INFO:trainer.default_trainer:------------------------------------------------------- INFO:trainer.default_trainer:Training on rank: 0 INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:modeling.language.LangEncoder.transformer:=> init weight of Linear/Conv2d from trunc norm INFO:modeling.language.LangEncoder.transformer:=> init bias of Linear/Conv2d to zeros INFO:base_dir.pipeline.XDecoderPipeline:GeneralizedSEEM( (backbone): D2FocalNet( (patch_embed): PatchEmbed( (proj): Conv2d(3, 192, kernel_size=(7, 7), stride=(4, 4), padding=(2, 2)) (norm): LayerNorm((192,), eps=1e-05, elementwise_affine=True) ) (pos_drop): Dropout(p=0.0, inplace=False) (layers): ModuleList( (0): BasicLayer( (blocks): ModuleList( (0): FocalModulationBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (modulation): FocalModulation( (f): Linear(in_features=192, out_features=389, bias=True) (h): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): GELU(approximate='none') (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (focal_layers): ModuleList( (0): Sequential( (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False) (1): GELU(approximate='none') ) (1): Sequential( (0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False) (1): GELU(approximate='none') ) (2): Sequential( (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192, bias=False) (1): GELU(approximate='none') ) (3): Sequential( (0): Conv2d(192, 192, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4), groups=192, bias=False) (1): GELU(approximate='none') ) ) ) (drop_path): Identity() (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=192, out_features=768, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=768, out_features=192, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) (1): FocalModulationBlock( (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (modulation): FocalModulation( (f): Linear(in_features=192, out_features=389, bias=True) (h): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1)) (act): GELU(approximate='none') (proj): Linear(in_features=192, out_features=192, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (focal_layers): ModuleList( (0): Sequential( (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False) (1): GELU(approximate='none') ) (1): Sequential( (0): Conv2d(192, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=192, bias=False) (1): GELU(approximate='none') ) (2): Sequential( (0): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192, bias=False) (1): GELU(approximate='none') ) (3): Sequential( (0): Conv2d(192, 192, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4), groups=192, bias=False) (1): GELU(approximate='none') ) ) ) (drop_path): DropPath() (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=192, out_features=768, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=768, out_features=192, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchEmbed( (proj): Conv2d(192, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): LayerNorm((384,), eps=1e-05, elementwise_affine=True) ) ) (1): BasicLayer( (blocks): ModuleList( (0-1): 2 x FocalModulationBlock( (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (modulation): FocalModulation( (f): Linear(in_features=384, out_features=773, bias=True) (h): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1)) (act): GELU(approximate='none') (proj): Linear(in_features=384, out_features=384, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (focal_layers): ModuleList( (0): Sequential( (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False) (1): GELU(approximate='none') ) (1): Sequential( (0): Conv2d(384, 384, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=384, bias=False) (1): GELU(approximate='none') ) (2): Sequential( (0): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384, bias=False) (1): GELU(approximate='none') ) (3): Sequential( (0): Conv2d(384, 384, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4), groups=384, bias=False) (1): GELU(approximate='none') ) ) ) (drop_path): DropPath() (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=384, out_features=1536, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=1536, out_features=384, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchEmbed( (proj): Conv2d(384, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) (2): BasicLayer( (blocks): ModuleList( (0-17): 18 x FocalModulationBlock( (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (modulation): FocalModulation( (f): Linear(in_features=768, out_features=1541, bias=True) (h): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1)) (act): GELU(approximate='none') (proj): Linear(in_features=768, out_features=768, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (focal_layers): ModuleList( (0): Sequential( (0): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=768, bias=False) (1): GELU(approximate='none') ) (1): Sequential( (0): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False) (1): GELU(approximate='none') ) (2): Sequential( (0): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768, bias=False) (1): GELU(approximate='none') ) (3): Sequential( (0): Conv2d(768, 768, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4), groups=768, bias=False) (1): GELU(approximate='none') ) ) ) (drop_path): DropPath() (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=768, out_features=3072, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=3072, out_features=768, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) (downsample): PatchEmbed( (proj): Conv2d(768, 1536, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) ) ) (3): BasicLayer( (blocks): ModuleList( (0-1): 2 x FocalModulationBlock( (norm1): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) (modulation): FocalModulation( (f): Linear(in_features=1536, out_features=3077, bias=True) (h): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1)) (act): GELU(approximate='none') (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Dropout(p=0.0, inplace=False) (focal_layers): ModuleList( (0): Sequential( (0): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1536, bias=False) (1): GELU(approximate='none') ) (1): Sequential( (0): Conv2d(1536, 1536, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1536, bias=False) (1): GELU(approximate='none') ) (2): Sequential( (0): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536, bias=False) (1): GELU(approximate='none') ) (3): Sequential( (0): Conv2d(1536, 1536, kernel_size=(9, 9), stride=(1, 1), padding=(4, 4), groups=1536, bias=False) (1): GELU(approximate='none') ) ) ) (drop_path): DropPath() (norm2): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='none') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Dropout(p=0.0, inplace=False) ) ) ) ) ) (norm0): LayerNorm((192,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (norm3): LayerNorm((1536,), eps=1e-05, elementwise_affine=True) ) (sem_seg_head): XdecoderHead( (pixel_decoder): TransformerEncoderPixelDecoder( (adapter_1): Conv2d( 192, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (layer_1): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (adapter_2): Conv2d( 384, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (layer_2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (adapter_3): Conv2d( 768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (layer_3): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) (mask_features): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (input_proj): Conv2d(1536, 512, kernel_size=(1, 1), stride=(1, 1)) (transformer): TransformerEncoderOnly( (encoder): TransformerEncoder( (layers): ModuleList( (0-5): 6 x TransformerEncoderLayer( (self_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) ) (linear1): Linear(in_features=512, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=512, bias=True) (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (dropout1): Dropout(p=0.0, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) ) ) ) ) (pe_layer): Positional encoding PositionEmbeddingSine num_pos_feats: 256 temperature: 10000 normalize: True scale: 6.283185307179586 (layer_4): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): GroupNorm(32, 512, eps=1e-05, affine=True) ) ) (predictor): SEEMDecoder( (pe_layer): Positional encoding PositionEmbeddingSine num_pos_feats: 256 temperature: 10000 normalize: True scale: 6.283185307179586 (transformer_self_attention_layers): ModuleList( (0-8): 9 x SelfAttentionLayer( (self_attn): MultiheadAttention( (out_proj): _LinearWithBias(in_features=512, out_features=512, bias=True) ) (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.0, inplace=False) ) ) (transformer_cross_attention_layers): ModuleList( (0-8): 9 x CrossAttentionLayer( (multihead_attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) ) (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (dropout): Dropout(p=0.0, inplace=False) ) ) (transformer_ffn_layers): ModuleList( (0-8): 9 x FFNLayer( (linear1): Linear(in_features=512, out_features=2048, bias=True) (dropout): Dropout(p=0.0, inplace=False) (linear2): Linear(in_features=2048, out_features=512, bias=True) (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) ) ) (decoder_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (query_feat): Embedding(101, 512) (query_embed): Embedding(101, 512) (level_embed): Embedding(3, 512) (input_proj): ModuleList( (0-2): 3 x Sequential() ) (lang_encoder): LanguageEncoder( (lang_encoder): Transformer( (token_embedding): Embedding(49408, 512) (resblocks): ModuleList( (0-11): 12 x ResidualAttentionBlock( (attn): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True) ) (ln_1): LayerNorm() (mlp): Sequential( (c_fc): Linear(in_features=512, out_features=2048, bias=True) (gelu): QuickGELU() (c_proj): Linear(in_features=2048, out_features=512, bias=True) ) (ln_2): LayerNorm() (drop_path): Identity() ) ) (ln_final): LayerNorm() ) ) (mask_embed): MLP( (layers): ModuleList( (0-2): 3 x Linear(in_features=512, out_features=512, bias=True) ) ) (mask_sptial_embed): ParameterList( (0): Parameter containing: [torch.float32 of size 512x512] (1): Parameter containing: [torch.float32 of size 512x512] (2): Parameter containing: [torch.float32 of size 512x512] ) (spatial_embed): Embedding(32, 512) (spatial_featured): Embedding(32, 512) (pn_indicator): Embedding(2, 512) (attention_data): AttentionDataStruct() ) ) (criterion): Criterion SetCriterion matcher: Matcher HungarianMatcher cost_class: 2.0 cost_mask: 5.0 cost_dice: 5.0 losses: [] weight_dict: {'loss_mask_ce_0': 2.0, 'loss_mask_dice_0': 5.0, 'loss_mask_bce_0': 5.0, 'loss_spatial_ce_0': 0.4, 'loss_spatial_dice_0': 1.0, 'loss_spatial_bce_0': 1.0, 'loss_grounding_ce_0': 0.4, 'loss_grounding_dice_0': 1.0, 'loss_grounding_bce_0': 1.0, 'loss_openimage_ce_0': 0.4, 'loss_openimage_dice_0': 1.0, 'loss_openimage_bce_0': 1.0, 'loss_mask_ce_1': 2.0, 'loss_mask_dice_1': 5.0, 'loss_mask_bce_1': 5.0, 'loss_spatial_ce_1': 0.4, 'loss_spatial_dice_1': 1.0, 'loss_spatial_bce_1': 1.0, 'loss_grounding_ce_1': 0.4, 'loss_grounding_dice_1': 1.0, 'loss_grounding_bce_1': 1.0, 'loss_openimage_ce_1': 0.4, 'loss_openimage_dice_1': 1.0, 'loss_openimage_bce_1': 1.0, 'loss_mask_ce_2': 2.0, 'loss_mask_dice_2': 5.0, 'loss_mask_bce_2': 5.0, 'loss_spatial_ce_2': 0.4, 'loss_spatial_dice_2': 1.0, 'loss_spatial_bce_2': 1.0, 'loss_grounding_ce_2': 0.4, 'loss_grounding_dice_2': 1.0, 'loss_grounding_bce_2': 1.0, 'loss_openimage_ce_2': 0.4, 'loss_openimage_dice_2': 1.0, 'loss_openimage_bce_2': 1.0, 'loss_mask_ce_3': 2.0, 'loss_mask_dice_3': 5.0, 'loss_mask_bce_3': 5.0, 'loss_spatial_ce_3': 0.4, 'loss_spatial_dice_3': 1.0, 'loss_spatial_bce_3': 1.0, 'loss_grounding_ce_3': 0.4, 'loss_grounding_dice_3': 1.0, 'loss_grounding_bce_3': 1.0, 'loss_openimage_ce_3': 0.4, 'loss_openimage_dice_3': 1.0, 'loss_openimage_bce_3': 1.0, 'loss_mask_ce_4': 2.0, 'loss_mask_dice_4': 5.0, 'loss_mask_bce_4': 5.0, 'loss_spatial_ce_4': 0.4, 'loss_spatial_dice_4': 1.0, 'loss_spatial_bce_4': 1.0, 'loss_grounding_ce_4': 0.4, 'loss_grounding_dice_4': 1.0, 'loss_grounding_bce_4': 1.0, 'loss_openimage_ce_4': 0.4, 'loss_openimage_dice_4': 1.0, 'loss_openimage_bce_4': 1.0, 'loss_mask_ce_5': 2.0, 'loss_mask_dice_5': 5.0, 'loss_mask_bce_5': 5.0, 'loss_spatial_ce_5': 0.4, 'loss_spatial_dice_5': 1.0, 'loss_spatial_bce_5': 1.0, 'loss_grounding_ce_5': 0.4, 'loss_grounding_dice_5': 1.0, 'loss_grounding_bce_5': 1.0, 'loss_openimage_ce_5': 0.4, 'loss_openimage_dice_5': 1.0, 'loss_openimage_bce_5': 1.0, 'loss_mask_ce_6': 2.0, 'loss_mask_dice_6': 5.0, 'loss_mask_bce_6': 5.0, 'loss_spatial_ce_6': 0.4, 'loss_spatial_dice_6': 1.0, 'loss_spatial_bce_6': 1.0, 'loss_grounding_ce_6': 0.4, 'loss_grounding_dice_6': 1.0, 'loss_grounding_bce_6': 1.0, 'loss_openimage_ce_6': 0.4, 'loss_openimage_dice_6': 1.0, 'loss_openimage_bce_6': 1.0, 'loss_mask_ce_7': 2.0, 'loss_mask_dice_7': 5.0, 'loss_mask_bce_7': 5.0, 'loss_spatial_ce_7': 0.4, 'loss_spatial_dice_7': 1.0, 'loss_spatial_bce_7': 1.0, 'loss_grounding_ce_7': 0.4, 'loss_grounding_dice_7': 1.0, 'loss_grounding_bce_7': 1.0, 'loss_openimage_ce_7': 0.4, 'loss_openimage_dice_7': 1.0, 'loss_openimage_bce_7': 1.0, 'loss_mask_ce_8': 2.0, 'loss_mask_dice_8': 5.0, 'loss_mask_bce_8': 5.0, 'loss_spatial_ce_8': 0.4, 'loss_spatial_dice_8': 1.0, 'loss_spatial_bce_8': 1.0, 'loss_grounding_ce_8': 0.4, 'loss_grounding_dice_8': 1.0, 'loss_grounding_bce_8': 1.0, 'loss_openimage_ce_8': 0.4, 'loss_openimage_dice_8': 1.0, 'loss_openimage_bce_8': 1.0, 'loss_mask_ce_9': 2.0, 'loss_mask_dice_9': 5.0, 'loss_mask_bce_9': 5.0, 'loss_spatial_ce_9': 0.4, 'loss_spatial_dice_9': 1.0, 'loss_spatial_bce_9': 1.0, 'loss_grounding_ce_9': 0.4, 'loss_grounding_dice_9': 1.0, 'loss_grounding_bce_9': 1.0, 'loss_openimage_ce_9': 0.4, 'loss_openimage_dice_9': 1.0, 'loss_openimage_bce_9': 1.0} num_classes: 133 eos_coef: 0.1 num_points: 12544 oversample_ratio: 3.0 importance_sample_ratio: 0.75 ) INFO:datasets.dataset_mappers.coco_panoptic_interactive_dataset_mapper:[COCOPanopticNewBaselineDatasetMapper] Full TransformGens used in training: [RandomFlip(), ResizeScale(min_scale=0.1, max_scale=2.0, target_height=1024, target_width=1024), FixedSizeCrop(crop_size=(1024, 1024))] INFO:datasets.build:Using training sampler TrainingSampler INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:2 to store for rank: 0 INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:2 with 64 nodes. INFO:detectron2.data.common:Serializing 116987 elements to byte tensors and concatenating them all ... INFO:detectron2.data.common:Serialized dataset takes 1458.79 MiB INFO:base_dir.pipeline.XDecoderPipeline:num of train samples: 1827 INFO:trainer.xdecoder_trainer:Calculate MAX_ITER @ 91350 and STEPS @ [81200, 87966] INFO:trainer.xdecoder_trainer:Total number of parameters in default module (on each GPU): 341181177 INFO:trainer.xdecoder_trainer:Number of trainable parameters in default module (on each GPU): 39812608 WARNING:trainer.utils_trainer:PyTorch AMP GradScaler initialized. INFO:utils.model:Loaded backbone.layers.0.blocks.0.gamma_1, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.gamma_2, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([768, 192]) <-> Ckpt Shape: torch.Size([768, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([192, 768]) <-> Ckpt Shape: torch.Size([192, 768]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.f.bias, Model Shape: torch.Size([389]) <-> Ckpt Shape: torch.Size([389]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.f.weight, Model Shape: torch.Size([389, 192]) <-> Ckpt Shape: torch.Size([389, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([192, 1, 5, 5]) <-> Ckpt Shape: torch.Size([192, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([192, 1, 7, 7]) <-> Ckpt Shape: torch.Size([192, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([192, 1, 9, 9]) <-> Ckpt Shape: torch.Size([192, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.h.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.h.weight, Model Shape: torch.Size([192, 192, 1, 1]) <-> Ckpt Shape: torch.Size([192, 192, 1, 1]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.modulation.proj.weight, Model Shape: torch.Size([192, 192]) <-> Ckpt Shape: torch.Size([192, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm1.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.0.norm2.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.gamma_1, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.gamma_2, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([768, 192]) <-> Ckpt Shape: torch.Size([768, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([192, 768]) <-> Ckpt Shape: torch.Size([192, 768]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.f.bias, Model Shape: torch.Size([389]) <-> Ckpt Shape: torch.Size([389]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.f.weight, Model Shape: torch.Size([389, 192]) <-> Ckpt Shape: torch.Size([389, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([192, 1, 3, 3]) <-> Ckpt Shape: torch.Size([192, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([192, 1, 5, 5]) <-> Ckpt Shape: torch.Size([192, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([192, 1, 7, 7]) <-> Ckpt Shape: torch.Size([192, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([192, 1, 9, 9]) <-> Ckpt Shape: torch.Size([192, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.h.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.h.weight, Model Shape: torch.Size([192, 192, 1, 1]) <-> Ckpt Shape: torch.Size([192, 192, 1, 1]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.modulation.proj.weight, Model Shape: torch.Size([192, 192]) <-> Ckpt Shape: torch.Size([192, 192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm1.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm1.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm2.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.blocks.1.norm2.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.layers.0.downsample.norm.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.0.downsample.norm.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.0.downsample.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.0.downsample.proj.weight, Model Shape: torch.Size([384, 192, 3, 3]) <-> Ckpt Shape: torch.Size([384, 192, 3, 3]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.f.bias, Model Shape: torch.Size([773]) <-> Ckpt Shape: torch.Size([773]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.f.weight, Model Shape: torch.Size([773, 384]) <-> Ckpt Shape: torch.Size([773, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([384, 1, 9, 9]) <-> Ckpt Shape: torch.Size([384, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.0.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.gamma_1, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.gamma_2, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([1536, 384]) <-> Ckpt Shape: torch.Size([1536, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([384, 1536]) <-> Ckpt Shape: torch.Size([384, 1536]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.f.bias, Model Shape: torch.Size([773]) <-> Ckpt Shape: torch.Size([773]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.f.weight, Model Shape: torch.Size([773, 384]) <-> Ckpt Shape: torch.Size([773, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([384, 1, 3, 3]) <-> Ckpt Shape: torch.Size([384, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([384, 1, 5, 5]) <-> Ckpt Shape: torch.Size([384, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([384, 1, 7, 7]) <-> Ckpt Shape: torch.Size([384, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([384, 1, 9, 9]) <-> Ckpt Shape: torch.Size([384, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.h.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.h.weight, Model Shape: torch.Size([384, 384, 1, 1]) <-> Ckpt Shape: torch.Size([384, 384, 1, 1]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.proj.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.modulation.proj.weight, Model Shape: torch.Size([384, 384]) <-> Ckpt Shape: torch.Size([384, 384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm2.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.blocks.1.norm2.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.layers.1.downsample.norm.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.1.downsample.norm.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.1.downsample.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.1.downsample.proj.weight, Model Shape: torch.Size([768, 384, 3, 3]) <-> Ckpt Shape: torch.Size([768, 384, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.0.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.1.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.10.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.11.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.12.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.13.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.14.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.15.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.16.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.17.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.2.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.3.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.4.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.5.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.6.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.7.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.8.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.gamma_1, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.gamma_2, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.mlp.fc1.bias, Model Shape: torch.Size([3072]) <-> Ckpt Shape: torch.Size([3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.mlp.fc1.weight, Model Shape: torch.Size([3072, 768]) <-> Ckpt Shape: torch.Size([3072, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.mlp.fc2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.mlp.fc2.weight, Model Shape: torch.Size([768, 3072]) <-> Ckpt Shape: torch.Size([768, 3072]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.f.bias, Model Shape: torch.Size([1541]) <-> Ckpt Shape: torch.Size([1541]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.f.weight, Model Shape: torch.Size([1541, 768]) <-> Ckpt Shape: torch.Size([1541, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([768, 1, 3, 3]) <-> Ckpt Shape: torch.Size([768, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([768, 1, 5, 5]) <-> Ckpt Shape: torch.Size([768, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([768, 1, 7, 7]) <-> Ckpt Shape: torch.Size([768, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([768, 1, 9, 9]) <-> Ckpt Shape: torch.Size([768, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.h.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.h.weight, Model Shape: torch.Size([768, 768, 1, 1]) <-> Ckpt Shape: torch.Size([768, 768, 1, 1]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.proj.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.modulation.proj.weight, Model Shape: torch.Size([768, 768]) <-> Ckpt Shape: torch.Size([768, 768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.norm1.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.norm1.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.blocks.9.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.layers.2.downsample.norm.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.2.downsample.norm.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.2.downsample.proj.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.2.downsample.proj.weight, Model Shape: torch.Size([1536, 768, 3, 3]) <-> Ckpt Shape: torch.Size([1536, 768, 3, 3]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.gamma_1, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.gamma_2, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc1.bias, Model Shape: torch.Size([6144]) <-> Ckpt Shape: torch.Size([6144]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc1.weight, Model Shape: torch.Size([6144, 1536]) <-> Ckpt Shape: torch.Size([6144, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc2.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.mlp.fc2.weight, Model Shape: torch.Size([1536, 6144]) <-> Ckpt Shape: torch.Size([1536, 6144]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.f.bias, Model Shape: torch.Size([3077]) <-> Ckpt Shape: torch.Size([3077]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.f.weight, Model Shape: torch.Size([3077, 1536]) <-> Ckpt Shape: torch.Size([3077, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([1536, 1, 3, 3]) <-> Ckpt Shape: torch.Size([1536, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([1536, 1, 5, 5]) <-> Ckpt Shape: torch.Size([1536, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([1536, 1, 7, 7]) <-> Ckpt Shape: torch.Size([1536, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([1536, 1, 9, 9]) <-> Ckpt Shape: torch.Size([1536, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.h.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.h.weight, Model Shape: torch.Size([1536, 1536, 1, 1]) <-> Ckpt Shape: torch.Size([1536, 1536, 1, 1]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.proj.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.modulation.proj.weight, Model Shape: torch.Size([1536, 1536]) <-> Ckpt Shape: torch.Size([1536, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm1.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm2.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.0.norm2.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.gamma_1, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.gamma_2, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc1.bias, Model Shape: torch.Size([6144]) <-> Ckpt Shape: torch.Size([6144]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc1.weight, Model Shape: torch.Size([6144, 1536]) <-> Ckpt Shape: torch.Size([6144, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc2.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.mlp.fc2.weight, Model Shape: torch.Size([1536, 6144]) <-> Ckpt Shape: torch.Size([1536, 6144]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.f.bias, Model Shape: torch.Size([3077]) <-> Ckpt Shape: torch.Size([3077]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.f.weight, Model Shape: torch.Size([3077, 1536]) <-> Ckpt Shape: torch.Size([3077, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.0.0.weight, Model Shape: torch.Size([1536, 1, 3, 3]) <-> Ckpt Shape: torch.Size([1536, 1, 3, 3]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.1.0.weight, Model Shape: torch.Size([1536, 1, 5, 5]) <-> Ckpt Shape: torch.Size([1536, 1, 5, 5]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.2.0.weight, Model Shape: torch.Size([1536, 1, 7, 7]) <-> Ckpt Shape: torch.Size([1536, 1, 7, 7]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.focal_layers.3.0.weight, Model Shape: torch.Size([1536, 1, 9, 9]) <-> Ckpt Shape: torch.Size([1536, 1, 9, 9]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.h.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.h.weight, Model Shape: torch.Size([1536, 1536, 1, 1]) <-> Ckpt Shape: torch.Size([1536, 1536, 1, 1]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.proj.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.modulation.proj.weight, Model Shape: torch.Size([1536, 1536]) <-> Ckpt Shape: torch.Size([1536, 1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm1.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm1.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm2.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.layers.3.blocks.1.norm2.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.norm0.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.norm0.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.norm1.bias, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.norm1.weight, Model Shape: torch.Size([384]) <-> Ckpt Shape: torch.Size([384]) INFO:utils.model:Loaded backbone.norm2.bias, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.norm2.weight, Model Shape: torch.Size([768]) <-> Ckpt Shape: torch.Size([768]) INFO:utils.model:Loaded backbone.norm3.bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.norm3.weight, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded backbone.patch_embed.norm.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.patch_embed.norm.weight, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.patch_embed.proj.bias, Model Shape: torch.Size([192]) <-> Ckpt Shape: torch.Size([192]) INFO:utils.model:Loaded backbone.patch_embed.proj.weight, Model Shape: torch.Size([192, 3, 7, 7]) <-> Ckpt Shape: torch.Size([192, 3, 7, 7]) INFO:utils.model:Loaded criterion.empty_weight, Model Shape: torch.Size([134]) <-> Ckpt Shape: torch.Size([134]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_1.weight, Model Shape: torch.Size([512, 192, 1, 1]) <-> Ckpt Shape: torch.Size([512, 192, 1, 1]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_2.weight, Model Shape: torch.Size([512, 384, 1, 1]) <-> Ckpt Shape: torch.Size([512, 384, 1, 1]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.adapter_3.weight, Model Shape: torch.Size([512, 768, 1, 1]) <-> Ckpt Shape: torch.Size([512, 768, 1, 1]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.input_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.input_proj.weight, Model Shape: torch.Size([512, 1536, 1, 1]) <-> Ckpt Shape: torch.Size([512, 1536, 1, 1]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_1.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_2.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_3.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.layer_4.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.mask_features.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.mask_features.weight, Model Shape: torch.Size([512, 512, 3, 3]) <-> Ckpt Shape: torch.Size([512, 512, 3, 3]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.0.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.1.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.2.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.3.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.4.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.norm2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.pixel_decoder.transformer.encoder.layers.5.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.class_embed, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.decoder_norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.decoder_norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.ln_final.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.ln_final.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.positional_embedding, Model Shape: torch.Size([77, 512]) <-> Ckpt Shape: torch.Size([77, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.0.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.1.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.10.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.11.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.2.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.3.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.4.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.5.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.6.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.7.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.8.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_1.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.ln_2.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_fc.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_fc.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.resblocks.9.mlp.c_proj.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_encoder.token_embedding.weight, Model Shape: torch.Size([49408, 512]) <-> Ckpt Shape: torch.Size([49408, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.lang_proj, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.lang_encoder.logit_scale, Model Shape: torch.Size([]) <-> Ckpt Shape: torch.Size([]) INFO:utils.model:Loaded sem_seg_head.predictor.level_embed.weight, Model Shape: torch.Size([3, 512]) <-> Ckpt Shape: torch.Size([3, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.0.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.0.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.1.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.1.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.mask_embed.layers.2.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.query_embed.weight, Model Shape: torch.Size([101, 512]) <-> Ckpt Shape: torch.Size([101, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.query_feat.weight, Model Shape: torch.Size([101, 512]) <-> Ckpt Shape: torch.Size([101, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.multihead_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_cross_attention_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear1.bias, Model Shape: torch.Size([2048]) <-> Ckpt Shape: torch.Size([2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear1.weight, Model Shape: torch.Size([2048, 512]) <-> Ckpt Shape: torch.Size([2048, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear2.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.linear2.weight, Model Shape: torch.Size([512, 2048]) <-> Ckpt Shape: torch.Size([512, 2048]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_ffn_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.0.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.1.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.2.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.3.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.4.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.5.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.6.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.7.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.norm.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.norm.weight, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.in_proj_bias, Model Shape: torch.Size([1536]) <-> Ckpt Shape: torch.Size([1536]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.in_proj_weight, Model Shape: torch.Size([1536, 512]) <-> Ckpt Shape: torch.Size([1536, 512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.out_proj.bias, Model Shape: torch.Size([512]) <-> Ckpt Shape: torch.Size([512]) INFO:utils.model:Loaded sem_seg_head.predictor.transformer_self_attention_layers.8.self_attn.out_proj.weight, Model Shape: torch.Size([512, 512]) <-> Ckpt Shape: torch.Size([512, 512]) WARNING:utils.model:*UNLOADED* dilation_kernel, Model Shape: torch.Size([1, 1, 3, 3]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.0, Model Shape: torch.Size([512, 512]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.1, Model Shape: torch.Size([512, 512]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.mask_sptial_embed.2, Model Shape: torch.Size([512, 512]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.pn_indicator.weight, Model Shape: torch.Size([2, 512]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.spatial_embed.weight, Model Shape: torch.Size([32, 512]) WARNING:utils.model:*UNLOADED* sem_seg_head.predictor.spatial_featured.weight, Model Shape: torch.Size([32, 512]) WARNING:utils.model:$UNUSED$ backbone_proj, Ckpt Shape: torch.Size([1536, 512]) WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.caping_embed, Ckpt Shape: torch.Size([512, 512]) WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.pos_embed_caping.weight, Ckpt Shape: torch.Size([77, 512]) WARNING:utils.model:$UNUSED$ sem_seg_head.predictor.self_attn_mask, Ckpt Shape: torch.Size([1, 278, 278]) WARNING:trainer.utils_trainer:Load weights from /mnt/output/xueyanz/pretrained/mask2former_vlp_focall_enc6_fpn_dec10_lang_capgTrue_retTrue_grdTrue_topc3_topr3_topg6_capgw8_rw8_cbs32_vbs1024_ep50_lr0.0001_preuTrue_gtw2.0_gcw0.5_1122_oq101/default/model_state_dict.pt... INFO:trainer.default_trainer:***** Running training ***** INFO:trainer.default_trainer: Num of GPUs = 64 INFO:trainer.default_trainer: Num Epochs = 50 INFO:trainer.default_trainer: Num of Mini Batches per Epoch = 1827 INFO:trainer.default_trainer: Total train batch size (w. parallel, distributed & accumulation) = 91350 INFO:trainer.default_trainer: Gradient Accumulation steps = 1 INFO:trainer.default_trainer: Total optimization steps = 91350 INFO:trainer.default_trainer:Start epoch: 0 training. INFO:trainer.default_trainer:epochs[ 0] optim steps[1] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95426/0.95426, loss_mask_bce_0: 0.05425/0.05425, loss_mask_dice_0: 0.74623/0.74623, loss_spatial_bce_0: 0.20393/0.20393, loss_spatial_dice_0: 0.79638/0.79638, loss_spatial_ce_0: 7.60275/7.60275, loss_grounding_bce_0: 0.01552/0.01552, loss_grounding_dice_0: 0.09088/0.09088, loss_grounding_ce_0: 0.04251/0.04251, loss_mask_ce_1: 0.97482/0.97482, loss_mask_bce_1: 0.05974/0.05974, loss_mask_dice_1: 0.66805/0.66805, loss_spatial_bce_1: 0.20445/0.20445, loss_spatial_dice_1: 0.80967/0.80967, loss_spatial_ce_1: 6.93220/6.93220, loss_grounding_bce_1: 0.01385/0.01385, loss_grounding_dice_1: 0.07940/0.07940, loss_grounding_ce_1: 0.05244/0.05244, loss_mask_ce_2: 1.08129/1.08129, loss_mask_bce_2: 0.05566/0.05566, loss_mask_dice_2: 0.79064/0.79064, loss_spatial_bce_2: 0.25918/0.25918, loss_spatial_dice_2: 0.80798/0.80798, loss_spatial_ce_2: 8.34506/8.34506, loss_grounding_bce_2: 0.01463/0.01463, loss_grounding_dice_2: 0.09518/0.09518, loss_grounding_ce_2: 0.06591/0.06591, loss_mask_ce_3: 1.14708/1.14708, loss_mask_bce_3: 0.06600/0.06600, loss_mask_dice_3: 0.76058/0.76058, loss_spatial_bce_3: 0.26760/0.26760, loss_spatial_dice_3: 0.78864/0.78864, loss_spatial_ce_3: 11.28311/11.28311, loss_grounding_bce_3: 0.03710/0.03710, loss_grounding_dice_3: 0.13825/0.13825, loss_grounding_ce_3: 0.09663/0.09663, loss_mask_ce_4: 1.06453/1.06453, loss_mask_bce_4: 0.07102/0.07102, loss_mask_dice_4: 0.75947/0.75947, loss_spatial_bce_4: 0.24777/0.24777, loss_spatial_dice_4: 0.80596/0.80596, loss_spatial_ce_4: 8.76293/8.76293, loss_grounding_bce_4: 0.02800/0.02800, loss_grounding_dice_4: 0.12020/0.12020, loss_grounding_ce_4: 0.04392/0.04392, loss_mask_ce_5: 1.10912/1.10912, loss_mask_bce_5: 0.05862/0.05862, loss_mask_dice_5: 0.67685/0.67685, loss_spatial_bce_5: 0.26795/0.26795, loss_spatial_dice_5: 0.80769/0.80769, loss_spatial_ce_5: 9.45201/9.45201, loss_grounding_bce_5: 0.04073/0.04073, loss_grounding_dice_5: 0.16229/0.16229, loss_grounding_ce_5: 0.02467/0.02467, loss_mask_ce_6: 1.44391/1.44391, loss_mask_bce_6: 0.06447/0.06447, loss_mask_dice_6: 0.66664/0.66664, loss_spatial_bce_6: 0.24954/0.24954, loss_spatial_dice_6: 0.81833/0.81833, loss_spatial_ce_6: 9.23953/9.23953, loss_grounding_bce_6: 0.02582/0.02582, loss_grounding_dice_6: 0.13621/0.13621, loss_grounding_ce_6: 0.01761/0.01761, loss_mask_ce_7: 1.43384/1.43384, loss_mask_bce_7: 0.05486/0.05486, loss_mask_dice_7: 0.60068/0.60068, loss_spatial_bce_7: 0.28589/0.28589, loss_spatial_dice_7: 0.87347/0.87347, loss_spatial_ce_7: 10.27557/10.27557, loss_grounding_bce_7: 0.01652/0.01652, loss_grounding_dice_7: 0.11537/0.11537, loss_grounding_ce_7: 0.01632/0.01632, loss_mask_ce_8: 2.14437/2.14437, loss_mask_bce_8: 0.10677/0.10677, loss_mask_dice_8: 0.98532/0.98532, loss_spatial_bce_8: 0.37247/0.37247, loss_spatial_dice_8: 0.89873/0.89873, loss_spatial_ce_8: 9.80396/9.80396, loss_grounding_bce_8: 0.01553/0.01553, loss_grounding_dice_8: 0.11306/0.11306, loss_grounding_ce_8: 0.31122/0.31122, loss_mask_ce_9: 7.00949/7.00949, loss_mask_bce_9: 0.20342/0.20342, loss_mask_dice_9: 1.28925/1.28925, loss_spatial_bce_9: 0.26348/0.26348, loss_spatial_dice_9: 0.83038/0.83038, loss_spatial_ce_9: 7.79116/7.79116, loss_grounding_bce_9: 0.04585/0.04585, loss_grounding_dice_9: 0.20480/0.20480, loss_grounding_ce_9: 0.45831/0.45831] items per batch[64] items per second[3.07] total items[64] mini batches[ 1] memory[4159] epoch remaining[10:34:12] INFO:trainer.default_trainer:epochs[ 0] optim steps[2] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10204/0.52815, loss_mask_bce_0: 0.23202/0.14313, loss_mask_dice_0: 0.30023/0.52323, loss_spatial_bce_0: 0.81615/0.51004, loss_spatial_dice_0: 0.68939/0.74288, loss_spatial_ce_0: 3.41211/5.50743, loss_grounding_bce_0: 0.07276/0.04414, loss_grounding_dice_0: 0.15834/0.12461, loss_grounding_ce_0: 0.00324/0.02287, loss_mask_ce_1: 0.12613/0.55048, loss_mask_bce_1: 0.24635/0.15305, loss_mask_dice_1: 0.24401/0.45603, loss_spatial_bce_1: 0.90900/0.55672, loss_spatial_dice_1: 0.67094/0.74031, loss_spatial_ce_1: 3.80520/5.36870, loss_grounding_bce_1: 0.07685/0.04535, loss_grounding_dice_1: 0.14779/0.11360, loss_grounding_ce_1: 0.00314/0.02779, loss_mask_ce_2: 0.11628/0.59879, loss_mask_bce_2: 0.23271/0.14419, loss_mask_dice_2: 0.25375/0.52220, loss_spatial_bce_2: 1.17978/0.71948, loss_spatial_dice_2: 0.67554/0.74176, loss_spatial_ce_2: 4.03453/6.18980, loss_grounding_bce_2: 0.06835/0.04149, loss_grounding_dice_2: 0.12804/0.11161, loss_grounding_ce_2: 0.00136/0.03363, loss_mask_ce_3: 0.10809/0.62758, loss_mask_bce_3: 0.22177/0.14388, loss_mask_dice_3: 0.28170/0.52114, loss_spatial_bce_3: 1.29597/0.78179, loss_spatial_dice_3: 0.67947/0.73405, loss_spatial_ce_3: 4.93524/8.10918, loss_grounding_bce_3: 0.07693/0.05701, loss_grounding_dice_3: 0.13738/0.13781, loss_grounding_ce_3: 0.00223/0.04943, loss_mask_ce_4: 0.13199/0.59826, loss_mask_bce_4: 0.20632/0.13867, loss_mask_dice_4: 0.23441/0.49694, loss_spatial_bce_4: 1.09758/0.67268, loss_spatial_dice_4: 0.62838/0.71717, loss_spatial_ce_4: 4.76794/6.76544, loss_grounding_bce_4: 0.08177/0.05488, loss_grounding_dice_4: 0.16429/0.14224, loss_grounding_ce_4: 0.00581/0.02487, loss_mask_ce_5: 0.11093/0.61003, loss_mask_bce_5: 0.20921/0.13392, loss_mask_dice_5: 0.21945/0.44815, loss_spatial_bce_5: 1.18726/0.72760, loss_spatial_dice_5: 0.72006/0.76387, loss_spatial_ce_5: 5.23798/7.34500, loss_grounding_bce_5: 0.09155/0.06614, loss_grounding_dice_5: 0.15148/0.15688, loss_grounding_ce_5: 0.00165/0.01316, loss_mask_ce_6: 0.09093/0.76742, loss_mask_bce_6: 0.20304/0.13376, loss_mask_dice_6: 0.26499/0.46582, loss_spatial_bce_6: 0.94986/0.59970, loss_spatial_dice_6: 0.68066/0.74950, loss_spatial_ce_6: 5.68233/7.46093, loss_grounding_bce_6: 0.07246/0.04914, loss_grounding_dice_6: 0.11464/0.12542, loss_grounding_ce_6: 0.00124/0.00942, loss_mask_ce_7: 0.36551/0.89967, loss_mask_bce_7: 0.22458/0.13972, loss_mask_dice_7: 0.41298/0.50683, loss_spatial_bce_7: 1.52798/0.90694, loss_spatial_dice_7: 0.80761/0.84054, loss_spatial_ce_7: 8.20937/9.24247, loss_grounding_bce_7: 0.08321/0.04987, loss_grounding_dice_7: 0.15335/0.13436, loss_grounding_ce_7: 0.13182/0.07407, loss_mask_ce_8: 0.75460/1.44948, loss_mask_bce_8: 0.20572/0.15625, loss_mask_dice_8: 0.30490/0.64511, loss_spatial_bce_8: 1.75489/1.06368, loss_spatial_dice_8: 0.85702/0.87788, loss_spatial_ce_8: 6.03855/7.92125, loss_grounding_bce_8: 0.08691/0.05122, loss_grounding_dice_8: 0.16686/0.13996, loss_grounding_ce_8: 0.18136/0.24629, loss_mask_ce_9: 2.39759/4.70354, loss_mask_bce_9: 0.20255/0.20299, loss_mask_dice_9: 0.22502/0.75714, loss_spatial_bce_9: 1.90434/1.08391, loss_spatial_dice_9: 0.94197/0.88617, loss_spatial_ce_9: 6.28857/7.03986, loss_grounding_bce_9: 0.07687/0.06136, loss_grounding_dice_9: 0.18072/0.19276, loss_grounding_ce_9: 0.32595/0.39213] items per batch[64] items per second[11.17] total items[128] mini batches[ 2] memory[4159] epoch remaining[6:44:05] INFO:trainer.default_trainer:epochs[ 0] optim steps[3] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04455/0.70028, loss_mask_bce_0: 0.11924/0.13517, loss_mask_dice_0: 0.49244/0.51296, loss_spatial_bce_0: 1.89933/0.97314, loss_spatial_dice_0: 0.82447/0.77008, loss_spatial_ce_0: 4.77425/5.26304, loss_grounding_bce_0: 0.06662/0.05163, loss_grounding_dice_0: 0.05152/0.10025, loss_grounding_ce_0: 0.06738/0.03771, loss_mask_ce_1: 0.85238/0.65111, loss_mask_bce_1: 0.16328/0.15646, loss_mask_dice_1: 0.57711/0.49639, loss_spatial_bce_1: 2.10982/1.07442, loss_spatial_dice_1: 0.84318/0.77460, loss_spatial_ce_1: 5.64176/5.45972, loss_grounding_bce_1: 0.05796/0.04956, loss_grounding_dice_1: 0.04553/0.09091, loss_grounding_ce_1: 0.04285/0.03281, loss_mask_ce_2: 0.52348/0.57369, loss_mask_bce_2: 0.11669/0.13502, loss_mask_dice_2: 0.49744/0.51394, loss_spatial_bce_2: 2.71396/1.38431, loss_spatial_dice_2: 0.84341/0.77564, loss_spatial_ce_2: 6.93352/6.43770, loss_grounding_bce_2: 0.07624/0.05307, loss_grounding_dice_2: 0.05684/0.09335, loss_grounding_ce_2: 0.02441/0.03056, loss_mask_ce_3: 1.21757/0.82424, loss_mask_bce_3: 0.13597/0.14125, loss_mask_dice_3: 0.46344/0.50191, loss_spatial_bce_3: 2.90216/1.48858, loss_spatial_dice_3: 0.84213/0.77008, loss_spatial_ce_3: 7.84853/8.02230, loss_grounding_bce_3: 0.08818/0.06740, loss_grounding_dice_3: 0.05661/0.11074, loss_grounding_ce_3: 0.02164/0.04017, loss_mask_ce_4: 0.88292/0.69315, loss_mask_bce_4: 0.12460/0.13398, loss_mask_dice_4: 0.54512/0.51300, loss_spatial_bce_4: 2.50876/1.28470, loss_spatial_dice_4: 0.84019/0.75818, loss_spatial_ce_4: 6.79505/6.77531, loss_grounding_bce_4: 0.09993/0.06990, loss_grounding_dice_4: 0.06387/0.11612, loss_grounding_ce_4: 0.01578/0.02184, loss_mask_ce_5: 1.27080/0.83028, loss_mask_bce_5: 0.11483/0.12755, loss_mask_dice_5: 0.42315/0.43981, loss_spatial_bce_5: 2.49477/1.31666, loss_spatial_dice_5: 0.84419/0.79065, loss_spatial_ce_5: 6.79130/7.16043, loss_grounding_bce_5: 0.13597/0.08942, loss_grounding_dice_5: 0.07283/0.12887, loss_grounding_ce_5: 0.01528/0.01387, loss_mask_ce_6: 1.03848/0.85778, loss_mask_bce_6: 0.12830/0.13194, loss_mask_dice_6: 0.46315/0.46493, loss_spatial_bce_6: 2.84830/1.34923, loss_spatial_dice_6: 0.84858/0.78253, loss_spatial_ce_6: 7.87560/7.59916, loss_grounding_bce_6: 0.12306/0.07378, loss_grounding_dice_6: 0.06799/0.10628, loss_grounding_ce_6: 0.02867/0.01584, loss_mask_ce_7: 1.09605/0.96513, loss_mask_bce_7: 0.26045/0.17996, loss_mask_dice_7: 0.53276/0.51547, loss_spatial_bce_7: 3.22440/1.67942, loss_spatial_dice_7: 0.87363/0.85157, loss_spatial_ce_7: 9.66790/9.38428, loss_grounding_bce_7: 0.43400/0.17791, loss_grounding_dice_7: 0.25777/0.17550, loss_grounding_ce_7: 0.07610/0.07475, loss_mask_ce_8: 1.83971/1.57956, loss_mask_bce_8: 0.79929/0.37059, loss_mask_dice_8: 0.71735/0.66919, loss_spatial_bce_8: 2.32594/1.48443, loss_spatial_dice_8: 0.97083/0.90886, loss_spatial_ce_8: 7.77186/7.87146, loss_grounding_bce_8: 1.68779/0.59675, loss_grounding_dice_8: 0.61540/0.29844, loss_grounding_ce_8: 0.49544/0.32934, loss_mask_ce_9: 3.60249/4.33652, loss_mask_bce_9: 1.40940/0.60513, loss_mask_dice_9: 0.86460/0.79296, loss_spatial_bce_9: 1.28103/1.14962, loss_spatial_dice_9: 0.81858/0.86364, loss_spatial_ce_9: 5.38694/6.48889, loss_grounding_bce_9: 2.98539/1.03604, loss_grounding_dice_9: 0.66088/0.34880, loss_grounding_ce_9: 0.75440/0.51289] items per batch[64] items per second[11.62] total items[192] mini batches[ 3] memory[4159] epoch remaining[5:25:04] INFO:trainer.default_trainer:epochs[ 0] optim steps[4] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00590/0.52669, loss_mask_bce_0: 0.06535/0.11771, loss_mask_dice_0: 0.04415/0.39576, loss_spatial_bce_0: 1.10605/1.00636, loss_spatial_dice_0: 0.60604/0.72907, loss_spatial_ce_0: 4.22721/5.00408, loss_grounding_bce_0: 0.07591/0.05770, loss_grounding_dice_0: 0.04787/0.08715, loss_grounding_ce_0: 0.00776/0.03022, loss_mask_ce_1: 0.00565/0.48975, loss_mask_bce_1: 0.06820/0.13439, loss_mask_dice_1: 0.04735/0.38413, loss_spatial_bce_1: 1.06407/1.07183, loss_spatial_dice_1: 0.54290/0.71667, loss_spatial_ce_1: 5.84097/5.55503, loss_grounding_bce_1: 0.07590/0.05614, loss_grounding_dice_1: 0.04989/0.08065, loss_grounding_ce_1: 0.00789/0.02658, loss_mask_ce_2: 0.00656/0.43191, loss_mask_bce_2: 0.06688/0.11799, loss_mask_dice_2: 0.04656/0.39710, loss_spatial_bce_2: 1.19823/1.33779, loss_spatial_dice_2: 0.52338/0.71258, loss_spatial_ce_2: 7.17408/6.62180, loss_grounding_bce_2: 0.07816/0.05934, loss_grounding_dice_2: 0.05009/0.08254, loss_grounding_ce_2: 0.00382/0.02388, loss_mask_ce_3: 0.00589/0.61966, loss_mask_bce_3: 0.06905/0.12320, loss_mask_dice_3: 0.04920/0.38873, loss_spatial_bce_3: 1.22069/1.42161, loss_spatial_dice_3: 0.52288/0.70828, loss_spatial_ce_3: 6.80481/7.71792, loss_grounding_bce_3: 0.07591/0.06953, loss_grounding_dice_3: 0.04762/0.09496, loss_grounding_ce_3: 0.00448/0.03125, loss_mask_ce_4: 0.00500/0.52111, loss_mask_bce_4: 0.07130/0.11831, loss_mask_dice_4: 0.04794/0.39673, loss_spatial_bce_4: 2.80946/1.66589, loss_spatial_dice_4: 0.51300/0.69688, loss_spatial_ce_4: 3.91904/6.06124, loss_grounding_bce_4: 0.08055/0.07256, loss_grounding_dice_4: 0.05032/0.09967, loss_grounding_ce_4: 0.01035/0.01897, loss_mask_ce_5: 0.00627/0.62428, loss_mask_bce_5: 0.06854/0.11280, loss_mask_dice_5: 0.04792/0.34184, loss_spatial_bce_5: 1.26907/1.30476, loss_spatial_dice_5: 0.54089/0.72821, loss_spatial_ce_5: 6.01844/6.87493, loss_grounding_bce_5: 0.08040/0.08716, loss_grounding_dice_5: 0.05142/0.10951, loss_grounding_ce_5: 0.00149/0.01077, loss_mask_ce_6: 0.00766/0.64525, loss_mask_bce_6: 0.06512/0.11523, loss_mask_dice_6: 0.04423/0.35975, loss_spatial_bce_6: 1.38883/1.35913, loss_spatial_dice_6: 0.58667/0.73356, loss_spatial_ce_6: 6.41467/7.30304, loss_grounding_bce_6: 0.07926/0.07515, loss_grounding_dice_6: 0.04963/0.09212, loss_grounding_ce_6: 0.00055/0.01202, loss_mask_ce_7: 0.00487/0.72507, loss_mask_bce_7: 0.06595/0.15146, loss_mask_dice_7: 0.04419/0.39765, loss_spatial_bce_7: 2.27933/1.82940, loss_spatial_dice_7: 0.82079/0.84387, loss_spatial_ce_7: 9.48347/9.40908, loss_grounding_bce_7: 0.08002/0.15344, loss_grounding_dice_7: 0.05092/0.14435, loss_grounding_ce_7: 0.00361/0.05696, loss_mask_ce_8: 0.00485/1.18588, loss_mask_bce_8: 0.07071/0.29562, loss_mask_dice_8: 0.04507/0.51316, loss_spatial_bce_8: 4.29050/2.18595, loss_spatial_dice_8: 0.95510/0.92042, loss_spatial_ce_8: 6.25733/7.46792, loss_grounding_bce_8: 0.08521/0.46886, loss_grounding_dice_8: 0.05393/0.23731, loss_grounding_ce_8: 0.00121/0.24731, loss_mask_ce_9: 1.63025/3.65995, loss_mask_bce_9: 0.07044/0.47145, loss_mask_dice_9: 0.04454/0.60585, loss_spatial_bce_9: 2.38512/1.45849, loss_spatial_dice_9: 0.99717/0.89702, loss_spatial_ce_9: 6.75328/6.55499, loss_grounding_bce_9: 0.08183/0.79749, loss_grounding_dice_9: 0.05650/0.27573, loss_grounding_ce_9: 0.40944/0.48702] items per batch[64] items per second[13.48] total items[256] mini batches[ 4] memory[4159] epoch remaining[4:39:44] INFO:trainer.default_trainer:epochs[ 0] optim steps[5] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03331/0.62801, loss_mask_bce_0: 0.10400/0.11497, loss_mask_dice_0: 0.21401/0.35941, loss_spatial_bce_0: 0.64908/0.93491, loss_spatial_dice_0: 0.68389/0.72003, loss_spatial_ce_0: 4.05973/4.81521, loss_grounding_bce_0: 0.12475/0.07111, loss_grounding_dice_0: 0.06801/0.08332, loss_grounding_ce_0: 0.01653/0.02748, loss_mask_ce_1: 1.01793/0.59538, loss_mask_bce_1: 0.11145/0.12981, loss_mask_dice_1: 0.22085/0.35148, loss_spatial_bce_1: 0.67893/0.99325, loss_spatial_dice_1: 0.68156/0.70965, loss_spatial_ce_1: 4.17763/5.27955, loss_grounding_bce_1: 0.12979/0.07087, loss_grounding_dice_1: 0.08456/0.08144, loss_grounding_ce_1: 0.01926/0.02512, loss_mask_ce_2: 1.01398/0.54832, loss_mask_bce_2: 0.11687/0.11776, loss_mask_dice_2: 0.20084/0.35785, loss_spatial_bce_2: 0.73799/1.21783, loss_spatial_dice_2: 0.68175/0.70641, loss_spatial_ce_2: 5.03259/6.30396, loss_grounding_bce_2: 0.13051/0.07358, loss_grounding_dice_2: 0.09856/0.08574, loss_grounding_ce_2: 0.00766/0.02063, loss_mask_ce_3: 0.97788/0.69130, loss_mask_bce_3: 0.12425/0.12341, loss_mask_dice_3: 0.22443/0.35587, loss_spatial_bce_3: 0.78662/1.29461, loss_spatial_dice_3: 0.67307/0.70124, loss_spatial_ce_3: 6.09535/7.39341, loss_grounding_bce_3: 0.13908/0.08344, loss_grounding_dice_3: 0.08016/0.09200, loss_grounding_ce_3: 0.00694/0.02638, loss_mask_ce_4: 1.58086/0.73306, loss_mask_bce_4: 1.03332/0.30131, loss_mask_dice_4: 0.49951/0.41729, loss_spatial_bce_4: 0.68138/1.46899, loss_spatial_dice_4: 0.67527/0.69256, loss_spatial_ce_4: 5.56088/5.96117, loss_grounding_bce_4: 0.15637/0.08932, loss_grounding_dice_4: 0.07623/0.09498, loss_grounding_ce_4: 0.00506/0.01618, loss_mask_ce_5: 1.89925/0.87927, loss_mask_bce_5: 0.45061/0.18036, loss_mask_dice_5: 0.41737/0.35695, loss_spatial_bce_5: 0.63264/1.17034, loss_spatial_dice_5: 0.67270/0.71711, loss_spatial_ce_5: 5.11144/6.52223, loss_grounding_bce_5: 0.16029/0.10179, loss_grounding_dice_5: 0.08442/0.10449, loss_grounding_ce_5: 0.00373/0.00936, loss_mask_ce_6: 2.09431/0.93506, loss_mask_bce_6: 0.68893/0.22997, loss_mask_dice_6: 0.45547/0.37890, loss_spatial_bce_6: 0.78062/1.24343, loss_spatial_dice_6: 0.67565/0.72198, loss_spatial_ce_6: 5.37463/6.91735, loss_grounding_bce_6: 0.15492/0.09110, loss_grounding_dice_6: 0.08126/0.08994, loss_grounding_ce_6: 0.00271/0.01015, loss_mask_ce_7: 2.04504/0.98906, loss_mask_bce_7: 0.90539/0.30224, loss_mask_dice_7: 0.61589/0.44130, loss_spatial_bce_7: 0.89575/1.64267, loss_spatial_dice_7: 0.71798/0.81870, loss_spatial_ce_7: 4.46777/8.42082, loss_grounding_bce_7: 0.13341/0.14943, loss_grounding_dice_7: 0.06840/0.12916, loss_grounding_ce_7: 0.00099/0.04577, loss_mask_ce_8: 1.18085/1.18488, loss_mask_bce_8: 1.86752/0.61000, loss_mask_dice_8: 0.60710/0.53194, loss_spatial_bce_8: 0.89322/1.92740, loss_spatial_dice_8: 0.84252/0.90484, loss_spatial_ce_8: 4.45132/6.86460, loss_grounding_bce_8: 0.11798/0.39869, loss_grounding_dice_8: 0.05867/0.20158, loss_grounding_ce_8: 0.01103/0.20005, loss_mask_ce_9: 4.21582/3.77113, loss_mask_bce_9: 1.67658/0.71248, loss_mask_dice_9: 0.70316/0.62532, loss_spatial_bce_9: 2.73832/1.71446, loss_spatial_dice_9: 0.88574/0.89477, loss_spatial_ce_9: 5.37281/6.31855, loss_grounding_bce_9: 0.15455/0.66890, loss_grounding_dice_9: 0.06064/0.23271, loss_grounding_ce_9: 0.03719/0.39706] items per batch[64] items per second[11.55] total items[320] mini batches[ 5] memory[4159] epoch remaining[4:17:20] INFO:trainer.default_trainer:epochs[ 0] optim steps[6] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.47889/0.76982, loss_mask_bce_0: 0.43422/0.16818, loss_mask_dice_0: 0.60542/0.40041, loss_spatial_bce_0: 1.05566/0.95503, loss_spatial_dice_0: 0.87702/0.74620, loss_spatial_ce_0: 3.45467/4.58846, loss_grounding_bce_0: 0.02193/0.06291, loss_grounding_dice_0: 0.29741/0.11900, loss_grounding_ce_0: 0.10138/0.03980, loss_mask_ce_1: 1.49174/0.74478, loss_mask_bce_1: 0.42694/0.17933, loss_mask_dice_1: 0.59482/0.39203, loss_spatial_bce_1: 1.25874/1.03750, loss_spatial_dice_1: 0.87578/0.73734, loss_spatial_ce_1: 3.65342/5.00853, loss_grounding_bce_1: 0.02745/0.06363, loss_grounding_dice_1: 0.31788/0.12084, loss_grounding_ce_1: 0.09445/0.03667, loss_mask_ce_2: 1.87834/0.76999, loss_mask_bce_2: 0.41431/0.16719, loss_mask_dice_2: 0.59569/0.39749, loss_spatial_bce_2: 1.32966/1.23647, loss_spatial_dice_2: 0.87774/0.73497, loss_spatial_ce_2: 5.36631/6.14768, loss_grounding_bce_2: 0.02242/0.06505, loss_grounding_dice_2: 0.30076/0.12158, loss_grounding_ce_2: 0.11473/0.03632, loss_mask_ce_3: 1.50125/0.82629, loss_mask_bce_3: 0.43001/0.17451, loss_mask_dice_3: 0.65843/0.40630, loss_spatial_bce_3: 1.78273/1.37596, loss_spatial_dice_3: 0.87621/0.73040, loss_spatial_ce_3: 5.09214/7.00986, loss_grounding_bce_3: 0.02208/0.07321, loss_grounding_dice_3: 0.31631/0.12939, loss_grounding_ce_3: 0.09850/0.03840, loss_mask_ce_4: 1.49679/0.86035, loss_mask_bce_4: 0.41013/0.31945, loss_mask_dice_4: 0.61603/0.45041, loss_spatial_bce_4: 1.52215/1.47785, loss_spatial_dice_4: 0.87000/0.72213, loss_spatial_ce_4: 5.49860/5.88407, loss_grounding_bce_4: 0.03639/0.08050, loss_grounding_dice_4: 0.40297/0.14631, loss_grounding_ce_4: 0.06212/0.02384, loss_mask_ce_5: 1.83253/1.03815, loss_mask_bce_5: 0.39895/0.21679, loss_mask_dice_5: 0.61524/0.40000, loss_spatial_bce_5: 1.41399/1.21095, loss_spatial_dice_5: 0.87274/0.74305, loss_spatial_ce_5: 5.36085/6.32867, loss_grounding_bce_5: 0.06553/0.09574, loss_grounding_dice_5: 0.61553/0.18966, loss_grounding_ce_5: 0.01484/0.01028, loss_mask_ce_6: 1.84088/1.08603, loss_mask_bce_6: 0.42140/0.26188, loss_mask_dice_6: 0.60186/0.41606, loss_spatial_bce_6: 1.54601/1.29386, loss_spatial_dice_6: 0.87335/0.74721, loss_spatial_ce_6: 7.38270/6.99491, loss_grounding_bce_6: 0.02108/0.07943, loss_grounding_dice_6: 0.27640/0.12102, loss_grounding_ce_6: 0.00409/0.00914, loss_mask_ce_7: 2.56576/1.25184, loss_mask_bce_7: 0.40496/0.31936, loss_mask_dice_7: 0.73832/0.49080, loss_spatial_bce_7: 1.67869/1.64867, loss_spatial_dice_7: 0.87781/0.82855, loss_spatial_ce_7: 6.99062/8.18245, loss_grounding_bce_7: 0.02421/0.12856, loss_grounding_dice_7: 0.31087/0.15945, loss_grounding_ce_7: 0.01096/0.03997, loss_mask_ce_8: 4.24634/1.69512, loss_mask_bce_8: 0.53381/0.59730, loss_mask_dice_8: 1.10796/0.62795, loss_spatial_bce_8: 1.17206/1.80151, loss_spatial_dice_8: 0.87741/0.90027, loss_spatial_ce_8: 4.38700/6.45167, loss_grounding_bce_8: 0.06461/0.34301, loss_grounding_dice_8: 0.63326/0.27353, loss_grounding_ce_8: 0.47679/0.24618, loss_mask_ce_9: 5.88673/4.12373, loss_mask_bce_9: 0.43416/0.66609, loss_mask_dice_9: 1.14512/0.71195, loss_spatial_bce_9: 1.45942/1.67195, loss_spatial_dice_9: 0.88071/0.89243, loss_spatial_ce_9: 4.69963/6.04873, loss_grounding_bce_9: 0.03765/0.56369, loss_grounding_dice_9: 0.62038/0.29732, loss_grounding_ce_9: 0.65219/0.43958] items per batch[64] items per second[13.88] total items[384] mini batches[ 6] memory[4180] epoch remaining[3:57:38] INFO:trainer.default_trainer:epochs[ 0] optim steps[7] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81545/0.77634, loss_mask_bce_0: 0.40560/0.20210, loss_mask_dice_0: 0.73375/0.44803, loss_spatial_bce_0: 1.07029/0.97150, loss_spatial_dice_0: 0.98247/0.77995, loss_spatial_ce_0: 5.55332/4.72629, loss_grounding_bce_0: 0.04672/0.06060, loss_grounding_dice_0: 0.12423/0.11975, loss_grounding_ce_0: 0.36790/0.08667, loss_mask_ce_1: 0.88293/0.76451, loss_mask_bce_1: 0.38143/0.20820, loss_mask_dice_1: 0.77732/0.44708, loss_spatial_bce_1: 1.19042/1.05935, loss_spatial_dice_1: 0.98352/0.77251, loss_spatial_ce_1: 5.23286/5.04058, loss_grounding_bce_1: 0.04803/0.06140, loss_grounding_dice_1: 0.15758/0.12609, loss_grounding_ce_1: 0.39093/0.08728, loss_mask_ce_2: 0.94952/0.79564, loss_mask_bce_2: 0.37380/0.19670, loss_mask_dice_2: 0.71664/0.44308, loss_spatial_bce_2: 1.27681/1.24223, loss_spatial_dice_2: 0.98575/0.77079, loss_spatial_ce_2: 6.30468/6.17011, loss_grounding_bce_2: 0.04438/0.06210, loss_grounding_dice_2: 0.12858/0.12258, loss_grounding_ce_2: 0.35958/0.08250, loss_mask_ce_3: 0.82620/0.82628, loss_mask_bce_3: 0.40276/0.20711, loss_mask_dice_3: 0.77257/0.45862, loss_spatial_bce_3: 1.51945/1.39646, loss_spatial_dice_3: 0.98772/0.76716, loss_spatial_ce_3: 6.95877/7.00256, loss_grounding_bce_3: 0.02250/0.06597, loss_grounding_dice_3: 0.12830/0.12923, loss_grounding_ce_3: 0.55659/0.11243, loss_mask_ce_4: 0.93543/0.87107, loss_mask_bce_4: 0.41197/0.33267, loss_mask_dice_4: 0.73942/0.49170, loss_spatial_bce_4: 1.25430/1.44591, loss_spatial_dice_4: 0.98459/0.75963, loss_spatial_ce_4: 6.18940/5.92769, loss_grounding_bce_4: 0.03187/0.07355, loss_grounding_dice_4: 0.15582/0.14767, loss_grounding_ce_4: 0.40123/0.07775, loss_mask_ce_5: 0.97771/1.02952, loss_mask_bce_5: 0.40238/0.24331, loss_mask_dice_5: 0.74479/0.44925, loss_spatial_bce_5: 1.43816/1.24341, loss_spatial_dice_5: 0.98503/0.77762, loss_spatial_ce_5: 6.27491/6.32099, loss_grounding_bce_5: 0.02408/0.08551, loss_grounding_dice_5: 0.14605/0.18343, loss_grounding_ce_5: 0.39758/0.06561, loss_mask_ce_6: 0.98401/1.07145, loss_mask_bce_6: 0.42040/0.28452, loss_mask_dice_6: 0.75008/0.46377, loss_spatial_bce_6: 1.39197/1.30788, loss_spatial_dice_6: 0.98662/0.78141, loss_spatial_ce_6: 6.30558/6.89644, loss_grounding_bce_6: 0.04358/0.07431, loss_grounding_dice_6: 0.16199/0.12687, loss_grounding_ce_6: 0.42950/0.06920, loss_mask_ce_7: 1.23026/1.24876, loss_mask_bce_7: 0.39569/0.33027, loss_mask_dice_7: 0.76712/0.53028, loss_spatial_bce_7: 1.21034/1.58605, loss_spatial_dice_7: 0.98705/0.85119, loss_spatial_ce_7: 6.33580/7.91864, loss_grounding_bce_7: 0.03169/0.11472, loss_grounding_dice_7: 0.14013/0.15669, loss_grounding_ce_7: 0.50676/0.10665, loss_mask_ce_8: 1.70501/1.69653, loss_mask_bce_8: 0.41901/0.57183, loss_mask_dice_8: 0.80536/0.65329, loss_spatial_bce_8: 1.35374/1.73755, loss_spatial_dice_8: 0.98406/0.91224, loss_spatial_ce_8: 6.28725/6.42818, loss_grounding_bce_8: 0.03641/0.29921, loss_grounding_dice_8: 0.15179/0.25614, loss_grounding_ce_8: 0.46838/0.27792, loss_mask_ce_9: 4.02584/4.10974, loss_mask_bce_9: 0.97623/0.71040, loss_mask_dice_9: 1.13504/0.77239, loss_spatial_bce_9: 1.48029/1.64457, loss_spatial_dice_9: 0.97507/0.90423, loss_spatial_ce_9: 6.84547/6.16255, loss_grounding_bce_9: 0.01706/0.48560, loss_grounding_dice_9: 0.12244/0.27234, loss_grounding_ce_9: 0.99313/0.51866] items per batch[64] items per second[9.56] total items[448] mini batches[ 7] memory[4180] epoch remaining[3:52:35] INFO:trainer.default_trainer:epochs[ 0] optim steps[8] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67434/0.76359, loss_mask_bce_0: 0.78148/0.27452, loss_mask_dice_0: 1.14633/0.53532, loss_spatial_bce_0: 1.47832/1.03485, loss_spatial_dice_0: 0.92374/0.79792, loss_spatial_ce_0: 6.85565/4.99246, loss_grounding_bce_0: 0.14080/0.07063, loss_grounding_dice_0: 0.10360/0.11773, loss_grounding_ce_0: 0.00014/0.07585, loss_mask_ce_1: 0.76194/0.76419, loss_mask_bce_1: 0.77586/0.27916, loss_mask_dice_1: 1.25787/0.54842, loss_spatial_bce_1: 1.44751/1.10787, loss_spatial_dice_1: 0.92541/0.79162, loss_spatial_ce_1: 6.53312/5.22714, loss_grounding_bce_1: 0.14307/0.07161, loss_grounding_dice_1: 0.10367/0.12329, loss_grounding_ce_1: 0.00008/0.07638, loss_mask_ce_2: 0.82361/0.79913, loss_mask_bce_2: 0.81344/0.27380, loss_mask_dice_2: 1.32235/0.55299, loss_spatial_bce_2: 1.95501/1.33133, loss_spatial_dice_2: 0.91489/0.78881, loss_spatial_ce_2: 8.23500/6.42822, loss_grounding_bce_2: 0.13455/0.07115, loss_grounding_dice_2: 0.10281/0.12011, loss_grounding_ce_2: 0.00003/0.07219, loss_mask_ce_3: 0.73661/0.81507, loss_mask_bce_3: 0.82426/0.28426, loss_mask_dice_3: 1.21127/0.55270, loss_spatial_bce_3: 2.26972/1.50562, loss_spatial_dice_3: 0.92186/0.78650, loss_spatial_ce_3: 10.12953/7.39344, loss_grounding_bce_3: 0.13540/0.07465, loss_grounding_dice_3: 0.10665/0.12641, loss_grounding_ce_3: 0.00014/0.09839, loss_mask_ce_4: 1.13660/0.90427, loss_mask_bce_4: 0.87401/0.40033, loss_mask_dice_4: 1.48958/0.61643, loss_spatial_bce_4: 1.90604/1.50343, loss_spatial_dice_4: 0.92539/0.78035, loss_spatial_ce_4: 9.62037/6.38928, loss_grounding_bce_4: 0.13689/0.08147, loss_grounding_dice_4: 0.10895/0.14283, loss_grounding_ce_4: 0.00039/0.06808, loss_mask_ce_5: 1.47016/1.08460, loss_mask_bce_5: 0.95688/0.33250, loss_mask_dice_5: 1.59661/0.59267, loss_spatial_bce_5: 2.30898/1.37660, loss_spatial_dice_5: 0.93717/0.79756, loss_spatial_ce_5: 9.16174/6.67608, loss_grounding_bce_5: 0.13909/0.09220, loss_grounding_dice_5: 0.11056/0.17432, loss_grounding_ce_5: 0.00009/0.05742, loss_mask_ce_6: 1.62365/1.14048, loss_mask_bce_6: 0.92567/0.36466, loss_mask_dice_6: 1.56489/0.60141, loss_spatial_bce_6: 2.21616/1.42141, loss_spatial_dice_6: 0.93928/0.80114, loss_spatial_ce_6: 9.21301/7.18601, loss_grounding_bce_6: 0.13891/0.08239, loss_grounding_dice_6: 0.10041/0.12357, loss_grounding_ce_6: 0.00006/0.06055, loss_mask_ce_7: 1.90593/1.33091, loss_mask_bce_7: 0.98047/0.41154, loss_mask_dice_7: 1.67573/0.67346, loss_spatial_bce_7: 1.98959/1.63650, loss_spatial_dice_7: 0.94879/0.86339, loss_spatial_ce_7: 8.16516/7.94946, loss_grounding_bce_7: 0.13748/0.11757, loss_grounding_dice_7: 0.09032/0.14839, loss_grounding_ce_7: 0.00000/0.09332, loss_mask_ce_8: 1.69678/1.69656, loss_mask_bce_8: 1.17657/0.64742, loss_mask_dice_8: 1.68309/0.78202, loss_spatial_bce_8: 1.63394/1.72459, loss_spatial_dice_8: 0.98003/0.92071, loss_spatial_ce_8: 7.24182/6.52989, loss_grounding_bce_8: 0.12661/0.27763, loss_grounding_dice_8: 0.08189/0.23436, loss_grounding_ce_8: 0.00009/0.24319, loss_mask_ce_9: 5.32589/4.26176, loss_mask_bce_9: 0.91812/0.73636, loss_mask_dice_9: 1.76423/0.89637, loss_spatial_bce_9: 1.62088/1.64161, loss_spatial_dice_9: 0.98890/0.91482, loss_spatial_ce_9: 6.35499/6.18660, loss_grounding_bce_9: 0.14288/0.44276, loss_grounding_dice_9: 0.08202/0.24855, loss_grounding_ce_9: 1.55121/0.64773] items per batch[64] items per second[12.19] total items[512] mini batches[ 8] memory[4293] epoch remaining[3:43:17] INFO:trainer.default_trainer:epochs[ 0] optim steps[9] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97172/0.78672, loss_mask_bce_0: 0.29921/0.27726, loss_mask_dice_0: 1.47282/0.63949, loss_spatial_bce_0: 0.46162/0.97116, loss_spatial_dice_0: 0.98331/0.81852, loss_spatial_ce_0: 2.26187/4.68906, loss_grounding_bce_0: 0.07318/0.07091, loss_grounding_dice_0: 0.45127/0.15479, loss_grounding_ce_0: 0.43929/0.11624, loss_mask_ce_1: 1.02554/0.79323, loss_mask_bce_1: 0.29415/0.28082, loss_mask_dice_1: 1.55962/0.66078, loss_spatial_bce_1: 0.65376/1.05741, loss_spatial_dice_1: 0.99313/0.81401, loss_spatial_ce_1: 2.70146/4.94651, loss_grounding_bce_1: 0.07036/0.07147, loss_grounding_dice_1: 0.43291/0.15769, loss_grounding_ce_1: 0.43020/0.11569, loss_mask_ce_2: 1.08967/0.83142, loss_mask_bce_2: 0.29265/0.27589, loss_mask_dice_2: 1.46985/0.65486, loss_spatial_bce_2: 0.86120/1.27909, loss_spatial_dice_2: 0.99246/0.81143, loss_spatial_ce_2: 3.83091/6.13963, loss_grounding_bce_2: 0.07232/0.07128, loss_grounding_dice_2: 0.44982/0.15674, loss_grounding_ce_2: 0.44466/0.11357, loss_mask_ce_3: 1.11786/0.84871, loss_mask_bce_3: 0.29287/0.28521, loss_mask_dice_3: 1.48044/0.65578, loss_spatial_bce_3: 1.12556/1.46339, loss_spatial_dice_3: 0.99389/0.80954, loss_spatial_ce_3: 4.81519/7.10696, loss_grounding_bce_3: 0.07189/0.07434, loss_grounding_dice_3: 0.48721/0.16650, loss_grounding_ce_3: 0.42805/0.13502, loss_mask_ce_4: 1.22236/0.93961, loss_mask_bce_4: 0.30918/0.39021, loss_mask_dice_4: 1.53613/0.71862, loss_spatial_bce_4: 0.68105/1.41205, loss_spatial_dice_4: 0.99310/0.80399, loss_spatial_ce_4: 4.49437/6.17873, loss_grounding_bce_4: 0.07457/0.08070, loss_grounding_dice_4: 0.46349/0.17846, loss_grounding_ce_4: 0.36124/0.10065, loss_mask_ce_5: 1.39296/1.11886, loss_mask_bce_5: 0.33779/0.33309, loss_mask_dice_5: 1.65147/0.71032, loss_spatial_bce_5: 0.46896/1.27575, loss_spatial_dice_5: 0.98346/0.81822, loss_spatial_ce_5: 4.30562/6.41270, loss_grounding_bce_5: 0.06282/0.08894, loss_grounding_dice_5: 0.44786/0.20472, loss_grounding_ce_5: 0.46035/0.10219, loss_mask_ce_6: 1.37365/1.16639, loss_mask_bce_6: 0.33216/0.36105, loss_mask_dice_6: 1.65271/0.71822, loss_spatial_bce_6: 0.55275/1.32489, loss_spatial_dice_6: 0.98942/0.82206, loss_spatial_ce_6: 5.08331/6.95237, loss_grounding_bce_6: 0.06966/0.08097, loss_grounding_dice_6: 0.47128/0.16220, loss_grounding_ce_6: 0.55472/0.11546, loss_mask_ce_7: 1.54985/1.35523, loss_mask_bce_7: 0.31660/0.40099, loss_mask_dice_7: 1.58619/0.77487, loss_spatial_bce_7: 0.59490/1.52076, loss_spatial_dice_7: 0.98761/0.87719, loss_spatial_ce_7: 4.60098/7.57741, loss_grounding_bce_7: 0.05775/0.11092, loss_grounding_dice_7: 0.43335/0.18005, loss_grounding_ce_7: 0.51421/0.14009, loss_mask_ce_8: 1.71356/1.69845, loss_mask_bce_8: 0.29190/0.60792, loss_mask_dice_8: 1.65600/0.87913, loss_spatial_bce_8: 0.69033/1.60968, loss_spatial_dice_8: 0.99564/0.92904, loss_spatial_ce_8: 4.00675/6.24954, loss_grounding_bce_8: 0.04387/0.25166, loss_grounding_dice_8: 0.48275/0.26195, loss_grounding_ce_8: 0.57042/0.27955, loss_mask_ce_9: 6.85956/4.55041, loss_mask_bce_9: 0.22132/0.67914, loss_mask_dice_9: 1.76907/0.99334, loss_spatial_bce_9: 0.51674/1.51663, loss_spatial_dice_9: 0.99160/0.92335, loss_spatial_ce_9: 6.97267/6.27394, loss_grounding_bce_9: 0.02177/0.39598, loss_grounding_dice_9: 0.43867/0.26967, loss_grounding_ce_9: 1.53638/0.74647] items per batch[64] items per second[18.62] total items[576] mini batches[ 9] memory[4603] epoch remaining[3:29:56] INFO:trainer.default_trainer:epochs[ 0] optim steps[10] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.51591/0.85964, loss_mask_bce_0: 0.98449/0.34799, loss_mask_dice_0: 2.00505/0.77604, loss_spatial_bce_0: 0.39657/0.91370, loss_spatial_dice_0: 0.88967/0.82564, loss_spatial_ce_0: 1.22765/4.34292, loss_grounding_bce_0: 0.01095/0.06491, loss_grounding_dice_0: 0.15092/0.15440, loss_grounding_ce_0: 0.00100/0.10471, loss_mask_ce_1: 1.41448/0.85535, loss_mask_bce_1: 0.98827/0.35157, loss_mask_dice_1: 2.03101/0.79780, loss_spatial_bce_1: 0.51835/1.00350, loss_spatial_dice_1: 0.87345/0.81995, loss_spatial_ce_1: 1.33523/4.58538, loss_grounding_bce_1: 0.01377/0.06570, loss_grounding_dice_1: 0.19155/0.16108, loss_grounding_ce_1: 0.00105/0.10423, loss_mask_ce_2: 1.45567/0.89384, loss_mask_bce_2: 0.96329/0.34463, loss_mask_dice_2: 2.06563/0.79594, loss_spatial_bce_2: 0.44960/1.19614, loss_spatial_dice_2: 0.87941/0.81823, loss_spatial_ce_2: 1.63323/5.68899, loss_grounding_bce_2: 0.01269/0.06542, loss_grounding_dice_2: 0.18444/0.15951, loss_grounding_ce_2: 0.00062/0.10228, loss_mask_ce_3: 1.63492/0.92733, loss_mask_bce_3: 0.96527/0.35322, loss_mask_dice_3: 2.01928/0.79213, loss_spatial_bce_3: 0.50102/1.36715, loss_spatial_dice_3: 0.91248/0.81983, loss_spatial_ce_3: 2.16077/6.61234, loss_grounding_bce_3: 0.00830/0.06774, loss_grounding_dice_3: 0.13513/0.16336, loss_grounding_ce_3: 0.00180/0.12170, loss_mask_ce_4: 1.50783/0.99643, loss_mask_bce_4: 0.99168/0.45035, loss_mask_dice_4: 2.08688/0.85545, loss_spatial_bce_4: 0.50120/1.32097, loss_spatial_dice_4: 0.94117/0.81771, loss_spatial_ce_4: 2.21408/5.78227, loss_grounding_bce_4: 0.00798/0.07343, loss_grounding_dice_4: 0.11173/0.17179, loss_grounding_ce_4: 0.00570/0.09116, loss_mask_ce_5: 1.64248/1.17122, loss_mask_bce_5: 0.97317/0.39710, loss_mask_dice_5: 2.03021/0.84231, loss_spatial_bce_5: 0.53641/1.20182, loss_spatial_dice_5: 0.96187/0.83258, loss_spatial_ce_5: 2.65631/6.03706, loss_grounding_bce_5: 0.00843/0.08089, loss_grounding_dice_5: 0.11121/0.19537, loss_grounding_ce_5: 0.02612/0.09458, loss_mask_ce_6: 1.78515/1.22826, loss_mask_bce_6: 1.00375/0.42532, loss_mask_dice_6: 2.22360/0.86876, loss_spatial_bce_6: 0.68050/1.26045, loss_spatial_dice_6: 0.96814/0.83667, loss_spatial_ce_6: 2.80742/6.53788, loss_grounding_bce_6: 0.00545/0.07342, loss_grounding_dice_6: 0.06691/0.15267, loss_grounding_ce_6: 0.00378/0.10429, loss_mask_ce_7: 1.81655/1.40137, loss_mask_bce_7: 1.04045/0.46494, loss_mask_dice_7: 2.38282/0.93567, loss_spatial_bce_7: 0.42752/1.41144, loss_spatial_dice_7: 0.97237/0.88671, loss_spatial_ce_7: 3.33166/7.15283, loss_grounding_bce_7: 0.00456/0.10028, loss_grounding_dice_7: 0.05919/0.16797, loss_grounding_ce_7: 0.21787/0.14786, loss_mask_ce_8: 2.26845/1.75545, loss_mask_bce_8: 1.36079/0.68321, loss_mask_dice_8: 3.01816/1.09303, loss_spatial_bce_8: 0.56240/1.50495, loss_spatial_dice_8: 0.97934/0.93407, loss_spatial_ce_8: 3.14450/5.93903, loss_grounding_bce_8: 0.00669/0.22716, loss_grounding_dice_8: 0.07369/0.24313, loss_grounding_ce_8: 0.03642/0.25524, loss_mask_ce_9: 6.23030/4.71840, loss_mask_bce_9: 1.50531/0.76175, loss_mask_dice_9: 3.23189/1.21719, loss_spatial_bce_9: 0.69072/1.43403, loss_spatial_dice_9: 0.97791/0.92880, loss_spatial_ce_9: 3.88738/6.03529, loss_grounding_bce_9: 0.03751/0.36014, loss_grounding_dice_9: 0.33730/0.27644, loss_grounding_ce_9: 6.29537/1.30136] items per batch[64] items per second[13.49] total items[640] mini batches[ 10] memory[4603] epoch remaining[3:23:12] INFO:trainer.default_trainer:epochs[ 0] optim steps[100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67309/0.77832, loss_mask_bce_0: 0.50206/0.28844, loss_mask_dice_0: 2.46159/0.94154, loss_spatial_bce_0: 0.05610/0.26769, loss_spatial_dice_0: 0.44941/0.49734, loss_spatial_ce_0: 0.19125/0.87355, loss_grounding_bce_0: 0.14365/0.08124, loss_grounding_dice_0: 0.22399/0.14251, loss_grounding_ce_0: 0.24178/0.15782, loss_mask_ce_1: 0.69184/0.77412, loss_mask_bce_1: 0.49393/0.29537, loss_mask_dice_1: 2.55573/0.94246, loss_spatial_bce_1: 0.05599/0.28035, loss_spatial_dice_1: 0.51233/0.51469, loss_spatial_ce_1: 0.20300/0.90365, loss_grounding_bce_1: 0.14873/0.08179, loss_grounding_dice_1: 0.21582/0.14241, loss_grounding_ce_1: 0.22521/0.16029, loss_mask_ce_2: 0.73122/0.79389, loss_mask_bce_2: 0.52339/0.28924, loss_mask_dice_2: 2.54139/0.94353, loss_spatial_bce_2: 0.05287/0.29788, loss_spatial_dice_2: 0.54627/0.52612, loss_spatial_ce_2: 0.21318/1.07856, loss_grounding_bce_2: 0.14126/0.08271, loss_grounding_dice_2: 0.21213/0.14788, loss_grounding_ce_2: 0.26043/0.15030, loss_mask_ce_3: 0.89133/0.79657, loss_mask_bce_3: 0.47964/0.28605, loss_mask_dice_3: 2.52539/0.95302, loss_spatial_bce_3: 0.05934/0.31862, loss_spatial_dice_3: 0.58958/0.53219, loss_spatial_ce_3: 0.32840/1.20533, loss_grounding_bce_3: 0.14435/0.08188, loss_grounding_dice_3: 0.21719/0.14300, loss_grounding_ce_3: 0.25810/0.15828, loss_mask_ce_4: 0.56184/0.81145, loss_mask_bce_4: 0.61959/0.29823, loss_mask_dice_4: 2.61034/0.96755, loss_spatial_bce_4: 0.06022/0.31768, loss_spatial_dice_4: 0.59567/0.56182, loss_spatial_ce_4: 0.33859/1.13893, loss_grounding_bce_4: 0.15084/0.08145, loss_grounding_dice_4: 0.23213/0.14786, loss_grounding_ce_4: 0.27134/0.20682, loss_mask_ce_5: 0.51459/0.82022, loss_mask_bce_5: 0.61090/0.29525, loss_mask_dice_5: 2.82304/0.97865, loss_spatial_bce_5: 0.06516/0.31175, loss_spatial_dice_5: 0.66237/0.58670, loss_spatial_ce_5: 0.35728/1.20148, loss_grounding_bce_5: 0.14367/0.08316, loss_grounding_dice_5: 0.24358/0.15551, loss_grounding_ce_5: 0.23602/0.23933, loss_mask_ce_6: 0.75304/0.87847, loss_mask_bce_6: 0.51562/0.29356, loss_mask_dice_6: 2.95339/1.02679, loss_spatial_bce_6: 0.06649/0.33358, loss_spatial_dice_6: 0.71905/0.60255, loss_spatial_ce_6: 0.47062/1.33281, loss_grounding_bce_6: 0.14825/0.08372, loss_grounding_dice_6: 0.25666/0.15324, loss_grounding_ce_6: 0.20772/0.23332, loss_mask_ce_7: 0.68151/0.96169, loss_mask_bce_7: 0.63669/0.30936, loss_mask_dice_7: 3.16689/1.08217, loss_spatial_bce_7: 0.07844/0.35906, loss_spatial_dice_7: 0.76548/0.65690, loss_spatial_ce_7: 0.50671/1.44242, loss_grounding_bce_7: 0.15447/0.08921, loss_grounding_dice_7: 0.28163/0.16066, loss_grounding_ce_7: 0.14559/0.30134, loss_mask_ce_8: 0.88488/1.19674, loss_mask_bce_8: 0.60063/0.35680, loss_mask_dice_8: 3.83403/1.20644, loss_spatial_bce_8: 0.24248/0.40410, loss_spatial_dice_8: 0.92249/0.73311, loss_spatial_ce_8: 0.78944/1.41757, loss_grounding_bce_8: 0.17719/0.11341, loss_grounding_dice_8: 0.35714/0.19413, loss_grounding_ce_8: 0.28937/0.40506, loss_mask_ce_9: 3.57288/4.24103, loss_mask_bce_9: 0.97153/0.49712, loss_mask_dice_9: 5.86912/1.57571, loss_spatial_bce_9: 0.40057/0.72537, loss_spatial_dice_9: 0.98607/0.85237, loss_spatial_ce_9: 1.80693/3.02882, loss_grounding_bce_9: 0.23151/0.19368, loss_grounding_dice_9: 0.47763/0.27531, loss_grounding_ce_9: 0.41989/1.16627] items per batch[64] items per second[0.28] total items[6400] mini batches[ 100] memory[4924] epoch remaining[1:25:20] INFO:trainer.default_trainer:epochs[ 0] optim steps[200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28686/0.77544, loss_mask_bce_0: 0.15222/0.29541, loss_mask_dice_0: 2.68482/0.98662, loss_spatial_bce_0: 0.01842/0.18525, loss_spatial_dice_0: 0.38967/0.36649, loss_spatial_ce_0: 0.11906/0.51304, loss_grounding_bce_0: 0.02517/0.07549, loss_grounding_dice_0: 0.23705/0.14646, loss_grounding_ce_0: 0.00746/0.22911, loss_mask_ce_1: 1.39026/0.78081, loss_mask_bce_1: 0.16062/0.29899, loss_mask_dice_1: 2.86458/0.99299, loss_spatial_bce_1: 0.01853/0.19171, loss_spatial_dice_1: 0.37907/0.37737, loss_spatial_ce_1: 0.10437/0.52290, loss_grounding_bce_1: 0.02399/0.07600, loss_grounding_dice_1: 0.24838/0.14545, loss_grounding_ce_1: 0.00845/0.23690, loss_mask_ce_2: 1.40282/0.78465, loss_mask_bce_2: 0.13903/0.29677, loss_mask_dice_2: 2.57287/0.99233, loss_spatial_bce_2: 0.01519/0.19910, loss_spatial_dice_2: 0.34357/0.38786, loss_spatial_ce_2: 0.10463/0.61194, loss_grounding_bce_2: 0.02703/0.07544, loss_grounding_dice_2: 0.26673/0.14675, loss_grounding_ce_2: 0.01021/0.22815, loss_mask_ce_3: 1.46628/0.77967, loss_mask_bce_3: 0.14113/0.29666, loss_mask_dice_3: 2.50939/0.99986, loss_spatial_bce_3: 0.01416/0.21239, loss_spatial_dice_3: 0.34831/0.39589, loss_spatial_ce_3: 0.14132/0.68483, loss_grounding_bce_3: 0.02674/0.07441, loss_grounding_dice_3: 0.26057/0.14376, loss_grounding_ce_3: 0.01030/0.23223, loss_mask_ce_4: 1.26590/0.79539, loss_mask_bce_4: 0.14081/0.30241, loss_mask_dice_4: 3.01016/1.02389, loss_spatial_bce_4: 0.02008/0.21609, loss_spatial_dice_4: 0.43732/0.41792, loss_spatial_ce_4: 0.12091/0.65803, loss_grounding_bce_4: 0.02742/0.07829, loss_grounding_dice_4: 0.25593/0.15266, loss_grounding_ce_4: 0.01768/0.26077, loss_mask_ce_5: 1.52436/0.81144, loss_mask_bce_5: 0.13522/0.30253, loss_mask_dice_5: 2.63341/1.04105, loss_spatial_bce_5: 0.01679/0.21628, loss_spatial_dice_5: 0.45327/0.44148, loss_spatial_ce_5: 0.22755/0.69943, loss_grounding_bce_5: 0.03314/0.07840, loss_grounding_dice_5: 0.25928/0.15482, loss_grounding_ce_5: 0.01382/0.29386, loss_mask_ce_6: 1.23661/0.84968, loss_mask_bce_6: 0.14148/0.30283, loss_mask_dice_6: 2.69891/1.06600, loss_spatial_bce_6: 0.02045/0.22764, loss_spatial_dice_6: 0.47921/0.45861, loss_spatial_ce_6: 0.22917/0.80385, loss_grounding_bce_6: 0.03090/0.07908, loss_grounding_dice_6: 0.27664/0.15395, loss_grounding_ce_6: 0.00630/0.31225, loss_mask_ce_7: 1.42640/0.93387, loss_mask_bce_7: 0.17309/0.31533, loss_mask_dice_7: 3.21582/1.11198, loss_spatial_bce_7: 0.02426/0.24559, loss_spatial_dice_7: 0.52668/0.50818, loss_spatial_ce_7: 0.14303/0.89352, loss_grounding_bce_7: 0.01999/0.08131, loss_grounding_dice_7: 0.22873/0.15783, loss_grounding_ce_7: 0.05786/0.39515, loss_mask_ce_8: 2.24187/1.17074, loss_mask_bce_8: 0.14927/0.35198, loss_mask_dice_8: 3.12995/1.21985, loss_spatial_bce_8: 0.02574/0.28942, loss_spatial_dice_8: 0.62516/0.60979, loss_spatial_ce_8: 0.48304/0.92976, loss_grounding_bce_8: 0.05086/0.09648, loss_grounding_dice_8: 0.39930/0.18020, loss_grounding_ce_8: 0.24237/0.50530, loss_mask_ce_9: 5.38288/4.12147, loss_mask_bce_9: 0.17493/0.44246, loss_mask_dice_9: 4.37978/1.75800, loss_spatial_bce_9: 0.08569/0.55020, loss_spatial_dice_9: 0.94763/0.83699, loss_spatial_ce_9: 2.08683/2.34230, loss_grounding_bce_9: 0.05308/0.14807, loss_grounding_dice_9: 0.65173/0.27193, loss_grounding_ce_9: 0.41510/1.21387] items per batch[64] items per second[0.30] total items[12800] mini batches[ 200] memory[4924] epoch remaining[1:08:47] INFO:trainer.default_trainer:epochs[ 0] optim steps[300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95461/0.75544, loss_mask_bce_0: 0.39380/0.28919, loss_mask_dice_0: 0.49250/1.00243, loss_spatial_bce_0: 0.21350/0.15801, loss_spatial_dice_0: 0.24455/0.32360, loss_spatial_ce_0: 0.02516/0.39811, loss_grounding_bce_0: 0.02692/0.07483, loss_grounding_dice_0: 0.04987/0.14972, loss_grounding_ce_0: 3.13343/0.24200, loss_mask_ce_1: 0.93225/0.76372, loss_mask_bce_1: 0.39692/0.29219, loss_mask_dice_1: 0.49099/1.01061, loss_spatial_bce_1: 0.20874/0.16216, loss_spatial_dice_1: 0.22847/0.33161, loss_spatial_ce_1: 0.01365/0.39867, loss_grounding_bce_1: 0.02665/0.07541, loss_grounding_dice_1: 0.05231/0.15208, loss_grounding_ce_1: 3.16915/0.23756, loss_mask_ce_2: 0.92885/0.76716, loss_mask_bce_2: 0.48527/0.29049, loss_mask_dice_2: 0.47628/1.01613, loss_spatial_bce_2: 0.20914/0.16515, loss_spatial_dice_2: 0.23836/0.33865, loss_spatial_ce_2: 0.02437/0.46407, loss_grounding_bce_2: 0.02558/0.07468, loss_grounding_dice_2: 0.05439/0.15168, loss_grounding_ce_2: 3.18650/0.23770, loss_mask_ce_3: 0.99019/0.76811, loss_mask_bce_3: 0.41755/0.29085, loss_mask_dice_3: 0.51337/1.02399, loss_spatial_bce_3: 0.25058/0.17531, loss_spatial_dice_3: 0.29477/0.34562, loss_spatial_ce_3: 0.03280/0.51066, loss_grounding_bce_3: 0.02709/0.07419, loss_grounding_dice_3: 0.05303/0.15145, loss_grounding_ce_3: 3.31603/0.23124, loss_mask_ce_4: 1.21690/0.76552, loss_mask_bce_4: 0.41251/0.29609, loss_mask_dice_4: 0.48936/1.03690, loss_spatial_bce_4: 0.22492/0.17952, loss_spatial_dice_4: 0.28102/0.36334, loss_spatial_ce_4: 0.02430/0.49169, loss_grounding_bce_4: 0.02777/0.07692, loss_grounding_dice_4: 0.06103/0.15616, loss_grounding_ce_4: 3.20265/0.26532, loss_mask_ce_5: 0.94606/0.79154, loss_mask_bce_5: 0.41209/0.29672, loss_mask_dice_5: 0.58782/1.04376, loss_spatial_bce_5: 0.20050/0.18043, loss_spatial_dice_5: 0.27321/0.37922, loss_spatial_ce_5: 0.14134/0.52960, loss_grounding_bce_5: 0.02886/0.07813, loss_grounding_dice_5: 0.06011/0.15973, loss_grounding_ce_5: 2.23688/0.29322, loss_mask_ce_6: 1.32338/0.82963, loss_mask_bce_6: 0.42392/0.29614, loss_mask_dice_6: 0.54823/1.07315, loss_spatial_bce_6: 0.17297/0.18934, loss_spatial_dice_6: 0.25015/0.39411, loss_spatial_ce_6: 0.27622/0.61308, loss_grounding_bce_6: 0.03944/0.07876, loss_grounding_dice_6: 0.06637/0.15769, loss_grounding_ce_6: 3.04648/0.31127, loss_mask_ce_7: 1.47862/0.90657, loss_mask_bce_7: 0.43945/0.30405, loss_mask_dice_7: 0.50172/1.12604, loss_spatial_bce_7: 0.47705/0.20437, loss_spatial_dice_7: 0.29841/0.43360, loss_spatial_ce_7: 0.14617/0.68788, loss_grounding_bce_7: 0.03580/0.07988, loss_grounding_dice_7: 0.06053/0.16287, loss_grounding_ce_7: 2.83462/0.38796, loss_mask_ce_8: 1.94384/1.12745, loss_mask_bce_8: 0.49621/0.33436, loss_mask_dice_8: 0.49494/1.20989, loss_spatial_bce_8: 0.20290/0.23973, loss_spatial_dice_8: 0.36354/0.53039, loss_spatial_ce_8: 0.34410/0.73908, loss_grounding_bce_8: 0.03794/0.08934, loss_grounding_dice_8: 0.07112/0.17650, loss_grounding_ce_8: 3.01630/0.55218, loss_mask_ce_9: 4.20523/3.91925, loss_mask_bce_9: 0.58663/0.40748, loss_mask_dice_9: 0.93557/1.72409, loss_spatial_bce_9: 0.43670/0.49776, loss_spatial_dice_9: 0.87274/0.82765, loss_spatial_ce_9: 2.57714/2.09514, loss_grounding_bce_9: 0.04859/0.13032, loss_grounding_dice_9: 0.13928/0.26736, loss_grounding_ce_9: 3.57339/1.15744] items per batch[64] items per second[0.31] total items[19200] mini batches[ 300] memory[4924] epoch remaining[1:00:25] INFO:trainer.default_trainer:epochs[ 0] optim steps[400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.54988/0.77511, loss_mask_bce_0: 0.23078/0.29239, loss_mask_dice_0: 0.42310/1.03730, loss_spatial_bce_0: 0.10703/0.14399, loss_spatial_dice_0: 0.17495/0.29804, loss_spatial_ce_0: 0.06791/0.33466, loss_grounding_bce_0: 0.00784/0.07716, loss_grounding_dice_0: 0.17186/0.15077, loss_grounding_ce_0: 1.04498/0.23658, loss_mask_ce_1: 1.50815/0.77884, loss_mask_bce_1: 0.22202/0.29540, loss_mask_dice_1: 0.44479/1.04066, loss_spatial_bce_1: 0.08807/0.14772, loss_spatial_dice_1: 0.15300/0.30487, loss_spatial_ce_1: 0.04280/0.33419, loss_grounding_bce_1: 0.00542/0.07704, loss_grounding_dice_1: 0.14870/0.15525, loss_grounding_ce_1: 1.00416/0.23970, loss_mask_ce_2: 1.76070/0.78110, loss_mask_bce_2: 0.22510/0.29355, loss_mask_dice_2: 0.45501/1.05323, loss_spatial_bce_2: 0.08225/0.14935, loss_spatial_dice_2: 0.18257/0.30980, loss_spatial_ce_2: 0.04670/0.38614, loss_grounding_bce_2: 0.00871/0.07700, loss_grounding_dice_2: 0.17241/0.15449, loss_grounding_ce_2: 0.95499/0.24181, loss_mask_ce_3: 1.61247/0.78230, loss_mask_bce_3: 0.23854/0.29684, loss_mask_dice_3: 0.43533/1.05543, loss_spatial_bce_3: 0.07757/0.15727, loss_spatial_dice_3: 0.17290/0.31558, loss_spatial_ce_3: 0.01839/0.42052, loss_grounding_bce_3: 0.00597/0.07733, loss_grounding_dice_3: 0.13893/0.15374, loss_grounding_ce_3: 1.04510/0.23082, loss_mask_ce_4: 1.72120/0.78597, loss_mask_bce_4: 0.24963/0.30053, loss_mask_dice_4: 0.55821/1.06599, loss_spatial_bce_4: 0.07959/0.16194, loss_spatial_dice_4: 0.14347/0.33221, loss_spatial_ce_4: 0.10035/0.40759, loss_grounding_bce_4: 0.00451/0.07946, loss_grounding_dice_4: 0.14548/0.15700, loss_grounding_ce_4: 1.01558/0.25918, loss_mask_ce_5: 2.23132/0.80969, loss_mask_bce_5: 0.22685/0.29958, loss_mask_dice_5: 0.44157/1.07770, loss_spatial_bce_5: 0.06656/0.16367, loss_spatial_dice_5: 0.16238/0.34488, loss_spatial_ce_5: 0.11279/0.43915, loss_grounding_bce_5: 0.00392/0.07933, loss_grounding_dice_5: 0.09480/0.16102, loss_grounding_ce_5: 1.16833/0.29423, loss_mask_ce_6: 2.22555/0.84762, loss_mask_bce_6: 0.22687/0.29994, loss_mask_dice_6: 0.45006/1.10217, loss_spatial_bce_6: 0.07083/0.17104, loss_spatial_dice_6: 0.17157/0.35743, loss_spatial_ce_6: 0.09013/0.50849, loss_grounding_bce_6: 0.00739/0.08141, loss_grounding_dice_6: 0.20746/0.15959, loss_grounding_ce_6: 1.04154/0.31525, loss_mask_ce_7: 2.42153/0.91681, loss_mask_bce_7: 0.16773/0.30883, loss_mask_dice_7: 0.46271/1.15756, loss_spatial_bce_7: 0.05750/0.18744, loss_spatial_dice_7: 0.24314/0.39475, loss_spatial_ce_7: 0.34093/0.57782, loss_grounding_bce_7: 0.00379/0.08308, loss_grounding_dice_7: 0.15361/0.16454, loss_grounding_ce_7: 1.08268/0.40002, loss_mask_ce_8: 2.26168/1.14145, loss_mask_bce_8: 0.19576/0.33425, loss_mask_dice_8: 0.48536/1.24045, loss_spatial_bce_8: 0.08905/0.21775, loss_spatial_dice_8: 0.29182/0.48559, loss_spatial_ce_8: 0.32584/0.62405, loss_grounding_bce_8: 0.00334/0.08990, loss_grounding_dice_8: 0.18959/0.17766, loss_grounding_ce_8: 1.16031/0.55441, loss_mask_ce_9: 3.86436/3.85995, loss_mask_bce_9: 0.18309/0.39343, loss_mask_dice_9: 0.83033/1.80191, loss_spatial_bce_9: 0.39904/0.46254, loss_spatial_dice_9: 0.82197/0.82333, loss_spatial_ce_9: 1.19690/1.94167, loss_grounding_bce_9: 0.00471/0.12219, loss_grounding_dice_9: 0.34470/0.26951, loss_grounding_ce_9: 1.23291/1.06407] items per batch[64] items per second[0.32] total items[25600] mini batches[ 400] memory[4924] epoch remaining[0:54:10] INFO:trainer.default_trainer:epochs[ 0] optim steps[500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34901/0.79755, loss_mask_bce_0: 0.10523/0.29402, loss_mask_dice_0: 0.63803/1.02573, loss_spatial_bce_0: 0.02763/0.13507, loss_spatial_dice_0: 0.15576/0.28198, loss_spatial_ce_0: 0.02174/0.29463, loss_grounding_bce_0: 0.01954/0.07656, loss_grounding_dice_0: 0.13019/0.15217, loss_grounding_ce_0: 0.48568/0.25311, loss_mask_ce_1: 0.32478/0.80593, loss_mask_bce_1: 0.10073/0.29441, loss_mask_dice_1: 0.65751/1.02444, loss_spatial_bce_1: 0.03059/0.13869, loss_spatial_dice_1: 0.15460/0.28853, loss_spatial_ce_1: 0.05861/0.29374, loss_grounding_bce_1: 0.02041/0.07625, loss_grounding_dice_1: 0.13859/0.15622, loss_grounding_ce_1: 0.48881/0.25153, loss_mask_ce_2: 0.32467/0.80937, loss_mask_bce_2: 0.10042/0.29275, loss_mask_dice_2: 0.60537/1.03374, loss_spatial_bce_2: 0.02974/0.13995, loss_spatial_dice_2: 0.15713/0.29240, loss_spatial_ce_2: 0.08402/0.33294, loss_grounding_bce_2: 0.01919/0.07704, loss_grounding_dice_2: 0.13149/0.15608, loss_grounding_ce_2: 0.49404/0.25367, loss_mask_ce_3: 0.35814/0.80919, loss_mask_bce_3: 0.10873/0.29698, loss_mask_dice_3: 0.63618/1.03702, loss_spatial_bce_3: 0.02548/0.14635, loss_spatial_dice_3: 0.14448/0.29754, loss_spatial_ce_3: 0.05529/0.36525, loss_grounding_bce_3: 0.02019/0.07748, loss_grounding_dice_3: 0.12777/0.15586, loss_grounding_ce_3: 0.47048/0.24235, loss_mask_ce_4: 0.34260/0.81490, loss_mask_bce_4: 0.10902/0.30004, loss_mask_dice_4: 0.58590/1.05188, loss_spatial_bce_4: 0.03702/0.15085, loss_spatial_dice_4: 0.15884/0.31223, loss_spatial_ce_4: 0.05314/0.35157, loss_grounding_bce_4: 0.02216/0.07985, loss_grounding_dice_4: 0.12574/0.15885, loss_grounding_ce_4: 0.46581/0.27344, loss_mask_ce_5: 0.35659/0.83768, loss_mask_bce_5: 0.09875/0.30020, loss_mask_dice_5: 0.61306/1.06038, loss_spatial_bce_5: 0.03278/0.15253, loss_spatial_dice_5: 0.16860/0.32287, loss_spatial_ce_5: 0.06363/0.38053, loss_grounding_bce_5: 0.02273/0.07929, loss_grounding_dice_5: 0.13383/0.16362, loss_grounding_ce_5: 0.47958/0.30670, loss_mask_ce_6: 0.46183/0.87009, loss_mask_bce_6: 0.09508/0.30088, loss_mask_dice_6: 0.60533/1.07676, loss_spatial_bce_6: 0.03362/0.16016, loss_spatial_dice_6: 0.17194/0.33339, loss_spatial_ce_6: 0.03944/0.44284, loss_grounding_bce_6: 0.02217/0.08199, loss_grounding_dice_6: 0.12597/0.16146, loss_grounding_ce_6: 0.60974/0.32805, loss_mask_ce_7: 0.44367/0.94461, loss_mask_bce_7: 0.09898/0.30835, loss_mask_dice_7: 0.71666/1.13535, loss_spatial_bce_7: 0.04387/0.17670, loss_spatial_dice_7: 0.17628/0.36904, loss_spatial_ce_7: 0.05903/0.50283, loss_grounding_bce_7: 0.02046/0.08300, loss_grounding_dice_7: 0.13869/0.16570, loss_grounding_ce_7: 0.50878/0.41858, loss_mask_ce_8: 0.54407/1.16420, loss_mask_bce_8: 0.10296/0.33302, loss_mask_dice_8: 0.76526/1.21542, loss_spatial_bce_8: 0.08669/0.20376, loss_spatial_dice_8: 0.30730/0.45319, loss_spatial_ce_8: 0.14766/0.54850, loss_grounding_bce_8: 0.02134/0.08918, loss_grounding_dice_8: 0.19740/0.17855, loss_grounding_ce_8: 0.54256/0.58466, loss_mask_ce_9: 2.15565/3.83830, loss_mask_bce_9: 0.12633/0.39146, loss_mask_dice_9: 1.24744/1.81583, loss_spatial_bce_9: 0.15454/0.43775, loss_spatial_dice_9: 0.80830/0.82253, loss_spatial_ce_9: 1.50927/1.84858, loss_grounding_bce_9: 0.02387/0.11959, loss_grounding_dice_9: 0.25015/0.27351, loss_grounding_ce_9: 0.54800/1.05006] items per batch[64] items per second[0.32] total items[32000] mini batches[ 500] memory[4924] epoch remaining[0:49:12] INFO:trainer.default_trainer:epochs[ 0] optim steps[600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85087/0.80306, loss_mask_bce_0: 0.47880/0.29760, loss_mask_dice_0: 1.12020/1.04139, loss_spatial_bce_0: 0.07582/0.12646, loss_spatial_dice_0: 0.15306/0.27063, loss_spatial_ce_0: 0.07047/0.26431, loss_grounding_bce_0: 0.06872/0.07589, loss_grounding_dice_0: 0.07566/0.15169, loss_grounding_ce_0: 0.35449/0.25580, loss_mask_ce_1: 0.51402/0.80828, loss_mask_bce_1: 0.49803/0.29756, loss_mask_dice_1: 1.23642/1.04477, loss_spatial_bce_1: 0.07714/0.12962, loss_spatial_dice_1: 0.15354/0.27686, loss_spatial_ce_1: 0.05790/0.26358, loss_grounding_bce_1: 0.07095/0.07552, loss_grounding_dice_1: 0.07975/0.15537, loss_grounding_ce_1: 0.26571/0.25518, loss_mask_ce_2: 0.75762/0.81510, loss_mask_bce_2: 0.48997/0.29659, loss_mask_dice_2: 1.16111/1.04897, loss_spatial_bce_2: 0.08149/0.13050, loss_spatial_dice_2: 0.15578/0.28062, loss_spatial_ce_2: 0.08566/0.29718, loss_grounding_bce_2: 0.06885/0.07644, loss_grounding_dice_2: 0.06992/0.15544, loss_grounding_ce_2: 0.36314/0.25664, loss_mask_ce_3: 0.43413/0.81140, loss_mask_bce_3: 0.56880/0.30123, loss_mask_dice_3: 1.28527/1.05023, loss_spatial_bce_3: 0.07893/0.13542, loss_spatial_dice_3: 0.16148/0.28471, loss_spatial_ce_3: 0.16602/0.32472, loss_grounding_bce_3: 0.07021/0.07683, loss_grounding_dice_3: 0.08659/0.15542, loss_grounding_ce_3: 0.20716/0.24698, loss_mask_ce_4: 0.53170/0.81985, loss_mask_bce_4: 0.49721/0.30263, loss_mask_dice_4: 1.20568/1.06648, loss_spatial_bce_4: 0.08230/0.14044, loss_spatial_dice_4: 0.16909/0.29813, loss_spatial_ce_4: 0.08356/0.31617, loss_grounding_bce_4: 0.06894/0.07873, loss_grounding_dice_4: 0.08645/0.15809, loss_grounding_ce_4: 0.23767/0.27395, loss_mask_ce_5: 0.58295/0.84325, loss_mask_bce_5: 0.51583/0.30274, loss_mask_dice_5: 1.29207/1.07504, loss_spatial_bce_5: 0.07535/0.14196, loss_spatial_dice_5: 0.17817/0.30819, loss_spatial_ce_5: 0.07831/0.34218, loss_grounding_bce_5: 0.06982/0.07825, loss_grounding_dice_5: 0.08865/0.16149, loss_grounding_ce_5: 0.32101/0.30397, loss_mask_ce_6: 0.51964/0.86956, loss_mask_bce_6: 0.51939/0.30485, loss_mask_dice_6: 1.30106/1.09190, loss_spatial_bce_6: 0.08005/0.14909, loss_spatial_dice_6: 0.17923/0.31807, loss_spatial_ce_6: 0.09010/0.39694, loss_grounding_bce_6: 0.07067/0.08098, loss_grounding_dice_6: 0.08848/0.15972, loss_grounding_ce_6: 0.30092/0.32386, loss_mask_ce_7: 0.81232/0.94269, loss_mask_bce_7: 0.49634/0.31152, loss_mask_dice_7: 1.30387/1.14540, loss_spatial_bce_7: 0.07350/0.16595, loss_spatial_dice_7: 0.19798/0.35211, loss_spatial_ce_7: 0.18711/0.46002, loss_grounding_bce_7: 0.06901/0.08233, loss_grounding_dice_7: 0.11329/0.16513, loss_grounding_ce_7: 0.33341/0.41456, loss_mask_ce_8: 1.34509/1.15780, loss_mask_bce_8: 0.41174/0.33321, loss_mask_dice_8: 1.19583/1.23253, loss_spatial_bce_8: 0.22797/0.19220, loss_spatial_dice_8: 0.31661/0.43363, loss_spatial_ce_8: 0.23911/0.50630, loss_grounding_bce_8: 0.07264/0.08773, loss_grounding_dice_8: 0.07394/0.17718, loss_grounding_ce_8: 0.27855/0.57019, loss_mask_ce_9: 4.93155/3.81581, loss_mask_bce_9: 0.43072/0.38725, loss_mask_dice_9: 2.45314/1.84760, loss_spatial_bce_9: 0.35840/0.42530, loss_spatial_dice_9: 0.87366/0.82180, loss_spatial_ce_9: 1.35101/1.78070, loss_grounding_bce_9: 0.08957/0.11476, loss_grounding_dice_9: 0.36436/0.27066, loss_grounding_ce_9: 0.08372/1.02314] items per batch[64] items per second[0.32] total items[38400] mini batches[ 600] memory[4924] epoch remaining[0:44:38] INFO:trainer.default_trainer:epochs[ 0] optim steps[700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26547/0.79465, loss_mask_bce_0: 0.12649/0.29463, loss_mask_dice_0: 0.19577/1.01644, loss_spatial_bce_0: 0.04274/0.12217, loss_spatial_dice_0: 0.09331/0.26251, loss_spatial_ce_0: 0.00471/0.24655, loss_grounding_bce_0: 0.03746/0.07685, loss_grounding_dice_0: 0.06993/0.15263, loss_grounding_ce_0: 0.22487/0.25967, loss_mask_ce_1: 0.27404/0.80410, loss_mask_bce_1: 0.12371/0.29486, loss_mask_dice_1: 0.21028/1.01964, loss_spatial_bce_1: 0.04021/0.12499, loss_spatial_dice_1: 0.07551/0.26798, loss_spatial_ce_1: 0.00546/0.24737, loss_grounding_bce_1: 0.03820/0.07640, loss_grounding_dice_1: 0.06139/0.15531, loss_grounding_ce_1: 0.24862/0.25578, loss_mask_ce_2: 0.26199/0.80606, loss_mask_bce_2: 0.13468/0.29465, loss_mask_dice_2: 0.37264/1.02232, loss_spatial_bce_2: 0.03790/0.12547, loss_spatial_dice_2: 0.07542/0.27104, loss_spatial_ce_2: 0.00538/0.27564, loss_grounding_bce_2: 0.03808/0.07721, loss_grounding_dice_2: 0.04894/0.15638, loss_grounding_ce_2: 0.24033/0.25513, loss_mask_ce_3: 0.27127/0.80377, loss_mask_bce_3: 0.12017/0.29834, loss_mask_dice_3: 0.23534/1.02541, loss_spatial_bce_3: 0.03425/0.12981, loss_spatial_dice_3: 0.06056/0.27485, loss_spatial_ce_3: 0.01039/0.30069, loss_grounding_bce_3: 0.03816/0.07776, loss_grounding_dice_3: 0.17381/0.15532, loss_grounding_ce_3: 0.26997/0.24824, loss_mask_ce_4: 0.26066/0.81411, loss_mask_bce_4: 0.12971/0.29891, loss_mask_dice_4: 0.37474/1.04185, loss_spatial_bce_4: 0.03522/0.13478, loss_spatial_dice_4: 0.06085/0.28716, loss_spatial_ce_4: 0.00678/0.29735, loss_grounding_bce_4: 0.03668/0.07941, loss_grounding_dice_4: 0.09908/0.15838, loss_grounding_ce_4: 0.23764/0.26926, loss_mask_ce_5: 0.21909/0.83481, loss_mask_bce_5: 0.12292/0.29915, loss_mask_dice_5: 0.34917/1.04443, loss_spatial_bce_5: 0.03821/0.13607, loss_spatial_dice_5: 0.06952/0.29673, loss_spatial_ce_5: 0.01191/0.31745, loss_grounding_bce_5: 0.03606/0.07899, loss_grounding_dice_5: 0.06574/0.16123, loss_grounding_ce_5: 0.24519/0.30307, loss_mask_ce_6: 0.26081/0.86755, loss_mask_bce_6: 0.11874/0.30113, loss_mask_dice_6: 0.43348/1.05970, loss_spatial_bce_6: 0.04017/0.14298, loss_spatial_dice_6: 0.09572/0.30516, loss_spatial_ce_6: 0.05095/0.36573, loss_grounding_bce_6: 0.03581/0.08182, loss_grounding_dice_6: 0.06803/0.16072, loss_grounding_ce_6: 0.28435/0.32478, loss_mask_ce_7: 0.41307/0.93555, loss_mask_bce_7: 0.13381/0.30741, loss_mask_dice_7: 0.45783/1.11212, loss_spatial_bce_7: 0.04197/0.15953, loss_spatial_dice_7: 0.13208/0.33838, loss_spatial_ce_7: 0.10298/0.42954, loss_grounding_bce_7: 0.04081/0.08286, loss_grounding_dice_7: 0.18474/0.16622, loss_grounding_ce_7: 0.37690/0.41166, loss_mask_ce_8: 0.38652/1.13107, loss_mask_bce_8: 0.12287/0.32728, loss_mask_dice_8: 0.26599/1.19845, loss_spatial_bce_8: 0.06332/0.18579, loss_spatial_dice_8: 0.10386/0.41587, loss_spatial_ce_8: 0.12522/0.47241, loss_grounding_bce_8: 0.03746/0.08692, loss_grounding_dice_8: 0.09890/0.17662, loss_grounding_ce_8: 0.40384/0.56728, loss_mask_ce_9: 3.34964/3.76067, loss_mask_bce_9: 0.21849/0.37665, loss_mask_dice_9: 1.07177/1.80168, loss_spatial_bce_9: 0.33178/0.42187, loss_spatial_dice_9: 0.70739/0.81895, loss_spatial_ce_9: 2.40942/1.74058, loss_grounding_bce_9: 0.06499/0.11167, loss_grounding_dice_9: 0.26150/0.26654, loss_grounding_ce_9: 0.58292/0.98027] items per batch[64] items per second[0.33] total items[44800] mini batches[ 700] memory[4924] epoch remaining[0:40:22] INFO:trainer.default_trainer:epochs[ 0] optim steps[800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.00865/0.79790, loss_mask_bce_0: 0.38824/0.29506, loss_mask_dice_0: 0.47936/1.01896, loss_spatial_bce_0: 0.07364/0.11850, loss_spatial_dice_0: 0.17650/0.25471, loss_spatial_ce_0: 0.47442/0.23033, loss_grounding_bce_0: 0.06544/0.07672, loss_grounding_dice_0: 0.12465/0.15316, loss_grounding_ce_0: 0.54804/0.26266, loss_mask_ce_1: 1.01714/0.80795, loss_mask_bce_1: 0.37466/0.29483, loss_mask_dice_1: 0.46401/1.02048, loss_spatial_bce_1: 0.05914/0.12100, loss_spatial_dice_1: 0.13340/0.25950, loss_spatial_ce_1: 0.47729/0.23251, loss_grounding_bce_1: 0.06687/0.07682, loss_grounding_dice_1: 0.11936/0.15597, loss_grounding_ce_1: 0.52185/0.25466, loss_mask_ce_2: 0.91497/0.81089, loss_mask_bce_2: 0.33458/0.29488, loss_mask_dice_2: 0.47881/1.02418, loss_spatial_bce_2: 0.05381/0.12117, loss_spatial_dice_2: 0.13538/0.26294, loss_spatial_ce_2: 0.42645/0.25681, loss_grounding_bce_2: 0.06538/0.07731, loss_grounding_dice_2: 0.12555/0.15638, loss_grounding_ce_2: 0.49854/0.25487, loss_mask_ce_3: 0.95883/0.80688, loss_mask_bce_3: 0.38920/0.29860, loss_mask_dice_3: 0.46865/1.02510, loss_spatial_bce_3: 0.05606/0.12546, loss_spatial_dice_3: 0.13599/0.26585, loss_spatial_ce_3: 0.46397/0.27992, loss_grounding_bce_3: 0.09086/0.07796, loss_grounding_dice_3: 0.14761/0.15484, loss_grounding_ce_3: 0.48637/0.25028, loss_mask_ce_4: 0.89077/0.81885, loss_mask_bce_4: 0.39342/0.29857, loss_mask_dice_4: 0.42444/1.03813, loss_spatial_bce_4: 0.05066/0.13035, loss_spatial_dice_4: 0.13423/0.27774, loss_spatial_ce_4: 0.53432/0.27785, loss_grounding_bce_4: 0.07476/0.07939, loss_grounding_dice_4: 0.11283/0.15800, loss_grounding_ce_4: 0.51266/0.27230, loss_mask_ce_5: 0.95125/0.83374, loss_mask_bce_5: 0.30812/0.29925, loss_mask_dice_5: 0.40569/1.04642, loss_spatial_bce_5: 0.05191/0.13161, loss_spatial_dice_5: 0.12092/0.28704, loss_spatial_ce_5: 0.63311/0.29634, loss_grounding_bce_5: 0.05710/0.07942, loss_grounding_dice_5: 0.10474/0.16128, loss_grounding_ce_5: 0.58113/0.30682, loss_mask_ce_6: 1.58068/0.87167, loss_mask_bce_6: 0.13004/0.30054, loss_mask_dice_6: 0.34423/1.06054, loss_spatial_bce_6: 0.05044/0.13857, loss_spatial_dice_6: 0.11743/0.29432, loss_spatial_ce_6: 0.50416/0.34233, loss_grounding_bce_6: 0.04995/0.08199, loss_grounding_dice_6: 0.08916/0.16095, loss_grounding_ce_6: 0.55574/0.32183, loss_mask_ce_7: 1.22480/0.93831, loss_mask_bce_7: 0.13873/0.30878, loss_mask_dice_7: 0.27531/1.11146, loss_spatial_bce_7: 0.06402/0.15567, loss_spatial_dice_7: 0.15775/0.32694, loss_spatial_ce_7: 0.47849/0.40253, loss_grounding_bce_7: 0.06930/0.08313, loss_grounding_dice_7: 0.09500/0.16603, loss_grounding_ce_7: 0.53585/0.40884, loss_mask_ce_8: 1.11323/1.13291, loss_mask_bce_8: 0.22645/0.32875, loss_mask_dice_8: 0.37103/1.19752, loss_spatial_bce_8: 0.19673/0.18068, loss_spatial_dice_8: 0.26266/0.40193, loss_spatial_ce_8: 0.77631/0.44843, loss_grounding_bce_8: 0.06577/0.08730, loss_grounding_dice_8: 0.10674/0.17502, loss_grounding_ce_8: 0.35715/0.56714, loss_mask_ce_9: 2.41352/3.74626, loss_mask_bce_9: 0.34696/0.37558, loss_mask_dice_9: 0.51487/1.81475, loss_spatial_bce_9: 0.37544/0.41483, loss_spatial_dice_9: 0.72282/0.81735, loss_spatial_ce_9: 1.21427/1.69885, loss_grounding_bce_9: 0.08720/0.10945, loss_grounding_dice_9: 0.18214/0.26464, loss_grounding_ce_9: 0.43661/0.95790] items per batch[64] items per second[0.33] total items[51200] mini batches[ 800] memory[4924] epoch remaining[0:36:18] INFO:trainer.default_trainer:epochs[ 0] optim steps[900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06890/0.81022, loss_mask_bce_0: 0.03424/0.29634, loss_mask_dice_0: 0.03078/1.01100, loss_spatial_bce_0: 0.02245/0.11564, loss_spatial_dice_0: 0.02451/0.24819, loss_spatial_ce_0: 0.03691/0.21748, loss_grounding_bce_0: 0.02706/0.07689, loss_grounding_dice_0: 0.02063/0.15249, loss_grounding_ce_0: 0.00258/0.26214, loss_mask_ce_1: 0.06209/0.81862, loss_mask_bce_1: 0.03959/0.29657, loss_mask_dice_1: 0.03565/1.01382, loss_spatial_bce_1: 0.02485/0.11835, loss_spatial_dice_1: 0.02536/0.25290, loss_spatial_ce_1: 0.05082/0.21847, loss_grounding_bce_1: 0.02901/0.07673, loss_grounding_dice_1: 0.02271/0.15533, loss_grounding_ce_1: 0.00156/0.25669, loss_mask_ce_2: 0.04980/0.82368, loss_mask_bce_2: 0.03297/0.29573, loss_mask_dice_2: 0.03126/1.01797, loss_spatial_bce_2: 0.02549/0.11810, loss_spatial_dice_2: 0.02914/0.25612, loss_spatial_ce_2: 0.03812/0.24045, loss_grounding_bce_2: 0.02582/0.07698, loss_grounding_dice_2: 0.02176/0.15579, loss_grounding_ce_2: 0.00217/0.25566, loss_mask_ce_3: 0.04652/0.81711, loss_mask_bce_3: 0.03375/0.30001, loss_mask_dice_3: 0.03161/1.01789, loss_spatial_bce_3: 0.02269/0.12235, loss_spatial_dice_3: 0.02283/0.25892, loss_spatial_ce_3: 0.09074/0.26144, loss_grounding_bce_3: 0.02755/0.07772, loss_grounding_dice_3: 0.02242/0.15475, loss_grounding_ce_3: 0.00311/0.25376, loss_mask_ce_4: 0.05304/0.82704, loss_mask_bce_4: 0.03451/0.30043, loss_mask_dice_4: 0.03327/1.03064, loss_spatial_bce_4: 0.02386/0.12668, loss_spatial_dice_4: 0.02407/0.26987, loss_spatial_ce_4: 0.11287/0.26204, loss_grounding_bce_4: 0.02847/0.07877, loss_grounding_dice_4: 0.02213/0.15644, loss_grounding_ce_4: 0.00594/0.27104, loss_mask_ce_5: 0.03601/0.83931, loss_mask_bce_5: 0.03407/0.30103, loss_mask_dice_5: 0.03241/1.04118, loss_spatial_bce_5: 0.02775/0.12848, loss_spatial_dice_5: 0.03269/0.27852, loss_spatial_ce_5: 0.09816/0.27949, loss_grounding_bce_5: 0.02756/0.07904, loss_grounding_dice_5: 0.02255/0.16047, loss_grounding_ce_5: 0.00072/0.30386, loss_mask_ce_6: 0.05138/0.87759, loss_mask_bce_6: 0.03309/0.30129, loss_mask_dice_6: 0.02906/1.05393, loss_spatial_bce_6: 0.07288/0.13493, loss_spatial_dice_6: 0.09282/0.28520, loss_spatial_ce_6: 0.13958/0.32238, loss_grounding_bce_6: 0.02655/0.08148, loss_grounding_dice_6: 0.02188/0.16028, loss_grounding_ce_6: 0.00224/0.31879, loss_mask_ce_7: 0.08343/0.95006, loss_mask_bce_7: 0.03575/0.31047, loss_mask_dice_7: 0.03142/1.10236, loss_spatial_bce_7: 0.24647/0.15194, loss_spatial_dice_7: 0.18066/0.31685, loss_spatial_ce_7: 0.02081/0.38158, loss_grounding_bce_7: 0.02956/0.08303, loss_grounding_dice_7: 0.02371/0.16541, loss_grounding_ce_7: 0.00465/0.40271, loss_mask_ce_8: 0.06442/1.13710, loss_mask_bce_8: 0.04160/0.32860, loss_mask_dice_8: 0.03421/1.18721, loss_spatial_bce_8: 0.50935/0.17603, loss_spatial_dice_8: 0.17844/0.38874, loss_spatial_ce_8: 0.14519/0.42656, loss_grounding_bce_8: 0.03239/0.08710, loss_grounding_dice_8: 0.02297/0.17408, loss_grounding_ce_8: 0.00226/0.56202, loss_mask_ce_9: 1.96195/3.72836, loss_mask_bce_9: 0.03551/0.37372, loss_mask_dice_9: 0.04382/1.80982, loss_spatial_bce_9: 0.59178/0.40850, loss_spatial_dice_9: 0.62121/0.81563, loss_spatial_ce_9: 0.94997/1.66974, loss_grounding_bce_9: 0.02281/0.10804, loss_grounding_dice_9: 0.02268/0.26256, loss_grounding_ce_9: 0.06767/0.95162] items per batch[64] items per second[0.32] total items[57600] mini batches[ 900] memory[4924] epoch remaining[0:32:32] INFO:trainer.default_trainer:epochs[ 0] optim steps[1000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36175/0.81817, loss_mask_bce_0: 0.04357/0.29479, loss_mask_dice_0: 0.61217/1.01748, loss_spatial_bce_0: 0.01561/0.11238, loss_spatial_dice_0: 0.19483/0.24254, loss_spatial_ce_0: 0.01219/0.20447, loss_grounding_bce_0: 0.00518/0.07695, loss_grounding_dice_0: 0.04388/0.15231, loss_grounding_ce_0: 0.12764/0.26200, loss_mask_ce_1: 0.29306/0.82435, loss_mask_bce_1: 0.04459/0.29512, loss_mask_dice_1: 0.70963/1.02059, loss_spatial_bce_1: 0.01377/0.11519, loss_spatial_dice_1: 0.24165/0.24695, loss_spatial_ce_1: 0.01331/0.20645, loss_grounding_bce_1: 0.00651/0.07696, loss_grounding_dice_1: 0.04665/0.15514, loss_grounding_ce_1: 0.10315/0.25623, loss_mask_ce_2: 0.27590/0.83087, loss_mask_bce_2: 0.04327/0.29464, loss_mask_dice_2: 0.53828/1.02831, loss_spatial_bce_2: 0.01264/0.11481, loss_spatial_dice_2: 0.22078/0.25018, loss_spatial_ce_2: 0.00855/0.22617, loss_grounding_bce_2: 0.00625/0.07708, loss_grounding_dice_2: 0.04357/0.15527, loss_grounding_ce_2: 0.14314/0.25849, loss_mask_ce_3: 0.25304/0.82104, loss_mask_bce_3: 0.03845/0.29867, loss_mask_dice_3: 0.54318/1.02501, loss_spatial_bce_3: 0.01384/0.11892, loss_spatial_dice_3: 0.17768/0.25252, loss_spatial_ce_3: 0.02859/0.24462, loss_grounding_bce_3: 0.00575/0.07780, loss_grounding_dice_3: 0.04255/0.15418, loss_grounding_ce_3: 0.14600/0.25468, loss_mask_ce_4: 0.20669/0.83041, loss_mask_bce_4: 0.04075/0.29962, loss_mask_dice_4: 0.77915/1.03944, loss_spatial_bce_4: 0.01406/0.12244, loss_spatial_dice_4: 0.19257/0.26323, loss_spatial_ce_4: 0.06979/0.24764, loss_grounding_bce_4: 0.00612/0.07885, loss_grounding_dice_4: 0.04342/0.15644, loss_grounding_ce_4: 0.17112/0.27467, loss_mask_ce_5: 0.17523/0.84401, loss_mask_bce_5: 0.04441/0.29991, loss_mask_dice_5: 0.71753/1.05122, loss_spatial_bce_5: 0.01460/0.12482, loss_spatial_dice_5: 0.22647/0.27131, loss_spatial_ce_5: 0.09510/0.26261, loss_grounding_bce_5: 0.00613/0.07913, loss_grounding_dice_5: 0.04322/0.16012, loss_grounding_ce_5: 0.26801/0.30400, loss_mask_ce_6: 0.21563/0.88144, loss_mask_bce_6: 0.04462/0.30005, loss_mask_dice_6: 0.78578/1.06222, loss_spatial_bce_6: 0.01008/0.13073, loss_spatial_dice_6: 0.24279/0.27741, loss_spatial_ce_6: 0.08522/0.30373, loss_grounding_bce_6: 0.00525/0.08151, loss_grounding_dice_6: 0.04214/0.16008, loss_grounding_ce_6: 0.25133/0.31895, loss_mask_ce_7: 0.40624/0.95292, loss_mask_bce_7: 0.04181/0.30981, loss_mask_dice_7: 0.70333/1.11111, loss_spatial_bce_7: 0.01427/0.14636, loss_spatial_dice_7: 0.20521/0.30815, loss_spatial_ce_7: 0.09768/0.36429, loss_grounding_bce_7: 0.00520/0.08289, loss_grounding_dice_7: 0.03375/0.16536, loss_grounding_ce_7: 0.38725/0.40265, loss_mask_ce_8: 0.91700/1.14082, loss_mask_bce_8: 0.03655/0.32798, loss_mask_dice_8: 0.79468/1.19726, loss_spatial_bce_8: 0.02314/0.17136, loss_spatial_dice_8: 0.26409/0.37833, loss_spatial_ce_8: 0.11192/0.40863, loss_grounding_bce_8: 0.00559/0.08690, loss_grounding_dice_8: 0.03524/0.17432, loss_grounding_ce_8: 0.50834/0.55295, loss_mask_ce_9: 3.07030/3.71436, loss_mask_bce_9: 0.03992/0.37181, loss_mask_dice_9: 0.85844/1.82591, loss_spatial_bce_9: 0.11316/0.40425, loss_spatial_dice_9: 0.69412/0.81140, loss_spatial_ce_9: 1.21160/1.64782, loss_grounding_bce_9: 0.00800/0.10730, loss_grounding_dice_9: 0.06138/0.26321, loss_grounding_ce_9: 0.78145/0.92445] items per batch[64] items per second[0.33] total items[64000] mini batches[ 1000] memory[4924] epoch remaining[0:28:45] INFO:trainer.default_trainer:epochs[ 0] optim steps[1100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11611/0.81548, loss_mask_bce_0: 0.13548/0.29626, loss_mask_dice_0: 0.19760/1.02132, loss_spatial_bce_0: 0.10123/0.10982, loss_spatial_dice_0: 0.14358/0.23883, loss_spatial_ce_0: 0.00156/0.19436, loss_grounding_bce_0: 0.09970/0.07675, loss_grounding_dice_0: 0.14637/0.15261, loss_grounding_ce_0: 0.01085/0.25813, loss_mask_ce_1: 0.11226/0.82211, loss_mask_bce_1: 0.13946/0.29655, loss_mask_dice_1: 0.18568/1.02439, loss_spatial_bce_1: 0.09534/0.11220, loss_spatial_dice_1: 0.12797/0.24304, loss_spatial_ce_1: 0.00114/0.19575, loss_grounding_bce_1: 0.09571/0.07681, loss_grounding_dice_1: 0.13744/0.15510, loss_grounding_ce_1: 0.00959/0.25251, loss_mask_ce_2: 0.12000/0.82889, loss_mask_bce_2: 0.12999/0.29607, loss_mask_dice_2: 0.20775/1.03294, loss_spatial_bce_2: 0.10200/0.11183, loss_spatial_dice_2: 0.13212/0.24609, loss_spatial_ce_2: 0.00043/0.21438, loss_grounding_bce_2: 0.09845/0.07692, loss_grounding_dice_2: 0.14356/0.15495, loss_grounding_ce_2: 0.01008/0.25579, loss_mask_ce_3: 0.13380/0.82075, loss_mask_bce_3: 0.12621/0.29980, loss_mask_dice_3: 0.18610/1.02778, loss_spatial_bce_3: 0.09606/0.11569, loss_spatial_dice_3: 0.12737/0.24819, loss_spatial_ce_3: 0.00082/0.23082, loss_grounding_bce_3: 0.09704/0.07760, loss_grounding_dice_3: 0.14443/0.15358, loss_grounding_ce_3: 0.00870/0.25205, loss_mask_ce_4: 0.15399/0.82961, loss_mask_bce_4: 0.13187/0.30115, loss_mask_dice_4: 0.19329/1.04277, loss_spatial_bce_4: 0.10312/0.11912, loss_spatial_dice_4: 0.14560/0.25846, loss_spatial_ce_4: 0.00043/0.23491, loss_grounding_bce_4: 0.09711/0.07856, loss_grounding_dice_4: 0.14458/0.15596, loss_grounding_ce_4: 0.00912/0.27051, loss_mask_ce_5: 0.17103/0.84625, loss_mask_bce_5: 0.13354/0.30136, loss_mask_dice_5: 0.19819/1.05746, loss_spatial_bce_5: 0.09936/0.12129, loss_spatial_dice_5: 0.12116/0.26577, loss_spatial_ce_5: 0.00026/0.24941, loss_grounding_bce_5: 0.09870/0.07874, loss_grounding_dice_5: 0.12356/0.15926, loss_grounding_ce_5: 0.00707/0.29983, loss_mask_ce_6: 0.21113/0.87935, loss_mask_bce_6: 0.12880/0.30128, loss_mask_dice_6: 0.19340/1.06747, loss_spatial_bce_6: 0.10470/0.12700, loss_spatial_dice_6: 0.12803/0.27147, loss_spatial_ce_6: 0.00096/0.28784, loss_grounding_bce_6: 0.09674/0.08114, loss_grounding_dice_6: 0.13875/0.15896, loss_grounding_ce_6: 0.01415/0.31465, loss_mask_ce_7: 0.13598/0.95044, loss_mask_bce_7: 0.13632/0.31089, loss_mask_dice_7: 0.19453/1.11390, loss_spatial_bce_7: 0.10960/0.14205, loss_spatial_dice_7: 0.14493/0.30194, loss_spatial_ce_7: 0.07618/0.34888, loss_grounding_bce_7: 0.09944/0.08231, loss_grounding_dice_7: 0.14937/0.16450, loss_grounding_ce_7: 0.01147/0.39637, loss_mask_ce_8: 0.31174/1.13458, loss_mask_bce_8: 0.13147/0.32859, loss_mask_dice_8: 0.19681/1.19692, loss_spatial_bce_8: 0.11923/0.16624, loss_spatial_dice_8: 0.17645/0.37004, loss_spatial_ce_8: 0.12562/0.39340, loss_grounding_bce_8: 0.10042/0.08653, loss_grounding_dice_8: 0.14881/0.17378, loss_grounding_ce_8: 0.08028/0.54446, loss_mask_ce_9: 1.83632/3.69344, loss_mask_bce_9: 0.15113/0.37108, loss_mask_dice_9: 0.28157/1.83282, loss_spatial_bce_9: 0.52128/0.40016, loss_spatial_dice_9: 0.83137/0.81118, loss_spatial_ce_9: 1.29442/1.62647, loss_grounding_bce_9: 0.11393/0.10562, loss_grounding_dice_9: 0.22826/0.26057, loss_grounding_ce_9: 0.14039/0.91563] items per batch[64] items per second[0.34] total items[70400] mini batches[ 1100] memory[4924] epoch remaining[0:25:02] INFO:trainer.default_trainer:epochs[ 0] optim steps[1200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89166/0.80796, loss_mask_bce_0: 0.35145/0.29481, loss_mask_dice_0: 0.28819/1.01576, loss_spatial_bce_0: 0.27754/0.10763, loss_spatial_dice_0: 0.19165/0.23342, loss_spatial_ce_0: 0.39611/0.18450, loss_grounding_bce_0: 0.07304/0.07562, loss_grounding_dice_0: 0.12703/0.15066, loss_grounding_ce_0: 0.00161/0.25159, loss_mask_ce_1: 0.91681/0.81485, loss_mask_bce_1: 0.35288/0.29489, loss_mask_dice_1: 0.28473/1.01808, loss_spatial_bce_1: 0.28901/0.10980, loss_spatial_dice_1: 0.19736/0.23754, loss_spatial_ce_1: 0.39665/0.18672, loss_grounding_bce_1: 0.07494/0.07560, loss_grounding_dice_1: 0.13127/0.15299, loss_grounding_ce_1: 0.00235/0.24666, loss_mask_ce_2: 0.91564/0.82282, loss_mask_bce_2: 0.33714/0.29460, loss_mask_dice_2: 0.26714/1.02713, loss_spatial_bce_2: 0.27873/0.10961, loss_spatial_dice_2: 0.20357/0.24062, loss_spatial_ce_2: 0.37635/0.20333, loss_grounding_bce_2: 0.08122/0.07573, loss_grounding_dice_2: 0.11839/0.15284, loss_grounding_ce_2: 0.00286/0.24901, loss_mask_ce_3: 0.89924/0.81418, loss_mask_bce_3: 0.36899/0.29812, loss_mask_dice_3: 0.28593/1.02258, loss_spatial_bce_3: 0.27420/0.11321, loss_spatial_dice_3: 0.19069/0.24236, loss_spatial_ce_3: 0.42188/0.21869, loss_grounding_bce_3: 0.07744/0.07635, loss_grounding_dice_3: 0.12606/0.15130, loss_grounding_ce_3: 0.00367/0.24538, loss_mask_ce_4: 0.93599/0.82211, loss_mask_bce_4: 0.34961/0.29909, loss_mask_dice_4: 0.28567/1.03845, loss_spatial_bce_4: 0.28807/0.11689, loss_spatial_dice_4: 0.22680/0.25240, loss_spatial_ce_4: 0.44750/0.22448, loss_grounding_bce_4: 0.07613/0.07736, loss_grounding_dice_4: 0.10941/0.15377, loss_grounding_ce_4: 0.00353/0.26246, loss_mask_ce_5: 0.79910/0.84067, loss_mask_bce_5: 0.33561/0.29916, loss_mask_dice_5: 0.29767/1.05115, loss_spatial_bce_5: 0.31265/0.11876, loss_spatial_dice_5: 0.22895/0.25934, loss_spatial_ce_5: 0.40584/0.23684, loss_grounding_bce_5: 0.08021/0.07759, loss_grounding_dice_5: 0.13061/0.15734, loss_grounding_ce_5: 0.00495/0.29084, loss_mask_ce_6: 0.86577/0.87346, loss_mask_bce_6: 0.36333/0.29906, loss_mask_dice_6: 0.29872/1.05921, loss_spatial_bce_6: 0.32120/0.12438, loss_spatial_dice_6: 0.26877/0.26488, loss_spatial_ce_6: 0.41555/0.27356, loss_grounding_bce_6: 0.07561/0.07966, loss_grounding_dice_6: 0.12364/0.15637, loss_grounding_ce_6: 0.00078/0.30907, loss_mask_ce_7: 0.98700/0.94627, loss_mask_bce_7: 0.34360/0.30851, loss_mask_dice_7: 0.28420/1.10665, loss_spatial_bce_7: 0.49195/0.13945, loss_spatial_dice_7: 0.44753/0.29521, loss_spatial_ce_7: 0.43907/0.33311, loss_grounding_bce_7: 0.06984/0.08097, loss_grounding_dice_7: 0.09649/0.16222, loss_grounding_ce_7: 0.00184/0.38906, loss_mask_ce_8: 1.02189/1.12731, loss_mask_bce_8: 0.30289/0.32638, loss_mask_dice_8: 0.29336/1.19253, loss_spatial_bce_8: 0.48606/0.16278, loss_spatial_dice_8: 0.48562/0.36025, loss_spatial_ce_8: 0.50131/0.37690, loss_grounding_bce_8: 0.07872/0.08508, loss_grounding_dice_8: 0.13839/0.17199, loss_grounding_ce_8: 0.12185/0.53196, loss_mask_ce_9: 2.66209/3.68380, loss_mask_bce_9: 0.31904/0.36709, loss_mask_dice_9: 0.49319/1.81897, loss_spatial_bce_9: 0.51250/0.39684, loss_spatial_dice_9: 0.55210/0.80824, loss_spatial_ce_9: 1.01751/1.59947, loss_grounding_bce_9: 0.07530/0.10336, loss_grounding_dice_9: 0.09594/0.25759, loss_grounding_ce_9: 0.38453/0.90547] items per batch[64] items per second[0.33] total items[76800] mini batches[ 1200] memory[4924] epoch remaining[0:21:30] INFO:trainer.default_trainer:epochs[ 0] optim steps[1300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24983/0.81212, loss_mask_bce_0: 0.00768/0.29424, loss_mask_dice_0: 0.00293/1.02015, loss_spatial_bce_0: 0.01589/0.10684, loss_spatial_dice_0: 0.00606/0.23140, loss_spatial_ce_0: 0.00001/0.17757, loss_grounding_bce_0: 0.00486/0.07549, loss_grounding_dice_0: 0.00183/0.15140, loss_grounding_ce_0: 0.09943/0.25530, loss_mask_ce_1: 0.23697/0.81883, loss_mask_bce_1: 0.00807/0.29445, loss_mask_dice_1: 0.00330/1.02133, loss_spatial_bce_1: 0.01683/0.10881, loss_spatial_dice_1: 0.00682/0.23545, loss_spatial_ce_1: 0.00000/0.17958, loss_grounding_bce_1: 0.00494/0.07554, loss_grounding_dice_1: 0.00194/0.15339, loss_grounding_ce_1: 0.09739/0.24972, loss_mask_ce_2: 0.29033/0.82838, loss_mask_bce_2: 0.00827/0.29420, loss_mask_dice_2: 0.00332/1.03105, loss_spatial_bce_2: 0.02010/0.10862, loss_spatial_dice_2: 0.00820/0.23834, loss_spatial_ce_2: 0.00000/0.19555, loss_grounding_bce_2: 0.00557/0.07570, loss_grounding_dice_2: 0.00224/0.15309, loss_grounding_ce_2: 0.10704/0.25252, loss_mask_ce_3: 0.22379/0.81708, loss_mask_bce_3: 0.00847/0.29786, loss_mask_dice_3: 0.00351/1.02744, loss_spatial_bce_3: 0.01716/0.11217, loss_spatial_dice_3: 0.00657/0.23957, loss_spatial_ce_3: 0.00002/0.20957, loss_grounding_bce_3: 0.00580/0.07626, loss_grounding_dice_3: 0.00231/0.15198, loss_grounding_ce_3: 0.10610/0.24985, loss_mask_ce_4: 0.24468/0.82544, loss_mask_bce_4: 0.00938/0.29913, loss_mask_dice_4: 0.00384/1.04339, loss_spatial_bce_4: 0.01807/0.11547, loss_spatial_dice_4: 0.00702/0.24933, loss_spatial_ce_4: 0.00004/0.21497, loss_grounding_bce_4: 0.00682/0.07734, loss_grounding_dice_4: 0.00280/0.15482, loss_grounding_ce_4: 0.09264/0.26654, loss_mask_ce_5: 0.24845/0.84162, loss_mask_bce_5: 0.00943/0.29973, loss_mask_dice_5: 0.00389/1.05690, loss_spatial_bce_5: 0.01495/0.11753, loss_spatial_dice_5: 0.00545/0.25613, loss_spatial_ce_5: 0.00005/0.22731, loss_grounding_bce_5: 0.00644/0.07775, loss_grounding_dice_5: 0.00275/0.15807, loss_grounding_ce_5: 0.09209/0.29335, loss_mask_ce_6: 0.24140/0.87337, loss_mask_bce_6: 0.00856/0.29978, loss_mask_dice_6: 0.00363/1.06398, loss_spatial_bce_6: 0.01903/0.12306, loss_spatial_dice_6: 0.00776/0.26139, loss_spatial_ce_6: 0.00133/0.26183, loss_grounding_bce_6: 0.00633/0.07951, loss_grounding_dice_6: 0.00271/0.15730, loss_grounding_ce_6: 0.08676/0.31206, loss_mask_ce_7: 0.28213/0.94474, loss_mask_bce_7: 0.00861/0.30948, loss_mask_dice_7: 0.00347/1.11184, loss_spatial_bce_7: 0.02053/0.13800, loss_spatial_dice_7: 0.00828/0.29142, loss_spatial_ce_7: 0.07797/0.32049, loss_grounding_bce_7: 0.00560/0.08125, loss_grounding_dice_7: 0.00222/0.16313, loss_grounding_ce_7: 0.12138/0.38983, loss_mask_ce_8: 0.29737/1.12699, loss_mask_bce_8: 0.00671/0.32657, loss_mask_dice_8: 0.00251/1.19582, loss_spatial_bce_8: 0.01164/0.16035, loss_spatial_dice_8: 0.00350/0.35422, loss_spatial_ce_8: 0.21650/0.36616, loss_grounding_bce_8: 0.00442/0.08516, loss_grounding_dice_8: 0.00171/0.17256, loss_grounding_ce_8: 0.14732/0.53224, loss_mask_ce_9: 1.85925/3.68711, loss_mask_bce_9: 0.01815/0.36671, loss_mask_dice_9: 0.03387/1.82236, loss_spatial_bce_9: 0.71455/0.39168, loss_spatial_dice_9: 0.34171/0.80702, loss_spatial_ce_9: 1.57697/1.58319, loss_grounding_bce_9: 0.01362/0.10307, loss_grounding_dice_9: 0.13861/0.25856, loss_grounding_ce_9: 0.27992/0.90571] items per batch[64] items per second[0.34] total items[83200] mini batches[ 1300] memory[4924] epoch remaining[0:17:57] INFO:trainer.default_trainer:epochs[ 0] optim steps[1400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57345/0.81328, loss_mask_bce_0: 0.44080/0.29481, loss_mask_dice_0: 0.77967/1.03332, loss_spatial_bce_0: 0.04765/0.10547, loss_spatial_dice_0: 0.10036/0.22951, loss_spatial_ce_0: 0.03872/0.17137, loss_grounding_bce_0: 0.13949/0.07535, loss_grounding_dice_0: 0.14873/0.15097, loss_grounding_ce_0: 0.03783/0.25557, loss_mask_ce_1: 0.90087/0.82015, loss_mask_bce_1: 0.40090/0.29490, loss_mask_dice_1: 0.70911/1.03552, loss_spatial_bce_1: 0.04529/0.10737, loss_spatial_dice_1: 0.09480/0.23370, loss_spatial_ce_1: 0.03770/0.17326, loss_grounding_bce_1: 0.12759/0.07513, loss_grounding_dice_1: 0.10760/0.15267, loss_grounding_ce_1: 0.05098/0.25159, loss_mask_ce_2: 0.56210/0.82989, loss_mask_bce_2: 0.42328/0.29476, loss_mask_dice_2: 0.72032/1.04758, loss_spatial_bce_2: 0.04224/0.10701, loss_spatial_dice_2: 0.09004/0.23597, loss_spatial_ce_2: 0.03486/0.18864, loss_grounding_bce_2: 0.13697/0.07529, loss_grounding_dice_2: 0.14239/0.15227, loss_grounding_ce_2: 0.05660/0.25452, loss_mask_ce_3: 0.52736/0.81868, loss_mask_bce_3: 0.42215/0.29799, loss_mask_dice_3: 0.79267/1.04049, loss_spatial_bce_3: 0.04747/0.11035, loss_spatial_dice_3: 0.09520/0.23749, loss_spatial_ce_3: 0.03232/0.20173, loss_grounding_bce_3: 0.15139/0.07568, loss_grounding_dice_3: 0.14172/0.15201, loss_grounding_ce_3: 0.04896/0.25206, loss_mask_ce_4: 0.58370/0.82656, loss_mask_bce_4: 0.44079/0.29963, loss_mask_dice_4: 0.83382/1.05705, loss_spatial_bce_4: 0.05067/0.11351, loss_spatial_dice_4: 0.09813/0.24684, loss_spatial_ce_4: 0.03407/0.20701, loss_grounding_bce_4: 0.14737/0.07691, loss_grounding_dice_4: 0.14849/0.15469, loss_grounding_ce_4: 0.06624/0.26845, loss_mask_ce_5: 1.14335/0.84396, loss_mask_bce_5: 0.44124/0.30045, loss_mask_dice_5: 0.81669/1.07228, loss_spatial_bce_5: 0.04929/0.11542, loss_spatial_dice_5: 0.10472/0.25390, loss_spatial_ce_5: 0.04292/0.21883, loss_grounding_bce_5: 0.15525/0.07734, loss_grounding_dice_5: 0.15142/0.15729, loss_grounding_ce_5: 0.07411/0.29458, loss_mask_ce_6: 0.67725/0.87359, loss_mask_bce_6: 0.43660/0.30045, loss_mask_dice_6: 0.81886/1.07722, loss_spatial_bce_6: 0.05305/0.12088, loss_spatial_dice_6: 0.10174/0.25912, loss_spatial_ce_6: 0.07050/0.25166, loss_grounding_bce_6: 0.15357/0.07887, loss_grounding_dice_6: 0.13828/0.15636, loss_grounding_ce_6: 0.11287/0.31530, loss_mask_ce_7: 0.81724/0.94519, loss_mask_bce_7: 0.39687/0.30942, loss_mask_dice_7: 0.77736/1.12432, loss_spatial_bce_7: 0.07856/0.13551, loss_spatial_dice_7: 0.09837/0.28881, loss_spatial_ce_7: 0.09216/0.31001, loss_grounding_bce_7: 0.18042/0.08056, loss_grounding_dice_7: 0.18479/0.16239, loss_grounding_ce_7: 0.14710/0.39166, loss_mask_ce_8: 1.01368/1.12988, loss_mask_bce_8: 0.42460/0.32675, loss_mask_dice_8: 0.88216/1.20571, loss_spatial_bce_8: 0.08668/0.15725, loss_spatial_dice_8: 0.18015/0.35012, loss_spatial_ce_8: 0.19864/0.35659, loss_grounding_bce_8: 0.12136/0.08485, loss_grounding_dice_8: 0.12233/0.17133, loss_grounding_ce_8: 0.79053/0.52555, loss_mask_ce_9: 3.10038/3.67752, loss_mask_bce_9: 0.39261/0.36538, loss_mask_dice_9: 1.73685/1.84670, loss_spatial_bce_9: 0.29622/0.38767, loss_spatial_dice_9: 0.83736/0.80654, loss_spatial_ce_9: 1.60729/1.57582, loss_grounding_bce_9: 0.10826/0.10218, loss_grounding_dice_9: 0.21223/0.25693, loss_grounding_ce_9: 0.23227/0.89897] items per batch[64] items per second[0.34] total items[89600] mini batches[ 1400] memory[4924] epoch remaining[0:14:27] INFO:trainer.default_trainer:epochs[ 0] optim steps[1500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64408/0.82656, loss_mask_bce_0: 0.38501/0.29932, loss_mask_dice_0: 0.80238/1.03227, loss_spatial_bce_0: 0.06977/0.10474, loss_spatial_dice_0: 0.12149/0.22725, loss_spatial_ce_0: 0.27696/0.16685, loss_grounding_bce_0: 0.03114/0.07563, loss_grounding_dice_0: 0.23170/0.15043, loss_grounding_ce_0: 0.40576/0.25917, loss_mask_ce_1: 0.67790/0.83145, loss_mask_bce_1: 0.39400/0.29954, loss_mask_dice_1: 0.82955/1.03550, loss_spatial_bce_1: 0.07210/0.10676, loss_spatial_dice_1: 0.12445/0.23156, loss_spatial_ce_1: 0.29661/0.16842, loss_grounding_bce_1: 0.03795/0.07555, loss_grounding_dice_1: 0.25568/0.15218, loss_grounding_ce_1: 0.52184/0.25646, loss_mask_ce_2: 0.61506/0.84237, loss_mask_bce_2: 0.38731/0.29927, loss_mask_dice_2: 0.81514/1.04787, loss_spatial_bce_2: 0.07099/0.10620, loss_spatial_dice_2: 0.13378/0.23359, loss_spatial_ce_2: 0.31609/0.18355, loss_grounding_bce_2: 0.02428/0.07567, loss_grounding_dice_2: 0.21557/0.15170, loss_grounding_ce_2: 0.39045/0.25847, loss_mask_ce_3: 0.65698/0.82954, loss_mask_bce_3: 0.37658/0.30240, loss_mask_dice_3: 0.75664/1.03985, loss_spatial_bce_3: 0.07982/0.10947, loss_spatial_dice_3: 0.16653/0.23524, loss_spatial_ce_3: 0.08884/0.19564, loss_grounding_bce_3: 0.02497/0.07617, loss_grounding_dice_3: 0.22288/0.15108, loss_grounding_ce_3: 0.38917/0.25847, loss_mask_ce_4: 0.62547/0.83891, loss_mask_bce_4: 0.38581/0.30370, loss_mask_dice_4: 0.80232/1.05722, loss_spatial_bce_4: 0.08938/0.11252, loss_spatial_dice_4: 0.17147/0.24425, loss_spatial_ce_4: 0.12371/0.20091, loss_grounding_bce_4: 0.02580/0.07739, loss_grounding_dice_4: 0.22660/0.15422, loss_grounding_ce_4: 0.33619/0.27240, loss_mask_ce_5: 0.50961/0.85423, loss_mask_bce_5: 0.40560/0.30458, loss_mask_dice_5: 0.88015/1.07208, loss_spatial_bce_5: 0.08795/0.11433, loss_spatial_dice_5: 0.16639/0.25095, loss_spatial_ce_5: 0.10706/0.21347, loss_grounding_bce_5: 0.03318/0.07794, loss_grounding_dice_5: 0.23782/0.15676, loss_grounding_ce_5: 0.24962/0.29941, loss_mask_ce_6: 0.58648/0.88389, loss_mask_bce_6: 0.42827/0.30518, loss_mask_dice_6: 0.84473/1.07655, loss_spatial_bce_6: 0.08777/0.11974, loss_spatial_dice_6: 0.16553/0.25605, loss_spatial_ce_6: 0.11758/0.24366, loss_grounding_bce_6: 0.03234/0.07908, loss_grounding_dice_6: 0.23828/0.15655, loss_grounding_ce_6: 0.27293/0.32288, loss_mask_ce_7: 0.78897/0.95772, loss_mask_bce_7: 0.41588/0.31400, loss_mask_dice_7: 0.80095/1.12287, loss_spatial_bce_7: 0.08876/0.13418, loss_spatial_dice_7: 0.17068/0.28538, loss_spatial_ce_7: 0.16054/0.30195, loss_grounding_bce_7: 0.02361/0.08126, loss_grounding_dice_7: 0.20274/0.16272, loss_grounding_ce_7: 0.32554/0.39943, loss_mask_ce_8: 1.02311/1.14057, loss_mask_bce_8: 0.50301/0.33141, loss_mask_dice_8: 1.05664/1.20471, loss_spatial_bce_8: 0.15934/0.15594, loss_spatial_dice_8: 0.22453/0.34631, loss_spatial_ce_8: 0.17480/0.34922, loss_grounding_bce_8: 0.02255/0.08493, loss_grounding_dice_8: 0.24984/0.17018, loss_grounding_ce_8: 0.44030/0.52879, loss_mask_ce_9: 4.11426/3.68884, loss_mask_bce_9: 0.80314/0.36931, loss_mask_dice_9: 2.18103/1.84943, loss_spatial_bce_9: 0.43862/0.38387, loss_spatial_dice_9: 0.88098/0.80701, loss_spatial_ce_9: 1.91693/1.56987, loss_grounding_bce_9: 0.05843/0.10171, loss_grounding_dice_9: 0.51700/0.25586, loss_grounding_ce_9: 0.41868/0.90339] items per batch[64] items per second[0.35] total items[96000] mini batches[ 1500] memory[4924] epoch remaining[0:11:00] INFO:trainer.default_trainer:epochs[ 0] optim steps[1600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.36991/0.82556, loss_mask_bce_0: 0.68337/0.29848, loss_mask_dice_0: 0.68256/1.03855, loss_spatial_bce_0: 0.34720/0.10369, loss_spatial_dice_0: 0.34201/0.22548, loss_spatial_ce_0: 0.20936/0.16284, loss_grounding_bce_0: 0.28106/0.07560, loss_grounding_dice_0: 0.09721/0.15064, loss_grounding_ce_0: 0.02947/0.26286, loss_mask_ce_1: 1.19602/0.82995, loss_mask_bce_1: 0.66842/0.29882, loss_mask_dice_1: 0.77427/1.04190, loss_spatial_bce_1: 0.32911/0.10568, loss_spatial_dice_1: 0.31989/0.22969, loss_spatial_ce_1: 0.25856/0.16454, loss_grounding_bce_1: 0.27151/0.07550, loss_grounding_dice_1: 0.09392/0.15199, loss_grounding_ce_1: 0.02499/0.26111, loss_mask_ce_2: 1.10665/0.83920, loss_mask_bce_2: 0.66463/0.29848, loss_mask_dice_2: 0.87067/1.05248, loss_spatial_bce_2: 0.33766/0.10511, loss_spatial_dice_2: 0.33765/0.23159, loss_spatial_ce_2: 0.20874/0.17870, loss_grounding_bce_2: 0.28372/0.07554, loss_grounding_dice_2: 0.09365/0.15145, loss_grounding_ce_2: 0.03838/0.26287, loss_mask_ce_3: 1.17494/0.82699, loss_mask_bce_3: 0.68208/0.30168, loss_mask_dice_3: 0.80514/1.04664, loss_spatial_bce_3: 0.34001/0.10816, loss_spatial_dice_3: 0.35306/0.23331, loss_spatial_ce_3: 0.25105/0.19039, loss_grounding_bce_3: 0.26808/0.07616, loss_grounding_dice_3: 0.09870/0.15116, loss_grounding_ce_3: 0.07181/0.26185, loss_mask_ce_4: 1.17515/0.83607, loss_mask_bce_4: 0.66316/0.30271, loss_mask_dice_4: 0.74157/1.06120, loss_spatial_bce_4: 0.32983/0.11119, loss_spatial_dice_4: 0.36975/0.24210, loss_spatial_ce_4: 0.25505/0.19563, loss_grounding_bce_4: 0.26079/0.07734, loss_grounding_dice_4: 0.09342/0.15374, loss_grounding_ce_4: 0.03421/0.27386, loss_mask_ce_5: 1.18011/0.85135, loss_mask_bce_5: 0.67464/0.30396, loss_mask_dice_5: 0.77772/1.07712, loss_spatial_bce_5: 0.33824/0.11292, loss_spatial_dice_5: 0.37935/0.24864, loss_spatial_ce_5: 0.32338/0.20870, loss_grounding_bce_5: 0.27301/0.07779, loss_grounding_dice_5: 0.09588/0.15635, loss_grounding_ce_5: 0.12687/0.30550, loss_mask_ce_6: 1.13674/0.88018, loss_mask_bce_6: 0.67355/0.30446, loss_mask_dice_6: 0.74614/1.07976, loss_spatial_bce_6: 0.34255/0.11835, loss_spatial_dice_6: 0.41191/0.25348, loss_spatial_ce_6: 0.23512/0.23719, loss_grounding_bce_6: 0.31694/0.07926, loss_grounding_dice_6: 0.09442/0.15624, loss_grounding_ce_6: 0.42880/0.32504, loss_mask_ce_7: 1.24824/0.95475, loss_mask_bce_7: 0.71078/0.31336, loss_mask_dice_7: 0.74267/1.12580, loss_spatial_bce_7: 0.42933/0.13286, loss_spatial_dice_7: 0.44356/0.28248, loss_spatial_ce_7: 0.27340/0.29528, loss_grounding_bce_7: 0.29496/0.08144, loss_grounding_dice_7: 0.09678/0.16231, loss_grounding_ce_7: 0.13587/0.40162, loss_mask_ce_8: 1.64864/1.13649, loss_mask_bce_8: 0.62268/0.33043, loss_mask_dice_8: 0.69420/1.21014, loss_spatial_bce_8: 0.63703/0.15470, loss_spatial_dice_8: 0.44151/0.34192, loss_spatial_ce_8: 0.35118/0.34221, loss_grounding_bce_8: 0.32708/0.08498, loss_grounding_dice_8: 0.11415/0.17044, loss_grounding_ce_8: 0.66904/0.52852, loss_mask_ce_9: 3.47697/3.68377, loss_mask_bce_9: 0.61590/0.36627, loss_mask_dice_9: 1.24187/1.85162, loss_spatial_bce_9: 0.51570/0.38309, loss_spatial_dice_9: 0.77842/0.80758, loss_spatial_ce_9: 1.13694/1.56469, loss_grounding_bce_9: 0.28621/0.10065, loss_grounding_dice_9: 0.13467/0.25528, loss_grounding_ce_9: 4.35833/0.89554] items per batch[64] items per second[0.34] total items[102400] mini batches[ 1600] memory[4924] epoch remaining[0:07:36] INFO:trainer.default_trainer:epochs[ 0] optim steps[1700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03701/0.82284, loss_mask_bce_0: 0.03749/0.29739, loss_mask_dice_0: 0.24064/1.03109, loss_spatial_bce_0: 0.02230/0.10390, loss_spatial_dice_0: 0.13731/0.22375, loss_spatial_ce_0: 0.10949/0.15780, loss_grounding_bce_0: 0.03637/0.07549, loss_grounding_dice_0: 0.10494/0.15006, loss_grounding_ce_0: 0.00183/0.26787, loss_mask_ce_1: 0.03777/0.82645, loss_mask_bce_1: 0.04051/0.29792, loss_mask_dice_1: 0.22327/1.03463, loss_spatial_bce_1: 0.02209/0.10579, loss_spatial_dice_1: 0.14655/0.22764, loss_spatial_ce_1: 0.12261/0.16046, loss_grounding_bce_1: 0.03179/0.07540, loss_grounding_dice_1: 0.09532/0.15147, loss_grounding_ce_1: 0.00138/0.26742, loss_mask_ce_2: 0.03766/0.83619, loss_mask_bce_2: 0.03977/0.29730, loss_mask_dice_2: 0.21142/1.04453, loss_spatial_bce_2: 0.02258/0.10531, loss_spatial_dice_2: 0.13724/0.22953, loss_spatial_ce_2: 0.14373/0.17345, loss_grounding_bce_2: 0.03615/0.07548, loss_grounding_dice_2: 0.10871/0.15106, loss_grounding_ce_2: 0.00149/0.26470, loss_mask_ce_3: 0.04469/0.82432, loss_mask_bce_3: 0.03234/0.30062, loss_mask_dice_3: 0.16102/1.03820, loss_spatial_bce_3: 0.02267/0.10833, loss_spatial_dice_3: 0.16862/0.23112, loss_spatial_ce_3: 0.12140/0.18540, loss_grounding_bce_3: 0.03784/0.07615, loss_grounding_dice_3: 0.11442/0.15087, loss_grounding_ce_3: 0.00302/0.26389, loss_mask_ce_4: 0.03955/0.83262, loss_mask_bce_4: 0.03584/0.30111, loss_mask_dice_4: 0.16889/1.05370, loss_spatial_bce_4: 0.02223/0.11114, loss_spatial_dice_4: 0.12866/0.23971, loss_spatial_ce_4: 0.16255/0.18988, loss_grounding_bce_4: 0.03320/0.07709, loss_grounding_dice_4: 0.10126/0.15320, loss_grounding_ce_4: 0.00349/0.27965, loss_mask_ce_5: 0.04558/0.84791, loss_mask_bce_5: 0.04101/0.30251, loss_mask_dice_5: 0.20604/1.06809, loss_spatial_bce_5: 0.02163/0.11284, loss_spatial_dice_5: 0.18122/0.24597, loss_spatial_ce_5: 0.16501/0.20328, loss_grounding_bce_5: 0.03524/0.07748, loss_grounding_dice_5: 0.10879/0.15543, loss_grounding_ce_5: 0.00399/0.30500, loss_mask_ce_6: 0.04294/0.87687, loss_mask_bce_6: 0.04248/0.30271, loss_mask_dice_6: 0.24793/1.07092, loss_spatial_bce_6: 0.02094/0.11840, loss_spatial_dice_6: 0.15968/0.25096, loss_spatial_ce_6: 0.18543/0.22977, loss_grounding_bce_6: 0.03369/0.07910, loss_grounding_dice_6: 0.10432/0.15576, loss_grounding_ce_6: 0.01009/0.32738, loss_mask_ce_7: 0.04779/0.94943, loss_mask_bce_7: 0.03871/0.31155, loss_mask_dice_7: 0.21504/1.11569, loss_spatial_bce_7: 0.02148/0.13219, loss_spatial_dice_7: 0.13010/0.27928, loss_spatial_ce_7: 0.44765/0.28809, loss_grounding_bce_7: 0.03714/0.08114, loss_grounding_dice_7: 0.12121/0.16122, loss_grounding_ce_7: 0.01847/0.40050, loss_mask_ce_8: 0.06726/1.12831, loss_mask_bce_8: 0.03517/0.32915, loss_mask_dice_8: 0.20939/1.19853, loss_spatial_bce_8: 0.02585/0.15365, loss_spatial_dice_8: 0.14541/0.33751, loss_spatial_ce_8: 0.23389/0.33799, loss_grounding_bce_8: 0.03884/0.08464, loss_grounding_dice_8: 0.12227/0.16923, loss_grounding_ce_8: 0.02356/0.52476, loss_mask_ce_9: 2.03213/3.65923, loss_mask_bce_9: 0.03838/0.36382, loss_mask_dice_9: 0.25180/1.83275, loss_spatial_bce_9: 0.30143/0.38184, loss_spatial_dice_9: 0.67657/0.80650, loss_spatial_ce_9: 0.57976/1.55758, loss_grounding_bce_9: 0.03608/0.09988, loss_grounding_dice_9: 0.12829/0.25303, loss_grounding_ce_9: 0.13157/0.88531] items per batch[64] items per second[0.34] total items[108800] mini batches[ 1700] memory[4924] epoch remaining[0:04:14] INFO:trainer.default_trainer:epochs[ 0] optim steps[1800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29304/0.81973, loss_mask_bce_0: 0.05597/0.29857, loss_mask_dice_0: 0.23473/1.01787, loss_spatial_bce_0: 0.02318/0.10541, loss_spatial_dice_0: 0.12870/0.22284, loss_spatial_ce_0: 0.20569/0.15506, loss_grounding_bce_0: 0.03085/0.07673, loss_grounding_dice_0: 0.14018/0.15069, loss_grounding_ce_0: 0.07657/0.26785, loss_mask_ce_1: 0.29120/0.82285, loss_mask_bce_1: 0.05811/0.29911, loss_mask_dice_1: 0.23747/1.02277, loss_spatial_bce_1: 0.02571/0.10737, loss_spatial_dice_1: 0.13215/0.22680, loss_spatial_ce_1: 0.21313/0.15735, loss_grounding_bce_1: 0.02341/0.07664, loss_grounding_dice_1: 0.12071/0.15200, loss_grounding_ce_1: 0.11690/0.26758, loss_mask_ce_2: 0.28544/0.83243, loss_mask_bce_2: 0.06147/0.29889, loss_mask_dice_2: 0.24918/1.03186, loss_spatial_bce_2: 0.02788/0.10705, loss_spatial_dice_2: 0.14300/0.22873, loss_spatial_ce_2: 0.16788/0.16946, loss_grounding_bce_2: 0.03434/0.07721, loss_grounding_dice_2: 0.15603/0.15175, loss_grounding_ce_2: 0.12806/0.26439, loss_mask_ce_3: 0.29224/0.82122, loss_mask_bce_3: 0.05579/0.30179, loss_mask_dice_3: 0.21858/1.02506, loss_spatial_bce_3: 0.02972/0.10973, loss_spatial_dice_3: 0.15124/0.23012, loss_spatial_ce_3: 0.29041/0.18119, loss_grounding_bce_3: 0.02613/0.07734, loss_grounding_dice_3: 0.13637/0.15159, loss_grounding_ce_3: 0.09105/0.26391, loss_mask_ce_4: 0.24129/0.82970, loss_mask_bce_4: 0.06245/0.30243, loss_mask_dice_4: 0.24310/1.04154, loss_spatial_bce_4: 0.03347/0.11289, loss_spatial_dice_4: 0.19534/0.23860, loss_spatial_ce_4: 0.51732/0.18593, loss_grounding_bce_4: 0.02975/0.07859, loss_grounding_dice_4: 0.15050/0.15386, loss_grounding_ce_4: 0.04374/0.27777, loss_mask_ce_5: 0.36795/0.84428, loss_mask_bce_5: 0.06989/0.30382, loss_mask_dice_5: 0.24231/1.05503, loss_spatial_bce_5: 0.03951/0.11426, loss_spatial_dice_5: 0.21988/0.24448, loss_spatial_ce_5: 0.41313/0.19901, loss_grounding_bce_5: 0.03402/0.07941, loss_grounding_dice_5: 0.14222/0.15584, loss_grounding_ce_5: 0.12889/0.30238, loss_mask_ce_6: 0.35806/0.87361, loss_mask_bce_6: 0.06272/0.30371, loss_mask_dice_6: 0.24683/1.05678, loss_spatial_bce_6: 0.03598/0.11998, loss_spatial_dice_6: 0.19718/0.24938, loss_spatial_ce_6: 0.44703/0.22521, loss_grounding_bce_6: 0.03126/0.08084, loss_grounding_dice_6: 0.15553/0.15617, loss_grounding_ce_6: 0.12623/0.32628, loss_mask_ce_7: 0.72520/0.94409, loss_mask_bce_7: 0.06203/0.31287, loss_mask_dice_7: 0.24340/1.10224, loss_spatial_bce_7: 0.03620/0.13356, loss_spatial_dice_7: 0.20364/0.27706, loss_spatial_ce_7: 0.29899/0.28245, loss_grounding_bce_7: 0.03254/0.08322, loss_grounding_dice_7: 0.14498/0.16199, loss_grounding_ce_7: 0.35424/0.39684, loss_mask_ce_8: 1.74910/1.12099, loss_mask_bce_8: 0.05370/0.33016, loss_mask_dice_8: 0.31270/1.18394, loss_spatial_bce_8: 0.03912/0.15433, loss_spatial_dice_8: 0.34205/0.33373, loss_spatial_ce_8: 0.65297/0.33422, loss_grounding_bce_8: 0.02880/0.08669, loss_grounding_dice_8: 0.22334/0.17008, loss_grounding_ce_8: 0.70937/0.51884, loss_mask_ce_9: 2.14702/3.64068, loss_mask_bce_9: 0.05211/0.36451, loss_mask_dice_9: 0.41939/1.80837, loss_spatial_bce_9: 0.09744/0.38223, loss_spatial_dice_9: 0.64742/0.80589, loss_spatial_ce_9: 0.62388/1.54791, loss_grounding_bce_9: 0.02709/0.10086, loss_grounding_dice_9: 0.29598/0.25318, loss_grounding_ce_9: 0.21447/0.87997] items per batch[64] items per second[0.35] total items[115200] mini batches[ 1800] memory[4924] epoch remaining[0:00:53] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00001827. INFO:trainer.default_trainer:Evaluation start ... INFO:detectron2.data.dataset_mapper:[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(800, 800), max_size=1333, sample_style='choice')] INFO:detectron2.data.common:Serializing 5000 elements to byte tensors and concatenating them all ... INFO:detectron2.data.common:Serialized dataset takes 3.69 MiB INFO:detectron2.data.common:Serializing 1581 elements to byte tensors and concatenating them all ... INFO:detectron2.data.common:Serialized dataset takes 0.53 MiB INFO:detectron2.data.common:Serializing 1300 elements to byte tensors and concatenating them all ... INFO:detectron2.data.common:Serialized dataset takes 3.11 MiB INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0029 s/iter. Inference: 0.3909 s/iter. Eval: 0.0681 s/iter. Total: 0.4619 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3846 s/iter. Eval: 0.0646 s/iter. Total: 0.4519 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3917 s/iter. Eval: 0.0651 s/iter. Total: 0.4597 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3926 s/iter. Eval: 0.0637 s/iter. Total: 0.4592 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0029 s/iter. Inference: 0.3933 s/iter. Eval: 0.0623 s/iter. Total: 0.4586 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0029 s/iter. Inference: 0.3917 s/iter. Eval: 0.0616 s/iter. Total: 0.4562 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalcctyf2yx ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.304 | 82.452 | 65.816 | 133 | | Things | 61.709 | 84.053 | 72.926 | 80 | | Stuff | 45.638 | 80.035 | 55.083 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.48s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.18 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.33 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.24s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.95 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.54 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.760 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 44.754 | 68.487 | 48.328 | 25.796 | 49.067 | 67.166 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.606 | bicycle | 22.252 | car | 43.308 | | motorcycle | 40.283 | airplane | 59.879 | bus | 70.119 | | train | 74.719 | truck | 42.460 | boat | 30.782 | | traffic light | 27.635 | fire hydrant | 70.104 | stop sign | 68.721 | | parking meter | 51.418 | bench | 26.248 | bird | 33.385 | | cat | 77.128 | dog | 70.857 | horse | 49.391 | | sheep | 52.214 | cow | 55.430 | elephant | 63.196 | | bear | 80.126 | zebra | 64.798 | giraffe | 61.722 | | backpack | 20.901 | umbrella | 54.801 | handbag | 24.903 | | tie | 39.899 | suitcase | 50.058 | frisbee | 70.029 | | skis | 8.733 | snowboard | 34.083 | sports ball | 49.446 | | kite | 36.577 | baseball bat | 37.019 | baseball glove | 49.119 | | skateboard | 43.513 | surfboard | 44.884 | tennis racket | 61.504 | | bottle | 41.401 | wine glass | 37.646 | cup | 48.864 | | fork | 25.240 | knife | 23.952 | spoon | 20.323 | | bowl | 39.302 | banana | 21.500 | apple | 24.697 | | sandwich | 49.209 | orange | 30.376 | broccoli | 24.246 | | carrot | 22.228 | hot dog | 34.912 | pizza | 51.008 | | donut | 53.569 | cake | 46.857 | chair | 27.878 | | couch | 42.799 | potted plant | 21.476 | bed | 43.974 | | dining table | 16.299 | toilet | 70.213 | tv | 64.404 | | laptop | 67.476 | mouse | 61.663 | remote | 41.936 | | keyboard | 56.912 | cell phone | 43.954 | microwave | 61.778 | | oven | 32.673 | toaster | 45.533 | sink | 42.752 | | refrigerator | 70.143 | book | 13.764 | clock | 54.343 | | vase | 40.124 | scissors | 35.600 | teddy bear | 56.658 | | hair drier | 35.161 | toothbrush | 27.214 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.17046864414569, 'fwIoU': 71.9566471839312, 'IoU-person': 89.12456542417758, 'IoU-bicycle': 79.64856102211651, 'IoU-car': 70.51818316844417, 'IoU-motorcycle': 88.48220510293076, 'IoU-airplane': 87.02410811857843, 'IoU-bus': 88.51401937090758, 'IoU-train': 88.53366197985662, 'IoU-truck': 68.86792501856554, 'IoU-boat': 73.73826701667386, 'IoU-traffic light': 79.60830836836836, 'IoU-fire hydrant': 92.96829627478635, 'IoU-stop sign': 95.79751207809693, 'IoU-parking meter': 85.02355544787989, 'IoU-bench': 65.32950082412349, 'IoU-bird': 73.05660407592612, 'IoU-cat': 91.96337585283511, 'IoU-dog': 87.39274277844193, 'IoU-horse': 87.92718599512061, 'IoU-sheep': 90.36913709228898, 'IoU-cow': 90.16276470881728, 'IoU-elephant': 90.8956655058068, 'IoU-bear': 78.73848085643723, 'IoU-zebra': 90.31509525916256, 'IoU-giraffe': 89.27836715248833, 'IoU-backpack': 47.77283734628466, 'IoU-umbrella': 87.74138477184442, 'IoU-handbag': 52.195160572914226, 'IoU-tie': 76.68173367931053, 'IoU-suitcase': 86.57886081484676, 'IoU-frisbee': 84.85422262988928, 'IoU-skis': 59.99133336680204, 'IoU-snowboard': 73.58131268249333, 'IoU-sports ball': 78.67090996351573, 'IoU-kite': 79.32344139884805, 'IoU-baseball bat': 71.67891249454159, 'IoU-baseball glove': 82.07515679062274, 'IoU-skateboard': 86.00471146295733, 'IoU-surfboard': 86.71021657847608, 'IoU-tennis racket': 91.27753346356296, 'IoU-bottle': 70.71405286843957, 'IoU-wine glass': 81.89274071458233, 'IoU-cup': 71.00224917358857, 'IoU-fork': 67.96753338681516, 'IoU-knife': 62.89397853884135, 'IoU-spoon': 61.70328902261245, 'IoU-bowl': 62.84369600576812, 'IoU-banana': 83.16571325377265, 'IoU-apple': 58.0185502601499, 'IoU-sandwich': 69.93802710869966, 'IoU-orange': 80.99678737124361, 'IoU-broccoli': 68.95573560956579, 'IoU-carrot': 64.68255108098275, 'IoU-hot dog': 62.01952224901992, 'IoU-pizza': 77.72318608281117, 'IoU-donut': 76.2107519022988, 'IoU-cake': 80.25772780524723, 'IoU-chair': 63.06950850111978, 'IoU-couch': 69.28296845370465, 'IoU-potted plant': 44.101389537744076, 'IoU-bed': 75.00290213734891, 'IoU-dining table': 52.98296109099464, 'IoU-toilet': 89.69424405639258, 'IoU-tv': 82.69493112891567, 'IoU-laptop': 82.10821215037303, 'IoU-mouse': 76.24326797581722, 'IoU-remote': 71.60994730170702, 'IoU-keyboard': 68.95213692131938, 'IoU-cell phone': 73.54036755199618, 'IoU-microwave': 70.65291622953941, 'IoU-oven': 69.30525792373074, 'IoU-toaster': 84.91389858451076, 'IoU-sink': 76.8054274764494, 'IoU-refrigerator': 84.75474046000802, 'IoU-book': 55.129666540437185, 'IoU-clock': 73.313674087353, 'IoU-vase': 71.5493048830241, 'IoU-scissors': 84.48354049875184, 'IoU-teddy bear': 84.63733529388777, 'IoU-hair drier': 28.866389540546844, 'IoU-toothbrush': 77.51362653323439, 'IoU-banner': 30.426812167761508, 'IoU-blanket': 17.845105551522302, 'IoU-bridge': 38.46272568855869, 'IoU-cardboard': 54.13363855158464, 'IoU-counter': 32.61554549695252, 'IoU-curtain': 72.18478385672805, 'IoU-door-stuff': 47.23609660074002, 'IoU-floor-wood': 65.05098867258498, 'IoU-flower': 50.49634956605741, 'IoU-fruit': 48.43776719091355, 'IoU-gravel': 32.3861462379712, 'IoU-house': 29.690204760228234, 'IoU-light': 42.762448512624424, 'IoU-mirror-stuff': 56.362643973906856, 'IoU-net': 51.41582656258502, 'IoU-pillow': 20.92077694242161, 'IoU-platform': 29.41180814777578, 'IoU-playingfield': 71.2168208731558, 'IoU-railroad': 62.07184431542827, 'IoU-river': 53.145935369747676, 'IoU-road': 68.42275689800081, 'IoU-roof': 13.44618222609365, 'IoU-sand': 65.60171333585679, 'IoU-sea': 85.44216688651223, 'IoU-shelf': 38.73616336226232, 'IoU-snow': 92.5240429351609, 'IoU-stairs': 34.004167697158934, 'IoU-tent': 10.22394298295012, 'IoU-towel': 45.22860836813012, 'IoU-wall-brick': 50.54198876002859, 'IoU-wall-stone': 34.78924243288548, 'IoU-wall-tile': 70.60289417775795, 'IoU-wall-wood': 43.22943211838842, 'IoU-water-other': 25.26193484363512, 'IoU-window-blind': 51.19351780092376, 'IoU-window-other': 49.63382840420492, 'IoU-tree-merged': 81.75936889980355, 'IoU-fence-merged': 55.694160055005696, 'IoU-ceiling-merged': 67.83818105731595, 'IoU-sky-other-merged': 94.15216539520725, 'IoU-cabinet-merged': 64.7068805880007, 'IoU-table-merged': 43.94279444213654, 'IoU-floor-other-merged': 54.36066677415879, 'IoU-pavement-merged': 58.11728042547024, 'IoU-mountain-merged': 57.72856855515527, 'IoU-grass-merged': 71.52431927883487, 'IoU-dirt-merged': 45.82906074641821, 'IoU-paper-merged': 33.08189097137849, 'IoU-food-other-merged': 41.41257176121326, 'IoU-building-other-merged': 59.3935397181024, 'IoU-rock-merged': 68.24850732283338, 'IoU-wall-other-merged': 68.5011844684149, 'IoU-rug-merged': 68.61381313964218, 'mACC': 77.18390940837178, 'pACC': 82.42015999537858, 'ACC-person': 92.98163300018994, 'ACC-bicycle': 88.71399399503336, 'ACC-car': 85.72327741857391, 'ACC-motorcycle': 92.72933937464724, 'ACC-airplane': 90.73790324825009, 'ACC-bus': 93.34853423953717, 'ACC-train': 95.52705268418462, 'ACC-truck': 76.91310986337216, 'ACC-boat': 82.37708068582862, 'ACC-traffic light': 90.48537566779613, 'ACC-fire hydrant': 95.69676156234121, 'ACC-stop sign': 98.19081456225699, 'ACC-parking meter': 87.77981822588148, 'ACC-bench': 76.88909122102967, 'ACC-bird': 78.05069077089004, 'ACC-cat': 95.50702094249417, 'ACC-dog': 89.99514984930616, 'ACC-horse': 92.29477952903817, 'ACC-sheep': 94.94436207544167, 'ACC-cow': 93.48081842524032, 'ACC-elephant': 92.9463112789873, 'ACC-bear': 80.12005315973046, 'ACC-zebra': 92.47106510560609, 'ACC-giraffe': 92.87231563390583, 'ACC-backpack': 61.9833524684271, 'ACC-umbrella': 92.42265763680541, 'ACC-handbag': 72.73415509697372, 'ACC-tie': 85.51515043768539, 'ACC-suitcase': 93.69942793326771, 'ACC-frisbee': 94.19127272727272, 'ACC-skis': 77.35854199531852, 'ACC-snowboard': 79.49254243099605, 'ACC-sports ball': 87.262897840626, 'ACC-kite': 85.135250780898, 'ACC-baseball bat': 84.86015479249863, 'ACC-baseball glove': 92.12763811129605, 'ACC-skateboard': 90.3332046626659, 'ACC-surfboard': 92.37757626612509, 'ACC-tennis racket': 95.22177996175255, 'ACC-bottle': 84.17176100185021, 'ACC-wine glass': 90.8240104440536, 'ACC-cup': 86.32011525737857, 'ACC-fork': 82.38415981176792, 'ACC-knife': 77.3289001245695, 'ACC-spoon': 76.19904889908219, 'ACC-bowl': 72.38381833065391, 'ACC-banana': 89.06225765510281, 'ACC-apple': 69.04646807729934, 'ACC-sandwich': 80.3866890821793, 'ACC-orange': 88.7722384392393, 'ACC-broccoli': 81.04828828302671, 'ACC-carrot': 78.65918177544454, 'ACC-hot dog': 68.48186544302528, 'ACC-pizza': 82.36367086487171, 'ACC-donut': 82.69251505739764, 'ACC-cake': 87.08282180389371, 'ACC-chair': 82.54765039819517, 'ACC-couch': 76.03359830548311, 'ACC-potted plant': 57.99915431423124, 'ACC-bed': 85.37207523272977, 'ACC-dining table': 84.3950459958608, 'ACC-toilet': 94.15715983158994, 'ACC-tv': 88.45931086780504, 'ACC-laptop': 94.90439644688001, 'ACC-mouse': 91.66910165254374, 'ACC-remote': 75.39315113551235, 'ACC-keyboard': 75.63035661974689, 'ACC-cell phone': 80.22041561869814, 'ACC-microwave': 74.50948061245936, 'ACC-oven': 91.82492533474296, 'ACC-toaster': 90.22704566916202, 'ACC-sink': 83.51368149813516, 'ACC-refrigerator': 94.37552444793896, 'ACC-book': 70.46324703706817, 'ACC-clock': 76.66669441736032, 'ACC-vase': 81.13831752600841, 'ACC-scissors': 89.80502070535951, 'ACC-teddy bear': 89.92914959233062, 'ACC-hair drier': 29.944781259784246, 'ACC-toothbrush': 84.37282835302294, 'ACC-banner': 81.95858270171023, 'ACC-blanket': 25.58242944376235, 'ACC-bridge': 55.53480186503865, 'ACC-cardboard': 71.48404827686491, 'ACC-counter': 49.740610539311184, 'ACC-curtain': 83.52554240831645, 'ACC-door-stuff': 74.50021539109683, 'ACC-floor-wood': 82.54636800284239, 'ACC-flower': 74.59157928034003, 'ACC-fruit': 70.0913993072639, 'ACC-gravel': 52.15950850790519, 'ACC-house': 43.2548131634792, 'ACC-light': 60.63125645982468, 'ACC-mirror-stuff': 66.05411687912014, 'ACC-net': 66.5117513906348, 'ACC-pillow': 42.12300649695216, 'ACC-platform': 41.770250188155586, 'ACC-playingfield': 92.41898104843776, 'ACC-railroad': 75.11939108910761, 'ACC-river': 75.71813829945539, 'ACC-road': 83.19763120816461, 'ACC-roof': 17.308975785060323, 'ACC-sand': 71.72643641064575, 'ACC-sea': 89.99888462113131, 'ACC-shelf': 52.68523936833114, 'ACC-snow': 95.71246534395709, 'ACC-stairs': 59.621852404880826, 'ACC-tent': 14.378939070138975, 'ACC-towel': 51.69137199893849, 'ACC-wall-brick': 67.8463763110249, 'ACC-wall-stone': 45.41399308097555, 'ACC-wall-tile': 86.3862201056528, 'ACC-wall-wood': 61.88702912972055, 'ACC-water-other': 42.80990324897878, 'ACC-window-blind': 66.19191250893547, 'ACC-window-other': 73.80510489735383, 'ACC-tree-merged': 89.01687909218377, 'ACC-fence-merged': 74.06860652502911, 'ACC-ceiling-merged': 82.62747425817581, 'ACC-sky-other-merged': 96.89540443194852, 'ACC-cabinet-merged': 79.2797214325773, 'ACC-table-merged': 56.86579652202519, 'ACC-floor-other-merged': 65.84125174476131, 'ACC-pavement-merged': 71.82131215779317, 'ACC-mountain-merged': 67.46798154076825, 'ACC-grass-merged': 85.14434688642375, 'ACC-dirt-merged': 63.51340809715691, 'ACC-paper-merged': 40.41561268287535, 'ACC-food-other-merged': 49.98504763242719, 'ACC-building-other-merged': 72.98061349691206, 'ACC-rock-merged': 81.96379554748336, 'ACC-wall-other-merged': 81.25075847390649, 'ACC-rug-merged': 84.09093567183066})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3297 s/iter. Inference: 0.1740 s/iter. Eval: 0.0000 s/iter. Total: 0.5037 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3640 s/iter. Inference: 0.3433 s/iter. Eval: 0.0000 s/iter. Total: 0.7074 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3774 s/iter. Inference: 0.5581 s/iter. Eval: 0.0000 s/iter. Total: 0.9357 s/iter. ETA=0:00:03 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 25/25. Dataloading: 0.3790 s/iter. Inference: 0.6280 s/iter. Eval: 0.0000 s/iter. Total: 1.0072 s/iter. ETA=0:00:00 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5346795434591747, 'noc@0.8': 2.8498683055311678, 'noc@0.85': 3.3786947614866842, 'noc@0.9': 4.265437518290899, 'miou@iter1': 0.8744241040737475} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1690 s/iter. Eval: 0.0010 s/iter. Total: 0.1718 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.24290466308594, 'precision@0.6': 72.36688995361328, 'precision@0.7': 68.13058471679688, 'precision@0.8': 59.463661193847656, 'precision@0.9': 33.30742263793945, 'cIoU': 61.294944763183594, 'mIoU': 66.84134674072266} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.30443084209452, 'SQ': 82.45175587781559, 'RQ': 65.8157363125467, 'PQ_th': 61.70873872727787, 'SQ_th': 84.05307248749786, 'RQ_th': 72.92584618167582, 'PQ_st': 45.63755101540263, 'SQ_st': 80.0346742028234, 'RQ_st': 55.08349500065369}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 44.753800740082696, 'AP50': 68.48686786858514, 'AP75': 48.32774540409417, 'APs': 25.795916550305513, 'APm': 49.0667262068247, 'APl': 67.16649346874355, 'AP-person': 48.60636288050145, 'AP-bicycle': 22.251636330933184, 'AP-car': 43.30812747756721, 'AP-motorcycle': 40.283329241253455, 'AP-airplane': 59.878530102281246, 'AP-bus': 70.11854824622165, 'AP-train': 74.7188851104744, 'AP-truck': 42.46036521652613, 'AP-boat': 30.782353495995462, 'AP-traffic light': 27.63500237107494, 'AP-fire hydrant': 70.10415888477644, 'AP-stop sign': 68.72094251321609, 'AP-parking meter': 51.417969671774785, 'AP-bench': 26.248389234308068, 'AP-bird': 33.384686037275934, 'AP-cat': 77.12769335857543, 'AP-dog': 70.85688420896842, 'AP-horse': 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56.658102289782796, 'AP-hair drier': 35.161213947481706, 'AP-toothbrush': 27.21381291524139}), ('sem_seg', {'mIoU': 66.17046864414569, 'fwIoU': 71.9566471839312, 'IoU-person': 89.12456542417758, 'IoU-bicycle': 79.64856102211651, 'IoU-car': 70.51818316844417, 'IoU-motorcycle': 88.48220510293076, 'IoU-airplane': 87.02410811857843, 'IoU-bus': 88.51401937090758, 'IoU-train': 88.53366197985662, 'IoU-truck': 68.86792501856554, 'IoU-boat': 73.73826701667386, 'IoU-traffic light': 79.60830836836836, 'IoU-fire hydrant': 92.96829627478635, 'IoU-stop sign': 95.79751207809693, 'IoU-parking meter': 85.02355544787989, 'IoU-bench': 65.32950082412349, 'IoU-bird': 73.05660407592612, 'IoU-cat': 91.96337585283511, 'IoU-dog': 87.39274277844193, 'IoU-horse': 87.92718599512061, 'IoU-sheep': 90.36913709228898, 'IoU-cow': 90.16276470881728, 'IoU-elephant': 90.8956655058068, 'IoU-bear': 78.73848085643723, 'IoU-zebra': 90.31509525916256, 'IoU-giraffe': 89.27836715248833, 'IoU-backpack': 47.77283734628466, 'IoU-umbrella': 87.74138477184442, 'IoU-handbag': 52.195160572914226, 'IoU-tie': 76.68173367931053, 'IoU-suitcase': 86.57886081484676, 'IoU-frisbee': 84.85422262988928, 'IoU-skis': 59.99133336680204, 'IoU-snowboard': 73.58131268249333, 'IoU-sports ball': 78.67090996351573, 'IoU-kite': 79.32344139884805, 'IoU-baseball bat': 71.67891249454159, 'IoU-baseball glove': 82.07515679062274, 'IoU-skateboard': 86.00471146295733, 'IoU-surfboard': 86.71021657847608, 'IoU-tennis racket': 91.27753346356296, 'IoU-bottle': 70.71405286843957, 'IoU-wine glass': 81.89274071458233, 'IoU-cup': 71.00224917358857, 'IoU-fork': 67.96753338681516, 'IoU-knife': 62.89397853884135, 'IoU-spoon': 61.70328902261245, 'IoU-bowl': 62.84369600576812, 'IoU-banana': 83.16571325377265, 'IoU-apple': 58.0185502601499, 'IoU-sandwich': 69.93802710869966, 'IoU-orange': 80.99678737124361, 'IoU-broccoli': 68.95573560956579, 'IoU-carrot': 64.68255108098275, 'IoU-hot dog': 62.01952224901992, 'IoU-pizza': 77.72318608281117, 'IoU-donut': 76.2107519022988, 'IoU-cake': 80.25772780524723, 'IoU-chair': 63.06950850111978, 'IoU-couch': 69.28296845370465, 'IoU-potted plant': 44.101389537744076, 'IoU-bed': 75.00290213734891, 'IoU-dining table': 52.98296109099464, 'IoU-toilet': 89.69424405639258, 'IoU-tv': 82.69493112891567, 'IoU-laptop': 82.10821215037303, 'IoU-mouse': 76.24326797581722, 'IoU-remote': 71.60994730170702, 'IoU-keyboard': 68.95213692131938, 'IoU-cell phone': 73.54036755199618, 'IoU-microwave': 70.65291622953941, 'IoU-oven': 69.30525792373074, 'IoU-toaster': 84.91389858451076, 'IoU-sink': 76.8054274764494, 'IoU-refrigerator': 84.75474046000802, 'IoU-book': 55.129666540437185, 'IoU-clock': 73.313674087353, 'IoU-vase': 71.5493048830241, 'IoU-scissors': 84.48354049875184, 'IoU-teddy bear': 84.63733529388777, 'IoU-hair drier': 28.866389540546844, 'IoU-toothbrush': 77.51362653323439, 'IoU-banner': 30.426812167761508, 'IoU-blanket': 17.845105551522302, 'IoU-bridge': 38.46272568855869, 'IoU-cardboard': 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'IoU-water-other': 25.26193484363512, 'IoU-window-blind': 51.19351780092376, 'IoU-window-other': 49.63382840420492, 'IoU-tree-merged': 81.75936889980355, 'IoU-fence-merged': 55.694160055005696, 'IoU-ceiling-merged': 67.83818105731595, 'IoU-sky-other-merged': 94.15216539520725, 'IoU-cabinet-merged': 64.7068805880007, 'IoU-table-merged': 43.94279444213654, 'IoU-floor-other-merged': 54.36066677415879, 'IoU-pavement-merged': 58.11728042547024, 'IoU-mountain-merged': 57.72856855515527, 'IoU-grass-merged': 71.52431927883487, 'IoU-dirt-merged': 45.82906074641821, 'IoU-paper-merged': 33.08189097137849, 'IoU-food-other-merged': 41.41257176121326, 'IoU-building-other-merged': 59.3935397181024, 'IoU-rock-merged': 68.24850732283338, 'IoU-wall-other-merged': 68.5011844684149, 'IoU-rug-merged': 68.61381313964218, 'mACC': 77.18390940837178, 'pACC': 82.42015999537858, 'ACC-person': 92.98163300018994, 'ACC-bicycle': 88.71399399503336, 'ACC-car': 85.72327741857391, 'ACC-motorcycle': 92.72933937464724, 'ACC-airplane': 90.73790324825009, 'ACC-bus': 93.34853423953717, 'ACC-train': 95.52705268418462, 'ACC-truck': 76.91310986337216, 'ACC-boat': 82.37708068582862, 'ACC-traffic light': 90.48537566779613, 'ACC-fire hydrant': 95.69676156234121, 'ACC-stop sign': 98.19081456225699, 'ACC-parking meter': 87.77981822588148, 'ACC-bench': 76.88909122102967, 'ACC-bird': 78.05069077089004, 'ACC-cat': 95.50702094249417, 'ACC-dog': 89.99514984930616, 'ACC-horse': 92.29477952903817, 'ACC-sheep': 94.94436207544167, 'ACC-cow': 93.48081842524032, 'ACC-elephant': 92.9463112789873, 'ACC-bear': 80.12005315973046, 'ACC-zebra': 92.47106510560609, 'ACC-giraffe': 92.87231563390583, 'ACC-backpack': 61.9833524684271, 'ACC-umbrella': 92.42265763680541, 'ACC-handbag': 72.73415509697372, 'ACC-tie': 85.51515043768539, 'ACC-suitcase': 93.69942793326771, 'ACC-frisbee': 94.19127272727272, 'ACC-skis': 77.35854199531852, 'ACC-snowboard': 79.49254243099605, 'ACC-sports ball': 87.262897840626, 'ACC-kite': 85.135250780898, 'ACC-baseball bat': 84.86015479249863, 'ACC-baseball glove': 92.12763811129605, 'ACC-skateboard': 90.3332046626659, 'ACC-surfboard': 92.37757626612509, 'ACC-tennis racket': 95.22177996175255, 'ACC-bottle': 84.17176100185021, 'ACC-wine glass': 90.8240104440536, 'ACC-cup': 86.32011525737857, 'ACC-fork': 82.38415981176792, 'ACC-knife': 77.3289001245695, 'ACC-spoon': 76.19904889908219, 'ACC-bowl': 72.38381833065391, 'ACC-banana': 89.06225765510281, 'ACC-apple': 69.04646807729934, 'ACC-sandwich': 80.3866890821793, 'ACC-orange': 88.7722384392393, 'ACC-broccoli': 81.04828828302671, 'ACC-carrot': 78.65918177544454, 'ACC-hot dog': 68.48186544302528, 'ACC-pizza': 82.36367086487171, 'ACC-donut': 82.69251505739764, 'ACC-cake': 87.08282180389371, 'ACC-chair': 82.54765039819517, 'ACC-couch': 76.03359830548311, 'ACC-potted plant': 57.99915431423124, 'ACC-bed': 85.37207523272977, 'ACC-dining table': 84.3950459958608, 'ACC-toilet': 94.15715983158994, 'ACC-tv': 88.45931086780504, 'ACC-laptop': 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66.84134674072266}}} INFO:trainer.default_trainer:This epoch takes 1:05:22.004980 INFO:trainer.default_trainer:PROGRESS: 2.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 1 training. INFO:trainer.default_trainer:epochs[ 1] optim steps[1900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07329/0.82251, loss_mask_bce_0: 0.31196/0.29903, loss_mask_dice_0: 0.29767/1.01968, loss_spatial_bce_0: 0.12723/0.10484, loss_spatial_dice_0: 0.12084/0.22140, loss_spatial_ce_0: 0.07241/0.15128, loss_grounding_bce_0: 0.05298/0.07720, loss_grounding_dice_0: 0.04712/0.15185, loss_grounding_ce_0: 0.00001/0.26711, loss_mask_ce_1: 0.07036/0.82453, loss_mask_bce_1: 0.30144/0.29955, loss_mask_dice_1: 0.28363/1.02392, loss_spatial_bce_1: 0.12608/0.10672, loss_spatial_dice_1: 0.12354/0.22543, loss_spatial_ce_1: 0.04639/0.15376, loss_grounding_bce_1: 0.05058/0.07706, loss_grounding_dice_1: 0.04544/0.15320, loss_grounding_ce_1: 0.00002/0.26763, loss_mask_ce_2: 0.06969/0.83434, loss_mask_bce_2: 0.30506/0.29936, loss_mask_dice_2: 0.27740/1.03323, loss_spatial_bce_2: 0.12395/0.10637, loss_spatial_dice_2: 0.11996/0.22731, loss_spatial_ce_2: 0.03365/0.16591, loss_grounding_bce_2: 0.05395/0.07759, loss_grounding_dice_2: 0.04599/0.15281, loss_grounding_ce_2: 0.00001/0.26457, loss_mask_ce_3: 0.07460/0.82294, loss_mask_bce_3: 0.30168/0.30229, loss_mask_dice_3: 0.28567/1.02646, loss_spatial_bce_3: 0.13200/0.10903, loss_spatial_dice_3: 0.12096/0.22845, loss_spatial_ce_3: 0.02699/0.17689, loss_grounding_bce_3: 0.05969/0.07779, loss_grounding_dice_3: 0.05126/0.15291, loss_grounding_ce_3: 0.00001/0.26355, loss_mask_ce_4: 0.09774/0.83071, loss_mask_bce_4: 0.30731/0.30326, loss_mask_dice_4: 0.29030/1.04233, loss_spatial_bce_4: 0.14452/0.11208, loss_spatial_dice_4: 0.12939/0.23676, loss_spatial_ce_4: 0.02099/0.18174, loss_grounding_bce_4: 0.05579/0.07929, loss_grounding_dice_4: 0.04893/0.15523, loss_grounding_ce_4: 0.00001/0.27653, loss_mask_ce_5: 0.09894/0.84503, loss_mask_bce_5: 0.32596/0.30466, loss_mask_dice_5: 0.30327/1.05606, loss_spatial_bce_5: 0.14109/0.11341, loss_spatial_dice_5: 0.12908/0.24251, loss_spatial_ce_5: 0.07462/0.19458, loss_grounding_bce_5: 0.06483/0.08008, loss_grounding_dice_5: 0.06105/0.15718, loss_grounding_ce_5: 0.00003/0.30030, loss_mask_ce_6: 0.13006/0.87474, loss_mask_bce_6: 0.34504/0.30443, loss_mask_dice_6: 0.28328/1.05817, loss_spatial_bce_6: 0.14250/0.11899, loss_spatial_dice_6: 0.12636/0.24727, loss_spatial_ce_6: 0.03450/0.22026, loss_grounding_bce_6: 0.07516/0.08126, loss_grounding_dice_6: 0.04825/0.15719, loss_grounding_ce_6: 0.00006/0.32464, loss_mask_ce_7: 0.24091/0.94416, loss_mask_bce_7: 0.32136/0.31368, loss_mask_dice_7: 0.32763/1.10365, loss_spatial_bce_7: 0.20020/0.13245, loss_spatial_dice_7: 0.22316/0.27495, loss_spatial_ce_7: 0.05874/0.27716, loss_grounding_bce_7: 0.06609/0.08377, loss_grounding_dice_7: 0.04580/0.16284, loss_grounding_ce_7: 0.00088/0.39443, loss_mask_ce_8: 0.27463/1.11949, loss_mask_bce_8: 0.34777/0.33068, loss_mask_dice_8: 0.33658/1.18446, loss_spatial_bce_8: 0.24904/0.15393, loss_spatial_dice_8: 0.21771/0.33117, loss_spatial_ce_8: 0.13186/0.32931, loss_grounding_bce_8: 0.05841/0.08712, loss_grounding_dice_8: 0.05033/0.17084, loss_grounding_ce_8: 0.01419/0.51563, loss_mask_ce_9: 2.94966/3.64071, loss_mask_bce_9: 0.34803/0.36542, loss_mask_dice_9: 0.58928/1.80463, loss_spatial_bce_9: 1.15926/0.38221, loss_spatial_dice_9: 0.85596/0.80528, loss_spatial_ce_9: 1.27449/1.54198, loss_grounding_bce_9: 0.08265/0.10119, loss_grounding_dice_9: 0.12039/0.25451, loss_grounding_ce_9: 0.54527/0.87016] items per batch[64] items per second[0.13] total items[121600] mini batches[ 1900] memory[4924] epoch remaining[0:57:20] INFO:trainer.default_trainer:epochs[ 1] optim steps[2000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11649/0.81901, loss_mask_bce_0: 0.42936/0.29911, loss_mask_dice_0: 1.13883/1.01822, loss_spatial_bce_0: 0.10767/0.10400, loss_spatial_dice_0: 0.47357/0.21947, loss_spatial_ce_0: 0.09916/0.14782, loss_grounding_bce_0: 0.19807/0.07704, loss_grounding_dice_0: 0.23286/0.15232, loss_grounding_ce_0: 0.00494/0.26472, loss_mask_ce_1: 1.05765/0.82078, loss_mask_bce_1: 0.41942/0.29964, loss_mask_dice_1: 1.09287/1.02295, loss_spatial_bce_1: 0.11391/0.10579, loss_spatial_dice_1: 0.46705/0.22333, loss_spatial_ce_1: 0.12639/0.15061, loss_grounding_bce_1: 0.19185/0.07692, loss_grounding_dice_1: 0.24135/0.15329, loss_grounding_ce_1: 0.00559/0.26524, loss_mask_ce_2: 1.05836/0.83007, loss_mask_bce_2: 0.42486/0.29961, loss_mask_dice_2: 0.77339/1.03081, loss_spatial_bce_2: 0.11136/0.10543, loss_spatial_dice_2: 0.47172/0.22507, loss_spatial_ce_2: 0.15004/0.16201, loss_grounding_bce_2: 0.18659/0.07737, loss_grounding_dice_2: 0.24522/0.15303, loss_grounding_ce_2: 0.00450/0.26366, loss_mask_ce_3: 1.07909/0.81863, loss_mask_bce_3: 0.41099/0.30252, loss_mask_dice_3: 1.00207/1.02503, loss_spatial_bce_3: 0.13182/0.10789, loss_spatial_dice_3: 0.47591/0.22635, loss_spatial_ce_3: 0.18005/0.17253, loss_grounding_bce_3: 0.18673/0.07759, loss_grounding_dice_3: 0.24226/0.15302, loss_grounding_ce_3: 0.00417/0.26188, loss_mask_ce_4: 1.10852/0.82611, loss_mask_bce_4: 0.41978/0.30359, loss_mask_dice_4: 1.14916/1.04197, loss_spatial_bce_4: 0.14339/0.11099, loss_spatial_dice_4: 0.48831/0.23441, loss_spatial_ce_4: 0.14041/0.17788, loss_grounding_bce_4: 0.19100/0.07906, loss_grounding_dice_4: 0.23946/0.15534, loss_grounding_ce_4: 0.00284/0.27485, loss_mask_ce_5: 1.26045/0.84177, loss_mask_bce_5: 0.41757/0.30469, loss_mask_dice_5: 0.95459/1.05468, loss_spatial_bce_5: 0.09159/0.11228, loss_spatial_dice_5: 0.47680/0.24002, loss_spatial_ce_5: 0.13436/0.19119, loss_grounding_bce_5: 0.18577/0.07975, loss_grounding_dice_5: 0.23416/0.15731, loss_grounding_ce_5: 0.00345/0.29702, loss_mask_ce_6: 1.33644/0.86919, loss_mask_bce_6: 0.41485/0.30460, loss_mask_dice_6: 1.08477/1.05767, loss_spatial_bce_6: 0.13230/0.11758, loss_spatial_dice_6: 0.48048/0.24457, loss_spatial_ce_6: 0.19682/0.21617, loss_grounding_bce_6: 0.17614/0.08097, loss_grounding_dice_6: 0.23280/0.15744, loss_grounding_ce_6: 0.00416/0.32183, loss_mask_ce_7: 1.36692/0.93894, loss_mask_bce_7: 0.42270/0.31348, loss_mask_dice_7: 1.09476/1.10228, loss_spatial_bce_7: 0.10025/0.13110, loss_spatial_dice_7: 0.49658/0.27248, loss_spatial_ce_7: 0.74044/0.27256, loss_grounding_bce_7: 0.17956/0.08348, loss_grounding_dice_7: 0.24274/0.16302, loss_grounding_ce_7: 0.00394/0.39057, loss_mask_ce_8: 1.26816/1.11431, loss_mask_bce_8: 0.43591/0.33079, loss_mask_dice_8: 1.19712/1.18254, loss_spatial_bce_8: 0.14514/0.15328, loss_spatial_dice_8: 0.47629/0.32846, loss_spatial_ce_8: 0.68971/0.32494, loss_grounding_bce_8: 0.19120/0.08710, loss_grounding_dice_8: 0.25744/0.17139, loss_grounding_ce_8: 0.01091/0.51284, loss_mask_ce_9: 3.66719/3.62847, loss_mask_bce_9: 0.54282/0.36548, loss_mask_dice_9: 4.06281/1.80484, loss_spatial_bce_9: 0.38026/0.38227, loss_spatial_dice_9: 0.72692/0.80503, loss_spatial_ce_9: 1.23783/1.53358, loss_grounding_bce_9: 0.17824/0.10079, loss_grounding_dice_9: 0.24669/0.25441, loss_grounding_ce_9: 0.02351/0.86045] items per batch[64] items per second[0.33] total items[128000] mini batches[ 2000] memory[4924] epoch remaining[0:53:20] INFO:trainer.default_trainer:epochs[ 1] optim steps[2100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02386/0.81183, loss_mask_bce_0: 0.08902/0.30063, loss_mask_dice_0: 0.07210/1.01104, loss_spatial_bce_0: 0.06030/0.10387, loss_spatial_dice_0: 0.05391/0.21788, loss_spatial_ce_0: 0.00151/0.14342, loss_grounding_bce_0: 0.04837/0.07717, loss_grounding_dice_0: 0.02554/0.15201, loss_grounding_ce_0: 0.00376/0.26599, loss_mask_ce_1: 0.02230/0.81290, loss_mask_bce_1: 0.09897/0.30149, loss_mask_dice_1: 0.07684/1.01531, loss_spatial_bce_1: 0.06294/0.10548, loss_spatial_dice_1: 0.05947/0.22152, loss_spatial_ce_1: 0.00300/0.14650, loss_grounding_bce_1: 0.05168/0.07708, loss_grounding_dice_1: 0.03090/0.15308, loss_grounding_ce_1: 0.00369/0.26620, loss_mask_ce_2: 0.02271/0.82277, loss_mask_bce_2: 0.09124/0.30111, loss_mask_dice_2: 0.07555/1.02296, loss_spatial_bce_2: 0.06217/0.10512, loss_spatial_dice_2: 0.05309/0.22316, loss_spatial_ce_2: 0.00410/0.15742, loss_grounding_bce_2: 0.05035/0.07753, loss_grounding_dice_2: 0.02348/0.15290, loss_grounding_ce_2: 0.00317/0.26376, loss_mask_ce_3: 0.02574/0.81124, loss_mask_bce_3: 0.08745/0.30408, loss_mask_dice_3: 0.06966/1.01703, loss_spatial_bce_3: 0.06783/0.10763, loss_spatial_dice_3: 0.05577/0.22430, loss_spatial_ce_3: 0.00151/0.16770, loss_grounding_bce_3: 0.04985/0.07774, loss_grounding_dice_3: 0.03261/0.15276, loss_grounding_ce_3: 0.00368/0.26345, loss_mask_ce_4: 0.02697/0.81813, loss_mask_bce_4: 0.09268/0.30539, loss_mask_dice_4: 0.07707/1.03350, loss_spatial_bce_4: 0.06935/0.11051, loss_spatial_dice_4: 0.05422/0.23225, loss_spatial_ce_4: 0.00253/0.17337, loss_grounding_bce_4: 0.05203/0.07909, loss_grounding_dice_4: 0.03175/0.15517, loss_grounding_ce_4: 0.00719/0.27549, loss_mask_ce_5: 0.02276/0.83445, loss_mask_bce_5: 0.08977/0.30626, loss_mask_dice_5: 0.07587/1.04608, loss_spatial_bce_5: 0.06674/0.11165, loss_spatial_dice_5: 0.05571/0.23762, loss_spatial_ce_5: 0.00184/0.18643, loss_grounding_bce_5: 0.04741/0.07993, loss_grounding_dice_5: 0.02891/0.15696, loss_grounding_ce_5: 0.00350/0.29876, loss_mask_ce_6: 0.01590/0.86075, loss_mask_bce_6: 0.09897/0.30616, loss_mask_dice_6: 0.08325/1.04956, loss_spatial_bce_6: 0.06546/0.11711, loss_spatial_dice_6: 0.05593/0.24186, loss_spatial_ce_6: 0.00215/0.21066, loss_grounding_bce_6: 0.05244/0.08116, loss_grounding_dice_6: 0.02828/0.15714, loss_grounding_ce_6: 0.00091/0.32243, loss_mask_ce_7: 0.01134/0.93117, loss_mask_bce_7: 0.08246/0.31506, loss_mask_dice_7: 0.07158/1.09411, loss_spatial_bce_7: 0.06416/0.13033, loss_spatial_dice_7: 0.05874/0.26964, loss_spatial_ce_7: 0.03524/0.26664, loss_grounding_bce_7: 0.04315/0.08370, loss_grounding_dice_7: 0.02531/0.16261, loss_grounding_ce_7: 0.00142/0.39014, loss_mask_ce_8: 0.01281/1.10604, loss_mask_bce_8: 0.10203/0.33232, loss_mask_dice_8: 0.07193/1.17375, loss_spatial_bce_8: 0.06282/0.15264, loss_spatial_dice_8: 0.06886/0.32472, loss_spatial_ce_8: 0.11210/0.32034, loss_grounding_bce_8: 0.04896/0.08725, loss_grounding_dice_8: 0.02317/0.17093, loss_grounding_ce_8: 0.00122/0.51442, loss_mask_ce_9: 1.96244/3.61547, loss_mask_bce_9: 0.10706/0.36673, loss_mask_dice_9: 0.08431/1.79005, loss_spatial_bce_9: 0.33298/0.38270, loss_spatial_dice_9: 0.34093/0.80420, loss_spatial_ce_9: 0.43590/1.52190, loss_grounding_bce_9: 0.06106/0.10145, loss_grounding_dice_9: 0.06153/0.25320, loss_grounding_ce_9: 0.11699/0.86210] items per batch[64] items per second[0.35] total items[134400] mini batches[ 2100] memory[4924] epoch remaining[0:49:14] INFO:trainer.default_trainer:epochs[ 1] optim steps[2200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80227/0.81080, loss_mask_bce_0: 0.29415/0.30087, loss_mask_dice_0: 0.29619/1.01365, loss_spatial_bce_0: 0.10100/0.10354, loss_spatial_dice_0: 0.10313/0.21760, loss_spatial_ce_0: 0.20244/0.14149, loss_grounding_bce_0: 0.17046/0.07751, loss_grounding_dice_0: 0.10113/0.15246, loss_grounding_ce_0: 0.00017/0.26493, loss_mask_ce_1: 0.84622/0.81327, loss_mask_bce_1: 0.31365/0.30177, loss_mask_dice_1: 0.31089/1.01885, loss_spatial_bce_1: 0.10540/0.10510, loss_spatial_dice_1: 0.10564/0.22109, loss_spatial_ce_1: 0.15645/0.14411, loss_grounding_bce_1: 0.16634/0.07748, loss_grounding_dice_1: 0.10556/0.15352, loss_grounding_ce_1: 0.00032/0.26649, loss_mask_ce_2: 0.83261/0.82247, loss_mask_bce_2: 0.31121/0.30131, loss_mask_dice_2: 0.30345/1.02661, loss_spatial_bce_2: 0.10474/0.10468, loss_spatial_dice_2: 0.10744/0.22264, loss_spatial_ce_2: 0.14350/0.15432, loss_grounding_bce_2: 0.16428/0.07790, loss_grounding_dice_2: 0.10190/0.15314, loss_grounding_ce_2: 0.00042/0.26408, loss_mask_ce_3: 0.64803/0.81171, loss_mask_bce_3: 0.30598/0.30430, loss_mask_dice_3: 0.30519/1.02017, loss_spatial_bce_3: 0.11189/0.10720, loss_spatial_dice_3: 0.10885/0.22366, loss_spatial_ce_3: 0.15332/0.16437, loss_grounding_bce_3: 0.16098/0.07814, loss_grounding_dice_3: 0.09720/0.15323, loss_grounding_ce_3: 0.00058/0.26319, loss_mask_ce_4: 0.94139/0.81638, loss_mask_bce_4: 0.32577/0.30543, loss_mask_dice_4: 0.31674/1.03680, loss_spatial_bce_4: 0.10877/0.11001, loss_spatial_dice_4: 0.10930/0.23147, loss_spatial_ce_4: 0.14896/0.17044, loss_grounding_bce_4: 0.15890/0.07939, loss_grounding_dice_4: 0.09976/0.15566, loss_grounding_ce_4: 0.00032/0.27481, loss_mask_ce_5: 0.73317/0.83363, loss_mask_bce_5: 0.28498/0.30641, loss_mask_dice_5: 0.30281/1.05087, loss_spatial_bce_5: 0.10232/0.11127, loss_spatial_dice_5: 0.11878/0.23683, loss_spatial_ce_5: 0.15903/0.18323, loss_grounding_bce_5: 0.15808/0.08021, loss_grounding_dice_5: 0.09969/0.15706, loss_grounding_ce_5: 0.00118/0.29658, loss_mask_ce_6: 0.71232/0.85976, loss_mask_bce_6: 0.27408/0.30643, loss_mask_dice_6: 0.30519/1.05267, loss_spatial_bce_6: 0.10765/0.11674, loss_spatial_dice_6: 0.11437/0.24079, loss_spatial_ce_6: 0.18004/0.20688, loss_grounding_bce_6: 0.17254/0.08146, loss_grounding_dice_6: 0.10192/0.15744, loss_grounding_ce_6: 0.00245/0.32069, loss_mask_ce_7: 0.72904/0.92890, loss_mask_bce_7: 0.26023/0.31527, loss_mask_dice_7: 0.30893/1.09706, loss_spatial_bce_7: 0.10252/0.12990, loss_spatial_dice_7: 0.11888/0.26858, loss_spatial_ce_7: 0.29222/0.26280, loss_grounding_bce_7: 0.16053/0.08388, loss_grounding_dice_7: 0.10404/0.16273, loss_grounding_ce_7: 0.00636/0.38622, loss_mask_ce_8: 0.74310/1.10212, loss_mask_bce_8: 0.27367/0.33241, loss_mask_dice_8: 0.31604/1.17792, loss_spatial_bce_8: 0.11208/0.15207, loss_spatial_dice_8: 0.11062/0.32314, loss_spatial_ce_8: 0.35588/0.31781, loss_grounding_bce_8: 0.14864/0.08758, loss_grounding_dice_8: 0.10363/0.17121, loss_grounding_ce_8: 0.00837/0.50996, loss_mask_ce_9: 2.70897/3.60996, loss_mask_bce_9: 0.37238/0.36579, loss_mask_dice_9: 0.43977/1.78900, loss_spatial_bce_9: 1.01839/0.38173, loss_spatial_dice_9: 0.73512/0.80416, loss_spatial_ce_9: 2.07876/1.51749, loss_grounding_bce_9: 0.16393/0.10159, loss_grounding_dice_9: 0.08714/0.25334, loss_grounding_ce_9: 0.22524/0.85297] items per batch[64] items per second[0.35] total items[140800] mini batches[ 2200] memory[4924] epoch remaining[0:45:37] INFO:trainer.default_trainer:epochs[ 1] optim steps[2300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63348/0.81234, loss_mask_bce_0: 0.42157/0.30291, loss_mask_dice_0: 1.04374/1.02121, loss_spatial_bce_0: 0.08371/0.10345, loss_spatial_dice_0: 0.22817/0.21722, loss_spatial_ce_0: 0.11791/0.14042, loss_grounding_bce_0: 0.02277/0.07848, loss_grounding_dice_0: 0.18734/0.15311, loss_grounding_ce_0: 0.41935/0.26607, loss_mask_ce_1: 1.72736/0.81399, loss_mask_bce_1: 0.46917/0.30360, loss_mask_dice_1: 1.16487/1.02585, loss_spatial_bce_1: 0.08952/0.10488, loss_spatial_dice_1: 0.25209/0.22051, loss_spatial_ce_1: 0.10779/0.14292, loss_grounding_bce_1: 0.02292/0.07840, loss_grounding_dice_1: 0.19861/0.15411, loss_grounding_ce_1: 0.37344/0.26725, loss_mask_ce_2: 1.99296/0.82354, loss_mask_bce_2: 0.44027/0.30344, loss_mask_dice_2: 1.14224/1.03519, loss_spatial_bce_2: 0.09191/0.10456, loss_spatial_dice_2: 0.23356/0.22190, loss_spatial_ce_2: 0.11617/0.15254, loss_grounding_bce_2: 0.02397/0.07864, loss_grounding_dice_2: 0.20640/0.15366, loss_grounding_ce_2: 0.43868/0.26549, loss_mask_ce_3: 2.18228/0.81355, loss_mask_bce_3: 0.44975/0.30607, loss_mask_dice_3: 1.24246/1.02823, loss_spatial_bce_3: 0.08617/0.10698, loss_spatial_dice_3: 0.21649/0.22270, loss_spatial_ce_3: 0.14658/0.16247, loss_grounding_bce_3: 0.02170/0.07893, loss_grounding_dice_3: 0.21622/0.15356, loss_grounding_ce_3: 0.45474/0.26492, loss_mask_ce_4: 2.22711/0.81871, loss_mask_bce_4: 0.47561/0.30741, loss_mask_dice_4: 1.22402/1.04492, loss_spatial_bce_4: 0.07540/0.10976, loss_spatial_dice_4: 0.25003/0.23042, loss_spatial_ce_4: 0.33733/0.16872, loss_grounding_bce_4: 0.02554/0.08016, loss_grounding_dice_4: 0.22646/0.15627, loss_grounding_ce_4: 0.45410/0.27741, loss_mask_ce_5: 1.53630/0.83644, loss_mask_bce_5: 0.50017/0.30836, loss_mask_dice_5: 1.22448/1.05993, loss_spatial_bce_5: 0.08294/0.11118, loss_spatial_dice_5: 0.29267/0.23568, loss_spatial_ce_5: 0.20467/0.18125, loss_grounding_bce_5: 0.02725/0.08095, loss_grounding_dice_5: 0.25119/0.15744, loss_grounding_ce_5: 0.37495/0.29743, loss_mask_ce_6: 2.07064/0.86217, loss_mask_bce_6: 0.50241/0.30843, loss_mask_dice_6: 1.25272/1.06198, loss_spatial_bce_6: 0.08670/0.11646, loss_spatial_dice_6: 0.26934/0.23961, loss_spatial_ce_6: 0.19099/0.20447, loss_grounding_bce_6: 0.01752/0.08209, loss_grounding_dice_6: 0.22369/0.15784, loss_grounding_ce_6: 0.52815/0.32139, loss_mask_ce_7: 1.57927/0.93230, loss_mask_bce_7: 0.50291/0.31687, loss_mask_dice_7: 1.10662/1.10587, loss_spatial_bce_7: 0.12511/0.12971, loss_spatial_dice_7: 0.31039/0.26717, loss_spatial_ce_7: 0.37606/0.26068, loss_grounding_bce_7: 0.02297/0.08440, loss_grounding_dice_7: 0.21676/0.16345, loss_grounding_ce_7: 0.50453/0.38924, loss_mask_ce_8: 2.29346/1.10659, loss_mask_bce_8: 0.57754/0.33429, loss_mask_dice_8: 1.40548/1.18893, loss_spatial_bce_8: 0.12889/0.15196, loss_spatial_dice_8: 0.43941/0.32157, loss_spatial_ce_8: 0.47007/0.31503, loss_grounding_bce_8: 0.03040/0.08838, loss_grounding_dice_8: 0.26779/0.17245, loss_grounding_ce_8: 0.57822/0.51768, loss_mask_ce_9: 5.09902/3.61792, loss_mask_bce_9: 0.38775/0.36763, loss_mask_dice_9: 2.38598/1.80533, loss_spatial_bce_9: 0.37239/0.38014, loss_spatial_dice_9: 0.93456/0.80454, loss_spatial_ce_9: 2.12268/1.51256, loss_grounding_bce_9: 0.03331/0.10227, loss_grounding_dice_9: 0.42896/0.25465, loss_grounding_ce_9: 0.68572/0.85311] items per batch[64] items per second[0.34] total items[147200] mini batches[ 2300] memory[4929] epoch remaining[0:42:26] INFO:trainer.default_trainer:epochs[ 1] optim steps[2400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55257/0.81220, loss_mask_bce_0: 0.38158/0.30282, loss_mask_dice_0: 0.57096/1.02201, loss_spatial_bce_0: 0.13583/0.10274, loss_spatial_dice_0: 0.15052/0.21658, loss_spatial_ce_0: 0.01605/0.13843, loss_grounding_bce_0: 0.07140/0.07824, loss_grounding_dice_0: 0.11178/0.15389, loss_grounding_ce_0: 0.01681/0.26603, loss_mask_ce_1: 0.22243/0.81419, loss_mask_bce_1: 0.50377/0.30376, loss_mask_dice_1: 0.67326/1.02655, loss_spatial_bce_1: 0.13717/0.10411, loss_spatial_dice_1: 0.14869/0.21988, loss_spatial_ce_1: 0.01102/0.14076, loss_grounding_bce_1: 0.07107/0.07830, loss_grounding_dice_1: 0.10045/0.15478, loss_grounding_ce_1: 0.01613/0.26818, loss_mask_ce_2: 0.24238/0.82353, loss_mask_bce_2: 0.49314/0.30343, loss_mask_dice_2: 0.69654/1.03611, loss_spatial_bce_2: 0.10054/0.10383, loss_spatial_dice_2: 0.12743/0.22115, loss_spatial_ce_2: 0.13188/0.15020, loss_grounding_bce_2: 0.05058/0.07841, loss_grounding_dice_2: 0.08156/0.15470, loss_grounding_ce_2: 0.01458/0.26587, loss_mask_ce_3: 0.71456/0.81425, loss_mask_bce_3: 0.37708/0.30603, loss_mask_dice_3: 0.53407/1.02908, loss_spatial_bce_3: 0.14579/0.10611, loss_spatial_dice_3: 0.15153/0.22189, loss_spatial_ce_3: 0.02779/0.16035, loss_grounding_bce_3: 0.07049/0.07867, loss_grounding_dice_3: 0.10142/0.15433, loss_grounding_ce_3: 0.00856/0.26522, loss_mask_ce_4: 0.58488/0.81850, loss_mask_bce_4: 0.37007/0.30750, loss_mask_dice_4: 0.51461/1.04585, loss_spatial_bce_4: 0.12119/0.10884, loss_spatial_dice_4: 0.15315/0.22966, loss_spatial_ce_4: 0.09045/0.16685, loss_grounding_bce_4: 0.06978/0.07988, loss_grounding_dice_4: 0.10095/0.15713, loss_grounding_ce_4: 0.01339/0.27721, loss_mask_ce_5: 0.55810/0.83703, loss_mask_bce_5: 0.37292/0.30861, loss_mask_dice_5: 0.48334/1.06046, loss_spatial_bce_5: 0.12841/0.11024, loss_spatial_dice_5: 0.13873/0.23487, loss_spatial_ce_5: 0.01049/0.17839, loss_grounding_bce_5: 0.05934/0.08063, loss_grounding_dice_5: 0.08940/0.15815, loss_grounding_ce_5: 0.00791/0.29580, loss_mask_ce_6: 0.58277/0.86271, loss_mask_bce_6: 0.35134/0.30864, loss_mask_dice_6: 0.49463/1.06335, loss_spatial_bce_6: 0.16289/0.11533, loss_spatial_dice_6: 0.15537/0.23856, loss_spatial_ce_6: 0.05650/0.20140, loss_grounding_bce_6: 0.07296/0.08192, loss_grounding_dice_6: 0.10247/0.15847, loss_grounding_ce_6: 0.00771/0.31986, loss_mask_ce_7: 0.35843/0.93273, loss_mask_bce_7: 0.49989/0.31758, loss_mask_dice_7: 0.62519/1.10701, loss_spatial_bce_7: 0.15641/0.12838, loss_spatial_dice_7: 0.16872/0.26582, loss_spatial_ce_7: 0.05907/0.25755, loss_grounding_bce_7: 0.07735/0.08425, loss_grounding_dice_7: 0.09846/0.16413, loss_grounding_ce_7: 0.00580/0.38708, loss_mask_ce_8: 0.69134/1.10597, loss_mask_bce_8: 0.49692/0.33486, loss_mask_dice_8: 0.52118/1.19174, loss_spatial_bce_8: 0.18479/0.15022, loss_spatial_dice_8: 0.24246/0.31996, loss_spatial_ce_8: 0.11168/0.31179, loss_grounding_bce_8: 0.07247/0.08815, loss_grounding_dice_8: 0.09947/0.17314, loss_grounding_ce_8: 0.00391/0.51182, loss_mask_ce_9: 2.87893/3.61723, loss_mask_bce_9: 0.48739/0.36744, loss_mask_dice_9: 1.12357/1.80700, loss_spatial_bce_9: 0.45668/0.37860, loss_spatial_dice_9: 0.82079/0.80531, loss_spatial_ce_9: 1.32433/1.51248, loss_grounding_bce_9: 0.09211/0.10182, loss_grounding_dice_9: 0.34964/0.25580, loss_grounding_ce_9: 0.03796/0.84452] items per batch[64] items per second[0.33] total items[153600] mini batches[ 2400] memory[4929] epoch remaining[0:39:26] INFO:trainer.default_trainer:epochs[ 1] optim steps[2500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10855/0.80996, loss_mask_bce_0: 0.23053/0.30276, loss_mask_dice_0: 0.26532/1.01323, loss_spatial_bce_0: 0.08561/0.10273, loss_spatial_dice_0: 0.09658/0.21558, loss_spatial_ce_0: 0.00580/0.13585, loss_grounding_bce_0: 0.08721/0.07833, loss_grounding_dice_0: 0.15915/0.15302, loss_grounding_ce_0: 0.01085/0.26801, loss_mask_ce_1: 0.09968/0.81214, loss_mask_bce_1: 0.23121/0.30368, loss_mask_dice_1: 0.26100/1.01801, loss_spatial_bce_1: 0.08907/0.10406, loss_spatial_dice_1: 0.10490/0.21885, loss_spatial_ce_1: 0.00540/0.13830, loss_grounding_bce_1: 0.08539/0.07840, loss_grounding_dice_1: 0.13476/0.15427, loss_grounding_ce_1: 0.01414/0.27063, loss_mask_ce_2: 0.08290/0.82078, loss_mask_bce_2: 0.22577/0.30345, loss_mask_dice_2: 0.24289/1.02743, loss_spatial_bce_2: 0.08744/0.10373, loss_spatial_dice_2: 0.09164/0.22010, loss_spatial_ce_2: 0.00777/0.14735, loss_grounding_bce_2: 0.08612/0.07850, loss_grounding_dice_2: 0.15240/0.15409, loss_grounding_ce_2: 0.00786/0.26807, loss_mask_ce_3: 0.09614/0.81178, loss_mask_bce_3: 0.22025/0.30583, loss_mask_dice_3: 0.24263/1.02045, loss_spatial_bce_3: 0.08676/0.10587, loss_spatial_dice_3: 0.09772/0.22073, loss_spatial_ce_3: 0.00886/0.15729, loss_grounding_bce_3: 0.08707/0.07875, loss_grounding_dice_3: 0.15255/0.15355, loss_grounding_ce_3: 0.00795/0.26797, loss_mask_ce_4: 0.09441/0.81603, loss_mask_bce_4: 0.21939/0.30741, loss_mask_dice_4: 0.23603/1.03778, loss_spatial_bce_4: 0.08487/0.10872, loss_spatial_dice_4: 0.09711/0.22850, loss_spatial_ce_4: 0.01369/0.16419, loss_grounding_bce_4: 0.08278/0.07991, loss_grounding_dice_4: 0.13266/0.15665, loss_grounding_ce_4: 0.00978/0.28058, loss_mask_ce_5: 0.11987/0.83567, loss_mask_bce_5: 0.21426/0.30844, loss_mask_dice_5: 0.23173/1.05158, loss_spatial_bce_5: 0.08706/0.11019, loss_spatial_dice_5: 0.10572/0.23366, loss_spatial_ce_5: 0.03816/0.17532, loss_grounding_bce_5: 0.08762/0.08065, loss_grounding_dice_5: 0.13322/0.15754, loss_grounding_ce_5: 0.01267/0.29817, loss_mask_ce_6: 0.08595/0.86124, loss_mask_bce_6: 0.23467/0.30874, loss_mask_dice_6: 0.22911/1.05487, loss_spatial_bce_6: 0.09418/0.11522, loss_spatial_dice_6: 0.10765/0.23711, loss_spatial_ce_6: 0.06277/0.19811, loss_grounding_bce_6: 0.08335/0.08204, loss_grounding_dice_6: 0.13608/0.15793, loss_grounding_ce_6: 0.00539/0.32287, loss_mask_ce_7: 0.06070/0.93150, loss_mask_bce_7: 0.22421/0.31770, loss_mask_dice_7: 0.26769/1.09779, loss_spatial_bce_7: 0.08774/0.12848, loss_spatial_dice_7: 0.10302/0.26436, loss_spatial_ce_7: 0.14055/0.25324, loss_grounding_bce_7: 0.07888/0.08418, loss_grounding_dice_7: 0.13253/0.16315, loss_grounding_ce_7: 0.00814/0.39119, loss_mask_ce_8: 0.08034/1.10212, loss_mask_bce_8: 0.20702/0.33517, loss_mask_dice_8: 0.27660/1.18192, loss_spatial_bce_8: 0.10893/0.14989, loss_spatial_dice_8: 0.12670/0.31794, loss_spatial_ce_8: 0.19276/0.30749, loss_grounding_bce_8: 0.07646/0.08810, loss_grounding_dice_8: 0.15898/0.17220, loss_grounding_ce_8: 0.01788/0.51233, loss_mask_ce_9: 2.19135/3.60781, loss_mask_bce_9: 0.23613/0.36685, loss_mask_dice_9: 0.59663/1.79747, loss_spatial_bce_9: 0.48821/0.37827, loss_spatial_dice_9: 0.79594/0.80517, loss_spatial_ce_9: 1.25032/1.50797, loss_grounding_bce_9: 0.09932/0.10137, loss_grounding_dice_9: 0.37473/0.25393, loss_grounding_ce_9: 0.02465/0.84216] items per batch[64] items per second[0.34] total items[160000] mini batches[ 2500] memory[4929] epoch remaining[0:36:14] INFO:trainer.default_trainer:epochs[ 1] optim steps[2600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27627/0.81186, loss_mask_bce_0: 0.00323/0.30277, loss_mask_dice_0: 0.25424/1.01475, loss_spatial_bce_0: 0.00437/0.10238, loss_spatial_dice_0: 0.31710/0.21504, loss_spatial_ce_0: 0.21500/0.13389, loss_grounding_bce_0: 0.00267/0.07883, loss_grounding_dice_0: 0.36946/0.15317, loss_grounding_ce_0: 0.13938/0.26744, loss_mask_ce_1: 0.26301/0.81401, loss_mask_bce_1: 0.00389/0.30360, loss_mask_dice_1: 0.33048/1.01971, loss_spatial_bce_1: 0.00565/0.10355, loss_spatial_dice_1: 0.33433/0.21821, loss_spatial_ce_1: 0.20920/0.13644, loss_grounding_bce_1: 0.00262/0.07894, loss_grounding_dice_1: 0.31437/0.15466, loss_grounding_ce_1: 0.12319/0.26935, loss_mask_ce_2: 0.28960/0.82311, loss_mask_bce_2: 0.00270/0.30346, loss_mask_dice_2: 0.27616/1.02872, loss_spatial_bce_2: 0.00491/0.10327, loss_spatial_dice_2: 0.31424/0.21935, loss_spatial_ce_2: 0.16353/0.14506, loss_grounding_bce_2: 0.00445/0.07905, loss_grounding_dice_2: 0.41499/0.15475, loss_grounding_ce_2: 0.11425/0.26725, loss_mask_ce_3: 0.23204/0.81403, loss_mask_bce_3: 0.00305/0.30606, loss_mask_dice_3: 0.25700/1.02265, loss_spatial_bce_3: 0.00327/0.10549, loss_spatial_dice_3: 0.33322/0.22009, loss_spatial_ce_3: 0.26650/0.15476, loss_grounding_bce_3: 0.00368/0.07943, loss_grounding_dice_3: 0.38757/0.15406, loss_grounding_ce_3: 0.10885/0.26678, loss_mask_ce_4: 0.28114/0.81731, loss_mask_bce_4: 0.00389/0.30793, loss_mask_dice_4: 0.33996/1.03997, loss_spatial_bce_4: 0.00844/0.10820, loss_spatial_dice_4: 0.35273/0.22766, loss_spatial_ce_4: 0.35327/0.16198, loss_grounding_bce_4: 0.00266/0.08045, loss_grounding_dice_4: 0.26521/0.15690, loss_grounding_ce_4: 0.12549/0.27863, loss_mask_ce_5: 0.39143/0.83676, loss_mask_bce_5: 0.00600/0.30918, loss_mask_dice_5: 0.39010/1.05259, loss_spatial_bce_5: 0.03512/0.10960, loss_spatial_dice_5: 0.34798/0.23269, loss_spatial_ce_5: 0.39818/0.17273, loss_grounding_bce_5: 0.00476/0.08121, loss_grounding_dice_5: 0.39636/0.15808, loss_grounding_ce_5: 0.11461/0.29657, loss_mask_ce_6: 0.56475/0.86183, loss_mask_bce_6: 0.00521/0.30880, loss_mask_dice_6: 0.38499/1.05600, loss_spatial_bce_6: 0.06942/0.11448, loss_spatial_dice_6: 0.34983/0.23610, loss_spatial_ce_6: 0.46885/0.19487, loss_grounding_bce_6: 0.00465/0.08249, loss_grounding_dice_6: 0.39981/0.15836, loss_grounding_ce_6: 0.16233/0.32030, loss_mask_ce_7: 0.92245/0.93209, loss_mask_bce_7: 0.00410/0.31829, loss_mask_dice_7: 0.25643/1.09905, loss_spatial_bce_7: 0.03211/0.12776, loss_spatial_dice_7: 0.37291/0.26322, loss_spatial_ce_7: 0.41255/0.24988, loss_grounding_bce_7: 0.00412/0.08461, loss_grounding_dice_7: 0.37573/0.16373, loss_grounding_ce_7: 0.47304/0.38737, loss_mask_ce_8: 0.68506/1.10304, loss_mask_bce_8: 0.00336/0.33556, loss_mask_dice_8: 0.22228/1.18183, loss_spatial_bce_8: 0.16535/0.14921, loss_spatial_dice_8: 0.38716/0.31662, loss_spatial_ce_8: 0.18858/0.30458, loss_grounding_bce_8: 0.00678/0.08848, loss_grounding_dice_8: 0.41438/0.17247, loss_grounding_ce_8: 0.32890/0.50892, loss_mask_ce_9: 2.96635/3.60998, loss_mask_bce_9: 0.00748/0.36646, loss_mask_dice_9: 0.44453/1.79583, loss_spatial_bce_9: 0.05747/0.37811, loss_spatial_dice_9: 0.74347/0.80544, loss_spatial_ce_9: 0.67390/1.50383, loss_grounding_bce_9: 0.00578/0.10138, loss_grounding_dice_9: 0.36463/0.25390, loss_grounding_ce_9: 0.75016/0.83673] items per batch[64] items per second[0.35] total items[166400] mini batches[ 2600] memory[4929] epoch remaining[0:32:59] INFO:trainer.default_trainer:epochs[ 1] optim steps[2700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79973/0.81459, loss_mask_bce_0: 0.10192/0.30281, loss_mask_dice_0: 0.16678/1.01358, loss_spatial_bce_0: 0.05944/0.10198, loss_spatial_dice_0: 0.08635/0.21412, loss_spatial_ce_0: 0.03189/0.13163, loss_grounding_bce_0: 0.07008/0.07956, loss_grounding_dice_0: 0.12874/0.15344, loss_grounding_ce_0: 0.33111/0.26608, loss_mask_ce_1: 0.63010/0.81697, loss_mask_bce_1: 0.11019/0.30363, loss_mask_dice_1: 0.15811/1.01844, loss_spatial_bce_1: 0.05385/0.10315, loss_spatial_dice_1: 0.09415/0.21730, loss_spatial_ce_1: 0.01627/0.13505, loss_grounding_bce_1: 0.08185/0.07977, loss_grounding_dice_1: 0.11761/0.15482, loss_grounding_ce_1: 0.33973/0.26798, loss_mask_ce_2: 0.95482/0.82631, loss_mask_bce_2: 0.08015/0.30371, loss_mask_dice_2: 0.13665/1.02590, loss_spatial_bce_2: 0.05476/0.10283, loss_spatial_dice_2: 0.09903/0.21851, loss_spatial_ce_2: 0.00612/0.14312, loss_grounding_bce_2: 0.07071/0.07986, loss_grounding_dice_2: 0.11500/0.15484, loss_grounding_ce_2: 0.37456/0.26571, loss_mask_ce_3: 0.94498/0.81783, loss_mask_bce_3: 0.07702/0.30614, loss_mask_dice_3: 0.14410/1.02062, loss_spatial_bce_3: 0.06273/0.10501, loss_spatial_dice_3: 0.10136/0.21909, loss_spatial_ce_3: 0.00042/0.15253, loss_grounding_bce_3: 0.05934/0.08022, loss_grounding_dice_3: 0.10694/0.15420, loss_grounding_ce_3: 0.29809/0.26577, loss_mask_ce_4: 1.08804/0.82098, loss_mask_bce_4: 0.10395/0.30828, loss_mask_dice_4: 0.21915/1.03857, loss_spatial_bce_4: 0.06930/0.10781, loss_spatial_dice_4: 0.12534/0.22673, loss_spatial_ce_4: 0.01331/0.16003, loss_grounding_bce_4: 0.07348/0.08118, loss_grounding_dice_4: 0.16680/0.15735, loss_grounding_ce_4: 0.30442/0.27858, loss_mask_ce_5: 0.97063/0.84055, loss_mask_bce_5: 0.07409/0.30920, loss_mask_dice_5: 0.12923/1.05126, loss_spatial_bce_5: 0.06630/0.10913, loss_spatial_dice_5: 0.10697/0.23163, loss_spatial_ce_5: 0.00715/0.17096, loss_grounding_bce_5: 0.05419/0.08185, loss_grounding_dice_5: 0.09835/0.15829, loss_grounding_ce_5: 0.48928/0.29616, loss_mask_ce_6: 1.35216/0.86524, loss_mask_bce_6: 0.08512/0.30908, loss_mask_dice_6: 0.12572/1.05435, loss_spatial_bce_6: 0.08570/0.11399, loss_spatial_dice_6: 0.18661/0.23489, loss_spatial_ce_6: 0.00443/0.19307, loss_grounding_bce_6: 0.06981/0.08316, loss_grounding_dice_6: 0.10198/0.15867, loss_grounding_ce_6: 0.52887/0.31986, loss_mask_ce_7: 0.94245/0.93701, loss_mask_bce_7: 0.12332/0.31823, loss_mask_dice_7: 0.15361/1.09760, loss_spatial_bce_7: 0.17080/0.12744, loss_spatial_dice_7: 0.34174/0.26198, loss_spatial_ce_7: 0.09471/0.24790, loss_grounding_bce_7: 0.09642/0.08535, loss_grounding_dice_7: 0.11836/0.16405, loss_grounding_ce_7: 0.31906/0.38453, loss_mask_ce_8: 0.42135/1.10703, loss_mask_bce_8: 0.33502/0.33582, loss_mask_dice_8: 0.20468/1.18126, loss_spatial_bce_8: 0.22675/0.14934, loss_spatial_dice_8: 0.38497/0.31533, loss_spatial_ce_8: 0.53020/0.30212, loss_grounding_bce_8: 0.25185/0.08920, loss_grounding_dice_8: 0.15812/0.17248, loss_grounding_ce_8: 0.11159/0.51057, loss_mask_ce_9: 3.62865/3.61290, loss_mask_bce_9: 0.27550/0.36645, loss_mask_dice_9: 0.26525/1.79524, loss_spatial_bce_9: 0.77945/0.37706, loss_spatial_dice_9: 0.71566/0.80518, loss_spatial_ce_9: 0.77644/1.49802, loss_grounding_bce_9: 0.22763/0.10200, loss_grounding_dice_9: 0.31819/0.25359, loss_grounding_ce_9: 0.11032/0.83537] items per batch[64] items per second[0.35] total items[172800] mini batches[ 2700] memory[4929] epoch remaining[0:29:49] INFO:trainer.default_trainer:epochs[ 1] optim steps[2800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62738/0.81350, loss_mask_bce_0: 0.18219/0.30325, loss_mask_dice_0: 0.32659/1.01622, loss_spatial_bce_0: 0.04218/0.10177, loss_spatial_dice_0: 0.20693/0.21316, loss_spatial_ce_0: 0.07091/0.12933, loss_grounding_bce_0: 0.06256/0.07990, loss_grounding_dice_0: 0.07107/0.15325, loss_grounding_ce_0: 0.04282/0.26561, loss_mask_ce_1: 0.66909/0.81534, loss_mask_bce_1: 0.20116/0.30404, loss_mask_dice_1: 0.33359/1.02090, loss_spatial_bce_1: 0.04381/0.10299, loss_spatial_dice_1: 0.18839/0.21629, loss_spatial_ce_1: 0.07993/0.13287, loss_grounding_bce_1: 0.05880/0.08010, loss_grounding_dice_1: 0.07206/0.15457, loss_grounding_ce_1: 0.03558/0.26792, loss_mask_ce_2: 0.66981/0.82366, loss_mask_bce_2: 0.17578/0.30456, loss_mask_dice_2: 0.33519/1.02889, loss_spatial_bce_2: 0.04077/0.10263, loss_spatial_dice_2: 0.20601/0.21743, loss_spatial_ce_2: 0.10209/0.14054, loss_grounding_bce_2: 0.06640/0.08019, loss_grounding_dice_2: 0.07448/0.15475, loss_grounding_ce_2: 0.03525/0.26566, loss_mask_ce_3: 0.73399/0.81465, loss_mask_bce_3: 0.16089/0.30685, loss_mask_dice_3: 0.37842/1.02313, loss_spatial_bce_3: 0.06377/0.10483, loss_spatial_dice_3: 0.23260/0.21813, loss_spatial_ce_3: 0.04572/0.14976, loss_grounding_bce_3: 0.07114/0.08051, loss_grounding_dice_3: 0.07470/0.15409, loss_grounding_ce_3: 0.02225/0.26562, loss_mask_ce_4: 0.84236/0.81869, loss_mask_bce_4: 0.15405/0.30910, loss_mask_dice_4: 0.35248/1.04193, loss_spatial_bce_4: 0.04243/0.10757, loss_spatial_dice_4: 0.22649/0.22571, loss_spatial_ce_4: 0.07879/0.15752, loss_grounding_bce_4: 0.06834/0.08151, loss_grounding_dice_4: 0.07056/0.15710, loss_grounding_ce_4: 0.02129/0.27669, loss_mask_ce_5: 0.69696/0.83890, loss_mask_bce_5: 0.13819/0.30969, loss_mask_dice_5: 0.32317/1.05391, loss_spatial_bce_5: 0.05226/0.10896, loss_spatial_dice_5: 0.22954/0.23043, loss_spatial_ce_5: 0.07409/0.16824, loss_grounding_bce_5: 0.06686/0.08219, loss_grounding_dice_5: 0.06929/0.15813, loss_grounding_ce_5: 0.02478/0.29547, loss_mask_ce_6: 0.60825/0.86334, loss_mask_bce_6: 0.12282/0.30961, loss_mask_dice_6: 0.36721/1.05661, loss_spatial_bce_6: 0.04337/0.11379, loss_spatial_dice_6: 0.24022/0.23370, loss_spatial_ce_6: 0.08448/0.19060, loss_grounding_bce_6: 0.05862/0.08363, loss_grounding_dice_6: 0.06738/0.15852, loss_grounding_ce_6: 0.01757/0.31831, loss_mask_ce_7: 0.82980/0.93531, loss_mask_bce_7: 0.14346/0.31877, loss_mask_dice_7: 0.38296/1.10100, loss_spatial_bce_7: 0.06262/0.12701, loss_spatial_dice_7: 0.19213/0.26055, loss_spatial_ce_7: 0.11811/0.24475, loss_grounding_bce_7: 0.07785/0.08559, loss_grounding_dice_7: 0.07481/0.16380, loss_grounding_ce_7: 0.02177/0.38171, loss_mask_ce_8: 1.21554/1.10480, loss_mask_bce_8: 0.17263/0.33619, loss_mask_dice_8: 0.38559/1.18571, loss_spatial_bce_8: 0.09235/0.14879, loss_spatial_dice_8: 0.27441/0.31353, loss_spatial_ce_8: 0.19260/0.30035, loss_grounding_bce_8: 0.06481/0.08951, loss_grounding_dice_8: 0.06780/0.17259, loss_grounding_ce_8: 0.01728/0.51049, loss_mask_ce_9: 3.28051/3.60561, loss_mask_bce_9: 0.18914/0.36718, loss_mask_dice_9: 0.94447/1.80021, loss_spatial_bce_9: 0.23130/0.37616, loss_spatial_dice_9: 0.87615/0.80435, loss_spatial_ce_9: 0.97504/1.49545, loss_grounding_bce_9: 0.06411/0.10203, loss_grounding_dice_9: 0.09086/0.25310, loss_grounding_ce_9: 0.15044/0.83516] items per batch[64] items per second[0.34] total items[179200] mini batches[ 2800] memory[4929] epoch remaining[0:26:39] INFO:trainer.default_trainer:epochs[ 1] optim steps[2900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30516/0.81632, loss_mask_bce_0: 0.08262/0.30271, loss_mask_dice_0: 0.30049/1.02158, loss_spatial_bce_0: 0.02909/0.10137, loss_spatial_dice_0: 0.10085/0.21293, loss_spatial_ce_0: 0.00012/0.12764, loss_grounding_bce_0: 0.02428/0.08015, loss_grounding_dice_0: 0.07861/0.15338, loss_grounding_ce_0: 0.00828/0.26334, loss_mask_ce_1: 0.33592/0.81792, loss_mask_bce_1: 0.08161/0.30332, loss_mask_dice_1: 0.29951/1.02659, loss_spatial_bce_1: 0.02459/0.10258, loss_spatial_dice_1: 0.09580/0.21606, loss_spatial_ce_1: 0.00130/0.13118, loss_grounding_bce_1: 0.02391/0.08030, loss_grounding_dice_1: 0.07543/0.15448, loss_grounding_ce_1: 0.01072/0.26712, loss_mask_ce_2: 0.34882/0.82576, loss_mask_bce_2: 0.07439/0.30383, loss_mask_dice_2: 0.28708/1.03392, loss_spatial_bce_2: 0.02233/0.10221, loss_spatial_dice_2: 0.08640/0.21721, loss_spatial_ce_2: 0.00078/0.13901, loss_grounding_bce_2: 0.02400/0.08031, loss_grounding_dice_2: 0.07530/0.15477, loss_grounding_ce_2: 0.01422/0.26440, loss_mask_ce_3: 0.33132/0.81682, loss_mask_bce_3: 0.07610/0.30599, loss_mask_dice_3: 0.29093/1.02817, loss_spatial_bce_3: 0.02801/0.10444, loss_spatial_dice_3: 0.09943/0.21779, loss_spatial_ce_3: 0.00223/0.14758, loss_grounding_bce_3: 0.02538/0.08060, loss_grounding_dice_3: 0.07496/0.15411, loss_grounding_ce_3: 0.00829/0.26468, loss_mask_ce_4: 0.39850/0.82178, loss_mask_bce_4: 0.06515/0.30820, loss_mask_dice_4: 0.26661/1.04762, loss_spatial_bce_4: 0.02679/0.10700, loss_spatial_dice_4: 0.09244/0.22533, loss_spatial_ce_4: 0.00042/0.15559, loss_grounding_bce_4: 0.02464/0.08160, loss_grounding_dice_4: 0.07117/0.15701, loss_grounding_ce_4: 0.05279/0.27526, loss_mask_ce_5: 0.30774/0.84162, loss_mask_bce_5: 0.06357/0.30884, loss_mask_dice_5: 0.25486/1.05953, loss_spatial_bce_5: 0.02671/0.10833, loss_spatial_dice_5: 0.09876/0.22985, loss_spatial_ce_5: 0.01960/0.16648, loss_grounding_bce_5: 0.01416/0.08226, loss_grounding_dice_5: 0.06570/0.15800, loss_grounding_ce_5: 0.03766/0.29477, loss_mask_ce_6: 0.30699/0.86665, loss_mask_bce_6: 0.07577/0.30882, loss_mask_dice_6: 0.28531/1.06245, loss_spatial_bce_6: 0.02968/0.11310, loss_spatial_dice_6: 0.09808/0.23320, loss_spatial_ce_6: 0.04598/0.18912, loss_grounding_bce_6: 0.01839/0.08368, loss_grounding_dice_6: 0.07112/0.15833, loss_grounding_ce_6: 0.03186/0.31841, loss_mask_ce_7: 0.32086/0.93810, loss_mask_bce_7: 0.06785/0.31830, loss_mask_dice_7: 0.26153/1.10697, loss_spatial_bce_7: 0.03459/0.12626, loss_spatial_dice_7: 0.10933/0.26000, loss_spatial_ce_7: 0.05850/0.24252, loss_grounding_bce_7: 0.02194/0.08570, loss_grounding_dice_7: 0.08275/0.16370, loss_grounding_ce_7: 0.00773/0.38021, loss_mask_ce_8: 0.35732/1.10798, loss_mask_bce_8: 0.06575/0.33535, loss_mask_dice_8: 0.26844/1.19148, loss_spatial_bce_8: 0.04145/0.14793, loss_spatial_dice_8: 0.17823/0.31231, loss_spatial_ce_8: 0.19295/0.29783, loss_grounding_bce_8: 0.01996/0.08955, loss_grounding_dice_8: 0.07457/0.17242, loss_grounding_ce_8: 0.03316/0.50558, loss_mask_ce_9: 1.75280/3.60274, loss_mask_bce_9: 0.07584/0.36579, loss_mask_dice_9: 0.34858/1.80409, loss_spatial_bce_9: 0.19913/0.37627, loss_spatial_dice_9: 0.80587/0.80424, loss_spatial_ce_9: 1.46186/1.49544, loss_grounding_bce_9: 0.01318/0.10195, loss_grounding_dice_9: 0.08230/0.25250, loss_grounding_ce_9: 0.06250/0.82900] items per batch[64] items per second[0.34] total items[185600] mini batches[ 2900] memory[4929] epoch remaining[0:23:33] INFO:trainer.default_trainer:epochs[ 1] optim steps[3000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.51198/0.81584, loss_mask_bce_0: 0.49641/0.30215, loss_mask_dice_0: 1.40403/1.02438, loss_spatial_bce_0: 0.07021/0.10049, loss_spatial_dice_0: 0.14654/0.21221, loss_spatial_ce_0: 0.00387/0.12638, loss_grounding_bce_0: 0.05916/0.07958, loss_grounding_dice_0: 0.10531/0.15331, loss_grounding_ce_0: 0.40623/0.26095, loss_mask_ce_1: 0.53368/0.81807, loss_mask_bce_1: 0.49166/0.30258, loss_mask_dice_1: 1.41142/1.03004, loss_spatial_bce_1: 0.07370/0.10164, loss_spatial_dice_1: 0.15644/0.21534, loss_spatial_ce_1: 0.00701/0.12975, loss_grounding_bce_1: 0.05922/0.07976, loss_grounding_dice_1: 0.10731/0.15469, loss_grounding_ce_1: 0.40776/0.26466, loss_mask_ce_2: 0.56641/0.82601, loss_mask_bce_2: 0.54809/0.30320, loss_mask_dice_2: 1.40514/1.03684, loss_spatial_bce_2: 0.06998/0.10121, loss_spatial_dice_2: 0.14113/0.21643, loss_spatial_ce_2: 0.00212/0.13714, loss_grounding_bce_2: 0.06083/0.07976, loss_grounding_dice_2: 0.11095/0.15493, loss_grounding_ce_2: 0.39836/0.26234, loss_mask_ce_3: 0.55782/0.81670, loss_mask_bce_3: 0.58705/0.30531, loss_mask_dice_3: 1.36177/1.03055, loss_spatial_bce_3: 0.07385/0.10344, loss_spatial_dice_3: 0.15178/0.21696, loss_spatial_ce_3: 0.00317/0.14566, loss_grounding_bce_3: 0.05984/0.08005, loss_grounding_dice_3: 0.10530/0.15415, loss_grounding_ce_3: 0.40202/0.26263, loss_mask_ce_4: 0.56250/0.82238, loss_mask_bce_4: 0.47870/0.30771, loss_mask_dice_4: 1.39079/1.05101, loss_spatial_bce_4: 0.07254/0.10595, loss_spatial_dice_4: 0.14298/0.22441, loss_spatial_ce_4: 0.00154/0.15433, loss_grounding_bce_4: 0.05529/0.08101, loss_grounding_dice_4: 0.10536/0.15710, loss_grounding_ce_4: 0.39252/0.27327, loss_mask_ce_5: 0.75744/0.84178, loss_mask_bce_5: 0.49244/0.30840, loss_mask_dice_5: 1.22673/1.06238, loss_spatial_bce_5: 0.07430/0.10738, loss_spatial_dice_5: 0.13610/0.22894, loss_spatial_ce_5: 0.00129/0.16486, loss_grounding_bce_5: 0.05829/0.08165, loss_grounding_dice_5: 0.10558/0.15839, loss_grounding_ce_5: 0.40178/0.29216, loss_mask_ce_6: 0.59297/0.86727, loss_mask_bce_6: 0.52691/0.30822, loss_mask_dice_6: 1.42338/1.06541, loss_spatial_bce_6: 0.07869/0.11216, loss_spatial_dice_6: 0.14263/0.23220, loss_spatial_ce_6: 0.00455/0.18727, loss_grounding_bce_6: 0.05796/0.08304, loss_grounding_dice_6: 0.10343/0.15838, loss_grounding_ce_6: 0.40741/0.31547, loss_mask_ce_7: 1.04678/0.93801, loss_mask_bce_7: 0.53791/0.31754, loss_mask_dice_7: 1.26775/1.10999, loss_spatial_bce_7: 0.08547/0.12550, loss_spatial_dice_7: 0.16623/0.25902, loss_spatial_ce_7: 0.07148/0.24101, loss_grounding_bce_7: 0.05631/0.08502, loss_grounding_dice_7: 0.10684/0.16394, loss_grounding_ce_7: 0.42984/0.37634, loss_mask_ce_8: 1.26997/1.10707, loss_mask_bce_8: 0.51594/0.33410, loss_mask_dice_8: 1.46435/1.19398, loss_spatial_bce_8: 0.07547/0.14690, loss_spatial_dice_8: 0.15023/0.31118, loss_spatial_ce_8: 0.13360/0.29569, loss_grounding_bce_8: 0.06256/0.08894, loss_grounding_dice_8: 0.11378/0.17268, loss_grounding_ce_8: 0.43928/0.49915, loss_mask_ce_9: 4.24079/3.59870, loss_mask_bce_9: 0.67312/0.36495, loss_mask_dice_9: 2.33919/1.80527, loss_spatial_bce_9: 0.28783/0.37474, loss_spatial_dice_9: 0.93667/0.80356, loss_spatial_ce_9: 1.31472/1.49128, loss_grounding_bce_9: 0.07799/0.10131, loss_grounding_dice_9: 0.19481/0.25283, loss_grounding_ce_9: 0.61233/0.82088] items per batch[64] items per second[0.35] total items[192000] mini batches[ 3000] memory[4929] epoch remaining[0:20:24] INFO:trainer.default_trainer:epochs[ 1] optim steps[3100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12380/0.81692, loss_mask_bce_0: 0.50322/0.30284, loss_mask_dice_0: 1.71364/1.02472, loss_spatial_bce_0: 0.30298/0.10036, loss_spatial_dice_0: 0.34347/0.21191, loss_spatial_ce_0: 0.35326/0.12533, loss_grounding_bce_0: 0.15804/0.07913, loss_grounding_dice_0: 0.26021/0.15235, loss_grounding_ce_0: 0.16109/0.26093, loss_mask_ce_1: 1.07314/0.81940, loss_mask_bce_1: 0.50822/0.30341, loss_mask_dice_1: 1.71294/1.03208, loss_spatial_bce_1: 0.30726/0.10149, loss_spatial_dice_1: 0.37957/0.21506, loss_spatial_ce_1: 0.35820/0.12862, loss_grounding_bce_1: 0.16070/0.07933, loss_grounding_dice_1: 0.26738/0.15352, loss_grounding_ce_1: 0.16847/0.26452, loss_mask_ce_2: 1.12317/0.82753, loss_mask_bce_2: 0.49782/0.30384, loss_mask_dice_2: 1.60893/1.03861, loss_spatial_bce_2: 0.24352/0.10099, loss_spatial_dice_2: 0.35527/0.21611, loss_spatial_ce_2: 0.36312/0.13611, loss_grounding_bce_2: 0.17240/0.07930, loss_grounding_dice_2: 0.28223/0.15401, loss_grounding_ce_2: 0.16988/0.26242, loss_mask_ce_3: 1.05655/0.81869, loss_mask_bce_3: 0.49505/0.30581, loss_mask_dice_3: 1.67753/1.03088, loss_spatial_bce_3: 0.28621/0.10312, loss_spatial_dice_3: 0.34318/0.21661, loss_spatial_ce_3: 0.33229/0.14442, loss_grounding_bce_3: 0.16227/0.07958, loss_grounding_dice_3: 0.26046/0.15311, loss_grounding_ce_3: 0.17059/0.26285, loss_mask_ce_4: 1.06826/0.82392, loss_mask_bce_4: 0.46800/0.30853, loss_mask_dice_4: 1.71999/1.05243, loss_spatial_bce_4: 0.26428/0.10565, loss_spatial_dice_4: 0.43214/0.22403, loss_spatial_ce_4: 0.58361/0.15335, loss_grounding_bce_4: 0.15759/0.08070, loss_grounding_dice_4: 0.25336/0.15602, loss_grounding_ce_4: 0.17601/0.27362, loss_mask_ce_5: 1.72550/0.84321, loss_mask_bce_5: 0.49792/0.30914, loss_mask_dice_5: 1.73794/1.06368, loss_spatial_bce_5: 0.29041/0.10708, loss_spatial_dice_5: 0.37700/0.22846, loss_spatial_ce_5: 0.63201/0.16405, loss_grounding_bce_5: 0.15613/0.08119, loss_grounding_dice_5: 0.26085/0.15730, loss_grounding_ce_5: 0.19137/0.29231, loss_mask_ce_6: 1.23654/0.86862, loss_mask_bce_6: 0.48196/0.30876, loss_mask_dice_6: 1.70479/1.06670, loss_spatial_bce_6: 0.39107/0.11183, loss_spatial_dice_6: 0.42201/0.23174, loss_spatial_ce_6: 0.45611/0.18595, loss_grounding_bce_6: 0.15481/0.08256, loss_grounding_dice_6: 0.26546/0.15717, loss_grounding_ce_6: 0.20863/0.31563, loss_mask_ce_7: 1.88386/0.93963, loss_mask_bce_7: 0.43703/0.31790, loss_mask_dice_7: 1.67894/1.11064, loss_spatial_bce_7: 0.38264/0.12516, loss_spatial_dice_7: 0.45234/0.25822, loss_spatial_ce_7: 0.59802/0.24005, loss_grounding_bce_7: 0.13842/0.08452, loss_grounding_dice_7: 0.26408/0.16275, loss_grounding_ce_7: 0.26231/0.37752, loss_mask_ce_8: 1.04055/1.10847, loss_mask_bce_8: 0.53867/0.33469, loss_mask_dice_8: 1.71703/1.19378, loss_spatial_bce_8: 0.38519/0.14669, loss_spatial_dice_8: 0.48244/0.31018, loss_spatial_ce_8: 0.45437/0.29353, loss_grounding_bce_8: 0.17411/0.08836, loss_grounding_dice_8: 0.26501/0.17137, loss_grounding_ce_8: 0.29305/0.49790, loss_mask_ce_9: 2.97767/3.60253, loss_mask_bce_9: 0.46188/0.36524, loss_mask_dice_9: 2.08421/1.80674, loss_spatial_bce_9: 0.25812/0.37420, loss_spatial_dice_9: 0.90491/0.80348, loss_spatial_ce_9: 1.82747/1.48833, loss_grounding_bce_9: 0.14677/0.10086, loss_grounding_dice_9: 0.34337/0.25128, loss_grounding_ce_9: 0.37145/0.81982] items per batch[64] items per second[0.34] total items[198400] mini batches[ 3100] memory[4929] epoch remaining[0:17:16] INFO:trainer.default_trainer:epochs[ 1] optim steps[3200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07941/0.81384, loss_mask_bce_0: 0.08771/0.30186, loss_mask_dice_0: 0.16633/1.02936, loss_spatial_bce_0: 0.07139/0.10003, loss_spatial_dice_0: 0.10788/0.21124, loss_spatial_ce_0: 0.00006/0.12369, loss_grounding_bce_0: 0.08515/0.07905, loss_grounding_dice_0: 0.11558/0.15214, loss_grounding_ce_0: 0.00589/0.25903, loss_mask_ce_1: 0.07600/0.81704, loss_mask_bce_1: 0.08040/0.30230, loss_mask_dice_1: 0.14822/1.03676, loss_spatial_bce_1: 0.07133/0.10118, loss_spatial_dice_1: 0.12275/0.21438, loss_spatial_ce_1: 0.00006/0.12719, loss_grounding_bce_1: 0.08164/0.07923, loss_grounding_dice_1: 0.11507/0.15324, loss_grounding_ce_1: 0.00404/0.26264, loss_mask_ce_2: 0.07932/0.82475, loss_mask_bce_2: 0.08613/0.30276, loss_mask_dice_2: 0.15610/1.04279, loss_spatial_bce_2: 0.06925/0.10072, loss_spatial_dice_2: 0.11114/0.21542, loss_spatial_ce_2: 0.00010/0.13461, loss_grounding_bce_2: 0.08314/0.07922, loss_grounding_dice_2: 0.11372/0.15368, loss_grounding_ce_2: 0.00417/0.26035, loss_mask_ce_3: 0.07406/0.81616, loss_mask_bce_3: 0.09291/0.30474, loss_mask_dice_3: 0.16309/1.03508, loss_spatial_bce_3: 0.06789/0.10275, loss_spatial_dice_3: 0.11081/0.21584, loss_spatial_ce_3: 0.00013/0.14248, loss_grounding_bce_3: 0.08276/0.07951, loss_grounding_dice_3: 0.10692/0.15283, loss_grounding_ce_3: 0.00255/0.26094, loss_mask_ce_4: 0.07612/0.82190, loss_mask_bce_4: 0.08241/0.30729, loss_mask_dice_4: 0.15705/1.05691, loss_spatial_bce_4: 0.07634/0.10530, loss_spatial_dice_4: 0.11661/0.22315, loss_spatial_ce_4: 0.00187/0.15179, loss_grounding_bce_4: 0.07835/0.08060, loss_grounding_dice_4: 0.11707/0.15572, loss_grounding_ce_4: 0.00441/0.27133, loss_mask_ce_5: 0.07515/0.84043, loss_mask_bce_5: 0.08712/0.30794, loss_mask_dice_5: 0.16304/1.06811, loss_spatial_bce_5: 0.07406/0.10667, loss_spatial_dice_5: 0.12150/0.22745, loss_spatial_ce_5: 0.00141/0.16248, loss_grounding_bce_5: 0.08131/0.08116, loss_grounding_dice_5: 0.12332/0.15716, loss_grounding_ce_5: 0.00466/0.28987, loss_mask_ce_6: 0.07855/0.86568, loss_mask_bce_6: 0.08479/0.30770, loss_mask_dice_6: 0.16234/1.07067, loss_spatial_bce_6: 0.07385/0.11139, loss_spatial_dice_6: 0.11942/0.23082, loss_spatial_ce_6: 0.00083/0.18379, loss_grounding_bce_6: 0.07637/0.08240, loss_grounding_dice_6: 0.11158/0.15679, loss_grounding_ce_6: 0.00478/0.31358, loss_mask_ce_7: 0.10312/0.93671, loss_mask_bce_7: 0.09275/0.31660, loss_mask_dice_7: 0.15307/1.11427, loss_spatial_bce_7: 0.07344/0.12461, loss_spatial_dice_7: 0.18642/0.25718, loss_spatial_ce_7: 0.02432/0.23759, loss_grounding_bce_7: 0.08436/0.08448, loss_grounding_dice_7: 0.11094/0.16228, loss_grounding_ce_7: 0.00145/0.37482, loss_mask_ce_8: 0.08717/1.10377, loss_mask_bce_8: 0.09424/0.33365, loss_mask_dice_8: 0.15841/1.19741, loss_spatial_bce_8: 0.15596/0.14632, loss_spatial_dice_8: 0.35054/0.30907, loss_spatial_ce_8: 0.09091/0.29121, loss_grounding_bce_8: 0.08668/0.08815, loss_grounding_dice_8: 0.12223/0.17058, loss_grounding_ce_8: 0.00402/0.49436, loss_mask_ce_9: 1.63837/3.59492, loss_mask_bce_9: 0.06902/0.36427, loss_mask_dice_9: 0.19763/1.81458, loss_spatial_bce_9: 0.34711/0.37369, loss_spatial_dice_9: 0.65459/0.80297, loss_spatial_ce_9: 1.01584/1.48868, loss_grounding_bce_9: 0.06597/0.10064, loss_grounding_dice_9: 0.13023/0.25082, loss_grounding_ce_9: 0.08067/0.81579] items per batch[64] items per second[0.35] total items[204800] mini batches[ 3200] memory[4929] epoch remaining[0:14:08] INFO:trainer.default_trainer:epochs[ 1] optim steps[3300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26167/0.81757, loss_mask_bce_0: 0.63442/0.30321, loss_mask_dice_0: 2.86739/1.03257, loss_spatial_bce_0: 0.03977/0.09956, loss_spatial_dice_0: 0.13676/0.21072, loss_spatial_ce_0: 0.08256/0.12226, loss_grounding_bce_0: 0.03753/0.07912, loss_grounding_dice_0: 0.30556/0.15175, loss_grounding_ce_0: 0.81768/0.25977, loss_mask_ce_1: 1.18684/0.82069, loss_mask_bce_1: 0.67026/0.30358, loss_mask_dice_1: 2.84820/1.04033, loss_spatial_bce_1: 0.04179/0.10068, loss_spatial_dice_1: 0.15751/0.21391, loss_spatial_ce_1: 0.05189/0.12628, loss_grounding_bce_1: 0.06387/0.07929, loss_grounding_dice_1: 0.31149/0.15269, loss_grounding_ce_1: 0.73195/0.26390, loss_mask_ce_2: 1.13017/0.82822, loss_mask_bce_2: 0.62372/0.30390, loss_mask_dice_2: 2.86752/1.04497, loss_spatial_bce_2: 0.03909/0.10016, loss_spatial_dice_2: 0.12098/0.21484, loss_spatial_ce_2: 0.02712/0.13308, loss_grounding_bce_2: 0.06591/0.07920, loss_grounding_dice_2: 0.30408/0.15330, loss_grounding_ce_2: 0.71889/0.26122, loss_mask_ce_3: 1.15908/0.81931, loss_mask_bce_3: 0.64491/0.30583, loss_mask_dice_3: 2.89147/1.03847, loss_spatial_bce_3: 0.03975/0.10220, loss_spatial_dice_3: 0.11286/0.21525, loss_spatial_ce_3: 0.01143/0.14054, loss_grounding_bce_3: 0.04132/0.07954, loss_grounding_dice_3: 0.31150/0.15236, loss_grounding_ce_3: 0.82867/0.26143, loss_mask_ce_4: 1.17866/0.82577, loss_mask_bce_4: 0.68501/0.30855, loss_mask_dice_4: 2.93419/1.05977, loss_spatial_bce_4: 0.03951/0.10467, loss_spatial_dice_4: 0.10436/0.22249, loss_spatial_ce_4: 0.01228/0.15062, loss_grounding_bce_4: 0.05118/0.08061, loss_grounding_dice_4: 0.28262/0.15527, loss_grounding_ce_4: 0.75412/0.27223, loss_mask_ce_5: 1.20148/0.84429, loss_mask_bce_5: 0.66363/0.30920, loss_mask_dice_5: 2.98446/1.07096, loss_spatial_bce_5: 0.03780/0.10605, loss_spatial_dice_5: 0.10013/0.22668, loss_spatial_ce_5: 0.02641/0.16117, loss_grounding_bce_5: 0.05617/0.08117, loss_grounding_dice_5: 0.29882/0.15660, loss_grounding_ce_5: 0.76313/0.29097, loss_mask_ce_6: 1.34244/0.86960, loss_mask_bce_6: 0.74093/0.30904, loss_mask_dice_6: 2.99247/1.07388, loss_spatial_bce_6: 0.04101/0.11067, loss_spatial_dice_6: 0.10320/0.23009, loss_spatial_ce_6: 0.07591/0.18297, loss_grounding_bce_6: 0.03760/0.08243, loss_grounding_dice_6: 0.29635/0.15642, loss_grounding_ce_6: 0.89765/0.31419, loss_mask_ce_7: 1.36812/0.94082, loss_mask_bce_7: 0.90335/0.31830, loss_mask_dice_7: 3.77066/1.11777, loss_spatial_bce_7: 0.05055/0.12387, loss_spatial_dice_7: 0.12610/0.25641, loss_spatial_ce_7: 0.04748/0.23623, loss_grounding_bce_7: 0.05881/0.08478, loss_grounding_dice_7: 0.33570/0.16197, loss_grounding_ce_7: 0.86199/0.37695, loss_mask_ce_8: 1.31587/1.10938, loss_mask_bce_8: 0.74911/0.33497, loss_mask_dice_8: 3.69441/1.20015, loss_spatial_bce_8: 0.09667/0.14550, loss_spatial_dice_8: 0.23743/0.30824, loss_spatial_ce_8: 0.18658/0.29002, loss_grounding_bce_8: 0.05957/0.08835, loss_grounding_dice_8: 0.39248/0.17037, loss_grounding_ce_8: 0.90991/0.49712, loss_mask_ce_9: 4.20310/3.60057, loss_mask_bce_9: 0.69013/0.36555, loss_mask_dice_9: 4.69930/1.81893, loss_spatial_bce_9: 0.18246/0.37250, loss_spatial_dice_9: 0.84749/0.80369, loss_spatial_ce_9: 1.13455/1.48748, loss_grounding_bce_9: 0.04340/0.10073, loss_grounding_dice_9: 0.49600/0.25046, loss_grounding_ce_9: 0.96498/0.81577] items per batch[64] items per second[0.34] total items[211200] mini batches[ 3300] memory[4929] epoch remaining[0:11:01] INFO:trainer.default_trainer:epochs[ 1] optim steps[3400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43037/0.81750, loss_mask_bce_0: 0.53694/0.30278, loss_mask_dice_0: 0.38311/1.03341, loss_spatial_bce_0: 0.22409/0.09955, loss_spatial_dice_0: 0.15201/0.21022, loss_spatial_ce_0: 0.01064/0.12109, loss_grounding_bce_0: 0.03591/0.07928, loss_grounding_dice_0: 0.12265/0.15216, loss_grounding_ce_0: 0.11589/0.26175, loss_mask_ce_1: 0.39518/0.82064, loss_mask_bce_1: 0.53959/0.30308, loss_mask_dice_1: 0.38976/1.04050, loss_spatial_bce_1: 0.23194/0.10067, loss_spatial_dice_1: 0.16922/0.21338, loss_spatial_ce_1: 0.01193/0.12521, loss_grounding_bce_1: 0.03874/0.07945, loss_grounding_dice_1: 0.13164/0.15287, loss_grounding_ce_1: 0.10854/0.26575, loss_mask_ce_2: 0.39461/0.82748, loss_mask_bce_2: 0.53692/0.30342, loss_mask_dice_2: 0.40581/1.04530, loss_spatial_bce_2: 0.23103/0.10012, loss_spatial_dice_2: 0.16274/0.21420, loss_spatial_ce_2: 0.01573/0.13229, loss_grounding_bce_2: 0.03884/0.07931, loss_grounding_dice_2: 0.12507/0.15345, loss_grounding_ce_2: 0.10769/0.26315, loss_mask_ce_3: 0.44828/0.81890, loss_mask_bce_3: 0.53757/0.30528, loss_mask_dice_3: 0.38740/1.03809, loss_spatial_bce_3: 0.23053/0.10203, loss_spatial_dice_3: 0.15641/0.21460, loss_spatial_ce_3: 0.01450/0.13935, loss_grounding_bce_3: 0.03851/0.07970, loss_grounding_dice_3: 0.12190/0.15262, loss_grounding_ce_3: 0.10181/0.26331, loss_mask_ce_4: 0.46042/0.82559, loss_mask_bce_4: 0.52529/0.30798, loss_mask_dice_4: 0.39851/1.06009, loss_spatial_bce_4: 0.25364/0.10465, loss_spatial_dice_4: 0.17823/0.22187, loss_spatial_ce_4: 0.03689/0.14983, loss_grounding_bce_4: 0.04422/0.08072, loss_grounding_dice_4: 0.12958/0.15539, loss_grounding_ce_4: 0.11859/0.27377, loss_mask_ce_5: 0.47061/0.84419, loss_mask_bce_5: 0.53258/0.30876, loss_mask_dice_5: 0.40997/1.07076, loss_spatial_bce_5: 0.21649/0.10608, loss_spatial_dice_5: 0.15934/0.22594, loss_spatial_ce_5: 0.08659/0.15993, loss_grounding_bce_5: 0.04525/0.08120, loss_grounding_dice_5: 0.13768/0.15666, loss_grounding_ce_5: 0.12063/0.29354, loss_mask_ce_6: 0.45475/0.86968, loss_mask_bce_6: 0.51719/0.30866, loss_mask_dice_6: 0.38418/1.07415, loss_spatial_bce_6: 0.21618/0.11062, loss_spatial_dice_6: 0.14852/0.22920, loss_spatial_ce_6: 0.12535/0.18164, loss_grounding_bce_6: 0.04641/0.08259, loss_grounding_dice_6: 0.13297/0.15648, loss_grounding_ce_6: 0.11968/0.31599, loss_mask_ce_7: 0.54520/0.94153, loss_mask_bce_7: 0.53564/0.31775, loss_mask_dice_7: 0.40882/1.11745, loss_spatial_bce_7: 0.20582/0.12358, loss_spatial_dice_7: 0.14437/0.25539, loss_spatial_ce_7: 0.09779/0.23529, loss_grounding_bce_7: 0.04264/0.08482, loss_grounding_dice_7: 0.13137/0.16216, loss_grounding_ce_7: 0.15487/0.37899, loss_mask_ce_8: 0.64271/1.10970, loss_mask_bce_8: 0.56286/0.33438, loss_mask_dice_8: 0.38435/1.19969, loss_spatial_bce_8: 0.26311/0.14529, loss_spatial_dice_8: 0.19593/0.30700, loss_spatial_ce_8: 0.23634/0.28865, loss_grounding_bce_8: 0.06997/0.08837, loss_grounding_dice_8: 0.12719/0.17067, loss_grounding_ce_8: 0.17330/0.49915, loss_mask_ce_9: 2.72803/3.60664, loss_mask_bce_9: 0.46236/0.36502, loss_mask_dice_9: 0.71499/1.81588, loss_spatial_bce_9: 0.37659/0.37328, loss_spatial_dice_9: 0.52104/0.80367, loss_spatial_ce_9: 0.85088/1.48918, loss_grounding_bce_9: 0.05361/0.10088, loss_grounding_dice_9: 0.30174/0.25073, loss_grounding_ce_9: 0.26204/0.81815] items per batch[64] items per second[0.34] total items[217600] mini batches[ 3400] memory[4929] epoch remaining[0:07:54] INFO:trainer.default_trainer:epochs[ 1] optim steps[3500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06401/0.81246, loss_mask_bce_0: 0.22067/0.30205, loss_mask_dice_0: 0.16246/1.03791, loss_spatial_bce_0: 0.10857/0.09900, loss_spatial_dice_0: 0.08712/0.20991, loss_spatial_ce_0: 0.00006/0.12032, loss_grounding_bce_0: 0.11406/0.07945, loss_grounding_dice_0: 0.08752/0.15250, loss_grounding_ce_0: 0.08065/0.26040, loss_mask_ce_1: 0.07783/0.81585, loss_mask_bce_1: 0.24590/0.30243, loss_mask_dice_1: 0.16921/1.04489, loss_spatial_bce_1: 0.11354/0.10013, loss_spatial_dice_1: 0.07982/0.21307, loss_spatial_ce_1: 0.00003/0.12448, loss_grounding_bce_1: 0.11611/0.07958, loss_grounding_dice_1: 0.08661/0.15306, loss_grounding_ce_1: 0.07769/0.26467, loss_mask_ce_2: 0.08887/0.82266, loss_mask_bce_2: 0.22358/0.30266, loss_mask_dice_2: 0.16239/1.04939, loss_spatial_bce_2: 0.10597/0.09965, loss_spatial_dice_2: 0.08104/0.21383, loss_spatial_ce_2: 0.00002/0.13159, loss_grounding_bce_2: 0.11807/0.07940, loss_grounding_dice_2: 0.08505/0.15363, loss_grounding_ce_2: 0.08175/0.26232, loss_mask_ce_3: 0.08907/0.81452, loss_mask_bce_3: 0.21890/0.30454, loss_mask_dice_3: 0.16020/1.04196, loss_spatial_bce_3: 0.10668/0.10169, loss_spatial_dice_3: 0.08022/0.21422, loss_spatial_ce_3: 0.00002/0.13885, loss_grounding_bce_3: 0.11553/0.07987, loss_grounding_dice_3: 0.08658/0.15303, loss_grounding_ce_3: 0.08058/0.26170, loss_mask_ce_4: 0.10640/0.82084, loss_mask_bce_4: 0.24013/0.30734, loss_mask_dice_4: 0.17032/1.06433, loss_spatial_bce_4: 0.12277/0.10423, loss_spatial_dice_4: 0.08375/0.22159, loss_spatial_ce_4: 0.00000/0.14895, loss_grounding_bce_4: 0.12374/0.08090, loss_grounding_dice_4: 0.08504/0.15565, loss_grounding_ce_4: 0.08386/0.27283, loss_mask_ce_5: 0.12569/0.83930, loss_mask_bce_5: 0.24910/0.30813, loss_mask_dice_5: 0.17104/1.07484, loss_spatial_bce_5: 0.12204/0.10578, loss_spatial_dice_5: 0.08795/0.22557, loss_spatial_ce_5: 0.00001/0.15896, loss_grounding_bce_5: 0.12003/0.08130, loss_grounding_dice_5: 0.09043/0.15683, loss_grounding_ce_5: 0.09127/0.29147, loss_mask_ce_6: 0.19273/0.86449, loss_mask_bce_6: 0.23271/0.30806, loss_mask_dice_6: 0.17633/1.07841, loss_spatial_bce_6: 0.12254/0.11030, loss_spatial_dice_6: 0.08788/0.22860, loss_spatial_ce_6: 0.00000/0.18092, loss_grounding_bce_6: 0.11754/0.08258, loss_grounding_dice_6: 0.08835/0.15667, loss_grounding_ce_6: 0.09688/0.31388, loss_mask_ce_7: 0.22907/0.93677, loss_mask_bce_7: 0.24945/0.31673, loss_mask_dice_7: 0.18393/1.12174, loss_spatial_bce_7: 0.13317/0.12307, loss_spatial_dice_7: 0.09668/0.25497, loss_spatial_ce_7: 0.00118/0.23458, loss_grounding_bce_7: 0.12564/0.08479, loss_grounding_dice_7: 0.09318/0.16226, loss_grounding_ce_7: 0.08910/0.37612, loss_mask_ce_8: 0.31322/1.10515, loss_mask_bce_8: 0.26192/0.33319, loss_mask_dice_8: 0.19283/1.20310, loss_spatial_bce_8: 0.14760/0.14464, loss_spatial_dice_8: 0.09590/0.30603, loss_spatial_ce_8: 0.01649/0.28722, loss_grounding_bce_8: 0.12640/0.08826, loss_grounding_dice_8: 0.09572/0.17057, loss_grounding_ce_8: 0.10171/0.49634, loss_mask_ce_9: 3.07846/3.59664, loss_mask_bce_9: 0.23332/0.36353, loss_mask_dice_9: 0.21997/1.81593, loss_spatial_bce_9: 0.52549/0.37333, loss_spatial_dice_9: 0.62779/0.80327, loss_spatial_ce_9: 1.13855/1.48617, loss_grounding_bce_9: 0.14226/0.10067, loss_grounding_dice_9: 0.16973/0.25043, loss_grounding_ce_9: 0.21197/0.81530] items per batch[64] items per second[0.34] total items[224000] mini batches[ 3500] memory[4929] epoch remaining[0:04:47] INFO:trainer.default_trainer:epochs[ 1] optim steps[3600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45215/0.81158, loss_mask_bce_0: 0.01915/0.30253, loss_mask_dice_0: 0.30327/1.02844, loss_spatial_bce_0: 0.01026/0.09966, loss_spatial_dice_0: 0.18290/0.20959, loss_spatial_ce_0: 0.00152/0.11941, loss_grounding_bce_0: 0.00593/0.07993, loss_grounding_dice_0: 0.03110/0.15249, loss_grounding_ce_0: 0.00010/0.26099, loss_mask_ce_1: 0.63189/0.81527, loss_mask_bce_1: 0.01936/0.30297, loss_mask_dice_1: 0.28421/1.03527, loss_spatial_bce_1: 0.00856/0.10071, loss_spatial_dice_1: 0.13683/0.21268, loss_spatial_ce_1: 0.00249/0.12399, loss_grounding_bce_1: 0.00666/0.08006, loss_grounding_dice_1: 0.04066/0.15319, loss_grounding_ce_1: 0.00007/0.26524, loss_mask_ce_2: 0.43260/0.82196, loss_mask_bce_2: 0.02057/0.30318, loss_mask_dice_2: 0.36763/1.04003, loss_spatial_bce_2: 0.01082/0.10019, loss_spatial_dice_2: 0.13766/0.21333, loss_spatial_ce_2: 0.00423/0.13111, loss_grounding_bce_2: 0.00671/0.07992, loss_grounding_dice_2: 0.03570/0.15368, loss_grounding_ce_2: 0.00008/0.26308, loss_mask_ce_3: 0.69827/0.81406, loss_mask_bce_3: 0.02000/0.30495, loss_mask_dice_3: 0.31965/1.03280, loss_spatial_bce_3: 0.01017/0.10224, loss_spatial_dice_3: 0.11295/0.21379, loss_spatial_ce_3: 0.00404/0.13801, loss_grounding_bce_3: 0.00791/0.08033, loss_grounding_dice_3: 0.04259/0.15312, loss_grounding_ce_3: 0.00002/0.26221, loss_mask_ce_4: 0.62952/0.82033, loss_mask_bce_4: 0.01785/0.30765, loss_mask_dice_4: 0.35464/1.05400, loss_spatial_bce_4: 0.01085/0.10495, loss_spatial_dice_4: 0.12104/0.22106, loss_spatial_ce_4: 0.01524/0.14780, loss_grounding_bce_4: 0.00523/0.08137, loss_grounding_dice_4: 0.03287/0.15571, loss_grounding_ce_4: 0.00019/0.27271, loss_mask_ce_5: 0.20801/0.83854, loss_mask_bce_5: 0.01749/0.30842, loss_mask_dice_5: 0.48651/1.06518, loss_spatial_bce_5: 0.01051/0.10639, loss_spatial_dice_5: 0.10271/0.22486, loss_spatial_ce_5: 0.02225/0.15805, loss_grounding_bce_5: 0.00674/0.08175, loss_grounding_dice_5: 0.03351/0.15687, loss_grounding_ce_5: 0.00018/0.29134, loss_mask_ce_6: 0.83282/0.86336, loss_mask_bce_6: 0.01804/0.30821, loss_mask_dice_6: 0.27860/1.06912, loss_spatial_bce_6: 0.01008/0.11090, loss_spatial_dice_6: 0.12687/0.22787, loss_spatial_ce_6: 0.02393/0.17980, loss_grounding_bce_6: 0.00610/0.08303, loss_grounding_dice_6: 0.03624/0.15678, loss_grounding_ce_6: 0.00158/0.31318, loss_mask_ce_7: 0.37790/0.93606, loss_mask_bce_7: 0.02110/0.31673, loss_mask_dice_7: 0.35521/1.11220, loss_spatial_bce_7: 0.01292/0.12372, loss_spatial_dice_7: 0.12741/0.25416, loss_spatial_ce_7: 0.04755/0.23372, loss_grounding_bce_7: 0.00516/0.08512, loss_grounding_dice_7: 0.03296/0.16233, loss_grounding_ce_7: 0.03249/0.37595, loss_mask_ce_8: 0.25492/1.10350, loss_mask_bce_8: 0.01754/0.33332, loss_mask_dice_8: 0.24804/1.19270, loss_spatial_bce_8: 0.01311/0.14560, loss_spatial_dice_8: 0.15978/0.30475, loss_spatial_ce_8: 0.07853/0.28600, loss_grounding_bce_8: 0.00666/0.08851, loss_grounding_dice_8: 0.03818/0.17055, loss_grounding_ce_8: 0.09245/0.49756, loss_mask_ce_9: 2.30100/3.58793, loss_mask_bce_9: 0.02617/0.36356, loss_mask_dice_9: 0.44117/1.79770, loss_spatial_bce_9: 0.11606/0.37438, loss_spatial_dice_9: 0.78251/0.80215, loss_spatial_ce_9: 1.49312/1.48288, loss_grounding_bce_9: 0.00663/0.10098, loss_grounding_dice_9: 0.05260/0.24968, loss_grounding_ce_9: 0.45750/0.81279] items per batch[64] items per second[0.34] total items[230400] mini batches[ 3600] memory[4929] epoch remaining[0:01:40] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00003654. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3629 s/iter. Eval: 0.0931 s/iter. Total: 0.4588 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0026 s/iter. Inference: 0.3624 s/iter. Eval: 0.0843 s/iter. Total: 0.4495 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3683 s/iter. Eval: 0.0832 s/iter. Total: 0.4544 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0028 s/iter. Inference: 0.3709 s/iter. Eval: 0.0787 s/iter. Total: 0.4526 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0029 s/iter. Inference: 0.3742 s/iter. Eval: 0.0760 s/iter. Total: 0.4532 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0030 s/iter. Inference: 0.3719 s/iter. Eval: 0.0740 s/iter. Total: 0.4491 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalixhsn8pz ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 54.929 | 82.644 | 65.249 | 133 | | Things | 61.401 | 84.122 | 72.484 | 80 | | Stuff | 45.161 | 80.413 | 54.328 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.60s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.37 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.46 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=5.16s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 22.79 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.680 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.478 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.369 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.608 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.763 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 44.458 | 68.001 | 47.832 | 25.514 | 49.190 | 67.128 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.786 | bicycle | 22.064 | car | 41.093 | | motorcycle | 39.692 | airplane | 60.921 | bus | 70.613 | | train | 75.096 | truck | 44.930 | boat | 28.609 | | traffic light | 27.683 | fire hydrant | 70.911 | stop sign | 68.898 | | parking meter | 50.076 | bench | 25.540 | bird | 32.227 | | cat | 76.980 | dog | 72.162 | horse | 49.268 | | sheep | 51.874 | cow | 56.187 | elephant | 65.363 | | bear | 79.493 | zebra | 65.010 | giraffe | 61.885 | | backpack | 23.642 | umbrella | 55.361 | handbag | 23.461 | | tie | 38.153 | suitcase | 49.155 | frisbee | 70.338 | | skis | 8.757 | snowboard | 34.937 | sports ball | 49.490 | | kite | 37.778 | baseball bat | 37.777 | baseball glove | 48.886 | | skateboard | 43.335 | surfboard | 43.912 | tennis racket | 62.287 | | bottle | 40.736 | wine glass | 36.338 | cup | 49.621 | | fork | 25.388 | knife | 23.131 | spoon | 19.470 | | bowl | 36.311 | banana | 19.693 | apple | 25.701 | | sandwich | 47.817 | orange | 30.138 | broccoli | 23.822 | | carrot | 21.485 | hot dog | 35.543 | pizza | 50.468 | | donut | 54.651 | cake | 46.919 | chair | 27.769 | | couch | 44.497 | potted plant | 22.001 | bed | 44.171 | | dining table | 13.509 | toilet | 68.434 | tv | 65.711 | | laptop | 66.233 | mouse | 63.789 | remote | 40.727 | | keyboard | 56.789 | cell phone | 45.153 | microwave | 62.334 | | oven | 31.243 | toaster | 43.338 | sink | 43.235 | | refrigerator | 68.129 | book | 12.882 | clock | 53.270 | | vase | 40.481 | scissors | 33.536 | teddy bear | 56.508 | | hair drier | 24.677 | toothbrush | 28.348 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.73501391430798, 'fwIoU': 72.31384486852362, 'IoU-person': 89.13148334043701, 'IoU-bicycle': 79.35148171014046, 'IoU-car': 70.46193121898634, 'IoU-motorcycle': 88.53120154430397, 'IoU-airplane': 87.38999757202889, 'IoU-bus': 87.80601238754973, 'IoU-train': 88.29715227336672, 'IoU-truck': 67.0191741892386, 'IoU-boat': 72.29895511862222, 'IoU-traffic light': 79.77807769789588, 'IoU-fire hydrant': 93.22342569829743, 'IoU-stop sign': 94.67271427502575, 'IoU-parking meter': 84.73517839812183, 'IoU-bench': 64.71186882130807, 'IoU-bird': 76.6780804598043, 'IoU-cat': 88.99467626360932, 'IoU-dog': 86.8906617029394, 'IoU-horse': 89.45909127295394, 'IoU-sheep': 92.61297023423342, 'IoU-cow': 89.8113081471428, 'IoU-elephant': 93.60703016393107, 'IoU-bear': 93.53649199831972, 'IoU-zebra': 93.14993474935233, 'IoU-giraffe': 89.75057227109028, 'IoU-backpack': 52.332027107941414, 'IoU-umbrella': 88.87043738730853, 'IoU-handbag': 50.846747464533394, 'IoU-tie': 74.44004520780342, 'IoU-suitcase': 84.29800152756222, 'IoU-frisbee': 84.78860729318717, 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76.00669159422834, 'IoU-dining table': 55.54888367830472, 'IoU-toilet': 90.16671491737034, 'IoU-tv': 82.00775408886479, 'IoU-laptop': 82.31710615886507, 'IoU-mouse': 84.55152211181571, 'IoU-remote': 72.52587645619295, 'IoU-keyboard': 69.76676654032417, 'IoU-cell phone': 81.4554330292165, 'IoU-microwave': 72.28381356449042, 'IoU-oven': 74.44716369500183, 'IoU-toaster': 82.92988901547639, 'IoU-sink': 74.29525792791156, 'IoU-refrigerator': 82.13653553267298, 'IoU-book': 54.90909290003435, 'IoU-clock': 78.0623973727422, 'IoU-vase': 61.98786260793874, 'IoU-scissors': 89.18542884257809, 'IoU-teddy bear': 86.4507623185209, 'IoU-hair drier': 51.487944890929974, 'IoU-toothbrush': 73.80827115026935, 'IoU-banner': 33.99017752193722, 'IoU-blanket': 17.14014118068975, 'IoU-bridge': 38.99981675662955, 'IoU-cardboard': 54.047225788986566, 'IoU-counter': 35.79987496396791, 'IoU-curtain': 71.39965166587353, 'IoU-door-stuff': 47.11537987828524, 'IoU-floor-wood': 66.08058123010304, 'IoU-flower': 50.43525890569064, 'IoU-fruit': 49.2537009377067, 'IoU-gravel': 24.369554354954563, 'IoU-house': 21.55312330587626, 'IoU-light': 43.74521579796155, 'IoU-mirror-stuff': 61.902722614613026, 'IoU-net': 45.49794800683273, 'IoU-pillow': 23.930043603612187, 'IoU-platform': 31.13687890119064, 'IoU-playingfield': 71.09070050707041, 'IoU-railroad': 60.285006603785504, 'IoU-river': 55.02487703474173, 'IoU-road': 67.3048632285391, 'IoU-roof': 18.86903033212077, 'IoU-sand': 66.45221040109418, 'IoU-sea': 85.86767867377726, 'IoU-shelf': 37.09673038303979, 'IoU-snow': 92.12920011645464, 'IoU-stairs': 36.452477485746655, 'IoU-tent': 11.312126529698798, 'IoU-towel': 46.6764654364895, 'IoU-wall-brick': 48.88108299739907, 'IoU-wall-stone': 28.85351209615535, 'IoU-wall-tile': 71.55395496670052, 'IoU-wall-wood': 42.89961951757359, 'IoU-water-other': 29.812342727616443, 'IoU-window-blind': 51.075431052547756, 'IoU-window-other': 49.39325105160449, 'IoU-tree-merged': 82.00598498461642, 'IoU-fence-merged': 54.13742260972953, 'IoU-ceiling-merged': 67.72417049365853, 'IoU-sky-other-merged': 94.3042960782591, 'IoU-cabinet-merged': 63.52277982692313, 'IoU-table-merged': 44.61405206933863, 'IoU-floor-other-merged': 54.39797788369853, 'IoU-pavement-merged': 57.357137936687344, 'IoU-mountain-merged': 57.97883551838077, 'IoU-grass-merged': 73.55480202194764, 'IoU-dirt-merged': 48.31809718601042, 'IoU-paper-merged': 40.83979380664511, 'IoU-food-other-merged': 45.423236774869494, 'IoU-building-other-merged': 59.724489891857004, 'IoU-rock-merged': 65.3266640568754, 'IoU-wall-other-merged': 69.17293011789344, 'IoU-rug-merged': 68.20407428541687, 'mACC': 78.47453117765312, 'pACC': 82.80162744663744, 'ACC-person': 93.55433111946233, 'ACC-bicycle': 88.97011360981561, 'ACC-car': 83.48577502294758, 'ACC-motorcycle': 93.42212377679743, 'ACC-airplane': 94.01569299738954, 'ACC-bus': 93.86646406561366, 'ACC-train': 95.39425652058272, 'ACC-truck': 76.97705418784541, 'ACC-boat': 81.22247131499527, 'ACC-traffic light': 91.14370650292797, 'ACC-fire hydrant': 96.20324783186086, 'ACC-stop sign': 98.57792198014643, 'ACC-parking meter': 88.0314974916312, 'ACC-bench': 77.79080182362243, 'ACC-bird': 82.73457963124648, 'ACC-cat': 94.17546085193841, 'ACC-dog': 89.5746994432841, 'ACC-horse': 94.72320034535369, 'ACC-sheep': 95.53428428897749, 'ACC-cow': 93.15497109285168, 'ACC-elephant': 96.11707538793374, 'ACC-bear': 95.47606313697736, 'ACC-zebra': 95.80250076707541, 'ACC-giraffe': 93.97857790108912, 'ACC-backpack': 69.80043996108711, 'ACC-umbrella': 94.38270230375728, 'ACC-handbag': 71.62547383566043, 'ACC-tie': 84.12872504386428, 'ACC-suitcase': 94.800398279097, 'ACC-frisbee': 94.41781818181818, 'ACC-skis': 76.51185531709012, 'ACC-snowboard': 83.4238691190517, 'ACC-sports ball': 87.69963097342388, 'ACC-kite': 86.14381108434705, 'ACC-baseball bat': 88.582742059954, 'ACC-baseball glove': 92.16078338124987, 'ACC-skateboard': 90.68914363686146, 'ACC-surfboard': 93.39335443935936, 'ACC-tennis racket': 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74.93186637436843, 'ACC-oven': 92.49208116525773, 'ACC-toaster': 88.66140223624245, 'ACC-sink': 84.6082584835736, 'ACC-refrigerator': 89.89848641117332, 'ACC-book': 70.24799669384439, 'ACC-clock': 83.57950647186986, 'ACC-vase': 71.18158150509032, 'ACC-scissors': 95.34886923945679, 'ACC-teddy bear': 92.64530397580731, 'ACC-hair drier': 63.82318617823699, 'ACC-toothbrush': 86.52275886031967, 'ACC-banner': 77.6095952056405, 'ACC-blanket': 23.70499524339971, 'ACC-bridge': 53.23848572270248, 'ACC-cardboard': 68.7471281081867, 'ACC-counter': 59.80792358971247, 'ACC-curtain': 82.83271255620649, 'ACC-door-stuff': 72.59533936500698, 'ACC-floor-wood': 81.33769326505464, 'ACC-flower': 75.08182975294167, 'ACC-fruit': 65.6972485391663, 'ACC-gravel': 32.124779607529334, 'ACC-house': 24.471678639745353, 'ACC-light': 63.37351184779696, 'ACC-mirror-stuff': 77.3350592129244, 'ACC-net': 68.3962860240371, 'ACC-pillow': 53.02737906983498, 'ACC-platform': 49.26301962496189, 'ACC-playingfield': 89.48831753121006, 'ACC-railroad': 88.54538272983618, 'ACC-river': 71.42400038191332, 'ACC-road': 85.17105008158599, 'ACC-roof': 27.9023497822163, 'ACC-sand': 71.4359792451175, 'ACC-sea': 90.79620628164047, 'ACC-shelf': 49.988121291271774, 'ACC-snow': 95.32198709243174, 'ACC-stairs': 58.86255900086643, 'ACC-tent': 14.137053502348282, 'ACC-towel': 55.32333566215165, 'ACC-wall-brick': 68.95547741186411, 'ACC-wall-stone': 34.35285469570169, 'ACC-wall-tile': 86.09281795156863, 'ACC-wall-wood': 58.55946768965209, 'ACC-water-other': 51.850433827087805, 'ACC-window-blind': 67.0977107740533, 'ACC-window-other': 71.27164976574207, 'ACC-tree-merged': 90.02197087346777, 'ACC-fence-merged': 70.88826292462002, 'ACC-ceiling-merged': 83.82885194681988, 'ACC-sky-other-merged': 97.18577979757089, 'ACC-cabinet-merged': 79.42257676380197, 'ACC-table-merged': 57.88461250904153, 'ACC-floor-other-merged': 65.9410577382881, 'ACC-pavement-merged': 70.71656048798957, 'ACC-mountain-merged': 66.78618632922586, 'ACC-grass-merged': 84.53319190929975, 'ACC-dirt-merged': 68.56500432152254, 'ACC-paper-merged': 56.4015932600192, 'ACC-food-other-merged': 63.6542427097384, 'ACC-building-other-merged': 74.14036646315476, 'ACC-rock-merged': 84.7068541070857, 'ACC-wall-other-merged': 82.16373644029146, 'ACC-rug-merged': 82.75677409470165})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2762 s/iter. Inference: 0.1748 s/iter. Eval: 0.0000 s/iter. Total: 0.4510 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3082 s/iter. Inference: 0.3414 s/iter. Eval: 0.0000 s/iter. Total: 0.6497 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3134 s/iter. Inference: 0.5682 s/iter. Eval: 0.0000 s/iter. Total: 0.8817 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.5288264559555165, 'noc@0.8': 2.7977758267486097, 'noc@0.85': 3.332455370207785, 'noc@0.9': 4.181153058238221, 'miou@iter1': 0.8764543737065977} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0017 s/iter. Inference: 0.1184 s/iter. Eval: 0.0008 s/iter. Total: 0.1209 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.35950469970703, 'precision@0.6': 72.48348236083984, 'precision@0.7': 68.28604888916016, 'precision@0.8': 58.841819763183594, 'precision@0.9': 32.29692840576172, 'cIoU': 61.32187271118164, 'mIoU': 66.62837982177734} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 54.929071287045005, 'SQ': 82.64372874018667, 'RQ': 65.24902281322872, 'PQ_th': 61.400658245145955, 'SQ_th': 84.1218222798588, 'RQ_th': 72.48395508460331, 'PQ_st': 45.1606381427416, 'SQ_st': 80.41264415200229, 'RQ_st': 54.32837032813488}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 44.45785304693965, 'AP50': 68.00053282737866, 'AP75': 47.83229189840308, 'APs': 25.513649130651316, 'APm': 49.189666534187296, 'APl': 67.12765657332946, 'AP-person': 48.78597646464545, 'AP-bicycle': 22.0644972576906, 'AP-car': 41.09266632135072, 'AP-motorcycle': 39.69238164039233, 'AP-airplane': 60.920925331857156, 'AP-bus': 70.61306772900573, 'AP-train': 75.09611379214289, 'AP-truck': 44.92952911697597, 'AP-boat': 28.60886466718377, 'AP-traffic light': 27.683213017160323, 'AP-fire hydrant': 70.91080890164278, 'AP-stop sign': 68.89803126559984, 'AP-parking meter': 50.07581629601962, 'AP-bench': 25.539743219803746, 'AP-bird': 32.22662845156531, 'AP-cat': 76.97986181867005, 'AP-dog': 72.16207124625824, 'AP-horse': 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56.50765590345381, 'AP-hair drier': 24.676857045310445, 'AP-toothbrush': 28.348224624546166}), ('sem_seg', {'mIoU': 66.73501391430798, 'fwIoU': 72.31384486852362, 'IoU-person': 89.13148334043701, 'IoU-bicycle': 79.35148171014046, 'IoU-car': 70.46193121898634, 'IoU-motorcycle': 88.53120154430397, 'IoU-airplane': 87.38999757202889, 'IoU-bus': 87.80601238754973, 'IoU-train': 88.29715227336672, 'IoU-truck': 67.0191741892386, 'IoU-boat': 72.29895511862222, 'IoU-traffic light': 79.77807769789588, 'IoU-fire hydrant': 93.22342569829743, 'IoU-stop sign': 94.67271427502575, 'IoU-parking meter': 84.73517839812183, 'IoU-bench': 64.71186882130807, 'IoU-bird': 76.6780804598043, 'IoU-cat': 88.99467626360932, 'IoU-dog': 86.8906617029394, 'IoU-horse': 89.45909127295394, 'IoU-sheep': 92.61297023423342, 'IoU-cow': 89.8113081471428, 'IoU-elephant': 93.60703016393107, 'IoU-bear': 93.53649199831972, 'IoU-zebra': 93.14993474935233, 'IoU-giraffe': 89.75057227109028, 'IoU-backpack': 52.332027107941414, 'IoU-umbrella': 88.87043738730853, 'IoU-handbag': 50.846747464533394, 'IoU-tie': 74.44004520780342, 'IoU-suitcase': 84.29800152756222, 'IoU-frisbee': 84.78860729318717, 'IoU-skis': 60.852575584719645, 'IoU-snowboard': 75.71664829106946, 'IoU-sports ball': 80.06105443942211, 'IoU-kite': 79.73397043132249, 'IoU-baseball bat': 68.60285307284127, 'IoU-baseball glove': 76.39633020947193, 'IoU-skateboard': 85.85972442074174, 'IoU-surfboard': 85.64593894319712, 'IoU-tennis racket': 90.61065957125287, 'IoU-bottle': 71.88525033704865, 'IoU-wine glass': 82.11517155970937, 'IoU-cup': 70.80066702224134, 'IoU-fork': 70.00712693723112, 'IoU-knife': 64.68558792970242, 'IoU-spoon': 56.546933810775336, 'IoU-bowl': 56.44873343789091, 'IoU-banana': 83.8429563823845, 'IoU-apple': 61.10882047789533, 'IoU-sandwich': 69.68713910151875, 'IoU-orange': 80.40013365333488, 'IoU-broccoli': 70.33469801712687, 'IoU-carrot': 61.47605371114114, 'IoU-hot dog': 64.18231898567652, 'IoU-pizza': 84.9641031969151, 'IoU-donut': 74.92225194037965, 'IoU-cake': 79.59847847010313, 'IoU-chair': 64.4240944270652, 'IoU-couch': 71.66638274954705, 'IoU-potted plant': 45.33908550057745, 'IoU-bed': 76.00669159422834, 'IoU-dining table': 55.54888367830472, 'IoU-toilet': 90.16671491737034, 'IoU-tv': 82.00775408886479, 'IoU-laptop': 82.31710615886507, 'IoU-mouse': 84.55152211181571, 'IoU-remote': 72.52587645619295, 'IoU-keyboard': 69.76676654032417, 'IoU-cell phone': 81.4554330292165, 'IoU-microwave': 72.28381356449042, 'IoU-oven': 74.44716369500183, 'IoU-toaster': 82.92988901547639, 'IoU-sink': 74.29525792791156, 'IoU-refrigerator': 82.13653553267298, 'IoU-book': 54.90909290003435, 'IoU-clock': 78.0623973727422, 'IoU-vase': 61.98786260793874, 'IoU-scissors': 89.18542884257809, 'IoU-teddy bear': 86.4507623185209, 'IoU-hair drier': 51.487944890929974, 'IoU-toothbrush': 73.80827115026935, 'IoU-banner': 33.99017752193722, 'IoU-blanket': 17.14014118068975, 'IoU-bridge': 38.99981675662955, 'IoU-cardboard': 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'IoU-water-other': 29.812342727616443, 'IoU-window-blind': 51.075431052547756, 'IoU-window-other': 49.39325105160449, 'IoU-tree-merged': 82.00598498461642, 'IoU-fence-merged': 54.13742260972953, 'IoU-ceiling-merged': 67.72417049365853, 'IoU-sky-other-merged': 94.3042960782591, 'IoU-cabinet-merged': 63.52277982692313, 'IoU-table-merged': 44.61405206933863, 'IoU-floor-other-merged': 54.39797788369853, 'IoU-pavement-merged': 57.357137936687344, 'IoU-mountain-merged': 57.97883551838077, 'IoU-grass-merged': 73.55480202194764, 'IoU-dirt-merged': 48.31809718601042, 'IoU-paper-merged': 40.83979380664511, 'IoU-food-other-merged': 45.423236774869494, 'IoU-building-other-merged': 59.724489891857004, 'IoU-rock-merged': 65.3266640568754, 'IoU-wall-other-merged': 69.17293011789344, 'IoU-rug-merged': 68.20407428541687, 'mACC': 78.47453117765312, 'pACC': 82.80162744663744, 'ACC-person': 93.55433111946233, 'ACC-bicycle': 88.97011360981561, 'ACC-car': 83.48577502294758, 'ACC-motorcycle': 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'ACC-laptop': 92.36948097789197, 'ACC-mouse': 91.62653449202587, 'ACC-remote': 76.6963857481717, 'ACC-keyboard': 82.43811537540456, 'ACC-cell phone': 93.4464764761288, 'ACC-microwave': 74.93186637436843, 'ACC-oven': 92.49208116525773, 'ACC-toaster': 88.66140223624245, 'ACC-sink': 84.6082584835736, 'ACC-refrigerator': 89.89848641117332, 'ACC-book': 70.24799669384439, 'ACC-clock': 83.57950647186986, 'ACC-vase': 71.18158150509032, 'ACC-scissors': 95.34886923945679, 'ACC-teddy bear': 92.64530397580731, 'ACC-hair drier': 63.82318617823699, 'ACC-toothbrush': 86.52275886031967, 'ACC-banner': 77.6095952056405, 'ACC-blanket': 23.70499524339971, 'ACC-bridge': 53.23848572270248, 'ACC-cardboard': 68.7471281081867, 'ACC-counter': 59.80792358971247, 'ACC-curtain': 82.83271255620649, 'ACC-door-stuff': 72.59533936500698, 'ACC-floor-wood': 81.33769326505464, 'ACC-flower': 75.08182975294167, 'ACC-fruit': 65.6972485391663, 'ACC-gravel': 32.124779607529334, 'ACC-house': 24.471678639745353, 'ACC-light': 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66.62837982177734}}} INFO:trainer.default_trainer:This epoch takes 1:00:32.941743 INFO:trainer.default_trainer:PROGRESS: 4.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 2 training. INFO:trainer.default_trainer:epochs[ 2] optim steps[3700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18000/0.81184, loss_mask_bce_0: 0.00795/0.30166, loss_mask_dice_0: 0.02793/1.03200, loss_spatial_bce_0: 0.01225/0.09905, loss_spatial_dice_0: 0.03203/0.20918, loss_spatial_ce_0: 0.00000/0.11804, loss_grounding_bce_0: 0.00848/0.07988, loss_grounding_dice_0: 0.03245/0.15248, loss_grounding_ce_0: 0.00395/0.25950, loss_mask_ce_1: 0.22617/0.81493, loss_mask_bce_1: 0.00971/0.30209, loss_mask_dice_1: 0.03125/1.03863, loss_spatial_bce_1: 0.00977/0.10005, loss_spatial_dice_1: 0.03302/0.21234, loss_spatial_ce_1: 0.00001/0.12269, loss_grounding_bce_1: 0.01141/0.07994, loss_grounding_dice_1: 0.03547/0.15305, loss_grounding_ce_1: 0.00471/0.26363, loss_mask_ce_2: 0.24083/0.82124, loss_mask_bce_2: 0.00842/0.30233, loss_mask_dice_2: 0.02709/1.04427, loss_spatial_bce_2: 0.01239/0.09950, loss_spatial_dice_2: 0.03339/0.21299, loss_spatial_ce_2: 0.00001/0.12982, loss_grounding_bce_2: 0.01116/0.07979, loss_grounding_dice_2: 0.03292/0.15360, loss_grounding_ce_2: 0.00265/0.26158, loss_mask_ce_3: 0.24007/0.81420, loss_mask_bce_3: 0.00703/0.30406, loss_mask_dice_3: 0.02370/1.03606, loss_spatial_bce_3: 0.01171/0.10152, loss_spatial_dice_3: 0.03097/0.21335, loss_spatial_ce_3: 0.00001/0.13673, loss_grounding_bce_3: 0.01147/0.08027, loss_grounding_dice_3: 0.03047/0.15310, loss_grounding_ce_3: 0.00231/0.26124, loss_mask_ce_4: 0.22926/0.82035, loss_mask_bce_4: 0.00857/0.30677, loss_mask_dice_4: 0.02812/1.05755, loss_spatial_bce_4: 0.01087/0.10415, loss_spatial_dice_4: 0.03159/0.22060, loss_spatial_ce_4: 0.00010/0.14647, loss_grounding_bce_4: 0.01007/0.08121, loss_grounding_dice_4: 0.02842/0.15564, loss_grounding_ce_4: 0.00288/0.27090, loss_mask_ce_5: 0.21705/0.83827, loss_mask_bce_5: 0.00705/0.30759, loss_mask_dice_5: 0.02336/1.06839, loss_spatial_bce_5: 0.01230/0.10568, loss_spatial_dice_5: 0.03345/0.22438, loss_spatial_ce_5: 0.00001/0.15676, loss_grounding_bce_5: 0.01064/0.08158, loss_grounding_dice_5: 0.02795/0.15671, loss_grounding_ce_5: 0.00375/0.28943, loss_mask_ce_6: 0.26492/0.86322, loss_mask_bce_6: 0.00774/0.30734, loss_mask_dice_6: 0.02417/1.07215, loss_spatial_bce_6: 0.01281/0.11016, loss_spatial_dice_6: 0.03267/0.22741, loss_spatial_ce_6: 0.00001/0.17801, loss_grounding_bce_6: 0.01032/0.08290, loss_grounding_dice_6: 0.02975/0.15672, loss_grounding_ce_6: 0.00685/0.31146, loss_mask_ce_7: 0.26359/0.93617, loss_mask_bce_7: 0.00887/0.31585, loss_mask_dice_7: 0.02691/1.11615, loss_spatial_bce_7: 0.01406/0.12300, loss_spatial_dice_7: 0.03238/0.25380, loss_spatial_ce_7: 0.00037/0.23144, loss_grounding_bce_7: 0.01097/0.08494, loss_grounding_dice_7: 0.03594/0.16229, loss_grounding_ce_7: 0.00318/0.37322, loss_mask_ce_8: 0.26321/1.10229, loss_mask_bce_8: 0.01024/0.33270, loss_mask_dice_8: 0.02625/1.19764, loss_spatial_bce_8: 0.01671/0.14466, loss_spatial_dice_8: 0.04018/0.30406, loss_spatial_ce_8: 0.04048/0.28439, loss_grounding_bce_8: 0.01020/0.08845, loss_grounding_dice_8: 0.02665/0.17054, loss_grounding_ce_8: 0.00594/0.49339, loss_mask_ce_9: 1.57317/3.58687, loss_mask_bce_9: 0.00923/0.36241, loss_mask_dice_9: 0.03363/1.80038, loss_spatial_bce_9: 0.06950/0.37358, loss_spatial_dice_9: 0.40860/0.80244, loss_spatial_ce_9: 0.19186/1.48111, loss_grounding_bce_9: 0.01071/0.10071, loss_grounding_dice_9: 0.03694/0.24930, loss_grounding_ce_9: 0.05913/0.80856] items per batch[64] items per second[0.16] total items[236800] mini batches[ 3700] memory[4929] epoch remaining[1:00:18] INFO:trainer.default_trainer:epochs[ 2] optim steps[3800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60548/0.80925, loss_mask_bce_0: 0.68907/0.30147, loss_mask_dice_0: 0.60318/1.02962, loss_spatial_bce_0: 0.21133/0.09898, loss_spatial_dice_0: 0.18385/0.20827, loss_spatial_ce_0: 0.04577/0.11642, loss_grounding_bce_0: 0.03088/0.08009, loss_grounding_dice_0: 0.03818/0.15241, loss_grounding_ce_0: 0.22620/0.25818, loss_mask_ce_1: 0.56405/0.81183, loss_mask_bce_1: 0.69501/0.30195, loss_mask_dice_1: 0.61208/1.03572, loss_spatial_bce_1: 0.21265/0.09992, loss_spatial_dice_1: 0.19169/0.21142, loss_spatial_ce_1: 0.05612/0.12133, loss_grounding_bce_1: 0.03041/0.08013, loss_grounding_dice_1: 0.04623/0.15291, loss_grounding_ce_1: 0.23207/0.26247, loss_mask_ce_2: 0.57132/0.81777, loss_mask_bce_2: 0.69014/0.30221, loss_mask_dice_2: 0.61779/1.04156, loss_spatial_bce_2: 0.20454/0.09944, loss_spatial_dice_2: 0.18382/0.21205, loss_spatial_ce_2: 0.05554/0.12825, loss_grounding_bce_2: 0.03052/0.08000, loss_grounding_dice_2: 0.04209/0.15355, loss_grounding_ce_2: 0.21642/0.25933, loss_mask_ce_3: 0.60015/0.81130, loss_mask_bce_3: 0.67523/0.30387, loss_mask_dice_3: 0.60401/1.03354, loss_spatial_bce_3: 0.20531/0.10137, loss_spatial_dice_3: 0.19024/0.21236, loss_spatial_ce_3: 0.07114/0.13504, loss_grounding_bce_3: 0.02773/0.08048, loss_grounding_dice_3: 0.04617/0.15298, loss_grounding_ce_3: 0.19536/0.25948, loss_mask_ce_4: 0.58452/0.81783, loss_mask_bce_4: 0.68888/0.30659, loss_mask_dice_4: 0.58602/1.05506, loss_spatial_bce_4: 0.20235/0.10392, loss_spatial_dice_4: 0.18641/0.21962, loss_spatial_ce_4: 0.09953/0.14533, loss_grounding_bce_4: 0.03212/0.08140, loss_grounding_dice_4: 0.04454/0.15536, loss_grounding_ce_4: 0.15586/0.26917, loss_mask_ce_5: 0.62769/0.83565, loss_mask_bce_5: 0.66447/0.30739, loss_mask_dice_5: 0.59118/1.06557, loss_spatial_bce_5: 0.20058/0.10553, loss_spatial_dice_5: 0.17190/0.22335, loss_spatial_ce_5: 0.10239/0.15484, loss_grounding_bce_5: 0.02809/0.08175, loss_grounding_dice_5: 0.03875/0.15646, loss_grounding_ce_5: 0.18902/0.28694, loss_mask_ce_6: 0.70072/0.86029, loss_mask_bce_6: 0.68031/0.30729, loss_mask_dice_6: 0.60021/1.06960, loss_spatial_bce_6: 0.21950/0.10999, loss_spatial_dice_6: 0.17496/0.22616, loss_spatial_ce_6: 0.16388/0.17641, loss_grounding_bce_6: 0.02734/0.08306, loss_grounding_dice_6: 0.04420/0.15644, loss_grounding_ce_6: 0.21363/0.30914, loss_mask_ce_7: 0.77173/0.93279, loss_mask_bce_7: 0.67911/0.31574, loss_mask_dice_7: 0.62983/1.11308, loss_spatial_bce_7: 0.24255/0.12277, loss_spatial_dice_7: 0.21322/0.25257, loss_spatial_ce_7: 0.06827/0.22944, loss_grounding_bce_7: 0.02989/0.08519, loss_grounding_dice_7: 0.05516/0.16207, loss_grounding_ce_7: 0.14542/0.36996, loss_mask_ce_8: 1.38486/1.09936, loss_mask_bce_8: 0.70604/0.33265, loss_mask_dice_8: 0.63569/1.19460, loss_spatial_bce_8: 0.25452/0.14451, loss_spatial_dice_8: 0.27674/0.30271, loss_spatial_ce_8: 0.20476/0.28216, loss_grounding_bce_8: 0.02898/0.08876, loss_grounding_dice_8: 0.04659/0.17030, loss_grounding_ce_8: 0.10952/0.49065, loss_mask_ce_9: 4.10917/3.57962, loss_mask_bce_9: 0.75013/0.36202, loss_mask_dice_9: 0.77011/1.79468, loss_spatial_bce_9: 0.58604/0.37414, loss_spatial_dice_9: 0.85688/0.80239, loss_spatial_ce_9: 2.13077/1.47855, loss_grounding_bce_9: 0.03624/0.10076, loss_grounding_dice_9: 0.05522/0.24896, loss_grounding_ce_9: 0.41197/0.80585] items per batch[64] items per second[0.35] total items[243200] mini batches[ 3800] memory[4929] epoch remaining[0:53:19] INFO:trainer.default_trainer:epochs[ 2] optim steps[3900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31514/0.80854, loss_mask_bce_0: 0.19001/0.30193, loss_mask_dice_0: 0.38031/1.03080, loss_spatial_bce_0: 0.07301/0.09856, loss_spatial_dice_0: 0.15539/0.20791, loss_spatial_ce_0: 0.10682/0.11527, loss_grounding_bce_0: 0.06129/0.08032, loss_grounding_dice_0: 0.08018/0.15234, loss_grounding_ce_0: 0.05851/0.25833, loss_mask_ce_1: 0.32638/0.81064, loss_mask_bce_1: 0.17287/0.30242, loss_mask_dice_1: 0.39020/1.03661, loss_spatial_bce_1: 0.06876/0.09951, loss_spatial_dice_1: 0.15801/0.21103, loss_spatial_ce_1: 0.08078/0.12012, loss_grounding_bce_1: 0.06057/0.08035, loss_grounding_dice_1: 0.07777/0.15309, loss_grounding_ce_1: 0.04705/0.26254, loss_mask_ce_2: 0.32278/0.81712, loss_mask_bce_2: 0.18247/0.30259, loss_mask_dice_2: 0.38855/1.04227, loss_spatial_bce_2: 0.06841/0.09904, loss_spatial_dice_2: 0.15813/0.21157, loss_spatial_ce_2: 0.06317/0.12689, loss_grounding_bce_2: 0.05896/0.08018, loss_grounding_dice_2: 0.08072/0.15365, loss_grounding_ce_2: 0.04164/0.25963, loss_mask_ce_3: 0.32036/0.81015, loss_mask_bce_3: 0.17382/0.30420, loss_mask_dice_3: 0.37250/1.03490, loss_spatial_bce_3: 0.06868/0.10101, loss_spatial_dice_3: 0.15263/0.21189, loss_spatial_ce_3: 0.04935/0.13389, loss_grounding_bce_3: 0.05910/0.08068, loss_grounding_dice_3: 0.07508/0.15320, loss_grounding_ce_3: 0.05062/0.25943, loss_mask_ce_4: 0.36789/0.81597, loss_mask_bce_4: 0.17865/0.30697, loss_mask_dice_4: 0.41005/1.05613, loss_spatial_bce_4: 0.07553/0.10356, loss_spatial_dice_4: 0.16584/0.21914, loss_spatial_ce_4: 0.07409/0.14427, loss_grounding_bce_4: 0.06003/0.08153, loss_grounding_dice_4: 0.07920/0.15554, loss_grounding_ce_4: 0.05312/0.26895, loss_mask_ce_5: 0.36400/0.83474, loss_mask_bce_5: 0.19911/0.30779, loss_mask_dice_5: 0.43704/1.06670, loss_spatial_bce_5: 0.06876/0.10519, loss_spatial_dice_5: 0.14823/0.22283, loss_spatial_ce_5: 0.12095/0.15383, loss_grounding_bce_5: 0.06234/0.08195, loss_grounding_dice_5: 0.08754/0.15658, loss_grounding_ce_5: 0.04818/0.28782, loss_mask_ce_6: 0.44625/0.85931, loss_mask_bce_6: 0.19295/0.30778, loss_mask_dice_6: 0.41792/1.07079, loss_spatial_bce_6: 0.06462/0.10972, loss_spatial_dice_6: 0.12693/0.22567, loss_spatial_ce_6: 0.08339/0.17521, loss_grounding_bce_6: 0.06021/0.08323, loss_grounding_dice_6: 0.08341/0.15641, loss_grounding_ce_6: 0.07933/0.30841, loss_mask_ce_7: 0.38958/0.93202, loss_mask_bce_7: 0.17825/0.31607, loss_mask_dice_7: 0.41922/1.11330, loss_spatial_bce_7: 0.07031/0.12258, loss_spatial_dice_7: 0.14134/0.25198, loss_spatial_ce_7: 0.11528/0.22797, loss_grounding_bce_7: 0.05822/0.08528, loss_grounding_dice_7: 0.08068/0.16211, loss_grounding_ce_7: 0.06679/0.36911, loss_mask_ce_8: 0.55900/1.09845, loss_mask_bce_8: 0.16785/0.33328, loss_mask_dice_8: 0.40890/1.19539, loss_spatial_bce_8: 0.11341/0.14425, loss_spatial_dice_8: 0.17035/0.30192, loss_spatial_ce_8: 0.21685/0.28052, loss_grounding_bce_8: 0.06223/0.08879, loss_grounding_dice_8: 0.09853/0.17023, loss_grounding_ce_8: 0.05850/0.48955, loss_mask_ce_9: 3.41830/3.57940, loss_mask_bce_9: 0.20733/0.36262, loss_mask_dice_9: 0.61304/1.79483, loss_spatial_bce_9: 0.58346/0.37349, loss_spatial_dice_9: 0.79635/0.80217, loss_spatial_ce_9: 1.33422/1.47717, loss_grounding_bce_9: 0.09000/0.10090, loss_grounding_dice_9: 0.15271/0.24891, loss_grounding_ce_9: 0.23616/0.80443] items per batch[64] items per second[0.35] total items[249600] mini batches[ 3900] memory[4929] epoch remaining[0:49:37] INFO:trainer.default_trainer:epochs[ 2] optim steps[4000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48703/0.80623, loss_mask_bce_0: 0.36978/0.30134, loss_mask_dice_0: 0.25380/1.03023, loss_spatial_bce_0: 0.17583/0.09853, loss_spatial_dice_0: 0.10001/0.20763, loss_spatial_ce_0: 0.00028/0.11473, loss_grounding_bce_0: 0.05400/0.08066, loss_grounding_dice_0: 0.10691/0.15242, loss_grounding_ce_0: 0.00704/0.25738, loss_mask_ce_1: 0.44288/0.80878, loss_mask_bce_1: 0.39536/0.30197, loss_mask_dice_1: 0.26068/1.03642, loss_spatial_bce_1: 0.18613/0.09953, loss_spatial_dice_1: 0.09971/0.21081, loss_spatial_ce_1: 0.00031/0.11938, loss_grounding_bce_1: 0.06022/0.08068, loss_grounding_dice_1: 0.12353/0.15327, loss_grounding_ce_1: 0.00535/0.26170, loss_mask_ce_2: 0.45076/0.81504, loss_mask_bce_2: 0.39262/0.30212, loss_mask_dice_2: 0.27016/1.04253, loss_spatial_bce_2: 0.18752/0.09898, loss_spatial_dice_2: 0.10168/0.21118, loss_spatial_ce_2: 0.00025/0.12634, loss_grounding_bce_2: 0.05966/0.08047, loss_grounding_dice_2: 0.10530/0.15379, loss_grounding_ce_2: 0.00520/0.25887, loss_mask_ce_3: 0.41796/0.80787, loss_mask_bce_3: 0.38784/0.30373, loss_mask_dice_3: 0.26524/1.03404, loss_spatial_bce_3: 0.18441/0.10099, loss_spatial_dice_3: 0.09470/0.21146, loss_spatial_ce_3: 0.00068/0.13305, loss_grounding_bce_3: 0.05153/0.08096, loss_grounding_dice_3: 0.09181/0.15339, loss_grounding_ce_3: 0.00271/0.25905, loss_mask_ce_4: 0.41028/0.81424, loss_mask_bce_4: 0.38560/0.30626, loss_mask_dice_4: 0.25516/1.05579, loss_spatial_bce_4: 0.17947/0.10339, loss_spatial_dice_4: 0.10185/0.21868, loss_spatial_ce_4: 0.00052/0.14349, loss_grounding_bce_4: 0.05200/0.08183, loss_grounding_dice_4: 0.10696/0.15566, loss_grounding_ce_4: 0.01318/0.26744, loss_mask_ce_5: 0.40902/0.83404, loss_mask_bce_5: 0.38203/0.30701, loss_mask_dice_5: 0.24428/1.06627, loss_spatial_bce_5: 0.18057/0.10508, loss_spatial_dice_5: 0.10201/0.22230, loss_spatial_ce_5: 0.00224/0.15287, loss_grounding_bce_5: 0.05597/0.08220, loss_grounding_dice_5: 0.10984/0.15677, loss_grounding_ce_5: 0.00173/0.28694, loss_mask_ce_6: 0.45049/0.85775, loss_mask_bce_6: 0.42759/0.30724, loss_mask_dice_6: 0.25928/1.07038, loss_spatial_bce_6: 0.18695/0.10986, loss_spatial_dice_6: 0.10475/0.22526, loss_spatial_ce_6: 0.01251/0.17380, loss_grounding_bce_6: 0.05792/0.08350, loss_grounding_dice_6: 0.10809/0.15651, loss_grounding_ce_6: 0.00411/0.30771, loss_mask_ce_7: 0.45738/0.92883, loss_mask_bce_7: 0.46927/0.31562, loss_mask_dice_7: 0.30849/1.11368, loss_spatial_bce_7: 0.19262/0.12268, loss_spatial_dice_7: 0.10736/0.25147, loss_spatial_ce_7: 0.10829/0.22635, loss_grounding_bce_7: 0.06644/0.08555, loss_grounding_dice_7: 0.11316/0.16221, loss_grounding_ce_7: 0.01402/0.36726, loss_mask_ce_8: 1.44956/1.09558, loss_mask_bce_8: 0.35888/0.33266, loss_mask_dice_8: 0.24429/1.19466, loss_spatial_bce_8: 0.21173/0.14421, loss_spatial_dice_8: 0.12876/0.30115, loss_spatial_ce_8: 0.09068/0.27995, loss_grounding_bce_8: 0.05907/0.08903, loss_grounding_dice_8: 0.11517/0.17021, loss_grounding_ce_8: 0.00066/0.48953, loss_mask_ce_9: 2.01829/3.57330, loss_mask_bce_9: 0.59682/0.36141, loss_mask_dice_9: 0.52631/1.79113, loss_spatial_bce_9: 0.51810/0.37365, loss_spatial_dice_9: 0.76702/0.80219, loss_spatial_ce_9: 1.07758/1.47526, loss_grounding_bce_9: 0.03090/0.10105, loss_grounding_dice_9: 0.14233/0.24902, loss_grounding_ce_9: 0.14967/0.80031] items per batch[64] items per second[0.34] total items[256000] mini batches[ 4000] memory[4929] epoch remaining[0:46:24] INFO:trainer.default_trainer:epochs[ 2] optim steps[4100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72826/0.80956, loss_mask_bce_0: 0.09383/0.30197, loss_mask_dice_0: 1.65726/1.03414, loss_spatial_bce_0: 0.01844/0.09866, loss_spatial_dice_0: 0.26694/0.20757, loss_spatial_ce_0: 0.06681/0.11411, loss_grounding_bce_0: 0.01124/0.08116, loss_grounding_dice_0: 0.21538/0.15280, loss_grounding_ce_0: 0.65150/0.25874, loss_mask_ce_1: 0.59485/0.81300, loss_mask_bce_1: 0.09781/0.30249, loss_mask_dice_1: 1.91385/1.04176, loss_spatial_bce_1: 0.02072/0.09965, loss_spatial_dice_1: 0.27979/0.21077, loss_spatial_ce_1: 0.07859/0.11866, loss_grounding_bce_1: 0.00995/0.08113, loss_grounding_dice_1: 0.19573/0.15366, loss_grounding_ce_1: 0.65686/0.26307, loss_mask_ce_2: 0.63235/0.81835, loss_mask_bce_2: 0.08568/0.30265, loss_mask_dice_2: 1.33609/1.04710, loss_spatial_bce_2: 0.01959/0.09903, loss_spatial_dice_2: 0.24765/0.21106, loss_spatial_ce_2: 0.04300/0.12547, loss_grounding_bce_2: 0.00998/0.08084, loss_grounding_dice_2: 0.14912/0.15414, loss_grounding_ce_2: 0.59574/0.26052, loss_mask_ce_3: 0.57216/0.81151, loss_mask_bce_3: 0.08961/0.30416, loss_mask_dice_3: 1.36043/1.03890, loss_spatial_bce_3: 0.01837/0.10112, loss_spatial_dice_3: 0.20074/0.21129, loss_spatial_ce_3: 0.05218/0.13222, loss_grounding_bce_3: 0.01148/0.08130, loss_grounding_dice_3: 0.20685/0.15373, loss_grounding_ce_3: 0.61563/0.26083, loss_mask_ce_4: 0.70646/0.81747, loss_mask_bce_4: 0.08414/0.30675, loss_mask_dice_4: 1.38170/1.06075, loss_spatial_bce_4: 0.01787/0.10354, loss_spatial_dice_4: 0.26978/0.21851, loss_spatial_ce_4: 0.13476/0.14312, loss_grounding_bce_4: 0.01048/0.08221, loss_grounding_dice_4: 0.21918/0.15593, loss_grounding_ce_4: 0.77384/0.26900, loss_mask_ce_5: 0.71284/0.83780, loss_mask_bce_5: 0.09271/0.30754, loss_mask_dice_5: 1.61367/1.07169, loss_spatial_bce_5: 0.01792/0.10523, loss_spatial_dice_5: 0.22106/0.22204, loss_spatial_ce_5: 0.10045/0.15248, loss_grounding_bce_5: 0.00994/0.08248, loss_grounding_dice_5: 0.25032/0.15710, loss_grounding_ce_5: 0.68662/0.28827, loss_mask_ce_6: 0.85939/0.86075, loss_mask_bce_6: 0.08592/0.30780, loss_mask_dice_6: 1.36819/1.07516, loss_spatial_bce_6: 0.01971/0.10992, loss_spatial_dice_6: 0.22899/0.22502, loss_spatial_ce_6: 0.03795/0.17319, loss_grounding_bce_6: 0.01464/0.08390, loss_grounding_dice_6: 0.27763/0.15682, loss_grounding_ce_6: 0.66319/0.30829, loss_mask_ce_7: 0.93711/0.93040, loss_mask_bce_7: 0.08343/0.31634, loss_mask_dice_7: 1.65881/1.11930, loss_spatial_bce_7: 0.02593/0.12267, loss_spatial_dice_7: 0.26034/0.25129, loss_spatial_ce_7: 0.16441/0.22633, loss_grounding_bce_7: 0.00967/0.08611, loss_grounding_dice_7: 0.21256/0.16282, loss_grounding_ce_7: 0.61112/0.36738, loss_mask_ce_8: 0.81054/1.09748, loss_mask_bce_8: 0.09078/0.33302, loss_mask_dice_8: 1.64036/1.20023, loss_spatial_bce_8: 0.12117/0.14436, loss_spatial_dice_8: 0.36307/0.30071, loss_spatial_ce_8: 0.22146/0.27963, loss_grounding_bce_8: 0.01104/0.08956, loss_grounding_dice_8: 0.19685/0.17059, loss_grounding_ce_8: 0.70628/0.48989, loss_mask_ce_9: 2.59727/3.57813, loss_mask_bce_9: 0.09185/0.36169, loss_mask_dice_9: 2.44135/1.79860, loss_spatial_bce_9: 0.13522/0.37339, loss_spatial_dice_9: 0.82596/0.80227, loss_spatial_ce_9: 2.09576/1.47290, loss_grounding_bce_9: 0.01228/0.10150, loss_grounding_dice_9: 0.43533/0.24967, loss_grounding_ce_9: 0.67764/0.79985] items per batch[64] items per second[0.34] total items[262400] mini batches[ 4100] memory[4929] epoch remaining[0:43:11] INFO:trainer.default_trainer:epochs[ 2] optim steps[4200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22752/0.81091, loss_mask_bce_0: 0.98985/0.30244, loss_mask_dice_0: 8.12409/1.03521, loss_spatial_bce_0: 0.01309/0.09849, loss_spatial_dice_0: 0.32882/0.20752, loss_spatial_ce_0: 0.11770/0.11304, loss_grounding_bce_0: 0.01804/0.08098, loss_grounding_dice_0: 0.28652/0.15299, loss_grounding_ce_0: 0.22183/0.25986, loss_mask_ce_1: 1.34853/0.81412, loss_mask_bce_1: 0.85361/0.30300, loss_mask_dice_1: 8.03383/1.04315, loss_spatial_bce_1: 0.02177/0.09943, loss_spatial_dice_1: 0.35802/0.21069, loss_spatial_ce_1: 0.07238/0.11764, loss_grounding_bce_1: 0.01194/0.08096, loss_grounding_dice_1: 0.23320/0.15382, loss_grounding_ce_1: 0.25621/0.26414, loss_mask_ce_2: 1.32033/0.81941, loss_mask_bce_2: 0.85097/0.30321, loss_mask_dice_2: 8.24847/1.04843, loss_spatial_bce_2: 0.02399/0.09880, loss_spatial_dice_2: 0.37898/0.21096, loss_spatial_ce_2: 0.06441/0.12427, loss_grounding_bce_2: 0.04063/0.08067, loss_grounding_dice_2: 0.26274/0.15428, loss_grounding_ce_2: 0.37160/0.26221, loss_mask_ce_3: 1.41041/0.81236, loss_mask_bce_3: 0.93353/0.30473, loss_mask_dice_3: 8.43203/1.03971, loss_spatial_bce_3: 0.02000/0.10087, loss_spatial_dice_3: 0.38826/0.21122, loss_spatial_ce_3: 0.05336/0.13108, loss_grounding_bce_3: 0.01766/0.08115, loss_grounding_dice_3: 0.27188/0.15388, loss_grounding_ce_3: 0.23916/0.26165, loss_mask_ce_4: 1.35219/0.81925, loss_mask_bce_4: 1.01170/0.30729, loss_mask_dice_4: 7.93941/1.06140, loss_spatial_bce_4: 0.01816/0.10323, loss_spatial_dice_4: 0.34552/0.21845, loss_spatial_ce_4: 0.08620/0.14204, loss_grounding_bce_4: 0.02636/0.08202, loss_grounding_dice_4: 0.24965/0.15609, loss_grounding_ce_4: 0.37901/0.27051, loss_mask_ce_5: 1.19283/0.83946, loss_mask_bce_5: 1.13771/0.30813, loss_mask_dice_5: 8.59713/1.07295, loss_spatial_bce_5: 0.01840/0.10493, loss_spatial_dice_5: 0.36733/0.22179, loss_spatial_ce_5: 0.08871/0.15148, loss_grounding_bce_5: 0.02363/0.08233, loss_grounding_dice_5: 0.31729/0.15738, loss_grounding_ce_5: 0.23052/0.28884, loss_mask_ce_6: 1.99222/0.86265, loss_mask_bce_6: 1.01970/0.30838, loss_mask_dice_6: 8.66740/1.07644, loss_spatial_bce_6: 0.01542/0.10954, loss_spatial_dice_6: 0.37903/0.22481, loss_spatial_ce_6: 0.05644/0.17176, loss_grounding_bce_6: 0.01727/0.08368, loss_grounding_dice_6: 0.27254/0.15702, loss_grounding_ce_6: 0.25353/0.30965, loss_mask_ce_7: 1.85604/0.93216, loss_mask_bce_7: 0.97615/0.31682, loss_mask_dice_7: 9.79792/1.12087, loss_spatial_bce_7: 0.01919/0.12218, loss_spatial_dice_7: 0.41276/0.25101, loss_spatial_ce_7: 0.26503/0.22552, loss_grounding_bce_7: 0.01880/0.08594, loss_grounding_dice_7: 0.24188/0.16318, loss_grounding_ce_7: 0.41550/0.36810, loss_mask_ce_8: 1.76873/1.09863, loss_mask_bce_8: 0.98127/0.33384, loss_mask_dice_8: 10.22846/1.20212, loss_spatial_bce_8: 0.02070/0.14383, loss_spatial_dice_8: 0.51466/0.30050, loss_spatial_ce_8: 0.46601/0.27862, loss_grounding_bce_8: 0.02828/0.08948, loss_grounding_dice_8: 0.38216/0.17101, loss_grounding_ce_8: 0.28187/0.49056, loss_mask_ce_9: 8.68306/3.57998, loss_mask_bce_9: 1.38455/0.36216, loss_mask_dice_9: 16.11801/1.80145, loss_spatial_bce_9: 0.10237/0.37249, loss_spatial_dice_9: 0.96186/0.80263, loss_spatial_ce_9: 1.65552/1.47156, loss_grounding_bce_9: 0.01716/0.10147, loss_grounding_dice_9: 0.52222/0.25034, loss_grounding_ce_9: 0.40762/0.79643] items per batch[64] items per second[0.34] total items[268800] mini batches[ 4200] memory[4929] epoch remaining[0:40:00] INFO:trainer.default_trainer:epochs[ 2] optim steps[4300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16089/0.81025, loss_mask_bce_0: 0.18483/0.30186, loss_mask_dice_0: 0.71019/1.03149, loss_spatial_bce_0: 0.06845/0.09838, loss_spatial_dice_0: 0.11267/0.20709, loss_spatial_ce_0: 0.00323/0.11247, loss_grounding_bce_0: 0.04751/0.08069, loss_grounding_dice_0: 0.16942/0.15233, loss_grounding_ce_0: 0.55769/0.25945, loss_mask_ce_1: 0.14572/0.81349, loss_mask_bce_1: 0.19335/0.30244, loss_mask_dice_1: 0.60380/1.03927, loss_spatial_bce_1: 0.05897/0.09931, loss_spatial_dice_1: 0.09781/0.21023, loss_spatial_ce_1: 0.00493/0.11712, loss_grounding_bce_1: 0.05482/0.08064, loss_grounding_dice_1: 0.20073/0.15328, loss_grounding_ce_1: 0.49925/0.26383, loss_mask_ce_2: 0.16094/0.81830, loss_mask_bce_2: 0.20698/0.30267, loss_mask_dice_2: 0.85493/1.04486, loss_spatial_bce_2: 0.05402/0.09866, loss_spatial_dice_2: 0.10881/0.21051, loss_spatial_ce_2: 0.00999/0.12374, loss_grounding_bce_2: 0.04582/0.08033, loss_grounding_dice_2: 0.19051/0.15376, loss_grounding_ce_2: 0.50143/0.26177, loss_mask_ce_3: 0.17316/0.81200, loss_mask_bce_3: 0.18787/0.30425, loss_mask_dice_3: 0.67976/1.03581, loss_spatial_bce_3: 0.04926/0.10070, loss_spatial_dice_3: 0.08735/0.21073, loss_spatial_ce_3: 0.00288/0.13026, loss_grounding_bce_3: 0.03808/0.08082, loss_grounding_dice_3: 0.20989/0.15327, loss_grounding_ce_3: 0.47903/0.26123, loss_mask_ce_4: 0.17367/0.81862, loss_mask_bce_4: 0.18948/0.30669, loss_mask_dice_4: 0.68745/1.05740, loss_spatial_bce_4: 0.05395/0.10309, loss_spatial_dice_4: 0.09677/0.21794, loss_spatial_ce_4: 0.00258/0.14116, loss_grounding_bce_4: 0.05307/0.08169, loss_grounding_dice_4: 0.19204/0.15557, loss_grounding_ce_4: 0.48076/0.26944, loss_mask_ce_5: 0.17562/0.83841, loss_mask_bce_5: 0.19448/0.30745, loss_mask_dice_5: 0.67432/1.06862, loss_spatial_bce_5: 0.05990/0.10469, loss_spatial_dice_5: 0.10159/0.22117, loss_spatial_ce_5: 0.00389/0.15088, loss_grounding_bce_5: 0.04110/0.08197, loss_grounding_dice_5: 0.19843/0.15671, loss_grounding_ce_5: 0.59145/0.28763, loss_mask_ce_6: 0.33097/0.86161, loss_mask_bce_6: 0.19120/0.30786, loss_mask_dice_6: 0.55580/1.07265, loss_spatial_bce_6: 0.04571/0.10932, loss_spatial_dice_6: 0.10092/0.22418, loss_spatial_ce_6: 0.01899/0.17096, loss_grounding_bce_6: 0.04686/0.08336, loss_grounding_dice_6: 0.17212/0.15647, loss_grounding_ce_6: 0.47354/0.30859, loss_mask_ce_7: 0.43736/0.93109, loss_mask_bce_7: 0.21271/0.31625, loss_mask_dice_7: 0.92031/1.11691, loss_spatial_bce_7: 0.04929/0.12197, loss_spatial_dice_7: 0.12372/0.25043, loss_spatial_ce_7: 0.29709/0.22423, loss_grounding_bce_7: 0.04461/0.08557, loss_grounding_dice_7: 0.16003/0.16241, loss_grounding_ce_7: 0.53105/0.36698, loss_mask_ce_8: 0.37345/1.09607, loss_mask_bce_8: 0.25131/0.33317, loss_mask_dice_8: 0.89765/1.19759, loss_spatial_bce_8: 0.11674/0.14381, loss_spatial_dice_8: 0.18228/0.29971, loss_spatial_ce_8: 0.09125/0.27711, loss_grounding_bce_8: 0.03908/0.08913, loss_grounding_dice_8: 0.17680/0.17044, loss_grounding_ce_8: 0.54231/0.48942, loss_mask_ce_9: 4.56243/3.57290, loss_mask_bce_9: 0.28675/0.36117, loss_mask_dice_9: 0.91447/1.79482, loss_spatial_bce_9: 0.36437/0.37222, loss_spatial_dice_9: 0.88878/0.80224, loss_spatial_ce_9: 1.52186/1.46688, loss_grounding_bce_9: 0.04525/0.10112, loss_grounding_dice_9: 0.17267/0.24938, loss_grounding_ce_9: 0.50955/0.79542] items per batch[64] items per second[0.34] total items[275200] mini batches[ 4300] memory[4929] epoch remaining[0:36:55] INFO:trainer.default_trainer:epochs[ 2] optim steps[4400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31139/0.81066, loss_mask_bce_0: 0.34020/0.30213, loss_mask_dice_0: 0.66619/1.03236, loss_spatial_bce_0: 0.28428/0.09807, loss_spatial_dice_0: 0.38322/0.20669, loss_spatial_ce_0: 0.10756/0.11168, loss_grounding_bce_0: 0.11597/0.08062, loss_grounding_dice_0: 0.30388/0.15252, loss_grounding_ce_0: 0.28819/0.26031, loss_mask_ce_1: 1.43413/0.81378, loss_mask_bce_1: 0.35870/0.30275, loss_mask_dice_1: 0.67143/1.03881, loss_spatial_bce_1: 0.27787/0.09899, loss_spatial_dice_1: 0.38379/0.20981, loss_spatial_ce_1: 0.11017/0.11621, loss_grounding_bce_1: 0.11850/0.08059, loss_grounding_dice_1: 0.30324/0.15341, loss_grounding_ce_1: 0.29820/0.26474, loss_mask_ce_2: 1.42664/0.81889, loss_mask_bce_2: 0.34760/0.30286, loss_mask_dice_2: 0.67289/1.04451, loss_spatial_bce_2: 0.27484/0.09840, loss_spatial_dice_2: 0.39373/0.21004, loss_spatial_ce_2: 0.09667/0.12283, loss_grounding_bce_2: 0.11712/0.08031, loss_grounding_dice_2: 0.30901/0.15390, loss_grounding_ce_2: 0.29639/0.26276, loss_mask_ce_3: 1.41057/0.81290, loss_mask_bce_3: 0.32811/0.30457, loss_mask_dice_3: 0.66290/1.03546, loss_spatial_bce_3: 0.25844/0.10040, loss_spatial_dice_3: 0.39454/0.21028, loss_spatial_ce_3: 0.15341/0.12944, loss_grounding_bce_3: 0.12337/0.08084, loss_grounding_dice_3: 0.28301/0.15339, loss_grounding_ce_3: 0.42096/0.26221, loss_mask_ce_4: 1.50711/0.81964, loss_mask_bce_4: 0.33296/0.30702, loss_mask_dice_4: 0.67676/1.05698, loss_spatial_bce_4: 0.35385/0.10279, loss_spatial_dice_4: 0.42460/0.21748, loss_spatial_ce_4: 0.09185/0.14055, loss_grounding_bce_4: 0.11578/0.08167, loss_grounding_dice_4: 0.31094/0.15575, loss_grounding_ce_4: 0.30769/0.27046, loss_mask_ce_5: 1.38815/0.83905, loss_mask_bce_5: 0.34641/0.30788, loss_mask_dice_5: 0.68217/1.06807, loss_spatial_bce_5: 0.41013/0.10432, loss_spatial_dice_5: 0.42596/0.22055, loss_spatial_ce_5: 0.08746/0.15043, loss_grounding_bce_5: 0.11412/0.08199, loss_grounding_dice_5: 0.31248/0.15700, loss_grounding_ce_5: 0.31264/0.28825, loss_mask_ce_6: 1.43004/0.86202, loss_mask_bce_6: 0.34019/0.30844, loss_mask_dice_6: 0.68464/1.07257, loss_spatial_bce_6: 0.40300/0.10898, loss_spatial_dice_6: 0.42902/0.22357, loss_spatial_ce_6: 0.15603/0.17040, loss_grounding_bce_6: 0.12429/0.08342, loss_grounding_dice_6: 0.31518/0.15672, loss_grounding_ce_6: 0.27759/0.30877, loss_mask_ce_7: 1.64744/0.93115, loss_mask_bce_7: 0.33736/0.31662, loss_mask_dice_7: 0.67337/1.11697, loss_spatial_bce_7: 0.42487/0.12159, loss_spatial_dice_7: 0.44774/0.25001, loss_spatial_ce_7: 0.20301/0.22320, loss_grounding_bce_7: 0.12209/0.08561, loss_grounding_dice_7: 0.30721/0.16263, loss_grounding_ce_7: 0.37323/0.36776, loss_mask_ce_8: 1.88285/1.09542, loss_mask_bce_8: 0.30554/0.33356, loss_mask_dice_8: 0.60457/1.19712, loss_spatial_bce_8: 0.36041/0.14342, loss_spatial_dice_8: 0.44437/0.29914, loss_spatial_ce_8: 0.32761/0.27606, loss_grounding_bce_8: 0.12287/0.08919, loss_grounding_dice_8: 0.31453/0.17056, loss_grounding_ce_8: 0.40087/0.49003, loss_mask_ce_9: 4.97797/3.57482, loss_mask_bce_9: 0.52394/0.36142, loss_mask_dice_9: 1.19327/1.79246, loss_spatial_bce_9: 0.46703/0.37200, loss_spatial_dice_9: 0.85780/0.80260, loss_spatial_ce_9: 2.02218/1.46552, loss_grounding_bce_9: 0.12180/0.10100, loss_grounding_dice_9: 0.36310/0.24984, loss_grounding_ce_9: 0.29406/0.79324] items per batch[64] items per second[0.34] total items[281600] mini batches[ 4400] memory[4929] epoch remaining[0:33:45] INFO:trainer.default_trainer:epochs[ 2] optim steps[4500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49657/0.81149, loss_mask_bce_0: 0.18048/0.30258, loss_mask_dice_0: 0.12255/1.03379, loss_spatial_bce_0: 0.05337/0.09809, loss_spatial_dice_0: 0.05299/0.20657, loss_spatial_ce_0: 0.00002/0.11079, loss_grounding_bce_0: 0.05062/0.08100, loss_grounding_dice_0: 0.03693/0.15264, loss_grounding_ce_0: 0.27258/0.25851, loss_mask_ce_1: 0.54080/0.81468, loss_mask_bce_1: 0.16194/0.30315, loss_mask_dice_1: 0.13530/1.03960, loss_spatial_bce_1: 0.05495/0.09896, loss_spatial_dice_1: 0.05273/0.20971, loss_spatial_ce_1: 0.00009/0.11555, loss_grounding_bce_1: 0.04527/0.08096, loss_grounding_dice_1: 0.04050/0.15344, loss_grounding_ce_1: 0.26538/0.26304, loss_mask_ce_2: 0.50423/0.81997, loss_mask_bce_2: 0.17209/0.30330, loss_mask_dice_2: 0.12876/1.04518, loss_spatial_bce_2: 0.05665/0.09832, loss_spatial_dice_2: 0.05604/0.20987, loss_spatial_ce_2: 0.00012/0.12222, loss_grounding_bce_2: 0.04730/0.08066, loss_grounding_dice_2: 0.04124/0.15383, loss_grounding_ce_2: 0.26719/0.26114, loss_mask_ce_3: 0.45134/0.81360, loss_mask_bce_3: 0.17208/0.30494, loss_mask_dice_3: 0.12333/1.03621, loss_spatial_bce_3: 0.06074/0.10026, loss_spatial_dice_3: 0.05478/0.21012, loss_spatial_ce_3: 0.00034/0.12869, loss_grounding_bce_3: 0.05005/0.08117, loss_grounding_dice_3: 0.04018/0.15343, loss_grounding_ce_3: 0.27912/0.26093, loss_mask_ce_4: 0.48103/0.82064, loss_mask_bce_4: 0.19234/0.30748, loss_mask_dice_4: 0.12180/1.05751, loss_spatial_bce_4: 0.06667/0.10274, loss_spatial_dice_4: 0.06022/0.21745, loss_spatial_ce_4: 0.02180/0.13991, loss_grounding_bce_4: 0.05519/0.08200, loss_grounding_dice_4: 0.04067/0.15577, loss_grounding_ce_4: 0.27257/0.26864, loss_mask_ce_5: 0.46216/0.84042, loss_mask_bce_5: 0.18943/0.30834, loss_mask_dice_5: 0.12218/1.06856, loss_spatial_bce_5: 0.07417/0.10425, loss_spatial_dice_5: 0.05028/0.22033, loss_spatial_ce_5: 0.02094/0.15044, loss_grounding_bce_5: 0.05543/0.08235, loss_grounding_dice_5: 0.03772/0.15696, loss_grounding_ce_5: 0.27612/0.28701, loss_mask_ce_6: 0.55114/0.86322, loss_mask_bce_6: 0.18407/0.30890, loss_mask_dice_6: 0.13337/1.07323, loss_spatial_bce_6: 0.07694/0.10891, loss_spatial_dice_6: 0.05124/0.22336, loss_spatial_ce_6: 0.04703/0.17006, loss_grounding_bce_6: 0.05015/0.08375, loss_grounding_dice_6: 0.04032/0.15671, loss_grounding_ce_6: 0.30955/0.30706, loss_mask_ce_7: 0.57931/0.93189, loss_mask_bce_7: 0.17204/0.31713, loss_mask_dice_7: 0.12770/1.11799, loss_spatial_bce_7: 0.05822/0.12176, loss_spatial_dice_7: 0.05189/0.24982, loss_spatial_ce_7: 0.08129/0.22267, loss_grounding_bce_7: 0.05406/0.08585, loss_grounding_dice_7: 0.03940/0.16259, loss_grounding_ce_7: 0.29857/0.36715, loss_mask_ce_8: 0.64768/1.09595, loss_mask_bce_8: 0.15695/0.33412, loss_mask_dice_8: 0.12637/1.19779, loss_spatial_bce_8: 0.06897/0.14390, loss_spatial_dice_8: 0.06017/0.29870, loss_spatial_ce_8: 0.06275/0.27487, loss_grounding_bce_8: 0.04888/0.08940, loss_grounding_dice_8: 0.03639/0.17044, loss_grounding_ce_8: 0.36449/0.48882, loss_mask_ce_9: 4.01813/3.57488, loss_mask_bce_9: 0.10687/0.36192, loss_mask_dice_9: 0.20831/1.79446, loss_spatial_bce_9: 0.55640/0.37175, loss_spatial_dice_9: 0.89838/0.80243, loss_spatial_ce_9: 1.56102/1.46379, loss_grounding_bce_9: 0.03585/0.10107, loss_grounding_dice_9: 0.07452/0.24946, loss_grounding_ce_9: 0.51806/0.79199] items per batch[64] items per second[0.34] total items[288000] mini batches[ 4500] memory[4929] epoch remaining[0:30:40] INFO:trainer.default_trainer:epochs[ 2] optim steps[4600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18014/0.81074, loss_mask_bce_0: 0.20197/0.30289, loss_mask_dice_0: 1.50608/1.03321, loss_spatial_bce_0: 0.03126/0.09820, loss_spatial_dice_0: 0.28773/0.20604, loss_spatial_ce_0: 0.15867/0.10982, loss_grounding_bce_0: 0.01293/0.08151, loss_grounding_dice_0: 0.36095/0.15244, loss_grounding_ce_0: 0.70997/0.25840, loss_mask_ce_1: 1.12754/0.81384, loss_mask_bce_1: 0.17721/0.30334, loss_mask_dice_1: 1.78184/1.03867, loss_spatial_bce_1: 0.04643/0.09907, loss_spatial_dice_1: 0.34515/0.20918, loss_spatial_ce_1: 0.10221/0.11453, loss_grounding_bce_1: 0.01404/0.08148, loss_grounding_dice_1: 0.57254/0.15330, loss_grounding_ce_1: 0.57553/0.26289, loss_mask_ce_2: 1.06447/0.81911, loss_mask_bce_2: 0.21080/0.30358, loss_mask_dice_2: 1.93495/1.04418, loss_spatial_bce_2: 0.04170/0.09840, loss_spatial_dice_2: 0.34573/0.20933, loss_spatial_ce_2: 0.09982/0.12124, loss_grounding_bce_2: 0.01306/0.08124, loss_grounding_dice_2: 0.41995/0.15367, loss_grounding_ce_2: 0.62133/0.26067, loss_mask_ce_3: 0.78637/0.81272, loss_mask_bce_3: 0.19586/0.30521, loss_mask_dice_3: 1.78020/1.03524, loss_spatial_bce_3: 0.03429/0.10034, loss_spatial_dice_3: 0.33391/0.20955, loss_spatial_ce_3: 0.10815/0.12771, loss_grounding_bce_3: 0.01361/0.08175, loss_grounding_dice_3: 0.42952/0.15325, loss_grounding_ce_3: 0.59804/0.26104, loss_mask_ce_4: 0.87660/0.81984, loss_mask_bce_4: 0.20249/0.30767, loss_mask_dice_4: 1.57243/1.05671, loss_spatial_bce_4: 0.04481/0.10279, loss_spatial_dice_4: 0.36950/0.21685, loss_spatial_ce_4: 0.16917/0.13883, loss_grounding_bce_4: 0.01150/0.08262, loss_grounding_dice_4: 0.48282/0.15568, loss_grounding_ce_4: 0.44935/0.26876, loss_mask_ce_5: 0.74622/0.83977, loss_mask_bce_5: 0.18698/0.30841, loss_mask_dice_5: 1.85197/1.06749, loss_spatial_bce_5: 0.04291/0.10428, loss_spatial_dice_5: 0.31018/0.21961, loss_spatial_ce_5: 0.26997/0.14952, loss_grounding_bce_5: 0.00937/0.08261, loss_grounding_dice_5: 0.42747/0.15683, loss_grounding_ce_5: 0.54070/0.28830, loss_mask_ce_6: 0.95157/0.86198, loss_mask_bce_6: 0.19244/0.30916, loss_mask_dice_6: 1.79889/1.07193, loss_spatial_bce_6: 0.06573/0.10895, loss_spatial_dice_6: 0.34919/0.22261, loss_spatial_ce_6: 0.11949/0.16917, loss_grounding_bce_6: 0.01196/0.08429, loss_grounding_dice_6: 0.42050/0.15660, loss_grounding_ce_6: 0.55994/0.30762, loss_mask_ce_7: 1.04247/0.93016, loss_mask_bce_7: 0.21772/0.31718, loss_mask_dice_7: 1.97456/1.11678, loss_spatial_bce_7: 0.04231/0.12167, loss_spatial_dice_7: 0.30054/0.24890, loss_spatial_ce_7: 0.16806/0.22109, loss_grounding_bce_7: 0.01545/0.08642, loss_grounding_dice_7: 0.57074/0.16246, loss_grounding_ce_7: 0.55322/0.36628, loss_mask_ce_8: 0.81571/1.09358, loss_mask_bce_8: 0.26108/0.33408, loss_mask_dice_8: 1.94400/1.19676, loss_spatial_bce_8: 0.03727/0.14396, loss_spatial_dice_8: 0.33399/0.29760, loss_spatial_ce_8: 0.30967/0.27379, loss_grounding_bce_8: 0.02400/0.08985, loss_grounding_dice_8: 0.47862/0.17017, loss_grounding_ce_8: 0.46148/0.48667, loss_mask_ce_9: 3.19591/3.57111, loss_mask_bce_9: 0.38485/0.36198, loss_mask_dice_9: 2.33021/1.79622, loss_spatial_bce_9: 0.13702/0.37229, loss_spatial_dice_9: 0.91380/0.80210, loss_spatial_ce_9: 1.47801/1.46148, loss_grounding_bce_9: 0.05084/0.10136, loss_grounding_dice_9: 0.71350/0.24908, loss_grounding_ce_9: 0.57055/0.79083] items per batch[64] items per second[0.34] total items[294400] mini batches[ 4600] memory[4929] epoch remaining[0:27:34] INFO:trainer.default_trainer:epochs[ 2] optim steps[4700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.30501/0.81210, loss_mask_bce_0: 0.02802/0.30322, loss_mask_dice_0: 2.56780/1.03394, loss_spatial_bce_0: 0.00449/0.09770, loss_spatial_dice_0: 0.19338/0.20550, loss_spatial_ce_0: 0.49692/0.10891, loss_grounding_bce_0: 0.00180/0.08124, loss_grounding_dice_0: 0.19648/0.15201, loss_grounding_ce_0: 0.64522/0.25815, loss_mask_ce_1: 1.45859/0.81512, loss_mask_bce_1: 0.02445/0.30375, loss_mask_dice_1: 2.35252/1.04005, loss_spatial_bce_1: 0.00455/0.09860, loss_spatial_dice_1: 0.24526/0.20876, loss_spatial_ce_1: 0.48020/0.11367, loss_grounding_bce_1: 0.00252/0.08123, loss_grounding_dice_1: 0.29030/0.15295, loss_grounding_ce_1: 0.58301/0.26274, loss_mask_ce_2: 1.22736/0.82008, loss_mask_bce_2: 0.02297/0.30400, loss_mask_dice_2: 1.84761/1.04422, loss_spatial_bce_2: 0.00412/0.09795, loss_spatial_dice_2: 0.26587/0.20887, loss_spatial_ce_2: 0.48793/0.12044, loss_grounding_bce_2: 0.00211/0.08104, loss_grounding_dice_2: 0.35619/0.15333, loss_grounding_ce_2: 0.64099/0.26015, loss_mask_ce_3: 1.38698/0.81446, loss_mask_bce_3: 0.02413/0.30557, loss_mask_dice_3: 2.99868/1.03561, loss_spatial_bce_3: 0.00511/0.09981, loss_spatial_dice_3: 0.28698/0.20909, loss_spatial_ce_3: 0.55352/0.12675, loss_grounding_bce_3: 0.00266/0.08151, loss_grounding_dice_3: 0.31527/0.15283, loss_grounding_ce_3: 0.64313/0.26126, loss_mask_ce_4: 1.26263/0.82120, loss_mask_bce_4: 0.02551/0.30808, loss_mask_dice_4: 2.69139/1.05749, loss_spatial_bce_4: 0.00338/0.10220, loss_spatial_dice_4: 0.22427/0.21639, loss_spatial_ce_4: 0.81819/0.13811, loss_grounding_bce_4: 0.00202/0.08237, loss_grounding_dice_4: 0.30325/0.15535, loss_grounding_ce_4: 0.63602/0.26881, loss_mask_ce_5: 1.28739/0.84138, loss_mask_bce_5: 0.02402/0.30886, loss_mask_dice_5: 2.59980/1.06805, loss_spatial_bce_5: 0.00440/0.10366, loss_spatial_dice_5: 0.24654/0.21910, loss_spatial_ce_5: 0.44497/0.14909, loss_grounding_bce_5: 0.00213/0.08235, loss_grounding_dice_5: 0.23004/0.15648, loss_grounding_ce_5: 0.54846/0.28880, loss_mask_ce_6: 0.92534/0.86412, loss_mask_bce_6: 0.03962/0.30962, loss_mask_dice_6: 3.13484/1.07295, loss_spatial_bce_6: 0.00464/0.10836, loss_spatial_dice_6: 0.28635/0.22204, loss_spatial_ce_6: 0.47198/0.16851, loss_grounding_bce_6: 0.00223/0.08402, loss_grounding_dice_6: 0.26903/0.15641, loss_grounding_ce_6: 0.57340/0.30838, loss_mask_ce_7: 1.51304/0.93158, loss_mask_bce_7: 0.02983/0.31760, loss_mask_dice_7: 3.01203/1.11722, loss_spatial_bce_7: 0.01565/0.12099, loss_spatial_dice_7: 0.55156/0.24854, loss_spatial_ce_7: 0.54976/0.21973, loss_grounding_bce_7: 0.00399/0.08614, loss_grounding_dice_7: 0.41589/0.16217, loss_grounding_ce_7: 0.67090/0.36675, loss_mask_ce_8: 1.49845/1.09387, loss_mask_bce_8: 0.02552/0.33452, loss_mask_dice_8: 2.83523/1.19818, loss_spatial_bce_8: 0.00804/0.14321, loss_spatial_dice_8: 0.50465/0.29698, loss_spatial_ce_8: 0.23533/0.27275, loss_grounding_bce_8: 0.00199/0.08959, loss_grounding_dice_8: 0.19586/0.16988, loss_grounding_ce_8: 0.65296/0.48617, loss_mask_ce_9: 4.30952/3.57474, loss_mask_bce_9: 0.01838/0.36227, loss_mask_dice_9: 3.50748/1.79933, loss_spatial_bce_9: 0.01439/0.37142, loss_spatial_dice_9: 0.89967/0.80226, loss_spatial_ce_9: 3.69991/1.46232, loss_grounding_bce_9: 0.00180/0.10101, loss_grounding_dice_9: 0.32860/0.24850, loss_grounding_ce_9: 0.59190/0.78933] items per batch[64] items per second[0.35] total items[300800] mini batches[ 4700] memory[4929] epoch remaining[0:24:23] INFO:trainer.default_trainer:epochs[ 2] optim steps[4800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74577/0.80939, loss_mask_bce_0: 0.22858/0.30323, loss_mask_dice_0: 0.28468/1.03134, loss_spatial_bce_0: 0.16181/0.09741, loss_spatial_dice_0: 0.21374/0.20482, loss_spatial_ce_0: 0.06441/0.10781, loss_grounding_bce_0: 0.03028/0.08131, loss_grounding_dice_0: 0.05717/0.15175, loss_grounding_ce_0: 0.19454/0.25611, loss_mask_ce_1: 0.77580/0.81232, loss_mask_bce_1: 0.23511/0.30378, loss_mask_dice_1: 0.27434/1.03721, loss_spatial_bce_1: 0.13386/0.09833, loss_spatial_dice_1: 0.20322/0.20806, loss_spatial_ce_1: 0.10663/0.11269, loss_grounding_bce_1: 0.03437/0.08128, loss_grounding_dice_1: 0.06065/0.15269, loss_grounding_ce_1: 0.18135/0.26075, loss_mask_ce_2: 0.74609/0.81735, loss_mask_bce_2: 0.24063/0.30397, loss_mask_dice_2: 0.28886/1.04156, loss_spatial_bce_2: 0.14531/0.09765, loss_spatial_dice_2: 0.20376/0.20816, loss_spatial_ce_2: 0.10052/0.11930, loss_grounding_bce_2: 0.03421/0.08109, loss_grounding_dice_2: 0.06003/0.15308, loss_grounding_ce_2: 0.17936/0.25809, loss_mask_ce_3: 0.87893/0.81224, loss_mask_bce_3: 0.23474/0.30559, loss_mask_dice_3: 0.29023/1.03318, loss_spatial_bce_3: 0.12755/0.09949, loss_spatial_dice_3: 0.20492/0.20837, loss_spatial_ce_3: 0.16249/0.12577, loss_grounding_bce_3: 0.03013/0.08157, loss_grounding_dice_3: 0.06102/0.15252, loss_grounding_ce_3: 0.18898/0.25933, loss_mask_ce_4: 0.69638/0.81842, loss_mask_bce_4: 0.24149/0.30813, loss_mask_dice_4: 0.31134/1.05464, loss_spatial_bce_4: 0.16571/0.10186, loss_spatial_dice_4: 0.21873/0.21560, loss_spatial_ce_4: 0.11579/0.13715, loss_grounding_bce_4: 0.03736/0.08239, loss_grounding_dice_4: 0.07021/0.15497, loss_grounding_ce_4: 0.16653/0.26684, loss_mask_ce_5: 0.61988/0.83878, loss_mask_bce_5: 0.22610/0.30893, loss_mask_dice_5: 0.30531/1.06481, loss_spatial_bce_5: 0.12343/0.10331, loss_spatial_dice_5: 0.23280/0.21838, loss_spatial_ce_5: 0.12910/0.14790, loss_grounding_bce_5: 0.03238/0.08236, loss_grounding_dice_5: 0.07141/0.15608, loss_grounding_ce_5: 0.20633/0.28695, loss_mask_ce_6: 0.82538/0.86235, loss_mask_bce_6: 0.23332/0.30964, loss_mask_dice_6: 0.32910/1.07017, loss_spatial_bce_6: 0.13397/0.10800, loss_spatial_dice_6: 0.23020/0.22120, loss_spatial_ce_6: 0.13549/0.16743, loss_grounding_bce_6: 0.03601/0.08398, loss_grounding_dice_6: 0.07701/0.15609, loss_grounding_ce_6: 0.20103/0.30651, loss_mask_ce_7: 0.80434/0.92873, loss_mask_bce_7: 0.26945/0.31784, loss_mask_dice_7: 0.31223/1.11463, loss_spatial_bce_7: 0.16437/0.12053, loss_spatial_dice_7: 0.24257/0.24764, loss_spatial_ce_7: 0.15659/0.21848, loss_grounding_bce_7: 0.03998/0.08620, loss_grounding_dice_7: 0.07550/0.16192, loss_grounding_ce_7: 0.23696/0.36361, loss_mask_ce_8: 0.66253/1.08996, loss_mask_bce_8: 0.26213/0.33468, loss_mask_dice_8: 0.30352/1.19545, loss_spatial_bce_8: 0.18475/0.14263, loss_spatial_dice_8: 0.26493/0.29589, loss_spatial_ce_8: 0.26403/0.27160, loss_grounding_bce_8: 0.05332/0.08969, loss_grounding_dice_8: 0.08904/0.16959, loss_grounding_ce_8: 0.27731/0.48260, loss_mask_ce_9: 4.92318/3.56804, loss_mask_bce_9: 0.47928/0.36223, loss_mask_dice_9: 0.74503/1.79679, loss_spatial_bce_9: 0.53205/0.37187, loss_spatial_dice_9: 0.81999/0.80188, loss_spatial_ce_9: 1.37519/1.45966, loss_grounding_bce_9: 0.10319/0.10110, loss_grounding_dice_9: 0.22201/0.24806, loss_grounding_ce_9: 0.81152/0.78437] items per batch[64] items per second[0.34] total items[307200] mini batches[ 4800] memory[4929] epoch remaining[0:21:15] INFO:trainer.default_trainer:epochs[ 2] optim steps[4900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27305/0.80925, loss_mask_bce_0: 0.26455/0.30397, loss_mask_dice_0: 0.16672/1.02991, loss_spatial_bce_0: 0.19867/0.09718, loss_spatial_dice_0: 0.10297/0.20405, loss_spatial_ce_0: 0.00075/0.10681, loss_grounding_bce_0: 0.19643/0.08093, loss_grounding_dice_0: 0.12377/0.15134, loss_grounding_ce_0: 0.02585/0.25577, loss_mask_ce_1: 0.24163/0.81208, loss_mask_bce_1: 0.25466/0.30464, loss_mask_dice_1: 0.16248/1.03598, loss_spatial_bce_1: 0.25613/0.09807, loss_spatial_dice_1: 0.11331/0.20721, loss_spatial_ce_1: 0.00059/0.11162, loss_grounding_bce_1: 0.19845/0.08093, loss_grounding_dice_1: 0.13004/0.15220, loss_grounding_ce_1: 0.01606/0.26031, loss_mask_ce_2: 0.23278/0.81727, loss_mask_bce_2: 0.26687/0.30471, loss_mask_dice_2: 0.17167/1.04030, loss_spatial_bce_2: 0.26883/0.09740, loss_spatial_dice_2: 0.11859/0.20735, loss_spatial_ce_2: 0.00043/0.11834, loss_grounding_bce_2: 0.20881/0.08072, loss_grounding_dice_2: 0.13231/0.15265, loss_grounding_ce_2: 0.02051/0.25782, loss_mask_ce_3: 0.25769/0.81185, loss_mask_bce_3: 0.27880/0.30628, loss_mask_dice_3: 0.17369/1.03192, loss_spatial_bce_3: 0.27223/0.09925, loss_spatial_dice_3: 0.12221/0.20757, loss_spatial_ce_3: 0.00291/0.12456, loss_grounding_bce_3: 0.20039/0.08119, loss_grounding_dice_3: 0.12545/0.15215, loss_grounding_ce_3: 0.02003/0.25890, loss_mask_ce_4: 0.27794/0.81839, loss_mask_bce_4: 0.25456/0.30890, loss_mask_dice_4: 0.16476/1.05323, loss_spatial_bce_4: 0.26374/0.10163, loss_spatial_dice_4: 0.11121/0.21482, loss_spatial_ce_4: 0.00891/0.13599, loss_grounding_bce_4: 0.18789/0.08200, loss_grounding_dice_4: 0.12282/0.15453, loss_grounding_ce_4: 0.02814/0.26619, loss_mask_ce_5: 0.30475/0.83923, loss_mask_bce_5: 0.22877/0.30974, loss_mask_dice_5: 0.15285/1.06391, loss_spatial_bce_5: 0.29895/0.10308, loss_spatial_dice_5: 0.11178/0.21754, loss_spatial_ce_5: 0.03902/0.14699, loss_grounding_bce_5: 0.17396/0.08196, loss_grounding_dice_5: 0.12136/0.15564, loss_grounding_ce_5: 0.03177/0.28676, loss_mask_ce_6: 0.29174/0.86227, loss_mask_bce_6: 0.26447/0.31038, loss_mask_dice_6: 0.17643/1.06881, loss_spatial_bce_6: 0.24847/0.10773, loss_spatial_dice_6: 0.12152/0.22034, loss_spatial_ce_6: 0.04451/0.16625, loss_grounding_bce_6: 0.20514/0.08360, loss_grounding_dice_6: 0.13032/0.15553, loss_grounding_ce_6: 0.03630/0.30679, loss_mask_ce_7: 0.31566/0.92924, loss_mask_bce_7: 0.22450/0.31857, loss_mask_dice_7: 0.16069/1.11327, loss_spatial_bce_7: 0.28364/0.12016, loss_spatial_dice_7: 0.13181/0.24677, loss_spatial_ce_7: 0.12546/0.21722, loss_grounding_bce_7: 0.16824/0.08574, loss_grounding_dice_7: 0.12386/0.16129, loss_grounding_ce_7: 0.04337/0.36485, loss_mask_ce_8: 0.50780/1.08974, loss_mask_bce_8: 0.21766/0.33553, loss_mask_dice_8: 0.14901/1.19441, loss_spatial_bce_8: 0.39483/0.14219, loss_spatial_dice_8: 0.12308/0.29468, loss_spatial_ce_8: 0.12930/0.27046, loss_grounding_bce_8: 0.16454/0.08926, loss_grounding_dice_8: 0.11780/0.16895, loss_grounding_ce_8: 0.06989/0.48117, loss_mask_ce_9: 3.00038/3.57165, loss_mask_bce_9: 0.21335/0.36318, loss_mask_dice_9: 0.18684/1.79810, loss_spatial_bce_9: 0.59227/0.37205, loss_spatial_dice_9: 0.68781/0.80200, loss_spatial_ce_9: 0.82390/1.45766, loss_grounding_bce_9: 0.15581/0.10079, loss_grounding_dice_9: 0.12664/0.24740, loss_grounding_ce_9: 0.35589/0.78370] items per batch[64] items per second[0.34] total items[313600] mini batches[ 4900] memory[4929] epoch remaining[0:18:08] INFO:trainer.default_trainer:epochs[ 2] optim steps[5000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.28962/0.80985, loss_mask_bce_0: 0.11335/0.30322, loss_mask_dice_0: 5.56411/1.02942, loss_spatial_bce_0: 0.00446/0.09725, loss_spatial_dice_0: 0.48422/0.20377, loss_spatial_ce_0: 0.15543/0.10635, loss_grounding_bce_0: 0.00211/0.08088, loss_grounding_dice_0: 0.04220/0.15120, loss_grounding_ce_0: 0.08971/0.25579, loss_mask_ce_1: 2.34058/0.81253, loss_mask_bce_1: 0.10079/0.30393, loss_mask_dice_1: 5.00362/1.03494, loss_spatial_bce_1: 0.00359/0.09814, loss_spatial_dice_1: 0.58423/0.20689, loss_spatial_ce_1: 0.24298/0.11093, loss_grounding_bce_1: 0.00239/0.08090, loss_grounding_dice_1: 0.05729/0.15217, loss_grounding_ce_1: 0.28281/0.26017, loss_mask_ce_2: 2.29307/0.81786, loss_mask_bce_2: 0.08444/0.30397, loss_mask_dice_2: 4.81666/1.03945, loss_spatial_bce_2: 0.00298/0.09748, loss_spatial_dice_2: 0.55792/0.20699, loss_spatial_ce_2: 0.32004/0.11770, loss_grounding_bce_2: 0.00216/0.08068, loss_grounding_dice_2: 0.04663/0.15254, loss_grounding_ce_2: 0.19121/0.25747, loss_mask_ce_3: 2.52158/0.81227, loss_mask_bce_3: 0.14116/0.30560, loss_mask_dice_3: 5.41722/1.03131, loss_spatial_bce_3: 0.00315/0.09930, loss_spatial_dice_3: 0.55349/0.20722, loss_spatial_ce_3: 0.30139/0.12377, loss_grounding_bce_3: 0.00268/0.08117, loss_grounding_dice_3: 0.04951/0.15198, loss_grounding_ce_3: 0.30811/0.25853, loss_mask_ce_4: 2.60403/0.81894, loss_mask_bce_4: 0.05902/0.30823, loss_mask_dice_4: 4.52289/1.05214, loss_spatial_bce_4: 0.00627/0.10163, loss_spatial_dice_4: 0.58548/0.21448, loss_spatial_ce_4: 0.17881/0.13513, loss_grounding_bce_4: 0.00384/0.08203, loss_grounding_dice_4: 0.06636/0.15446, loss_grounding_ce_4: 0.23493/0.26609, loss_mask_ce_5: 2.35080/0.84019, loss_mask_bce_5: 0.08341/0.30897, loss_mask_dice_5: 5.41530/1.06222, loss_spatial_bce_5: 0.00635/0.10306, loss_spatial_dice_5: 0.68668/0.21722, loss_spatial_ce_5: 0.34571/0.14618, loss_grounding_bce_5: 0.00184/0.08191, loss_grounding_dice_5: 0.04298/0.15562, loss_grounding_ce_5: 0.29857/0.28621, loss_mask_ce_6: 2.41968/0.86256, loss_mask_bce_6: 0.08383/0.30967, loss_mask_dice_6: 5.69245/1.06760, loss_spatial_bce_6: 0.00699/0.10760, loss_spatial_dice_6: 0.60420/0.21986, loss_spatial_ce_6: 0.46689/0.16537, loss_grounding_bce_6: 0.00279/0.08350, loss_grounding_dice_6: 0.06076/0.15532, loss_grounding_ce_6: 0.31833/0.30571, loss_mask_ce_7: 2.38113/0.92882, loss_mask_bce_7: 0.07747/0.31790, loss_mask_dice_7: 5.02078/1.11151, loss_spatial_bce_7: 0.01673/0.12006, loss_spatial_dice_7: 0.75424/0.24633, loss_spatial_ce_7: 0.16646/0.21615, loss_grounding_bce_7: 0.00310/0.08566, loss_grounding_dice_7: 0.07071/0.16103, loss_grounding_ce_7: 0.42458/0.36366, loss_mask_ce_8: 3.06566/1.08928, loss_mask_bce_8: 0.10387/0.33476, loss_mask_dice_8: 5.74748/1.19247, loss_spatial_bce_8: 0.00971/0.14236, loss_spatial_dice_8: 0.72887/0.29400, loss_spatial_ce_8: 0.26013/0.26927, loss_grounding_bce_8: 0.00282/0.08928, loss_grounding_dice_8: 0.07536/0.16877, loss_grounding_ce_8: 0.85201/0.47979, loss_mask_ce_9: 5.88816/3.56911, loss_mask_bce_9: 0.06773/0.36296, loss_mask_dice_9: 6.68514/1.79497, loss_spatial_bce_9: 0.00677/0.37222, loss_spatial_dice_9: 0.94688/0.80145, loss_spatial_ce_9: 3.12096/1.45567, loss_grounding_bce_9: 0.00319/0.10104, loss_grounding_dice_9: 0.06984/0.24731, loss_grounding_ce_9: 1.50796/0.78171] items per batch[64] items per second[0.34] total items[320000] mini batches[ 5000] memory[4929] epoch remaining[0:15:01] INFO:trainer.default_trainer:epochs[ 2] optim steps[5100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.15505/0.81202, loss_mask_bce_0: 0.53334/0.30357, loss_mask_dice_0: 1.31701/1.03380, loss_spatial_bce_0: 0.01902/0.09695, loss_spatial_dice_0: 0.17041/0.20397, loss_spatial_ce_0: 0.00011/0.10595, loss_grounding_bce_0: 0.17704/0.08072, loss_grounding_dice_0: 0.54810/0.15159, loss_grounding_ce_0: 1.46126/0.25624, loss_mask_ce_1: 1.22794/0.81478, loss_mask_bce_1: 0.50316/0.30433, loss_mask_dice_1: 1.40380/1.03994, loss_spatial_bce_1: 0.02065/0.09787, loss_spatial_dice_1: 0.19789/0.20711, loss_spatial_ce_1: 0.00062/0.11050, loss_grounding_bce_1: 0.16160/0.08075, loss_grounding_dice_1: 0.52147/0.15257, loss_grounding_ce_1: 1.39894/0.26061, loss_mask_ce_2: 1.26653/0.82007, loss_mask_bce_2: 0.51867/0.30431, loss_mask_dice_2: 1.33409/1.04391, loss_spatial_bce_2: 0.01892/0.09717, loss_spatial_dice_2: 0.17000/0.20715, loss_spatial_ce_2: 0.00026/0.11754, loss_grounding_bce_2: 0.15006/0.08054, loss_grounding_dice_2: 0.55277/0.15293, loss_grounding_ce_2: 1.34920/0.25808, loss_mask_ce_3: 1.19999/0.81422, loss_mask_bce_3: 0.51472/0.30588, loss_mask_dice_3: 1.29271/1.03583, loss_spatial_bce_3: 0.02317/0.09897, loss_spatial_dice_3: 0.21316/0.20741, loss_spatial_ce_3: 0.00571/0.12341, loss_grounding_bce_3: 0.18113/0.08102, loss_grounding_dice_3: 0.48546/0.15248, loss_grounding_ce_3: 1.40107/0.25901, loss_mask_ce_4: 1.44455/0.82092, loss_mask_bce_4: 0.54278/0.30858, loss_mask_dice_4: 1.39443/1.05645, loss_spatial_bce_4: 0.01703/0.10128, loss_spatial_dice_4: 0.16124/0.21466, loss_spatial_ce_4: 0.03101/0.13492, loss_grounding_bce_4: 0.24840/0.08194, loss_grounding_dice_4: 0.52301/0.15484, loss_grounding_ce_4: 1.38360/0.26655, loss_mask_ce_5: 1.12191/0.84235, loss_mask_bce_5: 0.56532/0.30943, loss_mask_dice_5: 1.46997/1.06697, loss_spatial_bce_5: 0.02131/0.10273, loss_spatial_dice_5: 0.17918/0.21733, loss_spatial_ce_5: 0.00775/0.14552, loss_grounding_bce_5: 0.23060/0.08183, loss_grounding_dice_5: 0.52375/0.15601, loss_grounding_ce_5: 0.70350/0.28657, loss_mask_ce_6: 1.20765/0.86452, loss_mask_bce_6: 0.52667/0.31006, loss_mask_dice_6: 1.43594/1.07180, loss_spatial_bce_6: 0.02456/0.10723, loss_spatial_dice_6: 0.16214/0.22000, loss_spatial_ce_6: 0.06549/0.16469, loss_grounding_bce_6: 0.23110/0.08334, loss_grounding_dice_6: 0.55367/0.15569, loss_grounding_ce_6: 0.66676/0.30639, loss_mask_ce_7: 1.31006/0.93084, loss_mask_bce_7: 0.54386/0.31841, loss_mask_dice_7: 1.45211/1.11622, loss_spatial_bce_7: 0.02469/0.11964, loss_spatial_dice_7: 0.15294/0.24657, loss_spatial_ce_7: 0.04747/0.21534, loss_grounding_bce_7: 0.24129/0.08564, loss_grounding_dice_7: 0.59752/0.16141, loss_grounding_ce_7: 0.71571/0.36521, loss_mask_ce_8: 1.92923/1.09108, loss_mask_bce_8: 0.40733/0.33511, loss_mask_dice_8: 1.51482/1.19707, loss_spatial_bce_8: 0.02100/0.14186, loss_spatial_dice_8: 0.19882/0.29408, loss_spatial_ce_8: 0.22974/0.26860, loss_grounding_bce_8: 0.17151/0.08918, loss_grounding_dice_8: 0.62764/0.16933, loss_grounding_ce_8: 0.94865/0.47990, loss_mask_ce_9: 4.37533/3.57101, loss_mask_bce_9: 0.55296/0.36313, loss_mask_dice_9: 3.49157/1.80121, loss_spatial_bce_9: 0.21276/0.37120, loss_spatial_dice_9: 0.80562/0.80177, loss_spatial_ce_9: 0.95719/1.45743, loss_grounding_bce_9: 0.42721/0.10094, loss_grounding_dice_9: 0.77802/0.24775, loss_grounding_ce_9: 0.09171/0.78125] items per batch[64] items per second[0.33] total items[326400] mini batches[ 5100] memory[4929] epoch remaining[0:11:55] INFO:trainer.default_trainer:epochs[ 2] optim steps[5200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84789/0.81325, loss_mask_bce_0: 0.38862/0.30400, loss_mask_dice_0: 0.31726/1.03374, loss_spatial_bce_0: 0.16658/0.09675, loss_spatial_dice_0: 0.13714/0.20350, loss_spatial_ce_0: 0.26764/0.10546, loss_grounding_bce_0: 0.19630/0.08062, loss_grounding_dice_0: 0.13155/0.15103, loss_grounding_ce_0: 0.02291/0.25559, loss_mask_ce_1: 0.79386/0.81571, loss_mask_bce_1: 0.41770/0.30468, loss_mask_dice_1: 0.33714/1.03977, loss_spatial_bce_1: 0.17574/0.09767, loss_spatial_dice_1: 0.14273/0.20670, loss_spatial_ce_1: 0.34554/0.10995, loss_grounding_bce_1: 0.20587/0.08063, loss_grounding_dice_1: 0.13392/0.15198, loss_grounding_ce_1: 0.01643/0.26026, loss_mask_ce_2: 0.87256/0.82177, loss_mask_bce_2: 0.41380/0.30462, loss_mask_dice_2: 0.32815/1.04372, loss_spatial_bce_2: 0.17447/0.09701, loss_spatial_dice_2: 0.13635/0.20672, loss_spatial_ce_2: 0.37058/0.11684, loss_grounding_bce_2: 0.21339/0.08043, loss_grounding_dice_2: 0.13397/0.15231, loss_grounding_ce_2: 0.01222/0.25807, loss_mask_ce_3: 0.86086/0.81598, loss_mask_bce_3: 0.40673/0.30625, loss_mask_dice_3: 0.32903/1.03566, loss_spatial_bce_3: 0.18601/0.09868, loss_spatial_dice_3: 0.14187/0.20695, loss_spatial_ce_3: 0.39992/0.12301, loss_grounding_bce_3: 0.20402/0.08091, loss_grounding_dice_3: 0.13108/0.15192, loss_grounding_ce_3: 0.00854/0.25890, loss_mask_ce_4: 0.67255/0.82209, loss_mask_bce_4: 0.57303/0.30901, loss_mask_dice_4: 0.40486/1.05664, loss_spatial_bce_4: 0.18946/0.10108, loss_spatial_dice_4: 0.14539/0.21421, loss_spatial_ce_4: 0.40967/0.13414, loss_grounding_bce_4: 0.22922/0.08184, loss_grounding_dice_4: 0.15135/0.15429, loss_grounding_ce_4: 0.01156/0.26657, loss_mask_ce_5: 0.62807/0.84324, loss_mask_bce_5: 0.51536/0.30988, loss_mask_dice_5: 0.40407/1.06716, loss_spatial_bce_5: 0.18719/0.10249, loss_spatial_dice_5: 0.15124/0.21690, loss_spatial_ce_5: 0.35353/0.14482, loss_grounding_bce_5: 0.21552/0.08173, loss_grounding_dice_5: 0.14768/0.15547, loss_grounding_ce_5: 0.01287/0.28607, loss_mask_ce_6: 0.57717/0.86555, loss_mask_bce_6: 0.57945/0.31055, loss_mask_dice_6: 0.45295/1.07202, loss_spatial_bce_6: 0.17814/0.10702, loss_spatial_dice_6: 0.16516/0.21952, loss_spatial_ce_6: 0.30825/0.16389, loss_grounding_bce_6: 0.21308/0.08321, loss_grounding_dice_6: 0.16748/0.15507, loss_grounding_ce_6: 0.02527/0.30629, loss_mask_ce_7: 0.86690/0.93239, loss_mask_bce_7: 0.49063/0.31893, loss_mask_dice_7: 0.39873/1.11590, loss_spatial_bce_7: 0.22795/0.11943, loss_spatial_dice_7: 0.24668/0.24612, loss_spatial_ce_7: 0.20705/0.21440, loss_grounding_bce_7: 0.25144/0.08558, loss_grounding_dice_7: 0.17226/0.16094, loss_grounding_ce_7: 0.03179/0.36495, loss_mask_ce_8: 1.12301/1.09226, loss_mask_bce_8: 0.45566/0.33558, loss_mask_dice_8: 0.38057/1.19726, loss_spatial_bce_8: 0.28742/0.14175, loss_spatial_dice_8: 0.20813/0.29347, loss_spatial_ce_8: 0.45367/0.26781, loss_grounding_bce_8: 0.21152/0.08901, loss_grounding_dice_8: 0.16443/0.16875, loss_grounding_ce_8: 0.09695/0.47929, loss_mask_ce_9: 4.53075/3.57569, loss_mask_bce_9: 0.55339/0.36380, loss_mask_dice_9: 0.70379/1.80112, loss_spatial_bce_9: 0.53845/0.37138, loss_spatial_dice_9: 0.77563/0.80219, loss_spatial_ce_9: 0.99283/1.45638, loss_grounding_bce_9: 0.27747/0.10109, loss_grounding_dice_9: 0.31732/0.24719, loss_grounding_ce_9: 0.31951/0.78322] items per batch[64] items per second[0.34] total items[332800] mini batches[ 5200] memory[4929] epoch remaining[0:08:47] INFO:trainer.default_trainer:epochs[ 2] optim steps[5300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96989/0.81314, loss_mask_bce_0: 1.16325/0.30398, loss_mask_dice_0: 1.68445/1.03553, loss_spatial_bce_0: 0.14617/0.09659, loss_spatial_dice_0: 0.16551/0.20318, loss_spatial_ce_0: 0.03076/0.10456, loss_grounding_bce_0: 0.16343/0.08056, loss_grounding_dice_0: 0.13820/0.15058, loss_grounding_ce_0: 0.66016/0.25448, loss_mask_ce_1: 0.96365/0.81577, loss_mask_bce_1: 1.15585/0.30466, loss_mask_dice_1: 1.66352/1.04142, loss_spatial_bce_1: 0.15036/0.09749, loss_spatial_dice_1: 0.17321/0.20631, loss_spatial_ce_1: 0.05339/0.10899, loss_grounding_bce_1: 0.18726/0.08055, loss_grounding_dice_1: 0.13899/0.15159, loss_grounding_ce_1: 0.68502/0.25905, loss_mask_ce_2: 0.94797/0.82201, loss_mask_bce_2: 1.14194/0.30450, loss_mask_dice_2: 1.68001/1.04544, loss_spatial_bce_2: 0.15215/0.09681, loss_spatial_dice_2: 0.16796/0.20639, loss_spatial_ce_2: 0.05339/0.11586, loss_grounding_bce_2: 0.18663/0.08034, loss_grounding_dice_2: 0.13944/0.15188, loss_grounding_ce_2: 0.55938/0.25700, loss_mask_ce_3: 0.94672/0.81646, loss_mask_bce_3: 1.16861/0.30613, loss_mask_dice_3: 1.66449/1.03804, loss_spatial_bce_3: 0.14511/0.09848, loss_spatial_dice_3: 0.16286/0.20656, loss_spatial_ce_3: 0.03751/0.12232, loss_grounding_bce_3: 0.17651/0.08084, loss_grounding_dice_3: 0.14198/0.15150, loss_grounding_ce_3: 0.71329/0.25779, loss_mask_ce_4: 1.01748/0.82236, loss_mask_bce_4: 1.10719/0.30896, loss_mask_dice_4: 1.74142/1.05851, loss_spatial_bce_4: 0.15777/0.10090, loss_spatial_dice_4: 0.21146/0.21386, loss_spatial_ce_4: 0.03473/0.13327, loss_grounding_bce_4: 0.16502/0.08177, loss_grounding_dice_4: 0.14710/0.15389, loss_grounding_ce_4: 0.64531/0.26535, loss_mask_ce_5: 0.96899/0.84363, loss_mask_bce_5: 1.13932/0.30989, loss_mask_dice_5: 1.77932/1.06873, loss_spatial_bce_5: 0.15391/0.10226, loss_spatial_dice_5: 0.21673/0.21655, loss_spatial_ce_5: 0.02947/0.14399, loss_grounding_bce_5: 0.18535/0.08166, loss_grounding_dice_5: 0.16389/0.15504, loss_grounding_ce_5: 0.70775/0.28478, loss_mask_ce_6: 0.98095/0.86526, loss_mask_bce_6: 1.21636/0.31054, loss_mask_dice_6: 1.74583/1.07340, loss_spatial_bce_6: 0.16573/0.10680, loss_spatial_dice_6: 0.20100/0.21909, loss_spatial_ce_6: 0.04780/0.16266, loss_grounding_bce_6: 0.18946/0.08313, loss_grounding_dice_6: 0.14803/0.15478, loss_grounding_ce_6: 1.19669/0.30485, loss_mask_ce_7: 1.19899/0.93134, loss_mask_bce_7: 1.11327/0.31890, loss_mask_dice_7: 1.75224/1.11774, loss_spatial_bce_7: 0.17872/0.11909, loss_spatial_dice_7: 0.24214/0.24569, loss_spatial_ce_7: 0.08550/0.21326, loss_grounding_bce_7: 0.16934/0.08552, loss_grounding_dice_7: 0.14341/0.16054, loss_grounding_ce_7: 0.88714/0.36344, loss_mask_ce_8: 1.47579/1.09050, loss_mask_bce_8: 1.16811/0.33544, loss_mask_dice_8: 1.80653/1.19877, loss_spatial_bce_8: 0.20601/0.14151, loss_spatial_dice_8: 0.23480/0.29282, loss_spatial_ce_8: 0.18379/0.26657, loss_grounding_bce_8: 0.16343/0.08897, loss_grounding_dice_8: 0.15305/0.16832, loss_grounding_ce_8: 1.09922/0.47683, loss_mask_ce_9: 3.40700/3.57167, loss_mask_bce_9: 1.51371/0.36324, loss_mask_dice_9: 3.38624/1.80191, loss_spatial_bce_9: 0.27256/0.37097, loss_spatial_dice_9: 0.94236/0.80237, loss_spatial_ce_9: 1.59790/1.45647, loss_grounding_bce_9: 0.17360/0.10103, loss_grounding_dice_9: 0.14265/0.24663, loss_grounding_ce_9: 1.55009/0.78128] items per batch[64] items per second[0.35] total items[339200] mini batches[ 5300] memory[4929] epoch remaining[0:05:39] INFO:trainer.default_trainer:epochs[ 2] optim steps[5400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53440/0.81205, loss_mask_bce_0: 0.30479/0.30432, loss_mask_dice_0: 0.78976/1.03038, loss_spatial_bce_0: 0.18226/0.09674, loss_spatial_dice_0: 0.41739/0.20293, loss_spatial_ce_0: 0.67207/0.10407, loss_grounding_bce_0: 0.14828/0.08073, loss_grounding_dice_0: 0.47939/0.15067, loss_grounding_ce_0: 0.57594/0.25396, loss_mask_ce_1: 1.61042/0.81496, loss_mask_bce_1: 0.25580/0.30497, loss_mask_dice_1: 0.76454/1.03626, loss_spatial_bce_1: 0.17574/0.09763, loss_spatial_dice_1: 0.40573/0.20598, loss_spatial_ce_1: 0.67696/0.10845, loss_grounding_bce_1: 0.14861/0.08073, loss_grounding_dice_1: 0.47198/0.15160, loss_grounding_ce_1: 0.55019/0.25839, loss_mask_ce_2: 1.59906/0.82109, loss_mask_bce_2: 0.28010/0.30486, loss_mask_dice_2: 0.80156/1.04012, loss_spatial_bce_2: 0.18519/0.09696, loss_spatial_dice_2: 0.42741/0.20613, loss_spatial_ce_2: 0.76290/0.11526, loss_grounding_bce_2: 0.19797/0.08052, loss_grounding_dice_2: 0.47201/0.15189, loss_grounding_ce_2: 0.68076/0.25632, loss_mask_ce_3: 1.63060/0.81542, loss_mask_bce_3: 0.25553/0.30656, loss_mask_dice_3: 0.80836/1.03263, loss_spatial_bce_3: 0.35711/0.09864, loss_spatial_dice_3: 0.45112/0.20626, loss_spatial_ce_3: 0.24698/0.12168, loss_grounding_bce_3: 0.13893/0.08102, loss_grounding_dice_3: 0.44866/0.15156, loss_grounding_ce_3: 0.57892/0.25711, loss_mask_ce_4: 1.61480/0.82163, loss_mask_bce_4: 0.32546/0.30926, loss_mask_dice_4: 0.93253/1.05318, loss_spatial_bce_4: 0.16780/0.10099, loss_spatial_dice_4: 0.42216/0.21358, loss_spatial_ce_4: 0.79983/0.13271, loss_grounding_bce_4: 0.15747/0.08190, loss_grounding_dice_4: 0.50382/0.15388, loss_grounding_ce_4: 0.53781/0.26494, loss_mask_ce_5: 1.29664/0.84235, loss_mask_bce_5: 0.33939/0.31032, loss_mask_dice_5: 1.01984/1.06344, loss_spatial_bce_5: 0.19183/0.10235, loss_spatial_dice_5: 0.44271/0.21620, loss_spatial_ce_5: 0.97746/0.14351, loss_grounding_bce_5: 0.16215/0.08182, loss_grounding_dice_5: 0.47295/0.15508, loss_grounding_ce_5: 0.65438/0.28399, loss_mask_ce_6: 1.60396/0.86382, loss_mask_bce_6: 0.39011/0.31081, loss_mask_dice_6: 1.03735/1.06813, loss_spatial_bce_6: 0.40301/0.10700, loss_spatial_dice_6: 0.47035/0.21874, loss_spatial_ce_6: 0.59647/0.16179, loss_grounding_bce_6: 0.17414/0.08325, loss_grounding_dice_6: 0.51035/0.15476, loss_grounding_ce_6: 0.50860/0.30417, loss_mask_ce_7: 1.50085/0.92917, loss_mask_bce_7: 0.37258/0.31909, loss_mask_dice_7: 1.05666/1.11229, loss_spatial_bce_7: 0.19947/0.11936, loss_spatial_dice_7: 0.46505/0.24529, loss_spatial_ce_7: 1.44072/0.21260, loss_grounding_bce_7: 0.17040/0.08558, loss_grounding_dice_7: 0.50938/0.16050, loss_grounding_ce_7: 0.68910/0.36269, loss_mask_ce_8: 1.78003/1.08887, loss_mask_bce_8: 0.41875/0.33594, loss_mask_dice_8: 1.09417/1.19292, loss_spatial_bce_8: 0.66211/0.14187, loss_spatial_dice_8: 0.51631/0.29208, loss_spatial_ce_8: 0.61629/0.26570, loss_grounding_bce_8: 0.19490/0.08911, loss_grounding_dice_8: 0.52878/0.16847, loss_grounding_ce_8: 0.71228/0.47537, loss_mask_ce_9: 3.41203/3.56557, loss_mask_bce_9: 0.61605/0.36367, loss_mask_dice_9: 1.42431/1.79327, loss_spatial_bce_9: 0.38451/0.37115, loss_spatial_dice_9: 0.71840/0.80219, loss_spatial_ce_9: 1.26249/1.45624, loss_grounding_bce_9: 0.29990/0.10110, loss_grounding_dice_9: 0.71499/0.24677, loss_grounding_ce_9: 0.26712/0.78084] items per batch[64] items per second[0.35] total items[345600] mini batches[ 5400] memory[4929] epoch remaining[0:02:31] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00005481. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0034 s/iter. Inference: 0.3577 s/iter. Eval: 0.0902 s/iter. Total: 0.4513 s/iter. ETA=0:00:30 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0031 s/iter. Inference: 0.3674 s/iter. Eval: 0.0854 s/iter. Total: 0.4560 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0031 s/iter. Inference: 0.3744 s/iter. Eval: 0.0812 s/iter. Total: 0.4589 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0031 s/iter. Inference: 0.3759 s/iter. Eval: 0.0766 s/iter. Total: 0.4557 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0032 s/iter. Inference: 0.3760 s/iter. Eval: 0.0731 s/iter. Total: 0.4523 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0031 s/iter. Inference: 0.3754 s/iter. Eval: 0.0709 s/iter. Total: 0.4495 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalnhmswgku ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.143 | 83.332 | 65.517 | 133 | | Things | 61.647 | 84.160 | 72.771 | 80 | | Stuff | 45.326 | 82.083 | 54.568 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.00 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.41 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.63s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 22.12 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.46 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.451 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.686 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.485 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.493 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.545 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.563 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.761 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.084 | 68.625 | 48.502 | 25.493 | 49.300 | 67.238 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.099 | bicycle | 22.193 | car | 43.051 | | motorcycle | 40.927 | airplane | 62.145 | bus | 70.758 | | train | 75.127 | truck | 43.839 | boat | 29.517 | | traffic light | 27.683 | fire hydrant | 71.371 | stop sign | 68.077 | | parking meter | 53.298 | bench | 26.437 | bird | 32.623 | | cat | 77.134 | dog | 71.206 | horse | 50.639 | | sheep | 51.975 | cow | 56.162 | elephant | 65.998 | | bear | 79.700 | zebra | 65.144 | giraffe | 62.012 | | backpack | 23.592 | umbrella | 54.110 | handbag | 24.581 | | tie | 39.900 | suitcase | 48.771 | frisbee | 70.891 | | skis | 9.026 | snowboard | 33.860 | sports ball | 48.598 | | kite | 36.574 | baseball bat | 39.058 | baseball glove | 50.027 | | skateboard | 44.617 | surfboard | 44.067 | tennis racket | 61.972 | | bottle | 40.252 | wine glass | 37.175 | cup | 47.754 | | fork | 25.435 | knife | 23.495 | spoon | 21.052 | | bowl | 37.636 | banana | 20.502 | apple | 27.059 | | sandwich | 48.623 | orange | 29.909 | broccoli | 23.751 | | carrot | 21.690 | hot dog | 39.982 | pizza | 54.335 | | donut | 55.133 | cake | 45.615 | chair | 28.276 | | couch | 43.717 | potted plant | 23.489 | bed | 44.627 | | dining table | 16.299 | toilet | 69.492 | tv | 65.351 | | laptop | 67.747 | mouse | 61.811 | remote | 43.567 | | keyboard | 58.406 | cell phone | 45.033 | microwave | 64.372 | | oven | 33.560 | toaster | 47.389 | sink | 44.920 | | refrigerator | 68.869 | book | 13.999 | clock | 53.499 | | vase | 39.667 | scissors | 33.586 | teddy bear | 56.747 | | hair drier | 30.455 | toothbrush | 27.716 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.21806963411615, 'fwIoU': 71.67842382947536, 'IoU-person': 88.94250368548911, 'IoU-bicycle': 78.84375066311974, 'IoU-car': 70.74591165894724, 'IoU-motorcycle': 88.63857930365788, 'IoU-airplane': 89.29277311026578, 'IoU-bus': 87.76828142238026, 'IoU-train': 85.13635050313546, 'IoU-truck': 67.77920165303574, 'IoU-boat': 72.18729492684089, 'IoU-traffic light': 79.37360787401741, 'IoU-fire hydrant': 92.98213545289263, 'IoU-stop sign': 95.91278338818816, 'IoU-parking meter': 84.56015600131337, 'IoU-bench': 62.493445186369776, 'IoU-bird': 77.56906765946925, 'IoU-cat': 87.47939474751256, 'IoU-dog': 87.93814568664868, 'IoU-horse': 89.11437432139986, 'IoU-sheep': 88.41141922479835, 'IoU-cow': 89.19810090536996, 'IoU-elephant': 91.3984925957881, 'IoU-bear': 85.96021263168718, 'IoU-zebra': 80.98588295489459, 'IoU-giraffe': 89.66067966708194, 'IoU-backpack': 53.57240524904265, 'IoU-umbrella': 89.37537865221567, 'IoU-handbag': 49.66493118677664, 'IoU-tie': 76.10873493426136, 'IoU-suitcase': 87.65066467259379, 'IoU-frisbee': 83.86619949633199, 'IoU-skis': 61.096245026976945, 'IoU-snowboard': 72.98817254174396, 'IoU-sports ball': 78.77537446956, 'IoU-kite': 79.26785548467784, 'IoU-baseball bat': 69.49957631670028, 'IoU-baseball glove': 79.92452783384961, 'IoU-skateboard': 86.25812807462509, 'IoU-surfboard': 86.34542029890866, 'IoU-tennis racket': 90.89441820135849, 'IoU-bottle': 70.6626236408956, 'IoU-wine glass': 82.20541489626669, 'IoU-cup': 71.86266514780124, 'IoU-fork': 68.77895182625244, 'IoU-knife': 64.27669413378638, 'IoU-spoon': 59.1017080796016, 'IoU-bowl': 59.08996971310763, 'IoU-banana': 82.75564288958758, 'IoU-apple': 59.07333400912924, 'IoU-sandwich': 70.74973817816836, 'IoU-orange': 76.37846865697358, 'IoU-broccoli': 70.72175783807685, 'IoU-carrot': 65.43134628509696, 'IoU-hot dog': 64.31689179260587, 'IoU-pizza': 86.40003461755353, 'IoU-donut': 75.25408262044047, 'IoU-cake': 78.84812364247117, 'IoU-chair': 63.54292735376434, 'IoU-couch': 62.738396084191805, 'IoU-potted plant': 43.99917405548005, 'IoU-bed': 74.30409505118138, 'IoU-dining table': 54.33918551754463, 'IoU-toilet': 85.75545019227366, 'IoU-tv': 81.73925246415025, 'IoU-laptop': 78.7736225824881, 'IoU-mouse': 80.39117846166559, 'IoU-remote': 74.07730895191317, 'IoU-keyboard': 69.6666044223017, 'IoU-cell phone': 75.72645594165216, 'IoU-microwave': 72.02661519118065, 'IoU-oven': 73.88261848441232, 'IoU-toaster': 85.73276562550785, 'IoU-sink': 75.18072959383282, 'IoU-refrigerator': 85.12965319273805, 'IoU-book': 54.223563457194835, 'IoU-clock': 76.9705288915108, 'IoU-vase': 68.80449469994204, 'IoU-scissors': 87.74117928942162, 'IoU-teddy bear': 87.58968445166381, 'IoU-hair drier': 49.46814542650427, 'IoU-toothbrush': 77.37406778467293, 'IoU-banner': 32.5663711473921, 'IoU-blanket': 18.813791376617704, 'IoU-bridge': 39.914625746751575, 'IoU-cardboard': 52.73174097960309, 'IoU-counter': 34.38490743044634, 'IoU-curtain': 70.82791508237416, 'IoU-door-stuff': 47.28803749633108, 'IoU-floor-wood': 63.33885009885317, 'IoU-flower': 47.50058854434322, 'IoU-fruit': 48.30993096018063, 'IoU-gravel': 27.324735023293556, 'IoU-house': 24.603122186266404, 'IoU-light': 43.20532616726659, 'IoU-mirror-stuff': 58.88128804662672, 'IoU-net': 45.21353153420844, 'IoU-pillow': 21.568360056674905, 'IoU-platform': 30.352669935852084, 'IoU-playingfield': 68.11975155592886, 'IoU-railroad': 63.946013681018535, 'IoU-river': 53.54828661304359, 'IoU-road': 68.45905347034474, 'IoU-roof': 15.471514822012871, 'IoU-sand': 65.18771990248406, 'IoU-sea': 85.80887300012138, 'IoU-shelf': 37.38626026985703, 'IoU-snow': 92.12623427557742, 'IoU-stairs': 36.672030076113245, 'IoU-tent': 10.642355470985907, 'IoU-towel': 46.37436660666792, 'IoU-wall-brick': 52.05214738282592, 'IoU-wall-stone': 28.726748360855836, 'IoU-wall-tile': 71.19305016401397, 'IoU-wall-wood': 42.23064270965147, 'IoU-water-other': 27.309579982345188, 'IoU-window-blind': 50.05306526802895, 'IoU-window-other': 50.10677388923534, 'IoU-tree-merged': 82.16641490569772, 'IoU-fence-merged': 55.66620576496849, 'IoU-ceiling-merged': 67.98954543838612, 'IoU-sky-other-merged': 93.83095776147641, 'IoU-cabinet-merged': 63.01508861421373, 'IoU-table-merged': 41.81227027156053, 'IoU-floor-other-merged': 54.796010211705806, 'IoU-pavement-merged': 56.573549333572316, 'IoU-mountain-merged': 57.62152418595514, 'IoU-grass-merged': 71.3422922878584, 'IoU-dirt-merged': 45.06866003426101, 'IoU-paper-merged': 37.241510440857276, 'IoU-food-other-merged': 44.837488098826455, 'IoU-building-other-merged': 59.59282169029175, 'IoU-rock-merged': 64.5004449979448, 'IoU-wall-other-merged': 68.34429576250048, 'IoU-rug-merged': 67.61819942424657, 'mACC': 77.5483250548638, 'pACC': 82.27987237462288, 'ACC-person': 93.36060412928026, 'ACC-bicycle': 88.41158249041715, 'ACC-car': 83.97622277811962, 'ACC-motorcycle': 92.97928997027208, 'ACC-airplane': 93.3998128226801, 'ACC-bus': 93.57753554769151, 'ACC-train': 91.94568234276228, 'ACC-truck': 76.62826960157622, 'ACC-boat': 80.8806604493601, 'ACC-traffic light': 89.93155126966363, 'ACC-fire hydrant': 96.11544406820191, 'ACC-stop sign': 98.29012826984, 'ACC-parking meter': 87.44506665871799, 'ACC-bench': 76.02420360519669, 'ACC-bird': 82.57258658175405, 'ACC-cat': 96.12704541187355, 'ACC-dog': 90.65345435872528, 'ACC-horse': 94.78309253432283, 'ACC-sheep': 91.63869186711626, 'ACC-cow': 92.58270422649805, 'ACC-elephant': 93.51727552885352, 'ACC-bear': 87.68724627524568, 'ACC-zebra': 82.91342511733447, 'ACC-giraffe': 93.70260231223081, 'ACC-backpack': 74.47009666874085, 'ACC-umbrella': 93.78345894450092, 'ACC-handbag': 72.4138394995506, 'ACC-tie': 85.53675777761671, 'ACC-suitcase': 93.1720919220372, 'ACC-frisbee': 94.69927272727273, 'ACC-skis': 76.39294439125443, 'ACC-snowboard': 80.16311136146555, 'ACC-sports ball': 86.92643243299119, 'ACC-kite': 84.87058864985816, 'ACC-baseball bat': 87.26071222531074, 'ACC-baseball glove': 92.86403954374816, 'ACC-skateboard': 90.98044804323334, 'ACC-surfboard': 92.44844765313692, 'ACC-tennis racket': 94.92102053415918, 'ACC-bottle': 87.26439028980911, 'ACC-wine glass': 90.66526445576689, 'ACC-cup': 86.0538571929533, 'ACC-fork': 81.34715186106443, 'ACC-knife': 77.76434381182678, 'ACC-spoon': 74.43700497745553, 'ACC-bowl': 68.69315149983095, 'ACC-banana': 89.89316231663747, 'ACC-apple': 71.39398196847651, 'ACC-sandwich': 81.39202052638251, 'ACC-orange': 83.34306354358523, 'ACC-broccoli': 82.04915920607492, 'ACC-carrot': 78.27814511703997, 'ACC-hot dog': 71.76435534406198, 'ACC-pizza': 92.16598719692799, 'ACC-donut': 83.1202401162595, 'ACC-cake': 87.17957399463974, 'ACC-chair': 79.50407700783566, 'ACC-couch': 67.58956637348034, 'ACC-potted plant': 59.711270665870884, 'ACC-bed': 85.21750251565038, 'ACC-dining table': 77.76937177788412, 'ACC-toilet': 90.56359334326943, 'ACC-tv': 88.38066584905856, 'ACC-laptop': 89.07067934863068, 'ACC-mouse': 91.24595521790418, 'ACC-remote': 79.02131340662399, 'ACC-keyboard': 78.96634661281338, 'ACC-cell phone': 84.2603981292868, 'ACC-microwave': 74.24403713303955, 'ACC-oven': 88.78819555649774, 'ACC-toaster': 90.24825849539477, 'ACC-sink': 83.83428836579873, 'ACC-refrigerator': 93.69147773774749, 'ACC-book': 69.42345409206803, 'ACC-clock': 82.10609155476199, 'ACC-vase': 77.49738908654018, 'ACC-scissors': 93.30702696746826, 'ACC-teddy bear': 93.37872069891368, 'ACC-hair drier': 62.08123416730709, 'ACC-toothbrush': 85.88776928422516, 'ACC-banner': 77.7926630515572, 'ACC-blanket': 29.783381576914636, 'ACC-bridge': 55.66654053614782, 'ACC-cardboard': 66.60229751345064, 'ACC-counter': 54.189507219438795, 'ACC-curtain': 83.5060939350056, 'ACC-door-stuff': 69.88391924501151, 'ACC-floor-wood': 81.12479261424556, 'ACC-flower': 66.45901482952449, 'ACC-fruit': 67.71405759387812, 'ACC-gravel': 37.75476597712743, 'ACC-house': 29.568187224661553, 'ACC-light': 61.10396011177889, 'ACC-mirror-stuff': 70.53630422308368, 'ACC-net': 64.26556302696136, 'ACC-pillow': 49.269125881418084, 'ACC-platform': 50.070144690829906, 'ACC-playingfield': 86.8422506494683, 'ACC-railroad': 80.11809320360229, 'ACC-river': 75.4695018493639, 'ACC-road': 87.18641289320547, 'ACC-roof': 21.42743055979986, 'ACC-sand': 71.156243919327, 'ACC-sea': 91.2773978759441, 'ACC-shelf': 54.516761105020805, 'ACC-snow': 95.5900818172726, 'ACC-stairs': 60.57382762642256, 'ACC-tent': 14.385212307254788, 'ACC-towel': 55.60450888110209, 'ACC-wall-brick': 68.27815558703489, 'ACC-wall-stone': 33.642889099426284, 'ACC-wall-tile': 85.25886002122128, 'ACC-wall-wood': 65.47869293017918, 'ACC-water-other': 41.332258081643225, 'ACC-window-blind': 66.32253796029757, 'ACC-window-other': 73.57985253384558, 'ACC-tree-merged': 89.68025577888909, 'ACC-fence-merged': 72.46948278154315, 'ACC-ceiling-merged': 82.15776089986863, 'ACC-sky-other-merged': 97.164418182317, 'ACC-cabinet-merged': 78.28515684370254, 'ACC-table-merged': 55.43489414961152, 'ACC-floor-other-merged': 68.52519962101157, 'ACC-pavement-merged': 67.3278159132673, 'ACC-mountain-merged': 69.34108636436336, 'ACC-grass-merged': 83.32743635218867, 'ACC-dirt-merged': 68.36665515419452, 'ACC-paper-merged': 48.374460816287986, 'ACC-food-other-merged': 65.20652834907982, 'ACC-building-other-merged': 73.57328367942668, 'ACC-rock-merged': 85.18336062940486, 'ACC-wall-other-merged': 82.14303751158998, 'ACC-rug-merged': 81.39413371819673})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3005 s/iter. Inference: 0.1758 s/iter. Eval: 0.0000 s/iter. Total: 0.4764 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3180 s/iter. Inference: 0.3374 s/iter. Eval: 0.0000 s/iter. Total: 0.6554 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3347 s/iter. Inference: 0.5543 s/iter. Eval: 0.0000 s/iter. Total: 0.8891 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4834650278021657, 'noc@0.8': 2.7383669885864794, 'noc@0.85': 3.228563067017852, 'noc@0.9': 4.069359086918349, 'miou@iter1': 0.8696873084055181} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1471 s/iter. Eval: 0.0010 s/iter. Total: 0.1498 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.048583984375, 'precision@0.6': 72.17256164550781, 'precision@0.7': 67.6253433227539, 'precision@0.8': 58.1033821105957, 'precision@0.9': 31.51962661743164, 'cIoU': 60.77846908569336, 'mIoU': 66.3112564086914} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.14299365594911, 'SQ': 83.33190126127661, 'RQ': 65.51716563691265, 'PQ_th': 61.64704792349981, 'SQ_th': 84.1596056225644, 'RQ_th': 72.77108407800932, 'PQ_st': 45.325553252099006, 'SQ_st': 82.08253618763466, 'RQ_st': 54.56785478242707}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.084393036041405, 'AP50': 68.62522156707553, 'AP75': 48.50161810167049, 'APs': 25.493299292262567, 'APm': 49.29971132026563, 'APl': 67.23767104247567, 'AP-person': 48.09873222995664, 'AP-bicycle': 22.1933705573003, 'AP-car': 43.05060473947428, 'AP-motorcycle': 40.92736547830266, 'AP-airplane': 62.144505711066, 'AP-bus': 70.7581847850801, 'AP-train': 75.12689942849812, 'AP-truck': 43.83868073613888, 'AP-boat': 29.517482584452264, 'AP-traffic light': 27.68340524031248, 'AP-fire hydrant': 71.37137523341846, 'AP-stop sign': 68.07656244467985, 'AP-parking meter': 53.29816189817574, 'AP-bench': 26.437003649811157, 'AP-bird': 32.62263612954123, 'AP-cat': 77.13425246798167, 'AP-dog': 71.2055440778092, 'AP-horse': 50.638643482295684, 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'ACC-laptop': 89.07067934863068, 'ACC-mouse': 91.24595521790418, 'ACC-remote': 79.02131340662399, 'ACC-keyboard': 78.96634661281338, 'ACC-cell phone': 84.2603981292868, 'ACC-microwave': 74.24403713303955, 'ACC-oven': 88.78819555649774, 'ACC-toaster': 90.24825849539477, 'ACC-sink': 83.83428836579873, 'ACC-refrigerator': 93.69147773774749, 'ACC-book': 69.42345409206803, 'ACC-clock': 82.10609155476199, 'ACC-vase': 77.49738908654018, 'ACC-scissors': 93.30702696746826, 'ACC-teddy bear': 93.37872069891368, 'ACC-hair drier': 62.08123416730709, 'ACC-toothbrush': 85.88776928422516, 'ACC-banner': 77.7926630515572, 'ACC-blanket': 29.783381576914636, 'ACC-bridge': 55.66654053614782, 'ACC-cardboard': 66.60229751345064, 'ACC-counter': 54.189507219438795, 'ACC-curtain': 83.5060939350056, 'ACC-door-stuff': 69.88391924501151, 'ACC-floor-wood': 81.12479261424556, 'ACC-flower': 66.45901482952449, 'ACC-fruit': 67.71405759387812, 'ACC-gravel': 37.75476597712743, 'ACC-house': 29.568187224661553, 'ACC-light': 61.10396011177889, 'ACC-mirror-stuff': 70.53630422308368, 'ACC-net': 64.26556302696136, 'ACC-pillow': 49.269125881418084, 'ACC-platform': 50.070144690829906, 'ACC-playingfield': 86.8422506494683, 'ACC-railroad': 80.11809320360229, 'ACC-river': 75.4695018493639, 'ACC-road': 87.18641289320547, 'ACC-roof': 21.42743055979986, 'ACC-sand': 71.156243919327, 'ACC-sea': 91.2773978759441, 'ACC-shelf': 54.516761105020805, 'ACC-snow': 95.5900818172726, 'ACC-stairs': 60.57382762642256, 'ACC-tent': 14.385212307254788, 'ACC-towel': 55.60450888110209, 'ACC-wall-brick': 68.27815558703489, 'ACC-wall-stone': 33.642889099426284, 'ACC-wall-tile': 85.25886002122128, 'ACC-wall-wood': 65.47869293017918, 'ACC-water-other': 41.332258081643225, 'ACC-window-blind': 66.32253796029757, 'ACC-window-other': 73.57985253384558, 'ACC-tree-merged': 89.68025577888909, 'ACC-fence-merged': 72.46948278154315, 'ACC-ceiling-merged': 82.15776089986863, 'ACC-sky-other-merged': 97.164418182317, 'ACC-cabinet-merged': 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66.3112564086914}}} INFO:trainer.default_trainer:This epoch takes 1:00:31.989656 INFO:trainer.default_trainer:PROGRESS: 6.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 3 training. INFO:trainer.default_trainer:epochs[ 3] optim steps[5500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81053/0.81161, loss_mask_bce_0: 0.33954/0.30458, loss_mask_dice_0: 2.40660/1.02928, loss_spatial_bce_0: 0.07260/0.09707, loss_spatial_dice_0: 0.16795/0.20271, loss_spatial_ce_0: 0.05331/0.10371, loss_grounding_bce_0: 0.06265/0.08100, loss_grounding_dice_0: 0.27379/0.15097, loss_grounding_ce_0: 0.17216/0.25342, loss_mask_ce_1: 0.81618/0.81452, loss_mask_bce_1: 0.33983/0.30520, loss_mask_dice_1: 2.43919/1.03555, loss_spatial_bce_1: 0.09260/0.09799, loss_spatial_dice_1: 0.17678/0.20584, loss_spatial_ce_1: 0.10138/0.10801, loss_grounding_bce_1: 0.06275/0.08100, loss_grounding_dice_1: 0.29366/0.15190, loss_grounding_ce_1: 0.16434/0.25782, loss_mask_ce_2: 0.84295/0.82069, loss_mask_bce_2: 0.33266/0.30519, loss_mask_dice_2: 2.47254/1.03956, loss_spatial_bce_2: 0.09235/0.09733, loss_spatial_dice_2: 0.22001/0.20602, loss_spatial_ce_2: 0.09170/0.11471, loss_grounding_bce_2: 0.07270/0.08077, loss_grounding_dice_2: 0.29982/0.15214, loss_grounding_ce_2: 0.15897/0.25564, loss_mask_ce_3: 0.78824/0.81545, loss_mask_bce_3: 0.37098/0.30680, loss_mask_dice_3: 2.78482/1.03214, loss_spatial_bce_3: 0.09410/0.09892, loss_spatial_dice_3: 0.18026/0.20601, loss_spatial_ce_3: 0.09403/0.12129, loss_grounding_bce_3: 0.06242/0.08131, loss_grounding_dice_3: 0.27505/0.15172, loss_grounding_ce_3: 0.18177/0.25686, loss_mask_ce_4: 0.72849/0.82112, loss_mask_bce_4: 0.43163/0.30951, loss_mask_dice_4: 2.53718/1.05246, loss_spatial_bce_4: 0.06513/0.10125, loss_spatial_dice_4: 0.19823/0.21335, loss_spatial_ce_4: 0.11317/0.13243, loss_grounding_bce_4: 0.06161/0.08217, loss_grounding_dice_4: 0.27848/0.15407, loss_grounding_ce_4: 0.13321/0.26431, loss_mask_ce_5: 0.56782/0.84205, loss_mask_bce_5: 0.42121/0.31057, loss_mask_dice_5: 2.69829/1.06241, loss_spatial_bce_5: 0.05020/0.10265, loss_spatial_dice_5: 0.18966/0.21599, loss_spatial_ce_5: 0.18792/0.14304, loss_grounding_bce_5: 0.05495/0.08209, loss_grounding_dice_5: 0.26923/0.15523, loss_grounding_ce_5: 0.08014/0.28360, loss_mask_ce_6: 0.77998/0.86368, loss_mask_bce_6: 0.36018/0.31119, loss_mask_dice_6: 2.44384/1.06741, loss_spatial_bce_6: 0.04994/0.10738, loss_spatial_dice_6: 0.18182/0.21859, loss_spatial_ce_6: 0.12748/0.16130, loss_grounding_bce_6: 0.05623/0.08346, loss_grounding_dice_6: 0.24862/0.15491, loss_grounding_ce_6: 0.10705/0.30348, loss_mask_ce_7: 0.91410/0.92884, loss_mask_bce_7: 0.35032/0.31949, loss_mask_dice_7: 2.46681/1.11125, loss_spatial_bce_7: 0.08559/0.11958, loss_spatial_dice_7: 0.25385/0.24504, loss_spatial_ce_7: 0.19563/0.21221, loss_grounding_bce_7: 0.05818/0.08588, loss_grounding_dice_7: 0.28821/0.16091, loss_grounding_ce_7: 0.09188/0.36078, loss_mask_ce_8: 0.79646/1.08788, loss_mask_bce_8: 0.39919/0.33639, loss_mask_dice_8: 3.06271/1.19175, loss_spatial_bce_8: 0.05850/0.14207, loss_spatial_dice_8: 0.22832/0.29165, loss_spatial_ce_8: 0.10254/0.26486, loss_grounding_bce_8: 0.07332/0.08933, loss_grounding_dice_8: 0.43698/0.16894, loss_grounding_ce_8: 0.01655/0.47532, loss_mask_ce_9: 4.33261/3.56431, loss_mask_bce_9: 0.37850/0.36382, loss_mask_dice_9: 3.94887/1.79100, loss_spatial_bce_9: 0.21813/0.37090, loss_spatial_dice_9: 0.85120/0.80208, loss_spatial_ce_9: 1.20843/1.45400, loss_grounding_bce_9: 0.11823/0.10145, loss_grounding_dice_9: 0.65899/0.24720, loss_grounding_ce_9: 0.03158/0.77963] items per batch[64] items per second[0.16] total items[352000] mini batches[ 5500] memory[4929] epoch remaining[1:06:27] INFO:trainer.default_trainer:epochs[ 3] optim steps[5600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61854/0.81197, loss_mask_bce_0: 0.37135/0.30476, loss_mask_dice_0: 2.88549/1.02831, loss_spatial_bce_0: 0.03891/0.09694, loss_spatial_dice_0: 0.21281/0.20233, loss_spatial_ce_0: 0.16601/0.10314, loss_grounding_bce_0: 0.03076/0.08125, loss_grounding_dice_0: 0.07758/0.15078, loss_grounding_ce_0: 0.00100/0.25402, loss_mask_ce_1: 0.63915/0.81554, loss_mask_bce_1: 0.37977/0.30522, loss_mask_dice_1: 2.64690/1.03433, loss_spatial_bce_1: 0.03939/0.09787, loss_spatial_dice_1: 0.22356/0.20543, loss_spatial_ce_1: 0.18434/0.10764, loss_grounding_bce_1: 0.02729/0.08126, loss_grounding_dice_1: 0.07747/0.15176, loss_grounding_ce_1: 0.00091/0.25854, loss_mask_ce_2: 0.59859/0.82120, loss_mask_bce_2: 0.37671/0.30528, loss_mask_dice_2: 2.79943/1.03778, loss_spatial_bce_2: 0.04067/0.09719, loss_spatial_dice_2: 0.21528/0.20559, loss_spatial_ce_2: 0.11895/0.11402, loss_grounding_bce_2: 0.02837/0.08106, loss_grounding_dice_2: 0.07320/0.15200, loss_grounding_ce_2: 0.00139/0.25603, loss_mask_ce_3: 0.59058/0.81625, loss_mask_bce_3: 0.40479/0.30685, loss_mask_dice_3: 2.73865/1.03094, loss_spatial_bce_3: 0.03818/0.09870, loss_spatial_dice_3: 0.20527/0.20558, loss_spatial_ce_3: 0.14148/0.12086, loss_grounding_bce_3: 0.02778/0.08163, loss_grounding_dice_3: 0.07666/0.15159, loss_grounding_ce_3: 0.00062/0.25727, loss_mask_ce_4: 0.59662/0.82203, loss_mask_bce_4: 0.37311/0.30952, loss_mask_dice_4: 2.80234/1.05067, loss_spatial_bce_4: 0.03893/0.10107, loss_spatial_dice_4: 0.22976/0.21297, loss_spatial_ce_4: 0.24938/0.13170, loss_grounding_bce_4: 0.02704/0.08240, loss_grounding_dice_4: 0.07335/0.15394, loss_grounding_ce_4: 0.00108/0.26435, loss_mask_ce_5: 0.63908/0.84244, loss_mask_bce_5: 0.35396/0.31067, loss_mask_dice_5: 2.66332/1.06058, loss_spatial_bce_5: 0.03379/0.10245, loss_spatial_dice_5: 0.24016/0.21555, loss_spatial_ce_5: 0.14073/0.14241, loss_grounding_bce_5: 0.02474/0.08237, loss_grounding_dice_5: 0.07055/0.15506, loss_grounding_ce_5: 0.01117/0.28351, loss_mask_ce_6: 0.69054/0.86444, loss_mask_bce_6: 0.39239/0.31123, loss_mask_dice_6: 2.57769/1.06594, loss_spatial_bce_6: 0.04170/0.10715, loss_spatial_dice_6: 0.23825/0.21814, loss_spatial_ce_6: 0.15024/0.16087, loss_grounding_bce_6: 0.02427/0.08372, loss_grounding_dice_6: 0.06340/0.15477, loss_grounding_ce_6: 0.11402/0.30321, loss_mask_ce_7: 0.74661/0.92886, loss_mask_bce_7: 0.37457/0.31958, loss_mask_dice_7: 2.73363/1.11019, loss_spatial_bce_7: 0.04808/0.11951, loss_spatial_dice_7: 0.28265/0.24466, loss_spatial_ce_7: 0.29312/0.21156, loss_grounding_bce_7: 0.02774/0.08616, loss_grounding_dice_7: 0.07143/0.16077, loss_grounding_ce_7: 0.25451/0.35953, loss_mask_ce_8: 1.12270/1.08819, loss_mask_bce_8: 0.41110/0.33671, loss_mask_dice_8: 2.78546/1.19057, loss_spatial_bce_8: 0.09468/0.14228, loss_spatial_dice_8: 0.42531/0.29123, loss_spatial_ce_8: 0.21125/0.26416, loss_grounding_bce_8: 0.04043/0.08962, loss_grounding_dice_8: 0.08087/0.16878, loss_grounding_ce_8: 0.25264/0.47492, loss_mask_ce_9: 3.14852/3.56192, loss_mask_bce_9: 0.43553/0.36423, loss_mask_dice_9: 4.33053/1.78989, loss_spatial_bce_9: 0.27652/0.37131, loss_spatial_dice_9: 0.93588/0.80195, loss_spatial_ce_9: 1.33050/1.45270, loss_grounding_bce_9: 0.03180/0.10165, loss_grounding_dice_9: 0.12669/0.24730, loss_grounding_ce_9: 1.41325/0.77717] items per batch[64] items per second[0.34] total items[358400] mini batches[ 5600] memory[4929] epoch remaining[0:54:52] INFO:trainer.default_trainer:epochs[ 3] optim steps[5700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.44536/0.81071, loss_mask_bce_0: 0.42969/0.30437, loss_mask_dice_0: 0.65787/1.02928, loss_spatial_bce_0: 0.25408/0.09669, loss_spatial_dice_0: 0.43877/0.20214, loss_spatial_ce_0: 0.13603/0.10256, loss_grounding_bce_0: 0.20153/0.08127, loss_grounding_dice_0: 0.21588/0.15083, loss_grounding_ce_0: 0.29871/0.25448, loss_mask_ce_1: 2.41225/0.81472, loss_mask_bce_1: 0.43529/0.30478, loss_mask_dice_1: 0.70701/1.03527, loss_spatial_bce_1: 0.22308/0.09761, loss_spatial_dice_1: 0.43055/0.20522, loss_spatial_ce_1: 0.13416/0.10718, loss_grounding_bce_1: 0.18960/0.08132, loss_grounding_dice_1: 0.21241/0.15193, loss_grounding_ce_1: 0.33006/0.25903, loss_mask_ce_2: 2.40054/0.82046, loss_mask_bce_2: 0.36862/0.30478, loss_mask_dice_2: 0.66167/1.03779, loss_spatial_bce_2: 0.24079/0.09697, loss_spatial_dice_2: 0.45968/0.20541, loss_spatial_ce_2: 0.15297/0.11307, loss_grounding_bce_2: 0.18702/0.08111, loss_grounding_dice_2: 0.18756/0.15204, loss_grounding_ce_2: 0.30928/0.25639, loss_mask_ce_3: 2.32293/0.81561, loss_mask_bce_3: 0.38349/0.30640, loss_mask_dice_3: 0.67398/1.03176, loss_spatial_bce_3: 0.23289/0.09847, loss_spatial_dice_3: 0.42899/0.20538, loss_spatial_ce_3: 0.12691/0.12002, loss_grounding_bce_3: 0.18644/0.08164, loss_grounding_dice_3: 0.20475/0.15165, loss_grounding_ce_3: 0.34333/0.25753, loss_mask_ce_4: 2.09588/0.82167, loss_mask_bce_4: 0.39052/0.30902, loss_mask_dice_4: 0.58528/1.05111, loss_spatial_bce_4: 0.21064/0.10082, loss_spatial_dice_4: 0.40433/0.21272, loss_spatial_ce_4: 0.13179/0.13083, loss_grounding_bce_4: 0.20696/0.08237, loss_grounding_dice_4: 0.20484/0.15397, loss_grounding_ce_4: 0.30505/0.26408, loss_mask_ce_5: 2.27878/0.84208, loss_mask_bce_5: 0.35690/0.31016, loss_mask_dice_5: 0.62372/1.06117, loss_spatial_bce_5: 0.19984/0.10222, loss_spatial_dice_5: 0.39789/0.21527, loss_spatial_ce_5: 0.16283/0.14144, loss_grounding_bce_5: 0.20188/0.08236, loss_grounding_dice_5: 0.22454/0.15499, loss_grounding_ce_5: 0.30702/0.28350, loss_mask_ce_6: 2.53331/0.86415, loss_mask_bce_6: 0.42120/0.31074, loss_mask_dice_6: 0.71338/1.06659, loss_spatial_bce_6: 0.21591/0.10689, loss_spatial_dice_6: 0.39887/0.21782, loss_spatial_ce_6: 0.19718/0.15973, loss_grounding_bce_6: 0.18820/0.08372, loss_grounding_dice_6: 0.18184/0.15481, loss_grounding_ce_6: 0.53924/0.30276, loss_mask_ce_7: 2.15290/0.92866, loss_mask_bce_7: 0.35957/0.31894, loss_mask_dice_7: 0.91873/1.11127, loss_spatial_bce_7: 0.22847/0.11917, loss_spatial_dice_7: 0.43038/0.24433, loss_spatial_ce_7: 0.40859/0.21025, loss_grounding_bce_7: 0.17048/0.08616, loss_grounding_dice_7: 0.31710/0.16080, loss_grounding_ce_7: 0.25584/0.35934, loss_mask_ce_8: 2.24832/1.08779, loss_mask_bce_8: 0.25008/0.33608, loss_mask_dice_8: 0.96987/1.19090, loss_spatial_bce_8: 0.28665/0.14198, loss_spatial_dice_8: 0.58372/0.29078, loss_spatial_ce_8: 0.36962/0.26333, loss_grounding_bce_8: 0.12487/0.08967, loss_grounding_dice_8: 0.29961/0.16884, loss_grounding_ce_8: 0.35932/0.47370, loss_mask_ce_9: 3.57018/3.55960, loss_mask_bce_9: 0.48864/0.36346, loss_mask_dice_9: 1.16413/1.78889, loss_spatial_bce_9: 0.46326/0.37066, loss_spatial_dice_9: 0.77819/0.80172, loss_spatial_ce_9: 1.40091/1.45144, loss_grounding_bce_9: 0.13278/0.10166, loss_grounding_dice_9: 0.34172/0.24714, loss_grounding_ce_9: 0.40080/0.77666] items per batch[64] items per second[0.35] total items[364800] mini batches[ 5700] memory[4929] epoch remaining[0:50:18] INFO:trainer.default_trainer:epochs[ 3] optim steps[5800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10277/0.80969, loss_mask_bce_0: 0.01272/0.30382, loss_mask_dice_0: 0.05559/1.02776, loss_spatial_bce_0: 0.00983/0.09637, loss_spatial_dice_0: 0.03470/0.20179, loss_spatial_ce_0: 0.00014/0.10214, loss_grounding_bce_0: 0.01084/0.08111, loss_grounding_dice_0: 0.07580/0.15087, loss_grounding_ce_0: 0.00752/0.25621, loss_mask_ce_1: 0.10741/0.81339, loss_mask_bce_1: 0.01230/0.30419, loss_mask_dice_1: 0.04164/1.03364, loss_spatial_bce_1: 0.00917/0.09730, loss_spatial_dice_1: 0.04417/0.20489, loss_spatial_ce_1: 0.00028/0.10680, loss_grounding_bce_1: 0.01004/0.08115, loss_grounding_dice_1: 0.03045/0.15192, loss_grounding_ce_1: 0.00775/0.26017, loss_mask_ce_2: 0.09205/0.81917, loss_mask_bce_2: 0.01254/0.30426, loss_mask_dice_2: 0.05507/1.03559, loss_spatial_bce_2: 0.00924/0.09666, loss_spatial_dice_2: 0.05167/0.20503, loss_spatial_ce_2: 0.00007/0.11254, loss_grounding_bce_2: 0.01143/0.08095, loss_grounding_dice_2: 0.03991/0.15223, loss_grounding_ce_2: 0.00923/0.25776, loss_mask_ce_3: 0.10223/0.81465, loss_mask_bce_3: 0.01213/0.30584, loss_mask_dice_3: 0.06100/1.02995, loss_spatial_bce_3: 0.01028/0.09814, loss_spatial_dice_3: 0.04331/0.20503, loss_spatial_ce_3: 0.00040/0.11935, loss_grounding_bce_3: 0.01176/0.08151, loss_grounding_dice_3: 0.03943/0.15181, loss_grounding_ce_3: 0.01091/0.25833, loss_mask_ce_4: 0.09682/0.82003, loss_mask_bce_4: 0.01296/0.30857, loss_mask_dice_4: 0.06239/1.04929, loss_spatial_bce_4: 0.01064/0.10047, loss_spatial_dice_4: 0.03738/0.21232, loss_spatial_ce_4: 0.00086/0.13024, loss_grounding_bce_4: 0.00951/0.08223, loss_grounding_dice_4: 0.03274/0.15405, loss_grounding_ce_4: 0.00647/0.26565, loss_mask_ce_5: 0.09933/0.84128, loss_mask_bce_5: 0.01555/0.30962, loss_mask_dice_5: 0.06464/1.05906, loss_spatial_bce_5: 0.01515/0.10190, loss_spatial_dice_5: 0.04236/0.21491, loss_spatial_ce_5: 0.00648/0.14086, loss_grounding_bce_5: 0.00944/0.08230, loss_grounding_dice_5: 0.06934/0.15509, loss_grounding_ce_5: 0.00544/0.28451, loss_mask_ce_6: 0.11632/0.86300, loss_mask_bce_6: 0.01326/0.31026, loss_mask_dice_6: 0.04796/1.06418, loss_spatial_bce_6: 0.01260/0.10657, loss_spatial_dice_6: 0.03520/0.21739, loss_spatial_ce_6: 0.02898/0.15895, loss_grounding_bce_6: 0.00946/0.08367, loss_grounding_dice_6: 0.01367/0.15482, loss_grounding_ce_6: 0.00749/0.30375, loss_mask_ce_7: 0.17090/0.92725, loss_mask_bce_7: 0.01357/0.31847, loss_mask_dice_7: 0.04393/1.10919, loss_spatial_bce_7: 0.01176/0.11871, loss_spatial_dice_7: 0.04883/0.24382, loss_spatial_ce_7: 0.00597/0.20943, loss_grounding_bce_7: 0.00981/0.08605, loss_grounding_dice_7: 0.04052/0.16086, loss_grounding_ce_7: 0.03941/0.36052, loss_mask_ce_8: 0.12503/1.08504, loss_mask_bce_8: 0.01570/0.33567, loss_mask_dice_8: 0.03943/1.18888, loss_spatial_bce_8: 0.10448/0.14154, loss_spatial_dice_8: 0.07132/0.29018, loss_spatial_ce_8: 0.02945/0.26220, loss_grounding_bce_8: 0.01175/0.08958, loss_grounding_dice_8: 0.04696/0.16895, loss_grounding_ce_8: 0.00811/0.47257, loss_mask_ce_9: 2.26073/3.55601, loss_mask_bce_9: 0.02096/0.36292, loss_mask_dice_9: 0.08397/1.78552, loss_spatial_bce_9: 0.14887/0.37006, loss_spatial_dice_9: 0.59352/0.80129, loss_spatial_ce_9: 0.78764/1.45045, loss_grounding_bce_9: 0.01808/0.10140, loss_grounding_dice_9: 0.07756/0.24693, loss_grounding_ce_9: 0.13970/0.77525] items per batch[64] items per second[0.36] total items[371200] mini batches[ 5800] memory[4929] epoch remaining[0:46:27] INFO:trainer.default_trainer:epochs[ 3] optim steps[5900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53538/0.80880, loss_mask_bce_0: 0.02886/0.30360, loss_mask_dice_0: 1.25353/1.03196, loss_spatial_bce_0: 0.00400/0.09606, loss_spatial_dice_0: 0.19237/0.20167, loss_spatial_ce_0: 0.02553/0.10174, loss_grounding_bce_0: 0.02075/0.08089, loss_grounding_dice_0: 0.28429/0.15072, loss_grounding_ce_0: 0.66170/0.25551, loss_mask_ce_1: 0.41434/0.81264, loss_mask_bce_1: 0.03054/0.30398, loss_mask_dice_1: 1.27129/1.03823, loss_spatial_bce_1: 0.00395/0.09700, loss_spatial_dice_1: 0.18792/0.20481, loss_spatial_ce_1: 0.03299/0.10622, loss_grounding_bce_1: 0.00556/0.08093, loss_grounding_dice_1: 0.21183/0.15185, loss_grounding_ce_1: 5.12794/0.26025, loss_mask_ce_2: 0.38277/0.81836, loss_mask_bce_2: 0.03234/0.30398, loss_mask_dice_2: 1.79997/1.04000, loss_spatial_bce_2: 0.00397/0.09635, loss_spatial_dice_2: 0.18754/0.20491, loss_spatial_ce_2: 0.03378/0.11202, loss_grounding_bce_2: 0.00406/0.08074, loss_grounding_dice_2: 0.11704/0.15213, loss_grounding_ce_2: 2.91151/0.25752, loss_mask_ce_3: 0.73172/0.81373, loss_mask_bce_3: 0.03569/0.30561, loss_mask_dice_3: 1.28312/1.03423, loss_spatial_bce_3: 0.00429/0.09779, loss_spatial_dice_3: 0.15532/0.20493, loss_spatial_ce_3: 0.03436/0.11864, loss_grounding_bce_3: 0.00269/0.08128, loss_grounding_dice_3: 0.08711/0.15173, loss_grounding_ce_3: 2.54224/0.25828, loss_mask_ce_4: 0.43791/0.81917, loss_mask_bce_4: 0.02574/0.30840, loss_mask_dice_4: 1.20893/1.05356, loss_spatial_bce_4: 0.00400/0.10014, loss_spatial_dice_4: 0.18134/0.21226, loss_spatial_ce_4: 0.11447/0.13017, loss_grounding_bce_4: 0.01385/0.08200, loss_grounding_dice_4: 0.21208/0.15395, loss_grounding_ce_4: 1.06811/0.26539, loss_mask_ce_5: 0.91086/0.84086, loss_mask_bce_5: 0.03343/0.30944, loss_mask_dice_5: 1.57081/1.06358, loss_spatial_bce_5: 0.00484/0.10160, loss_spatial_dice_5: 0.18500/0.21481, loss_spatial_ce_5: 0.11801/0.14032, loss_grounding_bce_5: 0.00888/0.08207, loss_grounding_dice_5: 0.17886/0.15501, loss_grounding_ce_5: 3.45340/0.28410, loss_mask_ce_6: 0.85948/0.86254, loss_mask_bce_6: 0.02135/0.31017, loss_mask_dice_6: 1.26472/1.06845, loss_spatial_bce_6: 0.00471/0.10628, loss_spatial_dice_6: 0.18325/0.21728, loss_spatial_ce_6: 0.13620/0.15836, loss_grounding_bce_6: 0.00851/0.08350, loss_grounding_dice_6: 0.25410/0.15491, loss_grounding_ce_6: 0.88811/0.30302, loss_mask_ce_7: 0.93705/0.92690, loss_mask_bce_7: 0.03686/0.31820, loss_mask_dice_7: 1.71571/1.11327, loss_spatial_bce_7: 0.00466/0.11845, loss_spatial_dice_7: 0.21994/0.24370, loss_spatial_ce_7: 0.12959/0.20907, loss_grounding_bce_7: 0.01235/0.08588, loss_grounding_dice_7: 0.24203/0.16085, loss_grounding_ce_7: 1.57846/0.35978, loss_mask_ce_8: 0.90358/1.08371, loss_mask_bce_8: 0.04203/0.33567, loss_mask_dice_8: 1.22555/1.19304, loss_spatial_bce_8: 0.00521/0.14114, loss_spatial_dice_8: 0.26771/0.28998, loss_spatial_ce_8: 0.16395/0.26215, loss_grounding_bce_8: 0.00486/0.08941, loss_grounding_dice_8: 0.18977/0.16893, loss_grounding_ce_8: 4.52483/0.47278, loss_mask_ce_9: 3.09376/3.55560, loss_mask_bce_9: 0.03558/0.36275, loss_mask_dice_9: 1.55984/1.79141, loss_spatial_bce_9: 0.06550/0.36943, loss_spatial_dice_9: 0.78446/0.80151, loss_spatial_ce_9: 3.25885/1.44900, loss_grounding_bce_9: 0.00840/0.10134, loss_grounding_dice_9: 0.32381/0.24693, loss_grounding_ce_9: 4.96177/0.77367] items per batch[64] items per second[0.34] total items[377600] mini batches[ 5900] memory[4929] epoch remaining[0:43:25] INFO:trainer.default_trainer:epochs[ 3] optim steps[6000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00535/0.80843, loss_mask_bce_0: 0.00524/0.30400, loss_mask_dice_0: 0.04022/1.03156, loss_spatial_bce_0: 0.00697/0.09593, loss_spatial_dice_0: 0.02964/0.20152, loss_spatial_ce_0: 0.00000/0.10102, loss_grounding_bce_0: 0.00193/0.08106, loss_grounding_dice_0: 0.05934/0.15083, loss_grounding_ce_0: 0.00121/0.25494, loss_mask_ce_1: 0.00504/0.81238, loss_mask_bce_1: 0.00329/0.30442, loss_mask_dice_1: 0.02548/1.03748, loss_spatial_bce_1: 0.00685/0.09686, loss_spatial_dice_1: 0.04019/0.20463, loss_spatial_ce_1: 0.00000/0.10547, loss_grounding_bce_1: 0.00410/0.08109, loss_grounding_dice_1: 0.09504/0.15199, loss_grounding_ce_1: 0.00088/0.25939, loss_mask_ce_2: 0.00466/0.81801, loss_mask_bce_2: 0.00355/0.30441, loss_mask_dice_2: 0.02700/1.03937, loss_spatial_bce_2: 0.00536/0.09622, loss_spatial_dice_2: 0.03114/0.20469, loss_spatial_ce_2: 0.00000/0.11145, loss_grounding_bce_2: 0.00256/0.08088, loss_grounding_dice_2: 0.07281/0.15236, loss_grounding_ce_2: 0.00061/0.25688, loss_mask_ce_3: 0.00663/0.81385, loss_mask_bce_3: 0.00360/0.30596, loss_mask_dice_3: 0.02119/1.03351, loss_spatial_bce_3: 0.00593/0.09761, loss_spatial_dice_3: 0.02540/0.20469, loss_spatial_ce_3: 0.00000/0.11793, loss_grounding_bce_3: 0.00369/0.08142, loss_grounding_dice_3: 0.07264/0.15185, loss_grounding_ce_3: 0.00050/0.25725, loss_mask_ce_4: 0.00300/0.81907, loss_mask_bce_4: 0.00404/0.30868, loss_mask_dice_4: 0.03062/1.05292, loss_spatial_bce_4: 0.00632/0.09997, loss_spatial_dice_4: 0.04227/0.21208, loss_spatial_ce_4: 0.00000/0.12953, loss_grounding_bce_4: 0.00223/0.08215, loss_grounding_dice_4: 0.06411/0.15409, loss_grounding_ce_4: 0.00014/0.26456, loss_mask_ce_5: 0.00474/0.84083, loss_mask_bce_5: 0.00340/0.30975, loss_mask_dice_5: 0.02763/1.06275, loss_spatial_bce_5: 0.00710/0.10145, loss_spatial_dice_5: 0.04219/0.21460, loss_spatial_ce_5: 0.00001/0.13961, loss_grounding_bce_5: 0.00166/0.08223, loss_grounding_dice_5: 0.05201/0.15517, loss_grounding_ce_5: 0.00013/0.28370, loss_mask_ce_6: 0.00369/0.86193, loss_mask_bce_6: 0.00438/0.31063, loss_mask_dice_6: 0.03401/1.06805, loss_spatial_bce_6: 0.00647/0.10618, loss_spatial_dice_6: 0.02673/0.21709, loss_spatial_ce_6: 0.00000/0.15774, loss_grounding_bce_6: 0.00405/0.08366, loss_grounding_dice_6: 0.05211/0.15505, loss_grounding_ce_6: 0.00081/0.30221, loss_mask_ce_7: 0.00605/0.92612, loss_mask_bce_7: 0.00574/0.31867, loss_mask_dice_7: 0.03843/1.11292, loss_spatial_bce_7: 0.00572/0.11834, loss_spatial_dice_7: 0.04250/0.24357, loss_spatial_ce_7: 0.00005/0.20838, loss_grounding_bce_7: 0.00551/0.08598, loss_grounding_dice_7: 0.08824/0.16092, loss_grounding_ce_7: 0.00082/0.35873, loss_mask_ce_8: 0.02470/1.08392, loss_mask_bce_8: 0.00532/0.33618, loss_mask_dice_8: 0.05270/1.19275, loss_spatial_bce_8: 0.00785/0.14105, loss_spatial_dice_8: 0.04197/0.28978, loss_spatial_ce_8: 0.16754/0.26170, loss_grounding_bce_8: 0.00198/0.08951, loss_grounding_dice_8: 0.04963/0.16899, loss_grounding_ce_8: 0.00688/0.47192, loss_mask_ce_9: 1.37405/3.55568, loss_mask_bce_9: 0.00915/0.36317, loss_mask_dice_9: 0.04336/1.78895, loss_spatial_bce_9: 0.11163/0.36903, loss_spatial_dice_9: 0.53833/0.80159, loss_spatial_ce_9: 0.37271/1.44884, loss_grounding_bce_9: 0.00226/0.10144, loss_grounding_dice_9: 0.08578/0.24690, loss_grounding_ce_9: 0.05538/0.77214] items per batch[64] items per second[0.35] total items[384000] mini batches[ 6000] memory[4929] epoch remaining[0:40:21] INFO:trainer.default_trainer:epochs[ 3] optim steps[6100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44452/0.80864, loss_mask_bce_0: 0.16085/0.30403, loss_mask_dice_0: 0.62251/1.03155, loss_spatial_bce_0: 0.04843/0.09583, loss_spatial_dice_0: 0.16264/0.20146, loss_spatial_ce_0: 0.00172/0.10060, loss_grounding_bce_0: 0.03869/0.08090, loss_grounding_dice_0: 0.19784/0.15082, loss_grounding_ce_0: 0.19725/0.25595, loss_mask_ce_1: 1.37654/0.81239, loss_mask_bce_1: 0.15527/0.30450, loss_mask_dice_1: 0.62113/1.03768, loss_spatial_bce_1: 0.04990/0.09676, loss_spatial_dice_1: 0.15385/0.20457, loss_spatial_ce_1: 0.00401/0.10489, loss_grounding_bce_1: 0.03911/0.08093, loss_grounding_dice_1: 0.20507/0.15211, loss_grounding_ce_1: 0.18368/0.26058, loss_mask_ce_2: 1.51134/0.81807, loss_mask_bce_2: 0.15154/0.30448, loss_mask_dice_2: 0.63239/1.03996, loss_spatial_bce_2: 0.04984/0.09613, loss_spatial_dice_2: 0.15614/0.20459, loss_spatial_ce_2: 0.00174/0.11087, loss_grounding_bce_2: 0.03835/0.08074, loss_grounding_dice_2: 0.19076/0.15240, loss_grounding_ce_2: 0.18630/0.25772, loss_mask_ce_3: 1.52511/0.81407, loss_mask_bce_3: 0.15933/0.30608, loss_mask_dice_3: 0.62469/1.03382, loss_spatial_bce_3: 0.04971/0.09749, loss_spatial_dice_3: 0.16215/0.20460, loss_spatial_ce_3: 0.00531/0.11731, loss_grounding_bce_3: 0.04209/0.08131, loss_grounding_dice_3: 0.20841/0.15195, loss_grounding_ce_3: 0.20858/0.25790, loss_mask_ce_4: 1.51445/0.81938, loss_mask_bce_4: 0.15162/0.30876, loss_mask_dice_4: 0.61284/1.05305, loss_spatial_bce_4: 0.04830/0.09986, loss_spatial_dice_4: 0.17524/0.21197, loss_spatial_ce_4: 0.14065/0.12877, loss_grounding_bce_4: 0.03708/0.08207, loss_grounding_dice_4: 0.18868/0.15432, loss_grounding_ce_4: 0.20949/0.26526, loss_mask_ce_5: 1.71119/0.84139, loss_mask_bce_5: 0.15788/0.30983, loss_mask_dice_5: 0.62994/1.06351, loss_spatial_bce_5: 0.05248/0.10130, loss_spatial_dice_5: 0.18192/0.21448, loss_spatial_ce_5: 0.26967/0.13886, loss_grounding_bce_5: 0.03769/0.08215, loss_grounding_dice_5: 0.18917/0.15528, loss_grounding_ce_5: 0.23428/0.28417, loss_mask_ce_6: 1.81292/0.86216, loss_mask_bce_6: 0.17505/0.31075, loss_mask_dice_6: 0.65803/1.06847, loss_spatial_bce_6: 0.05556/0.10603, loss_spatial_dice_6: 0.16946/0.21699, loss_spatial_ce_6: 0.17456/0.15680, loss_grounding_bce_6: 0.03346/0.08351, loss_grounding_dice_6: 0.25205/0.15519, loss_grounding_ce_6: 0.22607/0.30250, loss_mask_ce_7: 1.89483/0.92624, loss_mask_bce_7: 0.15135/0.31874, loss_mask_dice_7: 0.65845/1.11330, loss_spatial_bce_7: 0.06102/0.11819, loss_spatial_dice_7: 0.19918/0.24337, loss_spatial_ce_7: 0.09844/0.20753, loss_grounding_bce_7: 0.04170/0.08578, loss_grounding_dice_7: 0.22685/0.16108, loss_grounding_ce_7: 0.26648/0.35919, loss_mask_ce_8: 2.00171/1.08402, loss_mask_bce_8: 0.14711/0.33626, loss_mask_dice_8: 0.71539/1.19317, loss_spatial_bce_8: 0.05473/0.14095, loss_spatial_dice_8: 0.18249/0.28963, loss_spatial_ce_8: 0.14873/0.26114, loss_grounding_bce_8: 0.03383/0.08936, loss_grounding_dice_8: 0.29821/0.16911, loss_grounding_ce_8: 0.03137/0.47282, loss_mask_ce_9: 2.94206/3.55638, loss_mask_bce_9: 0.19164/0.36328, loss_mask_dice_9: 1.08093/1.78985, loss_spatial_bce_9: 0.30140/0.36892, loss_spatial_dice_9: 0.77591/0.80161, loss_spatial_ce_9: 1.89148/1.44947, loss_grounding_bce_9: 0.02798/0.10129, loss_grounding_dice_9: 0.28731/0.24676, loss_grounding_ce_9: 0.03533/0.77342] items per batch[64] items per second[0.35] total items[390400] mini batches[ 6100] memory[4929] epoch remaining[0:37:09] INFO:trainer.default_trainer:epochs[ 3] optim steps[6200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.98054/0.80795, loss_mask_bce_0: 0.78974/0.30409, loss_mask_dice_0: 0.52493/1.02967, loss_spatial_bce_0: 0.26116/0.09567, loss_spatial_dice_0: 0.17514/0.20127, loss_spatial_ce_0: 0.51369/0.10007, loss_grounding_bce_0: 0.35380/0.08081, loss_grounding_dice_0: 0.19586/0.15065, loss_grounding_ce_0: 0.85572/0.25549, loss_mask_ce_1: 1.97335/0.81175, loss_mask_bce_1: 0.81587/0.30452, loss_mask_dice_1: 0.55515/1.03553, loss_spatial_bce_1: 0.26648/0.09658, loss_spatial_dice_1: 0.15390/0.20430, loss_spatial_ce_1: 0.51464/0.10431, loss_grounding_bce_1: 0.37067/0.08084, loss_grounding_dice_1: 0.22917/0.15201, loss_grounding_ce_1: 0.86684/0.26031, loss_mask_ce_2: 1.95251/0.81735, loss_mask_bce_2: 0.88408/0.30449, loss_mask_dice_2: 0.59147/1.03764, loss_spatial_bce_2: 0.26726/0.09596, loss_spatial_dice_2: 0.16835/0.20439, loss_spatial_ce_2: 0.48120/0.11017, loss_grounding_bce_2: 0.39928/0.08066, loss_grounding_dice_2: 0.23215/0.15226, loss_grounding_ce_2: 0.85300/0.25743, loss_mask_ce_3: 1.91327/0.81379, loss_mask_bce_3: 0.92054/0.30608, loss_mask_dice_3: 0.57495/1.03136, loss_spatial_bce_3: 0.34543/0.09731, loss_spatial_dice_3: 0.19937/0.20439, loss_spatial_ce_3: 0.47198/0.11675, loss_grounding_bce_3: 0.44159/0.08119, loss_grounding_dice_3: 0.22988/0.15181, loss_grounding_ce_3: 0.75962/0.25774, loss_mask_ce_4: 1.80210/0.81897, loss_mask_bce_4: 0.95691/0.30870, loss_mask_dice_4: 0.67179/1.05088, loss_spatial_bce_4: 0.31925/0.09963, loss_spatial_dice_4: 0.17726/0.21174, loss_spatial_ce_4: 0.45971/0.12797, loss_grounding_bce_4: 0.45029/0.08197, loss_grounding_dice_4: 0.26651/0.15414, loss_grounding_ce_4: 0.68654/0.26514, loss_mask_ce_5: 1.87805/0.84043, loss_mask_bce_5: 0.85362/0.30981, loss_mask_dice_5: 0.74906/1.06110, loss_spatial_bce_5: 0.32956/0.10110, loss_spatial_dice_5: 0.16890/0.21420, loss_spatial_ce_5: 0.40267/0.13831, loss_grounding_bce_5: 0.43604/0.08204, loss_grounding_dice_5: 0.30064/0.15509, loss_grounding_ce_5: 0.74734/0.28326, loss_mask_ce_6: 1.92077/0.86186, loss_mask_bce_6: 0.85966/0.31070, loss_mask_dice_6: 0.65027/1.06587, loss_spatial_bce_6: 0.28815/0.10579, loss_spatial_dice_6: 0.16310/0.21672, loss_spatial_ce_6: 0.34542/0.15619, loss_grounding_bce_6: 0.50528/0.08343, loss_grounding_dice_6: 0.24448/0.15512, loss_grounding_ce_6: 0.76559/0.30199, loss_mask_ce_7: 1.89508/0.92571, loss_mask_bce_7: 0.88363/0.31871, loss_mask_dice_7: 0.68854/1.11081, loss_spatial_bce_7: 0.37952/0.11789, loss_spatial_dice_7: 0.28719/0.24304, loss_spatial_ce_7: 0.45448/0.20697, loss_grounding_bce_7: 0.40091/0.08560, loss_grounding_dice_7: 0.26402/0.16094, loss_grounding_ce_7: 0.65604/0.35780, loss_mask_ce_8: 2.13735/1.08262, loss_mask_bce_8: 0.90403/0.33618, loss_mask_dice_8: 0.59057/1.19029, loss_spatial_bce_8: 0.28614/0.14064, loss_spatial_dice_8: 0.26418/0.28917, loss_spatial_ce_8: 0.32277/0.26102, loss_grounding_bce_8: 0.49035/0.08927, loss_grounding_dice_8: 0.27349/0.16899, loss_grounding_ce_8: 0.80829/0.47081, loss_mask_ce_9: 4.57818/3.55338, loss_mask_bce_9: 1.31344/0.36341, loss_mask_dice_9: 1.27723/1.78645, loss_spatial_bce_9: 0.49707/0.36851, loss_spatial_dice_9: 0.77681/0.80158, loss_spatial_ce_9: 1.46361/1.44938, loss_grounding_bce_9: 0.62413/0.10116, loss_grounding_dice_9: 0.38916/0.24652, loss_grounding_ce_9: 1.07294/0.76964] items per batch[64] items per second[0.35] total items[396800] mini batches[ 6200] memory[4929] epoch remaining[0:34:03] INFO:trainer.default_trainer:epochs[ 3] optim steps[6300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14047/0.80914, loss_mask_bce_0: 0.41695/0.30390, loss_mask_dice_0: 1.25683/1.02891, loss_spatial_bce_0: 0.07539/0.09555, loss_spatial_dice_0: 0.20421/0.20105, loss_spatial_ce_0: 0.20062/0.09967, loss_grounding_bce_0: 0.08513/0.08076, loss_grounding_dice_0: 0.08930/0.15052, loss_grounding_ce_0: 0.00006/0.25536, loss_mask_ce_1: 1.07916/0.81266, loss_mask_bce_1: 0.49400/0.30446, loss_mask_dice_1: 1.10066/1.03445, loss_spatial_bce_1: 0.07690/0.09648, loss_spatial_dice_1: 0.20741/0.20407, loss_spatial_ce_1: 0.21686/0.10389, loss_grounding_bce_1: 0.09120/0.08077, loss_grounding_dice_1: 0.08907/0.15185, loss_grounding_ce_1: 0.00010/0.25994, loss_mask_ce_2: 1.35343/0.81789, loss_mask_bce_2: 0.43678/0.30441, loss_mask_dice_2: 1.46400/1.03688, loss_spatial_bce_2: 0.07325/0.09579, loss_spatial_dice_2: 0.19287/0.20409, loss_spatial_ce_2: 0.26860/0.10968, loss_grounding_bce_2: 0.08424/0.08056, loss_grounding_dice_2: 0.08400/0.15215, loss_grounding_ce_2: 0.00008/0.25731, loss_mask_ce_3: 1.41372/0.81457, loss_mask_bce_3: 0.49362/0.30595, loss_mask_dice_3: 1.24345/1.03048, loss_spatial_bce_3: 0.06751/0.09718, loss_spatial_dice_3: 0.20363/0.20406, loss_spatial_ce_3: 0.30874/0.11637, loss_grounding_bce_3: 0.08952/0.08119, loss_grounding_dice_3: 0.08793/0.15171, loss_grounding_ce_3: 0.00010/0.25739, loss_mask_ce_4: 1.27971/0.81974, loss_mask_bce_4: 0.40302/0.30862, loss_mask_dice_4: 1.22783/1.04997, loss_spatial_bce_4: 0.05925/0.09954, loss_spatial_dice_4: 0.18735/0.21146, loss_spatial_ce_4: 0.32587/0.12750, loss_grounding_bce_4: 0.08814/0.08196, loss_grounding_dice_4: 0.08716/0.15408, loss_grounding_ce_4: 0.00008/0.26503, loss_mask_ce_5: 1.26225/0.84115, loss_mask_bce_5: 0.41909/0.30977, loss_mask_dice_5: 1.16238/1.06026, loss_spatial_bce_5: 0.06432/0.10102, loss_spatial_dice_5: 0.18468/0.21389, loss_spatial_ce_5: 0.45419/0.13766, loss_grounding_bce_5: 0.08165/0.08209, loss_grounding_dice_5: 0.08345/0.15505, loss_grounding_ce_5: 0.00025/0.28279, loss_mask_ce_6: 1.23670/0.86289, loss_mask_bce_6: 0.38735/0.31061, loss_mask_dice_6: 1.10849/1.06482, loss_spatial_bce_6: 0.07001/0.10566, loss_spatial_dice_6: 0.19508/0.21644, loss_spatial_ce_6: 0.30113/0.15583, loss_grounding_bce_6: 0.08608/0.08343, loss_grounding_dice_6: 0.08774/0.15511, loss_grounding_ce_6: 0.00381/0.30141, loss_mask_ce_7: 2.07059/0.92690, loss_mask_bce_7: 0.39781/0.31855, loss_mask_dice_7: 1.27833/1.11008, loss_spatial_bce_7: 0.06925/0.11763, loss_spatial_dice_7: 0.19998/0.24272, loss_spatial_ce_7: 0.23073/0.20651, loss_grounding_bce_7: 0.09640/0.08556, loss_grounding_dice_7: 0.08988/0.16084, loss_grounding_ce_7: 0.02780/0.35713, loss_mask_ce_8: 1.38616/1.08396, loss_mask_bce_8: 0.50060/0.33609, loss_mask_dice_8: 1.36152/1.18924, loss_spatial_bce_8: 0.08821/0.14047, loss_spatial_dice_8: 0.28505/0.28875, loss_spatial_ce_8: 0.18150/0.26039, loss_grounding_bce_8: 0.09317/0.08914, loss_grounding_dice_8: 0.09353/0.16888, loss_grounding_ce_8: 0.12856/0.47043, loss_mask_ce_9: 4.05529/3.55374, loss_mask_bce_9: 0.63598/0.36362, loss_mask_dice_9: 1.90744/1.78681, loss_spatial_bce_9: 0.30181/0.36883, loss_spatial_dice_9: 0.94274/0.80145, loss_spatial_ce_9: 1.53205/1.44736, loss_grounding_bce_9: 0.14665/0.10118, loss_grounding_dice_9: 0.18015/0.24626, loss_grounding_ce_9: 2.51977/0.76863] items per batch[64] items per second[0.35] total items[403200] mini batches[ 6300] memory[4929] epoch remaining[0:30:55] INFO:trainer.default_trainer:epochs[ 3] optim steps[6400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35834/0.80968, loss_mask_bce_0: 0.08438/0.30368, loss_mask_dice_0: 0.23322/1.03011, loss_spatial_bce_0: 0.05151/0.09531, loss_spatial_dice_0: 0.15128/0.20089, loss_spatial_ce_0: 0.12713/0.09946, loss_grounding_bce_0: 0.03657/0.08076, loss_grounding_dice_0: 0.20641/0.15083, loss_grounding_ce_0: 0.42730/0.25644, loss_mask_ce_1: 0.34525/0.81302, loss_mask_bce_1: 0.09138/0.30425, loss_mask_dice_1: 0.27266/1.03593, loss_spatial_bce_1: 0.05251/0.09622, loss_spatial_dice_1: 0.16785/0.20386, loss_spatial_ce_1: 0.08206/0.10350, loss_grounding_bce_1: 0.04154/0.08076, loss_grounding_dice_1: 0.23064/0.15215, loss_grounding_ce_1: 0.51737/0.26091, loss_mask_ce_2: 0.35189/0.81828, loss_mask_bce_2: 0.08921/0.30423, loss_mask_dice_2: 0.26420/1.03815, loss_spatial_bce_2: 0.05250/0.09555, loss_spatial_dice_2: 0.17203/0.20385, loss_spatial_ce_2: 0.15721/0.10938, loss_grounding_bce_2: 0.03687/0.08056, loss_grounding_dice_2: 0.23013/0.15238, loss_grounding_ce_2: 0.52634/0.25843, loss_mask_ce_3: 0.35209/0.81523, loss_mask_bce_3: 0.08685/0.30577, loss_mask_dice_3: 0.27412/1.03232, loss_spatial_bce_3: 0.05535/0.09694, loss_spatial_dice_3: 0.17191/0.20393, loss_spatial_ce_3: 0.13765/0.11584, loss_grounding_bce_3: 0.04554/0.08121, loss_grounding_dice_3: 0.22711/0.15199, loss_grounding_ce_3: 0.51393/0.25877, loss_mask_ce_4: 0.57214/0.82014, loss_mask_bce_4: 0.08835/0.30851, loss_mask_dice_4: 0.24377/1.05132, loss_spatial_bce_4: 0.05164/0.09929, loss_spatial_dice_4: 0.18420/0.21133, loss_spatial_ce_4: 0.15708/0.12699, loss_grounding_bce_4: 0.03767/0.08195, loss_grounding_dice_4: 0.21194/0.15429, loss_grounding_ce_4: 0.60991/0.26649, loss_mask_ce_5: 0.48305/0.84150, loss_mask_bce_5: 0.08626/0.30960, loss_mask_dice_5: 0.23871/1.06163, loss_spatial_bce_5: 0.06472/0.10078, loss_spatial_dice_5: 0.17433/0.21373, loss_spatial_ce_5: 0.14396/0.13712, loss_grounding_bce_5: 0.05694/0.08209, loss_grounding_dice_5: 0.44614/0.15531, loss_grounding_ce_5: 0.00404/0.28395, loss_mask_ce_6: 0.36276/0.86300, loss_mask_bce_6: 0.09485/0.31043, loss_mask_dice_6: 0.28239/1.06648, loss_spatial_bce_6: 0.06574/0.10548, loss_spatial_dice_6: 0.19172/0.21631, loss_spatial_ce_6: 0.14578/0.15514, loss_grounding_bce_6: 0.04837/0.08344, loss_grounding_dice_6: 0.24808/0.15540, loss_grounding_ce_6: 0.49947/0.30307, loss_mask_ce_7: 0.65971/0.92702, loss_mask_bce_7: 0.09594/0.31847, loss_mask_dice_7: 0.33882/1.11234, loss_spatial_bce_7: 0.07804/0.11741, loss_spatial_dice_7: 0.23635/0.24258, loss_spatial_ce_7: 0.06757/0.20620, loss_grounding_bce_7: 0.04321/0.08560, loss_grounding_dice_7: 0.29069/0.16121, loss_grounding_ce_7: 0.64103/0.35874, loss_mask_ce_8: 0.09405/1.08402, loss_mask_bce_8: 0.12628/0.33608, loss_mask_dice_8: 0.43098/1.19151, loss_spatial_bce_8: 0.08074/0.14024, loss_spatial_dice_8: 0.25863/0.28868, loss_spatial_ce_8: 0.05008/0.26008, loss_grounding_bce_8: 0.08016/0.08917, loss_grounding_dice_8: 0.48830/0.16933, loss_grounding_ce_8: 0.00506/0.47233, loss_mask_ce_9: 1.66881/3.55439, loss_mask_bce_9: 0.10884/0.36368, loss_mask_dice_9: 0.52215/1.78997, loss_spatial_bce_9: 0.17178/0.36809, loss_spatial_dice_9: 0.70078/0.80144, loss_spatial_ce_9: 1.25473/1.44708, loss_grounding_bce_9: 0.05511/0.10118, loss_grounding_dice_9: 0.47425/0.24685, loss_grounding_ce_9: 0.03155/0.76922] items per batch[64] items per second[0.35] total items[409600] mini batches[ 6400] memory[4929] epoch remaining[0:27:50] INFO:trainer.default_trainer:epochs[ 3] optim steps[6500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42340/0.80886, loss_mask_bce_0: 0.70530/0.30391, loss_mask_dice_0: 0.55828/1.02824, loss_spatial_bce_0: 0.61395/0.09530, loss_spatial_dice_0: 0.42176/0.20053, loss_spatial_ce_0: 0.65751/0.09889, loss_grounding_bce_0: 0.64544/0.08085, loss_grounding_dice_0: 0.48814/0.15080, loss_grounding_ce_0: 1.41451/0.25654, loss_mask_ce_1: 1.25607/0.81184, loss_mask_bce_1: 0.70425/0.30445, loss_mask_dice_1: 0.52775/1.03424, loss_spatial_bce_1: 0.53002/0.09619, loss_spatial_dice_1: 0.38606/0.20352, loss_spatial_ce_1: 0.61327/0.10308, loss_grounding_bce_1: 0.60552/0.08084, loss_grounding_dice_1: 0.55394/0.15216, loss_grounding_ce_1: 1.42957/0.26106, loss_mask_ce_2: 1.27724/0.81720, loss_mask_bce_2: 0.69376/0.30446, loss_mask_dice_2: 0.47211/1.03627, loss_spatial_bce_2: 0.57196/0.09554, loss_spatial_dice_2: 0.39841/0.20344, loss_spatial_ce_2: 0.72828/0.10891, loss_grounding_bce_2: 0.57654/0.08062, loss_grounding_dice_2: 0.47339/0.15238, loss_grounding_ce_2: 1.51441/0.25864, loss_mask_ce_3: 1.16606/0.81421, loss_mask_bce_3: 0.68516/0.30587, loss_mask_dice_3: 0.45699/1.03049, loss_spatial_bce_3: 0.57596/0.09695, loss_spatial_dice_3: 0.38395/0.20357, loss_spatial_ce_3: 0.72303/0.11534, loss_grounding_bce_3: 0.53529/0.08123, loss_grounding_dice_3: 0.40796/0.15203, loss_grounding_ce_3: 1.30146/0.25898, loss_mask_ce_4: 1.17352/0.81932, loss_mask_bce_4: 0.65737/0.30860, loss_mask_dice_4: 0.43735/1.04921, loss_spatial_bce_4: 0.63634/0.09926, loss_spatial_dice_4: 0.44717/0.21098, loss_spatial_ce_4: 0.68233/0.12629, loss_grounding_bce_4: 0.67352/0.08201, loss_grounding_dice_4: 0.63976/0.15431, loss_grounding_ce_4: 1.29669/0.26680, loss_mask_ce_5: 1.25056/0.84082, loss_mask_bce_5: 0.71893/0.30973, loss_mask_dice_5: 0.43500/1.06000, loss_spatial_bce_5: 0.48076/0.10067, loss_spatial_dice_5: 0.41721/0.21329, loss_spatial_ce_5: 0.48710/0.13645, loss_grounding_bce_5: 0.67715/0.08216, loss_grounding_dice_5: 0.54058/0.15533, loss_grounding_ce_5: 1.36461/0.28426, loss_mask_ce_6: 1.16204/0.86223, loss_mask_bce_6: 0.76305/0.31066, loss_mask_dice_6: 0.48937/1.06463, loss_spatial_bce_6: 0.52421/0.10534, loss_spatial_dice_6: 0.42367/0.21590, loss_spatial_ce_6: 0.50258/0.15435, loss_grounding_bce_6: 0.60714/0.08346, loss_grounding_dice_6: 0.53728/0.15537, loss_grounding_ce_6: 1.07976/0.30299, loss_mask_ce_7: 1.37097/0.92620, loss_mask_bce_7: 0.72462/0.31864, loss_mask_dice_7: 0.46006/1.11051, loss_spatial_bce_7: 0.52306/0.11735, loss_spatial_dice_7: 0.50968/0.24215, loss_spatial_ce_7: 0.57050/0.20538, loss_grounding_bce_7: 0.58275/0.08555, loss_grounding_dice_7: 0.43586/0.16122, loss_grounding_ce_7: 1.09082/0.35822, loss_mask_ce_8: 1.28033/1.08270, loss_mask_bce_8: 0.60240/0.33633, loss_mask_dice_8: 0.43499/1.19049, loss_spatial_bce_8: 0.51330/0.14018, loss_spatial_dice_8: 0.48570/0.28818, loss_spatial_ce_8: 0.76011/0.25912, loss_grounding_bce_8: 0.44842/0.08911, loss_grounding_dice_8: 0.39734/0.16937, loss_grounding_ce_8: 1.39985/0.47148, loss_mask_ce_9: 3.05950/3.55252, loss_mask_bce_9: 0.63689/0.36391, loss_mask_dice_9: 0.51751/1.78927, loss_spatial_bce_9: 0.55131/0.36806, loss_spatial_dice_9: 0.60758/0.80124, loss_spatial_ce_9: 1.11727/1.44561, loss_grounding_bce_9: 0.57189/0.10100, loss_grounding_dice_9: 0.46624/0.24663, loss_grounding_ce_9: 1.17781/0.76800] items per batch[64] items per second[0.35] total items[416000] mini batches[ 6500] memory[4929] epoch remaining[0:24:46] INFO:trainer.default_trainer:epochs[ 3] optim steps[6600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07069/0.80782, loss_mask_bce_0: 0.01289/0.30365, loss_mask_dice_0: 0.21022/1.02914, loss_spatial_bce_0: 0.00378/0.09496, loss_spatial_dice_0: 0.07080/0.20019, loss_spatial_ce_0: 0.03444/0.09828, loss_grounding_bce_0: 0.00477/0.08078, loss_grounding_dice_0: 0.15030/0.15077, loss_grounding_ce_0: 0.14866/0.25581, loss_mask_ce_1: 0.06555/0.81089, loss_mask_bce_1: 0.00950/0.30420, loss_mask_dice_1: 0.20380/1.03539, loss_spatial_bce_1: 0.00523/0.09584, loss_spatial_dice_1: 0.15456/0.20320, loss_spatial_ce_1: 0.05099/0.10262, loss_grounding_bce_1: 0.00310/0.08080, loss_grounding_dice_1: 0.18868/0.15213, loss_grounding_ce_1: 0.76185/0.26039, loss_mask_ce_2: 0.05031/0.81646, loss_mask_bce_2: 0.01132/0.30418, loss_mask_dice_2: 0.16017/1.03747, loss_spatial_bce_2: 0.00419/0.09521, loss_spatial_dice_2: 0.14980/0.20310, loss_spatial_ce_2: 0.07673/0.10848, loss_grounding_bce_2: 0.00400/0.08061, loss_grounding_dice_2: 0.18016/0.15234, loss_grounding_ce_2: 0.66851/0.25804, loss_mask_ce_3: 0.06971/0.81350, loss_mask_bce_3: 0.00784/0.30561, loss_mask_dice_3: 0.16272/1.03169, loss_spatial_bce_3: 0.00399/0.09659, loss_spatial_dice_3: 0.09156/0.20322, loss_spatial_ce_3: 0.16606/0.11484, loss_grounding_bce_3: 0.00493/0.08124, loss_grounding_dice_3: 0.21631/0.15204, loss_grounding_ce_3: 0.14988/0.25789, loss_mask_ce_4: 0.08804/0.81792, loss_mask_bce_4: 0.00834/0.30831, loss_mask_dice_4: 0.18815/1.05055, loss_spatial_bce_4: 0.01576/0.09887, loss_spatial_dice_4: 0.23321/0.21062, loss_spatial_ce_4: 0.07143/0.12556, loss_grounding_bce_4: 0.00436/0.08192, loss_grounding_dice_4: 0.18529/0.15425, loss_grounding_ce_4: 0.17412/0.26585, loss_mask_ce_5: 0.09535/0.83971, loss_mask_bce_5: 0.00763/0.30944, loss_mask_dice_5: 0.14045/1.06126, loss_spatial_bce_5: 0.00355/0.10030, loss_spatial_dice_5: 0.10898/0.21290, loss_spatial_ce_5: 0.26348/0.13594, loss_grounding_bce_5: 0.00325/0.08207, loss_grounding_dice_5: 0.11398/0.15519, loss_grounding_ce_5: 0.19060/0.28359, loss_mask_ce_6: 0.08808/0.86127, loss_mask_bce_6: 0.00942/0.31030, loss_mask_dice_6: 0.17955/1.06598, loss_spatial_bce_6: 0.00468/0.10494, loss_spatial_dice_6: 0.08627/0.21543, loss_spatial_ce_6: 0.29217/0.15361, loss_grounding_bce_6: 0.00410/0.08343, loss_grounding_dice_6: 0.19358/0.15533, loss_grounding_ce_6: 0.54094/0.30195, loss_mask_ce_7: 0.23406/0.92531, loss_mask_bce_7: 0.00999/0.31820, loss_mask_dice_7: 0.16776/1.11202, loss_spatial_bce_7: 0.00437/0.11694, loss_spatial_dice_7: 0.09780/0.24171, loss_spatial_ce_7: 0.18334/0.20430, loss_grounding_bce_7: 0.00460/0.08548, loss_grounding_dice_7: 0.14155/0.16117, loss_grounding_ce_7: 0.35910/0.35645, loss_mask_ce_8: 0.32366/1.08024, loss_mask_bce_8: 0.00729/0.33590, loss_mask_dice_8: 0.17528/1.19174, loss_spatial_bce_8: 0.01288/0.13974, loss_spatial_dice_8: 0.22962/0.28765, loss_spatial_ce_8: 0.12047/0.25831, loss_grounding_bce_8: 0.00161/0.08900, loss_grounding_dice_8: 0.13884/0.16919, loss_grounding_ce_8: 0.47738/0.46962, loss_mask_ce_9: 1.67081/3.54957, loss_mask_bce_9: 0.01136/0.36327, loss_mask_dice_9: 0.38594/1.78979, loss_spatial_bce_9: 0.12565/0.36818, loss_spatial_dice_9: 0.82668/0.80119, loss_spatial_ce_9: 1.17848/1.44505, loss_grounding_bce_9: 0.00179/0.10088, loss_grounding_dice_9: 0.15612/0.24639, loss_grounding_ce_9: 0.32006/0.76612] items per batch[64] items per second[0.35] total items[422400] mini batches[ 6600] memory[4929] epoch remaining[0:21:41] INFO:trainer.default_trainer:epochs[ 3] optim steps[6700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03136/0.80806, loss_mask_bce_0: 0.52256/0.30429, loss_mask_dice_0: 6.98908/1.02847, loss_spatial_bce_0: 0.03610/0.09490, loss_spatial_dice_0: 0.35709/0.19975, loss_spatial_ce_0: 0.02860/0.09771, loss_grounding_bce_0: 0.01749/0.08082, loss_grounding_dice_0: 0.30878/0.15063, loss_grounding_ce_0: 0.62508/0.25537, loss_mask_ce_1: 1.00067/0.81130, loss_mask_bce_1: 0.49571/0.30485, loss_mask_dice_1: 7.52653/1.03459, loss_spatial_bce_1: 0.03214/0.09576, loss_spatial_dice_1: 0.33754/0.20273, loss_spatial_ce_1: 0.07493/0.10199, loss_grounding_bce_1: 0.01815/0.08090, loss_grounding_dice_1: 0.29101/0.15193, loss_grounding_ce_1: 0.61980/0.25965, loss_mask_ce_2: 0.92673/0.81706, loss_mask_bce_2: 0.47400/0.30484, loss_mask_dice_2: 7.18217/1.03674, loss_spatial_bce_2: 0.03681/0.09513, loss_spatial_dice_2: 0.34661/0.20262, loss_spatial_ce_2: 0.07720/0.10786, loss_grounding_bce_2: 0.01840/0.08067, loss_grounding_dice_2: 0.30812/0.15214, loss_grounding_ce_2: 0.61368/0.25752, loss_mask_ce_3: 0.99996/0.81400, loss_mask_bce_3: 0.45576/0.30631, loss_mask_dice_3: 6.96752/1.03097, loss_spatial_bce_3: 0.03576/0.09653, loss_spatial_dice_3: 0.28234/0.20276, loss_spatial_ce_3: 0.05312/0.11416, loss_grounding_bce_3: 0.02000/0.08133, loss_grounding_dice_3: 0.32303/0.15189, loss_grounding_ce_3: 0.62121/0.25718, loss_mask_ce_4: 1.00889/0.81851, loss_mask_bce_4: 0.50712/0.30896, loss_mask_dice_4: 6.37414/1.04965, loss_spatial_bce_4: 0.03623/0.09877, loss_spatial_dice_4: 0.33484/0.21015, loss_spatial_ce_4: 0.07088/0.12497, loss_grounding_bce_4: 0.02254/0.08202, loss_grounding_dice_4: 0.29675/0.15409, loss_grounding_ce_4: 0.69649/0.26501, loss_mask_ce_5: 0.92957/0.84014, loss_mask_bce_5: 0.52187/0.31004, loss_mask_dice_5: 6.86007/1.06027, loss_spatial_bce_5: 0.03717/0.10023, loss_spatial_dice_5: 0.36422/0.21247, loss_spatial_ce_5: 0.07601/0.13530, loss_grounding_bce_5: 0.02204/0.08220, loss_grounding_dice_5: 0.29113/0.15504, loss_grounding_ce_5: 0.68170/0.28251, loss_mask_ce_6: 1.15732/0.86155, loss_mask_bce_6: 0.44300/0.31098, loss_mask_dice_6: 6.76446/1.06523, loss_spatial_bce_6: 0.04390/0.10493, loss_spatial_dice_6: 0.33038/0.21502, loss_spatial_ce_6: 0.15967/0.15280, loss_grounding_bce_6: 0.02197/0.08356, loss_grounding_dice_6: 0.28703/0.15520, loss_grounding_ce_6: 0.72554/0.30078, loss_mask_ce_7: 1.23392/0.92562, loss_mask_bce_7: 0.47144/0.31878, loss_mask_dice_7: 7.22543/1.11105, loss_spatial_bce_7: 0.03335/0.11685, loss_spatial_dice_7: 0.42145/0.24123, loss_spatial_ce_7: 0.12937/0.20353, loss_grounding_bce_7: 0.02030/0.08558, loss_grounding_dice_7: 0.28740/0.16090, loss_grounding_ce_7: 0.65842/0.35502, loss_mask_ce_8: 1.43375/1.07921, loss_mask_bce_8: 0.61383/0.33651, loss_mask_dice_8: 9.11023/1.19147, loss_spatial_bce_8: 0.02914/0.13988, loss_spatial_dice_8: 0.50242/0.28708, loss_spatial_ce_8: 0.12436/0.25760, loss_grounding_bce_8: 0.02849/0.08913, loss_grounding_dice_8: 0.37596/0.16896, loss_grounding_ce_8: 0.72441/0.46707, loss_mask_ce_9: 7.67118/3.54959, loss_mask_bce_9: 0.69663/0.36415, loss_mask_dice_9: 14.04270/1.78971, loss_spatial_bce_9: 0.08153/0.36855, loss_spatial_dice_9: 0.85946/0.80093, loss_spatial_ce_9: 1.71244/1.44307, loss_grounding_bce_9: 0.02354/0.10096, loss_grounding_dice_9: 0.56353/0.24615, loss_grounding_ce_9: 0.66404/0.76395] items per batch[64] items per second[0.35] total items[428800] mini batches[ 6700] memory[4929] epoch remaining[0:18:37] INFO:trainer.default_trainer:epochs[ 3] optim steps[6800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04438/0.80742, loss_mask_bce_0: 0.07562/0.30447, loss_mask_dice_0: 0.30123/1.02790, loss_spatial_bce_0: 0.03991/0.09484, loss_spatial_dice_0: 0.15138/0.19955, loss_spatial_ce_0: 0.00199/0.09700, loss_grounding_bce_0: 0.03602/0.08076, loss_grounding_dice_0: 0.07677/0.15052, loss_grounding_ce_0: 0.00320/0.25542, loss_mask_ce_1: 0.04122/0.81049, loss_mask_bce_1: 0.07999/0.30509, loss_mask_dice_1: 0.33602/1.03450, loss_spatial_bce_1: 0.03619/0.09569, loss_spatial_dice_1: 0.11574/0.20251, loss_spatial_ce_1: 0.00191/0.10140, loss_grounding_bce_1: 0.03686/0.08085, loss_grounding_dice_1: 0.07446/0.15183, loss_grounding_ce_1: 0.00431/0.25973, loss_mask_ce_2: 0.03663/0.81627, loss_mask_bce_2: 0.08288/0.30505, loss_mask_dice_2: 0.33288/1.03640, loss_spatial_bce_2: 0.03942/0.09506, loss_spatial_dice_2: 0.15462/0.20242, loss_spatial_ce_2: 0.00200/0.10711, loss_grounding_bce_2: 0.03726/0.08060, loss_grounding_dice_2: 0.07841/0.15200, loss_grounding_ce_2: 0.00338/0.25790, loss_mask_ce_3: 0.03669/0.81334, loss_mask_bce_3: 0.08871/0.30652, loss_mask_dice_3: 0.33598/1.03076, loss_spatial_bce_3: 0.03996/0.09645, loss_spatial_dice_3: 0.15085/0.20252, loss_spatial_ce_3: 0.00277/0.11343, loss_grounding_bce_3: 0.03656/0.08126, loss_grounding_dice_3: 0.08377/0.15182, loss_grounding_ce_3: 0.00278/0.25744, loss_mask_ce_4: 0.03110/0.81776, loss_mask_bce_4: 0.07793/0.30918, loss_mask_dice_4: 0.33002/1.04934, loss_spatial_bce_4: 0.04133/0.09865, loss_spatial_dice_4: 0.14839/0.20991, loss_spatial_ce_4: 0.03290/0.12422, loss_grounding_bce_4: 0.04224/0.08200, loss_grounding_dice_4: 0.09173/0.15407, loss_grounding_ce_4: 0.00148/0.26522, loss_mask_ce_5: 0.04168/0.83945, loss_mask_bce_5: 0.07795/0.31025, loss_mask_dice_5: 0.33520/1.05964, loss_spatial_bce_5: 0.03783/0.10009, loss_spatial_dice_5: 0.14086/0.21224, loss_spatial_ce_5: 0.04521/0.13466, loss_grounding_bce_5: 0.03811/0.08216, loss_grounding_dice_5: 0.09035/0.15492, loss_grounding_ce_5: 0.00546/0.28275, loss_mask_ce_6: 0.03864/0.86054, loss_mask_bce_6: 0.08131/0.31124, loss_mask_dice_6: 0.28813/1.06466, loss_spatial_bce_6: 0.04310/0.10478, loss_spatial_dice_6: 0.13755/0.21480, loss_spatial_ce_6: 0.03939/0.15201, loss_grounding_bce_6: 0.04139/0.08348, loss_grounding_dice_6: 0.08248/0.15510, loss_grounding_ce_6: 0.00289/0.30102, loss_mask_ce_7: 0.05425/0.92477, loss_mask_bce_7: 0.08852/0.31899, loss_mask_dice_7: 0.32140/1.11051, loss_spatial_bce_7: 0.03770/0.11665, loss_spatial_dice_7: 0.16312/0.24096, loss_spatial_ce_7: 0.08665/0.20290, loss_grounding_bce_7: 0.04119/0.08553, loss_grounding_dice_7: 0.07682/0.16077, loss_grounding_ce_7: 0.00228/0.35525, loss_mask_ce_8: 0.06100/1.07775, loss_mask_bce_8: 0.08550/0.33671, loss_mask_dice_8: 0.31238/1.19100, loss_spatial_bce_8: 0.03664/0.13961, loss_spatial_dice_8: 0.16685/0.28667, loss_spatial_ce_8: 0.13362/0.25672, loss_grounding_bce_8: 0.04319/0.08908, loss_grounding_dice_8: 0.08942/0.16895, loss_grounding_ce_8: 0.00315/0.46699, loss_mask_ce_9: 2.03915/3.54861, loss_mask_bce_9: 0.08131/0.36461, loss_mask_dice_9: 0.33303/1.78810, loss_spatial_bce_9: 0.29134/0.36860, loss_spatial_dice_9: 0.68188/0.80094, loss_spatial_ce_9: 0.91988/1.44264, loss_grounding_bce_9: 0.04015/0.10089, loss_grounding_dice_9: 0.09845/0.24586, loss_grounding_ce_9: 0.07793/0.76413] items per batch[64] items per second[0.36] total items[435200] mini batches[ 6800] memory[4929] epoch remaining[0:15:31] INFO:trainer.default_trainer:epochs[ 3] optim steps[6900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61949/0.80682, loss_mask_bce_0: 0.38778/0.30501, loss_mask_dice_0: 0.58314/1.03206, loss_spatial_bce_0: 0.13602/0.09475, loss_spatial_dice_0: 0.23237/0.19949, loss_spatial_ce_0: 0.02525/0.09657, loss_grounding_bce_0: 0.25259/0.08086, loss_grounding_dice_0: 0.08965/0.15075, loss_grounding_ce_0: 0.13139/0.25446, loss_mask_ce_1: 0.73276/0.80979, loss_mask_bce_1: 0.32143/0.30566, loss_mask_dice_1: 0.46911/1.03897, loss_spatial_bce_1: 0.15663/0.09557, loss_spatial_dice_1: 0.23370/0.20238, loss_spatial_ce_1: 0.03882/0.10086, loss_grounding_bce_1: 0.22668/0.08094, loss_grounding_dice_1: 0.08721/0.15201, loss_grounding_ce_1: 0.14262/0.25861, loss_mask_ce_2: 1.17023/0.81564, loss_mask_bce_2: 0.25429/0.30556, loss_mask_dice_2: 0.54600/1.04101, loss_spatial_bce_2: 0.14606/0.09493, loss_spatial_dice_2: 0.23025/0.20237, loss_spatial_ce_2: 0.03628/0.10667, loss_grounding_bce_2: 0.22030/0.08071, loss_grounding_dice_2: 0.08476/0.15219, loss_grounding_ce_2: 0.16052/0.25692, loss_mask_ce_3: 1.07351/0.81291, loss_mask_bce_3: 0.22095/0.30701, loss_mask_dice_3: 0.48841/1.03484, loss_spatial_bce_3: 0.18266/0.09633, loss_spatial_dice_3: 0.24232/0.20250, loss_spatial_ce_3: 0.04437/0.11272, loss_grounding_bce_3: 0.16451/0.08136, loss_grounding_dice_3: 0.08234/0.15196, loss_grounding_ce_3: 0.46823/0.25645, loss_mask_ce_4: 1.03189/0.81765, loss_mask_bce_4: 0.23657/0.30963, loss_mask_dice_4: 0.50506/1.05371, loss_spatial_bce_4: 0.16594/0.09850, loss_spatial_dice_4: 0.25069/0.20991, loss_spatial_ce_4: 0.05510/0.12327, loss_grounding_bce_4: 0.15687/0.08208, loss_grounding_dice_4: 0.07572/0.15420, loss_grounding_ce_4: 0.45109/0.26433, loss_mask_ce_5: 1.06202/0.83888, loss_mask_bce_5: 0.26915/0.31074, loss_mask_dice_5: 0.51099/1.06433, loss_spatial_bce_5: 0.10341/0.09991, loss_spatial_dice_5: 0.21747/0.21215, loss_spatial_ce_5: 0.08770/0.13409, loss_grounding_bce_5: 0.19564/0.08221, loss_grounding_dice_5: 0.07250/0.15505, loss_grounding_ce_5: 0.44706/0.28203, loss_mask_ce_6: 0.70487/0.86057, loss_mask_bce_6: 0.32787/0.31169, loss_mask_dice_6: 0.51253/1.06907, loss_spatial_bce_6: 0.13719/0.10457, loss_spatial_dice_6: 0.21051/0.21473, loss_spatial_ce_6: 0.10515/0.15120, loss_grounding_bce_6: 0.17649/0.08355, loss_grounding_dice_6: 0.08353/0.15524, loss_grounding_ce_6: 0.41771/0.30047, loss_mask_ce_7: 0.58331/0.92463, loss_mask_bce_7: 0.26039/0.31946, loss_mask_dice_7: 0.60204/1.11505, loss_spatial_bce_7: 0.15369/0.11636, loss_spatial_dice_7: 0.21721/0.24077, loss_spatial_ce_7: 0.12493/0.20185, loss_grounding_bce_7: 0.19689/0.08554, loss_grounding_dice_7: 0.10807/0.16088, loss_grounding_ce_7: 0.09869/0.35415, loss_mask_ce_8: 0.87707/1.07710, loss_mask_bce_8: 0.25249/0.33733, loss_mask_dice_8: 0.59745/1.19540, loss_spatial_bce_8: 0.11346/0.13929, loss_spatial_dice_8: 0.31228/0.28657, loss_spatial_ce_8: 0.21972/0.25582, loss_grounding_bce_8: 0.18992/0.08919, loss_grounding_dice_8: 0.06782/0.16913, loss_grounding_ce_8: 0.09743/0.46478, loss_mask_ce_9: 4.77638/3.54804, loss_mask_bce_9: 0.22284/0.36508, loss_mask_dice_9: 1.40393/1.79457, loss_spatial_bce_9: 0.27139/0.36823, loss_spatial_dice_9: 0.84274/0.80120, loss_spatial_ce_9: 1.17347/1.44308, loss_grounding_bce_9: 0.14992/0.10098, loss_grounding_dice_9: 0.12343/0.24593, loss_grounding_ce_9: 0.31998/0.76269] items per batch[64] items per second[0.35] total items[441600] mini batches[ 6900] memory[4929] epoch remaining[0:12:28] INFO:trainer.default_trainer:epochs[ 3] optim steps[7000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26954/0.80804, loss_mask_bce_0: 0.00893/0.30492, loss_mask_dice_0: 0.24844/1.03107, loss_spatial_bce_0: 0.00721/0.09454, loss_spatial_dice_0: 0.13447/0.19933, loss_spatial_ce_0: 0.00076/0.09605, loss_grounding_bce_0: 0.00448/0.08080, loss_grounding_dice_0: 0.10005/0.15067, loss_grounding_ce_0: 0.13271/0.25462, loss_mask_ce_1: 0.29485/0.81100, loss_mask_bce_1: 0.01044/0.30552, loss_mask_dice_1: 0.23237/1.03756, loss_spatial_bce_1: 0.00357/0.09535, loss_spatial_dice_1: 0.08564/0.20222, loss_spatial_ce_1: 0.01097/0.10033, loss_grounding_bce_1: 0.00508/0.08086, loss_grounding_dice_1: 0.27653/0.15193, loss_grounding_ce_1: 0.07902/0.25904, loss_mask_ce_2: 0.19914/0.81712, loss_mask_bce_2: 0.01044/0.30544, loss_mask_dice_2: 0.21458/1.03968, loss_spatial_bce_2: 0.00422/0.09470, loss_spatial_dice_2: 0.17951/0.20221, loss_spatial_ce_2: 0.00408/0.10608, loss_grounding_bce_2: 0.00457/0.08063, loss_grounding_dice_2: 0.09620/0.15207, loss_grounding_ce_2: 0.15604/0.25730, loss_mask_ce_3: 0.22246/0.81447, loss_mask_bce_3: 0.01082/0.30685, loss_mask_dice_3: 0.23963/1.03385, loss_spatial_bce_3: 0.00409/0.09607, loss_spatial_dice_3: 0.11829/0.20233, loss_spatial_ce_3: 0.00377/0.11213, loss_grounding_bce_3: 0.00422/0.08127, loss_grounding_dice_3: 0.19148/0.15195, loss_grounding_ce_3: 0.14907/0.25683, loss_mask_ce_4: 0.18910/0.81897, loss_mask_bce_4: 0.01272/0.30937, loss_mask_dice_4: 0.52887/1.05247, loss_spatial_bce_4: 0.00480/0.09827, loss_spatial_dice_4: 0.13000/0.20973, loss_spatial_ce_4: 0.03332/0.12284, loss_grounding_bce_4: 0.00436/0.08199, loss_grounding_dice_4: 0.08831/0.15413, loss_grounding_ce_4: 0.06504/0.26488, loss_mask_ce_5: 0.21671/0.84022, loss_mask_bce_5: 0.01116/0.31055, loss_mask_dice_5: 0.51316/1.06326, loss_spatial_bce_5: 0.00530/0.09965, loss_spatial_dice_5: 0.12166/0.21191, loss_spatial_ce_5: 0.05514/0.13366, loss_grounding_bce_5: 0.00565/0.08212, loss_grounding_dice_5: 0.17041/0.15497, loss_grounding_ce_5: 0.06430/0.28277, loss_mask_ce_6: 0.21462/0.86193, loss_mask_bce_6: 0.01237/0.31156, loss_mask_dice_6: 0.52159/1.06773, loss_spatial_bce_6: 0.00642/0.10434, loss_spatial_dice_6: 0.15558/0.21448, loss_spatial_ce_6: 0.05054/0.15097, loss_grounding_bce_6: 0.00694/0.08349, loss_grounding_dice_6: 0.28520/0.15517, loss_grounding_ce_6: 0.03425/0.30106, loss_mask_ce_7: 0.34500/0.92653, loss_mask_bce_7: 0.00794/0.31919, loss_mask_dice_7: 0.24502/1.11352, loss_spatial_bce_7: 0.00620/0.11612, loss_spatial_dice_7: 0.17554/0.24051, loss_spatial_ce_7: 0.06323/0.20158, loss_grounding_bce_7: 0.00455/0.08543, loss_grounding_dice_7: 0.11189/0.16082, loss_grounding_ce_7: 0.14244/0.35445, loss_mask_ce_8: 0.52244/1.07817, loss_mask_bce_8: 0.01435/0.33720, loss_mask_dice_8: 0.41471/1.19404, loss_spatial_bce_8: 0.01221/0.13909, loss_spatial_dice_8: 0.26710/0.28621, loss_spatial_ce_8: 0.05695/0.25521, loss_grounding_bce_8: 0.00563/0.08913, loss_grounding_dice_8: 0.12472/0.16914, loss_grounding_ce_8: 0.09832/0.46561, loss_mask_ce_9: 2.96429/3.55098, loss_mask_bce_9: 0.01068/0.36476, loss_mask_dice_9: 0.27995/1.79287, loss_spatial_bce_9: 0.03543/0.36756, loss_spatial_dice_9: 0.82224/0.80112, loss_spatial_ce_9: 1.38101/1.44367, loss_grounding_bce_9: 0.00383/0.10088, loss_grounding_dice_9: 0.23073/0.24603, loss_grounding_ce_9: 0.16513/0.76372] items per batch[64] items per second[0.36] total items[448000] mini batches[ 7000] memory[4929] epoch remaining[0:09:24] INFO:trainer.default_trainer:epochs[ 3] optim steps[7100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19801/0.80740, loss_mask_bce_0: 0.63431/0.30485, loss_mask_dice_0: 1.34681/1.03356, loss_spatial_bce_0: 0.08318/0.09458, loss_spatial_dice_0: 0.19410/0.19929, loss_spatial_ce_0: 0.01987/0.09554, loss_grounding_bce_0: 0.06607/0.08074, loss_grounding_dice_0: 0.11558/0.15065, loss_grounding_ce_0: 0.93833/0.25335, loss_mask_ce_1: 0.51268/0.81041, loss_mask_bce_1: 0.62220/0.30544, loss_mask_dice_1: 1.61590/1.04016, loss_spatial_bce_1: 0.09102/0.09539, loss_spatial_dice_1: 0.21046/0.20217, loss_spatial_ce_1: 0.04431/0.10011, loss_grounding_bce_1: 0.06656/0.08081, loss_grounding_dice_1: 0.11744/0.15190, loss_grounding_ce_1: 0.95573/0.25779, loss_mask_ce_2: 0.25422/0.81640, loss_mask_bce_2: 0.60026/0.30540, loss_mask_dice_2: 1.35533/1.04277, loss_spatial_bce_2: 0.08457/0.09473, loss_spatial_dice_2: 0.19823/0.20214, loss_spatial_ce_2: 0.04447/0.10563, loss_grounding_bce_2: 0.06661/0.08058, loss_grounding_dice_2: 0.10907/0.15200, loss_grounding_ce_2: 1.51177/0.25620, loss_mask_ce_3: 0.26705/0.81401, loss_mask_bce_3: 0.59990/0.30678, loss_mask_dice_3: 1.29039/1.03661, loss_spatial_bce_3: 0.09394/0.09606, loss_spatial_dice_3: 0.21556/0.20228, loss_spatial_ce_3: 0.03299/0.11192, loss_grounding_bce_3: 0.07734/0.08122, loss_grounding_dice_3: 0.11441/0.15190, loss_grounding_ce_3: 1.63076/0.25578, loss_mask_ce_4: 0.21011/0.81830, loss_mask_bce_4: 0.81154/0.30938, loss_mask_dice_4: 1.52484/1.05549, loss_spatial_bce_4: 0.08277/0.09826, loss_spatial_dice_4: 0.20453/0.20968, loss_spatial_ce_4: 0.06536/0.12234, loss_grounding_bce_4: 0.07204/0.08191, loss_grounding_dice_4: 0.11444/0.15407, loss_grounding_ce_4: 2.10153/0.26383, loss_mask_ce_5: 0.20623/0.83968, loss_mask_bce_5: 0.79509/0.31054, loss_mask_dice_5: 1.39507/1.06554, loss_spatial_bce_5: 0.08294/0.09964, loss_spatial_dice_5: 0.24219/0.21186, loss_spatial_ce_5: 0.04910/0.13316, loss_grounding_bce_5: 0.07277/0.08216, loss_grounding_dice_5: 0.12311/0.15501, loss_grounding_ce_5: 2.12828/0.28170, loss_mask_ce_6: 0.45236/0.86127, loss_mask_bce_6: 0.64601/0.31161, loss_mask_dice_6: 1.41172/1.07003, loss_spatial_bce_6: 0.09091/0.10436, loss_spatial_dice_6: 0.22981/0.21442, loss_spatial_ce_6: 0.04522/0.15030, loss_grounding_bce_6: 0.10167/0.08348, loss_grounding_dice_6: 0.13891/0.15515, loss_grounding_ce_6: 2.33933/0.29998, loss_mask_ce_7: 0.40458/0.92602, loss_mask_bce_7: 0.71708/0.31919, loss_mask_dice_7: 1.44358/1.11633, loss_spatial_bce_7: 0.11821/0.11600, loss_spatial_dice_7: 0.23766/0.24040, loss_spatial_ce_7: 0.03809/0.20092, loss_grounding_bce_7: 0.08239/0.08538, loss_grounding_dice_7: 0.15283/0.16077, loss_grounding_ce_7: 1.46659/0.35274, loss_mask_ce_8: 0.51937/1.07812, loss_mask_bce_8: 0.75478/0.33708, loss_mask_dice_8: 1.60833/1.19716, loss_spatial_bce_8: 0.16453/0.13897, loss_spatial_dice_8: 0.30807/0.28608, loss_spatial_ce_8: 0.07501/0.25476, loss_grounding_bce_8: 0.16036/0.08906, loss_grounding_dice_8: 0.18234/0.16904, loss_grounding_ce_8: 3.92610/0.46352, loss_mask_ce_9: 3.58587/3.55148, loss_mask_bce_9: 0.99490/0.36461, loss_mask_dice_9: 2.59458/1.79494, loss_spatial_bce_9: 0.31656/0.36739, loss_spatial_dice_9: 0.84260/0.80107, loss_spatial_ce_9: 1.40288/1.44396, loss_grounding_bce_9: 0.65230/0.10089, loss_grounding_dice_9: 0.67637/0.24599, loss_grounding_ce_9: 0.86774/0.76096] items per batch[64] items per second[0.35] total items[454400] mini batches[ 7100] memory[4929] epoch remaining[0:06:21] INFO:trainer.default_trainer:epochs[ 3] optim steps[7200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05263/0.80735, loss_mask_bce_0: 0.30279/0.30450, loss_mask_dice_0: 0.90539/1.03253, loss_spatial_bce_0: 0.14972/0.09464, loss_spatial_dice_0: 0.30408/0.19936, loss_spatial_ce_0: 0.10987/0.09512, loss_grounding_bce_0: 0.00185/0.08084, loss_grounding_dice_0: 0.05159/0.15066, loss_grounding_ce_0: 2.42997/0.25283, loss_mask_ce_1: 1.14771/0.81017, loss_mask_bce_1: 0.31868/0.30513, loss_mask_dice_1: 0.88382/1.03912, loss_spatial_bce_1: 0.16725/0.09542, loss_spatial_dice_1: 0.32023/0.20217, loss_spatial_ce_1: 0.07430/0.09982, loss_grounding_bce_1: 0.00184/0.08089, loss_grounding_dice_1: 0.06395/0.15194, loss_grounding_ce_1: 2.31234/0.25767, loss_mask_ce_2: 1.14188/0.81626, loss_mask_bce_2: 0.34441/0.30520, loss_mask_dice_2: 0.96340/1.04155, loss_spatial_bce_2: 0.15086/0.09476, loss_spatial_dice_2: 0.31616/0.20217, loss_spatial_ce_2: 0.04343/0.10541, loss_grounding_bce_2: 0.00201/0.08068, loss_grounding_dice_2: 0.05671/0.15202, loss_grounding_ce_2: 2.79788/0.25608, loss_mask_ce_3: 1.22503/0.81368, loss_mask_bce_3: 0.37106/0.30655, loss_mask_dice_3: 0.94750/1.03570, loss_spatial_bce_3: 0.16867/0.09608, loss_spatial_dice_3: 0.32443/0.20226, loss_spatial_ce_3: 0.05336/0.11145, loss_grounding_bce_3: 0.00299/0.08131, loss_grounding_dice_3: 0.05454/0.15191, loss_grounding_ce_3: 2.97533/0.25585, loss_mask_ce_4: 1.29494/0.81778, loss_mask_bce_4: 0.38929/0.30914, loss_mask_dice_4: 1.04876/1.05402, loss_spatial_bce_4: 0.16459/0.09828, loss_spatial_dice_4: 0.33013/0.20966, loss_spatial_ce_4: 0.18008/0.12204, loss_grounding_bce_4: 0.00210/0.08200, loss_grounding_dice_4: 0.06795/0.15407, loss_grounding_ce_4: 2.67248/0.26405, loss_mask_ce_5: 0.78199/0.83901, loss_mask_bce_5: 0.84753/0.31037, loss_mask_dice_5: 1.03131/1.06401, loss_spatial_bce_5: 0.20255/0.09963, loss_spatial_dice_5: 0.33814/0.21182, loss_spatial_ce_5: 0.17021/0.13277, loss_grounding_bce_5: 0.00269/0.08225, loss_grounding_dice_5: 0.07091/0.15493, loss_grounding_ce_5: 2.33544/0.28152, loss_mask_ce_6: 1.92676/0.86035, loss_mask_bce_6: 0.38225/0.31145, loss_mask_dice_6: 0.95538/1.06928, loss_spatial_bce_6: 0.18674/0.10436, loss_spatial_dice_6: 0.31874/0.21440, loss_spatial_ce_6: 0.11012/0.14975, loss_grounding_bce_6: 0.00124/0.08359, loss_grounding_dice_6: 0.03919/0.15516, loss_grounding_ce_6: 3.53581/0.29999, loss_mask_ce_7: 1.35389/0.92584, loss_mask_bce_7: 0.32876/0.31871, loss_mask_dice_7: 0.92921/1.11469, loss_spatial_bce_7: 0.26941/0.11597, loss_spatial_dice_7: 0.40318/0.24033, loss_spatial_ce_7: 0.35292/0.20048, loss_grounding_bce_7: 0.00146/0.08543, loss_grounding_dice_7: 0.03331/0.16078, loss_grounding_ce_7: 3.11526/0.35278, loss_mask_ce_8: 1.30333/1.07678, loss_mask_bce_8: 0.29616/0.33654, loss_mask_dice_8: 0.93262/1.19539, loss_spatial_bce_8: 0.25978/0.13893, loss_spatial_dice_8: 0.39896/0.28586, loss_spatial_ce_8: 0.26982/0.25416, loss_grounding_bce_8: 0.00151/0.08913, loss_grounding_dice_8: 0.06187/0.16903, loss_grounding_ce_8: 2.45452/0.46174, loss_mask_ce_9: 5.60736/3.54718, loss_mask_bce_9: 0.53682/0.36415, loss_mask_dice_9: 1.46586/1.79070, loss_spatial_bce_9: 0.41995/0.36757, loss_spatial_dice_9: 0.83437/0.80052, loss_spatial_ce_9: 2.11073/1.44369, loss_grounding_bce_9: 0.00319/0.10092, loss_grounding_dice_9: 0.22267/0.24571, loss_grounding_ce_9: 3.10499/0.75955] items per batch[64] items per second[0.34] total items[460800] mini batches[ 7200] memory[4929] epoch remaining[0:03:18] INFO:trainer.default_trainer:epochs[ 3] optim steps[7300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.99970/0.80646, loss_mask_bce_0: 0.08223/0.30430, loss_mask_dice_0: 1.66294/1.03005, loss_spatial_bce_0: 0.00470/0.09477, loss_spatial_dice_0: 0.23590/0.19925, loss_spatial_ce_0: 0.02359/0.09493, loss_grounding_bce_0: 0.02021/0.08096, loss_grounding_dice_0: 0.56640/0.15066, loss_grounding_ce_0: 1.18961/0.25351, loss_mask_ce_1: 1.99544/0.80948, loss_mask_bce_1: 0.08138/0.30484, loss_mask_dice_1: 1.79644/1.03635, loss_spatial_bce_1: 0.00522/0.09555, loss_spatial_dice_1: 0.22920/0.20202, loss_spatial_ce_1: 0.03998/0.09957, loss_grounding_bce_1: 0.01640/0.08102, loss_grounding_dice_1: 0.43732/0.15186, loss_grounding_ce_1: 1.28078/0.25830, loss_mask_ce_2: 2.27740/0.81505, loss_mask_bce_2: 0.07767/0.30493, loss_mask_dice_2: 1.79515/1.03917, loss_spatial_bce_2: 0.00450/0.09487, loss_spatial_dice_2: 0.23783/0.20203, loss_spatial_ce_2: 0.04104/0.10509, loss_grounding_bce_2: 0.01430/0.08083, loss_grounding_dice_2: 0.47810/0.15194, loss_grounding_ce_2: 1.11782/0.25683, loss_mask_ce_3: 2.29493/0.81269, loss_mask_bce_3: 0.08546/0.30621, loss_mask_dice_3: 1.93043/1.03317, loss_spatial_bce_3: 0.00475/0.09617, loss_spatial_dice_3: 0.30783/0.20208, loss_spatial_ce_3: 0.04425/0.11127, loss_grounding_bce_3: 0.01313/0.08149, loss_grounding_dice_3: 0.49994/0.15181, loss_grounding_ce_3: 1.10190/0.25642, loss_mask_ce_4: 2.25717/0.81673, loss_mask_bce_4: 0.09177/0.30879, loss_mask_dice_4: 2.13145/1.05130, loss_spatial_bce_4: 0.00624/0.09835, loss_spatial_dice_4: 0.22613/0.20947, loss_spatial_ce_4: 0.07116/0.12163, loss_grounding_bce_4: 0.01637/0.08212, loss_grounding_dice_4: 0.58546/0.15410, loss_grounding_ce_4: 1.13075/0.26460, loss_mask_ce_5: 2.42123/0.83806, loss_mask_bce_5: 0.10336/0.31001, loss_mask_dice_5: 3.03135/1.06138, loss_spatial_bce_5: 0.00490/0.09974, loss_spatial_dice_5: 0.19985/0.21161, loss_spatial_ce_5: 0.15878/0.13238, loss_grounding_bce_5: 0.02080/0.08237, loss_grounding_dice_5: 0.73828/0.15488, loss_grounding_ce_5: 1.64857/0.28180, loss_mask_ce_6: 2.26939/0.85931, loss_mask_bce_6: 0.08038/0.31112, loss_mask_dice_6: 1.79784/1.06649, loss_spatial_bce_6: 0.00562/0.10448, loss_spatial_dice_6: 0.16845/0.21418, loss_spatial_ce_6: 0.09257/0.14932, loss_grounding_bce_6: 0.01329/0.08371, loss_grounding_dice_6: 0.88553/0.15513, loss_grounding_ce_6: 1.56911/0.30071, loss_mask_ce_7: 2.09949/0.92496, loss_mask_bce_7: 0.09629/0.31837, loss_mask_dice_7: 3.07007/1.11196, loss_spatial_bce_7: 0.00596/0.11618, loss_spatial_dice_7: 0.24386/0.24008, loss_spatial_ce_7: 0.33626/0.19984, loss_grounding_bce_7: 0.02421/0.08553, loss_grounding_dice_7: 0.70064/0.16074, loss_grounding_ce_7: 1.25651/0.35313, loss_mask_ce_8: 2.46193/1.07547, loss_mask_bce_8: 0.12059/0.33629, loss_mask_dice_8: 2.52596/1.19216, loss_spatial_bce_8: 0.01836/0.13914, loss_spatial_dice_8: 0.48115/0.28553, loss_spatial_ce_8: 0.35795/0.25375, loss_grounding_bce_8: 0.11768/0.08924, loss_grounding_dice_8: 0.99022/0.16896, loss_grounding_ce_8: 0.09218/0.46149, loss_mask_ce_9: 4.22517/3.54482, loss_mask_bce_9: 0.08190/0.36406, loss_mask_dice_9: 3.45782/1.78538, loss_spatial_bce_9: 0.03031/0.36755, loss_spatial_dice_9: 0.84130/0.80023, loss_spatial_ce_9: 2.15186/1.44282, loss_grounding_bce_9: 0.05038/0.10112, loss_grounding_dice_9: 0.97978/0.24553, loss_grounding_ce_9: 0.06028/0.75821] items per batch[64] items per second[0.35] total items[467200] mini batches[ 7300] memory[4929] epoch remaining[0:00:14] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00007308. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0018 s/iter. Inference: 0.3645 s/iter. Eval: 0.1034 s/iter. Total: 0.4698 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0024 s/iter. Inference: 0.3666 s/iter. Eval: 0.0981 s/iter. Total: 0.4673 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0028 s/iter. Inference: 0.3744 s/iter. Eval: 0.0855 s/iter. Total: 0.4629 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0029 s/iter. Inference: 0.3751 s/iter. Eval: 0.0804 s/iter. Total: 0.4585 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0030 s/iter. Inference: 0.3778 s/iter. Eval: 0.0756 s/iter. Total: 0.4566 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0030 s/iter. Inference: 0.3780 s/iter. Eval: 0.0722 s/iter. Total: 0.4534 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalzlpgtp1m ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.081 | 82.794 | 65.679 | 133 | | Things | 61.439 | 83.780 | 72.837 | 80 | | Stuff | 45.484 | 81.305 | 54.873 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.54s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.37 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.33 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.57s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.50 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.446 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.687 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.481 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.489 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.668 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.542 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.559 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.369 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.755 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 44.588 | 68.655 | 48.107 | 26.365 | 48.881 | 66.787 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 47.916 | bicycle | 21.376 | car | 42.845 | | motorcycle | 40.254 | airplane | 57.989 | bus | 70.939 | | train | 74.436 | truck | 41.819 | boat | 28.291 | | traffic light | 27.209 | fire hydrant | 69.356 | stop sign | 68.643 | | parking meter | 51.245 | bench | 25.924 | bird | 33.716 | | cat | 75.093 | dog | 70.668 | horse | 49.866 | | sheep | 52.258 | cow | 54.266 | elephant | 64.299 | | bear | 78.178 | zebra | 64.732 | giraffe | 60.666 | | backpack | 23.176 | umbrella | 55.121 | handbag | 23.646 | | tie | 40.358 | suitcase | 50.100 | frisbee | 69.546 | | skis | 7.952 | snowboard | 34.154 | sports ball | 49.231 | | kite | 35.105 | baseball bat | 37.849 | baseball glove | 49.304 | | skateboard | 43.747 | surfboard | 43.111 | tennis racket | 62.219 | | bottle | 41.649 | wine glass | 37.053 | cup | 48.518 | | fork | 25.515 | knife | 24.109 | spoon | 21.048 | | bowl | 37.259 | banana | 21.291 | apple | 24.395 | | sandwich | 50.729 | orange | 30.864 | broccoli | 23.958 | | carrot | 21.253 | hot dog | 35.629 | pizza | 48.688 | | donut | 53.496 | cake | 45.293 | chair | 28.089 | | couch | 40.838 | potted plant | 22.879 | bed | 42.674 | | dining table | 15.312 | toilet | 69.903 | tv | 66.166 | | laptop | 67.951 | mouse | 64.095 | remote | 43.039 | | keyboard | 58.935 | cell phone | 46.381 | microwave | 61.118 | | oven | 30.199 | toaster | 48.891 | sink | 43.565 | | refrigerator | 68.554 | book | 13.547 | clock | 53.801 | | vase | 40.642 | scissors | 36.604 | teddy bear | 57.390 | | hair drier | 30.520 | toothbrush | 28.621 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.04271541054608, 'fwIoU': 71.8742107749599, 'IoU-person': 88.68008001266402, 'IoU-bicycle': 79.24913482848287, 'IoU-car': 71.79926072383172, 'IoU-motorcycle': 88.72294177811007, 'IoU-airplane': 89.32413674424909, 'IoU-bus': 87.90485346513258, 'IoU-train': 87.6724621636805, 'IoU-truck': 66.78808347945078, 'IoU-boat': 74.69785279110754, 'IoU-traffic light': 79.66779078779246, 'IoU-fire hydrant': 92.99249130785017, 'IoU-stop sign': 95.80265523008657, 'IoU-parking meter': 85.02009517778433, 'IoU-bench': 61.28056469094923, 'IoU-bird': 78.21437974683543, 'IoU-cat': 91.71005233355473, 'IoU-dog': 87.00832708651676, 'IoU-horse': 89.4003396296282, 'IoU-sheep': 90.78482649747428, 'IoU-cow': 89.87485366625727, 'IoU-elephant': 93.5399916370315, 'IoU-bear': 93.65716111672852, 'IoU-zebra': 90.68196883726415, 'IoU-giraffe': 89.45315567116177, 'IoU-backpack': 50.005840701929024, 'IoU-umbrella': 89.57956152409339, 'IoU-handbag': 46.61897236656852, 'IoU-tie': 76.74803847923397, 'IoU-suitcase': 86.72674250076412, 'IoU-frisbee': 84.0535538286889, 'IoU-skis': 60.27682826117749, 'IoU-snowboard': 74.87801371278631, 'IoU-sports ball': 76.80489512729802, 'IoU-kite': 79.72367982186228, 'IoU-baseball bat': 68.53867146407282, 'IoU-baseball glove': 82.54701193536123, 'IoU-skateboard': 86.05501498481276, 'IoU-surfboard': 86.57588524903595, 'IoU-tennis racket': 90.71502154096535, 'IoU-bottle': 71.44581686800113, 'IoU-wine glass': 81.69903112993833, 'IoU-cup': 70.56087192532632, 'IoU-fork': 67.36262231217567, 'IoU-knife': 61.29314110965487, 'IoU-spoon': 56.08561689248746, 'IoU-bowl': 56.50516549276151, 'IoU-banana': 82.84400576280538, 'IoU-apple': 59.26539145372387, 'IoU-sandwich': 70.30335125701666, 'IoU-orange': 78.20781173498507, 'IoU-broccoli': 69.99934063080102, 'IoU-carrot': 64.65492941379091, 'IoU-hot dog': 67.08661995048413, 'IoU-pizza': 87.85597264050159, 'IoU-donut': 74.71838691670378, 'IoU-cake': 79.79696554044084, 'IoU-chair': 61.196867281517974, 'IoU-couch': 65.71728505291964, 'IoU-potted plant': 43.36740587387347, 'IoU-bed': 72.89750787190724, 'IoU-dining table': 53.46832016014184, 'IoU-toilet': 89.57848291037, 'IoU-tv': 83.70241770255609, 'IoU-laptop': 77.59903102132894, 'IoU-mouse': 79.39216824962689, 'IoU-remote': 74.40436106665649, 'IoU-keyboard': 69.6981047568364, 'IoU-cell phone': 73.75540670870261, 'IoU-microwave': 75.49579086699734, 'IoU-oven': 74.02084392025357, 'IoU-toaster': 86.07617911686157, 'IoU-sink': 75.20423749927075, 'IoU-refrigerator': 82.61949430869609, 'IoU-book': 57.62267700368457, 'IoU-clock': 76.9686357410881, 'IoU-vase': 72.29355657882175, 'IoU-scissors': 85.80871889957311, 'IoU-teddy bear': 87.19093283601762, 'IoU-hair drier': 30.57862255574957, 'IoU-toothbrush': 76.48757671359733, 'IoU-banner': 34.649910546113844, 'IoU-blanket': 20.396481954265884, 'IoU-bridge': 36.305486483265426, 'IoU-cardboard': 46.5342377885853, 'IoU-counter': 29.741531781744456, 'IoU-curtain': 71.64825647036866, 'IoU-door-stuff': 46.4689647023076, 'IoU-floor-wood': 64.2073455166446, 'IoU-flower': 49.86270570209139, 'IoU-fruit': 47.48631531228463, 'IoU-gravel': 27.98220059293791, 'IoU-house': 23.699679212101106, 'IoU-light': 41.421905023332414, 'IoU-mirror-stuff': 60.930372064026486, 'IoU-net': 45.126886437741845, 'IoU-pillow': 15.43305854626886, 'IoU-platform': 29.26242473520614, 'IoU-playingfield': 70.82488207532765, 'IoU-railroad': 63.83440198314412, 'IoU-river': 52.80466891719626, 'IoU-road': 67.67537412079564, 'IoU-roof': 13.203943206791577, 'IoU-sand': 66.55029353186337, 'IoU-sea': 84.50451305003469, 'IoU-shelf': 38.177969653122055, 'IoU-snow': 92.04333256842897, 'IoU-stairs': 32.0353040446181, 'IoU-tent': 11.395727587038126, 'IoU-towel': 41.184058753816046, 'IoU-wall-brick': 49.223720725945036, 'IoU-wall-stone': 29.622480276167106, 'IoU-wall-tile': 71.35488604771852, 'IoU-wall-wood': 42.010526239283216, 'IoU-water-other': 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'ACC-mouse': 91.729345006836, 'ACC-remote': 79.08912681542147, 'ACC-keyboard': 76.23162363368863, 'ACC-cell phone': 82.50001532366649, 'ACC-microwave': 78.25764164532241, 'ACC-oven': 87.77847273969981, 'ACC-toaster': 90.86479902557856, 'ACC-sink': 83.64517275674159, 'ACC-refrigerator': 90.51039819170542, 'ACC-book': 73.91457230114035, 'ACC-clock': 81.62729414576265, 'ACC-vase': 81.92559790710445, 'ACC-scissors': 91.36021816378812, 'ACC-teddy bear': 92.6191360410097, 'ACC-hair drier': 32.64879173426693, 'ACC-toothbrush': 84.66122307157748, 'ACC-banner': 80.64755745870839, 'ACC-blanket': 34.78147080585753, 'ACC-bridge': 57.0543995002184, 'ACC-cardboard': 53.86453395375891, 'ACC-counter': 44.45969753844249, 'ACC-curtain': 83.44789257783832, 'ACC-door-stuff': 68.65819615310687, 'ACC-floor-wood': 82.11329656540315, 'ACC-flower': 71.91101783208269, 'ACC-fruit': 68.79727438235078, 'ACC-gravel': 36.734286925427746, 'ACC-house': 29.242920323349196, 'ACC-light': 60.29241473031428, 'ACC-mirror-stuff': 68.82304058122942, 'ACC-net': 65.72403908188174, 'ACC-pillow': 37.49752503346155, 'ACC-platform': 50.99658263890873, 'ACC-playingfield': 92.30890936611543, 'ACC-railroad': 79.12801438156475, 'ACC-river': 74.06225398656021, 'ACC-road': 88.5104484927612, 'ACC-roof': 17.792459672326192, 'ACC-sand': 72.93385274690284, 'ACC-sea': 92.3210240371873, 'ACC-shelf': 54.21099101664232, 'ACC-snow': 95.27867431359832, 'ACC-stairs': 55.37141855318399, 'ACC-tent': 13.987802735967813, 'ACC-towel': 56.36742253971177, 'ACC-wall-brick': 69.05819639540708, 'ACC-wall-stone': 35.75532085420764, 'ACC-wall-tile': 83.25322814085571, 'ACC-wall-wood': 61.03900987275023, 'ACC-water-other': 27.387081366151715, 'ACC-window-blind': 66.15951996637783, 'ACC-window-other': 72.03009847170814, 'ACC-tree-merged': 90.27021745518107, 'ACC-fence-merged': 74.25693905575427, 'ACC-ceiling-merged': 83.49460596935255, 'ACC-sky-other-merged': 97.08942976784726, 'ACC-cabinet-merged': 76.88072663442304, 'ACC-table-merged': 60.16234477072947, 'ACC-floor-other-merged': 64.07857290527697, 'ACC-pavement-merged': 67.62841635410778, 'ACC-mountain-merged': 71.55999816387498, 'ACC-grass-merged': 83.3975393221999, 'ACC-dirt-merged': 66.07730429275804, 'ACC-paper-merged': 49.233580855086856, 'ACC-food-other-merged': 59.66042799924157, 'ACC-building-other-merged': 74.70708034816286, 'ACC-rock-merged': 83.25295260405733, 'ACC-wall-other-merged': 82.3083533654332, 'ACC-rug-merged': 82.77646657045082})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2826 s/iter. Inference: 0.1694 s/iter. Eval: 0.0000 s/iter. Total: 0.4521 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.2964 s/iter. Inference: 0.4388 s/iter. Eval: 0.0000 s/iter. Total: 0.7353 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/25. Dataloading: 0.2973 s/iter. Inference: 0.4776 s/iter. Eval: 0.0000 s/iter. Total: 0.7750 s/iter. ETA=0:00:00 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4679543459174715, 'noc@0.8': 2.650570676031607, 'noc@0.85': 3.206028680128768, 'noc@0.9': 4.047410008779631, 'miou@iter1': 0.8641007071459611} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1381 s/iter. Eval: 0.0010 s/iter. Total: 0.1406 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 74.77652740478516, 'precision@0.6': 71.93936920166016, 'precision@0.7': 67.23668670654297, 'precision@0.8': 57.559268951416016, 'precision@0.9': 31.325302124023438, 'cIoU': 60.5244140625, 'mIoU': 65.86559295654297} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.081085153376684, 'SQ': 82.79394060397722, 'RQ': 65.6785523947862, 'PQ_th': 61.43924985460581, 'SQ_th': 83.78011404645058, 'RQ_th': 72.83710503030115, 'PQ_st': 45.48385541567231, 'SQ_st': 81.30537691722506, 'RQ_st': 54.87318992608438}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-tv': 88.26884217943079, 'ACC-laptop': 88.48766704015854, 'ACC-mouse': 91.729345006836, 'ACC-remote': 79.08912681542147, 'ACC-keyboard': 76.23162363368863, 'ACC-cell phone': 82.50001532366649, 'ACC-microwave': 78.25764164532241, 'ACC-oven': 87.77847273969981, 'ACC-toaster': 90.86479902557856, 'ACC-sink': 83.64517275674159, 'ACC-refrigerator': 90.51039819170542, 'ACC-book': 73.91457230114035, 'ACC-clock': 81.62729414576265, 'ACC-vase': 81.92559790710445, 'ACC-scissors': 91.36021816378812, 'ACC-teddy bear': 92.6191360410097, 'ACC-hair drier': 32.64879173426693, 'ACC-toothbrush': 84.66122307157748, 'ACC-banner': 80.64755745870839, 'ACC-blanket': 34.78147080585753, 'ACC-bridge': 57.0543995002184, 'ACC-cardboard': 53.86453395375891, 'ACC-counter': 44.45969753844249, 'ACC-curtain': 83.44789257783832, 'ACC-door-stuff': 68.65819615310687, 'ACC-floor-wood': 82.11329656540315, 'ACC-flower': 71.91101783208269, 'ACC-fruit': 68.79727438235078, 'ACC-gravel': 36.734286925427746, 'ACC-house': 29.242920323349196, 'ACC-light': 60.29241473031428, 'ACC-mirror-stuff': 68.82304058122942, 'ACC-net': 65.72403908188174, 'ACC-pillow': 37.49752503346155, 'ACC-platform': 50.99658263890873, 'ACC-playingfield': 92.30890936611543, 'ACC-railroad': 79.12801438156475, 'ACC-river': 74.06225398656021, 'ACC-road': 88.5104484927612, 'ACC-roof': 17.792459672326192, 'ACC-sand': 72.93385274690284, 'ACC-sea': 92.3210240371873, 'ACC-shelf': 54.21099101664232, 'ACC-snow': 95.27867431359832, 'ACC-stairs': 55.37141855318399, 'ACC-tent': 13.987802735967813, 'ACC-towel': 56.36742253971177, 'ACC-wall-brick': 69.05819639540708, 'ACC-wall-stone': 35.75532085420764, 'ACC-wall-tile': 83.25322814085571, 'ACC-wall-wood': 61.03900987275023, 'ACC-water-other': 27.387081366151715, 'ACC-window-blind': 66.15951996637783, 'ACC-window-other': 72.03009847170814, 'ACC-tree-merged': 90.27021745518107, 'ACC-fence-merged': 74.25693905575427, 'ACC-ceiling-merged': 83.49460596935255, 'ACC-sky-other-merged': 97.08942976784726, 'ACC-cabinet-merged': 76.88072663442304, 'ACC-table-merged': 60.16234477072947, 'ACC-floor-other-merged': 64.07857290527697, 'ACC-pavement-merged': 67.62841635410778, 'ACC-mountain-merged': 71.55999816387498, 'ACC-grass-merged': 83.3975393221999, 'ACC-dirt-merged': 66.07730429275804, 'ACC-paper-merged': 49.233580855086856, 'ACC-food-other-merged': 59.66042799924157, 'ACC-building-other-merged': 74.70708034816286, 'ACC-rock-merged': 83.25295260405733, 'ACC-wall-other-merged': 82.3083533654332, 'ACC-rug-merged': 82.77646657045082})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4679543459174715, 'noc@0.8': 2.650570676031607, 'noc@0.85': 3.206028680128768, 'noc@0.9': 4.047410008779631, 'miou@iter1': 0.8641007071459611}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 74.77652740478516, 'precision@0.6': 71.93936920166016, 'precision@0.7': 67.23668670654297, 'precision@0.8': 57.559268951416016, 'precision@0.9': 31.325302124023438, 'cIoU': 60.5244140625, 'mIoU': 65.86559295654297}}} INFO:trainer.default_trainer:This epoch takes 0:59:19.580175 INFO:trainer.default_trainer:PROGRESS: 8.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 4 training. INFO:trainer.default_trainer:epochs[ 4] optim steps[7400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23041/0.80659, loss_mask_bce_0: 0.06069/0.30374, loss_mask_dice_0: 0.98064/1.03015, loss_spatial_bce_0: 0.01597/0.09456, loss_spatial_dice_0: 0.33302/0.19907, loss_spatial_ce_0: 0.08261/0.09477, loss_grounding_bce_0: 0.02428/0.08083, loss_grounding_dice_0: 0.55558/0.15075, loss_grounding_ce_0: 0.02162/0.25396, loss_mask_ce_1: 1.03585/0.80938, loss_mask_bce_1: 0.07484/0.30429, loss_mask_dice_1: 1.06988/1.03643, loss_spatial_bce_1: 0.01609/0.09532, loss_spatial_dice_1: 0.31439/0.20185, loss_spatial_ce_1: 0.14822/0.09941, loss_grounding_bce_1: 0.03297/0.08090, loss_grounding_dice_1: 0.60261/0.15189, loss_grounding_ce_1: 0.01177/0.25860, loss_mask_ce_2: 0.98043/0.81538, loss_mask_bce_2: 0.05789/0.30434, loss_mask_dice_2: 0.98903/1.03910, loss_spatial_bce_2: 0.01533/0.09465, loss_spatial_dice_2: 0.33380/0.20190, loss_spatial_ce_2: 0.12077/0.10474, loss_grounding_bce_2: 0.02907/0.08072, loss_grounding_dice_2: 0.59578/0.15190, loss_grounding_ce_2: 0.01298/0.25689, loss_mask_ce_3: 1.18757/0.81270, loss_mask_bce_3: 0.06449/0.30559, loss_mask_dice_3: 1.08004/1.03304, loss_spatial_bce_3: 0.01512/0.09593, loss_spatial_dice_3: 0.32322/0.20193, loss_spatial_ce_3: 0.11788/0.11101, loss_grounding_bce_3: 0.03129/0.08138, loss_grounding_dice_3: 0.50302/0.15179, loss_grounding_ce_3: 0.01138/0.25661, loss_mask_ce_4: 1.08065/0.81714, loss_mask_bce_4: 0.06127/0.30811, loss_mask_dice_4: 1.00612/1.05131, loss_spatial_bce_4: 0.01321/0.09812, loss_spatial_dice_4: 0.28622/0.20930, loss_spatial_ce_4: 0.16316/0.12133, loss_grounding_bce_4: 0.02484/0.08198, loss_grounding_dice_4: 0.57229/0.15416, loss_grounding_ce_4: 0.01467/0.26484, loss_mask_ce_5: 1.23437/0.83836, loss_mask_bce_5: 0.06395/0.30935, loss_mask_dice_5: 1.02034/1.06127, loss_spatial_bce_5: 0.01908/0.09955, loss_spatial_dice_5: 0.35186/0.21147, loss_spatial_ce_5: 0.10553/0.13204, loss_grounding_bce_5: 0.02151/0.08220, loss_grounding_dice_5: 0.54954/0.15491, loss_grounding_ce_5: 0.02496/0.28208, loss_mask_ce_6: 1.24762/0.85934, loss_mask_bce_6: 0.06213/0.31048, loss_mask_dice_6: 1.04834/1.06626, loss_spatial_bce_6: 0.02029/0.10427, loss_spatial_dice_6: 0.37138/0.21402, loss_spatial_ce_6: 0.12733/0.14895, loss_grounding_bce_6: 0.02425/0.08354, loss_grounding_dice_6: 0.56156/0.15523, loss_grounding_ce_6: 0.01163/0.30089, loss_mask_ce_7: 1.18455/0.92561, loss_mask_bce_7: 0.06125/0.31769, loss_mask_dice_7: 0.96185/1.11184, loss_spatial_bce_7: 0.01908/0.11591, loss_spatial_dice_7: 0.42951/0.23998, loss_spatial_ce_7: 0.45796/0.19938, loss_grounding_bce_7: 0.02826/0.08535, loss_grounding_dice_7: 0.49641/0.16087, loss_grounding_ce_7: 0.00397/0.35304, loss_mask_ce_8: 1.73729/1.07579, loss_mask_bce_8: 0.05617/0.33573, loss_mask_dice_8: 1.13160/1.19210, loss_spatial_bce_8: 0.04691/0.13886, loss_spatial_dice_8: 0.53935/0.28538, loss_spatial_ce_8: 0.28391/0.25340, loss_grounding_bce_8: 0.02288/0.08903, loss_grounding_dice_8: 0.56608/0.16894, loss_grounding_ce_8: 0.02695/0.46040, loss_mask_ce_9: 2.75607/3.54459, loss_mask_bce_9: 0.03770/0.36337, loss_mask_dice_9: 1.39532/1.78377, loss_spatial_bce_9: 0.10563/0.36772, loss_spatial_dice_9: 0.76109/0.80058, loss_spatial_ce_9: 1.89453/1.44301, loss_grounding_bce_9: 0.01988/0.10090, loss_grounding_dice_9: 0.58560/0.24549, loss_grounding_ce_9: 0.01242/0.75756] items per batch[64] items per second[0.16] total items[473600] mini batches[ 7400] memory[4929] epoch remaining[0:55:03] INFO:trainer.default_trainer:epochs[ 4] optim steps[7500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78345/0.80553, loss_mask_bce_0: 0.19613/0.30356, loss_mask_dice_0: 0.34795/1.03204, loss_spatial_bce_0: 0.04881/0.09429, loss_spatial_dice_0: 0.07617/0.19881, loss_spatial_ce_0: 0.05087/0.09432, loss_grounding_bce_0: 0.04574/0.08082, loss_grounding_dice_0: 0.12685/0.15088, loss_grounding_ce_0: 0.22419/0.25485, loss_mask_ce_1: 0.84554/0.80830, loss_mask_bce_1: 0.20061/0.30408, loss_mask_dice_1: 0.37005/1.03785, loss_spatial_bce_1: 0.05327/0.09507, loss_spatial_dice_1: 0.09749/0.20160, loss_spatial_ce_1: 0.03033/0.09902, loss_grounding_bce_1: 0.04425/0.08087, loss_grounding_dice_1: 0.10845/0.15190, loss_grounding_ce_1: 0.21772/0.25929, loss_mask_ce_2: 0.83386/0.81449, loss_mask_bce_2: 0.19385/0.30410, loss_mask_dice_2: 0.39327/1.04039, loss_spatial_bce_2: 0.05333/0.09439, loss_spatial_dice_2: 0.08307/0.20161, loss_spatial_ce_2: 0.02053/0.10434, loss_grounding_bce_2: 0.04175/0.08070, loss_grounding_dice_2: 0.10854/0.15195, loss_grounding_ce_2: 0.22521/0.25763, loss_mask_ce_3: 0.82150/0.81177, loss_mask_bce_3: 0.19770/0.30535, loss_mask_dice_3: 0.40154/1.03418, loss_spatial_bce_3: 0.05171/0.09564, loss_spatial_dice_3: 0.09087/0.20164, loss_spatial_ce_3: 0.01829/0.11076, loss_grounding_bce_3: 0.04163/0.08134, loss_grounding_dice_3: 0.10104/0.15184, loss_grounding_ce_3: 0.20623/0.25746, loss_mask_ce_4: 1.25525/0.81607, loss_mask_bce_4: 0.19202/0.30789, loss_mask_dice_4: 0.50067/1.05270, loss_spatial_bce_4: 0.05721/0.09784, loss_spatial_dice_4: 0.08982/0.20903, loss_spatial_ce_4: 0.04020/0.12092, loss_grounding_bce_4: 0.04506/0.08192, loss_grounding_dice_4: 0.14018/0.15412, loss_grounding_ce_4: 0.24155/0.26593, loss_mask_ce_5: 0.90632/0.83736, loss_mask_bce_5: 0.19752/0.30911, loss_mask_dice_5: 0.39454/1.06245, loss_spatial_bce_5: 0.05979/0.09930, loss_spatial_dice_5: 0.12090/0.21123, loss_spatial_ce_5: 0.11109/0.13150, loss_grounding_bce_5: 0.04547/0.08216, loss_grounding_dice_5: 0.10530/0.15479, loss_grounding_ce_5: 0.25720/0.28290, loss_mask_ce_6: 1.22572/0.85866, loss_mask_bce_6: 0.19982/0.31019, loss_mask_dice_6: 0.41388/1.06765, loss_spatial_bce_6: 0.06323/0.10401, loss_spatial_dice_6: 0.13075/0.21378, loss_spatial_ce_6: 0.01043/0.14839, loss_grounding_bce_6: 0.04333/0.08346, loss_grounding_dice_6: 0.11134/0.15518, loss_grounding_ce_6: 0.27422/0.30181, loss_mask_ce_7: 0.83902/0.92441, loss_mask_bce_7: 0.21598/0.31748, loss_mask_dice_7: 0.42295/1.11315, loss_spatial_bce_7: 0.06283/0.11553, loss_spatial_dice_7: 0.08428/0.23972, loss_spatial_ce_7: 0.06057/0.19892, loss_grounding_bce_7: 0.04683/0.08536, loss_grounding_dice_7: 0.11008/0.16080, loss_grounding_ce_7: 0.36952/0.35364, loss_mask_ce_8: 1.01892/1.07505, loss_mask_bce_8: 0.21246/0.33545, loss_mask_dice_8: 0.41729/1.19373, loss_spatial_bce_8: 0.05905/0.13850, loss_spatial_dice_8: 0.11783/0.28506, loss_spatial_ce_8: 0.15582/0.25273, loss_grounding_bce_8: 0.04792/0.08901, loss_grounding_dice_8: 0.11773/0.16894, loss_grounding_ce_8: 0.35669/0.46079, loss_mask_ce_9: 2.48232/3.54354, loss_mask_bce_9: 0.22325/0.36303, loss_mask_dice_9: 0.76130/1.78498, loss_spatial_bce_9: 0.38676/0.36734, loss_spatial_dice_9: 0.81607/0.80055, loss_spatial_ce_9: 1.37456/1.44347, loss_grounding_bce_9: 0.05501/0.10082, loss_grounding_dice_9: 0.18856/0.24546, loss_grounding_ce_9: 0.45125/0.75645] items per batch[64] items per second[0.35] total items[480000] mini batches[ 7500] memory[4929] epoch remaining[0:50:28] INFO:trainer.default_trainer:epochs[ 4] optim steps[7600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04500/0.80320, loss_mask_bce_0: 0.05954/0.30326, loss_mask_dice_0: 0.41171/1.03046, loss_spatial_bce_0: 0.01601/0.09418, loss_spatial_dice_0: 0.13954/0.19849, loss_spatial_ce_0: 0.02949/0.09377, loss_grounding_bce_0: 0.00393/0.08100, loss_grounding_dice_0: 0.04853/0.15084, loss_grounding_ce_0: 0.00168/0.25357, loss_mask_ce_1: 1.01128/0.80609, loss_mask_bce_1: 0.05085/0.30369, loss_mask_dice_1: 0.42963/1.03642, loss_spatial_bce_1: 0.01491/0.09493, loss_spatial_dice_1: 0.13585/0.20130, loss_spatial_ce_1: 0.06227/0.09847, loss_grounding_bce_1: 0.00351/0.08103, loss_grounding_dice_1: 0.04634/0.15186, loss_grounding_ce_1: 0.00503/0.25798, loss_mask_ce_2: 1.06365/0.81249, loss_mask_bce_2: 0.05133/0.30372, loss_mask_dice_2: 0.35234/1.03866, loss_spatial_bce_2: 0.01443/0.09427, loss_spatial_dice_2: 0.12925/0.20131, loss_spatial_ce_2: 0.06654/0.10373, loss_grounding_bce_2: 0.00382/0.08089, loss_grounding_dice_2: 0.04437/0.15189, loss_grounding_ce_2: 0.00553/0.25640, loss_mask_ce_3: 0.96803/0.80960, loss_mask_bce_3: 0.04981/0.30501, loss_mask_dice_3: 0.41473/1.03304, loss_spatial_bce_3: 0.01603/0.09549, loss_spatial_dice_3: 0.12255/0.20133, loss_spatial_ce_3: 0.08214/0.11014, loss_grounding_bce_3: 0.00522/0.08152, loss_grounding_dice_3: 0.06227/0.15191, loss_grounding_ce_3: 0.00361/0.25630, loss_mask_ce_4: 1.07600/0.81374, loss_mask_bce_4: 0.04806/0.30756, loss_mask_dice_4: 0.44026/1.05135, loss_spatial_bce_4: 0.01489/0.09768, loss_spatial_dice_4: 0.12208/0.20871, loss_spatial_ce_4: 0.20985/0.12057, loss_grounding_bce_4: 0.00658/0.08210, loss_grounding_dice_4: 0.08371/0.15412, loss_grounding_ce_4: 0.00319/0.26462, loss_mask_ce_5: 1.07210/0.83489, loss_mask_bce_5: 0.05155/0.30882, loss_mask_dice_5: 0.40441/1.06074, loss_spatial_bce_5: 0.01524/0.09912, loss_spatial_dice_5: 0.12635/0.21085, loss_spatial_ce_5: 0.10038/0.13094, loss_grounding_bce_5: 0.00610/0.08232, loss_grounding_dice_5: 0.06885/0.15471, loss_grounding_ce_5: 0.01024/0.28205, loss_mask_ce_6: 1.06273/0.85655, loss_mask_bce_6: 0.05300/0.30981, loss_mask_dice_6: 0.40484/1.06598, loss_spatial_bce_6: 0.01709/0.10383, loss_spatial_dice_6: 0.12129/0.21339, loss_spatial_ce_6: 0.13098/0.14784, loss_grounding_bce_6: 0.00538/0.08365, loss_grounding_dice_6: 0.06375/0.15519, loss_grounding_ce_6: 0.01074/0.30094, loss_mask_ce_7: 1.18018/0.92197, loss_mask_bce_7: 0.05908/0.31708, loss_mask_dice_7: 0.41234/1.11119, loss_spatial_bce_7: 0.02267/0.11530, loss_spatial_dice_7: 0.13484/0.23932, loss_spatial_ce_7: 0.49647/0.19834, loss_grounding_bce_7: 0.00520/0.08551, loss_grounding_dice_7: 0.05621/0.16074, loss_grounding_ce_7: 0.23922/0.35252, loss_mask_ce_8: 1.16828/1.07181, loss_mask_bce_8: 0.07178/0.33505, loss_mask_dice_8: 0.41917/1.19176, loss_spatial_bce_8: 0.04130/0.13814, loss_spatial_dice_8: 0.23262/0.28449, loss_spatial_ce_8: 0.07921/0.25211, loss_grounding_bce_8: 0.00408/0.08912, loss_grounding_dice_8: 0.05328/0.16884, loss_grounding_ce_8: 0.85440/0.45979, loss_mask_ce_9: 2.11101/3.53913, loss_mask_bce_9: 0.05548/0.36240, loss_mask_dice_9: 0.53596/1.78131, loss_spatial_bce_9: 0.19006/0.36757, loss_spatial_dice_9: 0.86921/0.80029, loss_spatial_ce_9: 1.62919/1.44216, loss_grounding_bce_9: 0.00425/0.10087, loss_grounding_dice_9: 0.07693/0.24522, loss_grounding_ce_9: 2.85068/0.75557] items per batch[64] items per second[0.36] total items[486400] mini batches[ 7600] memory[4929] epoch remaining[0:46:57] INFO:trainer.default_trainer:epochs[ 4] optim steps[7700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.99549/0.80374, loss_mask_bce_0: 0.27495/0.30325, loss_mask_dice_0: 6.30416/1.03147, loss_spatial_bce_0: 0.00630/0.09404, loss_spatial_dice_0: 0.45270/0.19841, loss_spatial_ce_0: 0.14781/0.09355, loss_grounding_bce_0: 0.00963/0.08110, loss_grounding_dice_0: 0.38105/0.15088, loss_grounding_ce_0: 0.23678/0.25321, loss_mask_ce_1: 2.13886/0.80646, loss_mask_bce_1: 0.23223/0.30374, loss_mask_dice_1: 6.11093/1.03707, loss_spatial_bce_1: 0.00544/0.09476, loss_spatial_dice_1: 0.46084/0.20119, loss_spatial_ce_1: 0.07608/0.09812, loss_grounding_bce_1: 0.00799/0.08114, loss_grounding_dice_1: 0.38044/0.15187, loss_grounding_ce_1: 0.23160/0.25780, loss_mask_ce_2: 2.37360/0.81276, loss_mask_bce_2: 0.21964/0.30382, loss_mask_dice_2: 6.11021/1.03997, loss_spatial_bce_2: 0.00640/0.09412, loss_spatial_dice_2: 0.41179/0.20118, loss_spatial_ce_2: 0.11961/0.10350, loss_grounding_bce_2: 0.00943/0.08101, loss_grounding_dice_2: 0.36118/0.15189, loss_grounding_ce_2: 0.26042/0.25635, loss_mask_ce_3: 2.36106/0.81044, loss_mask_bce_3: 0.24563/0.30504, loss_mask_dice_3: 6.31986/1.03390, loss_spatial_bce_3: 0.00777/0.09535, loss_spatial_dice_3: 0.41001/0.20115, loss_spatial_ce_3: 0.10345/0.10985, loss_grounding_bce_3: 0.01077/0.08162, loss_grounding_dice_3: 0.39531/0.15203, loss_grounding_ce_3: 0.26068/0.25606, loss_mask_ce_4: 2.21253/0.81427, loss_mask_bce_4: 0.24187/0.30763, loss_mask_dice_4: 6.60811/1.05238, loss_spatial_bce_4: 0.00991/0.09751, loss_spatial_dice_4: 0.55604/0.20856, loss_spatial_ce_4: 0.06964/0.12030, loss_grounding_bce_4: 0.02155/0.08221, loss_grounding_dice_4: 0.42892/0.15415, loss_grounding_ce_4: 0.18907/0.26443, loss_mask_ce_5: 2.08553/0.83534, loss_mask_bce_5: 0.22480/0.30894, loss_mask_dice_5: 5.95723/1.06128, loss_spatial_bce_5: 0.00928/0.09895, loss_spatial_dice_5: 0.49392/0.21072, loss_spatial_ce_5: 0.05571/0.13058, loss_grounding_bce_5: 0.01097/0.08239, loss_grounding_dice_5: 0.39039/0.15482, loss_grounding_ce_5: 0.28289/0.28192, loss_mask_ce_6: 2.18384/0.85703, loss_mask_bce_6: 0.22788/0.30991, loss_mask_dice_6: 5.93577/1.06671, loss_spatial_bce_6: 0.01101/0.10374, loss_spatial_dice_6: 0.49860/0.21326, loss_spatial_ce_6: 0.08504/0.14750, loss_grounding_bce_6: 0.00977/0.08372, loss_grounding_dice_6: 0.37750/0.15527, loss_grounding_ce_6: 0.29281/0.30070, loss_mask_ce_7: 2.07549/0.92171, loss_mask_bce_7: 0.24021/0.31727, loss_mask_dice_7: 6.31794/1.11221, loss_spatial_bce_7: 0.01132/0.11515, loss_spatial_dice_7: 0.57649/0.23916, loss_spatial_ce_7: 0.16148/0.19825, loss_grounding_bce_7: 0.01118/0.08552, loss_grounding_dice_7: 0.41648/0.16070, loss_grounding_ce_7: 0.37404/0.35213, loss_mask_ce_8: 2.96166/1.07150, loss_mask_bce_8: 0.26207/0.33528, loss_mask_dice_8: 5.94215/1.19239, loss_spatial_bce_8: 0.01517/0.13802, loss_spatial_dice_8: 0.71516/0.28427, loss_spatial_ce_8: 0.32737/0.25170, loss_grounding_bce_8: 0.02638/0.08920, loss_grounding_dice_8: 0.45190/0.16882, loss_grounding_ce_8: 0.37426/0.45988, loss_mask_ce_9: 6.52454/3.53863, loss_mask_bce_9: 0.19520/0.36232, loss_mask_dice_9: 6.84924/1.78194, loss_spatial_bce_9: 0.04110/0.36741, loss_spatial_dice_9: 0.98245/0.80051, loss_spatial_ce_9: 1.27767/1.44208, loss_grounding_bce_9: 0.01108/0.10101, loss_grounding_dice_9: 0.60793/0.24498, loss_grounding_ce_9: 0.37728/0.75484] items per batch[64] items per second[0.36] total items[492800] mini batches[ 7700] memory[4929] epoch remaining[0:43:36] INFO:trainer.default_trainer:epochs[ 4] optim steps[7800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77951/0.80368, loss_mask_bce_0: 0.01860/0.30333, loss_mask_dice_0: 0.49723/1.03266, loss_spatial_bce_0: 0.01378/0.09386, loss_spatial_dice_0: 0.20936/0.19822, loss_spatial_ce_0: 0.00303/0.09351, loss_grounding_bce_0: 0.00394/0.08104, loss_grounding_dice_0: 0.07849/0.15113, loss_grounding_ce_0: 0.02243/0.25450, loss_mask_ce_1: 0.90580/0.80675, loss_mask_bce_1: 0.01972/0.30382, loss_mask_dice_1: 0.53869/1.03817, loss_spatial_bce_1: 0.01420/0.09459, loss_spatial_dice_1: 0.26844/0.20098, loss_spatial_ce_1: 0.00318/0.09798, loss_grounding_bce_1: 0.00267/0.08106, loss_grounding_dice_1: 0.06700/0.15213, loss_grounding_ce_1: 0.03206/0.25899, loss_mask_ce_2: 0.83486/0.81298, loss_mask_bce_2: 0.02788/0.30396, loss_mask_dice_2: 0.47486/1.04155, loss_spatial_bce_2: 0.01203/0.09393, loss_spatial_dice_2: 0.22845/0.20101, loss_spatial_ce_2: 0.00340/0.10327, loss_grounding_bce_2: 0.00353/0.08094, loss_grounding_dice_2: 0.07474/0.15216, loss_grounding_ce_2: 0.03873/0.25753, loss_mask_ce_3: 0.31758/0.81090, loss_mask_bce_3: 0.02118/0.30514, loss_mask_dice_3: 0.72775/1.03523, loss_spatial_bce_3: 0.01176/0.09519, loss_spatial_dice_3: 0.23597/0.20098, loss_spatial_ce_3: 0.00948/0.10967, loss_grounding_bce_3: 0.00290/0.08152, loss_grounding_dice_3: 0.07143/0.15228, loss_grounding_ce_3: 0.04264/0.25726, loss_mask_ce_4: 0.86745/0.81455, loss_mask_bce_4: 0.02025/0.30775, loss_mask_dice_4: 0.48567/1.05344, loss_spatial_bce_4: 0.01682/0.09736, loss_spatial_dice_4: 0.25192/0.20841, loss_spatial_ce_4: 0.00635/0.12019, loss_grounding_bce_4: 0.00496/0.08211, loss_grounding_dice_4: 0.10416/0.15437, loss_grounding_ce_4: 0.02160/0.26562, loss_mask_ce_5: 0.72389/0.83552, loss_mask_bce_5: 0.02728/0.30909, loss_mask_dice_5: 0.57754/1.06286, loss_spatial_bce_5: 0.01503/0.09882, loss_spatial_dice_5: 0.24072/0.21054, loss_spatial_ce_5: 0.03488/0.13055, loss_grounding_bce_5: 0.00373/0.08229, loss_grounding_dice_5: 0.09344/0.15504, loss_grounding_ce_5: 0.05504/0.28364, loss_mask_ce_6: 0.72431/0.85762, loss_mask_bce_6: 0.01909/0.31009, loss_mask_dice_6: 0.48302/1.06791, loss_spatial_bce_6: 0.01654/0.10364, loss_spatial_dice_6: 0.24276/0.21313, loss_spatial_ce_6: 0.05265/0.14752, loss_grounding_bce_6: 0.00277/0.08362, loss_grounding_dice_6: 0.06389/0.15556, loss_grounding_ce_6: 0.05626/0.30236, loss_mask_ce_7: 1.00300/0.92244, loss_mask_bce_7: 0.02256/0.31748, loss_mask_dice_7: 0.55228/1.11393, loss_spatial_bce_7: 0.01725/0.11501, loss_spatial_dice_7: 0.25483/0.23892, loss_spatial_ce_7: 0.00952/0.19799, loss_grounding_bce_7: 0.00211/0.08537, loss_grounding_dice_7: 0.05924/0.16091, loss_grounding_ce_7: 0.05617/0.35425, loss_mask_ce_8: 0.63902/1.07163, loss_mask_bce_8: 0.02345/0.33576, loss_mask_dice_8: 0.58267/1.19423, loss_spatial_bce_8: 0.01961/0.13801, loss_spatial_dice_8: 0.25214/0.28404, loss_spatial_ce_8: 0.05523/0.25144, loss_grounding_bce_8: 0.00260/0.08922, loss_grounding_dice_8: 0.05043/0.16913, loss_grounding_ce_8: 0.07581/0.46200, loss_mask_ce_9: 1.58540/3.54103, loss_mask_bce_9: 0.01789/0.36276, loss_mask_dice_9: 0.48904/1.78534, loss_spatial_bce_9: 0.25413/0.36708, loss_spatial_dice_9: 0.89457/0.80066, loss_spatial_ce_9: 1.08325/1.44096, loss_grounding_bce_9: 0.00396/0.10112, loss_grounding_dice_9: 0.12230/0.24547, loss_grounding_ce_9: 0.16209/0.75576] items per batch[64] items per second[0.34] total items[499200] mini batches[ 7800] memory[4929] epoch remaining[0:40:45] INFO:trainer.default_trainer:epochs[ 4] optim steps[7900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.03885/0.80414, loss_mask_bce_0: 0.04943/0.30355, loss_mask_dice_0: 1.72781/1.03433, loss_spatial_bce_0: 0.00841/0.09371, loss_spatial_dice_0: 0.36780/0.19822, loss_spatial_ce_0: 0.38524/0.09309, loss_grounding_bce_0: 0.00696/0.08112, loss_grounding_dice_0: 0.78205/0.15130, loss_grounding_ce_0: 0.78354/0.25536, loss_mask_ce_1: 1.42472/0.80749, loss_mask_bce_1: 0.05183/0.30407, loss_mask_dice_1: 1.87562/1.04002, loss_spatial_bce_1: 0.00879/0.09445, loss_spatial_dice_1: 0.34249/0.20098, loss_spatial_ce_1: 0.05085/0.09759, loss_grounding_bce_1: 0.00309/0.08114, loss_grounding_dice_1: 0.67252/0.15229, loss_grounding_ce_1: 0.41571/0.25963, loss_mask_ce_2: 1.86816/0.81337, loss_mask_bce_2: 0.04219/0.30421, loss_mask_dice_2: 1.50337/1.04288, loss_spatial_bce_2: 0.00974/0.09377, loss_spatial_dice_2: 0.33951/0.20096, loss_spatial_ce_2: 0.05264/0.10294, loss_grounding_bce_2: 0.01445/0.08104, loss_grounding_dice_2: 0.81974/0.15240, loss_grounding_ce_2: 0.52784/0.25858, loss_mask_ce_3: 1.86928/0.81117, loss_mask_bce_3: 0.05409/0.30545, loss_mask_dice_3: 1.83670/1.03681, loss_spatial_bce_3: 0.00872/0.09506, loss_spatial_dice_3: 0.34581/0.20095, loss_spatial_ce_3: 0.06663/0.10925, loss_grounding_bce_3: 0.00653/0.08159, loss_grounding_dice_3: 0.71395/0.15244, loss_grounding_ce_3: 0.45909/0.25831, loss_mask_ce_4: 1.95162/0.81526, loss_mask_bce_4: 0.04670/0.30796, loss_mask_dice_4: 1.89067/1.05481, loss_spatial_bce_4: 0.01267/0.09718, loss_spatial_dice_4: 0.41962/0.20834, loss_spatial_ce_4: 0.16990/0.11975, loss_grounding_bce_4: 0.00237/0.08218, loss_grounding_dice_4: 0.46451/0.15453, loss_grounding_ce_4: 0.65616/0.26743, loss_mask_ce_5: 1.96481/0.83605, loss_mask_bce_5: 0.04893/0.30944, loss_mask_dice_5: 1.53280/1.06466, loss_spatial_bce_5: 0.01166/0.09867, loss_spatial_dice_5: 0.37904/0.21048, loss_spatial_ce_5: 0.08827/0.13024, loss_grounding_bce_5: 0.00202/0.08244, loss_grounding_dice_5: 0.27889/0.15519, loss_grounding_ce_5: 0.74730/0.28508, loss_mask_ce_6: 1.97497/0.85832, loss_mask_bce_6: 0.04346/0.31039, loss_mask_dice_6: 1.76995/1.06958, loss_spatial_bce_6: 0.01146/0.10346, loss_spatial_dice_6: 0.39436/0.21307, loss_spatial_ce_6: 0.12288/0.14733, loss_grounding_bce_6: 0.00378/0.08379, loss_grounding_dice_6: 0.65600/0.15586, loss_grounding_ce_6: 0.46229/0.30356, loss_mask_ce_7: 1.63440/0.92286, loss_mask_bce_7: 0.04888/0.31796, loss_mask_dice_7: 2.12781/1.11603, loss_spatial_bce_7: 0.01442/0.11479, loss_spatial_dice_7: 0.45493/0.23898, loss_spatial_ce_7: 0.38255/0.19790, loss_grounding_bce_7: 0.00510/0.08558, loss_grounding_dice_7: 0.64843/0.16124, loss_grounding_ce_7: 1.08728/0.35605, loss_mask_ce_8: 2.26003/1.07249, loss_mask_bce_8: 0.04812/0.33597, loss_mask_dice_8: 1.89640/1.19639, loss_spatial_bce_8: 0.02497/0.13789, loss_spatial_dice_8: 0.59680/0.28398, loss_spatial_ce_8: 0.51925/0.25121, loss_grounding_bce_8: 0.00695/0.08933, loss_grounding_dice_8: 0.69914/0.16951, loss_grounding_ce_8: 0.85376/0.46271, loss_mask_ce_9: 3.93124/3.54127, loss_mask_bce_9: 0.04123/0.36318, loss_mask_dice_9: 2.73233/1.78786, loss_spatial_bce_9: 0.01671/0.36679, loss_spatial_dice_9: 0.87789/0.80062, loss_spatial_ce_9: 1.44509/1.43969, loss_grounding_bce_9: 0.00350/0.10128, loss_grounding_dice_9: 0.85039/0.24601, loss_grounding_ce_9: 0.36703/0.75525] items per batch[64] items per second[0.35] total items[505600] mini batches[ 7900] memory[4929] epoch remaining[0:37:37] INFO:trainer.default_trainer:epochs[ 4] optim steps[8000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.76690/0.80414, loss_mask_bce_0: 0.50760/0.30383, loss_mask_dice_0: 0.41330/1.03298, loss_spatial_bce_0: 0.19560/0.09375, loss_spatial_dice_0: 0.15503/0.19809, loss_spatial_ce_0: 0.02048/0.09267, loss_grounding_bce_0: 0.24497/0.08126, loss_grounding_dice_0: 0.18730/0.15133, loss_grounding_ce_0: 0.26519/0.25565, loss_mask_ce_1: 1.74352/0.80700, loss_mask_bce_1: 0.50136/0.30438, loss_mask_dice_1: 0.41482/1.03860, loss_spatial_bce_1: 0.18259/0.09448, loss_spatial_dice_1: 0.14848/0.20082, loss_spatial_ce_1: 0.08814/0.09722, loss_grounding_bce_1: 0.24060/0.08126, loss_grounding_dice_1: 0.18626/0.15232, loss_grounding_ce_1: 0.43232/0.25999, loss_mask_ce_2: 1.71129/0.81316, loss_mask_bce_2: 0.52894/0.30455, loss_mask_dice_2: 0.41789/1.04189, loss_spatial_bce_2: 0.19766/0.09383, loss_spatial_dice_2: 0.16016/0.20081, loss_spatial_ce_2: 0.05380/0.10256, loss_grounding_bce_2: 0.25183/0.08117, loss_grounding_dice_2: 0.18760/0.15237, loss_grounding_ce_2: 0.30423/0.25885, loss_mask_ce_3: 1.73245/0.81097, loss_mask_bce_3: 0.53149/0.30575, loss_mask_dice_3: 0.41480/1.03558, loss_spatial_bce_3: 0.19087/0.09509, loss_spatial_dice_3: 0.16511/0.20079, loss_spatial_ce_3: 0.04107/0.10898, loss_grounding_bce_3: 0.25151/0.08172, loss_grounding_dice_3: 0.18507/0.15243, loss_grounding_ce_3: 0.34990/0.25860, loss_mask_ce_4: 1.65541/0.81515, loss_mask_bce_4: 0.52279/0.30829, loss_mask_dice_4: 0.40753/1.05357, loss_spatial_bce_4: 0.17687/0.09721, loss_spatial_dice_4: 0.16614/0.20821, loss_spatial_ce_4: 0.01066/0.11940, loss_grounding_bce_4: 0.25397/0.08231, loss_grounding_dice_4: 0.18572/0.15452, loss_grounding_ce_4: 0.47504/0.26761, loss_mask_ce_5: 1.70836/0.83585, loss_mask_bce_5: 0.52186/0.30971, loss_mask_dice_5: 0.40785/1.06311, loss_spatial_bce_5: 0.19072/0.09873, loss_spatial_dice_5: 0.15462/0.21036, loss_spatial_ce_5: 0.03811/0.12993, loss_grounding_bce_5: 0.24563/0.08257, loss_grounding_dice_5: 0.17812/0.15519, loss_grounding_ce_5: 0.34374/0.28538, loss_mask_ce_6: 1.63632/0.85841, loss_mask_bce_6: 0.51159/0.31067, loss_mask_dice_6: 0.39514/1.06809, loss_spatial_bce_6: 0.20750/0.10353, loss_spatial_dice_6: 0.15913/0.21292, loss_spatial_ce_6: 0.07057/0.14698, loss_grounding_bce_6: 0.24317/0.08390, loss_grounding_dice_6: 0.17918/0.15579, loss_grounding_ce_6: 0.39321/0.30364, loss_mask_ce_7: 1.82353/0.92229, loss_mask_bce_7: 0.54500/0.31826, loss_mask_dice_7: 0.42380/1.11449, loss_spatial_bce_7: 0.20789/0.11481, loss_spatial_dice_7: 0.16592/0.23876, loss_spatial_ce_7: 0.09415/0.19733, loss_grounding_bce_7: 0.25572/0.08574, loss_grounding_dice_7: 0.19394/0.16122, loss_grounding_ce_7: 0.53032/0.35602, loss_mask_ce_8: 1.75606/1.07163, loss_mask_bce_8: 0.54072/0.33622, loss_mask_dice_8: 0.45568/1.19514, loss_spatial_bce_8: 0.20858/0.13795, loss_spatial_dice_8: 0.16213/0.28353, loss_spatial_ce_8: 0.16452/0.25063, loss_grounding_bce_8: 0.25456/0.08950, loss_grounding_dice_8: 0.20222/0.16948, loss_grounding_ce_8: 0.46138/0.46235, loss_mask_ce_9: 4.07328/3.53955, loss_mask_bce_9: 0.50054/0.36328, loss_mask_dice_9: 0.61136/1.78429, loss_spatial_bce_9: 0.44375/0.36713, loss_spatial_dice_9: 0.90347/0.80061, loss_spatial_ce_9: 1.41859/1.43939, loss_grounding_bce_9: 0.23897/0.10142, loss_grounding_dice_9: 0.25297/0.24589, loss_grounding_ce_9: 0.41945/0.75467] items per batch[64] items per second[0.36] total items[512000] mini batches[ 8000] memory[4929] epoch remaining[0:34:23] INFO:trainer.default_trainer:epochs[ 4] optim steps[8100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05493/0.80396, loss_mask_bce_0: 0.12068/0.30352, loss_mask_dice_0: 0.33652/1.03396, loss_spatial_bce_0: 0.05573/0.09356, loss_spatial_dice_0: 0.14516/0.19800, loss_spatial_ce_0: 0.02258/0.09233, loss_grounding_bce_0: 0.08320/0.08118, loss_grounding_dice_0: 0.05174/0.15133, loss_grounding_ce_0: 0.01675/0.25598, loss_mask_ce_1: 1.09618/0.80692, loss_mask_bce_1: 0.12468/0.30410, loss_mask_dice_1: 0.33707/1.03921, loss_spatial_bce_1: 0.05022/0.09430, loss_spatial_dice_1: 0.14115/0.20077, loss_spatial_ce_1: 0.02279/0.09688, loss_grounding_bce_1: 0.07962/0.08119, loss_grounding_dice_1: 0.05191/0.15237, loss_grounding_ce_1: 0.01454/0.26017, loss_mask_ce_2: 1.09649/0.81292, loss_mask_bce_2: 0.13375/0.30432, loss_mask_dice_2: 0.40807/1.04296, loss_spatial_bce_2: 0.05125/0.09368, loss_spatial_dice_2: 0.14895/0.20075, loss_spatial_ce_2: 0.01982/0.10233, loss_grounding_bce_2: 0.08044/0.08108, loss_grounding_dice_2: 0.05108/0.15237, loss_grounding_ce_2: 0.01060/0.25918, loss_mask_ce_3: 0.92895/0.81067, loss_mask_bce_3: 0.13215/0.30556, loss_mask_dice_3: 0.41872/1.03649, loss_spatial_bce_3: 0.04732/0.09493, loss_spatial_dice_3: 0.14657/0.20075, loss_spatial_ce_3: 0.04859/0.10860, loss_grounding_bce_3: 0.07389/0.08164, loss_grounding_dice_3: 0.04983/0.15246, loss_grounding_ce_3: 0.01180/0.25892, loss_mask_ce_4: 0.86939/0.81514, loss_mask_bce_4: 0.13348/0.30799, loss_mask_dice_4: 0.40055/1.05494, loss_spatial_bce_4: 0.06388/0.09706, loss_spatial_dice_4: 0.17823/0.20819, loss_spatial_ce_4: 0.09769/0.11905, loss_grounding_bce_4: 0.07678/0.08223, loss_grounding_dice_4: 0.05143/0.15457, loss_grounding_ce_4: 0.01123/0.26777, loss_mask_ce_5: 0.83719/0.83541, loss_mask_bce_5: 0.13782/0.30947, loss_mask_dice_5: 0.42783/1.06395, loss_spatial_bce_5: 0.11133/0.09855, loss_spatial_dice_5: 0.19042/0.21035, loss_spatial_ce_5: 0.05448/0.12965, loss_grounding_bce_5: 0.07938/0.08254, loss_grounding_dice_5: 0.04982/0.15533, loss_grounding_ce_5: 0.00868/0.28508, loss_mask_ce_6: 0.86392/0.85836, loss_mask_bce_6: 0.14273/0.31039, loss_mask_dice_6: 0.46527/1.06886, loss_spatial_bce_6: 0.07270/0.10335, loss_spatial_dice_6: 0.17264/0.21289, loss_spatial_ce_6: 0.10613/0.14668, loss_grounding_bce_6: 0.08292/0.08380, loss_grounding_dice_6: 0.05143/0.15578, loss_grounding_ce_6: 0.02796/0.30368, loss_mask_ce_7: 0.84679/0.92183, loss_mask_bce_7: 0.18299/0.31804, loss_mask_dice_7: 0.48322/1.11534, loss_spatial_bce_7: 0.15294/0.11466, loss_spatial_dice_7: 0.20694/0.23875, loss_spatial_ce_7: 0.12416/0.19719, loss_grounding_bce_7: 0.07417/0.08569, loss_grounding_dice_7: 0.04094/0.16126, loss_grounding_ce_7: 0.00417/0.35601, loss_mask_ce_8: 1.38283/1.07086, loss_mask_bce_8: 0.13666/0.33610, loss_mask_dice_8: 0.53291/1.19642, loss_spatial_bce_8: 0.70513/0.13782, loss_spatial_dice_8: 0.22305/0.28342, loss_spatial_ce_8: 0.09886/0.25031, loss_grounding_bce_8: 0.07374/0.08948, loss_grounding_dice_8: 0.04425/0.16968, loss_grounding_ce_8: 0.00437/0.46146, loss_mask_ce_9: 3.44165/3.53931, loss_mask_bce_9: 0.14504/0.36291, loss_mask_dice_9: 0.62954/1.78455, loss_spatial_bce_9: 0.49817/0.36698, loss_spatial_dice_9: 0.88624/0.80048, loss_spatial_ce_9: 1.58495/1.43967, loss_grounding_bce_9: 0.08219/0.10140, loss_grounding_dice_9: 0.05750/0.24608, loss_grounding_ce_9: 0.03048/0.75333] items per batch[64] items per second[0.35] total items[518400] mini batches[ 8100] memory[4929] epoch remaining[0:31:22] INFO:trainer.default_trainer:epochs[ 4] optim steps[8200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06384/0.80418, loss_mask_bce_0: 0.01791/0.30391, loss_mask_dice_0: 0.23516/1.03378, loss_spatial_bce_0: 0.00681/0.09356, loss_spatial_dice_0: 0.08217/0.19805, loss_spatial_ce_0: 0.00012/0.09197, loss_grounding_bce_0: 0.00064/0.08133, loss_grounding_dice_0: 0.09094/0.15140, loss_grounding_ce_0: 0.01259/0.25638, loss_mask_ce_1: 0.06996/0.80745, loss_mask_bce_1: 0.01978/0.30445, loss_mask_dice_1: 0.24132/1.03895, loss_spatial_bce_1: 0.00843/0.09429, loss_spatial_dice_1: 0.08323/0.20081, loss_spatial_ce_1: 0.00035/0.09642, loss_grounding_bce_1: 0.00056/0.08136, loss_grounding_dice_1: 0.06676/0.15248, loss_grounding_ce_1: 0.01645/0.26052, loss_mask_ce_2: 0.06985/0.81340, loss_mask_bce_2: 0.01720/0.30469, loss_mask_dice_2: 0.20755/1.04266, loss_spatial_bce_2: 0.00611/0.09368, loss_spatial_dice_2: 0.09897/0.20074, loss_spatial_ce_2: 0.00044/0.10188, loss_grounding_bce_2: 0.00188/0.08121, loss_grounding_dice_2: 0.16446/0.15243, loss_grounding_ce_2: 0.01847/0.26009, loss_mask_ce_3: 0.07049/0.81116, loss_mask_bce_3: 0.02291/0.30594, loss_mask_dice_3: 0.26235/1.03629, loss_spatial_bce_3: 0.01068/0.09497, loss_spatial_dice_3: 0.14624/0.20077, loss_spatial_ce_3: 0.00032/0.10822, loss_grounding_bce_3: 0.00227/0.08178, loss_grounding_dice_3: 0.07402/0.15248, loss_grounding_ce_3: 0.02235/0.25937, loss_mask_ce_4: 0.06883/0.81571, loss_mask_bce_4: 0.02096/0.30831, loss_mask_dice_4: 0.24974/1.05511, loss_spatial_bce_4: 0.00927/0.09708, loss_spatial_dice_4: 0.12682/0.20823, loss_spatial_ce_4: 0.00096/0.11872, loss_grounding_bce_4: 0.00103/0.08236, loss_grounding_dice_4: 0.11573/0.15464, loss_grounding_ce_4: 0.03006/0.26804, loss_mask_ce_5: 0.09351/0.83583, loss_mask_bce_5: 0.02167/0.30980, loss_mask_dice_5: 0.27610/1.06412, loss_spatial_bce_5: 0.01264/0.09857, loss_spatial_dice_5: 0.09742/0.21037, loss_spatial_ce_5: 0.00324/0.12927, loss_grounding_bce_5: 0.00294/0.08271, loss_grounding_dice_5: 0.19545/0.15540, loss_grounding_ce_5: 0.04906/0.28572, loss_mask_ce_6: 0.08243/0.85905, loss_mask_bce_6: 0.01850/0.31071, loss_mask_dice_6: 0.21603/1.06904, loss_spatial_bce_6: 0.01199/0.10337, loss_spatial_dice_6: 0.11392/0.21286, loss_spatial_ce_6: 0.00024/0.14621, loss_grounding_bce_6: 0.00121/0.08393, loss_grounding_dice_6: 0.03696/0.15583, loss_grounding_ce_6: 0.02064/0.30474, loss_mask_ce_7: 0.08946/0.92203, loss_mask_bce_7: 0.02186/0.31839, loss_mask_dice_7: 0.22669/1.11524, loss_spatial_bce_7: 0.02268/0.11468, loss_spatial_dice_7: 0.14352/0.23878, loss_spatial_ce_7: 0.02140/0.19700, loss_grounding_bce_7: 0.00205/0.08583, loss_grounding_dice_7: 0.08071/0.16133, loss_grounding_ce_7: 0.00194/0.35657, loss_mask_ce_8: 0.16397/1.07108, loss_mask_bce_8: 0.02582/0.33645, loss_mask_dice_8: 0.27983/1.19630, loss_spatial_bce_8: 0.02860/0.13791, loss_spatial_dice_8: 0.17857/0.28342, loss_spatial_ce_8: 0.16106/0.24995, loss_grounding_bce_8: 0.00579/0.08964, loss_grounding_dice_8: 0.13467/0.16979, loss_grounding_ce_8: 1.38099/0.46308, loss_mask_ce_9: 1.76641/3.54019, loss_mask_bce_9: 0.02142/0.36321, loss_mask_dice_9: 0.30497/1.78481, loss_spatial_bce_9: 0.07479/0.36671, loss_spatial_dice_9: 0.64928/0.80069, loss_spatial_ce_9: 0.71328/1.43958, loss_grounding_bce_9: 0.00066/0.10146, loss_grounding_dice_9: 0.06335/0.24627, loss_grounding_ce_9: 0.09136/0.75449] items per batch[64] items per second[0.36] total items[524800] mini batches[ 8200] memory[4929] epoch remaining[0:28:17] INFO:trainer.default_trainer:epochs[ 4] optim steps[8300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15499/0.80414, loss_mask_bce_0: 0.19865/0.30379, loss_mask_dice_0: 0.10897/1.03552, loss_spatial_bce_0: 0.23369/0.09368, loss_spatial_dice_0: 0.13150/0.19778, loss_spatial_ce_0: 0.00265/0.09159, loss_grounding_bce_0: 0.09294/0.08144, loss_grounding_dice_0: 0.07401/0.15151, loss_grounding_ce_0: 0.00865/0.25656, loss_mask_ce_1: 0.11651/0.80723, loss_mask_bce_1: 0.20897/0.30433, loss_mask_dice_1: 0.10796/1.04106, loss_spatial_bce_1: 0.22703/0.09441, loss_spatial_dice_1: 0.12125/0.20054, loss_spatial_ce_1: 0.00462/0.09610, loss_grounding_bce_1: 0.08861/0.08145, loss_grounding_dice_1: 0.06763/0.15257, loss_grounding_ce_1: 0.00477/0.26074, loss_mask_ce_2: 0.10907/0.81303, loss_mask_bce_2: 0.21285/0.30457, loss_mask_dice_2: 0.11198/1.04421, loss_spatial_bce_2: 0.24101/0.09381, loss_spatial_dice_2: 0.12374/0.20045, loss_spatial_ce_2: 0.00203/0.10153, loss_grounding_bce_2: 0.08724/0.08132, loss_grounding_dice_2: 0.06990/0.15251, loss_grounding_ce_2: 0.00331/0.26061, loss_mask_ce_3: 0.12969/0.81131, loss_mask_bce_3: 0.20608/0.30577, loss_mask_dice_3: 0.11141/1.03776, loss_spatial_bce_3: 0.23987/0.09512, loss_spatial_dice_3: 0.12468/0.20048, loss_spatial_ce_3: 0.00346/0.10782, loss_grounding_bce_3: 0.10629/0.08187, loss_grounding_dice_3: 0.07620/0.15252, loss_grounding_ce_3: 0.00645/0.25975, loss_mask_ce_4: 0.13785/0.81581, loss_mask_bce_4: 0.21562/0.30822, loss_mask_dice_4: 0.12077/1.05702, loss_spatial_bce_4: 0.24199/0.09712, loss_spatial_dice_4: 0.12979/0.20792, loss_spatial_ce_4: 0.00447/0.11860, loss_grounding_bce_4: 0.11062/0.08246, loss_grounding_dice_4: 0.09263/0.15474, loss_grounding_ce_4: 0.01167/0.26839, loss_mask_ce_5: 0.21764/0.83588, loss_mask_bce_5: 0.21117/0.30979, loss_mask_dice_5: 0.11535/1.06611, loss_spatial_bce_5: 0.24566/0.09865, loss_spatial_dice_5: 0.13126/0.21005, loss_spatial_ce_5: 0.00209/0.12915, loss_grounding_bce_5: 0.10616/0.08280, loss_grounding_dice_5: 0.08574/0.15542, loss_grounding_ce_5: 0.01762/0.28621, loss_mask_ce_6: 0.17012/0.85887, loss_mask_bce_6: 0.22874/0.31087, loss_mask_dice_6: 0.12189/1.07096, loss_spatial_bce_6: 0.25215/0.10346, loss_spatial_dice_6: 0.12659/0.21249, loss_spatial_ce_6: 0.00403/0.14603, loss_grounding_bce_6: 0.10847/0.08420, loss_grounding_dice_6: 0.08533/0.15588, loss_grounding_ce_6: 0.00774/0.30465, loss_mask_ce_7: 0.22759/0.92163, loss_mask_bce_7: 0.20899/0.31831, loss_mask_dice_7: 0.11063/1.11706, loss_spatial_bce_7: 0.28323/0.11475, loss_spatial_dice_7: 0.13342/0.23836, loss_spatial_ce_7: 0.09991/0.19672, loss_grounding_bce_7: 0.11123/0.08594, loss_grounding_dice_7: 0.07726/0.16142, loss_grounding_ce_7: 0.00216/0.35673, loss_mask_ce_8: 0.50497/1.07006, loss_mask_bce_8: 0.21885/0.33654, loss_mask_dice_8: 0.12488/1.19808, loss_spatial_bce_8: 0.25962/0.13791, loss_spatial_dice_8: 0.13787/0.28283, loss_spatial_ce_8: 0.19515/0.24985, loss_grounding_bce_8: 0.11139/0.08978, loss_grounding_dice_8: 0.10538/0.16989, loss_grounding_ce_8: 0.02356/0.46263, loss_mask_ce_9: 2.12684/3.54024, loss_mask_bce_9: 0.19284/0.36314, loss_mask_dice_9: 0.12176/1.78603, loss_spatial_bce_9: 0.54646/0.36701, loss_spatial_dice_9: 0.69312/0.80062, loss_spatial_ce_9: 2.55580/1.43851, loss_grounding_bce_9: 0.09772/0.10163, loss_grounding_dice_9: 0.07585/0.24615, loss_grounding_ce_9: 0.75488/0.75343] items per batch[64] items per second[0.35] total items[531200] mini batches[ 8300] memory[4929] epoch remaining[0:25:15] INFO:trainer.default_trainer:epochs[ 4] optim steps[8400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68706/0.80396, loss_mask_bce_0: 0.48955/0.30373, loss_mask_dice_0: 0.74835/1.03626, loss_spatial_bce_0: 0.10014/0.09354, loss_spatial_dice_0: 0.12507/0.19756, loss_spatial_ce_0: 0.00138/0.09131, loss_grounding_bce_0: 0.17687/0.08140, loss_grounding_dice_0: 0.25603/0.15159, loss_grounding_ce_0: 0.10988/0.25609, loss_mask_ce_1: 0.63672/0.80702, loss_mask_bce_1: 0.47962/0.30430, loss_mask_dice_1: 0.63429/1.04198, loss_spatial_bce_1: 0.09726/0.09426, loss_spatial_dice_1: 0.14567/0.20033, loss_spatial_ce_1: 0.00100/0.09597, loss_grounding_bce_1: 0.16646/0.08140, loss_grounding_dice_1: 0.25245/0.15271, loss_grounding_ce_1: 0.12964/0.26026, loss_mask_ce_2: 0.70958/0.81270, loss_mask_bce_2: 0.47845/0.30457, loss_mask_dice_2: 0.68294/1.04525, loss_spatial_bce_2: 0.09475/0.09368, loss_spatial_dice_2: 0.13195/0.20029, loss_spatial_ce_2: 0.00202/0.10119, loss_grounding_bce_2: 0.17259/0.08126, loss_grounding_dice_2: 0.26640/0.15262, loss_grounding_ce_2: 0.09199/0.26004, loss_mask_ce_3: 0.65431/0.81111, loss_mask_bce_3: 0.47567/0.30570, loss_mask_dice_3: 0.64307/1.03858, loss_spatial_bce_3: 0.09420/0.09499, loss_spatial_dice_3: 0.18216/0.20032, loss_spatial_ce_3: 0.00742/0.10741, loss_grounding_bce_3: 0.16952/0.08181, loss_grounding_dice_3: 0.24857/0.15261, loss_grounding_ce_3: 0.15137/0.25931, loss_mask_ce_4: 0.63809/0.81569, loss_mask_bce_4: 0.47842/0.30812, loss_mask_dice_4: 0.60986/1.05787, loss_spatial_bce_4: 0.11300/0.09701, loss_spatial_dice_4: 0.16398/0.20773, loss_spatial_ce_4: 0.03014/0.11826, loss_grounding_bce_4: 0.19042/0.08241, loss_grounding_dice_4: 0.26802/0.15482, loss_grounding_ce_4: 0.11603/0.26801, loss_mask_ce_5: 0.70848/0.83553, loss_mask_bce_5: 0.50672/0.30968, loss_mask_dice_5: 0.82441/1.06700, loss_spatial_bce_5: 0.10884/0.09855, loss_spatial_dice_5: 0.19200/0.20984, loss_spatial_ce_5: 0.04960/0.12893, loss_grounding_bce_5: 0.16397/0.08274, loss_grounding_dice_5: 0.26131/0.15541, loss_grounding_ce_5: 0.19664/0.28583, loss_mask_ce_6: 0.63985/0.85867, loss_mask_bce_6: 0.45200/0.31076, loss_mask_dice_6: 0.60859/1.07177, loss_spatial_bce_6: 0.11456/0.10332, loss_spatial_dice_6: 0.19361/0.21227, loss_spatial_ce_6: 0.03310/0.14567, loss_grounding_bce_6: 0.11120/0.08415, loss_grounding_dice_6: 0.20467/0.15594, loss_grounding_ce_6: 0.30017/0.30437, loss_mask_ce_7: 0.91202/0.92114, loss_mask_bce_7: 0.46176/0.31821, loss_mask_dice_7: 0.72214/1.11800, loss_spatial_bce_7: 0.12873/0.11461, loss_spatial_dice_7: 0.23560/0.23807, loss_spatial_ce_7: 0.04379/0.19634, loss_grounding_bce_7: 0.12461/0.08589, loss_grounding_dice_7: 0.22433/0.16151, loss_grounding_ce_7: 0.39666/0.35613, loss_mask_ce_8: 0.98091/1.06944, loss_mask_bce_8: 0.42872/0.33642, loss_mask_dice_8: 0.57621/1.19917, loss_spatial_bce_8: 0.11251/0.13768, loss_spatial_dice_8: 0.26753/0.28251, loss_spatial_ce_8: 0.11361/0.24942, loss_grounding_bce_8: 0.08267/0.08970, loss_grounding_dice_8: 0.24904/0.16996, loss_grounding_ce_8: 0.82273/0.46187, loss_mask_ce_9: 3.60442/3.53766, loss_mask_bce_9: 0.54360/0.36300, loss_mask_dice_9: 1.31726/1.78654, loss_spatial_bce_9: 0.38580/0.36726, loss_spatial_dice_9: 0.76175/0.80039, loss_spatial_ce_9: 1.50906/1.43904, loss_grounding_bce_9: 0.19940/0.10157, loss_grounding_dice_9: 0.61336/0.24617, loss_grounding_ce_9: 0.93700/0.75230] items per batch[64] items per second[0.36] total items[537600] mini batches[ 8400] memory[4929] epoch remaining[0:22:12] INFO:trainer.default_trainer:epochs[ 4] optim steps[8500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14706/0.80462, loss_mask_bce_0: 0.39781/0.30324, loss_mask_dice_0: 1.43292/1.03568, loss_spatial_bce_0: 0.02825/0.09329, loss_spatial_dice_0: 0.13588/0.19735, loss_spatial_ce_0: 0.01391/0.09103, loss_grounding_bce_0: 0.11197/0.08118, loss_grounding_dice_0: 0.07521/0.15123, loss_grounding_ce_0: 0.00454/0.25560, loss_mask_ce_1: 1.13987/0.80740, loss_mask_bce_1: 0.40282/0.30389, loss_mask_dice_1: 1.40805/1.04175, loss_spatial_bce_1: 0.03133/0.09402, loss_spatial_dice_1: 0.14041/0.20010, loss_spatial_ce_1: 0.01613/0.09558, loss_grounding_bce_1: 0.10832/0.08120, loss_grounding_dice_1: 0.07786/0.15236, loss_grounding_ce_1: 0.00506/0.26001, loss_mask_ce_2: 1.02507/0.81326, loss_mask_bce_2: 0.40474/0.30410, loss_mask_dice_2: 1.44699/1.04479, loss_spatial_bce_2: 0.03233/0.09342, loss_spatial_dice_2: 0.14520/0.20006, loss_spatial_ce_2: 0.01942/0.10106, loss_grounding_bce_2: 0.10294/0.08106, loss_grounding_dice_2: 0.06666/0.15239, loss_grounding_ce_2: 0.01597/0.25965, loss_mask_ce_3: 1.07473/0.81139, loss_mask_bce_3: 0.39236/0.30529, loss_mask_dice_3: 1.33437/1.03808, loss_spatial_bce_3: 0.03781/0.09474, loss_spatial_dice_3: 0.15131/0.20009, loss_spatial_ce_3: 0.01770/0.10698, loss_grounding_bce_3: 0.10004/0.08161, loss_grounding_dice_3: 0.06341/0.15227, loss_grounding_ce_3: 0.00886/0.25890, loss_mask_ce_4: 1.26626/0.81624, loss_mask_bce_4: 0.43824/0.30774, loss_mask_dice_4: 1.52193/1.05753, loss_spatial_bce_4: 0.03619/0.09676, loss_spatial_dice_4: 0.15054/0.20754, loss_spatial_ce_4: 0.02104/0.11791, loss_grounding_bce_4: 0.12444/0.08218, loss_grounding_dice_4: 0.08168/0.15440, loss_grounding_ce_4: 0.00616/0.26750, loss_mask_ce_5: 1.33022/0.83585, loss_mask_bce_5: 0.43780/0.30936, loss_mask_dice_5: 1.40557/1.06670, loss_spatial_bce_5: 0.03927/0.09830, loss_spatial_dice_5: 0.15341/0.20963, loss_spatial_ce_5: 0.04670/0.12843, loss_grounding_bce_5: 0.11258/0.08253, loss_grounding_dice_5: 0.07604/0.15510, loss_grounding_ce_5: 0.04025/0.28529, loss_mask_ce_6: 1.21970/0.85899, loss_mask_bce_6: 0.52390/0.31039, loss_mask_dice_6: 1.55535/1.07137, loss_spatial_bce_6: 0.04439/0.10310, loss_spatial_dice_6: 0.16374/0.21205, loss_spatial_ce_6: 0.03083/0.14511, loss_grounding_bce_6: 0.09059/0.08391, loss_grounding_dice_6: 0.05969/0.15559, loss_grounding_ce_6: 0.06671/0.30374, loss_mask_ce_7: 1.06498/0.92194, loss_mask_bce_7: 0.60371/0.31783, loss_mask_dice_7: 1.81277/1.11789, loss_spatial_bce_7: 0.04746/0.11441, loss_spatial_dice_7: 0.19767/0.23785, loss_spatial_ce_7: 0.07157/0.19572, loss_grounding_bce_7: 0.08665/0.08563, loss_grounding_dice_7: 0.05696/0.16111, loss_grounding_ce_7: 0.49182/0.35539, loss_mask_ce_8: 1.31973/1.06914, loss_mask_bce_8: 0.45889/0.33612, loss_mask_dice_8: 1.90413/1.19912, loss_spatial_bce_8: 0.06139/0.13739, loss_spatial_dice_8: 0.29698/0.28225, loss_spatial_ce_8: 0.13811/0.24884, loss_grounding_bce_8: 0.10121/0.08949, loss_grounding_dice_8: 0.05437/0.16975, loss_grounding_ce_8: 0.06739/0.46050, loss_mask_ce_9: 4.25985/3.53797, loss_mask_bce_9: 0.67230/0.36273, loss_mask_dice_9: 4.04448/1.78707, loss_spatial_bce_9: 0.35700/0.36742, loss_spatial_dice_9: 0.96585/0.80038, loss_spatial_ce_9: 1.95163/1.43830, loss_grounding_bce_9: 0.10732/0.10140, loss_grounding_dice_9: 0.05283/0.24580, loss_grounding_ce_9: 1.35197/0.75088] items per batch[64] items per second[0.36] total items[544000] mini batches[ 8500] memory[4929] epoch remaining[0:19:10] INFO:trainer.default_trainer:epochs[ 4] optim steps[8600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16958/0.80430, loss_mask_bce_0: 0.04210/0.30328, loss_mask_dice_0: 0.09274/1.03468, loss_spatial_bce_0: 0.00666/0.09321, loss_spatial_dice_0: 0.04910/0.19719, loss_spatial_ce_0: 0.02258/0.09069, loss_grounding_bce_0: 0.04879/0.08120, loss_grounding_dice_0: 0.09711/0.15117, loss_grounding_ce_0: 0.00924/0.25524, loss_mask_ce_1: 0.12934/0.80719, loss_mask_bce_1: 0.04708/0.30390, loss_mask_dice_1: 0.09146/1.04041, loss_spatial_bce_1: 0.00656/0.09396, loss_spatial_dice_1: 0.04771/0.19994, loss_spatial_ce_1: 0.00876/0.09524, loss_grounding_bce_1: 0.04922/0.08122, loss_grounding_dice_1: 0.09298/0.15229, loss_grounding_ce_1: 0.00595/0.25966, loss_mask_ce_2: 0.09758/0.81314, loss_mask_bce_2: 0.04872/0.30410, loss_mask_dice_2: 0.08969/1.04370, loss_spatial_bce_2: 0.01014/0.09335, loss_spatial_dice_2: 0.06772/0.19988, loss_spatial_ce_2: 0.01336/0.10070, loss_grounding_bce_2: 0.05345/0.08109, loss_grounding_dice_2: 0.09248/0.15240, loss_grounding_ce_2: 0.01507/0.25944, loss_mask_ce_3: 0.13153/0.81106, loss_mask_bce_3: 0.04657/0.30530, loss_mask_dice_3: 0.08841/1.03695, loss_spatial_bce_3: 0.01539/0.09469, loss_spatial_dice_3: 0.08852/0.19991, loss_spatial_ce_3: 0.00749/0.10664, loss_grounding_bce_3: 0.04690/0.08162, loss_grounding_dice_3: 0.09696/0.15220, loss_grounding_ce_3: 0.01285/0.25860, loss_mask_ce_4: 0.14609/0.81574, loss_mask_bce_4: 0.05236/0.30774, loss_mask_dice_4: 0.09879/1.05644, loss_spatial_bce_4: 0.02055/0.09670, loss_spatial_dice_4: 0.08604/0.20734, loss_spatial_ce_4: 0.00436/0.11761, loss_grounding_bce_4: 0.05056/0.08220, loss_grounding_dice_4: 0.10666/0.15432, loss_grounding_ce_4: 0.00845/0.26723, loss_mask_ce_5: 0.09801/0.83548, loss_mask_bce_5: 0.04350/0.30940, loss_mask_dice_5: 0.09340/1.06568, loss_spatial_bce_5: 0.01738/0.09823, loss_spatial_dice_5: 0.08946/0.20943, loss_spatial_ce_5: 0.00622/0.12829, loss_grounding_bce_5: 0.04007/0.08252, loss_grounding_dice_5: 0.08446/0.15505, loss_grounding_ce_5: 0.00840/0.28493, loss_mask_ce_6: 0.12931/0.85860, loss_mask_bce_6: 0.05030/0.31042, loss_mask_dice_6: 0.08954/1.07044, loss_spatial_bce_6: 0.01198/0.10301, loss_spatial_dice_6: 0.07162/0.21183, loss_spatial_ce_6: 0.01870/0.14493, loss_grounding_bce_6: 0.04957/0.08389, loss_grounding_dice_6: 0.09313/0.15552, loss_grounding_ce_6: 0.00613/0.30335, loss_mask_ce_7: 0.16516/0.92151, loss_mask_bce_7: 0.03597/0.31790, loss_mask_dice_7: 0.09384/1.11686, loss_spatial_bce_7: 0.00743/0.11430, loss_spatial_dice_7: 0.05103/0.23755, loss_spatial_ce_7: 0.01585/0.19526, loss_grounding_bce_7: 0.03916/0.08564, loss_grounding_dice_7: 0.08482/0.16097, loss_grounding_ce_7: 0.02373/0.35510, loss_mask_ce_8: 1.37873/1.06875, loss_mask_bce_8: 0.00351/0.33612, loss_mask_dice_8: 0.03008/1.19784, loss_spatial_bce_8: 0.02146/0.13732, loss_spatial_dice_8: 0.08599/0.28190, loss_spatial_ce_8: 0.09154/0.24835, loss_grounding_bce_8: 0.04758/0.08950, loss_grounding_dice_8: 0.07983/0.16979, loss_grounding_ce_8: 0.05098/0.45938, loss_mask_ce_9: 2.03639/3.53788, loss_mask_bce_9: 0.00986/0.36277, loss_mask_dice_9: 0.07749/1.78572, loss_spatial_bce_9: 0.04122/0.36733, loss_spatial_dice_9: 0.29356/0.80037, loss_spatial_ce_9: 0.33761/1.43815, loss_grounding_bce_9: 0.06000/0.10141, loss_grounding_dice_9: 0.12358/0.24575, loss_grounding_ce_9: 0.17439/0.74995] items per batch[64] items per second[0.35] total items[550400] mini batches[ 8600] memory[4929] epoch remaining[0:16:10] INFO:trainer.default_trainer:epochs[ 4] optim steps[8700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16494/0.80466, loss_mask_bce_0: 0.30978/0.30308, loss_mask_dice_0: 0.54227/1.03784, loss_spatial_bce_0: 0.10723/0.09317, loss_spatial_dice_0: 0.16649/0.19714, loss_spatial_ce_0: 0.57011/0.09049, loss_grounding_bce_0: 0.13121/0.08132, loss_grounding_dice_0: 0.14446/0.15141, loss_grounding_ce_0: 0.01642/0.25479, loss_mask_ce_1: 0.32266/0.80704, loss_mask_bce_1: 0.29517/0.30370, loss_mask_dice_1: 0.40416/1.04348, loss_spatial_bce_1: 0.11076/0.09393, loss_spatial_dice_1: 0.16069/0.19986, loss_spatial_ce_1: 0.24758/0.09501, loss_grounding_bce_1: 0.12827/0.08130, loss_grounding_dice_1: 0.13995/0.15254, loss_grounding_ce_1: 0.01825/0.25913, loss_mask_ce_2: 0.28321/0.81298, loss_mask_bce_2: 0.31015/0.30397, loss_mask_dice_2: 0.42223/1.04677, loss_spatial_bce_2: 0.10998/0.09329, loss_spatial_dice_2: 0.16167/0.19978, loss_spatial_ce_2: 0.05217/0.10042, loss_grounding_bce_2: 0.13052/0.08123, loss_grounding_dice_2: 0.14126/0.15259, loss_grounding_ce_2: 0.01610/0.25883, loss_mask_ce_3: 0.20001/0.81099, loss_mask_bce_3: 0.30630/0.30523, loss_mask_dice_3: 0.51220/1.04016, loss_spatial_bce_3: 0.11737/0.09468, loss_spatial_dice_3: 0.15932/0.19983, loss_spatial_ce_3: 0.05201/0.10616, loss_grounding_bce_3: 0.12871/0.08184, loss_grounding_dice_3: 0.14333/0.15247, loss_grounding_ce_3: 0.01628/0.25794, loss_mask_ce_4: 0.18883/0.81563, loss_mask_bce_4: 0.31006/0.30763, loss_mask_dice_4: 0.53955/1.05929, loss_spatial_bce_4: 0.12579/0.09667, loss_spatial_dice_4: 0.20452/0.20726, loss_spatial_ce_4: 0.04680/0.11734, loss_grounding_bce_4: 0.13127/0.08240, loss_grounding_dice_4: 0.14864/0.15458, loss_grounding_ce_4: 0.02041/0.26655, loss_mask_ce_5: 0.34842/0.83522, loss_mask_bce_5: 0.29847/0.30930, loss_mask_dice_5: 0.40818/1.06867, loss_spatial_bce_5: 0.12988/0.09818, loss_spatial_dice_5: 0.16329/0.20933, loss_spatial_ce_5: 0.14448/0.12791, loss_grounding_bce_5: 0.12596/0.08264, loss_grounding_dice_5: 0.14045/0.15512, loss_grounding_ce_5: 0.02004/0.28453, loss_mask_ce_6: 0.37020/0.85883, loss_mask_bce_6: 0.29630/0.31030, loss_mask_dice_6: 0.40903/1.07369, loss_spatial_bce_6: 0.12152/0.10293, loss_spatial_dice_6: 0.15613/0.21179, loss_spatial_ce_6: 0.12651/0.14475, loss_grounding_bce_6: 0.13016/0.08397, loss_grounding_dice_6: 0.13927/0.15563, loss_grounding_ce_6: 0.03283/0.30276, loss_mask_ce_7: 0.25784/0.92144, loss_mask_bce_7: 0.31044/0.31770, loss_mask_dice_7: 0.45444/1.11940, loss_spatial_bce_7: 0.12333/0.11418, loss_spatial_dice_7: 0.17079/0.23745, loss_spatial_ce_7: 0.20165/0.19476, loss_grounding_bce_7: 0.13390/0.08570, loss_grounding_dice_7: 0.13519/0.16114, loss_grounding_ce_7: 0.01950/0.35412, loss_mask_ce_8: 0.21803/1.06871, loss_mask_bce_8: 0.30721/0.33598, loss_mask_dice_8: 0.50134/1.20070, loss_spatial_bce_8: 0.18390/0.13721, loss_spatial_dice_8: 0.20215/0.28196, loss_spatial_ce_8: 0.19378/0.24821, loss_grounding_bce_8: 0.13298/0.08953, loss_grounding_dice_8: 0.14540/0.16998, loss_grounding_ce_8: 0.03509/0.45830, loss_mask_ce_9: 2.90112/3.53738, loss_mask_bce_9: 0.34855/0.36253, loss_mask_dice_9: 0.72049/1.78879, loss_spatial_bce_9: 0.82913/0.36695, loss_spatial_dice_9: 0.84926/0.80030, loss_spatial_ce_9: 2.10465/1.43826, loss_grounding_bce_9: 0.14703/0.10146, loss_grounding_dice_9: 0.18539/0.24571, loss_grounding_ce_9: 0.27454/0.74877] items per batch[64] items per second[0.35] total items[556800] mini batches[ 8700] memory[4929] epoch remaining[0:13:08] INFO:trainer.default_trainer:epochs[ 4] optim steps[8800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88997/0.80573, loss_mask_bce_0: 0.31207/0.30305, loss_mask_dice_0: 0.37723/1.03863, loss_spatial_bce_0: 0.11371/0.09298, loss_spatial_dice_0: 0.15090/0.19701, loss_spatial_ce_0: 0.11557/0.09013, loss_grounding_bce_0: 0.18057/0.08130, loss_grounding_dice_0: 0.14806/0.15147, loss_grounding_ce_0: 1.57242/0.25512, loss_mask_ce_1: 0.93583/0.80826, loss_mask_bce_1: 0.29614/0.30360, loss_mask_dice_1: 0.35337/1.04387, loss_spatial_bce_1: 0.10897/0.09373, loss_spatial_dice_1: 0.14285/0.19970, loss_spatial_ce_1: 0.12692/0.09470, loss_grounding_bce_1: 0.16112/0.08129, loss_grounding_dice_1: 0.12409/0.15259, loss_grounding_ce_1: 1.83432/0.25973, loss_mask_ce_2: 1.00332/0.81407, loss_mask_bce_2: 0.29350/0.30395, loss_mask_dice_2: 0.35090/1.04704, loss_spatial_bce_2: 0.11428/0.09307, loss_spatial_dice_2: 0.14825/0.19962, loss_spatial_ce_2: 0.13055/0.10010, loss_grounding_bce_2: 0.15532/0.08119, loss_grounding_dice_2: 0.12253/0.15261, loss_grounding_ce_2: 1.31747/0.25921, loss_mask_ce_3: 1.20889/0.81242, loss_mask_bce_3: 0.28836/0.30520, loss_mask_dice_3: 0.31105/1.04066, loss_spatial_bce_3: 0.12175/0.09447, loss_spatial_dice_3: 0.14935/0.19968, loss_spatial_ce_3: 0.14509/0.10569, loss_grounding_bce_3: 0.13728/0.08179, loss_grounding_dice_3: 0.08574/0.15250, loss_grounding_ce_3: 1.12933/0.25841, loss_mask_ce_4: 1.15665/0.81650, loss_mask_bce_4: 0.29194/0.30756, loss_mask_dice_4: 0.34184/1.06000, loss_spatial_bce_4: 0.10570/0.09646, loss_spatial_dice_4: 0.14584/0.20713, loss_spatial_ce_4: 0.14471/0.11699, loss_grounding_bce_4: 0.13730/0.08236, loss_grounding_dice_4: 0.07952/0.15457, loss_grounding_ce_4: 1.34084/0.26703, loss_mask_ce_5: 1.33275/0.83647, loss_mask_bce_5: 0.28359/0.30934, loss_mask_dice_5: 0.31073/1.06946, loss_spatial_bce_5: 0.10309/0.09796, loss_spatial_dice_5: 0.12824/0.20918, loss_spatial_ce_5: 0.11895/0.12750, loss_grounding_bce_5: 0.13782/0.08263, loss_grounding_dice_5: 0.08339/0.15519, loss_grounding_ce_5: 0.63607/0.28478, loss_mask_ce_6: 1.44631/0.85995, loss_mask_bce_6: 0.30164/0.31033, loss_mask_dice_6: 0.31093/1.07447, loss_spatial_bce_6: 0.16173/0.10272, loss_spatial_dice_6: 0.17445/0.21161, loss_spatial_ce_6: 0.13093/0.14435, loss_grounding_bce_6: 0.13428/0.08404, loss_grounding_dice_6: 0.07147/0.15574, loss_grounding_ce_6: 1.61677/0.30280, loss_mask_ce_7: 1.21223/0.92248, loss_mask_bce_7: 0.44925/0.31776, loss_mask_dice_7: 0.57184/1.12008, loss_spatial_bce_7: 0.16676/0.11392, loss_spatial_dice_7: 0.24643/0.23719, loss_spatial_ce_7: 0.42760/0.19418, loss_grounding_bce_7: 0.14499/0.08567, loss_grounding_dice_7: 0.08274/0.16123, loss_grounding_ce_7: 1.09831/0.35423, loss_mask_ce_8: 1.59419/1.07002, loss_mask_bce_8: 0.50467/0.33604, loss_mask_dice_8: 0.83533/1.20142, loss_spatial_bce_8: 0.19964/0.13684, loss_spatial_dice_8: 0.32600/0.28167, loss_spatial_ce_8: 0.22587/0.24783, loss_grounding_bce_8: 0.14080/0.08957, loss_grounding_dice_8: 0.07578/0.17012, loss_grounding_ce_8: 1.83658/0.45828, loss_mask_ce_9: 4.87951/3.53901, loss_mask_bce_9: 0.57506/0.36256, loss_mask_dice_9: 0.92032/1.78990, loss_spatial_bce_9: 0.88519/0.36637, loss_spatial_dice_9: 0.90534/0.80046, loss_spatial_ce_9: 1.80545/1.43922, loss_grounding_bce_9: 0.17978/0.10143, loss_grounding_dice_9: 0.13931/0.24585, loss_grounding_ce_9: 4.31952/0.74982] items per batch[64] items per second[0.36] total items[563200] mini batches[ 8800] memory[4929] epoch remaining[0:10:06] INFO:trainer.default_trainer:epochs[ 4] optim steps[8900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00343/0.80451, loss_mask_bce_0: 0.00620/0.30278, loss_mask_dice_0: 0.09221/1.03915, loss_spatial_bce_0: 0.00771/0.09298, loss_spatial_dice_0: 0.06146/0.19692, loss_spatial_ce_0: 0.00010/0.08991, loss_grounding_bce_0: 0.00564/0.08123, loss_grounding_dice_0: 0.11493/0.15154, loss_grounding_ce_0: 0.00016/0.25508, loss_mask_ce_1: 0.00368/0.80707, loss_mask_bce_1: 0.00652/0.30336, loss_mask_dice_1: 0.12105/1.04430, loss_spatial_bce_1: 0.01179/0.09368, loss_spatial_dice_1: 0.06592/0.19964, loss_spatial_ce_1: 0.00014/0.09444, loss_grounding_bce_1: 0.00318/0.08119, loss_grounding_dice_1: 0.08644/0.15264, loss_grounding_ce_1: 0.00013/0.25978, loss_mask_ce_2: 0.00471/0.81304, loss_mask_bce_2: 0.00663/0.30365, loss_mask_dice_2: 0.11881/1.04763, loss_spatial_bce_2: 0.01287/0.09304, loss_spatial_dice_2: 0.08143/0.19953, loss_spatial_ce_2: 0.00013/0.09997, loss_grounding_bce_2: 0.00200/0.08109, loss_grounding_dice_2: 0.05722/0.15262, loss_grounding_ce_2: 0.00010/0.25918, loss_mask_ce_3: 0.00523/0.81157, loss_mask_bce_3: 0.00764/0.30493, loss_mask_dice_3: 0.12567/1.04140, loss_spatial_bce_3: 0.01189/0.09449, loss_spatial_dice_3: 0.06383/0.19962, loss_spatial_ce_3: 0.00010/0.10539, loss_grounding_bce_3: 0.00314/0.08170, loss_grounding_dice_3: 0.09868/0.15254, loss_grounding_ce_3: 0.00016/0.25822, loss_mask_ce_4: 0.00533/0.81572, loss_mask_bce_4: 0.00662/0.30727, loss_mask_dice_4: 0.09644/1.06036, loss_spatial_bce_4: 0.00819/0.09635, loss_spatial_dice_4: 0.08417/0.20709, loss_spatial_ce_4: 0.00011/0.11661, loss_grounding_bce_4: 0.00345/0.08229, loss_grounding_dice_4: 0.10328/0.15458, loss_grounding_ce_4: 0.00024/0.26707, loss_mask_ce_5: 0.00757/0.83570, loss_mask_bce_5: 0.00741/0.30907, loss_mask_dice_5: 0.09984/1.06995, loss_spatial_bce_5: 0.00827/0.09795, loss_spatial_dice_5: 0.07860/0.20914, loss_spatial_ce_5: 0.00015/0.12725, loss_grounding_bce_5: 0.00286/0.08249, loss_grounding_dice_5: 0.07026/0.15523, loss_grounding_ce_5: 0.00046/0.28474, loss_mask_ce_6: 0.01115/0.85908, loss_mask_bce_6: 0.00799/0.31010, loss_mask_dice_6: 0.09624/1.07524, loss_spatial_bce_6: 0.00485/0.10273, loss_spatial_dice_6: 0.03935/0.21157, loss_spatial_ce_6: 0.00013/0.14402, loss_grounding_bce_6: 0.00222/0.08393, loss_grounding_dice_6: 0.07690/0.15579, loss_grounding_ce_6: 0.00076/0.30275, loss_mask_ce_7: 0.04314/0.92185, loss_mask_bce_7: 0.00795/0.31748, loss_mask_dice_7: 0.09935/1.12071, loss_spatial_bce_7: 0.00606/0.11385, loss_spatial_dice_7: 0.06486/0.23708, loss_spatial_ce_7: 0.00018/0.19381, loss_grounding_bce_7: 0.00436/0.08560, loss_grounding_dice_7: 0.10307/0.16136, loss_grounding_ce_7: 0.00087/0.35433, loss_mask_ce_8: 0.01741/1.06935, loss_mask_bce_8: 0.00647/0.33574, loss_mask_dice_8: 0.09342/1.20175, loss_spatial_bce_8: 0.01274/0.13661, loss_spatial_dice_8: 0.08714/0.28153, loss_spatial_ce_8: 0.02983/0.24768, loss_grounding_bce_8: 0.00370/0.08951, loss_grounding_dice_8: 0.13826/0.17018, loss_grounding_ce_8: 0.00599/0.45756, loss_mask_ce_9: 1.63737/3.54122, loss_mask_bce_9: 0.00841/0.36213, loss_mask_dice_9: 0.14828/1.79048, loss_spatial_bce_9: 0.03576/0.36590, loss_spatial_dice_9: 0.48355/0.80046, loss_spatial_ce_9: 0.40819/1.43945, loss_grounding_bce_9: 0.00328/0.10135, loss_grounding_dice_9: 0.09201/0.24602, loss_grounding_ce_9: 0.16432/0.74927] items per batch[64] items per second[0.35] total items[569600] mini batches[ 8900] memory[4929] epoch remaining[0:07:05] INFO:trainer.default_trainer:epochs[ 4] optim steps[9000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14908/0.80349, loss_mask_bce_0: 0.17212/0.30245, loss_mask_dice_0: 0.99777/1.03817, loss_spatial_bce_0: 0.01528/0.09283, loss_spatial_dice_0: 0.10339/0.19664, loss_spatial_ce_0: 0.00085/0.08977, loss_grounding_bce_0: 0.01116/0.08118, loss_grounding_dice_0: 0.22812/0.15162, loss_grounding_ce_0: 0.30049/0.25489, loss_mask_ce_1: 1.21222/0.80593, loss_mask_bce_1: 0.18525/0.30299, loss_mask_dice_1: 1.37193/1.04296, loss_spatial_bce_1: 0.01585/0.09350, loss_spatial_dice_1: 0.08269/0.19942, loss_spatial_ce_1: 0.00056/0.09420, loss_grounding_bce_1: 0.00991/0.08114, loss_grounding_dice_1: 0.19946/0.15275, loss_grounding_ce_1: 0.29846/0.25949, loss_mask_ce_2: 0.94519/0.81181, loss_mask_bce_2: 0.17879/0.30327, loss_mask_dice_2: 1.05309/1.04655, loss_spatial_bce_2: 0.01734/0.09286, loss_spatial_dice_2: 0.09652/0.19931, loss_spatial_ce_2: 0.00032/0.09970, loss_grounding_bce_2: 0.01371/0.08107, loss_grounding_dice_2: 0.26246/0.15276, loss_grounding_ce_2: 0.18103/0.25880, loss_mask_ce_3: 0.64698/0.81016, loss_mask_bce_3: 0.18008/0.30458, loss_mask_dice_3: 1.62369/1.04066, loss_spatial_bce_3: 0.01658/0.09436, loss_spatial_dice_3: 0.10385/0.19938, loss_spatial_ce_3: 0.00112/0.10515, loss_grounding_bce_3: 0.01695/0.08167, loss_grounding_dice_3: 0.27427/0.15262, loss_grounding_ce_3: 0.18544/0.25791, loss_mask_ce_4: 0.60851/0.81398, loss_mask_bce_4: 0.15563/0.30686, loss_mask_dice_4: 1.28612/1.05945, loss_spatial_bce_4: 0.01459/0.09621, loss_spatial_dice_4: 0.10005/0.20686, loss_spatial_ce_4: 0.00291/0.11644, loss_grounding_bce_4: 0.00840/0.08228, loss_grounding_dice_4: 0.26976/0.15472, loss_grounding_ce_4: 0.27007/0.26671, loss_mask_ce_5: 0.81453/0.83430, loss_mask_bce_5: 0.16786/0.30868, loss_mask_dice_5: 1.46396/1.06886, loss_spatial_bce_5: 0.01833/0.09777, loss_spatial_dice_5: 0.08584/0.20884, loss_spatial_ce_5: 0.01558/0.12697, loss_grounding_bce_5: 0.01329/0.08247, loss_grounding_dice_5: 0.24325/0.15542, loss_grounding_ce_5: 0.24980/0.28440, loss_mask_ce_6: 0.78255/0.85755, loss_mask_bce_6: 0.19036/0.30969, loss_mask_dice_6: 1.31185/1.07397, loss_spatial_bce_6: 0.02032/0.10258, loss_spatial_dice_6: 0.12922/0.21133, loss_spatial_ce_6: 0.04520/0.14391, loss_grounding_bce_6: 0.01545/0.08391, loss_grounding_dice_6: 0.21007/0.15590, loss_grounding_ce_6: 0.45487/0.30223, loss_mask_ce_7: 1.14520/0.92054, loss_mask_bce_7: 0.21591/0.31703, loss_mask_dice_7: 1.37599/1.11943, loss_spatial_bce_7: 0.01860/0.11365, loss_spatial_dice_7: 0.10645/0.23682, loss_spatial_ce_7: 0.02490/0.19339, loss_grounding_bce_7: 0.00872/0.08559, loss_grounding_dice_7: 0.25763/0.16150, loss_grounding_ce_7: 0.41782/0.35362, loss_mask_ce_8: 1.05188/1.06822, loss_mask_bce_8: 0.26327/0.33514, loss_mask_dice_8: 1.98445/1.20006, loss_spatial_bce_8: 0.01885/0.13633, loss_spatial_dice_8: 0.10864/0.28119, loss_spatial_ce_8: 0.06609/0.24761, loss_grounding_bce_8: 0.04329/0.08944, loss_grounding_dice_8: 0.30072/0.17027, loss_grounding_ce_8: 0.29219/0.45724, loss_mask_ce_9: 5.81068/3.53725, loss_mask_bce_9: 0.12516/0.36140, loss_mask_dice_9: 1.84127/1.78661, loss_spatial_bce_9: 0.09756/0.36568, loss_spatial_dice_9: 0.81667/0.80016, loss_spatial_ce_9: 2.27238/1.44001, loss_grounding_bce_9: 0.01147/0.10131, loss_grounding_dice_9: 0.41263/0.24594, loss_grounding_ce_9: 0.40214/0.74774] items per batch[64] items per second[0.35] total items[576000] mini batches[ 9000] memory[4929] epoch remaining[0:04:04] INFO:trainer.default_trainer:epochs[ 4] optim steps[9100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58380/0.80257, loss_mask_bce_0: 0.26552/0.30242, loss_mask_dice_0: 0.24331/1.03534, loss_spatial_bce_0: 0.16662/0.09305, loss_spatial_dice_0: 0.24748/0.19652, loss_spatial_ce_0: 0.34133/0.08956, loss_grounding_bce_0: 0.10986/0.08123, loss_grounding_dice_0: 0.04920/0.15151, loss_grounding_ce_0: 0.00548/0.25453, loss_mask_ce_1: 0.60714/0.80494, loss_mask_bce_1: 0.25574/0.30297, loss_mask_dice_1: 0.24516/1.04010, loss_spatial_bce_1: 0.17424/0.09375, loss_spatial_dice_1: 0.24748/0.19934, loss_spatial_ce_1: 0.36843/0.09382, loss_grounding_bce_1: 0.10752/0.08119, loss_grounding_dice_1: 0.04840/0.15264, loss_grounding_ce_1: 0.00744/0.25911, loss_mask_ce_2: 0.59503/0.81081, loss_mask_bce_2: 0.26486/0.30328, loss_mask_dice_2: 0.25017/1.04385, loss_spatial_bce_2: 0.20698/0.09314, loss_spatial_dice_2: 0.16800/0.19924, loss_spatial_ce_2: 0.18285/0.09922, loss_grounding_bce_2: 0.11004/0.08110, loss_grounding_dice_2: 0.05419/0.15267, loss_grounding_ce_2: 0.00230/0.25848, loss_mask_ce_3: 0.58020/0.80938, loss_mask_bce_3: 0.25539/0.30456, loss_mask_dice_3: 0.23794/1.03802, loss_spatial_bce_3: 0.19994/0.09462, loss_spatial_dice_3: 0.16591/0.19933, loss_spatial_ce_3: 0.19469/0.10475, loss_grounding_bce_3: 0.11331/0.08169, loss_grounding_dice_3: 0.05304/0.15248, loss_grounding_ce_3: 0.00147/0.25772, loss_mask_ce_4: 0.60824/0.81330, loss_mask_bce_4: 0.26313/0.30684, loss_mask_dice_4: 0.25322/1.05668, loss_spatial_bce_4: 0.18697/0.09644, loss_spatial_dice_4: 0.24413/0.20681, loss_spatial_ce_4: 0.31084/0.11607, loss_grounding_bce_4: 0.11222/0.08232, loss_grounding_dice_4: 0.05113/0.15460, loss_grounding_ce_4: 0.00380/0.26625, loss_mask_ce_5: 0.67293/0.83363, loss_mask_bce_5: 0.27731/0.30867, loss_mask_dice_5: 0.25527/1.06613, loss_spatial_bce_5: 0.18222/0.09800, loss_spatial_dice_5: 0.24784/0.20875, loss_spatial_ce_5: 0.33261/0.12666, loss_grounding_bce_5: 0.11300/0.08249, loss_grounding_dice_5: 0.06218/0.15535, loss_grounding_ce_5: 0.00151/0.28381, loss_mask_ce_6: 0.62548/0.85678, loss_mask_bce_6: 0.28016/0.30972, loss_mask_dice_6: 0.23858/1.07133, loss_spatial_bce_6: 0.19903/0.10282, loss_spatial_dice_6: 0.25076/0.21124, loss_spatial_ce_6: 0.26314/0.14357, loss_grounding_bce_6: 0.11115/0.08393, loss_grounding_dice_6: 0.04980/0.15579, loss_grounding_ce_6: 0.00569/0.30175, loss_mask_ce_7: 0.73473/0.91941, loss_mask_bce_7: 0.28545/0.31701, loss_mask_dice_7: 0.25092/1.11656, loss_spatial_bce_7: 0.24287/0.11391, loss_spatial_dice_7: 0.23867/0.23668, loss_spatial_ce_7: 0.29441/0.19308, loss_grounding_bce_7: 0.10661/0.08562, loss_grounding_dice_7: 0.04585/0.16138, loss_grounding_ce_7: 0.00473/0.35296, loss_mask_ce_8: 0.86149/1.06666, loss_mask_bce_8: 0.30436/0.33501, loss_mask_dice_8: 0.27654/1.19690, loss_spatial_bce_8: 0.24948/0.13662, loss_spatial_dice_8: 0.23083/0.28096, loss_spatial_ce_8: 0.26280/0.24727, loss_grounding_bce_8: 0.10310/0.08946, loss_grounding_dice_8: 0.05326/0.17029, loss_grounding_ce_8: 0.08101/0.45630, loss_mask_ce_9: 1.99619/3.53279, loss_mask_bce_9: 0.40978/0.36136, loss_mask_dice_9: 0.41171/1.78187, loss_spatial_bce_9: 0.66918/0.36598, loss_spatial_dice_9: 0.78066/0.79997, loss_spatial_ce_9: 2.29979/1.43797, loss_grounding_bce_9: 0.12104/0.10139, loss_grounding_dice_9: 0.04359/0.24577, loss_grounding_ce_9: 1.05859/0.74702] items per batch[64] items per second[0.36] total items[582400] mini batches[ 9100] memory[4929] epoch remaining[0:01:03] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00009135. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3653 s/iter. Eval: 0.0958 s/iter. Total: 0.4638 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0026 s/iter. Inference: 0.3712 s/iter. Eval: 0.0895 s/iter. Total: 0.4635 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0027 s/iter. Inference: 0.3740 s/iter. Eval: 0.0843 s/iter. Total: 0.4611 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3756 s/iter. Eval: 0.0799 s/iter. Total: 0.4585 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3710 s/iter. Eval: 0.0753 s/iter. Total: 0.4492 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0029 s/iter. Inference: 0.3712 s/iter. Eval: 0.0727 s/iter. Total: 0.4470 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9j7db4pq ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 54.931 | 82.481 | 65.354 | 133 | | Things | 61.327 | 83.952 | 72.530 | 80 | | Stuff | 45.278 | 80.261 | 54.523 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.55s) creating index... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.76 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.42 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.96s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 21.94 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.448 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.683 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.483 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.251 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.489 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.543 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.561 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.597 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.757 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.47 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 44.850 | 68.339 | 48.312 | 25.093 | 48.919 | 67.180 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.128 | bicycle | 21.526 | car | 41.053 | | motorcycle | 40.703 | airplane | 60.313 | bus | 70.266 | | train | 74.530 | truck | 43.661 | boat | 29.733 | | traffic light | 26.787 | fire hydrant | 71.529 | stop sign | 68.584 | | parking meter | 50.184 | bench | 26.196 | bird | 33.423 | | cat | 76.662 | dog | 71.191 | horse | 50.781 | | sheep | 54.110 | cow | 55.379 | elephant | 66.462 | | bear | 79.741 | zebra | 65.794 | giraffe | 61.386 | | backpack | 22.756 | umbrella | 55.093 | handbag | 23.908 | | tie | 39.760 | suitcase | 50.856 | frisbee | 70.000 | | skis | 8.061 | snowboard | 34.274 | sports ball | 48.979 | | kite | 38.764 | baseball bat | 37.357 | baseball glove | 50.500 | | skateboard | 43.338 | surfboard | 44.963 | tennis racket | 62.821 | | bottle | 40.324 | wine glass | 36.925 | cup | 49.490 | | fork | 24.189 | knife | 24.574 | spoon | 20.166 | | bowl | 37.246 | banana | 20.785 | apple | 23.999 | | sandwich | 49.341 | orange | 28.613 | broccoli | 23.092 | | carrot | 20.919 | hot dog | 35.824 | pizza | 53.498 | | donut | 52.460 | cake | 45.848 | chair | 27.462 | | couch | 42.506 | potted plant | 22.347 | bed | 40.732 | | dining table | 15.749 | toilet | 69.157 | tv | 65.685 | | laptop | 70.956 | mouse | 62.243 | remote | 41.614 | | keyboard | 58.936 | cell phone | 46.149 | microwave | 64.283 | | oven | 33.937 | toaster | 47.551 | sink | 44.650 | | refrigerator | 68.989 | book | 14.003 | clock | 53.922 | | vase | 39.744 | scissors | 33.221 | teddy bear | 57.122 | | hair drier | 32.019 | toothbrush | 27.163 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.26978070198744, 'fwIoU': 71.33033205878928, 'IoU-person': 88.72147257545362, 'IoU-bicycle': 77.81422413251391, 'IoU-car': 74.09602712063902, 'IoU-motorcycle': 79.28666576697655, 'IoU-airplane': 86.27934305395624, 'IoU-bus': 87.79945684759414, 'IoU-train': 88.7691447164794, 'IoU-truck': 73.57804077550894, 'IoU-boat': 74.0778019123483, 'IoU-traffic light': 79.5872621924865, 'IoU-fire hydrant': 93.28292584364675, 'IoU-stop sign': 95.4338364816723, 'IoU-parking meter': 88.73173184135075, 'IoU-bench': 61.34766612613555, 'IoU-bird': 76.84223534244123, 'IoU-cat': 88.13006954128068, 'IoU-dog': 81.52696896802195, 'IoU-horse': 89.05507011844206, 'IoU-sheep': 81.45629259973619, 'IoU-cow': 86.36453563581732, 'IoU-elephant': 81.40859992606735, 'IoU-bear': 80.99954421805423, 'IoU-zebra': 74.23175885560399, 'IoU-giraffe': 84.07524903013118, 'IoU-backpack': 52.29518313592799, 'IoU-umbrella': 88.1356902114161, 'IoU-handbag': 51.50931467723561, 'IoU-tie': 75.64088541476777, 'IoU-suitcase': 87.49019729738407, 'IoU-frisbee': 84.1871221939629, 'IoU-skis': 59.187708028578086, 'IoU-snowboard': 72.08163512281142, 'IoU-sports ball': 80.18229918677982, 'IoU-kite': 78.1304669838397, 'IoU-baseball bat': 68.52618657457168, 'IoU-baseball glove': 74.14815844658395, 'IoU-skateboard': 86.24432710420858, 'IoU-surfboard': 86.73887140921504, 'IoU-tennis racket': 91.18969775983537, 'IoU-bottle': 69.63875787525961, 'IoU-wine glass': 82.15871000193779, 'IoU-cup': 71.9156898352012, 'IoU-fork': 68.052387551675, 'IoU-knife': 61.784584598438904, 'IoU-spoon': 58.21199332119513, 'IoU-bowl': 62.7346707457548, 'IoU-banana': 83.13722306843833, 'IoU-apple': 58.98994032839147, 'IoU-sandwich': 69.60072058971373, 'IoU-orange': 76.84157682792495, 'IoU-broccoli': 71.44665679689962, 'IoU-carrot': 63.73403933582983, 'IoU-hot dog': 63.37779447125725, 'IoU-pizza': 83.91491716937603, 'IoU-donut': 75.26007230044605, 'IoU-cake': 78.55879789132871, 'IoU-chair': 61.597589651086906, 'IoU-couch': 67.5381685970927, 'IoU-potted plant': 45.20703169573901, 'IoU-bed': 71.55776041124001, 'IoU-dining table': 53.326478185086124, 'IoU-toilet': 75.57927870943912, 'IoU-tv': 79.13705056270395, 'IoU-laptop': 80.74489988736725, 'IoU-mouse': 79.41544911461251, 'IoU-remote': 70.17702914184689, 'IoU-keyboard': 67.80263037742881, 'IoU-cell phone': 76.57169318563618, 'IoU-microwave': 69.69033136755077, 'IoU-oven': 71.93774180204878, 'IoU-toaster': 83.06891609886104, 'IoU-sink': 75.34858036735143, 'IoU-refrigerator': 83.80728728296846, 'IoU-book': 55.47173626702382, 'IoU-clock': 78.59407113884185, 'IoU-vase': 69.72574308709379, 'IoU-scissors': 68.60774153957848, 'IoU-teddy bear': 80.4021425903778, 'IoU-hair drier': 34.142135563762764, 'IoU-toothbrush': 77.47945599310009, 'IoU-banner': 30.463252429538397, 'IoU-blanket': 14.464939935669241, 'IoU-bridge': 34.826547126317855, 'IoU-cardboard': 53.92740532394779, 'IoU-counter': 38.103075602466156, 'IoU-curtain': 70.7570574836924, 'IoU-door-stuff': 47.150011180886935, 'IoU-floor-wood': 68.15003924488914, 'IoU-flower': 48.93874643874644, 'IoU-fruit': 48.91442719480359, 'IoU-gravel': 29.935990989672412, 'IoU-house': 24.345068169945062, 'IoU-light': 43.86253129843056, 'IoU-mirror-stuff': 63.38759959689333, 'IoU-net': 49.85752020480427, 'IoU-pillow': 16.12391221060697, 'IoU-platform': 34.12453788669966, 'IoU-playingfield': 71.01773904957739, 'IoU-railroad': 61.50051346904951, 'IoU-river': 51.98756231885433, 'IoU-road': 67.40962839918542, 'IoU-roof': 19.059072275362855, 'IoU-sand': 59.537238143008686, 'IoU-sea': 85.46153711881863, 'IoU-shelf': 36.42895845450323, 'IoU-snow': 92.14444614087117, 'IoU-stairs': 31.33609870852232, 'IoU-tent': 11.31781782080598, 'IoU-towel': 43.21204030749338, 'IoU-wall-brick': 48.991336225906124, 'IoU-wall-stone': 30.965076903723364, 'IoU-wall-tile': 67.91107541612905, 'IoU-wall-wood': 42.94542889175184, 'IoU-water-other': 23.295082346707545, 'IoU-window-blind': 51.79822044101095, 'IoU-window-other': 50.558739761314754, 'IoU-tree-merged': 81.4252622808604, 'IoU-fence-merged': 54.06475167964548, 'IoU-ceiling-merged': 67.97512584592444, 'IoU-sky-other-merged': 94.24421730843216, 'IoU-cabinet-merged': 64.57851239669421, 'IoU-table-merged': 42.416519660028776, 'IoU-floor-other-merged': 54.15358584765845, 'IoU-pavement-merged': 58.22263000493755, 'IoU-mountain-merged': 58.39538288009908, 'IoU-grass-merged': 70.14568805300348, 'IoU-dirt-merged': 45.782460057989724, 'IoU-paper-merged': 34.355752692703035, 'IoU-food-other-merged': 43.024304593421256, 'IoU-building-other-merged': 59.66141639829592, 'IoU-rock-merged': 67.07958684651788, 'IoU-wall-other-merged': 68.76760240864151, 'IoU-rug-merged': 67.50461740248764, 'mACC': 76.73996532508939, 'pACC': 82.01785544128425, 'ACC-person': 93.17568535082023, 'ACC-bicycle': 88.39708923518876, 'ACC-car': 85.21539202533816, 'ACC-motorcycle': 83.27635599015609, 'ACC-airplane': 90.02255033471663, 'ACC-bus': 93.96326014510147, 'ACC-train': 95.7618490695971, 'ACC-truck': 83.2272455347433, 'ACC-boat': 83.24196185386326, 'ACC-traffic light': 92.14983476039362, 'ACC-fire hydrant': 95.85945206181385, 'ACC-stop sign': 98.10557629773949, 'ACC-parking meter': 92.40776188186626, 'ACC-bench': 79.61551453232543, 'ACC-bird': 82.16856692859467, 'ACC-cat': 91.88495086041797, 'ACC-dog': 83.9183248190012, 'ACC-horse': 94.35320763015797, 'ACC-sheep': 84.6109042509014, 'ACC-cow': 89.34135697233185, 'ACC-elephant': 82.95792741484092, 'ACC-bear': 82.60252151401495, 'ACC-zebra': 75.8398210335299, 'ACC-giraffe': 87.43223634087101, 'ACC-backpack': 70.36802920219806, 'ACC-umbrella': 91.60110978970988, 'ACC-handbag': 71.98606434298077, 'ACC-tie': 85.11760273401228, 'ACC-suitcase': 92.81113797657137, 'ACC-frisbee': 94.24763636363636, 'ACC-skis': 76.0982224690542, 'ACC-snowboard': 81.69087213146874, 'ACC-sports ball': 88.97623551544847, 'ACC-kite': 86.16231100506168, 'ACC-baseball bat': 87.41505518135297, 'ACC-baseball glove': 92.22959584387137, 'ACC-skateboard': 90.7782405932017, 'ACC-surfboard': 92.5118873408434, 'ACC-tennis racket': 95.2831164137751, 'ACC-bottle': 82.54624406246148, 'ACC-wine glass': 90.32936846113398, 'ACC-cup': 88.8683398515343, 'ACC-fork': 80.39176886533583, 'ACC-knife': 78.50608192276691, 'ACC-spoon': 71.29023374723074, 'ACC-bowl': 72.41241268628012, 'ACC-banana': 89.72288209671949, 'ACC-apple': 71.15777009717034, 'ACC-sandwich': 81.82478700129484, 'ACC-orange': 85.84216079256495, 'ACC-broccoli': 80.7090347512897, 'ACC-carrot': 75.45289947356395, 'ACC-hot dog': 70.22118581656709, 'ACC-pizza': 89.74095835448577, 'ACC-donut': 82.17039158881792, 'ACC-cake': 87.19491719919658, 'ACC-chair': 79.16501349628241, 'ACC-couch': 74.67262825806817, 'ACC-potted plant': 61.80478209789461, 'ACC-bed': 82.45789298406582, 'ACC-dining table': 83.39390535890756, 'ACC-toilet': 78.52889740173218, 'ACC-tv': 88.60195365624921, 'ACC-laptop': 94.93000146553815, 'ACC-mouse': 91.01941134667345, 'ACC-remote': 74.13874797320501, 'ACC-keyboard': 74.93438814180561, 'ACC-cell phone': 84.63036251708589, 'ACC-microwave': 74.57868216325757, 'ACC-oven': 91.17542783180865, 'ACC-toaster': 90.43233108431757, 'ACC-sink': 82.9156860256461, 'ACC-refrigerator': 94.47862358817945, 'ACC-book': 70.13455773164104, 'ACC-clock': 84.25179151132399, 'ACC-vase': 78.7945841483714, 'ACC-scissors': 72.8860792037389, 'ACC-teddy bear': 85.47988612796256, 'ACC-hair drier': 35.78544388466684, 'ACC-toothbrush': 84.27640722724114, 'ACC-banner': 76.97381282934418, 'ACC-blanket': 22.09684763430281, 'ACC-bridge': 49.58065835051756, 'ACC-cardboard': 75.43193252871892, 'ACC-counter': 64.51696022444771, 'ACC-curtain': 83.04635763709533, 'ACC-door-stuff': 69.68079554545729, 'ACC-floor-wood': 77.47980155460715, 'ACC-flower': 65.20366432604368, 'ACC-fruit': 66.13317160626087, 'ACC-gravel': 41.44320805242205, 'ACC-house': 28.63469162152609, 'ACC-light': 62.585054358228376, 'ACC-mirror-stuff': 77.58201253879017, 'ACC-net': 64.51747355632264, 'ACC-pillow': 42.775341677380524, 'ACC-platform': 64.1332600843617, 'ACC-playingfield': 90.46900391933839, 'ACC-railroad': 85.55428598913645, 'ACC-river': 72.85060073545507, 'ACC-road': 83.91838671732101, 'ACC-roof': 25.23255387549047, 'ACC-sand': 63.786628891134335, 'ACC-sea': 89.01225156268943, 'ACC-shelf': 52.64342811000987, 'ACC-snow': 95.53696096267252, 'ACC-stairs': 56.285111497133485, 'ACC-tent': 13.87399575929171, 'ACC-towel': 56.77225781643496, 'ACC-wall-brick': 68.53601446901399, 'ACC-wall-stone': 39.677417232485915, 'ACC-wall-tile': 87.3655698776457, 'ACC-wall-wood': 63.425060215042095, 'ACC-water-other': 44.52389534505295, 'ACC-window-blind': 64.88643798571609, 'ACC-window-other': 75.440682578645, 'ACC-tree-merged': 90.39846435689942, 'ACC-fence-merged': 72.24636472243894, 'ACC-ceiling-merged': 82.45593287835067, 'ACC-sky-other-merged': 97.00623784003491, 'ACC-cabinet-merged': 77.98001709910649, 'ACC-table-merged': 54.030500962905215, 'ACC-floor-other-merged': 65.31024404818967, 'ACC-pavement-merged': 71.37870561887958, 'ACC-mountain-merged': 67.34638971918197, 'ACC-grass-merged': 85.19741520303907, 'ACC-dirt-merged': 64.78550905449701, 'ACC-paper-merged': 45.07150766434353, 'ACC-food-other-merged': 53.114090887209834, 'ACC-building-other-merged': 76.77796938511608, 'ACC-rock-merged': 81.7584094991539, 'ACC-wall-other-merged': 79.37338374669376, 'ACC-rug-merged': 81.92327214972246})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2898 s/iter. Inference: 0.1696 s/iter. Eval: 0.0000 s/iter. Total: 0.4594 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3308 s/iter. Inference: 0.3370 s/iter. Eval: 0.0000 s/iter. Total: 0.6679 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3533 s/iter. Inference: 0.5413 s/iter. Eval: 0.0000 s/iter. Total: 0.8948 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4623939127889962, 'noc@0.8': 2.594673690371671, 'noc@0.85': 3.086625695054141, 'noc@0.9': 3.986830553116769, 'miou@iter1': 0.8725527539251839} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1425 s/iter. Eval: 0.0010 s/iter. Total: 0.1450 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.28176879882812, 'precision@0.6': 72.6000747680664, 'precision@0.7': 67.6642074584961, 'precision@0.8': 58.95841598510742, 'precision@0.9': 32.56898498535156, 'cIoU': 61.54738235473633, 'mIoU': 66.62947845458984} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 54.931288391627874, 'SQ': 82.48092584079157, 'RQ': 65.35419671941126, 'PQ_th': 61.326644315743486, 'SQ_th': 83.95157731324113, 'RQ_th': 72.5299632519995, 'PQ_st': 45.27792095900054, 'SQ_st': 80.26107456162238, 'RQ_st': 54.52285100984403}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 44.849848889973025, 'AP50': 68.3391996907976, 'AP75': 48.31179276692071, 'APs': 25.093056035739814, 'APm': 48.91934368925116, 'APl': 67.18045618900884, 'AP-person': 49.12787345333327, 'AP-bicycle': 21.526255962857967, 'AP-car': 41.0534265524848, 'AP-motorcycle': 40.703424301702256, 'AP-airplane': 60.31266311629094, 'AP-bus': 70.26639142450465, 'AP-train': 74.53006880283777, 'AP-truck': 43.661056649401246, 'AP-boat': 29.733054532208243, 'AP-traffic light': 26.787304578534606, 'AP-fire hydrant': 71.52910786263995, 'AP-stop sign': 68.58350279192413, 'AP-parking meter': 50.18359606808568, 'AP-bench': 26.196035427629518, 'AP-bird': 33.42257199146591, 'AP-cat': 76.66242875994233, 'AP-dog': 71.19084709539875, 'AP-horse': 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'ACC-laptop': 94.93000146553815, 'ACC-mouse': 91.01941134667345, 'ACC-remote': 74.13874797320501, 'ACC-keyboard': 74.93438814180561, 'ACC-cell phone': 84.63036251708589, 'ACC-microwave': 74.57868216325757, 'ACC-oven': 91.17542783180865, 'ACC-toaster': 90.43233108431757, 'ACC-sink': 82.9156860256461, 'ACC-refrigerator': 94.47862358817945, 'ACC-book': 70.13455773164104, 'ACC-clock': 84.25179151132399, 'ACC-vase': 78.7945841483714, 'ACC-scissors': 72.8860792037389, 'ACC-teddy bear': 85.47988612796256, 'ACC-hair drier': 35.78544388466684, 'ACC-toothbrush': 84.27640722724114, 'ACC-banner': 76.97381282934418, 'ACC-blanket': 22.09684763430281, 'ACC-bridge': 49.58065835051756, 'ACC-cardboard': 75.43193252871892, 'ACC-counter': 64.51696022444771, 'ACC-curtain': 83.04635763709533, 'ACC-door-stuff': 69.68079554545729, 'ACC-floor-wood': 77.47980155460715, 'ACC-flower': 65.20366432604368, 'ACC-fruit': 66.13317160626087, 'ACC-gravel': 41.44320805242205, 'ACC-house': 28.63469162152609, 'ACC-light': 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77.98001709910649, 'ACC-table-merged': 54.030500962905215, 'ACC-floor-other-merged': 65.31024404818967, 'ACC-pavement-merged': 71.37870561887958, 'ACC-mountain-merged': 67.34638971918197, 'ACC-grass-merged': 85.19741520303907, 'ACC-dirt-merged': 64.78550905449701, 'ACC-paper-merged': 45.07150766434353, 'ACC-food-other-merged': 53.114090887209834, 'ACC-building-other-merged': 76.77796938511608, 'ACC-rock-merged': 81.7584094991539, 'ACC-wall-other-merged': 79.37338374669376, 'ACC-rug-merged': 81.92327214972246})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4623939127889962, 'noc@0.8': 2.594673690371671, 'noc@0.85': 3.086625695054141, 'noc@0.9': 3.986830553116769, 'miou@iter1': 0.8725527539251839}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.28176879882812, 'precision@0.6': 72.6000747680664, 'precision@0.7': 67.6642074584961, 'precision@0.8': 58.95841598510742, 'precision@0.9': 32.56898498535156, 'cIoU': 61.54738235473633, 'mIoU': 66.62947845458984}}} INFO:trainer.default_trainer:This epoch takes 0:58:38.191375 INFO:trainer.default_trainer:PROGRESS: 10.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 5 training. INFO:trainer.default_trainer:epochs[ 5] optim steps[9200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17423/0.80253, loss_mask_bce_0: 0.38079/0.30293, loss_mask_dice_0: 0.49102/1.03444, loss_spatial_bce_0: 0.13338/0.09318, loss_spatial_dice_0: 0.16923/0.19641, loss_spatial_ce_0: 0.02575/0.08906, loss_grounding_bce_0: 0.03874/0.08126, loss_grounding_dice_0: 0.12041/0.15153, loss_grounding_ce_0: 0.13594/0.25515, loss_mask_ce_1: 0.19561/0.80495, loss_mask_bce_1: 0.38074/0.30350, loss_mask_dice_1: 0.46324/1.03925, loss_spatial_bce_1: 0.13374/0.09386, loss_spatial_dice_1: 0.16872/0.19922, loss_spatial_ce_1: 0.01587/0.09338, loss_grounding_bce_1: 0.03718/0.08124, loss_grounding_dice_1: 0.11382/0.15267, loss_grounding_ce_1: 0.06022/0.25953, loss_mask_ce_2: 0.18097/0.81086, loss_mask_bce_2: 0.40093/0.30380, loss_mask_dice_2: 0.47440/1.04290, loss_spatial_bce_2: 0.13340/0.09326, loss_spatial_dice_2: 0.16529/0.19913, loss_spatial_ce_2: 0.01277/0.09879, loss_grounding_bce_2: 0.03600/0.08115, loss_grounding_dice_2: 0.12699/0.15266, loss_grounding_ce_2: 0.07769/0.25907, loss_mask_ce_3: 0.14222/0.80920, loss_mask_bce_3: 0.40608/0.30508, loss_mask_dice_3: 0.46694/1.03726, loss_spatial_bce_3: 0.13068/0.09474, loss_spatial_dice_3: 0.15822/0.19924, loss_spatial_ce_3: 0.00825/0.10430, loss_grounding_bce_3: 0.03632/0.08175, loss_grounding_dice_3: 0.12634/0.15256, loss_grounding_ce_3: 0.07685/0.25799, loss_mask_ce_4: 0.18236/0.81340, loss_mask_bce_4: 0.41322/0.30728, loss_mask_dice_4: 0.48678/1.05585, loss_spatial_bce_4: 0.12784/0.09658, loss_spatial_dice_4: 0.15349/0.20670, loss_spatial_ce_4: 0.00633/0.11550, loss_grounding_bce_4: 0.03785/0.08240, loss_grounding_dice_4: 0.12255/0.15461, loss_grounding_ce_4: 0.13600/0.26654, loss_mask_ce_5: 0.18295/0.83322, loss_mask_bce_5: 0.40392/0.30916, loss_mask_dice_5: 0.44757/1.06530, loss_spatial_bce_5: 0.12792/0.09813, loss_spatial_dice_5: 0.14850/0.20866, loss_spatial_ce_5: 0.00755/0.12620, loss_grounding_bce_5: 0.03603/0.08255, loss_grounding_dice_5: 0.13513/0.15542, loss_grounding_ce_5: 0.29082/0.28386, loss_mask_ce_6: 0.17750/0.85670, loss_mask_bce_6: 0.40170/0.31023, loss_mask_dice_6: 0.49447/1.07044, loss_spatial_bce_6: 0.13028/0.10294, loss_spatial_dice_6: 0.15731/0.21112, loss_spatial_ce_6: 0.01003/0.14309, loss_grounding_bce_6: 0.03909/0.08398, loss_grounding_dice_6: 0.12009/0.15584, loss_grounding_ce_6: 0.16773/0.30201, loss_mask_ce_7: 0.22223/0.91944, loss_mask_bce_7: 0.42222/0.31743, loss_mask_dice_7: 0.45618/1.11588, loss_spatial_bce_7: 0.14925/0.11396, loss_spatial_dice_7: 0.17214/0.23654, loss_spatial_ce_7: 0.11226/0.19263, loss_grounding_bce_7: 0.04286/0.08569, loss_grounding_dice_7: 0.13100/0.16138, loss_grounding_ce_7: 1.11691/0.35321, loss_mask_ce_8: 0.54612/1.06669, loss_mask_bce_8: 0.39478/0.33553, loss_mask_dice_8: 0.48254/1.19621, loss_spatial_bce_8: 0.16561/0.13670, loss_spatial_dice_8: 0.21439/0.28090, loss_spatial_ce_8: 0.06918/0.24690, loss_grounding_bce_8: 0.03390/0.08947, loss_grounding_dice_8: 0.11691/0.17039, loss_grounding_ce_8: 2.53501/0.45712, loss_mask_ce_9: 2.75326/3.53341, loss_mask_bce_9: 0.41888/0.36187, loss_mask_dice_9: 0.82501/1.78111, loss_spatial_bce_9: 0.47662/0.36611, loss_spatial_dice_9: 0.78055/0.80004, loss_spatial_ce_9: 1.02967/1.43732, loss_grounding_bce_9: 0.06435/0.10149, loss_grounding_dice_9: 0.30604/0.24595, loss_grounding_ce_9: 2.26186/0.74560] items per batch[64] items per second[0.16] total items[588800] mini batches[ 9200] memory[4929] epoch remaining[0:56:49] INFO:trainer.default_trainer:epochs[ 5] optim steps[9300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23217/0.80129, loss_mask_bce_0: 0.18296/0.30287, loss_mask_dice_0: 1.92649/1.03267, loss_spatial_bce_0: 0.02490/0.09306, loss_spatial_dice_0: 0.22905/0.19603, loss_spatial_ce_0: 0.12808/0.08869, loss_grounding_bce_0: 0.03641/0.08121, loss_grounding_dice_0: 0.05124/0.15155, loss_grounding_ce_0: 0.00033/0.25410, loss_mask_ce_1: 1.15527/0.80386, loss_mask_bce_1: 0.19481/0.30344, loss_mask_dice_1: 1.65922/1.03763, loss_spatial_bce_1: 0.02501/0.09375, loss_spatial_dice_1: 0.22080/0.19884, loss_spatial_ce_1: 0.11632/0.09297, loss_grounding_bce_1: 0.03181/0.08119, loss_grounding_dice_1: 0.04324/0.15271, loss_grounding_ce_1: 0.00044/0.25869, loss_mask_ce_2: 1.14835/0.80944, loss_mask_bce_2: 0.20867/0.30376, loss_mask_dice_2: 1.86105/1.04123, loss_spatial_bce_2: 0.02645/0.09317, loss_spatial_dice_2: 0.28014/0.19876, loss_spatial_ce_2: 0.09195/0.09838, loss_grounding_bce_2: 0.03693/0.08109, loss_grounding_dice_2: 0.04897/0.15265, loss_grounding_ce_2: 0.00071/0.25811, loss_mask_ce_3: 1.43747/0.80802, loss_mask_bce_3: 0.19246/0.30504, loss_mask_dice_3: 1.73880/1.03585, loss_spatial_bce_3: 0.02703/0.09463, loss_spatial_dice_3: 0.22477/0.19887, loss_spatial_ce_3: 0.15009/0.10395, loss_grounding_bce_3: 0.03171/0.08170, loss_grounding_dice_3: 0.04339/0.15249, loss_grounding_ce_3: 0.00084/0.25700, loss_mask_ce_4: 1.38406/0.81198, loss_mask_bce_4: 0.20233/0.30722, loss_mask_dice_4: 1.98016/1.05418, loss_spatial_bce_4: 0.02655/0.09646, loss_spatial_dice_4: 0.28574/0.20632, loss_spatial_ce_4: 0.13014/0.11503, loss_grounding_bce_4: 0.03790/0.08233, loss_grounding_dice_4: 0.04852/0.15469, loss_grounding_ce_4: 0.00037/0.26568, loss_mask_ce_5: 2.06215/0.83187, loss_mask_bce_5: 0.23532/0.30909, loss_mask_dice_5: 2.06758/1.06352, loss_spatial_bce_5: 0.02586/0.09801, loss_spatial_dice_5: 0.26160/0.20825, loss_spatial_ce_5: 0.20373/0.12599, loss_grounding_bce_5: 0.03316/0.08250, loss_grounding_dice_5: 0.04343/0.15537, loss_grounding_ce_5: 0.00045/0.28319, loss_mask_ce_6: 1.07857/0.85523, loss_mask_bce_6: 0.18805/0.31016, loss_mask_dice_6: 1.66939/1.06873, loss_spatial_bce_6: 0.03608/0.10279, loss_spatial_dice_6: 0.29030/0.21074, loss_spatial_ce_6: 0.13692/0.14272, loss_grounding_bce_6: 0.03567/0.08391, loss_grounding_dice_6: 0.04784/0.15586, loss_grounding_ce_6: 0.00024/0.30144, loss_mask_ce_7: 0.98263/0.91828, loss_mask_bce_7: 0.18825/0.31729, loss_mask_dice_7: 1.94754/1.11395, loss_spatial_bce_7: 0.03031/0.11384, loss_spatial_dice_7: 0.32263/0.23615, loss_spatial_ce_7: 0.20574/0.19210, loss_grounding_bce_7: 0.03751/0.08561, loss_grounding_dice_7: 0.05062/0.16142, loss_grounding_ce_7: 0.00026/0.35222, loss_mask_ce_8: 1.42469/1.06553, loss_mask_bce_8: 0.18530/0.33537, loss_mask_dice_8: 1.72792/1.19437, loss_spatial_bce_8: 0.04326/0.13659, loss_spatial_dice_8: 0.40165/0.28043, loss_spatial_ce_8: 0.33484/0.24635, loss_grounding_bce_8: 0.04155/0.08940, loss_grounding_dice_8: 0.05153/0.17036, loss_grounding_ce_8: 0.00030/0.45615, loss_mask_ce_9: 2.35683/3.53086, loss_mask_bce_9: 0.15577/0.36182, loss_mask_dice_9: 2.12113/1.77936, loss_spatial_bce_9: 0.48869/0.36661, loss_spatial_dice_9: 0.86036/0.80005, loss_spatial_ce_9: 1.83905/1.43660, loss_grounding_bce_9: 0.04248/0.10145, loss_grounding_dice_9: 0.05395/0.24589, loss_grounding_ce_9: 0.03592/0.74407] items per batch[64] items per second[0.36] total items[595200] mini batches[ 9300] memory[4929] epoch remaining[0:51:22] INFO:trainer.default_trainer:epochs[ 5] optim steps[9400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35530/0.79987, loss_mask_bce_0: 0.14038/0.30272, loss_mask_dice_0: 0.35318/1.03163, loss_spatial_bce_0: 0.21140/0.09296, loss_spatial_dice_0: 0.38010/0.19582, loss_spatial_ce_0: 0.08629/0.08817, loss_grounding_bce_0: 0.09559/0.08115, loss_grounding_dice_0: 0.31568/0.15147, loss_grounding_ce_0: 0.15243/0.25511, loss_mask_ce_1: 0.32279/0.80261, loss_mask_bce_1: 0.15617/0.30329, loss_mask_dice_1: 0.06895/1.03638, loss_spatial_bce_1: 0.26812/0.09365, loss_spatial_dice_1: 0.37749/0.19862, loss_spatial_ce_1: 0.11841/0.09247, loss_grounding_bce_1: 0.10028/0.08115, loss_grounding_dice_1: 0.24919/0.15262, loss_grounding_ce_1: 0.14905/0.25952, loss_mask_ce_2: 0.39019/0.80813, loss_mask_bce_2: 0.16322/0.30360, loss_mask_dice_2: 0.07212/1.03995, loss_spatial_bce_2: 0.29710/0.09308, loss_spatial_dice_2: 0.37814/0.19855, loss_spatial_ce_2: 0.06611/0.09789, loss_grounding_bce_2: 0.10391/0.08102, loss_grounding_dice_2: 0.05076/0.15259, loss_grounding_ce_2: 0.15903/0.25896, loss_mask_ce_3: 0.44063/0.80664, loss_mask_bce_3: 0.17750/0.30491, loss_mask_dice_3: 0.21706/1.03481, loss_spatial_bce_3: 0.33115/0.09454, loss_spatial_dice_3: 0.37963/0.19869, loss_spatial_ce_3: 0.12608/0.10338, loss_grounding_bce_3: 0.11011/0.08165, loss_grounding_dice_3: 0.11789/0.15242, loss_grounding_ce_3: 0.17032/0.25785, loss_mask_ce_4: 0.43835/0.81088, loss_mask_bce_4: 0.19936/0.30703, loss_mask_dice_4: 0.43805/1.05306, loss_spatial_bce_4: 0.46237/0.09636, loss_spatial_dice_4: 0.38145/0.20610, loss_spatial_ce_4: 0.08153/0.11463, loss_grounding_bce_4: 0.11896/0.08227, loss_grounding_dice_4: 0.27255/0.15458, loss_grounding_ce_4: 0.16765/0.26659, loss_mask_ce_5: 0.39605/0.83087, loss_mask_bce_5: 0.17410/0.30894, loss_mask_dice_5: 0.29292/1.06206, loss_spatial_bce_5: 0.18787/0.09790, loss_spatial_dice_5: 0.38236/0.20807, loss_spatial_ce_5: 0.12961/0.12552, loss_grounding_bce_5: 0.11012/0.08247, loss_grounding_dice_5: 0.12192/0.15526, loss_grounding_ce_5: 0.15463/0.28415, loss_mask_ce_6: 0.39645/0.85468, loss_mask_bce_6: 0.19596/0.31003, loss_mask_dice_6: 0.07681/1.06715, loss_spatial_bce_6: 0.18181/0.10267, loss_spatial_dice_6: 0.38561/0.21050, loss_spatial_ce_6: 0.26142/0.14237, loss_grounding_bce_6: 0.12697/0.08389, loss_grounding_dice_6: 0.27793/0.15576, loss_grounding_ce_6: 0.15150/0.30227, loss_mask_ce_7: 0.41412/0.91732, loss_mask_bce_7: 0.11915/0.31704, loss_mask_dice_7: 0.36256/1.11201, loss_spatial_bce_7: 0.18889/0.11368, loss_spatial_dice_7: 0.38168/0.23589, loss_spatial_ce_7: 0.45531/0.19149, loss_grounding_bce_7: 0.14520/0.08555, loss_grounding_dice_7: 0.26581/0.16127, loss_grounding_ce_7: 0.20195/0.35314, loss_mask_ce_8: 0.46387/1.06393, loss_mask_bce_8: 0.15216/0.33519, loss_mask_dice_8: 0.07440/1.19252, loss_spatial_bce_8: 0.35900/0.13648, loss_spatial_dice_8: 0.38656/0.28023, loss_spatial_ce_8: 0.60528/0.24584, loss_grounding_bce_8: 0.12056/0.08939, loss_grounding_dice_8: 0.32113/0.17018, loss_grounding_ce_8: 0.13899/0.45662, loss_mask_ce_9: 2.01404/3.53030, loss_mask_bce_9: 0.12990/0.36167, loss_mask_dice_9: 0.37596/1.77786, loss_spatial_bce_9: 0.31108/0.36695, loss_spatial_dice_9: 0.57846/0.79995, loss_spatial_ce_9: 1.79045/1.43651, loss_grounding_bce_9: 0.10172/0.10139, loss_grounding_dice_9: 0.19261/0.24564, loss_grounding_ce_9: 0.27231/0.74562] items per batch[64] items per second[0.35] total items[601600] mini batches[ 9400] memory[4929] epoch remaining[0:48:05] INFO:trainer.default_trainer:epochs[ 5] optim steps[9500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95444/0.80013, loss_mask_bce_0: 0.41608/0.30284, loss_mask_dice_0: 2.61984/1.03124, loss_spatial_bce_0: 0.09996/0.09302, loss_spatial_dice_0: 0.11546/0.19573, loss_spatial_ce_0: 0.01577/0.08807, loss_grounding_bce_0: 0.03889/0.08131, loss_grounding_dice_0: 0.05213/0.15142, loss_grounding_ce_0: 0.00285/0.25505, loss_mask_ce_1: 0.88176/0.80265, loss_mask_bce_1: 0.43725/0.30338, loss_mask_dice_1: 2.40484/1.03570, loss_spatial_bce_1: 0.10018/0.09367, loss_spatial_dice_1: 0.11976/0.19849, loss_spatial_ce_1: 0.01867/0.09234, loss_grounding_bce_1: 0.04512/0.08131, loss_grounding_dice_1: 0.06009/0.15255, loss_grounding_ce_1: 0.00280/0.25937, loss_mask_ce_2: 0.95948/0.80822, loss_mask_bce_2: 0.46110/0.30375, loss_mask_dice_2: 2.28390/1.03940, loss_spatial_bce_2: 0.10141/0.09309, loss_spatial_dice_2: 0.11198/0.19842, loss_spatial_ce_2: 0.02037/0.09780, loss_grounding_bce_2: 0.03978/0.08118, loss_grounding_dice_2: 0.05293/0.15249, loss_grounding_ce_2: 0.00319/0.25878, loss_mask_ce_3: 0.95364/0.80675, loss_mask_bce_3: 0.44301/0.30500, loss_mask_dice_3: 2.38467/1.03399, loss_spatial_bce_3: 0.10272/0.09459, loss_spatial_dice_3: 0.11018/0.19858, loss_spatial_ce_3: 0.03571/0.10325, loss_grounding_bce_3: 0.03896/0.08181, loss_grounding_dice_3: 0.05199/0.15236, loss_grounding_ce_3: 0.00343/0.25733, loss_mask_ce_4: 0.84366/0.81116, loss_mask_bce_4: 0.46695/0.30711, loss_mask_dice_4: 2.64662/1.05232, loss_spatial_bce_4: 0.10729/0.09635, loss_spatial_dice_4: 0.10831/0.20599, loss_spatial_ce_4: 0.12067/0.11462, loss_grounding_bce_4: 0.03756/0.08245, loss_grounding_dice_4: 0.05408/0.15450, loss_grounding_ce_4: 0.00397/0.26619, loss_mask_ce_5: 0.87024/0.83097, loss_mask_bce_5: 0.46168/0.30901, loss_mask_dice_5: 2.49330/1.06132, loss_spatial_bce_5: 0.10637/0.09791, loss_spatial_dice_5: 0.10388/0.20797, loss_spatial_ce_5: 0.12629/0.12542, loss_grounding_bce_5: 0.04070/0.08266, loss_grounding_dice_5: 0.05611/0.15515, loss_grounding_ce_5: 0.00648/0.28392, loss_mask_ce_6: 1.02430/0.85472, loss_mask_bce_6: 0.45043/0.31011, loss_mask_dice_6: 2.07248/1.06642, loss_spatial_bce_6: 0.10682/0.10269, loss_spatial_dice_6: 0.11379/0.21040, loss_spatial_ce_6: 0.09120/0.14249, loss_grounding_bce_6: 0.03604/0.08410, loss_grounding_dice_6: 0.04964/0.15569, loss_grounding_ce_6: 0.00882/0.30186, loss_mask_ce_7: 1.04241/0.91736, loss_mask_bce_7: 0.47727/0.31720, loss_mask_dice_7: 2.53550/1.11159, loss_spatial_bce_7: 0.13238/0.11382, loss_spatial_dice_7: 0.14486/0.23579, loss_spatial_ce_7: 0.07440/0.19106, loss_grounding_bce_7: 0.04379/0.08576, loss_grounding_dice_7: 0.05418/0.16118, loss_grounding_ce_7: 0.01078/0.35252, loss_mask_ce_8: 1.21203/1.06367, loss_mask_bce_8: 0.46647/0.33526, loss_mask_dice_8: 2.78296/1.19196, loss_spatial_bce_8: 0.21167/0.13654, loss_spatial_dice_8: 0.21423/0.27998, loss_spatial_ce_8: 0.10195/0.24561, loss_grounding_bce_8: 0.04886/0.08961, loss_grounding_dice_8: 0.07481/0.17010, loss_grounding_ce_8: 0.01790/0.45612, loss_mask_ce_9: 4.13690/3.52873, loss_mask_bce_9: 0.49836/0.36177, loss_mask_dice_9: 5.64659/1.77767, loss_spatial_bce_9: 0.58148/0.36708, loss_spatial_dice_9: 0.88563/0.80004, loss_spatial_ce_9: 1.70437/1.43602, loss_grounding_bce_9: 0.06749/0.10174, loss_grounding_dice_9: 0.11388/0.24557, loss_grounding_ce_9: 0.07519/0.74455] items per batch[64] items per second[0.36] total items[608000] mini batches[ 9500] memory[4929] epoch remaining[0:44:23] INFO:trainer.default_trainer:epochs[ 5] optim steps[9600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22845/0.80134, loss_mask_bce_0: 0.01126/0.30294, loss_mask_dice_0: 0.15960/1.03256, loss_spatial_bce_0: 0.00651/0.09309, loss_spatial_dice_0: 0.06388/0.19578, loss_spatial_ce_0: 0.00301/0.08799, loss_grounding_bce_0: 0.00500/0.08143, loss_grounding_dice_0: 0.09119/0.15147, loss_grounding_ce_0: 0.01182/0.25573, loss_mask_ce_1: 0.17344/0.80400, loss_mask_bce_1: 0.01059/0.30345, loss_mask_dice_1: 0.17200/1.03664, loss_spatial_bce_1: 0.00596/0.09371, loss_spatial_dice_1: 0.08434/0.19858, loss_spatial_ce_1: 0.00473/0.09219, loss_grounding_bce_1: 0.00446/0.08144, loss_grounding_dice_1: 0.27289/0.15259, loss_grounding_ce_1: 0.08927/0.26001, loss_mask_ce_2: 0.14616/0.80953, loss_mask_bce_2: 0.00828/0.30384, loss_mask_dice_2: 0.11464/1.04052, loss_spatial_bce_2: 0.00504/0.09315, loss_spatial_dice_2: 0.04914/0.19849, loss_spatial_ce_2: 0.00238/0.09766, loss_grounding_bce_2: 0.00386/0.08130, loss_grounding_dice_2: 0.06932/0.15252, loss_grounding_ce_2: 0.01195/0.25957, loss_mask_ce_3: 0.17854/0.80792, loss_mask_bce_3: 0.01175/0.30515, loss_mask_dice_3: 0.15669/1.03508, loss_spatial_bce_3: 0.00557/0.09466, loss_spatial_dice_3: 0.06410/0.19865, loss_spatial_ce_3: 0.00456/0.10310, loss_grounding_bce_3: 0.00622/0.08196, loss_grounding_dice_3: 0.09957/0.15239, loss_grounding_ce_3: 0.01458/0.25822, loss_mask_ce_4: 0.16421/0.81255, loss_mask_bce_4: 0.00858/0.30723, loss_mask_dice_4: 0.11807/1.05345, loss_spatial_bce_4: 0.00405/0.09642, loss_spatial_dice_4: 0.06263/0.20605, loss_spatial_ce_4: 0.01956/0.11485, loss_grounding_bce_4: 0.00507/0.08257, loss_grounding_dice_4: 0.10344/0.15454, loss_grounding_ce_4: 0.00690/0.26676, loss_mask_ce_5: 0.20467/0.83218, loss_mask_bce_5: 0.00925/0.30922, loss_mask_dice_5: 0.15517/1.06251, loss_spatial_bce_5: 0.00546/0.09797, loss_spatial_dice_5: 0.07644/0.20806, loss_spatial_ce_5: 0.02537/0.12544, loss_grounding_bce_5: 0.00275/0.08277, loss_grounding_dice_5: 0.07150/0.15520, loss_grounding_ce_5: 0.00593/0.28479, loss_mask_ce_6: 0.36115/0.85607, loss_mask_bce_6: 0.01109/0.31038, loss_mask_dice_6: 0.14559/1.06775, loss_spatial_bce_6: 0.00525/0.10280, loss_spatial_dice_6: 0.08885/0.21051, loss_spatial_ce_6: 0.03857/0.14241, loss_grounding_bce_6: 0.00338/0.08426, loss_grounding_dice_6: 0.09171/0.15574, loss_grounding_ce_6: 0.00833/0.30284, loss_mask_ce_7: 0.27709/0.91929, loss_mask_bce_7: 0.00795/0.31745, loss_mask_dice_7: 0.14114/1.11260, loss_spatial_bce_7: 0.00609/0.11389, loss_spatial_dice_7: 0.10726/0.23582, loss_spatial_ce_7: 0.05012/0.19102, loss_grounding_bce_7: 0.00460/0.08587, loss_grounding_dice_7: 0.09536/0.16130, loss_grounding_ce_7: 0.04980/0.35344, loss_mask_ce_8: 0.16579/1.06544, loss_mask_bce_8: 0.00932/0.33547, loss_mask_dice_8: 0.15223/1.19355, loss_spatial_bce_8: 0.01031/0.13673, loss_spatial_dice_8: 0.29491/0.28003, loss_spatial_ce_8: 0.25951/0.24539, loss_grounding_bce_8: 0.00291/0.08974, loss_grounding_dice_8: 0.08683/0.17017, loss_grounding_ce_8: 0.00899/0.45694, loss_mask_ce_9: 2.17352/3.53139, loss_mask_bce_9: 0.00738/0.36210, loss_mask_dice_9: 0.10650/1.78071, loss_spatial_bce_9: 0.23584/0.36717, loss_spatial_dice_9: 0.94480/0.80014, loss_spatial_ce_9: 1.02431/1.43602, loss_grounding_bce_9: 0.00208/0.10195, loss_grounding_dice_9: 0.08446/0.24563, loss_grounding_ce_9: 0.18944/0.74520] items per batch[64] items per second[0.35] total items[614400] mini batches[ 9600] memory[4929] epoch remaining[0:41:28] INFO:trainer.default_trainer:epochs[ 5] optim steps[9700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55346/0.80177, loss_mask_bce_0: 0.29513/0.30321, loss_mask_dice_0: 0.33019/1.03312, loss_spatial_bce_0: 0.15627/0.09299, loss_spatial_dice_0: 0.15864/0.19563, loss_spatial_ce_0: 0.18281/0.08768, loss_grounding_bce_0: 0.09288/0.08135, loss_grounding_dice_0: 0.47230/0.15156, loss_grounding_ce_0: 0.03536/0.25625, loss_mask_ce_1: 0.66606/0.80420, loss_mask_bce_1: 0.29543/0.30373, loss_mask_dice_1: 0.31926/1.03734, loss_spatial_bce_1: 0.15971/0.09363, loss_spatial_dice_1: 0.15605/0.19845, loss_spatial_ce_1: 0.18900/0.09184, loss_grounding_bce_1: 0.10397/0.08138, loss_grounding_dice_1: 0.51247/0.15270, loss_grounding_ce_1: 0.02870/0.26042, loss_mask_ce_2: 0.78588/0.80949, loss_mask_bce_2: 0.30524/0.30415, loss_mask_dice_2: 0.34904/1.04125, loss_spatial_bce_2: 0.15926/0.09307, loss_spatial_dice_2: 0.15151/0.19831, loss_spatial_ce_2: 0.18906/0.09730, loss_grounding_bce_2: 0.11200/0.08125, loss_grounding_dice_2: 0.55774/0.15263, loss_grounding_ce_2: 0.03503/0.26005, loss_mask_ce_3: 0.68964/0.80808, loss_mask_bce_3: 0.29785/0.30544, loss_mask_dice_3: 0.35462/1.03559, loss_spatial_bce_3: 0.16227/0.09460, loss_spatial_dice_3: 0.17996/0.19852, loss_spatial_ce_3: 0.16052/0.10265, loss_grounding_bce_3: 0.12186/0.08191, loss_grounding_dice_3: 0.50722/0.15254, loss_grounding_ce_3: 0.03156/0.25852, loss_mask_ce_4: 0.82146/0.81278, loss_mask_bce_4: 0.30064/0.30750, loss_mask_dice_4: 0.35322/1.05402, loss_spatial_bce_4: 0.15442/0.09643, loss_spatial_dice_4: 0.18454/0.20589, loss_spatial_ce_4: 0.14562/0.11443, loss_grounding_bce_4: 0.12245/0.08249, loss_grounding_dice_4: 0.58150/0.15464, loss_grounding_ce_4: 0.03551/0.26694, loss_mask_ce_5: 0.69984/0.83265, loss_mask_bce_5: 0.29946/0.30953, loss_mask_dice_5: 0.36589/1.06307, loss_spatial_bce_5: 0.15058/0.09799, loss_spatial_dice_5: 0.18758/0.20791, loss_spatial_ce_5: 0.14551/0.12509, loss_grounding_bce_5: 0.13284/0.08272, loss_grounding_dice_5: 0.58681/0.15528, loss_grounding_ce_5: 0.05171/0.28513, loss_mask_ce_6: 0.93768/0.85633, loss_mask_bce_6: 0.30856/0.31061, loss_mask_dice_6: 0.37165/1.06832, loss_spatial_bce_6: 0.17164/0.10292, loss_spatial_dice_6: 0.18997/0.21033, loss_spatial_ce_6: 0.14622/0.14228, loss_grounding_bce_6: 0.13175/0.08421, loss_grounding_dice_6: 0.56238/0.15590, loss_grounding_ce_6: 0.02314/0.30267, loss_mask_ce_7: 1.19880/0.91988, loss_mask_bce_7: 0.30860/0.31781, loss_mask_dice_7: 0.40112/1.11322, loss_spatial_bce_7: 0.17974/0.11402, loss_spatial_dice_7: 0.21619/0.23564, loss_spatial_ce_7: 0.14975/0.19044, loss_grounding_bce_7: 0.14619/0.08581, loss_grounding_dice_7: 0.64367/0.16150, loss_grounding_ce_7: 0.00956/0.35347, loss_mask_ce_8: 0.81639/1.06560, loss_mask_bce_8: 0.33288/0.33588, loss_mask_dice_8: 0.39250/1.19461, loss_spatial_bce_8: 0.17491/0.13666, loss_spatial_dice_8: 0.20627/0.27979, loss_spatial_ce_8: 0.51273/0.24478, loss_grounding_bce_8: 0.20676/0.08972, loss_grounding_dice_8: 0.68064/0.17040, loss_grounding_ce_8: 0.00550/0.45595, loss_mask_ce_9: 2.83711/3.53128, loss_mask_bce_9: 0.47261/0.36223, loss_mask_dice_9: 0.52780/1.78113, loss_spatial_bce_9: 0.48777/0.36687, loss_spatial_dice_9: 0.71891/0.80018, loss_spatial_ce_9: 0.82676/1.43565, loss_grounding_bce_9: 0.22544/0.10186, loss_grounding_dice_9: 0.80529/0.24590, loss_grounding_ce_9: 0.06863/0.74322] items per batch[64] items per second[0.35] total items[620800] mini batches[ 9700] memory[4929] epoch remaining[0:38:25] INFO:trainer.default_trainer:epochs[ 5] optim steps[9800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08887/0.80090, loss_mask_bce_0: 0.85869/0.30325, loss_mask_dice_0: 4.09170/1.03204, loss_spatial_bce_0: 0.04830/0.09289, loss_spatial_dice_0: 0.18112/0.19554, loss_spatial_ce_0: 0.11094/0.08754, loss_grounding_bce_0: 0.10943/0.08125, loss_grounding_dice_0: 0.09605/0.15141, loss_grounding_ce_0: 0.12340/0.25558, loss_mask_ce_1: 1.19383/0.80337, loss_mask_bce_1: 0.85321/0.30373, loss_mask_dice_1: 4.02936/1.03600, loss_spatial_bce_1: 0.04992/0.09349, loss_spatial_dice_1: 0.16590/0.19837, loss_spatial_ce_1: 0.10440/0.09180, loss_grounding_bce_1: 0.10929/0.08129, loss_grounding_dice_1: 0.09031/0.15255, loss_grounding_ce_1: 0.13628/0.26008, loss_mask_ce_2: 1.26200/0.80885, loss_mask_bce_2: 0.84067/0.30413, loss_mask_dice_2: 4.10856/1.03986, loss_spatial_bce_2: 0.05097/0.09295, loss_spatial_dice_2: 0.17346/0.19824, loss_spatial_ce_2: 0.11304/0.09711, loss_grounding_bce_2: 0.11492/0.08116, loss_grounding_dice_2: 0.09532/0.15249, loss_grounding_ce_2: 0.19650/0.25963, loss_mask_ce_3: 1.17200/0.80739, loss_mask_bce_3: 0.85448/0.30543, loss_mask_dice_3: 3.50975/1.03419, loss_spatial_bce_3: 0.04476/0.09446, loss_spatial_dice_3: 0.14443/0.19843, loss_spatial_ce_3: 0.08424/0.10243, loss_grounding_bce_3: 0.11658/0.08182, loss_grounding_dice_3: 0.09417/0.15240, loss_grounding_ce_3: 0.17826/0.25785, loss_mask_ce_4: 1.26391/0.81219, loss_mask_bce_4: 0.85808/0.30753, loss_mask_dice_4: 4.33052/1.05289, loss_spatial_bce_4: 0.05072/0.09630, loss_spatial_dice_4: 0.16824/0.20577, loss_spatial_ce_4: 0.15933/0.11426, loss_grounding_bce_4: 0.13445/0.08240, loss_grounding_dice_4: 0.09364/0.15455, loss_grounding_ce_4: 0.24904/0.26626, loss_mask_ce_5: 1.30804/0.83216, loss_mask_bce_5: 0.80621/0.30948, loss_mask_dice_5: 4.26392/1.06175, loss_spatial_bce_5: 0.05985/0.09787, loss_spatial_dice_5: 0.18073/0.20786, loss_spatial_ce_5: 0.15126/0.12497, loss_grounding_bce_5: 0.13449/0.08262, loss_grounding_dice_5: 0.09395/0.15515, loss_grounding_ce_5: 0.23656/0.28497, loss_mask_ce_6: 1.48350/0.85583, loss_mask_bce_6: 0.86033/0.31052, loss_mask_dice_6: 4.24450/1.06697, loss_spatial_bce_6: 0.04944/0.10282, loss_spatial_dice_6: 0.21718/0.21026, loss_spatial_ce_6: 0.15226/0.14207, loss_grounding_bce_6: 0.11680/0.08409, loss_grounding_dice_6: 0.10006/0.15576, loss_grounding_ce_6: 0.23973/0.30236, loss_mask_ce_7: 1.56912/0.91975, loss_mask_bce_7: 0.86776/0.31774, loss_mask_dice_7: 4.79181/1.11219, loss_spatial_bce_7: 0.07851/0.11393, loss_spatial_dice_7: 0.24211/0.23558, loss_spatial_ce_7: 0.24235/0.19024, loss_grounding_bce_7: 0.11482/0.08567, loss_grounding_dice_7: 0.09560/0.16140, loss_grounding_ce_7: 0.31117/0.35337, loss_mask_ce_8: 1.63519/1.06465, loss_mask_bce_8: 1.06569/0.33566, loss_mask_dice_8: 5.34730/1.19325, loss_spatial_bce_8: 0.06707/0.13655, loss_spatial_dice_8: 0.28163/0.27967, loss_spatial_ce_8: 0.15903/0.24489, loss_grounding_bce_8: 0.10796/0.08958, loss_grounding_dice_8: 0.10299/0.17026, loss_grounding_ce_8: 1.14279/0.45626, loss_mask_ce_9: 5.07864/3.52966, loss_mask_bce_9: 1.04236/0.36193, loss_mask_dice_9: 8.09633/1.77925, loss_spatial_bce_9: 0.21452/0.36680, loss_spatial_dice_9: 0.93689/0.80028, loss_spatial_ce_9: 1.14183/1.43599, loss_grounding_bce_9: 0.33646/0.10171, loss_grounding_dice_9: 0.44814/0.24563, loss_grounding_ce_9: 1.21609/0.74361] items per batch[64] items per second[0.35] total items[627200] mini batches[ 9800] memory[4929] epoch remaining[0:35:22] INFO:trainer.default_trainer:epochs[ 5] optim steps[9900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64333/0.80120, loss_mask_bce_0: 0.36162/0.30292, loss_mask_dice_0: 0.55238/1.03233, loss_spatial_bce_0: 0.08610/0.09288, loss_spatial_dice_0: 0.14176/0.19554, loss_spatial_ce_0: 0.00622/0.08733, loss_grounding_bce_0: 0.25750/0.08115, loss_grounding_dice_0: 0.14899/0.15136, loss_grounding_ce_0: 0.06221/0.25553, loss_mask_ce_1: 0.65953/0.80389, loss_mask_bce_1: 0.36493/0.30340, loss_mask_dice_1: 0.52514/1.03631, loss_spatial_bce_1: 0.08458/0.09348, loss_spatial_dice_1: 0.13792/0.19838, loss_spatial_ce_1: 0.01183/0.09170, loss_grounding_bce_1: 0.26215/0.08118, loss_grounding_dice_1: 0.14630/0.15248, loss_grounding_ce_1: 0.04630/0.26007, loss_mask_ce_2: 0.68245/0.80930, loss_mask_bce_2: 0.37140/0.30376, loss_mask_dice_2: 0.54792/1.04019, loss_spatial_bce_2: 0.08811/0.09293, loss_spatial_dice_2: 0.13292/0.19823, loss_spatial_ce_2: 0.01302/0.09689, loss_grounding_bce_2: 0.27104/0.08104, loss_grounding_dice_2: 0.14335/0.15241, loss_grounding_ce_2: 0.03892/0.25964, loss_mask_ce_3: 0.67794/0.80768, loss_mask_bce_3: 0.36788/0.30509, loss_mask_dice_3: 0.53552/1.03456, loss_spatial_bce_3: 0.08854/0.09442, loss_spatial_dice_3: 0.13803/0.19840, loss_spatial_ce_3: 0.01792/0.10223, loss_grounding_bce_3: 0.27070/0.08172, loss_grounding_dice_3: 0.14743/0.15237, loss_grounding_ce_3: 0.03138/0.25772, loss_mask_ce_4: 0.82737/0.81285, loss_mask_bce_4: 0.38145/0.30719, loss_mask_dice_4: 0.54757/1.05336, loss_spatial_bce_4: 0.08459/0.09627, loss_spatial_dice_4: 0.13263/0.20578, loss_spatial_ce_4: 0.02915/0.11386, loss_grounding_bce_4: 0.27500/0.08232, loss_grounding_dice_4: 0.14515/0.15443, loss_grounding_ce_4: 0.03138/0.26620, loss_mask_ce_5: 0.79881/0.83276, loss_mask_bce_5: 0.40522/0.30922, loss_mask_dice_5: 0.58316/1.06201, loss_spatial_bce_5: 0.12058/0.09788, loss_spatial_dice_5: 0.20955/0.20788, loss_spatial_ce_5: 0.05162/0.12454, loss_grounding_bce_5: 0.27879/0.08255, loss_grounding_dice_5: 0.15168/0.15509, loss_grounding_ce_5: 0.02597/0.28448, loss_mask_ce_6: 0.93432/0.85657, loss_mask_bce_6: 0.37572/0.31023, loss_mask_dice_6: 0.58353/1.06730, loss_spatial_bce_6: 0.13098/0.10278, loss_spatial_dice_6: 0.23877/0.21028, loss_spatial_ce_6: 0.10269/0.14165, loss_grounding_bce_6: 0.26364/0.08401, loss_grounding_dice_6: 0.14399/0.15577, loss_grounding_ce_6: 0.01388/0.30214, loss_mask_ce_7: 1.06400/0.92065, loss_mask_bce_7: 0.43657/0.31745, loss_mask_dice_7: 0.58556/1.11236, loss_spatial_bce_7: 0.10931/0.11391, loss_spatial_dice_7: 0.17248/0.23563, loss_spatial_ce_7: 0.16462/0.18966, loss_grounding_bce_7: 0.25435/0.08561, loss_grounding_dice_7: 0.13698/0.16146, loss_grounding_ce_7: 0.01489/0.35281, loss_mask_ce_8: 0.83849/1.06493, loss_mask_bce_8: 0.46211/0.33543, loss_mask_dice_8: 0.69548/1.19343, loss_spatial_bce_8: 0.11232/0.13645, loss_spatial_dice_8: 0.13233/0.27970, loss_spatial_ce_8: 0.36742/0.24454, loss_grounding_bce_8: 0.27757/0.08951, loss_grounding_dice_8: 0.12621/0.17021, loss_grounding_ce_8: 0.01625/0.45574, loss_mask_ce_9: 3.79317/3.53049, loss_mask_bce_9: 0.51320/0.36155, loss_mask_dice_9: 1.14320/1.77853, loss_spatial_bce_9: 0.46278/0.36647, loss_spatial_dice_9: 0.83021/0.80045, loss_spatial_ce_9: 1.30672/1.43544, loss_grounding_bce_9: 0.27729/0.10161, loss_grounding_dice_9: 0.14243/0.24549, loss_grounding_ce_9: 0.02782/0.74283] items per batch[64] items per second[0.36] total items[633600] mini batches[ 9900] memory[4929] epoch remaining[0:32:12] INFO:trainer.default_trainer:epochs[ 5] optim steps[10000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17185/0.80063, loss_mask_bce_0: 0.22486/0.30329, loss_mask_dice_0: 0.21682/1.03305, loss_spatial_bce_0: 0.13966/0.09281, loss_spatial_dice_0: 0.15705/0.19548, loss_spatial_ce_0: 0.00361/0.08715, loss_grounding_bce_0: 0.14816/0.08124, loss_grounding_dice_0: 0.14789/0.15148, loss_grounding_ce_0: 0.02112/0.25552, loss_mask_ce_1: 0.15547/0.80336, loss_mask_bce_1: 0.23181/0.30374, loss_mask_dice_1: 0.22971/1.03712, loss_spatial_bce_1: 0.13651/0.09342, loss_spatial_dice_1: 0.14580/0.19833, loss_spatial_ce_1: 0.00409/0.09148, loss_grounding_bce_1: 0.15924/0.08126, loss_grounding_dice_1: 0.15161/0.15253, loss_grounding_ce_1: 0.02550/0.26004, loss_mask_ce_2: 0.13746/0.80878, loss_mask_bce_2: 0.23099/0.30405, loss_mask_dice_2: 0.22792/1.04081, loss_spatial_bce_2: 0.13869/0.09287, loss_spatial_dice_2: 0.14788/0.19817, loss_spatial_ce_2: 0.00270/0.09668, loss_grounding_bce_2: 0.16048/0.08112, loss_grounding_dice_2: 0.15560/0.15247, loss_grounding_ce_2: 0.02086/0.25996, loss_mask_ce_3: 0.13238/0.80716, loss_mask_bce_3: 0.22498/0.30537, loss_mask_dice_3: 0.23546/1.03515, loss_spatial_bce_3: 0.14669/0.09439, loss_spatial_dice_3: 0.14483/0.19832, loss_spatial_ce_3: 0.00339/0.10201, loss_grounding_bce_3: 0.15805/0.08175, loss_grounding_dice_3: 0.15425/0.15241, loss_grounding_ce_3: 0.01827/0.25808, loss_mask_ce_4: 0.16231/0.81252, loss_mask_bce_4: 0.23672/0.30748, loss_mask_dice_4: 0.23547/1.05445, loss_spatial_bce_4: 0.15154/0.09623, loss_spatial_dice_4: 0.15193/0.20573, loss_spatial_ce_4: 0.00447/0.11360, loss_grounding_bce_4: 0.16960/0.08233, loss_grounding_dice_4: 0.16189/0.15450, loss_grounding_ce_4: 0.03262/0.26636, loss_mask_ce_5: 0.20623/0.83261, loss_mask_bce_5: 0.24005/0.30953, loss_mask_dice_5: 0.23412/1.06286, loss_spatial_bce_5: 0.14783/0.09786, loss_spatial_dice_5: 0.13374/0.20782, loss_spatial_ce_5: 0.00740/0.12439, loss_grounding_bce_5: 0.16864/0.08263, loss_grounding_dice_5: 0.15120/0.15518, loss_grounding_ce_5: 0.05071/0.28465, loss_mask_ce_6: 0.15749/0.85647, loss_mask_bce_6: 0.23471/0.31051, loss_mask_dice_6: 0.22345/1.06796, loss_spatial_bce_6: 0.15502/0.10276, loss_spatial_dice_6: 0.14278/0.21021, loss_spatial_ce_6: 0.01072/0.14149, loss_grounding_bce_6: 0.16320/0.08405, loss_grounding_dice_6: 0.16614/0.15586, loss_grounding_ce_6: 0.02926/0.30226, loss_mask_ce_7: 0.16084/0.92044, loss_mask_bce_7: 0.22991/0.31773, loss_mask_dice_7: 0.24394/1.11320, loss_spatial_bce_7: 0.16282/0.11389, loss_spatial_dice_7: 0.15030/0.23556, loss_spatial_ce_7: 0.06669/0.18931, loss_grounding_bce_7: 0.17083/0.08564, loss_grounding_dice_7: 0.16610/0.16148, loss_grounding_ce_7: 0.00881/0.35349, loss_mask_ce_8: 0.14784/1.06435, loss_mask_bce_8: 0.23750/0.33567, loss_mask_dice_8: 0.21209/1.19436, loss_spatial_bce_8: 0.20938/0.13648, loss_spatial_dice_8: 0.24644/0.27958, loss_spatial_ce_8: 0.18968/0.24416, loss_grounding_bce_8: 0.16797/0.08953, loss_grounding_dice_8: 0.14906/0.17025, loss_grounding_ce_8: 0.01210/0.45615, loss_mask_ce_9: 1.48787/3.53082, loss_mask_bce_9: 0.24112/0.36176, loss_mask_dice_9: 0.24510/1.77959, loss_spatial_bce_9: 0.59998/0.36651, loss_spatial_dice_9: 0.67411/0.80043, loss_spatial_ce_9: 0.92310/1.43375, loss_grounding_bce_9: 0.17017/0.10160, loss_grounding_dice_9: 0.17656/0.24538, loss_grounding_ce_9: 0.09977/0.74252] items per batch[64] items per second[0.35] total items[640000] mini batches[ 10000] memory[4929] epoch remaining[0:29:09] INFO:trainer.default_trainer:epochs[ 5] optim steps[10100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90339/0.80002, loss_mask_bce_0: 0.00922/0.30308, loss_mask_dice_0: 0.24184/1.03331, loss_spatial_bce_0: 0.00287/0.09275, loss_spatial_dice_0: 0.08691/0.19522, loss_spatial_ce_0: 0.08124/0.08686, loss_grounding_bce_0: 0.00548/0.08127, loss_grounding_dice_0: 0.09563/0.15154, loss_grounding_ce_0: 1.23608/0.25567, loss_mask_ce_1: 0.96276/0.80283, loss_mask_bce_1: 0.01203/0.30356, loss_mask_dice_1: 0.30914/1.03746, loss_spatial_bce_1: 0.00309/0.09335, loss_spatial_dice_1: 0.09331/0.19809, loss_spatial_ce_1: 0.09702/0.09115, loss_grounding_bce_1: 0.00681/0.08128, loss_grounding_dice_1: 0.09729/0.15256, loss_grounding_ce_1: 1.17153/0.26022, loss_mask_ce_2: 0.75864/0.80840, loss_mask_bce_2: 0.02289/0.30384, loss_mask_dice_2: 0.70224/1.04103, loss_spatial_bce_2: 0.00232/0.09281, loss_spatial_dice_2: 0.12841/0.19793, loss_spatial_ce_2: 0.10016/0.09648, loss_grounding_bce_2: 0.00490/0.08113, loss_grounding_dice_2: 0.16937/0.15253, loss_grounding_ce_2: 3.42892/0.26038, loss_mask_ce_3: 0.98552/0.80671, loss_mask_bce_3: 0.01157/0.30515, loss_mask_dice_3: 0.45901/1.03558, loss_spatial_bce_3: 0.00268/0.09431, loss_spatial_dice_3: 0.06100/0.19805, loss_spatial_ce_3: 0.11936/0.10167, loss_grounding_bce_3: 0.00573/0.08176, loss_grounding_dice_3: 0.12966/0.15239, loss_grounding_ce_3: 3.46439/0.25862, loss_mask_ce_4: 0.88337/0.81209, loss_mask_bce_4: 0.01085/0.30735, loss_mask_dice_4: 0.38292/1.05459, loss_spatial_bce_4: 0.00298/0.09613, loss_spatial_dice_4: 0.12297/0.20548, loss_spatial_ce_4: 0.15887/0.11328, loss_grounding_bce_4: 0.00571/0.08237, loss_grounding_dice_4: 0.14435/0.15456, loss_grounding_ce_4: 4.68619/0.26686, loss_mask_ce_5: 0.89021/0.83252, loss_mask_bce_5: 0.01012/0.30939, loss_mask_dice_5: 0.32431/1.06302, loss_spatial_bce_5: 0.00357/0.09777, loss_spatial_dice_5: 0.10924/0.20758, loss_spatial_ce_5: 0.10064/0.12408, loss_grounding_bce_5: 0.00741/0.08266, loss_grounding_dice_5: 0.13157/0.15518, loss_grounding_ce_5: 0.87571/0.28493, loss_mask_ce_6: 1.24199/0.85637, loss_mask_bce_6: 0.00887/0.31035, loss_mask_dice_6: 0.27543/1.06810, loss_spatial_bce_6: 0.00295/0.10268, loss_spatial_dice_6: 0.07733/0.20996, loss_spatial_ce_6: 0.13887/0.14118, loss_grounding_bce_6: 0.00575/0.08407, loss_grounding_dice_6: 0.13233/0.15597, loss_grounding_ce_6: 0.90791/0.30253, loss_mask_ce_7: 0.97648/0.92046, loss_mask_bce_7: 0.01110/0.31752, loss_mask_dice_7: 0.27263/1.11341, loss_spatial_bce_7: 0.00321/0.11382, loss_spatial_dice_7: 0.14910/0.23529, loss_spatial_ce_7: 0.15329/0.18865, loss_grounding_bce_7: 0.00504/0.08576, loss_grounding_dice_7: 0.11478/0.16157, loss_grounding_ce_7: 1.04188/0.35388, loss_mask_ce_8: 1.08868/1.06415, loss_mask_bce_8: 0.01084/0.33534, loss_mask_dice_8: 0.57726/1.19451, loss_spatial_bce_8: 0.00470/0.13622, loss_spatial_dice_8: 0.25756/0.27924, loss_spatial_ce_8: 0.12505/0.24377, loss_grounding_bce_8: 0.01017/0.08956, loss_grounding_dice_8: 0.14042/0.17036, loss_grounding_ce_8: 1.66723/0.45644, loss_mask_ce_9: 2.56232/3.52956, loss_mask_bce_9: 0.00995/0.36160, loss_mask_dice_9: 0.58758/1.78026, loss_spatial_bce_9: 0.02086/0.36629, loss_spatial_dice_9: 0.77592/0.80029, loss_spatial_ce_9: 1.52666/1.43387, loss_grounding_bce_9: 0.00531/0.10167, loss_grounding_dice_9: 0.13406/0.24526, loss_grounding_ce_9: 1.32796/0.74124] items per batch[64] items per second[0.36] total items[646400] mini batches[ 10100] memory[4929] epoch remaining[0:26:05] INFO:trainer.default_trainer:epochs[ 5] optim steps[10200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98551/0.79981, loss_mask_bce_0: 0.64663/0.30297, loss_mask_dice_0: 0.75560/1.03452, loss_spatial_bce_0: 0.13683/0.09263, loss_spatial_dice_0: 0.19793/0.19524, loss_spatial_ce_0: 0.16343/0.08660, loss_grounding_bce_0: 0.04821/0.08122, loss_grounding_dice_0: 0.17621/0.15166, loss_grounding_ce_0: 0.59209/0.25532, loss_mask_ce_1: 0.63555/0.80256, loss_mask_bce_1: 0.64992/0.30345, loss_mask_dice_1: 0.72462/1.03887, loss_spatial_bce_1: 0.14877/0.09323, loss_spatial_dice_1: 0.18630/0.19808, loss_spatial_ce_1: 0.16615/0.09088, loss_grounding_bce_1: 0.07103/0.08124, loss_grounding_dice_1: 0.18730/0.15260, loss_grounding_ce_1: 0.39938/0.25987, loss_mask_ce_2: 1.14513/0.80813, loss_mask_bce_2: 0.50935/0.30374, loss_mask_dice_2: 0.64097/1.04234, loss_spatial_bce_2: 0.14542/0.09268, loss_spatial_dice_2: 0.20070/0.19792, loss_spatial_ce_2: 0.16365/0.09610, loss_grounding_bce_2: 0.06299/0.08108, loss_grounding_dice_2: 0.17610/0.15258, loss_grounding_ce_2: 0.40728/0.26000, loss_mask_ce_3: 1.10277/0.80663, loss_mask_bce_3: 0.50290/0.30507, loss_mask_dice_3: 0.62513/1.03678, loss_spatial_bce_3: 0.15753/0.09419, loss_spatial_dice_3: 0.21942/0.19803, loss_spatial_ce_3: 0.15997/0.10136, loss_grounding_bce_3: 0.07280/0.08171, loss_grounding_dice_3: 0.18764/0.15241, loss_grounding_ce_3: 0.36913/0.25834, loss_mask_ce_4: 0.89956/0.81227, loss_mask_bce_4: 0.65940/0.30728, loss_mask_dice_4: 0.70418/1.05600, loss_spatial_bce_4: 0.14964/0.09600, loss_spatial_dice_4: 0.20652/0.20550, loss_spatial_ce_4: 0.13534/0.11311, loss_grounding_bce_4: 0.05700/0.08236, loss_grounding_dice_4: 0.17694/0.15472, loss_grounding_ce_4: 0.57595/0.26699, loss_mask_ce_5: 1.26476/0.83283, loss_mask_bce_5: 0.52752/0.30924, loss_mask_dice_5: 0.66056/1.06449, loss_spatial_bce_5: 0.14613/0.09763, loss_spatial_dice_5: 0.19439/0.20762, loss_spatial_ce_5: 0.14097/0.12374, loss_grounding_bce_5: 0.07704/0.08264, loss_grounding_dice_5: 0.17700/0.15519, loss_grounding_ce_5: 0.33396/0.28452, loss_mask_ce_6: 1.08106/0.85659, loss_mask_bce_6: 0.52455/0.31020, loss_mask_dice_6: 0.63114/1.06950, loss_spatial_bce_6: 0.17018/0.10252, loss_spatial_dice_6: 0.22659/0.20994, loss_spatial_ce_6: 0.18498/0.14101, loss_grounding_bce_6: 0.07536/0.08404, loss_grounding_dice_6: 0.18920/0.15604, loss_grounding_ce_6: 0.32779/0.30211, loss_mask_ce_7: 1.56987/0.92037, loss_mask_bce_7: 0.49871/0.31748, loss_mask_dice_7: 0.59331/1.11497, loss_spatial_bce_7: 0.19202/0.11363, loss_spatial_dice_7: 0.23805/0.23538, loss_spatial_ce_7: 0.25779/0.18838, loss_grounding_bce_7: 0.06305/0.08569, loss_grounding_dice_7: 0.18072/0.16162, loss_grounding_ce_7: 0.39523/0.35350, loss_mask_ce_8: 1.05951/1.06411, loss_mask_bce_8: 0.59900/0.33524, loss_mask_dice_8: 0.69085/1.19614, loss_spatial_bce_8: 0.25555/0.13603, loss_spatial_dice_8: 0.30631/0.27921, loss_spatial_ce_8: 0.46654/0.24347, loss_grounding_bce_8: 0.07415/0.08951, loss_grounding_dice_8: 0.20339/0.17041, loss_grounding_ce_8: 0.34076/0.45638, loss_mask_ce_9: 3.75941/3.52980, loss_mask_bce_9: 0.80562/0.36137, loss_mask_dice_9: 1.28642/1.78166, loss_spatial_bce_9: 0.67609/0.36589, loss_spatial_dice_9: 0.79337/0.80031, loss_spatial_ce_9: 1.30405/1.43349, loss_grounding_bce_9: 0.18629/0.10159, loss_grounding_dice_9: 0.50161/0.24529, loss_grounding_ce_9: 0.85940/0.74092] items per batch[64] items per second[0.35] total items[652800] mini batches[ 10200] memory[4929] epoch remaining[0:23:04] INFO:trainer.default_trainer:epochs[ 5] optim steps[10300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79663/0.79996, loss_mask_bce_0: 0.33136/0.30308, loss_mask_dice_0: 0.18641/1.03337, loss_spatial_bce_0: 0.20149/0.09264, loss_spatial_dice_0: 0.19467/0.19508, loss_spatial_ce_0: 0.25095/0.08649, loss_grounding_bce_0: 0.33734/0.08128, loss_grounding_dice_0: 0.17350/0.15176, loss_grounding_ce_0: 0.65571/0.25506, loss_mask_ce_1: 0.68567/0.80279, loss_mask_bce_1: 0.28829/0.30357, loss_mask_dice_1: 0.13385/1.03767, loss_spatial_bce_1: 0.26187/0.09325, loss_spatial_dice_1: 0.26928/0.19789, loss_spatial_ce_1: 0.28441/0.09075, loss_grounding_bce_1: 0.33619/0.08131, loss_grounding_dice_1: 0.20643/0.15273, loss_grounding_ce_1: 0.56563/0.25963, loss_mask_ce_2: 0.73678/0.80842, loss_mask_bce_2: 0.32363/0.30389, loss_mask_dice_2: 0.20241/1.04103, loss_spatial_bce_2: 0.22238/0.09268, loss_spatial_dice_2: 0.24413/0.19773, loss_spatial_ce_2: 0.28492/0.09587, loss_grounding_bce_2: 0.33191/0.08115, loss_grounding_dice_2: 0.22065/0.15266, loss_grounding_ce_2: 0.53656/0.25948, loss_mask_ce_3: 0.83793/0.80658, loss_mask_bce_3: 0.31482/0.30524, loss_mask_dice_3: 0.19377/1.03567, loss_spatial_bce_3: 0.21359/0.09418, loss_spatial_dice_3: 0.25117/0.19788, loss_spatial_ce_3: 0.34449/0.10113, loss_grounding_bce_3: 0.30939/0.08178, loss_grounding_dice_3: 0.18241/0.15250, loss_grounding_ce_3: 0.52587/0.25788, loss_mask_ce_4: 0.84747/0.81224, loss_mask_bce_4: 0.32390/0.30743, loss_mask_dice_4: 0.20479/1.05480, loss_spatial_bce_4: 0.34652/0.09608, loss_spatial_dice_4: 0.27559/0.20531, loss_spatial_ce_4: 0.21210/0.11291, loss_grounding_bce_4: 0.31501/0.08241, loss_grounding_dice_4: 0.20050/0.15481, loss_grounding_ce_4: 0.58051/0.26653, loss_mask_ce_5: 0.91910/0.83275, loss_mask_bce_5: 0.31150/0.30941, loss_mask_dice_5: 0.19307/1.06302, loss_spatial_bce_5: 0.30030/0.09767, loss_spatial_dice_5: 0.26772/0.20744, loss_spatial_ce_5: 0.33144/0.12348, loss_grounding_bce_5: 0.31238/0.08268, loss_grounding_dice_5: 0.24265/0.15532, loss_grounding_ce_5: 0.60774/0.28404, loss_mask_ce_6: 0.88416/0.85663, loss_mask_bce_6: 0.29089/0.31035, loss_mask_dice_6: 0.24187/1.06823, loss_spatial_bce_6: 0.49097/0.10266, loss_spatial_dice_6: 0.27424/0.20975, loss_spatial_ce_6: 0.27027/0.14083, loss_grounding_bce_6: 0.30728/0.08412, loss_grounding_dice_6: 0.26986/0.15621, loss_grounding_ce_6: 0.58856/0.30137, loss_mask_ce_7: 0.89518/0.92082, loss_mask_bce_7: 0.25862/0.31760, loss_mask_dice_7: 0.19904/1.11372, loss_spatial_bce_7: 0.41667/0.11374, loss_spatial_dice_7: 0.27361/0.23518, loss_spatial_ce_7: 0.52127/0.18799, loss_grounding_bce_7: 0.27689/0.08578, loss_grounding_dice_7: 0.23467/0.16177, loss_grounding_ce_7: 0.54334/0.35259, loss_mask_ce_8: 0.75990/1.06376, loss_mask_bce_8: 0.27826/0.33543, loss_mask_dice_8: 0.21579/1.19468, loss_spatial_bce_8: 0.69795/0.13607, loss_spatial_dice_8: 0.25965/0.27895, loss_spatial_ce_8: 0.19577/0.24310, loss_grounding_bce_8: 0.28270/0.08955, loss_grounding_dice_8: 0.28001/0.17060, loss_grounding_ce_8: 0.49590/0.45439, loss_mask_ce_9: 2.40250/3.52800, loss_mask_bce_9: 0.32186/0.36163, loss_mask_dice_9: 0.33185/1.78078, loss_spatial_bce_9: 0.64088/0.36604, loss_spatial_dice_9: 0.65045/0.80025, loss_spatial_ce_9: 0.62427/1.43255, loss_grounding_bce_9: 0.34889/0.10163, loss_grounding_dice_9: 0.34574/0.24533, loss_grounding_ce_9: 0.38409/0.73861] items per batch[64] items per second[0.36] total items[659200] mini batches[ 10300] memory[4929] epoch remaining[0:19:59] INFO:trainer.default_trainer:epochs[ 5] optim steps[10400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24500/0.79960, loss_mask_bce_0: 0.14468/0.30310, loss_mask_dice_0: 0.33191/1.03370, loss_spatial_bce_0: 0.07706/0.09248, loss_spatial_dice_0: 0.25176/0.19502, loss_spatial_ce_0: 0.00461/0.08613, loss_grounding_bce_0: 0.08581/0.08122, loss_grounding_dice_0: 0.05891/0.15163, loss_grounding_ce_0: 0.02239/0.25457, loss_mask_ce_1: 0.32657/0.80230, loss_mask_bce_1: 0.14049/0.30365, loss_mask_dice_1: 0.42389/1.03800, loss_spatial_bce_1: 0.07730/0.09309, loss_spatial_dice_1: 0.23268/0.19787, loss_spatial_ce_1: 0.00423/0.09039, loss_grounding_bce_1: 0.07774/0.08124, loss_grounding_dice_1: 0.05829/0.15263, loss_grounding_ce_1: 0.01750/0.25908, loss_mask_ce_2: 0.19069/0.80808, loss_mask_bce_2: 0.13291/0.30391, loss_mask_dice_2: 0.17562/1.04115, loss_spatial_bce_2: 0.07156/0.09253, loss_spatial_dice_2: 0.14860/0.19770, loss_spatial_ce_2: 0.00646/0.09548, loss_grounding_bce_2: 0.08033/0.08106, loss_grounding_dice_2: 0.05801/0.15251, loss_grounding_ce_2: 0.01250/0.25893, loss_mask_ce_3: 0.20164/0.80629, loss_mask_bce_3: 0.14169/0.30532, loss_mask_dice_3: 0.35923/1.03619, loss_spatial_bce_3: 0.07903/0.09402, loss_spatial_dice_3: 0.26052/0.19785, loss_spatial_ce_3: 0.00137/0.10073, loss_grounding_bce_3: 0.08710/0.08171, loss_grounding_dice_3: 0.05842/0.15237, loss_grounding_ce_3: 0.01280/0.25751, loss_mask_ce_4: 0.16667/0.81196, loss_mask_bce_4: 0.14575/0.30748, loss_mask_dice_4: 0.51197/1.05509, loss_spatial_bce_4: 0.07472/0.09596, loss_spatial_dice_4: 0.24773/0.20526, loss_spatial_ce_4: 0.00208/0.11252, loss_grounding_bce_4: 0.07707/0.08234, loss_grounding_dice_4: 0.05865/0.15469, loss_grounding_ce_4: 0.01903/0.26597, loss_mask_ce_5: 0.22020/0.83220, loss_mask_bce_5: 0.13785/0.30947, loss_mask_dice_5: 0.19470/1.06333, loss_spatial_bce_5: 0.07141/0.09749, loss_spatial_dice_5: 0.30934/0.20734, loss_spatial_ce_5: 0.00349/0.12325, loss_grounding_bce_5: 0.08448/0.08261, loss_grounding_dice_5: 0.07226/0.15521, loss_grounding_ce_5: 0.01646/0.28377, loss_mask_ce_6: 0.31715/0.85623, loss_mask_bce_6: 0.14503/0.31040, loss_mask_dice_6: 0.16945/1.06872, loss_spatial_bce_6: 0.07259/0.10250, loss_spatial_dice_6: 0.31263/0.20969, loss_spatial_ce_6: 0.00814/0.14055, loss_grounding_bce_6: 0.07806/0.08401, loss_grounding_dice_6: 0.06224/0.15608, loss_grounding_ce_6: 0.02403/0.30083, loss_mask_ce_7: 0.27314/0.92047, loss_mask_bce_7: 0.14213/0.31769, loss_mask_dice_7: 0.21181/1.11380, loss_spatial_bce_7: 0.08168/0.11353, loss_spatial_dice_7: 0.27103/0.23509, loss_spatial_ce_7: 0.07156/0.18759, loss_grounding_bce_7: 0.07621/0.08567, loss_grounding_dice_7: 0.05771/0.16167, loss_grounding_ce_7: 0.03557/0.35214, loss_mask_ce_8: 0.39401/1.06340, loss_mask_bce_8: 0.13674/0.33540, loss_mask_dice_8: 0.17645/1.19499, loss_spatial_bce_8: 0.08864/0.13587, loss_spatial_dice_8: 0.22535/0.27874, loss_spatial_ce_8: 0.03060/0.24268, loss_grounding_bce_8: 0.07864/0.08943, loss_grounding_dice_8: 0.04745/0.17049, loss_grounding_ce_8: 0.04483/0.45373, loss_mask_ce_9: 2.66433/3.52881, loss_mask_bce_9: 0.13732/0.36160, loss_mask_dice_9: 0.25707/1.78182, loss_spatial_bce_9: 0.88594/0.36565, loss_spatial_dice_9: 0.97407/0.80024, loss_spatial_ce_9: 1.33011/1.43214, loss_grounding_bce_9: 0.08778/0.10147, loss_grounding_dice_9: 0.05958/0.24515, loss_grounding_ce_9: 0.06004/0.73792] items per batch[64] items per second[0.36] total items[665600] mini batches[ 10400] memory[4929] epoch remaining[0:16:57] INFO:trainer.default_trainer:epochs[ 5] optim steps[10500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58158/0.79957, loss_mask_bce_0: 0.46183/0.30307, loss_mask_dice_0: 0.73171/1.03541, loss_spatial_bce_0: 0.15029/0.09245, loss_spatial_dice_0: 0.23562/0.19491, loss_spatial_ce_0: 0.02022/0.08572, loss_grounding_bce_0: 0.03725/0.08111, loss_grounding_dice_0: 0.07347/0.15150, loss_grounding_ce_0: 0.00907/0.25408, loss_mask_ce_1: 0.56914/0.80226, loss_mask_bce_1: 0.47144/0.30365, loss_mask_dice_1: 0.71034/1.03978, loss_spatial_bce_1: 0.15169/0.09307, loss_spatial_dice_1: 0.23938/0.19778, loss_spatial_ce_1: 0.03060/0.08994, loss_grounding_bce_1: 0.03976/0.08113, loss_grounding_dice_1: 0.07862/0.15249, loss_grounding_ce_1: 0.00477/0.25848, loss_mask_ce_2: 0.62925/0.80828, loss_mask_bce_2: 0.46225/0.30383, loss_mask_dice_2: 0.71874/1.04268, loss_spatial_bce_2: 0.15844/0.09254, loss_spatial_dice_2: 0.23046/0.19761, loss_spatial_ce_2: 0.03412/0.09500, loss_grounding_bce_2: 0.04119/0.08094, loss_grounding_dice_2: 0.08058/0.15237, loss_grounding_ce_2: 0.00205/0.25844, loss_mask_ce_3: 0.59952/0.80612, loss_mask_bce_3: 0.46489/0.30528, loss_mask_dice_3: 0.71162/1.03762, loss_spatial_bce_3: 0.15387/0.09402, loss_spatial_dice_3: 0.22478/0.19779, loss_spatial_ce_3: 0.04349/0.10028, loss_grounding_bce_3: 0.03431/0.08159, loss_grounding_dice_3: 0.07033/0.15222, loss_grounding_ce_3: 0.00157/0.25701, loss_mask_ce_4: 0.63527/0.81189, loss_mask_bce_4: 0.47462/0.30740, loss_mask_dice_4: 0.72616/1.05660, loss_spatial_bce_4: 0.15462/0.09595, loss_spatial_dice_4: 0.21997/0.20516, loss_spatial_ce_4: 0.10722/0.11201, loss_grounding_bce_4: 0.04091/0.08223, loss_grounding_dice_4: 0.07552/0.15453, loss_grounding_ce_4: 0.00046/0.26539, loss_mask_ce_5: 0.73780/0.83210, loss_mask_bce_5: 0.51159/0.30937, loss_mask_dice_5: 0.71077/1.06494, loss_spatial_bce_5: 0.19833/0.09751, loss_spatial_dice_5: 0.25033/0.20725, loss_spatial_ce_5: 0.25733/0.12269, loss_grounding_bce_5: 0.04052/0.08254, loss_grounding_dice_5: 0.07822/0.15509, loss_grounding_ce_5: 0.00088/0.28315, loss_mask_ce_6: 0.69942/0.85609, loss_mask_bce_6: 0.47711/0.31027, loss_mask_dice_6: 0.71469/1.06995, loss_spatial_bce_6: 0.16385/0.10252, loss_spatial_dice_6: 0.27394/0.20957, loss_spatial_ce_6: 0.11676/0.14003, loss_grounding_bce_6: 0.03800/0.08388, loss_grounding_dice_6: 0.07471/0.15599, loss_grounding_ce_6: 0.00386/0.30011, loss_mask_ce_7: 0.69741/0.92029, loss_mask_bce_7: 0.44830/0.31752, loss_mask_dice_7: 0.71398/1.11535, loss_spatial_bce_7: 0.16282/0.11348, loss_spatial_dice_7: 0.26337/0.23499, loss_spatial_ce_7: 0.10630/0.18697, loss_grounding_bce_7: 0.03350/0.08557, loss_grounding_dice_7: 0.07486/0.16149, loss_grounding_ce_7: 0.00917/0.35160, loss_mask_ce_8: 0.82451/1.06305, loss_mask_bce_8: 0.47573/0.33519, loss_mask_dice_8: 0.77776/1.19679, loss_spatial_bce_8: 0.23910/0.13580, loss_spatial_dice_8: 0.29614/0.27858, loss_spatial_ce_8: 0.18817/0.24217, loss_grounding_bce_8: 0.03585/0.08932, loss_grounding_dice_8: 0.06308/0.17033, loss_grounding_ce_8: 0.08032/0.45317, loss_mask_ce_9: 3.24865/3.52773, loss_mask_bce_9: 0.46650/0.36150, loss_mask_dice_9: 1.00454/1.78391, loss_spatial_bce_9: 0.31512/0.36547, loss_spatial_dice_9: 0.85928/0.80029, loss_spatial_ce_9: 1.74586/1.43231, loss_grounding_bce_9: 0.06452/0.10141, loss_grounding_dice_9: 0.12962/0.24493, loss_grounding_ce_9: 0.44772/0.73707] items per batch[64] items per second[0.36] total items[672000] mini batches[ 10500] memory[4929] epoch remaining[0:13:55] INFO:trainer.default_trainer:epochs[ 5] optim steps[10600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35823/0.79991, loss_mask_bce_0: 0.33630/0.30331, loss_mask_dice_0: 0.27076/1.03660, loss_spatial_bce_0: 0.10560/0.09232, loss_spatial_dice_0: 0.10315/0.19489, loss_spatial_ce_0: 0.02588/0.08548, loss_grounding_bce_0: 0.20136/0.08116, loss_grounding_dice_0: 0.12672/0.15160, loss_grounding_ce_0: 0.04001/0.25369, loss_mask_ce_1: 0.36985/0.80280, loss_mask_bce_1: 0.33691/0.30388, loss_mask_dice_1: 0.26786/1.04091, loss_spatial_bce_1: 0.10540/0.09295, loss_spatial_dice_1: 0.10482/0.19778, loss_spatial_ce_1: 0.03044/0.08971, loss_grounding_bce_1: 0.19524/0.08117, loss_grounding_dice_1: 0.12409/0.15254, loss_grounding_ce_1: 0.03095/0.25798, loss_mask_ce_2: 0.40427/0.80851, loss_mask_bce_2: 0.32641/0.30404, loss_mask_dice_2: 0.26564/1.04376, loss_spatial_bce_2: 0.10653/0.09241, loss_spatial_dice_2: 0.11448/0.19760, loss_spatial_ce_2: 0.03199/0.09475, loss_grounding_bce_2: 0.18214/0.08098, loss_grounding_dice_2: 0.11034/0.15244, loss_grounding_ce_2: 0.03067/0.25798, loss_mask_ce_3: 0.42315/0.80646, loss_mask_bce_3: 0.34102/0.30551, loss_mask_dice_3: 0.28379/1.03883, loss_spatial_bce_3: 0.10828/0.09391, loss_spatial_dice_3: 0.10356/0.19782, loss_spatial_ce_3: 0.04998/0.10012, loss_grounding_bce_3: 0.19905/0.08164, loss_grounding_dice_3: 0.11136/0.15227, loss_grounding_ce_3: 0.03294/0.25656, loss_mask_ce_4: 0.46083/0.81233, loss_mask_bce_4: 0.42208/0.30761, loss_mask_dice_4: 0.33968/1.05755, loss_spatial_bce_4: 0.10068/0.09581, loss_spatial_dice_4: 0.08951/0.20520, loss_spatial_ce_4: 0.06781/0.11193, loss_grounding_bce_4: 0.17676/0.08227, loss_grounding_dice_4: 0.11026/0.15457, loss_grounding_ce_4: 0.06072/0.26482, loss_mask_ce_5: 0.48657/0.83250, loss_mask_bce_5: 0.43143/0.30963, loss_mask_dice_5: 0.36464/1.06620, loss_spatial_bce_5: 0.10503/0.09737, loss_spatial_dice_5: 0.09577/0.20728, loss_spatial_ce_5: 0.05431/0.12248, loss_grounding_bce_5: 0.19584/0.08260, loss_grounding_dice_5: 0.12801/0.15515, loss_grounding_ce_5: 0.11411/0.28257, loss_mask_ce_6: 0.51314/0.85671, loss_mask_bce_6: 0.37822/0.31050, loss_mask_dice_6: 0.29094/1.07072, loss_spatial_bce_6: 0.10428/0.10238, loss_spatial_dice_6: 0.08985/0.20960, loss_spatial_ce_6: 0.04509/0.13985, loss_grounding_bce_6: 0.21103/0.08392, loss_grounding_dice_6: 0.13444/0.15595, loss_grounding_ce_6: 0.20032/0.29970, loss_mask_ce_7: 0.62149/0.92090, loss_mask_bce_7: 0.50727/0.31772, loss_mask_dice_7: 0.34033/1.11652, loss_spatial_bce_7: 0.10570/0.11334, loss_spatial_dice_7: 0.09118/0.23500, loss_spatial_ce_7: 0.06770/0.18655, loss_grounding_bce_7: 0.17432/0.08559, loss_grounding_dice_7: 0.12046/0.16146, loss_grounding_ce_7: 0.52589/0.35087, loss_mask_ce_8: 0.89148/1.06355, loss_mask_bce_8: 0.39959/0.33544, loss_mask_dice_8: 0.38787/1.19795, loss_spatial_bce_8: 0.13785/0.13566, loss_spatial_dice_8: 0.12659/0.27859, loss_spatial_ce_8: 0.16056/0.24184, loss_grounding_bce_8: 0.19394/0.08930, loss_grounding_dice_8: 0.20505/0.17036, loss_grounding_ce_8: 1.51607/0.45286, loss_mask_ce_9: 3.55625/3.52898, loss_mask_bce_9: 0.51577/0.36169, loss_mask_dice_9: 0.63788/1.78510, loss_spatial_bce_9: 0.53516/0.36485, loss_spatial_dice_9: 0.63529/0.80044, loss_spatial_ce_9: 0.96115/1.43364, loss_grounding_bce_9: 0.19190/0.10136, loss_grounding_dice_9: 0.24397/0.24497, loss_grounding_ce_9: 1.95457/0.73824] items per batch[64] items per second[0.36] total items[678400] mini batches[ 10600] memory[4929] epoch remaining[0:10:54] INFO:trainer.default_trainer:epochs[ 5] optim steps[10700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42660/0.79958, loss_mask_bce_0: 0.12240/0.30289, loss_mask_dice_0: 0.15988/1.03575, loss_spatial_bce_0: 0.05037/0.09224, loss_spatial_dice_0: 0.06041/0.19481, loss_spatial_ce_0: 0.04681/0.08557, loss_grounding_bce_0: 0.08851/0.08108, loss_grounding_dice_0: 0.04925/0.15157, loss_grounding_ce_0: 0.19728/0.25393, loss_mask_ce_1: 0.44543/0.80253, loss_mask_bce_1: 0.12076/0.30349, loss_mask_dice_1: 0.16722/1.04002, loss_spatial_bce_1: 0.05232/0.09288, loss_spatial_dice_1: 0.06074/0.19769, loss_spatial_ce_1: 0.04673/0.08978, loss_grounding_bce_1: 0.09299/0.08110, loss_grounding_dice_1: 0.05229/0.15250, loss_grounding_ce_1: 0.16803/0.25789, loss_mask_ce_2: 0.46456/0.80823, loss_mask_bce_2: 0.12879/0.30364, loss_mask_dice_2: 0.16286/1.04310, loss_spatial_bce_2: 0.05042/0.09233, loss_spatial_dice_2: 0.06017/0.19750, loss_spatial_ce_2: 0.04687/0.09479, loss_grounding_bce_2: 0.09882/0.08091, loss_grounding_dice_2: 0.05704/0.15243, loss_grounding_ce_2: 0.14486/0.25852, loss_mask_ce_3: 0.42275/0.80632, loss_mask_bce_3: 0.12518/0.30508, loss_mask_dice_3: 0.16916/1.03796, loss_spatial_bce_3: 0.05257/0.09384, loss_spatial_dice_3: 0.06207/0.19773, loss_spatial_ce_3: 0.04705/0.10021, loss_grounding_bce_3: 0.09228/0.08153, loss_grounding_dice_3: 0.05188/0.15219, loss_grounding_ce_3: 0.13386/0.25663, loss_mask_ce_4: 0.44906/0.81214, loss_mask_bce_4: 0.13103/0.30727, loss_mask_dice_4: 0.17398/1.05650, loss_spatial_bce_4: 0.05180/0.09574, loss_spatial_dice_4: 0.06427/0.20509, loss_spatial_ce_4: 0.04832/0.11198, loss_grounding_bce_4: 0.09977/0.08222, loss_grounding_dice_4: 0.05554/0.15454, loss_grounding_ce_4: 0.11284/0.26452, loss_mask_ce_5: 0.41245/0.83254, loss_mask_bce_5: 0.13101/0.30926, loss_mask_dice_5: 0.16539/1.06520, loss_spatial_bce_5: 0.05035/0.09730, loss_spatial_dice_5: 0.06278/0.20717, loss_spatial_ce_5: 0.04849/0.12252, loss_grounding_bce_5: 0.10492/0.08255, loss_grounding_dice_5: 0.06164/0.15515, loss_grounding_ce_5: 0.16876/0.28240, loss_mask_ce_6: 0.41221/0.85687, loss_mask_bce_6: 0.13283/0.31015, loss_mask_dice_6: 0.16894/1.06953, loss_spatial_bce_6: 0.04920/0.10231, loss_spatial_dice_6: 0.06081/0.20950, loss_spatial_ce_6: 0.05781/0.13994, loss_grounding_bce_6: 0.10208/0.08383, loss_grounding_dice_6: 0.05686/0.15590, loss_grounding_ce_6: 0.14318/0.29953, loss_mask_ce_7: 0.41560/0.92102, loss_mask_bce_7: 0.12706/0.31741, loss_mask_dice_7: 0.16688/1.11547, loss_spatial_bce_7: 0.05294/0.11329, loss_spatial_dice_7: 0.06485/0.23487, loss_spatial_ce_7: 0.11293/0.18652, loss_grounding_bce_7: 0.10259/0.08554, loss_grounding_dice_7: 0.05749/0.16147, loss_grounding_ce_7: 0.09770/0.35068, loss_mask_ce_8: 0.36297/1.06362, loss_mask_bce_8: 0.17315/0.33505, loss_mask_dice_8: 0.18999/1.19699, loss_spatial_bce_8: 0.05483/0.13556, loss_spatial_dice_8: 0.06797/0.27840, loss_spatial_ce_8: 0.16207/0.24190, loss_grounding_bce_8: 0.24471/0.08926, loss_grounding_dice_8: 0.14781/0.17038, loss_grounding_ce_8: 0.04133/0.45309, loss_mask_ce_9: 3.64184/3.52994, loss_mask_bce_9: 0.14615/0.36129, loss_mask_dice_9: 0.23022/1.78437, loss_spatial_bce_9: 0.33717/0.36474, loss_spatial_dice_9: 0.58016/0.80044, loss_spatial_ce_9: 0.83024/1.43413, loss_grounding_bce_9: 0.11896/0.10130, loss_grounding_dice_9: 0.07060/0.24495, loss_grounding_ce_9: 1.00589/0.73906] items per batch[64] items per second[0.36] total items[684800] mini batches[ 10700] memory[4929] epoch remaining[0:07:52] INFO:trainer.default_trainer:epochs[ 5] optim steps[10800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17818/0.79870, loss_mask_bce_0: 0.47354/0.30326, loss_mask_dice_0: 0.53584/1.03623, loss_spatial_bce_0: 0.11285/0.09224, loss_spatial_dice_0: 0.13316/0.19471, loss_spatial_ce_0: 0.00194/0.08532, loss_grounding_bce_0: 0.26054/0.08104, loss_grounding_dice_0: 0.21232/0.15152, loss_grounding_ce_0: 0.00735/0.25326, loss_mask_ce_1: 0.17485/0.80142, loss_mask_bce_1: 0.47894/0.30382, loss_mask_dice_1: 0.55764/1.04045, loss_spatial_bce_1: 0.11466/0.09287, loss_spatial_dice_1: 0.13270/0.19759, loss_spatial_ce_1: 0.00199/0.08955, loss_grounding_bce_1: 0.25511/0.08106, loss_grounding_dice_1: 0.21646/0.15251, loss_grounding_ce_1: 0.00693/0.25726, loss_mask_ce_2: 0.17845/0.80732, loss_mask_bce_2: 0.46538/0.30396, loss_mask_dice_2: 0.57304/1.04355, loss_spatial_bce_2: 0.11359/0.09233, loss_spatial_dice_2: 0.13049/0.19742, loss_spatial_ce_2: 0.00111/0.09454, loss_grounding_bce_2: 0.25442/0.08087, loss_grounding_dice_2: 0.22057/0.15246, loss_grounding_ce_2: 0.00668/0.25814, loss_mask_ce_3: 0.22420/0.80543, loss_mask_bce_3: 0.46308/0.30538, loss_mask_dice_3: 0.57135/1.03846, loss_spatial_bce_3: 0.11818/0.09381, loss_spatial_dice_3: 0.12822/0.19763, loss_spatial_ce_3: 0.00161/0.10017, loss_grounding_bce_3: 0.25059/0.08149, loss_grounding_dice_3: 0.21860/0.15212, loss_grounding_ce_3: 0.00650/0.25620, loss_mask_ce_4: 0.19987/0.81135, loss_mask_bce_4: 0.47784/0.30766, loss_mask_dice_4: 0.67332/1.05686, loss_spatial_bce_4: 0.11237/0.09570, loss_spatial_dice_4: 0.12990/0.20499, loss_spatial_ce_4: 0.00672/0.11194, loss_grounding_bce_4: 0.26002/0.08219, loss_grounding_dice_4: 0.21442/0.15452, loss_grounding_ce_4: 0.01197/0.26428, loss_mask_ce_5: 0.19570/0.83188, loss_mask_bce_5: 0.48783/0.30956, loss_mask_dice_5: 0.64308/1.06549, loss_spatial_bce_5: 0.11480/0.09727, loss_spatial_dice_5: 0.13605/0.20706, loss_spatial_ce_5: 0.02049/0.12232, loss_grounding_bce_5: 0.26360/0.08251, loss_grounding_dice_5: 0.21866/0.15518, loss_grounding_ce_5: 0.01427/0.28210, loss_mask_ce_6: 0.18277/0.85622, loss_mask_bce_6: 0.49067/0.31051, loss_mask_dice_6: 0.62435/1.06981, loss_spatial_bce_6: 0.12865/0.10230, loss_spatial_dice_6: 0.13195/0.20939, loss_spatial_ce_6: 0.02304/0.13997, loss_grounding_bce_6: 0.26744/0.08378, loss_grounding_dice_6: 0.21663/0.15587, loss_grounding_ce_6: 0.00603/0.29897, loss_mask_ce_7: 0.24318/0.92024, loss_mask_bce_7: 0.48263/0.31782, loss_mask_dice_7: 0.58326/1.11578, loss_spatial_bce_7: 0.12715/0.11325, loss_spatial_dice_7: 0.14665/0.23471, loss_spatial_ce_7: 0.06253/0.18645, loss_grounding_bce_7: 0.25759/0.08550, loss_grounding_dice_7: 0.20961/0.16141, loss_grounding_ce_7: 0.01541/0.34976, loss_mask_ce_8: 0.23870/1.06253, loss_mask_bce_8: 0.50567/0.33544, loss_mask_dice_8: 0.58999/1.19736, loss_spatial_bce_8: 0.12149/0.13547, loss_spatial_dice_8: 0.15887/0.27827, loss_spatial_ce_8: 0.08761/0.24167, loss_grounding_bce_8: 0.26971/0.08919, loss_grounding_dice_8: 0.19836/0.17027, loss_grounding_ce_8: 0.00208/0.45273, loss_mask_ce_9: 2.74207/3.52818, loss_mask_bce_9: 0.54329/0.36165, loss_mask_dice_9: 0.96488/1.78518, loss_spatial_bce_9: 0.37292/0.36488, loss_spatial_dice_9: 0.83051/0.80042, loss_spatial_ce_9: 1.39049/1.43348, loss_grounding_bce_9: 0.27078/0.10136, loss_grounding_dice_9: 0.20508/0.24492, loss_grounding_ce_9: 0.03304/0.73841] items per batch[64] items per second[0.35] total items[691200] mini batches[ 10800] memory[4929] epoch remaining[0:04:52] INFO:trainer.default_trainer:epochs[ 5] optim steps[10900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08792/0.79817, loss_mask_bce_0: 0.04393/0.30299, loss_mask_dice_0: 0.89702/1.03345, loss_spatial_bce_0: 0.01063/0.09249, loss_spatial_dice_0: 0.17099/0.19463, loss_spatial_ce_0: 0.02409/0.08526, loss_grounding_bce_0: 0.00203/0.08112, loss_grounding_dice_0: 0.35325/0.15176, loss_grounding_ce_0: 0.27375/0.25353, loss_mask_ce_1: 0.09277/0.80081, loss_mask_bce_1: 0.04935/0.30356, loss_mask_dice_1: 1.14526/1.03743, loss_spatial_bce_1: 0.01103/0.09310, loss_spatial_dice_1: 0.17696/0.19755, loss_spatial_ce_1: 0.14841/0.08946, loss_grounding_bce_1: 0.00181/0.08113, loss_grounding_dice_1: 0.23892/0.15275, loss_grounding_ce_1: 0.28082/0.25757, loss_mask_ce_2: 0.09241/0.80703, loss_mask_bce_2: 0.05075/0.30365, loss_mask_dice_2: 1.15316/1.04043, loss_spatial_bce_2: 0.00952/0.09254, loss_spatial_dice_2: 0.15343/0.19737, loss_spatial_ce_2: 0.11738/0.09436, loss_grounding_bce_2: 0.00201/0.08094, loss_grounding_dice_2: 0.36574/0.15274, loss_grounding_ce_2: 0.45218/0.25853, loss_mask_ce_3: 0.11438/0.80494, loss_mask_bce_3: 0.04971/0.30506, loss_mask_dice_3: 1.02416/1.03541, loss_spatial_bce_3: 0.00996/0.09407, loss_spatial_dice_3: 0.15424/0.19753, loss_spatial_ce_3: 0.04708/0.09998, loss_grounding_bce_3: 0.00199/0.08155, loss_grounding_dice_3: 0.25188/0.15224, loss_grounding_ce_3: 0.46173/0.25664, loss_mask_ce_4: 0.09732/0.81103, loss_mask_bce_4: 0.04755/0.30735, loss_mask_dice_4: 1.11856/1.05393, loss_spatial_bce_4: 0.01171/0.09593, loss_spatial_dice_4: 0.14156/0.20488, loss_spatial_ce_4: 0.18491/0.11196, loss_grounding_bce_4: 0.00249/0.08227, loss_grounding_dice_4: 0.28971/0.15470, loss_grounding_ce_4: 0.32764/0.26475, loss_mask_ce_5: 0.15867/0.83148, loss_mask_bce_5: 0.04655/0.30929, loss_mask_dice_5: 1.20065/1.06249, loss_spatial_bce_5: 0.01108/0.09754, loss_spatial_dice_5: 0.19264/0.20698, loss_spatial_ce_5: 0.23989/0.12232, loss_grounding_bce_5: 0.00139/0.08260, loss_grounding_dice_5: 0.32652/0.15537, loss_grounding_ce_5: 0.28429/0.28265, loss_mask_ce_6: 0.13762/0.85575, loss_mask_bce_6: 0.04515/0.31026, loss_mask_dice_6: 0.70274/1.06678, loss_spatial_bce_6: 0.01076/0.10257, loss_spatial_dice_6: 0.13891/0.20935, loss_spatial_ce_6: 0.08242/0.13994, loss_grounding_bce_6: 0.00255/0.08387, loss_grounding_dice_6: 0.23099/0.15607, loss_grounding_ce_6: 0.46167/0.29926, loss_mask_ce_7: 0.59680/0.91997, loss_mask_bce_7: 0.04654/0.31750, loss_mask_dice_7: 0.89458/1.11259, loss_spatial_bce_7: 0.01367/0.11358, loss_spatial_dice_7: 0.17525/0.23462, loss_spatial_ce_7: 0.13662/0.18617, loss_grounding_bce_7: 0.00183/0.08559, loss_grounding_dice_7: 0.34320/0.16161, loss_grounding_ce_7: 0.49235/0.35014, loss_mask_ce_8: 0.28468/1.06190, loss_mask_bce_8: 0.04355/0.33510, loss_mask_dice_8: 1.05437/1.19388, loss_spatial_bce_8: 0.01647/0.13579, loss_spatial_dice_8: 0.25876/0.27812, loss_spatial_ce_8: 0.15168/0.24152, loss_grounding_bce_8: 0.00369/0.08927, loss_grounding_dice_8: 0.23856/0.17039, loss_grounding_ce_8: 0.40095/0.45306, loss_mask_ce_9: 3.52704/3.52515, loss_mask_bce_9: 0.04722/0.36122, loss_mask_dice_9: 1.30179/1.77941, loss_spatial_bce_9: 0.11428/0.36529, loss_spatial_dice_9: 0.80250/0.80006, loss_spatial_ce_9: 0.99193/1.43264, loss_grounding_bce_9: 0.00231/0.10143, loss_grounding_dice_9: 0.49682/0.24522, loss_grounding_ce_9: 0.60122/0.73814] items per batch[64] items per second[0.36] total items[697600] mini batches[ 10900] memory[4929] epoch remaining[0:01:51] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00010962. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0020 s/iter. Inference: 0.3562 s/iter. Eval: 0.0962 s/iter. Total: 0.4545 s/iter. ETA=0:00:30 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3611 s/iter. Eval: 0.0878 s/iter. Total: 0.4514 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0027 s/iter. Inference: 0.3641 s/iter. Eval: 0.0808 s/iter. Total: 0.4477 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0028 s/iter. Inference: 0.3694 s/iter. Eval: 0.0775 s/iter. Total: 0.4498 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0028 s/iter. Inference: 0.3718 s/iter. Eval: 0.0755 s/iter. Total: 0.4503 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0029 s/iter. Inference: 0.3701 s/iter. Eval: 0.0752 s/iter. Total: 0.4483 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaloax28rlq ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.168 | 83.041 | 65.614 | 133 | | Things | 61.490 | 84.038 | 72.681 | 80 | | Stuff | 45.625 | 81.536 | 54.946 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.58s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.77 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.42 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=5.26s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.47 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.54 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.280 | 68.980 | 48.872 | 25.614 | 49.392 | 67.562 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.379 | bicycle | 22.837 | car | 40.729 | | motorcycle | 40.976 | airplane | 61.571 | bus | 69.406 | | train | 74.761 | truck | 42.730 | boat | 30.016 | | traffic light | 27.681 | fire hydrant | 72.081 | stop sign | 69.445 | | parking meter | 52.163 | bench | 25.717 | bird | 33.904 | | cat | 77.126 | dog | 71.469 | horse | 50.637 | | sheep | 53.838 | cow | 56.437 | elephant | 65.696 | | bear | 80.315 | zebra | 65.915 | giraffe | 62.158 | | backpack | 22.321 | umbrella | 54.837 | handbag | 24.197 | | tie | 40.861 | suitcase | 51.361 | frisbee | 69.612 | | skis | 9.458 | snowboard | 34.509 | sports ball | 48.972 | | kite | 37.497 | baseball bat | 38.734 | baseball glove | 49.137 | | skateboard | 44.626 | surfboard | 44.921 | tennis racket | 63.146 | | bottle | 41.270 | wine glass | 37.863 | cup | 50.021 | | fork | 25.672 | knife | 24.993 | spoon | 21.006 | | bowl | 37.351 | banana | 21.045 | apple | 24.926 | | sandwich | 45.739 | orange | 30.007 | broccoli | 24.320 | | carrot | 21.274 | hot dog | 34.136 | pizza | 50.562 | | donut | 55.040 | cake | 46.144 | chair | 28.136 | | couch | 40.719 | potted plant | 22.072 | bed | 42.438 | | dining table | 15.710 | toilet | 69.684 | tv | 65.747 | | laptop | 69.019 | mouse | 63.066 | remote | 43.032 | | keyboard | 59.689 | cell phone | 45.160 | microwave | 67.564 | | oven | 32.431 | toaster | 59.020 | sink | 43.245 | | refrigerator | 69.756 | book | 13.823 | clock | 54.457 | | vase | 41.710 | scissors | 37.328 | teddy bear | 56.806 | | hair drier | 30.624 | toothbrush | 29.625 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.690 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.489 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.86025161467768, 'fwIoU': 71.43188701174721, 'IoU-person': 88.48277261489248, 'IoU-bicycle': 75.04429641147605, 'IoU-car': 71.53630621675734, 'IoU-motorcycle': 85.59552168116637, 'IoU-airplane': 89.37969148925696, 'IoU-bus': 88.31943997586886, 'IoU-train': 86.89391956633793, 'IoU-truck': 68.38614806934004, 'IoU-boat': 74.70840653074553, 'IoU-traffic light': 78.89653703784894, 'IoU-fire hydrant': 93.03329156333885, 'IoU-stop sign': 95.32079917779123, 'IoU-parking meter': 88.33599298202644, 'IoU-bench': 64.96719944265365, 'IoU-bird': 77.83308757410495, 'IoU-cat': 89.92585238136051, 'IoU-dog': 77.16356218041092, 'IoU-horse': 87.04046903440576, 'IoU-sheep': 88.37951931995669, 'IoU-cow': 86.23606032380867, 'IoU-elephant': 90.57978112790855, 'IoU-bear': 86.07092300915656, 'IoU-zebra': 87.65440345574278, 'IoU-giraffe': 89.59601141707981, 'IoU-backpack': 52.514945514106735, 'IoU-umbrella': 83.92798915603494, 'IoU-handbag': 51.15404199836584, 'IoU-tie': 73.05443410573233, 'IoU-suitcase': 83.82949362051629, 'IoU-frisbee': 84.61714330507036, 'IoU-skis': 59.293866700625586, 'IoU-snowboard': 74.98754851591771, 'IoU-sports ball': 80.25668224343721, 'IoU-kite': 79.8339234167517, 'IoU-baseball bat': 67.84819528359829, 'IoU-baseball glove': 79.32806993243763, 'IoU-skateboard': 86.22406391605057, 'IoU-surfboard': 86.60322062166779, 'IoU-tennis racket': 90.48794051182117, 'IoU-bottle': 72.87722927148701, 'IoU-wine glass': 81.769179205498, 'IoU-cup': 70.79127022148623, 'IoU-fork': 68.6251952108277, 'IoU-knife': 63.02226752616542, 'IoU-spoon': 61.89612311475384, 'IoU-bowl': 57.2243861660093, 'IoU-banana': 83.06446614017042, 'IoU-apple': 58.93043616262855, 'IoU-sandwich': 69.31290187675762, 'IoU-orange': 80.89435270782757, 'IoU-broccoli': 68.9093230499175, 'IoU-carrot': 63.465409448880905, 'IoU-hot dog': 68.3404745808874, 'IoU-pizza': 84.60940032890039, 'IoU-donut': 74.95929189610206, 'IoU-cake': 77.41614759757724, 'IoU-chair': 61.846287276631074, 'IoU-couch': 64.74507131814867, 'IoU-potted plant': 43.03083848710506, 'IoU-bed': 72.08024228279292, 'IoU-dining table': 54.69443271044439, 'IoU-toilet': 86.14297400417401, 'IoU-tv': 83.98144795499896, 'IoU-laptop': 78.27290531723298, 'IoU-mouse': 77.40076382826165, 'IoU-remote': 71.56801852991991, 'IoU-keyboard': 70.31172757092567, 'IoU-cell phone': 80.14867665452421, 'IoU-microwave': 72.57309448333226, 'IoU-oven': 73.18627623169847, 'IoU-toaster': 86.49982216186504, 'IoU-sink': 74.84817250892597, 'IoU-refrigerator': 79.08942706787357, 'IoU-book': 54.89870189567018, 'IoU-clock': 79.23368410201469, 'IoU-vase': 71.60048945094553, 'IoU-scissors': 62.512788524215104, 'IoU-teddy bear': 86.09128720292138, 'IoU-hair drier': 48.8482532126469, 'IoU-toothbrush': 76.7265101848315, 'IoU-banner': 31.99408954472756, 'IoU-blanket': 16.682779444995607, 'IoU-bridge': 39.116045800919025, 'IoU-cardboard': 53.15361190475427, 'IoU-counter': 32.93980928215597, 'IoU-curtain': 70.02647041444658, 'IoU-door-stuff': 47.016368593903806, 'IoU-floor-wood': 63.96137825604256, 'IoU-flower': 47.53269917824932, 'IoU-fruit': 48.834071167773416, 'IoU-gravel': 32.371780796592034, 'IoU-house': 21.93460457223927, 'IoU-light': 44.46929434304083, 'IoU-mirror-stuff': 66.49649968007829, 'IoU-net': 52.625418700179694, 'IoU-pillow': 20.412527993219097, 'IoU-platform': 31.168985479766555, 'IoU-playingfield': 70.0077548187957, 'IoU-railroad': 63.21445722138136, 'IoU-river': 54.0918997912396, 'IoU-road': 66.51611694964569, 'IoU-roof': 17.252072392884255, 'IoU-sand': 67.68436916383506, 'IoU-sea': 85.04699402658515, 'IoU-shelf': 37.105198057149146, 'IoU-snow': 92.44543332477359, 'IoU-stairs': 31.703139459163037, 'IoU-tent': 11.595669114296408, 'IoU-towel': 44.838390378033594, 'IoU-wall-brick': 48.42079428510615, 'IoU-wall-stone': 29.722025807088453, 'IoU-wall-tile': 71.88440066249267, 'IoU-wall-wood': 43.53644412005759, 'IoU-water-other': 16.579431941397928, 'IoU-window-blind': 49.58311281068631, 'IoU-window-other': 49.38566292675074, 'IoU-tree-merged': 82.08823919149677, 'IoU-fence-merged': 55.51216015889645, 'IoU-ceiling-merged': 66.9473788318424, 'IoU-sky-other-merged': 94.15610576590603, 'IoU-cabinet-merged': 63.35973579050949, 'IoU-table-merged': 42.277214515452066, 'IoU-floor-other-merged': 52.29937660653685, 'IoU-pavement-merged': 55.01208810113107, 'IoU-mountain-merged': 57.205304323728754, 'IoU-grass-merged': 71.5593896316282, 'IoU-dirt-merged': 48.0455536578268, 'IoU-paper-merged': 38.31554640588798, 'IoU-food-other-merged': 44.06550753957955, 'IoU-building-other-merged': 58.740212931944114, 'IoU-rock-merged': 65.52209361011094, 'IoU-wall-other-merged': 68.09089460062307, 'IoU-rug-merged': 65.08556179103633, 'mACC': 77.43305308307187, 'pACC': 82.18366998334665, 'ACC-person': 93.22827299110132, 'ACC-bicycle': 82.01633627813206, 'ACC-car': 86.45899948557076, 'ACC-motorcycle': 90.21189145538891, 'ACC-airplane': 93.83662616459041, 'ACC-bus': 93.72170698846017, 'ACC-train': 92.5496854343266, 'ACC-truck': 76.53391290948417, 'ACC-boat': 83.4657407526607, 'ACC-traffic light': 90.2699132536855, 'ACC-fire hydrant': 96.17666651009664, 'ACC-stop sign': 98.30282890218746, 'ACC-parking meter': 91.61177112479378, 'ACC-bench': 76.69512253825991, 'ACC-bird': 82.79567476417921, 'ACC-cat': 93.49748146186923, 'ACC-dog': 80.11458565807048, 'ACC-horse': 95.09746925593691, 'ACC-sheep': 92.94635857861405, 'ACC-cow': 89.39386520075546, 'ACC-elephant': 92.80297233733988, 'ACC-bear': 88.0076512205299, 'ACC-zebra': 89.8479524930457, 'ACC-giraffe': 93.57401393903328, 'ACC-backpack': 66.80627251294905, 'ACC-umbrella': 88.65518897403459, 'ACC-handbag': 73.62163830081143, 'ACC-tie': 82.5780565155019, 'ACC-suitcase': 92.46842767866748, 'ACC-frisbee': 94.20872727272727, 'ACC-skis': 76.14864887886418, 'ACC-snowboard': 81.85986137983396, 'ACC-sports ball': 86.81220992133635, 'ACC-kite': 86.35297345324302, 'ACC-baseball bat': 88.97421873243975, 'ACC-baseball glove': 92.85179020485218, 'ACC-skateboard': 90.8748558065631, 'ACC-surfboard': 92.75483634722521, 'ACC-tennis racket': 94.90576171139745, 'ACC-bottle': 85.66306771155405, 'ACC-wine glass': 91.1267248100669, 'ACC-cup': 88.69412995748264, 'ACC-fork': 81.51921355592624, 'ACC-knife': 80.4149263574412, 'ACC-spoon': 80.28417941857072, 'ACC-bowl': 66.40083415720227, 'ACC-banana': 90.81569487195098, 'ACC-apple': 71.48716314612004, 'ACC-sandwich': 83.30142202885222, 'ACC-orange': 88.82420462087484, 'ACC-broccoli': 83.80809228390592, 'ACC-carrot': 76.24466395856044, 'ACC-hot dog': 76.5412630349618, 'ACC-pizza': 90.89985270227258, 'ACC-donut': 83.60263679918639, 'ACC-cake': 84.91291552815875, 'ACC-chair': 84.19619359276719, 'ACC-couch': 74.07092952466125, 'ACC-potted plant': 61.60005253122283, 'ACC-bed': 82.52308741853447, 'ACC-dining table': 78.38063999355622, 'ACC-toilet': 91.26724795153747, 'ACC-tv': 89.33045010615172, 'ACC-laptop': 89.21196318612837, 'ACC-mouse': 93.06804613125837, 'ACC-remote': 76.4703410521801, 'ACC-keyboard': 78.28657994063703, 'ACC-cell phone': 89.68354585556116, 'ACC-microwave': 74.98919259731362, 'ACC-oven': 86.14728187396659, 'ACC-toaster': 91.52923948596532, 'ACC-sink': 84.31287247184386, 'ACC-refrigerator': 87.91229602781073, 'ACC-book': 72.27942618587319, 'ACC-clock': 84.31486720558514, 'ACC-vase': 81.34886557111668, 'ACC-scissors': 66.48337143854043, 'ACC-teddy bear': 91.79199755585695, 'ACC-hair drier': 60.75199954458771, 'ACC-toothbrush': 85.49687282835302, 'ACC-banner': 78.00969574880784, 'ACC-blanket': 27.448073780155624, 'ACC-bridge': 56.15235466208872, 'ACC-cardboard': 72.31109495419514, 'ACC-counter': 46.58703509247023, 'ACC-curtain': 82.70989104533064, 'ACC-door-stuff': 71.69591548535922, 'ACC-floor-wood': 79.96926608711763, 'ACC-flower': 66.98205243430648, 'ACC-fruit': 71.32609068934237, 'ACC-gravel': 42.91248046941586, 'ACC-house': 25.448057249993894, 'ACC-light': 63.04459910806569, 'ACC-mirror-stuff': 78.34506683127266, 'ACC-net': 64.02069158169883, 'ACC-pillow': 46.538082805120474, 'ACC-platform': 45.06442859473005, 'ACC-playingfield': 89.47648802951548, 'ACC-railroad': 82.51684179625262, 'ACC-river': 86.75226077428394, 'ACC-road': 87.32439507964386, 'ACC-roof': 23.71552437180913, 'ACC-sand': 71.73417635152039, 'ACC-sea': 91.46466092800553, 'ACC-shelf': 51.63223338137212, 'ACC-snow': 95.44379083138618, 'ACC-stairs': 62.674694652253784, 'ACC-tent': 14.14071289066584, 'ACC-towel': 54.136992286014994, 'ACC-wall-brick': 69.35005906676237, 'ACC-wall-stone': 35.838907789405184, 'ACC-wall-tile': 85.9814912474695, 'ACC-wall-wood': 63.7973201900629, 'ACC-water-other': 21.22182650810047, 'ACC-window-blind': 65.8089372217843, 'ACC-window-other': 73.2050700842509, 'ACC-tree-merged': 89.69431361873929, 'ACC-fence-merged': 73.02962617152168, 'ACC-ceiling-merged': 83.58820072334208, 'ACC-sky-other-merged': 97.13794044414321, 'ACC-cabinet-merged': 79.11986920338113, 'ACC-table-merged': 55.440687872973726, 'ACC-floor-other-merged': 70.53570199211299, 'ACC-pavement-merged': 64.54483799500689, 'ACC-mountain-merged': 67.05233182731286, 'ACC-grass-merged': 84.6726070514221, 'ACC-dirt-merged': 66.92556999773561, 'ACC-paper-merged': 50.18177465903849, 'ACC-food-other-merged': 59.49551969301473, 'ACC-building-other-merged': 71.4693729966012, 'ACC-rock-merged': 81.92300269290315, 'ACC-wall-other-merged': 82.65402331113646, 'ACC-rug-merged': 83.3301072954848})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2850 s/iter. Inference: 0.4482 s/iter. Eval: 0.0000 s/iter. Total: 0.7333 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3220 s/iter. Inference: 0.4741 s/iter. Eval: 0.0000 s/iter. Total: 0.7962 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3355 s/iter. Inference: 0.5872 s/iter. Eval: 0.0000 s/iter. Total: 0.9228 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4685396546678373, 'noc@0.8': 2.621012584138133, 'noc@0.85': 3.096868598185543, 'noc@0.9': 3.9353233830845773, 'miou@iter1': 0.8696474349390598} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1466 s/iter. Eval: 0.0010 s/iter. Total: 0.1489 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.35950469970703, 'precision@0.6': 72.67781066894531, 'precision@0.7': 68.55810546875, 'precision@0.8': 58.95841598510742, 'precision@0.9': 32.530120849609375, 'cIoU': 61.579830169677734, 'mIoU': 66.79682159423828} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.167918050214716, 'SQ': 83.04104978584745, 'RQ': 65.614112045285, 'PQ_th': 61.48982073547759, 'SQ_th': 84.038387155302, 'RQ_th': 72.68144262421131, 'PQ_st': 45.62542343095007, 'SQ_st': 81.53563488855757, 'RQ_st': 54.946443246905574}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.280077011790425, 'AP50': 68.98044053637625, 'AP75': 48.871853520219375, 'APs': 25.613942712593353, 'APm': 49.39231302907945, 'APl': 67.56189194143907, 'AP-person': 48.378501833562886, 'AP-bicycle': 22.837177789498174, 'AP-car': 40.72940670674056, 'AP-motorcycle': 40.97592889716189, 'AP-airplane': 61.57083539590488, 'AP-bus': 69.40563732873422, 'AP-train': 74.76080691805441, 'AP-truck': 42.73028825529254, 'AP-boat': 30.016142038833678, 'AP-traffic light': 27.68127884891885, 'AP-fire hydrant': 72.0805392571121, 'AP-stop sign': 69.44522364077143, 'AP-parking meter': 52.16272336801543, 'AP-bench': 25.71741404604207, 'AP-bird': 33.90410954067528, 'AP-cat': 77.12571641559583, 'AP-dog': 71.46898558842058, 'AP-horse': 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'ACC-tv': 89.33045010615172, 'ACC-laptop': 89.21196318612837, 'ACC-mouse': 93.06804613125837, 'ACC-remote': 76.4703410521801, 'ACC-keyboard': 78.28657994063703, 'ACC-cell phone': 89.68354585556116, 'ACC-microwave': 74.98919259731362, 'ACC-oven': 86.14728187396659, 'ACC-toaster': 91.52923948596532, 'ACC-sink': 84.31287247184386, 'ACC-refrigerator': 87.91229602781073, 'ACC-book': 72.27942618587319, 'ACC-clock': 84.31486720558514, 'ACC-vase': 81.34886557111668, 'ACC-scissors': 66.48337143854043, 'ACC-teddy bear': 91.79199755585695, 'ACC-hair drier': 60.75199954458771, 'ACC-toothbrush': 85.49687282835302, 'ACC-banner': 78.00969574880784, 'ACC-blanket': 27.448073780155624, 'ACC-bridge': 56.15235466208872, 'ACC-cardboard': 72.31109495419514, 'ACC-counter': 46.58703509247023, 'ACC-curtain': 82.70989104533064, 'ACC-door-stuff': 71.69591548535922, 'ACC-floor-wood': 79.96926608711763, 'ACC-flower': 66.98205243430648, 'ACC-fruit': 71.32609068934237, 'ACC-gravel': 42.91248046941586, 'ACC-house': 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'ACC-cabinet-merged': 79.11986920338113, 'ACC-table-merged': 55.440687872973726, 'ACC-floor-other-merged': 70.53570199211299, 'ACC-pavement-merged': 64.54483799500689, 'ACC-mountain-merged': 67.05233182731286, 'ACC-grass-merged': 84.6726070514221, 'ACC-dirt-merged': 66.92556999773561, 'ACC-paper-merged': 50.18177465903849, 'ACC-food-other-merged': 59.49551969301473, 'ACC-building-other-merged': 71.4693729966012, 'ACC-rock-merged': 81.92300269290315, 'ACC-wall-other-merged': 82.65402331113646, 'ACC-rug-merged': 83.3301072954848})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4685396546678373, 'noc@0.8': 2.621012584138133, 'noc@0.85': 3.096868598185543, 'noc@0.9': 3.9353233830845773, 'miou@iter1': 0.8696474349390598}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.35950469970703, 'precision@0.6': 72.67781066894531, 'precision@0.7': 68.55810546875, 'precision@0.8': 58.95841598510742, 'precision@0.9': 32.530120849609375, 'cIoU': 61.579830169677734, 'mIoU': 66.79682159423828}}} INFO:trainer.default_trainer:This epoch takes 0:58:24.448702 INFO:trainer.default_trainer:PROGRESS: 12.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 6 training. INFO:trainer.default_trainer:epochs[ 6] optim steps[11000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37454/0.79820, loss_mask_bce_0: 0.30311/0.30312, loss_mask_dice_0: 0.57668/1.03412, loss_spatial_bce_0: 0.06751/0.09254, loss_spatial_dice_0: 0.09980/0.19462, loss_spatial_ce_0: 0.03008/0.08506, loss_grounding_bce_0: 0.05983/0.08113, loss_grounding_dice_0: 0.11656/0.15178, loss_grounding_ce_0: 0.00360/0.25322, loss_mask_ce_1: 0.33919/0.80081, loss_mask_bce_1: 0.28747/0.30366, loss_mask_dice_1: 0.45264/1.03819, loss_spatial_bce_1: 0.07193/0.09312, loss_spatial_dice_1: 0.10184/0.19753, loss_spatial_ce_1: 0.01050/0.08927, loss_grounding_bce_1: 0.05604/0.08111, loss_grounding_dice_1: 0.11774/0.15279, loss_grounding_ce_1: 0.00566/0.25729, loss_mask_ce_2: 0.37530/0.80713, loss_mask_bce_2: 0.29471/0.30377, loss_mask_dice_2: 0.46840/1.04106, loss_spatial_bce_2: 0.06980/0.09258, loss_spatial_dice_2: 0.10335/0.19737, loss_spatial_ce_2: 0.00907/0.09412, loss_grounding_bce_2: 0.05726/0.08092, loss_grounding_dice_2: 0.11206/0.15276, loss_grounding_ce_2: 0.00716/0.25823, loss_mask_ce_3: 0.37310/0.80498, loss_mask_bce_3: 0.30224/0.30516, loss_mask_dice_3: 0.52558/1.03597, loss_spatial_bce_3: 0.07156/0.09409, loss_spatial_dice_3: 0.09898/0.19749, loss_spatial_ce_3: 0.01230/0.09970, loss_grounding_bce_3: 0.05580/0.08154, loss_grounding_dice_3: 0.11231/0.15228, loss_grounding_ce_3: 0.00625/0.25648, loss_mask_ce_4: 0.38983/0.81135, loss_mask_bce_4: 0.30432/0.30742, loss_mask_dice_4: 0.53197/1.05466, loss_spatial_bce_4: 0.07510/0.09597, loss_spatial_dice_4: 0.11032/0.20487, loss_spatial_ce_4: 0.00354/0.11172, loss_grounding_bce_4: 0.05677/0.08225, loss_grounding_dice_4: 0.11000/0.15470, loss_grounding_ce_4: 0.00536/0.26486, loss_mask_ce_5: 0.35330/0.83148, loss_mask_bce_5: 0.30728/0.30939, loss_mask_dice_5: 0.52871/1.06324, loss_spatial_bce_5: 0.08416/0.09760, loss_spatial_dice_5: 0.10901/0.20696, loss_spatial_ce_5: 0.00743/0.12222, loss_grounding_bce_5: 0.05599/0.08257, loss_grounding_dice_5: 0.12088/0.15545, loss_grounding_ce_5: 0.00316/0.28282, loss_mask_ce_6: 0.37370/0.85589, loss_mask_bce_6: 0.30874/0.31040, loss_mask_dice_6: 0.52233/1.06737, loss_spatial_bce_6: 0.08428/0.10261, loss_spatial_dice_6: 0.12777/0.20930, loss_spatial_ce_6: 0.02994/0.14005, loss_grounding_bce_6: 0.05351/0.08387, loss_grounding_dice_6: 0.10650/0.15607, loss_grounding_ce_6: 0.00331/0.29920, loss_mask_ce_7: 0.38472/0.92010, loss_mask_bce_7: 0.28420/0.31762, loss_mask_dice_7: 0.42150/1.11320, loss_spatial_bce_7: 0.08911/0.11366, loss_spatial_dice_7: 0.11836/0.23461, loss_spatial_ce_7: 0.07090/0.18602, loss_grounding_bce_7: 0.05460/0.08553, loss_grounding_dice_7: 0.11441/0.16163, loss_grounding_ce_7: 0.00452/0.35011, loss_mask_ce_8: 0.61284/1.06192, loss_mask_bce_8: 0.31924/0.33535, loss_mask_dice_8: 0.50534/1.19446, loss_spatial_bce_8: 0.09472/0.13586, loss_spatial_dice_8: 0.15844/0.27813, loss_spatial_ce_8: 0.09125/0.24129, loss_grounding_bce_8: 0.06485/0.08928, loss_grounding_dice_8: 0.13601/0.17040, loss_grounding_ce_8: 0.05727/0.45265, loss_mask_ce_9: 1.74591/3.52638, loss_mask_bce_9: 0.28298/0.36140, loss_mask_dice_9: 0.55640/1.78027, loss_spatial_bce_9: 0.36894/0.36498, loss_spatial_dice_9: 0.75229/0.80006, loss_spatial_ce_9: 0.95229/1.43256, loss_grounding_bce_9: 0.05352/0.10143, loss_grounding_dice_9: 0.13090/0.24524, loss_grounding_ce_9: 0.06950/0.73764] items per batch[64] items per second[0.16] total items[704000] mini batches[ 11000] memory[4929] epoch remaining[1:01:53] INFO:trainer.default_trainer:epochs[ 6] optim steps[11100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15696/0.79748, loss_mask_bce_0: 0.27163/0.30277, loss_mask_dice_0: 0.42816/1.03302, loss_spatial_bce_0: 0.08176/0.09235, loss_spatial_dice_0: 0.15012/0.19437, loss_spatial_ce_0: 0.00002/0.08484, loss_grounding_bce_0: 0.04778/0.08104, loss_grounding_dice_0: 0.05116/0.15184, loss_grounding_ce_0: 0.02745/0.25322, loss_mask_ce_1: 0.17277/0.80012, loss_mask_bce_1: 0.26480/0.30334, loss_mask_dice_1: 0.45205/1.03720, loss_spatial_bce_1: 0.07533/0.09293, loss_spatial_dice_1: 0.12903/0.19727, loss_spatial_ce_1: 0.00004/0.08909, loss_grounding_bce_1: 0.04654/0.08104, loss_grounding_dice_1: 0.04828/0.15285, loss_grounding_ce_1: 0.01181/0.25718, loss_mask_ce_2: 0.17202/0.80654, loss_mask_bce_2: 0.26262/0.30344, loss_mask_dice_2: 0.49788/1.04012, loss_spatial_bce_2: 0.08149/0.09241, loss_spatial_dice_2: 0.12573/0.19712, loss_spatial_ce_2: 0.00008/0.09394, loss_grounding_bce_2: 0.03446/0.08084, loss_grounding_dice_2: 0.04088/0.15279, loss_grounding_ce_2: 0.02231/0.25818, loss_mask_ce_3: 0.18111/0.80449, loss_mask_bce_3: 0.26992/0.30485, loss_mask_dice_3: 0.49336/1.03498, loss_spatial_bce_3: 0.08789/0.09392, loss_spatial_dice_3: 0.17607/0.19727, loss_spatial_ce_3: 0.00051/0.09947, loss_grounding_bce_3: 0.04187/0.08144, loss_grounding_dice_3: 0.05348/0.15233, loss_grounding_ce_3: 0.06994/0.25662, loss_mask_ce_4: 0.24990/0.81082, loss_mask_bce_4: 0.26757/0.30706, loss_mask_dice_4: 0.47068/1.05344, loss_spatial_bce_4: 0.07950/0.09578, loss_spatial_dice_4: 0.11671/0.20464, loss_spatial_ce_4: 0.00154/0.11147, loss_grounding_bce_4: 0.04442/0.08216, loss_grounding_dice_4: 0.05139/0.15470, loss_grounding_ce_4: 0.04581/0.26469, loss_mask_ce_5: 0.30460/0.83077, loss_mask_bce_5: 0.27575/0.30908, loss_mask_dice_5: 0.49531/1.06205, loss_spatial_bce_5: 0.07702/0.09743, loss_spatial_dice_5: 0.11565/0.20674, loss_spatial_ce_5: 0.01015/0.12190, loss_grounding_bce_5: 0.04068/0.08251, loss_grounding_dice_5: 0.04854/0.15548, loss_grounding_ce_5: 0.08541/0.28272, loss_mask_ce_6: 0.39658/0.85529, loss_mask_bce_6: 0.26626/0.31007, loss_mask_dice_6: 0.47892/1.06630, loss_spatial_bce_6: 0.08884/0.10244, loss_spatial_dice_6: 0.14681/0.20910, loss_spatial_ce_6: 0.01346/0.13969, loss_grounding_bce_6: 0.05733/0.08382, loss_grounding_dice_6: 0.05579/0.15611, loss_grounding_ce_6: 0.08659/0.29873, loss_mask_ce_7: 0.42918/0.91956, loss_mask_bce_7: 0.28634/0.31726, loss_mask_dice_7: 0.50299/1.11181, loss_spatial_bce_7: 0.08989/0.11345, loss_spatial_dice_7: 0.18419/0.23437, loss_spatial_ce_7: 0.21946/0.18568, loss_grounding_bce_7: 0.04792/0.08550, loss_grounding_dice_7: 0.06022/0.16171, loss_grounding_ce_7: 0.02892/0.34961, loss_mask_ce_8: 0.15975/1.06102, loss_mask_bce_8: 0.38206/0.33496, loss_mask_dice_8: 0.64210/1.19294, loss_spatial_bce_8: 0.16547/0.13562, loss_spatial_dice_8: 0.23816/0.27782, loss_spatial_ce_8: 0.14926/0.24133, loss_grounding_bce_8: 0.07660/0.08922, loss_grounding_dice_8: 0.06549/0.17045, loss_grounding_ce_8: 1.16084/0.45197, loss_mask_ce_9: 2.84639/3.52418, loss_mask_bce_9: 0.50383/0.36102, loss_mask_dice_9: 1.40200/1.77840, loss_spatial_bce_9: 0.33148/0.36519, loss_spatial_dice_9: 0.81984/0.80007, loss_spatial_ce_9: 0.85062/1.43220, loss_grounding_bce_9: 0.09097/0.10131, loss_grounding_dice_9: 0.09552/0.24519, loss_grounding_ce_9: 4.71592/0.73649] items per batch[64] items per second[0.35] total items[710400] mini batches[ 11100] memory[4929] epoch remaining[0:53:04] INFO:trainer.default_trainer:epochs[ 6] optim steps[11200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44202/0.79687, loss_mask_bce_0: 0.09370/0.30253, loss_mask_dice_0: 0.10053/1.03374, loss_spatial_bce_0: 0.11301/0.09216, loss_spatial_dice_0: 0.08684/0.19432, loss_spatial_ce_0: 0.00000/0.08467, loss_grounding_bce_0: 0.07089/0.08089, loss_grounding_dice_0: 0.07675/0.15183, loss_grounding_ce_0: 0.10517/0.25313, loss_mask_ce_1: 0.46432/0.79958, loss_mask_bce_1: 0.09080/0.30310, loss_mask_dice_1: 0.09825/1.03785, loss_spatial_bce_1: 0.09621/0.09275, loss_spatial_dice_1: 0.07643/0.19726, loss_spatial_ce_1: 0.00000/0.08890, loss_grounding_bce_1: 0.07602/0.08087, loss_grounding_dice_1: 0.07753/0.15285, loss_grounding_ce_1: 0.13561/0.25721, loss_mask_ce_2: 0.48188/0.80608, loss_mask_bce_2: 0.09587/0.30318, loss_mask_dice_2: 0.10791/1.04076, loss_spatial_bce_2: 0.09716/0.09222, loss_spatial_dice_2: 0.07740/0.19708, loss_spatial_ce_2: 0.00001/0.09364, loss_grounding_bce_2: 0.08100/0.08067, loss_grounding_dice_2: 0.07794/0.15274, loss_grounding_ce_2: 0.15328/0.25841, loss_mask_ce_3: 0.50558/0.80395, loss_mask_bce_3: 0.08719/0.30458, loss_mask_dice_3: 0.09372/1.03562, loss_spatial_bce_3: 0.11181/0.09373, loss_spatial_dice_3: 0.09215/0.19724, loss_spatial_ce_3: 0.00003/0.09943, loss_grounding_bce_3: 0.07692/0.08128, loss_grounding_dice_3: 0.08037/0.15228, loss_grounding_ce_3: 0.13239/0.25666, loss_mask_ce_4: 0.52126/0.81054, loss_mask_bce_4: 0.09563/0.30676, loss_mask_dice_4: 0.10238/1.05402, loss_spatial_bce_4: 0.09505/0.09559, loss_spatial_dice_4: 0.08070/0.20466, loss_spatial_ce_4: 0.00005/0.11130, loss_grounding_bce_4: 0.08053/0.08204, loss_grounding_dice_4: 0.08351/0.15471, loss_grounding_ce_4: 0.12489/0.26455, loss_mask_ce_5: 0.55347/0.83058, loss_mask_bce_5: 0.10387/0.30882, loss_mask_dice_5: 0.10490/1.06272, loss_spatial_bce_5: 0.08036/0.09724, loss_spatial_dice_5: 0.06260/0.20673, loss_spatial_ce_5: 0.00014/0.12169, loss_grounding_bce_5: 0.08289/0.08236, loss_grounding_dice_5: 0.08071/0.15541, loss_grounding_ce_5: 0.16897/0.28261, loss_mask_ce_6: 0.62605/0.85504, loss_mask_bce_6: 0.10543/0.30983, loss_mask_dice_6: 0.10637/1.06700, loss_spatial_bce_6: 0.09533/0.10223, loss_spatial_dice_6: 0.09247/0.20911, loss_spatial_ce_6: 0.00524/0.13934, loss_grounding_bce_6: 0.08671/0.08366, loss_grounding_dice_6: 0.08612/0.15605, loss_grounding_ce_6: 0.20929/0.29897, loss_mask_ce_7: 0.57215/0.91922, loss_mask_bce_7: 0.12343/0.31698, loss_mask_dice_7: 0.11163/1.11274, loss_spatial_bce_7: 0.10520/0.11322, loss_spatial_dice_7: 0.11193/0.23434, loss_spatial_ce_7: 0.06066/0.18543, loss_grounding_bce_7: 0.09719/0.08540, loss_grounding_dice_7: 0.09059/0.16169, loss_grounding_ce_7: 0.18715/0.34953, loss_mask_ce_8: 0.58264/1.06030, loss_mask_bce_8: 0.09861/0.33477, loss_mask_dice_8: 0.09969/1.19372, loss_spatial_bce_8: 0.15512/0.13550, loss_spatial_dice_8: 0.12523/0.27782, loss_spatial_ce_8: 0.01943/0.24098, loss_grounding_bce_8: 0.07257/0.08916, loss_grounding_dice_8: 0.07104/0.17051, loss_grounding_ce_8: 0.30380/0.45185, loss_mask_ce_9: 2.59945/3.52378, loss_mask_bce_9: 0.12309/0.36066, loss_mask_dice_9: 0.19553/1.77816, loss_spatial_bce_9: 0.43376/0.36472, loss_spatial_dice_9: 0.52880/0.80006, loss_spatial_ce_9: 0.55614/1.43111, loss_grounding_bce_9: 0.09358/0.10117, loss_grounding_dice_9: 0.15356/0.24525, loss_grounding_ce_9: 0.53343/0.73600] items per batch[64] items per second[0.36] total items[716800] mini batches[ 11200] memory[4929] epoch remaining[0:48:52] INFO:trainer.default_trainer:epochs[ 6] optim steps[11300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19787/0.79629, loss_mask_bce_0: 0.03810/0.30253, loss_mask_dice_0: 0.96921/1.03327, loss_spatial_bce_0: 0.00792/0.09211, loss_spatial_dice_0: 0.20445/0.19415, loss_spatial_ce_0: 0.00008/0.08449, loss_grounding_bce_0: 0.00179/0.08094, loss_grounding_dice_0: 0.03699/0.15192, loss_grounding_ce_0: 0.01737/0.25311, loss_mask_ce_1: 0.45182/0.79863, loss_mask_bce_1: 0.02453/0.30308, loss_mask_dice_1: 0.81429/1.03732, loss_spatial_bce_1: 0.00763/0.09269, loss_spatial_dice_1: 0.14905/0.19707, loss_spatial_ce_1: 0.00010/0.08880, loss_grounding_bce_1: 0.00302/0.08092, loss_grounding_dice_1: 0.06396/0.15296, loss_grounding_ce_1: 0.00715/0.25719, loss_mask_ce_2: 1.28498/0.80517, loss_mask_bce_2: 0.03654/0.30312, loss_mask_dice_2: 1.03496/1.04058, loss_spatial_bce_2: 0.00930/0.09217, loss_spatial_dice_2: 0.19556/0.19694, loss_spatial_ce_2: 0.00004/0.09362, loss_grounding_bce_2: 0.00169/0.08073, loss_grounding_dice_2: 0.03105/0.15286, loss_grounding_ce_2: 0.01166/0.25820, loss_mask_ce_3: 1.33257/0.80328, loss_mask_bce_3: 0.03772/0.30452, loss_mask_dice_3: 0.97608/1.03520, loss_spatial_bce_3: 0.01315/0.09369, loss_spatial_dice_3: 0.20861/0.19712, loss_spatial_ce_3: 0.00015/0.09922, loss_grounding_bce_3: 0.00194/0.08134, loss_grounding_dice_3: 0.04208/0.15246, loss_grounding_ce_3: 0.00709/0.25637, loss_mask_ce_4: 0.84775/0.80956, loss_mask_bce_4: 0.03414/0.30673, loss_mask_dice_4: 0.89058/1.05373, loss_spatial_bce_4: 0.01564/0.09555, loss_spatial_dice_4: 0.30557/0.20450, loss_spatial_ce_4: 0.00734/0.11101, loss_grounding_bce_4: 0.00204/0.08211, loss_grounding_dice_4: 0.03114/0.15483, loss_grounding_ce_4: 0.00067/0.26444, loss_mask_ce_5: 0.79226/0.82965, loss_mask_bce_5: 0.04168/0.30878, loss_mask_dice_5: 0.93244/1.06230, loss_spatial_bce_5: 0.01691/0.09721, loss_spatial_dice_5: 0.32757/0.20660, loss_spatial_ce_5: 0.02852/0.12146, loss_grounding_bce_5: 0.00192/0.08242, loss_grounding_dice_5: 0.04048/0.15555, loss_grounding_ce_5: 0.00568/0.28249, loss_mask_ce_6: 1.46890/0.85405, loss_mask_bce_6: 0.05071/0.30987, loss_mask_dice_6: 0.74664/1.06664, loss_spatial_bce_6: 0.01428/0.10221, loss_spatial_dice_6: 0.28680/0.20897, loss_spatial_ce_6: 0.06963/0.13908, loss_grounding_bce_6: 0.00092/0.08373, loss_grounding_dice_6: 0.03113/0.15618, loss_grounding_ce_6: 0.02348/0.29888, loss_mask_ce_7: 0.74971/0.91823, loss_mask_bce_7: 0.03801/0.31703, loss_mask_dice_7: 0.95509/1.11257, loss_spatial_bce_7: 0.00874/0.11322, loss_spatial_dice_7: 0.29258/0.23413, loss_spatial_ce_7: 0.07609/0.18528, loss_grounding_bce_7: 0.00171/0.08547, loss_grounding_dice_7: 0.04884/0.16191, loss_grounding_ce_7: 0.14624/0.34975, loss_mask_ce_8: 0.84867/1.05893, loss_mask_bce_8: 0.04456/0.33483, loss_mask_dice_8: 1.19851/1.19340, loss_spatial_bce_8: 0.01659/0.13546, loss_spatial_dice_8: 0.43159/0.27748, loss_spatial_ce_8: 0.08843/0.24091, loss_grounding_bce_8: 0.00213/0.08927, loss_grounding_dice_8: 0.05534/0.17074, loss_grounding_ce_8: 0.03780/0.45161, loss_mask_ce_9: 3.58980/3.52287, loss_mask_bce_9: 0.03194/0.36066, loss_mask_dice_9: 1.20820/1.77778, loss_spatial_bce_9: 0.02184/0.36464, loss_spatial_dice_9: 0.72214/0.79993, loss_spatial_ce_9: 2.15585/1.42999, loss_grounding_bce_9: 0.00195/0.10123, loss_grounding_dice_9: 0.05792/0.24551, loss_grounding_ce_9: 1.94213/0.73541] items per batch[64] items per second[0.36] total items[723200] mini batches[ 11300] memory[4943] epoch remaining[0:45:14] INFO:trainer.default_trainer:epochs[ 6] optim steps[11400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32615/0.79651, loss_mask_bce_0: 0.34990/0.30258, loss_mask_dice_0: 0.88886/1.03497, loss_spatial_bce_0: 0.06799/0.09209, loss_spatial_dice_0: 0.14922/0.19421, loss_spatial_ce_0: 0.00440/0.08427, loss_grounding_bce_0: 0.03814/0.08110, loss_grounding_dice_0: 0.13130/0.15204, loss_grounding_ce_0: 0.71808/0.25401, loss_mask_ce_1: 0.32206/0.79879, loss_mask_bce_1: 0.34705/0.30314, loss_mask_dice_1: 0.83868/1.03910, loss_spatial_bce_1: 0.08049/0.09269, loss_spatial_dice_1: 0.15884/0.19712, loss_spatial_ce_1: 0.00515/0.08864, loss_grounding_bce_1: 0.03785/0.08112, loss_grounding_dice_1: 0.13331/0.15317, loss_grounding_ce_1: 0.71547/0.25775, loss_mask_ce_2: 0.34426/0.80544, loss_mask_bce_2: 0.33846/0.30316, loss_mask_dice_2: 0.81725/1.04234, loss_spatial_bce_2: 0.06746/0.09216, loss_spatial_dice_2: 0.15258/0.19698, loss_spatial_ce_2: 0.00467/0.09346, loss_grounding_bce_2: 0.04033/0.08093, loss_grounding_dice_2: 0.12454/0.15300, loss_grounding_ce_2: 0.70493/0.25874, loss_mask_ce_3: 0.38420/0.80358, loss_mask_bce_3: 0.33471/0.30457, loss_mask_dice_3: 0.79583/1.03680, loss_spatial_bce_3: 0.06851/0.09370, loss_spatial_dice_3: 0.15319/0.19717, loss_spatial_ce_3: 0.00679/0.09915, loss_grounding_bce_3: 0.04405/0.08153, loss_grounding_dice_3: 0.12604/0.15268, loss_grounding_ce_3: 0.70566/0.25699, loss_mask_ce_4: 0.39369/0.81006, loss_mask_bce_4: 0.35673/0.30679, loss_mask_dice_4: 0.85106/1.05518, loss_spatial_bce_4: 0.07713/0.09563, loss_spatial_dice_4: 0.15619/0.20456, loss_spatial_ce_4: 0.01985/0.11088, loss_grounding_bce_4: 0.04359/0.08227, loss_grounding_dice_4: 0.12730/0.15499, loss_grounding_ce_4: 0.74239/0.26526, loss_mask_ce_5: 0.47588/0.82999, loss_mask_bce_5: 0.34162/0.30885, loss_mask_dice_5: 0.86673/1.06360, loss_spatial_bce_5: 0.07400/0.09725, loss_spatial_dice_5: 0.15252/0.20665, loss_spatial_ce_5: 0.02186/0.12134, loss_grounding_bce_5: 0.04384/0.08257, loss_grounding_dice_5: 0.11725/0.15570, loss_grounding_ce_5: 0.77465/0.28329, loss_mask_ce_6: 0.60077/0.85445, loss_mask_bce_6: 0.34077/0.30999, loss_mask_dice_6: 0.92920/1.06826, loss_spatial_bce_6: 0.07687/0.10226, loss_spatial_dice_6: 0.13869/0.20901, loss_spatial_ce_6: 0.05085/0.13915, loss_grounding_bce_6: 0.03944/0.08386, loss_grounding_dice_6: 0.12505/0.15626, loss_grounding_ce_6: 0.76665/0.29991, loss_mask_ce_7: 0.93590/0.91869, loss_mask_bce_7: 0.35332/0.31711, loss_mask_dice_7: 0.95420/1.11388, loss_spatial_bce_7: 0.09014/0.11320, loss_spatial_dice_7: 0.15223/0.23422, loss_spatial_ce_7: 0.08249/0.18539, loss_grounding_bce_7: 0.03388/0.08562, loss_grounding_dice_7: 0.11983/0.16200, loss_grounding_ce_7: 0.78873/0.35036, loss_mask_ce_8: 0.79584/1.05970, loss_mask_bce_8: 0.43507/0.33486, loss_mask_dice_8: 1.12925/1.19489, loss_spatial_bce_8: 0.07739/0.13544, loss_spatial_dice_8: 0.18351/0.27755, loss_spatial_ce_8: 0.07252/0.24105, loss_grounding_bce_8: 0.04427/0.08943, loss_grounding_dice_8: 0.12507/0.17077, loss_grounding_ce_8: 0.83210/0.45330, loss_mask_ce_9: 6.59640/3.52310, loss_mask_bce_9: 0.76864/0.36072, loss_mask_dice_9: 3.02066/1.77974, loss_spatial_bce_9: 0.34815/0.36453, loss_spatial_dice_9: 0.90746/0.79991, loss_spatial_ce_9: 1.08967/1.43011, loss_grounding_bce_9: 0.09456/0.10137, loss_grounding_dice_9: 0.42164/0.24563, loss_grounding_ce_9: 0.77747/0.73586] items per batch[64] items per second[0.36] total items[729600] mini batches[ 11400] memory[4943] epoch remaining[0:42:05] INFO:trainer.default_trainer:epochs[ 6] optim steps[11500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13063/0.79665, loss_mask_bce_0: 0.40891/0.30281, loss_mask_dice_0: 0.72995/1.03533, loss_spatial_bce_0: 0.11225/0.09210, loss_spatial_dice_0: 0.17922/0.19428, loss_spatial_ce_0: 0.00405/0.08410, loss_grounding_bce_0: 0.06016/0.08113, loss_grounding_dice_0: 0.10802/0.15220, loss_grounding_ce_0: 0.26939/0.25470, loss_mask_ce_1: 0.24089/0.79934, loss_mask_bce_1: 0.42371/0.30335, loss_mask_dice_1: 0.96213/1.03931, loss_spatial_bce_1: 0.10999/0.09268, loss_spatial_dice_1: 0.19598/0.19720, loss_spatial_ce_1: 0.00503/0.08848, loss_grounding_bce_1: 0.06088/0.08119, loss_grounding_dice_1: 0.12245/0.15336, loss_grounding_ce_1: 0.29960/0.25833, loss_mask_ce_2: 0.12341/0.80565, loss_mask_bce_2: 0.42107/0.30333, loss_mask_dice_2: 0.75092/1.04265, loss_spatial_bce_2: 0.10833/0.09216, loss_spatial_dice_2: 0.19956/0.19708, loss_spatial_ce_2: 0.00315/0.09323, loss_grounding_bce_2: 0.05823/0.08096, loss_grounding_dice_2: 0.11562/0.15310, loss_grounding_ce_2: 0.27869/0.25936, loss_mask_ce_3: 0.13756/0.80396, loss_mask_bce_3: 0.39974/0.30478, loss_mask_dice_3: 0.62879/1.03720, loss_spatial_bce_3: 0.10586/0.09368, loss_spatial_dice_3: 0.20226/0.19725, loss_spatial_ce_3: 0.01224/0.09897, loss_grounding_bce_3: 0.05914/0.08157, loss_grounding_dice_3: 0.11679/0.15276, loss_grounding_ce_3: 0.29081/0.25749, loss_mask_ce_4: 0.23415/0.81047, loss_mask_bce_4: 0.40846/0.30694, loss_mask_dice_4: 0.93976/1.05546, loss_spatial_bce_4: 0.12683/0.09562, loss_spatial_dice_4: 0.21013/0.20469, loss_spatial_ce_4: 0.02254/0.11077, loss_grounding_bce_4: 0.05871/0.08232, loss_grounding_dice_4: 0.10904/0.15510, loss_grounding_ce_4: 0.27872/0.26588, loss_mask_ce_5: 0.11890/0.83023, loss_mask_bce_5: 0.40856/0.30903, loss_mask_dice_5: 0.64002/1.06396, loss_spatial_bce_5: 0.12856/0.09727, loss_spatial_dice_5: 0.20672/0.20677, loss_spatial_ce_5: 0.07324/0.12132, loss_grounding_bce_5: 0.05879/0.08260, loss_grounding_dice_5: 0.13326/0.15581, loss_grounding_ce_5: 0.27476/0.28421, loss_mask_ce_6: 0.25289/0.85455, loss_mask_bce_6: 0.41665/0.31010, loss_mask_dice_6: 0.96176/1.06850, loss_spatial_bce_6: 0.12423/0.10230, loss_spatial_dice_6: 0.20553/0.20912, loss_spatial_ce_6: 0.09169/0.13908, loss_grounding_bce_6: 0.05821/0.08388, loss_grounding_dice_6: 0.14324/0.15640, loss_grounding_ce_6: 0.33418/0.30045, loss_mask_ce_7: 0.18256/0.91881, loss_mask_bce_7: 0.40586/0.31734, loss_mask_dice_7: 0.68330/1.11424, loss_spatial_bce_7: 0.13251/0.11319, loss_spatial_dice_7: 0.23018/0.23431, loss_spatial_ce_7: 0.10716/0.18510, loss_grounding_bce_7: 0.06038/0.08565, loss_grounding_dice_7: 0.13700/0.16214, loss_grounding_ce_7: 0.33278/0.35090, loss_mask_ce_8: 0.21400/1.06019, loss_mask_bce_8: 0.42715/0.33513, loss_mask_dice_8: 0.66356/1.19553, loss_spatial_bce_8: 0.14385/0.13546, loss_spatial_dice_8: 0.26170/0.27771, loss_spatial_ce_8: 0.23708/0.24089, loss_grounding_bce_8: 0.06051/0.08950, loss_grounding_dice_8: 0.11121/0.17104, loss_grounding_ce_8: 0.34518/0.45328, loss_mask_ce_9: 2.66171/3.52337, loss_mask_bce_9: 0.42461/0.36093, loss_mask_dice_9: 0.88548/1.77978, loss_spatial_bce_9: 0.28000/0.36416, loss_spatial_dice_9: 0.67190/0.79993, loss_spatial_ce_9: 1.00584/1.43028, loss_grounding_bce_9: 0.06042/0.10140, loss_grounding_dice_9: 0.15895/0.24591, loss_grounding_ce_9: 0.36968/0.73511] items per batch[64] items per second[0.35] total items[736000] mini batches[ 11500] memory[4943] epoch remaining[0:39:01] INFO:trainer.default_trainer:epochs[ 6] optim steps[11600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.61581/0.79699, loss_mask_bce_0: 0.66809/0.30281, loss_mask_dice_0: 1.28512/1.03543, loss_spatial_bce_0: 0.26480/0.09204, loss_spatial_dice_0: 0.46931/0.19422, loss_spatial_ce_0: 0.06933/0.08388, loss_grounding_bce_0: 0.24386/0.08109, loss_grounding_dice_0: 0.15980/0.15220, loss_grounding_ce_0: 0.92359/0.25503, loss_mask_ce_1: 2.65907/0.79977, loss_mask_bce_1: 0.64854/0.30336, loss_mask_dice_1: 1.26634/1.03937, loss_spatial_bce_1: 0.23621/0.09262, loss_spatial_dice_1: 0.41315/0.19714, loss_spatial_ce_1: 0.09452/0.08820, loss_grounding_bce_1: 0.25466/0.08110, loss_grounding_dice_1: 0.17949/0.15335, loss_grounding_ce_1: 0.90908/0.25854, loss_mask_ce_2: 2.67131/0.80599, loss_mask_bce_2: 0.66994/0.30331, loss_mask_dice_2: 1.27387/1.04264, loss_spatial_bce_2: 0.26308/0.09209, loss_spatial_dice_2: 0.42457/0.19696, loss_spatial_ce_2: 0.10331/0.09291, loss_grounding_bce_2: 0.22360/0.08090, loss_grounding_dice_2: 0.17271/0.15312, loss_grounding_ce_2: 0.90323/0.25959, loss_mask_ce_3: 2.63582/0.80431, loss_mask_bce_3: 0.75953/0.30479, loss_mask_dice_3: 1.27562/1.03707, loss_spatial_bce_3: 0.34218/0.09362, loss_spatial_dice_3: 0.40054/0.19717, loss_spatial_ce_3: 0.15228/0.09877, loss_grounding_bce_3: 0.18022/0.08150, loss_grounding_dice_3: 0.13537/0.15271, loss_grounding_ce_3: 0.81794/0.25792, loss_mask_ce_4: 2.52749/0.81079, loss_mask_bce_4: 0.76521/0.30693, loss_mask_dice_4: 1.29383/1.05549, loss_spatial_bce_4: 0.35367/0.09561, loss_spatial_dice_4: 0.51247/0.20466, loss_spatial_ce_4: 0.01764/0.11053, loss_grounding_bce_4: 0.20738/0.08224, loss_grounding_dice_4: 0.16997/0.15512, loss_grounding_ce_4: 0.84747/0.26619, loss_mask_ce_5: 2.45654/0.83077, loss_mask_bce_5: 0.68004/0.30900, loss_mask_dice_5: 1.55073/1.06407, loss_spatial_bce_5: 0.34819/0.09727, loss_spatial_dice_5: 0.50085/0.20672, loss_spatial_ce_5: 0.13231/0.12109, loss_grounding_bce_5: 0.37035/0.08253, loss_grounding_dice_5: 0.30616/0.15583, loss_grounding_ce_5: 1.11886/0.28506, loss_mask_ce_6: 2.57324/0.85516, loss_mask_bce_6: 0.68083/0.31007, loss_mask_dice_6: 1.39671/1.06830, loss_spatial_bce_6: 0.37007/0.10232, loss_spatial_dice_6: 0.48968/0.20908, loss_spatial_ce_6: 0.31578/0.13889, loss_grounding_bce_6: 0.23477/0.08379, loss_grounding_dice_6: 0.20498/0.15632, loss_grounding_ce_6: 0.85520/0.30080, loss_mask_ce_7: 2.59407/0.91893, loss_mask_bce_7: 0.75258/0.31737, loss_mask_dice_7: 1.39436/1.11420, loss_spatial_bce_7: 0.40577/0.11317, loss_spatial_dice_7: 0.52396/0.23422, loss_spatial_ce_7: 0.13692/0.18486, loss_grounding_bce_7: 0.25926/0.08558, loss_grounding_dice_7: 0.18689/0.16215, loss_grounding_ce_7: 0.95806/0.35081, loss_mask_ce_8: 2.11677/1.06000, loss_mask_bce_8: 0.85924/0.33521, loss_mask_dice_8: 1.54872/1.19544, loss_spatial_bce_8: 0.51245/0.13543, loss_spatial_dice_8: 0.60353/0.27755, loss_spatial_ce_8: 0.34798/0.24069, loss_grounding_bce_8: 0.49852/0.08944, loss_grounding_dice_8: 0.35567/0.17107, loss_grounding_ce_8: 0.01571/0.45382, loss_mask_ce_9: 4.70735/3.52235, loss_mask_bce_9: 0.92091/0.36089, loss_mask_dice_9: 2.22463/1.77915, loss_spatial_bce_9: 0.29699/0.36416, loss_spatial_dice_9: 0.91650/0.79983, loss_spatial_ce_9: 1.27808/1.42968, loss_grounding_bce_9: 0.19548/0.10133, loss_grounding_dice_9: 0.18764/0.24585, loss_grounding_ce_9: 0.04010/0.73533] items per batch[64] items per second[0.35] total items[742400] mini batches[ 11600] memory[4943] epoch remaining[0:36:02] INFO:trainer.default_trainer:epochs[ 6] optim steps[11700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00256/0.79630, loss_mask_bce_0: 0.02103/0.30292, loss_mask_dice_0: 0.08193/1.03501, loss_spatial_bce_0: 0.02071/0.09203, loss_spatial_dice_0: 0.08062/0.19411, loss_spatial_ce_0: 0.00002/0.08362, loss_grounding_bce_0: 0.00542/0.08106, loss_grounding_dice_0: 0.07922/0.15234, loss_grounding_ce_0: 0.00066/0.25517, loss_mask_ce_1: 0.00294/0.79910, loss_mask_bce_1: 0.02210/0.30347, loss_mask_dice_1: 0.08857/1.03915, loss_spatial_bce_1: 0.02060/0.09260, loss_spatial_dice_1: 0.08973/0.19699, loss_spatial_ce_1: 0.00001/0.08806, loss_grounding_bce_1: 0.00578/0.08109, loss_grounding_dice_1: 0.09927/0.15342, loss_grounding_ce_1: 0.00135/0.25856, loss_mask_ce_2: 0.00398/0.80557, loss_mask_bce_2: 0.02704/0.30341, loss_mask_dice_2: 0.08722/1.04230, loss_spatial_bce_2: 0.01842/0.09206, loss_spatial_dice_2: 0.08329/0.19681, loss_spatial_ce_2: 0.00003/0.09262, loss_grounding_bce_2: 0.00536/0.08088, loss_grounding_dice_2: 0.10228/0.15319, loss_grounding_ce_2: 0.00190/0.25962, loss_mask_ce_3: 0.00322/0.80376, loss_mask_bce_3: 0.02689/0.30486, loss_mask_dice_3: 0.09172/1.03657, loss_spatial_bce_3: 0.02199/0.09360, loss_spatial_dice_3: 0.07838/0.19703, loss_spatial_ce_3: 0.00012/0.09849, loss_grounding_bce_3: 0.00642/0.08146, loss_grounding_dice_3: 0.08668/0.15279, loss_grounding_ce_3: 0.00079/0.25799, loss_mask_ce_4: 0.00373/0.81017, loss_mask_bce_4: 0.02767/0.30706, loss_mask_dice_4: 0.08807/1.05511, loss_spatial_bce_4: 0.02307/0.09558, loss_spatial_dice_4: 0.07757/0.20454, loss_spatial_ce_4: 0.00014/0.11021, loss_grounding_bce_4: 0.00449/0.08221, loss_grounding_dice_4: 0.09370/0.15518, loss_grounding_ce_4: 0.00045/0.26623, loss_mask_ce_5: 0.00332/0.83017, loss_mask_bce_5: 0.02933/0.30915, loss_mask_dice_5: 0.09938/1.06352, loss_spatial_bce_5: 0.02190/0.09724, loss_spatial_dice_5: 0.07584/0.20657, loss_spatial_ce_5: 0.00008/0.12077, loss_grounding_bce_5: 0.00800/0.08249, loss_grounding_dice_5: 0.11317/0.15589, loss_grounding_ce_5: 0.00027/0.28560, loss_mask_ce_6: 0.00557/0.85461, loss_mask_bce_6: 0.02824/0.31022, loss_mask_dice_6: 0.10619/1.06769, loss_spatial_bce_6: 0.02935/0.10228, loss_spatial_dice_6: 0.08227/0.20892, loss_spatial_ce_6: 0.01299/0.13857, loss_grounding_bce_6: 0.00371/0.08375, loss_grounding_dice_6: 0.08226/0.15638, loss_grounding_ce_6: 0.00233/0.30087, loss_mask_ce_7: 0.01107/0.91836, loss_mask_bce_7: 0.02894/0.31742, loss_mask_dice_7: 0.12769/1.11363, loss_spatial_bce_7: 0.03752/0.11316, loss_spatial_dice_7: 0.08122/0.23399, loss_spatial_ce_7: 0.01769/0.18441, loss_grounding_bce_7: 0.00567/0.08556, loss_grounding_dice_7: 0.09143/0.16225, loss_grounding_ce_7: 0.00177/0.35090, loss_mask_ce_8: 0.00863/1.05913, loss_mask_bce_8: 0.02802/0.33528, loss_mask_dice_8: 0.10503/1.19483, loss_spatial_bce_8: 0.03374/0.13540, loss_spatial_dice_8: 0.09527/0.27736, loss_spatial_ce_8: 0.06989/0.24023, loss_grounding_bce_8: 0.00411/0.08939, loss_grounding_dice_8: 0.10200/0.17111, loss_grounding_ce_8: 0.00638/0.45349, loss_mask_ce_9: 1.53558/3.52267, loss_mask_bce_9: 0.02516/0.36086, loss_mask_dice_9: 0.10539/1.77845, loss_spatial_bce_9: 0.29503/0.36400, loss_spatial_dice_9: 0.68756/0.79998, loss_spatial_ce_9: 0.56609/1.42927, loss_grounding_bce_9: 0.00473/0.10125, loss_grounding_dice_9: 0.16550/0.24600, loss_grounding_ce_9: 0.24159/0.73480] items per batch[64] items per second[0.35] total items[748800] mini batches[ 11700] memory[4943] epoch remaining[0:33:00] INFO:trainer.default_trainer:epochs[ 6] optim steps[11800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.81072/0.79637, loss_mask_bce_0: 0.47060/0.30298, loss_mask_dice_0: 0.37003/1.03514, loss_spatial_bce_0: 0.26317/0.09201, loss_spatial_dice_0: 0.15096/0.19406, loss_spatial_ce_0: 0.15885/0.08341, loss_grounding_bce_0: 0.09108/0.08106, loss_grounding_dice_0: 0.08316/0.15219, loss_grounding_ce_0: 0.91462/0.25489, loss_mask_ce_1: 1.87290/0.79925, loss_mask_bce_1: 0.43391/0.30352, loss_mask_dice_1: 0.35037/1.03902, loss_spatial_bce_1: 0.25956/0.09261, loss_spatial_dice_1: 0.16096/0.19693, loss_spatial_ce_1: 0.16607/0.08781, loss_grounding_bce_1: 0.17133/0.08110, loss_grounding_dice_1: 0.21007/0.15326, loss_grounding_ce_1: 0.31276/0.25840, loss_mask_ce_2: 2.01195/0.80583, loss_mask_bce_2: 0.44220/0.30346, loss_mask_dice_2: 0.36020/1.04241, loss_spatial_bce_2: 0.26998/0.09206, loss_spatial_dice_2: 0.15256/0.19677, loss_spatial_ce_2: 0.17687/0.09236, loss_grounding_bce_2: 0.08632/0.08088, loss_grounding_dice_2: 0.08231/0.15301, loss_grounding_ce_2: 1.10287/0.25965, loss_mask_ce_3: 1.67449/0.80400, loss_mask_bce_3: 0.57132/0.30491, loss_mask_dice_3: 0.49572/1.03649, loss_spatial_bce_3: 0.25667/0.09359, loss_spatial_dice_3: 0.14381/0.19699, loss_spatial_ce_3: 0.17984/0.09827, loss_grounding_bce_3: 0.13011/0.08148, loss_grounding_dice_3: 0.14184/0.15263, loss_grounding_ce_3: 0.28145/0.25781, loss_mask_ce_4: 1.82602/0.81045, loss_mask_bce_4: 0.44010/0.30714, loss_mask_dice_4: 0.36624/1.05517, loss_spatial_bce_4: 0.22568/0.09559, loss_spatial_dice_4: 0.13360/0.20448, loss_spatial_ce_4: 0.16619/0.11003, loss_grounding_bce_4: 0.12585/0.08219, loss_grounding_dice_4: 0.13204/0.15507, loss_grounding_ce_4: 0.21935/0.26605, loss_mask_ce_5: 2.02575/0.83042, loss_mask_bce_5: 0.54458/0.30925, loss_mask_dice_5: 0.43868/1.06370, loss_spatial_bce_5: 0.20534/0.09722, loss_spatial_dice_5: 0.11539/0.20654, loss_spatial_ce_5: 0.20380/0.12058, loss_grounding_bce_5: 0.11973/0.08246, loss_grounding_dice_5: 0.11329/0.15568, loss_grounding_ce_5: 0.29905/0.28596, loss_mask_ce_6: 1.72767/0.85483, loss_mask_bce_6: 0.57557/0.31033, loss_mask_dice_6: 0.41114/1.06788, loss_spatial_bce_6: 0.22730/0.10227, loss_spatial_dice_6: 0.12806/0.20892, loss_spatial_ce_6: 0.20465/0.13841, loss_grounding_bce_6: 0.12363/0.08373, loss_grounding_dice_6: 0.11942/0.15620, loss_grounding_ce_6: 0.33597/0.30067, loss_mask_ce_7: 1.69328/0.91830, loss_mask_bce_7: 0.66132/0.31751, loss_mask_dice_7: 0.50203/1.11382, loss_spatial_bce_7: 0.22106/0.11313, loss_spatial_dice_7: 0.12169/0.23397, loss_spatial_ce_7: 0.17767/0.18416, loss_grounding_bce_7: 0.18630/0.08551, loss_grounding_dice_7: 0.19562/0.16202, loss_grounding_ce_7: 0.26111/0.35123, loss_mask_ce_8: 1.83739/1.05908, loss_mask_bce_8: 1.14835/0.33545, loss_mask_dice_8: 0.79932/1.19522, loss_spatial_bce_8: 0.25408/0.13536, loss_spatial_dice_8: 0.16448/0.27734, loss_spatial_ce_8: 0.15037/0.24008, loss_grounding_bce_8: 0.11327/0.08936, loss_grounding_dice_8: 0.11013/0.17100, loss_grounding_ce_8: 1.19325/0.45420, loss_mask_ce_9: 3.52695/3.52276, loss_mask_bce_9: 1.02052/0.36104, loss_mask_dice_9: 1.03629/1.77924, loss_spatial_bce_9: 0.51969/0.36396, loss_spatial_dice_9: 0.80823/0.79992, loss_spatial_ce_9: 1.20309/1.42838, loss_grounding_bce_9: 0.17688/0.10124, loss_grounding_dice_9: 0.21054/0.24580, loss_grounding_ce_9: 1.99174/0.73441] items per batch[64] items per second[0.37] total items[755200] mini batches[ 11800] memory[4943] epoch remaining[0:29:51] INFO:trainer.default_trainer:epochs[ 6] optim steps[11900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89575/0.79606, loss_mask_bce_0: 0.09275/0.30262, loss_mask_dice_0: 0.06146/1.03565, loss_spatial_bce_0: 0.06483/0.09200, loss_spatial_dice_0: 0.04123/0.19391, loss_spatial_ce_0: 0.00010/0.08314, loss_grounding_bce_0: 0.01608/0.08108, loss_grounding_dice_0: 0.02102/0.15217, loss_grounding_ce_0: 0.00022/0.25522, loss_mask_ce_1: 0.67914/0.79887, loss_mask_bce_1: 0.10346/0.30317, loss_mask_dice_1: 0.06839/1.03972, loss_spatial_bce_1: 0.07001/0.09262, loss_spatial_dice_1: 0.04271/0.19680, loss_spatial_ce_1: 0.00020/0.08750, loss_grounding_bce_1: 0.01713/0.08111, loss_grounding_dice_1: 0.02427/0.15325, loss_grounding_ce_1: 0.00019/0.25867, loss_mask_ce_2: 0.97841/0.80557, loss_mask_bce_2: 0.09365/0.30313, loss_mask_dice_2: 0.05767/1.04326, loss_spatial_bce_2: 0.08568/0.09207, loss_spatial_dice_2: 0.06007/0.19664, loss_spatial_ce_2: 0.00022/0.09208, loss_grounding_bce_2: 0.01328/0.08089, loss_grounding_dice_2: 0.02303/0.15302, loss_grounding_ce_2: 0.00018/0.25937, loss_mask_ce_3: 0.88617/0.80358, loss_mask_bce_3: 0.08596/0.30455, loss_mask_dice_3: 0.05506/1.03697, loss_spatial_bce_3: 0.07481/0.09350, loss_spatial_dice_3: 0.04956/0.19684, loss_spatial_ce_3: 0.00038/0.09814, loss_grounding_bce_3: 0.01616/0.08150, loss_grounding_dice_3: 0.02376/0.15262, loss_grounding_ce_3: 0.00035/0.25770, loss_mask_ce_4: 0.85315/0.81006, loss_mask_bce_4: 0.10071/0.30683, loss_mask_dice_4: 0.06925/1.05583, loss_spatial_bce_4: 0.06504/0.09560, loss_spatial_dice_4: 0.05253/0.20433, loss_spatial_ce_4: 0.00216/0.10971, loss_grounding_bce_4: 0.01498/0.08219, loss_grounding_dice_4: 0.02124/0.15508, loss_grounding_ce_4: 0.00060/0.26584, loss_mask_ce_5: 0.73315/0.82977, loss_mask_bce_5: 0.11245/0.30894, loss_mask_dice_5: 0.07917/1.06435, loss_spatial_bce_5: 0.06968/0.09712, loss_spatial_dice_5: 0.04942/0.20637, loss_spatial_ce_5: 0.00257/0.12044, loss_grounding_bce_5: 0.01575/0.08246, loss_grounding_dice_5: 0.02123/0.15570, loss_grounding_ce_5: 0.00038/0.28567, loss_mask_ce_6: 0.56894/0.85427, loss_mask_bce_6: 0.10018/0.31001, loss_mask_dice_6: 0.07095/1.06868, loss_spatial_bce_6: 0.06756/0.10220, loss_spatial_dice_6: 0.04768/0.20873, loss_spatial_ce_6: 0.00552/0.13850, loss_grounding_bce_6: 0.01195/0.08375, loss_grounding_dice_6: 0.01882/0.15623, loss_grounding_ce_6: 0.00021/0.30055, loss_mask_ce_7: 0.52519/0.91790, loss_mask_bce_7: 0.10137/0.31715, loss_mask_dice_7: 0.07114/1.11451, loss_spatial_bce_7: 0.06927/0.11323, loss_spatial_dice_7: 0.05226/0.23379, loss_spatial_ce_7: 0.03577/0.18383, loss_grounding_bce_7: 0.01345/0.08553, loss_grounding_dice_7: 0.02187/0.16201, loss_grounding_ce_7: 0.00075/0.35109, loss_mask_ce_8: 0.37001/1.05887, loss_mask_bce_8: 0.11728/0.33502, loss_mask_dice_8: 0.06819/1.19563, loss_spatial_bce_8: 0.08464/0.13514, loss_spatial_dice_8: 0.05746/0.27708, loss_spatial_ce_8: 0.15920/0.24014, loss_grounding_bce_8: 0.01166/0.08940, loss_grounding_dice_8: 0.01515/0.17097, loss_grounding_ce_8: 0.00702/0.45370, loss_mask_ce_9: 2.64957/3.52186, loss_mask_bce_9: 0.15465/0.36066, loss_mask_dice_9: 0.13227/1.78018, loss_spatial_bce_9: 1.26039/0.36389, loss_spatial_dice_9: 0.78121/0.79975, loss_spatial_ce_9: 1.89455/1.42841, loss_grounding_bce_9: 0.02512/0.10119, loss_grounding_dice_9: 0.03734/0.24565, loss_grounding_ce_9: 0.43867/0.73458] items per batch[64] items per second[0.35] total items[761600] mini batches[ 11900] memory[4943] epoch remaining[0:26:50] INFO:trainer.default_trainer:epochs[ 6] optim steps[12000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06572/0.79624, loss_mask_bce_0: 0.11848/0.30236, loss_mask_dice_0: 2.15267/1.03657, loss_spatial_bce_0: 0.01708/0.09186, loss_spatial_dice_0: 0.27357/0.19388, loss_spatial_ce_0: 0.40082/0.08296, loss_grounding_bce_0: 0.06124/0.08104, loss_grounding_dice_0: 0.17780/0.15222, loss_grounding_ce_0: 0.00318/0.25484, loss_mask_ce_1: 1.07462/0.79895, loss_mask_bce_1: 0.12139/0.30292, loss_mask_dice_1: 2.33376/1.04061, loss_spatial_bce_1: 0.01983/0.09247, loss_spatial_dice_1: 0.32394/0.19675, loss_spatial_ce_1: 0.14990/0.08727, loss_grounding_bce_1: 0.08041/0.08107, loss_grounding_dice_1: 0.18521/0.15331, loss_grounding_ce_1: 0.00243/0.25854, loss_mask_ce_2: 1.18367/0.80593, loss_mask_bce_2: 0.12297/0.30286, loss_mask_dice_2: 2.20763/1.04402, loss_spatial_bce_2: 0.01596/0.09192, loss_spatial_dice_2: 0.29595/0.19661, loss_spatial_ce_2: 0.28030/0.09191, loss_grounding_bce_2: 0.06675/0.08084, loss_grounding_dice_2: 0.16758/0.15306, loss_grounding_ce_2: 0.00229/0.25906, loss_mask_ce_3: 1.35856/0.80375, loss_mask_bce_3: 0.11401/0.30427, loss_mask_dice_3: 2.02498/1.03792, loss_spatial_bce_3: 0.01721/0.09336, loss_spatial_dice_3: 0.25912/0.19680, loss_spatial_ce_3: 0.25196/0.09784, loss_grounding_bce_3: 0.06235/0.08145, loss_grounding_dice_3: 0.16782/0.15271, loss_grounding_ce_3: 0.00333/0.25755, loss_mask_ce_4: 1.48391/0.81038, loss_mask_bce_4: 0.12522/0.30654, loss_mask_dice_4: 1.90524/1.05677, loss_spatial_bce_4: 0.01640/0.09546, loss_spatial_dice_4: 0.30247/0.20430, loss_spatial_ce_4: 0.18398/0.10946, loss_grounding_bce_4: 0.07036/0.08216, loss_grounding_dice_4: 0.17106/0.15515, loss_grounding_ce_4: 0.00060/0.26545, loss_mask_ce_5: 1.35808/0.83008, loss_mask_bce_5: 0.12704/0.30861, loss_mask_dice_5: 1.71848/1.06485, loss_spatial_bce_5: 0.01699/0.09695, loss_spatial_dice_5: 0.31593/0.20635, loss_spatial_ce_5: 0.36118/0.12026, loss_grounding_bce_5: 0.07778/0.08241, loss_grounding_dice_5: 0.19131/0.15570, loss_grounding_ce_5: 0.00129/0.28525, loss_mask_ce_6: 1.23197/0.85444, loss_mask_bce_6: 0.14223/0.30971, loss_mask_dice_6: 2.24971/1.06944, loss_spatial_bce_6: 0.02080/0.10205, loss_spatial_dice_6: 0.35172/0.20871, loss_spatial_ce_6: 0.37601/0.13819, loss_grounding_bce_6: 0.06587/0.08368, loss_grounding_dice_6: 0.17833/0.15627, loss_grounding_ce_6: 0.00132/0.30022, loss_mask_ce_7: 1.11328/0.91788, loss_mask_bce_7: 0.12683/0.31690, loss_mask_dice_7: 2.10809/1.11554, loss_spatial_bce_7: 0.02698/0.11306, loss_spatial_dice_7: 0.44099/0.23375, loss_spatial_ce_7: 0.11829/0.18357, loss_grounding_bce_7: 0.06360/0.08546, loss_grounding_dice_7: 0.18066/0.16208, loss_grounding_ce_7: 0.00321/0.35062, loss_mask_ce_8: 1.24957/1.05891, loss_mask_bce_8: 0.12164/0.33482, loss_mask_dice_8: 1.66441/1.19650, loss_spatial_bce_8: 0.03002/0.13500, loss_spatial_dice_8: 0.43168/0.27704, loss_spatial_ce_8: 0.33832/0.23977, loss_grounding_bce_8: 0.06040/0.08933, loss_grounding_dice_8: 0.16334/0.17101, loss_grounding_ce_8: 0.00276/0.45340, loss_mask_ce_9: 4.61417/3.52268, loss_mask_bce_9: 0.14350/0.36037, loss_mask_dice_9: 2.51242/1.78097, loss_spatial_bce_9: 0.24536/0.36355, loss_spatial_dice_9: 0.92127/0.79983, loss_spatial_ce_9: 2.80082/1.42827, loss_grounding_bce_9: 0.04577/0.10112, loss_grounding_dice_9: 0.17909/0.24563, loss_grounding_ce_9: 0.02679/0.73458] items per batch[64] items per second[0.36] total items[768000] mini batches[ 12000] memory[4943] epoch remaining[0:23:47] INFO:trainer.default_trainer:epochs[ 6] optim steps[12100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97350/0.79593, loss_mask_bce_0: 0.92238/0.30234, loss_mask_dice_0: 1.99776/1.03581, loss_spatial_bce_0: 0.16098/0.09183, loss_spatial_dice_0: 0.29209/0.19373, loss_spatial_ce_0: 0.01438/0.08281, loss_grounding_bce_0: 0.00428/0.08100, loss_grounding_dice_0: 0.10228/0.15211, loss_grounding_ce_0: 0.41608/0.25468, loss_mask_ce_1: 0.95378/0.79877, loss_mask_bce_1: 0.92292/0.30287, loss_mask_dice_1: 2.09555/1.03979, loss_spatial_bce_1: 0.16067/0.09243, loss_spatial_dice_1: 0.29505/0.19659, loss_spatial_ce_1: 0.02331/0.08710, loss_grounding_bce_1: 0.00505/0.08105, loss_grounding_dice_1: 0.13349/0.15320, loss_grounding_ce_1: 0.43043/0.25834, loss_mask_ce_2: 0.95217/0.80562, loss_mask_bce_2: 0.91557/0.30278, loss_mask_dice_2: 2.06032/1.04322, loss_spatial_bce_2: 0.13752/0.09186, loss_spatial_dice_2: 0.28148/0.19645, loss_spatial_ce_2: 0.05785/0.09171, loss_grounding_bce_2: 0.00674/0.08081, loss_grounding_dice_2: 0.12619/0.15300, loss_grounding_ce_2: 0.44002/0.25879, loss_mask_ce_3: 0.97761/0.80366, loss_mask_bce_3: 0.88840/0.30417, loss_mask_dice_3: 2.04665/1.03709, loss_spatial_bce_3: 0.14798/0.09330, loss_spatial_dice_3: 0.29998/0.19662, loss_spatial_ce_3: 0.04729/0.09766, loss_grounding_bce_3: 0.00588/0.08140, loss_grounding_dice_3: 0.14032/0.15260, loss_grounding_ce_3: 0.43248/0.25731, loss_mask_ce_4: 1.00581/0.81010, loss_mask_bce_4: 0.91837/0.30647, loss_mask_dice_4: 2.01541/1.05599, loss_spatial_bce_4: 0.13527/0.09541, loss_spatial_dice_4: 0.26685/0.20412, loss_spatial_ce_4: 0.05814/0.10928, loss_grounding_bce_4: 0.00429/0.08211, loss_grounding_dice_4: 0.11323/0.15503, loss_grounding_ce_4: 0.55540/0.26522, loss_mask_ce_5: 1.01177/0.82980, loss_mask_bce_5: 0.86952/0.30856, loss_mask_dice_5: 2.06868/1.06391, loss_spatial_bce_5: 0.15096/0.09693, loss_spatial_dice_5: 0.30662/0.20618, loss_spatial_ce_5: 0.15379/0.11998, loss_grounding_bce_5: 0.00417/0.08237, loss_grounding_dice_5: 0.10113/0.15561, loss_grounding_ce_5: 0.39848/0.28511, loss_mask_ce_6: 0.96912/0.85406, loss_mask_bce_6: 0.83948/0.30963, loss_mask_dice_6: 1.99932/1.06852, loss_spatial_bce_6: 0.15709/0.10198, loss_spatial_dice_6: 0.29842/0.20850, loss_spatial_ce_6: 0.16416/0.13800, loss_grounding_bce_6: 0.00546/0.08362, loss_grounding_dice_6: 0.09953/0.15616, loss_grounding_ce_6: 0.56443/0.30008, loss_mask_ce_7: 1.02323/0.91781, loss_mask_bce_7: 0.83944/0.31677, loss_mask_dice_7: 2.01868/1.11470, loss_spatial_bce_7: 0.15296/0.11297, loss_spatial_dice_7: 0.30742/0.23354, loss_spatial_ce_7: 0.13943/0.18335, loss_grounding_bce_7: 0.00470/0.08541, loss_grounding_dice_7: 0.14882/0.16202, loss_grounding_ce_7: 0.45556/0.35030, loss_mask_ce_8: 1.40937/1.05846, loss_mask_bce_8: 0.95488/0.33469, loss_mask_dice_8: 2.18688/1.19545, loss_spatial_bce_8: 0.19378/0.13499, loss_spatial_dice_8: 0.41469/0.27679, loss_spatial_ce_8: 0.36541/0.23966, loss_grounding_bce_8: 0.00437/0.08931, loss_grounding_dice_8: 0.13036/0.17094, loss_grounding_ce_8: 0.51595/0.45232, loss_mask_ce_9: 3.82292/3.52090, loss_mask_bce_9: 0.99074/0.36020, loss_mask_dice_9: 3.41470/1.77998, loss_spatial_bce_9: 0.33143/0.36344, loss_spatial_dice_9: 0.92064/0.79955, loss_spatial_ce_9: 1.15645/1.42703, loss_grounding_bce_9: 0.00731/0.10108, loss_grounding_dice_9: 0.22807/0.24541, loss_grounding_ce_9: 0.61425/0.73328] items per batch[64] items per second[0.36] total items[774400] mini batches[ 12100] memory[4943] epoch remaining[0:20:45] INFO:trainer.default_trainer:epochs[ 6] optim steps[12200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62305/0.79631, loss_mask_bce_0: 0.26184/0.30263, loss_mask_dice_0: 1.42241/1.03618, loss_spatial_bce_0: 0.01871/0.09184, loss_spatial_dice_0: 0.25554/0.19364, loss_spatial_ce_0: 0.00992/0.08250, loss_grounding_bce_0: 0.06298/0.08100, loss_grounding_dice_0: 0.11148/0.15206, loss_grounding_ce_0: 0.23995/0.25459, loss_mask_ce_1: 0.77681/0.79931, loss_mask_bce_1: 0.24357/0.30312, loss_mask_dice_1: 1.40382/1.04034, loss_spatial_bce_1: 0.01858/0.09243, loss_spatial_dice_1: 0.25215/0.19650, loss_spatial_ce_1: 0.01268/0.08682, loss_grounding_bce_1: 0.05414/0.08103, loss_grounding_dice_1: 0.10426/0.15313, loss_grounding_ce_1: 0.23519/0.25821, loss_mask_ce_2: 0.67882/0.80605, loss_mask_bce_2: 0.27140/0.30305, loss_mask_dice_2: 1.32656/1.04372, loss_spatial_bce_2: 0.01745/0.09188, loss_spatial_dice_2: 0.25411/0.19640, loss_spatial_ce_2: 0.00974/0.09149, loss_grounding_bce_2: 0.06089/0.08080, loss_grounding_dice_2: 0.11389/0.15294, loss_grounding_ce_2: 0.23580/0.25853, loss_mask_ce_3: 0.74021/0.80412, loss_mask_bce_3: 0.29203/0.30441, loss_mask_dice_3: 1.30570/1.03730, loss_spatial_bce_3: 0.01963/0.09333, loss_spatial_dice_3: 0.24840/0.19655, loss_spatial_ce_3: 0.01921/0.09734, loss_grounding_bce_3: 0.06129/0.08138, loss_grounding_dice_3: 0.12171/0.15253, loss_grounding_ce_3: 0.16604/0.25713, loss_mask_ce_4: 0.84497/0.81051, loss_mask_bce_4: 0.31634/0.30675, loss_mask_dice_4: 1.54643/1.05642, loss_spatial_bce_4: 0.01961/0.09542, loss_spatial_dice_4: 0.25051/0.20405, loss_spatial_ce_4: 0.03793/0.10904, loss_grounding_bce_4: 0.06230/0.08210, loss_grounding_dice_4: 0.11172/0.15498, loss_grounding_ce_4: 0.36163/0.26514, loss_mask_ce_5: 1.02546/0.83015, loss_mask_bce_5: 0.39649/0.30882, loss_mask_dice_5: 1.69643/1.06444, loss_spatial_bce_5: 0.01801/0.09693, loss_spatial_dice_5: 0.25188/0.20610, loss_spatial_ce_5: 0.06756/0.11976, loss_grounding_bce_5: 0.06508/0.08235, loss_grounding_dice_5: 0.12476/0.15553, loss_grounding_ce_5: 0.43017/0.28507, loss_mask_ce_6: 0.74231/0.85452, loss_mask_bce_6: 0.36927/0.30989, loss_mask_dice_6: 1.69321/1.06909, loss_spatial_bce_6: 0.02485/0.10197, loss_spatial_dice_6: 0.25615/0.20840, loss_spatial_ce_6: 0.11562/0.13776, loss_grounding_bce_6: 0.04967/0.08359, loss_grounding_dice_6: 0.11551/0.15615, loss_grounding_ce_6: 0.53899/0.29983, loss_mask_ce_7: 0.91608/0.91816, loss_mask_bce_7: 0.36457/0.31699, loss_mask_dice_7: 1.71604/1.11536, loss_spatial_bce_7: 0.02235/0.11296, loss_spatial_dice_7: 0.26045/0.23344, loss_spatial_ce_7: 0.15258/0.18291, loss_grounding_bce_7: 0.04556/0.08537, loss_grounding_dice_7: 0.12093/0.16200, loss_grounding_ce_7: 0.34089/0.34970, loss_mask_ce_8: 1.07456/1.05886, loss_mask_bce_8: 0.50483/0.33497, loss_mask_dice_8: 2.08587/1.19607, loss_spatial_bce_8: 0.02317/0.13494, loss_spatial_dice_8: 0.29650/0.27656, loss_spatial_ce_8: 0.15125/0.23942, loss_grounding_bce_8: 0.04813/0.08924, loss_grounding_dice_8: 0.11547/0.17090, loss_grounding_ce_8: 1.21678/0.45201, loss_mask_ce_9: 3.72262/3.52318, loss_mask_bce_9: 0.29388/0.36059, loss_mask_dice_9: 3.04002/1.78126, loss_spatial_bce_9: 0.16440/0.36347, loss_spatial_dice_9: 0.96292/0.79958, loss_spatial_ce_9: 1.60891/1.42645, loss_grounding_bce_9: 0.05949/0.10108, loss_grounding_dice_9: 0.25524/0.24544, loss_grounding_ce_9: 2.40006/0.73270] items per batch[64] items per second[0.36] total items[780800] mini batches[ 12200] memory[4943] epoch remaining[0:17:43] INFO:trainer.default_trainer:epochs[ 6] optim steps[12300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99550/0.79540, loss_mask_bce_0: 0.34077/0.30257, loss_mask_dice_0: 0.33853/1.03506, loss_spatial_bce_0: 0.09997/0.09177, loss_spatial_dice_0: 0.10326/0.19348, loss_spatial_ce_0: 0.13890/0.08214, loss_grounding_bce_0: 0.05456/0.08095, loss_grounding_dice_0: 0.10844/0.15196, loss_grounding_ce_0: 0.00238/0.25436, loss_mask_ce_1: 1.00907/0.79839, loss_mask_bce_1: 0.37173/0.30303, loss_mask_dice_1: 0.38653/1.03926, loss_spatial_bce_1: 0.14220/0.09236, loss_spatial_dice_1: 0.15588/0.19633, loss_spatial_ce_1: 0.00784/0.08646, loss_grounding_bce_1: 0.05648/0.08099, loss_grounding_dice_1: 0.10415/0.15302, loss_grounding_ce_1: 0.00240/0.25784, loss_mask_ce_2: 1.00528/0.80515, loss_mask_bce_2: 0.31206/0.30295, loss_mask_dice_2: 0.31324/1.04250, loss_spatial_bce_2: 0.11907/0.09181, loss_spatial_dice_2: 0.15332/0.19620, loss_spatial_ce_2: 0.01296/0.09109, loss_grounding_bce_2: 0.05451/0.08075, loss_grounding_dice_2: 0.10712/0.15279, loss_grounding_ce_2: 0.00250/0.25820, loss_mask_ce_3: 1.03432/0.80304, loss_mask_bce_3: 0.43442/0.30435, loss_mask_dice_3: 0.43646/1.03599, loss_spatial_bce_3: 0.14884/0.09326, loss_spatial_dice_3: 0.13310/0.19637, loss_spatial_ce_3: 0.01069/0.09693, loss_grounding_bce_3: 0.05813/0.08131, loss_grounding_dice_3: 0.12134/0.15242, loss_grounding_ce_3: 0.00184/0.25677, loss_mask_ce_4: 1.13009/0.80957, loss_mask_bce_4: 0.92006/0.30667, loss_mask_dice_4: 0.57292/1.05520, loss_spatial_bce_4: 0.13038/0.09536, loss_spatial_dice_4: 0.16729/0.20384, loss_spatial_ce_4: 0.00502/0.10866, loss_grounding_bce_4: 0.05809/0.08205, loss_grounding_dice_4: 0.09409/0.15486, loss_grounding_ce_4: 0.00242/0.26477, loss_mask_ce_5: 0.95387/0.82921, loss_mask_bce_5: 0.79256/0.30875, loss_mask_dice_5: 0.58691/1.06339, loss_spatial_bce_5: 0.11122/0.09690, loss_spatial_dice_5: 0.15100/0.20591, loss_spatial_ce_5: 0.09467/0.11942, loss_grounding_bce_5: 0.05744/0.08229, loss_grounding_dice_5: 0.11927/0.15541, loss_grounding_ce_5: 0.00349/0.28473, loss_mask_ce_6: 1.22149/0.85359, loss_mask_bce_6: 0.72610/0.30982, loss_mask_dice_6: 0.48146/1.06786, loss_spatial_bce_6: 0.20621/0.10193, loss_spatial_dice_6: 0.17913/0.20819, loss_spatial_ce_6: 0.02175/0.13742, loss_grounding_bce_6: 0.05803/0.08353, loss_grounding_dice_6: 0.10864/0.15608, loss_grounding_ce_6: 0.00465/0.29946, loss_mask_ce_7: 1.59636/0.91734, loss_mask_bce_7: 0.31400/0.31689, loss_mask_dice_7: 0.34875/1.11402, loss_spatial_bce_7: 0.12406/0.11291, loss_spatial_dice_7: 0.23294/0.23322, loss_spatial_ce_7: 0.06399/0.18255, loss_grounding_bce_7: 0.05106/0.08535, loss_grounding_dice_7: 0.11176/0.16188, loss_grounding_ce_7: 0.06705/0.34910, loss_mask_ce_8: 1.08203/1.05820, loss_mask_bce_8: 0.44498/0.33485, loss_mask_dice_8: 0.58053/1.19457, loss_spatial_bce_8: 0.13737/0.13485, loss_spatial_dice_8: 0.28259/0.27628, loss_spatial_ce_8: 0.06472/0.23912, loss_grounding_bce_8: 0.06005/0.08919, loss_grounding_dice_8: 0.12716/0.17080, loss_grounding_ce_8: 0.00517/0.45112, loss_mask_ce_9: 3.73030/3.52092, loss_mask_bce_9: 0.50742/0.36064, loss_mask_dice_9: 0.75860/1.78008, loss_spatial_bce_9: 0.38174/0.36340, loss_spatial_dice_9: 0.88121/0.79929, loss_spatial_ce_9: 1.32102/1.42489, loss_grounding_bce_9: 0.05471/0.10104, loss_grounding_dice_9: 0.12657/0.24547, loss_grounding_ce_9: 0.06710/0.73222] items per batch[64] items per second[0.35] total items[787200] mini batches[ 12300] memory[4943] epoch remaining[0:14:43] INFO:trainer.default_trainer:epochs[ 6] optim steps[12400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26541/0.79555, loss_mask_bce_0: 0.12029/0.30256, loss_mask_dice_0: 0.15106/1.03659, loss_spatial_bce_0: 0.05191/0.09159, loss_spatial_dice_0: 0.05390/0.19349, loss_spatial_ce_0: 0.00267/0.08193, loss_grounding_bce_0: 0.06268/0.08092, loss_grounding_dice_0: 0.08254/0.15216, loss_grounding_ce_0: 0.20902/0.25431, loss_mask_ce_1: 0.27687/0.79866, loss_mask_bce_1: 0.13386/0.30304, loss_mask_dice_1: 0.17077/1.04094, loss_spatial_bce_1: 0.05100/0.09218, loss_spatial_dice_1: 0.05799/0.19636, loss_spatial_ce_1: 0.00955/0.08632, loss_grounding_bce_1: 0.22544/0.08097, loss_grounding_dice_1: 0.16813/0.15318, loss_grounding_ce_1: 0.00542/0.25786, loss_mask_ce_2: 0.25289/0.80536, loss_mask_bce_2: 0.13498/0.30297, loss_mask_dice_2: 0.16524/1.04407, loss_spatial_bce_2: 0.05763/0.09163, loss_spatial_dice_2: 0.05630/0.19622, loss_spatial_ce_2: 0.00819/0.09082, loss_grounding_bce_2: 0.07297/0.08073, loss_grounding_dice_2: 0.08850/0.15290, loss_grounding_ce_2: 0.21843/0.25831, loss_mask_ce_3: 0.27132/0.80330, loss_mask_bce_3: 0.13145/0.30436, loss_mask_dice_3: 0.15449/1.03742, loss_spatial_bce_3: 0.05197/0.09307, loss_spatial_dice_3: 0.05001/0.19636, loss_spatial_ce_3: 0.00980/0.09677, loss_grounding_bce_3: 0.19701/0.08129, loss_grounding_dice_3: 0.15780/0.15258, loss_grounding_ce_3: 0.00403/0.25671, loss_mask_ce_4: 0.34204/0.81017, loss_mask_bce_4: 0.16615/0.30667, loss_mask_dice_4: 0.18466/1.05701, loss_spatial_bce_4: 0.05307/0.09517, loss_spatial_dice_4: 0.05865/0.20389, loss_spatial_ce_4: 0.01803/0.10833, loss_grounding_bce_4: 0.20482/0.08202, loss_grounding_dice_4: 0.17270/0.15495, loss_grounding_ce_4: 0.00169/0.26473, loss_mask_ce_5: 0.38868/0.82975, loss_mask_bce_5: 0.15296/0.30874, loss_mask_dice_5: 0.14870/1.06495, loss_spatial_bce_5: 0.05721/0.09670, loss_spatial_dice_5: 0.05526/0.20590, loss_spatial_ce_5: 0.04259/0.11917, loss_grounding_bce_5: 0.21227/0.08225, loss_grounding_dice_5: 0.15728/0.15554, loss_grounding_ce_5: 0.00193/0.28460, loss_mask_ce_6: 0.38889/0.85408, loss_mask_bce_6: 0.14652/0.30977, loss_mask_dice_6: 0.14947/1.06938, loss_spatial_bce_6: 0.05064/0.10172, loss_spatial_dice_6: 0.05609/0.20820, loss_spatial_ce_6: 0.09744/0.13735, loss_grounding_bce_6: 0.20520/0.08350, loss_grounding_dice_6: 0.15470/0.15622, loss_grounding_ce_6: 0.00557/0.29934, loss_mask_ce_7: 0.21381/0.91779, loss_mask_bce_7: 0.29042/0.31682, loss_mask_dice_7: 0.26877/1.11579, loss_spatial_bce_7: 0.10395/0.11276, loss_spatial_dice_7: 0.10134/0.23331, loss_spatial_ce_7: 0.09146/0.18240, loss_grounding_bce_7: 0.17956/0.08531, loss_grounding_dice_7: 0.16213/0.16199, loss_grounding_ce_7: 0.00361/0.34894, loss_mask_ce_8: 0.20963/1.05871, loss_mask_bce_8: 0.21853/0.33483, loss_mask_dice_8: 0.28866/1.19615, loss_spatial_bce_8: 0.08344/0.13464, loss_spatial_dice_8: 0.12721/0.27638, loss_spatial_ce_8: 0.10376/0.23871, loss_grounding_bce_8: 0.12923/0.08918, loss_grounding_dice_8: 0.19820/0.17099, loss_grounding_ce_8: 0.00249/0.45112, loss_mask_ce_9: 2.62331/3.52145, loss_mask_bce_9: 0.17791/0.36065, loss_mask_dice_9: 0.41290/1.78301, loss_spatial_bce_9: 0.49642/0.36295, loss_spatial_dice_9: 0.83968/0.79948, loss_spatial_ce_9: 1.22658/1.42591, loss_grounding_bce_9: 0.07988/0.10111, loss_grounding_dice_9: 0.27306/0.24574, loss_grounding_ce_9: 0.28495/0.73227] items per batch[64] items per second[0.36] total items[793600] mini batches[ 12400] memory[4943] epoch remaining[0:11:42] INFO:trainer.default_trainer:epochs[ 6] optim steps[12500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64996/0.79587, loss_mask_bce_0: 0.15622/0.30262, loss_mask_dice_0: 0.23017/1.03717, loss_spatial_bce_0: 0.07249/0.09159, loss_spatial_dice_0: 0.09044/0.19346, loss_spatial_ce_0: 0.00288/0.08179, loss_grounding_bce_0: 0.04705/0.08089, loss_grounding_dice_0: 0.02434/0.15207, loss_grounding_ce_0: 0.00019/0.25441, loss_mask_ce_1: 0.66344/0.79876, loss_mask_bce_1: 0.15200/0.30311, loss_mask_dice_1: 0.22888/1.04160, loss_spatial_bce_1: 0.07007/0.09214, loss_spatial_dice_1: 0.08579/0.19633, loss_spatial_ce_1: 0.00738/0.08620, loss_grounding_bce_1: 0.05426/0.08094, loss_grounding_dice_1: 0.02689/0.15310, loss_grounding_ce_1: 0.00022/0.25801, loss_mask_ce_2: 0.60982/0.80546, loss_mask_bce_2: 0.15141/0.30305, loss_mask_dice_2: 0.22649/1.04448, loss_spatial_bce_2: 0.06496/0.09159, loss_spatial_dice_2: 0.08287/0.19618, loss_spatial_ce_2: 0.00735/0.09062, loss_grounding_bce_2: 0.05015/0.08071, loss_grounding_dice_2: 0.02623/0.15280, loss_grounding_ce_2: 0.00025/0.25863, loss_mask_ce_3: 0.60136/0.80332, loss_mask_bce_3: 0.16751/0.30443, loss_mask_dice_3: 0.22240/1.03800, loss_spatial_bce_3: 0.06908/0.09301, loss_spatial_dice_3: 0.08386/0.19628, loss_spatial_ce_3: 0.00830/0.09662, loss_grounding_bce_3: 0.05503/0.08127, loss_grounding_dice_3: 0.02621/0.15245, loss_grounding_ce_3: 0.00011/0.25687, loss_mask_ce_4: 0.63677/0.81051, loss_mask_bce_4: 0.16167/0.30673, loss_mask_dice_4: 0.22846/1.05731, loss_spatial_bce_4: 0.07086/0.09519, loss_spatial_dice_4: 0.09322/0.20387, loss_spatial_ce_4: 0.00389/0.10816, loss_grounding_bce_4: 0.05868/0.08199, loss_grounding_dice_4: 0.02899/0.15484, loss_grounding_ce_4: 0.00022/0.26510, loss_mask_ce_5: 0.55496/0.83007, loss_mask_bce_5: 0.16275/0.30883, loss_mask_dice_5: 0.22062/1.06528, loss_spatial_bce_5: 0.06678/0.09670, loss_spatial_dice_5: 0.08162/0.20586, loss_spatial_ce_5: 0.00265/0.11900, loss_grounding_bce_5: 0.06224/0.08224, loss_grounding_dice_5: 0.02917/0.15542, loss_grounding_ce_5: 0.00035/0.28487, loss_mask_ce_6: 0.42565/0.85464, loss_mask_bce_6: 0.17279/0.30990, loss_mask_dice_6: 0.22473/1.06967, loss_spatial_bce_6: 0.06703/0.10173, loss_spatial_dice_6: 0.08566/0.20815, loss_spatial_ce_6: 0.00296/0.13717, loss_grounding_bce_6: 0.05801/0.08346, loss_grounding_dice_6: 0.02644/0.15609, loss_grounding_ce_6: 0.00010/0.29969, loss_mask_ce_7: 0.38412/0.91817, loss_mask_bce_7: 0.24803/0.31699, loss_mask_dice_7: 0.28763/1.11609, loss_spatial_bce_7: 0.07575/0.11265, loss_spatial_dice_7: 0.09940/0.23328, loss_spatial_ce_7: 0.03928/0.18238, loss_grounding_bce_7: 0.06484/0.08536, loss_grounding_dice_7: 0.03034/0.16193, loss_grounding_ce_7: 0.00006/0.34928, loss_mask_ce_8: 0.54950/1.05923, loss_mask_bce_8: 0.16601/0.33494, loss_mask_dice_8: 0.22627/1.19655, loss_spatial_bce_8: 0.12955/0.13454, loss_spatial_dice_8: 0.09980/0.27626, loss_spatial_ce_8: 0.03689/0.23854, loss_grounding_bce_8: 0.05765/0.08917, loss_grounding_dice_8: 0.03075/0.17102, loss_grounding_ce_8: 0.00090/0.45178, loss_mask_ce_9: 2.22276/3.52338, loss_mask_bce_9: 0.25106/0.36073, loss_mask_dice_9: 0.35114/1.78312, loss_spatial_bce_9: 0.72779/0.36285, loss_spatial_dice_9: 0.81391/0.79947, loss_spatial_ce_9: 1.38173/1.42608, loss_grounding_bce_9: 0.05259/0.10109, loss_grounding_dice_9: 0.03536/0.24581, loss_grounding_ce_9: 0.00794/0.73336] items per batch[64] items per second[0.36] total items[800000] mini batches[ 12500] memory[4943] epoch remaining[0:08:41] INFO:trainer.default_trainer:epochs[ 6] optim steps[12600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08117/0.79562, loss_mask_bce_0: 0.06990/0.30292, loss_mask_dice_0: 0.11372/1.03724, loss_spatial_bce_0: 0.03515/0.09165, loss_spatial_dice_0: 0.05739/0.19342, loss_spatial_ce_0: 0.40620/0.08171, loss_grounding_bce_0: 0.04705/0.08090, loss_grounding_dice_0: 0.06343/0.15206, loss_grounding_ce_0: 0.00540/0.25431, loss_mask_ce_1: 0.07995/0.79865, loss_mask_bce_1: 0.06971/0.30334, loss_mask_dice_1: 0.11460/1.04194, loss_spatial_bce_1: 0.03538/0.09219, loss_spatial_dice_1: 0.06552/0.19625, loss_spatial_ce_1: 0.41004/0.08611, loss_grounding_bce_1: 0.04724/0.08094, loss_grounding_dice_1: 0.06129/0.15305, loss_grounding_ce_1: 0.00333/0.25784, loss_mask_ce_2: 0.08226/0.80525, loss_mask_bce_2: 0.07036/0.30329, loss_mask_dice_2: 0.12155/1.04466, loss_spatial_bce_2: 0.03635/0.09162, loss_spatial_dice_2: 0.06743/0.19612, loss_spatial_ce_2: 0.38479/0.09044, loss_grounding_bce_2: 0.04828/0.08073, loss_grounding_dice_2: 0.05827/0.15275, loss_grounding_ce_2: 0.00394/0.25839, loss_mask_ce_3: 0.06668/0.80326, loss_mask_bce_3: 0.07777/0.30466, loss_mask_dice_3: 0.12067/1.03826, loss_spatial_bce_3: 0.03536/0.09304, loss_spatial_dice_3: 0.05988/0.19619, loss_spatial_ce_3: 0.34251/0.09649, loss_grounding_bce_3: 0.04927/0.08129, loss_grounding_dice_3: 0.05322/0.15244, loss_grounding_ce_3: 0.00546/0.25666, loss_mask_ce_4: 0.08404/0.81033, loss_mask_bce_4: 0.07680/0.30700, loss_mask_dice_4: 0.11741/1.05748, loss_spatial_bce_4: 0.03457/0.09523, loss_spatial_dice_4: 0.05987/0.20380, loss_spatial_ce_4: 0.37408/0.10796, loss_grounding_bce_4: 0.05314/0.08200, loss_grounding_dice_4: 0.06811/0.15475, loss_grounding_ce_4: 0.00637/0.26528, loss_mask_ce_5: 0.09162/0.83004, loss_mask_bce_5: 0.07853/0.30904, loss_mask_dice_5: 0.12541/1.06537, loss_spatial_bce_5: 0.03799/0.09673, loss_spatial_dice_5: 0.07284/0.20578, loss_spatial_ce_5: 0.27839/0.11885, loss_grounding_bce_5: 0.05252/0.08223, loss_grounding_dice_5: 0.06686/0.15533, loss_grounding_ce_5: 0.00575/0.28460, loss_mask_ce_6: 0.07629/0.85468, loss_mask_bce_6: 0.07026/0.31008, loss_mask_dice_6: 0.10662/1.06984, loss_spatial_bce_6: 0.03601/0.10178, loss_spatial_dice_6: 0.05730/0.20810, loss_spatial_ce_6: 0.42495/0.13703, loss_grounding_bce_6: 0.05081/0.08347, loss_grounding_dice_6: 0.07053/0.15605, loss_grounding_ce_6: 0.00450/0.29922, loss_mask_ce_7: 0.08280/0.91807, loss_mask_bce_7: 0.07248/0.31725, loss_mask_dice_7: 0.10582/1.11630, loss_spatial_bce_7: 0.04123/0.11268, loss_spatial_dice_7: 0.05539/0.23317, loss_spatial_ce_7: 0.89510/0.18214, loss_grounding_bce_7: 0.04843/0.08538, loss_grounding_dice_7: 0.06803/0.16189, loss_grounding_ce_7: 0.00721/0.34878, loss_mask_ce_8: 0.07804/1.05930, loss_mask_bce_8: 0.07297/0.33509, loss_mask_dice_8: 0.11181/1.19654, loss_spatial_bce_8: 0.04644/0.13454, loss_spatial_dice_8: 0.17316/0.27610, loss_spatial_ce_8: 0.79554/0.23832, loss_grounding_bce_8: 0.04852/0.08920, loss_grounding_dice_8: 0.05447/0.17098, loss_grounding_ce_8: 0.01646/0.45122, loss_mask_ce_9: 1.73215/3.52302, loss_mask_bce_9: 0.08453/0.36101, loss_mask_dice_9: 0.17281/1.78416, loss_spatial_bce_9: 0.43570/0.36280, loss_spatial_dice_9: 0.61328/0.79940, loss_spatial_ce_9: 1.40753/1.42599, loss_grounding_bce_9: 0.05845/0.10114, loss_grounding_dice_9: 0.09552/0.24586, loss_grounding_ce_9: 0.09466/0.73304] items per batch[64] items per second[0.35] total items[806400] mini batches[ 12600] memory[4943] epoch remaining[0:05:41] INFO:trainer.default_trainer:epochs[ 6] optim steps[12700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52094/0.79471, loss_mask_bce_0: 0.12489/0.30304, loss_mask_dice_0: 0.49083/1.03521, loss_spatial_bce_0: 0.03032/0.09180, loss_spatial_dice_0: 0.16809/0.19341, loss_spatial_ce_0: 0.03547/0.08150, loss_grounding_bce_0: 0.01383/0.08103, loss_grounding_dice_0: 0.07764/0.15211, loss_grounding_ce_0: 0.21636/0.25395, loss_mask_ce_1: 0.54428/0.79773, loss_mask_bce_1: 0.13983/0.30345, loss_mask_dice_1: 0.80473/1.03996, loss_spatial_bce_1: 0.03403/0.09234, loss_spatial_dice_1: 0.17418/0.19620, loss_spatial_ce_1: 0.04029/0.08586, loss_grounding_bce_1: 0.01243/0.08109, loss_grounding_dice_1: 0.06772/0.15313, loss_grounding_ce_1: 0.27401/0.25763, loss_mask_ce_2: 0.58873/0.80434, loss_mask_bce_2: 0.13571/0.30341, loss_mask_dice_2: 0.75826/1.04270, loss_spatial_bce_2: 0.03383/0.09174, loss_spatial_dice_2: 0.17941/0.19604, loss_spatial_ce_2: 0.03237/0.09020, loss_grounding_bce_2: 0.01287/0.08086, loss_grounding_dice_2: 0.06311/0.15280, loss_grounding_ce_2: 0.28753/0.25823, loss_mask_ce_3: 0.59227/0.80227, loss_mask_bce_3: 0.12445/0.30473, loss_mask_dice_3: 0.78760/1.03641, loss_spatial_bce_3: 0.03007/0.09318, loss_spatial_dice_3: 0.18148/0.19612, loss_spatial_ce_3: 0.08681/0.09623, loss_grounding_bce_3: 0.01710/0.08143, loss_grounding_dice_3: 0.07388/0.15251, loss_grounding_ce_3: 0.47321/0.25649, loss_mask_ce_4: 0.56598/0.80926, loss_mask_bce_4: 0.12075/0.30709, loss_mask_dice_4: 0.84353/1.05557, loss_spatial_bce_4: 0.03367/0.09540, loss_spatial_dice_4: 0.18710/0.20373, loss_spatial_ce_4: 0.12157/0.10771, loss_grounding_bce_4: 0.01332/0.08212, loss_grounding_dice_4: 0.07658/0.15479, loss_grounding_ce_4: 0.45714/0.26553, loss_mask_ce_5: 0.77315/0.82907, loss_mask_bce_5: 0.07703/0.30912, loss_mask_dice_5: 0.60016/1.06328, loss_spatial_bce_5: 0.03979/0.09688, loss_spatial_dice_5: 0.18733/0.20574, loss_spatial_ce_5: 0.06805/0.11860, loss_grounding_bce_5: 0.01743/0.08236, loss_grounding_dice_5: 0.09391/0.15536, loss_grounding_ce_5: 0.05804/0.28443, loss_mask_ce_6: 0.60007/0.85365, loss_mask_bce_6: 0.11693/0.31017, loss_mask_dice_6: 0.79481/1.06777, loss_spatial_bce_6: 0.02576/0.10196, loss_spatial_dice_6: 0.20095/0.20804, loss_spatial_ce_6: 0.21353/0.13670, loss_grounding_bce_6: 0.01602/0.08358, loss_grounding_dice_6: 0.07804/0.15603, loss_grounding_ce_6: 0.30937/0.29897, loss_mask_ce_7: 1.17393/0.91693, loss_mask_bce_7: 0.11701/0.31726, loss_mask_dice_7: 0.77365/1.11404, loss_spatial_bce_7: 0.04525/0.11283, loss_spatial_dice_7: 0.19573/0.23310, loss_spatial_ce_7: 0.14950/0.18181, loss_grounding_bce_7: 0.02268/0.08548, loss_grounding_dice_7: 0.09809/0.16191, loss_grounding_ce_7: 0.51102/0.34832, loss_mask_ce_8: 0.89717/1.05781, loss_mask_bce_8: 0.10752/0.33506, loss_mask_dice_8: 0.86166/1.19424, loss_spatial_bce_8: 0.05703/0.13465, loss_spatial_dice_8: 0.21903/0.27594, loss_spatial_ce_8: 0.21002/0.23814, loss_grounding_bce_8: 0.01688/0.08930, loss_grounding_dice_8: 0.06800/0.17099, loss_grounding_ce_8: 0.42558/0.45084, loss_mask_ce_9: 4.30760/3.51945, loss_mask_bce_9: 0.23023/0.36099, loss_mask_dice_9: 1.24776/1.78000, loss_spatial_bce_9: 0.25393/0.36304, loss_spatial_dice_9: 0.89080/0.79929, loss_spatial_ce_9: 2.32813/1.42530, loss_grounding_bce_9: 0.02371/0.10123, loss_grounding_dice_9: 0.13796/0.24585, loss_grounding_ce_9: 4.22191/0.73211] items per batch[64] items per second[0.36] total items[812800] mini batches[ 12700] memory[4943] epoch remaining[0:02:40] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00012789. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3725 s/iter. Eval: 0.0946 s/iter. Total: 0.4700 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3557 s/iter. Eval: 0.0843 s/iter. Total: 0.4426 s/iter. ETA=0:00:24 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 36/79. Dataloading: 0.0026 s/iter. Inference: 0.3390 s/iter. Eval: 0.0801 s/iter. Total: 0.4219 s/iter. ETA=0:00:18 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 47/79. Dataloading: 0.0027 s/iter. Inference: 0.3525 s/iter. Eval: 0.0756 s/iter. Total: 0.4310 s/iter. ETA=0:00:13 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0029 s/iter. Inference: 0.3599 s/iter. Eval: 0.0747 s/iter. Total: 0.4376 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0030 s/iter. Inference: 0.3652 s/iter. Eval: 0.0717 s/iter. Total: 0.4400 s/iter. ETA=0:00:03 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval8dtaub9e ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.463 | 82.941 | 66.031 | 133 | | Things | 61.781 | 84.001 | 73.052 | 80 | | Stuff | 45.925 | 81.340 | 55.434 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.31 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.36 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.58 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.44 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.221 | 68.683 | 48.637 | 26.068 | 49.439 | 67.821 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.772 | bicycle | 22.259 | car | 42.556 | | motorcycle | 40.719 | airplane | 61.779 | bus | 70.983 | | train | 74.357 | truck | 43.286 | boat | 30.995 | | traffic light | 28.533 | fire hydrant | 70.716 | stop sign | 68.764 | | parking meter | 52.692 | bench | 25.930 | bird | 33.908 | | cat | 77.790 | dog | 72.188 | horse | 50.714 | | sheep | 52.312 | cow | 56.234 | elephant | 65.830 | | bear | 79.836 | zebra | 65.627 | giraffe | 61.923 | | backpack | 24.878 | umbrella | 55.281 | handbag | 23.439 | | tie | 40.269 | suitcase | 50.677 | frisbee | 70.088 | | skis | 8.362 | snowboard | 35.075 | sports ball | 48.502 | | kite | 36.536 | baseball bat | 37.810 | baseball glove | 49.310 | | skateboard | 43.413 | surfboard | 44.701 | tennis racket | 63.023 | | bottle | 41.802 | wine glass | 38.069 | cup | 49.128 | | fork | 26.524 | knife | 23.981 | spoon | 20.879 | | bowl | 36.660 | banana | 21.918 | apple | 25.632 | | sandwich | 47.294 | orange | 30.968 | broccoli | 24.101 | | carrot | 22.614 | hot dog | 34.015 | pizza | 50.093 | | donut | 55.331 | cake | 47.723 | chair | 27.945 | | couch | 42.931 | potted plant | 21.625 | bed | 41.535 | | dining table | 15.694 | toilet | 69.472 | tv | 65.247 | | laptop | 70.471 | mouse | 62.744 | remote | 43.900 | | keyboard | 58.557 | cell phone | 45.932 | microwave | 64.575 | | oven | 32.036 | toaster | 49.076 | sink | 43.923 | | refrigerator | 69.172 | book | 13.904 | clock | 54.203 | | vase | 40.642 | scissors | 36.581 | teddy bear | 58.236 | | hair drier | 32.742 | toothbrush | 29.755 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.687 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.261 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.544 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.562 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.600 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.87837033579686, 'fwIoU': 71.76539482194202, 'IoU-person': 88.96661177118304, 'IoU-bicycle': 73.1496779034311, 'IoU-car': 71.80241713066067, 'IoU-motorcycle': 89.0673318839978, 'IoU-airplane': 89.48815467308658, 'IoU-bus': 87.13028756415436, 'IoU-train': 86.97391108810221, 'IoU-truck': 66.66376064675494, 'IoU-boat': 74.52529692254168, 'IoU-traffic light': 79.90714573230743, 'IoU-fire hydrant': 93.01806892706274, 'IoU-stop sign': 95.16853554059836, 'IoU-parking meter': 84.19566043495324, 'IoU-bench': 64.26101235009027, 'IoU-bird': 77.25980936024733, 'IoU-cat': 88.12057952520132, 'IoU-dog': 88.13519748859903, 'IoU-horse': 89.65936137288053, 'IoU-sheep': 91.08303678763725, 'IoU-cow': 88.22214127019849, 'IoU-elephant': 91.45428841238137, 'IoU-bear': 93.0404428146812, 'IoU-zebra': 93.01979858052108, 'IoU-giraffe': 89.395680285765, 'IoU-backpack': 52.56202536875922, 'IoU-umbrella': 85.01531591151662, 'IoU-handbag': 49.601199335289714, 'IoU-tie': 76.20766237572644, 'IoU-suitcase': 86.26397945285596, 'IoU-frisbee': 84.289753339183, 'IoU-skis': 59.52601669720141, 'IoU-snowboard': 71.93235504961955, 'IoU-sports ball': 81.06172746386582, 'IoU-kite': 79.45285459567178, 'IoU-baseball bat': 67.5187659607398, 'IoU-baseball glove': 78.94495779462304, 'IoU-skateboard': 86.03582741089728, 'IoU-surfboard': 86.74465133278784, 'IoU-tennis racket': 90.64494923811858, 'IoU-bottle': 71.44801006787168, 'IoU-wine glass': 82.28172956010745, 'IoU-cup': 67.09440480623996, 'IoU-fork': 67.72071884439409, 'IoU-knife': 63.001844546232796, 'IoU-spoon': 62.41505082373977, 'IoU-bowl': 57.36853615316628, 'IoU-banana': 81.9230699698997, 'IoU-apple': 60.3902726256829, 'IoU-sandwich': 69.93451061294135, 'IoU-orange': 79.53839415002987, 'IoU-broccoli': 69.90228044384976, 'IoU-carrot': 65.27056667768936, 'IoU-hot dog': 62.99971334483237, 'IoU-pizza': 87.20821728781965, 'IoU-donut': 65.76646929172276, 'IoU-cake': 79.57023703498739, 'IoU-chair': 62.951759992797584, 'IoU-couch': 70.78597307325816, 'IoU-potted plant': 44.9248384543318, 'IoU-bed': 75.55352576167034, 'IoU-dining table': 54.138278698825395, 'IoU-toilet': 86.10404602176376, 'IoU-tv': 80.57289537393886, 'IoU-laptop': 77.96021364747646, 'IoU-mouse': 83.12457580057999, 'IoU-remote': 71.98864678302711, 'IoU-keyboard': 69.53524466162722, 'IoU-cell phone': 68.2129890897812, 'IoU-microwave': 70.40582157838841, 'IoU-oven': 72.55959303990457, 'IoU-toaster': 85.5158101616897, 'IoU-sink': 75.35147980952085, 'IoU-refrigerator': 82.77571082610781, 'IoU-book': 52.420567104293006, 'IoU-clock': 74.85841098786169, 'IoU-vase': 61.986948548235446, 'IoU-scissors': 57.36933409279429, 'IoU-teddy bear': 82.58596303371255, 'IoU-hair drier': 49.00583581297719, 'IoU-toothbrush': 76.55079852921322, 'IoU-banner': 31.55001823017564, 'IoU-blanket': 20.151233403179837, 'IoU-bridge': 38.692523942090496, 'IoU-cardboard': 55.17690843105301, 'IoU-counter': 33.24581374639946, 'IoU-curtain': 70.77039677726714, 'IoU-door-stuff': 49.67408830218997, 'IoU-floor-wood': 64.59574619016372, 'IoU-flower': 44.16276264328693, 'IoU-fruit': 45.36565434266365, 'IoU-gravel': 32.55955507709866, 'IoU-house': 26.804599550347486, 'IoU-light': 43.90520997792407, 'IoU-mirror-stuff': 62.9138192238352, 'IoU-net': 49.042308117631066, 'IoU-pillow': 19.907787619637315, 'IoU-platform': 30.3799147556767, 'IoU-playingfield': 71.18475151718737, 'IoU-railroad': 64.02231972550024, 'IoU-river': 51.25320716912489, 'IoU-road': 67.95632540506108, 'IoU-roof': 18.86495194600474, 'IoU-sand': 65.95612350091945, 'IoU-sea': 83.79886498995769, 'IoU-shelf': 36.85370254072087, 'IoU-snow': 91.99039467565989, 'IoU-stairs': 31.533728930794712, 'IoU-tent': 10.920377613758111, 'IoU-towel': 43.961994381578016, 'IoU-wall-brick': 49.10394561836355, 'IoU-wall-stone': 29.95729975203318, 'IoU-wall-tile': 71.12427692192388, 'IoU-wall-wood': 45.42701471393277, 'IoU-water-other': 21.988980261305, 'IoU-window-blind': 52.80227236068916, 'IoU-window-other': 51.331609775044114, 'IoU-tree-merged': 81.78489239470149, 'IoU-fence-merged': 54.835618158557665, 'IoU-ceiling-merged': 69.13985442937141, 'IoU-sky-other-merged': 94.27128931115858, 'IoU-cabinet-merged': 63.73920469351937, 'IoU-table-merged': 40.09685620070817, 'IoU-floor-other-merged': 54.27865204657657, 'IoU-pavement-merged': 56.061149204120575, 'IoU-mountain-merged': 57.60308955158121, 'IoU-grass-merged': 72.67931970897284, 'IoU-dirt-merged': 47.98450269283273, 'IoU-paper-merged': 36.18464530864506, 'IoU-food-other-merged': 41.17157492444309, 'IoU-building-other-merged': 59.74899033912915, 'IoU-rock-merged': 66.42016419143957, 'IoU-wall-other-merged': 67.92293634515619, 'IoU-rug-merged': 68.38649611101236, 'mACC': 77.38588466415631, 'pACC': 82.33626395045422, 'ACC-person': 93.01081900637669, 'ACC-bicycle': 83.7659101331649, 'ACC-car': 86.75818799866853, 'ACC-motorcycle': 93.6193858503026, 'ACC-airplane': 93.75289731248672, 'ACC-bus': 93.9994821925457, 'ACC-train': 93.99861817267075, 'ACC-truck': 75.7791319213347, 'ACC-boat': 83.04543807735526, 'ACC-traffic light': 90.57184727966758, 'ACC-fire hydrant': 95.9882487072297, 'ACC-stop sign': 98.01870135379576, 'ACC-parking meter': 87.5780341801697, 'ACC-bench': 77.47186127238025, 'ACC-bird': 82.40540419084793, 'ACC-cat': 93.24979941770889, 'ACC-dog': 90.9533361235832, 'ACC-horse': 94.94563886613625, 'ACC-sheep': 95.75038459209662, 'ACC-cow': 91.6482034074852, 'ACC-elephant': 93.62019369073651, 'ACC-bear': 94.94533031188118, 'ACC-zebra': 95.4877375610002, 'ACC-giraffe': 93.32818085934578, 'ACC-backpack': 72.46660853103829, 'ACC-umbrella': 88.94706020227507, 'ACC-handbag': 69.77220008646997, 'ACC-tie': 84.98344576899567, 'ACC-suitcase': 94.0813012612712, 'ACC-frisbee': 94.31563636363637, 'ACC-skis': 73.94930782938157, 'ACC-snowboard': 81.5889887585413, 'ACC-sports ball': 88.87286672662057, 'ACC-kite': 85.8014996262261, 'ACC-baseball bat': 88.25082978069814, 'ACC-baseball glove': 92.52538135074181, 'ACC-skateboard': 90.67579031468955, 'ACC-surfboard': 92.67997886694961, 'ACC-tennis racket': 95.3752716722967, 'ACC-bottle': 85.90409832813101, 'ACC-wine glass': 91.47458794944788, 'ACC-cup': 84.5915644104819, 'ACC-fork': 78.91850026512651, 'ACC-knife': 78.46284897779732, 'ACC-spoon': 79.19703078952884, 'ACC-bowl': 64.94280701384395, 'ACC-banana': 89.88506291772856, 'ACC-apple': 73.90347661146406, 'ACC-sandwich': 81.74217337353227, 'ACC-orange': 90.55272570288497, 'ACC-broccoli': 80.236597472942, 'ACC-carrot': 78.28382136978166, 'ACC-hot dog': 69.67369563228007, 'ACC-pizza': 93.60121979761769, 'ACC-donut': 73.03149047711113, 'ACC-cake': 88.49032510236124, 'ACC-chair': 80.15255101954725, 'ACC-couch': 78.93610658298887, 'ACC-potted plant': 59.74581747323982, 'ACC-bed': 82.66049082689919, 'ACC-dining table': 80.21687522649921, 'ACC-toilet': 90.86976809274915, 'ACC-tv': 88.78523484694895, 'ACC-laptop': 89.3803976543196, 'ACC-mouse': 92.33755036813379, 'ACC-remote': 76.22256128926621, 'ACC-keyboard': 77.3537486996278, 'ACC-cell phone': 75.03907534953284, 'ACC-microwave': 74.64907979024241, 'ACC-oven': 91.16649587050986, 'ACC-toaster': 90.5137609656626, 'ACC-sink': 85.17017752136121, 'ACC-refrigerator': 91.84990189879356, 'ACC-book': 70.66623938808104, 'ACC-clock': 79.35745064498325, 'ACC-vase': 69.33015300609281, 'ACC-scissors': 60.80581035543435, 'ACC-teddy bear': 87.49203616080283, 'ACC-hair drier': 60.47163635328609, 'ACC-toothbrush': 83.73175816539263, 'ACC-banner': 79.63527875953515, 'ACC-blanket': 32.9981014253539, 'ACC-bridge': 53.89736954379691, 'ACC-cardboard': 72.54910571470118, 'ACC-counter': 53.72720296545164, 'ACC-curtain': 82.41622710182372, 'ACC-door-stuff': 72.02443773977339, 'ACC-floor-wood': 79.54885875723771, 'ACC-flower': 60.7503382898975, 'ACC-fruit': 64.83228235789284, 'ACC-gravel': 43.19978669932645, 'ACC-house': 32.698009867573354, 'ACC-light': 61.394293343031045, 'ACC-mirror-stuff': 77.6292170306889, 'ACC-net': 64.40091297475018, 'ACC-pillow': 60.858569192144515, 'ACC-platform': 56.23817585467481, 'ACC-playingfield': 92.5804664835827, 'ACC-railroad': 78.04474398195455, 'ACC-river': 67.19533763600187, 'ACC-road': 88.32359603590501, 'ACC-roof': 26.175400424566163, 'ACC-sand': 72.25589313105519, 'ACC-sea': 92.04841526055247, 'ACC-shelf': 50.15294118175413, 'ACC-snow': 95.71572445377298, 'ACC-stairs': 58.690458142310135, 'ACC-tent': 14.802539406384483, 'ACC-towel': 55.1748509285486, 'ACC-wall-brick': 70.35959676631231, 'ACC-wall-stone': 36.72956471393231, 'ACC-wall-tile': 85.62576281724775, 'ACC-wall-wood': 64.85496329362093, 'ACC-water-other': 34.92800159517954, 'ACC-window-blind': 65.48680118595247, 'ACC-window-other': 77.10349105896907, 'ACC-tree-merged': 89.3268518569612, 'ACC-fence-merged': 71.8562001889342, 'ACC-ceiling-merged': 83.50435746466349, 'ACC-sky-other-merged': 97.07907593701192, 'ACC-cabinet-merged': 77.70639164751992, 'ACC-table-merged': 55.462751335669715, 'ACC-floor-other-merged': 66.2472363708016, 'ACC-pavement-merged': 66.41971315823956, 'ACC-mountain-merged': 69.97163680870769, 'ACC-grass-merged': 84.08341025708569, 'ACC-dirt-merged': 68.00559509306377, 'ACC-paper-merged': 48.221087259592466, 'ACC-food-other-merged': 51.85572438915845, 'ACC-building-other-merged': 73.54386556963829, 'ACC-rock-merged': 83.20694153114928, 'ACC-wall-other-merged': 81.45116582727464, 'ACC-rug-merged': 81.55829616080054})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2955 s/iter. Inference: 0.1741 s/iter. Eval: 0.0000 s/iter. Total: 0.4697 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.2842 s/iter. Inference: 0.4647 s/iter. Eval: 0.0000 s/iter. Total: 0.7490 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/25. Dataloading: 0.3166 s/iter. Inference: 0.5102 s/iter. Eval: 0.0000 s/iter. Total: 0.8269 s/iter. ETA=0:00:00 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4266900790166812, 'noc@0.8': 2.572724612232953, 'noc@0.85': 2.9821480831138425, 'noc@0.9': 3.843429909277144, 'miou@iter1': 0.870579460820574} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1453 s/iter. Eval: 0.0010 s/iter. Total: 0.1478 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.12631225585938, 'precision@0.6': 72.09483337402344, 'precision@0.7': 67.78079986572266, 'precision@0.8': 58.725223541259766, 'precision@0.9': 32.1414680480957, 'cIoU': 61.21421432495117, 'mIoU': 66.40081787109375} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.462590847948725, 'SQ': 82.94101342428485, 'RQ': 66.03107507961037, 'PQ_th': 61.781451847517374, 'SQ_th': 84.00140534995359, 'RQ_th': 73.05150877023581, 'PQ_st': 45.92468745237343, 'SQ_st': 81.3404218383699, 'RQ_st': 55.43419403715688}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.22123568652706, 'AP50': 68.68306579532461, 'AP75': 48.63712753417006, 'APs': 26.068071159311728, 'APm': 49.4390583923625, 'APl': 67.82073220651054, 'AP-person': 48.7718650898467, 'AP-bicycle': 22.258856066212264, 'AP-car': 42.55613542060332, 'AP-motorcycle': 40.71857456136687, 'AP-airplane': 61.77921806228185, 'AP-bus': 70.98264088853455, 'AP-train': 74.35705459367664, 'AP-truck': 43.28571008781618, 'AP-boat': 30.994559415592203, 'AP-traffic light': 28.533183506579228, 'AP-fire hydrant': 70.71571119344978, 'AP-stop sign': 68.76407831289201, 'AP-parking meter': 52.69229379805158, 'AP-bench': 25.930156028694924, 'AP-bird': 33.9076818161416, 'AP-cat': 77.79011554795709, 'AP-dog': 72.18809259271978, 'AP-horse': 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'IoU-umbrella': 85.01531591151662, 'IoU-handbag': 49.601199335289714, 'IoU-tie': 76.20766237572644, 'IoU-suitcase': 86.26397945285596, 'IoU-frisbee': 84.289753339183, 'IoU-skis': 59.52601669720141, 'IoU-snowboard': 71.93235504961955, 'IoU-sports ball': 81.06172746386582, 'IoU-kite': 79.45285459567178, 'IoU-baseball bat': 67.5187659607398, 'IoU-baseball glove': 78.94495779462304, 'IoU-skateboard': 86.03582741089728, 'IoU-surfboard': 86.74465133278784, 'IoU-tennis racket': 90.64494923811858, 'IoU-bottle': 71.44801006787168, 'IoU-wine glass': 82.28172956010745, 'IoU-cup': 67.09440480623996, 'IoU-fork': 67.72071884439409, 'IoU-knife': 63.001844546232796, 'IoU-spoon': 62.41505082373977, 'IoU-bowl': 57.36853615316628, 'IoU-banana': 81.9230699698997, 'IoU-apple': 60.3902726256829, 'IoU-sandwich': 69.93451061294135, 'IoU-orange': 79.53839415002987, 'IoU-broccoli': 69.90228044384976, 'IoU-carrot': 65.27056667768936, 'IoU-hot dog': 62.99971334483237, 'IoU-pizza': 87.20821728781965, 'IoU-donut': 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'IoU-water-other': 21.988980261305, 'IoU-window-blind': 52.80227236068916, 'IoU-window-other': 51.331609775044114, 'IoU-tree-merged': 81.78489239470149, 'IoU-fence-merged': 54.835618158557665, 'IoU-ceiling-merged': 69.13985442937141, 'IoU-sky-other-merged': 94.27128931115858, 'IoU-cabinet-merged': 63.73920469351937, 'IoU-table-merged': 40.09685620070817, 'IoU-floor-other-merged': 54.27865204657657, 'IoU-pavement-merged': 56.061149204120575, 'IoU-mountain-merged': 57.60308955158121, 'IoU-grass-merged': 72.67931970897284, 'IoU-dirt-merged': 47.98450269283273, 'IoU-paper-merged': 36.18464530864506, 'IoU-food-other-merged': 41.17157492444309, 'IoU-building-other-merged': 59.74899033912915, 'IoU-rock-merged': 66.42016419143957, 'IoU-wall-other-merged': 67.92293634515619, 'IoU-rug-merged': 68.38649611101236, 'mACC': 77.38588466415631, 'pACC': 82.33626395045422, 'ACC-person': 93.01081900637669, 'ACC-bicycle': 83.7659101331649, 'ACC-car': 86.75818799866853, 'ACC-motorcycle': 93.6193858503026, 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'ACC-baseball bat': 88.25082978069814, 'ACC-baseball glove': 92.52538135074181, 'ACC-skateboard': 90.67579031468955, 'ACC-surfboard': 92.67997886694961, 'ACC-tennis racket': 95.3752716722967, 'ACC-bottle': 85.90409832813101, 'ACC-wine glass': 91.47458794944788, 'ACC-cup': 84.5915644104819, 'ACC-fork': 78.91850026512651, 'ACC-knife': 78.46284897779732, 'ACC-spoon': 79.19703078952884, 'ACC-bowl': 64.94280701384395, 'ACC-banana': 89.88506291772856, 'ACC-apple': 73.90347661146406, 'ACC-sandwich': 81.74217337353227, 'ACC-orange': 90.55272570288497, 'ACC-broccoli': 80.236597472942, 'ACC-carrot': 78.28382136978166, 'ACC-hot dog': 69.67369563228007, 'ACC-pizza': 93.60121979761769, 'ACC-donut': 73.03149047711113, 'ACC-cake': 88.49032510236124, 'ACC-chair': 80.15255101954725, 'ACC-couch': 78.93610658298887, 'ACC-potted plant': 59.74581747323982, 'ACC-bed': 82.66049082689919, 'ACC-dining table': 80.21687522649921, 'ACC-toilet': 90.86976809274915, 'ACC-tv': 88.78523484694895, 'ACC-laptop': 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66.40081787109375}}} INFO:trainer.default_trainer:This epoch takes 0:58:15.550590 INFO:trainer.default_trainer:PROGRESS: 14.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 7 training. INFO:trainer.default_trainer:epochs[ 7] optim steps[12800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07105/0.79438, loss_mask_bce_0: 0.25938/0.30314, loss_mask_dice_0: 0.10572/1.03426, loss_spatial_bce_0: 0.25487/0.09190, loss_spatial_dice_0: 0.06834/0.19335, loss_spatial_ce_0: 0.14344/0.08139, loss_grounding_bce_0: 0.18619/0.08104, loss_grounding_dice_0: 0.07602/0.15219, loss_grounding_ce_0: 0.69351/0.25410, loss_mask_ce_1: 1.04591/0.79750, loss_mask_bce_1: 0.28648/0.30349, loss_mask_dice_1: 0.10228/1.03899, loss_spatial_bce_1: 0.25001/0.09242, loss_spatial_dice_1: 0.07013/0.19613, loss_spatial_ce_1: 0.14685/0.08575, loss_grounding_bce_1: 0.18893/0.08110, loss_grounding_dice_1: 0.06997/0.15324, loss_grounding_ce_1: 0.55285/0.25780, loss_mask_ce_2: 1.14186/0.80395, loss_mask_bce_2: 0.25178/0.30351, loss_mask_dice_2: 0.10947/1.04175, loss_spatial_bce_2: 0.25602/0.09186, loss_spatial_dice_2: 0.07388/0.19599, loss_spatial_ce_2: 0.14000/0.09006, loss_grounding_bce_2: 0.18900/0.08090, loss_grounding_dice_2: 0.07446/0.15289, loss_grounding_ce_2: 0.49837/0.25835, loss_mask_ce_3: 0.91659/0.80209, loss_mask_bce_3: 0.27385/0.30478, loss_mask_dice_3: 0.11407/1.03548, loss_spatial_bce_3: 0.24381/0.09330, loss_spatial_dice_3: 0.07317/0.19608, loss_spatial_ce_3: 0.13811/0.09608, loss_grounding_bce_3: 0.18383/0.08142, loss_grounding_dice_3: 0.07371/0.15258, loss_grounding_ce_3: 0.46051/0.25676, loss_mask_ce_4: 0.96413/0.80905, loss_mask_bce_4: 0.25407/0.30714, loss_mask_dice_4: 0.10513/1.05453, loss_spatial_bce_4: 0.22056/0.09548, loss_spatial_dice_4: 0.06987/0.20366, loss_spatial_ce_4: 0.15290/0.10753, loss_grounding_bce_4: 0.19454/0.08216, loss_grounding_dice_4: 0.07832/0.15489, loss_grounding_ce_4: 0.47546/0.26568, loss_mask_ce_5: 1.43684/0.82890, loss_mask_bce_5: 0.27586/0.30919, loss_mask_dice_5: 0.10528/1.06225, loss_spatial_bce_5: 0.23963/0.09698, loss_spatial_dice_5: 0.06634/0.20567, loss_spatial_ce_5: 0.15445/0.11831, loss_grounding_bce_5: 0.19849/0.08238, loss_grounding_dice_5: 0.07435/0.15548, loss_grounding_ce_5: 0.40153/0.28461, loss_mask_ce_6: 1.34893/0.85363, loss_mask_bce_6: 0.29182/0.31028, loss_mask_dice_6: 0.11292/1.06672, loss_spatial_bce_6: 0.23712/0.10198, loss_spatial_dice_6: 0.07156/0.20796, loss_spatial_ce_6: 0.15437/0.13665, loss_grounding_bce_6: 0.21995/0.08359, loss_grounding_dice_6: 0.07379/0.15615, loss_grounding_ce_6: 0.54803/0.29915, loss_mask_ce_7: 0.82800/0.91645, loss_mask_bce_7: 0.29839/0.31734, loss_mask_dice_7: 0.13756/1.11300, loss_spatial_bce_7: 0.26273/0.11287, loss_spatial_dice_7: 0.09557/0.23303, loss_spatial_ce_7: 0.26349/0.18162, loss_grounding_bce_7: 0.23353/0.08550, loss_grounding_dice_7: 0.08779/0.16206, loss_grounding_ce_7: 0.61755/0.34830, loss_mask_ce_8: 1.63332/1.05769, loss_mask_bce_8: 0.31107/0.33510, loss_mask_dice_8: 0.12984/1.19307, loss_spatial_bce_8: 0.24624/0.13473, loss_spatial_dice_8: 0.08305/0.27581, loss_spatial_ce_8: 0.49744/0.23842, loss_grounding_bce_8: 0.21701/0.08935, loss_grounding_dice_8: 0.08755/0.17112, loss_grounding_ce_8: 0.90538/0.45088, loss_mask_ce_9: 3.69956/3.51929, loss_mask_bce_9: 0.28252/0.36105, loss_mask_dice_9: 0.14026/1.77822, loss_spatial_bce_9: 0.81892/0.36289, loss_spatial_dice_9: 0.60568/0.79921, loss_spatial_ce_9: 1.78165/1.42450, loss_grounding_bce_9: 0.29996/0.10123, loss_grounding_dice_9: 0.14492/0.24593, loss_grounding_ce_9: 0.23907/0.73153] items per batch[64] items per second[0.16] total items[819200] mini batches[ 12800] memory[4943] epoch remaining[1:13:25] INFO:trainer.default_trainer:epochs[ 7] optim steps[12900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63375/0.79444, loss_mask_bce_0: 0.75193/0.30309, loss_mask_dice_0: 1.90883/1.03423, loss_spatial_bce_0: 0.06037/0.09184, loss_spatial_dice_0: 0.19480/0.19327, loss_spatial_ce_0: 0.02457/0.08110, loss_grounding_bce_0: 0.07044/0.08115, loss_grounding_dice_0: 0.25890/0.15221, loss_grounding_ce_0: 0.02237/0.25388, loss_mask_ce_1: 0.62396/0.79751, loss_mask_bce_1: 0.76754/0.30346, loss_mask_dice_1: 1.92840/1.03894, loss_spatial_bce_1: 0.06580/0.09235, loss_spatial_dice_1: 0.20724/0.19604, loss_spatial_ce_1: 0.03389/0.08546, loss_grounding_bce_1: 0.07114/0.08121, loss_grounding_dice_1: 0.28328/0.15331, loss_grounding_ce_1: 0.01634/0.25756, loss_mask_ce_2: 0.67441/0.80389, loss_mask_bce_2: 0.76558/0.30348, loss_mask_dice_2: 1.90663/1.04165, loss_spatial_bce_2: 0.06780/0.09181, loss_spatial_dice_2: 0.22728/0.19593, loss_spatial_ce_2: 0.06373/0.08976, loss_grounding_bce_2: 0.06303/0.08101, loss_grounding_dice_2: 0.24792/0.15293, loss_grounding_ce_2: 0.01647/0.25799, loss_mask_ce_3: 0.65658/0.80185, loss_mask_bce_3: 0.77973/0.30477, loss_mask_dice_3: 2.04197/1.03540, loss_spatial_bce_3: 0.06986/0.09326, loss_spatial_dice_3: 0.21742/0.19604, loss_spatial_ce_3: 0.07380/0.09578, loss_grounding_bce_3: 0.06752/0.08154, loss_grounding_dice_3: 0.25109/0.15265, loss_grounding_ce_3: 0.01981/0.25650, loss_mask_ce_4: 0.59782/0.80897, loss_mask_bce_4: 0.80228/0.30714, loss_mask_dice_4: 1.96396/1.05448, loss_spatial_bce_4: 0.06378/0.09544, loss_spatial_dice_4: 0.20109/0.20358, loss_spatial_ce_4: 0.07387/0.10718, loss_grounding_bce_4: 0.06623/0.08228, loss_grounding_dice_4: 0.24648/0.15497, loss_grounding_ce_4: 0.02058/0.26527, loss_mask_ce_5: 0.73480/0.82898, loss_mask_bce_5: 0.76958/0.30915, loss_mask_dice_5: 1.94686/1.06215, loss_spatial_bce_5: 0.07447/0.09693, loss_spatial_dice_5: 0.22062/0.20562, loss_spatial_ce_5: 0.13371/0.11801, loss_grounding_bce_5: 0.06520/0.08251, loss_grounding_dice_5: 0.25469/0.15557, loss_grounding_ce_5: 0.02780/0.28434, loss_mask_ce_6: 0.74669/0.85358, loss_mask_bce_6: 0.80750/0.31027, loss_mask_dice_6: 1.86951/1.06650, loss_spatial_bce_6: 0.09562/0.10192, loss_spatial_dice_6: 0.25151/0.20787, loss_spatial_ce_6: 0.16255/0.13631, loss_grounding_bce_6: 0.07164/0.08377, loss_grounding_dice_6: 0.28019/0.15625, loss_grounding_ce_6: 0.02779/0.29887, loss_mask_ce_7: 0.96879/0.91658, loss_mask_bce_7: 0.80275/0.31736, loss_mask_dice_7: 1.87612/1.11277, loss_spatial_bce_7: 0.06400/0.11277, loss_spatial_dice_7: 0.22222/0.23294, loss_spatial_ce_7: 0.18231/0.18123, loss_grounding_bce_7: 0.06557/0.08564, loss_grounding_dice_7: 0.26797/0.16217, loss_grounding_ce_7: 0.02591/0.34782, loss_mask_ce_8: 1.64921/1.05782, loss_mask_bce_8: 0.81333/0.33506, loss_mask_dice_8: 2.13441/1.19279, loss_spatial_bce_8: 0.10379/0.13457, loss_spatial_dice_8: 0.31733/0.27571, loss_spatial_ce_8: 0.14265/0.23826, loss_grounding_bce_8: 0.06550/0.08940, loss_grounding_dice_8: 0.33878/0.17117, loss_grounding_ce_8: 0.09307/0.45082, loss_mask_ce_9: 4.39651/3.51776, loss_mask_bce_9: 1.05808/0.36113, loss_mask_dice_9: 4.40828/1.77881, loss_spatial_bce_9: 0.26480/0.36281, loss_spatial_dice_9: 0.86957/0.79918, loss_spatial_ce_9: 0.96906/1.42365, loss_grounding_bce_9: 0.07940/0.10135, loss_grounding_dice_9: 0.56739/0.24597, loss_grounding_ce_9: 0.53310/0.73008] items per batch[64] items per second[0.36] total items[825600] mini batches[ 12900] memory[4943] epoch remaining[0:52:48] INFO:trainer.default_trainer:epochs[ 7] optim steps[13000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.41289/0.79375, loss_mask_bce_0: 0.41708/0.30286, loss_mask_dice_0: 0.68116/1.03457, loss_spatial_bce_0: 0.13829/0.09176, loss_spatial_dice_0: 0.19285/0.19319, loss_spatial_ce_0: 0.08516/0.08103, loss_grounding_bce_0: 0.23202/0.08110, loss_grounding_dice_0: 0.13575/0.15219, loss_grounding_ce_0: 0.06979/0.25341, loss_mask_ce_1: 0.42971/0.79713, loss_mask_bce_1: 0.41438/0.30322, loss_mask_dice_1: 0.67987/1.03910, loss_spatial_bce_1: 0.14524/0.09230, loss_spatial_dice_1: 0.19738/0.19597, loss_spatial_ce_1: 0.07860/0.08525, loss_grounding_bce_1: 0.23382/0.08117, loss_grounding_dice_1: 0.13819/0.15330, loss_grounding_ce_1: 0.06346/0.25723, loss_mask_ce_2: 0.73469/0.80323, loss_mask_bce_2: 0.40020/0.30328, loss_mask_dice_2: 0.67149/1.04206, loss_spatial_bce_2: 0.15515/0.09172, loss_spatial_dice_2: 0.19689/0.19584, loss_spatial_ce_2: 0.07569/0.08947, loss_grounding_bce_2: 0.23064/0.08097, loss_grounding_dice_2: 0.13499/0.15291, loss_grounding_ce_2: 0.06473/0.25761, loss_mask_ce_3: 0.37975/0.80126, loss_mask_bce_3: 0.42180/0.30455, loss_mask_dice_3: 0.68821/1.03578, loss_spatial_bce_3: 0.13400/0.09320, loss_spatial_dice_3: 0.19383/0.19596, loss_spatial_ce_3: 0.09828/0.09549, loss_grounding_bce_3: 0.23294/0.08149, loss_grounding_dice_3: 0.13592/0.15260, loss_grounding_ce_3: 0.07636/0.25625, loss_mask_ce_4: 0.36097/0.80836, loss_mask_bce_4: 0.42561/0.30692, loss_mask_dice_4: 0.68374/1.05513, loss_spatial_bce_4: 0.15324/0.09537, loss_spatial_dice_4: 0.19780/0.20348, loss_spatial_ce_4: 0.09681/0.10696, loss_grounding_bce_4: 0.23859/0.08224, loss_grounding_dice_4: 0.13176/0.15494, loss_grounding_ce_4: 0.06756/0.26506, loss_mask_ce_5: 0.42600/0.82844, loss_mask_bce_5: 0.43123/0.30891, loss_mask_dice_5: 0.67556/1.06253, loss_spatial_bce_5: 0.13970/0.09684, loss_spatial_dice_5: 0.19510/0.20550, loss_spatial_ce_5: 0.16593/0.11785, loss_grounding_bce_5: 0.23900/0.08246, loss_grounding_dice_5: 0.13054/0.15552, loss_grounding_ce_5: 0.03317/0.28412, loss_mask_ce_6: 0.42565/0.85311, loss_mask_bce_6: 0.45070/0.31000, loss_mask_dice_6: 0.68647/1.06686, loss_spatial_bce_6: 0.18590/0.10181, loss_spatial_dice_6: 0.20247/0.20775, loss_spatial_ce_6: 0.10755/0.13621, loss_grounding_bce_6: 0.24762/0.08373, loss_grounding_dice_6: 0.13171/0.15622, loss_grounding_ce_6: 0.04550/0.29852, loss_mask_ce_7: 0.59771/0.91603, loss_mask_bce_7: 0.41059/0.31719, loss_mask_dice_7: 0.68082/1.11332, loss_spatial_bce_7: 0.22800/0.11263, loss_spatial_dice_7: 0.19994/0.23278, loss_spatial_ce_7: 0.23831/0.18112, loss_grounding_bce_7: 0.23902/0.08560, loss_grounding_dice_7: 0.13386/0.16208, loss_grounding_ce_7: 0.04729/0.34722, loss_mask_ce_8: 0.78200/1.05685, loss_mask_bce_8: 0.38139/0.33487, loss_mask_dice_8: 0.72296/1.19315, loss_spatial_bce_8: 0.25660/0.13446, loss_spatial_dice_8: 0.22492/0.27554, loss_spatial_ce_8: 0.18527/0.23812, loss_grounding_bce_8: 0.23063/0.08936, loss_grounding_dice_8: 0.13959/0.17110, loss_grounding_ce_8: 0.04632/0.45047, loss_mask_ce_9: 3.47282/3.51757, loss_mask_bce_9: 0.52035/0.36095, loss_mask_dice_9: 0.87518/1.77890, loss_spatial_bce_9: 0.43286/0.36281, loss_spatial_dice_9: 0.82941/0.79915, loss_spatial_ce_9: 1.87671/1.42417, loss_grounding_bce_9: 0.38080/0.10127, loss_grounding_dice_9: 0.27802/0.24585, loss_grounding_ce_9: 0.04908/0.73005] items per batch[64] items per second[0.36] total items[832000] mini batches[ 13000] memory[4943] epoch remaining[0:48:53] INFO:trainer.default_trainer:epochs[ 7] optim steps[13100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70139/0.79295, loss_mask_bce_0: 1.64277/0.30275, loss_mask_dice_0: 1.00604/1.03342, loss_spatial_bce_0: 0.29336/0.09182, loss_spatial_dice_0: 0.31685/0.19310, loss_spatial_ce_0: 0.04842/0.08081, loss_grounding_bce_0: 0.06448/0.08105, loss_grounding_dice_0: 0.08048/0.15204, loss_grounding_ce_0: 0.01584/0.25347, loss_mask_ce_1: 0.66706/0.79637, loss_mask_bce_1: 1.62703/0.30311, loss_mask_dice_1: 1.01685/1.03792, loss_spatial_bce_1: 0.35452/0.09233, loss_spatial_dice_1: 0.33143/0.19587, loss_spatial_ce_1: 0.02581/0.08515, loss_grounding_bce_1: 0.05830/0.08114, loss_grounding_dice_1: 0.08051/0.15314, loss_grounding_ce_1: 0.01413/0.25698, loss_mask_ce_2: 0.96676/0.80243, loss_mask_bce_2: 1.44828/0.30315, loss_mask_dice_2: 1.03233/1.04092, loss_spatial_bce_2: 0.32099/0.09176, loss_spatial_dice_2: 0.32379/0.19572, loss_spatial_ce_2: 0.03813/0.08930, loss_grounding_bce_2: 0.05789/0.08094, loss_grounding_dice_2: 0.07610/0.15276, loss_grounding_ce_2: 0.01579/0.25725, loss_mask_ce_3: 0.99136/0.80039, loss_mask_bce_3: 1.41410/0.30442, loss_mask_dice_3: 1.06281/1.03459, loss_spatial_bce_3: 0.38186/0.09323, loss_spatial_dice_3: 0.32606/0.19585, loss_spatial_ce_3: 0.04531/0.09535, loss_grounding_bce_3: 0.05698/0.08145, loss_grounding_dice_3: 0.07840/0.15243, loss_grounding_ce_3: 0.00901/0.25595, loss_mask_ce_4: 0.60282/0.80738, loss_mask_bce_4: 1.80631/0.30684, loss_mask_dice_4: 1.02204/1.05372, loss_spatial_bce_4: 0.43073/0.09540, loss_spatial_dice_4: 0.32929/0.20337, loss_spatial_ce_4: 0.11174/0.10679, loss_grounding_bce_4: 0.06606/0.08219, loss_grounding_dice_4: 0.08020/0.15480, loss_grounding_ce_4: 0.00638/0.26477, loss_mask_ce_5: 0.92137/0.82763, loss_mask_bce_5: 1.44408/0.30877, loss_mask_dice_5: 1.06982/1.06144, loss_spatial_bce_5: 0.41673/0.09687, loss_spatial_dice_5: 0.32188/0.20539, loss_spatial_ce_5: 0.06554/0.11774, loss_grounding_bce_5: 0.06424/0.08241, loss_grounding_dice_5: 0.08626/0.15536, loss_grounding_ce_5: 0.01054/0.28390, loss_mask_ce_6: 0.81760/0.85239, loss_mask_bce_6: 1.26124/0.30986, loss_mask_dice_6: 1.00835/1.06569, loss_spatial_bce_6: 0.36596/0.10182, loss_spatial_dice_6: 0.34842/0.20764, loss_spatial_ce_6: 0.11176/0.13612, loss_grounding_bce_6: 0.05731/0.08367, loss_grounding_dice_6: 0.08880/0.15606, loss_grounding_ce_6: 0.00638/0.29817, loss_mask_ce_7: 0.80965/0.91484, loss_mask_bce_7: 1.40124/0.31712, loss_mask_dice_7: 0.99502/1.11223, loss_spatial_bce_7: 0.33166/0.11261, loss_spatial_dice_7: 0.33870/0.23270, loss_spatial_ce_7: 0.39831/0.18090, loss_grounding_bce_7: 0.06320/0.08555, loss_grounding_dice_7: 0.07370/0.16193, loss_grounding_ce_7: 0.02409/0.34685, loss_mask_ce_8: 1.00223/1.05559, loss_mask_bce_8: 1.52037/0.33482, loss_mask_dice_8: 1.02952/1.19187, loss_spatial_bce_8: 0.53554/0.13442, loss_spatial_dice_8: 0.35754/0.27540, loss_spatial_ce_8: 0.20146/0.23792, loss_grounding_bce_8: 0.04946/0.08935, loss_grounding_dice_8: 0.09930/0.17097, loss_grounding_ce_8: 0.00734/0.44973, loss_mask_ce_9: 2.04864/3.51473, loss_mask_bce_9: 1.18741/0.36103, loss_mask_dice_9: 1.02852/1.77646, loss_spatial_bce_9: 0.45634/0.36292, loss_spatial_dice_9: 0.89935/0.79893, loss_spatial_ce_9: 1.67640/1.42382, loss_grounding_bce_9: 0.06174/0.10130, loss_grounding_dice_9: 0.09163/0.24567, loss_grounding_ce_9: 0.14683/0.72995] items per batch[64] items per second[0.35] total items[838400] mini batches[ 13100] memory[4943] epoch remaining[0:45:48] INFO:trainer.default_trainer:epochs[ 7] optim steps[13200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74876/0.79308, loss_mask_bce_0: 0.04256/0.30297, loss_mask_dice_0: 3.00311/1.03472, loss_spatial_bce_0: 0.00293/0.09176, loss_spatial_dice_0: 0.26513/0.19315, loss_spatial_ce_0: 0.11021/0.08061, loss_grounding_bce_0: 0.00159/0.08102, loss_grounding_dice_0: 0.45108/0.15216, loss_grounding_ce_0: 0.42973/0.25412, loss_mask_ce_1: 0.68912/0.79629, loss_mask_bce_1: 0.03974/0.30332, loss_mask_dice_1: 2.28026/1.03926, loss_spatial_bce_1: 0.00342/0.09228, loss_spatial_dice_1: 0.39400/0.19591, loss_spatial_ce_1: 0.05403/0.08497, loss_grounding_bce_1: 0.00055/0.08110, loss_grounding_dice_1: 0.21979/0.15324, loss_grounding_ce_1: 0.51175/0.25748, loss_mask_ce_2: 0.63349/0.80250, loss_mask_bce_2: 0.04779/0.30335, loss_mask_dice_2: 3.36012/1.04207, loss_spatial_bce_2: 0.00344/0.09172, loss_spatial_dice_2: 0.36061/0.19575, loss_spatial_ce_2: 0.14880/0.08911, loss_grounding_bce_2: 0.00213/0.08091, loss_grounding_dice_2: 0.52611/0.15288, loss_grounding_ce_2: 0.44870/0.25785, loss_mask_ce_3: 0.91330/0.80052, loss_mask_bce_3: 0.05637/0.30462, loss_mask_dice_3: 3.38595/1.03576, loss_spatial_bce_3: 0.00423/0.09319, loss_spatial_dice_3: 0.39021/0.19590, loss_spatial_ce_3: 0.20198/0.09520, loss_grounding_bce_3: 0.00086/0.08140, loss_grounding_dice_3: 0.32780/0.15257, loss_grounding_ce_3: 0.43450/0.25653, loss_mask_ce_4: 0.74385/0.80738, loss_mask_bce_4: 0.05801/0.30702, loss_mask_dice_4: 4.07130/1.05521, loss_spatial_bce_4: 0.00354/0.09537, loss_spatial_dice_4: 0.31707/0.20341, loss_spatial_ce_4: 0.28299/0.10660, loss_grounding_bce_4: 0.00113/0.08215, loss_grounding_dice_4: 0.20068/0.15488, loss_grounding_ce_4: 0.44786/0.26527, loss_mask_ce_5: 0.76609/0.82769, loss_mask_bce_5: 0.05051/0.30897, loss_mask_dice_5: 2.44914/1.06262, loss_spatial_bce_5: 0.00332/0.09688, loss_spatial_dice_5: 0.22431/0.20545, loss_spatial_ce_5: 0.20918/0.11755, loss_grounding_bce_5: 0.00138/0.08238, loss_grounding_dice_5: 0.42017/0.15550, loss_grounding_ce_5: 0.45040/0.28452, loss_mask_ce_6: 1.16792/0.85258, loss_mask_bce_6: 0.07152/0.31005, loss_mask_dice_6: 3.18923/1.06689, loss_spatial_bce_6: 0.00265/0.10183, loss_spatial_dice_6: 0.28572/0.20771, loss_spatial_ce_6: 0.21685/0.13598, loss_grounding_bce_6: 0.00053/0.08363, loss_grounding_dice_6: 0.13893/0.15616, loss_grounding_ce_6: 0.47130/0.29849, loss_mask_ce_7: 1.05120/0.91503, loss_mask_bce_7: 0.05665/0.31737, loss_mask_dice_7: 3.40119/1.11361, loss_spatial_bce_7: 0.00380/0.11263, loss_spatial_dice_7: 0.38092/0.23275, loss_spatial_ce_7: 0.81802/0.18081, loss_grounding_bce_7: 0.00029/0.08553, loss_grounding_dice_7: 0.14461/0.16204, loss_grounding_ce_7: 0.51552/0.34724, loss_mask_ce_8: 0.93229/1.05555, loss_mask_bce_8: 0.06073/0.33517, loss_mask_dice_8: 4.01872/1.19347, loss_spatial_bce_8: 0.00564/0.13436, loss_spatial_dice_8: 0.44061/0.27536, loss_spatial_ce_8: 0.53137/0.23768, loss_grounding_bce_8: 0.00177/0.08935, loss_grounding_dice_8: 0.26594/0.17109, loss_grounding_ce_8: 0.49775/0.44997, loss_mask_ce_9: 3.77912/3.51562, loss_mask_bce_9: 0.03330/0.36126, loss_mask_dice_9: 3.34513/1.77864, loss_spatial_bce_9: 0.01327/0.36285, loss_spatial_dice_9: 0.85957/0.79905, loss_spatial_ce_9: 2.62861/1.42389, loss_grounding_bce_9: 0.00054/0.10125, loss_grounding_dice_9: 0.33724/0.24572, loss_grounding_ce_9: 0.54949/0.72971] items per batch[64] items per second[0.35] total items[844800] mini batches[ 13200] memory[4943] epoch remaining[0:42:47] INFO:trainer.default_trainer:epochs[ 7] optim steps[13300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.33730/0.79312, loss_mask_bce_0: 0.04167/0.30311, loss_mask_dice_0: 1.50601/1.03504, loss_spatial_bce_0: 0.00653/0.09165, loss_spatial_dice_0: 0.46361/0.19316, loss_spatial_ce_0: 0.17827/0.08046, loss_grounding_bce_0: 0.00769/0.08099, loss_grounding_dice_0: 0.17176/0.15223, loss_grounding_ce_0: 1.85043/0.25474, loss_mask_ce_1: 2.98110/0.79648, loss_mask_bce_1: 0.04376/0.30344, loss_mask_dice_1: 2.59442/1.03963, loss_spatial_bce_1: 0.00395/0.09216, loss_spatial_dice_1: 0.40040/0.19592, loss_spatial_ce_1: 0.85761/0.08485, loss_grounding_bce_1: 0.00919/0.08107, loss_grounding_dice_1: 0.17226/0.15333, loss_grounding_ce_1: 2.01166/0.25802, loss_mask_ce_2: 2.28297/0.80259, loss_mask_bce_2: 0.03830/0.30348, loss_mask_dice_2: 1.96493/1.04216, loss_spatial_bce_2: 0.00490/0.09161, loss_spatial_dice_2: 0.45598/0.19576, loss_spatial_ce_2: 0.16829/0.08891, loss_grounding_bce_2: 0.00941/0.08087, loss_grounding_dice_2: 0.24211/0.15293, loss_grounding_ce_2: 1.89687/0.25839, loss_mask_ce_3: 2.28454/0.80061, loss_mask_bce_3: 0.03407/0.30479, loss_mask_dice_3: 1.95576/1.03614, loss_spatial_bce_3: 0.00630/0.09307, loss_spatial_dice_3: 0.44303/0.19592, loss_spatial_ce_3: 0.08700/0.09502, loss_grounding_bce_3: 0.01528/0.08139, loss_grounding_dice_3: 0.19323/0.15264, loss_grounding_ce_3: 1.69957/0.25695, loss_mask_ce_4: 2.61162/0.80750, loss_mask_bce_4: 0.03373/0.30716, loss_mask_dice_4: 1.89058/1.05550, loss_spatial_bce_4: 0.00657/0.09526, loss_spatial_dice_4: 0.42284/0.20343, loss_spatial_ce_4: 0.09192/0.10646, loss_grounding_bce_4: 0.01607/0.08213, loss_grounding_dice_4: 0.22994/0.15494, loss_grounding_ce_4: 1.17033/0.26575, loss_mask_ce_5: 2.77587/0.82811, loss_mask_bce_5: 0.03705/0.30908, loss_mask_dice_5: 2.46027/1.06298, loss_spatial_bce_5: 0.00868/0.09678, loss_spatial_dice_5: 0.47956/0.20547, loss_spatial_ce_5: 0.10576/0.11734, loss_grounding_bce_5: 0.00800/0.08235, loss_grounding_dice_5: 0.12206/0.15560, loss_grounding_ce_5: 1.90407/0.28504, loss_mask_ce_6: 2.55842/0.85300, loss_mask_bce_6: 0.04674/0.31016, loss_mask_dice_6: 1.97186/1.06718, loss_spatial_bce_6: 0.00734/0.10173, loss_spatial_dice_6: 0.49090/0.20777, loss_spatial_ce_6: 0.09773/0.13578, loss_grounding_bce_6: 0.00527/0.08359, loss_grounding_dice_6: 0.14416/0.15624, loss_grounding_ce_6: 2.22331/0.29908, loss_mask_ce_7: 2.82996/0.91557, loss_mask_bce_7: 0.04410/0.31748, loss_mask_dice_7: 2.06974/1.11374, loss_spatial_bce_7: 0.00734/0.11245, loss_spatial_dice_7: 0.44643/0.23276, loss_spatial_ce_7: 0.10591/0.18063, loss_grounding_bce_7: 0.00789/0.08550, loss_grounding_dice_7: 0.37335/0.16216, loss_grounding_ce_7: 1.61219/0.34763, loss_mask_ce_8: 3.78476/1.05581, loss_mask_bce_8: 0.05471/0.33532, loss_mask_dice_8: 2.42246/1.19380, loss_spatial_bce_8: 0.00781/0.13420, loss_spatial_dice_8: 0.45373/0.27545, loss_spatial_ce_8: 0.29805/0.23743, loss_grounding_bce_8: 0.00442/0.08936, loss_grounding_dice_8: 0.11709/0.17121, loss_grounding_ce_8: 1.37103/0.45040, loss_mask_ce_9: 5.69343/3.51668, loss_mask_bce_9: 0.02969/0.36148, loss_mask_dice_9: 2.59307/1.77916, loss_spatial_bce_9: 0.01885/0.36256, loss_spatial_dice_9: 0.82826/0.79906, loss_spatial_ce_9: 1.57688/1.42428, loss_grounding_bce_9: 0.00480/0.10131, loss_grounding_dice_9: 0.18837/0.24605, loss_grounding_ce_9: 1.58521/0.72946] items per batch[64] items per second[0.36] total items[851200] mini batches[ 13300] memory[4943] epoch remaining[0:39:38] INFO:trainer.default_trainer:epochs[ 7] optim steps[13400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33810/0.79365, loss_mask_bce_0: 0.00880/0.30299, loss_mask_dice_0: 0.41745/1.03539, loss_spatial_bce_0: 0.00429/0.09152, loss_spatial_dice_0: 0.05976/0.19312, loss_spatial_ce_0: 0.00001/0.08046, loss_grounding_bce_0: 0.00409/0.08095, loss_grounding_dice_0: 0.02939/0.15209, loss_grounding_ce_0: 0.00301/0.25522, loss_mask_ce_1: 0.03305/0.79694, loss_mask_bce_1: 0.00811/0.30333, loss_mask_dice_1: 0.12309/1.03978, loss_spatial_bce_1: 0.00448/0.09203, loss_spatial_dice_1: 0.04433/0.19587, loss_spatial_ce_1: 0.00000/0.08484, loss_grounding_bce_1: 0.00482/0.08105, loss_grounding_dice_1: 0.08009/0.15321, loss_grounding_ce_1: 0.00314/0.25837, loss_mask_ce_2: 0.04736/0.80320, loss_mask_bce_2: 0.01070/0.30337, loss_mask_dice_2: 0.23167/1.04226, loss_spatial_bce_2: 0.00474/0.09147, loss_spatial_dice_2: 0.16723/0.19573, loss_spatial_ce_2: 0.00001/0.08885, loss_grounding_bce_2: 0.00586/0.08087, loss_grounding_dice_2: 0.05835/0.15284, loss_grounding_ce_2: 0.00378/0.25877, loss_mask_ce_3: 0.04716/0.80112, loss_mask_bce_3: 0.00797/0.30468, loss_mask_dice_3: 0.16013/1.03641, loss_spatial_bce_3: 0.00541/0.09295, loss_spatial_dice_3: 0.07719/0.19591, loss_spatial_ce_3: 0.00005/0.09498, loss_grounding_bce_3: 0.00609/0.08137, loss_grounding_dice_3: 0.04774/0.15252, loss_grounding_ce_3: 0.00206/0.25720, loss_mask_ce_4: 0.04732/0.80797, loss_mask_bce_4: 0.00932/0.30704, loss_mask_dice_4: 0.26227/1.05565, loss_spatial_bce_4: 0.00555/0.09513, loss_spatial_dice_4: 0.09951/0.20341, loss_spatial_ce_4: 0.00065/0.10642, loss_grounding_bce_4: 0.00599/0.08211, loss_grounding_dice_4: 0.05122/0.15484, loss_grounding_ce_4: 0.00182/0.26589, loss_mask_ce_5: 0.06104/0.82857, loss_mask_bce_5: 0.01029/0.30896, loss_mask_dice_5: 0.22360/1.06297, loss_spatial_bce_5: 0.00718/0.09666, loss_spatial_dice_5: 0.07206/0.20546, loss_spatial_ce_5: 0.02204/0.11728, loss_grounding_bce_5: 0.00428/0.08236, loss_grounding_dice_5: 0.03487/0.15550, loss_grounding_ce_5: 0.00281/0.28534, loss_mask_ce_6: 0.05912/0.85356, loss_mask_bce_6: 0.00888/0.31002, loss_mask_dice_6: 0.15567/1.06726, loss_spatial_bce_6: 0.00603/0.10158, loss_spatial_dice_6: 0.07690/0.20774, loss_spatial_ce_6: 0.03845/0.13575, loss_grounding_bce_6: 0.00550/0.08358, loss_grounding_dice_6: 0.03289/0.15614, loss_grounding_ce_6: 0.00177/0.29917, loss_mask_ce_7: 0.08458/0.91620, loss_mask_bce_7: 0.01216/0.31736, loss_mask_dice_7: 0.24076/1.11389, loss_spatial_bce_7: 0.01489/0.11227, loss_spatial_dice_7: 0.10420/0.23272, loss_spatial_ce_7: 0.10002/0.18060, loss_grounding_bce_7: 0.00542/0.08548, loss_grounding_dice_7: 0.04121/0.16205, loss_grounding_ce_7: 0.00339/0.34805, loss_mask_ce_8: 0.20923/1.05658, loss_mask_bce_8: 0.00898/0.33521, loss_mask_dice_8: 0.23971/1.19406, loss_spatial_bce_8: 0.01219/0.13397, loss_spatial_dice_8: 0.04961/0.27540, loss_spatial_ce_8: 0.04597/0.23732, loss_grounding_bce_8: 0.00833/0.08933, loss_grounding_dice_8: 0.07959/0.17108, loss_grounding_ce_8: 0.05303/0.45118, loss_mask_ce_9: 2.26386/3.51743, loss_mask_bce_9: 0.00862/0.36125, loss_mask_dice_9: 0.33094/1.78010, loss_spatial_bce_9: 0.15122/0.36234, loss_spatial_dice_9: 0.64085/0.79905, loss_spatial_ce_9: 0.77719/1.42460, loss_grounding_bce_9: 0.00666/0.10122, loss_grounding_dice_9: 0.10909/0.24597, loss_grounding_ce_9: 0.14022/0.72981] items per batch[64] items per second[0.37] total items[857600] mini batches[ 13400] memory[4943] epoch remaining[0:36:25] INFO:trainer.default_trainer:epochs[ 7] optim steps[13500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45239/0.79340, loss_mask_bce_0: 0.64862/0.30293, loss_mask_dice_0: 1.02341/1.03419, loss_spatial_bce_0: 0.10859/0.09151, loss_spatial_dice_0: 0.19737/0.19303, loss_spatial_ce_0: 0.04396/0.08024, loss_grounding_bce_0: 0.01067/0.08100, loss_grounding_dice_0: 0.18628/0.15205, loss_grounding_ce_0: 0.17031/0.25515, loss_mask_ce_1: 0.43871/0.79655, loss_mask_bce_1: 0.67054/0.30329, loss_mask_dice_1: 1.02254/1.03851, loss_spatial_bce_1: 0.11980/0.09203, loss_spatial_dice_1: 0.18390/0.19577, loss_spatial_ce_1: 0.08244/0.08463, loss_grounding_bce_1: 0.01202/0.08111, loss_grounding_dice_1: 0.16425/0.15315, loss_grounding_ce_1: 0.16306/0.25826, loss_mask_ce_2: 0.44539/0.80286, loss_mask_bce_2: 0.69474/0.30334, loss_mask_dice_2: 1.01730/1.04105, loss_spatial_bce_2: 0.11695/0.09147, loss_spatial_dice_2: 0.19360/0.19564, loss_spatial_ce_2: 0.06266/0.08862, loss_grounding_bce_2: 0.00953/0.08091, loss_grounding_dice_2: 0.15249/0.15280, loss_grounding_ce_2: 0.26427/0.25866, loss_mask_ce_3: 0.40014/0.80082, loss_mask_bce_3: 0.68700/0.30462, loss_mask_dice_3: 1.03317/1.03513, loss_spatial_bce_3: 0.12049/0.09296, loss_spatial_dice_3: 0.21509/0.19582, loss_spatial_ce_3: 0.09224/0.09472, loss_grounding_bce_3: 0.01522/0.08142, loss_grounding_dice_3: 0.20307/0.15247, loss_grounding_ce_3: 0.16457/0.25709, loss_mask_ce_4: 0.39702/0.80761, loss_mask_bce_4: 0.67680/0.30699, loss_mask_dice_4: 0.99322/1.05432, loss_spatial_bce_4: 0.11968/0.09515, loss_spatial_dice_4: 0.20267/0.20336, loss_spatial_ce_4: 0.08418/0.10619, loss_grounding_bce_4: 0.01696/0.08213, loss_grounding_dice_4: 0.17436/0.15477, loss_grounding_ce_4: 0.13031/0.26587, loss_mask_ce_5: 0.46882/0.82806, loss_mask_bce_5: 0.68277/0.30888, loss_mask_dice_5: 1.02591/1.06162, loss_spatial_bce_5: 0.11102/0.09667, loss_spatial_dice_5: 0.20588/0.20539, loss_spatial_ce_5: 0.08737/0.11715, loss_grounding_bce_5: 0.01052/0.08240, loss_grounding_dice_5: 0.11928/0.15541, loss_grounding_ce_5: 0.40045/0.28515, loss_mask_ce_6: 0.56843/0.85314, loss_mask_bce_6: 0.71145/0.30995, loss_mask_dice_6: 1.01069/1.06594, loss_spatial_bce_6: 0.13412/0.10161, loss_spatial_dice_6: 0.25309/0.20770, loss_spatial_ce_6: 0.14239/0.13558, loss_grounding_bce_6: 0.01567/0.08363, loss_grounding_dice_6: 0.17448/0.15605, loss_grounding_ce_6: 0.38657/0.29881, loss_mask_ce_7: 0.75919/0.91585, loss_mask_bce_7: 0.73850/0.31733, loss_mask_dice_7: 1.04609/1.11243, loss_spatial_bce_7: 0.13862/0.11231, loss_spatial_dice_7: 0.22928/0.23267, loss_spatial_ce_7: 0.08744/0.18043, loss_grounding_bce_7: 0.01775/0.08552, loss_grounding_dice_7: 0.16331/0.16201, loss_grounding_ce_7: 1.03112/0.34768, loss_mask_ce_8: 1.12619/1.05632, loss_mask_bce_8: 0.70247/0.33510, loss_mask_dice_8: 1.02175/1.19246, loss_spatial_bce_8: 0.19248/0.13406, loss_spatial_dice_8: 0.36466/0.27544, loss_spatial_ce_8: 0.28334/0.23732, loss_grounding_bce_8: 0.00766/0.08941, loss_grounding_dice_8: 0.18489/0.17109, loss_grounding_ce_8: 1.21920/0.45065, loss_mask_ce_9: 4.22190/3.51588, loss_mask_bce_9: 0.58603/0.36110, loss_mask_dice_9: 1.67956/1.77767, loss_spatial_bce_9: 0.35128/0.36220, loss_spatial_dice_9: 0.76042/0.79897, loss_spatial_ce_9: 1.15878/1.42428, loss_grounding_bce_9: 0.01081/0.10129, loss_grounding_dice_9: 0.45828/0.24601, loss_grounding_ce_9: 1.09597/0.72878] items per batch[64] items per second[0.36] total items[864000] mini batches[ 13500] memory[4943] epoch remaining[0:33:25] INFO:trainer.default_trainer:epochs[ 7] optim steps[13600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.76104/0.79384, loss_mask_bce_0: 0.21773/0.30286, loss_mask_dice_0: 0.43399/1.03447, loss_spatial_bce_0: 0.06569/0.09152, loss_spatial_dice_0: 0.10823/0.19300, loss_spatial_ce_0: 0.00039/0.08015, loss_grounding_bce_0: 0.15158/0.08093, loss_grounding_dice_0: 0.08721/0.15208, loss_grounding_ce_0: 0.43214/0.25471, loss_mask_ce_1: 0.82732/0.79706, loss_mask_bce_1: 0.20521/0.30321, loss_mask_dice_1: 0.39736/1.03849, loss_spatial_bce_1: 0.06819/0.09206, loss_spatial_dice_1: 0.07886/0.19573, loss_spatial_ce_1: 0.00049/0.08458, loss_grounding_bce_1: 0.15332/0.08106, loss_grounding_dice_1: 0.09193/0.15318, loss_grounding_ce_1: 0.44198/0.25797, loss_mask_ce_2: 0.89742/0.80331, loss_mask_bce_2: 0.20416/0.30327, loss_mask_dice_2: 0.42768/1.04098, loss_spatial_bce_2: 0.06685/0.09150, loss_spatial_dice_2: 0.07108/0.19563, loss_spatial_ce_2: 0.00050/0.08856, loss_grounding_bce_2: 0.15780/0.08085, loss_grounding_dice_2: 0.09065/0.15284, loss_grounding_ce_2: 0.09428/0.25836, loss_mask_ce_3: 0.92635/0.80147, loss_mask_bce_3: 0.21229/0.30457, loss_mask_dice_3: 0.36241/1.03497, loss_spatial_bce_3: 0.06737/0.09295, loss_spatial_dice_3: 0.09447/0.19578, loss_spatial_ce_3: 0.00049/0.09465, loss_grounding_bce_3: 0.15960/0.08136, loss_grounding_dice_3: 0.08709/0.15253, loss_grounding_ce_3: 0.06154/0.25666, loss_mask_ce_4: 0.87786/0.80800, loss_mask_bce_4: 0.22254/0.30691, loss_mask_dice_4: 0.55180/1.05440, loss_spatial_bce_4: 0.07004/0.09515, loss_spatial_dice_4: 0.09717/0.20330, loss_spatial_ce_4: 0.00142/0.10611, loss_grounding_bce_4: 0.14620/0.08207, loss_grounding_dice_4: 0.08605/0.15480, loss_grounding_ce_4: 0.06349/0.26584, loss_mask_ce_5: 0.75241/0.82864, loss_mask_bce_5: 0.21898/0.30882, loss_mask_dice_5: 0.36812/1.06152, loss_spatial_bce_5: 0.06536/0.09667, loss_spatial_dice_5: 0.07736/0.20538, loss_spatial_ce_5: 0.00235/0.11719, loss_grounding_bce_5: 0.13936/0.08236, loss_grounding_dice_5: 0.08822/0.15547, loss_grounding_ce_5: 0.10336/0.28512, loss_mask_ce_6: 0.80144/0.85388, loss_mask_bce_6: 0.22238/0.30992, loss_mask_dice_6: 0.45019/1.06582, loss_spatial_bce_6: 0.07113/0.10161, loss_spatial_dice_6: 0.08644/0.20768, loss_spatial_ce_6: 0.03204/0.13552, loss_grounding_bce_6: 0.17048/0.08358, loss_grounding_dice_6: 0.09099/0.15612, loss_grounding_ce_6: 0.40107/0.29852, loss_mask_ce_7: 0.94497/0.91639, loss_mask_bce_7: 0.21258/0.31728, loss_mask_dice_7: 0.48783/1.11244, loss_spatial_bce_7: 0.06865/0.11230, loss_spatial_dice_7: 0.08980/0.23262, loss_spatial_ce_7: 0.12294/0.18037, loss_grounding_bce_7: 0.14547/0.08546, loss_grounding_dice_7: 0.09282/0.16204, loss_grounding_ce_7: 0.73418/0.34725, loss_mask_ce_8: 1.21264/1.05684, loss_mask_bce_8: 0.21301/0.33506, loss_mask_dice_8: 0.50982/1.19236, loss_spatial_bce_8: 0.08573/0.13403, loss_spatial_dice_8: 0.11330/0.27537, loss_spatial_ce_8: 0.16558/0.23726, loss_grounding_bce_8: 0.16672/0.08932, loss_grounding_dice_8: 0.07785/0.17116, loss_grounding_ce_8: 1.70703/0.45100, loss_mask_ce_9: 4.52676/3.51615, loss_mask_bce_9: 0.22365/0.36114, loss_mask_dice_9: 0.65261/1.77838, loss_spatial_bce_9: 0.44652/0.36215, loss_spatial_dice_9: 0.87395/0.79898, loss_spatial_ce_9: 1.84450/1.42388, loss_grounding_bce_9: 0.13301/0.10120, loss_grounding_dice_9: 0.07537/0.24597, loss_grounding_ce_9: 3.35974/0.72805] items per batch[64] items per second[0.36] total items[870400] mini batches[ 13600] memory[4943] epoch remaining[0:30:26] INFO:trainer.default_trainer:epochs[ 7] optim steps[13700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27636/0.79406, loss_mask_bce_0: 0.01523/0.30296, loss_mask_dice_0: 0.05090/1.03339, loss_spatial_bce_0: 0.00877/0.09161, loss_spatial_dice_0: 0.02593/0.19293, loss_spatial_ce_0: 0.12580/0.08004, loss_grounding_bce_0: 0.00399/0.08111, loss_grounding_dice_0: 0.03187/0.15205, loss_grounding_ce_0: 0.00350/0.25487, loss_mask_ce_1: 0.31231/0.79714, loss_mask_bce_1: 0.01469/0.30334, loss_mask_dice_1: 0.04249/1.03750, loss_spatial_bce_1: 0.00892/0.09214, loss_spatial_dice_1: 0.02561/0.19567, loss_spatial_ce_1: 0.13511/0.08444, loss_grounding_bce_1: 0.00431/0.08122, loss_grounding_dice_1: 0.03704/0.15312, loss_grounding_ce_1: 0.00188/0.25806, loss_mask_ce_2: 0.36764/0.80348, loss_mask_bce_2: 0.01499/0.30338, loss_mask_dice_2: 0.04983/1.03991, loss_spatial_bce_2: 0.00836/0.09157, loss_spatial_dice_2: 0.02248/0.19556, loss_spatial_ce_2: 0.12767/0.08839, loss_grounding_bce_2: 0.00447/0.08102, loss_grounding_dice_2: 0.03412/0.15277, loss_grounding_ce_2: 0.00202/0.25839, loss_mask_ce_3: 0.38415/0.80167, loss_mask_bce_3: 0.01330/0.30469, loss_mask_dice_3: 0.04146/1.03391, loss_spatial_bce_3: 0.00962/0.09299, loss_spatial_dice_3: 0.02691/0.19572, loss_spatial_ce_3: 0.12575/0.09467, loss_grounding_bce_3: 0.00442/0.08155, loss_grounding_dice_3: 0.03030/0.15248, loss_grounding_ce_3: 0.00169/0.25665, loss_mask_ce_4: 0.27977/0.80831, loss_mask_bce_4: 0.02725/0.30703, loss_mask_dice_4: 0.07303/1.05324, loss_spatial_bce_4: 0.00978/0.09521, loss_spatial_dice_4: 0.02962/0.20323, loss_spatial_ce_4: 0.13112/0.10606, loss_grounding_bce_4: 0.00316/0.08224, loss_grounding_dice_4: 0.03298/0.15473, loss_grounding_ce_4: 0.00220/0.26597, loss_mask_ce_5: 0.38570/0.82891, loss_mask_bce_5: 0.01681/0.30899, loss_mask_dice_5: 0.04788/1.06038, loss_spatial_bce_5: 0.00950/0.09673, loss_spatial_dice_5: 0.03058/0.20534, loss_spatial_ce_5: 0.13268/0.11706, loss_grounding_bce_5: 0.00424/0.08263, loss_grounding_dice_5: 0.03627/0.15542, loss_grounding_ce_5: 0.00918/0.28506, loss_mask_ce_6: 0.48684/0.85387, loss_mask_bce_6: 0.01397/0.31012, loss_mask_dice_6: 0.04274/1.06462, loss_spatial_bce_6: 0.01006/0.10168, loss_spatial_dice_6: 0.02798/0.20764, loss_spatial_ce_6: 0.13672/0.13550, loss_grounding_bce_6: 0.00485/0.08386, loss_grounding_dice_6: 0.04803/0.15610, loss_grounding_ce_6: 0.00357/0.29827, loss_mask_ce_7: 0.79067/0.91694, loss_mask_bce_7: 0.01418/0.31750, loss_mask_dice_7: 0.04433/1.11134, loss_spatial_bce_7: 0.01182/0.11234, loss_spatial_dice_7: 0.03235/0.23255, loss_spatial_ce_7: 0.20012/0.18028, loss_grounding_bce_7: 0.00468/0.08575, loss_grounding_dice_7: 0.03732/0.16201, loss_grounding_ce_7: 0.01504/0.34743, loss_mask_ce_8: 0.71421/1.05708, loss_mask_bce_8: 0.02128/0.33527, loss_mask_dice_8: 0.05353/1.19117, loss_spatial_bce_8: 0.01188/0.13401, loss_spatial_dice_8: 0.03785/0.27526, loss_spatial_ce_8: 0.39488/0.23745, loss_grounding_bce_8: 0.00518/0.08957, loss_grounding_dice_8: 0.04276/0.17112, loss_grounding_ce_8: 0.02993/0.45060, loss_mask_ce_9: 2.92655/3.51669, loss_mask_bce_9: 0.04025/0.36151, loss_mask_dice_9: 0.11429/1.77773, loss_spatial_bce_9: 0.42833/0.36211, loss_spatial_dice_9: 0.75145/0.79893, loss_spatial_ce_9: 1.22296/1.42344, loss_grounding_bce_9: 0.00550/0.10147, loss_grounding_dice_9: 0.06402/0.24600, loss_grounding_ce_9: 0.18135/0.72811] items per batch[64] items per second[0.36] total items[876800] mini batches[ 13700] memory[4943] epoch remaining[0:27:24] INFO:trainer.default_trainer:epochs[ 7] optim steps[13800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15614/0.79410, loss_mask_bce_0: 0.00555/0.30299, loss_mask_dice_0: 0.06189/1.03370, loss_spatial_bce_0: 0.00459/0.09162, loss_spatial_dice_0: 0.09996/0.19280, loss_spatial_ce_0: 0.04904/0.07972, loss_grounding_bce_0: 0.00098/0.08109, loss_grounding_dice_0: 0.02692/0.15199, loss_grounding_ce_0: 0.07097/0.25483, loss_mask_ce_1: 0.15810/0.79723, loss_mask_bce_1: 0.00735/0.30336, loss_mask_dice_1: 0.10362/1.03760, loss_spatial_bce_1: 0.00477/0.09216, loss_spatial_dice_1: 0.12862/0.19555, loss_spatial_ce_1: 0.04803/0.08410, loss_grounding_bce_1: 0.00278/0.08119, loss_grounding_dice_1: 0.06529/0.15301, loss_grounding_ce_1: 0.07075/0.25808, loss_mask_ce_2: 0.13911/0.80347, loss_mask_bce_2: 0.00523/0.30342, loss_mask_dice_2: 0.13975/1.04022, loss_spatial_bce_2: 0.00378/0.09160, loss_spatial_dice_2: 0.13117/0.19544, loss_spatial_ce_2: 0.00463/0.08807, loss_grounding_bce_2: 0.00147/0.08099, loss_grounding_dice_2: 0.03633/0.15269, loss_grounding_ce_2: 0.05505/0.25839, loss_mask_ce_3: 0.15065/0.80153, loss_mask_bce_3: 0.00545/0.30475, loss_mask_dice_3: 0.10418/1.03409, loss_spatial_bce_3: 0.00422/0.09306, loss_spatial_dice_3: 0.12512/0.19561, loss_spatial_ce_3: 0.00735/0.09434, loss_grounding_bce_3: 0.00261/0.08151, loss_grounding_dice_3: 0.05320/0.15237, loss_grounding_ce_3: 0.05902/0.25674, loss_mask_ce_4: 0.18964/0.80826, loss_mask_bce_4: 0.00780/0.30708, loss_mask_dice_4: 0.12261/1.05347, loss_spatial_bce_4: 0.00338/0.09527, loss_spatial_dice_4: 0.10388/0.20310, loss_spatial_ce_4: 0.04506/0.10579, loss_grounding_bce_4: 0.00220/0.08222, loss_grounding_dice_4: 0.05684/0.15464, loss_grounding_ce_4: 0.10337/0.26602, loss_mask_ce_5: 0.14349/0.82859, loss_mask_bce_5: 0.00574/0.30906, loss_mask_dice_5: 0.06959/1.06069, loss_spatial_bce_5: 0.00330/0.09679, loss_spatial_dice_5: 0.09741/0.20519, loss_spatial_ce_5: 0.06404/0.11685, loss_grounding_bce_5: 0.00112/0.08261, loss_grounding_dice_5: 0.04023/0.15532, loss_grounding_ce_5: 0.11511/0.28497, loss_mask_ce_6: 0.18992/0.85361, loss_mask_bce_6: 0.00670/0.31020, loss_mask_dice_6: 0.09607/1.06502, loss_spatial_bce_6: 0.00335/0.10171, loss_spatial_dice_6: 0.11544/0.20747, loss_spatial_ce_6: 0.14471/0.13532, loss_grounding_bce_6: 0.00185/0.08384, loss_grounding_dice_6: 0.04280/0.15604, loss_grounding_ce_6: 0.25532/0.29814, loss_mask_ce_7: 0.28584/0.91683, loss_mask_bce_7: 0.00727/0.31758, loss_mask_dice_7: 0.20682/1.11181, loss_spatial_bce_7: 0.00486/0.11247, loss_spatial_dice_7: 0.14858/0.23243, loss_spatial_ce_7: 0.26259/0.18007, loss_grounding_bce_7: 0.00291/0.08574, loss_grounding_dice_7: 0.06506/0.16194, loss_grounding_ce_7: 0.72461/0.34736, loss_mask_ce_8: 0.51196/1.05678, loss_mask_bce_8: 0.01848/0.33534, loss_mask_dice_8: 0.30823/1.19183, loss_spatial_bce_8: 0.00678/0.13413, loss_spatial_dice_8: 0.17087/0.27508, loss_spatial_ce_8: 0.02331/0.23741, loss_grounding_bce_8: 0.00631/0.08955, loss_grounding_dice_8: 0.15137/0.17100, loss_grounding_ce_8: 2.18731/0.45081, loss_mask_ce_9: 3.86476/3.51712, loss_mask_bce_9: 0.00991/0.36164, loss_mask_dice_9: 0.28838/1.77911, loss_spatial_bce_9: 0.04531/0.36207, loss_spatial_dice_9: 0.80999/0.79876, loss_spatial_ce_9: 1.74559/1.42317, loss_grounding_bce_9: 0.00317/0.10150, loss_grounding_dice_9: 0.11075/0.24593, loss_grounding_ce_9: 2.63948/0.72787] items per batch[64] items per second[0.36] total items[883200] mini batches[ 13800] memory[4943] epoch remaining[0:24:22] INFO:trainer.default_trainer:epochs[ 7] optim steps[13900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37094/0.79425, loss_mask_bce_0: 0.45084/0.30305, loss_mask_dice_0: 2.33948/1.03417, loss_spatial_bce_0: 0.06866/0.09156, loss_spatial_dice_0: 0.24123/0.19266, loss_spatial_ce_0: 0.39224/0.07954, loss_grounding_bce_0: 0.06046/0.08104, loss_grounding_dice_0: 0.08489/0.15198, loss_grounding_ce_0: 0.17941/0.25485, loss_mask_ce_1: 0.53981/0.79743, loss_mask_bce_1: 0.42364/0.30341, loss_mask_dice_1: 1.86287/1.03809, loss_spatial_bce_1: 0.07539/0.09209, loss_spatial_dice_1: 0.27644/0.19541, loss_spatial_ce_1: 0.03090/0.08396, loss_grounding_bce_1: 0.06122/0.08114, loss_grounding_dice_1: 0.08025/0.15302, loss_grounding_ce_1: 0.21388/0.25802, loss_mask_ce_2: 0.34738/0.80362, loss_mask_bce_2: 0.44604/0.30346, loss_mask_dice_2: 2.52433/1.04068, loss_spatial_bce_2: 0.07700/0.09152, loss_spatial_dice_2: 0.26283/0.19531, loss_spatial_ce_2: 0.02489/0.08791, loss_grounding_bce_2: 0.06572/0.08093, loss_grounding_dice_2: 0.08200/0.15270, loss_grounding_ce_2: 0.15746/0.25836, loss_mask_ce_3: 0.32205/0.80178, loss_mask_bce_3: 0.44613/0.30479, loss_mask_dice_3: 1.95320/1.03457, loss_spatial_bce_3: 0.08160/0.09297, loss_spatial_dice_3: 0.27716/0.19546, loss_spatial_ce_3: 0.04152/0.09411, loss_grounding_bce_3: 0.06068/0.08146, loss_grounding_dice_3: 0.07591/0.15237, loss_grounding_ce_3: 0.12957/0.25657, loss_mask_ce_4: 0.41503/0.80861, loss_mask_bce_4: 0.45417/0.30714, loss_mask_dice_4: 2.17431/1.05389, loss_spatial_bce_4: 0.07834/0.09517, loss_spatial_dice_4: 0.28611/0.20294, loss_spatial_ce_4: 0.04427/0.10564, loss_grounding_bce_4: 0.07044/0.08217, loss_grounding_dice_4: 0.08191/0.15464, loss_grounding_ce_4: 0.18896/0.26572, loss_mask_ce_5: 0.55463/0.82890, loss_mask_bce_5: 0.45076/0.30915, loss_mask_dice_5: 2.41524/1.06118, loss_spatial_bce_5: 0.07880/0.09671, loss_spatial_dice_5: 0.26713/0.20507, loss_spatial_ce_5: 0.04791/0.11661, loss_grounding_bce_5: 0.07728/0.08256, loss_grounding_dice_5: 0.10067/0.15535, loss_grounding_ce_5: 0.29390/0.28454, loss_mask_ce_6: 0.56659/0.85404, loss_mask_bce_6: 0.48473/0.31032, loss_mask_dice_6: 2.07443/1.06540, loss_spatial_bce_6: 0.07734/0.10161, loss_spatial_dice_6: 0.34746/0.20736, loss_spatial_ce_6: 0.08567/0.13517, loss_grounding_bce_6: 0.08302/0.08379, loss_grounding_dice_6: 0.09035/0.15602, loss_grounding_ce_6: 0.41022/0.29804, loss_mask_ce_7: 0.71553/0.91679, loss_mask_bce_7: 0.49361/0.31771, loss_mask_dice_7: 2.00556/1.11238, loss_spatial_bce_7: 0.08511/0.11233, loss_spatial_dice_7: 0.28104/0.23226, loss_spatial_ce_7: 0.18665/0.17989, loss_grounding_bce_7: 0.09833/0.08569, loss_grounding_dice_7: 0.10446/0.16198, loss_grounding_ce_7: 0.92693/0.34665, loss_mask_ce_8: 0.89384/1.05704, loss_mask_bce_8: 0.50926/0.33545, loss_mask_dice_8: 2.23472/1.19237, loss_spatial_bce_8: 0.10103/0.13400, loss_spatial_dice_8: 0.36686/0.27484, loss_spatial_ce_8: 0.33159/0.23727, loss_grounding_bce_8: 0.09932/0.08949, loss_grounding_dice_8: 0.08833/0.17098, loss_grounding_ce_8: 0.45748/0.45031, loss_mask_ce_9: 5.31963/3.51607, loss_mask_bce_9: 0.48977/0.36179, loss_mask_dice_9: 2.52705/1.78037, loss_spatial_bce_9: 0.34392/0.36216, loss_spatial_dice_9: 0.84928/0.79876, loss_spatial_ce_9: 1.50278/1.42330, loss_grounding_bce_9: 0.11904/0.10142, loss_grounding_dice_9: 0.16491/0.24590, loss_grounding_ce_9: 2.02970/0.72734] items per batch[64] items per second[0.34] total items[889600] mini batches[ 13900] memory[4943] epoch remaining[0:21:28] INFO:trainer.default_trainer:epochs[ 7] optim steps[14000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24193/0.79371, loss_mask_bce_0: 0.24961/0.30283, loss_mask_dice_0: 0.24523/1.03281, loss_spatial_bce_0: 0.10051/0.09156, loss_spatial_dice_0: 0.08958/0.19246, loss_spatial_ce_0: 0.04638/0.07947, loss_grounding_bce_0: 0.08913/0.08096, loss_grounding_dice_0: 0.09433/0.15195, loss_grounding_ce_0: 0.06859/0.25426, loss_mask_ce_1: 0.24738/0.79706, loss_mask_bce_1: 0.25510/0.30319, loss_mask_dice_1: 0.24656/1.03676, loss_spatial_bce_1: 0.09859/0.09209, loss_spatial_dice_1: 0.08913/0.19521, loss_spatial_ce_1: 0.04631/0.08392, loss_grounding_bce_1: 0.09440/0.08106, loss_grounding_dice_1: 0.08937/0.15299, loss_grounding_ce_1: 0.06823/0.25746, loss_mask_ce_2: 0.22878/0.80318, loss_mask_bce_2: 0.26269/0.30325, loss_mask_dice_2: 0.26613/1.03937, loss_spatial_bce_2: 0.09859/0.09148, loss_spatial_dice_2: 0.09836/0.19511, loss_spatial_ce_2: 0.04634/0.08787, loss_grounding_bce_2: 0.09975/0.08086, loss_grounding_dice_2: 0.09539/0.15267, loss_grounding_ce_2: 0.04774/0.25777, loss_mask_ce_3: 0.22279/0.80147, loss_mask_bce_3: 0.26472/0.30455, loss_mask_dice_3: 0.26089/1.03331, loss_spatial_bce_3: 0.10178/0.09290, loss_spatial_dice_3: 0.09663/0.19526, loss_spatial_ce_3: 0.04652/0.09405, loss_grounding_bce_3: 0.09107/0.08139, loss_grounding_dice_3: 0.09345/0.15234, loss_grounding_ce_3: 0.05119/0.25598, loss_mask_ce_4: 0.22739/0.80820, loss_mask_bce_4: 0.26809/0.30697, loss_mask_dice_4: 0.25703/1.05259, loss_spatial_bce_4: 0.09813/0.09514, loss_spatial_dice_4: 0.09442/0.20277, loss_spatial_ce_4: 0.04669/0.10556, loss_grounding_bce_4: 0.09720/0.08208, loss_grounding_dice_4: 0.09034/0.15457, loss_grounding_ce_4: 0.08507/0.26518, loss_mask_ce_5: 0.30843/0.82855, loss_mask_bce_5: 0.27128/0.30896, loss_mask_dice_5: 0.24909/1.05980, loss_spatial_bce_5: 0.10551/0.09667, loss_spatial_dice_5: 0.08666/0.20489, loss_spatial_ce_5: 0.04751/0.11657, loss_grounding_bce_5: 0.08968/0.08247, loss_grounding_dice_5: 0.08556/0.15532, loss_grounding_ce_5: 0.10737/0.28386, loss_mask_ce_6: 0.32523/0.85345, loss_mask_bce_6: 0.28517/0.31016, loss_mask_dice_6: 0.27975/1.06400, loss_spatial_bce_6: 0.11246/0.10156, loss_spatial_dice_6: 0.09468/0.20716, loss_spatial_ce_6: 0.06535/0.13511, loss_grounding_bce_6: 0.10789/0.08369, loss_grounding_dice_6: 0.10011/0.15595, loss_grounding_ce_6: 0.11189/0.29751, loss_mask_ce_7: 0.46420/0.91634, loss_mask_bce_7: 0.29520/0.31753, loss_mask_dice_7: 0.28982/1.11100, loss_spatial_bce_7: 0.10531/0.11231, loss_spatial_dice_7: 0.09796/0.23201, loss_spatial_ce_7: 0.06227/0.17953, loss_grounding_bce_7: 0.12231/0.08559, loss_grounding_dice_7: 0.13042/0.16193, loss_grounding_ce_7: 0.18253/0.34632, loss_mask_ce_8: 0.50458/1.05652, loss_mask_bce_8: 0.30891/0.33536, loss_mask_dice_8: 0.30537/1.19079, loss_spatial_bce_8: 0.22703/0.13397, loss_spatial_dice_8: 0.15575/0.27457, loss_spatial_ce_8: 0.17132/0.23698, loss_grounding_bce_8: 0.13211/0.08939, loss_grounding_dice_8: 0.11530/0.17094, loss_grounding_ce_8: 0.12485/0.45000, loss_mask_ce_9: 2.96580/3.51542, loss_mask_bce_9: 0.34139/0.36168, loss_mask_dice_9: 0.52988/1.77853, loss_spatial_bce_9: 0.52215/0.36230, loss_spatial_dice_9: 0.72477/0.79851, loss_spatial_ce_9: 1.21508/1.42281, loss_grounding_bce_9: 0.13932/0.10138, loss_grounding_dice_9: 0.21502/0.24582, loss_grounding_ce_9: 0.35041/0.72592] items per batch[64] items per second[0.36] total items[896000] mini batches[ 14000] memory[4943] epoch remaining[0:18:27] INFO:trainer.default_trainer:epochs[ 7] optim steps[14100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.66650/0.79336, loss_mask_bce_0: 0.16376/0.30275, loss_mask_dice_0: 1.16060/1.03324, loss_spatial_bce_0: 0.02413/0.09151, loss_spatial_dice_0: 0.14545/0.19241, loss_spatial_ce_0: 0.03987/0.07921, loss_grounding_bce_0: 0.00950/0.08097, loss_grounding_dice_0: 0.03611/0.15193, loss_grounding_ce_0: 0.08260/0.25421, loss_mask_ce_1: 1.56804/0.79681, loss_mask_bce_1: 0.17211/0.30309, loss_mask_dice_1: 1.24250/1.03725, loss_spatial_bce_1: 0.02429/0.09205, loss_spatial_dice_1: 0.14289/0.19517, loss_spatial_ce_1: 0.05475/0.08371, loss_grounding_bce_1: 0.00864/0.08108, loss_grounding_dice_1: 0.03076/0.15299, loss_grounding_ce_1: 0.10673/0.25744, loss_mask_ce_2: 1.63932/0.80295, loss_mask_bce_2: 0.18966/0.30314, loss_mask_dice_2: 1.25459/1.03977, loss_spatial_bce_2: 0.02517/0.09145, loss_spatial_dice_2: 0.13380/0.19504, loss_spatial_ce_2: 0.05080/0.08764, loss_grounding_bce_2: 0.00710/0.08088, loss_grounding_dice_2: 0.02342/0.15266, loss_grounding_ce_2: 0.08398/0.25771, loss_mask_ce_3: 1.44356/0.80137, loss_mask_bce_3: 0.21026/0.30446, loss_mask_dice_3: 1.28676/1.03407, loss_spatial_bce_3: 0.02180/0.09289, loss_spatial_dice_3: 0.15560/0.19521, loss_spatial_ce_3: 0.04366/0.09384, loss_grounding_bce_3: 0.00678/0.08140, loss_grounding_dice_3: 0.02653/0.15233, loss_grounding_ce_3: 0.12302/0.25587, loss_mask_ce_4: 1.87596/0.80811, loss_mask_bce_4: 0.21117/0.30685, loss_mask_dice_4: 1.12844/1.05327, loss_spatial_bce_4: 0.02820/0.09509, loss_spatial_dice_4: 0.13884/0.20272, loss_spatial_ce_4: 0.03234/0.10529, loss_grounding_bce_4: 0.00618/0.08211, loss_grounding_dice_4: 0.02625/0.15454, loss_grounding_ce_4: 0.07390/0.26519, loss_mask_ce_5: 1.91289/0.82854, loss_mask_bce_5: 0.20367/0.30886, loss_mask_dice_5: 1.16253/1.06035, loss_spatial_bce_5: 0.02764/0.09664, loss_spatial_dice_5: 0.16393/0.20483, loss_spatial_ce_5: 0.06370/0.11629, loss_grounding_bce_5: 0.00772/0.08253, loss_grounding_dice_5: 0.03362/0.15531, loss_grounding_ce_5: 0.06370/0.28415, loss_mask_ce_6: 1.83726/0.85362, loss_mask_bce_6: 0.17166/0.31007, loss_mask_dice_6: 1.11612/1.06440, loss_spatial_bce_6: 0.02573/0.10152, loss_spatial_dice_6: 0.15593/0.20709, loss_spatial_ce_6: 0.03145/0.13492, loss_grounding_bce_6: 0.00705/0.08375, loss_grounding_dice_6: 0.03263/0.15597, loss_grounding_ce_6: 0.20134/0.29781, loss_mask_ce_7: 1.93753/0.91659, loss_mask_bce_7: 0.16135/0.31744, loss_mask_dice_7: 1.17014/1.11147, loss_spatial_bce_7: 0.02669/0.11228, loss_spatial_dice_7: 0.16077/0.23194, loss_spatial_ce_7: 0.06518/0.17948, loss_grounding_bce_7: 0.00803/0.08563, loss_grounding_dice_7: 0.02999/0.16194, loss_grounding_ce_7: 0.12215/0.34631, loss_mask_ce_8: 2.55672/1.05664, loss_mask_bce_8: 0.25335/0.33529, loss_mask_dice_8: 1.52832/1.19142, loss_spatial_bce_8: 0.04480/0.13385, loss_spatial_dice_8: 0.23773/0.27436, loss_spatial_ce_8: 0.22237/0.23691, loss_grounding_bce_8: 0.00889/0.08945, loss_grounding_dice_8: 0.02696/0.17095, loss_grounding_ce_8: 0.29014/0.45052, loss_mask_ce_9: 5.15474/3.51577, loss_mask_bce_9: 0.18927/0.36164, loss_mask_dice_9: 1.87499/1.77924, loss_spatial_bce_9: 0.20425/0.36209, loss_spatial_dice_9: 0.88678/0.79856, loss_spatial_ce_9: 2.26562/1.42308, loss_grounding_bce_9: 0.01164/0.10142, loss_grounding_dice_9: 0.05095/0.24583, loss_grounding_ce_9: 0.94034/0.72572] items per batch[64] items per second[0.36] total items[902400] mini batches[ 14100] memory[4943] epoch remaining[0:15:26] INFO:trainer.default_trainer:epochs[ 7] optim steps[14200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12766/0.79303, loss_mask_bce_0: 0.05383/0.30269, loss_mask_dice_0: 3.41100/1.03583, loss_spatial_bce_0: 0.00573/0.09138, loss_spatial_dice_0: 0.44276/0.19242, loss_spatial_ce_0: 0.25606/0.07902, loss_grounding_bce_0: 0.00817/0.08081, loss_grounding_dice_0: 0.36657/0.15186, loss_grounding_ce_0: 0.07320/0.25352, loss_mask_ce_1: 1.16254/0.79640, loss_mask_bce_1: 0.05873/0.30299, loss_mask_dice_1: 3.27581/1.03978, loss_spatial_bce_1: 0.00617/0.09193, loss_spatial_dice_1: 0.35990/0.19514, loss_spatial_ce_1: 0.48716/0.08350, loss_grounding_bce_1: 0.00756/0.08092, loss_grounding_dice_1: 0.34036/0.15297, loss_grounding_ce_1: 0.04336/0.25675, loss_mask_ce_2: 1.15682/0.80259, loss_mask_bce_2: 0.06224/0.30306, loss_mask_dice_2: 3.71235/1.04246, loss_spatial_bce_2: 0.00543/0.09134, loss_spatial_dice_2: 0.43403/0.19503, loss_spatial_ce_2: 0.32281/0.08745, loss_grounding_bce_2: 0.00676/0.08071, loss_grounding_dice_2: 0.36450/0.15260, loss_grounding_ce_2: 0.04823/0.25704, loss_mask_ce_3: 1.13887/0.80088, loss_mask_bce_3: 0.05386/0.30442, loss_mask_dice_3: 3.67042/1.03671, loss_spatial_bce_3: 0.00443/0.09281, loss_spatial_dice_3: 0.39081/0.19520, loss_spatial_ce_3: 0.20085/0.09365, loss_grounding_bce_3: 0.01242/0.08122, loss_grounding_dice_3: 0.42745/0.15229, loss_grounding_ce_3: 0.06777/0.25515, loss_mask_ce_4: 1.19027/0.80777, loss_mask_bce_4: 0.05519/0.30676, loss_mask_dice_4: 2.82712/1.05585, loss_spatial_bce_4: 0.00579/0.09500, loss_spatial_dice_4: 0.35609/0.20274, loss_spatial_ce_4: 0.29746/0.10505, loss_grounding_bce_4: 0.00672/0.08192, loss_grounding_dice_4: 0.35019/0.15448, loss_grounding_ce_4: 0.05577/0.26446, loss_mask_ce_5: 1.27756/0.82814, loss_mask_bce_5: 0.05546/0.30874, loss_mask_dice_5: 2.58175/1.06284, loss_spatial_bce_5: 0.00473/0.09651, loss_spatial_dice_5: 0.35606/0.20484, loss_spatial_ce_5: 0.18527/0.11605, loss_grounding_bce_5: 0.00612/0.08235, loss_grounding_dice_5: 0.31765/0.15523, loss_grounding_ce_5: 0.06610/0.28345, loss_mask_ce_6: 1.62803/0.85329, loss_mask_bce_6: 0.04813/0.30998, loss_mask_dice_6: 2.82775/1.06710, loss_spatial_bce_6: 0.00589/0.10141, loss_spatial_dice_6: 0.38584/0.20709, loss_spatial_ce_6: 0.57301/0.13470, loss_grounding_bce_6: 0.00517/0.08357, loss_grounding_dice_6: 0.01724/0.15588, loss_grounding_ce_6: 0.06467/0.29710, loss_mask_ce_7: 1.78159/0.91614, loss_mask_bce_7: 0.05605/0.31731, loss_mask_dice_7: 3.84450/1.11406, loss_spatial_bce_7: 0.01087/0.11215, loss_spatial_dice_7: 0.44258/0.23198, loss_spatial_ce_7: 0.28812/0.17919, loss_grounding_bce_7: 0.00697/0.08547, loss_grounding_dice_7: 0.36583/0.16191, loss_grounding_ce_7: 0.05940/0.34562, loss_mask_ce_8: 1.38862/1.05615, loss_mask_bce_8: 0.06802/0.33508, loss_mask_dice_8: 3.34808/1.19442, loss_spatial_bce_8: 0.00953/0.13368, loss_spatial_dice_8: 0.56463/0.27440, loss_spatial_ce_8: 0.57227/0.23654, loss_grounding_bce_8: 0.00923/0.08925, loss_grounding_dice_8: 0.37753/0.17092, loss_grounding_ce_8: 0.05188/0.44958, loss_mask_ce_9: 4.34422/3.51536, loss_mask_bce_9: 0.04196/0.36132, loss_mask_dice_9: 3.51166/1.78242, loss_spatial_bce_9: 0.02625/0.36181, loss_spatial_dice_9: 0.91999/0.79861, loss_spatial_ce_9: 3.43211/1.42296, loss_grounding_bce_9: 0.00459/0.10119, loss_grounding_dice_9: 0.16055/0.24579, loss_grounding_ce_9: 0.20742/0.72491] items per batch[64] items per second[0.36] total items[908800] mini batches[ 14200] memory[4943] epoch remaining[0:12:26] INFO:trainer.default_trainer:epochs[ 7] optim steps[14300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67443/0.79395, loss_mask_bce_0: 0.39632/0.30272, loss_mask_dice_0: 1.39127/1.03613, loss_spatial_bce_0: 0.05390/0.09140, loss_spatial_dice_0: 0.24695/0.19248, loss_spatial_ce_0: 0.07575/0.07901, loss_grounding_bce_0: 0.09151/0.08085, loss_grounding_dice_0: 0.25119/0.15186, loss_grounding_ce_0: 1.19859/0.25446, loss_mask_ce_1: 0.77770/0.79723, loss_mask_bce_1: 0.41425/0.30303, loss_mask_dice_1: 1.44548/1.04016, loss_spatial_bce_1: 0.05719/0.09193, loss_spatial_dice_1: 0.29062/0.19520, loss_spatial_ce_1: 0.03474/0.08355, loss_grounding_bce_1: 0.04611/0.08097, loss_grounding_dice_1: 0.18322/0.15299, loss_grounding_ce_1: 3.50488/0.25744, loss_mask_ce_2: 0.70905/0.80358, loss_mask_bce_2: 0.40393/0.30306, loss_mask_dice_2: 1.41579/1.04282, loss_spatial_bce_2: 0.05058/0.09134, loss_spatial_dice_2: 0.27428/0.19509, loss_spatial_ce_2: 0.02842/0.08741, loss_grounding_bce_2: 0.05084/0.08076, loss_grounding_dice_2: 0.17519/0.15261, loss_grounding_ce_2: 3.10481/0.25768, loss_mask_ce_3: 0.64499/0.80184, loss_mask_bce_3: 0.43032/0.30442, loss_mask_dice_3: 1.42549/1.03715, loss_spatial_bce_3: 0.06229/0.09282, loss_spatial_dice_3: 0.27005/0.19526, loss_spatial_ce_3: 0.09163/0.09356, loss_grounding_bce_3: 0.05027/0.08127, loss_grounding_dice_3: 0.16216/0.15230, loss_grounding_ce_3: 3.32605/0.25581, loss_mask_ce_4: 0.77256/0.80861, loss_mask_bce_4: 0.43875/0.30675, loss_mask_dice_4: 1.52232/1.05629, loss_spatial_bce_4: 0.05349/0.09497, loss_spatial_dice_4: 0.26709/0.20276, loss_spatial_ce_4: 0.09823/0.10499, loss_grounding_bce_4: 0.05920/0.08198, loss_grounding_dice_4: 0.19242/0.15452, loss_grounding_ce_4: 4.08067/0.26518, loss_mask_ce_5: 0.72395/0.82936, loss_mask_bce_5: 0.44451/0.30874, loss_mask_dice_5: 1.49557/1.06321, loss_spatial_bce_5: 0.05174/0.09650, loss_spatial_dice_5: 0.27473/0.20488, loss_spatial_ce_5: 0.11788/0.11598, loss_grounding_bce_5: 0.07380/0.08240, loss_grounding_dice_5: 0.19866/0.15527, loss_grounding_ce_5: 2.92387/0.28436, loss_mask_ce_6: 0.83114/0.85428, loss_mask_bce_6: 0.42717/0.31005, loss_mask_dice_6: 1.45895/1.06746, loss_spatial_bce_6: 0.08283/0.10141, loss_spatial_dice_6: 0.30536/0.20713, loss_spatial_ce_6: 0.07606/0.13453, loss_grounding_bce_6: 0.06018/0.08362, loss_grounding_dice_6: 0.16364/0.15591, loss_grounding_ce_6: 1.84200/0.29785, loss_mask_ce_7: 0.83950/0.91727, loss_mask_bce_7: 0.49504/0.31730, loss_mask_dice_7: 1.47474/1.11460, loss_spatial_bce_7: 0.05078/0.11209, loss_spatial_dice_7: 0.32478/0.23203, loss_spatial_ce_7: 0.21601/0.17904, loss_grounding_bce_7: 0.03435/0.08551, loss_grounding_dice_7: 0.14470/0.16195, loss_grounding_ce_7: 4.44643/0.34623, loss_mask_ce_8: 1.24738/1.05676, loss_mask_bce_8: 0.24469/0.33502, loss_mask_dice_8: 1.46657/1.19486, loss_spatial_bce_8: 0.09069/0.13361, loss_spatial_dice_8: 0.37070/0.27438, loss_spatial_ce_8: 0.13377/0.23641, loss_grounding_bce_8: 0.03471/0.08933, loss_grounding_dice_8: 0.15784/0.17102, loss_grounding_ce_8: 1.79916/0.45035, loss_mask_ce_9: 5.06488/3.51710, loss_mask_bce_9: 0.40362/0.36127, loss_mask_dice_9: 2.31195/1.78221, loss_spatial_bce_9: 0.41596/0.36170, loss_spatial_dice_9: 0.92777/0.79849, loss_spatial_ce_9: 1.57033/1.42319, loss_grounding_bce_9: 0.12717/0.10125, loss_grounding_dice_9: 0.39131/0.24587, loss_grounding_ce_9: 2.75268/0.72595] items per batch[64] items per second[0.36] total items[915200] mini batches[ 14300] memory[4943] epoch remaining[0:09:26] INFO:trainer.default_trainer:epochs[ 7] optim steps[14400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26036/0.79339, loss_mask_bce_0: 0.03700/0.30272, loss_mask_dice_0: 0.18106/1.03639, loss_spatial_bce_0: 0.01174/0.09134, loss_spatial_dice_0: 0.06341/0.19244, loss_spatial_ce_0: 0.00478/0.07893, loss_grounding_bce_0: 0.00256/0.08088, loss_grounding_dice_0: 0.02332/0.15190, loss_grounding_ce_0: 0.04350/0.25440, loss_mask_ce_1: 0.26709/0.79663, loss_mask_bce_1: 0.03773/0.30305, loss_mask_dice_1: 0.19122/1.04033, loss_spatial_bce_1: 0.01299/0.09185, loss_spatial_dice_1: 0.07248/0.19519, loss_spatial_ce_1: 0.00239/0.08338, loss_grounding_bce_1: 0.00349/0.08101, loss_grounding_dice_1: 0.02941/0.15303, loss_grounding_ce_1: 0.09232/0.25743, loss_mask_ce_2: 0.24797/0.80296, loss_mask_bce_2: 0.03835/0.30307, loss_mask_dice_2: 0.17853/1.04320, loss_spatial_bce_2: 0.01034/0.09126, loss_spatial_dice_2: 0.06749/0.19507, loss_spatial_ce_2: 0.00783/0.08726, loss_grounding_bce_2: 0.00217/0.08080, loss_grounding_dice_2: 0.02104/0.15263, loss_grounding_ce_2: 0.13193/0.25781, loss_mask_ce_3: 0.24645/0.80126, loss_mask_bce_3: 0.03591/0.30444, loss_mask_dice_3: 0.16521/1.03771, loss_spatial_bce_3: 0.01052/0.09277, loss_spatial_dice_3: 0.06361/0.19524, loss_spatial_ce_3: 0.01373/0.09349, loss_grounding_bce_3: 0.00290/0.08127, loss_grounding_dice_3: 0.02774/0.15235, loss_grounding_ce_3: 0.10937/0.25593, loss_mask_ce_4: 0.23824/0.80812, loss_mask_bce_4: 0.03413/0.30673, loss_mask_dice_4: 0.17489/1.05659, loss_spatial_bce_4: 0.01224/0.09493, loss_spatial_dice_4: 0.06930/0.20276, loss_spatial_ce_4: 0.01054/0.10479, loss_grounding_bce_4: 0.00243/0.08200, loss_grounding_dice_4: 0.02471/0.15453, loss_grounding_ce_4: 0.04896/0.26516, loss_mask_ce_5: 0.24445/0.82876, loss_mask_bce_5: 0.03328/0.30876, loss_mask_dice_5: 0.17934/1.06345, loss_spatial_bce_5: 0.01257/0.09643, loss_spatial_dice_5: 0.06334/0.20487, loss_spatial_ce_5: 0.01679/0.11583, loss_grounding_bce_5: 0.00247/0.08244, loss_grounding_dice_5: 0.02261/0.15531, loss_grounding_ce_5: 0.07258/0.28433, loss_mask_ce_6: 0.27643/0.85382, loss_mask_bce_6: 0.02933/0.31005, loss_mask_dice_6: 0.17110/1.06786, loss_spatial_bce_6: 0.01153/0.10134, loss_spatial_dice_6: 0.06935/0.20709, loss_spatial_ce_6: 0.00959/0.13445, loss_grounding_bce_6: 0.00255/0.08365, loss_grounding_dice_6: 0.02493/0.15594, loss_grounding_ce_6: 0.02231/0.29774, loss_mask_ce_7: 0.40996/0.91691, loss_mask_bce_7: 0.02578/0.31731, loss_mask_dice_7: 0.18912/1.11479, loss_spatial_bce_7: 0.01630/0.11203, loss_spatial_dice_7: 0.08047/0.23204, loss_spatial_ce_7: 0.03714/0.17871, loss_grounding_bce_7: 0.00286/0.08549, loss_grounding_dice_7: 0.02909/0.16196, loss_grounding_ce_7: 0.09065/0.34620, loss_mask_ce_8: 0.35483/1.05625, loss_mask_bce_8: 0.06380/0.33498, loss_mask_dice_8: 0.33026/1.19502, loss_spatial_bce_8: 0.01397/0.13352, loss_spatial_dice_8: 0.08301/0.27430, loss_spatial_ce_8: 0.10494/0.23621, loss_grounding_bce_8: 0.00676/0.08932, loss_grounding_dice_8: 0.05806/0.17104, loss_grounding_ce_8: 0.14917/0.45017, loss_mask_ce_9: 3.18269/3.51763, loss_mask_bce_9: 0.05612/0.36114, loss_mask_dice_9: 0.35934/1.78245, loss_spatial_bce_9: 0.10422/0.36156, loss_spatial_dice_9: 0.65412/0.79847, loss_spatial_ce_9: 1.10230/1.42305, loss_grounding_bce_9: 0.00496/0.10130, loss_grounding_dice_9: 0.06051/0.24582, loss_grounding_ce_9: 2.13880/0.72579] items per batch[64] items per second[0.36] total items[921600] mini batches[ 14400] memory[4967] epoch remaining[0:06:27] INFO:trainer.default_trainer:epochs[ 7] optim steps[14500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60332/0.79274, loss_mask_bce_0: 0.14031/0.30248, loss_mask_dice_0: 0.25462/1.03555, loss_spatial_bce_0: 0.25362/0.09132, loss_spatial_dice_0: 0.37038/0.19234, loss_spatial_ce_0: 0.07947/0.07878, loss_grounding_bce_0: 0.21162/0.08085, loss_grounding_dice_0: 0.30026/0.15204, loss_grounding_ce_0: 0.46477/0.25438, loss_mask_ce_1: 0.59958/0.79612, loss_mask_bce_1: 0.15547/0.30279, loss_mask_dice_1: 0.25839/1.03921, loss_spatial_bce_1: 0.22815/0.09183, loss_spatial_dice_1: 0.32087/0.19508, loss_spatial_ce_1: 0.08516/0.08327, loss_grounding_bce_1: 0.16618/0.08096, loss_grounding_dice_1: 0.27640/0.15315, loss_grounding_ce_1: 0.60138/0.25751, loss_mask_ce_2: 0.54027/0.80237, loss_mask_bce_2: 0.13789/0.30283, loss_mask_dice_2: 0.23511/1.04211, loss_spatial_bce_2: 0.18853/0.09126, loss_spatial_dice_2: 0.29500/0.19498, loss_spatial_ce_2: 0.11439/0.08707, loss_grounding_bce_2: 0.17953/0.08075, loss_grounding_dice_2: 0.27905/0.15276, loss_grounding_ce_2: 0.70500/0.25797, loss_mask_ce_3: 0.51431/0.80076, loss_mask_bce_3: 0.15272/0.30423, loss_mask_dice_3: 0.24806/1.03693, loss_spatial_bce_3: 0.28006/0.09280, loss_spatial_dice_3: 0.34887/0.19515, loss_spatial_ce_3: 0.05873/0.09329, loss_grounding_bce_3: 0.18801/0.08123, loss_grounding_dice_3: 0.28640/0.15246, loss_grounding_ce_3: 0.58407/0.25617, loss_mask_ce_4: 0.51801/0.80773, loss_mask_bce_4: 0.13994/0.30648, loss_mask_dice_4: 0.24278/1.05561, loss_spatial_bce_4: 0.27601/0.09493, loss_spatial_dice_4: 0.34207/0.20265, loss_spatial_ce_4: 0.04371/0.10461, loss_grounding_bce_4: 0.21227/0.08199, loss_grounding_dice_4: 0.30015/0.15466, loss_grounding_ce_4: 0.87269/0.26522, loss_mask_ce_5: 0.44965/0.82805, loss_mask_bce_5: 0.14060/0.30849, loss_mask_dice_5: 0.22716/1.06256, loss_spatial_bce_5: 0.26784/0.09647, loss_spatial_dice_5: 0.38398/0.20479, loss_spatial_ce_5: 0.06030/0.11563, loss_grounding_bce_5: 0.13599/0.08240, loss_grounding_dice_5: 0.25581/0.15547, loss_grounding_ce_5: 0.68934/0.28440, loss_mask_ce_6: 0.72867/0.85314, loss_mask_bce_6: 0.12106/0.30979, loss_mask_dice_6: 0.21807/1.06690, loss_spatial_bce_6: 0.41692/0.10143, loss_spatial_dice_6: 0.48964/0.20702, loss_spatial_ce_6: 0.06035/0.13420, loss_grounding_bce_6: 0.16320/0.08360, loss_grounding_dice_6: 0.25315/0.15606, loss_grounding_ce_6: 0.64625/0.29781, loss_mask_ce_7: 0.61436/0.91617, loss_mask_bce_7: 0.23211/0.31706, loss_mask_dice_7: 0.32002/1.11376, loss_spatial_bce_7: 0.36094/0.11205, loss_spatial_dice_7: 0.57722/0.23198, loss_spatial_ce_7: 0.46804/0.17853, loss_grounding_bce_7: 0.26528/0.08546, loss_grounding_dice_7: 0.30248/0.16209, loss_grounding_ce_7: 0.50010/0.34608, loss_mask_ce_8: 0.04453/1.05534, loss_mask_bce_8: 0.42258/0.33467, loss_mask_dice_8: 0.32995/1.19387, loss_spatial_bce_8: 0.59848/0.13362, loss_spatial_dice_8: 0.61136/0.27420, loss_spatial_ce_8: 1.08701/0.23617, loss_grounding_bce_8: 0.47265/0.08931, loss_grounding_dice_8: 0.37480/0.17115, loss_grounding_ce_8: 0.00099/0.44972, loss_mask_ce_9: 1.91579/3.51454, loss_mask_bce_9: 0.25239/0.36085, loss_mask_dice_9: 0.31249/1.78061, loss_spatial_bce_9: 0.26490/0.36147, loss_spatial_dice_9: 0.52626/0.79841, loss_spatial_ce_9: 0.61219/1.42331, loss_grounding_bce_9: 0.28822/0.10127, loss_grounding_dice_9: 0.35840/0.24585, loss_grounding_ce_9: 0.17816/0.72528] items per batch[64] items per second[0.36] total items[928000] mini batches[ 14500] memory[4967] epoch remaining[0:03:27] INFO:trainer.default_trainer:epochs[ 7] optim steps[14600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11265/0.79267, loss_mask_bce_0: 0.07223/0.30247, loss_mask_dice_0: 0.40573/1.03438, loss_spatial_bce_0: 0.01451/0.09146, loss_spatial_dice_0: 0.10556/0.19237, loss_spatial_ce_0: 0.09469/0.07867, loss_grounding_bce_0: 0.00866/0.08087, loss_grounding_dice_0: 0.07203/0.15213, loss_grounding_ce_0: 0.29594/0.25491, loss_mask_ce_1: 0.14536/0.79584, loss_mask_bce_1: 0.06954/0.30279, loss_mask_dice_1: 0.38316/1.03812, loss_spatial_bce_1: 0.01532/0.09195, loss_spatial_dice_1: 0.13809/0.19510, loss_spatial_ce_1: 0.12716/0.08317, loss_grounding_bce_1: 0.01015/0.08098, loss_grounding_dice_1: 0.06684/0.15319, loss_grounding_ce_1: 0.45656/0.25831, loss_mask_ce_2: 0.12965/0.80221, loss_mask_bce_2: 0.07015/0.30283, loss_mask_dice_2: 0.41395/1.04098, loss_spatial_bce_2: 0.01588/0.09137, loss_spatial_dice_2: 0.14442/0.19499, loss_spatial_ce_2: 0.14932/0.08699, loss_grounding_bce_2: 0.00810/0.08075, loss_grounding_dice_2: 0.06519/0.15280, loss_grounding_ce_2: 0.39935/0.25870, loss_mask_ce_3: 0.10818/0.80077, loss_mask_bce_3: 0.06732/0.30419, loss_mask_dice_3: 0.38270/1.03570, loss_spatial_bce_3: 0.01397/0.09294, loss_spatial_dice_3: 0.11377/0.19519, loss_spatial_ce_3: 0.12355/0.09311, loss_grounding_bce_3: 0.00853/0.08123, loss_grounding_dice_3: 0.07136/0.15252, loss_grounding_ce_3: 0.45580/0.25692, loss_mask_ce_4: 0.09296/0.80769, loss_mask_bce_4: 0.06434/0.30646, loss_mask_dice_4: 0.43572/1.05436, loss_spatial_bce_4: 0.01544/0.09508, loss_spatial_dice_4: 0.12294/0.20267, loss_spatial_ce_4: 0.09794/0.10442, loss_grounding_bce_4: 0.00636/0.08199, loss_grounding_dice_4: 0.05954/0.15469, loss_grounding_ce_4: 0.43609/0.26585, loss_mask_ce_5: 0.13088/0.82792, loss_mask_bce_5: 0.06928/0.30856, loss_mask_dice_5: 0.42705/1.06141, loss_spatial_bce_5: 0.01519/0.09663, loss_spatial_dice_5: 0.11539/0.20480, loss_spatial_ce_5: 0.13837/0.11544, loss_grounding_bce_5: 0.00887/0.08241, loss_grounding_dice_5: 0.06877/0.15551, loss_grounding_ce_5: 0.30667/0.28494, loss_mask_ce_6: 0.15430/0.85302, loss_mask_bce_6: 0.06618/0.30981, loss_mask_dice_6: 0.44620/1.06579, loss_spatial_bce_6: 0.01728/0.10160, loss_spatial_dice_6: 0.11850/0.20704, loss_spatial_ce_6: 0.13644/0.13399, loss_grounding_bce_6: 0.00684/0.08360, loss_grounding_dice_6: 0.05786/0.15611, loss_grounding_ce_6: 0.49126/0.29879, loss_mask_ce_7: 0.22953/0.91579, loss_mask_bce_7: 0.07106/0.31706, loss_mask_dice_7: 0.45185/1.11270, loss_spatial_bce_7: 0.02006/0.11224, loss_spatial_dice_7: 0.11108/0.23203, loss_spatial_ce_7: 0.26636/0.17847, loss_grounding_bce_7: 0.00763/0.08546, loss_grounding_dice_7: 0.07022/0.16211, loss_grounding_ce_7: 0.19350/0.34642, loss_mask_ce_8: 0.63628/1.05533, loss_mask_bce_8: 0.09253/0.33467, loss_mask_dice_8: 0.52670/1.19290, loss_spatial_bce_8: 0.02511/0.13386, loss_spatial_dice_8: 0.19067/0.27415, loss_spatial_ce_8: 0.16317/0.23603, loss_grounding_bce_8: 0.01148/0.08930, loss_grounding_dice_8: 0.08007/0.17120, loss_grounding_ce_8: 0.79394/0.45021, loss_mask_ce_9: 2.52786/3.51356, loss_mask_bce_9: 0.08653/0.36090, loss_mask_dice_9: 0.76417/1.77835, loss_spatial_bce_9: 0.20574/0.36165, loss_spatial_dice_9: 0.91456/0.79836, loss_spatial_ce_9: 1.85213/1.42319, loss_grounding_bce_9: 0.02929/0.10131, loss_grounding_dice_9: 0.21155/0.24589, loss_grounding_ce_9: 0.92950/0.72503] items per batch[64] items per second[0.36] total items[934400] mini batches[ 14600] memory[4967] epoch remaining[0:00:28] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00014616. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0015 s/iter. Inference: 0.3692 s/iter. Eval: 0.0853 s/iter. Total: 0.4560 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0021 s/iter. Inference: 0.3763 s/iter. Eval: 0.0842 s/iter. Total: 0.4628 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0024 s/iter. Inference: 0.3807 s/iter. Eval: 0.0779 s/iter. Total: 0.4611 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0026 s/iter. Inference: 0.3692 s/iter. Eval: 0.0743 s/iter. Total: 0.4462 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3732 s/iter. Eval: 0.0730 s/iter. Total: 0.4490 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0029 s/iter. Inference: 0.3721 s/iter. Eval: 0.0706 s/iter. Total: 0.4457 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalfe9yshpu ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.370 | 82.690 | 65.717 | 133 | | Things | 61.797 | 84.300 | 72.811 | 80 | | Stuff | 45.669 | 80.261 | 55.010 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.52s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.67 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.38 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.99s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 21.36 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.494 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.45 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.795 | 69.295 | 49.431 | 26.215 | 49.652 | 67.698 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.553 | bicycle | 22.550 | car | 42.647 | | motorcycle | 42.652 | airplane | 62.207 | bus | 70.907 | | train | 75.181 | truck | 43.600 | boat | 30.735 | | traffic light | 28.357 | fire hydrant | 72.086 | stop sign | 68.139 | | parking meter | 51.692 | bench | 26.362 | bird | 34.566 | | cat | 77.403 | dog | 71.866 | horse | 51.036 | | sheep | 54.505 | cow | 56.579 | elephant | 67.259 | | bear | 80.420 | zebra | 67.016 | giraffe | 63.127 | | backpack | 23.788 | umbrella | 55.330 | handbag | 23.091 | | tie | 40.200 | suitcase | 50.516 | frisbee | 70.840 | | skis | 7.740 | snowboard | 34.656 | sports ball | 49.117 | | kite | 36.497 | baseball bat | 38.598 | baseball glove | 51.252 | | skateboard | 43.211 | surfboard | 45.354 | tennis racket | 63.105 | | bottle | 41.464 | wine glass | 37.280 | cup | 50.462 | | fork | 25.646 | knife | 24.176 | spoon | 21.278 | | bowl | 40.539 | banana | 21.811 | apple | 25.391 | | sandwich | 48.542 | orange | 30.531 | broccoli | 24.098 | | carrot | 21.990 | hot dog | 37.354 | pizza | 51.420 | | donut | 55.181 | cake | 46.713 | chair | 28.099 | | couch | 44.740 | potted plant | 23.422 | bed | 44.481 | | dining table | 16.544 | toilet | 68.849 | tv | 67.121 | | laptop | 70.718 | mouse | 63.771 | remote | 43.436 | | keyboard | 58.635 | cell phone | 46.106 | microwave | 64.011 | | oven | 35.709 | toaster | 55.431 | sink | 44.108 | | refrigerator | 70.793 | book | 14.045 | clock | 53.268 | | vase | 40.622 | scissors | 37.553 | teddy bear | 58.308 | | hair drier | 36.466 | toothbrush | 30.742 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.5366726783509, 'fwIoU': 71.51680580116269, 'IoU-person': 88.7034342934035, 'IoU-bicycle': 77.92609178173122, 'IoU-car': 72.49004681979349, 'IoU-motorcycle': 89.11204677288933, 'IoU-airplane': 87.10784127004085, 'IoU-bus': 87.11193061210342, 'IoU-train': 87.9191125899059, 'IoU-truck': 67.65291125976483, 'IoU-boat': 73.45194881811611, 'IoU-traffic light': 79.23168246140563, 'IoU-fire hydrant': 93.35849283337546, 'IoU-stop sign': 95.4014887034019, 'IoU-parking meter': 84.65727940708895, 'IoU-bench': 60.63272196292176, 'IoU-bird': 77.68536966967997, 'IoU-cat': 88.08875384850217, 'IoU-dog': 81.0247577599273, 'IoU-horse': 88.74286502907458, 'IoU-sheep': 85.8700625384455, 'IoU-cow': 88.88137145712115, 'IoU-elephant': 91.43052868763752, 'IoU-bear': 86.57289233137581, 'IoU-zebra': 83.17100694475572, 'IoU-giraffe': 89.7704421658985, 'IoU-backpack': 49.98421055356246, 'IoU-umbrella': 86.38764507853425, 'IoU-handbag': 47.92551308986949, 'IoU-tie': 72.84931829012433, 'IoU-suitcase': 87.46688336799593, 'IoU-frisbee': 84.39577693733489, 'IoU-skis': 58.75304863077603, 'IoU-snowboard': 71.48827380209431, 'IoU-sports ball': 81.10797536151726, 'IoU-kite': 79.44274684057775, 'IoU-baseball bat': 68.5536847755064, 'IoU-baseball glove': 76.85758837621952, 'IoU-skateboard': 86.06724189736629, 'IoU-surfboard': 85.96275862330803, 'IoU-tennis racket': 90.96069297149273, 'IoU-bottle': 70.03681105927448, 'IoU-wine glass': 83.02908851022517, 'IoU-cup': 70.06059576601731, 'IoU-fork': 69.35741840902139, 'IoU-knife': 63.88445727373795, 'IoU-spoon': 60.41004115517317, 'IoU-bowl': 60.40431419319019, 'IoU-banana': 83.44467753890838, 'IoU-apple': 59.66480764189493, 'IoU-sandwich': 70.86687603459212, 'IoU-orange': 77.6796104811959, 'IoU-broccoli': 70.41308043981924, 'IoU-carrot': 63.88876879167661, 'IoU-hot dog': 67.75119655033957, 'IoU-pizza': 84.07607482097278, 'IoU-donut': 74.84918173030152, 'IoU-cake': 79.72344680759554, 'IoU-chair': 62.28858263601597, 'IoU-couch': 70.26824653584654, 'IoU-potted plant': 44.26663799293972, 'IoU-bed': 66.7276893601101, 'IoU-dining table': 54.0751884773508, 'IoU-toilet': 74.56623978861916, 'IoU-tv': 79.8448410425156, 'IoU-laptop': 77.95480283531091, 'IoU-mouse': 74.58120433948729, 'IoU-remote': 67.79287374827283, 'IoU-keyboard': 68.86683265527421, 'IoU-cell phone': 83.87466433421234, 'IoU-microwave': 70.65811809205242, 'IoU-oven': 70.84283696614528, 'IoU-toaster': 85.84341036328847, 'IoU-sink': 75.07270843083505, 'IoU-refrigerator': 76.8081649398882, 'IoU-book': 58.54100122650463, 'IoU-clock': 74.77234813859772, 'IoU-vase': 60.89432411512874, 'IoU-scissors': 75.0958286725464, 'IoU-teddy bear': 81.43804527687129, 'IoU-hair drier': 49.307862679955704, 'IoU-toothbrush': 76.75886904013083, 'IoU-banner': 31.411256457881713, 'IoU-blanket': 16.33334910593608, 'IoU-bridge': 36.02626109355923, 'IoU-cardboard': 51.6358105637126, 'IoU-counter': 30.665233578227063, 'IoU-curtain': 70.88078482128216, 'IoU-door-stuff': 47.39112270830041, 'IoU-floor-wood': 66.12105135170614, 'IoU-flower': 46.934658734950055, 'IoU-fruit': 47.05653298403224, 'IoU-gravel': 25.040344240856104, 'IoU-house': 25.492115911141124, 'IoU-light': 42.80948422233233, 'IoU-mirror-stuff': 64.61795364932867, 'IoU-net': 48.91730910088061, 'IoU-pillow': 21.364522631130477, 'IoU-platform': 29.633721696613023, 'IoU-playingfield': 68.86914479201144, 'IoU-railroad': 63.9785314409957, 'IoU-river': 54.997108725420276, 'IoU-road': 67.95175938036424, 'IoU-roof': 14.557953112898758, 'IoU-sand': 66.85968415674769, 'IoU-sea': 86.38498296686798, 'IoU-shelf': 36.554689667428214, 'IoU-snow': 92.05532986809686, 'IoU-stairs': 34.19045574351048, 'IoU-tent': 11.604269949729547, 'IoU-towel': 45.181983923652645, 'IoU-wall-brick': 48.012112941345094, 'IoU-wall-stone': 29.245231256194966, 'IoU-wall-tile': 68.02369345934575, 'IoU-wall-wood': 43.31001287881302, 'IoU-water-other': 26.005487076084137, 'IoU-window-blind': 50.7019277424199, 'IoU-window-other': 50.069934585430154, 'IoU-tree-merged': 82.00393659140205, 'IoU-fence-merged': 54.79734777822042, 'IoU-ceiling-merged': 68.29885689117987, 'IoU-sky-other-merged': 94.30079003690325, 'IoU-cabinet-merged': 62.92743895770546, 'IoU-table-merged': 39.66739630348134, 'IoU-floor-other-merged': 55.34447600573783, 'IoU-pavement-merged': 58.76254762975758, 'IoU-mountain-merged': 57.89612410246722, 'IoU-grass-merged': 72.5126238010403, 'IoU-dirt-merged': 47.08697959867651, 'IoU-paper-merged': 35.145085146817465, 'IoU-food-other-merged': 41.77795208371725, 'IoU-building-other-merged': 59.50079350439016, 'IoU-rock-merged': 64.60787120223112, 'IoU-wall-other-merged': 67.79509550286954, 'IoU-rug-merged': 68.18613705836793, 'mACC': 77.07146674452837, 'pACC': 82.20698636249662, 'ACC-person': 93.1811963530515, 'ACC-bicycle': 90.10132299625339, 'ACC-car': 86.53318825083973, 'ACC-motorcycle': 93.8492903541334, 'ACC-airplane': 93.64551287301282, 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'ACC-mouse': 91.88229819378158, 'ACC-remote': 71.87540300436585, 'ACC-keyboard': 75.11080780457895, 'ACC-cell phone': 92.23125864510185, 'ACC-microwave': 74.78637224966552, 'ACC-oven': 92.12418030408872, 'ACC-toaster': 91.17888571076654, 'ACC-sink': 84.31711675827933, 'ACC-refrigerator': 85.68065156205537, 'ACC-book': 75.69177103471463, 'ACC-clock': 79.6359043698038, 'ACC-vase': 70.01820283142078, 'ACC-scissors': 79.44201122037664, 'ACC-teddy bear': 85.9593483546935, 'ACC-hair drier': 60.83169669541457, 'ACC-toothbrush': 84.80281445448227, 'ACC-banner': 77.9550546816088, 'ACC-blanket': 27.365036548578303, 'ACC-bridge': 53.2404538667046, 'ACC-cardboard': 65.61867093209248, 'ACC-counter': 50.086463153562946, 'ACC-curtain': 84.06817882415011, 'ACC-door-stuff': 72.71633250095768, 'ACC-floor-wood': 81.06457947321124, 'ACC-flower': 66.20394801256253, 'ACC-fruit': 71.61342989935034, 'ACC-gravel': 28.635647585604907, 'ACC-house': 31.345437836564084, 'ACC-light': 61.75574445890595, 'ACC-mirror-stuff': 79.44903999056353, 'ACC-net': 67.03641257235846, 'ACC-pillow': 41.105300256340534, 'ACC-platform': 49.8400903580797, 'ACC-playingfield': 86.57995572932381, 'ACC-railroad': 84.01261236250974, 'ACC-river': 80.03196106901285, 'ACC-road': 84.85593642495398, 'ACC-roof': 18.886919532080825, 'ACC-sand': 71.70648007055476, 'ACC-sea': 90.81671095103577, 'ACC-shelf': 51.363154805398004, 'ACC-snow': 95.36190899151063, 'ACC-stairs': 55.57036782323751, 'ACC-tent': 13.97410616826495, 'ACC-towel': 54.64870046522208, 'ACC-wall-brick': 66.11392340426508, 'ACC-wall-stone': 36.82146548382929, 'ACC-wall-tile': 85.86253428612314, 'ACC-wall-wood': 61.43679184717198, 'ACC-water-other': 40.92874373122169, 'ACC-window-blind': 65.68317523998469, 'ACC-window-other': 69.92828047370338, 'ACC-tree-merged': 89.90774081372189, 'ACC-fence-merged': 73.18996087898562, 'ACC-ceiling-merged': 84.10692473773051, 'ACC-sky-other-merged': 97.10804095680895, 'ACC-cabinet-merged': 78.14203652743153, 'ACC-table-merged': 49.97071670098786, 'ACC-floor-other-merged': 65.22608398751193, 'ACC-pavement-merged': 72.10539344859409, 'ACC-mountain-merged': 68.00222805125588, 'ACC-grass-merged': 84.04858084546298, 'ACC-dirt-merged': 70.74656599741051, 'ACC-paper-merged': 45.81810306794259, 'ACC-food-other-merged': 52.11672277897905, 'ACC-building-other-merged': 73.48429780514223, 'ACC-rock-merged': 84.96667600400033, 'ACC-wall-other-merged': 81.9729833487669, 'ACC-rug-merged': 82.41030640147271})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3078 s/iter. Inference: 0.1725 s/iter. Eval: 0.0000 s/iter. Total: 0.4803 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3101 s/iter. Inference: 0.4522 s/iter. Eval: 0.0000 s/iter. Total: 0.7624 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3218 s/iter. Inference: 0.5203 s/iter. Eval: 0.0000 s/iter. Total: 0.8422 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4357623646473514, 'noc@0.8': 2.516827626573017, 'noc@0.85': 2.981270119988294, 'noc@0.9': 3.811237928007024, 'miou@iter1': 0.8695565224531139} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1452 s/iter. Eval: 0.0010 s/iter. Total: 0.1478 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 74.27127838134766, 'precision@0.6': 71.7839126586914, 'precision@0.7': 67.6642074584961, 'precision@0.8': 58.99728012084961, 'precision@0.9': 33.42401885986328, 'cIoU': 60.46744155883789, 'mIoU': 66.02411651611328} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.37035419652126, 'SQ': 82.69011956284638, 'RQ': 65.7171355921693, 'PQ_th': 61.797232671340275, 'SQ_th': 84.29950660825746, 'RQ_th': 72.81064010863557, 'PQ_st': 45.66940555528506, 'SQ_st': 80.26085609807497, 'RQ_st': 55.00995896354095}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-baseball bat': 88.04029400085413, 'ACC-baseball glove': 92.6950707219184, 'ACC-skateboard': 90.64100434936779, 'ACC-surfboard': 91.78168910364248, 'ACC-tennis racket': 94.8320776593769, 'ACC-bottle': 84.46149637734214, 'ACC-wine glass': 90.71422095700893, 'ACC-cup': 86.89916246868236, 'ACC-fork': 79.57412798112732, 'ACC-knife': 76.95006228475123, 'ACC-spoon': 77.25516549731398, 'ACC-bowl': 72.93647787647105, 'ACC-banana': 91.33457983266202, 'ACC-apple': 71.0143169499821, 'ACC-sandwich': 82.03038987663112, 'ACC-orange': 86.39543117204316, 'ACC-broccoli': 79.85022649129294, 'ACC-carrot': 78.50170214809779, 'ACC-hot dog': 74.72426670850056, 'ACC-pizza': 93.4239723852685, 'ACC-donut': 83.02270139872337, 'ACC-cake': 87.38645010747743, 'ACC-chair': 78.43693982272313, 'ACC-couch': 80.39628176601018, 'ACC-potted plant': 58.88641335966028, 'ACC-bed': 79.9973475253235, 'ACC-dining table': 81.40943013898863, 'ACC-toilet': 78.33031844749549, 'ACC-tv': 89.35134104004906, 'ACC-laptop': 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61.75574445890595, 'ACC-mirror-stuff': 79.44903999056353, 'ACC-net': 67.03641257235846, 'ACC-pillow': 41.105300256340534, 'ACC-platform': 49.8400903580797, 'ACC-playingfield': 86.57995572932381, 'ACC-railroad': 84.01261236250974, 'ACC-river': 80.03196106901285, 'ACC-road': 84.85593642495398, 'ACC-roof': 18.886919532080825, 'ACC-sand': 71.70648007055476, 'ACC-sea': 90.81671095103577, 'ACC-shelf': 51.363154805398004, 'ACC-snow': 95.36190899151063, 'ACC-stairs': 55.57036782323751, 'ACC-tent': 13.97410616826495, 'ACC-towel': 54.64870046522208, 'ACC-wall-brick': 66.11392340426508, 'ACC-wall-stone': 36.82146548382929, 'ACC-wall-tile': 85.86253428612314, 'ACC-wall-wood': 61.43679184717198, 'ACC-water-other': 40.92874373122169, 'ACC-window-blind': 65.68317523998469, 'ACC-window-other': 69.92828047370338, 'ACC-tree-merged': 89.90774081372189, 'ACC-fence-merged': 73.18996087898562, 'ACC-ceiling-merged': 84.10692473773051, 'ACC-sky-other-merged': 97.10804095680895, 'ACC-cabinet-merged': 78.14203652743153, 'ACC-table-merged': 49.97071670098786, 'ACC-floor-other-merged': 65.22608398751193, 'ACC-pavement-merged': 72.10539344859409, 'ACC-mountain-merged': 68.00222805125588, 'ACC-grass-merged': 84.04858084546298, 'ACC-dirt-merged': 70.74656599741051, 'ACC-paper-merged': 45.81810306794259, 'ACC-food-other-merged': 52.11672277897905, 'ACC-building-other-merged': 73.48429780514223, 'ACC-rock-merged': 84.96667600400033, 'ACC-wall-other-merged': 81.9729833487669, 'ACC-rug-merged': 82.41030640147271})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4357623646473514, 'noc@0.8': 2.516827626573017, 'noc@0.85': 2.981270119988294, 'noc@0.9': 3.811237928007024, 'miou@iter1': 0.8695565224531139}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 74.27127838134766, 'precision@0.6': 71.7839126586914, 'precision@0.7': 67.6642074584961, 'precision@0.8': 58.99728012084961, 'precision@0.9': 33.42401885986328, 'cIoU': 60.46744155883789, 'mIoU': 66.02411651611328}}} INFO:trainer.default_trainer:This epoch takes 0:57:58.794094 INFO:trainer.default_trainer:PROGRESS: 16.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 8 training. INFO:trainer.default_trainer:epochs[ 8] optim steps[14700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89552/0.79273, loss_mask_bce_0: 0.58925/0.30238, loss_mask_dice_0: 5.02212/1.03416, loss_spatial_bce_0: 0.01183/0.09140, loss_spatial_dice_0: 0.27969/0.19237, loss_spatial_ce_0: 0.00281/0.07864, loss_grounding_bce_0: 0.08465/0.08085, loss_grounding_dice_0: 0.09923/0.15228, loss_grounding_ce_0: 0.87143/0.25470, loss_mask_ce_1: 0.59374/0.79593, loss_mask_bce_1: 0.62142/0.30271, loss_mask_dice_1: 5.00278/1.03809, loss_spatial_bce_1: 0.01059/0.09190, loss_spatial_dice_1: 0.28008/0.19511, loss_spatial_ce_1: 0.00567/0.08311, loss_grounding_bce_1: 0.10270/0.08096, loss_grounding_dice_1: 0.09932/0.15333, loss_grounding_ce_1: 0.49844/0.25810, loss_mask_ce_2: 0.60884/0.80217, loss_mask_bce_2: 0.60143/0.30278, loss_mask_dice_2: 5.04983/1.04087, loss_spatial_bce_2: 0.00886/0.09132, loss_spatial_dice_2: 0.28145/0.19500, loss_spatial_ce_2: 0.00572/0.08689, loss_grounding_bce_2: 0.09858/0.08073, loss_grounding_dice_2: 0.10691/0.15298, loss_grounding_ce_2: 0.75499/0.25848, loss_mask_ce_3: 0.70395/0.80094, loss_mask_bce_3: 0.62823/0.30411, loss_mask_dice_3: 4.96580/1.03577, loss_spatial_bce_3: 0.01102/0.09287, loss_spatial_dice_3: 0.28924/0.19519, loss_spatial_ce_3: 0.01029/0.09296, loss_grounding_bce_3: 0.09281/0.08123, loss_grounding_dice_3: 0.10025/0.15272, loss_grounding_ce_3: 0.62149/0.25668, loss_mask_ce_4: 0.65618/0.80787, loss_mask_bce_4: 0.61688/0.30637, loss_mask_dice_4: 4.89589/1.05412, loss_spatial_bce_4: 0.01338/0.09499, loss_spatial_dice_4: 0.33248/0.20266, loss_spatial_ce_4: 0.05419/0.10428, loss_grounding_bce_4: 0.10653/0.08197, loss_grounding_dice_4: 0.10783/0.15485, loss_grounding_ce_4: 0.44239/0.26563, loss_mask_ce_5: 0.66950/0.82825, loss_mask_bce_5: 0.63451/0.30851, loss_mask_dice_5: 5.05370/1.06122, loss_spatial_bce_5: 0.01231/0.09657, loss_spatial_dice_5: 0.32858/0.20482, loss_spatial_ce_5: 0.07115/0.11532, loss_grounding_bce_5: 0.07445/0.08242, loss_grounding_dice_5: 0.10241/0.15571, loss_grounding_ce_5: 1.29001/0.28482, loss_mask_ce_6: 0.85247/0.85327, loss_mask_bce_6: 0.67754/0.30971, loss_mask_dice_6: 5.25558/1.06565, loss_spatial_bce_6: 0.01525/0.10152, loss_spatial_dice_6: 0.34268/0.20707, loss_spatial_ce_6: 0.06274/0.13379, loss_grounding_bce_6: 0.08818/0.08357, loss_grounding_dice_6: 0.11817/0.15628, loss_grounding_ce_6: 1.10349/0.29859, loss_mask_ce_7: 0.84860/0.91627, loss_mask_bce_7: 0.68755/0.31702, loss_mask_dice_7: 5.65321/1.11234, loss_spatial_bce_7: 0.03605/0.11216, loss_spatial_dice_7: 0.35274/0.23204, loss_spatial_ce_7: 0.12526/0.17838, loss_grounding_bce_7: 0.09969/0.08543, loss_grounding_dice_7: 0.09683/0.16226, loss_grounding_ce_7: 1.04542/0.34615, loss_mask_ce_8: 0.61171/1.05536, loss_mask_bce_8: 0.76464/0.33461, loss_mask_dice_8: 6.17997/1.19280, loss_spatial_bce_8: 0.01241/0.13378, loss_spatial_dice_8: 0.41038/0.27412, loss_spatial_ce_8: 0.17512/0.23579, loss_grounding_bce_8: 0.13921/0.08928, loss_grounding_dice_8: 0.09193/0.17144, loss_grounding_ce_8: 2.09190/0.44996, loss_mask_ce_9: 5.26592/3.51394, loss_mask_bce_9: 1.33478/0.36086, loss_mask_dice_9: 10.63966/1.77820, loss_spatial_bce_9: 0.11738/0.36154, loss_spatial_dice_9: 0.94518/0.79834, loss_spatial_ce_9: 1.10242/1.42332, loss_grounding_bce_9: 0.22297/0.10129, loss_grounding_dice_9: 0.31875/0.24611, loss_grounding_ce_9: 1.94765/0.72434] items per batch[64] items per second[0.16] total items[940800] mini batches[ 14700] memory[4967] epoch remaining[0:54:27] INFO:trainer.default_trainer:epochs[ 8] optim steps[14800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72556/0.79236, loss_mask_bce_0: 0.03701/0.30226, loss_mask_dice_0: 0.46356/1.03453, loss_spatial_bce_0: 0.00925/0.09127, loss_spatial_dice_0: 0.16665/0.19230, loss_spatial_ce_0: 0.00006/0.07847, loss_grounding_bce_0: 0.00513/0.08073, loss_grounding_dice_0: 0.07192/0.15220, loss_grounding_ce_0: 0.01036/0.25459, loss_mask_ce_1: 0.53804/0.79548, loss_mask_bce_1: 0.03254/0.30258, loss_mask_dice_1: 0.48610/1.03862, loss_spatial_bce_1: 0.01021/0.09177, loss_spatial_dice_1: 0.14367/0.19503, loss_spatial_ce_1: 0.00012/0.08300, loss_grounding_bce_1: 0.00443/0.08084, loss_grounding_dice_1: 0.05508/0.15322, loss_grounding_ce_1: 0.01635/0.25800, loss_mask_ce_2: 0.65049/0.80174, loss_mask_bce_2: 0.03269/0.30267, loss_mask_dice_2: 0.51038/1.04131, loss_spatial_bce_2: 0.00950/0.09119, loss_spatial_dice_2: 0.13998/0.19493, loss_spatial_ce_2: 0.00054/0.08667, loss_grounding_bce_2: 0.00593/0.08062, loss_grounding_dice_2: 0.09555/0.15287, loss_grounding_ce_2: 0.01770/0.25831, loss_mask_ce_3: 0.68562/0.80045, loss_mask_bce_3: 0.03533/0.30404, loss_mask_dice_3: 0.43932/1.03625, loss_spatial_bce_3: 0.00771/0.09274, loss_spatial_dice_3: 0.13339/0.19513, loss_spatial_ce_3: 0.00479/0.09273, loss_grounding_bce_3: 0.00435/0.08112, loss_grounding_dice_3: 0.07423/0.15264, loss_grounding_ce_3: 0.07284/0.25659, loss_mask_ce_4: 0.86055/0.80769, loss_mask_bce_4: 0.03532/0.30621, loss_mask_dice_4: 0.37500/1.05433, loss_spatial_bce_4: 0.00807/0.09486, loss_spatial_dice_4: 0.11873/0.20260, loss_spatial_ce_4: 0.00866/0.10402, loss_grounding_bce_4: 0.00282/0.08187, loss_grounding_dice_4: 0.05124/0.15476, loss_grounding_ce_4: 0.07271/0.26571, loss_mask_ce_5: 0.71161/0.82802, loss_mask_bce_5: 0.04303/0.30836, loss_mask_dice_5: 0.51016/1.06150, loss_spatial_bce_5: 0.00891/0.09643, loss_spatial_dice_5: 0.12885/0.20474, loss_spatial_ce_5: 0.01262/0.11511, loss_grounding_bce_5: 0.00505/0.08232, loss_grounding_dice_5: 0.08430/0.15561, loss_grounding_ce_5: 0.00549/0.28495, loss_mask_ce_6: 0.77362/0.85300, loss_mask_bce_6: 0.03421/0.30959, loss_mask_dice_6: 0.40340/1.06604, loss_spatial_bce_6: 0.00967/0.10139, loss_spatial_dice_6: 0.12763/0.20700, loss_spatial_ce_6: 0.00805/0.13352, loss_grounding_bce_6: 0.00397/0.08347, loss_grounding_dice_6: 0.08017/0.15615, loss_grounding_ce_6: 0.00428/0.29840, loss_mask_ce_7: 0.69749/0.91561, loss_mask_bce_7: 0.03652/0.31688, loss_mask_dice_7: 0.53154/1.11274, loss_spatial_bce_7: 0.04529/0.11199, loss_spatial_dice_7: 0.23902/0.23194, loss_spatial_ce_7: 0.05757/0.17821, loss_grounding_bce_7: 0.00435/0.08532, loss_grounding_dice_7: 0.07441/0.16216, loss_grounding_ce_7: 0.00221/0.34577, loss_mask_ce_8: 0.75307/1.05478, loss_mask_bce_8: 0.03820/0.33448, loss_mask_dice_8: 0.49308/1.19306, loss_spatial_bce_8: 0.04052/0.13362, loss_spatial_dice_8: 0.25390/0.27400, loss_spatial_ce_8: 0.07831/0.23562, loss_grounding_bce_8: 0.00122/0.08915, loss_grounding_dice_8: 0.03638/0.17131, loss_grounding_ce_8: 0.00060/0.44995, loss_mask_ce_9: 2.25762/3.51286, loss_mask_bce_9: 0.02673/0.36071, loss_mask_dice_9: 0.59728/1.77855, loss_spatial_bce_9: 0.06147/0.36141, loss_spatial_dice_9: 0.71435/0.79832, loss_spatial_ce_9: 0.80992/1.42348, loss_grounding_bce_9: 0.00204/0.10116, loss_grounding_dice_9: 0.07313/0.24590, loss_grounding_ce_9: 0.03030/0.72435] items per batch[64] items per second[0.36] total items[947200] mini batches[ 14800] memory[4967] epoch remaining[0:49:52] INFO:trainer.default_trainer:epochs[ 8] optim steps[14900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66420/0.79133, loss_mask_bce_0: 0.47407/0.30209, loss_mask_dice_0: 0.22365/1.03358, loss_spatial_bce_0: 0.27547/0.09123, loss_spatial_dice_0: 0.18465/0.19212, loss_spatial_ce_0: 0.15585/0.07825, loss_grounding_bce_0: 0.00351/0.08064, loss_grounding_dice_0: 0.06692/0.15213, loss_grounding_ce_0: 0.02904/0.25429, loss_mask_ce_1: 0.71493/0.79449, loss_mask_bce_1: 0.46009/0.30244, loss_mask_dice_1: 0.23944/1.03767, loss_spatial_bce_1: 0.31623/0.09172, loss_spatial_dice_1: 0.17927/0.19485, loss_spatial_ce_1: 0.23260/0.08278, loss_grounding_bce_1: 0.00509/0.08077, loss_grounding_dice_1: 0.06601/0.15318, loss_grounding_ce_1: 0.03192/0.25772, loss_mask_ce_2: 0.70017/0.80074, loss_mask_bce_2: 0.44983/0.30254, loss_mask_dice_2: 0.22531/1.04047, loss_spatial_bce_2: 0.27936/0.09117, loss_spatial_dice_2: 0.18562/0.19476, loss_spatial_ce_2: 0.20165/0.08643, loss_grounding_bce_2: 0.00575/0.08055, loss_grounding_dice_2: 0.05274/0.15283, loss_grounding_ce_2: 0.03344/0.25811, loss_mask_ce_3: 1.04970/0.79946, loss_mask_bce_3: 0.50726/0.30390, loss_mask_dice_3: 0.22711/1.03536, loss_spatial_bce_3: 0.27658/0.09271, loss_spatial_dice_3: 0.19428/0.19498, loss_spatial_ce_3: 0.25465/0.09245, loss_grounding_bce_3: 0.00415/0.08105, loss_grounding_dice_3: 0.05604/0.15260, loss_grounding_ce_3: 0.03700/0.25623, loss_mask_ce_4: 1.03783/0.80668, loss_mask_bce_4: 0.44842/0.30610, loss_mask_dice_4: 0.26678/1.05330, loss_spatial_bce_4: 0.29171/0.09484, loss_spatial_dice_4: 0.19284/0.20245, loss_spatial_ce_4: 0.32494/0.10375, loss_grounding_bce_4: 0.00086/0.08179, loss_grounding_dice_4: 0.04464/0.15470, loss_grounding_ce_4: 0.03715/0.26530, loss_mask_ce_5: 1.35467/0.82701, loss_mask_bce_5: 0.40545/0.30821, loss_mask_dice_5: 0.24256/1.06051, loss_spatial_bce_5: 0.22185/0.09643, loss_spatial_dice_5: 0.18814/0.20457, loss_spatial_ce_5: 0.45993/0.11486, loss_grounding_bce_5: 0.00363/0.08223, loss_grounding_dice_5: 0.05165/0.15555, loss_grounding_ce_5: 0.04973/0.28447, loss_mask_ce_6: 1.53070/0.85199, loss_mask_bce_6: 0.40161/0.30948, loss_mask_dice_6: 0.24401/1.06513, loss_spatial_bce_6: 0.29282/0.10139, loss_spatial_dice_6: 0.21086/0.20685, loss_spatial_ce_6: 0.68278/0.13330, loss_grounding_bce_6: 0.00479/0.08340, loss_grounding_dice_6: 0.06800/0.15611, loss_grounding_ce_6: 0.06763/0.29783, loss_mask_ce_7: 1.36265/0.91442, loss_mask_bce_7: 0.36693/0.31677, loss_mask_dice_7: 0.24385/1.11188, loss_spatial_bce_7: 0.27987/0.11195, loss_spatial_dice_7: 0.37086/0.23183, loss_spatial_ce_7: 0.63568/0.17792, loss_grounding_bce_7: 0.00404/0.08523, loss_grounding_dice_7: 0.06386/0.16211, loss_grounding_ce_7: 0.06684/0.34517, loss_mask_ce_8: 1.52150/1.05341, loss_mask_bce_8: 0.39687/0.33432, loss_mask_dice_8: 0.24892/1.19216, loss_spatial_bce_8: 0.35468/0.13356, loss_spatial_dice_8: 0.36866/0.27379, loss_spatial_ce_8: 0.36073/0.23532, loss_grounding_bce_8: 0.00274/0.08905, loss_grounding_dice_8: 0.06183/0.17126, loss_grounding_ce_8: 0.15799/0.44945, loss_mask_ce_9: 3.54173/3.51126, loss_mask_bce_9: 0.47073/0.36060, loss_mask_dice_9: 0.56697/1.77678, loss_spatial_bce_9: 0.64077/0.36156, loss_spatial_dice_9: 0.62483/0.79827, loss_spatial_ce_9: 1.01540/1.42298, loss_grounding_bce_9: 0.00588/0.10104, loss_grounding_dice_9: 0.29577/0.24577, loss_grounding_ce_9: 0.54998/0.72389] items per batch[64] items per second[0.36] total items[953600] mini batches[ 14900] memory[4967] epoch remaining[0:46:29] INFO:trainer.default_trainer:epochs[ 8] optim steps[15000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17626/0.79112, loss_mask_bce_0: 0.14395/0.30197, loss_mask_dice_0: 0.15053/1.03382, loss_spatial_bce_0: 0.08981/0.09118, loss_spatial_dice_0: 0.09814/0.19209, loss_spatial_ce_0: 0.07954/0.07814, loss_grounding_bce_0: 0.09793/0.08064, loss_grounding_dice_0: 0.09838/0.15219, loss_grounding_ce_0: 0.00193/0.25415, loss_mask_ce_1: 0.18899/0.79424, loss_mask_bce_1: 0.14865/0.30235, loss_mask_dice_1: 0.16542/1.03787, loss_spatial_bce_1: 0.09734/0.09167, loss_spatial_dice_1: 0.10497/0.19480, loss_spatial_ce_1: 0.07803/0.08275, loss_grounding_bce_1: 0.10094/0.08076, loss_grounding_dice_1: 0.10232/0.15319, loss_grounding_ce_1: 0.00191/0.25761, loss_mask_ce_2: 0.21844/0.80053, loss_mask_bce_2: 0.13712/0.30245, loss_mask_dice_2: 0.14604/1.04091, loss_spatial_bce_2: 0.09066/0.09110, loss_spatial_dice_2: 0.09610/0.19470, loss_spatial_ce_2: 0.07840/0.08647, loss_grounding_bce_2: 0.09854/0.08049, loss_grounding_dice_2: 0.10999/0.15283, loss_grounding_ce_2: 0.00271/0.25807, loss_mask_ce_3: 0.13081/0.79923, loss_mask_bce_3: 0.12936/0.30384, loss_mask_dice_3: 0.13652/1.03552, loss_spatial_bce_3: 0.08870/0.09266, loss_spatial_dice_3: 0.10220/0.19491, loss_spatial_ce_3: 0.08149/0.09232, loss_grounding_bce_3: 0.10033/0.08105, loss_grounding_dice_3: 0.10527/0.15263, loss_grounding_ce_3: 0.00267/0.25624, loss_mask_ce_4: 0.13283/0.80646, loss_mask_bce_4: 0.14435/0.30601, loss_mask_dice_4: 0.16063/1.05367, loss_spatial_bce_4: 0.09245/0.09480, loss_spatial_dice_4: 0.10660/0.20242, loss_spatial_ce_4: 0.09583/0.10369, loss_grounding_bce_4: 0.10047/0.08177, loss_grounding_dice_4: 0.12707/0.15471, loss_grounding_ce_4: 0.00337/0.26522, loss_mask_ce_5: 0.09591/0.82676, loss_mask_bce_5: 0.14601/0.30811, loss_mask_dice_5: 0.14636/1.06087, loss_spatial_bce_5: 0.09594/0.09639, loss_spatial_dice_5: 0.10059/0.20455, loss_spatial_ce_5: 0.08392/0.11477, loss_grounding_bce_5: 0.10566/0.08221, loss_grounding_dice_5: 0.11106/0.15558, loss_grounding_ce_5: 0.00282/0.28467, loss_mask_ce_6: 0.12200/0.85169, loss_mask_bce_6: 0.13996/0.30937, loss_mask_dice_6: 0.14456/1.06530, loss_spatial_bce_6: 0.09289/0.10136, loss_spatial_dice_6: 0.09652/0.20679, loss_spatial_ce_6: 0.08585/0.13310, loss_grounding_bce_6: 0.10288/0.08339, loss_grounding_dice_6: 0.09987/0.15613, loss_grounding_ce_6: 0.00394/0.29764, loss_mask_ce_7: 0.11058/0.91432, loss_mask_bce_7: 0.14991/0.31665, loss_mask_dice_7: 0.13757/1.11213, loss_spatial_bce_7: 0.09271/0.11191, loss_spatial_dice_7: 0.11547/0.23182, loss_spatial_ce_7: 0.13574/0.17773, loss_grounding_bce_7: 0.11245/0.08521, loss_grounding_dice_7: 0.12388/0.16209, loss_grounding_ce_7: 0.00349/0.34471, loss_mask_ce_8: 0.20172/1.05298, loss_mask_bce_8: 0.14328/0.33413, loss_mask_dice_8: 0.15885/1.19234, loss_spatial_bce_8: 0.10446/0.13348, loss_spatial_dice_8: 0.13508/0.27370, loss_spatial_ce_8: 0.14245/0.23513, loss_grounding_bce_8: 0.10801/0.08904, loss_grounding_dice_8: 0.11992/0.17125, loss_grounding_ce_8: 0.00966/0.44911, loss_mask_ce_9: 1.58215/3.51029, loss_mask_bce_9: 0.14882/0.36034, loss_mask_dice_9: 0.17187/1.77700, loss_spatial_bce_9: 0.40603/0.36138, loss_spatial_dice_9: 0.69169/0.79823, loss_spatial_ce_9: 1.34424/1.42302, loss_grounding_bce_9: 0.10441/0.10100, loss_grounding_dice_9: 0.12110/0.24569, loss_grounding_ce_9: 0.08323/0.72379] items per batch[64] items per second[0.36] total items[960000] mini batches[ 15000] memory[4967] epoch remaining[0:43:11] INFO:trainer.default_trainer:epochs[ 8] optim steps[15100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25447/0.79139, loss_mask_bce_0: 0.12064/0.30227, loss_mask_dice_0: 0.47040/1.03461, loss_spatial_bce_0: 0.06119/0.09118, loss_spatial_dice_0: 0.22864/0.19207, loss_spatial_ce_0: 0.00710/0.07796, loss_grounding_bce_0: 0.03489/0.08064, loss_grounding_dice_0: 0.02182/0.15216, loss_grounding_ce_0: 0.00869/0.25471, loss_mask_ce_1: 1.33149/0.79436, loss_mask_bce_1: 0.11355/0.30274, loss_mask_dice_1: 0.49344/1.03871, loss_spatial_bce_1: 0.05655/0.09167, loss_spatial_dice_1: 0.20629/0.19476, loss_spatial_ce_1: 0.02663/0.08265, loss_grounding_bce_1: 0.03599/0.08076, loss_grounding_dice_1: 0.02199/0.15312, loss_grounding_ce_1: 0.01021/0.25826, loss_mask_ce_2: 0.91786/0.80075, loss_mask_bce_2: 0.12425/0.30279, loss_mask_dice_2: 0.89252/1.04183, loss_spatial_bce_2: 0.05977/0.09111, loss_spatial_dice_2: 0.23493/0.19467, loss_spatial_ce_2: 0.12306/0.08631, loss_grounding_bce_2: 0.03593/0.08050, loss_grounding_dice_2: 0.02134/0.15278, loss_grounding_ce_2: 0.01312/0.25854, loss_mask_ce_3: 1.33061/0.79959, loss_mask_bce_3: 0.10881/0.30420, loss_mask_dice_3: 0.55278/1.03644, loss_spatial_bce_3: 0.06392/0.09267, loss_spatial_dice_3: 0.25013/0.19490, loss_spatial_ce_3: 0.00962/0.09211, loss_grounding_bce_3: 0.03721/0.08104, loss_grounding_dice_3: 0.02227/0.15257, loss_grounding_ce_3: 0.01094/0.25674, loss_mask_ce_4: 1.30471/0.80673, loss_mask_bce_4: 0.11605/0.30636, loss_mask_dice_4: 0.51520/1.05455, loss_spatial_bce_4: 0.05370/0.09478, loss_spatial_dice_4: 0.20121/0.20238, loss_spatial_ce_4: 0.00562/0.10360, loss_grounding_bce_4: 0.03454/0.08177, loss_grounding_dice_4: 0.02121/0.15467, loss_grounding_ce_4: 0.01088/0.26581, loss_mask_ce_5: 1.32264/0.82716, loss_mask_bce_5: 0.11113/0.30845, loss_mask_dice_5: 0.43755/1.06173, loss_spatial_bce_5: 0.05299/0.09639, loss_spatial_dice_5: 0.19896/0.20449, loss_spatial_ce_5: 0.14335/0.11462, loss_grounding_bce_5: 0.03273/0.08222, loss_grounding_dice_5: 0.02043/0.15552, loss_grounding_ce_5: 0.02038/0.28525, loss_mask_ce_6: 1.19498/0.85212, loss_mask_bce_6: 0.11297/0.30973, loss_mask_dice_6: 0.83699/1.06608, loss_spatial_bce_6: 0.05501/0.10136, loss_spatial_dice_6: 0.20344/0.20676, loss_spatial_ce_6: 0.18974/0.13298, loss_grounding_bce_6: 0.03343/0.08338, loss_grounding_dice_6: 0.02021/0.15606, loss_grounding_ce_6: 0.01850/0.29799, loss_mask_ce_7: 1.26895/0.91478, loss_mask_bce_7: 0.12279/0.31704, loss_mask_dice_7: 0.76517/1.11275, loss_spatial_bce_7: 0.05077/0.11195, loss_spatial_dice_7: 0.21782/0.23180, loss_spatial_ce_7: 0.21302/0.17764, loss_grounding_bce_7: 0.03213/0.08521, loss_grounding_dice_7: 0.01884/0.16201, loss_grounding_ce_7: 0.03711/0.34518, loss_mask_ce_8: 0.95696/1.05342, loss_mask_bce_8: 0.11648/0.33458, loss_mask_dice_8: 0.88759/1.19337, loss_spatial_bce_8: 0.13032/0.13350, loss_spatial_dice_8: 0.29776/0.27363, loss_spatial_ce_8: 0.31816/0.23494, loss_grounding_bce_8: 0.03447/0.08905, loss_grounding_dice_8: 0.01965/0.17120, loss_grounding_ce_8: 0.03745/0.44977, loss_mask_ce_9: 3.16782/3.51175, loss_mask_bce_9: 0.12282/0.36084, loss_mask_dice_9: 1.58455/1.77943, loss_spatial_bce_9: 0.51889/0.36129, loss_spatial_dice_9: 0.83144/0.79830, loss_spatial_ce_9: 2.50047/1.42254, loss_grounding_bce_9: 0.03950/0.10104, loss_grounding_dice_9: 0.02657/0.24575, loss_grounding_ce_9: 0.21498/0.72436] items per batch[64] items per second[0.36] total items[966400] mini batches[ 15100] memory[4967] epoch remaining[0:40:12] INFO:trainer.default_trainer:epochs[ 8] optim steps[15200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11388/0.79112, loss_mask_bce_0: 0.00569/0.30226, loss_mask_dice_0: 0.33407/1.03521, loss_spatial_bce_0: 0.01821/0.09106, loss_spatial_dice_0: 0.32047/0.19200, loss_spatial_ce_0: 0.00246/0.07768, loss_grounding_bce_0: 0.00406/0.08058, loss_grounding_dice_0: 0.01864/0.15224, loss_grounding_ce_0: 0.00093/0.25465, loss_mask_ce_1: 0.10409/0.79414, loss_mask_bce_1: 0.00323/0.30275, loss_mask_dice_1: 0.24303/1.03935, loss_spatial_bce_1: 0.02618/0.09154, loss_spatial_dice_1: 0.33844/0.19470, loss_spatial_ce_1: 0.17353/0.08244, loss_grounding_bce_1: 0.00414/0.08068, loss_grounding_dice_1: 0.02357/0.15322, loss_grounding_ce_1: 0.00116/0.25831, loss_mask_ce_2: 0.12415/0.80050, loss_mask_bce_2: 0.00538/0.30282, loss_mask_dice_2: 0.33039/1.04257, loss_spatial_bce_2: 0.02658/0.09099, loss_spatial_dice_2: 0.33998/0.19460, loss_spatial_ce_2: 0.21295/0.08606, loss_grounding_bce_2: 0.00299/0.08041, loss_grounding_dice_2: 0.00985/0.15282, loss_grounding_ce_2: 0.00142/0.25864, loss_mask_ce_3: 0.11831/0.79929, loss_mask_bce_3: 0.00685/0.30422, loss_mask_dice_3: 0.38617/1.03706, loss_spatial_bce_3: 0.04430/0.09255, loss_spatial_dice_3: 0.33789/0.19484, loss_spatial_ce_3: 0.24134/0.09187, loss_grounding_bce_3: 0.00306/0.08096, loss_grounding_dice_3: 0.02365/0.15265, loss_grounding_ce_3: 0.00082/0.25692, loss_mask_ce_4: 0.15552/0.80638, loss_mask_bce_4: 0.00622/0.30636, loss_mask_dice_4: 0.37843/1.05526, loss_spatial_bce_4: 0.01427/0.09465, loss_spatial_dice_4: 0.33340/0.20233, loss_spatial_ce_4: 0.36459/0.10339, loss_grounding_bce_4: 0.00326/0.08168, loss_grounding_dice_4: 0.02060/0.15478, loss_grounding_ce_4: 0.00073/0.26582, loss_mask_ce_5: 0.15600/0.82707, loss_mask_bce_5: 0.00524/0.30844, loss_mask_dice_5: 0.34608/1.06225, loss_spatial_bce_5: 0.01698/0.09625, loss_spatial_dice_5: 0.33197/0.20441, loss_spatial_ce_5: 0.02624/0.11464, loss_grounding_bce_5: 0.00290/0.08215, loss_grounding_dice_5: 0.02152/0.15559, loss_grounding_ce_5: 0.00092/0.28518, loss_mask_ce_6: 0.14722/0.85204, loss_mask_bce_6: 0.00283/0.30974, loss_mask_dice_6: 0.24649/1.06660, loss_spatial_bce_6: 0.03415/0.10123, loss_spatial_dice_6: 0.33710/0.20669, loss_spatial_ce_6: 0.03736/0.13288, loss_grounding_bce_6: 0.00180/0.08331, loss_grounding_dice_6: 0.01497/0.15617, loss_grounding_ce_6: 0.00020/0.29791, loss_mask_ce_7: 0.23258/0.91446, loss_mask_bce_7: 0.00309/0.31707, loss_mask_dice_7: 0.20922/1.11345, loss_spatial_bce_7: 0.15962/0.11182, loss_spatial_dice_7: 0.33894/0.23177, loss_spatial_ce_7: 0.48272/0.17751, loss_grounding_bce_7: 0.00211/0.08513, loss_grounding_dice_7: 0.01481/0.16208, loss_grounding_ce_7: 0.00191/0.34489, loss_mask_ce_8: 0.78052/1.05312, loss_mask_bce_8: 0.00758/0.33465, loss_mask_dice_8: 0.27062/1.19394, loss_spatial_bce_8: 0.05142/0.13336, loss_spatial_dice_8: 0.34128/0.27357, loss_spatial_ce_8: 0.13145/0.23467, loss_grounding_bce_8: 0.02369/0.08900, loss_grounding_dice_8: 0.35159/0.17132, loss_grounding_ce_8: 0.00082/0.44932, loss_mask_ce_9: 1.53104/3.51169, loss_mask_bce_9: 0.00380/0.36077, loss_mask_dice_9: 0.31655/1.78009, loss_spatial_bce_9: 0.23808/0.36101, loss_spatial_dice_9: 0.80005/0.79837, loss_spatial_ce_9: 2.35246/1.42263, loss_grounding_bce_9: 0.00212/0.10098, loss_grounding_dice_9: 0.01608/0.24587, loss_grounding_ce_9: 0.01958/0.72343] items per batch[64] items per second[0.36] total items[972800] mini batches[ 15200] memory[4967] epoch remaining[0:37:10] INFO:trainer.default_trainer:epochs[ 8] optim steps[15300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31745/0.79080, loss_mask_bce_0: 0.23562/0.30226, loss_mask_dice_0: 0.58458/1.03375, loss_spatial_bce_0: 0.07379/0.09110, loss_spatial_dice_0: 0.15131/0.19198, loss_spatial_ce_0: 0.00006/0.07768, loss_grounding_bce_0: 0.08055/0.08060, loss_grounding_dice_0: 0.17873/0.15201, loss_grounding_ce_0: 0.15464/0.25465, loss_mask_ce_1: 0.27293/0.79394, loss_mask_bce_1: 0.23122/0.30276, loss_mask_dice_1: 0.58260/1.03786, loss_spatial_bce_1: 0.07742/0.09158, loss_spatial_dice_1: 0.14991/0.19470, loss_spatial_ce_1: 0.00004/0.08242, loss_grounding_bce_1: 0.08041/0.08070, loss_grounding_dice_1: 0.17979/0.15304, loss_grounding_ce_1: 0.15375/0.25816, loss_mask_ce_2: 0.29748/0.80036, loss_mask_bce_2: 0.23170/0.30283, loss_mask_dice_2: 0.58802/1.04098, loss_spatial_bce_2: 0.07969/0.09105, loss_spatial_dice_2: 0.15354/0.19459, loss_spatial_ce_2: 0.00007/0.08599, loss_grounding_bce_2: 0.08630/0.08044, loss_grounding_dice_2: 0.17666/0.15261, loss_grounding_ce_2: 0.15899/0.25874, loss_mask_ce_3: 0.32715/0.79914, loss_mask_bce_3: 0.22533/0.30421, loss_mask_dice_3: 0.58509/1.03555, loss_spatial_bce_3: 0.07285/0.09260, loss_spatial_dice_3: 0.15671/0.19483, loss_spatial_ce_3: 0.00078/0.09181, loss_grounding_bce_3: 0.08622/0.08099, loss_grounding_dice_3: 0.17824/0.15244, loss_grounding_ce_3: 0.13333/0.25691, loss_mask_ce_4: 0.32802/0.80622, loss_mask_bce_4: 0.24633/0.30634, loss_mask_dice_4: 0.60016/1.05374, loss_spatial_bce_4: 0.07828/0.09470, loss_spatial_dice_4: 0.16586/0.20232, loss_spatial_ce_4: 0.00146/0.10335, loss_grounding_bce_4: 0.08673/0.08171, loss_grounding_dice_4: 0.17686/0.15463, loss_grounding_ce_4: 0.15032/0.26606, loss_mask_ce_5: 0.36147/0.82681, loss_mask_bce_5: 0.23859/0.30842, loss_mask_dice_5: 0.59676/1.06070, loss_spatial_bce_5: 0.08318/0.09629, loss_spatial_dice_5: 0.18651/0.20437, loss_spatial_ce_5: 0.00528/0.11452, loss_grounding_bce_5: 0.08673/0.08219, loss_grounding_dice_5: 0.17725/0.15539, loss_grounding_ce_5: 0.13761/0.28530, loss_mask_ce_6: 0.35525/0.85189, loss_mask_bce_6: 0.24209/0.30975, loss_mask_dice_6: 0.60234/1.06503, loss_spatial_bce_6: 0.08232/0.10127, loss_spatial_dice_6: 0.18059/0.20664, loss_spatial_ce_6: 0.01475/0.13279, loss_grounding_bce_6: 0.08933/0.08337, loss_grounding_dice_6: 0.19159/0.15603, loss_grounding_ce_6: 0.12868/0.29798, loss_mask_ce_7: 0.45336/0.91425, loss_mask_bce_7: 0.22500/0.31708, loss_mask_dice_7: 0.58192/1.11187, loss_spatial_bce_7: 0.07903/0.11190, loss_spatial_dice_7: 0.16624/0.23170, loss_spatial_ce_7: 0.01280/0.17747, loss_grounding_bce_7: 0.07732/0.08514, loss_grounding_dice_7: 0.17429/0.16189, loss_grounding_ce_7: 0.16696/0.34489, loss_mask_ce_8: 0.45032/1.05285, loss_mask_bce_8: 0.22252/0.33469, loss_mask_dice_8: 0.54977/1.19222, loss_spatial_bce_8: 0.11188/0.13335, loss_spatial_dice_8: 0.19056/0.27345, loss_spatial_ce_8: 0.06619/0.23454, loss_grounding_bce_8: 0.07992/0.08906, loss_grounding_dice_8: 0.18124/0.17118, loss_grounding_ce_8: 0.14244/0.44935, loss_mask_ce_9: 1.85620/3.51050, loss_mask_bce_9: 0.22866/0.36074, loss_mask_dice_9: 0.78774/1.77799, loss_spatial_bce_9: 0.21132/0.36122, loss_spatial_dice_9: 0.64746/0.79840, loss_spatial_ce_9: 0.65357/1.42245, loss_grounding_bce_9: 0.07628/0.10098, loss_grounding_dice_9: 0.21903/0.24566, loss_grounding_ce_9: 0.23080/0.72365] items per batch[64] items per second[0.37] total items[979200] mini batches[ 15300] memory[4967] epoch remaining[0:34:03] INFO:trainer.default_trainer:epochs[ 8] optim steps[15400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17077/0.79092, loss_mask_bce_0: 0.23640/0.30201, loss_mask_dice_0: 0.14516/1.03374, loss_spatial_bce_0: 0.12882/0.09110, loss_spatial_dice_0: 0.08855/0.19201, loss_spatial_ce_0: 0.00454/0.07748, loss_grounding_bce_0: 0.12099/0.08047, loss_grounding_dice_0: 0.07526/0.15190, loss_grounding_ce_0: 0.01584/0.25434, loss_mask_ce_1: 0.17277/0.79429, loss_mask_bce_1: 0.23544/0.30251, loss_mask_dice_1: 0.14525/1.03762, loss_spatial_bce_1: 0.12564/0.09156, loss_spatial_dice_1: 0.08840/0.19472, loss_spatial_ce_1: 0.00559/0.08232, loss_grounding_bce_1: 0.12058/0.08059, loss_grounding_dice_1: 0.07523/0.15294, loss_grounding_ce_1: 0.01101/0.25784, loss_mask_ce_2: 0.19378/0.80063, loss_mask_bce_2: 0.24628/0.30258, loss_mask_dice_2: 0.14467/1.04069, loss_spatial_bce_2: 0.12172/0.09104, loss_spatial_dice_2: 0.08088/0.19462, loss_spatial_ce_2: 0.00578/0.08593, loss_grounding_bce_2: 0.12251/0.08031, loss_grounding_dice_2: 0.07408/0.15249, loss_grounding_ce_2: 0.01130/0.25840, loss_mask_ce_3: 0.20481/0.79932, loss_mask_bce_3: 0.24385/0.30396, loss_mask_dice_3: 0.14398/1.03546, loss_spatial_bce_3: 0.12831/0.09258, loss_spatial_dice_3: 0.08321/0.19486, loss_spatial_ce_3: 0.00235/0.09174, loss_grounding_bce_3: 0.12085/0.08086, loss_grounding_dice_3: 0.07551/0.15232, loss_grounding_ce_3: 0.01492/0.25659, loss_mask_ce_4: 0.20759/0.80649, loss_mask_bce_4: 0.25174/0.30609, loss_mask_dice_4: 0.15522/1.05371, loss_spatial_bce_4: 0.12556/0.09468, loss_spatial_dice_4: 0.07961/0.20233, loss_spatial_ce_4: 0.00120/0.10327, loss_grounding_bce_4: 0.12039/0.08157, loss_grounding_dice_4: 0.07841/0.15454, loss_grounding_ce_4: 0.01710/0.26582, loss_mask_ce_5: 0.20037/0.82696, loss_mask_bce_5: 0.22626/0.30816, loss_mask_dice_5: 0.14392/1.06045, loss_spatial_bce_5: 0.12362/0.09627, loss_spatial_dice_5: 0.07937/0.20438, loss_spatial_ce_5: 0.00109/0.11452, loss_grounding_bce_5: 0.12177/0.08206, loss_grounding_dice_5: 0.07879/0.15529, loss_grounding_ce_5: 0.01479/0.28495, loss_mask_ce_6: 0.19003/0.85193, loss_mask_bce_6: 0.24144/0.30949, loss_mask_dice_6: 0.14320/1.06495, loss_spatial_bce_6: 0.13484/0.10124, loss_spatial_dice_6: 0.08383/0.20667, loss_spatial_ce_6: 0.00049/0.13268, loss_grounding_bce_6: 0.12233/0.08323, loss_grounding_dice_6: 0.07486/0.15592, loss_grounding_ce_6: 0.01572/0.29760, loss_mask_ce_7: 0.24483/0.91467, loss_mask_bce_7: 0.23276/0.31684, loss_mask_dice_7: 0.15497/1.11165, loss_spatial_bce_7: 0.13893/0.11185, loss_spatial_dice_7: 0.09873/0.23171, loss_spatial_ce_7: 0.01934/0.17734, loss_grounding_bce_7: 0.12948/0.08500, loss_grounding_dice_7: 0.08641/0.16181, loss_grounding_ce_7: 0.01198/0.34436, loss_mask_ce_8: 0.31447/1.05317, loss_mask_bce_8: 0.23435/0.33446, loss_mask_dice_8: 0.16055/1.19192, loss_spatial_bce_8: 0.14174/0.13338, loss_spatial_dice_8: 0.10182/0.27347, loss_spatial_ce_8: 0.13047/0.23449, loss_grounding_bce_8: 0.12159/0.08892, loss_grounding_dice_8: 0.08597/0.17109, loss_grounding_ce_8: 0.03323/0.44920, loss_mask_ce_9: 1.60442/3.51116, loss_mask_bce_9: 0.23191/0.36051, loss_mask_dice_9: 0.21578/1.77752, loss_spatial_bce_9: 0.45426/0.36107, loss_spatial_dice_9: 0.54420/0.79843, loss_spatial_ce_9: 0.67965/1.42258, loss_grounding_bce_9: 0.11976/0.10084, loss_grounding_dice_9: 0.10979/0.24561, loss_grounding_ce_9: 0.11547/0.72311] items per batch[64] items per second[0.35] total items[985600] mini batches[ 15400] memory[4967] epoch remaining[0:31:08] INFO:trainer.default_trainer:epochs[ 8] optim steps[15500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.57585/0.79120, loss_mask_bce_0: 0.16959/0.30202, loss_mask_dice_0: 3.45274/1.03335, loss_spatial_bce_0: 0.03503/0.09105, loss_spatial_dice_0: 0.34568/0.19197, loss_spatial_ce_0: 0.00465/0.07736, loss_grounding_bce_0: 0.01033/0.08046, loss_grounding_dice_0: 0.28739/0.15196, loss_grounding_ce_0: 0.46077/0.25485, loss_mask_ce_1: 1.34864/0.79441, loss_mask_bce_1: 0.37181/0.30258, loss_mask_dice_1: 3.87088/1.03730, loss_spatial_bce_1: 0.02460/0.09152, loss_spatial_dice_1: 0.33326/0.19467, loss_spatial_ce_1: 0.25609/0.08226, loss_grounding_bce_1: 0.01010/0.08060, loss_grounding_dice_1: 0.31647/0.15300, loss_grounding_ce_1: 0.49709/0.25824, loss_mask_ce_2: 1.89633/0.80084, loss_mask_bce_2: 0.17283/0.30263, loss_mask_dice_2: 4.28113/1.04055, loss_spatial_bce_2: 0.02423/0.09100, loss_spatial_dice_2: 0.37925/0.19462, loss_spatial_ce_2: 0.23921/0.08578, loss_grounding_bce_2: 0.00935/0.08032, loss_grounding_dice_2: 0.27503/0.15255, loss_grounding_ce_2: 0.49068/0.25901, loss_mask_ce_3: 1.65497/0.79966, loss_mask_bce_3: 0.42250/0.30402, loss_mask_dice_3: 3.83212/1.03512, loss_spatial_bce_3: 0.03094/0.09255, loss_spatial_dice_3: 0.41268/0.19487, loss_spatial_ce_3: 0.01160/0.09155, loss_grounding_bce_3: 0.00981/0.08086, loss_grounding_dice_3: 0.25589/0.15234, loss_grounding_ce_3: 0.46012/0.25710, loss_mask_ce_4: 1.47809/0.80676, loss_mask_bce_4: 0.27168/0.30610, loss_mask_dice_4: 3.85583/1.05341, loss_spatial_bce_4: 0.03774/0.09466, loss_spatial_dice_4: 0.41953/0.20233, loss_spatial_ce_4: 0.01792/0.10311, loss_grounding_bce_4: 0.00960/0.08156, loss_grounding_dice_4: 0.29774/0.15455, loss_grounding_ce_4: 0.42710/0.26636, loss_mask_ce_5: 1.74805/0.82733, loss_mask_bce_5: 0.28656/0.30823, loss_mask_dice_5: 4.03495/1.06004, loss_spatial_bce_5: 0.02876/0.09623, loss_spatial_dice_5: 0.36461/0.20436, loss_spatial_ce_5: 0.02422/0.11443, loss_grounding_bce_5: 0.01007/0.08206, loss_grounding_dice_5: 0.26464/0.15536, loss_grounding_ce_5: 0.45699/0.28550, loss_mask_ce_6: 1.82496/0.85215, loss_mask_bce_6: 0.28575/0.30954, loss_mask_dice_6: 3.48617/1.06467, loss_spatial_bce_6: 0.03486/0.10120, loss_spatial_dice_6: 0.43583/0.20663, loss_spatial_ce_6: 0.06351/0.13265, loss_grounding_bce_6: 0.00876/0.08323, loss_grounding_dice_6: 0.21214/0.15602, loss_grounding_ce_6: 0.46360/0.29798, loss_mask_ce_7: 1.61692/0.91498, loss_mask_bce_7: 0.27007/0.31694, loss_mask_dice_7: 4.39639/1.11153, loss_spatial_bce_7: 0.02788/0.11183, loss_spatial_dice_7: 0.45294/0.23171, loss_spatial_ce_7: 0.29330/0.17725, loss_grounding_bce_7: 0.00953/0.08500, loss_grounding_dice_7: 0.26868/0.16189, loss_grounding_ce_7: 0.46300/0.34476, loss_mask_ce_8: 2.47767/1.05365, loss_mask_bce_8: 0.24699/0.33455, loss_mask_dice_8: 3.55796/1.19170, loss_spatial_bce_8: 0.03674/0.13335, loss_spatial_dice_8: 0.42689/0.27341, loss_spatial_ce_8: 0.30740/0.23432, loss_grounding_bce_8: 0.01217/0.08890, loss_grounding_dice_8: 0.26619/0.17116, loss_grounding_ce_8: 0.56690/0.44945, loss_mask_ce_9: 4.51967/3.51183, loss_mask_bce_9: 0.24118/0.36064, loss_mask_dice_9: 4.82149/1.77740, loss_spatial_bce_9: 0.11955/0.36099, loss_spatial_dice_9: 0.94517/0.79856, loss_spatial_ce_9: 1.41629/1.42271, loss_grounding_bce_9: 0.00792/0.10085, loss_grounding_dice_9: 0.35925/0.24567, loss_grounding_ce_9: 0.57273/0.72375] items per batch[64] items per second[0.36] total items[992000] mini batches[ 15500] memory[4967] epoch remaining[0:28:06] INFO:trainer.default_trainer:epochs[ 8] optim steps[15600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.10505/0.79069, loss_mask_bce_0: 0.07270/0.30197, loss_mask_dice_0: 1.04750/1.03321, loss_spatial_bce_0: 0.01505/0.09107, loss_spatial_dice_0: 0.21659/0.19178, loss_spatial_ce_0: 0.00136/0.07721, loss_grounding_bce_0: 0.00516/0.08052, loss_grounding_dice_0: 0.32210/0.15192, loss_grounding_ce_0: 0.40380/0.25463, loss_mask_ce_1: 1.44061/0.79380, loss_mask_bce_1: 0.07415/0.30251, loss_mask_dice_1: 1.16312/1.03700, loss_spatial_bce_1: 0.01775/0.09155, loss_spatial_dice_1: 0.22811/0.19451, loss_spatial_ce_1: 0.00095/0.08203, loss_grounding_bce_1: 0.00817/0.08064, loss_grounding_dice_1: 0.32372/0.15296, loss_grounding_ce_1: 0.38258/0.25809, loss_mask_ce_2: 1.06768/0.80013, loss_mask_bce_2: 0.07289/0.30257, loss_mask_dice_2: 1.01813/1.04035, loss_spatial_bce_2: 0.01674/0.09104, loss_spatial_dice_2: 0.20357/0.19445, loss_spatial_ce_2: 0.00088/0.08550, loss_grounding_bce_2: 0.00818/0.08037, loss_grounding_dice_2: 0.29673/0.15252, loss_grounding_ce_2: 0.35457/0.25873, loss_mask_ce_3: 1.03432/0.79903, loss_mask_bce_3: 0.07805/0.30394, loss_mask_dice_3: 1.09458/1.03476, loss_spatial_bce_3: 0.01671/0.09257, loss_spatial_dice_3: 0.23286/0.19469, loss_spatial_ce_3: 0.00162/0.09128, loss_grounding_bce_3: 0.02784/0.08091, loss_grounding_dice_3: 0.34512/0.15229, loss_grounding_ce_3: 0.03004/0.25679, loss_mask_ce_4: 1.01822/0.80618, loss_mask_bce_4: 0.07592/0.30602, loss_mask_dice_4: 0.91313/1.05310, loss_spatial_bce_4: 0.01321/0.09467, loss_spatial_dice_4: 0.23505/0.20214, loss_spatial_ce_4: 0.02081/0.10288, loss_grounding_bce_4: 0.02544/0.08160, loss_grounding_dice_4: 0.34910/0.15454, loss_grounding_ce_4: 0.03410/0.26612, loss_mask_ce_5: 1.27026/0.82685, loss_mask_bce_5: 0.06675/0.30818, loss_mask_dice_5: 0.86328/1.05978, loss_spatial_bce_5: 0.01250/0.09624, loss_spatial_dice_5: 0.20175/0.20416, loss_spatial_ce_5: 0.30050/0.11429, loss_grounding_bce_5: 0.00571/0.08211, loss_grounding_dice_5: 0.31827/0.15534, loss_grounding_ce_5: 0.30079/0.28527, loss_mask_ce_6: 1.21034/0.85173, loss_mask_bce_6: 0.07211/0.30948, loss_mask_dice_6: 1.18195/1.06440, loss_spatial_bce_6: 0.01522/0.10122, loss_spatial_dice_6: 0.22430/0.20646, loss_spatial_ce_6: 0.04494/0.13251, loss_grounding_bce_6: 0.02491/0.08327, loss_grounding_dice_6: 0.34941/0.15598, loss_grounding_ce_6: 0.05795/0.29769, loss_mask_ce_7: 1.26819/0.91474, loss_mask_bce_7: 0.07672/0.31693, loss_mask_dice_7: 0.97198/1.11130, loss_spatial_bce_7: 0.01562/0.11188, loss_spatial_dice_7: 0.21608/0.23151, loss_spatial_ce_7: 0.33127/0.17714, loss_grounding_bce_7: 0.02680/0.08512, loss_grounding_dice_7: 0.35573/0.16186, loss_grounding_ce_7: 0.04507/0.34436, loss_mask_ce_8: 1.54947/1.05304, loss_mask_bce_8: 0.16332/0.33461, loss_mask_dice_8: 1.11056/1.19175, loss_spatial_bce_8: 0.03102/0.13337, loss_spatial_dice_8: 0.33831/0.27315, loss_spatial_ce_8: 0.30181/0.23405, loss_grounding_bce_8: 0.00863/0.08897, loss_grounding_dice_8: 0.36808/0.17115, loss_grounding_ce_8: 0.38667/0.44887, loss_mask_ce_9: 3.35087/3.51152, loss_mask_bce_9: 0.07102/0.36066, loss_mask_dice_9: 1.69781/1.77816, loss_spatial_bce_9: 0.07453/0.36105, loss_spatial_dice_9: 0.81751/0.79834, loss_spatial_ce_9: 1.03135/1.42190, loss_grounding_bce_9: 0.01248/0.10093, loss_grounding_dice_9: 0.37412/0.24568, loss_grounding_ce_9: 0.12068/0.72275] items per batch[64] items per second[0.36] total items[998400] mini batches[ 15600] memory[4967] epoch remaining[0:25:07] INFO:trainer.default_trainer:epochs[ 8] optim steps[15700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49840/0.79076, loss_mask_bce_0: 0.68900/0.30193, loss_mask_dice_0: 1.08063/1.03396, loss_spatial_bce_0: 0.22560/0.09101, loss_spatial_dice_0: 0.30083/0.19171, loss_spatial_ce_0: 0.03916/0.07698, loss_grounding_bce_0: 0.17231/0.08042, loss_grounding_dice_0: 0.10764/0.15195, loss_grounding_ce_0: 0.42190/0.25460, loss_mask_ce_1: 1.54360/0.79382, loss_mask_bce_1: 0.65333/0.30248, loss_mask_dice_1: 1.07363/1.03773, loss_spatial_bce_1: 0.22807/0.09149, loss_spatial_dice_1: 0.30248/0.19444, loss_spatial_ce_1: 0.04820/0.08184, loss_grounding_bce_1: 0.19056/0.08055, loss_grounding_dice_1: 0.14110/0.15300, loss_grounding_ce_1: 0.29023/0.25783, loss_mask_ce_2: 1.45993/0.80027, loss_mask_bce_2: 0.66148/0.30251, loss_mask_dice_2: 1.07225/1.04116, loss_spatial_bce_2: 0.23163/0.09097, loss_spatial_dice_2: 0.30245/0.19436, loss_spatial_ce_2: 0.05245/0.08521, loss_grounding_bce_2: 0.19406/0.08029, loss_grounding_dice_2: 0.13686/0.15256, loss_grounding_ce_2: 0.22748/0.25852, loss_mask_ce_3: 1.25530/0.79910, loss_mask_bce_3: 0.67252/0.30392, loss_mask_dice_3: 1.05895/1.03555, loss_spatial_bce_3: 0.21889/0.09250, loss_spatial_dice_3: 0.30212/0.19462, loss_spatial_ce_3: 0.07367/0.09102, loss_grounding_bce_3: 0.18221/0.08082, loss_grounding_dice_3: 0.13869/0.15236, loss_grounding_ce_3: 0.23293/0.25652, loss_mask_ce_4: 1.29537/0.80622, loss_mask_bce_4: 0.69567/0.30600, loss_mask_dice_4: 1.09074/1.05399, loss_spatial_bce_4: 0.19853/0.09460, loss_spatial_dice_4: 0.29545/0.20204, loss_spatial_ce_4: 0.17387/0.10266, loss_grounding_bce_4: 0.19746/0.08152, loss_grounding_dice_4: 0.14131/0.15461, loss_grounding_ce_4: 0.25624/0.26598, loss_mask_ce_5: 1.52092/0.82694, loss_mask_bce_5: 0.64002/0.30813, loss_mask_dice_5: 1.07906/1.06062, loss_spatial_bce_5: 0.20838/0.09614, loss_spatial_dice_5: 0.32110/0.20408, loss_spatial_ce_5: 0.12454/0.11394, loss_grounding_bce_5: 0.18782/0.08201, loss_grounding_dice_5: 0.14370/0.15540, loss_grounding_ce_5: 0.31841/0.28518, loss_mask_ce_6: 1.67041/0.85195, loss_mask_bce_6: 0.62140/0.30943, loss_mask_dice_6: 1.07596/1.06517, loss_spatial_bce_6: 0.22168/0.10114, loss_spatial_dice_6: 0.32818/0.20643, loss_spatial_ce_6: 0.13566/0.13214, loss_grounding_bce_6: 0.18275/0.08317, loss_grounding_dice_6: 0.14112/0.15607, loss_grounding_ce_6: 0.33956/0.29764, loss_mask_ce_7: 1.73676/0.91471, loss_mask_bce_7: 0.70638/0.31688, loss_mask_dice_7: 1.12484/1.11207, loss_spatial_bce_7: 0.24750/0.11181, loss_spatial_dice_7: 0.35867/0.23144, loss_spatial_ce_7: 0.19341/0.17677, loss_grounding_bce_7: 0.19058/0.08502, loss_grounding_dice_7: 0.14504/0.16191, loss_grounding_ce_7: 0.42739/0.34425, loss_mask_ce_8: 1.74344/1.05257, loss_mask_bce_8: 0.67343/0.33458, loss_mask_dice_8: 1.05202/1.19255, loss_spatial_bce_8: 0.33353/0.13327, loss_spatial_dice_8: 0.36971/0.27307, loss_spatial_ce_8: 0.31187/0.23365, loss_grounding_bce_8: 0.17417/0.08889, loss_grounding_dice_8: 0.12258/0.17117, loss_grounding_ce_8: 0.65294/0.44841, loss_mask_ce_9: 3.09698/3.51066, loss_mask_bce_9: 0.75202/0.36057, loss_mask_dice_9: 1.21978/1.77833, loss_spatial_bce_9: 0.45652/0.36094, loss_spatial_dice_9: 0.73777/0.79830, loss_spatial_ce_9: 0.95927/1.42130, loss_grounding_bce_9: 0.23015/0.10081, loss_grounding_dice_9: 0.17092/0.24568, loss_grounding_ce_9: 0.65317/0.72210] items per batch[64] items per second[0.36] total items[1004800] mini batches[ 15700] memory[4967] epoch remaining[0:22:07] INFO:trainer.default_trainer:epochs[ 8] optim steps[15800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58898/0.79042, loss_mask_bce_0: 1.11839/0.30171, loss_mask_dice_0: 1.25396/1.03347, loss_spatial_bce_0: 0.20080/0.09089, loss_spatial_dice_0: 0.22647/0.19148, loss_spatial_ce_0: 0.22266/0.07687, loss_grounding_bce_0: 0.17997/0.08035, loss_grounding_dice_0: 0.13155/0.15192, loss_grounding_ce_0: 0.01191/0.25412, loss_mask_ce_1: 0.59564/0.79347, loss_mask_bce_1: 1.06320/0.30224, loss_mask_dice_1: 1.23479/1.03715, loss_spatial_bce_1: 0.27469/0.09136, loss_spatial_dice_1: 0.23569/0.19422, loss_spatial_ce_1: 0.07768/0.08167, loss_grounding_bce_1: 0.19173/0.08048, loss_grounding_dice_1: 0.15251/0.15294, loss_grounding_ce_1: 0.01202/0.25729, loss_mask_ce_2: 0.59680/0.80007, loss_mask_bce_2: 1.11148/0.30226, loss_mask_dice_2: 1.28386/1.04060, loss_spatial_bce_2: 0.23341/0.09084, loss_spatial_dice_2: 0.24075/0.19414, loss_spatial_ce_2: 0.08871/0.08503, loss_grounding_bce_2: 0.19303/0.08022, loss_grounding_dice_2: 0.15643/0.15250, loss_grounding_ce_2: 0.01457/0.25802, loss_mask_ce_3: 0.58912/0.79884, loss_mask_bce_3: 1.14986/0.30372, loss_mask_dice_3: 1.27195/1.03498, loss_spatial_bce_3: 0.18714/0.09237, loss_spatial_dice_3: 0.22236/0.19440, loss_spatial_ce_3: 0.20340/0.09085, loss_grounding_bce_3: 0.18228/0.08075, loss_grounding_dice_3: 0.12974/0.15230, loss_grounding_ce_3: 0.01912/0.25601, loss_mask_ce_4: 0.55095/0.80581, loss_mask_bce_4: 1.11328/0.30579, loss_mask_dice_4: 1.31448/1.05348, loss_spatial_bce_4: 0.18350/0.09448, loss_spatial_dice_4: 0.24142/0.20181, loss_spatial_ce_4: 0.26178/0.10248, loss_grounding_bce_4: 0.19035/0.08145, loss_grounding_dice_4: 0.13077/0.15458, loss_grounding_ce_4: 0.01281/0.26550, loss_mask_ce_5: 0.52125/0.82672, loss_mask_bce_5: 1.18523/0.30791, loss_mask_dice_5: 1.28770/1.06021, loss_spatial_bce_5: 0.18632/0.09602, loss_spatial_dice_5: 0.23184/0.20384, loss_spatial_ce_5: 0.27768/0.11379, loss_grounding_bce_5: 0.20451/0.08194, loss_grounding_dice_5: 0.18416/0.15536, loss_grounding_ce_5: 0.06135/0.28457, loss_mask_ce_6: 0.55359/0.85171, loss_mask_bce_6: 1.19633/0.30921, loss_mask_dice_6: 1.29965/1.06477, loss_spatial_bce_6: 0.20426/0.10103, loss_spatial_dice_6: 0.23176/0.20621, loss_spatial_ce_6: 0.23922/0.13201, loss_grounding_bce_6: 0.19039/0.08309, loss_grounding_dice_6: 0.13800/0.15604, loss_grounding_ce_6: 0.16678/0.29701, loss_mask_ce_7: 0.67310/0.91464, loss_mask_bce_7: 1.05158/0.31668, loss_mask_dice_7: 1.26264/1.11158, loss_spatial_bce_7: 0.19525/0.11169, loss_spatial_dice_7: 0.25074/0.23122, loss_spatial_ce_7: 0.42803/0.17656, loss_grounding_bce_7: 0.18573/0.08495, loss_grounding_dice_7: 0.14233/0.16187, loss_grounding_ce_7: 0.10035/0.34381, loss_mask_ce_8: 0.75017/1.05243, loss_mask_bce_8: 1.13687/0.33434, loss_mask_dice_8: 1.33942/1.19215, loss_spatial_bce_8: 0.33225/0.13315, loss_spatial_dice_8: 0.29647/0.27279, loss_spatial_ce_8: 0.33386/0.23333, loss_grounding_bce_8: 0.20097/0.08882, loss_grounding_dice_8: 0.11313/0.17113, loss_grounding_ce_8: 0.01225/0.44770, loss_mask_ce_9: 3.16182/3.50986, loss_mask_bce_9: 0.98336/0.36039, loss_mask_dice_9: 1.84216/1.77811, loss_spatial_bce_9: 0.39593/0.36096, loss_spatial_dice_9: 0.90480/0.79823, loss_spatial_ce_9: 1.24230/1.42074, loss_grounding_bce_9: 0.22509/0.10074, loss_grounding_dice_9: 0.09502/0.24562, loss_grounding_ce_9: 0.04549/0.72134] items per batch[64] items per second[0.36] total items[1011200] mini batches[ 15800] memory[4967] epoch remaining[0:19:07] INFO:trainer.default_trainer:epochs[ 8] optim steps[15900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18510/0.79033, loss_mask_bce_0: 0.11017/0.30152, loss_mask_dice_0: 0.19806/1.03307, loss_spatial_bce_0: 0.04256/0.09076, loss_spatial_dice_0: 0.07560/0.19136, loss_spatial_ce_0: 0.00004/0.07682, loss_grounding_bce_0: 0.02188/0.08023, loss_grounding_dice_0: 0.06107/0.15192, loss_grounding_ce_0: 0.00002/0.25412, loss_mask_ce_1: 0.16218/0.79319, loss_mask_bce_1: 0.11610/0.30209, loss_mask_dice_1: 0.19983/1.03676, loss_spatial_bce_1: 0.04737/0.09124, loss_spatial_dice_1: 0.08322/0.19408, loss_spatial_ce_1: 0.00009/0.08153, loss_grounding_bce_1: 0.02111/0.08035, loss_grounding_dice_1: 0.05618/0.15290, loss_grounding_ce_1: 0.00010/0.25732, loss_mask_ce_2: 0.16074/0.79978, loss_mask_bce_2: 0.11427/0.30209, loss_mask_dice_2: 0.20079/1.04023, loss_spatial_bce_2: 0.04166/0.09072, loss_spatial_dice_2: 0.07259/0.19403, loss_spatial_ce_2: 0.00008/0.08486, loss_grounding_bce_2: 0.01691/0.08010, loss_grounding_dice_2: 0.04841/0.15252, loss_grounding_ce_2: 0.00004/0.25822, loss_mask_ce_3: 0.17101/0.79867, loss_mask_bce_3: 0.11483/0.30355, loss_mask_dice_3: 0.21001/1.03458, loss_spatial_bce_3: 0.04844/0.09224, loss_spatial_dice_3: 0.08149/0.19428, loss_spatial_ce_3: 0.00072/0.09070, loss_grounding_bce_3: 0.02029/0.08062, loss_grounding_dice_3: 0.05723/0.15234, loss_grounding_ce_3: 0.00000/0.25607, loss_mask_ce_4: 0.16390/0.80566, loss_mask_bce_4: 0.11425/0.30562, loss_mask_dice_4: 0.19956/1.05311, loss_spatial_bce_4: 0.04895/0.09434, loss_spatial_dice_4: 0.08638/0.20168, loss_spatial_ce_4: 0.01056/0.10237, loss_grounding_bce_4: 0.02073/0.08132, loss_grounding_dice_4: 0.05897/0.15453, loss_grounding_ce_4: 0.00000/0.26567, loss_mask_ce_5: 0.15383/0.82662, loss_mask_bce_5: 0.11474/0.30776, loss_mask_dice_5: 0.20422/1.05972, loss_spatial_bce_5: 0.04748/0.09591, loss_spatial_dice_5: 0.08477/0.20370, loss_spatial_ce_5: 0.03595/0.11365, loss_grounding_bce_5: 0.01779/0.08181, loss_grounding_dice_5: 0.04917/0.15536, loss_grounding_ce_5: 0.00001/0.28475, loss_mask_ce_6: 0.19765/0.85164, loss_mask_bce_6: 0.11311/0.30907, loss_mask_dice_6: 0.18780/1.06437, loss_spatial_bce_6: 0.06035/0.10089, loss_spatial_dice_6: 0.09606/0.20607, loss_spatial_ce_6: 0.06079/0.13193, loss_grounding_bce_6: 0.01659/0.08294, loss_grounding_dice_6: 0.04653/0.15600, loss_grounding_ce_6: 0.00001/0.29715, loss_mask_ce_7: 0.22089/0.91452, loss_mask_bce_7: 0.10880/0.31653, loss_mask_dice_7: 0.21503/1.11131, loss_spatial_bce_7: 0.08049/0.11156, loss_spatial_dice_7: 0.09537/0.23109, loss_spatial_ce_7: 0.08453/0.17641, loss_grounding_bce_7: 0.01811/0.08478, loss_grounding_dice_7: 0.04488/0.16179, loss_grounding_ce_7: 0.00840/0.34420, loss_mask_ce_8: 0.24752/1.05218, loss_mask_bce_8: 0.14759/0.33418, loss_mask_dice_8: 0.23862/1.19185, loss_spatial_bce_8: 0.15180/0.13300, loss_spatial_dice_8: 0.12570/0.27258, loss_spatial_ce_8: 0.14677/0.23318, loss_grounding_bce_8: 0.01848/0.08868, loss_grounding_dice_8: 0.04877/0.17111, loss_grounding_ce_8: 1.22357/0.44805, loss_mask_ce_9: 1.92762/3.51038, loss_mask_bce_9: 0.12752/0.36032, loss_mask_dice_9: 0.27720/1.77794, loss_spatial_bce_9: 0.39251/0.36079, loss_spatial_dice_9: 0.69770/0.79825, loss_spatial_ce_9: 1.06935/1.42048, loss_grounding_bce_9: 0.01472/0.10063, loss_grounding_dice_9: 0.04611/0.24562, loss_grounding_ce_9: 0.88902/0.72099] items per batch[64] items per second[0.36] total items[1017600] mini batches[ 15900] memory[4967] epoch remaining[0:16:09] INFO:trainer.default_trainer:epochs[ 8] optim steps[16000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94051/0.79024, loss_mask_bce_0: 0.36847/0.30149, loss_mask_dice_0: 1.18869/1.03381, loss_spatial_bce_0: 0.08227/0.09079, loss_spatial_dice_0: 0.17539/0.19134, loss_spatial_ce_0: 0.04213/0.07670, loss_grounding_bce_0: 0.04572/0.08027, loss_grounding_dice_0: 0.07672/0.15189, loss_grounding_ce_0: 0.00053/0.25364, loss_mask_ce_1: 1.10497/0.79304, loss_mask_bce_1: 0.36507/0.30207, loss_mask_dice_1: 1.04906/1.03748, loss_spatial_bce_1: 0.07976/0.09126, loss_spatial_dice_1: 0.19207/0.19409, loss_spatial_ce_1: 0.06319/0.08139, loss_grounding_bce_1: 0.05365/0.08040, loss_grounding_dice_1: 0.07832/0.15282, loss_grounding_ce_1: 0.00083/0.25694, loss_mask_ce_2: 1.08062/0.79963, loss_mask_bce_2: 0.37391/0.30207, loss_mask_dice_2: 1.08572/1.04080, loss_spatial_bce_2: 0.08169/0.09074, loss_spatial_dice_2: 0.18830/0.19404, loss_spatial_ce_2: 0.07133/0.08468, loss_grounding_bce_2: 0.04608/0.08014, loss_grounding_dice_2: 0.06982/0.15249, loss_grounding_ce_2: 0.00156/0.25782, loss_mask_ce_3: 1.20064/0.79855, loss_mask_bce_3: 0.36426/0.30352, loss_mask_dice_3: 1.08138/1.03550, loss_spatial_bce_3: 0.08374/0.09228, loss_spatial_dice_3: 0.19572/0.19429, loss_spatial_ce_3: 0.04274/0.09048, loss_grounding_bce_3: 0.04571/0.08066, loss_grounding_dice_3: 0.07233/0.15226, loss_grounding_ce_3: 0.00121/0.25569, loss_mask_ce_4: 1.07533/0.80545, loss_mask_bce_4: 0.37164/0.30557, loss_mask_dice_4: 1.05669/1.05382, loss_spatial_bce_4: 0.09562/0.09436, loss_spatial_dice_4: 0.23162/0.20166, loss_spatial_ce_4: 0.02369/0.10214, loss_grounding_bce_4: 0.04120/0.08134, loss_grounding_dice_4: 0.07094/0.15446, loss_grounding_ce_4: 0.00100/0.26544, loss_mask_ce_5: 1.12172/0.82664, loss_mask_bce_5: 0.36056/0.30771, loss_mask_dice_5: 1.06111/1.06047, loss_spatial_bce_5: 0.09840/0.09591, loss_spatial_dice_5: 0.22622/0.20368, loss_spatial_ce_5: 0.01453/0.11352, loss_grounding_bce_5: 0.03963/0.08184, loss_grounding_dice_5: 0.06977/0.15535, loss_grounding_ce_5: 0.00101/0.28433, loss_mask_ce_6: 1.08893/0.85148, loss_mask_bce_6: 0.36812/0.30902, loss_mask_dice_6: 1.05509/1.06498, loss_spatial_bce_6: 0.09965/0.10088, loss_spatial_dice_6: 0.23727/0.20603, loss_spatial_ce_6: 0.03753/0.13178, loss_grounding_bce_6: 0.04111/0.08298, loss_grounding_dice_6: 0.07453/0.15595, loss_grounding_ce_6: 0.00873/0.29664, loss_mask_ce_7: 1.14128/0.91449, loss_mask_bce_7: 0.37001/0.31644, loss_mask_dice_7: 1.02128/1.11185, loss_spatial_bce_7: 0.10095/0.11152, loss_spatial_dice_7: 0.24553/0.23105, loss_spatial_ce_7: 0.07926/0.17613, loss_grounding_bce_7: 0.04373/0.08480, loss_grounding_dice_7: 0.08122/0.16174, loss_grounding_ce_7: 0.04926/0.34378, loss_mask_ce_8: 1.33694/1.05197, loss_mask_bce_8: 0.38764/0.33403, loss_mask_dice_8: 1.00761/1.19221, loss_spatial_bce_8: 0.14918/0.13293, loss_spatial_dice_8: 0.30595/0.27250, loss_spatial_ce_8: 0.46635/0.23312, loss_grounding_bce_8: 0.04289/0.08870, loss_grounding_dice_8: 0.06991/0.17101, loss_grounding_ce_8: 0.04135/0.44725, loss_mask_ce_9: 2.96068/3.51045, loss_mask_bce_9: 0.35725/0.36029, loss_mask_dice_9: 1.36833/1.77964, loss_spatial_bce_9: 0.43463/0.36089, loss_spatial_dice_9: 0.86698/0.79822, loss_spatial_ce_9: 2.14682/1.42071, loss_grounding_bce_9: 0.03477/0.10066, loss_grounding_dice_9: 0.08173/0.24545, loss_grounding_ce_9: 0.15768/0.72028] items per batch[64] items per second[0.36] total items[1024000] mini batches[ 16000] memory[4967] epoch remaining[0:13:10] INFO:trainer.default_trainer:epochs[ 8] optim steps[16100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 3.70197/0.79138, loss_mask_bce_0: 0.88418/0.30168, loss_mask_dice_0: 0.95453/1.03472, loss_spatial_bce_0: 0.12673/0.09077, loss_spatial_dice_0: 0.17913/0.19130, loss_spatial_ce_0: 0.00050/0.07660, loss_grounding_bce_0: 0.20718/0.08023, loss_grounding_dice_0: 0.17350/0.15193, loss_grounding_ce_0: 0.00855/0.25348, loss_mask_ce_1: 3.62932/0.79413, loss_mask_bce_1: 0.90766/0.30226, loss_mask_dice_1: 0.92425/1.03832, loss_spatial_bce_1: 0.13226/0.09124, loss_spatial_dice_1: 0.19670/0.19406, loss_spatial_ce_1: 0.00280/0.08133, loss_grounding_bce_1: 0.20440/0.08035, loss_grounding_dice_1: 0.17719/0.15284, loss_grounding_ce_1: 0.00442/0.25675, loss_mask_ce_2: 3.75827/0.80068, loss_mask_bce_2: 0.89843/0.30228, loss_mask_dice_2: 0.91090/1.04165, loss_spatial_bce_2: 0.13910/0.09071, loss_spatial_dice_2: 0.21014/0.19401, loss_spatial_ce_2: 0.00333/0.08455, loss_grounding_bce_2: 0.21170/0.08010, loss_grounding_dice_2: 0.18004/0.15253, loss_grounding_ce_2: 0.00537/0.25759, loss_mask_ce_3: 3.74824/0.79966, loss_mask_bce_3: 0.93872/0.30377, loss_mask_dice_3: 0.94788/1.03632, loss_spatial_bce_3: 0.13139/0.09227, loss_spatial_dice_3: 0.19064/0.19424, loss_spatial_ce_3: 0.01154/0.09032, loss_grounding_bce_3: 0.23002/0.08061, loss_grounding_dice_3: 0.17765/0.15227, loss_grounding_ce_3: 0.00938/0.25569, loss_mask_ce_4: 3.82076/0.80652, loss_mask_bce_4: 1.01965/0.30578, loss_mask_dice_4: 0.99791/1.05460, loss_spatial_bce_4: 0.13266/0.09434, loss_spatial_dice_4: 0.18740/0.20162, loss_spatial_ce_4: 0.06076/0.10197, loss_grounding_bce_4: 0.20054/0.08129, loss_grounding_dice_4: 0.17696/0.15447, loss_grounding_ce_4: 0.00521/0.26555, loss_mask_ce_5: 3.86867/0.82782, loss_mask_bce_5: 0.99418/0.30792, loss_mask_dice_5: 1.00311/1.06150, loss_spatial_bce_5: 0.18255/0.09589, loss_spatial_dice_5: 0.21304/0.20363, loss_spatial_ce_5: 0.17390/0.11337, loss_grounding_bce_5: 0.19574/0.08180, loss_grounding_dice_5: 0.18096/0.15539, loss_grounding_ce_5: 0.32756/0.28466, loss_mask_ce_6: 3.71376/0.85256, loss_mask_bce_6: 0.92180/0.30926, loss_mask_dice_6: 0.96597/1.06571, loss_spatial_bce_6: 0.22202/0.10085, loss_spatial_dice_6: 0.23182/0.20599, loss_spatial_ce_6: 0.22786/0.13170, loss_grounding_bce_6: 0.22308/0.08300, loss_grounding_dice_6: 0.18914/0.15604, loss_grounding_ce_6: 0.37237/0.29700, loss_mask_ce_7: 3.81479/0.91565, loss_mask_bce_7: 1.05153/0.31667, loss_mask_dice_7: 1.26598/1.11262, loss_spatial_bce_7: 0.20869/0.11150, loss_spatial_dice_7: 0.27939/0.23106, loss_spatial_ce_7: 0.31115/0.17605, loss_grounding_bce_7: 0.25122/0.08480, loss_grounding_dice_7: 0.19749/0.16182, loss_grounding_ce_7: 0.24373/0.34382, loss_mask_ce_8: 3.93196/1.05317, loss_mask_bce_8: 1.19857/0.33429, loss_mask_dice_8: 1.40061/1.19293, loss_spatial_bce_8: 0.42754/0.13287, loss_spatial_dice_8: 0.55084/0.27244, loss_spatial_ce_8: 0.34710/0.23298, loss_grounding_bce_8: 0.31825/0.08871, loss_grounding_dice_8: 0.21568/0.17104, loss_grounding_ce_8: 1.52022/0.44750, loss_mask_ce_9: 4.07846/3.51152, loss_mask_bce_9: 1.58445/0.36049, loss_mask_dice_9: 8.10997/1.78083, loss_spatial_bce_9: 0.36888/0.36078, loss_spatial_dice_9: 0.94761/0.79835, loss_spatial_ce_9: 1.20695/1.42086, loss_grounding_bce_9: 0.46150/0.10062, loss_grounding_dice_9: 0.22716/0.24552, loss_grounding_ce_9: 1.56168/0.72039] items per batch[64] items per second[0.36] total items[1030400] mini batches[ 16100] memory[4967] epoch remaining[0:10:11] INFO:trainer.default_trainer:epochs[ 8] optim steps[16200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16399/0.79102, loss_mask_bce_0: 0.35347/0.30157, loss_mask_dice_0: 1.97281/1.03561, loss_spatial_bce_0: 0.07493/0.09072, loss_spatial_dice_0: 0.32314/0.19124, loss_spatial_ce_0: 0.06722/0.07661, loss_grounding_bce_0: 0.07471/0.08023, loss_grounding_dice_0: 0.24618/0.15190, loss_grounding_ce_0: 0.20915/0.25317, loss_mask_ce_1: 0.15941/0.79392, loss_mask_bce_1: 0.33213/0.30213, loss_mask_dice_1: 1.59309/1.03920, loss_spatial_bce_1: 0.06499/0.09118, loss_spatial_dice_1: 0.32567/0.19403, loss_spatial_ce_1: 0.43449/0.08130, loss_grounding_bce_1: 0.07534/0.08034, loss_grounding_dice_1: 0.40947/0.15277, loss_grounding_ce_1: 0.34219/0.25647, loss_mask_ce_2: 0.17543/0.80038, loss_mask_bce_2: 0.35904/0.30220, loss_mask_dice_2: 1.45005/1.04237, loss_spatial_bce_2: 0.08242/0.09066, loss_spatial_dice_2: 0.29248/0.19393, loss_spatial_ce_2: 0.43270/0.08448, loss_grounding_bce_2: 0.07314/0.08009, loss_grounding_dice_2: 0.28226/0.15248, loss_grounding_ce_2: 0.21985/0.25719, loss_mask_ce_3: 0.18294/0.79942, loss_mask_bce_3: 0.35774/0.30367, loss_mask_dice_3: 1.73119/1.03706, loss_spatial_bce_3: 0.07473/0.09223, loss_spatial_dice_3: 0.32218/0.19418, loss_spatial_ce_3: 0.42183/0.09023, loss_grounding_bce_3: 0.07473/0.08061, loss_grounding_dice_3: 0.30396/0.15220, loss_grounding_ce_3: 0.22646/0.25535, loss_mask_ce_4: 0.16767/0.80632, loss_mask_bce_4: 0.36309/0.30569, loss_mask_dice_4: 1.43963/1.05542, loss_spatial_bce_4: 0.09545/0.09431, loss_spatial_dice_4: 0.35366/0.20157, loss_spatial_ce_4: 0.12973/0.10179, loss_grounding_bce_4: 0.07890/0.08127, loss_grounding_dice_4: 0.39356/0.15435, loss_grounding_ce_4: 0.35391/0.26530, loss_mask_ce_5: 0.17136/0.82747, loss_mask_bce_5: 0.37506/0.30786, loss_mask_dice_5: 1.78916/1.06235, loss_spatial_bce_5: 0.06487/0.09587, loss_spatial_dice_5: 0.31895/0.20357, loss_spatial_ce_5: 0.56868/0.11326, loss_grounding_bce_5: 0.08042/0.08179, loss_grounding_dice_5: 0.30608/0.15536, loss_grounding_ce_5: 0.29740/0.28434, loss_mask_ce_6: 0.19554/0.85239, loss_mask_bce_6: 0.35527/0.30918, loss_mask_dice_6: 1.40861/1.06642, loss_spatial_bce_6: 0.06044/0.10083, loss_spatial_dice_6: 0.29378/0.20596, loss_spatial_ce_6: 0.51871/0.13161, loss_grounding_bce_6: 0.07771/0.08300, loss_grounding_dice_6: 0.27035/0.15597, loss_grounding_ce_6: 0.40180/0.29687, loss_mask_ce_7: 0.24000/0.91540, loss_mask_bce_7: 0.34956/0.31656, loss_mask_dice_7: 1.73795/1.11342, loss_spatial_bce_7: 0.06717/0.11147, loss_spatial_dice_7: 0.35764/0.23106, loss_spatial_ce_7: 0.37154/0.17600, loss_grounding_bce_7: 0.08312/0.08482, loss_grounding_dice_7: 0.30004/0.16177, loss_grounding_ce_7: 0.37223/0.34354, loss_mask_ce_8: 0.32321/1.05284, loss_mask_bce_8: 0.36267/0.33419, loss_mask_dice_8: 2.15146/1.19376, loss_spatial_bce_8: 0.22616/0.13282, loss_spatial_dice_8: 0.42129/0.27238, loss_spatial_ce_8: 0.43714/0.23260, loss_grounding_bce_8: 0.07851/0.08873, loss_grounding_dice_8: 0.28221/0.17096, loss_grounding_ce_8: 0.36198/0.44689, loss_mask_ce_9: 2.81269/3.51311, loss_mask_bce_9: 0.38399/0.36050, loss_mask_dice_9: 2.21856/1.78144, loss_spatial_bce_9: 0.29459/0.36067, loss_spatial_dice_9: 0.82964/0.79822, loss_spatial_ce_9: 1.34608/1.42066, loss_grounding_bce_9: 0.08605/0.10069, loss_grounding_dice_9: 0.36522/0.24550, loss_grounding_ce_9: 0.27900/0.72022] items per batch[64] items per second[0.36] total items[1036800] mini batches[ 16200] memory[4967] epoch remaining[0:07:13] INFO:trainer.default_trainer:epochs[ 8] optim steps[16300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78740/0.79057, loss_mask_bce_0: 0.82346/0.30179, loss_mask_dice_0: 1.77468/1.03508, loss_spatial_bce_0: 0.06861/0.09073, loss_spatial_dice_0: 0.16253/0.19121, loss_spatial_ce_0: 0.03206/0.07654, loss_grounding_bce_0: 0.38593/0.08030, loss_grounding_dice_0: 0.73470/0.15193, loss_grounding_ce_0: 0.09969/0.25276, loss_mask_ce_1: 0.96130/0.79358, loss_mask_bce_1: 0.90675/0.30233, loss_mask_dice_1: 1.72818/1.03864, loss_spatial_bce_1: 0.06975/0.09119, loss_spatial_dice_1: 0.16912/0.19400, loss_spatial_ce_1: 0.03441/0.08118, loss_grounding_bce_1: 0.40724/0.08040, loss_grounding_dice_1: 0.73460/0.15278, loss_grounding_ce_1: 0.11635/0.25612, loss_mask_ce_2: 0.98261/0.80013, loss_mask_bce_2: 1.00229/0.30238, loss_mask_dice_2: 1.79073/1.04205, loss_spatial_bce_2: 0.06458/0.09067, loss_spatial_dice_2: 0.16713/0.19390, loss_spatial_ce_2: 0.03441/0.08437, loss_grounding_bce_2: 0.38191/0.08015, loss_grounding_dice_2: 0.71108/0.15247, loss_grounding_ce_2: 0.11943/0.25692, loss_mask_ce_3: 1.02655/0.79905, loss_mask_bce_3: 0.96407/0.30390, loss_mask_dice_3: 1.76524/1.03666, loss_spatial_bce_3: 0.05414/0.09226, loss_spatial_dice_3: 0.16955/0.19415, loss_spatial_ce_3: 0.03701/0.09010, loss_grounding_bce_3: 0.36098/0.08067, loss_grounding_dice_3: 0.69924/0.15219, loss_grounding_ce_3: 0.07393/0.25496, loss_mask_ce_4: 0.82626/0.80590, loss_mask_bce_4: 0.73258/0.30587, loss_mask_dice_4: 1.74392/1.05507, loss_spatial_bce_4: 0.07192/0.09437, loss_spatial_dice_4: 0.14846/0.20152, loss_spatial_ce_4: 0.04352/0.10158, loss_grounding_bce_4: 0.36581/0.08134, loss_grounding_dice_4: 0.70468/0.15435, loss_grounding_ce_4: 0.07393/0.26476, loss_mask_ce_5: 0.92534/0.82714, loss_mask_bce_5: 0.71110/0.30807, loss_mask_dice_5: 1.75884/1.06181, loss_spatial_bce_5: 0.06054/0.09593, loss_spatial_dice_5: 0.14918/0.20355, loss_spatial_ce_5: 0.05623/0.11307, loss_grounding_bce_5: 0.33715/0.08184, loss_grounding_dice_5: 0.70644/0.15537, loss_grounding_ce_5: 0.08205/0.28385, loss_mask_ce_6: 0.77082/0.85197, loss_mask_bce_6: 0.86194/0.30938, loss_mask_dice_6: 1.78798/1.06590, loss_spatial_bce_6: 0.03962/0.10088, loss_spatial_dice_6: 0.16855/0.20591, loss_spatial_ce_6: 0.08828/0.13145, loss_grounding_bce_6: 0.33403/0.08306, loss_grounding_dice_6: 0.71428/0.15598, loss_grounding_ce_6: 0.09078/0.29634, loss_mask_ce_7: 0.85267/0.91486, loss_mask_bce_7: 0.74912/0.31674, loss_mask_dice_7: 1.88191/1.11278, loss_spatial_bce_7: 0.05163/0.11151, loss_spatial_dice_7: 0.17940/0.23098, loss_spatial_ce_7: 0.11196/0.17578, loss_grounding_bce_7: 0.27817/0.08485, loss_grounding_dice_7: 0.74278/0.16174, loss_grounding_ce_7: 0.08070/0.34304, loss_mask_ce_8: 1.25197/1.05223, loss_mask_bce_8: 0.94971/0.33439, loss_mask_dice_8: 2.15595/1.19306, loss_spatial_bce_8: 0.07279/0.13278, loss_spatial_dice_8: 0.23662/0.27223, loss_spatial_ce_8: 0.11910/0.23248, loss_grounding_bce_8: 0.34945/0.08878, loss_grounding_dice_8: 0.73226/0.17094, loss_grounding_ce_8: 0.06273/0.44610, loss_mask_ce_9: 7.88827/3.51127, loss_mask_bce_9: 1.16039/0.36066, loss_mask_dice_9: 4.01478/1.77990, loss_spatial_bce_9: 0.26534/0.36087, loss_spatial_dice_9: 0.95486/0.79817, loss_spatial_ce_9: 1.21337/1.42056, loss_grounding_bce_9: 0.30981/0.10074, loss_grounding_dice_9: 0.75781/0.24549, loss_grounding_ce_9: 0.12211/0.71959] items per batch[64] items per second[0.35] total items[1043200] mini batches[ 16300] memory[4967] epoch remaining[0:04:15] INFO:trainer.default_trainer:epochs[ 8] optim steps[16400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.23092/0.79023, loss_mask_bce_0: 0.40532/0.30187, loss_mask_dice_0: 0.82353/1.03366, loss_spatial_bce_0: 0.06784/0.09088, loss_spatial_dice_0: 0.10400/0.19123, loss_spatial_ce_0: 0.00016/0.07634, loss_grounding_bce_0: 0.03280/0.08047, loss_grounding_dice_0: 0.06125/0.15194, loss_grounding_ce_0: 0.00105/0.25332, loss_mask_ce_1: 0.23053/0.79331, loss_mask_bce_1: 0.41410/0.30243, loss_mask_dice_1: 0.80956/1.03718, loss_spatial_bce_1: 0.06835/0.09135, loss_spatial_dice_1: 0.09689/0.19401, loss_spatial_ce_1: 0.00003/0.08105, loss_grounding_bce_1: 0.03114/0.08056, loss_grounding_dice_1: 0.05880/0.15280, loss_grounding_ce_1: 0.00106/0.25623, loss_mask_ce_2: 0.22212/0.79979, loss_mask_bce_2: 0.40682/0.30246, loss_mask_dice_2: 0.81301/1.04059, loss_spatial_bce_2: 0.06517/0.09082, loss_spatial_dice_2: 0.09990/0.19391, loss_spatial_ce_2: 0.00005/0.08426, loss_grounding_bce_2: 0.03537/0.08032, loss_grounding_dice_2: 0.06733/0.15252, loss_grounding_ce_2: 0.00096/0.25736, loss_mask_ce_3: 0.22631/0.79862, loss_mask_bce_3: 0.40022/0.30400, loss_mask_dice_3: 0.76758/1.03532, loss_spatial_bce_3: 0.06600/0.09243, loss_spatial_dice_3: 0.09946/0.19417, loss_spatial_ce_3: 0.00016/0.08998, loss_grounding_bce_3: 0.03272/0.08085, loss_grounding_dice_3: 0.05977/0.15224, loss_grounding_ce_3: 0.00134/0.25513, loss_mask_ce_4: 0.24020/0.80547, loss_mask_bce_4: 0.39581/0.30595, loss_mask_dice_4: 0.74978/1.05366, loss_spatial_bce_4: 0.06585/0.09452, loss_spatial_dice_4: 0.10266/0.20155, loss_spatial_ce_4: 0.00113/0.10142, loss_grounding_bce_4: 0.03748/0.08155, loss_grounding_dice_4: 0.06629/0.15442, loss_grounding_ce_4: 0.00198/0.26478, loss_mask_ce_5: 0.21622/0.82659, loss_mask_bce_5: 0.39426/0.30817, loss_mask_dice_5: 0.81705/1.06047, loss_spatial_bce_5: 0.06617/0.09607, loss_spatial_dice_5: 0.09419/0.20359, loss_spatial_ce_5: 0.00441/0.11292, loss_grounding_bce_5: 0.03065/0.08204, loss_grounding_dice_5: 0.06403/0.15544, loss_grounding_ce_5: 0.00139/0.28429, loss_mask_ce_6: 0.23132/0.85147, loss_mask_bce_6: 0.39495/0.30947, loss_mask_dice_6: 0.72722/1.06443, loss_spatial_bce_6: 0.06286/0.10104, loss_spatial_dice_6: 0.10928/0.20594, loss_spatial_ce_6: 0.02386/0.13126, loss_grounding_bce_6: 0.03240/0.08325, loss_grounding_dice_6: 0.06138/0.15607, loss_grounding_ce_6: 0.00862/0.29637, loss_mask_ce_7: 0.42893/0.91437, loss_mask_bce_7: 0.41585/0.31683, loss_mask_dice_7: 0.74557/1.11139, loss_spatial_bce_7: 0.05487/0.11164, loss_spatial_dice_7: 0.11652/0.23096, loss_spatial_ce_7: 0.06166/0.17567, loss_grounding_bce_7: 0.03308/0.08505, loss_grounding_dice_7: 0.07143/0.16182, loss_grounding_ce_7: 0.04063/0.34338, loss_mask_ce_8: 0.45918/1.05175, loss_mask_bce_8: 0.43046/0.33442, loss_mask_dice_8: 0.89392/1.19138, loss_spatial_bce_8: 0.06864/0.13296, loss_spatial_dice_8: 0.13239/0.27216, loss_spatial_ce_8: 0.05257/0.23222, loss_grounding_bce_8: 0.03372/0.08892, loss_grounding_dice_8: 0.05830/0.17102, loss_grounding_ce_8: 0.48760/0.44639, loss_mask_ce_9: 2.77138/3.50968, loss_mask_bce_9: 0.46843/0.36055, loss_mask_dice_9: 1.56546/1.77689, loss_spatial_bce_9: 0.34064/0.36095, loss_spatial_dice_9: 0.93002/0.79805, loss_spatial_ce_9: 1.73534/1.42032, loss_grounding_bce_9: 0.04945/0.10084, loss_grounding_dice_9: 0.14538/0.24547, loss_grounding_ce_9: 4.04180/0.71961] items per batch[64] items per second[0.36] total items[1049600] mini batches[ 16400] memory[4967] epoch remaining[0:01:16] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00016443. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0022 s/iter. Inference: 0.3762 s/iter. Eval: 0.0938 s/iter. Total: 0.4722 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0023 s/iter. Inference: 0.3757 s/iter. Eval: 0.0774 s/iter. Total: 0.4555 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0025 s/iter. Inference: 0.3787 s/iter. Eval: 0.0823 s/iter. Total: 0.4637 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0026 s/iter. Inference: 0.3811 s/iter. Eval: 0.0767 s/iter. Total: 0.4606 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0027 s/iter. Inference: 0.3826 s/iter. Eval: 0.0736 s/iter. Total: 0.4591 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0028 s/iter. Inference: 0.3808 s/iter. Eval: 0.0713 s/iter. Total: 0.4551 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalu2ikfdm2 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.243 | 83.190 | 65.592 | 133 | | Things | 61.499 | 84.343 | 72.474 | 80 | | Stuff | 45.799 | 81.450 | 55.205 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.52s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.07 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.38 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.60s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.73 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.690 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.484 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.162 | 68.995 | 48.395 | 25.841 | 49.520 | 67.173 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.979 | bicycle | 23.907 | car | 43.647 | | motorcycle | 41.898 | airplane | 60.438 | bus | 71.173 | | train | 74.591 | truck | 42.011 | boat | 29.852 | | traffic light | 28.094 | fire hydrant | 69.944 | stop sign | 68.364 | | parking meter | 50.681 | bench | 26.886 | bird | 33.094 | | cat | 76.760 | dog | 71.312 | horse | 49.725 | | sheep | 54.087 | cow | 56.091 | elephant | 65.674 | | bear | 79.188 | zebra | 65.985 | giraffe | 61.442 | | backpack | 24.184 | umbrella | 54.306 | handbag | 23.730 | | tie | 40.055 | suitcase | 51.634 | frisbee | 71.621 | | skis | 10.471 | snowboard | 36.060 | sports ball | 49.337 | | kite | 38.145 | baseball bat | 38.428 | baseball glove | 49.419 | | skateboard | 44.140 | surfboard | 45.205 | tennis racket | 62.706 | | bottle | 41.584 | wine glass | 37.129 | cup | 49.595 | | fork | 25.096 | knife | 23.654 | spoon | 20.963 | | bowl | 39.455 | banana | 21.734 | apple | 24.687 | | sandwich | 47.814 | orange | 29.123 | broccoli | 23.975 | | carrot | 21.071 | hot dog | 34.606 | pizza | 51.489 | | donut | 55.871 | cake | 45.493 | chair | 29.078 | | couch | 43.996 | potted plant | 23.558 | bed | 43.489 | | dining table | 15.573 | toilet | 70.184 | tv | 64.728 | | laptop | 71.950 | mouse | 62.256 | remote | 42.164 | | keyboard | 57.771 | cell phone | 43.553 | microwave | 63.126 | | oven | 34.069 | toaster | 47.202 | sink | 43.381 | | refrigerator | 68.641 | book | 14.369 | clock | 54.953 | | vase | 39.195 | scissors | 35.492 | teddy bear | 57.798 | | hair drier | 30.230 | toothbrush | 29.631 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.33137320565615, 'fwIoU': 71.36785514122572, 'IoU-person': 88.80131525873925, 'IoU-bicycle': 72.7040201010996, 'IoU-car': 72.30382814642408, 'IoU-motorcycle': 86.07983195993587, 'IoU-airplane': 88.05231611105668, 'IoU-bus': 88.48287893201966, 'IoU-train': 87.48056654819372, 'IoU-truck': 69.79208107782645, 'IoU-boat': 70.6873090959039, 'IoU-traffic light': 78.99055328941799, 'IoU-fire hydrant': 93.29722578758893, 'IoU-stop sign': 94.79460193087303, 'IoU-parking meter': 84.3761744874568, 'IoU-bench': 62.65843667564673, 'IoU-bird': 77.71249403447467, 'IoU-cat': 87.06699863433754, 'IoU-dog': 81.84949243324203, 'IoU-horse': 88.26763967750321, 'IoU-sheep': 87.18002327311574, 'IoU-cow': 87.12213147462326, 'IoU-elephant': 92.11974307684075, 'IoU-bear': 68.22072061238215, 'IoU-zebra': 74.12231676067988, 'IoU-giraffe': 88.28462538787387, 'IoU-backpack': 52.544806616568515, 'IoU-umbrella': 84.70590446661451, 'IoU-handbag': 47.84753734886503, 'IoU-tie': 76.76092538603278, 'IoU-suitcase': 79.92573027076614, 'IoU-frisbee': 84.41565606248925, 'IoU-skis': 59.5107327946195, 'IoU-snowboard': 72.88174030619668, 'IoU-sports ball': 78.39241280660843, 'IoU-kite': 78.62734334980846, 'IoU-baseball bat': 70.54380664652568, 'IoU-baseball glove': 81.66867890957616, 'IoU-skateboard': 86.12984607523792, 'IoU-surfboard': 86.59611435792134, 'IoU-tennis racket': 91.06034591506871, 'IoU-bottle': 70.72620550767013, 'IoU-wine glass': 82.6954932547692, 'IoU-cup': 70.8229063543219, 'IoU-fork': 68.48308710745135, 'IoU-knife': 63.43945728104787, 'IoU-spoon': 60.060171081732115, 'IoU-bowl': 60.42855409577928, 'IoU-banana': 82.43035111658166, 'IoU-apple': 61.556319934551354, 'IoU-sandwich': 70.38052985312775, 'IoU-orange': 77.10330527168841, 'IoU-broccoli': 70.71292941638657, 'IoU-carrot': 64.17138175240702, 'IoU-hot dog': 63.57096537967458, 'IoU-pizza': 85.93123909806798, 'IoU-donut': 75.82512306325197, 'IoU-cake': 80.017087199025, 'IoU-chair': 63.142370301381156, 'IoU-couch': 67.79111954516824, 'IoU-potted plant': 45.09353257029494, 'IoU-bed': 69.72577658294095, 'IoU-dining table': 54.37884927076968, 'IoU-toilet': 83.8505112496151, 'IoU-tv': 83.92490862805928, 'IoU-laptop': 77.5690643495503, 'IoU-mouse': 82.95744263581093, 'IoU-remote': 70.90761519041034, 'IoU-keyboard': 66.60988873365075, 'IoU-cell phone': 80.51654955517577, 'IoU-microwave': 71.98084414164576, 'IoU-oven': 70.60501822449172, 'IoU-toaster': 84.4744836741467, 'IoU-sink': 74.61580189642417, 'IoU-refrigerator': 80.01060949480893, 'IoU-book': 57.453939050313586, 'IoU-clock': 69.45050217062537, 'IoU-vase': 59.002213950907, 'IoU-scissors': 83.92804552442084, 'IoU-teddy bear': 78.61835727622709, 'IoU-hair drier': 43.632003385606474, 'IoU-toothbrush': 75.73418034560574, 'IoU-banner': 29.90328747681134, 'IoU-blanket': 16.891631735217594, 'IoU-bridge': 35.62759133240405, 'IoU-cardboard': 54.43577467601634, 'IoU-counter': 33.79453265182497, 'IoU-curtain': 71.29101623684933, 'IoU-door-stuff': 49.20858362477824, 'IoU-floor-wood': 63.5359192871894, 'IoU-flower': 48.27502324840911, 'IoU-fruit': 49.87963217764913, 'IoU-gravel': 26.310161122907434, 'IoU-house': 24.470031997888956, 'IoU-light': 42.824666498700466, 'IoU-mirror-stuff': 64.10927435849335, 'IoU-net': 41.32680612611288, 'IoU-pillow': 17.913899005003454, 'IoU-platform': 29.743124357810895, 'IoU-playingfield': 69.71234117505651, 'IoU-railroad': 63.38912339206762, 'IoU-river': 52.17050046498657, 'IoU-road': 69.54669820549628, 'IoU-roof': 13.942707928900477, 'IoU-sand': 66.01267772929873, 'IoU-sea': 85.03639859244149, 'IoU-shelf': 40.064601514049656, 'IoU-snow': 91.9531593509739, 'IoU-stairs': 32.19190939306312, 'IoU-tent': 11.275365063633993, 'IoU-towel': 46.10219553920737, 'IoU-wall-brick': 49.74357262094292, 'IoU-wall-stone': 28.510994829114562, 'IoU-wall-tile': 70.02559795167322, 'IoU-wall-wood': 43.52857616678635, 'IoU-water-other': 19.917658578963334, 'IoU-window-blind': 51.02445543369537, 'IoU-window-other': 50.97996099014428, 'IoU-tree-merged': 81.78005220341788, 'IoU-fence-merged': 53.17099551443759, 'IoU-ceiling-merged': 67.43089718875684, 'IoU-sky-other-merged': 93.71337195370347, 'IoU-cabinet-merged': 64.43273689035485, 'IoU-table-merged': 42.88289457009558, 'IoU-floor-other-merged': 53.50680549355917, 'IoU-pavement-merged': 60.36474062309875, 'IoU-mountain-merged': 58.36244692680125, 'IoU-grass-merged': 70.7194852148216, 'IoU-dirt-merged': 46.73060059983183, 'IoU-paper-merged': 36.64753935273892, 'IoU-food-other-merged': 41.48804640092282, 'IoU-building-other-merged': 59.96718274552716, 'IoU-rock-merged': 63.690377121145524, 'IoU-wall-other-merged': 67.26781350068997, 'IoU-rug-merged': 65.85955862206103, 'mACC': 76.58579008651722, 'pACC': 82.08756932541563, 'ACC-person': 93.29772067564015, 'ACC-bicycle': 79.41689524188594, 'ACC-car': 87.2990195583978, 'ACC-motorcycle': 90.8662044217982, 'ACC-airplane': 92.19787044053463, 'ACC-bus': 94.38731333277275, 'ACC-train': 93.46560172196106, 'ACC-truck': 77.53402419901644, 'ACC-boat': 78.93259966418452, 'ACC-traffic light': 90.27134648482148, 'ACC-fire hydrant': 96.06382933296895, 'ACC-stop sign': 98.40155340517701, 'ACC-parking meter': 87.86716726044138, 'ACC-bench': 78.13230931144307, 'ACC-bird': 82.2365098522589, 'ACC-cat': 90.75710590412943, 'ACC-dog': 84.1559211521516, 'ACC-horse': 92.73734029810173, 'ACC-sheep': 91.82917948863584, 'ACC-cow': 90.57222417673702, 'ACC-elephant': 94.24523663090312, 'ACC-bear': 69.47565856053778, 'ACC-zebra': 75.75907334713425, 'ACC-giraffe': 91.94004322392908, 'ACC-backpack': 72.64123452029342, 'ACC-umbrella': 88.63640993472059, 'ACC-handbag': 66.80199275589817, 'ACC-tie': 84.54364067267205, 'ACC-suitcase': 85.83252385593411, 'ACC-frisbee': 94.59127272727272, 'ACC-skis': 73.31999986371241, 'ACC-snowboard': 83.43121647767627, 'ACC-sports ball': 88.0908818391375, 'ACC-kite': 86.34869456001651, 'ACC-baseball bat': 86.07391978661711, 'ACC-baseball glove': 92.60644315225929, 'ACC-skateboard': 90.79372595840945, 'ACC-surfboard': 92.59897334452141, 'ACC-tennis racket': 94.9287503325319, 'ACC-bottle': 85.22813105407702, 'ACC-wine glass': 91.15247230331272, 'ACC-cup': 87.73373630227177, 'ACC-fork': 79.99601150788192, 'ACC-knife': 77.22558071370997, 'ACC-spoon': 75.11909312486384, 'ACC-bowl': 75.93893271615124, 'ACC-banana': 89.71880484828917, 'ACC-apple': 75.28827926833463, 'ACC-sandwich': 83.09608179799727, 'ACC-orange': 84.37359165797538, 'ACC-broccoli': 81.82619974878585, 'ACC-carrot': 77.40123684091792, 'ACC-hot dog': 70.22031427712339, 'ACC-pizza': 93.76741446722335, 'ACC-donut': 82.93814009783384, 'ACC-cake': 88.08673024953494, 'ACC-chair': 80.30779060879229, 'ACC-couch': 79.02414577259627, 'ACC-potted plant': 58.16239425268272, 'ACC-bed': 81.58236427335204, 'ACC-dining table': 80.93416205904302, 'ACC-toilet': 88.1393718620576, 'ACC-tv': 88.71450333574307, 'ACC-laptop': 91.49771854628298, 'ACC-mouse': 91.55546897827993, 'ACC-remote': 75.57369709141335, 'ACC-keyboard': 69.77910447109832, 'ACC-cell phone': 87.98175053479595, 'ACC-microwave': 74.53041194572549, 'ACC-oven': 88.73151986344836, 'ACC-toaster': 90.57055659718895, 'ACC-sink': 84.25794161086179, 'ACC-refrigerator': 88.95770675647884, 'ACC-book': 74.8476156454008, 'ACC-clock': 73.67485948762751, 'ACC-vase': 66.41444058095145, 'ACC-scissors': 89.29381410168122, 'ACC-teddy bear': 82.46105274663857, 'ACC-hair drier': 52.822133037315346, 'ACC-toothbrush': 85.1259555246699, 'ACC-banner': 78.41430091416802, 'ACC-blanket': 27.57816883085204, 'ACC-bridge': 53.32254451814755, 'ACC-cardboard': 68.48829431438126, 'ACC-counter': 55.23175564918662, 'ACC-curtain': 81.56121458997256, 'ACC-door-stuff': 70.91145270654027, 'ACC-floor-wood': 83.01958642014847, 'ACC-flower': 68.05599419572361, 'ACC-fruit': 69.05905948152906, 'ACC-gravel': 31.24809840133847, 'ACC-house': 30.404181777245892, 'ACC-light': 61.813224935880264, 'ACC-mirror-stuff': 73.42387106100799, 'ACC-net': 64.8138910504974, 'ACC-pillow': 38.20450747505893, 'ACC-platform': 50.19925866157637, 'ACC-playingfield': 88.92464097939995, 'ACC-railroad': 82.73289464019032, 'ACC-river': 80.9452143224143, 'ACC-road': 81.76180598495752, 'ACC-roof': 18.87542935373859, 'ACC-sand': 70.92274607614961, 'ACC-sea': 90.42463755262182, 'ACC-shelf': 57.0053842661226, 'ACC-snow': 95.68753886389628, 'ACC-stairs': 58.825377762231476, 'ACC-tent': 13.631901083597159, 'ACC-towel': 54.43910681693863, 'ACC-wall-brick': 68.95128909360012, 'ACC-wall-stone': 34.21379235270756, 'ACC-wall-tile': 85.68996538615106, 'ACC-wall-wood': 66.03096886385158, 'ACC-water-other': 29.32286870386353, 'ACC-window-blind': 63.38023063857355, 'ACC-window-other': 71.63831251670437, 'ACC-tree-merged': 89.40619383082995, 'ACC-fence-merged': 72.57062659747648, 'ACC-ceiling-merged': 82.21437510882636, 'ACC-sky-other-merged': 96.941984512208, 'ACC-cabinet-merged': 77.59695282338728, 'ACC-table-merged': 54.85833386710279, 'ACC-floor-other-merged': 63.75631000626508, 'ACC-pavement-merged': 75.6982602159418, 'ACC-mountain-merged': 69.56826566670584, 'ACC-grass-merged': 83.23296127977244, 'ACC-dirt-merged': 71.0540843721114, 'ACC-paper-merged': 46.947759765150636, 'ACC-food-other-merged': 52.6828398495332, 'ACC-building-other-merged': 74.8439084264992, 'ACC-rock-merged': 83.81022807092904, 'ACC-wall-other-merged': 81.158264086286, 'ACC-rug-merged': 83.28256465915497})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3300 s/iter. Inference: 0.1756 s/iter. Eval: 0.0000 s/iter. Total: 0.5056 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3579 s/iter. Inference: 0.3397 s/iter. Eval: 0.0000 s/iter. Total: 0.6977 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3653 s/iter. Inference: 0.5502 s/iter. Eval: 0.0000 s/iter. Total: 0.9157 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.424348844015218, 'noc@0.8': 2.5858940591161836, 'noc@0.85': 3.0406789581504245, 'noc@0.9': 3.896107696810067, 'miou@iter1': 0.8768166225836366} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1429 s/iter. Eval: 0.0010 s/iter. Total: 0.1455 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.9813461303711, 'precision@0.6': 73.18305206298828, 'precision@0.7': 68.90789031982422, 'precision@0.8': 60.20209884643555, 'precision@0.9': 33.34628677368164, 'cIoU': 61.0471305847168, 'mIoU': 67.25298309326172} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.242539386102884, 'SQ': 83.19026284111088, 'RQ': 65.59240395288097, 'PQ_th': 61.49889066122193, 'SQ_th': 84.34307805546027, 'RQ_th': 72.47412477281489, 'PQ_st': 45.79899029158353, 'SQ_st': 81.45016440435714, 'RQ_st': 55.20490082845249}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.16236111614283, 'AP50': 68.99499946133585, 'AP75': 48.394925986469936, 'APs': 25.841189026565942, 'APm': 49.5202140113362, 'APl': 67.17290917932579, 'AP-person': 48.97850791073347, 'AP-bicycle': 23.906557698980137, 'AP-car': 43.64670250382162, 'AP-motorcycle': 41.89829393234427, 'AP-airplane': 60.43833863931724, 'AP-bus': 71.17294851694088, 'AP-train': 74.5907511152234, 'AP-truck': 42.0108441865078, 'AP-boat': 29.85165739607249, 'AP-traffic light': 28.093586564833732, 'AP-fire hydrant': 69.94374244741962, 'AP-stop sign': 68.36362185495697, 'AP-parking meter': 50.68148709292786, 'AP-bench': 26.885830384834776, 'AP-bird': 33.09413284792812, 'AP-cat': 76.75999531110749, 'AP-dog': 71.31205494490835, 'AP-horse': 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'IoU-umbrella': 84.70590446661451, 'IoU-handbag': 47.84753734886503, 'IoU-tie': 76.76092538603278, 'IoU-suitcase': 79.92573027076614, 'IoU-frisbee': 84.41565606248925, 'IoU-skis': 59.5107327946195, 'IoU-snowboard': 72.88174030619668, 'IoU-sports ball': 78.39241280660843, 'IoU-kite': 78.62734334980846, 'IoU-baseball bat': 70.54380664652568, 'IoU-baseball glove': 81.66867890957616, 'IoU-skateboard': 86.12984607523792, 'IoU-surfboard': 86.59611435792134, 'IoU-tennis racket': 91.06034591506871, 'IoU-bottle': 70.72620550767013, 'IoU-wine glass': 82.6954932547692, 'IoU-cup': 70.8229063543219, 'IoU-fork': 68.48308710745135, 'IoU-knife': 63.43945728104787, 'IoU-spoon': 60.060171081732115, 'IoU-bowl': 60.42855409577928, 'IoU-banana': 82.43035111658166, 'IoU-apple': 61.556319934551354, 'IoU-sandwich': 70.38052985312775, 'IoU-orange': 77.10330527168841, 'IoU-broccoli': 70.71292941638657, 'IoU-carrot': 64.17138175240702, 'IoU-hot dog': 63.57096537967458, 'IoU-pizza': 85.93123909806798, 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'IoU-water-other': 19.917658578963334, 'IoU-window-blind': 51.02445543369537, 'IoU-window-other': 50.97996099014428, 'IoU-tree-merged': 81.78005220341788, 'IoU-fence-merged': 53.17099551443759, 'IoU-ceiling-merged': 67.43089718875684, 'IoU-sky-other-merged': 93.71337195370347, 'IoU-cabinet-merged': 64.43273689035485, 'IoU-table-merged': 42.88289457009558, 'IoU-floor-other-merged': 53.50680549355917, 'IoU-pavement-merged': 60.36474062309875, 'IoU-mountain-merged': 58.36244692680125, 'IoU-grass-merged': 70.7194852148216, 'IoU-dirt-merged': 46.73060059983183, 'IoU-paper-merged': 36.64753935273892, 'IoU-food-other-merged': 41.48804640092282, 'IoU-building-other-merged': 59.96718274552716, 'IoU-rock-merged': 63.690377121145524, 'IoU-wall-other-merged': 67.26781350068997, 'IoU-rug-merged': 65.85955862206103, 'mACC': 76.58579008651722, 'pACC': 82.08756932541563, 'ACC-person': 93.29772067564015, 'ACC-bicycle': 79.41689524188594, 'ACC-car': 87.2990195583978, 'ACC-motorcycle': 90.8662044217982, 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77.59695282338728, 'ACC-table-merged': 54.85833386710279, 'ACC-floor-other-merged': 63.75631000626508, 'ACC-pavement-merged': 75.6982602159418, 'ACC-mountain-merged': 69.56826566670584, 'ACC-grass-merged': 83.23296127977244, 'ACC-dirt-merged': 71.0540843721114, 'ACC-paper-merged': 46.947759765150636, 'ACC-food-other-merged': 52.6828398495332, 'ACC-building-other-merged': 74.8439084264992, 'ACC-rock-merged': 83.81022807092904, 'ACC-wall-other-merged': 81.158264086286, 'ACC-rug-merged': 83.28256465915497})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.424348844015218, 'noc@0.8': 2.5858940591161836, 'noc@0.85': 3.0406789581504245, 'noc@0.9': 3.896107696810067, 'miou@iter1': 0.8768166225836366}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.9813461303711, 'precision@0.6': 73.18305206298828, 'precision@0.7': 68.90789031982422, 'precision@0.8': 60.20209884643555, 'precision@0.9': 33.34628677368164, 'cIoU': 61.0471305847168, 'mIoU': 67.25298309326172}}} INFO:trainer.default_trainer:This epoch takes 0:57:53.951630 INFO:trainer.default_trainer:PROGRESS: 18.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 9 training. INFO:trainer.default_trainer:epochs[ 9] optim steps[16500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55353/0.79071, loss_mask_bce_0: 0.04164/0.30196, loss_mask_dice_0: 0.47535/1.03400, loss_spatial_bce_0: 0.02749/0.09088, loss_spatial_dice_0: 0.29177/0.19134, loss_spatial_ce_0: 0.00158/0.07625, loss_grounding_bce_0: 0.01003/0.08050, loss_grounding_dice_0: 0.08998/0.15205, loss_grounding_ce_0: 0.12778/0.25283, loss_mask_ce_1: 0.75928/0.79375, loss_mask_bce_1: 0.00971/0.30252, loss_mask_dice_1: 0.41669/1.03760, loss_spatial_bce_1: 0.04355/0.09136, loss_spatial_dice_1: 0.32193/0.19412, loss_spatial_ce_1: 0.00321/0.08102, loss_grounding_bce_1: 0.01003/0.08060, loss_grounding_dice_1: 0.08709/0.15293, loss_grounding_ce_1: 0.14067/0.25578, loss_mask_ce_2: 0.73769/0.80013, loss_mask_bce_2: 0.00707/0.30255, loss_mask_dice_2: 0.35256/1.04117, loss_spatial_bce_2: 0.03531/0.09082, loss_spatial_dice_2: 0.34471/0.19403, loss_spatial_ce_2: 0.00748/0.08415, loss_grounding_bce_2: 0.01097/0.08035, loss_grounding_dice_2: 0.08507/0.15264, loss_grounding_ce_2: 0.09801/0.25695, loss_mask_ce_3: 0.67797/0.79898, loss_mask_bce_3: 0.00876/0.30411, loss_mask_dice_3: 0.31404/1.03567, loss_spatial_bce_3: 0.02602/0.09244, loss_spatial_dice_3: 0.27965/0.19427, loss_spatial_ce_3: 0.02560/0.08983, loss_grounding_bce_3: 0.01343/0.08087, loss_grounding_dice_3: 0.09426/0.15233, loss_grounding_ce_3: 0.11350/0.25489, loss_mask_ce_4: 0.78379/0.80601, loss_mask_bce_4: 0.00904/0.30600, loss_mask_dice_4: 0.40740/1.05416, loss_spatial_bce_4: 0.02973/0.09452, loss_spatial_dice_4: 0.32124/0.20164, loss_spatial_ce_4: 0.08551/0.10133, loss_grounding_bce_4: 0.01092/0.08158, loss_grounding_dice_4: 0.08607/0.15457, loss_grounding_ce_4: 0.52539/0.26438, loss_mask_ce_5: 0.49260/0.82700, loss_mask_bce_5: 0.02492/0.30827, loss_mask_dice_5: 0.45653/1.06084, loss_spatial_bce_5: 0.04024/0.09609, loss_spatial_dice_5: 0.37279/0.20371, loss_spatial_ce_5: 0.16281/0.11283, loss_grounding_bce_5: 0.01542/0.08208, loss_grounding_dice_5: 0.09887/0.15556, loss_grounding_ce_5: 0.16889/0.28390, loss_mask_ce_6: 0.84129/0.85179, loss_mask_bce_6: 0.00932/0.30957, loss_mask_dice_6: 0.42964/1.06469, loss_spatial_bce_6: 0.03595/0.10107, loss_spatial_dice_6: 0.37556/0.20607, loss_spatial_ce_6: 0.17257/0.13120, loss_grounding_bce_6: 0.01784/0.08328, loss_grounding_dice_6: 0.12481/0.15614, loss_grounding_ce_6: 0.44645/0.29605, loss_mask_ce_7: 0.50826/0.91472, loss_mask_bce_7: 0.03246/0.31690, loss_mask_dice_7: 0.57636/1.11161, loss_spatial_bce_7: 0.03279/0.11162, loss_spatial_dice_7: 0.37145/0.23107, loss_spatial_ce_7: 0.56609/0.17561, loss_grounding_bce_7: 0.01430/0.08508, loss_grounding_dice_7: 0.09087/0.16191, loss_grounding_ce_7: 0.48254/0.34279, loss_mask_ce_8: 0.65548/1.05232, loss_mask_bce_8: 0.04354/0.33447, loss_mask_dice_8: 0.67372/1.19170, loss_spatial_bce_8: 0.04948/0.13297, loss_spatial_dice_8: 0.39527/0.27229, loss_spatial_ce_8: 0.21324/0.23215, loss_grounding_bce_8: 0.00786/0.08895, loss_grounding_dice_8: 0.08714/0.17109, loss_grounding_ce_8: 1.86397/0.44595, loss_mask_ce_9: 3.71952/3.51031, loss_mask_bce_9: 0.01106/0.36061, loss_mask_dice_9: 0.47160/1.77695, loss_spatial_bce_9: 0.08373/0.36088, loss_spatial_dice_9: 0.72725/0.79810, loss_spatial_ce_9: 0.73749/1.42028, loss_grounding_bce_9: 0.00941/0.10084, loss_grounding_dice_9: 0.13502/0.24558, loss_grounding_ce_9: 1.52363/0.71834] items per batch[64] items per second[0.16] total items[1056000] mini batches[ 16500] memory[4967] epoch remaining[0:55:32] INFO:trainer.default_trainer:epochs[ 9] optim steps[16600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14467/0.79048, loss_mask_bce_0: 0.02597/0.30193, loss_mask_dice_0: 0.28067/1.03318, loss_spatial_bce_0: 0.01512/0.09086, loss_spatial_dice_0: 0.17221/0.19125, loss_spatial_ce_0: 0.05546/0.07612, loss_grounding_bce_0: 0.01029/0.08050, loss_grounding_dice_0: 0.10216/0.15202, loss_grounding_ce_0: 0.25376/0.25258, loss_mask_ce_1: 0.16893/0.79358, loss_mask_bce_1: 0.02689/0.30250, loss_mask_dice_1: 0.30056/1.03670, loss_spatial_bce_1: 0.01155/0.09134, loss_spatial_dice_1: 0.12183/0.19400, loss_spatial_ce_1: 0.05546/0.08088, loss_grounding_bce_1: 0.01160/0.08059, loss_grounding_dice_1: 0.10961/0.15289, loss_grounding_ce_1: 0.20930/0.25547, loss_mask_ce_2: 0.13784/0.80004, loss_mask_bce_2: 0.03278/0.30254, loss_mask_dice_2: 0.35671/1.04035, loss_spatial_bce_2: 0.01407/0.09081, loss_spatial_dice_2: 0.14891/0.19389, loss_spatial_ce_2: 0.05546/0.08400, loss_grounding_bce_2: 0.01530/0.08033, loss_grounding_dice_2: 0.11960/0.15260, loss_grounding_ce_2: 0.41069/0.25668, loss_mask_ce_3: 0.13488/0.79889, loss_mask_bce_3: 0.03303/0.30411, loss_mask_dice_3: 0.34333/1.03472, loss_spatial_bce_3: 0.01597/0.09243, loss_spatial_dice_3: 0.16428/0.19417, loss_spatial_ce_3: 0.05549/0.08968, loss_grounding_bce_3: 0.01496/0.08086, loss_grounding_dice_3: 0.11597/0.15231, loss_grounding_ce_3: 0.32951/0.25460, loss_mask_ce_4: 0.15329/0.80577, loss_mask_bce_4: 0.02495/0.30596, loss_mask_dice_4: 0.26239/1.05331, loss_spatial_bce_4: 0.01285/0.09451, loss_spatial_dice_4: 0.12485/0.20153, loss_spatial_ce_4: 0.05557/0.10123, loss_grounding_bce_4: 0.01303/0.08158, loss_grounding_dice_4: 0.12465/0.15452, loss_grounding_ce_4: 0.23284/0.26401, loss_mask_ce_5: 0.13935/0.82677, loss_mask_bce_5: 0.02514/0.30823, loss_mask_dice_5: 0.25357/1.05995, loss_spatial_bce_5: 0.01323/0.09610, loss_spatial_dice_5: 0.12844/0.20359, loss_spatial_ce_5: 0.05557/0.11268, loss_grounding_bce_5: 0.01253/0.08207, loss_grounding_dice_5: 0.10645/0.15548, loss_grounding_ce_5: 0.29902/0.28346, loss_mask_ce_6: 0.15036/0.85169, loss_mask_bce_6: 0.02875/0.30953, loss_mask_dice_6: 0.30959/1.06392, loss_spatial_bce_6: 0.01835/0.10107, loss_spatial_dice_6: 0.14736/0.20595, loss_spatial_ce_6: 0.06353/0.13106, loss_grounding_bce_6: 0.01825/0.08327, loss_grounding_dice_6: 0.11964/0.15605, loss_grounding_ce_6: 0.29187/0.29559, loss_mask_ce_7: 0.16476/0.91460, loss_mask_bce_7: 0.02777/0.31688, loss_mask_dice_7: 0.31607/1.11073, loss_spatial_bce_7: 0.01242/0.11155, loss_spatial_dice_7: 0.19402/0.23094, loss_spatial_ce_7: 0.06898/0.17536, loss_grounding_bce_7: 0.01453/0.08510, loss_grounding_dice_7: 0.13147/0.16187, loss_grounding_ce_7: 0.42694/0.34205, loss_mask_ce_8: 0.27377/1.05211, loss_mask_bce_8: 0.02826/0.33445, loss_mask_dice_8: 0.32321/1.19074, loss_spatial_bce_8: 0.01489/0.13290, loss_spatial_dice_8: 0.14150/0.27212, loss_spatial_ce_8: 0.20596/0.23201, loss_grounding_bce_8: 0.01686/0.08897, loss_grounding_dice_8: 0.11331/0.17102, loss_grounding_ce_8: 0.66887/0.44528, loss_mask_ce_9: 2.22811/3.50893, loss_mask_bce_9: 0.02015/0.36058, loss_mask_dice_9: 0.36704/1.77580, loss_spatial_bce_9: 0.02867/0.36079, loss_spatial_dice_9: 0.53766/0.79791, loss_spatial_ce_9: 0.88126/1.41993, loss_grounding_bce_9: 0.00987/0.10085, loss_grounding_dice_9: 0.12429/0.24545, loss_grounding_ce_9: 1.61876/0.71764] items per batch[64] items per second[0.35] total items[1062400] mini batches[ 16600] memory[4967] epoch remaining[0:51:13] INFO:trainer.default_trainer:epochs[ 9] optim steps[16700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24242/0.79026, loss_mask_bce_0: 0.03651/0.30184, loss_mask_dice_0: 0.41966/1.03302, loss_spatial_bce_0: 0.01087/0.09078, loss_spatial_dice_0: 0.12656/0.19121, loss_spatial_ce_0: 0.11791/0.07600, loss_grounding_bce_0: 0.01323/0.08043, loss_grounding_dice_0: 0.15951/0.15197, loss_grounding_ce_0: 0.04779/0.25263, loss_mask_ce_1: 1.18371/0.79331, loss_mask_bce_1: 0.03859/0.30240, loss_mask_dice_1: 0.43249/1.03642, loss_spatial_bce_1: 0.00967/0.09126, loss_spatial_dice_1: 0.12210/0.19394, loss_spatial_ce_1: 0.08557/0.08079, loss_grounding_bce_1: 0.01195/0.08052, loss_grounding_dice_1: 0.18203/0.15285, loss_grounding_ce_1: 0.09909/0.25554, loss_mask_ce_2: 1.11657/0.79984, loss_mask_bce_2: 0.04763/0.30242, loss_mask_dice_2: 0.49089/1.04019, loss_spatial_bce_2: 0.00932/0.09073, loss_spatial_dice_2: 0.10930/0.19384, loss_spatial_ce_2: 0.07807/0.08384, loss_grounding_bce_2: 0.01314/0.08027, loss_grounding_dice_2: 0.18742/0.15257, loss_grounding_ce_2: 0.01532/0.25671, loss_mask_ce_3: 1.19260/0.79860, loss_mask_bce_3: 0.03650/0.30400, loss_mask_dice_3: 0.46049/1.03446, loss_spatial_bce_3: 0.00966/0.09234, loss_spatial_dice_3: 0.10522/0.19412, loss_spatial_ce_3: 0.16095/0.08954, loss_grounding_bce_3: 0.01299/0.08079, loss_grounding_dice_3: 0.13846/0.15226, loss_grounding_ce_3: 0.01158/0.25465, loss_mask_ce_4: 1.07066/0.80561, loss_mask_bce_4: 0.04631/0.30582, loss_mask_dice_4: 0.46918/1.05313, loss_spatial_bce_4: 0.01073/0.09441, loss_spatial_dice_4: 0.14440/0.20148, loss_spatial_ce_4: 0.22459/0.10120, loss_grounding_bce_4: 0.01160/0.08152, loss_grounding_dice_4: 0.13068/0.15449, loss_grounding_ce_4: 0.00627/0.26401, loss_mask_ce_5: 1.14747/0.82656, loss_mask_bce_5: 0.04110/0.30811, loss_mask_dice_5: 0.39125/1.05979, loss_spatial_bce_5: 0.00892/0.09599, loss_spatial_dice_5: 0.12245/0.20353, loss_spatial_ce_5: 0.21796/0.11270, loss_grounding_bce_5: 0.00975/0.08200, loss_grounding_dice_5: 0.14191/0.15544, loss_grounding_ce_5: 0.03814/0.28376, loss_mask_ce_6: 1.20086/0.85150, loss_mask_bce_6: 0.03202/0.30940, loss_mask_dice_6: 0.46927/1.06367, loss_spatial_bce_6: 0.00873/0.10097, loss_spatial_dice_6: 0.09621/0.20587, loss_spatial_ce_6: 0.12833/0.13105, loss_grounding_bce_6: 0.00972/0.08322, loss_grounding_dice_6: 0.12991/0.15605, loss_grounding_ce_6: 0.01751/0.29556, loss_mask_ce_7: 1.12957/0.91471, loss_mask_bce_7: 0.04234/0.31672, loss_mask_dice_7: 0.44535/1.11036, loss_spatial_bce_7: 0.01444/0.11143, loss_spatial_dice_7: 0.18042/0.23087, loss_spatial_ce_7: 0.09759/0.17538, loss_grounding_bce_7: 0.01041/0.08503, loss_grounding_dice_7: 0.17247/0.16180, loss_grounding_ce_7: 0.26657/0.34198, loss_mask_ce_8: 1.12655/1.05223, loss_mask_bce_8: 0.05041/0.33424, loss_mask_dice_8: 0.77261/1.19057, loss_spatial_bce_8: 0.02835/0.13276, loss_spatial_dice_8: 0.29447/0.27204, loss_spatial_ce_8: 0.08386/0.23206, loss_grounding_bce_8: 0.00778/0.08890, loss_grounding_dice_8: 0.13632/0.17095, loss_grounding_ce_8: 2.61718/0.44544, loss_mask_ce_9: 2.79423/3.50870, loss_mask_bce_9: 0.04629/0.36054, loss_mask_dice_9: 0.89379/1.77567, loss_spatial_bce_9: 0.06755/0.36071, loss_spatial_dice_9: 0.83414/0.79792, loss_spatial_ce_9: 1.47526/1.42026, loss_grounding_bce_9: 0.00527/0.10080, loss_grounding_dice_9: 0.17478/0.24543, loss_grounding_ce_9: 3.21881/0.71735] items per batch[64] items per second[0.36] total items[1068800] mini batches[ 16700] memory[4967] epoch remaining[0:47:45] INFO:trainer.default_trainer:epochs[ 9] optim steps[16800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64303/0.78958, loss_mask_bce_0: 0.01296/0.30180, loss_mask_dice_0: 0.40744/1.03242, loss_spatial_bce_0: 0.00241/0.09080, loss_spatial_dice_0: 0.08597/0.19116, loss_spatial_ce_0: 0.00099/0.07595, loss_grounding_bce_0: 0.00477/0.08051, loss_grounding_dice_0: 0.26422/0.15198, loss_grounding_ce_0: 0.18867/0.25237, loss_mask_ce_1: 0.66373/0.79262, loss_mask_bce_1: 0.01575/0.30236, loss_mask_dice_1: 0.32951/1.03579, loss_spatial_bce_1: 0.00285/0.09127, loss_spatial_dice_1: 0.12593/0.19386, loss_spatial_ce_1: 0.00206/0.08073, loss_grounding_bce_1: 0.00510/0.08060, loss_grounding_dice_1: 0.20955/0.15289, loss_grounding_ce_1: 0.23617/0.25531, loss_mask_ce_2: 0.72572/0.79915, loss_mask_bce_2: 0.01323/0.30236, loss_mask_dice_2: 0.29841/1.03961, loss_spatial_bce_2: 0.00282/0.09074, loss_spatial_dice_2: 0.10113/0.19377, loss_spatial_ce_2: 0.00398/0.08373, loss_grounding_bce_2: 0.00192/0.08035, loss_grounding_dice_2: 0.09856/0.15262, loss_grounding_ce_2: 0.51150/0.25649, loss_mask_ce_3: 0.56316/0.79781, loss_mask_bce_3: 0.01109/0.30396, loss_mask_dice_3: 0.51695/1.03403, loss_spatial_bce_3: 0.00359/0.09235, loss_spatial_dice_3: 0.15819/0.19407, loss_spatial_ce_3: 0.01444/0.08959, loss_grounding_bce_3: 0.00090/0.08089, loss_grounding_dice_3: 0.04339/0.15230, loss_grounding_ce_3: 0.45851/0.25447, loss_mask_ce_4: 0.59339/0.80479, loss_mask_bce_4: 0.01461/0.30580, loss_mask_dice_4: 0.44016/1.05270, loss_spatial_bce_4: 0.00285/0.09443, loss_spatial_dice_4: 0.17359/0.20142, loss_spatial_ce_4: 0.32001/0.10114, loss_grounding_bce_4: 0.00355/0.08161, loss_grounding_dice_4: 0.28199/0.15450, loss_grounding_ce_4: 0.20879/0.26381, loss_mask_ce_5: 0.87035/0.82607, loss_mask_bce_5: 0.01316/0.30806, loss_mask_dice_5: 0.38392/1.05930, loss_spatial_bce_5: 0.01053/0.09601, loss_spatial_dice_5: 0.21984/0.20348, loss_spatial_ce_5: 0.18173/0.11266, loss_grounding_bce_5: 0.00291/0.08212, loss_grounding_dice_5: 0.13204/0.15549, loss_grounding_ce_5: 0.42454/0.28365, loss_mask_ce_6: 0.77083/0.85113, loss_mask_bce_6: 0.01944/0.30933, loss_mask_dice_6: 0.48748/1.06310, loss_spatial_bce_6: 0.00733/0.10098, loss_spatial_dice_6: 0.22967/0.20581, loss_spatial_ce_6: 0.07833/0.13104, loss_grounding_bce_6: 0.00164/0.08337, loss_grounding_dice_6: 0.18673/0.15609, loss_grounding_ce_6: 0.39887/0.29531, loss_mask_ce_7: 1.02555/0.91412, loss_mask_bce_7: 0.01578/0.31667, loss_mask_dice_7: 0.36128/1.10975, loss_spatial_bce_7: 0.00704/0.11144, loss_spatial_dice_7: 0.34640/0.23080, loss_spatial_ce_7: 0.26661/0.17535, loss_grounding_bce_7: 0.00195/0.08511, loss_grounding_dice_7: 0.17133/0.16180, loss_grounding_ce_7: 0.49063/0.34193, loss_mask_ce_8: 1.89614/1.05162, loss_mask_bce_8: 0.01295/0.33413, loss_mask_dice_8: 0.35627/1.19000, loss_spatial_bce_8: 0.01735/0.13278, loss_spatial_dice_8: 0.48109/0.27197, loss_spatial_ce_8: 0.33819/0.23205, loss_grounding_bce_8: 0.00190/0.08899, loss_grounding_dice_8: 0.12591/0.17097, loss_grounding_ce_8: 0.49683/0.44524, loss_mask_ce_9: 3.14478/3.50748, loss_mask_bce_9: 0.01510/0.36047, loss_mask_dice_9: 1.02350/1.77455, loss_spatial_bce_9: 0.01514/0.36090, loss_spatial_dice_9: 0.79995/0.79783, loss_spatial_ce_9: 0.97578/1.42024, loss_grounding_bce_9: 0.00251/0.10089, loss_grounding_dice_9: 0.29664/0.24535, loss_grounding_ce_9: 0.38385/0.71710] items per batch[64] items per second[0.36] total items[1075200] mini batches[ 16800] memory[4967] epoch remaining[0:44:26] INFO:trainer.default_trainer:epochs[ 9] optim steps[16900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71100/0.79019, loss_mask_bce_0: 0.12475/0.30190, loss_mask_dice_0: 0.15592/1.03345, loss_spatial_bce_0: 0.04981/0.09071, loss_spatial_dice_0: 0.07955/0.19113, loss_spatial_ce_0: 0.10160/0.07591, loss_grounding_bce_0: 0.06882/0.08049, loss_grounding_dice_0: 0.07481/0.15200, loss_grounding_ce_0: 0.01849/0.25260, loss_mask_ce_1: 0.73876/0.79326, loss_mask_bce_1: 0.12113/0.30249, loss_mask_dice_1: 0.15856/1.03693, loss_spatial_bce_1: 0.04897/0.09119, loss_spatial_dice_1: 0.07893/0.19384, loss_spatial_ce_1: 0.11725/0.08069, loss_grounding_bce_1: 0.07196/0.08058, loss_grounding_dice_1: 0.07502/0.15291, loss_grounding_ce_1: 0.02373/0.25546, loss_mask_ce_2: 0.78254/0.79969, loss_mask_bce_2: 0.12046/0.30246, loss_mask_dice_2: 0.16618/1.04044, loss_spatial_bce_2: 0.05518/0.09065, loss_spatial_dice_2: 0.07796/0.19374, loss_spatial_ce_2: 0.10719/0.08377, loss_grounding_bce_2: 0.07131/0.08033, loss_grounding_dice_2: 0.07479/0.15262, loss_grounding_ce_2: 0.02101/0.25669, loss_mask_ce_3: 0.87932/0.79838, loss_mask_bce_3: 0.12114/0.30409, loss_mask_dice_3: 0.16484/1.03526, loss_spatial_bce_3: 0.05100/0.09227, loss_spatial_dice_3: 0.08577/0.19407, loss_spatial_ce_3: 0.11543/0.08958, loss_grounding_bce_3: 0.06967/0.08087, loss_grounding_dice_3: 0.07539/0.15232, loss_grounding_ce_3: 0.02060/0.25474, loss_mask_ce_4: 0.94107/0.80540, loss_mask_bce_4: 0.12524/0.30598, loss_mask_dice_4: 0.15811/1.05392, loss_spatial_bce_4: 0.05277/0.09435, loss_spatial_dice_4: 0.08098/0.20141, loss_spatial_ce_4: 0.12313/0.10123, loss_grounding_bce_4: 0.07109/0.08159, loss_grounding_dice_4: 0.07354/0.15452, loss_grounding_ce_4: 0.02561/0.26407, loss_mask_ce_5: 0.82981/0.82688, loss_mask_bce_5: 0.12544/0.30819, loss_mask_dice_5: 0.17307/1.06069, loss_spatial_bce_5: 0.05199/0.09592, loss_spatial_dice_5: 0.07168/0.20345, loss_spatial_ce_5: 0.14515/0.11261, loss_grounding_bce_5: 0.06787/0.08209, loss_grounding_dice_5: 0.07490/0.15549, loss_grounding_ce_5: 0.01986/0.28387, loss_mask_ce_6: 1.04261/0.85188, loss_mask_bce_6: 0.12771/0.30954, loss_mask_dice_6: 0.17348/1.06422, loss_spatial_bce_6: 0.05423/0.10091, loss_spatial_dice_6: 0.08481/0.20583, loss_spatial_ce_6: 0.11753/0.13097, loss_grounding_bce_6: 0.06908/0.08334, loss_grounding_dice_6: 0.07534/0.15611, loss_grounding_ce_6: 0.05171/0.29570, loss_mask_ce_7: 1.25853/0.91487, loss_mask_bce_7: 0.13211/0.31688, loss_mask_dice_7: 0.18358/1.11086, loss_spatial_bce_7: 0.05341/0.11136, loss_spatial_dice_7: 0.09554/0.23083, loss_spatial_ce_7: 0.18668/0.17530, loss_grounding_bce_7: 0.07623/0.08508, loss_grounding_dice_7: 0.09082/0.16178, loss_grounding_ce_7: 0.03907/0.34225, loss_mask_ce_8: 1.14093/1.05224, loss_mask_bce_8: 0.14888/0.33432, loss_mask_dice_8: 0.20510/1.19098, loss_spatial_bce_8: 0.06245/0.13264, loss_spatial_dice_8: 0.10556/0.27190, loss_spatial_ce_8: 0.23969/0.23196, loss_grounding_bce_8: 0.08398/0.08895, loss_grounding_dice_8: 0.10887/0.17097, loss_grounding_ce_8: 0.11270/0.44558, loss_mask_ce_9: 3.70036/3.50880, loss_mask_bce_9: 0.26828/0.36083, loss_mask_dice_9: 0.55766/1.77692, loss_spatial_bce_9: 0.45360/0.36074, loss_spatial_dice_9: 0.59673/0.79779, loss_spatial_ce_9: 1.39020/1.42033, loss_grounding_bce_9: 0.09178/0.10088, loss_grounding_dice_9: 0.14039/0.24543, loss_grounding_ce_9: 0.13457/0.71756] items per batch[64] items per second[0.36] total items[1081600] mini batches[ 16900] memory[4967] epoch remaining[0:41:11] INFO:trainer.default_trainer:epochs[ 9] optim steps[17000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42811/0.79022, loss_mask_bce_0: 0.04151/0.30198, loss_mask_dice_0: 0.22301/1.03378, loss_spatial_bce_0: 0.01374/0.09063, loss_spatial_dice_0: 0.07549/0.19119, loss_spatial_ce_0: 0.00149/0.07587, loss_grounding_bce_0: 0.00873/0.08046, loss_grounding_dice_0: 0.09560/0.15220, loss_grounding_ce_0: 0.00554/0.25255, loss_mask_ce_1: 0.20264/0.79314, loss_mask_bce_1: 0.22539/0.30260, loss_mask_dice_1: 0.28183/1.03738, loss_spatial_bce_1: 0.01452/0.09112, loss_spatial_dice_1: 0.06261/0.19387, loss_spatial_ce_1: 0.00096/0.08065, loss_grounding_bce_1: 0.01170/0.08055, loss_grounding_dice_1: 0.11029/0.15313, loss_grounding_ce_1: 0.00356/0.25539, loss_mask_ce_2: 0.36723/0.79958, loss_mask_bce_2: 0.04164/0.30256, loss_mask_dice_2: 0.20526/1.04079, loss_spatial_bce_2: 0.01332/0.09058, loss_spatial_dice_2: 0.07084/0.19378, loss_spatial_ce_2: 0.00143/0.08371, loss_grounding_bce_2: 0.00956/0.08030, loss_grounding_dice_2: 0.11680/0.15283, loss_grounding_ce_2: 0.00316/0.25663, loss_mask_ce_3: 0.30009/0.79838, loss_mask_bce_3: 0.04576/0.30417, loss_mask_dice_3: 0.19008/1.03551, loss_spatial_bce_3: 0.02338/0.09219, loss_spatial_dice_3: 0.08548/0.19412, loss_spatial_ce_3: 0.00037/0.08948, loss_grounding_bce_3: 0.01057/0.08086, loss_grounding_dice_3: 0.10970/0.15249, loss_grounding_ce_3: 0.00273/0.25464, loss_mask_ce_4: 0.35738/0.80526, loss_mask_bce_4: 0.04939/0.30606, loss_mask_dice_4: 0.19974/1.05424, loss_spatial_bce_4: 0.03487/0.09427, loss_spatial_dice_4: 0.09637/0.20145, loss_spatial_ce_4: 0.03376/0.10114, loss_grounding_bce_4: 0.01191/0.08155, loss_grounding_dice_4: 0.11996/0.15475, loss_grounding_ce_4: 0.00483/0.26434, loss_mask_ce_5: 0.23685/0.82683, loss_mask_bce_5: 0.21285/0.30831, loss_mask_dice_5: 0.28145/1.06103, loss_spatial_bce_5: 0.03249/0.09586, loss_spatial_dice_5: 0.10320/0.20353, loss_spatial_ce_5: 0.00120/0.11249, loss_grounding_bce_5: 0.01111/0.08206, loss_grounding_dice_5: 0.12184/0.15570, loss_grounding_ce_5: 0.00869/0.28384, loss_mask_ce_6: 0.24018/0.85191, loss_mask_bce_6: 0.22624/0.30965, loss_mask_dice_6: 0.32692/1.06449, loss_spatial_bce_6: 0.01532/0.10082, loss_spatial_dice_6: 0.08130/0.20588, loss_spatial_ce_6: 0.07796/0.13096, loss_grounding_bce_6: 0.00998/0.08331, loss_grounding_dice_6: 0.08802/0.15633, loss_grounding_ce_6: 0.01036/0.29567, loss_mask_ce_7: 0.84032/0.91492, loss_mask_bce_7: 0.02958/0.31693, loss_mask_dice_7: 0.24153/1.11119, loss_spatial_bce_7: 0.07771/0.11128, loss_spatial_dice_7: 0.13564/0.23087, loss_spatial_ce_7: 0.06657/0.17525, loss_grounding_bce_7: 0.01028/0.08505, loss_grounding_dice_7: 0.12734/0.16200, loss_grounding_ce_7: 0.02597/0.34211, loss_mask_ce_8: 1.28966/1.05251, loss_mask_bce_8: 0.03745/0.33444, loss_mask_dice_8: 0.21155/1.19133, loss_spatial_bce_8: 0.05063/0.13261, loss_spatial_dice_8: 0.16715/0.27195, loss_spatial_ce_8: 0.17808/0.23184, loss_grounding_bce_8: 0.01115/0.08891, loss_grounding_dice_8: 0.14241/0.17121, loss_grounding_ce_8: 0.09072/0.44546, loss_mask_ce_9: 3.44899/3.50912, loss_mask_bce_9: 0.10415/0.36090, loss_mask_dice_9: 0.56896/1.77721, loss_spatial_bce_9: 0.19655/0.36060, loss_spatial_dice_9: 0.82570/0.79784, loss_spatial_ce_9: 1.94117/1.42038, loss_grounding_bce_9: 0.01396/0.10081, loss_grounding_dice_9: 0.43243/0.24571, loss_grounding_ce_9: 0.10179/0.71657] items per batch[64] items per second[0.36] total items[1088000] mini batches[ 17000] memory[4967] epoch remaining[0:38:09] INFO:trainer.default_trainer:epochs[ 9] optim steps[17100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32396/0.78976, loss_mask_bce_0: 0.34324/0.30203, loss_mask_dice_0: 0.56997/1.03328, loss_spatial_bce_0: 0.16372/0.09071, loss_spatial_dice_0: 0.27627/0.19122, loss_spatial_ce_0: 0.14496/0.07584, loss_grounding_bce_0: 0.14634/0.08057, loss_grounding_dice_0: 0.29594/0.15220, loss_grounding_ce_0: 0.00631/0.25288, loss_mask_ce_1: 0.34184/0.79266, loss_mask_bce_1: 0.34568/0.30269, loss_mask_dice_1: 0.56625/1.03689, loss_spatial_bce_1: 0.16408/0.09118, loss_spatial_dice_1: 0.29714/0.19391, loss_spatial_ce_1: 0.10780/0.08068, loss_grounding_bce_1: 0.14061/0.08067, loss_grounding_dice_1: 0.29411/0.15310, loss_grounding_ce_1: 0.00781/0.25557, loss_mask_ce_2: 0.35507/0.79929, loss_mask_bce_2: 0.34505/0.30263, loss_mask_dice_2: 0.58433/1.04020, loss_spatial_bce_2: 0.13335/0.09063, loss_spatial_dice_2: 0.26825/0.19381, loss_spatial_ce_2: 0.12222/0.08368, loss_grounding_bce_2: 0.13901/0.08043, loss_grounding_dice_2: 0.29919/0.15283, loss_grounding_ce_2: 0.01580/0.25682, loss_mask_ce_3: 0.31459/0.79809, loss_mask_bce_3: 0.32923/0.30421, loss_mask_dice_3: 0.54574/1.03501, loss_spatial_bce_3: 0.13672/0.09224, loss_spatial_dice_3: 0.24437/0.19414, loss_spatial_ce_3: 0.23674/0.08947, loss_grounding_bce_3: 0.14736/0.08098, loss_grounding_dice_3: 0.29071/0.15246, loss_grounding_ce_3: 0.01097/0.25483, loss_mask_ce_4: 0.30589/0.80467, loss_mask_bce_4: 0.35374/0.30610, loss_mask_dice_4: 0.51673/1.05371, loss_spatial_bce_4: 0.14713/0.09431, loss_spatial_dice_4: 0.25214/0.20146, loss_spatial_ce_4: 0.26294/0.10126, loss_grounding_bce_4: 0.13246/0.08167, loss_grounding_dice_4: 0.26948/0.15475, loss_grounding_ce_4: 0.01239/0.26451, loss_mask_ce_5: 0.35984/0.82631, loss_mask_bce_5: 0.34871/0.30832, loss_mask_dice_5: 0.55818/1.06056, loss_spatial_bce_5: 0.12349/0.09593, loss_spatial_dice_5: 0.22329/0.20354, loss_spatial_ce_5: 0.29413/0.11252, loss_grounding_bce_5: 0.13802/0.08219, loss_grounding_dice_5: 0.27379/0.15569, loss_grounding_ce_5: 0.01493/0.28408, loss_mask_ce_6: 0.39092/0.85131, loss_mask_bce_6: 0.36520/0.30968, loss_mask_dice_6: 0.55531/1.06393, loss_spatial_bce_6: 0.12259/0.10086, loss_spatial_dice_6: 0.18580/0.20590, loss_spatial_ce_6: 0.46347/0.13108, loss_grounding_bce_6: 0.16589/0.08342, loss_grounding_dice_6: 0.29308/0.15635, loss_grounding_ce_6: 0.00849/0.29600, loss_mask_ce_7: 0.48695/0.91437, loss_mask_bce_7: 0.34563/0.31691, loss_mask_dice_7: 0.52124/1.11050, loss_spatial_bce_7: 0.12963/0.11130, loss_spatial_dice_7: 0.21079/0.23084, loss_spatial_ce_7: 0.33261/0.17540, loss_grounding_bce_7: 0.14204/0.08513, loss_grounding_dice_7: 0.26786/0.16197, loss_grounding_ce_7: 0.00849/0.34227, loss_mask_ce_8: 0.54875/1.05185, loss_mask_bce_8: 0.31498/0.33439, loss_mask_dice_8: 0.46981/1.19076, loss_spatial_bce_8: 0.22955/0.13261, loss_spatial_dice_8: 0.28523/0.27188, loss_spatial_ce_8: 0.39714/0.23193, loss_grounding_bce_8: 0.12673/0.08900, loss_grounding_dice_8: 0.23275/0.17121, loss_grounding_ce_8: 0.09603/0.44583, loss_mask_ce_9: 2.24027/3.50751, loss_mask_bce_9: 0.35038/0.36074, loss_mask_dice_9: 0.73937/1.77583, loss_spatial_bce_9: 0.56131/0.36057, loss_spatial_dice_9: 0.90537/0.79776, loss_spatial_ce_9: 1.94910/1.41962, loss_grounding_bce_9: 0.13217/0.10084, loss_grounding_dice_9: 0.30958/0.24566, loss_grounding_ce_9: 0.07881/0.71644] items per batch[64] items per second[0.36] total items[1094400] mini batches[ 17100] memory[4967] epoch remaining[0:35:04] INFO:trainer.default_trainer:epochs[ 9] optim steps[17200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41660/0.78974, loss_mask_bce_0: 0.55432/0.30209, loss_mask_dice_0: 0.72570/1.03250, loss_spatial_bce_0: 0.14544/0.09071, loss_spatial_dice_0: 0.16521/0.19114, loss_spatial_ce_0: 0.25040/0.07565, loss_grounding_bce_0: 0.05551/0.08058, loss_grounding_dice_0: 0.09111/0.15217, loss_grounding_ce_0: 0.00100/0.25270, loss_mask_ce_1: 1.32755/0.79257, loss_mask_bce_1: 0.56722/0.30277, loss_mask_dice_1: 0.75581/1.03623, loss_spatial_bce_1: 0.14414/0.09119, loss_spatial_dice_1: 0.15961/0.19383, loss_spatial_ce_1: 0.23214/0.08052, loss_grounding_bce_1: 0.05633/0.08069, loss_grounding_dice_1: 0.09011/0.15307, loss_grounding_ce_1: 0.00058/0.25541, loss_mask_ce_2: 1.39693/0.79927, loss_mask_bce_2: 0.55239/0.30272, loss_mask_dice_2: 0.71558/1.03945, loss_spatial_bce_2: 0.19416/0.09065, loss_spatial_dice_2: 0.19603/0.19371, loss_spatial_ce_2: 0.01886/0.08349, loss_grounding_bce_2: 0.06133/0.08045, loss_grounding_dice_2: 0.07822/0.15280, loss_grounding_ce_2: 0.00032/0.25666, loss_mask_ce_3: 1.47228/0.79803, loss_mask_bce_3: 0.57236/0.30429, loss_mask_dice_3: 0.71195/1.03435, loss_spatial_bce_3: 0.18481/0.09227, loss_spatial_dice_3: 0.18276/0.19406, loss_spatial_ce_3: 0.01981/0.08928, loss_grounding_bce_3: 0.05768/0.08100, loss_grounding_dice_3: 0.07653/0.15242, loss_grounding_ce_3: 0.00080/0.25485, loss_mask_ce_4: 1.55365/0.80466, loss_mask_bce_4: 0.56592/0.30619, loss_mask_dice_4: 0.73375/1.05301, loss_spatial_bce_4: 0.15459/0.09434, loss_spatial_dice_4: 0.18715/0.20139, loss_spatial_ce_4: 0.15818/0.10109, loss_grounding_bce_4: 0.05578/0.08170, loss_grounding_dice_4: 0.08570/0.15473, loss_grounding_ce_4: 0.00183/0.26432, loss_mask_ce_5: 1.67271/0.82633, loss_mask_bce_5: 0.59637/0.30838, loss_mask_dice_5: 0.72570/1.05966, loss_spatial_bce_5: 0.17728/0.09597, loss_spatial_dice_5: 0.18862/0.20345, loss_spatial_ce_5: 0.06643/0.11236, loss_grounding_bce_5: 0.05484/0.08222, loss_grounding_dice_5: 0.07919/0.15567, loss_grounding_ce_5: 0.00189/0.28382, loss_mask_ce_6: 1.60829/0.85134, loss_mask_bce_6: 0.55300/0.30976, loss_mask_dice_6: 0.72037/1.06320, loss_spatial_bce_6: 0.17703/0.10089, loss_spatial_dice_6: 0.18964/0.20583, loss_spatial_ce_6: 0.13056/0.13086, loss_grounding_bce_6: 0.06156/0.08346, loss_grounding_dice_6: 0.08788/0.15631, loss_grounding_ce_6: 0.00377/0.29570, loss_mask_ce_7: 1.72860/0.91444, loss_mask_bce_7: 0.57386/0.31698, loss_mask_dice_7: 0.70751/1.10959, loss_spatial_bce_7: 0.16616/0.11129, loss_spatial_dice_7: 0.20719/0.23073, loss_spatial_ce_7: 0.15553/0.17517, loss_grounding_bce_7: 0.05116/0.08516, loss_grounding_dice_7: 0.07929/0.16195, loss_grounding_ce_7: 0.00251/0.34185, loss_mask_ce_8: 1.59305/1.05192, loss_mask_bce_8: 0.56466/0.33443, loss_mask_dice_8: 0.81142/1.18980, loss_spatial_bce_8: 0.27433/0.13259, loss_spatial_dice_8: 0.28892/0.27180, loss_spatial_ce_8: 0.20976/0.23180, loss_grounding_bce_8: 0.05585/0.08901, loss_grounding_dice_8: 0.09003/0.17120, loss_grounding_ce_8: 0.00071/0.44541, loss_mask_ce_9: 5.49752/3.50783, loss_mask_bce_9: 0.76312/0.36083, loss_mask_dice_9: 1.76530/1.77468, loss_spatial_bce_9: 0.37778/0.36064, loss_spatial_dice_9: 0.87645/0.79789, loss_spatial_ce_9: 1.65114/1.41980, loss_grounding_bce_9: 0.06194/0.10085, loss_grounding_dice_9: 0.16451/0.24564, loss_grounding_ce_9: 0.07657/0.71597] items per batch[64] items per second[0.36] total items[1100800] mini batches[ 17200] memory[4967] epoch remaining[0:32:01] INFO:trainer.default_trainer:epochs[ 9] optim steps[17300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84417/0.78984, loss_mask_bce_0: 0.17816/0.30202, loss_mask_dice_0: 0.59736/1.03275, loss_spatial_bce_0: 0.03409/0.09065, loss_spatial_dice_0: 0.15346/0.19112, loss_spatial_ce_0: 0.05081/0.07553, loss_grounding_bce_0: 0.07214/0.08052, loss_grounding_dice_0: 0.11207/0.15208, loss_grounding_ce_0: 0.82987/0.25349, loss_mask_ce_1: 1.01448/0.79281, loss_mask_bce_1: 0.16745/0.30264, loss_mask_dice_1: 0.57255/1.03658, loss_spatial_bce_1: 0.02928/0.09113, loss_spatial_dice_1: 0.14389/0.19381, loss_spatial_ce_1: 0.06009/0.08040, loss_grounding_bce_1: 0.08687/0.08063, loss_grounding_dice_1: 0.12114/0.15295, loss_grounding_ce_1: 1.01368/0.25608, loss_mask_ce_2: 0.83106/0.79946, loss_mask_bce_2: 0.18635/0.30263, loss_mask_dice_2: 0.59542/1.03965, loss_spatial_bce_2: 0.03086/0.09060, loss_spatial_dice_2: 0.15455/0.19371, loss_spatial_ce_2: 0.07069/0.08335, loss_grounding_bce_2: 0.06920/0.08040, loss_grounding_dice_2: 0.11524/0.15267, loss_grounding_ce_2: 0.89684/0.25739, loss_mask_ce_3: 0.95604/0.79811, loss_mask_bce_3: 0.16157/0.30419, loss_mask_dice_3: 0.60116/1.03472, loss_spatial_bce_3: 0.03895/0.09221, loss_spatial_dice_3: 0.18228/0.19405, loss_spatial_ce_3: 0.04750/0.08916, loss_grounding_bce_3: 0.06806/0.08095, loss_grounding_dice_3: 0.10320/0.15233, loss_grounding_ce_3: 0.91002/0.25533, loss_mask_ce_4: 0.40643/0.80483, loss_mask_bce_4: 0.41713/0.30610, loss_mask_dice_4: 0.83981/1.05336, loss_spatial_bce_4: 0.04892/0.09428, loss_spatial_dice_4: 0.17449/0.20136, loss_spatial_ce_4: 0.03841/0.10104, loss_grounding_bce_4: 0.07184/0.08167, loss_grounding_dice_4: 0.12823/0.15465, loss_grounding_ce_4: 0.54155/0.26458, loss_mask_ce_5: 0.43196/0.82637, loss_mask_bce_5: 0.35865/0.30829, loss_mask_dice_5: 0.84392/1.06006, loss_spatial_bce_5: 0.03556/0.09590, loss_spatial_dice_5: 0.15600/0.20343, loss_spatial_ce_5: 0.04898/0.11227, loss_grounding_bce_5: 0.06372/0.08217, loss_grounding_dice_5: 0.10636/0.15558, loss_grounding_ce_5: 0.63238/0.28464, loss_mask_ce_6: 0.66978/0.85138, loss_mask_bce_6: 0.16710/0.30968, loss_mask_dice_6: 0.63496/1.06352, loss_spatial_bce_6: 0.03720/0.10080, loss_spatial_dice_6: 0.16458/0.20581, loss_spatial_ce_6: 0.08779/0.13083, loss_grounding_bce_6: 0.05204/0.08342, loss_grounding_dice_6: 0.10393/0.15623, loss_grounding_ce_6: 0.74467/0.29620, loss_mask_ce_7: 0.84186/0.91462, loss_mask_bce_7: 0.11080/0.31688, loss_mask_dice_7: 0.59851/1.10994, loss_spatial_bce_7: 0.07294/0.11120, loss_spatial_dice_7: 0.18742/0.23073, loss_spatial_ce_7: 0.23236/0.17517, loss_grounding_bce_7: 0.05163/0.08511, loss_grounding_dice_7: 0.12545/0.16182, loss_grounding_ce_7: 1.02280/0.34239, loss_mask_ce_8: 0.80543/1.05193, loss_mask_bce_8: 0.24632/0.33431, loss_mask_dice_8: 0.90267/1.19026, loss_spatial_bce_8: 0.07872/0.13253, loss_spatial_dice_8: 0.21161/0.27175, loss_spatial_ce_8: 0.28741/0.23187, loss_grounding_bce_8: 0.05339/0.08893, loss_grounding_dice_8: 0.10932/0.17108, loss_grounding_ce_8: 1.79224/0.44629, loss_mask_ce_9: 4.73452/3.50876, loss_mask_bce_9: 0.20395/0.36081, loss_mask_dice_9: 1.36721/1.77577, loss_spatial_bce_9: 0.13545/0.36051, loss_spatial_dice_9: 0.76372/0.79787, loss_spatial_ce_9: 1.71582/1.41972, loss_grounding_bce_9: 0.05907/0.10086, loss_grounding_dice_9: 0.22309/0.24559, loss_grounding_ce_9: 2.67702/0.71677] items per batch[64] items per second[0.36] total items[1107200] mini batches[ 17300] memory[4967] epoch remaining[0:28:59] INFO:trainer.default_trainer:epochs[ 9] optim steps[17400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98008/0.78969, loss_mask_bce_0: 0.35419/0.30208, loss_mask_dice_0: 0.37348/1.03186, loss_spatial_bce_0: 0.12946/0.09064, loss_spatial_dice_0: 0.15156/0.19094, loss_spatial_ce_0: 0.03732/0.07546, loss_grounding_bce_0: 0.09184/0.08052, loss_grounding_dice_0: 0.10046/0.15196, loss_grounding_ce_0: 0.00885/0.25333, loss_mask_ce_1: 0.92503/0.79265, loss_mask_bce_1: 0.35053/0.30271, loss_mask_dice_1: 0.35391/1.03567, loss_spatial_bce_1: 0.11598/0.09113, loss_spatial_dice_1: 0.13798/0.19363, loss_spatial_ce_1: 0.02861/0.08031, loss_grounding_bce_1: 0.10802/0.08063, loss_grounding_dice_1: 0.12081/0.15284, loss_grounding_ce_1: 0.01169/0.25592, loss_mask_ce_2: 0.90503/0.79926, loss_mask_bce_2: 0.35518/0.30273, loss_mask_dice_2: 0.35738/1.03880, loss_spatial_bce_2: 0.11508/0.09061, loss_spatial_dice_2: 0.14552/0.19354, loss_spatial_ce_2: 0.03180/0.08316, loss_grounding_bce_2: 0.10651/0.08040, loss_grounding_dice_2: 0.10586/0.15259, loss_grounding_ce_2: 0.02543/0.25726, loss_mask_ce_3: 0.93581/0.79794, loss_mask_bce_3: 0.34600/0.30428, loss_mask_dice_3: 0.35756/1.03383, loss_spatial_bce_3: 0.11171/0.09222, loss_spatial_dice_3: 0.15184/0.19384, loss_spatial_ce_3: 0.03324/0.08907, loss_grounding_bce_3: 0.09942/0.08094, loss_grounding_dice_3: 0.09276/0.15221, loss_grounding_ce_3: 0.02280/0.25526, loss_mask_ce_4: 1.00442/0.80449, loss_mask_bce_4: 0.34838/0.30618, loss_mask_dice_4: 0.36658/1.05256, loss_spatial_bce_4: 0.11379/0.09427, loss_spatial_dice_4: 0.13176/0.20116, loss_spatial_ce_4: 0.01688/0.10107, loss_grounding_bce_4: 0.10333/0.08168, loss_grounding_dice_4: 0.10112/0.15453, loss_grounding_ce_4: 0.00868/0.26454, loss_mask_ce_5: 1.31391/0.82618, loss_mask_bce_5: 0.39075/0.30837, loss_mask_dice_5: 0.42605/1.05926, loss_spatial_bce_5: 0.11215/0.09593, loss_spatial_dice_5: 0.12758/0.20323, loss_spatial_ce_5: 0.01500/0.11209, loss_grounding_bce_5: 0.08936/0.08216, loss_grounding_dice_5: 0.08788/0.15545, loss_grounding_ce_5: 0.04938/0.28472, loss_mask_ce_6: 1.29753/0.85124, loss_mask_bce_6: 0.38374/0.30974, loss_mask_dice_6: 0.41553/1.06264, loss_spatial_bce_6: 0.11095/0.10081, loss_spatial_dice_6: 0.13523/0.20560, loss_spatial_ce_6: 0.01665/0.13088, loss_grounding_bce_6: 0.10347/0.08340, loss_grounding_dice_6: 0.10891/0.15610, loss_grounding_ce_6: 0.06246/0.29609, loss_mask_ce_7: 1.45810/0.91462, loss_mask_bce_7: 0.40054/0.31691, loss_mask_dice_7: 0.38622/1.10918, loss_spatial_bce_7: 0.14689/0.11123, loss_spatial_dice_7: 0.16651/0.23054, loss_spatial_ce_7: 0.04127/0.17484, loss_grounding_bce_7: 0.13356/0.08509, loss_grounding_dice_7: 0.21687/0.16173, loss_grounding_ce_7: 0.02372/0.34254, loss_mask_ce_8: 1.38900/1.05171, loss_mask_bce_8: 0.47101/0.33440, loss_mask_dice_8: 0.55638/1.18965, loss_spatial_bce_8: 0.21033/0.13251, loss_spatial_dice_8: 0.17360/0.27152, loss_spatial_ce_8: 0.13618/0.23175, loss_grounding_bce_8: 0.14901/0.08891, loss_grounding_dice_8: 0.20310/0.17097, loss_grounding_ce_8: 0.08926/0.44625, loss_mask_ce_9: 5.04064/3.50768, loss_mask_bce_9: 0.43191/0.36098, loss_mask_dice_9: 0.80602/1.77556, loss_spatial_bce_9: 0.55523/0.36067, loss_spatial_dice_9: 0.81635/0.79779, loss_spatial_ce_9: 1.25769/1.41923, loss_grounding_bce_9: 0.14541/0.10089, loss_grounding_dice_9: 0.16248/0.24550, loss_grounding_ce_9: 0.61506/0.71650] items per batch[64] items per second[0.36] total items[1113600] mini batches[ 17400] memory[4967] epoch remaining[0:26:01] INFO:trainer.default_trainer:epochs[ 9] optim steps[17500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21473/0.78984, loss_mask_bce_0: 0.42997/0.30211, loss_mask_dice_0: 0.33188/1.03271, loss_spatial_bce_0: 0.23768/0.09054, loss_spatial_dice_0: 0.15695/0.19091, loss_spatial_ce_0: 0.01839/0.07535, loss_grounding_bce_0: 0.24176/0.08047, loss_grounding_dice_0: 0.18022/0.15186, loss_grounding_ce_0: 0.07134/0.25318, loss_mask_ce_1: 0.21971/0.79282, loss_mask_bce_1: 0.40637/0.30276, loss_mask_dice_1: 0.32360/1.03659, loss_spatial_bce_1: 0.21361/0.09104, loss_spatial_dice_1: 0.14925/0.19363, loss_spatial_ce_1: 0.04834/0.08020, loss_grounding_bce_1: 0.18851/0.08058, loss_grounding_dice_1: 0.14515/0.15279, loss_grounding_ce_1: 0.17458/0.25584, loss_mask_ce_2: 0.21375/0.79946, loss_mask_bce_2: 0.37393/0.30277, loss_mask_dice_2: 0.27353/1.03972, loss_spatial_bce_2: 0.23330/0.09051, loss_spatial_dice_2: 0.17652/0.19354, loss_spatial_ce_2: 0.03285/0.08301, loss_grounding_bce_2: 0.24398/0.08035, loss_grounding_dice_2: 0.19406/0.15248, loss_grounding_ce_2: 0.05498/0.25723, loss_mask_ce_3: 0.18207/0.79803, loss_mask_bce_3: 0.38132/0.30433, loss_mask_dice_3: 0.26909/1.03495, loss_spatial_bce_3: 0.28208/0.09213, loss_spatial_dice_3: 0.17132/0.19382, loss_spatial_ce_3: 0.06052/0.08893, loss_grounding_bce_3: 0.24343/0.08088, loss_grounding_dice_3: 0.18505/0.15213, loss_grounding_ce_3: 0.05735/0.25525, loss_mask_ce_4: 0.17730/0.80476, loss_mask_bce_4: 0.37831/0.30623, loss_mask_dice_4: 0.29106/1.05359, loss_spatial_bce_4: 0.25839/0.09418, loss_spatial_dice_4: 0.21148/0.20116, loss_spatial_ce_4: 0.02777/0.10091, loss_grounding_bce_4: 0.24075/0.08162, loss_grounding_dice_4: 0.19632/0.15446, loss_grounding_ce_4: 0.05719/0.26445, loss_mask_ce_5: 0.21174/0.82634, loss_mask_bce_5: 0.39025/0.30841, loss_mask_dice_5: 0.27464/1.06026, loss_spatial_bce_5: 0.27935/0.09583, loss_spatial_dice_5: 0.24765/0.20322, loss_spatial_ce_5: 0.01140/0.11190, loss_grounding_bce_5: 0.24456/0.08212, loss_grounding_dice_5: 0.17300/0.15537, loss_grounding_ce_5: 0.08312/0.28458, loss_mask_ce_6: 0.27279/0.85142, loss_mask_bce_6: 0.46824/0.30977, loss_mask_dice_6: 0.36218/1.06367, loss_spatial_bce_6: 0.35431/0.10066, loss_spatial_dice_6: 0.31125/0.20558, loss_spatial_ce_6: 0.02051/0.13076, loss_grounding_bce_6: 0.22718/0.08336, loss_grounding_dice_6: 0.15937/0.15605, loss_grounding_ce_6: 0.20080/0.29604, loss_mask_ce_7: 0.36910/0.91481, loss_mask_bce_7: 0.38502/0.31693, loss_mask_dice_7: 0.26693/1.11023, loss_spatial_bce_7: 0.20313/0.11108, loss_spatial_dice_7: 0.14960/0.23050, loss_spatial_ce_7: 0.17226/0.17470, loss_grounding_bce_7: 0.20623/0.08505, loss_grounding_dice_7: 0.15203/0.16169, loss_grounding_ce_7: 0.26128/0.34237, loss_mask_ce_8: 0.67783/1.05190, loss_mask_bce_8: 0.33901/0.33444, loss_mask_dice_8: 0.25868/1.19078, loss_spatial_bce_8: 0.20464/0.13240, loss_spatial_dice_8: 0.14746/0.27150, loss_spatial_ce_8: 0.17624/0.23167, loss_grounding_bce_8: 0.20096/0.08886, loss_grounding_dice_8: 0.15245/0.17092, loss_grounding_ce_8: 0.24481/0.44604, loss_mask_ce_9: 1.53033/3.50859, loss_mask_bce_9: 0.36341/0.36092, loss_mask_dice_9: 0.28980/1.77676, loss_spatial_bce_9: 0.47477/0.36031, loss_spatial_dice_9: 0.60412/0.79780, loss_spatial_ce_9: 0.84409/1.41943, loss_grounding_bce_9: 0.21543/0.10082, loss_grounding_dice_9: 0.17036/0.24542, loss_grounding_ce_9: 0.08543/0.71620] items per batch[64] items per second[0.37] total items[1120000] mini batches[ 17500] memory[4967] epoch remaining[0:22:58] INFO:trainer.default_trainer:epochs[ 9] optim steps[17600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.40537/0.78970, loss_mask_bce_0: 0.55534/0.30192, loss_mask_dice_0: 1.16899/1.03213, loss_spatial_bce_0: 0.04953/0.09048, loss_spatial_dice_0: 0.13180/0.19085, loss_spatial_ce_0: 0.00180/0.07529, loss_grounding_bce_0: 0.20410/0.08044, loss_grounding_dice_0: 0.40019/0.15182, loss_grounding_ce_0: 0.00219/0.25279, loss_mask_ce_1: 0.40142/0.79268, loss_mask_bce_1: 0.54970/0.30255, loss_mask_dice_1: 1.27681/1.03587, loss_spatial_bce_1: 0.04867/0.09098, loss_spatial_dice_1: 0.12401/0.19357, loss_spatial_ce_1: 0.00022/0.08019, loss_grounding_bce_1: 0.19079/0.08054, loss_grounding_dice_1: 0.38744/0.15273, loss_grounding_ce_1: 0.00218/0.25553, loss_mask_ce_2: 0.43537/0.79937, loss_mask_bce_2: 0.51319/0.30257, loss_mask_dice_2: 1.38872/1.03906, loss_spatial_bce_2: 0.05062/0.09046, loss_spatial_dice_2: 0.14218/0.19349, loss_spatial_ce_2: 0.00045/0.08294, loss_grounding_bce_2: 0.16776/0.08031, loss_grounding_dice_2: 0.39585/0.15241, loss_grounding_ce_2: 0.00268/0.25693, loss_mask_ce_3: 0.48448/0.79801, loss_mask_bce_3: 0.51411/0.30411, loss_mask_dice_3: 1.31371/1.03426, loss_spatial_bce_3: 0.04907/0.09209, loss_spatial_dice_3: 0.17200/0.19376, loss_spatial_ce_3: 0.00157/0.08888, loss_grounding_bce_3: 0.15829/0.08085, loss_grounding_dice_3: 0.35361/0.15206, loss_grounding_ce_3: 0.00214/0.25499, loss_mask_ce_4: 0.45106/0.80477, loss_mask_bce_4: 0.50786/0.30601, loss_mask_dice_4: 1.04584/1.05297, loss_spatial_bce_4: 0.05519/0.09415, loss_spatial_dice_4: 0.16511/0.20110, loss_spatial_ce_4: 0.01254/0.10075, loss_grounding_bce_4: 0.16850/0.08161, loss_grounding_dice_4: 0.36627/0.15441, loss_grounding_ce_4: 0.00252/0.26412, loss_mask_ce_5: 0.50895/0.82640, loss_mask_bce_5: 0.50771/0.30819, loss_mask_dice_5: 1.24539/1.05959, loss_spatial_bce_5: 0.05507/0.09580, loss_spatial_dice_5: 0.15650/0.20317, loss_spatial_ce_5: 0.01863/0.11180, loss_grounding_bce_5: 0.15935/0.08207, loss_grounding_dice_5: 0.36494/0.15529, loss_grounding_ce_5: 0.00795/0.28433, loss_mask_ce_6: 0.53898/0.85161, loss_mask_bce_6: 0.51178/0.30957, loss_mask_dice_6: 1.07104/1.06295, loss_spatial_bce_6: 0.04806/0.10061, loss_spatial_dice_6: 0.15589/0.20551, loss_spatial_ce_6: 0.04165/0.13068, loss_grounding_bce_6: 0.17558/0.08332, loss_grounding_dice_6: 0.35046/0.15598, loss_grounding_ce_6: 0.00572/0.29585, loss_mask_ce_7: 0.54933/0.91473, loss_mask_bce_7: 0.53359/0.31673, loss_mask_dice_7: 1.24590/1.10949, loss_spatial_bce_7: 0.05474/0.11099, loss_spatial_dice_7: 0.16598/0.23039, loss_spatial_ce_7: 0.09299/0.17465, loss_grounding_bce_7: 0.16399/0.08500, loss_grounding_dice_7: 0.36557/0.16162, loss_grounding_ce_7: 0.00975/0.34224, loss_mask_ce_8: 0.66955/1.05162, loss_mask_bce_8: 0.55482/0.33421, loss_mask_dice_8: 1.30451/1.19001, loss_spatial_bce_8: 0.06532/0.13232, loss_spatial_dice_8: 0.17894/0.27134, loss_spatial_ce_8: 0.07746/0.23166, loss_grounding_bce_8: 0.17529/0.08882, loss_grounding_dice_8: 0.37655/0.17087, loss_grounding_ce_8: 0.00470/0.44531, loss_mask_ce_9: 3.08268/3.50742, loss_mask_bce_9: 0.52625/0.36072, loss_mask_dice_9: 2.74391/1.77582, loss_spatial_bce_9: 0.23294/0.36028, loss_spatial_dice_9: 0.87249/0.79766, loss_spatial_ce_9: 1.18632/1.41910, loss_grounding_bce_9: 0.16787/0.10077, loss_grounding_dice_9: 0.42044/0.24529, loss_grounding_ce_9: 0.03552/0.71525] items per batch[64] items per second[0.36] total items[1126400] mini batches[ 17600] memory[4967] epoch remaining[0:19:58] INFO:trainer.default_trainer:epochs[ 9] optim steps[17700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03513/0.79002, loss_mask_bce_0: 0.04808/0.30186, loss_mask_dice_0: 0.45493/1.03234, loss_spatial_bce_0: 0.01548/0.09048, loss_spatial_dice_0: 0.16607/0.19083, loss_spatial_ce_0: 0.03276/0.07518, loss_grounding_bce_0: 0.02382/0.08035, loss_grounding_dice_0: 0.26142/0.15187, loss_grounding_ce_0: 0.10203/0.25334, loss_mask_ce_1: 0.04031/0.79298, loss_mask_bce_1: 0.04098/0.30248, loss_mask_dice_1: 0.35237/1.03618, loss_spatial_bce_1: 0.01388/0.09097, loss_spatial_dice_1: 0.16275/0.19355, loss_spatial_ce_1: 0.05763/0.07999, loss_grounding_bce_1: 0.02010/0.08045, loss_grounding_dice_1: 0.21509/0.15277, loss_grounding_ce_1: 0.10642/0.25604, loss_mask_ce_2: 0.04156/0.79989, loss_mask_bce_2: 0.04919/0.30249, loss_mask_dice_2: 0.39002/1.03919, loss_spatial_bce_2: 0.01736/0.09046, loss_spatial_dice_2: 0.17378/0.19346, loss_spatial_ce_2: 0.01768/0.08274, loss_grounding_bce_2: 0.01842/0.08023, loss_grounding_dice_2: 0.22582/0.15246, loss_grounding_ce_2: 0.10265/0.25775, loss_mask_ce_3: 0.06157/0.79836, loss_mask_bce_3: 0.04164/0.30403, loss_mask_dice_3: 0.32823/1.03455, loss_spatial_bce_3: 0.01649/0.09210, loss_spatial_dice_3: 0.19010/0.19374, loss_spatial_ce_3: 0.02056/0.08872, loss_grounding_bce_3: 0.02722/0.08075, loss_grounding_dice_3: 0.22274/0.15208, loss_grounding_ce_3: 0.10481/0.25589, loss_mask_ce_4: 0.04186/0.80522, loss_mask_bce_4: 0.04368/0.30593, loss_mask_dice_4: 0.39122/1.05331, loss_spatial_bce_4: 0.01502/0.09413, loss_spatial_dice_4: 0.19059/0.20109, loss_spatial_ce_4: 0.04106/0.10051, loss_grounding_bce_4: 0.02208/0.08152, loss_grounding_dice_4: 0.21751/0.15446, loss_grounding_ce_4: 0.10716/0.26440, loss_mask_ce_5: 0.05836/0.82697, loss_mask_bce_5: 0.04860/0.30809, loss_mask_dice_5: 0.39951/1.05995, loss_spatial_bce_5: 0.01526/0.09579, loss_spatial_dice_5: 0.13910/0.20315, loss_spatial_ce_5: 0.03276/0.11158, loss_grounding_bce_5: 0.02328/0.08197, loss_grounding_dice_5: 0.20284/0.15534, loss_grounding_ce_5: 0.11493/0.28501, loss_mask_ce_6: 0.09913/0.85209, loss_mask_bce_6: 0.05230/0.30951, loss_mask_dice_6: 0.36426/1.06330, loss_spatial_bce_6: 0.01791/0.10061, loss_spatial_dice_6: 0.16743/0.20548, loss_spatial_ce_6: 0.06865/0.13044, loss_grounding_bce_6: 0.02326/0.08323, loss_grounding_dice_6: 0.25020/0.15607, loss_grounding_ce_6: 0.10807/0.29594, loss_mask_ce_7: 0.07563/0.91537, loss_mask_bce_7: 0.04954/0.31663, loss_mask_dice_7: 0.33984/1.10976, loss_spatial_bce_7: 0.01669/0.11094, loss_spatial_dice_7: 0.14361/0.23033, loss_spatial_ce_7: 0.18717/0.17451, loss_grounding_bce_7: 0.03004/0.08489, loss_grounding_dice_7: 0.21375/0.16166, loss_grounding_ce_7: 0.10906/0.34252, loss_mask_ce_8: 0.08071/1.05205, loss_mask_bce_8: 0.05850/0.33414, loss_mask_dice_8: 0.49932/1.19042, loss_spatial_bce_8: 0.01436/0.13223, loss_spatial_dice_8: 0.19105/0.27127, loss_spatial_ce_8: 0.23541/0.23154, loss_grounding_bce_8: 0.02236/0.08872, loss_grounding_dice_8: 0.24461/0.17090, loss_grounding_ce_8: 0.10479/0.44535, loss_mask_ce_9: 1.52900/3.50829, loss_mask_bce_9: 0.03673/0.36066, loss_mask_dice_9: 0.56548/1.77692, loss_spatial_bce_9: 0.36307/0.36021, loss_spatial_dice_9: 0.77435/0.79768, loss_spatial_ce_9: 1.35312/1.41939, loss_grounding_bce_9: 0.00964/0.10065, loss_grounding_dice_9: 0.21718/0.24536, loss_grounding_ce_9: 0.25054/0.71485] items per batch[64] items per second[0.36] total items[1132800] mini batches[ 17700] memory[4967] epoch remaining[0:16:58] INFO:trainer.default_trainer:epochs[ 9] optim steps[17800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69278/0.78971, loss_mask_bce_0: 0.05297/0.30172, loss_mask_dice_0: 2.77647/1.03278, loss_spatial_bce_0: 0.00637/0.09046, loss_spatial_dice_0: 0.27637/0.19080, loss_spatial_ce_0: 0.04843/0.07510, loss_grounding_bce_0: 0.00402/0.08037, loss_grounding_dice_0: 0.34589/0.15195, loss_grounding_ce_0: 0.12161/0.25309, loss_mask_ce_1: 0.66581/0.79263, loss_mask_bce_1: 0.04893/0.30233, loss_mask_dice_1: 2.79995/1.03669, loss_spatial_bce_1: 0.00473/0.09091, loss_spatial_dice_1: 0.27990/0.19351, loss_spatial_ce_1: 0.07157/0.07993, loss_grounding_bce_1: 0.00474/0.08047, loss_grounding_dice_1: 0.31107/0.15283, loss_grounding_ce_1: 0.12851/0.25576, loss_mask_ce_2: 0.60030/0.79960, loss_mask_bce_2: 0.05932/0.30235, loss_mask_dice_2: 2.84265/1.03966, loss_spatial_bce_2: 0.00497/0.09041, loss_spatial_dice_2: 0.27017/0.19342, loss_spatial_ce_2: 0.01501/0.08271, loss_grounding_bce_2: 0.00587/0.08022, loss_grounding_dice_2: 0.41972/0.15250, loss_grounding_ce_2: 0.11895/0.25747, loss_mask_ce_3: 0.58656/0.79800, loss_mask_bce_3: 0.05574/0.30390, loss_mask_dice_3: 3.12994/1.03513, loss_spatial_bce_3: 0.00530/0.09205, loss_spatial_dice_3: 0.25154/0.19371, loss_spatial_ce_3: 0.00991/0.08862, loss_grounding_bce_3: 0.00590/0.08075, loss_grounding_dice_3: 0.42105/0.15215, loss_grounding_ce_3: 0.12421/0.25571, loss_mask_ce_4: 0.65002/0.80489, loss_mask_bce_4: 0.04354/0.30581, loss_mask_dice_4: 2.75308/1.05386, loss_spatial_bce_4: 0.00453/0.09408, loss_spatial_dice_4: 0.29989/0.20104, loss_spatial_ce_4: 0.03964/0.10040, loss_grounding_bce_4: 0.00514/0.08153, loss_grounding_dice_4: 0.36675/0.15453, loss_grounding_ce_4: 0.12698/0.26423, loss_mask_ce_5: 0.54859/0.82672, loss_mask_bce_5: 0.05294/0.30796, loss_mask_dice_5: 2.41945/1.06065, loss_spatial_bce_5: 0.00438/0.09578, loss_spatial_dice_5: 0.24868/0.20312, loss_spatial_ce_5: 0.54809/0.11145, loss_grounding_bce_5: 0.00419/0.08197, loss_grounding_dice_5: 0.22486/0.15533, loss_grounding_ce_5: 0.10947/0.28476, loss_mask_ce_6: 0.76394/0.85166, loss_mask_bce_6: 0.05742/0.30940, loss_mask_dice_6: 2.71223/1.06388, loss_spatial_bce_6: 0.00647/0.10059, loss_spatial_dice_6: 0.27002/0.20545, loss_spatial_ce_6: 0.03863/0.13029, loss_grounding_bce_6: 0.00742/0.08321, loss_grounding_dice_6: 0.45314/0.15607, loss_grounding_ce_6: 0.07540/0.29557, loss_mask_ce_7: 0.80532/0.91510, loss_mask_bce_7: 0.05685/0.31655, loss_mask_dice_7: 3.10213/1.11040, loss_spatial_bce_7: 0.00681/0.11091, loss_spatial_dice_7: 0.25894/0.23027, loss_spatial_ce_7: 0.06057/0.17434, loss_grounding_bce_7: 0.00343/0.08488, loss_grounding_dice_7: 0.34099/0.16171, loss_grounding_ce_7: 0.06350/0.34228, loss_mask_ce_8: 0.73749/1.05184, loss_mask_bce_8: 0.11472/0.33405, loss_mask_dice_8: 2.94162/1.19122, loss_spatial_bce_8: 0.00793/0.13222, loss_spatial_dice_8: 0.35679/0.27118, loss_spatial_ce_8: 0.21018/0.23138, loss_grounding_bce_8: 0.00430/0.08871, loss_grounding_dice_8: 0.28693/0.17090, loss_grounding_ce_8: 0.07943/0.44484, loss_mask_ce_9: 6.40824/3.50762, loss_mask_bce_9: 0.06513/0.36061, loss_mask_dice_9: 5.54675/1.77758, loss_spatial_bce_9: 0.06071/0.36022, loss_spatial_dice_9: 0.94815/0.79763, loss_spatial_ce_9: 1.97221/1.41905, loss_grounding_bce_9: 0.00447/0.10064, loss_grounding_dice_9: 0.36202/0.24536, loss_grounding_ce_9: 0.26909/0.71438] items per batch[64] items per second[0.35] total items[1139200] mini batches[ 17800] memory[4967] epoch remaining[0:14:00] INFO:trainer.default_trainer:epochs[ 9] optim steps[17900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.28052/0.78991, loss_mask_bce_0: 0.04885/0.30200, loss_mask_dice_0: 1.37444/1.03324, loss_spatial_bce_0: 0.01517/0.09042, loss_spatial_dice_0: 0.29502/0.19080, loss_spatial_ce_0: 0.11412/0.07507, loss_grounding_bce_0: 0.00805/0.08038, loss_grounding_dice_0: 0.43249/0.15196, loss_grounding_ce_0: 0.31806/0.25279, loss_mask_ce_1: 2.14796/0.79270, loss_mask_bce_1: 0.04752/0.30259, loss_mask_dice_1: 1.44684/1.03719, loss_spatial_bce_1: 0.01488/0.09086, loss_spatial_dice_1: 0.33982/0.19353, loss_spatial_ce_1: 0.11962/0.07986, loss_grounding_bce_1: 0.00537/0.08048, loss_grounding_dice_1: 0.33447/0.15287, loss_grounding_ce_1: 0.31694/0.25548, loss_mask_ce_2: 2.38128/0.79961, loss_mask_bce_2: 0.05581/0.30264, loss_mask_dice_2: 1.47910/1.04012, loss_spatial_bce_2: 0.01362/0.09036, loss_spatial_dice_2: 0.33231/0.19342, loss_spatial_ce_2: 0.09851/0.08256, loss_grounding_bce_2: 0.01148/0.08024, loss_grounding_dice_2: 0.49778/0.15253, loss_grounding_ce_2: 0.28166/0.25718, loss_mask_ce_3: 2.44931/0.79810, loss_mask_bce_3: 0.05128/0.30415, loss_mask_dice_3: 1.33852/1.03565, loss_spatial_bce_3: 0.01189/0.09199, loss_spatial_dice_3: 0.31636/0.19372, loss_spatial_ce_3: 0.50802/0.08854, loss_grounding_bce_3: 0.00877/0.08077, loss_grounding_dice_3: 0.46791/0.15218, loss_grounding_ce_3: 0.31286/0.25548, loss_mask_ce_4: 2.08324/0.80497, loss_mask_bce_4: 0.04934/0.30609, loss_mask_dice_4: 1.40151/1.05443, loss_spatial_bce_4: 0.01599/0.09402, loss_spatial_dice_4: 0.31405/0.20105, loss_spatial_ce_4: 0.16201/0.10026, loss_grounding_bce_4: 0.00953/0.08155, loss_grounding_dice_4: 0.39160/0.15454, loss_grounding_ce_4: 0.45365/0.26396, loss_mask_ce_5: 2.95353/0.82690, loss_mask_bce_5: 0.04179/0.30817, loss_mask_dice_5: 1.31671/1.06112, loss_spatial_bce_5: 0.01608/0.09570, loss_spatial_dice_5: 0.35684/0.20314, loss_spatial_ce_5: 0.16112/0.11132, loss_grounding_bce_5: 0.00558/0.08196, loss_grounding_dice_5: 0.37285/0.15533, loss_grounding_ce_5: 0.29687/0.28449, loss_mask_ce_6: 2.82883/0.85183, loss_mask_bce_6: 0.05140/0.30961, loss_mask_dice_6: 1.51229/1.06444, loss_spatial_bce_6: 0.01552/0.10050, loss_spatial_dice_6: 0.31283/0.20546, loss_spatial_ce_6: 0.15101/0.13020, loss_grounding_bce_6: 0.00455/0.08322, loss_grounding_dice_6: 0.39452/0.15608, loss_grounding_ce_6: 0.31360/0.29526, loss_mask_ce_7: 2.22290/0.91531, loss_mask_bce_7: 0.05406/0.31679, loss_mask_dice_7: 1.59017/1.11114, loss_spatial_bce_7: 0.01612/0.11082, loss_spatial_dice_7: 0.33490/0.23031, loss_spatial_ce_7: 0.23603/0.17420, loss_grounding_bce_7: 0.00843/0.08486, loss_grounding_dice_7: 0.49076/0.16176, loss_grounding_ce_7: 0.40296/0.34222, loss_mask_ce_8: 2.59244/1.05230, loss_mask_bce_8: 0.06031/0.33423, loss_mask_dice_8: 1.64647/1.19165, loss_spatial_bce_8: 0.01842/0.13212, loss_spatial_dice_8: 0.42099/0.27120, loss_spatial_ce_8: 0.22982/0.23121, loss_grounding_bce_8: 0.01914/0.08871, loss_grounding_dice_8: 0.61367/0.17095, loss_grounding_ce_8: 0.46926/0.44454, loss_mask_ce_9: 4.98331/3.50802, loss_mask_bce_9: 0.03941/0.36076, loss_mask_dice_9: 1.76974/1.77853, loss_spatial_bce_9: 0.02899/0.36002, loss_spatial_dice_9: 0.83265/0.79772, loss_spatial_ce_9: 1.16188/1.41919, loss_grounding_bce_9: 0.00297/0.10063, loss_grounding_dice_9: 0.43539/0.24542, loss_grounding_ce_9: 0.24817/0.71496] items per batch[64] items per second[0.36] total items[1145600] mini batches[ 17900] memory[4967] epoch remaining[0:11:01] INFO:trainer.default_trainer:epochs[ 9] optim steps[18000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72488/0.79002, loss_mask_bce_0: 0.34752/0.30180, loss_mask_dice_0: 0.43665/1.03297, loss_spatial_bce_0: 0.20264/0.09041, loss_spatial_dice_0: 0.21581/0.19071, loss_spatial_ce_0: 0.00831/0.07509, loss_grounding_bce_0: 0.30077/0.08032, loss_grounding_dice_0: 0.16063/0.15189, loss_grounding_ce_0: 0.86703/0.25292, loss_mask_ce_1: 1.63291/0.79277, loss_mask_bce_1: 0.36529/0.30239, loss_mask_dice_1: 0.49552/1.03677, loss_spatial_bce_1: 0.19755/0.09086, loss_spatial_dice_1: 0.19898/0.19345, loss_spatial_ce_1: 0.02270/0.07988, loss_grounding_bce_1: 0.32947/0.08042, loss_grounding_dice_1: 0.28573/0.15279, loss_grounding_ce_1: 0.88695/0.25558, loss_mask_ce_2: 1.75071/0.79974, loss_mask_bce_2: 0.36907/0.30242, loss_mask_dice_2: 0.46334/1.03968, loss_spatial_bce_2: 0.19284/0.09035, loss_spatial_dice_2: 0.19885/0.19333, loss_spatial_ce_2: 0.03238/0.08257, loss_grounding_bce_2: 0.30299/0.08017, loss_grounding_dice_2: 0.20902/0.15242, loss_grounding_ce_2: 0.99569/0.25730, loss_mask_ce_3: 1.79611/0.79810, loss_mask_bce_3: 0.38906/0.30395, loss_mask_dice_3: 0.46887/1.03524, loss_spatial_bce_3: 0.19797/0.09198, loss_spatial_dice_3: 0.19216/0.19364, loss_spatial_ce_3: 0.03577/0.08850, loss_grounding_bce_3: 0.26994/0.08070, loss_grounding_dice_3: 0.15619/0.15206, loss_grounding_ce_3: 0.90076/0.25559, loss_mask_ce_4: 2.03427/0.80504, loss_mask_bce_4: 0.36256/0.30589, loss_mask_dice_4: 0.49788/1.05395, loss_spatial_bce_4: 0.21263/0.09401, loss_spatial_dice_4: 0.22670/0.20099, loss_spatial_ce_4: 0.04637/0.10023, loss_grounding_bce_4: 0.23420/0.08149, loss_grounding_dice_4: 0.14305/0.15444, loss_grounding_ce_4: 0.88978/0.26410, loss_mask_ce_5: 2.19386/0.82709, loss_mask_bce_5: 0.41709/0.30796, loss_mask_dice_5: 0.48685/1.06069, loss_spatial_bce_5: 0.19683/0.09569, loss_spatial_dice_5: 0.23394/0.20308, loss_spatial_ce_5: 0.02584/0.11124, loss_grounding_bce_5: 0.27665/0.08190, loss_grounding_dice_5: 0.29099/0.15526, loss_grounding_ce_5: 0.79344/0.28468, loss_mask_ce_6: 1.77385/0.85198, loss_mask_bce_6: 0.39887/0.30945, loss_mask_dice_6: 0.52611/1.06406, loss_spatial_bce_6: 0.22672/0.10054, loss_spatial_dice_6: 0.22103/0.20541, loss_spatial_ce_6: 0.04539/0.13013, loss_grounding_bce_6: 0.22683/0.08315, loss_grounding_dice_6: 0.25053/0.15597, loss_grounding_ce_6: 0.71063/0.29548, loss_mask_ce_7: 1.78347/0.91559, loss_mask_bce_7: 0.42844/0.31664, loss_mask_dice_7: 0.57384/1.11075, loss_spatial_bce_7: 0.22145/0.11084, loss_spatial_dice_7: 0.24634/0.23027, loss_spatial_ce_7: 0.06477/0.17417, loss_grounding_bce_7: 0.25771/0.08479, loss_grounding_dice_7: 0.25583/0.16167, loss_grounding_ce_7: 0.70522/0.34251, loss_mask_ce_8: 2.11856/1.05281, loss_mask_bce_8: 0.41984/0.33412, loss_mask_dice_8: 0.57468/1.19122, loss_spatial_bce_8: 0.33399/0.13213, loss_spatial_dice_8: 0.31243/0.27111, loss_spatial_ce_8: 0.07706/0.23109, loss_grounding_bce_8: 0.30580/0.08866, loss_grounding_dice_8: 0.34772/0.17088, loss_grounding_ce_8: 0.93542/0.44472, loss_mask_ce_9: 3.69569/3.50932, loss_mask_bce_9: 0.63774/0.36073, loss_mask_dice_9: 0.87902/1.77825, loss_spatial_bce_9: 0.45675/0.36023, loss_spatial_dice_9: 0.78836/0.79782, loss_spatial_ce_9: 1.01800/1.41976, loss_grounding_bce_9: 0.59789/0.10062, loss_grounding_dice_9: 0.44466/0.24539, loss_grounding_ce_9: 0.95982/0.71531] items per batch[64] items per second[0.37] total items[1152000] mini batches[ 18000] memory[4967] epoch remaining[0:08:02] INFO:trainer.default_trainer:epochs[ 9] optim steps[18100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 3.59276/0.78989, loss_mask_bce_0: 0.01421/0.30186, loss_mask_dice_0: 0.67343/1.03291, loss_spatial_bce_0: 0.00217/0.09040, loss_spatial_dice_0: 0.15405/0.19065, loss_spatial_ce_0: 0.04020/0.07502, loss_grounding_bce_0: 0.00646/0.08035, loss_grounding_dice_0: 0.21143/0.15192, loss_grounding_ce_0: 0.60716/0.25270, loss_mask_ce_1: 3.88727/0.79259, loss_mask_bce_1: 0.01632/0.30243, loss_mask_dice_1: 0.89775/1.03672, loss_spatial_bce_1: 0.00266/0.09087, loss_spatial_dice_1: 0.19133/0.19339, loss_spatial_ce_1: 0.13647/0.07982, loss_grounding_bce_1: 0.01395/0.08044, loss_grounding_dice_1: 0.44850/0.15282, loss_grounding_ce_1: 0.13265/0.25531, loss_mask_ce_2: 3.79204/0.79952, loss_mask_bce_2: 0.01083/0.30247, loss_mask_dice_2: 0.58197/1.03950, loss_spatial_bce_2: 0.00261/0.09036, loss_spatial_dice_2: 0.19760/0.19325, loss_spatial_ce_2: 0.01901/0.08244, loss_grounding_bce_2: 0.00668/0.08019, loss_grounding_dice_2: 0.19265/0.15241, loss_grounding_ce_2: 0.33911/0.25704, loss_mask_ce_3: 3.80786/0.79792, loss_mask_bce_3: 0.01674/0.30395, loss_mask_dice_3: 1.36567/1.03518, loss_spatial_bce_3: 0.00288/0.09199, loss_spatial_dice_3: 0.20145/0.19358, loss_spatial_ce_3: 0.01160/0.08846, loss_grounding_bce_3: 0.00906/0.08072, loss_grounding_dice_3: 0.28470/0.15204, loss_grounding_ce_3: 0.55595/0.25538, loss_mask_ce_4: 4.25648/0.80496, loss_mask_bce_4: 0.00999/0.30594, loss_mask_dice_4: 0.46657/1.05386, loss_spatial_bce_4: 0.00310/0.09402, loss_spatial_dice_4: 0.20804/0.20093, loss_spatial_ce_4: 0.04280/0.10015, loss_grounding_bce_4: 0.00514/0.08152, loss_grounding_dice_4: 0.18045/0.15444, loss_grounding_ce_4: 0.40672/0.26390, loss_mask_ce_5: 3.72156/0.82695, loss_mask_bce_5: 0.01605/0.30804, loss_mask_dice_5: 0.98188/1.06055, loss_spatial_bce_5: 0.00374/0.09571, loss_spatial_dice_5: 0.19518/0.20302, loss_spatial_ce_5: 0.05377/0.11113, loss_grounding_bce_5: 0.00494/0.08192, loss_grounding_dice_5: 0.21637/0.15528, loss_grounding_ce_5: 0.42662/0.28441, loss_mask_ce_6: 4.28613/0.85180, loss_mask_bce_6: 0.01506/0.30948, loss_mask_dice_6: 1.36052/1.06390, loss_spatial_bce_6: 0.00481/0.10055, loss_spatial_dice_6: 0.29200/0.20535, loss_spatial_ce_6: 0.05653/0.13004, loss_grounding_bce_6: 0.00618/0.08317, loss_grounding_dice_6: 0.23393/0.15594, loss_grounding_ce_6: 0.44986/0.29510, loss_mask_ce_7: 4.46314/0.91531, loss_mask_bce_7: 0.01832/0.31665, loss_mask_dice_7: 1.45305/1.11055, loss_spatial_bce_7: 0.00342/0.11086, loss_spatial_dice_7: 0.25973/0.23021, loss_spatial_ce_7: 0.16105/0.17403, loss_grounding_bce_7: 0.01569/0.08481, loss_grounding_dice_7: 0.29101/0.16166, loss_grounding_ce_7: 0.50927/0.34204, loss_mask_ce_8: 3.48986/1.05239, loss_mask_bce_8: 0.06720/0.33416, loss_mask_dice_8: 1.72823/1.19081, loss_spatial_bce_8: 0.00670/0.13213, loss_spatial_dice_8: 0.30326/0.27102, loss_spatial_ce_8: 0.15306/0.23096, loss_grounding_bce_8: 0.01436/0.08872, loss_grounding_dice_8: 0.29524/0.17089, loss_grounding_ce_8: 0.43902/0.44410, loss_mask_ce_9: 5.69445/3.50864, loss_mask_bce_9: 0.01844/0.36072, loss_mask_dice_9: 1.55350/1.77798, loss_spatial_bce_9: 0.01220/0.36015, loss_spatial_dice_9: 0.76685/0.79776, loss_spatial_ce_9: 1.63372/1.41988, loss_grounding_bce_9: 0.00983/0.10063, loss_grounding_dice_9: 0.56826/0.24533, loss_grounding_ce_9: 0.20533/0.71474] items per batch[64] items per second[0.35] total items[1158400] mini batches[ 18100] memory[4967] epoch remaining[0:05:03] INFO:trainer.default_trainer:epochs[ 9] optim steps[18200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08659/0.78912, loss_mask_bce_0: 0.23960/0.30201, loss_mask_dice_0: 0.28877/1.03140, loss_spatial_bce_0: 0.12766/0.09053, loss_spatial_dice_0: 0.15174/0.19059, loss_spatial_ce_0: 0.00033/0.07494, loss_grounding_bce_0: 0.07039/0.08036, loss_grounding_dice_0: 0.13448/0.15188, loss_grounding_ce_0: 0.00018/0.25286, loss_mask_ce_1: 0.09240/0.79185, loss_mask_bce_1: 0.22531/0.30257, loss_mask_dice_1: 0.27507/1.03520, loss_spatial_bce_1: 0.12349/0.09100, loss_spatial_dice_1: 0.15772/0.19333, loss_spatial_ce_1: 0.00065/0.07967, loss_grounding_bce_1: 0.05965/0.08046, loss_grounding_dice_1: 0.12416/0.15274, loss_grounding_ce_1: 0.00014/0.25549, loss_mask_ce_2: 0.10916/0.79863, loss_mask_bce_2: 0.22342/0.30259, loss_mask_dice_2: 0.24827/1.03789, loss_spatial_bce_2: 0.12668/0.09048, loss_spatial_dice_2: 0.15559/0.19318, loss_spatial_ce_2: 0.00105/0.08239, loss_grounding_bce_2: 0.07233/0.08021, loss_grounding_dice_2: 0.11399/0.15235, loss_grounding_ce_2: 0.00025/0.25724, loss_mask_ce_3: 0.09418/0.79724, loss_mask_bce_3: 0.22574/0.30412, loss_mask_dice_3: 0.26178/1.03371, loss_spatial_bce_3: 0.13184/0.09212, loss_spatial_dice_3: 0.15000/0.19351, loss_spatial_ce_3: 0.00228/0.08834, loss_grounding_bce_3: 0.07255/0.08074, loss_grounding_dice_3: 0.11629/0.15196, loss_grounding_ce_3: 0.00036/0.25554, loss_mask_ce_4: 0.16357/0.80428, loss_mask_bce_4: 0.24629/0.30609, loss_mask_dice_4: 0.28882/1.05231, loss_spatial_bce_4: 0.13696/0.09416, loss_spatial_dice_4: 0.18456/0.20086, loss_spatial_ce_4: 0.00678/0.10002, loss_grounding_bce_4: 0.07789/0.08153, loss_grounding_dice_4: 0.12251/0.15439, loss_grounding_ce_4: 0.00040/0.26408, loss_mask_ce_5: 0.17704/0.82616, loss_mask_bce_5: 0.25393/0.30821, loss_mask_dice_5: 0.29310/1.05908, loss_spatial_bce_5: 0.13205/0.09585, loss_spatial_dice_5: 0.18949/0.20296, loss_spatial_ce_5: 0.00993/0.11110, loss_grounding_bce_5: 0.05921/0.08193, loss_grounding_dice_5: 0.12673/0.15523, loss_grounding_ce_5: 0.00042/0.28429, loss_mask_ce_6: 0.07127/0.85079, loss_mask_bce_6: 0.26000/0.30968, loss_mask_dice_6: 0.32216/1.06244, loss_spatial_bce_6: 0.15433/0.10071, loss_spatial_dice_6: 0.18617/0.20527, loss_spatial_ce_6: 0.04479/0.13006, loss_grounding_bce_6: 0.06145/0.08317, loss_grounding_dice_6: 0.12736/0.15589, loss_grounding_ce_6: 0.01014/0.29526, loss_mask_ce_7: 0.08632/0.91436, loss_mask_bce_7: 0.24529/0.31687, loss_mask_dice_7: 0.27945/1.10901, loss_spatial_bce_7: 0.14480/0.11099, loss_spatial_dice_7: 0.18745/0.23011, loss_spatial_ce_7: 0.05156/0.17397, loss_grounding_bce_7: 0.05264/0.08481, loss_grounding_dice_7: 0.11031/0.16161, loss_grounding_ce_7: 0.00915/0.34198, loss_mask_ce_8: 0.15134/1.05145, loss_mask_bce_8: 0.22733/0.33433, loss_mask_dice_8: 0.28222/1.18911, loss_spatial_bce_8: 0.16693/0.13228, loss_spatial_dice_8: 0.13823/0.27088, loss_spatial_ce_8: 0.11044/0.23084, loss_grounding_bce_8: 0.04700/0.08873, loss_grounding_dice_8: 0.10319/0.17083, loss_grounding_ce_8: 0.02363/0.44375, loss_mask_ce_9: 2.19271/3.50685, loss_mask_bce_9: 0.27334/0.36086, loss_mask_dice_9: 0.38922/1.77523, loss_spatial_bce_9: 0.88230/0.36031, loss_spatial_dice_9: 0.73420/0.79765, loss_spatial_ce_9: 1.78826/1.41952, loss_grounding_bce_9: 0.08944/0.10063, loss_grounding_dice_9: 0.18514/0.24522, loss_grounding_ce_9: 0.13526/0.71430] items per batch[64] items per second[0.36] total items[1164800] mini batches[ 18200] memory[4967] epoch remaining[0:02:05] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00018270. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3692 s/iter. Eval: 0.1008 s/iter. Total: 0.4729 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3681 s/iter. Eval: 0.0908 s/iter. Total: 0.4615 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0026 s/iter. Inference: 0.3760 s/iter. Eval: 0.0852 s/iter. Total: 0.4640 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0027 s/iter. Inference: 0.3777 s/iter. Eval: 0.0825 s/iter. Total: 0.4630 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0027 s/iter. Inference: 0.3777 s/iter. Eval: 0.0785 s/iter. Total: 0.4590 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0028 s/iter. Inference: 0.3785 s/iter. Eval: 0.0759 s/iter. Total: 0.4573 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalu8lobc8i ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.507 | 83.206 | 66.018 | 133 | | Things | 61.733 | 84.032 | 72.986 | 80 | | Stuff | 46.109 | 81.959 | 55.501 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.59s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.37 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.63s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.12 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.692 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.678 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.570 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.767 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.479 | 69.200 | 49.188 | 25.533 | 49.595 | 67.833 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.965 | bicycle | 21.946 | car | 42.927 | | motorcycle | 41.211 | airplane | 61.669 | bus | 70.990 | | train | 74.941 | truck | 43.415 | boat | 30.620 | | traffic light | 28.624 | fire hydrant | 71.680 | stop sign | 68.724 | | parking meter | 50.615 | bench | 26.424 | bird | 34.135 | | cat | 75.225 | dog | 70.501 | horse | 49.944 | | sheep | 52.736 | cow | 56.649 | elephant | 66.691 | | bear | 81.001 | zebra | 65.758 | giraffe | 61.812 | | backpack | 23.120 | umbrella | 55.772 | handbag | 23.633 | | tie | 40.159 | suitcase | 51.045 | frisbee | 69.302 | | skis | 9.001 | snowboard | 34.546 | sports ball | 49.171 | | kite | 37.080 | baseball bat | 38.986 | baseball glove | 49.695 | | skateboard | 43.583 | surfboard | 45.077 | tennis racket | 63.835 | | bottle | 41.507 | wine glass | 38.042 | cup | 49.733 | | fork | 24.870 | knife | 23.849 | spoon | 23.346 | | bowl | 40.549 | banana | 22.665 | apple | 25.709 | | sandwich | 48.798 | orange | 30.019 | broccoli | 24.386 | | carrot | 22.478 | hot dog | 37.333 | pizza | 52.355 | | donut | 56.723 | cake | 47.657 | chair | 27.725 | | couch | 43.710 | potted plant | 23.359 | bed | 44.254 | | dining table | 15.924 | toilet | 68.554 | tv | 66.412 | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.96765041396885, 'fwIoU': 71.17204125249152, 'IoU-person': 88.86463908562548, 'IoU-bicycle': 72.74257153233921, 'IoU-car': 72.71894011484233, 'IoU-motorcycle': 81.93524387335948, 'IoU-airplane': 87.00161033462471, 'IoU-bus': 88.2431368021124, 'IoU-train': 88.03292933108266, 'IoU-truck': 71.26816328033334, 'IoU-boat': 73.14271880868102, 'IoU-traffic light': 80.37185686144382, 'IoU-fire hydrant': 92.71067960364017, 'IoU-stop sign': 94.33800364364727, 'IoU-parking meter': 88.81601886342828, 'IoU-bench': 58.87373512601892, 'IoU-bird': 76.19251974695716, 'IoU-cat': 90.02463163977387, 'IoU-dog': 85.74736912911706, 'IoU-horse': 89.41565790132164, 'IoU-sheep': 84.24097266961147, 'IoU-cow': 90.78051873503045, 'IoU-elephant': 90.2605238145257, 'IoU-bear': 78.18655133156112, 'IoU-zebra': 82.82026518581115, 'IoU-giraffe': 86.60823960901496, 'IoU-backpack': 53.59178103549692, 'IoU-umbrella': 85.14056090578666, 'IoU-handbag': 48.649475779125, 'IoU-tie': 73.3534822406838, 'IoU-suitcase': 87.86080784039059, 'IoU-frisbee': 74.41453644197239, 'IoU-skis': 58.74516863451051, 'IoU-snowboard': 72.51593390632742, 'IoU-sports ball': 71.30091128882276, 'IoU-kite': 79.47114324509383, 'IoU-baseball bat': 67.68455653322233, 'IoU-baseball glove': 73.53601352470486, 'IoU-skateboard': 85.99252173171979, 'IoU-surfboard': 86.4437681896313, 'IoU-tennis racket': 88.13035386317692, 'IoU-bottle': 70.42270580634302, 'IoU-wine glass': 82.36597014340697, 'IoU-cup': 68.59958327823406, 'IoU-fork': 67.76644485361415, 'IoU-knife': 63.05606093171519, 'IoU-spoon': 59.975736601055175, 'IoU-bowl': 57.47893141636947, 'IoU-banana': 83.4776564921982, 'IoU-apple': 56.75916087941288, 'IoU-sandwich': 70.288894239551, 'IoU-orange': 74.1928325751692, 'IoU-broccoli': 69.79105920693438, 'IoU-carrot': 63.72768978128052, 'IoU-hot dog': 71.99676549811873, 'IoU-pizza': 80.89863502541313, 'IoU-donut': 61.39342409493892, 'IoU-cake': 81.55520781772697, 'IoU-chair': 61.178689273536044, 'IoU-couch': 70.53446736274796, 'IoU-potted plant': 44.22063093484091, 'IoU-bed': 71.26645108205267, 'IoU-dining table': 53.81474773693352, 'IoU-toilet': 88.12032255193816, 'IoU-tv': 79.86117398862773, 'IoU-laptop': 80.1162724885878, 'IoU-mouse': 78.99577445362557, 'IoU-remote': 50.73648792700993, 'IoU-keyboard': 55.974760377523914, 'IoU-cell phone': 84.1623583208031, 'IoU-microwave': 70.55386279185399, 'IoU-oven': 71.07431472288339, 'IoU-toaster': 85.09328382263826, 'IoU-sink': 75.598011614093, 'IoU-refrigerator': 79.21871244137658, 'IoU-book': 57.2261778419626, 'IoU-clock': 72.12690567282735, 'IoU-vase': 58.41669188935863, 'IoU-scissors': 74.53921900941532, 'IoU-teddy bear': 79.04691312815343, 'IoU-hair drier': 51.684655427670855, 'IoU-toothbrush': 76.17295815477691, 'IoU-banner': 29.622955853052247, 'IoU-blanket': 20.27131012363363, 'IoU-bridge': 36.640738454302266, 'IoU-cardboard': 45.83336948373752, 'IoU-counter': 33.55817015279632, 'IoU-curtain': 69.62381120941718, 'IoU-door-stuff': 45.32607676589118, 'IoU-floor-wood': 61.5684772419414, 'IoU-flower': 46.61416188080148, 'IoU-fruit': 45.18587587579591, 'IoU-gravel': 28.81052964962652, 'IoU-house': 25.70246866105607, 'IoU-light': 44.16158419313844, 'IoU-mirror-stuff': 66.15464205821793, 'IoU-net': 42.60023485635517, 'IoU-pillow': 17.043032503799015, 'IoU-platform': 28.98674235289032, 'IoU-playingfield': 69.55020275973641, 'IoU-railroad': 61.8270006560146, 'IoU-river': 56.249337326237836, 'IoU-road': 68.45037079851632, 'IoU-roof': 10.423876537786867, 'IoU-sand': 64.30941146779531, 'IoU-sea': 84.90687325483152, 'IoU-shelf': 38.516649425554604, 'IoU-snow': 92.23891881730795, 'IoU-stairs': 31.330597624013784, 'IoU-tent': 11.957770793214364, 'IoU-towel': 64.19034547832987, 'IoU-wall-brick': 52.673715151376854, 'IoU-wall-stone': 31.34255888499106, 'IoU-wall-tile': 71.66261919835907, 'IoU-wall-wood': 43.02433499922872, 'IoU-water-other': 28.361600368612077, 'IoU-window-blind': 50.298319899175816, 'IoU-window-other': 50.91877992786662, 'IoU-tree-merged': 81.86778775848906, 'IoU-fence-merged': 54.34885853138106, 'IoU-ceiling-merged': 67.3713500242066, 'IoU-sky-other-merged': 93.47214145442648, 'IoU-cabinet-merged': 62.758316789886905, 'IoU-table-merged': 40.9695285531969, 'IoU-floor-other-merged': 52.86447252736771, 'IoU-pavement-merged': 56.025337030445634, 'IoU-mountain-merged': 57.26557710404645, 'IoU-grass-merged': 71.08984058137638, 'IoU-dirt-merged': 44.406285833671525, 'IoU-paper-merged': 30.717925867996076, 'IoU-food-other-merged': 43.89395363723004, 'IoU-building-other-merged': 59.61383626928727, 'IoU-rock-merged': 64.88402207648707, 'IoU-wall-other-merged': 67.8608967168992, 'IoU-rug-merged': 67.7007957747727, 'mACC': 76.99835458375898, 'pACC': 81.85207623562569, 'ACC-person': 93.06017065189052, 'ACC-bicycle': 83.01273675922161, 'ACC-car': 86.43769101967945, 'ACC-motorcycle': 85.83250281853736, 'ACC-airplane': 93.2647970668662, 'ACC-bus': 93.39094480280924, 'ACC-train': 95.31899871112273, 'ACC-truck': 79.48701764802415, 'ACC-boat': 82.23661197864463, 'ACC-traffic light': 91.12507449816009, 'ACC-fire hydrant': 95.96444294516775, 'ACC-stop sign': 98.17536430847348, 'ACC-parking meter': 91.77337979852699, 'ACC-bench': 74.45546208322654, 'ACC-bird': 82.16048867546608, 'ACC-cat': 95.17814203014878, 'ACC-dog': 88.89282930657768, 'ACC-horse': 94.14054327026115, 'ACC-sheep': 87.86178582547048, 'ACC-cow': 94.15980933310531, 'ACC-elephant': 92.53575835893845, 'ACC-bear': 79.75316802947069, 'ACC-zebra': 84.85593431073558, 'ACC-giraffe': 90.64501483392256, 'ACC-backpack': 74.55938160051183, 'ACC-umbrella': 89.40951160682779, 'ACC-handbag': 71.15266971524113, 'ACC-tie': 81.11915080418963, 'ACC-suitcase': 92.62686362238298, 'ACC-frisbee': 93.8949090909091, 'ACC-skis': 75.60690569239209, 'ACC-snowboard': 83.14956773040092, 'ACC-sports ball': 79.26008620956988, 'ACC-kite': 86.17930072816695, 'ACC-baseball bat': 88.3235058328151, 'ACC-baseball glove': 92.77216950202835, 'ACC-skateboard': 90.58141935715535, 'ACC-surfboard': 92.34893044334815, 'ACC-tennis racket': 93.78845449206692, 'ACC-bottle': 84.32043868695904, 'ACC-wine glass': 91.49879421950644, 'ACC-cup': 84.60111285899984, 'ACC-fork': 77.8348053136609, 'ACC-knife': 76.31622334579028, 'ACC-spoon': 78.63722117412053, 'ACC-bowl': 67.1429004347387, 'ACC-banana': 90.26388888506264, 'ACC-apple': 69.50814943026107, 'ACC-sandwich': 80.85376647706123, 'ACC-orange': 82.28792411646396, 'ACC-broccoli': 81.46622874535386, 'ACC-carrot': 75.26187173706434, 'ACC-hot dog': 79.24733853642381, 'ACC-pizza': 88.28432890364076, 'ACC-donut': 66.89725858694472, 'ACC-cake': 88.83331858299934, 'ACC-chair': 78.52917238737676, 'ACC-couch': 77.06635721292783, 'ACC-potted plant': 58.709747845948, 'ACC-bed': 82.48518443630304, 'ACC-dining table': 78.94097858093637, 'ACC-toilet': 92.23882961187387, 'ACC-tv': 89.64960404329415, 'ACC-laptop': 91.46825880845229, 'ACC-mouse': 90.90830384294883, 'ACC-remote': 53.79385593128241, 'ACC-keyboard': 59.90484030311015, 'ACC-cell phone': 91.83197497543105, 'ACC-microwave': 74.79790584146522, 'ACC-oven': 93.90821964309619, 'ACC-toaster': 90.32216124485076, 'ACC-sink': 85.38790125341124, 'ACC-refrigerator': 88.98304111059068, 'ACC-book': 76.84325180017372, 'ACC-clock': 79.01053432654697, 'ACC-vase': 67.71281264755, 'ACC-scissors': 79.25228860792038, 'ACC-teddy bear': 83.04072561076057, 'ACC-hair drier': 61.6073207525688, 'ACC-toothbrush': 83.99669909659487, 'ACC-banner': 79.14202262942005, 'ACC-blanket': 34.405821346971635, 'ACC-bridge': 59.34944587223063, 'ACC-cardboard': 61.453744365275554, 'ACC-counter': 52.06385827672837, 'ACC-curtain': 82.39388136623364, 'ACC-door-stuff': 70.51400259011523, 'ACC-floor-wood': 78.52687700322656, 'ACC-flower': 68.53539930760405, 'ACC-fruit': 68.67002416077582, 'ACC-gravel': 51.84354562839755, 'ACC-house': 33.69178463719808, 'ACC-light': 61.460805611912875, 'ACC-mirror-stuff': 78.31003484573952, 'ACC-net': 66.61523146947255, 'ACC-pillow': 36.477442824973004, 'ACC-platform': 52.870977530190785, 'ACC-playingfield': 88.2249052565179, 'ACC-railroad': 85.73557170876234, 'ACC-river': 83.99369238724984, 'ACC-road': 86.7765601063664, 'ACC-roof': 13.130503163873575, 'ACC-sand': 68.23852447268945, 'ACC-sea': 89.54684256422723, 'ACC-shelf': 56.7277278702351, 'ACC-snow': 95.56362680592349, 'ACC-stairs': 58.521491010016916, 'ACC-tent': 14.296080063234228, 'ACC-towel': 81.05533380631815, 'ACC-wall-brick': 71.95653175883166, 'ACC-wall-stone': 39.65154864610099, 'ACC-wall-tile': 87.09504181714534, 'ACC-wall-wood': 62.98159326953492, 'ACC-water-other': 43.62320490682889, 'ACC-window-blind': 63.63175460217278, 'ACC-window-other': 73.30499191850677, 'ACC-tree-merged': 89.19430128613445, 'ACC-fence-merged': 73.40483088333394, 'ACC-ceiling-merged': 83.43216046420385, 'ACC-sky-other-merged': 97.17482412100757, 'ACC-cabinet-merged': 79.88609239233611, 'ACC-table-merged': 55.91795587798454, 'ACC-floor-other-merged': 67.74131982055549, 'ACC-pavement-merged': 66.01200218372274, 'ACC-mountain-merged': 69.6899809944593, 'ACC-grass-merged': 83.70673588471337, 'ACC-dirt-merged': 65.00030260402218, 'ACC-paper-merged': 38.11105522984058, 'ACC-food-other-merged': 58.42231891376256, 'ACC-building-other-merged': 74.51741430175319, 'ACC-rock-merged': 83.27480861995217, 'ACC-wall-other-merged': 79.01040832596841, 'ACC-rug-merged': 83.76718669227225})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2940 s/iter. Inference: 0.1765 s/iter. Eval: 0.0000 s/iter. Total: 0.4705 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3183 s/iter. Inference: 0.3381 s/iter. Eval: 0.0000 s/iter. Total: 0.6565 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3463 s/iter. Inference: 0.5346 s/iter. Eval: 0.0000 s/iter. Total: 0.8811 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.424348844015218, 'noc@0.8': 2.5218027509511267, 'noc@0.85': 2.9792215393620136, 'noc@0.9': 3.7919227392449515, 'miou@iter1': 0.8722901783877031} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0011 s/iter. Inference: 0.1517 s/iter. Eval: 0.0011 s/iter. Total: 0.1539 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.4760971069336, 'precision@0.6': 72.36688995361328, 'precision@0.7': 68.20832061767578, 'precision@0.8': 58.99728012084961, 'precision@0.9': 32.41352462768555, 'cIoU': 61.54941940307617, 'mIoU': 66.68595886230469} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.50656720980175, 'SQ': 83.20625773633762, 'RQ': 66.0183637840185, 'PQ_th': 61.73275774199614, 'SQ_th': 84.03247568408389, 'RQ_th': 72.98607032184468, 'PQ_st': 46.10854376498006, 'SQ_st': 81.95913630577726, 'RQ_st': 55.50107089673377}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.478557305316485, 'AP50': 69.19991329887307, 'AP75': 49.187862620250755, 'APs': 25.533123272816937, 'APm': 49.59519527550673, 'APl': 67.83260507055698, 'AP-person': 48.965347108344154, 'AP-bicycle': 21.946294366259696, 'AP-car': 42.92654460450094, 'AP-motorcycle': 41.21095931091647, 'AP-airplane': 61.669171491748784, 'AP-bus': 70.99041934614299, 'AP-train': 74.94099019840776, 'AP-truck': 43.415401250676396, 'AP-boat': 30.620141669076023, 'AP-traffic light': 28.623531171751765, 'AP-fire hydrant': 71.68011531961317, 'AP-stop sign': 68.72424216150593, 'AP-parking meter': 50.61474204453579, 'AP-bench': 26.42401637365247, 'AP-bird': 34.13531140475633, 'AP-cat': 75.22469434456525, 'AP-dog': 70.50147092700527, 'AP-horse': 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'IoU-umbrella': 85.14056090578666, 'IoU-handbag': 48.649475779125, 'IoU-tie': 73.3534822406838, 'IoU-suitcase': 87.86080784039059, 'IoU-frisbee': 74.41453644197239, 'IoU-skis': 58.74516863451051, 'IoU-snowboard': 72.51593390632742, 'IoU-sports ball': 71.30091128882276, 'IoU-kite': 79.47114324509383, 'IoU-baseball bat': 67.68455653322233, 'IoU-baseball glove': 73.53601352470486, 'IoU-skateboard': 85.99252173171979, 'IoU-surfboard': 86.4437681896313, 'IoU-tennis racket': 88.13035386317692, 'IoU-bottle': 70.42270580634302, 'IoU-wine glass': 82.36597014340697, 'IoU-cup': 68.59958327823406, 'IoU-fork': 67.76644485361415, 'IoU-knife': 63.05606093171519, 'IoU-spoon': 59.975736601055175, 'IoU-bowl': 57.47893141636947, 'IoU-banana': 83.4776564921982, 'IoU-apple': 56.75916087941288, 'IoU-sandwich': 70.288894239551, 'IoU-orange': 74.1928325751692, 'IoU-broccoli': 69.79105920693438, 'IoU-carrot': 63.72768978128052, 'IoU-hot dog': 71.99676549811873, 'IoU-pizza': 80.89863502541313, 'IoU-donut': 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INFO:trainer.default_trainer:This epoch takes 0:57:46.315093 INFO:trainer.default_trainer:PROGRESS: 20.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 10 training. INFO:trainer.default_trainer:epochs[ 10] optim steps[18300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44991/0.78904, loss_mask_bce_0: 1.15157/0.30200, loss_mask_dice_0: 1.11701/1.03137, loss_spatial_bce_0: 0.18296/0.09049, loss_spatial_dice_0: 0.18765/0.19056, loss_spatial_ce_0: 0.00493/0.07479, loss_grounding_bce_0: 0.10609/0.08042, loss_grounding_dice_0: 0.14488/0.15190, loss_grounding_ce_0: 0.00014/0.25271, loss_mask_ce_1: 0.85175/0.79176, loss_mask_bce_1: 0.97614/0.30255, loss_mask_dice_1: 1.27869/1.03503, loss_spatial_bce_1: 0.17993/0.09096, loss_spatial_dice_1: 0.18655/0.19330, loss_spatial_ce_1: 0.00624/0.07952, loss_grounding_bce_1: 0.13196/0.08053, loss_grounding_dice_1: 0.16044/0.15276, loss_grounding_ce_1: 0.00032/0.25527, loss_mask_ce_2: 0.82933/0.79846, loss_mask_bce_2: 1.02950/0.30260, loss_mask_dice_2: 1.36291/1.03789, loss_spatial_bce_2: 0.17722/0.09045, loss_spatial_dice_2: 0.20101/0.19315, loss_spatial_ce_2: 0.00382/0.08224, loss_grounding_bce_2: 0.10848/0.08028, loss_grounding_dice_2: 0.14753/0.15234, loss_grounding_ce_2: 0.00019/0.25712, loss_mask_ce_3: 0.78865/0.79723, loss_mask_bce_3: 1.00521/0.30412, loss_mask_dice_3: 1.34486/1.03352, loss_spatial_bce_3: 0.18379/0.09209, loss_spatial_dice_3: 0.19548/0.19348, loss_spatial_ce_3: 0.00923/0.08818, loss_grounding_bce_3: 0.10494/0.08082, loss_grounding_dice_3: 0.15383/0.15198, loss_grounding_ce_3: 0.00007/0.25542, loss_mask_ce_4: 0.46558/0.80426, loss_mask_bce_4: 1.15943/0.30610, loss_mask_dice_4: 1.11357/1.05224, loss_spatial_bce_4: 0.18279/0.09412, loss_spatial_dice_4: 0.19808/0.20082, loss_spatial_ce_4: 0.01263/0.09995, loss_grounding_bce_4: 0.12884/0.08159, loss_grounding_dice_4: 0.15764/0.15441, loss_grounding_ce_4: 0.00006/0.26382, loss_mask_ce_5: 0.26376/0.82614, loss_mask_bce_5: 1.06650/0.30824, loss_mask_dice_5: 1.24693/1.05896, loss_spatial_bce_5: 0.18302/0.09581, loss_spatial_dice_5: 0.21234/0.20292, loss_spatial_ce_5: 0.04269/0.11104, loss_grounding_bce_5: 0.10963/0.08202, loss_grounding_dice_5: 0.14962/0.15528, loss_grounding_ce_5: 0.00042/0.28401, loss_mask_ce_6: 0.35580/0.85084, loss_mask_bce_6: 1.03353/0.30967, loss_mask_dice_6: 1.18074/1.06237, loss_spatial_bce_6: 0.17487/0.10067, loss_spatial_dice_6: 0.20043/0.20524, loss_spatial_ce_6: 0.09269/0.13012, loss_grounding_bce_6: 0.08559/0.08325, loss_grounding_dice_6: 0.13836/0.15594, loss_grounding_ce_6: 0.00012/0.29503, loss_mask_ce_7: 0.20712/0.91444, loss_mask_bce_7: 1.17500/0.31682, loss_mask_dice_7: 1.27953/1.10878, loss_spatial_bce_7: 0.21825/0.11097, loss_spatial_dice_7: 0.19830/0.23010, loss_spatial_ce_7: 0.09939/0.17388, loss_grounding_bce_7: 0.10442/0.08486, loss_grounding_dice_7: 0.13935/0.16164, loss_grounding_ce_7: 0.00079/0.34167, loss_mask_ce_8: 0.58417/1.05150, loss_mask_bce_8: 1.36069/0.33430, loss_mask_dice_8: 1.14124/1.18888, loss_spatial_bce_8: 0.20629/0.13222, loss_spatial_dice_8: 0.17937/0.27080, loss_spatial_ce_8: 0.15455/0.23081, loss_grounding_bce_8: 0.08990/0.08879, loss_grounding_dice_8: 0.13131/0.17085, loss_grounding_ce_8: 0.69985/0.44339, loss_mask_ce_9: 2.71001/3.50741, loss_mask_bce_9: 1.13028/0.36081, loss_mask_dice_9: 1.48532/1.77506, loss_spatial_bce_9: 0.40482/0.36015, loss_spatial_dice_9: 0.83614/0.79763, loss_spatial_ce_9: 1.39378/1.41913, loss_grounding_bce_9: 0.10289/0.10069, loss_grounding_dice_9: 0.13779/0.24527, loss_grounding_ce_9: 1.08966/0.71376] items per batch[64] items per second[0.16] total items[1171200] mini batches[ 18300] memory[4967] epoch remaining[1:01:09] INFO:trainer.default_trainer:epochs[ 10] optim steps[18400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08400/0.78894, loss_mask_bce_0: 0.52304/0.30177, loss_mask_dice_0: 0.43970/1.03120, loss_spatial_bce_0: 0.10401/0.09041, loss_spatial_dice_0: 0.11901/0.19055, loss_spatial_ce_0: 0.01086/0.07471, loss_grounding_bce_0: 0.03382/0.08040, loss_grounding_dice_0: 0.07883/0.15188, loss_grounding_ce_0: 0.10926/0.25254, loss_mask_ce_1: 1.05701/0.79161, loss_mask_bce_1: 0.50364/0.30233, loss_mask_dice_1: 0.43934/1.03475, loss_spatial_bce_1: 0.10747/0.09088, loss_spatial_dice_1: 0.12524/0.19328, loss_spatial_ce_1: 0.01026/0.07943, loss_grounding_bce_1: 0.03997/0.08051, loss_grounding_dice_1: 0.08165/0.15278, loss_grounding_ce_1: 0.08840/0.25534, loss_mask_ce_2: 1.14425/0.79837, loss_mask_bce_2: 0.51316/0.30238, loss_mask_dice_2: 0.46295/1.03765, loss_spatial_bce_2: 0.15086/0.09037, loss_spatial_dice_2: 0.14798/0.19314, loss_spatial_ce_2: 0.01360/0.08219, loss_grounding_bce_2: 0.03297/0.08025, loss_grounding_dice_2: 0.07256/0.15231, loss_grounding_ce_2: 0.13325/0.25704, loss_mask_ce_3: 1.02871/0.79713, loss_mask_bce_3: 0.51081/0.30388, loss_mask_dice_3: 0.45406/1.03333, loss_spatial_bce_3: 0.10880/0.09201, loss_spatial_dice_3: 0.11712/0.19346, loss_spatial_ce_3: 0.01216/0.08812, loss_grounding_bce_3: 0.02989/0.08080, loss_grounding_dice_3: 0.06465/0.15203, loss_grounding_ce_3: 0.10020/0.25563, loss_mask_ce_4: 1.39037/0.80429, loss_mask_bce_4: 0.49453/0.30586, loss_mask_dice_4: 0.51766/1.05194, loss_spatial_bce_4: 0.10944/0.09404, loss_spatial_dice_4: 0.12265/0.20081, loss_spatial_ce_4: 0.00956/0.09981, loss_grounding_bce_4: 0.03213/0.08156, loss_grounding_dice_4: 0.07166/0.15446, loss_grounding_ce_4: 0.13676/0.26378, loss_mask_ce_5: 1.16114/0.82608, loss_mask_bce_5: 0.52750/0.30798, loss_mask_dice_5: 0.50719/1.05882, loss_spatial_bce_5: 0.12462/0.09574, loss_spatial_dice_5: 0.14143/0.20292, loss_spatial_ce_5: 0.01578/0.11097, loss_grounding_bce_5: 0.03008/0.08197, loss_grounding_dice_5: 0.09967/0.15528, loss_grounding_ce_5: 0.15962/0.28401, loss_mask_ce_6: 1.11105/0.85077, loss_mask_bce_6: 0.51110/0.30944, loss_mask_dice_6: 0.52381/1.06219, loss_spatial_bce_6: 0.13840/0.10060, loss_spatial_dice_6: 0.16069/0.20526, loss_spatial_ce_6: 0.06019/0.13013, loss_grounding_bce_6: 0.03155/0.08322, loss_grounding_dice_6: 0.07767/0.15595, loss_grounding_ce_6: 0.13235/0.29498, loss_mask_ce_7: 1.28776/0.91438, loss_mask_bce_7: 0.49985/0.31658, loss_mask_dice_7: 0.47871/1.10854, loss_spatial_bce_7: 0.15808/0.11093, loss_spatial_dice_7: 0.19828/0.23011, loss_spatial_ce_7: 0.07710/0.17380, loss_grounding_bce_7: 0.02725/0.08484, loss_grounding_dice_7: 0.06553/0.16168, loss_grounding_ce_7: 0.08160/0.34128, loss_mask_ce_8: 0.78179/1.05148, loss_mask_bce_8: 0.49482/0.33407, loss_mask_dice_8: 0.50570/1.18851, loss_spatial_bce_8: 0.13630/0.13219, loss_spatial_dice_8: 0.19694/0.27078, loss_spatial_ce_8: 0.20337/0.23080, loss_grounding_bce_8: 0.03551/0.08875, loss_grounding_dice_8: 0.05621/0.17086, loss_grounding_ce_8: 0.08142/0.44289, loss_mask_ce_9: 4.49084/3.50620, loss_mask_bce_9: 0.75325/0.36062, loss_mask_dice_9: 0.90479/1.77476, loss_spatial_bce_9: 0.45471/0.35998, loss_spatial_dice_9: 0.71740/0.79753, loss_spatial_ce_9: 1.28356/1.41877, loss_grounding_bce_9: 0.04586/0.10071, loss_grounding_dice_9: 0.31451/0.24531, loss_grounding_ce_9: 0.58047/0.71279] items per batch[64] items per second[0.36] total items[1177600] mini batches[ 18400] memory[4967] epoch remaining[0:52:32] INFO:trainer.default_trainer:epochs[ 10] optim steps[18500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38968/0.78862, loss_mask_bce_0: 0.08606/0.30159, loss_mask_dice_0: 0.35039/1.03117, loss_spatial_bce_0: 0.03923/0.09031, loss_spatial_dice_0: 0.12999/0.19044, loss_spatial_ce_0: 0.02983/0.07449, loss_grounding_bce_0: 0.03999/0.08034, loss_grounding_dice_0: 0.16702/0.15190, loss_grounding_ce_0: 0.04146/0.25293, loss_mask_ce_1: 0.44193/0.79128, loss_mask_bce_1: 0.08956/0.30218, loss_mask_dice_1: 0.36990/1.03471, loss_spatial_bce_1: 0.03257/0.09077, loss_spatial_dice_1: 0.13381/0.19317, loss_spatial_ce_1: 0.02240/0.07921, loss_grounding_bce_1: 0.03887/0.08046, loss_grounding_dice_1: 0.16145/0.15279, loss_grounding_ce_1: 0.03908/0.25542, loss_mask_ce_2: 0.43722/0.79809, loss_mask_bce_2: 0.09025/0.30220, loss_mask_dice_2: 0.40101/1.03768, loss_spatial_bce_2: 0.03696/0.09027, loss_spatial_dice_2: 0.15192/0.19303, loss_spatial_ce_2: 0.03358/0.08200, loss_grounding_bce_2: 0.03712/0.08018, loss_grounding_dice_2: 0.16551/0.15231, loss_grounding_ce_2: 0.06899/0.25730, loss_mask_ce_3: 0.41010/0.79684, loss_mask_bce_3: 0.09493/0.30367, loss_mask_dice_3: 0.38767/1.03339, loss_spatial_bce_3: 0.02712/0.09190, loss_spatial_dice_3: 0.12850/0.19336, loss_spatial_ce_3: 0.02509/0.08791, loss_grounding_bce_3: 0.04126/0.08073, loss_grounding_dice_3: 0.16352/0.15207, loss_grounding_ce_3: 0.09728/0.25567, loss_mask_ce_4: 0.39294/0.80377, loss_mask_bce_4: 0.08839/0.30571, loss_mask_dice_4: 0.37779/1.05220, loss_spatial_bce_4: 0.02761/0.09393, loss_spatial_dice_4: 0.12414/0.20072, loss_spatial_ce_4: 0.05824/0.09966, loss_grounding_bce_4: 0.03787/0.08149, loss_grounding_dice_4: 0.16255/0.15448, loss_grounding_ce_4: 0.05834/0.26414, loss_mask_ce_5: 0.39491/0.82551, loss_mask_bce_5: 0.09036/0.30784, loss_mask_dice_5: 0.38314/1.05891, loss_spatial_bce_5: 0.02486/0.09563, loss_spatial_dice_5: 0.10430/0.20283, loss_spatial_ce_5: 0.05320/0.11077, loss_grounding_bce_5: 0.04045/0.08192, loss_grounding_dice_5: 0.16056/0.15527, loss_grounding_ce_5: 0.05616/0.28417, loss_mask_ce_6: 0.35166/0.85050, loss_mask_bce_6: 0.09136/0.30925, loss_mask_dice_6: 0.40028/1.06220, loss_spatial_bce_6: 0.03490/0.10049, loss_spatial_dice_6: 0.12388/0.20519, loss_spatial_ce_6: 0.10796/0.13001, loss_grounding_bce_6: 0.04754/0.08316, loss_grounding_dice_6: 0.17247/0.15598, loss_grounding_ce_6: 0.08104/0.29511, loss_mask_ce_7: 0.33828/0.91398, loss_mask_bce_7: 0.10462/0.31641, loss_mask_dice_7: 0.42360/1.10871, loss_spatial_bce_7: 0.04356/0.11081, loss_spatial_dice_7: 0.15125/0.23003, loss_spatial_ce_7: 0.14650/0.17365, loss_grounding_bce_7: 0.04574/0.08478, loss_grounding_dice_7: 0.17330/0.16171, loss_grounding_ce_7: 0.02394/0.34103, loss_mask_ce_8: 0.52985/1.05084, loss_mask_bce_8: 0.09227/0.33394, loss_mask_dice_8: 0.41106/1.18849, loss_spatial_bce_8: 0.04727/0.13204, loss_spatial_dice_8: 0.18023/0.27068, loss_spatial_ce_8: 0.15785/0.23056, loss_grounding_bce_8: 0.03768/0.08868, loss_grounding_dice_8: 0.15220/0.17092, loss_grounding_ce_8: 0.00939/0.44272, loss_mask_ce_9: 3.38091/3.50546, loss_mask_bce_9: 0.10680/0.36046, loss_mask_dice_9: 0.64031/1.77453, loss_spatial_bce_9: 0.32583/0.35976, loss_spatial_dice_9: 0.84481/0.79750, loss_spatial_ce_9: 1.02883/1.41850, loss_grounding_bce_9: 0.12758/0.10063, loss_grounding_dice_9: 0.31890/0.24526, loss_grounding_ce_9: 0.04235/0.71297] items per batch[64] items per second[0.36] total items[1184000] mini batches[ 18500] memory[4967] epoch remaining[0:48:23] INFO:trainer.default_trainer:epochs[ 10] optim steps[18600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69112/0.78821, loss_mask_bce_0: 0.21778/0.30165, loss_mask_dice_0: 0.47497/1.03093, loss_spatial_bce_0: 0.25840/0.09031, loss_spatial_dice_0: 0.17126/0.19037, loss_spatial_ce_0: 0.03215/0.07437, loss_grounding_bce_0: 0.05000/0.08040, loss_grounding_dice_0: 0.05377/0.15191, loss_grounding_ce_0: 0.00027/0.25277, loss_mask_ce_1: 0.65344/0.79094, loss_mask_bce_1: 0.22747/0.30223, loss_mask_dice_1: 0.36919/1.03426, loss_spatial_bce_1: 0.19698/0.09076, loss_spatial_dice_1: 0.19118/0.19310, loss_spatial_ce_1: 0.07344/0.07914, loss_grounding_bce_1: 0.05082/0.08052, loss_grounding_dice_1: 0.05218/0.15282, loss_grounding_ce_1: 0.00022/0.25526, loss_mask_ce_2: 0.75033/0.79766, loss_mask_bce_2: 0.25734/0.30225, loss_mask_dice_2: 0.40313/1.03723, loss_spatial_bce_2: 0.17941/0.09026, loss_spatial_dice_2: 0.16563/0.19297, loss_spatial_ce_2: 0.11475/0.08189, loss_grounding_bce_2: 0.05525/0.08025, loss_grounding_dice_2: 0.05620/0.15232, loss_grounding_ce_2: 0.00167/0.25707, loss_mask_ce_3: 0.66793/0.79657, loss_mask_bce_3: 0.23721/0.30370, loss_mask_dice_3: 0.36373/1.03308, loss_spatial_bce_3: 0.20519/0.09190, loss_spatial_dice_3: 0.17024/0.19331, loss_spatial_ce_3: 0.19565/0.08781, loss_grounding_bce_3: 0.05195/0.08079, loss_grounding_dice_3: 0.05314/0.15211, loss_grounding_ce_3: 0.00060/0.25556, loss_mask_ce_4: 0.78773/0.80347, loss_mask_bce_4: 0.34409/0.30576, loss_mask_dice_4: 0.48720/1.05175, loss_spatial_bce_4: 0.18697/0.09392, loss_spatial_dice_4: 0.18294/0.20066, loss_spatial_ce_4: 0.10048/0.09960, loss_grounding_bce_4: 0.05561/0.08156, loss_grounding_dice_4: 0.05740/0.15447, loss_grounding_ce_4: 0.00030/0.26391, loss_mask_ce_5: 1.03664/0.82508, loss_mask_bce_5: 0.24994/0.30791, loss_mask_dice_5: 0.41410/1.05862, loss_spatial_bce_5: 0.21096/0.09564, loss_spatial_dice_5: 0.17774/0.20278, loss_spatial_ce_5: 0.11593/0.11069, loss_grounding_bce_5: 0.05167/0.08199, loss_grounding_dice_5: 0.05147/0.15528, loss_grounding_ce_5: 0.00679/0.28388, loss_mask_ce_6: 1.17118/0.85015, loss_mask_bce_6: 0.21642/0.30929, loss_mask_dice_6: 0.39158/1.06167, loss_spatial_bce_6: 0.22823/0.10050, loss_spatial_dice_6: 0.17292/0.20515, loss_spatial_ce_6: 0.11124/0.13001, loss_grounding_bce_6: 0.05177/0.08321, loss_grounding_dice_6: 0.05082/0.15598, loss_grounding_ce_6: 0.00024/0.29483, loss_mask_ce_7: 1.07501/0.91347, loss_mask_bce_7: 0.36144/0.31648, loss_mask_dice_7: 0.57575/1.10837, loss_spatial_bce_7: 0.22486/0.11081, loss_spatial_dice_7: 0.18863/0.22996, loss_spatial_ce_7: 0.10948/0.17357, loss_grounding_bce_7: 0.05077/0.08485, loss_grounding_dice_7: 0.04885/0.16175, loss_grounding_ce_7: 0.00023/0.34082, loss_mask_ce_8: 1.57577/1.05012, loss_mask_bce_8: 0.53746/0.33411, loss_mask_dice_8: 0.69161/1.18824, loss_spatial_bce_8: 0.24265/0.13202, loss_spatial_dice_8: 0.23168/0.27056, loss_spatial_ce_8: 0.21232/0.23037, loss_grounding_bce_8: 0.05588/0.08874, loss_grounding_dice_8: 0.04913/0.17095, loss_grounding_ce_8: 0.34199/0.44239, loss_mask_ce_9: 3.53818/3.50492, loss_mask_bce_9: 0.50253/0.36052, loss_mask_dice_9: 0.93765/1.77389, loss_spatial_bce_9: 0.36315/0.35973, loss_spatial_dice_9: 0.81339/0.79737, loss_spatial_ce_9: 1.05455/1.41822, loss_grounding_bce_9: 0.05992/0.10069, loss_grounding_dice_9: 0.06096/0.24524, loss_grounding_ce_9: 0.17744/0.71243] items per batch[64] items per second[0.36] total items[1190400] mini batches[ 18600] memory[4967] epoch remaining[0:44:55] INFO:trainer.default_trainer:epochs[ 10] optim steps[18700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05792/0.78822, loss_mask_bce_0: 0.29855/0.30162, loss_mask_dice_0: 0.16828/1.03174, loss_spatial_bce_0: 0.21250/0.09025, loss_spatial_dice_0: 0.15927/0.19034, loss_spatial_ce_0: 0.23358/0.07435, loss_grounding_bce_0: 0.22877/0.08037, loss_grounding_dice_0: 0.13604/0.15195, loss_grounding_ce_0: 0.02962/0.25308, loss_mask_ce_1: 0.05425/0.79105, loss_mask_bce_1: 0.32049/0.30218, loss_mask_dice_1: 0.17373/1.03515, loss_spatial_bce_1: 0.20392/0.09071, loss_spatial_dice_1: 0.14657/0.19309, loss_spatial_ce_1: 0.28784/0.07911, loss_grounding_bce_1: 0.24833/0.08050, loss_grounding_dice_1: 0.14080/0.15289, loss_grounding_ce_1: 0.02830/0.25552, loss_mask_ce_2: 0.06023/0.79784, loss_mask_bce_2: 0.31708/0.30219, loss_mask_dice_2: 0.17476/1.03814, loss_spatial_bce_2: 0.19955/0.09022, loss_spatial_dice_2: 0.16370/0.19297, loss_spatial_ce_2: 0.25280/0.08184, loss_grounding_bce_2: 0.24720/0.08023, loss_grounding_dice_2: 0.14312/0.15238, loss_grounding_ce_2: 0.03814/0.25744, loss_mask_ce_3: 0.05161/0.79669, loss_mask_bce_3: 0.32723/0.30367, loss_mask_dice_3: 0.17467/1.03395, loss_spatial_bce_3: 0.19068/0.09185, loss_spatial_dice_3: 0.15484/0.19333, loss_spatial_ce_3: 0.23832/0.08773, loss_grounding_bce_3: 0.25858/0.08077, loss_grounding_dice_3: 0.14507/0.15219, loss_grounding_ce_3: 0.03255/0.25584, loss_mask_ce_4: 0.04965/0.80349, loss_mask_bce_4: 0.34495/0.30574, loss_mask_dice_4: 0.18383/1.05265, loss_spatial_bce_4: 0.19422/0.09386, loss_spatial_dice_4: 0.15243/0.20065, loss_spatial_ce_4: 0.17913/0.09958, loss_grounding_bce_4: 0.27324/0.08154, loss_grounding_dice_4: 0.15264/0.15452, loss_grounding_ce_4: 0.01720/0.26425, loss_mask_ce_5: 0.10859/0.82518, loss_mask_bce_5: 0.32035/0.30794, loss_mask_dice_5: 0.26307/1.05952, loss_spatial_bce_5: 0.20810/0.09557, loss_spatial_dice_5: 0.18720/0.20276, loss_spatial_ce_5: 0.15361/0.11072, loss_grounding_bce_5: 0.24434/0.08197, loss_grounding_dice_5: 0.14790/0.15534, loss_grounding_ce_5: 0.03375/0.28412, loss_mask_ce_6: 0.17161/0.85026, loss_mask_bce_6: 0.30516/0.30934, loss_mask_dice_6: 0.17119/1.06260, loss_spatial_bce_6: 0.20244/0.10043, loss_spatial_dice_6: 0.17362/0.20513, loss_spatial_ce_6: 0.17829/0.13007, loss_grounding_bce_6: 0.25012/0.08320, loss_grounding_dice_6: 0.15057/0.15601, loss_grounding_ce_6: 0.02321/0.29510, loss_mask_ce_7: 0.08128/0.91340, loss_mask_bce_7: 0.39996/0.31653, loss_mask_dice_7: 0.33800/1.10925, loss_spatial_bce_7: 0.20159/0.11073, loss_spatial_dice_7: 0.12717/0.22991, loss_spatial_ce_7: 0.52828/0.17359, loss_grounding_bce_7: 0.31605/0.08483, loss_grounding_dice_7: 0.25480/0.16179, loss_grounding_ce_7: 0.01626/0.34083, loss_mask_ce_8: 0.10414/1.04995, loss_mask_bce_8: 0.43310/0.33416, loss_mask_dice_8: 0.23123/1.18934, loss_spatial_bce_8: 0.30517/0.13198, loss_spatial_dice_8: 0.17045/0.27046, loss_spatial_ce_8: 0.49078/0.23024, loss_grounding_bce_8: 0.35878/0.08874, loss_grounding_dice_8: 0.19601/0.17099, loss_grounding_ce_8: 0.02860/0.44254, loss_mask_ce_9: 3.45810/3.50473, loss_mask_bce_9: 0.43728/0.36058, loss_mask_dice_9: 0.26208/1.77506, loss_spatial_bce_9: 0.64244/0.35956, loss_spatial_dice_9: 0.57470/0.79730, loss_spatial_ce_9: 0.63541/1.41795, loss_grounding_bce_9: 0.43438/0.10069, loss_grounding_dice_9: 0.26449/0.24527, loss_grounding_ce_9: 0.60749/0.71253] items per batch[64] items per second[0.37] total items[1196800] mini batches[ 18700] memory[4967] epoch remaining[0:41:33] INFO:trainer.default_trainer:epochs[ 10] optim steps[18800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61347/0.78815, loss_mask_bce_0: 0.13085/0.30179, loss_mask_dice_0: 0.14078/1.03156, loss_spatial_bce_0: 0.07681/0.09017, loss_spatial_dice_0: 0.08258/0.19028, loss_spatial_ce_0: 0.10725/0.07426, loss_grounding_bce_0: 0.07826/0.08035, loss_grounding_dice_0: 0.05253/0.15192, loss_grounding_ce_0: 0.00243/0.25360, loss_mask_ce_1: 0.61911/0.79114, loss_mask_bce_1: 0.13893/0.30235, loss_mask_dice_1: 0.15493/1.03498, loss_spatial_bce_1: 0.07851/0.09063, loss_spatial_dice_1: 0.08805/0.19300, loss_spatial_ce_1: 0.11742/0.07897, loss_grounding_bce_1: 0.07840/0.08050, loss_grounding_dice_1: 0.05451/0.15287, loss_grounding_ce_1: 0.00512/0.25612, loss_mask_ce_2: 0.70564/0.79795, loss_mask_bce_2: 0.13303/0.30234, loss_mask_dice_2: 0.14519/1.03791, loss_spatial_bce_2: 0.07918/0.09014, loss_spatial_dice_2: 0.08410/0.19290, loss_spatial_ce_2: 0.07305/0.08174, loss_grounding_bce_2: 0.07876/0.08021, loss_grounding_dice_2: 0.05714/0.15235, loss_grounding_ce_2: 0.00804/0.25804, loss_mask_ce_3: 0.73987/0.79672, loss_mask_bce_3: 0.13883/0.30386, loss_mask_dice_3: 0.14753/1.03381, loss_spatial_bce_3: 0.07756/0.09178, loss_spatial_dice_3: 0.08677/0.19327, loss_spatial_ce_3: 0.07600/0.08760, loss_grounding_bce_3: 0.07905/0.08076, loss_grounding_dice_3: 0.05561/0.15217, loss_grounding_ce_3: 0.01010/0.25638, loss_mask_ce_4: 0.65598/0.80351, loss_mask_bce_4: 0.13387/0.30594, loss_mask_dice_4: 0.15818/1.05241, loss_spatial_bce_4: 0.07749/0.09378, loss_spatial_dice_4: 0.08754/0.20058, loss_spatial_ce_4: 0.10162/0.09943, loss_grounding_bce_4: 0.08253/0.08153, loss_grounding_dice_4: 0.05999/0.15451, loss_grounding_ce_4: 0.00599/0.26473, loss_mask_ce_5: 0.61507/0.82552, loss_mask_bce_5: 0.13571/0.30817, loss_mask_dice_5: 0.14619/1.05920, loss_spatial_bce_5: 0.08482/0.09550, loss_spatial_dice_5: 0.09423/0.20273, loss_spatial_ce_5: 0.12038/0.11054, loss_grounding_bce_5: 0.07990/0.08197, loss_grounding_dice_5: 0.05556/0.15532, loss_grounding_ce_5: 0.00530/0.28433, loss_mask_ce_6: 0.63616/0.85045, loss_mask_bce_6: 0.13501/0.30954, loss_mask_dice_6: 0.14337/1.06242, loss_spatial_bce_6: 0.08947/0.10035, loss_spatial_dice_6: 0.10482/0.20508, loss_spatial_ce_6: 0.12548/0.13001, loss_grounding_bce_6: 0.07704/0.08319, loss_grounding_dice_6: 0.05355/0.15598, loss_grounding_ce_6: 0.00858/0.29533, loss_mask_ce_7: 0.59198/0.91354, loss_mask_bce_7: 0.13194/0.31669, loss_mask_dice_7: 0.13657/1.10895, loss_spatial_bce_7: 0.08134/0.11066, loss_spatial_dice_7: 0.11064/0.22988, loss_spatial_ce_7: 0.14809/0.17344, loss_grounding_bce_7: 0.07699/0.08481, loss_grounding_dice_7: 0.05086/0.16175, loss_grounding_ce_7: 0.01599/0.34088, loss_mask_ce_8: 0.55784/1.04989, loss_mask_bce_8: 0.15618/0.33433, loss_mask_dice_8: 0.21639/1.18909, loss_spatial_bce_8: 0.07062/0.13188, loss_spatial_dice_8: 0.07327/0.27039, loss_spatial_ce_8: 0.24659/0.23008, loss_grounding_bce_8: 0.08211/0.08873, loss_grounding_dice_8: 0.05798/0.17098, loss_grounding_ce_8: 0.02077/0.44254, loss_mask_ce_9: 2.80360/3.50511, loss_mask_bce_9: 0.23592/0.36073, loss_mask_dice_9: 0.28658/1.77522, loss_spatial_bce_9: 0.52469/0.35940, loss_spatial_dice_9: 0.64035/0.79738, loss_spatial_ce_9: 1.02374/1.41784, loss_grounding_bce_9: 0.08772/0.10069, loss_grounding_dice_9: 0.05072/0.24535, loss_grounding_ce_9: 0.08752/0.71189] items per batch[64] items per second[0.36] total items[1203200] mini batches[ 18800] memory[4967] epoch remaining[0:38:38] INFO:trainer.default_trainer:epochs[ 10] optim steps[18900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.23010/0.78802, loss_mask_bce_0: 0.03647/0.30152, loss_mask_dice_0: 0.09617/1.03164, loss_spatial_bce_0: 0.02702/0.09007, loss_spatial_dice_0: 0.06708/0.19024, loss_spatial_ce_0: 0.00118/0.07416, loss_grounding_bce_0: 0.01390/0.08026, loss_grounding_dice_0: 0.08648/0.15184, loss_grounding_ce_0: 0.00117/0.25383, loss_mask_ce_1: 0.19934/0.79099, loss_mask_bce_1: 0.04202/0.30208, loss_mask_dice_1: 0.10605/1.03510, loss_spatial_bce_1: 0.02616/0.09054, loss_spatial_dice_1: 0.08164/0.19296, loss_spatial_ce_1: 0.00096/0.07881, loss_grounding_bce_1: 0.01525/0.08040, loss_grounding_dice_1: 0.07935/0.15278, loss_grounding_ce_1: 0.00082/0.25622, loss_mask_ce_2: 0.24274/0.79783, loss_mask_bce_2: 0.03729/0.30208, loss_mask_dice_2: 0.10043/1.03793, loss_spatial_bce_2: 0.02614/0.09005, loss_spatial_dice_2: 0.07612/0.19287, loss_spatial_ce_2: 0.00106/0.08156, loss_grounding_bce_2: 0.01318/0.08012, loss_grounding_dice_2: 0.07953/0.15227, loss_grounding_ce_2: 0.00122/0.25822, loss_mask_ce_3: 0.19974/0.79656, loss_mask_bce_3: 0.04214/0.30359, loss_mask_dice_3: 0.12010/1.03388, loss_spatial_bce_3: 0.02631/0.09169, loss_spatial_dice_3: 0.05679/0.19324, loss_spatial_ce_3: 0.00186/0.08742, loss_grounding_bce_3: 0.01415/0.08066, loss_grounding_dice_3: 0.08253/0.15210, loss_grounding_ce_3: 0.00074/0.25644, loss_mask_ce_4: 0.17457/0.80334, loss_mask_bce_4: 0.04229/0.30568, loss_mask_dice_4: 0.11874/1.05245, loss_spatial_bce_4: 0.02602/0.09369, loss_spatial_dice_4: 0.06584/0.20057, loss_spatial_ce_4: 0.00720/0.09927, loss_grounding_bce_4: 0.01861/0.08143, loss_grounding_dice_4: 0.09989/0.15444, loss_grounding_ce_4: 0.00046/0.26482, loss_mask_ce_5: 0.14966/0.82522, loss_mask_bce_5: 0.03550/0.30791, loss_mask_dice_5: 0.12338/1.05927, loss_spatial_bce_5: 0.02881/0.09540, loss_spatial_dice_5: 0.06820/0.20269, loss_spatial_ce_5: 0.03736/0.11044, loss_grounding_bce_5: 0.02160/0.08187, loss_grounding_dice_5: 0.10452/0.15523, loss_grounding_ce_5: 0.00043/0.28431, loss_mask_ce_6: 0.14804/0.85034, loss_mask_bce_6: 0.03963/0.30926, loss_mask_dice_6: 0.10183/1.06245, loss_spatial_bce_6: 0.02615/0.10024, loss_spatial_dice_6: 0.06905/0.20503, loss_spatial_ce_6: 0.08071/0.12996, loss_grounding_bce_6: 0.01595/0.08307, loss_grounding_dice_6: 0.07686/0.15589, loss_grounding_ce_6: 0.00053/0.29535, loss_mask_ce_7: 0.15366/0.91337, loss_mask_bce_7: 0.04010/0.31644, loss_mask_dice_7: 0.11414/1.10896, loss_spatial_bce_7: 0.03196/0.11058, loss_spatial_dice_7: 0.07107/0.22984, loss_spatial_ce_7: 0.07598/0.17333, loss_grounding_bce_7: 0.01402/0.08470, loss_grounding_dice_7: 0.08725/0.16164, loss_grounding_ce_7: 0.00077/0.34102, loss_mask_ce_8: 0.19948/1.04983, loss_mask_bce_8: 0.03942/0.33405, loss_mask_dice_8: 0.10926/1.18903, loss_spatial_bce_8: 0.03333/0.13180, loss_spatial_dice_8: 0.08415/0.27040, loss_spatial_ce_8: 0.13859/0.22992, loss_grounding_bce_8: 0.01316/0.08860, loss_grounding_dice_8: 0.08617/0.17087, loss_grounding_ce_8: 0.00391/0.44281, loss_mask_ce_9: 1.77395/3.50446, loss_mask_bce_9: 0.03245/0.36043, loss_mask_dice_9: 0.10853/1.77511, loss_spatial_bce_9: 0.06094/0.35935, loss_spatial_dice_9: 0.29959/0.79733, loss_spatial_ce_9: 0.32652/1.41786, loss_grounding_bce_9: 0.01447/0.10055, loss_grounding_dice_9: 0.10369/0.24517, loss_grounding_ce_9: 0.14918/0.71188] items per batch[64] items per second[0.36] total items[1209600] mini batches[ 18900] memory[4967] epoch remaining[0:35:41] INFO:trainer.default_trainer:epochs[ 10] optim steps[19000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92314/0.78786, loss_mask_bce_0: 0.24595/0.30165, loss_mask_dice_0: 3.13523/1.03123, loss_spatial_bce_0: 0.01255/0.09009, loss_spatial_dice_0: 0.35207/0.19024, loss_spatial_ce_0: 0.00657/0.07405, loss_grounding_bce_0: 0.01014/0.08034, loss_grounding_dice_0: 0.06608/0.15183, loss_grounding_ce_0: 0.02165/0.25366, loss_mask_ce_1: 0.45679/0.79094, loss_mask_bce_1: 0.21196/0.30222, loss_mask_dice_1: 2.96774/1.03457, loss_spatial_bce_1: 0.01252/0.09054, loss_spatial_dice_1: 0.33672/0.19294, loss_spatial_ce_1: 0.01355/0.07870, loss_grounding_bce_1: 0.00885/0.08049, loss_grounding_dice_1: 0.06954/0.15278, loss_grounding_ce_1: 0.04351/0.25604, loss_mask_ce_2: 0.49315/0.79775, loss_mask_bce_2: 0.25084/0.30222, loss_mask_dice_2: 2.86595/1.03752, loss_spatial_bce_2: 0.01291/0.09007, loss_spatial_dice_2: 0.35165/0.19286, loss_spatial_ce_2: 0.01387/0.08141, loss_grounding_bce_2: 0.00838/0.08019, loss_grounding_dice_2: 0.06573/0.15224, loss_grounding_ce_2: 0.02400/0.25814, loss_mask_ce_3: 0.38973/0.79659, loss_mask_bce_3: 0.24001/0.30373, loss_mask_dice_3: 2.78028/1.03340, loss_spatial_bce_3: 0.01285/0.09171, loss_spatial_dice_3: 0.35602/0.19324, loss_spatial_ce_3: 0.02818/0.08729, loss_grounding_bce_3: 0.01013/0.08073, loss_grounding_dice_3: 0.07848/0.15207, loss_grounding_ce_3: 0.04510/0.25629, loss_mask_ce_4: 0.30503/0.80318, loss_mask_bce_4: 0.26468/0.30582, loss_mask_dice_4: 2.94725/1.05210, loss_spatial_bce_4: 0.01095/0.09368, loss_spatial_dice_4: 0.37472/0.20054, loss_spatial_ce_4: 0.04531/0.09915, loss_grounding_bce_4: 0.00951/0.08149, loss_grounding_dice_4: 0.07788/0.15444, loss_grounding_ce_4: 0.06113/0.26455, loss_mask_ce_5: 0.56520/0.82515, loss_mask_bce_5: 0.26134/0.30806, loss_mask_dice_5: 3.21816/1.05896, loss_spatial_bce_5: 0.01224/0.09541, loss_spatial_dice_5: 0.36194/0.20267, loss_spatial_ce_5: 0.63838/0.11035, loss_grounding_bce_5: 0.00888/0.08196, loss_grounding_dice_5: 0.07332/0.15520, loss_grounding_ce_5: 0.01304/0.28441, loss_mask_ce_6: 0.77344/0.85042, loss_mask_bce_6: 0.23119/0.30939, loss_mask_dice_6: 2.83611/1.06205, loss_spatial_bce_6: 0.01750/0.10025, loss_spatial_dice_6: 0.37279/0.20503, loss_spatial_ce_6: 0.19181/0.12985, loss_grounding_bce_6: 0.00908/0.08315, loss_grounding_dice_6: 0.06970/0.15586, loss_grounding_ce_6: 0.02552/0.29526, loss_mask_ce_7: 0.70953/0.91337, loss_mask_bce_7: 0.24448/0.31659, loss_mask_dice_7: 2.94929/1.10853, loss_spatial_bce_7: 0.01110/0.11057, loss_spatial_dice_7: 0.39655/0.22979, loss_spatial_ce_7: 0.13293/0.17313, loss_grounding_bce_7: 0.00928/0.08475, loss_grounding_dice_7: 0.08639/0.16162, loss_grounding_ce_7: 0.01458/0.34088, loss_mask_ce_8: 0.61686/1.04944, loss_mask_bce_8: 0.28193/0.33417, loss_mask_dice_8: 3.73784/1.18842, loss_spatial_bce_8: 0.02178/0.13179, loss_spatial_dice_8: 0.49837/0.27030, loss_spatial_ce_8: 0.11917/0.22965, loss_grounding_bce_8: 0.01081/0.08867, loss_grounding_dice_8: 0.08065/0.17086, loss_grounding_ce_8: 1.24345/0.44234, loss_mask_ce_9: 3.78386/3.50445, loss_mask_bce_9: 0.19907/0.36051, loss_mask_dice_9: 3.92821/1.77468, loss_spatial_bce_9: 0.06200/0.35931, loss_spatial_dice_9: 0.93413/0.79733, loss_spatial_ce_9: 1.58791/1.41772, loss_grounding_bce_9: 0.00834/0.10058, loss_grounding_dice_9: 0.11068/0.24509, loss_grounding_ce_9: 1.87950/0.71151] items per batch[64] items per second[0.36] total items[1216000] mini batches[ 19000] memory[4967] epoch remaining[0:32:40] INFO:trainer.default_trainer:epochs[ 10] optim steps[19100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72881/0.78780, loss_mask_bce_0: 0.51422/0.30160, loss_mask_dice_0: 1.16574/1.03113, loss_spatial_bce_0: 0.08919/0.09002, loss_spatial_dice_0: 0.38333/0.19018, loss_spatial_ce_0: 0.02778/0.07399, loss_grounding_bce_0: 0.16601/0.08037, loss_grounding_dice_0: 0.10290/0.15186, loss_grounding_ce_0: 0.00154/0.25360, loss_mask_ce_1: 1.76661/0.79088, loss_mask_bce_1: 0.52517/0.30215, loss_mask_dice_1: 1.27613/1.03454, loss_spatial_bce_1: 0.07871/0.09049, loss_spatial_dice_1: 0.37636/0.19288, loss_spatial_ce_1: 0.02581/0.07859, loss_grounding_bce_1: 0.15756/0.08052, loss_grounding_dice_1: 0.09954/0.15277, loss_grounding_ce_1: 0.00138/0.25596, loss_mask_ce_2: 1.80960/0.79771, loss_mask_bce_2: 0.49484/0.30214, loss_mask_dice_2: 1.17109/1.03745, loss_spatial_bce_2: 0.06919/0.09001, loss_spatial_dice_2: 0.35143/0.19279, loss_spatial_ce_2: 0.01536/0.08136, loss_grounding_bce_2: 0.15930/0.08023, loss_grounding_dice_2: 0.09094/0.15226, loss_grounding_ce_2: 0.00198/0.25801, loss_mask_ce_3: 1.85541/0.79659, loss_mask_bce_3: 0.50515/0.30367, loss_mask_dice_3: 0.93293/1.03337, loss_spatial_bce_3: 0.07186/0.09164, loss_spatial_dice_3: 0.36294/0.19315, loss_spatial_ce_3: 0.01775/0.08717, loss_grounding_bce_3: 0.15706/0.08079, loss_grounding_dice_3: 0.08317/0.15210, loss_grounding_ce_3: 0.00200/0.25621, loss_mask_ce_4: 1.84779/0.80313, loss_mask_bce_4: 0.50585/0.30575, loss_mask_dice_4: 1.16116/1.05208, loss_spatial_bce_4: 0.07129/0.09362, loss_spatial_dice_4: 0.33248/0.20047, loss_spatial_ce_4: 0.11983/0.09905, loss_grounding_bce_4: 0.15834/0.08154, loss_grounding_dice_4: 0.09333/0.15448, loss_grounding_ce_4: 0.00119/0.26437, loss_mask_ce_5: 1.91054/0.82524, loss_mask_bce_5: 0.50585/0.30799, loss_mask_dice_5: 1.26587/1.05903, loss_spatial_bce_5: 0.07859/0.09534, loss_spatial_dice_5: 0.35051/0.20262, loss_spatial_ce_5: 0.06669/0.11024, loss_grounding_bce_5: 0.15135/0.08201, loss_grounding_dice_5: 0.08393/0.15522, loss_grounding_ce_5: 0.00133/0.28430, loss_mask_ce_6: 1.73798/0.85042, loss_mask_bce_6: 0.53023/0.30930, loss_mask_dice_6: 1.25884/1.06192, loss_spatial_bce_6: 0.06673/0.10018, loss_spatial_dice_6: 0.35351/0.20498, loss_spatial_ce_6: 0.05757/0.12979, loss_grounding_bce_6: 0.17007/0.08318, loss_grounding_dice_6: 0.09047/0.15586, loss_grounding_ce_6: 0.00082/0.29502, loss_mask_ce_7: 1.89826/0.91338, loss_mask_bce_7: 0.57716/0.31655, loss_mask_dice_7: 1.04778/1.10851, loss_spatial_bce_7: 0.06025/0.11048, loss_spatial_dice_7: 0.38650/0.22972, loss_spatial_ce_7: 0.30713/0.17300, loss_grounding_bce_7: 0.16293/0.08482, loss_grounding_dice_7: 0.08847/0.16165, loss_grounding_ce_7: 0.11726/0.34052, loss_mask_ce_8: 2.14675/1.04959, loss_mask_bce_8: 0.67661/0.33416, loss_mask_dice_8: 1.07622/1.18839, loss_spatial_bce_8: 0.07705/0.13171, loss_spatial_dice_8: 0.37474/0.27020, loss_spatial_ce_8: 0.47439/0.22950, loss_grounding_bce_8: 0.16307/0.08871, loss_grounding_dice_8: 0.07972/0.17090, loss_grounding_ce_8: 0.53932/0.44221, loss_mask_ce_9: 3.46841/3.50450, loss_mask_bce_9: 0.84421/0.36045, loss_mask_dice_9: 2.12622/1.77459, loss_spatial_bce_9: 0.31140/0.35923, loss_spatial_dice_9: 0.82473/0.79730, loss_spatial_ce_9: 2.35136/1.41740, loss_grounding_bce_9: 0.19360/0.10060, loss_grounding_dice_9: 0.09636/0.24516, loss_grounding_ce_9: 1.03087/0.71106] items per batch[64] items per second[0.37] total items[1222400] mini batches[ 19100] memory[4967] epoch remaining[0:29:37] INFO:trainer.default_trainer:epochs[ 10] optim steps[19200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.69937/0.78744, loss_mask_bce_0: 0.17121/0.30167, loss_mask_dice_0: 1.75695/1.03118, loss_spatial_bce_0: 0.00280/0.08998, loss_spatial_dice_0: 0.06831/0.19005, loss_spatial_ce_0: 0.00126/0.07383, loss_grounding_bce_0: 0.00927/0.08039, loss_grounding_dice_0: 0.16022/0.15186, loss_grounding_ce_0: 0.47373/0.25358, loss_mask_ce_1: 1.87149/0.79045, loss_mask_bce_1: 0.15536/0.30221, loss_mask_dice_1: 1.43423/1.03450, loss_spatial_bce_1: 0.00335/0.09045, loss_spatial_dice_1: 0.06594/0.19274, loss_spatial_ce_1: 0.00232/0.07848, loss_grounding_bce_1: 0.01304/0.08053, loss_grounding_dice_1: 0.19490/0.15278, loss_grounding_ce_1: 0.40340/0.25592, loss_mask_ce_2: 1.64268/0.79737, loss_mask_bce_2: 0.15795/0.30221, loss_mask_dice_2: 1.51453/1.03742, loss_spatial_bce_2: 0.00393/0.08997, loss_spatial_dice_2: 0.07992/0.19267, loss_spatial_ce_2: 0.00146/0.08118, loss_grounding_bce_2: 0.01425/0.08026, loss_grounding_dice_2: 0.20322/0.15223, loss_grounding_ce_2: 0.40996/0.25802, loss_mask_ce_3: 1.78490/0.79642, loss_mask_bce_3: 0.16187/0.30374, loss_mask_dice_3: 1.50238/1.03329, loss_spatial_bce_3: 0.00318/0.09160, loss_spatial_dice_3: 0.07009/0.19303, loss_spatial_ce_3: 0.00665/0.08704, loss_grounding_bce_3: 0.01625/0.08082, loss_grounding_dice_3: 0.20360/0.15209, loss_grounding_ce_3: 0.43008/0.25631, loss_mask_ce_4: 1.53101/0.80296, loss_mask_bce_4: 0.17756/0.30582, loss_mask_dice_4: 1.74042/1.05204, loss_spatial_bce_4: 0.00304/0.09359, loss_spatial_dice_4: 0.06514/0.20035, loss_spatial_ce_4: 0.03011/0.09889, loss_grounding_bce_4: 0.02740/0.08156, loss_grounding_dice_4: 0.22073/0.15446, loss_grounding_ce_4: 0.44684/0.26437, loss_mask_ce_5: 1.63589/0.82504, loss_mask_bce_5: 0.19096/0.30807, loss_mask_dice_5: 1.69183/1.05901, loss_spatial_bce_5: 0.00309/0.09532, loss_spatial_dice_5: 0.06756/0.20250, loss_spatial_ce_5: 0.04045/0.11006, loss_grounding_bce_5: 0.02537/0.08204, loss_grounding_dice_5: 0.25638/0.15522, loss_grounding_ce_5: 0.50436/0.28422, loss_mask_ce_6: 1.39045/0.85009, loss_mask_bce_6: 0.20405/0.30940, loss_mask_dice_6: 1.81130/1.06182, loss_spatial_bce_6: 0.00307/0.10018, loss_spatial_dice_6: 0.07939/0.20487, loss_spatial_ce_6: 0.09312/0.12966, loss_grounding_bce_6: 0.02563/0.08320, loss_grounding_dice_6: 0.26250/0.15584, loss_grounding_ce_6: 0.37329/0.29494, loss_mask_ce_7: 1.67466/0.91300, loss_mask_bce_7: 0.22292/0.31662, loss_mask_dice_7: 1.84560/1.10858, loss_spatial_bce_7: 0.00330/0.11046, loss_spatial_dice_7: 0.07868/0.22958, loss_spatial_ce_7: 0.03327/0.17290, loss_grounding_bce_7: 0.05120/0.08484, loss_grounding_dice_7: 0.42369/0.16167, loss_grounding_ce_7: 0.37793/0.34036, loss_mask_ce_8: 1.33810/1.04915, loss_mask_bce_8: 0.38359/0.33425, loss_mask_dice_8: 2.13340/1.18848, loss_spatial_bce_8: 0.00493/0.13162, loss_spatial_dice_8: 0.11940/0.27003, loss_spatial_ce_8: 0.06298/0.22935, loss_grounding_bce_8: 0.10096/0.08876, loss_grounding_dice_8: 0.45356/0.17092, loss_grounding_ce_8: 0.54514/0.44220, loss_mask_ce_9: 6.86886/3.50447, loss_mask_bce_9: 0.25917/0.36057, loss_mask_dice_9: 4.34432/1.77514, loss_spatial_bce_9: 0.05873/0.35923, loss_spatial_dice_9: 0.87858/0.79734, loss_spatial_ce_9: 1.43339/1.41767, loss_grounding_bce_9: 0.05561/0.10063, loss_grounding_dice_9: 0.62804/0.24516, loss_grounding_ce_9: 0.64318/0.71109] items per batch[64] items per second[0.36] total items[1228800] mini batches[ 19200] memory[4967] epoch remaining[0:26:39] INFO:trainer.default_trainer:epochs[ 10] optim steps[19300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08730/0.78746, loss_mask_bce_0: 0.41714/0.30175, loss_mask_dice_0: 4.22093/1.03146, loss_spatial_bce_0: 0.06776/0.08999, loss_spatial_dice_0: 0.26367/0.19006, loss_spatial_ce_0: 0.03405/0.07377, loss_grounding_bce_0: 0.00441/0.08040, loss_grounding_dice_0: 0.51431/0.15193, loss_grounding_ce_0: 0.76931/0.25376, loss_mask_ce_1: 1.17202/0.79038, loss_mask_bce_1: 0.42309/0.30229, loss_mask_dice_1: 4.40341/1.03484, loss_spatial_bce_1: 0.06828/0.09045, loss_spatial_dice_1: 0.24331/0.19275, loss_spatial_ce_1: 0.03681/0.07838, loss_grounding_bce_1: 0.00333/0.08056, loss_grounding_dice_1: 0.51498/0.15287, loss_grounding_ce_1: 0.70636/0.25613, loss_mask_ce_2: 1.05377/0.79736, loss_mask_bce_2: 0.41740/0.30228, loss_mask_dice_2: 4.00322/1.03783, loss_spatial_bce_2: 0.07198/0.08998, loss_spatial_dice_2: 0.26482/0.19267, loss_spatial_ce_2: 0.03979/0.08107, loss_grounding_bce_2: 0.00415/0.08028, loss_grounding_dice_2: 0.57190/0.15232, loss_grounding_ce_2: 0.69747/0.25826, loss_mask_ce_3: 1.18994/0.79639, loss_mask_bce_3: 0.43644/0.30382, loss_mask_dice_3: 4.01486/1.03365, loss_spatial_bce_3: 0.07246/0.09160, loss_spatial_dice_3: 0.27337/0.19304, loss_spatial_ce_3: 0.05818/0.08692, loss_grounding_bce_3: 0.00291/0.08083, loss_grounding_dice_3: 0.45303/0.15218, loss_grounding_ce_3: 0.74200/0.25650, loss_mask_ce_4: 1.38679/0.80297, loss_mask_bce_4: 0.45256/0.30591, loss_mask_dice_4: 3.80442/1.05254, loss_spatial_bce_4: 0.07324/0.09361, loss_spatial_dice_4: 0.27077/0.20036, loss_spatial_ce_4: 0.08370/0.09881, loss_grounding_bce_4: 0.00337/0.08159, loss_grounding_dice_4: 0.57474/0.15451, loss_grounding_ce_4: 0.67161/0.26464, loss_mask_ce_5: 1.56195/0.82519, loss_mask_bce_5: 0.41973/0.30818, loss_mask_dice_5: 4.48961/1.05953, loss_spatial_bce_5: 0.07711/0.09536, loss_spatial_dice_5: 0.19771/0.20254, loss_spatial_ce_5: 0.10960/0.11006, loss_grounding_bce_5: 0.00287/0.08206, loss_grounding_dice_5: 0.52884/0.15528, loss_grounding_ce_5: 0.73534/0.28432, loss_mask_ce_6: 1.56813/0.85055, loss_mask_bce_6: 0.43212/0.30949, loss_mask_dice_6: 4.90046/1.06246, loss_spatial_bce_6: 0.08077/0.10018, loss_spatial_dice_6: 0.24577/0.20489, loss_spatial_ce_6: 0.07146/0.12965, loss_grounding_bce_6: 0.00355/0.08320, loss_grounding_dice_6: 0.54504/0.15587, loss_grounding_ce_6: 0.63020/0.29504, loss_mask_ce_7: 1.00105/0.91294, loss_mask_bce_7: 0.48239/0.31674, loss_mask_dice_7: 4.22240/1.10917, loss_spatial_bce_7: 0.09359/0.11044, loss_spatial_dice_7: 0.32796/0.22957, loss_spatial_ce_7: 0.16115/0.17293, loss_grounding_bce_7: 0.00311/0.08484, loss_grounding_dice_7: 0.48524/0.16172, loss_grounding_ce_7: 0.65110/0.34040, loss_mask_ce_8: 1.16943/1.04925, loss_mask_bce_8: 0.54583/0.33442, loss_mask_dice_8: 4.03869/1.18898, loss_spatial_bce_8: 0.09542/0.13163, loss_spatial_dice_8: 0.39489/0.27001, loss_spatial_ce_8: 0.13235/0.22937, loss_grounding_bce_8: 0.00580/0.08877, loss_grounding_dice_8: 0.56707/0.17097, loss_grounding_ce_8: 0.74352/0.44216, loss_mask_ce_9: 3.39414/3.50490, loss_mask_bce_9: 0.51478/0.36071, loss_mask_dice_9: 5.38177/1.77542, loss_spatial_bce_9: 0.21390/0.35927, loss_spatial_dice_9: 0.94528/0.79731, loss_spatial_ce_9: 2.10285/1.41758, loss_grounding_bce_9: 0.00215/0.10063, loss_grounding_dice_9: 0.60768/0.24519, loss_grounding_ce_9: 0.74467/0.71103] items per batch[64] items per second[0.37] total items[1235200] mini batches[ 19300] memory[4967] epoch remaining[0:23:37] INFO:trainer.default_trainer:epochs[ 10] optim steps[19400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02797/0.78727, loss_mask_bce_0: 0.05613/0.30162, loss_mask_dice_0: 0.13278/1.03091, loss_spatial_bce_0: 0.03157/0.08996, loss_spatial_dice_0: 0.07949/0.19002, loss_spatial_ce_0: 0.00005/0.07371, loss_grounding_bce_0: 0.03436/0.08041, loss_grounding_dice_0: 0.07235/0.15195, loss_grounding_ce_0: 0.00230/0.25350, loss_mask_ce_1: 0.02779/0.79003, loss_mask_bce_1: 0.05846/0.30217, loss_mask_dice_1: 0.14946/1.03423, loss_spatial_bce_1: 0.02990/0.09042, loss_spatial_dice_1: 0.07099/0.19270, loss_spatial_ce_1: 0.00004/0.07833, loss_grounding_bce_1: 0.03667/0.08057, loss_grounding_dice_1: 0.05960/0.15287, loss_grounding_ce_1: 0.00177/0.25584, loss_mask_ce_2: 0.03994/0.79702, loss_mask_bce_2: 0.05821/0.30216, loss_mask_dice_2: 0.14855/1.03735, loss_spatial_bce_2: 0.03267/0.08996, loss_spatial_dice_2: 0.07407/0.19263, loss_spatial_ce_2: 0.00004/0.08100, loss_grounding_bce_2: 0.03530/0.08028, loss_grounding_dice_2: 0.06206/0.15233, loss_grounding_ce_2: 0.00198/0.25797, loss_mask_ce_3: 0.03121/0.79606, loss_mask_bce_3: 0.05601/0.30370, loss_mask_dice_3: 0.17748/1.03303, loss_spatial_bce_3: 0.03358/0.09156, loss_spatial_dice_3: 0.08089/0.19299, loss_spatial_ce_3: 0.00031/0.08683, loss_grounding_bce_3: 0.03416/0.08084, loss_grounding_dice_3: 0.05969/0.15216, loss_grounding_ce_3: 0.00225/0.25624, loss_mask_ce_4: 0.02900/0.80256, loss_mask_bce_4: 0.06265/0.30581, loss_mask_dice_4: 0.17333/1.05212, loss_spatial_bce_4: 0.03527/0.09357, loss_spatial_dice_4: 0.08542/0.20031, loss_spatial_ce_4: 0.00038/0.09873, loss_grounding_bce_4: 0.03566/0.08159, loss_grounding_dice_4: 0.06672/0.15451, loss_grounding_ce_4: 0.00151/0.26424, loss_mask_ce_5: 0.03860/0.82492, loss_mask_bce_5: 0.05990/0.30808, loss_mask_dice_5: 0.15944/1.05903, loss_spatial_bce_5: 0.03502/0.09533, loss_spatial_dice_5: 0.08957/0.20248, loss_spatial_ce_5: 0.00013/0.11004, loss_grounding_bce_5: 0.03811/0.08207, loss_grounding_dice_5: 0.08111/0.15531, loss_grounding_ce_5: 0.00095/0.28393, loss_mask_ce_6: 0.03897/0.85021, loss_mask_bce_6: 0.06099/0.30939, loss_mask_dice_6: 0.14215/1.06195, loss_spatial_bce_6: 0.03187/0.10014, loss_spatial_dice_6: 0.07819/0.20485, loss_spatial_ce_6: 0.00960/0.12959, loss_grounding_bce_6: 0.03763/0.08321, loss_grounding_dice_6: 0.05846/0.15588, loss_grounding_ce_6: 0.00115/0.29471, loss_mask_ce_7: 0.04385/0.91274, loss_mask_bce_7: 0.06308/0.31662, loss_mask_dice_7: 0.15859/1.10861, loss_spatial_bce_7: 0.03639/0.11040, loss_spatial_dice_7: 0.09306/0.22950, loss_spatial_ce_7: 0.01965/0.17272, loss_grounding_bce_7: 0.03656/0.08484, loss_grounding_dice_7: 0.07330/0.16174, loss_grounding_ce_7: 0.00341/0.34001, loss_mask_ce_8: 0.11358/1.04876, loss_mask_bce_8: 0.06728/0.33426, loss_mask_dice_8: 0.18941/1.18824, loss_spatial_bce_8: 0.03963/0.13153, loss_spatial_dice_8: 0.12692/0.26989, loss_spatial_ce_8: 0.03440/0.22936, loss_grounding_bce_8: 0.03557/0.08876, loss_grounding_dice_8: 0.07996/0.17094, loss_grounding_ce_8: 0.02926/0.44155, loss_mask_ce_9: 1.66895/3.50358, loss_mask_bce_9: 0.05924/0.36056, loss_mask_dice_9: 0.23164/1.77461, loss_spatial_bce_9: 0.44735/0.35923, loss_spatial_dice_9: 0.62151/0.79728, loss_spatial_ce_9: 1.34293/1.41716, loss_grounding_bce_9: 0.03529/0.10061, loss_grounding_dice_9: 0.11440/0.24513, loss_grounding_ce_9: 0.12869/0.71041] items per batch[64] items per second[0.36] total items[1241600] mini batches[ 19400] memory[4967] epoch remaining[0:20:39] INFO:trainer.default_trainer:epochs[ 10] optim steps[19500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67713/0.78751, loss_mask_bce_0: 0.04586/0.30171, loss_mask_dice_0: 2.20624/1.03164, loss_spatial_bce_0: 0.01190/0.08986, loss_spatial_dice_0: 0.32203/0.18995, loss_spatial_ce_0: 0.14078/0.07357, loss_grounding_bce_0: 0.01636/0.08037, loss_grounding_dice_0: 0.23964/0.15192, loss_grounding_ce_0: 0.00873/0.25382, loss_mask_ce_1: 0.70294/0.79018, loss_mask_bce_1: 0.03142/0.30225, loss_mask_dice_1: 1.38247/1.03495, loss_spatial_bce_1: 0.00903/0.09032, loss_spatial_dice_1: 0.39671/0.19263, loss_spatial_ce_1: 0.44203/0.07826, loss_grounding_bce_1: 0.01127/0.08054, loss_grounding_dice_1: 0.20248/0.15282, loss_grounding_ce_1: 0.00918/0.25593, loss_mask_ce_2: 0.78150/0.79731, loss_mask_bce_2: 0.02985/0.30224, loss_mask_dice_2: 1.44654/1.03801, loss_spatial_bce_2: 0.01107/0.08987, loss_spatial_dice_2: 0.36989/0.19256, loss_spatial_ce_2: 0.10283/0.08084, loss_grounding_bce_2: 0.01419/0.08026, loss_grounding_dice_2: 0.19686/0.15227, loss_grounding_ce_2: 0.01960/0.25814, loss_mask_ce_3: 0.92855/0.79638, loss_mask_bce_3: 0.03257/0.30378, loss_mask_dice_3: 1.71998/1.03376, loss_spatial_bce_3: 0.01041/0.09146, loss_spatial_dice_3: 0.37630/0.19293, loss_spatial_ce_3: 0.09682/0.08670, loss_grounding_bce_3: 0.01744/0.08081, loss_grounding_dice_3: 0.23057/0.15213, loss_grounding_ce_3: 0.02106/0.25638, loss_mask_ce_4: 0.71574/0.80286, loss_mask_bce_4: 0.02769/0.30589, loss_mask_dice_4: 1.56879/1.05273, loss_spatial_bce_4: 0.01113/0.09348, loss_spatial_dice_4: 0.32725/0.20026, loss_spatial_ce_4: 0.03524/0.09863, loss_grounding_bce_4: 0.01649/0.08158, loss_grounding_dice_4: 0.24538/0.15447, loss_grounding_ce_4: 0.00354/0.26431, loss_mask_ce_5: 0.70552/0.82518, loss_mask_bce_5: 0.02332/0.30815, loss_mask_dice_5: 1.18082/1.05980, loss_spatial_bce_5: 0.01588/0.09524, loss_spatial_dice_5: 0.37452/0.20243, loss_spatial_ce_5: 0.10527/0.10990, loss_grounding_bce_5: 0.01719/0.08205, loss_grounding_dice_5: 0.25228/0.15529, loss_grounding_ce_5: 0.00781/0.28408, loss_mask_ce_6: 0.73816/0.85051, loss_mask_bce_6: 0.04011/0.30949, loss_mask_dice_6: 2.11947/1.06265, loss_spatial_bce_6: 0.01219/0.10006, loss_spatial_dice_6: 0.36278/0.20482, loss_spatial_ce_6: 0.12468/0.12951, loss_grounding_bce_6: 0.00790/0.08316, loss_grounding_dice_6: 0.17134/0.15587, loss_grounding_ce_6: 0.00427/0.29482, loss_mask_ce_7: 0.86759/0.91300, loss_mask_bce_7: 0.04858/0.31669, loss_mask_dice_7: 2.43902/1.10943, loss_spatial_bce_7: 0.00936/0.11030, loss_spatial_dice_7: 0.45302/0.22949, loss_spatial_ce_7: 0.17889/0.17251, loss_grounding_bce_7: 0.00686/0.08481, loss_grounding_dice_7: 0.18482/0.16174, loss_grounding_ce_7: 0.00027/0.33992, loss_mask_ce_8: 1.30373/1.04892, loss_mask_bce_8: 0.03301/0.33437, loss_mask_dice_8: 1.68886/1.18923, loss_spatial_bce_8: 0.01281/0.13142, loss_spatial_dice_8: 0.47149/0.26983, loss_spatial_ce_8: 0.15901/0.22916, loss_grounding_bce_8: 0.01276/0.08872, loss_grounding_dice_8: 0.25819/0.17095, loss_grounding_ce_8: 0.01007/0.44155, loss_mask_ce_9: 4.31493/3.50470, loss_mask_bce_9: 0.02815/0.36073, loss_mask_dice_9: 1.86825/1.77577, loss_spatial_bce_9: 0.04377/0.35907, loss_spatial_dice_9: 0.93159/0.79725, loss_spatial_ce_9: 1.93473/1.41688, loss_grounding_bce_9: 0.00352/0.10058, loss_grounding_dice_9: 0.13235/0.24507, loss_grounding_ce_9: 0.59936/0.71002] items per batch[64] items per second[0.36] total items[1248000] mini batches[ 19500] memory[4967] epoch remaining[0:17:40] INFO:trainer.default_trainer:epochs[ 10] optim steps[19600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80317/0.78717, loss_mask_bce_0: 0.73819/0.30168, loss_mask_dice_0: 2.37122/1.03053, loss_spatial_bce_0: 0.04775/0.08981, loss_spatial_dice_0: 0.14579/0.18982, loss_spatial_ce_0: 0.04827/0.07345, loss_grounding_bce_0: 0.08709/0.08040, loss_grounding_dice_0: 0.07876/0.15181, loss_grounding_ce_0: 1.59224/0.25389, loss_mask_ce_1: 0.82782/0.78978, loss_mask_bce_1: 0.77514/0.30225, loss_mask_dice_1: 2.67819/1.03381, loss_spatial_bce_1: 0.05430/0.09026, loss_spatial_dice_1: 0.14516/0.19250, loss_spatial_ce_1: 0.02894/0.07807, loss_grounding_bce_1: 0.08019/0.08056, loss_grounding_dice_1: 0.08240/0.15275, loss_grounding_ce_1: 1.66063/0.25596, loss_mask_ce_2: 0.86893/0.79695, loss_mask_bce_2: 0.75864/0.30220, loss_mask_dice_2: 2.62943/1.03688, loss_spatial_bce_2: 0.05331/0.08982, loss_spatial_dice_2: 0.15831/0.19245, loss_spatial_ce_2: 0.03147/0.08067, loss_grounding_bce_2: 0.08423/0.08028, loss_grounding_dice_2: 0.07586/0.15216, loss_grounding_ce_2: 1.02200/0.25828, loss_mask_ce_3: 0.83737/0.79599, loss_mask_bce_3: 0.78077/0.30377, loss_mask_dice_3: 2.58441/1.03264, loss_spatial_bce_3: 0.05393/0.09139, loss_spatial_dice_3: 0.15611/0.19278, loss_spatial_ce_3: 0.03422/0.08653, loss_grounding_bce_3: 0.08633/0.08084, loss_grounding_dice_3: 0.08344/0.15203, loss_grounding_ce_3: 0.76012/0.25642, loss_mask_ce_4: 0.82201/0.80238, loss_mask_bce_4: 0.80350/0.30587, loss_mask_dice_4: 2.53423/1.05167, loss_spatial_bce_4: 0.05156/0.09342, loss_spatial_dice_4: 0.15329/0.20012, loss_spatial_ce_4: 0.04248/0.09846, loss_grounding_bce_4: 0.08594/0.08160, loss_grounding_dice_4: 0.09584/0.15434, loss_grounding_ce_4: 0.80272/0.26428, loss_mask_ce_5: 1.10438/0.82484, loss_mask_bce_5: 0.76841/0.30810, loss_mask_dice_5: 2.79259/1.05864, loss_spatial_bce_5: 0.04741/0.09517, loss_spatial_dice_5: 0.16307/0.20228, loss_spatial_ce_5: 0.04630/0.10975, loss_grounding_bce_5: 0.07952/0.08207, loss_grounding_dice_5: 0.07182/0.15518, loss_grounding_ce_5: 0.54356/0.28385, loss_mask_ce_6: 1.01647/0.85015, loss_mask_bce_6: 0.79128/0.30943, loss_mask_dice_6: 2.75621/1.06153, loss_spatial_bce_6: 0.04197/0.09999, loss_spatial_dice_6: 0.15863/0.20468, loss_spatial_ce_6: 0.08561/0.12933, loss_grounding_bce_6: 0.07396/0.08318, loss_grounding_dice_6: 0.06983/0.15576, loss_grounding_ce_6: 0.59899/0.29454, loss_mask_ce_7: 0.80849/0.91249, loss_mask_bce_7: 0.78140/0.31661, loss_mask_dice_7: 3.13449/1.10826, loss_spatial_bce_7: 0.04874/0.11021, loss_spatial_dice_7: 0.20171/0.22933, loss_spatial_ce_7: 0.09141/0.17236, loss_grounding_bce_7: 0.08449/0.08482, loss_grounding_dice_7: 0.08304/0.16163, loss_grounding_ce_7: 1.25800/0.33957, loss_mask_ce_8: 1.23797/1.04834, loss_mask_bce_8: 0.87764/0.33433, loss_mask_dice_8: 3.01221/1.18806, loss_spatial_bce_8: 0.09782/0.13133, loss_spatial_dice_8: 0.29536/0.26965, loss_spatial_ce_8: 0.09985/0.22890, loss_grounding_bce_8: 0.07927/0.08871, loss_grounding_dice_8: 0.06356/0.17081, loss_grounding_ce_8: 0.22067/0.44109, loss_mask_ce_9: 4.32235/3.50307, loss_mask_bce_9: 0.98991/0.36064, loss_mask_dice_9: 5.19832/1.77424, loss_spatial_bce_9: 0.20993/0.35908, loss_spatial_dice_9: 0.96486/0.79714, loss_spatial_ce_9: 1.48399/1.41670, loss_grounding_bce_9: 0.08323/0.10056, loss_grounding_dice_9: 0.05670/0.24482, loss_grounding_ce_9: 1.00784/0.70948] items per batch[64] items per second[0.36] total items[1254400] mini batches[ 19600] memory[4967] epoch remaining[0:14:42] INFO:trainer.default_trainer:epochs[ 10] optim steps[19700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19849/0.78711, loss_mask_bce_0: 0.06605/0.30191, loss_mask_dice_0: 0.41482/1.03156, loss_spatial_bce_0: 0.01631/0.08978, loss_spatial_dice_0: 0.11698/0.18988, loss_spatial_ce_0: 0.00014/0.07339, loss_grounding_bce_0: 0.02028/0.08038, loss_grounding_dice_0: 0.07292/0.15178, loss_grounding_ce_0: 0.00009/0.25379, loss_mask_ce_1: 0.21833/0.78976, loss_mask_bce_1: 0.07873/0.30248, loss_mask_dice_1: 0.44765/1.03506, loss_spatial_bce_1: 0.01630/0.09025, loss_spatial_dice_1: 0.10279/0.19256, loss_spatial_ce_1: 0.00008/0.07800, loss_grounding_bce_1: 0.01982/0.08054, loss_grounding_dice_1: 0.06895/0.15273, loss_grounding_ce_1: 0.00013/0.25590, loss_mask_ce_2: 0.27329/0.79703, loss_mask_bce_2: 0.10189/0.30243, loss_mask_dice_2: 0.44739/1.03795, loss_spatial_bce_2: 0.01607/0.08982, loss_spatial_dice_2: 0.11240/0.19251, loss_spatial_ce_2: 0.00009/0.08059, loss_grounding_bce_2: 0.02251/0.08026, loss_grounding_dice_2: 0.07629/0.15213, loss_grounding_ce_2: 0.00025/0.25819, loss_mask_ce_3: 0.15266/0.79606, loss_mask_bce_3: 0.06897/0.30399, loss_mask_dice_3: 0.39177/1.03379, loss_spatial_bce_3: 0.01796/0.09139, loss_spatial_dice_3: 0.11624/0.19285, loss_spatial_ce_3: 0.00025/0.08640, loss_grounding_bce_3: 0.02405/0.08082, loss_grounding_dice_3: 0.07988/0.15202, loss_grounding_ce_3: 0.00019/0.25633, loss_mask_ce_4: 0.11589/0.80233, loss_mask_bce_4: 0.07852/0.30608, loss_mask_dice_4: 0.62134/1.05284, loss_spatial_bce_4: 0.01880/0.09341, loss_spatial_dice_4: 0.09351/0.20018, loss_spatial_ce_4: 0.00042/0.09836, loss_grounding_bce_4: 0.02501/0.08158, loss_grounding_dice_4: 0.08383/0.15436, loss_grounding_ce_4: 0.00016/0.26426, loss_mask_ce_5: 0.14266/0.82493, loss_mask_bce_5: 0.08872/0.30834, loss_mask_dice_5: 0.56059/1.05989, loss_spatial_bce_5: 0.02076/0.09515, loss_spatial_dice_5: 0.12753/0.20233, loss_spatial_ce_5: 0.00085/0.10978, loss_grounding_bce_5: 0.02262/0.08205, loss_grounding_dice_5: 0.07015/0.15524, loss_grounding_ce_5: 0.00012/0.28385, loss_mask_ce_6: 0.75375/0.85003, loss_mask_bce_6: 0.07963/0.30972, loss_mask_dice_6: 0.53522/1.06268, loss_spatial_bce_6: 0.02200/0.09998, loss_spatial_dice_6: 0.10561/0.20477, loss_spatial_ce_6: 0.00752/0.12935, loss_grounding_bce_6: 0.02093/0.08315, loss_grounding_dice_6: 0.06740/0.15577, loss_grounding_ce_6: 0.00022/0.29450, loss_mask_ce_7: 1.20988/0.91268, loss_mask_bce_7: 0.08569/0.31685, loss_mask_dice_7: 0.48665/1.10948, loss_spatial_bce_7: 0.02186/0.11019, loss_spatial_dice_7: 0.11712/0.22941, loss_spatial_ce_7: 0.07819/0.17235, loss_grounding_bce_7: 0.02753/0.08481, loss_grounding_dice_7: 0.08569/0.16165, loss_grounding_ce_7: 0.00021/0.33936, loss_mask_ce_8: 0.35147/1.04848, loss_mask_bce_8: 0.08010/0.33456, loss_mask_dice_8: 0.40141/1.18919, loss_spatial_bce_8: 0.02178/0.13132, loss_spatial_dice_8: 0.10478/0.26976, loss_spatial_ce_8: 0.06759/0.22863, loss_grounding_bce_8: 0.02473/0.08869, loss_grounding_dice_8: 0.07166/0.17082, loss_grounding_ce_8: 0.00011/0.44100, loss_mask_ce_9: 3.57986/3.50353, loss_mask_bce_9: 0.05361/0.36083, loss_mask_dice_9: 0.56380/1.77677, loss_spatial_bce_9: 0.21857/0.35884, loss_spatial_dice_9: 0.76972/0.79724, loss_spatial_ce_9: 0.98886/1.41652, loss_grounding_bce_9: 0.02285/0.10054, loss_grounding_dice_9: 0.09626/0.24480, loss_grounding_ce_9: 0.01425/0.70985] items per batch[64] items per second[0.35] total items[1260800] mini batches[ 19700] memory[4967] epoch remaining[0:11:45] INFO:trainer.default_trainer:epochs[ 10] optim steps[19800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86729/0.78734, loss_mask_bce_0: 0.21828/0.30202, loss_mask_dice_0: 0.22954/1.03144, loss_spatial_bce_0: 0.07636/0.08976, loss_spatial_dice_0: 0.08326/0.18977, loss_spatial_ce_0: 0.02617/0.07332, loss_grounding_bce_0: 0.08633/0.08046, loss_grounding_dice_0: 0.08907/0.15177, loss_grounding_ce_0: 0.25990/0.25418, loss_mask_ce_1: 0.94755/0.79001, loss_mask_bce_1: 0.21036/0.30258, loss_mask_dice_1: 0.22960/1.03483, loss_spatial_bce_1: 0.07943/0.09023, loss_spatial_dice_1: 0.08458/0.19248, loss_spatial_ce_1: 0.02453/0.07794, loss_grounding_bce_1: 0.08371/0.08061, loss_grounding_dice_1: 0.09162/0.15274, loss_grounding_ce_1: 0.27444/0.25660, loss_mask_ce_2: 0.70651/0.79721, loss_mask_bce_2: 0.21648/0.30253, loss_mask_dice_2: 0.25143/1.03780, loss_spatial_bce_2: 0.07720/0.08980, loss_spatial_dice_2: 0.08372/0.19241, loss_spatial_ce_2: 0.01483/0.08050, loss_grounding_bce_2: 0.09114/0.08034, loss_grounding_dice_2: 0.10802/0.15217, loss_grounding_ce_2: 0.17401/0.25848, loss_mask_ce_3: 0.76727/0.79630, loss_mask_bce_3: 0.21510/0.30410, loss_mask_dice_3: 0.25341/1.03356, loss_spatial_bce_3: 0.07500/0.09138, loss_spatial_dice_3: 0.07367/0.19276, loss_spatial_ce_3: 0.01241/0.08628, loss_grounding_bce_3: 0.07808/0.08090, loss_grounding_dice_3: 0.10008/0.15206, loss_grounding_ce_3: 0.28949/0.25700, loss_mask_ce_4: 0.82273/0.80258, loss_mask_bce_4: 0.21416/0.30620, loss_mask_dice_4: 0.26764/1.05264, loss_spatial_bce_4: 0.08225/0.09341, loss_spatial_dice_4: 0.09253/0.20011, loss_spatial_ce_4: 0.00755/0.09829, loss_grounding_bce_4: 0.08323/0.08167, loss_grounding_dice_4: 0.11617/0.15434, loss_grounding_ce_4: 0.14573/0.26465, loss_mask_ce_5: 0.64515/0.82518, loss_mask_bce_5: 0.23478/0.30849, loss_mask_dice_5: 0.36309/1.05971, loss_spatial_bce_5: 0.08132/0.09514, loss_spatial_dice_5: 0.08994/0.20224, loss_spatial_ce_5: 0.01465/0.10973, loss_grounding_bce_5: 0.07932/0.08213, loss_grounding_dice_5: 0.09491/0.15523, loss_grounding_ce_5: 0.33636/0.28387, loss_mask_ce_6: 1.00643/0.85034, loss_mask_bce_6: 0.21945/0.30988, loss_mask_dice_6: 0.25844/1.06236, loss_spatial_bce_6: 0.08354/0.09998, loss_spatial_dice_6: 0.10536/0.20470, loss_spatial_ce_6: 0.03622/0.12932, loss_grounding_bce_6: 0.08214/0.08322, loss_grounding_dice_6: 0.07955/0.15578, loss_grounding_ce_6: 0.32730/0.29515, loss_mask_ce_7: 0.96295/0.91309, loss_mask_bce_7: 0.21045/0.31698, loss_mask_dice_7: 0.21485/1.10925, loss_spatial_bce_7: 0.08592/0.11020, loss_spatial_dice_7: 0.11213/0.22936, loss_spatial_ce_7: 0.02611/0.17222, loss_grounding_bce_7: 0.08130/0.08487, loss_grounding_dice_7: 0.09801/0.16172, loss_grounding_ce_7: 0.32344/0.33962, loss_mask_ce_8: 0.67232/1.04909, loss_mask_bce_8: 0.23115/0.33474, loss_mask_dice_8: 0.32504/1.18902, loss_spatial_bce_8: 0.09533/0.13129, loss_spatial_dice_8: 0.12085/0.26968, loss_spatial_ce_8: 0.08558/0.22848, loss_grounding_bce_8: 0.08507/0.08879, loss_grounding_dice_8: 0.14391/0.17086, loss_grounding_ce_8: 0.05211/0.44115, loss_mask_ce_9: 2.89988/3.50499, loss_mask_bce_9: 0.27303/0.36104, loss_mask_dice_9: 0.47883/1.77655, loss_spatial_bce_9: 0.51903/0.35883, loss_spatial_dice_9: 0.81080/0.79725, loss_spatial_ce_9: 1.58702/1.41643, loss_grounding_bce_9: 0.11197/0.10065, loss_grounding_dice_9: 0.25898/0.24486, loss_grounding_ce_9: 0.02638/0.71067] items per batch[64] items per second[0.36] total items[1267200] mini batches[ 19800] memory[4967] epoch remaining[0:08:48] INFO:trainer.default_trainer:epochs[ 10] optim steps[19900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.83803/0.78743, loss_mask_bce_0: 0.17353/0.30191, loss_mask_dice_0: 0.16727/1.03178, loss_spatial_bce_0: 0.13557/0.08972, loss_spatial_dice_0: 0.14330/0.18972, loss_spatial_ce_0: 0.00008/0.07319, loss_grounding_bce_0: 0.00000/0.08045, loss_grounding_dice_0: 0.00003/0.15168, loss_grounding_ce_0: 0.87774/0.25386, loss_mask_ce_1: 1.76106/0.79011, loss_mask_bce_1: 0.17053/0.30249, loss_mask_dice_1: 0.14875/1.03507, loss_spatial_bce_1: 0.13431/0.09020, loss_spatial_dice_1: 0.09247/0.19239, loss_spatial_ce_1: 0.00014/0.07778, loss_grounding_bce_1: 0.00000/0.08061, loss_grounding_dice_1: 0.00003/0.15267, loss_grounding_ce_1: 0.92163/0.25630, loss_mask_ce_2: 1.64037/0.79729, loss_mask_bce_2: 0.16683/0.30244, loss_mask_dice_2: 0.13172/1.03822, loss_spatial_bce_2: 0.13627/0.08978, loss_spatial_dice_2: 0.09249/0.19233, loss_spatial_ce_2: 0.00025/0.08036, loss_grounding_bce_2: 0.00000/0.08033, loss_grounding_dice_2: 0.00006/0.15209, loss_grounding_ce_2: 0.91964/0.25817, loss_mask_ce_3: 1.63874/0.79640, loss_mask_bce_3: 0.17897/0.30401, loss_mask_dice_3: 0.12321/1.03382, loss_spatial_bce_3: 0.13620/0.09135, loss_spatial_dice_3: 0.11218/0.19267, loss_spatial_ce_3: 0.00030/0.08611, loss_grounding_bce_3: 0.00000/0.08089, loss_grounding_dice_3: 0.00019/0.15198, loss_grounding_ce_3: 0.84087/0.25667, loss_mask_ce_4: 1.65962/0.80270, loss_mask_bce_4: 0.16532/0.30609, loss_mask_dice_4: 0.11850/1.05292, loss_spatial_bce_4: 0.14261/0.09337, loss_spatial_dice_4: 0.09799/0.19999, loss_spatial_ce_4: 0.00019/0.09813, loss_grounding_bce_4: 0.00000/0.08166, loss_grounding_dice_4: 0.00002/0.15427, loss_grounding_ce_4: 0.71499/0.26432, loss_mask_ce_5: 1.63007/0.82537, loss_mask_bce_5: 0.17316/0.30839, loss_mask_dice_5: 0.13256/1.06002, loss_spatial_bce_5: 0.14559/0.09513, loss_spatial_dice_5: 0.16320/0.20215, loss_spatial_ce_5: 0.00013/0.10953, loss_grounding_bce_5: 0.00000/0.08212, loss_grounding_dice_5: 0.00004/0.15516, loss_grounding_ce_5: 0.51364/0.28347, loss_mask_ce_6: 1.59058/0.85047, loss_mask_bce_6: 0.17044/0.30978, loss_mask_dice_6: 0.12244/1.06270, loss_spatial_bce_6: 0.14692/0.09997, loss_spatial_dice_6: 0.17657/0.20464, loss_spatial_ce_6: 0.00029/0.12919, loss_grounding_bce_6: 0.00000/0.08321, loss_grounding_dice_6: 0.00001/0.15570, loss_grounding_ce_6: 0.51775/0.29468, loss_mask_ce_7: 1.47795/0.91329, loss_mask_bce_7: 0.16357/0.31685, loss_mask_dice_7: 0.13487/1.10955, loss_spatial_bce_7: 0.15465/0.11019, loss_spatial_dice_7: 0.16547/0.22928, loss_spatial_ce_7: 0.00248/0.17203, loss_grounding_bce_7: 0.00000/0.08486, loss_grounding_dice_7: 0.00001/0.16167, loss_grounding_ce_7: 0.62013/0.33903, loss_mask_ce_8: 1.32889/1.04909, loss_mask_bce_8: 0.17057/0.33466, loss_mask_dice_8: 0.15423/1.18932, loss_spatial_bce_8: 0.17447/0.13123, loss_spatial_dice_8: 0.20509/0.26954, loss_spatial_ce_8: 0.03105/0.22825, loss_grounding_bce_8: 0.00000/0.08880, loss_grounding_dice_8: 0.00036/0.17079, loss_grounding_ce_8: 0.56286/0.44052, loss_mask_ce_9: 3.06216/3.50504, loss_mask_bce_9: 0.19155/0.36091, loss_mask_dice_9: 0.40941/1.77723, loss_spatial_bce_9: 0.66638/0.35878, loss_spatial_dice_9: 0.71062/0.79724, loss_spatial_ce_9: 1.06230/1.41657, loss_grounding_bce_9: 0.00000/0.10060, loss_grounding_dice_9: 0.00360/0.24470, loss_grounding_ce_9: 0.48942/0.71042] items per batch[64] items per second[0.36] total items[1273600] mini batches[ 19900] memory[4967] epoch remaining[0:05:50] INFO:trainer.default_trainer:epochs[ 10] optim steps[20000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54533/0.78705, loss_mask_bce_0: 0.07810/0.30190, loss_mask_dice_0: 1.38501/1.03026, loss_spatial_bce_0: 0.01891/0.08977, loss_spatial_dice_0: 0.31846/0.18969, loss_spatial_ce_0: 0.03400/0.07307, loss_grounding_bce_0: 0.00148/0.08051, loss_grounding_dice_0: 0.03307/0.15171, loss_grounding_ce_0: 0.06040/0.25405, loss_mask_ce_1: 0.54504/0.78974, loss_mask_bce_1: 0.07789/0.30246, loss_mask_dice_1: 1.16798/1.03367, loss_spatial_bce_1: 0.02089/0.09024, loss_spatial_dice_1: 0.34154/0.19235, loss_spatial_ce_1: 0.03341/0.07769, loss_grounding_bce_1: 0.00275/0.08065, loss_grounding_dice_1: 0.04605/0.15266, loss_grounding_ce_1: 0.06448/0.25644, loss_mask_ce_2: 0.61985/0.79695, loss_mask_bce_2: 0.07226/0.30243, loss_mask_dice_2: 1.23418/1.03687, loss_spatial_bce_2: 0.02304/0.08983, loss_spatial_dice_2: 0.34047/0.19230, loss_spatial_ce_2: 0.03241/0.08027, loss_grounding_bce_2: 0.00217/0.08039, loss_grounding_dice_2: 0.06060/0.15212, loss_grounding_ce_2: 0.08902/0.25820, loss_mask_ce_3: 0.60909/0.79598, loss_mask_bce_3: 0.08411/0.30401, loss_mask_dice_3: 1.31641/1.03230, loss_spatial_bce_3: 0.02308/0.09140, loss_spatial_dice_3: 0.33220/0.19263, loss_spatial_ce_3: 0.03145/0.08606, loss_grounding_bce_3: 0.00113/0.08094, loss_grounding_dice_3: 0.03102/0.15204, loss_grounding_ce_3: 0.05714/0.25674, loss_mask_ce_4: 0.80695/0.80221, loss_mask_bce_4: 0.07164/0.30610, loss_mask_dice_4: 1.06868/1.05152, loss_spatial_bce_4: 0.01679/0.09339, loss_spatial_dice_4: 0.31488/0.19994, loss_spatial_ce_4: 0.17763/0.09802, loss_grounding_bce_4: 0.00197/0.08169, loss_grounding_dice_4: 0.05028/0.15428, loss_grounding_ce_4: 0.04863/0.26449, loss_mask_ce_5: 1.04333/0.82493, loss_mask_bce_5: 0.08330/0.30837, loss_mask_dice_5: 1.13969/1.05861, loss_spatial_bce_5: 0.01753/0.09515, loss_spatial_dice_5: 0.28300/0.20210, loss_spatial_ce_5: 0.15481/0.10942, loss_grounding_bce_5: 0.00371/0.08214, loss_grounding_dice_5: 0.06181/0.15519, loss_grounding_ce_5: 0.18691/0.28361, loss_mask_ce_6: 0.49016/0.85013, loss_mask_bce_6: 0.07642/0.30982, loss_mask_dice_6: 1.20470/1.06127, loss_spatial_bce_6: 0.01903/0.10000, loss_spatial_dice_6: 0.28647/0.20460, loss_spatial_ce_6: 0.15343/0.12905, loss_grounding_bce_6: 0.00217/0.08325, loss_grounding_dice_6: 0.04155/0.15572, loss_grounding_ce_6: 0.07905/0.29457, loss_mask_ce_7: 0.55994/0.91288, loss_mask_bce_7: 0.07506/0.31691, loss_mask_dice_7: 1.25492/1.10805, loss_spatial_bce_7: 0.01338/0.11022, loss_spatial_dice_7: 0.34128/0.22922, loss_spatial_ce_7: 0.26205/0.17201, loss_grounding_bce_7: 0.00318/0.08489, loss_grounding_dice_7: 0.06417/0.16170, loss_grounding_ce_7: 0.19310/0.33891, loss_mask_ce_8: 1.77275/1.04848, loss_mask_bce_8: 0.09448/0.33470, loss_mask_dice_8: 1.24180/1.18776, loss_spatial_bce_8: 0.02343/0.13132, loss_spatial_dice_8: 0.42422/0.26948, loss_spatial_ce_8: 0.12581/0.22824, loss_grounding_bce_8: 0.00236/0.08885, loss_grounding_dice_8: 0.04441/0.17084, loss_grounding_ce_8: 0.20459/0.44034, loss_mask_ce_9: 3.36134/3.50314, loss_mask_bce_9: 0.07038/0.36087, loss_mask_dice_9: 1.31181/1.77480, loss_spatial_bce_9: 0.04522/0.35885, loss_spatial_dice_9: 0.75281/0.79708, loss_spatial_ce_9: 1.69799/1.41634, loss_grounding_bce_9: 0.00346/0.10059, loss_grounding_dice_9: 0.10650/0.24467, loss_grounding_ce_9: 3.08847/0.71038] items per batch[64] items per second[0.36] total items[1280000] mini batches[ 20000] memory[4967] epoch remaining[0:02:52] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00020097. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0023 s/iter. Inference: 0.3644 s/iter. Eval: 0.1023 s/iter. Total: 0.4690 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0022 s/iter. Inference: 0.3695 s/iter. Eval: 0.0812 s/iter. Total: 0.4530 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0024 s/iter. Inference: 0.3710 s/iter. Eval: 0.0777 s/iter. Total: 0.4513 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 47/79. Dataloading: 0.0025 s/iter. Inference: 0.3715 s/iter. Eval: 0.0751 s/iter. Total: 0.4493 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 59/79. Dataloading: 0.0026 s/iter. Inference: 0.3714 s/iter. Eval: 0.0745 s/iter. Total: 0.4486 s/iter. ETA=0:00:08 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 71/79. Dataloading: 0.0027 s/iter. Inference: 0.3720 s/iter. Eval: 0.0721 s/iter. Total: 0.4469 s/iter. ETA=0:00:03 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalbbk_2ds1 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.601 | 83.090 | 66.211 | 133 | | Things | 61.787 | 83.971 | 73.072 | 80 | | Stuff | 46.263 | 81.758 | 55.855 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.57s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.02 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.45 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.83s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.37 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.373 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.761 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.158 | 68.872 | 48.783 | 25.868 | 49.622 | 67.429 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.011 | bicycle | 20.807 | car | 43.409 | | motorcycle | 40.613 | airplane | 59.417 | bus | 71.123 | | train | 74.756 | truck | 41.366 | boat | 30.026 | | traffic light | 27.597 | fire hydrant | 70.335 | stop sign | 67.591 | | parking meter | 52.655 | bench | 25.905 | bird | 32.911 | | cat | 75.432 | dog | 70.806 | horse | 50.617 | | sheep | 53.686 | cow | 57.090 | elephant | 65.660 | | bear | 78.857 | zebra | 65.078 | giraffe | 61.540 | | backpack | 22.281 | umbrella | 54.843 | handbag | 22.817 | | tie | 40.211 | suitcase | 49.957 | frisbee | 69.526 | | skis | 8.622 | snowboard | 34.622 | sports ball | 49.993 | | kite | 37.483 | baseball bat | 38.671 | baseball glove | 49.083 | | skateboard | 42.355 | surfboard | 43.784 | tennis racket | 62.450 | | bottle | 40.800 | wine glass | 37.053 | cup | 50.478 | | fork | 24.886 | knife | 24.844 | spoon | 21.248 | | bowl | 39.545 | banana | 21.933 | apple | 25.594 | | sandwich | 48.753 | orange | 30.103 | broccoli | 24.416 | | carrot | 21.684 | hot dog | 39.921 | pizza | 52.284 | | donut | 55.234 | cake | 45.851 | chair | 27.754 | | couch | 44.222 | potted plant | 22.140 | bed | 41.698 | | dining table | 15.465 | toilet | 69.427 | tv | 66.158 | | laptop | 68.876 | mouse | 64.273 | remote | 43.729 | | keyboard | 56.393 | cell phone | 43.995 | microwave | 65.208 | | oven | 32.962 | toaster | 54.756 | sink | 43.388 | | refrigerator | 69.264 | book | 13.685 | clock | 55.482 | | vase | 41.627 | scissors | 36.797 | teddy bear | 58.240 | | hair drier | 32.240 | toothbrush | 28.283 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.8198623230718, 'fwIoU': 70.98443094900428, 'IoU-person': 88.31038565699512, 'IoU-bicycle': 74.8978770400904, 'IoU-car': 72.24783483454557, 'IoU-motorcycle': 80.76721038111693, 'IoU-airplane': 84.23715981546471, 'IoU-bus': 88.0485798151487, 'IoU-train': 87.2792454172102, 'IoU-truck': 69.56107907269617, 'IoU-boat': 72.29627007441076, 'IoU-traffic light': 79.58237358951472, 'IoU-fire hydrant': 92.93090946624176, 'IoU-stop sign': 93.08153770965178, 'IoU-parking meter': 84.35812317914134, 'IoU-bench': 61.42687252619682, 'IoU-bird': 74.49140057952894, 'IoU-cat': 87.64993013582408, 'IoU-dog': 82.95884987389364, 'IoU-horse': 86.8095274730422, 'IoU-sheep': 89.52471157733251, 'IoU-cow': 87.31624221092919, 'IoU-elephant': 89.08226810482824, 'IoU-bear': 79.31245664913207, 'IoU-zebra': 76.3364693479225, 'IoU-giraffe': 89.54671310494331, 'IoU-backpack': 51.45770539732547, 'IoU-umbrella': 78.31621891645713, 'IoU-handbag': 47.607590369448005, 'IoU-tie': 75.90463305328325, 'IoU-suitcase': 86.84002853876095, 'IoU-frisbee': 83.67811423063408, 'IoU-skis': 59.656530679093734, 'IoU-snowboard': 71.84982559364825, 'IoU-sports ball': 79.79391737279322, 'IoU-kite': 79.42055957509096, 'IoU-baseball bat': 70.28035684534177, 'IoU-baseball glove': 77.95496276186378, 'IoU-skateboard': 86.30460184551015, 'IoU-surfboard': 80.28788045269751, 'IoU-tennis racket': 87.36781253186254, 'IoU-bottle': 70.86779809330815, 'IoU-wine glass': 82.45785316608118, 'IoU-cup': 72.58331177124356, 'IoU-fork': 69.98723668089147, 'IoU-knife': 63.97783744335841, 'IoU-spoon': 61.19207517456351, 'IoU-bowl': 59.82144258791363, 'IoU-banana': 83.31062169285146, 'IoU-apple': 59.95174583053663, 'IoU-sandwich': 70.8001090205856, 'IoU-orange': 79.90042128897701, 'IoU-broccoli': 69.03548636660241, 'IoU-carrot': 63.06348748873025, 'IoU-hot dog': 65.01452066972885, 'IoU-pizza': 86.31746311107335, 'IoU-donut': 69.99205219866033, 'IoU-cake': 71.7318380136544, 'IoU-chair': 61.44895070358886, 'IoU-couch': 67.33780031065274, 'IoU-potted plant': 45.783209288806795, 'IoU-bed': 65.76704875273795, 'IoU-dining table': 54.49542329251816, 'IoU-toilet': 83.37938849492635, 'IoU-tv': 76.40595041408682, 'IoU-laptop': 77.64566328371473, 'IoU-mouse': 81.17065656889801, 'IoU-remote': 74.07995659156535, 'IoU-keyboard': 61.84045720027633, 'IoU-cell phone': 75.17215041665524, 'IoU-microwave': 72.01348514642105, 'IoU-oven': 69.04168350354864, 'IoU-toaster': 86.20884143376705, 'IoU-sink': 74.44754118845071, 'IoU-refrigerator': 82.88039475430656, 'IoU-book': 54.268757964051105, 'IoU-clock': 68.52004804488122, 'IoU-vase': 63.711929204851224, 'IoU-scissors': 50.17574616273098, 'IoU-teddy bear': 82.04949255704128, 'IoU-hair drier': 49.28710041019325, 'IoU-toothbrush': 75.29193828260564, 'IoU-banner': 35.60857496816848, 'IoU-blanket': 18.06186234967514, 'IoU-bridge': 36.98680369019633, 'IoU-cardboard': 51.55993935546756, 'IoU-counter': 35.90111369740804, 'IoU-curtain': 69.8248854617132, 'IoU-door-stuff': 46.47413435471295, 'IoU-floor-wood': 64.64565602347622, 'IoU-flower': 38.56640040280032, 'IoU-fruit': 47.87576915338006, 'IoU-gravel': 27.898016290022447, 'IoU-house': 27.59751620386363, 'IoU-light': 44.79724375478425, 'IoU-mirror-stuff': 64.7629695093492, 'IoU-net': 41.14237779055412, 'IoU-pillow': 22.06072939181842, 'IoU-platform': 30.33695967356166, 'IoU-playingfield': 68.73414248378218, 'IoU-railroad': 63.176425871520934, 'IoU-river': 48.317858715467835, 'IoU-road': 67.85893700859775, 'IoU-roof': 20.047670896646544, 'IoU-sand': 67.06028472491879, 'IoU-sea': 84.45002961341585, 'IoU-shelf': 38.037234048320954, 'IoU-snow': 92.20447697951397, 'IoU-stairs': 34.96827816998536, 'IoU-tent': 10.73761608771183, 'IoU-towel': 44.62748026486388, 'IoU-wall-brick': 47.44310524472352, 'IoU-wall-stone': 27.648853544809114, 'IoU-wall-tile': 71.03653361525359, 'IoU-wall-wood': 44.59925278573046, 'IoU-water-other': 18.504176062614842, 'IoU-window-blind': 52.03888719877124, 'IoU-window-other': 48.73234401516601, 'IoU-tree-merged': 81.98325869208539, 'IoU-fence-merged': 54.04872674420951, 'IoU-ceiling-merged': 67.22591418292771, 'IoU-sky-other-merged': 93.71099105043591, 'IoU-cabinet-merged': 64.88654615611249, 'IoU-table-merged': 40.7884657112001, 'IoU-floor-other-merged': 54.264124249634236, 'IoU-pavement-merged': 57.46235615596229, 'IoU-mountain-merged': 58.4802513652056, 'IoU-grass-merged': 70.71619943300944, 'IoU-dirt-merged': 45.7548143921103, 'IoU-paper-merged': 39.589326053151616, 'IoU-food-other-merged': 39.707033035345, 'IoU-building-other-merged': 59.273139187547365, 'IoU-rock-merged': 63.003949783300904, 'IoU-wall-other-merged': 67.66082218345383, 'IoU-rug-merged': 66.95755082044207, 'mACC': 76.40029104201615, 'pACC': 81.71957900108451, 'ACC-person': 93.20863899745511, 'ACC-bicycle': 85.49592890950152, 'ACC-car': 86.62325119276974, 'ACC-motorcycle': 84.72689687920737, 'ACC-airplane': 90.28522674775033, 'ACC-bus': 93.76583075667892, 'ACC-train': 92.81165044212037, 'ACC-truck': 77.45229096104097, 'ACC-boat': 81.57892123416785, 'ACC-traffic light': 91.3614381996706, 'ACC-fire hydrant': 96.1143765452395, 'ACC-stop sign': 98.00403670613683, 'ACC-parking meter': 87.73510692480855, 'ACC-bench': 76.26071415199954, 'ACC-bird': 77.77394372827966, 'ACC-cat': 90.72147182149499, 'ACC-dog': 87.44201695721218, 'ACC-horse': 91.04523960184548, 'ACC-sheep': 92.55627422287212, 'ACC-cow': 91.41451542388404, 'ACC-elephant': 91.0763387344287, 'ACC-bear': 80.79371327402619, 'ACC-zebra': 77.99175515873169, 'ACC-giraffe': 93.49428646593293, 'ACC-backpack': 73.57812376753928, 'ACC-umbrella': 82.72447800944056, 'ACC-handbag': 71.36723003700986, 'ACC-tie': 84.47362195086929, 'ACC-suitcase': 93.03195368113563, 'ACC-frisbee': 94.388, 'ACC-skis': 73.84334422498357, 'ACC-snowboard': 81.8265533540692, 'ACC-sports ball': 89.21295004186436, 'ACC-kite': 86.03709045328581, 'ACC-baseball bat': 86.91381519304107, 'ACC-baseball glove': 92.69182824985769, 'ACC-skateboard': 90.91974092310731, 'ACC-surfboard': 86.39181540199414, 'ACC-tennis racket': 91.18100276566162, 'ACC-bottle': 86.45372117350728, 'ACC-wine glass': 90.88955775960544, 'ACC-cup': 88.81045766361576, 'ACC-fork': 80.44711305829227, 'ACC-knife': 77.56814684546055, 'ACC-spoon': 77.29914466680641, 'ACC-bowl': 69.86680255160451, 'ACC-banana': 89.87148127261943, 'ACC-apple': 73.52580315959027, 'ACC-sandwich': 81.80988789617234, 'ACC-orange': 89.04610989959795, 'ACC-broccoli': 80.07511957927353, 'ACC-carrot': 74.52155738909839, 'ACC-hot dog': 71.7031296981423, 'ACC-pizza': 92.3898175401811, 'ACC-donut': 76.30119411150666, 'ACC-cake': 78.4355760055424, 'ACC-chair': 77.93387355960441, 'ACC-couch': 74.70380961775766, 'ACC-potted plant': 55.60325375717442, 'ACC-bed': 70.69388231837705, 'ACC-dining table': 78.43913180243335, 'ACC-toilet': 87.25514605383353, 'ACC-tv': 84.92790812780751, 'ACC-laptop': 89.16466373347987, 'ACC-mouse': 91.24667669520109, 'ACC-remote': 78.73527992592689, 'ACC-keyboard': 67.14513817630947, 'ACC-cell phone': 83.9537204840644, 'ACC-microwave': 75.05984652633845, 'ACC-oven': 85.98506740546856, 'ACC-toaster': 90.99481312184373, 'ACC-sink': 84.61503301769173, 'ACC-refrigerator': 91.47333343801547, 'ACC-book': 71.53627665237735, 'ACC-clock': 72.80330814386548, 'ACC-vase': 71.62436164467402, 'ACC-scissors': 53.150221744759385, 'ACC-teddy bear': 86.8899612270386, 'ACC-hair drier': 60.36347593430678, 'ACC-toothbrush': 83.67529534398888, 'ACC-banner': 80.1977709327541, 'ACC-blanket': 43.68952612877784, 'ACC-bridge': 55.328908607011165, 'ACC-cardboard': 65.50379525956086, 'ACC-counter': 56.33820257957268, 'ACC-curtain': 82.52248139469404, 'ACC-door-stuff': 71.55267837270061, 'ACC-floor-wood': 77.43360101406668, 'ACC-flower': 52.61210010870466, 'ACC-fruit': 68.9189165750785, 'ACC-gravel': 41.12103784028565, 'ACC-house': 37.87785490140623, 'ACC-light': 61.64168070282893, 'ACC-mirror-stuff': 75.40681447503904, 'ACC-net': 70.24274309227519, 'ACC-pillow': 52.488034361775426, 'ACC-platform': 53.465010294759594, 'ACC-playingfield': 86.03017844397314, 'ACC-railroad': 83.16207257022894, 'ACC-river': 70.25498001828893, 'ACC-road': 85.3543118726845, 'ACC-roof': 28.327853088698475, 'ACC-sand': 72.71984915779407, 'ACC-sea': 92.58050340160254, 'ACC-shelf': 55.13854077361158, 'ACC-snow': 95.66499302739369, 'ACC-stairs': 58.62270189878402, 'ACC-tent': 14.53399258085157, 'ACC-towel': 56.856654914257966, 'ACC-wall-brick': 67.89747761879381, 'ACC-wall-stone': 34.83311322605496, 'ACC-wall-tile': 86.37455373418952, 'ACC-wall-wood': 62.290860982577236, 'ACC-water-other': 27.536314816704788, 'ACC-window-blind': 61.138721751729975, 'ACC-window-other': 77.76596442329273, 'ACC-tree-merged': 89.40905260176189, 'ACC-fence-merged': 69.6293363914223, 'ACC-ceiling-merged': 82.9756124412746, 'ACC-sky-other-merged': 96.93556221905018, 'ACC-cabinet-merged': 77.5342176136696, 'ACC-table-merged': 55.95728445775682, 'ACC-floor-other-merged': 69.6472099985865, 'ACC-pavement-merged': 69.02774064707808, 'ACC-mountain-merged': 72.78457210362085, 'ACC-grass-merged': 83.94440315223598, 'ACC-dirt-merged': 66.06491043263316, 'ACC-paper-merged': 52.668372912633046, 'ACC-food-other-merged': 57.99493859410413, 'ACC-building-other-merged': 72.42008335485416, 'ACC-rock-merged': 82.74487964217239, 'ACC-wall-other-merged': 81.57082150924444, 'ACC-rug-merged': 81.20140475917579})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2934 s/iter. Inference: 0.1915 s/iter. Eval: 0.0000 s/iter. Total: 0.4850 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3239 s/iter. Inference: 0.3436 s/iter. Eval: 0.0000 s/iter. Total: 0.6677 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3395 s/iter. Inference: 0.5559 s/iter. Eval: 0.0000 s/iter. Total: 0.8954 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4401521802750952, 'noc@0.8': 2.5715539947322212, 'noc@0.85': 3.0851624231782266, 'noc@0.9': 3.9672227099795143, 'miou@iter1': 0.8709015906258288} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1403 s/iter. Eval: 0.0010 s/iter. Total: 0.1430 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.59269714355469, 'precision@0.6': 72.67781066894531, 'precision@0.7': 68.32491302490234, 'precision@0.8': 59.075008392333984, 'precision@0.9': 32.102603912353516, 'cIoU': 61.46101379394531, 'mIoU': 66.75450134277344} INFO:trainer.default_trainer:This epoch takes 0:57:42.302997 INFO:trainer.default_trainer:PROGRESS: 22.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 11 training. INFO:trainer.default_trainer:epochs[ 11] optim steps[20100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11300/0.78680, loss_mask_bce_0: 0.50212/0.30188, loss_mask_dice_0: 0.63275/1.02958, loss_spatial_bce_0: 0.16228/0.08985, loss_spatial_dice_0: 0.23610/0.18973, loss_spatial_ce_0: 0.00273/0.07298, loss_grounding_bce_0: 0.16360/0.08051, loss_grounding_dice_0: 0.24879/0.15180, loss_grounding_ce_0: 0.20973/0.25417, loss_mask_ce_1: 1.13996/0.78945, loss_mask_bce_1: 0.49797/0.30244, loss_mask_dice_1: 0.63341/1.03313, loss_spatial_bce_1: 0.16831/0.09031, loss_spatial_dice_1: 0.26333/0.19237, loss_spatial_ce_1: 0.01212/0.07760, loss_grounding_bce_1: 0.16474/0.08066, loss_grounding_dice_1: 0.25650/0.15272, loss_grounding_ce_1: 0.18767/0.25647, loss_mask_ce_2: 1.20381/0.79667, loss_mask_bce_2: 0.39543/0.30242, loss_mask_dice_2: 0.55242/1.03622, loss_spatial_bce_2: 0.17273/0.08989, loss_spatial_dice_2: 0.27006/0.19231, loss_spatial_ce_2: 0.01183/0.08014, loss_grounding_bce_2: 0.16902/0.08040, loss_grounding_dice_2: 0.24420/0.15218, loss_grounding_ce_2: 0.26712/0.25827, loss_mask_ce_3: 1.03937/0.79579, loss_mask_bce_3: 0.50995/0.30398, loss_mask_dice_3: 0.62753/1.03161, loss_spatial_bce_3: 0.17593/0.09149, loss_spatial_dice_3: 0.29393/0.19266, loss_spatial_ce_3: 0.00898/0.08599, loss_grounding_bce_3: 0.16359/0.08094, loss_grounding_dice_3: 0.24870/0.15212, loss_grounding_ce_3: 0.24846/0.25681, loss_mask_ce_4: 1.50379/0.80200, loss_mask_bce_4: 0.40198/0.30610, loss_mask_dice_4: 0.52335/1.05079, loss_spatial_bce_4: 0.16043/0.09343, loss_spatial_dice_4: 0.24648/0.19994, loss_spatial_ce_4: 0.00795/0.09799, loss_grounding_bce_4: 0.16153/0.08170, loss_grounding_dice_4: 0.24740/0.15440, loss_grounding_ce_4: 0.20113/0.26450, loss_mask_ce_5: 1.18283/0.82473, loss_mask_bce_5: 0.40969/0.30836, loss_mask_dice_5: 0.55347/1.05792, loss_spatial_bce_5: 0.16347/0.09521, loss_spatial_dice_5: 0.21798/0.20212, loss_spatial_ce_5: 0.01035/0.10933, loss_grounding_bce_5: 0.15731/0.08216, loss_grounding_dice_5: 0.24257/0.15528, loss_grounding_ce_5: 0.17624/0.28344, loss_mask_ce_6: 1.56118/0.84981, loss_mask_bce_6: 0.38034/0.30983, loss_mask_dice_6: 0.54419/1.06069, loss_spatial_bce_6: 0.16315/0.10006, loss_spatial_dice_6: 0.22081/0.20461, loss_spatial_ce_6: 0.05252/0.12895, loss_grounding_bce_6: 0.16698/0.08326, loss_grounding_dice_6: 0.24311/0.15584, loss_grounding_ce_6: 0.22747/0.29456, loss_mask_ce_7: 1.14609/0.91280, loss_mask_bce_7: 0.52200/0.31685, loss_mask_dice_7: 0.63561/1.10734, loss_spatial_bce_7: 0.16802/0.11034, loss_spatial_dice_7: 0.21128/0.22923, loss_spatial_ce_7: 0.08990/0.17195, loss_grounding_bce_7: 0.16942/0.08496, loss_grounding_dice_7: 0.25154/0.16182, loss_grounding_ce_7: 0.27036/0.33864, loss_mask_ce_8: 1.59431/1.04834, loss_mask_bce_8: 0.53340/0.33464, loss_mask_dice_8: 0.72689/1.18709, loss_spatial_bce_8: 0.18666/0.13140, loss_spatial_dice_8: 0.22934/0.26947, loss_spatial_ce_8: 0.16738/0.22809, loss_grounding_bce_8: 0.17116/0.08892, loss_grounding_dice_8: 0.24948/0.17096, loss_grounding_ce_8: 0.30367/0.43971, loss_mask_ce_9: 2.83882/3.50309, loss_mask_bce_9: 0.62874/0.36084, loss_mask_dice_9: 0.97663/1.77385, loss_spatial_bce_9: 0.39080/0.35871, loss_spatial_dice_9: 0.89278/0.79711, loss_spatial_ce_9: 1.85475/1.41611, loss_grounding_bce_9: 0.17787/0.10064, loss_grounding_dice_9: 0.32840/0.24483, loss_grounding_ce_9: 0.47633/0.70957] items per batch[64] items per second[0.16] total items[1286400] mini batches[ 20100] memory[4967] epoch remaining[2:09:11] INFO:trainer.default_trainer:epochs[ 11] optim steps[20200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67921/0.78670, loss_mask_bce_0: 1.55364/0.30186, loss_mask_dice_0: 6.57692/1.02959, loss_spatial_bce_0: 0.04337/0.08983, loss_spatial_dice_0: 0.31268/0.18973, loss_spatial_ce_0: 0.02829/0.07287, loss_grounding_bce_0: 0.23814/0.08056, loss_grounding_dice_0: 0.43828/0.15183, loss_grounding_ce_0: 0.01126/0.25391, loss_mask_ce_1: 0.60480/0.78926, loss_mask_bce_1: 1.57303/0.30242, loss_mask_dice_1: 6.59139/1.03305, loss_spatial_bce_1: 0.04108/0.09028, loss_spatial_dice_1: 0.31718/0.19235, loss_spatial_ce_1: 0.04484/0.07748, loss_grounding_bce_1: 0.23138/0.08072, loss_grounding_dice_1: 0.44807/0.15275, loss_grounding_ce_1: 0.01394/0.25620, loss_mask_ce_2: 0.66354/0.79659, loss_mask_bce_2: 1.54041/0.30237, loss_mask_dice_2: 6.71601/1.03620, loss_spatial_bce_2: 0.03226/0.08988, loss_spatial_dice_2: 0.28930/0.19228, loss_spatial_ce_2: 0.03049/0.08007, loss_grounding_bce_2: 0.21250/0.08046, loss_grounding_dice_2: 0.44555/0.15219, loss_grounding_ce_2: 0.01261/0.25795, loss_mask_ce_3: 0.70281/0.79571, loss_mask_bce_3: 1.57469/0.30396, loss_mask_dice_3: 6.66853/1.03177, loss_spatial_bce_3: 0.04065/0.09146, loss_spatial_dice_3: 0.30579/0.19266, loss_spatial_ce_3: 0.01223/0.08591, loss_grounding_bce_3: 0.22035/0.08099, loss_grounding_dice_3: 0.42627/0.15214, loss_grounding_ce_3: 0.00654/0.25657, loss_mask_ce_4: 0.82604/0.80190, loss_mask_bce_4: 1.54380/0.30609, loss_mask_dice_4: 6.53380/1.05085, loss_spatial_bce_4: 0.04095/0.09341, loss_spatial_dice_4: 0.29821/0.19993, loss_spatial_ce_4: 0.02498/0.09785, loss_grounding_bce_4: 0.23201/0.08175, loss_grounding_dice_4: 0.42763/0.15443, loss_grounding_ce_4: 0.01109/0.26426, loss_mask_ce_5: 0.73350/0.82466, loss_mask_bce_5: 1.61261/0.30831, loss_mask_dice_5: 6.76503/1.05806, loss_spatial_bce_5: 0.03895/0.09519, loss_spatial_dice_5: 0.33108/0.20209, loss_spatial_ce_5: 0.08607/0.10916, loss_grounding_bce_5: 0.22362/0.08221, loss_grounding_dice_5: 0.43876/0.15531, loss_grounding_ce_5: 0.01536/0.28313, loss_mask_ce_6: 0.59086/0.84986, loss_mask_bce_6: 1.61702/0.30981, loss_mask_dice_6: 7.03196/1.06088, loss_spatial_bce_6: 0.03755/0.10005, loss_spatial_dice_6: 0.32256/0.20461, loss_spatial_ce_6: 0.11358/0.12883, loss_grounding_bce_6: 0.21701/0.08332, loss_grounding_dice_6: 0.45006/0.15590, loss_grounding_ce_6: 0.01183/0.29416, loss_mask_ce_7: 0.60840/0.91288, loss_mask_bce_7: 1.53408/0.31682, loss_mask_dice_7: 7.04857/1.10730, loss_spatial_bce_7: 0.04638/0.11034, loss_spatial_dice_7: 0.35223/0.22926, loss_spatial_ce_7: 0.16737/0.17180, loss_grounding_bce_7: 0.22941/0.08500, loss_grounding_dice_7: 0.43620/0.16182, loss_grounding_ce_7: 0.01675/0.33834, loss_mask_ce_8: 1.18405/1.04835, loss_mask_bce_8: 1.43534/0.33458, loss_mask_dice_8: 7.07905/1.18715, loss_spatial_bce_8: 0.07478/0.13136, loss_spatial_dice_8: 0.43031/0.26946, loss_spatial_ce_8: 0.09743/0.22783, loss_grounding_bce_8: 0.24766/0.08895, loss_grounding_dice_8: 0.44953/0.17096, loss_grounding_ce_8: 0.00349/0.43915, loss_mask_ce_9: 4.77495/3.50258, loss_mask_bce_9: 1.24181/0.36075, loss_mask_dice_9: 8.70718/1.77351, loss_spatial_bce_9: 0.17191/0.35866, loss_spatial_dice_9: 0.96668/0.79701, loss_spatial_ce_9: 1.10447/1.41587, loss_grounding_bce_9: 0.23369/0.10073, loss_grounding_dice_9: 0.55261/0.24486, loss_grounding_ce_9: 0.05575/0.70838] items per batch[64] items per second[0.36] total items[1292800] mini batches[ 20200] memory[4967] epoch remaining[0:53:10] INFO:trainer.default_trainer:epochs[ 11] optim steps[20300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25711/0.78634, loss_mask_bce_0: 0.35295/0.30185, loss_mask_dice_0: 1.32354/1.02984, loss_spatial_bce_0: 0.05707/0.08974, loss_spatial_dice_0: 0.15707/0.18967, loss_spatial_ce_0: 0.03370/0.07277, loss_grounding_bce_0: 0.02534/0.08055, loss_grounding_dice_0: 0.09832/0.15184, loss_grounding_ce_0: 0.00530/0.25390, loss_mask_ce_1: 1.15469/0.78898, loss_mask_bce_1: 0.35391/0.30236, loss_mask_dice_1: 1.26824/1.03325, loss_spatial_bce_1: 0.05273/0.09020, loss_spatial_dice_1: 0.13540/0.19228, loss_spatial_ce_1: 0.03047/0.07737, loss_grounding_bce_1: 0.02891/0.08071, loss_grounding_dice_1: 0.10808/0.15279, loss_grounding_ce_1: 0.00529/0.25612, loss_mask_ce_2: 1.16303/0.79638, loss_mask_bce_2: 0.35014/0.30234, loss_mask_dice_2: 1.26792/1.03636, loss_spatial_bce_2: 0.05158/0.08980, loss_spatial_dice_2: 0.13347/0.19223, loss_spatial_ce_2: 0.03009/0.07996, loss_grounding_bce_2: 0.02781/0.08044, loss_grounding_dice_2: 0.10300/0.15222, loss_grounding_ce_2: 0.00619/0.25802, loss_mask_ce_3: 1.25542/0.79544, loss_mask_bce_3: 0.35734/0.30395, loss_mask_dice_3: 1.33260/1.03189, loss_spatial_bce_3: 0.05163/0.09138, loss_spatial_dice_3: 0.13776/0.19262, loss_spatial_ce_3: 0.03200/0.08577, loss_grounding_bce_3: 0.03009/0.08097, loss_grounding_dice_3: 0.10743/0.15215, loss_grounding_ce_3: 0.00736/0.25670, loss_mask_ce_4: 1.38253/0.80160, loss_mask_bce_4: 0.35657/0.30609, loss_mask_dice_4: 1.35857/1.05102, loss_spatial_bce_4: 0.05549/0.09334, loss_spatial_dice_4: 0.13495/0.19989, loss_spatial_ce_4: 0.03750/0.09767, loss_grounding_bce_4: 0.02702/0.08173, loss_grounding_dice_4: 0.09146/0.15442, loss_grounding_ce_4: 0.01182/0.26445, loss_mask_ce_5: 1.49138/0.82456, loss_mask_bce_5: 0.36672/0.30828, loss_mask_dice_5: 1.38906/1.05819, loss_spatial_bce_5: 0.05733/0.09512, loss_spatial_dice_5: 0.16008/0.20204, loss_spatial_ce_5: 0.05625/0.10900, loss_grounding_bce_5: 0.02632/0.08219, loss_grounding_dice_5: 0.10932/0.15534, loss_grounding_ce_5: 0.07108/0.28321, loss_mask_ce_6: 1.62849/0.84957, loss_mask_bce_6: 0.37257/0.30979, loss_mask_dice_6: 1.32745/1.06114, loss_spatial_bce_6: 0.05894/0.09997, loss_spatial_dice_6: 0.19090/0.20456, loss_spatial_ce_6: 0.07528/0.12875, loss_grounding_bce_6: 0.02804/0.08330, loss_grounding_dice_6: 0.10907/0.15592, loss_grounding_ce_6: 0.01573/0.29423, loss_mask_ce_7: 1.15251/0.91271, loss_mask_bce_7: 0.36186/0.31677, loss_mask_dice_7: 1.45084/1.10743, loss_spatial_bce_7: 0.05155/0.11024, loss_spatial_dice_7: 0.15111/0.22921, loss_spatial_ce_7: 0.12656/0.17165, loss_grounding_bce_7: 0.02433/0.08498, loss_grounding_dice_7: 0.10742/0.16187, loss_grounding_ce_7: 0.26412/0.33847, loss_mask_ce_8: 1.57945/1.04805, loss_mask_bce_8: 0.36411/0.33446, loss_mask_dice_8: 1.36922/1.18729, loss_spatial_bce_8: 0.11874/0.13127, loss_spatial_dice_8: 0.27351/0.26945, loss_spatial_ce_8: 0.14158/0.22766, loss_grounding_bce_8: 0.02170/0.08893, loss_grounding_dice_8: 0.08949/0.17101, loss_grounding_ce_8: 0.99863/0.43917, loss_mask_ce_9: 3.93445/3.50268, loss_mask_bce_9: 0.36485/0.36064, loss_mask_dice_9: 2.28548/1.77353, loss_spatial_bce_9: 0.36616/0.35851, loss_spatial_dice_9: 0.85355/0.79704, loss_spatial_ce_9: 1.29538/1.41562, loss_grounding_bce_9: 0.05578/0.10072, loss_grounding_dice_9: 0.30666/0.24492, loss_grounding_ce_9: 1.16567/0.70811] items per batch[64] items per second[0.36] total items[1299200] mini batches[ 20300] memory[4967] epoch remaining[0:48:59] INFO:trainer.default_trainer:epochs[ 11] optim steps[20400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97727/0.78563, loss_mask_bce_0: 0.27024/0.30165, loss_mask_dice_0: 1.91957/1.02945, loss_spatial_bce_0: 0.02831/0.08972, loss_spatial_dice_0: 0.20694/0.18959, loss_spatial_ce_0: 0.18971/0.07266, loss_grounding_bce_0: 0.04054/0.08055, loss_grounding_dice_0: 0.47322/0.15188, loss_grounding_ce_0: 0.11635/0.25355, loss_mask_ce_1: 1.01116/0.78822, loss_mask_bce_1: 0.26102/0.30217, loss_mask_dice_1: 1.88814/1.03290, loss_spatial_bce_1: 0.02574/0.09017, loss_spatial_dice_1: 0.19988/0.19220, loss_spatial_ce_1: 0.14524/0.07729, loss_grounding_bce_1: 0.03414/0.08071, loss_grounding_dice_1: 0.44758/0.15281, loss_grounding_ce_1: 0.12268/0.25592, loss_mask_ce_2: 1.05777/0.79567, loss_mask_bce_2: 0.26933/0.30215, loss_mask_dice_2: 1.93812/1.03595, loss_spatial_bce_2: 0.02801/0.08977, loss_spatial_dice_2: 0.19135/0.19215, loss_spatial_ce_2: 0.09910/0.07980, loss_grounding_bce_2: 0.03800/0.08045, loss_grounding_dice_2: 0.47125/0.15225, loss_grounding_ce_2: 0.11422/0.25771, loss_mask_ce_3: 1.07045/0.79478, loss_mask_bce_3: 0.27695/0.30374, loss_mask_dice_3: 1.82838/1.03153, loss_spatial_bce_3: 0.02793/0.09136, loss_spatial_dice_3: 0.20512/0.19256, loss_spatial_ce_3: 0.19116/0.08565, loss_grounding_bce_3: 0.04073/0.08097, loss_grounding_dice_3: 0.47331/0.15215, loss_grounding_ce_3: 0.12311/0.25647, loss_mask_ce_4: 1.08257/0.80090, loss_mask_bce_4: 0.27157/0.30587, loss_mask_dice_4: 2.06785/1.05058, loss_spatial_bce_4: 0.02786/0.09331, loss_spatial_dice_4: 0.27808/0.19984, loss_spatial_ce_4: 0.03709/0.09755, loss_grounding_bce_4: 0.04135/0.08172, loss_grounding_dice_4: 0.48660/0.15446, loss_grounding_ce_4: 0.11850/0.26429, loss_mask_ce_5: 0.92634/0.82374, loss_mask_bce_5: 0.27898/0.30806, loss_mask_dice_5: 2.09763/1.05772, loss_spatial_bce_5: 0.02691/0.09509, loss_spatial_dice_5: 0.28912/0.20196, loss_spatial_ce_5: 0.02494/0.10894, loss_grounding_bce_5: 0.03795/0.08218, loss_grounding_dice_5: 0.44893/0.15537, loss_grounding_ce_5: 0.11748/0.28286, loss_mask_ce_6: 1.17976/0.84884, loss_mask_bce_6: 0.29685/0.30958, loss_mask_dice_6: 2.12092/1.06072, loss_spatial_bce_6: 0.03526/0.09994, loss_spatial_dice_6: 0.27171/0.20450, loss_spatial_ce_6: 0.04289/0.12864, loss_grounding_bce_6: 0.03584/0.08327, loss_grounding_dice_6: 0.44218/0.15589, loss_grounding_ce_6: 0.15093/0.29393, loss_mask_ce_7: 1.33574/0.91181, loss_mask_bce_7: 0.27211/0.31658, loss_mask_dice_7: 2.04469/1.10693, loss_spatial_bce_7: 0.03847/0.11020, loss_spatial_dice_7: 0.32252/0.22912, loss_spatial_ce_7: 0.05536/0.17156, loss_grounding_bce_7: 0.04272/0.08497, loss_grounding_dice_7: 0.47330/0.16188, loss_grounding_ce_7: 0.24994/0.33812, loss_mask_ce_8: 1.34842/1.04708, loss_mask_bce_8: 0.27264/0.33423, loss_mask_dice_8: 2.43431/1.18672, loss_spatial_bce_8: 0.03215/0.13124, loss_spatial_dice_8: 0.38863/0.26934, loss_spatial_ce_8: 0.23278/0.22744, loss_grounding_bce_8: 0.03281/0.08891, loss_grounding_dice_8: 0.46058/0.17104, loss_grounding_ce_8: 0.17911/0.43865, loss_mask_ce_9: 2.96588/3.50135, loss_mask_bce_9: 0.42260/0.36034, loss_mask_dice_9: 4.16217/1.77244, loss_spatial_bce_9: 0.19687/0.35834, loss_spatial_dice_9: 0.93505/0.79684, loss_spatial_ce_9: 1.68291/1.41505, loss_grounding_bce_9: 0.04753/0.10064, loss_grounding_dice_9: 0.58347/0.24483, loss_grounding_ce_9: 0.27897/0.70771] items per batch[64] items per second[0.37] total items[1305600] mini batches[ 20400] memory[4967] epoch remaining[0:45:23] INFO:trainer.default_trainer:epochs[ 11] optim steps[20500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82455/0.78590, loss_mask_bce_0: 0.26748/0.30188, loss_mask_dice_0: 0.27472/1.03026, loss_spatial_bce_0: 0.08929/0.08974, loss_spatial_dice_0: 0.12919/0.18960, loss_spatial_ce_0: 0.06932/0.07258, loss_grounding_bce_0: 0.08823/0.08063, loss_grounding_dice_0: 0.05249/0.15191, loss_grounding_ce_0: 0.51983/0.25362, loss_mask_ce_1: 0.87129/0.78852, loss_mask_bce_1: 0.10780/0.30242, loss_mask_dice_1: 0.17571/1.03372, loss_spatial_bce_1: 0.08337/0.09018, loss_spatial_dice_1: 0.12370/0.19219, loss_spatial_ce_1: 0.06932/0.07723, loss_grounding_bce_1: 0.08407/0.08079, loss_grounding_dice_1: 0.05116/0.15283, loss_grounding_ce_1: 0.51989/0.25580, loss_mask_ce_2: 1.15594/0.79603, loss_mask_bce_2: 0.08824/0.30239, loss_mask_dice_2: 0.16068/1.03673, loss_spatial_bce_2: 0.09243/0.08979, loss_spatial_dice_2: 0.12793/0.19215, loss_spatial_ce_2: 0.06932/0.07974, loss_grounding_bce_2: 0.08975/0.08053, loss_grounding_dice_2: 0.05340/0.15231, loss_grounding_ce_2: 0.50650/0.25757, loss_mask_ce_3: 0.86401/0.79505, loss_mask_bce_3: 0.25815/0.30400, loss_mask_dice_3: 0.25263/1.03232, loss_spatial_bce_3: 0.14125/0.09138, loss_spatial_dice_3: 0.14287/0.19255, loss_spatial_ce_3: 0.06937/0.08559, loss_grounding_bce_3: 0.09235/0.08105, loss_grounding_dice_3: 0.05758/0.15218, loss_grounding_ce_3: 0.47882/0.25632, loss_mask_ce_4: 1.16578/0.80113, loss_mask_bce_4: 0.08514/0.30617, loss_mask_dice_4: 0.14742/1.05132, loss_spatial_bce_4: 0.12212/0.09332, loss_spatial_dice_4: 0.13456/0.19982, loss_spatial_ce_4: 0.06943/0.09750, loss_grounding_bce_4: 0.10837/0.08181, loss_grounding_dice_4: 0.05805/0.15447, loss_grounding_ce_4: 0.29689/0.26426, loss_mask_ce_5: 0.61069/0.82387, loss_mask_bce_5: 0.21036/0.30838, loss_mask_dice_5: 0.25426/1.05872, loss_spatial_bce_5: 0.10321/0.09510, loss_spatial_dice_5: 0.13063/0.20195, loss_spatial_ce_5: 0.06987/0.10894, loss_grounding_bce_5: 0.07642/0.08228, loss_grounding_dice_5: 0.05009/0.15539, loss_grounding_ce_5: 0.10837/0.28274, loss_mask_ce_6: 0.65037/0.84908, loss_mask_bce_6: 0.22878/0.30990, loss_mask_dice_6: 0.25878/1.06161, loss_spatial_bce_6: 0.14882/0.09996, loss_spatial_dice_6: 0.14590/0.20452, loss_spatial_ce_6: 0.07321/0.12857, loss_grounding_bce_6: 0.06528/0.08336, loss_grounding_dice_6: 0.04557/0.15592, loss_grounding_ce_6: 0.20034/0.29360, loss_mask_ce_7: 1.45307/0.91180, loss_mask_bce_7: 0.09006/0.31693, loss_mask_dice_7: 0.15097/1.10785, loss_spatial_bce_7: 0.17612/0.11021, loss_spatial_dice_7: 0.15323/0.22912, loss_spatial_ce_7: 0.09583/0.17145, loss_grounding_bce_7: 0.10780/0.08506, loss_grounding_dice_7: 0.05560/0.16188, loss_grounding_ce_7: 0.21994/0.33781, loss_mask_ce_8: 0.96262/1.04725, loss_mask_bce_8: 0.35300/0.33457, loss_mask_dice_8: 0.27210/1.18775, loss_spatial_bce_8: 0.14021/0.13123, loss_spatial_dice_8: 0.13749/0.26934, loss_spatial_ce_8: 0.03793/0.22729, loss_grounding_bce_8: 0.05258/0.08903, loss_grounding_dice_8: 0.06800/0.17107, loss_grounding_ce_8: 0.46579/0.43824, loss_mask_ce_9: 2.71622/3.50181, loss_mask_bce_9: 0.21115/0.36065, loss_mask_dice_9: 0.27895/1.77349, loss_spatial_bce_9: 0.36143/0.35835, loss_spatial_dice_9: 0.66797/0.79689, loss_spatial_ce_9: 0.75399/1.41493, loss_grounding_bce_9: 0.05834/0.10071, loss_grounding_dice_9: 0.05842/0.24476, loss_grounding_ce_9: 1.00885/0.70713] items per batch[64] items per second[0.36] total items[1312000] mini batches[ 20500] memory[4967] epoch remaining[0:42:27] INFO:trainer.default_trainer:epochs[ 11] optim steps[20600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38377/0.78572, loss_mask_bce_0: 0.71904/0.30189, loss_mask_dice_0: 0.51411/1.03027, loss_spatial_bce_0: 0.31995/0.08972, loss_spatial_dice_0: 0.22998/0.18958, loss_spatial_ce_0: 0.10102/0.07260, loss_grounding_bce_0: 0.56736/0.08063, loss_grounding_dice_0: 0.35326/0.15190, loss_grounding_ce_0: 0.08254/0.25388, loss_mask_ce_1: 0.39995/0.78825, loss_mask_bce_1: 0.71245/0.30245, loss_mask_dice_1: 0.51845/1.03363, loss_spatial_bce_1: 0.31014/0.09016, loss_spatial_dice_1: 0.22386/0.19217, loss_spatial_ce_1: 0.07840/0.07722, loss_grounding_bce_1: 0.57481/0.08079, loss_grounding_dice_1: 0.36931/0.15283, loss_grounding_ce_1: 0.10029/0.25607, loss_mask_ce_2: 0.35906/0.79577, loss_mask_bce_2: 0.69130/0.30241, loss_mask_dice_2: 0.52020/1.03663, loss_spatial_bce_2: 0.31652/0.08977, loss_spatial_dice_2: 0.24091/0.19215, loss_spatial_ce_2: 0.08131/0.07973, loss_grounding_bce_2: 0.56963/0.08055, loss_grounding_dice_2: 0.37269/0.15233, loss_grounding_ce_2: 0.09634/0.25792, loss_mask_ce_3: 0.38748/0.79488, loss_mask_bce_3: 0.69058/0.30403, loss_mask_dice_3: 0.51561/1.03233, loss_spatial_bce_3: 0.34538/0.09135, loss_spatial_dice_3: 0.22844/0.19256, loss_spatial_ce_3: 0.11369/0.08555, loss_grounding_bce_3: 0.57185/0.08106, loss_grounding_dice_3: 0.36783/0.15219, loss_grounding_ce_3: 0.08278/0.25676, loss_mask_ce_4: 0.35865/0.80085, loss_mask_bce_4: 0.72884/0.30622, loss_mask_dice_4: 0.53624/1.05130, loss_spatial_bce_4: 0.33228/0.09331, loss_spatial_dice_4: 0.23687/0.19982, loss_spatial_ce_4: 0.10858/0.09744, loss_grounding_bce_4: 0.62188/0.08184, loss_grounding_dice_4: 0.41121/0.15448, loss_grounding_ce_4: 0.09728/0.26471, loss_mask_ce_5: 0.44597/0.82363, loss_mask_bce_5: 0.69829/0.30840, loss_mask_dice_5: 0.53481/1.05873, loss_spatial_bce_5: 0.36039/0.09509, loss_spatial_dice_5: 0.26634/0.20195, loss_spatial_ce_5: 0.08967/0.10885, loss_grounding_bce_5: 0.57070/0.08229, loss_grounding_dice_5: 0.40884/0.15541, loss_grounding_ce_5: 0.11822/0.28302, loss_mask_ce_6: 0.49311/0.84864, loss_mask_bce_6: 0.71269/0.30992, loss_mask_dice_6: 0.53070/1.06153, loss_spatial_bce_6: 0.34404/0.09996, loss_spatial_dice_6: 0.25314/0.20452, loss_spatial_ce_6: 0.07683/0.12850, loss_grounding_bce_6: 0.60888/0.08338, loss_grounding_dice_6: 0.41259/0.15595, loss_grounding_ce_6: 0.14521/0.29398, loss_mask_ce_7: 0.36366/0.91158, loss_mask_bce_7: 0.74331/0.31694, loss_mask_dice_7: 0.56943/1.10784, loss_spatial_bce_7: 0.32300/0.11019, loss_spatial_dice_7: 0.25399/0.22912, loss_spatial_ce_7: 0.11426/0.17138, loss_grounding_bce_7: 0.61673/0.08504, loss_grounding_dice_7: 0.43269/0.16184, loss_grounding_ce_7: 0.07195/0.33814, loss_mask_ce_8: 0.45639/1.04679, loss_mask_bce_8: 0.71911/0.33456, loss_mask_dice_8: 0.57055/1.18773, loss_spatial_bce_8: 0.37065/0.13118, loss_spatial_dice_8: 0.25448/0.26926, loss_spatial_ce_8: 0.19032/0.22721, loss_grounding_bce_8: 0.56224/0.08899, loss_grounding_dice_8: 0.40206/0.17103, loss_grounding_ce_8: 0.01591/0.43870, loss_mask_ce_9: 2.19239/3.50197, loss_mask_bce_9: 0.73491/0.36070, loss_mask_dice_9: 0.59052/1.77342, loss_spatial_bce_9: 0.56562/0.35828, loss_spatial_dice_9: 0.66864/0.79691, loss_spatial_ce_9: 0.84768/1.41434, loss_grounding_bce_9: 0.53741/0.10071, loss_grounding_dice_9: 0.43889/0.24477, loss_grounding_ce_9: 0.02862/0.70729] items per batch[64] items per second[0.36] total items[1318400] mini batches[ 20600] memory[4967] epoch remaining[0:39:31] INFO:trainer.default_trainer:epochs[ 11] optim steps[20700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06204/0.78571, loss_mask_bce_0: 0.06367/0.30183, loss_mask_dice_0: 1.61255/1.03103, loss_spatial_bce_0: 0.02445/0.08965, loss_spatial_dice_0: 0.38737/0.18958, loss_spatial_ce_0: 0.06221/0.07249, loss_grounding_bce_0: 0.00421/0.08059, loss_grounding_dice_0: 0.02519/0.15189, loss_grounding_ce_0: 0.39374/0.25383, loss_mask_ce_1: 1.12384/0.78827, loss_mask_bce_1: 0.05578/0.30240, loss_mask_dice_1: 1.46514/1.03448, loss_spatial_bce_1: 0.02167/0.09010, loss_spatial_dice_1: 0.34798/0.19218, loss_spatial_ce_1: 0.08178/0.07710, loss_grounding_bce_1: 0.00432/0.08077, loss_grounding_dice_1: 0.02694/0.15282, loss_grounding_ce_1: 0.36089/0.25602, loss_mask_ce_2: 1.11444/0.79577, loss_mask_bce_2: 0.05858/0.30236, loss_mask_dice_2: 1.55671/1.03735, loss_spatial_bce_2: 0.02246/0.08970, loss_spatial_dice_2: 0.33816/0.19216, loss_spatial_ce_2: 0.11294/0.07965, loss_grounding_bce_2: 0.00606/0.08052, loss_grounding_dice_2: 0.03057/0.15237, loss_grounding_ce_2: 0.28645/0.25786, loss_mask_ce_3: 1.36993/0.79501, loss_mask_bce_3: 0.06916/0.30397, loss_mask_dice_3: 1.46790/1.03310, loss_spatial_bce_3: 0.02409/0.09127, loss_spatial_dice_3: 0.38207/0.19257, loss_spatial_ce_3: 0.07785/0.08547, loss_grounding_bce_3: 0.00321/0.08103, loss_grounding_dice_3: 0.02469/0.15218, loss_grounding_ce_3: 0.16121/0.25675, loss_mask_ce_4: 1.21054/0.80100, loss_mask_bce_4: 0.06747/0.30617, loss_mask_dice_4: 1.57198/1.05215, loss_spatial_bce_4: 0.02824/0.09324, loss_spatial_dice_4: 0.41237/0.19984, loss_spatial_ce_4: 0.02265/0.09731, loss_grounding_bce_4: 0.00721/0.08180, loss_grounding_dice_4: 0.03617/0.15449, loss_grounding_ce_4: 0.09163/0.26472, loss_mask_ce_5: 1.60926/0.82383, loss_mask_bce_5: 0.06090/0.30833, loss_mask_dice_5: 1.39830/1.05938, loss_spatial_bce_5: 0.04275/0.09501, loss_spatial_dice_5: 0.39033/0.20197, loss_spatial_ce_5: 0.02346/0.10872, loss_grounding_bce_5: 0.00292/0.08224, loss_grounding_dice_5: 0.02182/0.15540, loss_grounding_ce_5: 0.16817/0.28294, loss_mask_ce_6: 1.41131/0.84886, loss_mask_bce_6: 0.05872/0.30986, loss_mask_dice_6: 1.51182/1.06228, loss_spatial_bce_6: 0.05640/0.09988, loss_spatial_dice_6: 0.40143/0.20454, loss_spatial_ce_6: 0.03619/0.12844, loss_grounding_bce_6: 0.00503/0.08335, loss_grounding_dice_6: 0.03049/0.15596, loss_grounding_ce_6: 0.10323/0.29400, loss_mask_ce_7: 1.21257/0.91165, loss_mask_bce_7: 0.15239/0.31687, loss_mask_dice_7: 1.93127/1.10861, loss_spatial_bce_7: 0.05312/0.11012, loss_spatial_dice_7: 0.38977/0.22918, loss_spatial_ce_7: 0.26098/0.17129, loss_grounding_bce_7: 0.00632/0.08501, loss_grounding_dice_7: 0.03369/0.16186, loss_grounding_ce_7: 0.30070/0.33818, loss_mask_ce_8: 1.92337/1.04708, loss_mask_bce_8: 0.15574/0.33448, loss_mask_dice_8: 1.81671/1.18846, loss_spatial_bce_8: 0.08803/0.13107, loss_spatial_dice_8: 0.45303/0.26929, loss_spatial_ce_8: 0.64026/0.22720, loss_grounding_bce_8: 0.00679/0.08896, loss_grounding_dice_8: 0.03783/0.17105, loss_grounding_ce_8: 0.10430/0.43877, loss_mask_ce_9: 5.54134/3.50294, loss_mask_bce_9: 0.08841/0.36065, loss_mask_dice_9: 2.45048/1.77447, loss_spatial_bce_9: 0.08529/0.35815, loss_spatial_dice_9: 0.85316/0.79700, loss_spatial_ce_9: 1.28862/1.41421, loss_grounding_bce_9: 0.00479/0.10068, loss_grounding_dice_9: 0.04126/0.24482, loss_grounding_ce_9: 2.13689/0.70723] items per batch[64] items per second[0.36] total items[1324800] mini batches[ 20700] memory[4967] epoch remaining[0:36:28] INFO:trainer.default_trainer:epochs[ 11] optim steps[20800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29357/0.78542, loss_mask_bce_0: 0.04040/0.30189, loss_mask_dice_0: 0.37851/1.03006, loss_spatial_bce_0: 0.00989/0.08967, loss_spatial_dice_0: 0.08265/0.18950, loss_spatial_ce_0: 0.00247/0.07245, loss_grounding_bce_0: 0.00936/0.08062, loss_grounding_dice_0: 0.10189/0.15186, loss_grounding_ce_0: 0.00481/0.25368, loss_mask_ce_1: 0.31641/0.78800, loss_mask_bce_1: 0.03954/0.30247, loss_mask_dice_1: 0.29628/1.03355, loss_spatial_bce_1: 0.00811/0.09012, loss_spatial_dice_1: 0.07694/0.19208, loss_spatial_ce_1: 0.00349/0.07709, loss_grounding_bce_1: 0.00996/0.08079, loss_grounding_dice_1: 0.10266/0.15276, loss_grounding_ce_1: 0.00398/0.25581, loss_mask_ce_2: 0.30027/0.79545, loss_mask_bce_2: 0.04337/0.30241, loss_mask_dice_2: 0.31552/1.03628, loss_spatial_bce_2: 0.00879/0.08971, loss_spatial_dice_2: 0.09136/0.19207, loss_spatial_ce_2: 0.00237/0.07961, loss_grounding_bce_2: 0.00812/0.08053, loss_grounding_dice_2: 0.05868/0.15233, loss_grounding_ce_2: 0.00382/0.25769, loss_mask_ce_3: 0.27539/0.79473, loss_mask_bce_3: 0.04244/0.30404, loss_mask_dice_3: 0.28662/1.03209, loss_spatial_bce_3: 0.01081/0.09128, loss_spatial_dice_3: 0.10574/0.19250, loss_spatial_ce_3: 0.00255/0.08543, loss_grounding_bce_3: 0.00983/0.08105, loss_grounding_dice_3: 0.12069/0.15214, loss_grounding_ce_3: 0.00379/0.25659, loss_mask_ce_4: 0.28874/0.80072, loss_mask_bce_4: 0.04522/0.30619, loss_mask_dice_4: 0.37920/1.05104, loss_spatial_bce_4: 0.01028/0.09324, loss_spatial_dice_4: 0.09663/0.19979, loss_spatial_ce_4: 0.01963/0.09731, loss_grounding_bce_4: 0.00998/0.08182, loss_grounding_dice_4: 0.13458/0.15445, loss_grounding_ce_4: 0.00951/0.26456, loss_mask_ce_5: 0.31855/0.82347, loss_mask_bce_5: 0.03858/0.30837, loss_mask_dice_5: 0.31657/1.05829, loss_spatial_bce_5: 0.00937/0.09503, loss_spatial_dice_5: 0.07142/0.20191, loss_spatial_ce_5: 0.07512/0.10874, loss_grounding_bce_5: 0.00958/0.08227, loss_grounding_dice_5: 0.11079/0.15535, loss_grounding_ce_5: 0.00999/0.28284, loss_mask_ce_6: 0.31868/0.84849, loss_mask_bce_6: 0.04448/0.30991, loss_mask_dice_6: 0.35504/1.06123, loss_spatial_bce_6: 0.01071/0.09990, loss_spatial_dice_6: 0.07852/0.20447, loss_spatial_ce_6: 0.05621/0.12847, loss_grounding_bce_6: 0.00826/0.08336, loss_grounding_dice_6: 0.12220/0.15591, loss_grounding_ce_6: 0.01581/0.29380, loss_mask_ce_7: 0.26420/0.91128, loss_mask_bce_7: 0.04083/0.31691, loss_mask_dice_7: 0.42532/1.10751, loss_spatial_bce_7: 0.00944/0.11015, loss_spatial_dice_7: 0.06072/0.22909, loss_spatial_ce_7: 0.07812/0.17138, loss_grounding_bce_7: 0.00886/0.08504, loss_grounding_dice_7: 0.12342/0.16182, loss_grounding_ce_7: 0.02833/0.33798, loss_mask_ce_8: 0.37029/1.04653, loss_mask_bce_8: 0.04390/0.33455, loss_mask_dice_8: 0.41950/1.18729, loss_spatial_bce_8: 0.01194/0.13108, loss_spatial_dice_8: 0.12490/0.26919, loss_spatial_ce_8: 0.05183/0.22721, loss_grounding_bce_8: 0.00917/0.08898, loss_grounding_dice_8: 0.13271/0.17102, loss_grounding_ce_8: 0.07212/0.43849, loss_mask_ce_9: 3.09948/3.50180, loss_mask_bce_9: 0.05834/0.36062, loss_mask_dice_9: 0.51925/1.77291, loss_spatial_bce_9: 0.12742/0.35824, loss_spatial_dice_9: 0.89871/0.79689, loss_spatial_ce_9: 2.16452/1.41379, loss_grounding_bce_9: 0.00789/0.10071, loss_grounding_dice_9: 0.15049/0.24474, loss_grounding_ce_9: 0.20250/0.70660] items per batch[64] items per second[0.36] total items[1331200] mini batches[ 20800] memory[4967] epoch remaining[0:33:27] INFO:trainer.default_trainer:epochs[ 11] optim steps[20900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43008/0.78535, loss_mask_bce_0: 0.20818/0.30182, loss_mask_dice_0: 1.01164/1.03026, loss_spatial_bce_0: 0.03307/0.08971, loss_spatial_dice_0: 0.19567/0.18948, loss_spatial_ce_0: 0.08444/0.07257, loss_grounding_bce_0: 0.03622/0.08062, loss_grounding_dice_0: 0.58223/0.15188, loss_grounding_ce_0: 0.70570/0.25368, loss_mask_ce_1: 2.00449/0.78802, loss_mask_bce_1: 0.17382/0.30238, loss_mask_dice_1: 0.79962/1.03383, loss_spatial_bce_1: 0.03114/0.09013, loss_spatial_dice_1: 0.18702/0.19206, loss_spatial_ce_1: 0.11924/0.07711, loss_grounding_bce_1: 0.04326/0.08079, loss_grounding_dice_1: 0.57201/0.15279, loss_grounding_ce_1: 0.47222/0.25580, loss_mask_ce_2: 1.53554/0.79551, loss_mask_bce_2: 0.20854/0.30235, loss_mask_dice_2: 0.90811/1.03651, loss_spatial_bce_2: 0.03643/0.08971, loss_spatial_dice_2: 0.21234/0.19204, loss_spatial_ce_2: 0.04171/0.07960, loss_grounding_bce_2: 0.05831/0.08054, loss_grounding_dice_2: 0.57716/0.15234, loss_grounding_ce_2: 0.20159/0.25771, loss_mask_ce_3: 1.82567/0.79479, loss_mask_bce_3: 0.18645/0.30396, loss_mask_dice_3: 0.90697/1.03228, loss_spatial_bce_3: 0.04061/0.09132, loss_spatial_dice_3: 0.22106/0.19249, loss_spatial_ce_3: 0.06639/0.08537, loss_grounding_bce_3: 0.05618/0.08106, loss_grounding_dice_3: 0.60134/0.15217, loss_grounding_ce_3: 0.13741/0.25662, loss_mask_ce_4: 1.70737/0.80071, loss_mask_bce_4: 0.17977/0.30610, loss_mask_dice_4: 0.94289/1.05125, loss_spatial_bce_4: 0.04755/0.09325, loss_spatial_dice_4: 0.26026/0.19977, loss_spatial_ce_4: 0.07687/0.09735, loss_grounding_bce_4: 0.01448/0.08182, loss_grounding_dice_4: 0.58970/0.15447, loss_grounding_ce_4: 0.91794/0.26454, loss_mask_ce_5: 2.75601/0.82359, loss_mask_bce_5: 0.17335/0.30822, loss_mask_dice_5: 0.96639/1.05838, loss_spatial_bce_5: 0.04056/0.09508, loss_spatial_dice_5: 0.25624/0.20191, loss_spatial_ce_5: 0.09118/0.10860, loss_grounding_bce_5: 0.04540/0.08225, loss_grounding_dice_5: 0.61311/0.15535, loss_grounding_ce_5: 0.28367/0.28292, loss_mask_ce_6: 2.94368/0.84854, loss_mask_bce_6: 0.18299/0.30980, loss_mask_dice_6: 1.03532/1.06135, loss_spatial_bce_6: 0.04134/0.09995, loss_spatial_dice_6: 0.23932/0.20448, loss_spatial_ce_6: 0.18411/0.12841, loss_grounding_bce_6: 0.04769/0.08335, loss_grounding_dice_6: 0.63024/0.15595, loss_grounding_ce_6: 0.30542/0.29389, loss_mask_ce_7: 2.10607/0.91131, loss_mask_bce_7: 0.22857/0.31679, loss_mask_dice_7: 0.97852/1.10766, loss_spatial_bce_7: 0.03797/0.11020, loss_spatial_dice_7: 0.26377/0.22908, loss_spatial_ce_7: 0.32918/0.17131, loss_grounding_bce_7: 0.02275/0.08502, loss_grounding_dice_7: 0.66932/0.16184, loss_grounding_ce_7: 0.69896/0.33797, loss_mask_ce_8: 2.40213/1.04658, loss_mask_bce_8: 0.23192/0.33438, loss_mask_dice_8: 1.17231/1.18752, loss_spatial_bce_8: 0.10071/0.13108, loss_spatial_dice_8: 0.36100/0.26915, loss_spatial_ce_8: 0.14816/0.22714, loss_grounding_bce_8: 0.06621/0.08894, loss_grounding_dice_8: 0.75356/0.17107, loss_grounding_ce_8: 0.46715/0.43879, loss_mask_ce_9: 4.80126/3.50247, loss_mask_bce_9: 0.36318/0.36054, loss_mask_dice_9: 2.15200/1.77303, loss_spatial_bce_9: 0.35758/0.35828, loss_spatial_dice_9: 0.88564/0.79679, loss_spatial_ce_9: 1.54420/1.41362, loss_grounding_bce_9: 0.11629/0.10072, loss_grounding_dice_9: 0.91937/0.24480, loss_grounding_ce_9: 0.21313/0.70639] items per batch[64] items per second[0.36] total items[1337600] mini batches[ 20900] memory[4967] epoch remaining[0:30:27] INFO:trainer.default_trainer:epochs[ 11] optim steps[21000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81863/0.78550, loss_mask_bce_0: 0.33306/0.30167, loss_mask_dice_0: 0.37411/1.02967, loss_spatial_bce_0: 0.09129/0.08965, loss_spatial_dice_0: 0.10842/0.18944, loss_spatial_ce_0: 0.00646/0.07253, loss_grounding_bce_0: 0.08860/0.08053, loss_grounding_dice_0: 0.06359/0.15178, loss_grounding_ce_0: 0.00266/0.25372, loss_mask_ce_1: 0.78329/0.78823, loss_mask_bce_1: 0.33317/0.30220, loss_mask_dice_1: 0.37956/1.03336, loss_spatial_bce_1: 0.09458/0.09007, loss_spatial_dice_1: 0.10683/0.19202, loss_spatial_ce_1: 0.00971/0.07706, loss_grounding_bce_1: 0.09232/0.08071, loss_grounding_dice_1: 0.06467/0.15269, loss_grounding_ce_1: 0.00297/0.25584, loss_mask_ce_2: 0.79047/0.79569, loss_mask_bce_2: 0.34070/0.30220, loss_mask_dice_2: 0.37364/1.03595, loss_spatial_bce_2: 0.09149/0.08964, loss_spatial_dice_2: 0.10203/0.19199, loss_spatial_ce_2: 0.01282/0.07954, loss_grounding_bce_2: 0.08391/0.08047, loss_grounding_dice_2: 0.06248/0.15225, loss_grounding_ce_2: 0.00389/0.25785, loss_mask_ce_3: 0.85832/0.79507, loss_mask_bce_3: 0.32930/0.30382, loss_mask_dice_3: 0.36163/1.03179, loss_spatial_bce_3: 0.08980/0.09125, loss_spatial_dice_3: 0.10218/0.19245, loss_spatial_ce_3: 0.01474/0.08530, loss_grounding_bce_3: 0.08666/0.08098, loss_grounding_dice_3: 0.06299/0.15209, loss_grounding_ce_3: 0.00608/0.25663, loss_mask_ce_4: 0.87821/0.80081, loss_mask_bce_4: 0.33110/0.30596, loss_mask_dice_4: 0.36487/1.05085, loss_spatial_bce_4: 0.09006/0.09319, loss_spatial_dice_4: 0.09831/0.19975, loss_spatial_ce_4: 0.01784/0.09731, loss_grounding_bce_4: 0.09271/0.08174, loss_grounding_dice_4: 0.07704/0.15438, loss_grounding_ce_4: 0.00805/0.26452, loss_mask_ce_5: 0.83046/0.82364, loss_mask_bce_5: 0.34063/0.30807, loss_mask_dice_5: 0.38176/1.05804, loss_spatial_bce_5: 0.09397/0.09502, loss_spatial_dice_5: 0.11099/0.20186, loss_spatial_ce_5: 0.02245/0.10859, loss_grounding_bce_5: 0.09078/0.08217, loss_grounding_dice_5: 0.07362/0.15527, loss_grounding_ce_5: 0.00210/0.28288, loss_mask_ce_6: 0.73099/0.84872, loss_mask_bce_6: 0.33193/0.30966, loss_mask_dice_6: 0.37734/1.06085, loss_spatial_bce_6: 0.09187/0.09989, loss_spatial_dice_6: 0.10591/0.20445, loss_spatial_ce_6: 0.01553/0.12839, loss_grounding_bce_6: 0.09298/0.08326, loss_grounding_dice_6: 0.07326/0.15586, loss_grounding_ce_6: 0.00560/0.29382, loss_mask_ce_7: 0.87969/0.91160, loss_mask_bce_7: 0.35690/0.31661, loss_mask_dice_7: 0.41803/1.10716, loss_spatial_bce_7: 0.09284/0.11019, loss_spatial_dice_7: 0.12364/0.22905, loss_spatial_ce_7: 0.06504/0.17132, loss_grounding_bce_7: 0.09614/0.08492, loss_grounding_dice_7: 0.07121/0.16174, loss_grounding_ce_7: 0.00658/0.33790, loss_mask_ce_8: 1.07983/1.04683, loss_mask_bce_8: 0.37068/0.33418, loss_mask_dice_8: 0.38104/1.18700, loss_spatial_bce_8: 0.11254/0.13105, loss_spatial_dice_8: 0.12702/0.26913, loss_spatial_ce_8: 0.14470/0.22717, loss_grounding_bce_8: 0.09377/0.08885, loss_grounding_dice_8: 0.05830/0.17097, loss_grounding_ce_8: 0.00304/0.43874, loss_mask_ce_9: 2.56447/3.50253, loss_mask_bce_9: 0.39883/0.36044, loss_mask_dice_9: 0.61425/1.77267, loss_spatial_bce_9: 0.49500/0.35808, loss_spatial_dice_9: 0.69242/0.79677, loss_spatial_ce_9: 0.98301/1.41384, loss_grounding_bce_9: 0.10645/0.10064, loss_grounding_dice_9: 0.10379/0.24472, loss_grounding_ce_9: 0.02648/0.70642] items per batch[64] items per second[0.37] total items[1344000] mini batches[ 21000] memory[4967] epoch remaining[0:27:24] INFO:trainer.default_trainer:epochs[ 11] optim steps[21100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53932/0.78513, loss_mask_bce_0: 0.28294/0.30166, loss_mask_dice_0: 2.31456/1.03013, loss_spatial_bce_0: 0.02765/0.08969, loss_spatial_dice_0: 0.30322/0.18939, loss_spatial_ce_0: 0.01058/0.07237, loss_grounding_bce_0: 0.03604/0.08062, loss_grounding_dice_0: 0.42938/0.15184, loss_grounding_ce_0: 0.44685/0.25378, loss_mask_ce_1: 0.53117/0.78781, loss_mask_bce_1: 0.28463/0.30220, loss_mask_dice_1: 2.59289/1.03389, loss_spatial_bce_1: 0.03146/0.09011, loss_spatial_dice_1: 0.30700/0.19196, loss_spatial_ce_1: 0.19601/0.07694, loss_grounding_bce_1: 0.03473/0.08080, loss_grounding_dice_1: 0.39497/0.15274, loss_grounding_ce_1: 0.45613/0.25597, loss_mask_ce_2: 0.57568/0.79529, loss_mask_bce_2: 0.27318/0.30221, loss_mask_dice_2: 3.43727/1.03664, loss_spatial_bce_2: 0.03441/0.08969, loss_spatial_dice_2: 0.31822/0.19193, loss_spatial_ce_2: 0.00495/0.07942, loss_grounding_bce_2: 0.03389/0.08056, loss_grounding_dice_2: 0.37102/0.15233, loss_grounding_ce_2: 0.53128/0.25797, loss_mask_ce_3: 0.50283/0.79466, loss_mask_bce_3: 0.28945/0.30381, loss_mask_dice_3: 3.41721/1.03246, loss_spatial_bce_3: 0.03493/0.09133, loss_spatial_dice_3: 0.30848/0.19240, loss_spatial_ce_3: 0.07296/0.08517, loss_grounding_bce_3: 0.03292/0.08107, loss_grounding_dice_3: 0.28603/0.15217, loss_grounding_ce_3: 0.38813/0.25677, loss_mask_ce_4: 0.66204/0.80041, loss_mask_bce_4: 0.29831/0.30597, loss_mask_dice_4: 3.20663/1.05130, loss_spatial_bce_4: 0.03484/0.09323, loss_spatial_dice_4: 0.34726/0.19967, loss_spatial_ce_4: 0.09443/0.09724, loss_grounding_bce_4: 0.03302/0.08182, loss_grounding_dice_4: 0.38560/0.15445, loss_grounding_ce_4: 0.40712/0.26459, loss_mask_ce_5: 0.67318/0.82344, loss_mask_bce_5: 0.29133/0.30805, loss_mask_dice_5: 3.57101/1.05861, loss_spatial_bce_5: 0.03071/0.09507, loss_spatial_dice_5: 0.31591/0.20179, loss_spatial_ce_5: 0.05612/0.10843, loss_grounding_bce_5: 0.03225/0.08225, loss_grounding_dice_5: 0.30269/0.15530, loss_grounding_ce_5: 0.48492/0.28302, loss_mask_ce_6: 0.50772/0.84844, loss_mask_bce_6: 0.27455/0.30962, loss_mask_dice_6: 3.48276/1.06141, loss_spatial_bce_6: 0.03121/0.09991, loss_spatial_dice_6: 0.29843/0.20439, loss_spatial_ce_6: 0.30119/0.12833, loss_grounding_bce_6: 0.03358/0.08333, loss_grounding_dice_6: 0.34736/0.15592, loss_grounding_ce_6: 0.43492/0.29404, loss_mask_ce_7: 0.70582/0.91140, loss_mask_bce_7: 0.26983/0.31656, loss_mask_dice_7: 3.08847/1.10772, loss_spatial_bce_7: 0.03084/0.11018, loss_spatial_dice_7: 0.34856/0.22898, loss_spatial_ce_7: 0.34825/0.17130, loss_grounding_bce_7: 0.03745/0.08500, loss_grounding_dice_7: 0.54410/0.16181, loss_grounding_ce_7: 0.42783/0.33844, loss_mask_ce_8: 0.78338/1.04661, loss_mask_bce_8: 0.30454/0.33416, loss_mask_dice_8: 3.28633/1.18770, loss_spatial_bce_8: 0.03226/0.13102, loss_spatial_dice_8: 0.31577/0.26898, loss_spatial_ce_8: 0.08741/0.22710, loss_grounding_bce_8: 0.03298/0.08892, loss_grounding_dice_8: 0.42914/0.17103, loss_grounding_ce_8: 0.46310/0.43862, loss_mask_ce_9: 3.86704/3.50256, loss_mask_bce_9: 0.29984/0.36045, loss_mask_dice_9: 3.82506/1.77352, loss_spatial_bce_9: 0.08891/0.35806, loss_spatial_dice_9: 0.94911/0.79666, loss_spatial_ce_9: 1.73998/1.41421, loss_grounding_bce_9: 0.04211/0.10072, loss_grounding_dice_9: 0.49777/0.24474, loss_grounding_ce_9: 0.58373/0.70623] items per batch[64] items per second[0.36] total items[1350400] mini batches[ 21100] memory[4967] epoch remaining[0:24:25] INFO:trainer.default_trainer:epochs[ 11] optim steps[21200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92459/0.78544, loss_mask_bce_0: 0.31566/0.30152, loss_mask_dice_0: 7.13676/1.03038, loss_spatial_bce_0: 0.00850/0.08962, loss_spatial_dice_0: 0.25601/0.18935, loss_spatial_ce_0: 0.01559/0.07225, loss_grounding_bce_0: 0.04514/0.08054, loss_grounding_dice_0: 0.39161/0.15183, loss_grounding_ce_0: 0.83924/0.25411, loss_mask_ce_1: 0.95734/0.78811, loss_mask_bce_1: 0.33798/0.30209, loss_mask_dice_1: 7.51762/1.03415, loss_spatial_bce_1: 0.00810/0.09002, loss_spatial_dice_1: 0.24224/0.19191, loss_spatial_ce_1: 0.01227/0.07688, loss_grounding_bce_1: 0.04193/0.08073, loss_grounding_dice_1: 0.30287/0.15273, loss_grounding_ce_1: 0.81514/0.25634, loss_mask_ce_2: 0.88553/0.79551, loss_mask_bce_2: 0.33986/0.30209, loss_mask_dice_2: 7.63309/1.03685, loss_spatial_bce_2: 0.00884/0.08960, loss_spatial_dice_2: 0.25024/0.19189, loss_spatial_ce_2: 0.01538/0.07929, loss_grounding_bce_2: 0.03613/0.08048, loss_grounding_dice_2: 0.23805/0.15232, loss_grounding_ce_2: 1.27283/0.25831, loss_mask_ce_3: 1.20050/0.79493, loss_mask_bce_3: 0.33483/0.30371, loss_mask_dice_3: 7.37221/1.03280, loss_spatial_bce_3: 0.01013/0.09124, loss_spatial_dice_3: 0.26727/0.19238, loss_spatial_ce_3: 0.03112/0.08505, loss_grounding_bce_3: 0.03518/0.08100, loss_grounding_dice_3: 0.26245/0.15220, loss_grounding_ce_3: 1.26355/0.25716, loss_mask_ce_4: 1.07721/0.80070, loss_mask_bce_4: 0.34081/0.30585, loss_mask_dice_4: 7.29273/1.05158, loss_spatial_bce_4: 0.01097/0.09316, loss_spatial_dice_4: 0.29680/0.19964, loss_spatial_ce_4: 0.14686/0.09713, loss_grounding_bce_4: 0.11946/0.08174, loss_grounding_dice_4: 0.45652/0.15448, loss_grounding_ce_4: 1.31867/0.26498, loss_mask_ce_5: 1.30826/0.82376, loss_mask_bce_5: 0.33113/0.30792, loss_mask_dice_5: 7.63895/1.05898, loss_spatial_bce_5: 0.01049/0.09499, loss_spatial_dice_5: 0.29532/0.20175, loss_spatial_ce_5: 0.12464/0.10829, loss_grounding_bce_5: 0.11733/0.08218, loss_grounding_dice_5: 0.41592/0.15530, loss_grounding_ce_5: 1.48847/0.28333, loss_mask_ce_6: 1.07558/0.84870, loss_mask_bce_6: 0.32793/0.30948, loss_mask_dice_6: 7.48622/1.06170, loss_spatial_bce_6: 0.01547/0.09984, loss_spatial_dice_6: 0.33341/0.20437, loss_spatial_ce_6: 0.09397/0.12820, loss_grounding_bce_6: 0.15395/0.08326, loss_grounding_dice_6: 0.43002/0.15595, loss_grounding_ce_6: 1.57622/0.29437, loss_mask_ce_7: 1.36045/0.91160, loss_mask_bce_7: 0.36477/0.31643, loss_mask_dice_7: 8.39929/1.10816, loss_spatial_bce_7: 0.01168/0.11008, loss_spatial_dice_7: 0.32613/0.22892, loss_spatial_ce_7: 0.08810/0.17120, loss_grounding_bce_7: 0.10907/0.08492, loss_grounding_dice_7: 0.42501/0.16182, loss_grounding_ce_7: 1.05164/0.33835, loss_mask_ce_8: 1.99626/1.04671, loss_mask_bce_8: 0.45001/0.33405, loss_mask_dice_8: 9.18271/1.18801, loss_spatial_bce_8: 0.01679/0.13088, loss_spatial_dice_8: 0.37712/0.26890, loss_spatial_ce_8: 0.07709/0.22695, loss_grounding_bce_8: 0.10890/0.08883, loss_grounding_dice_8: 0.44223/0.17103, loss_grounding_ce_8: 0.87298/0.43866, loss_mask_ce_9: 6.16459/3.50182, loss_mask_bce_9: 0.38510/0.36029, loss_mask_dice_9: 12.45259/1.77399, loss_spatial_bce_9: 0.05042/0.35784, loss_spatial_dice_9: 0.93228/0.79664, loss_spatial_ce_9: 1.35071/1.41447, loss_grounding_bce_9: 0.05165/0.10063, loss_grounding_dice_9: 0.41810/0.24472, loss_grounding_ce_9: 2.94359/0.70640] items per batch[64] items per second[0.36] total items[1356800] mini batches[ 21200] memory[4967] epoch remaining[0:21:26] INFO:trainer.default_trainer:epochs[ 11] optim steps[21300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08241/0.78519, loss_mask_bce_0: 0.38930/0.30152, loss_mask_dice_0: 1.87877/1.02970, loss_spatial_bce_0: 0.03834/0.08956, loss_spatial_dice_0: 0.20101/0.18920, loss_spatial_ce_0: 0.00164/0.07214, loss_grounding_bce_0: 0.02443/0.08047, loss_grounding_dice_0: 0.13181/0.15176, loss_grounding_ce_0: 0.33516/0.25386, loss_mask_ce_1: 1.11466/0.78777, loss_mask_bce_1: 0.39434/0.30210, loss_mask_dice_1: 1.97101/1.03354, loss_spatial_bce_1: 0.03746/0.08996, loss_spatial_dice_1: 0.21257/0.19176, loss_spatial_ce_1: 0.00127/0.07675, loss_grounding_bce_1: 0.02365/0.08066, loss_grounding_dice_1: 0.12136/0.15268, loss_grounding_ce_1: 0.33363/0.25602, loss_mask_ce_2: 1.16633/0.79513, loss_mask_bce_2: 0.42539/0.30210, loss_mask_dice_2: 1.86491/1.03617, loss_spatial_bce_2: 0.03888/0.08955, loss_spatial_dice_2: 0.21608/0.19175, loss_spatial_ce_2: 0.00095/0.07913, loss_grounding_bce_2: 0.02478/0.08041, loss_grounding_dice_2: 0.14132/0.15226, loss_grounding_ce_2: 0.32594/0.25807, loss_mask_ce_3: 1.10807/0.79461, loss_mask_bce_3: 0.42757/0.30372, loss_mask_dice_3: 1.96868/1.03207, loss_spatial_bce_3: 0.04190/0.09119, loss_spatial_dice_3: 0.21616/0.19224, loss_spatial_ce_3: 0.00332/0.08490, loss_grounding_bce_3: 0.02307/0.08094, loss_grounding_dice_3: 0.10840/0.15214, loss_grounding_ce_3: 0.34550/0.25691, loss_mask_ce_4: 1.34753/0.80042, loss_mask_bce_4: 0.39355/0.30586, loss_mask_dice_4: 1.85956/1.05089, loss_spatial_bce_4: 0.04722/0.09311, loss_spatial_dice_4: 0.21469/0.19948, loss_spatial_ce_4: 0.00895/0.09705, loss_grounding_bce_4: 0.02198/0.08167, loss_grounding_dice_4: 0.11257/0.15441, loss_grounding_ce_4: 0.34493/0.26466, loss_mask_ce_5: 1.14914/0.82337, loss_mask_bce_5: 0.39590/0.30795, loss_mask_dice_5: 1.99199/1.05837, loss_spatial_bce_5: 0.04450/0.09494, loss_spatial_dice_5: 0.20799/0.20159, loss_spatial_ce_5: 0.20540/0.10814, loss_grounding_bce_5: 0.02433/0.08212, loss_grounding_dice_5: 0.18363/0.15523, loss_grounding_ce_5: 0.37832/0.28301, loss_mask_ce_6: 1.15091/0.84839, loss_mask_bce_6: 0.41041/0.30949, loss_mask_dice_6: 1.87161/1.06095, loss_spatial_bce_6: 0.03948/0.09979, loss_spatial_dice_6: 0.17257/0.20421, loss_spatial_ce_6: 0.20811/0.12805, loss_grounding_bce_6: 0.02151/0.08318, loss_grounding_dice_6: 0.12544/0.15587, loss_grounding_ce_6: 0.39595/0.29404, loss_mask_ce_7: 1.23006/0.91122, loss_mask_bce_7: 0.41074/0.31645, loss_mask_dice_7: 1.94770/1.10746, loss_spatial_bce_7: 0.04950/0.11003, loss_spatial_dice_7: 0.23384/0.22873, loss_spatial_ce_7: 0.10670/0.17096, loss_grounding_bce_7: 0.02125/0.08483, loss_grounding_dice_7: 0.12338/0.16173, loss_grounding_ce_7: 0.55145/0.33792, loss_mask_ce_8: 1.62879/1.04620, loss_mask_bce_8: 0.40207/0.33406, loss_mask_dice_8: 2.49196/1.18726, loss_spatial_bce_8: 0.08289/0.13082, loss_spatial_dice_8: 0.35138/0.26868, loss_spatial_ce_8: 0.11695/0.22676, loss_grounding_bce_8: 0.02538/0.08875, loss_grounding_dice_8: 0.15331/0.17093, loss_grounding_ce_8: 0.49848/0.43812, loss_mask_ce_9: 4.90663/3.50151, loss_mask_bce_9: 0.52983/0.36029, loss_mask_dice_9: 3.92415/1.77304, loss_spatial_bce_9: 0.26876/0.35796, loss_spatial_dice_9: 0.87430/0.79649, loss_spatial_ce_9: 1.36367/1.41401, loss_grounding_bce_9: 0.03055/0.10057, loss_grounding_dice_9: 0.27013/0.24467, loss_grounding_ce_9: 0.53613/0.70579] items per batch[64] items per second[0.36] total items[1363200] mini batches[ 21300] memory[4967] epoch remaining[0:18:29] INFO:trainer.default_trainer:epochs[ 11] optim steps[21400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01988/0.78504, loss_mask_bce_0: 0.07769/0.30150, loss_mask_dice_0: 0.06067/1.02980, loss_spatial_bce_0: 0.04726/0.08954, loss_spatial_dice_0: 0.03726/0.18919, loss_spatial_ce_0: 0.00913/0.07198, loss_grounding_bce_0: 0.03416/0.08045, loss_grounding_dice_0: 0.02357/0.15174, loss_grounding_ce_0: 0.00351/0.25405, loss_mask_ce_1: 0.01816/0.78742, loss_mask_bce_1: 0.07991/0.30209, loss_mask_dice_1: 0.05952/1.03373, loss_spatial_bce_1: 0.04705/0.08994, loss_spatial_dice_1: 0.03597/0.19175, loss_spatial_ce_1: 0.02064/0.07660, loss_grounding_bce_1: 0.03663/0.08063, loss_grounding_dice_1: 0.02440/0.15264, loss_grounding_ce_1: 0.00247/0.25633, loss_mask_ce_2: 0.01720/0.79494, loss_mask_bce_2: 0.07585/0.30209, loss_mask_dice_2: 0.05777/1.03640, loss_spatial_bce_2: 0.04738/0.08954, loss_spatial_dice_2: 0.03576/0.19174, loss_spatial_ce_2: 0.03002/0.07894, loss_grounding_bce_2: 0.03580/0.08039, loss_grounding_dice_2: 0.02601/0.15221, loss_grounding_ce_2: 0.00221/0.25841, loss_mask_ce_3: 0.01879/0.79438, loss_mask_bce_3: 0.08551/0.30372, loss_mask_dice_3: 0.06036/1.03214, loss_spatial_bce_3: 0.05036/0.09118, loss_spatial_dice_3: 0.03849/0.19223, loss_spatial_ce_3: 0.02287/0.08470, loss_grounding_bce_3: 0.03431/0.08091, loss_grounding_dice_3: 0.02491/0.15211, loss_grounding_ce_3: 0.00203/0.25717, loss_mask_ce_4: 0.02866/0.80028, loss_mask_bce_4: 0.08462/0.30585, loss_mask_dice_4: 0.06058/1.05108, loss_spatial_bce_4: 0.05110/0.09310, loss_spatial_dice_4: 0.04029/0.19947, loss_spatial_ce_4: 0.01428/0.09686, loss_grounding_bce_4: 0.03661/0.08164, loss_grounding_dice_4: 0.02596/0.15437, loss_grounding_ce_4: 0.00712/0.26489, loss_mask_ce_5: 0.03344/0.82322, loss_mask_bce_5: 0.08102/0.30794, loss_mask_dice_5: 0.05628/1.05847, loss_spatial_bce_5: 0.04855/0.09491, loss_spatial_dice_5: 0.03740/0.20155, loss_spatial_ce_5: 0.00936/0.10798, loss_grounding_bce_5: 0.03731/0.08209, loss_grounding_dice_5: 0.02386/0.15519, loss_grounding_ce_5: 0.01036/0.28341, loss_mask_ce_6: 0.03149/0.84821, loss_mask_bce_6: 0.08778/0.30949, loss_mask_dice_6: 0.05666/1.06096, loss_spatial_bce_6: 0.04591/0.09977, loss_spatial_dice_6: 0.03747/0.20417, loss_spatial_ce_6: 0.01082/0.12800, loss_grounding_bce_6: 0.03616/0.08316, loss_grounding_dice_6: 0.02278/0.15584, loss_grounding_ce_6: 0.00339/0.29451, loss_mask_ce_7: 0.04389/0.91093, loss_mask_bce_7: 0.07989/0.31647, loss_mask_dice_7: 0.06401/1.10747, loss_spatial_bce_7: 0.04575/0.11002, loss_spatial_dice_7: 0.04507/0.22868, loss_spatial_ce_7: 0.00687/0.17088, loss_grounding_bce_7: 0.03378/0.08480, loss_grounding_dice_7: 0.02485/0.16170, loss_grounding_ce_7: 0.00722/0.33832, loss_mask_ce_8: 0.06950/1.04608, loss_mask_bce_8: 0.08478/0.33403, loss_mask_dice_8: 0.06878/1.18720, loss_spatial_bce_8: 0.05245/0.13078, loss_spatial_dice_8: 0.04885/0.26866, loss_spatial_ce_8: 0.02527/0.22667, loss_grounding_bce_8: 0.03666/0.08873, loss_grounding_dice_8: 0.03121/0.17093, loss_grounding_ce_8: 0.01383/0.43840, loss_mask_ce_9: 1.83078/3.50197, loss_mask_bce_9: 0.10553/0.36033, loss_mask_dice_9: 0.13076/1.77301, loss_spatial_bce_9: 0.61336/0.35801, loss_spatial_dice_9: 0.71480/0.79647, loss_spatial_ce_9: 0.87000/1.41385, loss_grounding_bce_9: 0.05120/0.10053, loss_grounding_dice_9: 0.06673/0.24469, loss_grounding_ce_9: 0.07551/0.70578] items per batch[64] items per second[0.36] total items[1369600] mini batches[ 21400] memory[4967] epoch remaining[0:15:31] INFO:trainer.default_trainer:epochs[ 11] optim steps[21500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.40901/0.78489, loss_mask_bce_0: 0.61064/0.30162, loss_mask_dice_0: 3.13444/1.03071, loss_spatial_bce_0: 0.04847/0.08950, loss_spatial_dice_0: 0.33605/0.18914, loss_spatial_ce_0: 0.01247/0.07181, loss_grounding_bce_0: 0.03780/0.08047, loss_grounding_dice_0: 0.17679/0.15175, loss_grounding_ce_0: 0.00157/0.25391, loss_mask_ce_1: 1.43843/0.78726, loss_mask_bce_1: 0.60888/0.30220, loss_mask_dice_1: 3.21405/1.03461, loss_spatial_bce_1: 0.05210/0.08990, loss_spatial_dice_1: 0.33020/0.19170, loss_spatial_ce_1: 0.02699/0.07641, loss_grounding_bce_1: 0.04163/0.08065, loss_grounding_dice_1: 0.17226/0.15263, loss_grounding_ce_1: 0.00146/0.25620, loss_mask_ce_2: 1.76657/0.79485, loss_mask_bce_2: 0.55833/0.30219, loss_mask_dice_2: 2.91539/1.03728, loss_spatial_bce_2: 0.05428/0.08951, loss_spatial_dice_2: 0.38401/0.19168, loss_spatial_ce_2: 0.02364/0.07880, loss_grounding_bce_2: 0.04018/0.08041, loss_grounding_dice_2: 0.18122/0.15223, loss_grounding_ce_2: 0.00313/0.25828, loss_mask_ce_3: 1.74080/0.79423, loss_mask_bce_3: 0.58697/0.30383, loss_mask_dice_3: 3.02080/1.03308, loss_spatial_bce_3: 0.06308/0.09114, loss_spatial_dice_3: 0.38214/0.19219, loss_spatial_ce_3: 0.04305/0.08451, loss_grounding_bce_3: 0.04816/0.08093, loss_grounding_dice_3: 0.16438/0.15211, loss_grounding_ce_3: 0.00382/0.25702, loss_mask_ce_4: 1.64100/0.80007, loss_mask_bce_4: 0.50372/0.30596, loss_mask_dice_4: 2.86962/1.05207, loss_spatial_bce_4: 0.04253/0.09306, loss_spatial_dice_4: 0.33856/0.19942, loss_spatial_ce_4: 0.08579/0.09670, loss_grounding_bce_4: 0.04163/0.08166, loss_grounding_dice_4: 0.17813/0.15437, loss_grounding_ce_4: 0.00160/0.26473, loss_mask_ce_5: 1.66401/0.82305, loss_mask_bce_5: 0.56096/0.30804, loss_mask_dice_5: 3.18526/1.05932, loss_spatial_bce_5: 0.04342/0.09488, loss_spatial_dice_5: 0.34097/0.20152, loss_spatial_ce_5: 0.08487/0.10782, loss_grounding_bce_5: 0.03814/0.08211, loss_grounding_dice_5: 0.15888/0.15520, loss_grounding_ce_5: 0.00586/0.28351, loss_mask_ce_6: 1.86553/0.84794, loss_mask_bce_6: 0.58689/0.30959, loss_mask_dice_6: 2.97644/1.06190, loss_spatial_bce_6: 0.06362/0.09973, loss_spatial_dice_6: 0.37118/0.20412, loss_spatial_ce_6: 0.08011/0.12782, loss_grounding_bce_6: 0.05238/0.08318, loss_grounding_dice_6: 0.18426/0.15586, loss_grounding_ce_6: 0.01235/0.29448, loss_mask_ce_7: 1.70815/0.91070, loss_mask_bce_7: 0.62630/0.31656, loss_mask_dice_7: 3.36361/1.10847, loss_spatial_bce_7: 0.13187/0.10997, loss_spatial_dice_7: 0.41044/0.22865, loss_spatial_ce_7: 0.14979/0.17063, loss_grounding_bce_7: 0.04385/0.08483, loss_grounding_dice_7: 0.15382/0.16173, loss_grounding_ce_7: 0.02754/0.33806, loss_mask_ce_8: 2.68705/1.04589, loss_mask_bce_8: 0.63021/0.33414, loss_mask_dice_8: 3.85971/1.18815, loss_spatial_bce_8: 0.08717/0.13067, loss_spatial_dice_8: 0.44439/0.26863, loss_spatial_ce_8: 0.13006/0.22630, loss_grounding_bce_8: 0.03496/0.08878, loss_grounding_dice_8: 0.17655/0.17099, loss_grounding_ce_8: 0.21055/0.43809, loss_mask_ce_9: 5.24292/3.50198, loss_mask_bce_9: 0.52446/0.36044, loss_mask_dice_9: 4.54151/1.77505, loss_spatial_bce_9: 0.18459/0.35792, loss_spatial_dice_9: 0.97458/0.79651, loss_spatial_ce_9: 1.34056/1.41377, loss_grounding_bce_9: 0.03636/0.10054, loss_grounding_dice_9: 0.37581/0.24469, loss_grounding_ce_9: 0.32857/0.70555] items per batch[64] items per second[0.36] total items[1376000] mini batches[ 21500] memory[4967] epoch remaining[0:12:33] INFO:trainer.default_trainer:epochs[ 11] optim steps[21600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05724/0.78560, loss_mask_bce_0: 0.08088/0.30181, loss_mask_dice_0: 0.08219/1.03043, loss_spatial_bce_0: 0.21122/0.08949, loss_spatial_dice_0: 0.18691/0.18909, loss_spatial_ce_0: 0.00007/0.07179, loss_grounding_bce_0: 0.18091/0.08045, loss_grounding_dice_0: 0.18729/0.15169, loss_grounding_ce_0: 0.00110/0.25393, loss_mask_ce_1: 0.05699/0.78793, loss_mask_bce_1: 0.08595/0.30241, loss_mask_dice_1: 0.08413/1.03432, loss_spatial_bce_1: 0.20009/0.08990, loss_spatial_dice_1: 0.18393/0.19166, loss_spatial_ce_1: 0.00005/0.07640, loss_grounding_bce_1: 0.19630/0.08063, loss_grounding_dice_1: 0.18919/0.15256, loss_grounding_ce_1: 0.00107/0.25614, loss_mask_ce_2: 0.03990/0.79556, loss_mask_bce_2: 0.08509/0.30238, loss_mask_dice_2: 0.08449/1.03699, loss_spatial_bce_2: 0.20705/0.08950, loss_spatial_dice_2: 0.18623/0.19163, loss_spatial_ce_2: 0.00005/0.07879, loss_grounding_bce_2: 0.21119/0.08039, loss_grounding_dice_2: 0.19422/0.15217, loss_grounding_ce_2: 0.00101/0.25822, loss_mask_ce_3: 0.06993/0.79500, loss_mask_bce_3: 0.08148/0.30402, loss_mask_dice_3: 0.08020/1.03277, loss_spatial_bce_3: 0.21720/0.09114, loss_spatial_dice_3: 0.18692/0.19216, loss_spatial_ce_3: 0.00009/0.08451, loss_grounding_bce_3: 0.21769/0.08091, loss_grounding_dice_3: 0.20383/0.15206, loss_grounding_ce_3: 0.00077/0.25695, loss_mask_ce_4: 0.06738/0.80083, loss_mask_bce_4: 0.08992/0.30616, loss_mask_dice_4: 0.08859/1.05196, loss_spatial_bce_4: 0.20819/0.09307, loss_spatial_dice_4: 0.18680/0.19938, loss_spatial_ce_4: 0.00022/0.09671, loss_grounding_bce_4: 0.20791/0.08164, loss_grounding_dice_4: 0.18453/0.15430, loss_grounding_ce_4: 0.00112/0.26469, loss_mask_ce_5: 0.06357/0.82365, loss_mask_bce_5: 0.08578/0.30825, loss_mask_dice_5: 0.08798/1.05911, loss_spatial_bce_5: 0.20982/0.09489, loss_spatial_dice_5: 0.18148/0.20148, loss_spatial_ce_5: 0.00040/0.10780, loss_grounding_bce_5: 0.21424/0.08208, loss_grounding_dice_5: 0.19423/0.15511, loss_grounding_ce_5: 0.00041/0.28352, loss_mask_ce_6: 0.08330/0.84858, loss_mask_bce_6: 0.08890/0.30983, loss_mask_dice_6: 0.08586/1.06179, loss_spatial_bce_6: 0.23205/0.09978, loss_spatial_dice_6: 0.17817/0.20408, loss_spatial_ce_6: 0.00753/0.12783, loss_grounding_bce_6: 0.21171/0.08316, loss_grounding_dice_6: 0.17957/0.15579, loss_grounding_ce_6: 0.00074/0.29458, loss_mask_ce_7: 0.08891/0.91147, loss_mask_bce_7: 0.08560/0.31678, loss_mask_dice_7: 0.08720/1.10830, loss_spatial_bce_7: 0.28451/0.10999, loss_spatial_dice_7: 0.19595/0.22860, loss_spatial_ce_7: 0.00437/0.17059, loss_grounding_bce_7: 0.20794/0.08482, loss_grounding_dice_7: 0.19803/0.16166, loss_grounding_ce_7: 0.00285/0.33796, loss_mask_ce_8: 0.09635/1.04678, loss_mask_bce_8: 0.10225/0.33435, loss_mask_dice_8: 0.08931/1.18800, loss_spatial_bce_8: 0.22167/0.13070, loss_spatial_dice_8: 0.19592/0.26859, loss_spatial_ce_8: 0.23288/0.22623, loss_grounding_bce_8: 0.22439/0.08878, loss_grounding_dice_8: 0.18945/0.17095, loss_grounding_ce_8: 0.00303/0.43819, loss_mask_ce_9: 2.22038/3.50319, loss_mask_bce_9: 0.06611/0.36066, loss_mask_dice_9: 0.10824/1.77489, loss_spatial_bce_9: 0.14974/0.35795, loss_spatial_dice_9: 0.24377/0.79652, loss_spatial_ce_9: 0.04658/1.41360, loss_grounding_bce_9: 0.23189/0.10056, loss_grounding_dice_9: 0.22594/0.24466, loss_grounding_ce_9: 0.21221/0.70590] items per batch[64] items per second[0.36] total items[1382400] mini batches[ 21600] memory[4967] epoch remaining[0:09:36] INFO:trainer.default_trainer:epochs[ 11] optim steps[21700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20709/0.78555, loss_mask_bce_0: 0.40774/0.30184, loss_mask_dice_0: 4.60150/1.03142, loss_spatial_bce_0: 0.01915/0.08944, loss_spatial_dice_0: 0.39456/0.18910, loss_spatial_ce_0: 0.05244/0.07174, loss_grounding_bce_0: 0.10578/0.08043, loss_grounding_dice_0: 0.25326/0.15168, loss_grounding_ce_0: 0.36342/0.25387, loss_mask_ce_1: 1.39170/0.78774, loss_mask_bce_1: 0.41133/0.30245, loss_mask_dice_1: 4.47835/1.03529, loss_spatial_bce_1: 0.01866/0.08985, loss_spatial_dice_1: 0.42564/0.19168, loss_spatial_ce_1: 0.18562/0.07636, loss_grounding_bce_1: 0.10832/0.08063, loss_grounding_dice_1: 0.27013/0.15254, loss_grounding_ce_1: 0.38246/0.25608, loss_mask_ce_2: 1.18776/0.79550, loss_mask_bce_2: 0.41223/0.30244, loss_mask_dice_2: 4.68905/1.03793, loss_spatial_bce_2: 0.02209/0.08945, loss_spatial_dice_2: 0.44178/0.19167, loss_spatial_ce_2: 0.03877/0.07870, loss_grounding_bce_2: 0.10338/0.08039, loss_grounding_dice_2: 0.26270/0.15220, loss_grounding_ce_2: 0.38601/0.25816, loss_mask_ce_3: 1.21340/0.79515, loss_mask_bce_3: 0.42879/0.30406, loss_mask_dice_3: 5.37242/1.03370, loss_spatial_bce_3: 0.02095/0.09109, loss_spatial_dice_3: 0.41857/0.19218, loss_spatial_ce_3: 0.25563/0.08443, loss_grounding_bce_3: 0.09597/0.08092, loss_grounding_dice_3: 0.24793/0.15203, loss_grounding_ce_3: 0.41426/0.25700, loss_mask_ce_4: 1.26758/0.80080, loss_mask_bce_4: 0.39925/0.30619, loss_mask_dice_4: 3.90322/1.05283, loss_spatial_bce_4: 0.01887/0.09303, loss_spatial_dice_4: 0.45866/0.19940, loss_spatial_ce_4: 0.50192/0.09663, loss_grounding_bce_4: 0.09373/0.08162, loss_grounding_dice_4: 0.26614/0.15429, loss_grounding_ce_4: 0.30743/0.26469, loss_mask_ce_5: 1.00834/0.82371, loss_mask_bce_5: 0.48500/0.30826, loss_mask_dice_5: 4.74564/1.05997, loss_spatial_bce_5: 0.02106/0.09484, loss_spatial_dice_5: 0.42297/0.20150, loss_spatial_ce_5: 0.31685/0.10770, loss_grounding_bce_5: 0.10124/0.08209, loss_grounding_dice_5: 0.26167/0.15510, loss_grounding_ce_5: 0.34283/0.28340, loss_mask_ce_6: 1.33576/0.84849, loss_mask_bce_6: 0.47204/0.30985, loss_mask_dice_6: 4.93898/1.06273, loss_spatial_bce_6: 0.02136/0.09974, loss_spatial_dice_6: 0.45573/0.20408, loss_spatial_ce_6: 0.41785/0.12781, loss_grounding_bce_6: 0.19173/0.08318, loss_grounding_dice_6: 0.31397/0.15578, loss_grounding_ce_6: 0.22984/0.29442, loss_mask_ce_7: 1.67895/0.91156, loss_mask_bce_7: 0.50662/0.31681, loss_mask_dice_7: 4.57300/1.10920, loss_spatial_bce_7: 0.02967/0.10994, loss_spatial_dice_7: 0.52552/0.22863, loss_spatial_ce_7: 0.25134/0.17051, loss_grounding_bce_7: 0.16740/0.08484, loss_grounding_dice_7: 0.30683/0.16165, loss_grounding_ce_7: 0.28037/0.33767, loss_mask_ce_8: 1.68562/1.04696, loss_mask_bce_8: 0.56464/0.33437, loss_mask_dice_8: 5.17113/1.18887, loss_spatial_bce_8: 0.05301/0.13066, loss_spatial_dice_8: 0.57026/0.26865, loss_spatial_ce_8: 0.47472/0.22626, loss_grounding_bce_8: 0.18793/0.08877, loss_grounding_dice_8: 0.32895/0.17097, loss_grounding_ce_8: 0.18244/0.43816, loss_mask_ce_9: 7.07878/3.50388, loss_mask_bce_9: 0.70958/0.36064, loss_mask_dice_9: 7.76451/1.77576, loss_spatial_bce_9: 0.21774/0.35791, loss_spatial_dice_9: 0.94761/0.79658, loss_spatial_ce_9: 1.44490/1.41354, loss_grounding_bce_9: 0.16051/0.10055, loss_grounding_dice_9: 0.35012/0.24465, loss_grounding_ce_9: 0.47868/0.70586] items per batch[64] items per second[0.36] total items[1388800] mini batches[ 21700] memory[4967] epoch remaining[0:06:38] INFO:trainer.default_trainer:epochs[ 11] optim steps[21800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58073/0.78496, loss_mask_bce_0: 0.01653/0.30182, loss_mask_dice_0: 0.46749/1.03043, loss_spatial_bce_0: 0.01637/0.08945, loss_spatial_dice_0: 0.16543/0.18904, loss_spatial_ce_0: 0.00202/0.07161, loss_grounding_bce_0: 0.00995/0.08042, loss_grounding_dice_0: 0.07707/0.15162, loss_grounding_ce_0: 0.00061/0.25408, loss_mask_ce_1: 0.23080/0.78713, loss_mask_bce_1: 0.01028/0.30242, loss_mask_dice_1: 0.14156/1.03428, loss_spatial_bce_1: 0.02253/0.08987, loss_spatial_dice_1: 0.15461/0.19160, loss_spatial_ce_1: 0.00668/0.07622, loss_grounding_bce_1: 0.01574/0.08062, loss_grounding_dice_1: 0.09025/0.15248, loss_grounding_ce_1: 0.00031/0.25622, loss_mask_ce_2: 0.23204/0.79482, loss_mask_bce_2: 0.01508/0.30242, loss_mask_dice_2: 0.20466/1.03685, loss_spatial_bce_2: 0.02000/0.08948, loss_spatial_dice_2: 0.18286/0.19159, loss_spatial_ce_2: 0.00199/0.07856, loss_grounding_bce_2: 0.01081/0.08037, loss_grounding_dice_2: 0.08274/0.15216, loss_grounding_ce_2: 0.00038/0.25827, loss_mask_ce_3: 0.52940/0.79458, loss_mask_bce_3: 0.01796/0.30404, loss_mask_dice_3: 0.46315/1.03265, loss_spatial_bce_3: 0.01529/0.09112, loss_spatial_dice_3: 0.14956/0.19211, loss_spatial_ce_3: 0.00697/0.08433, loss_grounding_bce_3: 0.01456/0.08090, loss_grounding_dice_3: 0.07203/0.15196, loss_grounding_ce_3: 0.00026/0.25718, loss_mask_ce_4: 0.22255/0.80009, loss_mask_bce_4: 0.01625/0.30617, loss_mask_dice_4: 0.21714/1.05169, loss_spatial_bce_4: 0.02847/0.09306, loss_spatial_dice_4: 0.11860/0.19934, loss_spatial_ce_4: 0.01375/0.09647, loss_grounding_bce_4: 0.01329/0.08161, loss_grounding_dice_4: 0.06766/0.15421, loss_grounding_ce_4: 0.00029/0.26458, loss_mask_ce_5: 0.60333/0.82307, loss_mask_bce_5: 0.02003/0.30822, loss_mask_dice_5: 0.48908/1.05884, loss_spatial_bce_5: 0.01928/0.09487, loss_spatial_dice_5: 0.13552/0.20143, loss_spatial_ce_5: 0.25983/0.10759, loss_grounding_bce_5: 0.01204/0.08206, loss_grounding_dice_5: 0.07496/0.15502, loss_grounding_ce_5: 0.00061/0.28319, loss_mask_ce_6: 0.26423/0.84777, loss_mask_bce_6: 0.01529/0.30982, loss_mask_dice_6: 0.22695/1.06169, loss_spatial_bce_6: 0.05992/0.09979, loss_spatial_dice_6: 0.17814/0.20403, loss_spatial_ce_6: 0.03236/0.12770, loss_grounding_bce_6: 0.01260/0.08315, loss_grounding_dice_6: 0.09306/0.15571, loss_grounding_ce_6: 0.00086/0.29417, loss_mask_ce_7: 0.45127/0.91065, loss_mask_bce_7: 0.01214/0.31676, loss_mask_dice_7: 0.14685/1.10794, loss_spatial_bce_7: 0.03951/0.10999, loss_spatial_dice_7: 0.27622/0.22857, loss_spatial_ce_7: 0.17793/0.17037, loss_grounding_bce_7: 0.01563/0.08480, loss_grounding_dice_7: 0.08345/0.16156, loss_grounding_ce_7: 0.00216/0.33737, loss_mask_ce_8: 0.36944/1.04625, loss_mask_bce_8: 0.01291/0.33433, loss_mask_dice_8: 0.14427/1.18766, loss_spatial_bce_8: 1.40103/0.13073, loss_spatial_dice_8: 0.32774/0.26851, loss_spatial_ce_8: 0.00645/0.22617, loss_grounding_bce_8: 0.01175/0.08876, loss_grounding_dice_8: 0.08082/0.17088, loss_grounding_ce_8: 0.00968/0.43784, loss_mask_ce_9: 2.22295/3.50202, loss_mask_bce_9: 0.11557/0.36062, loss_mask_dice_9: 0.27610/1.77417, loss_spatial_bce_9: 0.30189/0.35786, loss_spatial_dice_9: 0.59048/0.79644, loss_spatial_ce_9: 0.51303/1.41345, loss_grounding_bce_9: 0.00466/0.10055, loss_grounding_dice_9: 0.07776/0.24450, loss_grounding_ce_9: 0.16656/0.70562] items per batch[64] items per second[0.36] total items[1395200] mini batches[ 21800] memory[4967] epoch remaining[0:03:40] INFO:trainer.default_trainer:epochs[ 11] optim steps[21900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25953/0.78458, loss_mask_bce_0: 0.09791/0.30181, loss_mask_dice_0: 1.20550/1.02965, loss_spatial_bce_0: 0.02033/0.08953, loss_spatial_dice_0: 0.20211/0.18902, loss_spatial_ce_0: 0.04900/0.07161, loss_grounding_bce_0: 0.00941/0.08046, loss_grounding_dice_0: 0.22128/0.15161, loss_grounding_ce_0: 0.26363/0.25411, loss_mask_ce_1: 0.28061/0.78670, loss_mask_bce_1: 0.10362/0.30243, loss_mask_dice_1: 1.21276/1.03348, loss_spatial_bce_1: 0.02913/0.08995, loss_spatial_dice_1: 0.31312/0.19157, loss_spatial_ce_1: 0.05336/0.07618, loss_grounding_bce_1: 0.00976/0.08066, loss_grounding_dice_1: 0.21589/0.15247, loss_grounding_ce_1: 0.26668/0.25624, loss_mask_ce_2: 0.25935/0.79450, loss_mask_bce_2: 0.09997/0.30244, loss_mask_dice_2: 1.21502/1.03601, loss_spatial_bce_2: 0.03096/0.08956, loss_spatial_dice_2: 0.24411/0.19157, loss_spatial_ce_2: 0.03177/0.07856, loss_grounding_bce_2: 0.01016/0.08042, loss_grounding_dice_2: 0.28443/0.15218, loss_grounding_ce_2: 0.27354/0.25833, loss_mask_ce_3: 0.38111/0.79430, loss_mask_bce_3: 0.09858/0.30402, loss_mask_dice_3: 1.00267/1.03180, loss_spatial_bce_3: 0.02599/0.09120, loss_spatial_dice_3: 0.25057/0.19211, loss_spatial_ce_3: 0.02025/0.08426, loss_grounding_bce_3: 0.01050/0.08094, loss_grounding_dice_3: 0.23328/0.15194, loss_grounding_ce_3: 0.33245/0.25721, loss_mask_ce_4: 0.39805/0.79978, loss_mask_bce_4: 0.10110/0.30615, loss_mask_dice_4: 1.11175/1.05082, loss_spatial_bce_4: 0.02690/0.09313, loss_spatial_dice_4: 0.31572/0.19933, loss_spatial_ce_4: 0.06844/0.09642, loss_grounding_bce_4: 0.00891/0.08165, loss_grounding_dice_4: 0.22761/0.15422, loss_grounding_ce_4: 0.26915/0.26457, loss_mask_ce_5: 0.27167/0.82272, loss_mask_bce_5: 0.09832/0.30823, loss_mask_dice_5: 1.30234/1.05795, loss_spatial_bce_5: 0.02471/0.09496, loss_spatial_dice_5: 0.27811/0.20142, loss_spatial_ce_5: 0.05328/0.10760, loss_grounding_bce_5: 0.00936/0.08209, loss_grounding_dice_5: 0.24881/0.15504, loss_grounding_ce_5: 0.26951/0.28323, loss_mask_ce_6: 0.56043/0.84760, loss_mask_bce_6: 0.10237/0.30978, loss_mask_dice_6: 1.16303/1.06079, loss_spatial_bce_6: 0.02452/0.09987, loss_spatial_dice_6: 0.21375/0.20401, loss_spatial_ce_6: 0.25175/0.12773, loss_grounding_bce_6: 0.00943/0.08319, loss_grounding_dice_6: 0.18779/0.15571, loss_grounding_ce_6: 0.41687/0.29425, loss_mask_ce_7: 0.35441/0.91023, loss_mask_bce_7: 0.10513/0.31675, loss_mask_dice_7: 1.30763/1.10698, loss_spatial_bce_7: 0.02573/0.11012, loss_spatial_dice_7: 0.34834/0.22857, loss_spatial_ce_7: 0.12388/0.17027, loss_grounding_bce_7: 0.00914/0.08484, loss_grounding_dice_7: 0.14623/0.16153, loss_grounding_ce_7: 0.30026/0.33746, loss_mask_ce_8: 0.51864/1.04578, loss_mask_bce_8: 0.11032/0.33429, loss_mask_dice_8: 1.51991/1.18663, loss_spatial_bce_8: 0.07202/0.13088, loss_spatial_dice_8: 0.48407/0.26848, loss_spatial_ce_8: 0.20698/0.22607, loss_grounding_bce_8: 0.00988/0.08878, loss_grounding_dice_8: 0.27878/0.17088, loss_grounding_ce_8: 0.44195/0.43773, loss_mask_ce_9: 2.67241/3.50067, loss_mask_bce_9: 0.11293/0.36060, loss_mask_dice_9: 1.76498/1.77250, loss_spatial_bce_9: 0.21011/0.35798, loss_spatial_dice_9: 0.93327/0.79638, loss_spatial_ce_9: 1.62769/1.41309, loss_grounding_bce_9: 0.01464/0.10059, loss_grounding_dice_9: 0.36615/0.24452, loss_grounding_ce_9: 0.43087/0.70533] items per batch[64] items per second[0.36] total items[1401600] mini batches[ 21900] memory[4967] epoch remaining[0:00:42] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00021924. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0012 s/iter. Inference: 0.3688 s/iter. Eval: 0.0948 s/iter. Total: 0.4649 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0020 s/iter. Inference: 0.3749 s/iter. Eval: 0.0826 s/iter. Total: 0.4597 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0024 s/iter. Inference: 0.3789 s/iter. Eval: 0.0787 s/iter. Total: 0.4602 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0025 s/iter. Inference: 0.3808 s/iter. Eval: 0.0773 s/iter. Total: 0.4607 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0026 s/iter. Inference: 0.3801 s/iter. Eval: 0.0744 s/iter. Total: 0.4573 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0027 s/iter. Inference: 0.3800 s/iter. Eval: 0.0719 s/iter. Total: 0.4548 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalnq3i9wcq ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.564 | 82.896 | 66.221 | 133 | | Things | 61.731 | 83.879 | 73.093 | 80 | | Stuff | 46.255 | 81.412 | 55.850 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.51s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.12 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.47 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.60s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.05 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.260 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.758 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.164 | 68.782 | 48.751 | 26.024 | 49.395 | 67.360 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.152 | bicycle | 21.709 | car | 42.099 | | motorcycle | 39.568 | airplane | 60.399 | bus | 70.067 | | train | 74.111 | truck | 43.488 | boat | 29.415 | | traffic light | 28.573 | fire hydrant | 70.988 | stop sign | 70.161 | | parking meter | 50.656 | bench | 25.156 | bird | 34.215 | | cat | 77.177 | dog | 70.415 | horse | 48.250 | | sheep | 53.744 | cow | 55.707 | elephant | 65.707 | | bear | 80.095 | zebra | 66.131 | giraffe | 61.314 | | backpack | 23.577 | umbrella | 55.214 | handbag | 23.552 | | tie | 40.036 | suitcase | 51.538 | frisbee | 70.261 | | skis | 8.433 | snowboard | 34.903 | sports ball | 49.002 | | kite | 36.606 | baseball bat | 37.242 | baseball glove | 49.993 | | skateboard | 42.773 | surfboard | 44.426 | tennis racket | 63.012 | | bottle | 41.482 | wine glass | 36.269 | cup | 51.346 | | fork | 25.219 | knife | 23.768 | spoon | 21.960 | | bowl | 35.322 | banana | 22.506 | apple | 26.398 | | sandwich | 49.497 | orange | 30.441 | broccoli | 25.754 | | carrot | 23.420 | hot dog | 33.634 | pizza | 53.080 | | donut | 56.639 | cake | 46.270 | chair | 28.187 | | couch | 41.405 | potted plant | 22.948 | bed | 42.733 | | dining table | 14.577 | toilet | 68.820 | tv | 66.265 | | laptop | 69.203 | mouse | 63.423 | remote | 42.800 | | keyboard | 57.974 | cell phone | 45.743 | microwave | 63.663 | | oven | 32.650 | toaster | 55.228 | sink | 42.781 | | refrigerator | 70.142 | book | 13.969 | clock | 53.626 | | vase | 39.691 | scissors | 37.084 | teddy bear | 56.806 | | hair drier | 34.042 | toothbrush | 28.476 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.70524458728406, 'fwIoU': 71.62397262485372, 'IoU-person': 89.11617778093567, 'IoU-bicycle': 77.82088975341551, 'IoU-car': 74.55407691242696, 'IoU-motorcycle': 82.83851927798233, 'IoU-airplane': 88.8970642224871, 'IoU-bus': 88.26683330971295, 'IoU-train': 88.59045572179839, 'IoU-truck': 72.17377601939668, 'IoU-boat': 69.22469406236031, 'IoU-traffic light': 79.54451051892238, 'IoU-fire hydrant': 93.02585252366912, 'IoU-stop sign': 95.25820869187717, 'IoU-parking meter': 84.83490536573177, 'IoU-bench': 65.37429977059641, 'IoU-bird': 70.42742541802373, 'IoU-cat': 91.36929002021071, 'IoU-dog': 81.12049741647371, 'IoU-horse': 89.17361386059831, 'IoU-sheep': 85.09887757289715, 'IoU-cow': 86.74415213107638, 'IoU-elephant': 90.80159602104216, 'IoU-bear': 75.63776563668516, 'IoU-zebra': 79.09434332779041, 'IoU-giraffe': 85.68763980752415, 'IoU-backpack': 52.46416041727857, 'IoU-umbrella': 85.25562075957829, 'IoU-handbag': 49.086806991462204, 'IoU-tie': 76.36357892536981, 'IoU-suitcase': 86.64825845971087, 'IoU-frisbee': 84.5623570633929, 'IoU-skis': 59.10140790414451, 'IoU-snowboard': 72.38133506600096, 'IoU-sports ball': 77.27089331890623, 'IoU-kite': 76.92485008044464, 'IoU-baseball bat': 68.49110472741997, 'IoU-baseball glove': 77.04708521527849, 'IoU-skateboard': 86.07903559333246, 'IoU-surfboard': 86.82819074023365, 'IoU-tennis racket': 91.0947786007915, 'IoU-bottle': 70.71170263892093, 'IoU-wine glass': 82.68170673512037, 'IoU-cup': 69.8580150192268, 'IoU-fork': 69.92303819878067, 'IoU-knife': 62.55676940019356, 'IoU-spoon': 58.50470103111219, 'IoU-bowl': 60.67737970334337, 'IoU-banana': 83.30535861913471, 'IoU-apple': 57.87083551998814, 'IoU-sandwich': 69.62045894352146, 'IoU-orange': 78.56250051409772, 'IoU-broccoli': 69.18629546791843, 'IoU-carrot': 62.252433892530505, 'IoU-hot dog': 70.22973994734699, 'IoU-pizza': 84.46031430867123, 'IoU-donut': 73.58036473577046, 'IoU-cake': 80.54707052956232, 'IoU-chair': 64.40320660381106, 'IoU-couch': 69.73943188374898, 'IoU-potted plant': 43.68184230164137, 'IoU-bed': 70.1277470958672, 'IoU-dining table': 54.901051673343446, 'IoU-toilet': 86.70146903214058, 'IoU-tv': 79.4032833505269, 'IoU-laptop': 80.35317423478752, 'IoU-mouse': 73.20044936421894, 'IoU-remote': 72.61300811899682, 'IoU-keyboard': 70.79281549512231, 'IoU-cell phone': 75.53937926988009, 'IoU-microwave': 70.62910976277438, 'IoU-oven': 68.70126124605201, 'IoU-toaster': 82.72423206512383, 'IoU-sink': 74.81249537036703, 'IoU-refrigerator': 83.19117726204398, 'IoU-book': 54.773293480791985, 'IoU-clock': 76.68978988181196, 'IoU-vase': 66.18442027645665, 'IoU-scissors': 83.28283345827963, 'IoU-teddy bear': 81.75067721350507, 'IoU-hair drier': 48.52341450361252, 'IoU-toothbrush': 73.57927144499573, 'IoU-banner': 33.75389474778546, 'IoU-blanket': 19.85849051262511, 'IoU-bridge': 36.889071216440335, 'IoU-cardboard': 53.94711583456123, 'IoU-counter': 30.287364466411965, 'IoU-curtain': 71.51689083913857, 'IoU-door-stuff': 46.91637995237701, 'IoU-floor-wood': 64.64285071875992, 'IoU-flower': 49.70406986457137, 'IoU-fruit': 47.333384988408916, 'IoU-gravel': 29.394040012162748, 'IoU-house': 23.360203378057236, 'IoU-light': 44.181915668205654, 'IoU-mirror-stuff': 65.2626283045911, 'IoU-net': 54.106445652747716, 'IoU-pillow': 23.56682747705271, 'IoU-platform': 27.958483911627795, 'IoU-playingfield': 69.6644147540309, 'IoU-railroad': 63.82484907497565, 'IoU-river': 52.79633895622722, 'IoU-road': 68.07474313352273, 'IoU-roof': 19.790850820222, 'IoU-sand': 64.8967414719417, 'IoU-sea': 84.66453561214344, 'IoU-shelf': 39.816119022457194, 'IoU-snow': 92.1710942238921, 'IoU-stairs': 31.20831185868903, 'IoU-tent': 10.495570918896558, 'IoU-towel': 44.15779529010553, 'IoU-wall-brick': 49.32774076422907, 'IoU-wall-stone': 29.55693610442949, 'IoU-wall-tile': 71.49974783679947, 'IoU-wall-wood': 43.741465208567206, 'IoU-water-other': 23.527120367738068, 'IoU-window-blind': 48.09494454838815, 'IoU-window-other': 50.927586612110765, 'IoU-tree-merged': 81.83815967906335, 'IoU-fence-merged': 55.92885664254841, 'IoU-ceiling-merged': 68.1014628541271, 'IoU-sky-other-merged': 93.73580867000013, 'IoU-cabinet-merged': 63.37609343233566, 'IoU-table-merged': 40.01675796210337, 'IoU-floor-other-merged': 54.85914480723842, 'IoU-pavement-merged': 58.53818462220121, 'IoU-mountain-merged': 57.501191169101354, 'IoU-grass-merged': 70.31139348509372, 'IoU-dirt-merged': 47.51795878536865, 'IoU-paper-merged': 36.42792479933913, 'IoU-food-other-merged': 45.47720622676867, 'IoU-building-other-merged': 59.404323987755866, 'IoU-rock-merged': 62.72497737586819, 'IoU-wall-other-merged': 68.7644491574809, 'IoU-rug-merged': 68.25422372537398, 'mACC': 77.77965558473362, 'pACC': 82.23487557887232, 'ACC-person': 92.95297528545264, 'ACC-bicycle': 88.32466622249527, 'ACC-car': 86.43189108221789, 'ACC-motorcycle': 86.93270626504346, 'ACC-airplane': 92.89934805996354, 'ACC-bus': 93.74448562157785, 'ACC-train': 95.28785817398746, 'ACC-truck': 82.34077435169495, 'ACC-boat': 77.18154081555713, 'ACC-traffic light': 91.03143673060869, 'ACC-fire hydrant': 95.78659361962876, 'ACC-stop sign': 98.25804935308608, 'ACC-parking meter': 88.00492990693569, 'ACC-bench': 78.46774820647369, 'ACC-bird': 75.32147167582559, 'ACC-cat': 95.28230237088215, 'ACC-dog': 83.4850401680662, 'ACC-horse': 94.13336537086067, 'ACC-sheep': 89.54145262484441, 'ACC-cow': 89.91749101571908, 'ACC-elephant': 92.7720070341004, 'ACC-bear': 77.05823941243052, 'ACC-zebra': 80.95598855359671, 'ACC-giraffe': 89.45387619527519, 'ACC-backpack': 77.8831912077896, 'ACC-umbrella': 89.7149168559205, 'ACC-handbag': 67.29013542774824, 'ACC-tie': 85.94880154478804, 'ACC-suitcase': 92.7350822369577, 'ACC-frisbee': 94.25381818181819, 'ACC-skis': 75.76193283065926, 'ACC-snowboard': 82.9399230976464, 'ACC-sports ball': 87.10577728160759, 'ACC-kite': 82.73605017883257, 'ACC-baseball bat': 87.44240235560318, 'ACC-baseball glove': 92.30849599734839, 'ACC-skateboard': 90.52553738705782, 'ACC-surfboard': 92.76037634125282, 'ACC-tennis racket': 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69.17163590299579, 'ACC-grass-merged': 83.1311487583471, 'ACC-dirt-merged': 69.05583383947301, 'ACC-paper-merged': 49.31815302650815, 'ACC-food-other-merged': 62.809158865467005, 'ACC-building-other-merged': 76.45451606959297, 'ACC-rock-merged': 82.28602431054479, 'ACC-wall-other-merged': 80.63008782474483, 'ACC-rug-merged': 81.46569620430819})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3076 s/iter. Inference: 0.1754 s/iter. Eval: 0.0000 s/iter. Total: 0.4830 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3360 s/iter. Inference: 0.3344 s/iter. Eval: 0.0000 s/iter. Total: 0.6705 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3425 s/iter. Inference: 0.5366 s/iter. Eval: 0.0000 s/iter. Total: 0.8793 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4527363184079602, 'noc@0.8': 2.5709686859818555, 'noc@0.85': 3.065554580040972, 'noc@0.9': 3.883816213052385, 'miou@iter1': 0.8697165086459863} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1505 s/iter. Eval: 0.0011 s/iter. Total: 0.1529 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 74.50447082519531, 'precision@0.6': 71.7061767578125, 'precision@0.7': 66.84803771972656, 'precision@0.8': 57.71472930908203, 'precision@0.9': 31.403032302856445, 'cIoU': 60.420005798339844, 'mIoU': 65.95543670654297} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.564081845446154, 'SQ': 82.89611580658985, 'RQ': 66.22148760879786, 'PQ_th': 61.73125364444757, 'SQ_th': 83.87914417204185, 'RQ_th': 73.09266221928421, 'PQ_st': 46.255143280915654, 'SQ_st': 81.41229940590762, 'RQ_st': 55.849903291082526}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-laptop': 91.2298434965394, 'ACC-mouse': 91.4360644856408, 'ACC-remote': 77.17267351466175, 'ACC-keyboard': 79.50651590804576, 'ACC-cell phone': 83.97711461489583, 'ACC-microwave': 75.05472048553858, 'ACC-oven': 89.24322301713886, 'ACC-toaster': 92.06777155839, 'ACC-sink': 84.90752148772035, 'ACC-refrigerator': 92.64800107541748, 'ACC-book': 72.16451626460452, 'ACC-clock': 81.86975358853198, 'ACC-vase': 75.34958173324816, 'ACC-scissors': 88.61595460430267, 'ACC-teddy bear': 87.24262001474288, 'ACC-hair drier': 64.51626675774914, 'ACC-toothbrush': 82.83356497567755, 'ACC-banner': 79.56225883378936, 'ACC-blanket': 35.648991356810065, 'ACC-bridge': 53.43599849659194, 'ACC-cardboard': 72.0848625854297, 'ACC-counter': 58.94519955946007, 'ACC-curtain': 83.20850828288869, 'ACC-door-stuff': 74.81984547688185, 'ACC-floor-wood': 82.70101962189138, 'ACC-flower': 74.09419898726424, 'ACC-fruit': 69.9305587273359, 'ACC-gravel': 39.205802344377666, 'ACC-house': 27.986422268121874, 'ACC-light': 63.33415476782912, 'ACC-mirror-stuff': 76.30376633754338, 'ACC-net': 71.01011411075861, 'ACC-pillow': 52.320132631806814, 'ACC-platform': 46.11914868685305, 'ACC-playingfield': 88.21178613216212, 'ACC-railroad': 85.67367054307033, 'ACC-river': 86.28308750936465, 'ACC-road': 87.4876635475888, 'ACC-roof': 28.48863409486324, 'ACC-sand': 70.57164734027978, 'ACC-sea': 88.56303961679691, 'ACC-shelf': 57.343220131559505, 'ACC-snow': 95.75500946481507, 'ACC-stairs': 56.41376366452867, 'ACC-tent': 14.536135936866144, 'ACC-towel': 56.21812542762701, 'ACC-wall-brick': 68.76933630837804, 'ACC-wall-stone': 38.30958208326754, 'ACC-wall-tile': 84.86221173054902, 'ACC-wall-wood': 60.8298597523695, 'ACC-water-other': 35.297988071942754, 'ACC-window-blind': 65.25739224432603, 'ACC-window-other': 69.28684947212302, 'ACC-tree-merged': 89.78892009972368, 'ACC-fence-merged': 74.68107132184073, 'ACC-ceiling-merged': 83.25624969577622, 'ACC-sky-other-merged': 97.14480478560627, 'ACC-cabinet-merged': 77.11035510456783, 'ACC-table-merged': 51.954709866358264, 'ACC-floor-other-merged': 64.14311624592911, 'ACC-pavement-merged': 72.16687939989579, 'ACC-mountain-merged': 69.17163590299579, 'ACC-grass-merged': 83.1311487583471, 'ACC-dirt-merged': 69.05583383947301, 'ACC-paper-merged': 49.31815302650815, 'ACC-food-other-merged': 62.809158865467005, 'ACC-building-other-merged': 76.45451606959297, 'ACC-rock-merged': 82.28602431054479, 'ACC-wall-other-merged': 80.63008782474483, 'ACC-rug-merged': 81.46569620430819})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4527363184079602, 'noc@0.8': 2.5709686859818555, 'noc@0.85': 3.065554580040972, 'noc@0.9': 3.883816213052385, 'miou@iter1': 0.8697165086459863}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 74.50447082519531, 'precision@0.6': 71.7061767578125, 'precision@0.7': 66.84803771972656, 'precision@0.8': 57.71472930908203, 'precision@0.9': 31.403032302856445, 'cIoU': 60.420005798339844, 'mIoU': 65.95543670654297}}} INFO:trainer.default_trainer:This epoch takes 0:57:33.997298 INFO:trainer.default_trainer:PROGRESS: 24.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 12 training. INFO:trainer.default_trainer:epochs[ 12] optim steps[22000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85121/0.78473, loss_mask_bce_0: 0.57663/0.30191, loss_mask_dice_0: 0.94770/1.02943, loss_spatial_bce_0: 0.17661/0.08950, loss_spatial_dice_0: 0.27594/0.18898, loss_spatial_ce_0: 0.05045/0.07148, loss_grounding_bce_0: 0.28183/0.08048, loss_grounding_dice_0: 0.56357/0.15163, loss_grounding_ce_0: 0.09042/0.25396, loss_mask_ce_1: 0.88559/0.78697, loss_mask_bce_1: 0.59537/0.30253, loss_mask_dice_1: 0.90661/1.03331, loss_spatial_bce_1: 0.16186/0.08992, loss_spatial_dice_1: 0.25590/0.19151, loss_spatial_ce_1: 0.06051/0.07607, loss_grounding_bce_1: 0.28906/0.08068, loss_grounding_dice_1: 0.53435/0.15252, loss_grounding_ce_1: 0.10935/0.25608, loss_mask_ce_2: 0.84042/0.79466, loss_mask_bce_2: 0.61444/0.30254, loss_mask_dice_2: 0.92923/1.03573, loss_spatial_bce_2: 0.16844/0.08953, loss_spatial_dice_2: 0.27698/0.19153, loss_spatial_ce_2: 0.05061/0.07839, loss_grounding_bce_2: 0.27613/0.08045, loss_grounding_dice_2: 0.56082/0.15224, loss_grounding_ce_2: 0.11016/0.25822, loss_mask_ce_3: 0.87219/0.79449, loss_mask_bce_3: 0.61979/0.30413, loss_mask_dice_3: 0.94945/1.03162, loss_spatial_bce_3: 0.17615/0.09117, loss_spatial_dice_3: 0.25586/0.19205, loss_spatial_ce_3: 0.06501/0.08412, loss_grounding_bce_3: 0.29592/0.08096, loss_grounding_dice_3: 0.54490/0.15198, loss_grounding_ce_3: 0.08723/0.25709, loss_mask_ce_4: 0.81918/0.80009, loss_mask_bce_4: 0.62434/0.30627, loss_mask_dice_4: 0.92466/1.05061, loss_spatial_bce_4: 0.17664/0.09310, loss_spatial_dice_4: 0.23998/0.19930, loss_spatial_ce_4: 0.05232/0.09631, loss_grounding_bce_4: 0.30193/0.08167, loss_grounding_dice_4: 0.59730/0.15426, loss_grounding_ce_4: 0.08790/0.26446, loss_mask_ce_5: 0.82822/0.82302, loss_mask_bce_5: 0.58476/0.30833, loss_mask_dice_5: 0.87664/1.05765, loss_spatial_bce_5: 0.18573/0.09492, loss_spatial_dice_5: 0.27229/0.20138, loss_spatial_ce_5: 0.07128/0.10743, loss_grounding_bce_5: 0.28370/0.08211, loss_grounding_dice_5: 0.62064/0.15507, loss_grounding_ce_5: 0.13604/0.28303, loss_mask_ce_6: 0.91694/0.84788, loss_mask_bce_6: 0.57512/0.30986, loss_mask_dice_6: 0.85123/1.06054, loss_spatial_bce_6: 0.19631/0.09982, loss_spatial_dice_6: 0.24712/0.20396, loss_spatial_ce_6: 0.07979/0.12760, loss_grounding_bce_6: 0.28793/0.08321, loss_grounding_dice_6: 0.58105/0.15573, loss_grounding_ce_6: 0.12310/0.29406, loss_mask_ce_7: 0.79972/0.91029, loss_mask_bce_7: 0.60060/0.31687, loss_mask_dice_7: 0.88360/1.10684, loss_spatial_bce_7: 0.23202/0.11008, loss_spatial_dice_7: 0.31280/0.22851, loss_spatial_ce_7: 0.08019/0.17012, loss_grounding_bce_7: 0.32909/0.08483, loss_grounding_dice_7: 0.62471/0.16154, loss_grounding_ce_7: 0.10286/0.33721, loss_mask_ce_8: 1.06763/1.04592, loss_mask_bce_8: 0.56767/0.33442, loss_mask_dice_8: 0.91346/1.18638, loss_spatial_bce_8: 0.25708/0.13086, loss_spatial_dice_8: 0.33261/0.26844, loss_spatial_ce_8: 0.16006/0.22585, loss_grounding_bce_8: 0.28368/0.08877, loss_grounding_dice_8: 0.53721/0.17087, loss_grounding_ce_8: 0.23907/0.43738, loss_mask_ce_9: 2.63795/3.50095, loss_mask_bce_9: 0.45893/0.36074, loss_mask_dice_9: 0.93343/1.77220, loss_spatial_bce_9: 0.43478/0.35794, loss_spatial_dice_9: 0.87935/0.79639, loss_spatial_ce_9: 1.30634/1.41248, loss_grounding_bce_9: 0.20446/0.10061, loss_grounding_dice_9: 0.56001/0.24459, loss_grounding_ce_9: 0.07516/0.70497] items per batch[64] items per second[0.16] total items[1408000] mini batches[ 22000] memory[4967] epoch remaining[0:55:17] INFO:trainer.default_trainer:epochs[ 12] optim steps[22100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35565/0.78427, loss_mask_bce_0: 0.09374/0.30188, loss_mask_dice_0: 0.11523/1.02918, loss_spatial_bce_0: 0.13138/0.08951, loss_spatial_dice_0: 0.22411/0.18893, loss_spatial_ce_0: 0.11410/0.07138, loss_grounding_bce_0: 0.10656/0.08050, loss_grounding_dice_0: 0.12796/0.15161, loss_grounding_ce_0: 0.04194/0.25398, loss_mask_ce_1: 0.38561/0.78647, loss_mask_bce_1: 0.08656/0.30250, loss_mask_dice_1: 0.10593/1.03310, loss_spatial_bce_1: 0.13730/0.08992, loss_spatial_dice_1: 0.24377/0.19145, loss_spatial_ce_1: 0.11553/0.07591, loss_grounding_bce_1: 0.10061/0.08070, loss_grounding_dice_1: 0.11438/0.15248, loss_grounding_ce_1: 0.03766/0.25623, loss_mask_ce_2: 0.40234/0.79416, loss_mask_bce_2: 0.09578/0.30252, loss_mask_dice_2: 0.11590/1.03550, loss_spatial_bce_2: 0.14116/0.08952, loss_spatial_dice_2: 0.26674/0.19146, loss_spatial_ce_2: 0.11455/0.07830, loss_grounding_bce_2: 0.10711/0.08048, loss_grounding_dice_2: 0.11721/0.15219, loss_grounding_ce_2: 0.05141/0.25825, loss_mask_ce_3: 0.43436/0.79387, loss_mask_bce_3: 0.09487/0.30411, loss_mask_dice_3: 0.10835/1.03139, loss_spatial_bce_3: 0.10579/0.09116, loss_spatial_dice_3: 0.17261/0.19200, loss_spatial_ce_3: 0.31514/0.08403, loss_grounding_bce_3: 0.10349/0.08097, loss_grounding_dice_3: 0.11839/0.15193, loss_grounding_ce_3: 0.05427/0.25717, loss_mask_ce_4: 0.40756/0.79954, loss_mask_bce_4: 0.09479/0.30622, loss_mask_dice_4: 0.11416/1.05036, loss_spatial_bce_4: 0.18115/0.09310, loss_spatial_dice_4: 0.34268/0.19926, loss_spatial_ce_4: 0.07057/0.09618, loss_grounding_bce_4: 0.09577/0.08169, loss_grounding_dice_4: 0.11994/0.15419, loss_grounding_ce_4: 0.06656/0.26444, loss_mask_ce_5: 0.32831/0.82243, loss_mask_bce_5: 0.08768/0.30829, loss_mask_dice_5: 0.10514/1.05729, loss_spatial_bce_5: 0.23068/0.09495, loss_spatial_dice_5: 0.37123/0.20134, loss_spatial_ce_5: 0.06148/0.10734, loss_grounding_bce_5: 0.10978/0.08212, loss_grounding_dice_5: 0.11296/0.15501, loss_grounding_ce_5: 0.07144/0.28307, loss_mask_ce_6: 0.42443/0.84743, loss_mask_bce_6: 0.09136/0.30985, loss_mask_dice_6: 0.10473/1.06034, loss_spatial_bce_6: 0.11718/0.09982, loss_spatial_dice_6: 0.18025/0.20392, loss_spatial_ce_6: 0.16180/0.12756, loss_grounding_bce_6: 0.11488/0.08321, loss_grounding_dice_6: 0.11332/0.15568, loss_grounding_ce_6: 0.06456/0.29409, loss_mask_ce_7: 0.46802/0.90973, loss_mask_bce_7: 0.10024/0.31689, loss_mask_dice_7: 0.10722/1.10654, loss_spatial_bce_7: 0.13560/0.11007, loss_spatial_dice_7: 0.18694/0.22844, loss_spatial_ce_7: 0.35753/0.17005, loss_grounding_bce_7: 0.11244/0.08484, loss_grounding_dice_7: 0.12109/0.16149, loss_grounding_ce_7: 0.06055/0.33711, loss_mask_ce_8: 0.34610/1.04559, loss_mask_bce_8: 0.09145/0.33442, loss_mask_dice_8: 0.11498/1.18606, loss_spatial_bce_8: 0.12301/0.13081, loss_spatial_dice_8: 0.20778/0.26833, loss_spatial_ce_8: 0.25870/0.22577, loss_grounding_bce_8: 0.10403/0.08879, loss_grounding_dice_8: 0.12823/0.17082, loss_grounding_ce_8: 0.01171/0.43727, loss_mask_ce_9: 1.60268/3.50023, loss_mask_bce_9: 0.10735/0.36074, loss_mask_dice_9: 0.17938/1.77220, loss_spatial_bce_9: 0.34004/0.35794, loss_spatial_dice_9: 0.62574/0.79633, loss_spatial_ce_9: 1.27121/1.41177, loss_grounding_bce_9: 0.11302/0.10061, loss_grounding_dice_9: 0.19420/0.24453, loss_grounding_ce_9: 0.04298/0.70451] items per batch[64] items per second[0.36] total items[1414400] mini batches[ 22100] memory[4967] epoch remaining[0:49:58] INFO:trainer.default_trainer:epochs[ 12] optim steps[22200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75820/0.78381, loss_mask_bce_0: 0.80049/0.30191, loss_mask_dice_0: 0.99675/1.02893, loss_spatial_bce_0: 0.23639/0.08950, loss_spatial_dice_0: 0.25007/0.18888, loss_spatial_ce_0: 0.00702/0.07130, loss_grounding_bce_0: 0.27594/0.08051, loss_grounding_dice_0: 0.22956/0.15160, loss_grounding_ce_0: 0.19798/0.25413, loss_mask_ce_1: 0.69699/0.78607, loss_mask_bce_1: 0.80379/0.30252, loss_mask_dice_1: 1.04066/1.03287, loss_spatial_bce_1: 0.23129/0.08991, loss_spatial_dice_1: 0.27233/0.19140, loss_spatial_ce_1: 0.00953/0.07583, loss_grounding_bce_1: 0.26804/0.08071, loss_grounding_dice_1: 0.22726/0.15247, loss_grounding_ce_1: 0.20888/0.25645, loss_mask_ce_2: 0.79067/0.79376, loss_mask_bce_2: 0.75594/0.30253, loss_mask_dice_2: 1.03134/1.03532, loss_spatial_bce_2: 0.22341/0.08952, loss_spatial_dice_2: 0.25131/0.19144, loss_spatial_ce_2: 0.02161/0.07821, loss_grounding_bce_2: 0.26253/0.08050, loss_grounding_dice_2: 0.22804/0.15219, loss_grounding_ce_2: 0.20579/0.25846, loss_mask_ce_3: 0.84155/0.79357, loss_mask_bce_3: 0.77158/0.30412, loss_mask_dice_3: 0.98938/1.03122, loss_spatial_bce_3: 0.21613/0.09115, loss_spatial_dice_3: 0.25489/0.19196, loss_spatial_ce_3: 0.02540/0.08397, loss_grounding_bce_3: 0.26376/0.08099, loss_grounding_dice_3: 0.23429/0.15192, loss_grounding_ce_3: 0.18535/0.25737, loss_mask_ce_4: 0.73441/0.79930, loss_mask_bce_4: 0.73068/0.30619, loss_mask_dice_4: 0.95293/1.04994, loss_spatial_bce_4: 0.21798/0.09308, loss_spatial_dice_4: 0.28555/0.19924, loss_spatial_ce_4: 0.04485/0.09612, loss_grounding_bce_4: 0.23364/0.08169, loss_grounding_dice_4: 0.21769/0.15419, loss_grounding_ce_4: 0.19299/0.26445, loss_mask_ce_5: 0.78701/0.82211, loss_mask_bce_5: 0.71807/0.30828, loss_mask_dice_5: 0.96659/1.05697, loss_spatial_bce_5: 0.21706/0.09491, loss_spatial_dice_5: 0.26343/0.20130, loss_spatial_ce_5: 0.18552/0.10729, loss_grounding_bce_5: 0.23507/0.08214, loss_grounding_dice_5: 0.22032/0.15500, loss_grounding_ce_5: 0.17372/0.28319, loss_mask_ce_6: 1.01095/0.84712, loss_mask_bce_6: 0.73869/0.30984, loss_mask_dice_6: 0.92531/1.05997, loss_spatial_bce_6: 0.20183/0.09977, loss_spatial_dice_6: 0.26131/0.20388, loss_spatial_ce_6: 0.16077/0.12758, loss_grounding_bce_6: 0.23431/0.08322, loss_grounding_dice_6: 0.21750/0.15565, loss_grounding_ce_6: 0.17142/0.29426, loss_mask_ce_7: 0.79808/0.90948, loss_mask_bce_7: 0.75212/0.31683, loss_mask_dice_7: 0.93335/1.10615, loss_spatial_bce_7: 0.20192/0.11001, loss_spatial_dice_7: 0.29595/0.22841, loss_spatial_ce_7: 0.07450/0.17010, loss_grounding_bce_7: 0.21008/0.08485, loss_grounding_dice_7: 0.19107/0.16146, loss_grounding_ce_7: 0.09438/0.33735, loss_mask_ce_8: 1.43056/1.04522, loss_mask_bce_8: 0.67777/0.33431, loss_mask_dice_8: 0.96937/1.18556, loss_spatial_bce_8: 0.21368/0.13074, loss_spatial_dice_8: 0.29802/0.26830, loss_spatial_ce_8: 0.24205/0.22571, loss_grounding_bce_8: 0.22429/0.08879, loss_grounding_dice_8: 0.20131/0.17076, loss_grounding_ce_8: 0.24490/0.43722, loss_mask_ce_9: 3.31693/3.49951, loss_mask_bce_9: 0.72385/0.36062, loss_mask_dice_9: 1.25642/1.77130, loss_spatial_bce_9: 0.37178/0.35785, loss_spatial_dice_9: 0.86977/0.79625, loss_spatial_ce_9: 1.24268/1.41177, loss_grounding_bce_9: 0.23381/0.10062, loss_grounding_dice_9: 0.23331/0.24447, loss_grounding_ce_9: 0.21466/0.70440] items per batch[64] items per second[0.36] total items[1420800] mini batches[ 22200] memory[4967] epoch remaining[0:46:27] INFO:trainer.default_trainer:epochs[ 12] optim steps[22300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72410/0.78358, loss_mask_bce_0: 0.54120/0.30201, loss_mask_dice_0: 1.52624/1.02887, loss_spatial_bce_0: 0.08651/0.08955, loss_spatial_dice_0: 0.22730/0.18887, loss_spatial_ce_0: 0.01319/0.07124, loss_grounding_bce_0: 0.19242/0.08051, loss_grounding_dice_0: 0.19104/0.15164, loss_grounding_ce_0: 0.00131/0.25441, loss_mask_ce_1: 0.71204/0.78579, loss_mask_bce_1: 0.56822/0.30265, loss_mask_dice_1: 1.52936/1.03285, loss_spatial_bce_1: 0.08523/0.08995, loss_spatial_dice_1: 0.21862/0.19139, loss_spatial_ce_1: 0.02055/0.07580, loss_grounding_bce_1: 0.17990/0.08072, loss_grounding_dice_1: 0.17019/0.15250, loss_grounding_ce_1: 0.00204/0.25678, loss_mask_ce_2: 0.86485/0.79348, loss_mask_bce_2: 0.54618/0.30265, loss_mask_dice_2: 1.46013/1.03531, loss_spatial_bce_2: 0.08541/0.08955, loss_spatial_dice_2: 0.22379/0.19142, loss_spatial_ce_2: 0.05044/0.07818, loss_grounding_bce_2: 0.19113/0.08051, loss_grounding_dice_2: 0.16418/0.15223, loss_grounding_ce_2: 0.00140/0.25867, loss_mask_ce_3: 0.86391/0.79333, loss_mask_bce_3: 0.53347/0.30428, loss_mask_dice_3: 1.47476/1.03117, loss_spatial_bce_3: 0.09226/0.09119, loss_spatial_dice_3: 0.23000/0.19195, loss_spatial_ce_3: 0.05216/0.08389, loss_grounding_bce_3: 0.19114/0.08100, loss_grounding_dice_3: 0.17159/0.15197, loss_grounding_ce_3: 0.00116/0.25781, loss_mask_ce_4: 0.71004/0.79903, loss_mask_bce_4: 0.58119/0.30639, loss_mask_dice_4: 1.69475/1.04995, loss_spatial_bce_4: 0.09026/0.09311, loss_spatial_dice_4: 0.23591/0.19923, loss_spatial_ce_4: 0.06610/0.09609, loss_grounding_bce_4: 0.17548/0.08174, loss_grounding_dice_4: 0.17900/0.15425, loss_grounding_ce_4: 0.00123/0.26458, loss_mask_ce_5: 0.59624/0.82184, loss_mask_bce_5: 0.58190/0.30845, loss_mask_dice_5: 1.66815/1.05701, loss_spatial_bce_5: 0.10174/0.09496, loss_spatial_dice_5: 0.24932/0.20128, loss_spatial_ce_5: 0.04488/0.10723, loss_grounding_bce_5: 0.19418/0.08214, loss_grounding_dice_5: 0.17784/0.15503, loss_grounding_ce_5: 0.00199/0.28353, loss_mask_ce_6: 0.63502/0.84672, loss_mask_bce_6: 0.57118/0.31002, loss_mask_dice_6: 1.69668/1.06004, loss_spatial_bce_6: 0.10223/0.09982, loss_spatial_dice_6: 0.24081/0.20386, loss_spatial_ce_6: 0.09627/0.12757, loss_grounding_bce_6: 0.18480/0.08323, loss_grounding_dice_6: 0.17953/0.15572, loss_grounding_ce_6: 0.00597/0.29444, loss_mask_ce_7: 0.69196/0.90911, loss_mask_bce_7: 0.66710/0.31703, loss_mask_dice_7: 1.79348/1.10612, loss_spatial_bce_7: 0.14697/0.11005, loss_spatial_dice_7: 0.27598/0.22842, loss_spatial_ce_7: 0.12575/0.17013, loss_grounding_bce_7: 0.18844/0.08485, loss_grounding_dice_7: 0.15325/0.16151, loss_grounding_ce_7: 0.00237/0.33781, loss_mask_ce_8: 1.25742/1.04477, loss_mask_bce_8: 0.61846/0.33442, loss_mask_dice_8: 1.78781/1.18557, loss_spatial_bce_8: 0.13362/0.13072, loss_spatial_dice_8: 0.32322/0.26821, loss_spatial_ce_8: 0.22230/0.22570, loss_grounding_bce_8: 0.18337/0.08878, loss_grounding_dice_8: 0.18093/0.17079, loss_grounding_ce_8: 0.00410/0.43727, loss_mask_ce_9: 3.28923/3.49881, loss_mask_bce_9: 0.51574/0.36078, loss_mask_dice_9: 2.44282/1.77123, loss_spatial_bce_9: 0.29365/0.35790, loss_spatial_dice_9: 0.92138/0.79623, loss_spatial_ce_9: 1.14807/1.41149, loss_grounding_bce_9: 0.20594/0.10063, loss_grounding_dice_9: 0.14649/0.24453, loss_grounding_ce_9: 0.02662/0.70445] items per batch[64] items per second[0.36] total items[1427200] mini batches[ 22300] memory[4967] epoch remaining[0:43:27] INFO:trainer.default_trainer:epochs[ 12] optim steps[22400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04433/0.78353, loss_mask_bce_0: 0.10993/0.30210, loss_mask_dice_0: 0.17020/1.02936, loss_spatial_bce_0: 0.07266/0.08952, loss_spatial_dice_0: 0.10682/0.18882, loss_spatial_ce_0: 0.00898/0.07118, loss_grounding_bce_0: 0.05731/0.08053, loss_grounding_dice_0: 0.11292/0.15167, loss_grounding_ce_0: 0.02314/0.25467, loss_mask_ce_1: 0.04969/0.78568, loss_mask_bce_1: 0.10336/0.30274, loss_mask_dice_1: 0.16760/1.03333, loss_spatial_bce_1: 0.04895/0.08991, loss_spatial_dice_1: 0.07307/0.19135, loss_spatial_ce_1: 0.00824/0.07583, loss_grounding_bce_1: 0.05175/0.08075, loss_grounding_dice_1: 0.10276/0.15255, loss_grounding_ce_1: 0.02020/0.25697, loss_mask_ce_2: 0.05148/0.79332, loss_mask_bce_2: 0.11409/0.30274, loss_mask_dice_2: 0.17418/1.03579, loss_spatial_bce_2: 0.06407/0.08952, loss_spatial_dice_2: 0.09602/0.19139, loss_spatial_ce_2: 0.00712/0.07816, loss_grounding_bce_2: 0.05947/0.08053, loss_grounding_dice_2: 0.11547/0.15227, loss_grounding_ce_2: 0.02149/0.25886, loss_mask_ce_3: 0.04189/0.79325, loss_mask_bce_3: 0.10739/0.30437, loss_mask_dice_3: 0.17482/1.03169, loss_spatial_bce_3: 0.04126/0.09115, loss_spatial_dice_3: 0.06116/0.19192, loss_spatial_ce_3: 0.16033/0.08387, loss_grounding_bce_3: 0.05085/0.08101, loss_grounding_dice_3: 0.10117/0.15197, loss_grounding_ce_3: 0.01394/0.25807, loss_mask_ce_4: 0.03233/0.79887, loss_mask_bce_4: 0.10610/0.30650, loss_mask_dice_4: 0.16774/1.05039, loss_spatial_bce_4: 0.04367/0.09305, loss_spatial_dice_4: 0.06420/0.19917, loss_spatial_ce_4: 0.06562/0.09609, loss_grounding_bce_4: 0.05816/0.08175, loss_grounding_dice_4: 0.12464/0.15427, loss_grounding_ce_4: 0.00812/0.26480, loss_mask_ce_5: 0.07070/0.82166, loss_mask_bce_5: 0.10654/0.30854, loss_mask_dice_5: 0.17023/1.05752, loss_spatial_bce_5: 0.03906/0.09490, loss_spatial_dice_5: 0.05405/0.20124, loss_spatial_ce_5: 0.04429/0.10724, loss_grounding_bce_5: 0.05134/0.08215, loss_grounding_dice_5: 0.10757/0.15508, loss_grounding_ce_5: 0.00374/0.28378, loss_mask_ce_6: 0.04471/0.84656, loss_mask_bce_6: 0.09861/0.31011, loss_mask_dice_6: 0.15257/1.06059, loss_spatial_bce_6: 0.04502/0.09977, loss_spatial_dice_6: 0.06766/0.20382, loss_spatial_ce_6: 0.07144/0.12758, loss_grounding_bce_6: 0.05210/0.08325, loss_grounding_dice_6: 0.10508/0.15575, loss_grounding_ce_6: 0.00262/0.29480, loss_mask_ce_7: 0.09770/0.90891, loss_mask_bce_7: 0.10687/0.31720, loss_mask_dice_7: 0.16578/1.10670, loss_spatial_bce_7: 0.05003/0.11001, loss_spatial_dice_7: 0.08167/0.22837, loss_spatial_ce_7: 0.04718/0.17018, loss_grounding_bce_7: 0.05795/0.08489, loss_grounding_dice_7: 0.12283/0.16155, loss_grounding_ce_7: 0.00504/0.33792, loss_mask_ce_8: 0.09661/1.04491, loss_mask_bce_8: 0.10092/0.33463, loss_mask_dice_8: 0.17378/1.18639, loss_spatial_bce_8: 0.04945/0.13064, loss_spatial_dice_8: 0.05992/0.26813, loss_spatial_ce_8: 0.09938/0.22564, loss_grounding_bce_8: 0.04762/0.08879, loss_grounding_dice_8: 0.10863/0.17081, loss_grounding_ce_8: 0.00319/0.43741, loss_mask_ce_9: 2.15233/3.49956, loss_mask_bce_9: 0.11592/0.36101, loss_mask_dice_9: 0.43406/1.77236, loss_spatial_bce_9: 0.37202/0.35784, loss_spatial_dice_9: 0.57580/0.79632, loss_spatial_ce_9: 1.08870/1.41134, loss_grounding_bce_9: 0.05837/0.10067, loss_grounding_dice_9: 0.32846/0.24458, loss_grounding_ce_9: 0.02931/0.70476] items per batch[64] items per second[0.36] total items[1433600] mini batches[ 22400] memory[4967] epoch remaining[0:40:26] INFO:trainer.default_trainer:epochs[ 12] optim steps[22500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00085/0.78347, loss_mask_bce_0: 0.03261/0.30226, loss_mask_dice_0: 0.03228/1.02940, loss_spatial_bce_0: 0.02498/0.08948, loss_spatial_dice_0: 0.02424/0.18879, loss_spatial_ce_0: 0.00018/0.07113, loss_grounding_bce_0: 0.02604/0.08053, loss_grounding_dice_0: 0.02425/0.15172, loss_grounding_ce_0: 0.11583/0.25438, loss_mask_ce_1: 0.00134/0.78561, loss_mask_bce_1: 0.03439/0.30292, loss_mask_dice_1: 0.03126/1.03339, loss_spatial_bce_1: 0.02891/0.08988, loss_spatial_dice_1: 0.02618/0.19133, loss_spatial_ce_1: 0.00003/0.07573, loss_grounding_bce_1: 0.02874/0.08074, loss_grounding_dice_1: 0.02502/0.15255, loss_grounding_ce_1: 0.10511/0.25672, loss_mask_ce_2: 0.00174/0.79320, loss_mask_bce_2: 0.03146/0.30292, loss_mask_dice_2: 0.02967/1.03579, loss_spatial_bce_2: 0.02813/0.08950, loss_spatial_dice_2: 0.02574/0.19137, loss_spatial_ce_2: 0.00004/0.07807, loss_grounding_bce_2: 0.02589/0.08053, loss_grounding_dice_2: 0.02442/0.15229, loss_grounding_ce_2: 0.10065/0.25861, loss_mask_ce_3: 0.00117/0.79321, loss_mask_bce_3: 0.03422/0.30452, loss_mask_dice_3: 0.03241/1.03161, loss_spatial_bce_3: 0.02878/0.09111, loss_spatial_dice_3: 0.02769/0.19191, loss_spatial_ce_3: 0.00015/0.08376, loss_grounding_bce_3: 0.02734/0.08100, loss_grounding_dice_3: 0.02639/0.15199, loss_grounding_ce_3: 0.09689/0.25788, loss_mask_ce_4: 0.00069/0.79874, loss_mask_bce_4: 0.03420/0.30666, loss_mask_dice_4: 0.03360/1.05032, loss_spatial_bce_4: 0.02765/0.09303, loss_spatial_dice_4: 0.02935/0.19918, loss_spatial_ce_4: 0.00004/0.09597, loss_grounding_bce_4: 0.02778/0.08175, loss_grounding_dice_4: 0.02471/0.15431, loss_grounding_ce_4: 0.09309/0.26457, loss_mask_ce_5: 0.00130/0.82153, loss_mask_bce_5: 0.03411/0.30871, loss_mask_dice_5: 0.03208/1.05754, loss_spatial_bce_5: 0.02949/0.09488, loss_spatial_dice_5: 0.02682/0.20124, loss_spatial_ce_5: 0.00039/0.10715, loss_grounding_bce_5: 0.02763/0.08214, loss_grounding_dice_5: 0.02528/0.15511, loss_grounding_ce_5: 0.09413/0.28347, loss_mask_ce_6: 0.00232/0.84647, loss_mask_bce_6: 0.03247/0.31029, loss_mask_dice_6: 0.03375/1.06059, loss_spatial_bce_6: 0.02776/0.09974, loss_spatial_dice_6: 0.02608/0.20382, loss_spatial_ce_6: 0.03983/0.12751, loss_grounding_bce_6: 0.02656/0.08324, loss_grounding_dice_6: 0.02735/0.15581, loss_grounding_ce_6: 0.09422/0.29450, loss_mask_ce_7: 0.00202/0.90872, loss_mask_bce_7: 0.03626/0.31735, loss_mask_dice_7: 0.03704/1.10672, loss_spatial_bce_7: 0.02961/0.10999, loss_spatial_dice_7: 0.02666/0.22837, loss_spatial_ce_7: 0.02515/0.17012, loss_grounding_bce_7: 0.02556/0.08488, loss_grounding_dice_7: 0.02729/0.16164, loss_grounding_ce_7: 0.09338/0.33761, loss_mask_ce_8: 0.01685/1.04488, loss_mask_bce_8: 0.03663/0.33479, loss_mask_dice_8: 0.03783/1.18638, loss_spatial_bce_8: 0.03520/0.13058, loss_spatial_dice_8: 0.03898/0.26811, loss_spatial_ce_8: 0.15512/0.22552, loss_grounding_bce_8: 0.02581/0.08879, loss_grounding_dice_8: 0.02893/0.17092, loss_grounding_ce_8: 0.10578/0.43731, loss_mask_ce_9: 2.60768/3.49988, loss_mask_bce_9: 0.04137/0.36121, loss_mask_dice_9: 0.06801/1.77276, loss_spatial_bce_9: 0.52965/0.35774, loss_spatial_dice_9: 0.66149/0.79635, loss_spatial_ce_9: 0.82224/1.41136, loss_grounding_bce_9: 0.03279/0.10068, loss_grounding_dice_9: 0.05533/0.24469, loss_grounding_ce_9: 0.69906/0.70389] items per batch[64] items per second[0.36] total items[1440000] mini batches[ 22500] memory[4967] epoch remaining[0:37:22] INFO:trainer.default_trainer:epochs[ 12] optim steps[22600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09071/0.78335, loss_mask_bce_0: 0.06236/0.30227, loss_mask_dice_0: 0.10571/1.02884, loss_spatial_bce_0: 0.02751/0.08946, loss_spatial_dice_0: 0.05584/0.18872, loss_spatial_ce_0: 0.02370/0.07105, loss_grounding_bce_0: 0.03514/0.08048, loss_grounding_dice_0: 0.06040/0.15164, loss_grounding_ce_0: 0.00552/0.25447, loss_mask_ce_1: 0.08697/0.78549, loss_mask_bce_1: 0.06555/0.30293, loss_mask_dice_1: 0.11030/1.03285, loss_spatial_bce_1: 0.02790/0.08985, loss_spatial_dice_1: 0.05297/0.19125, loss_spatial_ce_1: 0.01532/0.07565, loss_grounding_bce_1: 0.03756/0.08070, loss_grounding_dice_1: 0.06436/0.15249, loss_grounding_ce_1: 0.00647/0.25679, loss_mask_ce_2: 0.10915/0.79316, loss_mask_bce_2: 0.06403/0.30291, loss_mask_dice_2: 0.11237/1.03528, loss_spatial_bce_2: 0.02642/0.08948, loss_spatial_dice_2: 0.06893/0.19129, loss_spatial_ce_2: 0.01618/0.07799, loss_grounding_bce_2: 0.03805/0.08050, loss_grounding_dice_2: 0.06260/0.15224, loss_grounding_ce_2: 0.00931/0.25869, loss_mask_ce_3: 0.10785/0.79316, loss_mask_bce_3: 0.06537/0.30454, loss_mask_dice_3: 0.11135/1.03109, loss_spatial_bce_3: 0.02927/0.09110, loss_spatial_dice_3: 0.05360/0.19185, loss_spatial_ce_3: 0.02740/0.08367, loss_grounding_bce_3: 0.03848/0.08097, loss_grounding_dice_3: 0.07190/0.15191, loss_grounding_ce_3: 0.00784/0.25796, loss_mask_ce_4: 0.12263/0.79870, loss_mask_bce_4: 0.05674/0.30667, loss_mask_dice_4: 0.10610/1.04988, loss_spatial_bce_4: 0.02954/0.09302, loss_spatial_dice_4: 0.05895/0.19911, loss_spatial_ce_4: 0.00306/0.09588, loss_grounding_bce_4: 0.04032/0.08172, loss_grounding_dice_4: 0.06405/0.15426, loss_grounding_ce_4: 0.00526/0.26459, loss_mask_ce_5: 0.14034/0.82141, loss_mask_bce_5: 0.05802/0.30871, loss_mask_dice_5: 0.10698/1.05697, loss_spatial_bce_5: 0.03359/0.09488, loss_spatial_dice_5: 0.06275/0.20119, loss_spatial_ce_5: 0.01414/0.10714, loss_grounding_bce_5: 0.04167/0.08211, loss_grounding_dice_5: 0.06450/0.15505, loss_grounding_ce_5: 0.00574/0.28348, loss_mask_ce_6: 0.15285/0.84641, loss_mask_bce_6: 0.05419/0.31028, loss_mask_dice_6: 0.09493/1.05992, loss_spatial_bce_6: 0.03333/0.09974, loss_spatial_dice_6: 0.04594/0.20377, loss_spatial_ce_6: 0.04575/0.12754, loss_grounding_bce_6: 0.03826/0.08321, loss_grounding_dice_6: 0.06482/0.15574, loss_grounding_ce_6: 0.00517/0.29446, loss_mask_ce_7: 0.12191/0.90848, loss_mask_bce_7: 0.07800/0.31731, loss_mask_dice_7: 0.13632/1.10612, loss_spatial_bce_7: 0.03674/0.10999, loss_spatial_dice_7: 0.05700/0.22830, loss_spatial_ce_7: 0.11469/0.17012, loss_grounding_bce_7: 0.03830/0.08484, loss_grounding_dice_7: 0.06290/0.16155, loss_grounding_ce_7: 0.00817/0.33761, loss_mask_ce_8: 0.30970/1.04456, loss_mask_bce_8: 0.05533/0.33473, loss_mask_dice_8: 0.11185/1.18571, loss_spatial_bce_8: 0.03397/0.13059, loss_spatial_dice_8: 0.07178/0.26803, loss_spatial_ce_8: 0.09668/0.22546, loss_grounding_bce_8: 0.03544/0.08875, loss_grounding_dice_8: 0.06946/0.17082, loss_grounding_ce_8: 0.09598/0.43739, loss_mask_ce_9: 2.57633/3.49924, loss_mask_bce_9: 0.04303/0.36114, loss_mask_dice_9: 0.12720/1.77202, loss_spatial_bce_9: 0.22933/0.35779, loss_spatial_dice_9: 0.75599/0.79632, loss_spatial_ce_9: 0.91319/1.41094, loss_grounding_bce_9: 0.03597/0.10065, loss_grounding_dice_9: 0.10309/0.24459, loss_grounding_ce_9: 0.20443/0.70378] items per batch[64] items per second[0.36] total items[1446400] mini batches[ 22600] memory[4967] epoch remaining[0:34:17] INFO:trainer.default_trainer:epochs[ 12] optim steps[22700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00668/0.78335, loss_mask_bce_0: 0.09260/0.30224, loss_mask_dice_0: 0.10830/1.02866, loss_spatial_bce_0: 0.07115/0.08941, loss_spatial_dice_0: 0.08036/0.18867, loss_spatial_ce_0: 0.00035/0.07096, loss_grounding_bce_0: 0.06717/0.08047, loss_grounding_dice_0: 0.08129/0.15169, loss_grounding_ce_0: 0.00178/0.25430, loss_mask_ce_1: 0.00667/0.78546, loss_mask_bce_1: 0.09623/0.30291, loss_mask_dice_1: 0.11551/1.03266, loss_spatial_bce_1: 0.07025/0.08981, loss_spatial_dice_1: 0.08438/0.19122, loss_spatial_ce_1: 0.00041/0.07558, loss_grounding_bce_1: 0.06411/0.08067, loss_grounding_dice_1: 0.08039/0.15254, loss_grounding_ce_1: 0.00196/0.25666, loss_mask_ce_2: 0.00586/0.79316, loss_mask_bce_2: 0.09239/0.30288, loss_mask_dice_2: 0.10918/1.03508, loss_spatial_bce_2: 0.07320/0.08942, loss_spatial_dice_2: 0.09281/0.19125, loss_spatial_ce_2: 0.00124/0.07790, loss_grounding_bce_2: 0.06685/0.08047, loss_grounding_dice_2: 0.08288/0.15227, loss_grounding_ce_2: 0.00183/0.25855, loss_mask_ce_3: 0.00535/0.79322, loss_mask_bce_3: 0.08878/0.30451, loss_mask_dice_3: 0.10477/1.03102, loss_spatial_bce_3: 0.07099/0.09105, loss_spatial_dice_3: 0.07323/0.19181, loss_spatial_ce_3: 0.00133/0.08357, loss_grounding_bce_3: 0.06460/0.08097, loss_grounding_dice_3: 0.08348/0.15194, loss_grounding_ce_3: 0.00147/0.25782, loss_mask_ce_4: 0.00493/0.79868, loss_mask_bce_4: 0.09376/0.30667, loss_mask_dice_4: 0.11027/1.04975, loss_spatial_bce_4: 0.07200/0.09297, loss_spatial_dice_4: 0.07966/0.19908, loss_spatial_ce_4: 0.00279/0.09578, loss_grounding_bce_4: 0.06347/0.08173, loss_grounding_dice_4: 0.07979/0.15431, loss_grounding_ce_4: 0.00125/0.26443, loss_mask_ce_5: 0.00442/0.82146, loss_mask_bce_5: 0.08757/0.30870, loss_mask_dice_5: 0.11474/1.05670, loss_spatial_bce_5: 0.06862/0.09485, loss_spatial_dice_5: 0.08613/0.20114, loss_spatial_ce_5: 0.00216/0.10705, loss_grounding_bce_5: 0.06518/0.08212, loss_grounding_dice_5: 0.08118/0.15511, loss_grounding_ce_5: 0.00083/0.28342, loss_mask_ce_6: 0.00391/0.84652, loss_mask_bce_6: 0.08880/0.31028, loss_mask_dice_6: 0.10553/1.05970, loss_spatial_bce_6: 0.07276/0.09970, loss_spatial_dice_6: 0.08660/0.20373, loss_spatial_ce_6: 0.00374/0.12748, loss_grounding_bce_6: 0.06709/0.08319, loss_grounding_dice_6: 0.07737/0.15579, loss_grounding_ce_6: 0.00138/0.29443, loss_mask_ce_7: 0.00581/0.90853, loss_mask_bce_7: 0.09146/0.31725, loss_mask_dice_7: 0.12168/1.10594, loss_spatial_bce_7: 0.07329/0.10994, loss_spatial_dice_7: 0.08907/0.22826, loss_spatial_ce_7: 0.06398/0.17008, loss_grounding_bce_7: 0.06322/0.08482, loss_grounding_dice_7: 0.07643/0.16157, loss_grounding_ce_7: 0.00288/0.33762, loss_mask_ce_8: 0.01521/1.04461, loss_mask_bce_8: 0.09303/0.33470, loss_mask_dice_8: 0.11717/1.18551, loss_spatial_bce_8: 0.08617/0.13062, loss_spatial_dice_8: 0.10317/0.26799, loss_spatial_ce_8: 0.07986/0.22543, loss_grounding_bce_8: 0.06698/0.08872, loss_grounding_dice_8: 0.08822/0.17086, loss_grounding_ce_8: 0.00371/0.43750, loss_mask_ce_9: 1.58708/3.49964, loss_mask_bce_9: 0.10049/0.36112, loss_mask_dice_9: 0.12268/1.77158, loss_spatial_bce_9: 0.31656/0.35790, loss_spatial_dice_9: 0.57449/0.79641, loss_spatial_ce_9: 1.00386/1.41091, loss_grounding_bce_9: 0.07132/0.10064, loss_grounding_dice_9: 0.08649/0.24464, loss_grounding_ce_9: 0.10418/0.70373] items per batch[64] items per second[0.36] total items[1452800] mini batches[ 22700] memory[4967] epoch remaining[0:31:19] INFO:trainer.default_trainer:epochs[ 12] optim steps[22800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.09841/0.78323, loss_mask_bce_0: 0.28003/0.30226, loss_mask_dice_0: 0.48573/1.02852, loss_spatial_bce_0: 0.06578/0.08938, loss_spatial_dice_0: 0.09939/0.18861, loss_spatial_ce_0: 0.00284/0.07087, loss_grounding_bce_0: 0.09565/0.08047, loss_grounding_dice_0: 0.08066/0.15167, loss_grounding_ce_0: 0.00231/0.25417, loss_mask_ce_1: 2.04712/0.78527, loss_mask_bce_1: 0.28150/0.30292, loss_mask_dice_1: 0.53345/1.03247, loss_spatial_bce_1: 0.06481/0.08977, loss_spatial_dice_1: 0.08959/0.19116, loss_spatial_ce_1: 0.00054/0.07551, loss_grounding_bce_1: 0.10007/0.08068, loss_grounding_dice_1: 0.08264/0.15251, loss_grounding_ce_1: 0.00118/0.25647, loss_mask_ce_2: 2.12924/0.79303, loss_mask_bce_2: 0.28708/0.30291, loss_mask_dice_2: 0.49727/1.03494, loss_spatial_bce_2: 0.06579/0.08939, loss_spatial_dice_2: 0.10093/0.19119, loss_spatial_ce_2: 0.00015/0.07781, loss_grounding_bce_2: 0.09883/0.08048, loss_grounding_dice_2: 0.08600/0.15226, loss_grounding_ce_2: 0.00130/0.25839, loss_mask_ce_3: 2.21737/0.79307, loss_mask_bce_3: 0.27819/0.30455, loss_mask_dice_3: 0.50978/1.03081, loss_spatial_bce_3: 0.06731/0.09102, loss_spatial_dice_3: 0.10325/0.19175, loss_spatial_ce_3: 0.00031/0.08351, loss_grounding_bce_3: 0.09658/0.08097, loss_grounding_dice_3: 0.07501/0.15190, loss_grounding_ce_3: 0.00663/0.25773, loss_mask_ce_4: 2.27963/0.79855, loss_mask_bce_4: 0.27527/0.30668, loss_mask_dice_4: 0.49772/1.04959, loss_spatial_bce_4: 0.06815/0.09294, loss_spatial_dice_4: 0.11531/0.19903, loss_spatial_ce_4: 0.00032/0.09568, loss_grounding_bce_4: 0.10106/0.08173, loss_grounding_dice_4: 0.09819/0.15427, loss_grounding_ce_4: 0.00554/0.26430, loss_mask_ce_5: 2.35246/0.82126, loss_mask_bce_5: 0.29397/0.30873, loss_mask_dice_5: 0.67418/1.05650, loss_spatial_bce_5: 0.07232/0.09483, loss_spatial_dice_5: 0.11623/0.20111, loss_spatial_ce_5: 0.00178/0.10695, loss_grounding_bce_5: 0.10009/0.08212, loss_grounding_dice_5: 0.09594/0.15509, loss_grounding_ce_5: 0.01872/0.28337, loss_mask_ce_6: 2.74693/0.84642, loss_mask_bce_6: 0.27855/0.31030, loss_mask_dice_6: 0.75117/1.05949, loss_spatial_bce_6: 0.08171/0.09968, loss_spatial_dice_6: 0.12331/0.20367, loss_spatial_ce_6: 0.02276/0.12743, loss_grounding_bce_6: 0.09367/0.08317, loss_grounding_dice_6: 0.07996/0.15576, loss_grounding_ce_6: 0.00510/0.29445, loss_mask_ce_7: 2.38736/0.90845, loss_mask_bce_7: 0.30159/0.31725, loss_mask_dice_7: 0.86155/1.10581, loss_spatial_bce_7: 0.07055/0.10995, loss_spatial_dice_7: 0.12005/0.22822, loss_spatial_ce_7: 0.01098/0.17009, loss_grounding_bce_7: 0.09263/0.08479, loss_grounding_dice_7: 0.07323/0.16152, loss_grounding_ce_7: 0.08786/0.33763, loss_mask_ce_8: 2.42160/1.04438, loss_mask_bce_8: 0.32479/0.33471, loss_mask_dice_8: 0.90651/1.18539, loss_spatial_bce_8: 0.13209/0.13058, loss_spatial_dice_8: 0.17833/0.26795, loss_spatial_ce_8: 0.06840/0.22526, loss_grounding_bce_8: 0.09243/0.08870, loss_grounding_dice_8: 0.06763/0.17081, loss_grounding_ce_8: 1.64991/0.43765, loss_mask_ce_9: 4.71784/3.49979, loss_mask_bce_9: 0.62027/0.36116, loss_mask_dice_9: 2.04917/1.77153, loss_spatial_bce_9: 0.62632/0.35790, loss_spatial_dice_9: 0.88812/0.79640, loss_spatial_ce_9: 1.69147/1.41113, loss_grounding_bce_9: 0.19192/0.10065, loss_grounding_dice_9: 0.17317/0.24464, loss_grounding_ce_9: 3.31029/0.70383] items per batch[64] items per second[0.36] total items[1459200] mini batches[ 22800] memory[4967] epoch remaining[0:28:19] INFO:trainer.default_trainer:epochs[ 12] optim steps[22900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20967/0.78319, loss_mask_bce_0: 0.20042/0.30215, loss_mask_dice_0: 0.92751/1.02879, loss_spatial_bce_0: 0.03781/0.08935, loss_spatial_dice_0: 0.10848/0.18855, loss_spatial_ce_0: 0.00054/0.07076, loss_grounding_bce_0: 0.03914/0.08046, loss_grounding_dice_0: 0.12874/0.15162, loss_grounding_ce_0: 0.08114/0.25417, loss_mask_ce_1: 0.25003/0.78523, loss_mask_bce_1: 0.20697/0.30280, loss_mask_dice_1: 0.98911/1.03270, loss_spatial_bce_1: 0.04036/0.08975, loss_spatial_dice_1: 0.12180/0.19109, loss_spatial_ce_1: 0.00030/0.07549, loss_grounding_bce_1: 0.04501/0.08066, loss_grounding_dice_1: 0.12253/0.15247, loss_grounding_ce_1: 0.09073/0.25642, loss_mask_ce_2: 0.29686/0.79295, loss_mask_bce_2: 0.19705/0.30279, loss_mask_dice_2: 0.90788/1.03526, loss_spatial_bce_2: 0.03841/0.08938, loss_spatial_dice_2: 0.10319/0.19113, loss_spatial_ce_2: 0.00070/0.07771, loss_grounding_bce_2: 0.04238/0.08047, loss_grounding_dice_2: 0.11979/0.15221, loss_grounding_ce_2: 0.09379/0.25829, loss_mask_ce_3: 0.23072/0.79307, loss_mask_bce_3: 0.20327/0.30442, loss_mask_dice_3: 0.84475/1.03098, loss_spatial_bce_3: 0.04199/0.09100, loss_spatial_dice_3: 0.12947/0.19170, loss_spatial_ce_3: 0.00085/0.08342, loss_grounding_bce_3: 0.04540/0.08096, loss_grounding_dice_3: 0.13176/0.15182, loss_grounding_ce_3: 0.09738/0.25774, loss_mask_ce_4: 0.37592/0.79859, loss_mask_bce_4: 0.19058/0.30655, loss_mask_dice_4: 0.95884/1.04991, loss_spatial_bce_4: 0.03741/0.09292, loss_spatial_dice_4: 0.11225/0.19898, loss_spatial_ce_4: 0.00179/0.09559, loss_grounding_bce_4: 0.04003/0.08172, loss_grounding_dice_4: 0.16652/0.15421, loss_grounding_ce_4: 0.07461/0.26416, loss_mask_ce_5: 0.40960/0.82119, loss_mask_bce_5: 0.20198/0.30862, loss_mask_dice_5: 1.02255/1.05673, loss_spatial_bce_5: 0.03868/0.09483, loss_spatial_dice_5: 0.12711/0.20105, loss_spatial_ce_5: 0.00652/0.10685, loss_grounding_bce_5: 0.04427/0.08210, loss_grounding_dice_5: 0.18062/0.15504, loss_grounding_ce_5: 0.06293/0.28326, loss_mask_ce_6: 0.27550/0.84644, loss_mask_bce_6: 0.20864/0.31019, loss_mask_dice_6: 1.12813/1.05976, loss_spatial_bce_6: 0.04282/0.09968, loss_spatial_dice_6: 0.13207/0.20361, loss_spatial_ce_6: 0.00460/0.12738, loss_grounding_bce_6: 0.04062/0.08316, loss_grounding_dice_6: 0.11151/0.15569, loss_grounding_ce_6: 0.34000/0.29443, loss_mask_ce_7: 0.50336/0.90837, loss_mask_bce_7: 0.19391/0.31712, loss_mask_dice_7: 0.94485/1.10616, loss_spatial_bce_7: 0.04896/0.10994, loss_spatial_dice_7: 0.13236/0.22816, loss_spatial_ce_7: 0.02130/0.17004, loss_grounding_bce_7: 0.03847/0.08480, loss_grounding_dice_7: 0.15583/0.16151, loss_grounding_ce_7: 0.07424/0.33763, loss_mask_ce_8: 1.50217/1.04422, loss_mask_bce_8: 0.19103/0.33461, loss_mask_dice_8: 1.31907/1.18564, loss_spatial_bce_8: 0.04491/0.13054, loss_spatial_dice_8: 0.16235/0.26790, loss_spatial_ce_8: 0.08214/0.22515, loss_grounding_bce_8: 0.04313/0.08870, loss_grounding_dice_8: 0.12932/0.17078, loss_grounding_ce_8: 0.19996/0.43784, loss_mask_ce_9: 5.12227/3.49980, loss_mask_bce_9: 0.20722/0.36109, loss_mask_dice_9: 1.77270/1.77229, loss_spatial_bce_9: 0.26058/0.35781, loss_spatial_dice_9: 0.77264/0.79632, loss_spatial_ce_9: 1.43395/1.41113, loss_grounding_bce_9: 0.05159/0.10065, loss_grounding_dice_9: 0.30506/0.24455, loss_grounding_ce_9: 0.34947/0.70365] items per batch[64] items per second[0.36] total items[1465600] mini batches[ 22900] memory[4967] epoch remaining[0:25:21] INFO:trainer.default_trainer:epochs[ 12] optim steps[23000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04162/0.78308, loss_mask_bce_0: 0.07080/0.30199, loss_mask_dice_0: 0.03598/1.02947, loss_spatial_bce_0: 0.05733/0.08928, loss_spatial_dice_0: 0.02639/0.18854, loss_spatial_ce_0: 0.00001/0.07070, loss_grounding_bce_0: 0.06104/0.08042, loss_grounding_dice_0: 0.02868/0.15163, loss_grounding_ce_0: 0.00151/0.25420, loss_mask_ce_1: 0.03908/0.78507, loss_mask_bce_1: 0.07769/0.30264, loss_mask_dice_1: 0.03715/1.03331, loss_spatial_bce_1: 0.05832/0.08967, loss_spatial_dice_1: 0.02672/0.19109, loss_spatial_ce_1: 0.00000/0.07543, loss_grounding_bce_1: 0.06227/0.08062, loss_grounding_dice_1: 0.03154/0.15245, loss_grounding_ce_1: 0.00201/0.25635, loss_mask_ce_2: 0.04117/0.79282, loss_mask_bce_2: 0.07356/0.30263, loss_mask_dice_2: 0.03464/1.03590, loss_spatial_bce_2: 0.05519/0.08931, loss_spatial_dice_2: 0.02620/0.19112, loss_spatial_ce_2: 0.00001/0.07766, loss_grounding_bce_2: 0.05770/0.08044, loss_grounding_dice_2: 0.02858/0.15222, loss_grounding_ce_2: 0.00296/0.25836, loss_mask_ce_3: 0.04468/0.79304, loss_mask_bce_3: 0.08009/0.30425, loss_mask_dice_3: 0.03706/1.03160, loss_spatial_bce_3: 0.05747/0.09093, loss_spatial_dice_3: 0.02695/0.19171, loss_spatial_ce_3: 0.00003/0.08341, loss_grounding_bce_3: 0.05800/0.08092, loss_grounding_dice_3: 0.02823/0.15182, loss_grounding_ce_3: 0.00302/0.25770, loss_mask_ce_4: 0.03822/0.79852, loss_mask_bce_4: 0.07713/0.30637, loss_mask_dice_4: 0.03836/1.05038, loss_spatial_bce_4: 0.05953/0.09286, loss_spatial_dice_4: 0.02829/0.19899, loss_spatial_ce_4: 0.00014/0.09550, loss_grounding_bce_4: 0.06445/0.08169, loss_grounding_dice_4: 0.03019/0.15421, loss_grounding_ce_4: 0.00259/0.26421, loss_mask_ce_5: 0.03758/0.82115, loss_mask_bce_5: 0.07993/0.30845, loss_mask_dice_5: 0.03596/1.05734, loss_spatial_bce_5: 0.06067/0.09475, loss_spatial_dice_5: 0.02953/0.20106, loss_spatial_ce_5: 0.00047/0.10672, loss_grounding_bce_5: 0.06134/0.08211, loss_grounding_dice_5: 0.03010/0.15504, loss_grounding_ce_5: 0.00365/0.28330, loss_mask_ce_6: 0.05181/0.84640, loss_mask_bce_6: 0.07897/0.31001, loss_mask_dice_6: 0.03898/1.06042, loss_spatial_bce_6: 0.06561/0.09960, loss_spatial_dice_6: 0.03029/0.20361, loss_spatial_ce_6: 0.00022/0.12726, loss_grounding_bce_6: 0.05951/0.08313, loss_grounding_dice_6: 0.03171/0.15569, loss_grounding_ce_6: 0.00341/0.29460, loss_mask_ce_7: 0.06735/0.90847, loss_mask_bce_7: 0.07346/0.31693, loss_mask_dice_7: 0.03814/1.10668, loss_spatial_bce_7: 0.05940/0.10986, loss_spatial_dice_7: 0.03490/0.22815, loss_spatial_ce_7: 0.00235/0.16999, loss_grounding_bce_7: 0.06010/0.08478, loss_grounding_dice_7: 0.02976/0.16149, loss_grounding_ce_7: 0.00543/0.33771, loss_mask_ce_8: 0.05762/1.04434, loss_mask_bce_8: 0.07200/0.33442, loss_mask_dice_8: 0.03781/1.18619, loss_spatial_bce_8: 0.07626/0.13047, loss_spatial_dice_8: 0.03348/0.26790, loss_spatial_ce_8: 0.04731/0.22507, loss_grounding_bce_8: 0.05704/0.08866, loss_grounding_dice_8: 0.02885/0.17080, loss_grounding_ce_8: 0.01687/0.43782, loss_mask_ce_9: 1.92168/3.49958, loss_mask_bce_9: 0.07198/0.36090, loss_mask_dice_9: 0.04542/1.77242, loss_spatial_bce_9: 0.90750/0.35774, loss_spatial_dice_9: 0.78068/0.79631, loss_spatial_ce_9: 0.92095/1.41117, loss_grounding_bce_9: 0.06084/0.10063, loss_grounding_dice_9: 0.03958/0.24454, loss_grounding_ce_9: 0.13303/0.70348] items per batch[64] items per second[0.36] total items[1472000] mini batches[ 23000] memory[4967] epoch remaining[0:22:20] INFO:trainer.default_trainer:epochs[ 12] optim steps[23100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01511/0.78278, loss_mask_bce_0: 0.07018/0.30197, loss_mask_dice_0: 0.91841/1.02938, loss_spatial_bce_0: 0.01576/0.08923, loss_spatial_dice_0: 0.19223/0.18846, loss_spatial_ce_0: 0.01327/0.07062, loss_grounding_bce_0: 0.01638/0.08040, loss_grounding_dice_0: 0.12079/0.15160, loss_grounding_ce_0: 0.00488/0.25387, loss_mask_ce_1: 1.33627/0.78471, loss_mask_bce_1: 0.07121/0.30264, loss_mask_dice_1: 0.82216/1.03328, loss_spatial_bce_1: 0.01663/0.08962, loss_spatial_dice_1: 0.22222/0.19100, loss_spatial_ce_1: 0.01129/0.07531, loss_grounding_bce_1: 0.01492/0.08061, loss_grounding_dice_1: 0.09249/0.15245, loss_grounding_ce_1: 0.00612/0.25599, loss_mask_ce_2: 1.27019/0.79245, loss_mask_bce_2: 0.07041/0.30264, loss_mask_dice_2: 0.71876/1.03589, loss_spatial_bce_2: 0.01857/0.08926, loss_spatial_dice_2: 0.23105/0.19105, loss_spatial_ce_2: 0.01225/0.07753, loss_grounding_bce_2: 0.01422/0.08043, loss_grounding_dice_2: 0.09751/0.15226, loss_grounding_ce_2: 0.01410/0.25795, loss_mask_ce_3: 0.80554/0.79267, loss_mask_bce_3: 0.05776/0.30425, loss_mask_dice_3: 0.84017/1.03154, loss_spatial_bce_3: 0.01835/0.09088, loss_spatial_dice_3: 0.21159/0.19164, loss_spatial_ce_3: 0.00857/0.08327, loss_grounding_bce_3: 0.01240/0.08092, loss_grounding_dice_3: 0.09146/0.15183, loss_grounding_ce_3: 0.01326/0.25740, loss_mask_ce_4: 1.50851/0.79824, loss_mask_bce_4: 0.07012/0.30639, loss_mask_dice_4: 0.77799/1.05045, loss_spatial_bce_4: 0.01715/0.09280, loss_spatial_dice_4: 0.20519/0.19893, loss_spatial_ce_4: 0.03540/0.09537, loss_grounding_bce_4: 0.01636/0.08167, loss_grounding_dice_4: 0.09797/0.15418, loss_grounding_ce_4: 0.01089/0.26399, loss_mask_ce_5: 1.60570/0.82081, loss_mask_bce_5: 0.07419/0.30845, loss_mask_dice_5: 0.82512/1.05741, loss_spatial_bce_5: 0.01832/0.09470, loss_spatial_dice_5: 0.20124/0.20098, loss_spatial_ce_5: 0.03213/0.10658, loss_grounding_bce_5: 0.02084/0.08208, loss_grounding_dice_5: 0.12527/0.15503, loss_grounding_ce_5: 0.00635/0.28305, loss_mask_ce_6: 1.63695/0.84619, loss_mask_bce_6: 0.06842/0.31000, loss_mask_dice_6: 0.89014/1.06039, loss_spatial_bce_6: 0.02109/0.09953, loss_spatial_dice_6: 0.23372/0.20355, loss_spatial_ce_6: 0.08770/0.12717, loss_grounding_bce_6: 0.01396/0.08310, loss_grounding_dice_6: 0.08964/0.15566, loss_grounding_ce_6: 0.01238/0.29443, loss_mask_ce_7: 1.38423/0.90820, loss_mask_bce_7: 0.06377/0.31693, loss_mask_dice_7: 0.79937/1.10664, loss_spatial_bce_7: 0.02142/0.10980, loss_spatial_dice_7: 0.20339/0.22807, loss_spatial_ce_7: 0.01302/0.16989, loss_grounding_bce_7: 0.01512/0.08475, loss_grounding_dice_7: 0.10875/0.16145, loss_grounding_ce_7: 0.10009/0.33741, loss_mask_ce_8: 0.76316/1.04399, loss_mask_bce_8: 0.08691/0.33443, loss_mask_dice_8: 0.98262/1.18623, loss_spatial_bce_8: 0.03721/0.13042, loss_spatial_dice_8: 0.34286/0.26782, loss_spatial_ce_8: 0.11491/0.22492, loss_grounding_bce_8: 0.01600/0.08866, loss_grounding_dice_8: 0.10546/0.17081, loss_grounding_ce_8: 0.10406/0.43730, loss_mask_ce_9: 3.61915/3.49931, loss_mask_bce_9: 0.07017/0.36090, loss_mask_dice_9: 1.00659/1.77267, loss_spatial_bce_9: 0.09128/0.35772, loss_spatial_dice_9: 0.78925/0.79630, loss_spatial_ce_9: 1.41946/1.41100, loss_grounding_bce_9: 0.01510/0.10064, loss_grounding_dice_9: 0.18101/0.24458, loss_grounding_ce_9: 0.39131/0.70303] items per batch[64] items per second[0.35] total items[1478400] mini batches[ 23100] memory[4967] epoch remaining[0:19:23] INFO:trainer.default_trainer:epochs[ 12] optim steps[23200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72349/0.78259, loss_mask_bce_0: 0.17303/0.30191, loss_mask_dice_0: 1.63723/1.02922, loss_spatial_bce_0: 0.04142/0.08915, loss_spatial_dice_0: 0.21727/0.18837, loss_spatial_ce_0: 0.00171/0.07048, loss_grounding_bce_0: 0.01688/0.08036, loss_grounding_dice_0: 0.20687/0.15151, loss_grounding_ce_0: 0.45464/0.25391, loss_mask_ce_1: 1.23225/0.78444, loss_mask_bce_1: 0.18454/0.30259, loss_mask_dice_1: 1.56229/1.03315, loss_spatial_bce_1: 0.04091/0.08954, loss_spatial_dice_1: 0.17497/0.19091, loss_spatial_ce_1: 0.00121/0.07517, loss_grounding_bce_1: 0.02135/0.08057, loss_grounding_dice_1: 0.37206/0.15237, loss_grounding_ce_1: 0.03874/0.25597, loss_mask_ce_2: 0.82033/0.79216, loss_mask_bce_2: 0.17368/0.30260, loss_mask_dice_2: 1.40628/1.03573, loss_spatial_bce_2: 0.04540/0.08918, loss_spatial_dice_2: 0.18359/0.19097, loss_spatial_ce_2: 0.00117/0.07738, loss_grounding_bce_2: 0.02214/0.08040, loss_grounding_dice_2: 0.43544/0.15216, loss_grounding_ce_2: 0.03307/0.25805, loss_mask_ce_3: 1.00175/0.79246, loss_mask_bce_3: 0.19919/0.30420, loss_mask_dice_3: 2.04418/1.03142, loss_spatial_bce_3: 0.04448/0.09080, loss_spatial_dice_3: 0.16135/0.19155, loss_spatial_ce_3: 0.00193/0.08311, loss_grounding_bce_3: 0.02513/0.08088, loss_grounding_dice_3: 0.40639/0.15173, loss_grounding_ce_3: 0.24773/0.25744, loss_mask_ce_4: 1.36135/0.79815, loss_mask_bce_4: 0.18297/0.30634, loss_mask_dice_4: 1.75305/1.05023, loss_spatial_bce_4: 0.05242/0.09272, loss_spatial_dice_4: 0.21484/0.19884, loss_spatial_ce_4: 0.03274/0.09521, loss_grounding_bce_4: 0.02836/0.08162, loss_grounding_dice_4: 0.35653/0.15409, loss_grounding_ce_4: 0.43212/0.26412, loss_mask_ce_5: 0.84416/0.82058, loss_mask_bce_5: 0.19502/0.30844, loss_mask_dice_5: 1.61301/1.05723, loss_spatial_bce_5: 0.05115/0.09461, loss_spatial_dice_5: 0.22028/0.20091, loss_spatial_ce_5: 0.03213/0.10646, loss_grounding_bce_5: 0.02438/0.08203, loss_grounding_dice_5: 0.35816/0.15494, loss_grounding_ce_5: 0.05647/0.28297, loss_mask_ce_6: 0.82483/0.84606, loss_mask_bce_6: 0.18115/0.30998, loss_mask_dice_6: 1.77231/1.06034, loss_spatial_bce_6: 0.05844/0.09944, loss_spatial_dice_6: 0.18866/0.20347, loss_spatial_ce_6: 0.09876/0.12703, loss_grounding_bce_6: 0.02314/0.08304, loss_grounding_dice_6: 0.37142/0.15558, loss_grounding_ce_6: 0.08648/0.29443, loss_mask_ce_7: 0.96415/0.90796, loss_mask_bce_7: 0.18442/0.31694, loss_mask_dice_7: 1.64371/1.10655, loss_spatial_bce_7: 0.07126/0.10971, loss_spatial_dice_7: 0.20291/0.22800, loss_spatial_ce_7: 0.12171/0.16970, loss_grounding_bce_7: 0.02812/0.08470, loss_grounding_dice_7: 0.43069/0.16137, loss_grounding_ce_7: 0.05178/0.33732, loss_mask_ce_8: 0.68086/1.04384, loss_mask_bce_8: 0.18681/0.33441, loss_mask_dice_8: 1.75604/1.18624, loss_spatial_bce_8: 0.08871/0.13036, loss_spatial_dice_8: 0.29516/0.26778, loss_spatial_ce_8: 0.06939/0.22459, loss_grounding_bce_8: 0.01648/0.08860, loss_grounding_dice_8: 0.35205/0.17072, loss_grounding_ce_8: 0.07334/0.43714, loss_mask_ce_9: 2.81050/3.50011, loss_mask_bce_9: 0.22068/0.36089, loss_mask_dice_9: 2.12839/1.77294, loss_spatial_bce_9: 0.24637/0.35762, loss_spatial_dice_9: 0.84916/0.79632, loss_spatial_ce_9: 1.60506/1.41097, loss_grounding_bce_9: 0.01577/0.10060, loss_grounding_dice_9: 0.47917/0.24443, loss_grounding_ce_9: 0.32978/0.70295] items per batch[64] items per second[0.37] total items[1484800] mini batches[ 23200] memory[4967] epoch remaining[0:16:22] INFO:trainer.default_trainer:epochs[ 12] optim steps[23300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09052/0.78215, loss_mask_bce_0: 0.11158/0.30179, loss_mask_dice_0: 0.28305/1.02955, loss_spatial_bce_0: 0.03623/0.08909, loss_spatial_dice_0: 0.10306/0.18825, loss_spatial_ce_0: 0.00175/0.07040, loss_grounding_bce_0: 0.02023/0.08027, loss_grounding_dice_0: 0.06556/0.15148, loss_grounding_ce_0: 0.40935/0.25384, loss_mask_ce_1: 0.06487/0.78405, loss_mask_bce_1: 0.11581/0.30246, loss_mask_dice_1: 0.31113/1.03365, loss_spatial_bce_1: 0.03526/0.08949, loss_spatial_dice_1: 0.09142/0.19078, loss_spatial_ce_1: 0.00405/0.07506, loss_grounding_bce_1: 0.01674/0.08049, loss_grounding_dice_1: 0.05790/0.15235, loss_grounding_ce_1: 0.43548/0.25592, loss_mask_ce_2: 0.09340/0.79184, loss_mask_bce_2: 0.11203/0.30246, loss_mask_dice_2: 0.30545/1.03614, loss_spatial_bce_2: 0.03811/0.08912, loss_spatial_dice_2: 0.09971/0.19085, loss_spatial_ce_2: 0.00211/0.07729, loss_grounding_bce_2: 0.01909/0.08031, loss_grounding_dice_2: 0.07197/0.15210, loss_grounding_ce_2: 0.46816/0.25803, loss_mask_ce_3: 0.09376/0.79202, loss_mask_bce_3: 0.10813/0.30406, loss_mask_dice_3: 0.27947/1.03186, loss_spatial_bce_3: 0.03762/0.09073, loss_spatial_dice_3: 0.08817/0.19140, loss_spatial_ce_3: 0.00320/0.08301, loss_grounding_bce_3: 0.02013/0.08080, loss_grounding_dice_3: 0.06639/0.15169, loss_grounding_ce_3: 0.57462/0.25744, loss_mask_ce_4: 0.10932/0.79771, loss_mask_bce_4: 0.12374/0.30620, loss_mask_dice_4: 0.31944/1.05062, loss_spatial_bce_4: 0.03611/0.09266, loss_spatial_dice_4: 0.09371/0.19872, loss_spatial_ce_4: 0.00628/0.09510, loss_grounding_bce_4: 0.02011/0.08155, loss_grounding_dice_4: 0.07131/0.15408, loss_grounding_ce_4: 0.35193/0.26396, loss_mask_ce_5: 0.06567/0.82027, loss_mask_bce_5: 0.10911/0.30827, loss_mask_dice_5: 0.28473/1.05768, loss_spatial_bce_5: 0.03457/0.09454, loss_spatial_dice_5: 0.09611/0.20077, loss_spatial_ce_5: 0.02889/0.10633, loss_grounding_bce_5: 0.02077/0.08195, loss_grounding_dice_5: 0.07117/0.15492, loss_grounding_ce_5: 0.30956/0.28285, loss_mask_ce_6: 0.06461/0.84565, loss_mask_bce_6: 0.10678/0.30982, loss_mask_dice_6: 0.28014/1.06072, loss_spatial_bce_6: 0.03845/0.09938, loss_spatial_dice_6: 0.09973/0.20331, loss_spatial_ce_6: 0.01320/0.12705, loss_grounding_bce_6: 0.01837/0.08295, loss_grounding_dice_6: 0.06241/0.15555, loss_grounding_ce_6: 0.31545/0.29426, loss_mask_ce_7: 0.15562/0.90761, loss_mask_bce_7: 0.11272/0.31679, loss_mask_dice_7: 0.30299/1.10700, loss_spatial_bce_7: 0.04240/0.10965, loss_spatial_dice_7: 0.11443/0.22786, loss_spatial_ce_7: 0.07178/0.16958, loss_grounding_bce_7: 0.02138/0.08462, loss_grounding_dice_7: 0.06755/0.16132, loss_grounding_ce_7: 0.35741/0.33709, loss_mask_ce_8: 0.11541/1.04331, loss_mask_bce_8: 0.11763/0.33424, loss_mask_dice_8: 0.28953/1.18677, loss_spatial_bce_8: 0.04340/0.13023, loss_spatial_dice_8: 0.15562/0.26766, loss_spatial_ce_8: 0.16610/0.22443, loss_grounding_bce_8: 0.02065/0.08851, loss_grounding_dice_8: 0.06158/0.17068, loss_grounding_ce_8: 0.37050/0.43677, loss_mask_ce_9: 1.99193/3.49938, loss_mask_bce_9: 0.11683/0.36074, loss_mask_dice_9: 0.40444/1.77346, loss_spatial_bce_9: 0.35537/0.35748, loss_spatial_dice_9: 0.71500/0.79621, loss_spatial_ce_9: 1.22020/1.41062, loss_grounding_bce_9: 0.01906/0.10054, loss_grounding_dice_9: 0.08205/0.24436, loss_grounding_ce_9: 2.10287/0.70261] items per batch[64] items per second[0.35] total items[1491200] mini batches[ 23300] memory[4967] epoch remaining[0:13:25] INFO:trainer.default_trainer:epochs[ 12] optim steps[23400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38307/0.78211, loss_mask_bce_0: 0.27455/0.30192, loss_mask_dice_0: 0.73928/1.03019, loss_spatial_bce_0: 0.04742/0.08904, loss_spatial_dice_0: 0.15091/0.18821, loss_spatial_ce_0: 0.00138/0.07031, loss_grounding_bce_0: 0.00915/0.08027, loss_grounding_dice_0: 0.22614/0.15157, loss_grounding_ce_0: 0.12682/0.25381, loss_mask_ce_1: 0.68372/0.78409, loss_mask_bce_1: 0.25907/0.30262, loss_mask_dice_1: 0.74403/1.03428, loss_spatial_bce_1: 0.04807/0.08944, loss_spatial_dice_1: 0.15081/0.19075, loss_spatial_ce_1: 0.00128/0.07496, loss_grounding_bce_1: 0.01117/0.08049, loss_grounding_dice_1: 0.23676/0.15241, loss_grounding_ce_1: 0.12241/0.25590, loss_mask_ce_2: 0.36780/0.79180, loss_mask_bce_2: 0.25246/0.30260, loss_mask_dice_2: 0.75928/1.03679, loss_spatial_bce_2: 0.04942/0.08908, loss_spatial_dice_2: 0.15421/0.19081, loss_spatial_ce_2: 0.00114/0.07719, loss_grounding_bce_2: 0.00955/0.08032, loss_grounding_dice_2: 0.26781/0.15218, loss_grounding_ce_2: 0.11453/0.25793, loss_mask_ce_3: 0.80006/0.79205, loss_mask_bce_3: 0.24992/0.30422, loss_mask_dice_3: 0.68875/1.03249, loss_spatial_bce_3: 0.04961/0.09070, loss_spatial_dice_3: 0.15261/0.19137, loss_spatial_ce_3: 0.00284/0.08291, loss_grounding_bce_3: 0.01161/0.08081, loss_grounding_dice_3: 0.26743/0.15176, loss_grounding_ce_3: 0.12926/0.25742, loss_mask_ce_4: 0.76361/0.79782, loss_mask_bce_4: 0.26730/0.30632, loss_mask_dice_4: 0.63959/1.05128, loss_spatial_bce_4: 0.05581/0.09263, loss_spatial_dice_4: 0.18107/0.19870, loss_spatial_ce_4: 0.00172/0.09499, loss_grounding_bce_4: 0.01224/0.08157, loss_grounding_dice_4: 0.24014/0.15419, loss_grounding_ce_4: 0.12746/0.26403, loss_mask_ce_5: 0.67281/0.82039, loss_mask_bce_5: 0.25355/0.30841, loss_mask_dice_5: 0.60765/1.05824, loss_spatial_bce_5: 0.05334/0.09449, loss_spatial_dice_5: 0.16164/0.20073, loss_spatial_ce_5: 0.00095/0.10624, loss_grounding_bce_5: 0.01109/0.08196, loss_grounding_dice_5: 0.28360/0.15499, loss_grounding_ce_5: 0.12391/0.28293, loss_mask_ce_6: 0.69172/0.84573, loss_mask_bce_6: 0.25767/0.31000, loss_mask_dice_6: 0.69858/1.06147, loss_spatial_bce_6: 0.05636/0.09931, loss_spatial_dice_6: 0.18014/0.20325, loss_spatial_ce_6: 0.00579/0.12696, loss_grounding_bce_6: 0.01284/0.08296, loss_grounding_dice_6: 0.23335/0.15563, loss_grounding_ce_6: 0.13085/0.29425, loss_mask_ce_7: 0.37638/0.90771, loss_mask_bce_7: 0.27533/0.31694, loss_mask_dice_7: 0.74179/1.10769, loss_spatial_bce_7: 0.05822/0.10959, loss_spatial_dice_7: 0.22402/0.22781, loss_spatial_ce_7: 0.06645/0.16942, loss_grounding_bce_7: 0.00941/0.08464, loss_grounding_dice_7: 0.24023/0.16145, loss_grounding_ce_7: 0.32477/0.33701, loss_mask_ce_8: 1.21629/1.04363, loss_mask_bce_8: 0.27692/0.33443, loss_mask_dice_8: 0.91692/1.18768, loss_spatial_bce_8: 0.12389/0.13013, loss_spatial_dice_8: 0.30283/0.26758, loss_spatial_ce_8: 0.07244/0.22426, loss_grounding_bce_8: 0.00604/0.08854, loss_grounding_dice_8: 0.20657/0.17076, loss_grounding_ce_8: 0.73256/0.43670, loss_mask_ce_9: 2.30153/3.50023, loss_mask_bce_9: 0.26591/0.36081, loss_mask_dice_9: 1.05935/1.77413, loss_spatial_bce_9: 0.28692/0.35757, loss_spatial_dice_9: 0.78806/0.79629, loss_spatial_ce_9: 1.46317/1.41136, loss_grounding_bce_9: 0.00478/0.10056, loss_grounding_dice_9: 0.45276/0.24449, loss_grounding_ce_9: 0.53685/0.70309] items per batch[64] items per second[0.37] total items[1497600] mini batches[ 23400] memory[4967] epoch remaining[0:10:25] INFO:trainer.default_trainer:epochs[ 12] optim steps[23500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43564/0.78208, loss_mask_bce_0: 0.37429/0.30189, loss_mask_dice_0: 0.75792/1.03036, loss_spatial_bce_0: 0.07708/0.08901, loss_spatial_dice_0: 0.15119/0.18814, loss_spatial_ce_0: 0.00774/0.07021, loss_grounding_bce_0: 0.08602/0.08027, loss_grounding_dice_0: 0.17976/0.15155, loss_grounding_ce_0: 0.01343/0.25395, loss_mask_ce_1: 0.41016/0.78394, loss_mask_bce_1: 0.36646/0.30258, loss_mask_dice_1: 0.75488/1.03440, loss_spatial_bce_1: 0.07829/0.08940, loss_spatial_dice_1: 0.15790/0.19067, loss_spatial_ce_1: 0.01720/0.07489, loss_grounding_bce_1: 0.08331/0.08049, loss_grounding_dice_1: 0.18296/0.15243, loss_grounding_ce_1: 0.01374/0.25616, loss_mask_ce_2: 0.47921/0.79169, loss_mask_bce_2: 0.37020/0.30255, loss_mask_dice_2: 0.72490/1.03681, loss_spatial_bce_2: 0.08093/0.08905, loss_spatial_dice_2: 0.15915/0.19074, loss_spatial_ce_2: 0.01230/0.07711, loss_grounding_bce_2: 0.08817/0.08033, loss_grounding_dice_2: 0.19548/0.15217, loss_grounding_ce_2: 0.00989/0.25807, loss_mask_ce_3: 0.55535/0.79196, loss_mask_bce_3: 0.34687/0.30416, loss_mask_dice_3: 0.68505/1.03264, loss_spatial_bce_3: 0.08665/0.09065, loss_spatial_dice_3: 0.14436/0.19130, loss_spatial_ce_3: 0.01549/0.08280, loss_grounding_bce_3: 0.08582/0.08081, loss_grounding_dice_3: 0.19615/0.15174, loss_grounding_ce_3: 0.00678/0.25757, loss_mask_ce_4: 0.55984/0.79767, loss_mask_bce_4: 0.35728/0.30628, loss_mask_dice_4: 0.72667/1.05140, loss_spatial_bce_4: 0.09316/0.09259, loss_spatial_dice_4: 0.17214/0.19863, loss_spatial_ce_4: 0.02205/0.09493, loss_grounding_bce_4: 0.08187/0.08156, loss_grounding_dice_4: 0.20218/0.15421, loss_grounding_ce_4: 0.00960/0.26408, loss_mask_ce_5: 0.56767/0.82027, loss_mask_bce_5: 0.34738/0.30839, loss_mask_dice_5: 0.73787/1.05829, loss_spatial_bce_5: 0.09247/0.09443, loss_spatial_dice_5: 0.16909/0.20068, loss_spatial_ce_5: 0.04691/0.10613, loss_grounding_bce_5: 0.08354/0.08197, loss_grounding_dice_5: 0.19826/0.15505, loss_grounding_ce_5: 0.02031/0.28311, loss_mask_ce_6: 0.65043/0.84574, loss_mask_bce_6: 0.35972/0.30996, loss_mask_dice_6: 0.71302/1.06162, loss_spatial_bce_6: 0.13117/0.09927, loss_spatial_dice_6: 0.21827/0.20318, loss_spatial_ce_6: 0.08182/0.12685, loss_grounding_bce_6: 0.08920/0.08296, loss_grounding_dice_6: 0.19861/0.15568, loss_grounding_ce_6: 0.03837/0.29440, loss_mask_ce_7: 0.98734/0.90765, loss_mask_bce_7: 0.37925/0.31690, loss_mask_dice_7: 0.78450/1.10784, loss_spatial_bce_7: 0.11274/0.10957, loss_spatial_dice_7: 0.18859/0.22775, loss_spatial_ce_7: 0.20254/0.16942, loss_grounding_bce_7: 0.07377/0.08464, loss_grounding_dice_7: 0.19915/0.16146, loss_grounding_ce_7: 0.08612/0.33710, loss_mask_ce_8: 1.12596/1.04343, loss_mask_bce_8: 0.42441/0.33438, loss_mask_dice_8: 0.85164/1.18776, loss_spatial_bce_8: 0.13986/0.13004, loss_spatial_dice_8: 0.25309/0.26749, loss_spatial_ce_8: 0.28623/0.22428, loss_grounding_bce_8: 0.07479/0.08853, loss_grounding_dice_8: 0.19174/0.17083, loss_grounding_ce_8: 0.16212/0.43696, loss_mask_ce_9: 3.88757/3.50115, loss_mask_bce_9: 0.57213/0.36076, loss_mask_dice_9: 1.49726/1.77429, loss_spatial_bce_9: 0.32571/0.35766, loss_spatial_dice_9: 0.81403/0.79629, loss_spatial_ce_9: 1.44000/1.41129, loss_grounding_bce_9: 0.11425/0.10054, loss_grounding_dice_9: 0.32352/0.24455, loss_grounding_ce_9: 0.41043/0.70343] items per batch[64] items per second[0.35] total items[1504000] mini batches[ 23500] memory[4967] epoch remaining[0:07:27] INFO:trainer.default_trainer:epochs[ 12] optim steps[23600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29909/0.78195, loss_mask_bce_0: 0.11376/0.30198, loss_mask_dice_0: 0.62562/1.03057, loss_spatial_bce_0: 0.01925/0.08905, loss_spatial_dice_0: 0.08676/0.18815, loss_spatial_ce_0: 0.00212/0.07016, loss_grounding_bce_0: 0.02096/0.08034, loss_grounding_dice_0: 0.05548/0.15155, loss_grounding_ce_0: 0.07970/0.25383, loss_mask_ce_1: 0.26329/0.78383, loss_mask_bce_1: 0.11642/0.30271, loss_mask_dice_1: 0.63879/1.03453, loss_spatial_bce_1: 0.01929/0.08945, loss_spatial_dice_1: 0.09477/0.19069, loss_spatial_ce_1: 0.00216/0.07483, loss_grounding_bce_1: 0.02245/0.08057, loss_grounding_dice_1: 0.06127/0.15241, loss_grounding_ce_1: 0.07709/0.25599, loss_mask_ce_2: 0.27109/0.79146, loss_mask_bce_2: 0.11477/0.30270, loss_mask_dice_2: 0.72404/1.03700, loss_spatial_bce_2: 0.02078/0.08909, loss_spatial_dice_2: 0.11532/0.19075, loss_spatial_ce_2: 0.00170/0.07707, loss_grounding_bce_2: 0.02077/0.08040, loss_grounding_dice_2: 0.05583/0.15215, loss_grounding_ce_2: 0.07550/0.25792, loss_mask_ce_3: 0.24551/0.79173, loss_mask_bce_3: 0.11282/0.30431, loss_mask_dice_3: 0.57643/1.03277, loss_spatial_bce_3: 0.02086/0.09070, loss_spatial_dice_3: 0.10231/0.19132, loss_spatial_ce_3: 0.00091/0.08273, loss_grounding_bce_3: 0.02309/0.08089, loss_grounding_dice_3: 0.05009/0.15171, loss_grounding_ce_3: 0.07542/0.25743, loss_mask_ce_4: 0.26628/0.79747, loss_mask_bce_4: 0.11362/0.30640, loss_mask_dice_4: 0.61973/1.05150, loss_spatial_bce_4: 0.01738/0.09264, loss_spatial_dice_4: 0.11104/0.19864, loss_spatial_ce_4: 0.00336/0.09491, loss_grounding_bce_4: 0.01955/0.08163, loss_grounding_dice_4: 0.05307/0.15419, loss_grounding_ce_4: 0.08133/0.26396, loss_mask_ce_5: 0.24622/0.82012, loss_mask_bce_5: 0.11662/0.30849, loss_mask_dice_5: 0.69612/1.05838, loss_spatial_bce_5: 0.02087/0.09447, loss_spatial_dice_5: 0.12771/0.20067, loss_spatial_ce_5: 0.00351/0.10612, loss_grounding_bce_5: 0.02346/0.08202, loss_grounding_dice_5: 0.05904/0.15502, loss_grounding_ce_5: 0.07356/0.28305, loss_mask_ce_6: 0.29692/0.84554, loss_mask_bce_6: 0.11105/0.31007, loss_mask_dice_6: 0.53749/1.06164, loss_spatial_bce_6: 0.02045/0.09931, loss_spatial_dice_6: 0.12479/0.20316, loss_spatial_ce_6: 0.00055/0.12684, loss_grounding_bce_6: 0.02014/0.08302, loss_grounding_dice_6: 0.05324/0.15565, loss_grounding_ce_6: 0.07350/0.29425, loss_mask_ce_7: 0.40682/0.90741, loss_mask_bce_7: 0.11301/0.31700, loss_mask_dice_7: 0.58059/1.10796, loss_spatial_bce_7: 0.02076/0.10961, loss_spatial_dice_7: 0.12070/0.22774, loss_spatial_ce_7: 0.02783/0.16935, loss_grounding_bce_7: 0.02275/0.08470, loss_grounding_dice_7: 0.06403/0.16143, loss_grounding_ce_7: 0.07641/0.33680, loss_mask_ce_8: 0.46728/1.04315, loss_mask_bce_8: 0.11256/0.33446, loss_mask_dice_8: 0.88286/1.18774, loss_spatial_bce_8: 0.02765/0.13009, loss_spatial_dice_8: 0.17579/0.26746, loss_spatial_ce_8: 0.08094/0.22425, loss_grounding_bce_8: 0.02093/0.08857, loss_grounding_dice_8: 0.06791/0.17081, loss_grounding_ce_8: 0.10017/0.43670, loss_mask_ce_9: 2.82927/3.49976, loss_mask_bce_9: 0.13737/0.36080, loss_mask_dice_9: 1.22701/1.77401, loss_spatial_bce_9: 0.23943/0.35779, loss_spatial_dice_9: 0.84784/0.79619, loss_spatial_ce_9: 1.55535/1.41087, loss_grounding_bce_9: 0.03015/0.10057, loss_grounding_dice_9: 0.21747/0.24449, loss_grounding_ce_9: 0.38794/0.70326] items per batch[64] items per second[0.36] total items[1510400] mini batches[ 23600] memory[4967] epoch remaining[0:04:29] INFO:trainer.default_trainer:epochs[ 12] optim steps[23700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70222/0.78177, loss_mask_bce_0: 0.44138/0.30200, loss_mask_dice_0: 0.57645/1.02907, loss_spatial_bce_0: 0.08679/0.08916, loss_spatial_dice_0: 0.12359/0.18809, loss_spatial_ce_0: 0.05122/0.07011, loss_grounding_bce_0: 0.04173/0.08042, loss_grounding_dice_0: 0.05252/0.15158, loss_grounding_ce_0: 0.04171/0.25396, loss_mask_ce_1: 0.72223/0.78357, loss_mask_bce_1: 0.43416/0.30274, loss_mask_dice_1: 0.56033/1.03308, loss_spatial_bce_1: 0.08656/0.08956, loss_spatial_dice_1: 0.12425/0.19061, loss_spatial_ce_1: 0.04727/0.07473, loss_grounding_bce_1: 0.04193/0.08065, loss_grounding_dice_1: 0.05512/0.15243, loss_grounding_ce_1: 0.04711/0.25610, loss_mask_ce_2: 0.67513/0.79125, loss_mask_bce_2: 0.43999/0.30274, loss_mask_dice_2: 0.54319/1.03549, loss_spatial_bce_2: 0.08502/0.08920, loss_spatial_dice_2: 0.11919/0.19068, loss_spatial_ce_2: 0.04717/0.07702, loss_grounding_bce_2: 0.04616/0.08049, loss_grounding_dice_2: 0.05834/0.15217, loss_grounding_ce_2: 0.05493/0.25796, loss_mask_ce_3: 0.73279/0.79156, loss_mask_bce_3: 0.44449/0.30434, loss_mask_dice_3: 0.54256/1.03127, loss_spatial_bce_3: 0.08816/0.09085, loss_spatial_dice_3: 0.11936/0.19127, loss_spatial_ce_3: 0.07756/0.08264, loss_grounding_bce_3: 0.04365/0.08098, loss_grounding_dice_3: 0.05553/0.15172, loss_grounding_ce_3: 0.02889/0.25738, loss_mask_ce_4: 0.78372/0.79729, loss_mask_bce_4: 0.43921/0.30645, loss_mask_dice_4: 0.55679/1.05000, loss_spatial_bce_4: 0.09531/0.09273, loss_spatial_dice_4: 0.14219/0.19858, loss_spatial_ce_4: 0.09277/0.09480, loss_grounding_bce_4: 0.04336/0.08172, loss_grounding_dice_4: 0.05715/0.15421, loss_grounding_ce_4: 0.01866/0.26385, loss_mask_ce_5: 0.86626/0.81998, loss_mask_bce_5: 0.40756/0.30850, loss_mask_dice_5: 0.54666/1.05692, loss_spatial_bce_5: 0.08917/0.09457, loss_spatial_dice_5: 0.14169/0.20062, loss_spatial_ce_5: 0.11442/0.10599, loss_grounding_bce_5: 0.04040/0.08210, loss_grounding_dice_5: 0.05558/0.15505, loss_grounding_ce_5: 0.03416/0.28301, loss_mask_ce_6: 0.98689/0.84532, loss_mask_bce_6: 0.32565/0.31010, loss_mask_dice_6: 0.48415/1.06023, loss_spatial_bce_6: 0.08882/0.09943, loss_spatial_dice_6: 0.12104/0.20310, loss_spatial_ce_6: 0.11346/0.12687, loss_grounding_bce_6: 0.04404/0.08311, loss_grounding_dice_6: 0.05451/0.15569, loss_grounding_ce_6: 0.06792/0.29414, loss_mask_ce_7: 1.21009/0.90727, loss_mask_bce_7: 0.42903/0.31709, loss_mask_dice_7: 0.61266/1.10643, loss_spatial_bce_7: 0.10392/0.10974, loss_spatial_dice_7: 0.16551/0.22767, loss_spatial_ce_7: 0.20071/0.16925, loss_grounding_bce_7: 0.04003/0.08479, loss_grounding_dice_7: 0.05037/0.16146, loss_grounding_ce_7: 0.06362/0.33663, loss_mask_ce_8: 0.95131/1.04287, loss_mask_bce_8: 0.55111/0.33451, loss_mask_dice_8: 0.74387/1.18611, loss_spatial_bce_8: 0.16579/0.13021, loss_spatial_dice_8: 0.21864/0.26735, loss_spatial_ce_8: 0.13241/0.22415, loss_grounding_bce_8: 0.04532/0.08867, loss_grounding_dice_8: 0.04107/0.17085, loss_grounding_ce_8: 0.09234/0.43641, loss_mask_ce_9: 3.00923/3.49892, loss_mask_bce_9: 0.77556/0.36077, loss_mask_dice_9: 1.12205/1.77161, loss_spatial_bce_9: 0.43761/0.35805, loss_spatial_dice_9: 0.83312/0.79609, loss_spatial_ce_9: 1.32541/1.41052, loss_grounding_bce_9: 0.05921/0.10065, loss_grounding_dice_9: 0.19381/0.24449, loss_grounding_ce_9: 0.72119/0.70314] items per batch[64] items per second[0.37] total items[1516800] mini batches[ 23700] memory[4967] epoch remaining[0:01:30] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00023751. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3721 s/iter. Eval: 0.0858 s/iter. Total: 0.4606 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0024 s/iter. Inference: 0.3713 s/iter. Eval: 0.0832 s/iter. Total: 0.4570 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0026 s/iter. Inference: 0.3758 s/iter. Eval: 0.0790 s/iter. Total: 0.4574 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 43/79. Dataloading: 0.0026 s/iter. Inference: 0.3891 s/iter. Eval: 0.0801 s/iter. Total: 0.4719 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 54/79. Dataloading: 0.0027 s/iter. Inference: 0.3888 s/iter. Eval: 0.0771 s/iter. Total: 0.4687 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 66/79. Dataloading: 0.0027 s/iter. Inference: 0.3837 s/iter. Eval: 0.0748 s/iter. Total: 0.4615 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 78/79. Dataloading: 0.0028 s/iter. Inference: 0.3818 s/iter. Eval: 0.0725 s/iter. Total: 0.4573 s/iter. ETA=0:00:00 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaln1mv9a80 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.129 | 82.996 | 65.654 | 133 | | Things | 61.243 | 83.778 | 72.585 | 80 | | Stuff | 45.899 | 81.815 | 55.191 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.55s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.33 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.41 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=5.01s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.14 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.447 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.686 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.480 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.253 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.665 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.349 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.545 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.563 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.755 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 44.698 | 68.637 | 48.025 | 25.287 | 48.996 | 66.547 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 47.645 | bicycle | 21.056 | car | 43.582 | | motorcycle | 39.542 | airplane | 61.366 | bus | 69.149 | | train | 72.678 | truck | 42.264 | boat | 28.934 | | traffic light | 28.845 | fire hydrant | 70.069 | stop sign | 69.533 | | parking meter | 51.518 | bench | 26.250 | bird | 33.274 | | cat | 76.253 | dog | 70.108 | horse | 47.713 | | sheep | 53.509 | cow | 55.603 | elephant | 63.583 | | bear | 79.165 | zebra | 64.512 | giraffe | 60.719 | | backpack | 22.624 | umbrella | 54.265 | handbag | 24.696 | | tie | 39.831 | suitcase | 48.822 | frisbee | 69.898 | | skis | 8.536 | snowboard | 34.554 | sports ball | 50.015 | | kite | 37.008 | baseball bat | 37.640 | baseball glove | 49.322 | | skateboard | 42.214 | surfboard | 43.941 | tennis racket | 63.357 | | bottle | 40.395 | wine glass | 36.886 | cup | 50.240 | | fork | 27.051 | knife | 23.624 | spoon | 21.531 | | bowl | 36.516 | banana | 21.350 | apple | 24.348 | | sandwich | 46.175 | orange | 29.966 | broccoli | 23.277 | | carrot | 22.187 | hot dog | 31.808 | pizza | 50.562 | | donut | 55.958 | cake | 46.638 | chair | 27.799 | | couch | 41.640 | potted plant | 21.838 | bed | 40.513 | | dining table | 14.668 | toilet | 68.757 | tv | 65.282 | | laptop | 67.547 | mouse | 65.344 | remote | 44.089 | | keyboard | 56.948 | cell phone | 46.193 | microwave | 65.264 | | oven | 31.486 | toaster | 47.078 | sink | 44.005 | | refrigerator | 69.364 | book | 13.566 | clock | 53.682 | | vase | 39.569 | scissors | 37.981 | teddy bear | 56.009 | | hair drier | 37.762 | toothbrush | 27.360 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.80720960909792, 'fwIoU': 71.74661049641405, 'IoU-person': 88.5456415012562, 'IoU-bicycle': 76.9859496458752, 'IoU-car': 71.39099176902347, 'IoU-motorcycle': 88.11782082936763, 'IoU-airplane': 84.44142807444256, 'IoU-bus': 87.50493353231096, 'IoU-train': 86.47218278587006, 'IoU-truck': 67.16008981286254, 'IoU-boat': 70.3682795143721, 'IoU-traffic light': 79.38020327504454, 'IoU-fire hydrant': 93.05375548697164, 'IoU-stop sign': 87.08140371244338, 'IoU-parking meter': 84.95020969889994, 'IoU-bench': 60.095643808043555, 'IoU-bird': 71.33602547366623, 'IoU-cat': 89.58135356324615, 'IoU-dog': 82.25332583344607, 'IoU-horse': 89.3769871414235, 'IoU-sheep': 90.4928260591192, 'IoU-cow': 90.04760366651061, 'IoU-elephant': 91.38986648639514, 'IoU-bear': 87.55434990149058, 'IoU-zebra': 87.30563193943523, 'IoU-giraffe': 89.12827243551659, 'IoU-backpack': 51.925773070978984, 'IoU-umbrella': 84.80981486315436, 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'ACC-mouse': 91.99448791345158, 'ACC-remote': 60.8636935993126, 'ACC-keyboard': 79.46603866940079, 'ACC-cell phone': 88.42409370728528, 'ACC-microwave': 82.37581738992255, 'ACC-oven': 90.4596145139117, 'ACC-toaster': 92.01918734346987, 'ACC-sink': 83.25575947628771, 'ACC-refrigerator': 90.93907154858202, 'ACC-book': 74.29506037936736, 'ACC-clock': 79.70740321578302, 'ACC-vase': 75.67099457355533, 'ACC-scissors': 88.71121762205144, 'ACC-teddy bear': 90.14867056269557, 'ACC-hair drier': 60.57268095522728, 'ACC-toothbrush': 83.06375955524669, 'ACC-banner': 79.30459444435935, 'ACC-blanket': 29.727887906851947, 'ACC-bridge': 56.12683227857745, 'ACC-cardboard': 72.93308128544423, 'ACC-counter': 55.65403356083722, 'ACC-curtain': 83.31437840838497, 'ACC-door-stuff': 70.90711506330655, 'ACC-floor-wood': 80.34212438352253, 'ACC-flower': 71.02877786341372, 'ACC-fruit': 70.2030395516352, 'ACC-gravel': 48.416036659927585, 'ACC-house': 27.828064144770014, 'ACC-light': 64.3459547142365, 'ACC-mirror-stuff': 70.9800574879317, 'ACC-net': 65.92955816722204, 'ACC-pillow': 57.559141800402855, 'ACC-platform': 44.60634524641781, 'ACC-playingfield': 84.91082179691564, 'ACC-railroad': 85.34241565639415, 'ACC-river': 78.415265172179, 'ACC-road': 80.0026692223569, 'ACC-roof': 27.995982512111546, 'ACC-sand': 68.69034352124712, 'ACC-sea': 90.64937078966067, 'ACC-shelf': 55.320493848894735, 'ACC-snow': 95.83126033597908, 'ACC-stairs': 57.53089472966529, 'ACC-tent': 14.299425789695999, 'ACC-towel': 55.786301132455606, 'ACC-wall-brick': 69.00279202092858, 'ACC-wall-stone': 37.87941590227237, 'ACC-wall-tile': 87.43162372207784, 'ACC-wall-wood': 62.16965615866275, 'ACC-water-other': 40.2606246488668, 'ACC-window-blind': 66.48762063469195, 'ACC-window-other': 73.97049652502929, 'ACC-tree-merged': 90.78354744973588, 'ACC-fence-merged': 72.98899340319178, 'ACC-ceiling-merged': 82.38947733618551, 'ACC-sky-other-merged': 96.98578116453362, 'ACC-cabinet-merged': 78.42615727960295, 'ACC-table-merged': 58.51662381260474, 'ACC-floor-other-merged': 65.1203166563538, 'ACC-pavement-merged': 78.0384464216141, 'ACC-mountain-merged': 67.41302129637425, 'ACC-grass-merged': 83.10038166470433, 'ACC-dirt-merged': 70.28171460021478, 'ACC-paper-merged': 56.98912728241068, 'ACC-food-other-merged': 65.59290263572319, 'ACC-building-other-merged': 73.0822482205274, 'ACC-rock-merged': 84.6706593347422, 'ACC-wall-other-merged': 82.65166611344641, 'ACC-rug-merged': 82.33162421551727})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3397 s/iter. Inference: 0.1725 s/iter. Eval: 0.0000 s/iter. Total: 0.5121 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3509 s/iter. Inference: 0.3368 s/iter. Eval: 0.0000 s/iter. Total: 0.6878 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3698 s/iter. Inference: 0.5429 s/iter. Eval: 0.0000 s/iter. Total: 0.9128 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4202516827626572, 'noc@0.8': 2.4925373134328357, 'noc@0.85': 2.9473222124670766, 'noc@0.9': 3.7913374304945857, 'miou@iter1': 0.8689947717190487} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1429 s/iter. Eval: 0.0010 s/iter. Total: 0.1455 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 74.85425567626953, 'precision@0.6': 71.7839126586914, 'precision@0.7': 67.547607421875, 'precision@0.8': 57.71472930908203, 'precision@0.9': 30.858919143676758, 'cIoU': 60.793880462646484, 'mIoU': 66.08995056152344} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.128810677376464, 'SQ': 82.99561293859047, 'RQ': 65.65361802507506, 'PQ_th': 61.24325970228035, 'SQ_th': 83.77793108914041, 'RQ_th': 72.58505834832528, 'PQ_st': 45.89945365865366, 'SQ_st': 81.81475535285466, 'RQ_st': 55.191066593754016}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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78.42615727960295, 'ACC-table-merged': 58.51662381260474, 'ACC-floor-other-merged': 65.1203166563538, 'ACC-pavement-merged': 78.0384464216141, 'ACC-mountain-merged': 67.41302129637425, 'ACC-grass-merged': 83.10038166470433, 'ACC-dirt-merged': 70.28171460021478, 'ACC-paper-merged': 56.98912728241068, 'ACC-food-other-merged': 65.59290263572319, 'ACC-building-other-merged': 73.0822482205274, 'ACC-rock-merged': 84.6706593347422, 'ACC-wall-other-merged': 82.65166611344641, 'ACC-rug-merged': 82.33162421551727})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4202516827626572, 'noc@0.8': 2.4925373134328357, 'noc@0.85': 2.9473222124670766, 'noc@0.9': 3.7913374304945857, 'miou@iter1': 0.8689947717190487}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 74.85425567626953, 'precision@0.6': 71.7839126586914, 'precision@0.7': 67.547607421875, 'precision@0.8': 57.71472930908203, 'precision@0.9': 30.858919143676758, 'cIoU': 60.793880462646484, 'mIoU': 66.08995056152344}}} INFO:trainer.default_trainer:This epoch takes 0:57:39.919220 INFO:trainer.default_trainer:PROGRESS: 26.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 13 training. INFO:trainer.default_trainer:epochs[ 13] optim steps[23800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65368/0.78210, loss_mask_bce_0: 0.34989/0.30202, loss_mask_dice_0: 0.93729/1.02938, loss_spatial_bce_0: 0.07661/0.08915, loss_spatial_dice_0: 0.18732/0.18811, loss_spatial_ce_0: 0.00020/0.07005, loss_grounding_bce_0: 0.00895/0.08042, loss_grounding_dice_0: 0.01381/0.15167, loss_grounding_ce_0: 0.09015/0.25408, loss_mask_ce_1: 0.66402/0.78377, loss_mask_bce_1: 0.35311/0.30277, loss_mask_dice_1: 0.91569/1.03355, loss_spatial_bce_1: 0.07656/0.08954, loss_spatial_dice_1: 0.19663/0.19063, loss_spatial_ce_1: 0.00027/0.07467, loss_grounding_bce_1: 0.00872/0.08065, loss_grounding_dice_1: 0.01495/0.15255, loss_grounding_ce_1: 0.08693/0.25617, loss_mask_ce_2: 0.70457/0.79151, loss_mask_bce_2: 0.37870/0.30278, loss_mask_dice_2: 1.07621/1.03579, loss_spatial_bce_2: 0.07900/0.08920, loss_spatial_dice_2: 0.19804/0.19069, loss_spatial_ce_2: 0.00035/0.07694, loss_grounding_bce_2: 0.01036/0.08050, loss_grounding_dice_2: 0.01714/0.15225, loss_grounding_ce_2: 0.08724/0.25805, loss_mask_ce_3: 0.77389/0.79178, loss_mask_bce_3: 0.36372/0.30437, loss_mask_dice_3: 0.86482/1.03154, loss_spatial_bce_3: 0.08648/0.09084, loss_spatial_dice_3: 0.20039/0.19129, loss_spatial_ce_3: 0.00049/0.08258, loss_grounding_bce_3: 0.00967/0.08100, loss_grounding_dice_3: 0.01767/0.15182, loss_grounding_ce_3: 0.07777/0.25749, loss_mask_ce_4: 0.74730/0.79760, loss_mask_bce_4: 0.36784/0.30647, loss_mask_dice_4: 0.87020/1.05043, loss_spatial_bce_4: 0.10137/0.09274, loss_spatial_dice_4: 0.21335/0.19860, loss_spatial_ce_4: 0.00124/0.09472, loss_grounding_bce_4: 0.00740/0.08172, loss_grounding_dice_4: 0.01236/0.15433, loss_grounding_ce_4: 0.05965/0.26398, loss_mask_ce_5: 0.80428/0.82029, loss_mask_bce_5: 0.40548/0.30850, loss_mask_dice_5: 0.91299/1.05729, loss_spatial_bce_5: 0.11598/0.09459, loss_spatial_dice_5: 0.19256/0.20066, loss_spatial_ce_5: 0.00046/0.10592, loss_grounding_bce_5: 0.01067/0.08211, loss_grounding_dice_5: 0.01717/0.15512, loss_grounding_ce_5: 0.07991/0.28310, loss_mask_ce_6: 0.74466/0.84572, loss_mask_bce_6: 0.38512/0.31011, loss_mask_dice_6: 0.90148/1.06056, loss_spatial_bce_6: 0.11929/0.09944, loss_spatial_dice_6: 0.18972/0.20315, loss_spatial_ce_6: 0.03221/0.12687, loss_grounding_bce_6: 0.01050/0.08312, loss_grounding_dice_6: 0.01845/0.15575, loss_grounding_ce_6: 0.09515/0.29429, loss_mask_ce_7: 0.66709/0.90743, loss_mask_bce_7: 0.40151/0.31713, loss_mask_dice_7: 0.84794/1.10687, loss_spatial_bce_7: 0.11791/0.10972, loss_spatial_dice_7: 0.20010/0.22770, loss_spatial_ce_7: 0.01615/0.16922, loss_grounding_bce_7: 0.01174/0.08478, loss_grounding_dice_7: 0.02041/0.16151, loss_grounding_ce_7: 0.05982/0.33654, loss_mask_ce_8: 1.08617/1.04304, loss_mask_bce_8: 0.51242/0.33456, loss_mask_dice_8: 0.91579/1.18648, loss_spatial_bce_8: 0.10549/0.13018, loss_spatial_dice_8: 0.23796/0.26738, loss_spatial_ce_8: 0.11987/0.22408, loss_grounding_bce_8: 0.01009/0.08867, loss_grounding_dice_8: 0.01769/0.17087, loss_grounding_ce_8: 0.06891/0.43650, loss_mask_ce_9: 5.23462/3.49915, loss_mask_bce_9: 0.41688/0.36077, loss_mask_dice_9: 1.31684/1.77218, loss_spatial_bce_9: 0.46193/0.35798, loss_spatial_dice_9: 0.90432/0.79605, loss_spatial_ce_9: 1.78195/1.41015, loss_grounding_bce_9: 0.02147/0.10065, loss_grounding_dice_9: 0.05985/0.24458, loss_grounding_ce_9: 0.49402/0.70298] items per batch[64] items per second[0.16] total items[1523200] mini batches[ 23800] memory[4967] epoch remaining[0:58:44] INFO:trainer.default_trainer:epochs[ 13] optim steps[23900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60052/0.78149, loss_mask_bce_0: 0.43245/0.30206, loss_mask_dice_0: 2.36941/1.02916, loss_spatial_bce_0: 0.03326/0.08915, loss_spatial_dice_0: 0.32290/0.18804, loss_spatial_ce_0: 0.04290/0.06997, loss_grounding_bce_0: 0.01958/0.08046, loss_grounding_dice_0: 0.27887/0.15165, loss_grounding_ce_0: 0.43919/0.25364, loss_mask_ce_1: 0.59072/0.78315, loss_mask_bce_1: 0.42339/0.30282, loss_mask_dice_1: 2.31274/1.03328, loss_spatial_bce_1: 0.03406/0.08955, loss_spatial_dice_1: 0.32931/0.19056, loss_spatial_ce_1: 0.11268/0.07457, loss_grounding_bce_1: 0.01807/0.08069, loss_grounding_dice_1: 0.24791/0.15252, loss_grounding_ce_1: 0.46149/0.25564, loss_mask_ce_2: 0.60892/0.79088, loss_mask_bce_2: 0.43813/0.30281, loss_mask_dice_2: 2.24757/1.03557, loss_spatial_bce_2: 0.02869/0.08920, loss_spatial_dice_2: 0.30093/0.19061, loss_spatial_ce_2: 0.07629/0.07685, loss_grounding_bce_2: 0.01854/0.08055, loss_grounding_dice_2: 0.28116/0.15222, loss_grounding_ce_2: 0.36412/0.25760, loss_mask_ce_3: 0.60443/0.79115, loss_mask_bce_3: 0.43079/0.30441, loss_mask_dice_3: 2.25297/1.03134, loss_spatial_bce_3: 0.03288/0.09085, loss_spatial_dice_3: 0.27552/0.19121, loss_spatial_ce_3: 0.09223/0.08248, loss_grounding_bce_3: 0.02169/0.08105, loss_grounding_dice_3: 0.33601/0.15179, loss_grounding_ce_3: 0.37176/0.25709, loss_mask_ce_4: 0.62593/0.79703, loss_mask_bce_4: 0.40508/0.30649, loss_mask_dice_4: 2.15339/1.05010, loss_spatial_bce_4: 0.02796/0.09276, loss_spatial_dice_4: 0.32410/0.19851, loss_spatial_ce_4: 0.13216/0.09457, loss_grounding_bce_4: 0.01549/0.08176, loss_grounding_dice_4: 0.25564/0.15434, loss_grounding_ce_4: 0.47018/0.26352, loss_mask_ce_5: 0.60749/0.81967, loss_mask_bce_5: 0.42069/0.30851, loss_mask_dice_5: 2.22661/1.05704, loss_spatial_bce_5: 0.02676/0.09460, loss_spatial_dice_5: 0.35925/0.20058, loss_spatial_ce_5: 0.23358/0.10580, loss_grounding_bce_5: 0.01771/0.08216, loss_grounding_dice_5: 0.28698/0.15509, loss_grounding_ce_5: 0.37971/0.28255, loss_mask_ce_6: 0.66260/0.84517, loss_mask_bce_6: 0.42270/0.31012, loss_mask_dice_6: 2.22591/1.06019, loss_spatial_bce_6: 0.03020/0.09945, loss_spatial_dice_6: 0.35667/0.20306, loss_spatial_ce_6: 0.31963/0.12678, loss_grounding_bce_6: 0.01572/0.08315, loss_grounding_dice_6: 0.30023/0.15573, loss_grounding_ce_6: 0.44080/0.29376, loss_mask_ce_7: 0.63849/0.90679, loss_mask_bce_7: 0.41905/0.31713, loss_mask_dice_7: 2.13622/1.10648, loss_spatial_bce_7: 0.04887/0.10973, loss_spatial_dice_7: 0.43526/0.22760, loss_spatial_ce_7: 0.20211/0.16915, loss_grounding_bce_7: 0.01955/0.08482, loss_grounding_dice_7: 0.32109/0.16145, loss_grounding_ce_7: 0.46208/0.33604, loss_mask_ce_8: 0.91995/1.04241, loss_mask_bce_8: 0.43879/0.33451, loss_mask_dice_8: 2.17867/1.18603, loss_spatial_bce_8: 0.08814/0.13014, loss_spatial_dice_8: 0.45691/0.26726, loss_spatial_ce_8: 0.20427/0.22389, loss_grounding_bce_8: 0.01652/0.08870, loss_grounding_dice_8: 0.27409/0.17086, loss_grounding_ce_8: 0.58700/0.43590, loss_mask_ce_9: 3.88549/3.49804, loss_mask_bce_9: 0.42299/0.36070, loss_mask_dice_9: 3.51664/1.77128, loss_spatial_bce_9: 0.19492/0.35812, loss_spatial_dice_9: 0.97298/0.79606, loss_spatial_ce_9: 1.23872/1.40973, loss_grounding_bce_9: 0.01546/0.10068, loss_grounding_dice_9: 0.44545/0.24453, loss_grounding_ce_9: 0.47706/0.70239] items per batch[64] items per second[0.36] total items[1529600] mini batches[ 23900] memory[4967] epoch remaining[0:51:50] INFO:trainer.default_trainer:epochs[ 13] optim steps[24000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56924/0.78136, loss_mask_bce_0: 0.05181/0.30218, loss_mask_dice_0: 1.93715/1.02889, loss_spatial_bce_0: 0.00967/0.08914, loss_spatial_dice_0: 0.28564/0.18793, loss_spatial_ce_0: 0.09310/0.06991, loss_grounding_bce_0: 0.00202/0.08050, loss_grounding_dice_0: 0.06871/0.15157, loss_grounding_ce_0: 0.06955/0.25385, loss_mask_ce_1: 0.74155/0.78307, loss_mask_bce_1: 0.04629/0.30293, loss_mask_dice_1: 2.53830/1.03305, loss_spatial_bce_1: 0.00888/0.08954, loss_spatial_dice_1: 0.31434/0.19047, loss_spatial_ce_1: 0.07754/0.07452, loss_grounding_bce_1: 0.00384/0.08073, loss_grounding_dice_1: 0.12120/0.15245, loss_grounding_ce_1: 0.09190/0.25578, loss_mask_ce_2: 0.99326/0.79087, loss_mask_bce_2: 0.06311/0.30293, loss_mask_dice_2: 1.57570/1.03533, loss_spatial_bce_2: 0.00819/0.08918, loss_spatial_dice_2: 0.26165/0.19053, loss_spatial_ce_2: 0.06122/0.07680, loss_grounding_bce_2: 0.00046/0.08059, loss_grounding_dice_2: 0.04787/0.15216, loss_grounding_ce_2: 0.15326/0.25775, loss_mask_ce_3: 1.43441/0.79101, loss_mask_bce_3: 0.05199/0.30453, loss_mask_dice_3: 1.98899/1.03110, loss_spatial_bce_3: 0.00840/0.09084, loss_spatial_dice_3: 0.30704/0.19113, loss_spatial_ce_3: 0.31375/0.08242, loss_grounding_bce_3: 0.00254/0.08108, loss_grounding_dice_3: 0.10956/0.15171, loss_grounding_ce_3: 0.17060/0.25736, loss_mask_ce_4: 1.31541/0.79684, loss_mask_bce_4: 0.05340/0.30662, loss_mask_dice_4: 2.14672/1.04988, loss_spatial_bce_4: 0.00685/0.09276, loss_spatial_dice_4: 0.33065/0.19843, loss_spatial_ce_4: 0.15960/0.09449, loss_grounding_bce_4: 0.00254/0.08179, loss_grounding_dice_4: 0.11683/0.15425, loss_grounding_ce_4: 0.20854/0.26357, loss_mask_ce_5: 0.74902/0.81955, loss_mask_bce_5: 0.05159/0.30863, loss_mask_dice_5: 1.92223/1.05680, loss_spatial_bce_5: 0.00801/0.09459, loss_spatial_dice_5: 0.28989/0.20049, loss_spatial_ce_5: 0.16282/0.10575, loss_grounding_bce_5: 0.00094/0.08218, loss_grounding_dice_5: 0.10430/0.15499, loss_grounding_ce_5: 0.29880/0.28284, loss_mask_ce_6: 1.00426/0.84505, loss_mask_bce_6: 0.07639/0.31025, loss_mask_dice_6: 1.75134/1.05987, loss_spatial_bce_6: 0.00915/0.09944, loss_spatial_dice_6: 0.30650/0.20295, loss_spatial_ce_6: 0.14346/0.12675, loss_grounding_bce_6: 0.00188/0.08318, loss_grounding_dice_6: 0.08465/0.15566, loss_grounding_ce_6: 0.29901/0.29378, loss_mask_ce_7: 1.74786/0.90644, loss_mask_bce_7: 0.04567/0.31725, loss_mask_dice_7: 1.87499/1.10633, loss_spatial_bce_7: 0.01073/0.10971, loss_spatial_dice_7: 0.31616/0.22749, loss_spatial_ce_7: 0.32628/0.16909, loss_grounding_bce_7: 0.00361/0.08484, loss_grounding_dice_7: 0.11062/0.16135, loss_grounding_ce_7: 0.48792/0.33582, loss_mask_ce_8: 1.76884/1.04222, loss_mask_bce_8: 0.05968/0.33462, loss_mask_dice_8: 2.50991/1.18585, loss_spatial_bce_8: 0.01298/0.13012, loss_spatial_dice_8: 0.44734/0.26712, loss_spatial_ce_8: 0.32470/0.22387, loss_grounding_bce_8: 0.00323/0.08872, loss_grounding_dice_8: 0.14437/0.17077, loss_grounding_ce_8: 0.62640/0.43579, loss_mask_ce_9: 3.58834/3.49796, loss_mask_bce_9: 0.03218/0.36086, loss_mask_dice_9: 2.00521/1.77119, loss_spatial_bce_9: 0.06905/0.35813, loss_spatial_dice_9: 0.86958/0.79598, loss_spatial_ce_9: 2.60760/1.40926, loss_grounding_bce_9: 0.00247/0.10069, loss_grounding_dice_9: 0.31531/0.24444, loss_grounding_ce_9: 0.68753/0.70227] items per batch[64] items per second[0.36] total items[1536000] mini batches[ 24000] memory[4967] epoch remaining[0:47:48] INFO:trainer.default_trainer:epochs[ 13] optim steps[24100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.09526/0.78085, loss_mask_bce_0: 0.09393/0.30205, loss_mask_dice_0: 0.65474/1.02849, loss_spatial_bce_0: 0.03901/0.08912, loss_spatial_dice_0: 0.22453/0.18785, loss_spatial_ce_0: 0.19520/0.06989, loss_grounding_bce_0: 0.06143/0.08054, loss_grounding_dice_0: 0.10968/0.15157, loss_grounding_ce_0: 0.05784/0.25380, loss_mask_ce_1: 0.85989/0.78265, loss_mask_bce_1: 0.10458/0.30279, loss_mask_dice_1: 0.65549/1.03263, loss_spatial_bce_1: 0.04070/0.08952, loss_spatial_dice_1: 0.27125/0.19040, loss_spatial_ce_1: 0.24045/0.07455, loss_grounding_bce_1: 0.05813/0.08077, loss_grounding_dice_1: 0.10261/0.15245, loss_grounding_ce_1: 0.04690/0.25574, loss_mask_ce_2: 0.65276/0.79034, loss_mask_bce_2: 0.11235/0.30279, loss_mask_dice_2: 0.76597/1.03482, loss_spatial_bce_2: 0.04273/0.08915, loss_spatial_dice_2: 0.21761/0.19044, loss_spatial_ce_2: 0.08080/0.07680, loss_grounding_bce_2: 0.05891/0.08062, loss_grounding_dice_2: 0.10413/0.15215, loss_grounding_ce_2: 0.03739/0.25790, loss_mask_ce_3: 0.83360/0.79055, loss_mask_bce_3: 0.09732/0.30439, loss_mask_dice_3: 0.66676/1.03060, loss_spatial_bce_3: 0.03993/0.09082, loss_spatial_dice_3: 0.21337/0.19106, loss_spatial_ce_3: 0.04013/0.08238, loss_grounding_bce_3: 0.06057/0.08112, loss_grounding_dice_3: 0.10207/0.15173, loss_grounding_ce_3: 0.05336/0.25738, loss_mask_ce_4: 1.00569/0.79640, loss_mask_bce_4: 0.09430/0.30652, loss_mask_dice_4: 0.79407/1.04942, loss_spatial_bce_4: 0.02665/0.09273, loss_spatial_dice_4: 0.27318/0.19836, loss_spatial_ce_4: 0.15124/0.09450, loss_grounding_bce_4: 0.06239/0.08184, loss_grounding_dice_4: 0.11007/0.15427, loss_grounding_ce_4: 0.04066/0.26369, loss_mask_ce_5: 1.23508/0.81899, loss_mask_bce_5: 0.09703/0.30855, loss_mask_dice_5: 0.72702/1.05635, loss_spatial_bce_5: 0.02530/0.09457, loss_spatial_dice_5: 0.25755/0.20042, loss_spatial_ce_5: 0.26910/0.10574, loss_grounding_bce_5: 0.05757/0.08222, loss_grounding_dice_5: 0.10743/0.15498, loss_grounding_ce_5: 0.03654/0.28305, loss_mask_ce_6: 0.88530/0.84446, loss_mask_bce_6: 0.09344/0.31018, loss_mask_dice_6: 0.74172/1.05938, loss_spatial_bce_6: 0.02812/0.09942, loss_spatial_dice_6: 0.27831/0.20287, loss_spatial_ce_6: 0.25100/0.12679, loss_grounding_bce_6: 0.06573/0.08322, loss_grounding_dice_6: 0.11444/0.15567, loss_grounding_ce_6: 0.03927/0.29394, loss_mask_ce_7: 0.86867/0.90572, loss_mask_bce_7: 0.07480/0.31720, loss_mask_dice_7: 0.70849/1.10587, loss_spatial_bce_7: 0.08122/0.10969, loss_spatial_dice_7: 0.34420/0.22741, loss_spatial_ce_7: 0.10696/0.16919, loss_grounding_bce_7: 0.05451/0.08489, loss_grounding_dice_7: 0.11717/0.16136, loss_grounding_ce_7: 0.02241/0.33597, loss_mask_ce_8: 1.19151/1.04147, loss_mask_bce_8: 0.08059/0.33454, loss_mask_dice_8: 0.73526/1.18542, loss_spatial_bce_8: 0.04653/0.13012, loss_spatial_dice_8: 0.32650/0.26704, loss_spatial_ce_8: 0.12870/0.22386, loss_grounding_bce_8: 0.05804/0.08876, loss_grounding_dice_8: 0.10804/0.17081, loss_grounding_ce_8: 0.02517/0.43600, loss_mask_ce_9: 2.49530/3.49707, loss_mask_bce_9: 0.08617/0.36077, loss_mask_dice_9: 0.97507/1.77057, loss_spatial_bce_9: 0.29338/0.35830, loss_spatial_dice_9: 0.86427/0.79600, loss_spatial_ce_9: 1.10699/1.40901, loss_grounding_bce_9: 0.06198/0.10076, loss_grounding_dice_9: 0.13403/0.24442, loss_grounding_ce_9: 0.02304/0.70192] items per batch[64] items per second[0.37] total items[1542400] mini batches[ 24100] memory[4967] epoch remaining[0:44:12] INFO:trainer.default_trainer:epochs[ 13] optim steps[24200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19624/0.78106, loss_mask_bce_0: 0.24964/0.30220, loss_mask_dice_0: 0.35141/1.02913, loss_spatial_bce_0: 0.08369/0.08913, loss_spatial_dice_0: 0.09183/0.18780, loss_spatial_ce_0: 0.00285/0.06987, loss_grounding_bce_0: 0.09055/0.08055, loss_grounding_dice_0: 0.05360/0.15154, loss_grounding_ce_0: 0.00175/0.25393, loss_mask_ce_1: 0.19300/0.78289, loss_mask_bce_1: 0.24756/0.30295, loss_mask_dice_1: 0.34546/1.03325, loss_spatial_bce_1: 0.07868/0.08953, loss_spatial_dice_1: 0.11157/0.19034, loss_spatial_ce_1: 0.00368/0.07453, loss_grounding_bce_1: 0.08578/0.08078, loss_grounding_dice_1: 0.05329/0.15241, loss_grounding_ce_1: 0.00110/0.25574, loss_mask_ce_2: 0.23710/0.79063, loss_mask_bce_2: 0.25855/0.30291, loss_mask_dice_2: 0.35550/1.03545, loss_spatial_bce_2: 0.08129/0.08916, loss_spatial_dice_2: 0.09968/0.19040, loss_spatial_ce_2: 0.00340/0.07674, loss_grounding_bce_2: 0.08800/0.08063, loss_grounding_dice_2: 0.05222/0.15212, loss_grounding_ce_2: 0.00231/0.25805, loss_mask_ce_3: 0.23566/0.79081, loss_mask_bce_3: 0.25869/0.30452, loss_mask_dice_3: 0.35961/1.03110, loss_spatial_bce_3: 0.08233/0.09083, loss_spatial_dice_3: 0.10496/0.19101, loss_spatial_ce_3: 0.00339/0.08236, loss_grounding_bce_3: 0.09427/0.08114, loss_grounding_dice_3: 0.05444/0.15169, loss_grounding_ce_3: 0.00107/0.25751, loss_mask_ce_4: 0.39820/0.79673, loss_mask_bce_4: 0.24981/0.30667, loss_mask_dice_4: 0.35955/1.04998, loss_spatial_bce_4: 0.07881/0.09275, loss_spatial_dice_4: 0.11721/0.19833, loss_spatial_ce_4: 0.00558/0.09446, loss_grounding_bce_4: 0.08367/0.08185, loss_grounding_dice_4: 0.05340/0.15423, loss_grounding_ce_4: 0.00575/0.26387, loss_mask_ce_5: 0.45801/0.81927, loss_mask_bce_5: 0.25246/0.30869, loss_mask_dice_5: 0.33028/1.05686, loss_spatial_bce_5: 0.08082/0.09459, loss_spatial_dice_5: 0.11184/0.20041, loss_spatial_ce_5: 0.03751/0.10572, loss_grounding_bce_5: 0.07904/0.08223, loss_grounding_dice_5: 0.05103/0.15494, loss_grounding_ce_5: 0.02280/0.28327, loss_mask_ce_6: 0.54772/0.84467, loss_mask_bce_6: 0.25500/0.31037, loss_mask_dice_6: 0.31600/1.06006, loss_spatial_bce_6: 0.08505/0.09944, loss_spatial_dice_6: 0.10933/0.20284, loss_spatial_ce_6: 0.04254/0.12676, loss_grounding_bce_6: 0.07862/0.08322, loss_grounding_dice_6: 0.05419/0.15562, loss_grounding_ce_6: 0.00531/0.29409, loss_mask_ce_7: 0.53680/0.90594, loss_mask_bce_7: 0.25808/0.31738, loss_mask_dice_7: 0.32430/1.10645, loss_spatial_bce_7: 0.09560/0.10970, loss_spatial_dice_7: 0.13236/0.22738, loss_spatial_ce_7: 0.09142/0.16919, loss_grounding_bce_7: 0.08976/0.08488, loss_grounding_dice_7: 0.05343/0.16132, loss_grounding_ce_7: 0.01698/0.33611, loss_mask_ce_8: 0.67584/1.04185, loss_mask_bce_8: 0.26652/0.33476, loss_mask_dice_8: 0.34126/1.18612, loss_spatial_bce_8: 0.11454/0.13011, loss_spatial_dice_8: 0.15989/0.26698, loss_spatial_ce_8: 0.09233/0.22384, loss_grounding_bce_8: 0.08082/0.08880, loss_grounding_dice_8: 0.05003/0.17076, loss_grounding_ce_8: 0.94112/0.43629, loss_mask_ce_9: 2.59026/3.49759, loss_mask_bce_9: 0.26856/0.36105, loss_mask_dice_9: 0.62301/1.77197, loss_spatial_bce_9: 0.41493/0.35829, loss_spatial_dice_9: 0.75267/0.79603, loss_spatial_ce_9: 1.04652/1.40855, loss_grounding_bce_9: 0.09039/0.10078, loss_grounding_dice_9: 0.06515/0.24441, loss_grounding_ce_9: 1.43521/0.70177] items per batch[64] items per second[0.36] total items[1548800] mini batches[ 24200] memory[4967] epoch remaining[0:41:03] INFO:trainer.default_trainer:epochs[ 13] optim steps[24300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55049/0.78104, loss_mask_bce_0: 1.20096/0.30234, loss_mask_dice_0: 0.90771/1.02902, loss_spatial_bce_0: 0.11272/0.08911, loss_spatial_dice_0: 0.17403/0.18777, loss_spatial_ce_0: 0.06166/0.06980, loss_grounding_bce_0: 0.05044/0.08061, loss_grounding_dice_0: 0.02504/0.15161, loss_grounding_ce_0: 0.43097/0.25395, loss_mask_ce_1: 0.59367/0.78289, loss_mask_bce_1: 0.74594/0.30304, loss_mask_dice_1: 1.06576/1.03322, loss_spatial_bce_1: 0.11626/0.08952, loss_spatial_dice_1: 0.17222/0.19032, loss_spatial_ce_1: 0.07253/0.07444, loss_grounding_bce_1: 0.05417/0.08086, loss_grounding_dice_1: 0.02593/0.15250, loss_grounding_ce_1: 0.25751/0.25572, loss_mask_ce_2: 0.51042/0.79062, loss_mask_bce_2: 0.86082/0.30303, loss_mask_dice_2: 1.12573/1.03537, loss_spatial_bce_2: 0.12516/0.08916, loss_spatial_dice_2: 0.17095/0.19038, loss_spatial_ce_2: 0.04179/0.07667, loss_grounding_bce_2: 0.04939/0.08070, loss_grounding_dice_2: 0.02448/0.15222, loss_grounding_ce_2: 0.86100/0.25818, loss_mask_ce_3: 0.54373/0.79090, loss_mask_bce_3: 0.82210/0.30463, loss_mask_dice_3: 1.08956/1.03098, loss_spatial_bce_3: 0.12195/0.09083, loss_spatial_dice_3: 0.16504/0.19100, loss_spatial_ce_3: 0.05403/0.08230, loss_grounding_bce_3: 0.05258/0.08121, loss_grounding_dice_3: 0.02659/0.15177, loss_grounding_ce_3: 0.53444/0.25765, loss_mask_ce_4: 0.78190/0.79682, loss_mask_bce_4: 0.61611/0.30676, loss_mask_dice_4: 1.02866/1.04986, loss_spatial_bce_4: 0.12255/0.09276, loss_spatial_dice_4: 0.16783/0.19832, loss_spatial_ce_4: 0.12777/0.09437, loss_grounding_bce_4: 0.05234/0.08193, loss_grounding_dice_4: 0.02658/0.15430, loss_grounding_ce_4: 0.46488/0.26401, loss_mask_ce_5: 0.55292/0.81936, loss_mask_bce_5: 1.25862/0.30882, loss_mask_dice_5: 0.99744/1.05681, loss_spatial_bce_5: 0.12987/0.09458, loss_spatial_dice_5: 0.16973/0.20041, loss_spatial_ce_5: 0.12754/0.10562, loss_grounding_bce_5: 0.05081/0.08231, loss_grounding_dice_5: 0.02674/0.15504, loss_grounding_ce_5: 0.70153/0.28321, loss_mask_ce_6: 0.94817/0.84472, loss_mask_bce_6: 0.70220/0.31047, loss_mask_dice_6: 1.11748/1.05989, loss_spatial_bce_6: 0.12312/0.09941, loss_spatial_dice_6: 0.17088/0.20284, loss_spatial_ce_6: 0.15708/0.12676, loss_grounding_bce_6: 0.05449/0.08330, loss_grounding_dice_6: 0.03164/0.15569, loss_grounding_ce_6: 0.39520/0.29396, loss_mask_ce_7: 1.11092/0.90591, loss_mask_bce_7: 0.62925/0.31749, loss_mask_dice_7: 0.87887/1.10625, loss_spatial_bce_7: 0.16188/0.10968, loss_spatial_dice_7: 0.23763/0.22740, loss_spatial_ce_7: 0.15490/0.16914, loss_grounding_bce_7: 0.06089/0.08493, loss_grounding_dice_7: 0.03255/0.16137, loss_grounding_ce_7: 1.08469/0.33612, loss_mask_ce_8: 0.71970/1.04197, loss_mask_bce_8: 0.65744/0.33487, loss_mask_dice_8: 0.99969/1.18599, loss_spatial_bce_8: 0.14866/0.13009, loss_spatial_dice_8: 0.20901/0.26697, loss_spatial_ce_8: 0.24483/0.22381, loss_grounding_bce_8: 0.14416/0.08886, loss_grounding_dice_8: 0.06222/0.17087, loss_grounding_ce_8: 0.94695/0.43633, loss_mask_ce_9: 5.42238/3.49827, loss_mask_bce_9: 0.68935/0.36117, loss_mask_dice_9: 1.13006/1.77204, loss_spatial_bce_9: 0.44259/0.35844, loss_spatial_dice_9: 0.86058/0.79608, loss_spatial_ce_9: 1.19383/1.40877, loss_grounding_bce_9: 0.21560/0.10089, loss_grounding_dice_9: 0.15372/0.24460, loss_grounding_ce_9: 1.37268/0.70112] items per batch[64] items per second[0.36] total items[1555200] mini batches[ 24300] memory[4967] epoch remaining[0:38:02] INFO:trainer.default_trainer:epochs[ 13] optim steps[24400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04522/0.78068, loss_mask_bce_0: 0.02845/0.30231, loss_mask_dice_0: 0.10423/1.02802, loss_spatial_bce_0: 0.00669/0.08909, loss_spatial_dice_0: 0.04055/0.18767, loss_spatial_ce_0: 0.00025/0.06971, loss_grounding_bce_0: 0.00864/0.08062, loss_grounding_dice_0: 0.04290/0.15158, loss_grounding_ce_0: 0.00113/0.25405, loss_mask_ce_1: 0.05459/0.78249, loss_mask_bce_1: 0.03020/0.30304, loss_mask_dice_1: 0.11681/1.03227, loss_spatial_bce_1: 0.01029/0.08949, loss_spatial_dice_1: 0.05837/0.19020, loss_spatial_ce_1: 0.00079/0.07434, loss_grounding_bce_1: 0.00800/0.08088, loss_grounding_dice_1: 0.05063/0.15247, loss_grounding_ce_1: 0.00160/0.25577, loss_mask_ce_2: 0.07145/0.79031, loss_mask_bce_2: 0.02374/0.30303, loss_mask_dice_2: 0.09795/1.03449, loss_spatial_bce_2: 0.00870/0.08913, loss_spatial_dice_2: 0.05145/0.19027, loss_spatial_ce_2: 0.00093/0.07658, loss_grounding_bce_2: 0.01004/0.08073, loss_grounding_dice_2: 0.05622/0.15222, loss_grounding_ce_2: 0.00117/0.25824, loss_mask_ce_3: 0.05891/0.79064, loss_mask_bce_3: 0.02595/0.30461, loss_mask_dice_3: 0.10701/1.03000, loss_spatial_bce_3: 0.00947/0.09080, loss_spatial_dice_3: 0.05951/0.19088, loss_spatial_ce_3: 0.00159/0.08219, loss_grounding_bce_3: 0.00925/0.08124, loss_grounding_dice_3: 0.05585/0.15174, loss_grounding_ce_3: 0.00113/0.25774, loss_mask_ce_4: 0.05686/0.79650, loss_mask_bce_4: 0.02252/0.30675, loss_mask_dice_4: 0.09983/1.04894, loss_spatial_bce_4: 0.01052/0.09274, loss_spatial_dice_4: 0.06112/0.19821, loss_spatial_ce_4: 0.02485/0.09425, loss_grounding_bce_4: 0.00928/0.08196, loss_grounding_dice_4: 0.04965/0.15428, loss_grounding_ce_4: 0.00120/0.26418, loss_mask_ce_5: 0.05185/0.81903, loss_mask_bce_5: 0.03072/0.30880, loss_mask_dice_5: 0.11984/1.05585, loss_spatial_bce_5: 0.00946/0.09454, loss_spatial_dice_5: 0.05240/0.20028, loss_spatial_ce_5: 0.06775/0.10549, loss_grounding_bce_5: 0.00919/0.08234, loss_grounding_dice_5: 0.05292/0.15502, loss_grounding_ce_5: 0.00100/0.28336, loss_mask_ce_6: 0.06896/0.84448, loss_mask_bce_6: 0.02497/0.31041, loss_mask_dice_6: 0.11158/1.05892, loss_spatial_bce_6: 0.01016/0.09938, loss_spatial_dice_6: 0.05505/0.20273, loss_spatial_ce_6: 0.05376/0.12677, loss_grounding_bce_6: 0.01031/0.08332, loss_grounding_dice_6: 0.05245/0.15565, loss_grounding_ce_6: 0.00147/0.29414, loss_mask_ce_7: 0.07008/0.90556, loss_mask_bce_7: 0.02445/0.31742, loss_mask_dice_7: 0.09021/1.10521, loss_spatial_bce_7: 0.01175/0.10968, loss_spatial_dice_7: 0.05806/0.22729, loss_spatial_ce_7: 0.05036/0.16900, loss_grounding_bce_7: 0.01057/0.08492, loss_grounding_dice_7: 0.05389/0.16136, loss_grounding_ce_7: 0.00094/0.33630, loss_mask_ce_8: 0.16226/1.04148, loss_mask_bce_8: 0.03317/0.33476, loss_mask_dice_8: 0.12595/1.18487, loss_spatial_bce_8: 0.03363/0.13010, loss_spatial_dice_8: 0.17490/0.26687, loss_spatial_ce_8: 0.11855/0.22361, loss_grounding_bce_8: 0.01185/0.08886, loss_grounding_dice_8: 0.05688/0.17083, loss_grounding_ce_8: 0.00075/0.43677, loss_mask_ce_9: 1.53571/3.49706, loss_mask_bce_9: 0.02331/0.36105, loss_mask_dice_9: 0.11531/1.77071, loss_spatial_bce_9: 0.06946/0.35843, loss_spatial_dice_9: 0.79092/0.79601, loss_spatial_ce_9: 0.85582/1.40814, loss_grounding_bce_9: 0.00661/0.10083, loss_grounding_dice_9: 0.04310/0.24449, loss_grounding_ce_9: 0.02104/0.70129] items per batch[64] items per second[0.36] total items[1561600] mini batches[ 24400] memory[4967] epoch remaining[0:34:58] INFO:trainer.default_trainer:epochs[ 13] optim steps[24500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34486/0.78078, loss_mask_bce_0: 0.15272/0.30239, loss_mask_dice_0: 0.61147/1.02799, loss_spatial_bce_0: 0.04065/0.08907, loss_spatial_dice_0: 0.16421/0.18766, loss_spatial_ce_0: 0.00039/0.06969, loss_grounding_bce_0: 0.05465/0.08054, loss_grounding_dice_0: 0.16881/0.15153, loss_grounding_ce_0: 0.04699/0.25402, loss_mask_ce_1: 0.38406/0.78260, loss_mask_bce_1: 0.14436/0.30313, loss_mask_dice_1: 0.60046/1.03232, loss_spatial_bce_1: 0.03868/0.08947, loss_spatial_dice_1: 0.15995/0.19020, loss_spatial_ce_1: 0.00017/0.07433, loss_grounding_bce_1: 0.04939/0.08083, loss_grounding_dice_1: 0.16758/0.15242, loss_grounding_ce_1: 0.05006/0.25577, loss_mask_ce_2: 0.40145/0.79044, loss_mask_bce_2: 0.14451/0.30314, loss_mask_dice_2: 0.55272/1.03447, loss_spatial_bce_2: 0.03902/0.08914, loss_spatial_dice_2: 0.15276/0.19026, loss_spatial_ce_2: 0.00009/0.07655, loss_grounding_bce_2: 0.05296/0.08067, loss_grounding_dice_2: 0.16383/0.15216, loss_grounding_ce_2: 0.04440/0.25831, loss_mask_ce_3: 0.37805/0.79089, loss_mask_bce_3: 0.16977/0.30471, loss_mask_dice_3: 0.57936/1.03000, loss_spatial_bce_3: 0.03813/0.09080, loss_spatial_dice_3: 0.17377/0.19088, loss_spatial_ce_3: 0.00156/0.08212, loss_grounding_bce_3: 0.05791/0.08118, loss_grounding_dice_3: 0.17385/0.15167, loss_grounding_ce_3: 0.04460/0.25778, loss_mask_ce_4: 0.45112/0.79669, loss_mask_bce_4: 0.13710/0.30684, loss_mask_dice_4: 0.57982/1.04894, loss_spatial_bce_4: 0.04154/0.09274, loss_spatial_dice_4: 0.17692/0.19822, loss_spatial_ce_4: 0.00113/0.09421, loss_grounding_bce_4: 0.05783/0.08190, loss_grounding_dice_4: 0.18585/0.15422, loss_grounding_ce_4: 0.05755/0.26411, loss_mask_ce_5: 0.38519/0.81911, loss_mask_bce_5: 0.14589/0.30887, loss_mask_dice_5: 0.61633/1.05578, loss_spatial_bce_5: 0.04811/0.09453, loss_spatial_dice_5: 0.19554/0.20030, loss_spatial_ce_5: 0.00196/0.10547, loss_grounding_bce_5: 0.05396/0.08229, loss_grounding_dice_5: 0.18768/0.15498, loss_grounding_ce_5: 0.04223/0.28328, loss_mask_ce_6: 0.56518/0.84468, loss_mask_bce_6: 0.14871/0.31046, loss_mask_dice_6: 0.60202/1.05883, loss_spatial_bce_6: 0.04042/0.09934, loss_spatial_dice_6: 0.15848/0.20274, loss_spatial_ce_6: 0.07527/0.12669, loss_grounding_bce_6: 0.05745/0.08326, loss_grounding_dice_6: 0.17252/0.15563, loss_grounding_ce_6: 0.10237/0.29410, loss_mask_ce_7: 0.44243/0.90562, loss_mask_bce_7: 0.14721/0.31749, loss_mask_dice_7: 0.68453/1.10511, loss_spatial_bce_7: 0.05690/0.10964, loss_spatial_dice_7: 0.22053/0.22730, loss_spatial_ce_7: 0.05803/0.16899, loss_grounding_bce_7: 0.03080/0.08487, loss_grounding_dice_7: 0.15345/0.16133, loss_grounding_ce_7: 0.26290/0.33620, loss_mask_ce_8: 0.61047/1.04140, loss_mask_bce_8: 0.12231/0.33484, loss_mask_dice_8: 0.58146/1.18469, loss_spatial_bce_8: 0.06556/0.13005, loss_spatial_dice_8: 0.22446/0.26688, loss_spatial_ce_8: 0.12836/0.22363, loss_grounding_bce_8: 0.03171/0.08880, loss_grounding_dice_8: 0.15285/0.17077, loss_grounding_ce_8: 0.13793/0.43666, loss_mask_ce_9: 2.56723/3.49743, loss_mask_bce_9: 0.15243/0.36107, loss_mask_dice_9: 0.72804/1.77037, loss_spatial_bce_9: 0.27478/0.35831, loss_spatial_dice_9: 0.80663/0.79602, loss_spatial_ce_9: 1.16925/1.40841, loss_grounding_bce_9: 0.05940/0.10078, loss_grounding_dice_9: 0.23292/0.24445, loss_grounding_ce_9: 0.44468/0.70130] items per batch[64] items per second[0.36] total items[1568000] mini batches[ 24500] memory[4967] epoch remaining[0:31:58] INFO:trainer.default_trainer:epochs[ 13] optim steps[24600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45548/0.78073, loss_mask_bce_0: 0.16361/0.30234, loss_mask_dice_0: 0.77223/1.02820, loss_spatial_bce_0: 0.05178/0.08902, loss_spatial_dice_0: 0.19553/0.18760, loss_spatial_ce_0: 0.00112/0.06962, loss_grounding_bce_0: 0.02651/0.08054, loss_grounding_dice_0: 0.10460/0.15151, loss_grounding_ce_0: 0.14539/0.25396, loss_mask_ce_1: 1.13030/0.78253, loss_mask_bce_1: 0.15383/0.30306, loss_mask_dice_1: 0.70994/1.03240, loss_spatial_bce_1: 0.05103/0.08942, loss_spatial_dice_1: 0.21742/0.19015, loss_spatial_ce_1: 0.00924/0.07430, loss_grounding_bce_1: 0.02377/0.08082, loss_grounding_dice_1: 0.10031/0.15242, loss_grounding_ce_1: 0.12909/0.25571, loss_mask_ce_2: 1.43368/0.79039, loss_mask_bce_2: 0.16886/0.30306, loss_mask_dice_2: 0.98427/1.03461, loss_spatial_bce_2: 0.04998/0.08909, loss_spatial_dice_2: 0.19436/0.19022, loss_spatial_ce_2: 0.05884/0.07648, loss_grounding_bce_2: 0.03042/0.08066, loss_grounding_dice_2: 0.10768/0.15214, loss_grounding_ce_2: 0.21944/0.25825, loss_mask_ce_3: 1.31514/0.79089, loss_mask_bce_3: 0.17948/0.30465, loss_mask_dice_3: 0.96865/1.03015, loss_spatial_bce_3: 0.04706/0.09074, loss_spatial_dice_3: 0.19848/0.19083, loss_spatial_ce_3: 0.02870/0.08203, loss_grounding_bce_3: 0.02882/0.08117, loss_grounding_dice_3: 0.10783/0.15168, loss_grounding_ce_3: 0.27491/0.25766, loss_mask_ce_4: 1.02462/0.79657, loss_mask_bce_4: 0.16807/0.30680, loss_mask_dice_4: 0.73909/1.04900, loss_spatial_bce_4: 0.05139/0.09269, loss_spatial_dice_4: 0.25666/0.19819, loss_spatial_ce_4: 0.03295/0.09412, loss_grounding_bce_4: 0.02437/0.08189, loss_grounding_dice_4: 0.09803/0.15418, loss_grounding_ce_4: 0.17041/0.26399, loss_mask_ce_5: 1.42478/0.81901, loss_mask_bce_5: 0.16335/0.30882, loss_mask_dice_5: 0.71051/1.05595, loss_spatial_bce_5: 0.05070/0.09448, loss_spatial_dice_5: 0.28461/0.20027, loss_spatial_ce_5: 0.08080/0.10540, loss_grounding_bce_5: 0.02831/0.08228, loss_grounding_dice_5: 0.10944/0.15495, loss_grounding_ce_5: 0.20894/0.28324, loss_mask_ce_6: 1.61536/0.84452, loss_mask_bce_6: 0.16399/0.31042, loss_mask_dice_6: 0.77125/1.05904, loss_spatial_bce_6: 0.06093/0.09929, loss_spatial_dice_6: 0.23382/0.20269, loss_spatial_ce_6: 0.05311/0.12665, loss_grounding_bce_6: 0.02546/0.08325, loss_grounding_dice_6: 0.10337/0.15562, loss_grounding_ce_6: 0.12723/0.29408, loss_mask_ce_7: 1.47607/0.90556, loss_mask_bce_7: 0.15780/0.31744, loss_mask_dice_7: 0.79239/1.10534, loss_spatial_bce_7: 0.06131/0.10964, loss_spatial_dice_7: 0.32974/0.22726, loss_spatial_ce_7: 0.15726/0.16898, loss_grounding_bce_7: 0.02275/0.08486, loss_grounding_dice_7: 0.09829/0.16130, loss_grounding_ce_7: 0.13244/0.33622, loss_mask_ce_8: 1.68329/1.04144, loss_mask_bce_8: 0.14818/0.33481, loss_mask_dice_8: 0.83544/1.18480, loss_spatial_bce_8: 0.07555/0.13002, loss_spatial_dice_8: 0.35857/0.26684, loss_spatial_ce_8: 0.16826/0.22363, loss_grounding_bce_8: 0.02466/0.08880, loss_grounding_dice_8: 0.10916/0.17077, loss_grounding_ce_8: 0.42926/0.43706, loss_mask_ce_9: 3.25062/3.49779, loss_mask_bce_9: 0.14863/0.36103, loss_mask_dice_9: 1.20962/1.77073, loss_spatial_bce_9: 0.22736/0.35820, loss_spatial_dice_9: 0.77834/0.79596, loss_spatial_ce_9: 1.66451/1.40802, loss_grounding_bce_9: 0.01655/0.10078, loss_grounding_dice_9: 0.12040/0.24444, loss_grounding_ce_9: 1.01631/0.70121] items per batch[64] items per second[0.36] total items[1574400] mini batches[ 24600] memory[4967] epoch remaining[0:28:58] INFO:trainer.default_trainer:epochs[ 13] optim steps[24700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14519/0.78064, loss_mask_bce_0: 0.11572/0.30230, loss_mask_dice_0: 0.25152/1.02839, loss_spatial_bce_0: 0.11242/0.08902, loss_spatial_dice_0: 0.16733/0.18752, loss_spatial_ce_0: 0.01013/0.06955, loss_grounding_bce_0: 0.05786/0.08057, loss_grounding_dice_0: 0.11476/0.15149, loss_grounding_ce_0: 0.11759/0.25399, loss_mask_ce_1: 0.14507/0.78244, loss_mask_bce_1: 0.10714/0.30305, loss_mask_dice_1: 0.25051/1.03263, loss_spatial_bce_1: 0.10563/0.08942, loss_spatial_dice_1: 0.16410/0.19007, loss_spatial_ce_1: 0.01041/0.07426, loss_grounding_bce_1: 0.06271/0.08085, loss_grounding_dice_1: 0.12345/0.15240, loss_grounding_ce_1: 0.11291/0.25578, loss_mask_ce_2: 0.15189/0.79027, loss_mask_bce_2: 0.11331/0.30304, loss_mask_dice_2: 0.24137/1.03481, loss_spatial_bce_2: 0.09704/0.08909, loss_spatial_dice_2: 0.15938/0.19015, loss_spatial_ce_2: 0.01867/0.07637, loss_grounding_bce_2: 0.06161/0.08071, loss_grounding_dice_2: 0.11929/0.15214, loss_grounding_ce_2: 0.13799/0.25820, loss_mask_ce_3: 0.17786/0.79082, loss_mask_bce_3: 0.11815/0.30461, loss_mask_dice_3: 0.25585/1.03031, loss_spatial_bce_3: 0.11000/0.09075, loss_spatial_dice_3: 0.16010/0.19077, loss_spatial_ce_3: 0.01419/0.08192, loss_grounding_bce_3: 0.06214/0.08120, loss_grounding_dice_3: 0.12012/0.15165, loss_grounding_ce_3: 0.13567/0.25769, loss_mask_ce_4: 0.19316/0.79638, loss_mask_bce_4: 0.12993/0.30682, loss_mask_dice_4: 0.27472/1.04924, loss_spatial_bce_4: 0.12305/0.09270, loss_spatial_dice_4: 0.18472/0.19813, loss_spatial_ce_4: 0.01787/0.09399, loss_grounding_bce_4: 0.07232/0.08195, loss_grounding_dice_4: 0.13382/0.15416, loss_grounding_ce_4: 0.18928/0.26407, loss_mask_ce_5: 0.21779/0.81876, loss_mask_bce_5: 0.14694/0.30881, loss_mask_dice_5: 0.26918/1.05620, loss_spatial_bce_5: 0.12126/0.09450, loss_spatial_dice_5: 0.19307/0.20021, loss_spatial_ce_5: 0.04430/0.10528, loss_grounding_bce_5: 0.07922/0.08236, loss_grounding_dice_5: 0.13656/0.15493, loss_grounding_ce_5: 0.18141/0.28328, loss_mask_ce_6: 0.80568/0.84429, loss_mask_bce_6: 0.13446/0.31039, loss_mask_dice_6: 0.24828/1.05925, loss_spatial_bce_6: 0.07718/0.09930, loss_spatial_dice_6: 0.19296/0.20262, loss_spatial_ce_6: 0.06527/0.12659, loss_grounding_bce_6: 0.07228/0.08332, loss_grounding_dice_6: 0.12946/0.15560, loss_grounding_ce_6: 0.39959/0.29402, loss_mask_ce_7: 0.28906/0.90530, loss_mask_bce_7: 0.14290/0.31744, loss_mask_dice_7: 0.31422/1.10568, loss_spatial_bce_7: 0.16247/0.10965, loss_spatial_dice_7: 0.24359/0.22721, loss_spatial_ce_7: 0.08997/0.16886, loss_grounding_bce_7: 0.08047/0.08491, loss_grounding_dice_7: 0.16533/0.16127, loss_grounding_ce_7: 0.19147/0.33617, loss_mask_ce_8: 0.75115/1.04124, loss_mask_bce_8: 0.11345/0.33476, loss_mask_dice_8: 0.24523/1.18503, loss_spatial_bce_8: 0.09611/0.13006, loss_spatial_dice_8: 0.20455/0.26677, loss_spatial_ce_8: 0.22699/0.22344, loss_grounding_bce_8: 0.06410/0.08887, loss_grounding_dice_8: 0.12513/0.17078, loss_grounding_ce_8: 0.43976/0.43699, loss_mask_ce_9: 2.92124/3.49729, loss_mask_bce_9: 0.16680/0.36103, loss_mask_dice_9: 0.49258/1.77112, loss_spatial_bce_9: 0.45793/0.35817, loss_spatial_dice_9: 0.86742/0.79591, loss_spatial_ce_9: 1.60267/1.40796, loss_grounding_bce_9: 0.09432/0.10085, loss_grounding_dice_9: 0.24527/0.24445, loss_grounding_ce_9: 0.62331/0.70103] items per batch[64] items per second[0.36] total items[1580800] mini batches[ 24700] memory[4967] epoch remaining[0:26:01] INFO:trainer.default_trainer:epochs[ 13] optim steps[24800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.48864/0.78081, loss_mask_bce_0: 0.08745/0.30209, loss_mask_dice_0: 5.04574/1.02900, loss_spatial_bce_0: 0.00701/0.08892, loss_spatial_dice_0: 0.49953/0.18749, loss_spatial_ce_0: 0.10286/0.06946, loss_grounding_bce_0: 0.00491/0.08052, loss_grounding_dice_0: 0.17461/0.15146, loss_grounding_ce_0: 0.32285/0.25395, loss_mask_ce_1: 1.69203/0.78276, loss_mask_bce_1: 0.07948/0.30284, loss_mask_dice_1: 4.96877/1.03327, loss_spatial_bce_1: 0.00297/0.08932, loss_spatial_dice_1: 0.46949/0.19005, loss_spatial_ce_1: 0.11153/0.07415, loss_grounding_bce_1: 0.00474/0.08081, loss_grounding_dice_1: 0.29864/0.15238, loss_grounding_ce_1: 0.30632/0.25578, loss_mask_ce_2: 1.28700/0.79055, loss_mask_bce_2: 0.07623/0.30284, loss_mask_dice_2: 4.63941/1.03543, loss_spatial_bce_2: 0.00320/0.08900, loss_spatial_dice_2: 0.46184/0.19012, loss_spatial_ce_2: 0.37162/0.07627, loss_grounding_bce_2: 0.00400/0.08066, loss_grounding_dice_2: 0.32579/0.15212, loss_grounding_ce_2: 0.32204/0.25818, loss_mask_ce_3: 1.30796/0.79123, loss_mask_bce_3: 0.08562/0.30441, loss_mask_dice_3: 5.24847/1.03101, loss_spatial_bce_3: 0.00310/0.09067, loss_spatial_dice_3: 0.40236/0.19075, loss_spatial_ce_3: 0.35445/0.08183, loss_grounding_bce_3: 0.00367/0.08115, loss_grounding_dice_3: 0.18064/0.15162, loss_grounding_ce_3: 0.35034/0.25768, loss_mask_ce_4: 1.31602/0.79678, loss_mask_bce_4: 0.08762/0.30660, loss_mask_dice_4: 6.27136/1.04991, loss_spatial_bce_4: 0.00295/0.09261, loss_spatial_dice_4: 0.47189/0.19812, loss_spatial_ce_4: 0.18502/0.09396, loss_grounding_bce_4: 0.00334/0.08190, loss_grounding_dice_4: 0.20286/0.15414, loss_grounding_ce_4: 0.26839/0.26397, loss_mask_ce_5: 1.37968/0.81915, loss_mask_bce_5: 0.10258/0.30858, loss_mask_dice_5: 5.77863/1.05688, loss_spatial_bce_5: 0.00206/0.09441, loss_spatial_dice_5: 0.45571/0.20019, loss_spatial_ce_5: 0.33621/0.10523, loss_grounding_bce_5: 0.00557/0.08231, loss_grounding_dice_5: 0.40503/0.15491, loss_grounding_ce_5: 0.28200/0.28317, loss_mask_ce_6: 1.62771/0.84475, loss_mask_bce_6: 0.08687/0.31017, loss_mask_dice_6: 5.75740/1.05994, loss_spatial_bce_6: 0.00492/0.09920, loss_spatial_dice_6: 0.47179/0.20259, loss_spatial_ce_6: 0.31497/0.12645, loss_grounding_bce_6: 0.00327/0.08326, loss_grounding_dice_6: 0.04695/0.15555, loss_grounding_ce_6: 0.24975/0.29384, loss_mask_ce_7: 1.16416/0.90573, loss_mask_bce_7: 0.09101/0.31722, loss_mask_dice_7: 5.50541/1.10645, loss_spatial_bce_7: 0.00364/0.10954, loss_spatial_dice_7: 0.53119/0.22720, loss_spatial_ce_7: 0.40400/0.16879, loss_grounding_bce_7: 0.00414/0.08485, loss_grounding_dice_7: 0.21722/0.16122, loss_grounding_ce_7: 0.21519/0.33584, loss_mask_ce_8: 1.81313/1.04181, loss_mask_bce_8: 0.10362/0.33454, loss_mask_dice_8: 5.91804/1.18571, loss_spatial_bce_8: 0.00244/0.12990, loss_spatial_dice_8: 0.47580/0.26676, loss_spatial_ce_8: 0.23960/0.22343, loss_grounding_bce_8: 0.00416/0.08881, loss_grounding_dice_8: 0.30152/0.17078, loss_grounding_ce_8: 0.36931/0.43663, loss_mask_ce_9: 6.49964/3.49826, loss_mask_bce_9: 0.06260/0.36076, loss_mask_dice_9: 6.81483/1.77219, loss_spatial_bce_9: 0.01231/0.35799, loss_spatial_dice_9: 0.90834/0.79587, loss_spatial_ce_9: 2.60104/1.40814, loss_grounding_bce_9: 0.00299/0.10080, loss_grounding_dice_9: 0.06820/0.24445, loss_grounding_ce_9: 0.59488/0.70090] items per batch[64] items per second[0.36] total items[1587200] mini batches[ 24800] memory[4967] epoch remaining[0:23:02] INFO:trainer.default_trainer:epochs[ 13] optim steps[24900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71677/0.78050, loss_mask_bce_0: 0.42910/0.30196, loss_mask_dice_0: 2.05481/1.02902, loss_spatial_bce_0: 0.05315/0.08888, loss_spatial_dice_0: 0.21711/0.18749, loss_spatial_ce_0: 0.16967/0.06934, loss_grounding_bce_0: 0.14631/0.08053, loss_grounding_dice_0: 0.19960/0.15143, loss_grounding_ce_0: 0.13337/0.25374, loss_mask_ce_1: 0.75817/0.78239, loss_mask_bce_1: 0.43905/0.30270, loss_mask_dice_1: 1.99852/1.03330, loss_spatial_bce_1: 0.05376/0.08929, loss_spatial_dice_1: 0.21969/0.19003, loss_spatial_ce_1: 0.14752/0.07403, loss_grounding_bce_1: 0.16624/0.08081, loss_grounding_dice_1: 0.22890/0.15237, loss_grounding_ce_1: 0.14513/0.25557, loss_mask_ce_2: 0.77909/0.79024, loss_mask_bce_2: 0.41488/0.30269, loss_mask_dice_2: 1.83696/1.03537, loss_spatial_bce_2: 0.05474/0.08896, loss_spatial_dice_2: 0.20847/0.19011, loss_spatial_ce_2: 0.14775/0.07615, loss_grounding_bce_2: 0.16499/0.08066, loss_grounding_dice_2: 0.23035/0.15207, loss_grounding_ce_2: 0.18838/0.25794, loss_mask_ce_3: 0.72535/0.79087, loss_mask_bce_3: 0.42845/0.30424, loss_mask_dice_3: 1.93840/1.03108, loss_spatial_bce_3: 0.04931/0.09063, loss_spatial_dice_3: 0.21498/0.19074, loss_spatial_ce_3: 0.15552/0.08173, loss_grounding_bce_3: 0.20578/0.08115, loss_grounding_dice_3: 0.27612/0.15161, loss_grounding_ce_3: 0.16941/0.25745, loss_mask_ce_4: 0.79857/0.79646, loss_mask_bce_4: 0.40498/0.30639, loss_mask_dice_4: 1.91627/1.04985, loss_spatial_bce_4: 0.05071/0.09259, loss_spatial_dice_4: 0.22289/0.19812, loss_spatial_ce_4: 0.09508/0.09387, loss_grounding_bce_4: 0.20635/0.08190, loss_grounding_dice_4: 0.25580/0.15411, loss_grounding_ce_4: 0.16581/0.26373, loss_mask_ce_5: 0.59484/0.81884, loss_mask_bce_5: 0.40938/0.30840, loss_mask_dice_5: 2.13427/1.05689, loss_spatial_bce_5: 0.05346/0.09438, loss_spatial_dice_5: 0.23062/0.20021, loss_spatial_ce_5: 0.08838/0.10511, loss_grounding_bce_5: 0.17853/0.08230, loss_grounding_dice_5: 0.20591/0.15486, loss_grounding_ce_5: 0.11842/0.28288, loss_mask_ce_6: 0.86912/0.84438, loss_mask_bce_6: 0.42520/0.30999, loss_mask_dice_6: 1.81431/1.06004, loss_spatial_bce_6: 0.05599/0.09918, loss_spatial_dice_6: 0.23394/0.20261, loss_spatial_ce_6: 0.11886/0.12639, loss_grounding_bce_6: 0.22315/0.08326, loss_grounding_dice_6: 0.24380/0.15552, loss_grounding_ce_6: 0.12438/0.29347, loss_mask_ce_7: 1.10987/0.90543, loss_mask_bce_7: 0.39156/0.31707, loss_mask_dice_7: 2.15837/1.10641, loss_spatial_bce_7: 0.06374/0.10955, loss_spatial_dice_7: 0.22867/0.22723, loss_spatial_ce_7: 0.16212/0.16876, loss_grounding_bce_7: 0.13902/0.08485, loss_grounding_dice_7: 0.14924/0.16118, loss_grounding_ce_7: 0.15012/0.33542, loss_mask_ce_8: 1.28044/1.04156, loss_mask_bce_8: 0.56994/0.33437, loss_mask_dice_8: 2.83286/1.18577, loss_spatial_bce_8: 0.07944/0.12986, loss_spatial_dice_8: 0.27595/0.26677, loss_spatial_ce_8: 0.19333/0.22347, loss_grounding_bce_8: 0.19926/0.08883, loss_grounding_dice_8: 0.21827/0.17075, loss_grounding_ce_8: 0.13848/0.43611, loss_mask_ce_9: 5.99410/3.49748, loss_mask_bce_9: 0.61327/0.36048, loss_mask_dice_9: 3.31474/1.77180, loss_spatial_bce_9: 0.24462/0.35798, loss_spatial_dice_9: 0.93468/0.79579, loss_spatial_ce_9: 1.20044/1.40793, loss_grounding_bce_9: 0.46647/0.10081, loss_grounding_dice_9: 0.53744/0.24439, loss_grounding_ce_9: 0.90758/0.70030] items per batch[64] items per second[0.37] total items[1593600] mini batches[ 24900] memory[4967] epoch remaining[0:20:03] INFO:trainer.default_trainer:epochs[ 13] optim steps[25000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06159/0.78062, loss_mask_bce_0: 0.66813/0.30205, loss_mask_dice_0: 0.98950/1.02904, loss_spatial_bce_0: 0.11480/0.08885, loss_spatial_dice_0: 0.19526/0.18739, loss_spatial_ce_0: 0.03650/0.06923, loss_grounding_bce_0: 0.14112/0.08056, loss_grounding_dice_0: 0.21366/0.15141, loss_grounding_ce_0: 0.12606/0.25379, loss_mask_ce_1: 1.10340/0.78245, loss_mask_bce_1: 0.63586/0.30281, loss_mask_dice_1: 0.95263/1.03336, loss_spatial_bce_1: 0.12565/0.08926, loss_spatial_dice_1: 0.22136/0.18993, loss_spatial_ce_1: 0.02262/0.07391, loss_grounding_bce_1: 0.13015/0.08084, loss_grounding_dice_1: 0.20700/0.15237, loss_grounding_ce_1: 0.11917/0.25555, loss_mask_ce_2: 1.18340/0.79033, loss_mask_bce_2: 0.69937/0.30278, loss_mask_dice_2: 0.99559/1.03546, loss_spatial_bce_2: 0.14328/0.08894, loss_spatial_dice_2: 0.25539/0.19002, loss_spatial_ce_2: 0.02039/0.07604, loss_grounding_bce_2: 0.14643/0.08070, loss_grounding_dice_2: 0.20524/0.15204, loss_grounding_ce_2: 0.12623/0.25786, loss_mask_ce_3: 1.22696/0.79091, loss_mask_bce_3: 0.67529/0.30434, loss_mask_dice_3: 1.01093/1.03113, loss_spatial_bce_3: 0.11415/0.09061, loss_spatial_dice_3: 0.22201/0.19065, loss_spatial_ce_3: 0.03545/0.08160, loss_grounding_bce_3: 0.15062/0.08119, loss_grounding_dice_3: 0.21948/0.15160, loss_grounding_ce_3: 0.15118/0.25744, loss_mask_ce_4: 1.59500/0.79648, loss_mask_bce_4: 0.71416/0.30645, loss_mask_dice_4: 1.13202/1.04994, loss_spatial_bce_4: 0.16037/0.09257, loss_spatial_dice_4: 0.24545/0.19803, loss_spatial_ce_4: 0.06538/0.09376, loss_grounding_bce_4: 0.18172/0.08193, loss_grounding_dice_4: 0.21318/0.15410, loss_grounding_ce_4: 0.14654/0.26374, loss_mask_ce_5: 1.05657/0.81889, loss_mask_bce_5: 1.08929/0.30851, loss_mask_dice_5: 1.37397/1.05697, loss_spatial_bce_5: 0.15965/0.09437, loss_spatial_dice_5: 0.25117/0.20013, loss_spatial_ce_5: 0.17192/0.10500, loss_grounding_bce_5: 0.09263/0.08233, loss_grounding_dice_5: 0.22253/0.15484, loss_grounding_ce_5: 0.21441/0.28282, loss_mask_ce_6: 1.10513/0.84450, loss_mask_bce_6: 1.04371/0.31009, loss_mask_dice_6: 1.37529/1.06013, loss_spatial_bce_6: 0.16733/0.09917, loss_spatial_dice_6: 0.26548/0.20254, loss_spatial_ce_6: 0.19634/0.12630, loss_grounding_bce_6: 0.10018/0.08330, loss_grounding_dice_6: 0.20561/0.15551, loss_grounding_ce_6: 0.21915/0.29337, loss_mask_ce_7: 1.30269/0.90563, loss_mask_bce_7: 0.90356/0.31716, loss_mask_dice_7: 1.26806/1.10647, loss_spatial_bce_7: 0.16938/0.10953, loss_spatial_dice_7: 0.28295/0.22717, loss_spatial_ce_7: 0.27115/0.16863, loss_grounding_bce_7: 0.05689/0.08487, loss_grounding_dice_7: 0.23134/0.16117, loss_grounding_ce_7: 0.22704/0.33538, loss_mask_ce_8: 1.26623/1.04150, loss_mask_bce_8: 0.80947/0.33450, loss_mask_dice_8: 1.31473/1.18590, loss_spatial_bce_8: 0.19650/0.12988, loss_spatial_dice_8: 0.29494/0.26668, loss_spatial_ce_8: 0.28394/0.22334, loss_grounding_bce_8: 0.10832/0.08885, loss_grounding_dice_8: 0.27451/0.17073, loss_grounding_ce_8: 0.10436/0.43603, loss_mask_ce_9: 3.31792/3.49796, loss_mask_bce_9: 0.85346/0.36063, loss_mask_dice_9: 1.71274/1.77227, loss_spatial_bce_9: 0.50006/0.35795, loss_spatial_dice_9: 0.93554/0.79576, loss_spatial_ce_9: 1.92703/1.40758, loss_grounding_bce_9: 0.09002/0.10082, loss_grounding_dice_9: 0.37360/0.24440, loss_grounding_ce_9: 0.29458/0.70028] items per batch[64] items per second[0.37] total items[1600000] mini batches[ 25000] memory[4967] epoch remaining[0:17:03] INFO:trainer.default_trainer:epochs[ 13] optim steps[25100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58887/0.78054, loss_mask_bce_0: 0.77480/0.30195, loss_mask_dice_0: 1.67405/1.02871, loss_spatial_bce_0: 0.10307/0.08883, loss_spatial_dice_0: 0.15438/0.18731, loss_spatial_ce_0: 0.00224/0.06915, loss_grounding_bce_0: 0.11542/0.08054, loss_grounding_dice_0: 0.09994/0.15135, loss_grounding_ce_0: 0.08881/0.25359, loss_mask_ce_1: 0.84213/0.78238, loss_mask_bce_1: 0.71087/0.30273, loss_mask_dice_1: 1.49203/1.03299, loss_spatial_bce_1: 0.10058/0.08923, loss_spatial_dice_1: 0.16839/0.18986, loss_spatial_ce_1: 0.00371/0.07383, loss_grounding_bce_1: 0.11370/0.08083, loss_grounding_dice_1: 0.10204/0.15232, loss_grounding_ce_1: 0.07100/0.25532, loss_mask_ce_2: 0.67374/0.79028, loss_mask_bce_2: 0.76066/0.30270, loss_mask_dice_2: 1.68566/1.03520, loss_spatial_bce_2: 0.10218/0.08891, loss_spatial_dice_2: 0.15825/0.18994, loss_spatial_ce_2: 0.00962/0.07596, loss_grounding_bce_2: 0.11033/0.08068, loss_grounding_dice_2: 0.10159/0.15198, loss_grounding_ce_2: 0.07520/0.25765, loss_mask_ce_3: 0.67724/0.79090, loss_mask_bce_3: 0.75746/0.30426, loss_mask_dice_3: 1.67919/1.03080, loss_spatial_bce_3: 0.10026/0.09058, loss_spatial_dice_3: 0.17509/0.19058, loss_spatial_ce_3: 0.01572/0.08151, loss_grounding_bce_3: 0.11635/0.08117, loss_grounding_dice_3: 0.10241/0.15155, loss_grounding_ce_3: 0.05871/0.25723, loss_mask_ce_4: 0.62794/0.79646, loss_mask_bce_4: 0.79698/0.30638, loss_mask_dice_4: 1.70062/1.04964, loss_spatial_bce_4: 0.10944/0.09253, loss_spatial_dice_4: 0.17735/0.19793, loss_spatial_ce_4: 0.01622/0.09364, loss_grounding_bce_4: 0.11289/0.08192, loss_grounding_dice_4: 0.10053/0.15403, loss_grounding_ce_4: 0.23068/0.26349, loss_mask_ce_5: 0.54647/0.81880, loss_mask_bce_5: 0.78467/0.30844, loss_mask_dice_5: 1.68212/1.05669, loss_spatial_bce_5: 0.11117/0.09434, loss_spatial_dice_5: 0.19056/0.20005, loss_spatial_ce_5: 0.06244/0.10488, loss_grounding_bce_5: 0.11820/0.08233, loss_grounding_dice_5: 0.12531/0.15477, loss_grounding_ce_5: 0.23537/0.28254, loss_mask_ce_6: 0.68436/0.84453, loss_mask_bce_6: 0.79817/0.31003, loss_mask_dice_6: 1.62312/1.05976, loss_spatial_bce_6: 0.13627/0.09914, loss_spatial_dice_6: 0.21709/0.20248, loss_spatial_ce_6: 0.23700/0.12620, loss_grounding_bce_6: 0.11852/0.08331, loss_grounding_dice_6: 0.10668/0.15546, loss_grounding_ce_6: 0.10511/0.29314, loss_mask_ce_7: 0.87654/0.90567, loss_mask_bce_7: 0.86379/0.31709, loss_mask_dice_7: 1.72997/1.10615, loss_spatial_bce_7: 0.12890/0.10954, loss_spatial_dice_7: 0.17703/0.22708, loss_spatial_ce_7: 0.12895/0.16855, loss_grounding_bce_7: 0.11799/0.08488, loss_grounding_dice_7: 0.10927/0.16111, loss_grounding_ce_7: 0.23252/0.33520, loss_mask_ce_8: 1.03328/1.04153, loss_mask_bce_8: 0.88144/0.33446, loss_mask_dice_8: 2.02164/1.18568, loss_spatial_bce_8: 0.13791/0.12990, loss_spatial_dice_8: 0.21838/0.26658, loss_spatial_ce_8: 0.12378/0.22313, loss_grounding_bce_8: 0.11729/0.08886, loss_grounding_dice_8: 0.09079/0.17065, loss_grounding_ce_8: 1.24382/0.43578, loss_mask_ce_9: 4.81162/3.49768, loss_mask_bce_9: 1.21200/0.36061, loss_mask_dice_9: 3.49971/1.77223, loss_spatial_bce_9: 0.32946/0.35797, loss_spatial_dice_9: 0.91642/0.79565, loss_spatial_ce_9: 1.52166/1.40709, loss_grounding_bce_9: 0.22484/0.10081, loss_grounding_dice_9: 0.25726/0.24430, loss_grounding_ce_9: 2.12204/0.70013] items per batch[64] items per second[0.36] total items[1606400] mini batches[ 25100] memory[4967] epoch remaining[0:14:07] INFO:trainer.default_trainer:epochs[ 13] optim steps[25200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.08186/0.78075, loss_mask_bce_0: 0.09624/0.30192, loss_mask_dice_0: 3.91927/1.02998, loss_spatial_bce_0: 0.00683/0.08874, loss_spatial_dice_0: 0.26603/0.18736, loss_spatial_ce_0: 0.01795/0.06905, loss_grounding_bce_0: 0.01102/0.08047, loss_grounding_dice_0: 0.39859/0.15141, loss_grounding_ce_0: 0.55158/0.25352, loss_mask_ce_1: 2.46141/0.78252, loss_mask_bce_1: 0.09173/0.30269, loss_mask_dice_1: 3.30132/1.03427, loss_spatial_bce_1: 0.00754/0.08914, loss_spatial_dice_1: 0.23377/0.18992, loss_spatial_ce_1: 0.05198/0.07372, loss_grounding_bce_1: 0.01705/0.08075, loss_grounding_dice_1: 0.51964/0.15234, loss_grounding_ce_1: 0.38168/0.25534, loss_mask_ce_2: 2.51850/0.79050, loss_mask_bce_2: 0.09782/0.30266, loss_mask_dice_2: 3.41923/1.03628, loss_spatial_bce_2: 0.00684/0.08882, loss_spatial_dice_2: 0.28700/0.19000, loss_spatial_ce_2: 0.01957/0.07595, loss_grounding_bce_2: 0.01259/0.08060, loss_grounding_dice_2: 0.44561/0.15204, loss_grounding_ce_2: 0.39703/0.25759, loss_mask_ce_3: 2.74480/0.79103, loss_mask_bce_3: 0.09018/0.30423, loss_mask_dice_3: 3.04022/1.03192, loss_spatial_bce_3: 0.00770/0.09049, loss_spatial_dice_3: 0.21850/0.19063, loss_spatial_ce_3: 0.18757/0.08147, loss_grounding_bce_3: 0.01829/0.08109, loss_grounding_dice_3: 0.28641/0.15159, loss_grounding_ce_3: 0.35521/0.25727, loss_mask_ce_4: 2.93162/0.79657, loss_mask_bce_4: 0.09369/0.30635, loss_mask_dice_4: 2.91563/1.05088, loss_spatial_bce_4: 0.00714/0.09244, loss_spatial_dice_4: 0.35672/0.19799, loss_spatial_ce_4: 0.14397/0.09361, loss_grounding_bce_4: 0.01701/0.08185, loss_grounding_dice_4: 0.42416/0.15412, loss_grounding_ce_4: 0.33761/0.26347, loss_mask_ce_5: 2.83060/0.81882, loss_mask_bce_5: 0.10437/0.30841, loss_mask_dice_5: 3.37228/1.05789, loss_spatial_bce_5: 0.00953/0.09425, loss_spatial_dice_5: 0.25162/0.20008, loss_spatial_ce_5: 0.06058/0.10483, loss_grounding_bce_5: 0.01495/0.08225, loss_grounding_dice_5: 0.38756/0.15483, loss_grounding_ce_5: 0.37645/0.28248, loss_mask_ce_6: 3.09988/0.84464, loss_mask_bce_6: 0.09217/0.31000, loss_mask_dice_6: 3.21049/1.06100, loss_spatial_bce_6: 0.00844/0.09904, loss_spatial_dice_6: 0.31340/0.20254, loss_spatial_ce_6: 0.09919/0.12615, loss_grounding_bce_6: 0.01947/0.08323, loss_grounding_dice_6: 0.38485/0.15550, loss_grounding_ce_6: 0.53957/0.29312, loss_mask_ce_7: 3.26560/0.90583, loss_mask_bce_7: 0.10783/0.31704, loss_mask_dice_7: 3.29390/1.10737, loss_spatial_bce_7: 0.00860/0.10944, loss_spatial_dice_7: 0.32275/0.22713, loss_spatial_ce_7: 0.12160/0.16848, loss_grounding_bce_7: 0.01277/0.08480, loss_grounding_dice_7: 0.30242/0.16114, loss_grounding_ce_7: 0.52223/0.33521, loss_mask_ce_8: 3.49818/1.04198, loss_mask_bce_8: 0.12450/0.33437, loss_mask_dice_8: 4.23287/1.18701, loss_spatial_bce_8: 0.01539/0.12975, loss_spatial_dice_8: 0.44639/0.26661, loss_spatial_ce_8: 0.10353/0.22299, loss_grounding_bce_8: 0.01818/0.08878, loss_grounding_dice_8: 0.65911/0.17072, loss_grounding_ce_8: 0.59928/0.43590, loss_mask_ce_9: 5.15068/3.49820, loss_mask_bce_9: 0.08912/0.36049, loss_mask_dice_9: 5.60970/1.77378, loss_spatial_bce_9: 0.02825/0.35773, loss_spatial_dice_9: 0.89464/0.79576, loss_spatial_ce_9: 3.81474/1.40730, loss_grounding_bce_9: 0.01030/0.10072, loss_grounding_dice_9: 0.43090/0.24440, loss_grounding_ce_9: 0.34895/0.70033] items per batch[64] items per second[0.36] total items[1612800] mini batches[ 25200] memory[4967] epoch remaining[0:11:10] INFO:trainer.default_trainer:epochs[ 13] optim steps[25300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14344/0.78051, loss_mask_bce_0: 0.25976/0.30187, loss_mask_dice_0: 0.23781/1.03009, loss_spatial_bce_0: 0.08499/0.08868, loss_spatial_dice_0: 0.14699/0.18731, loss_spatial_ce_0: 0.00190/0.06898, loss_grounding_bce_0: 0.07459/0.08046, loss_grounding_dice_0: 0.07062/0.15142, loss_grounding_ce_0: 0.00201/0.25340, loss_mask_ce_1: 0.14161/0.78225, loss_mask_bce_1: 0.25517/0.30262, loss_mask_dice_1: 0.24150/1.03425, loss_spatial_bce_1: 0.07131/0.08908, loss_spatial_dice_1: 0.13243/0.18989, loss_spatial_ce_1: 0.00211/0.07364, loss_grounding_bce_1: 0.07247/0.08074, loss_grounding_dice_1: 0.07007/0.15234, loss_grounding_ce_1: 0.00207/0.25522, loss_mask_ce_2: 0.13129/0.79027, loss_mask_bce_2: 0.27251/0.30261, loss_mask_dice_2: 0.26642/1.03642, loss_spatial_bce_2: 0.07324/0.08875, loss_spatial_dice_2: 0.13793/0.18996, loss_spatial_ce_2: 0.00202/0.07588, loss_grounding_bce_2: 0.07249/0.08061, loss_grounding_dice_2: 0.06848/0.15205, loss_grounding_ce_2: 0.00143/0.25747, loss_mask_ce_3: 0.13762/0.79085, loss_mask_bce_3: 0.27541/0.30419, loss_mask_dice_3: 0.25953/1.03205, loss_spatial_bce_3: 0.09920/0.09042, loss_spatial_dice_3: 0.15406/0.19058, loss_spatial_ce_3: 0.00368/0.08145, loss_grounding_bce_3: 0.07057/0.08109, loss_grounding_dice_3: 0.06937/0.15160, loss_grounding_ce_3: 0.00234/0.25718, loss_mask_ce_4: 0.14469/0.79626, loss_mask_bce_4: 0.28998/0.30629, loss_mask_dice_4: 0.25258/1.05096, loss_spatial_bce_4: 0.08512/0.09238, loss_spatial_dice_4: 0.16080/0.19794, loss_spatial_ce_4: 0.03206/0.09356, loss_grounding_bce_4: 0.07055/0.08185, loss_grounding_dice_4: 0.06503/0.15412, loss_grounding_ce_4: 0.00407/0.26362, loss_mask_ce_5: 0.14044/0.81855, loss_mask_bce_5: 0.27689/0.30834, loss_mask_dice_5: 0.29136/1.05792, loss_spatial_bce_5: 0.08651/0.09418, loss_spatial_dice_5: 0.16241/0.20002, loss_spatial_ce_5: 0.03906/0.10484, loss_grounding_bce_5: 0.06779/0.08225, loss_grounding_dice_5: 0.06667/0.15484, loss_grounding_ce_5: 0.00176/0.28242, loss_mask_ce_6: 0.15248/0.84445, loss_mask_bce_6: 0.27602/0.30995, loss_mask_dice_6: 0.29135/1.06109, loss_spatial_bce_6: 0.07013/0.09897, loss_spatial_dice_6: 0.11691/0.20250, loss_spatial_ce_6: 0.24186/0.12618, loss_grounding_bce_6: 0.07591/0.08325, loss_grounding_dice_6: 0.07612/0.15553, loss_grounding_ce_6: 0.00238/0.29307, loss_mask_ce_7: 0.57770/0.90564, loss_mask_bce_7: 0.15933/0.31697, loss_mask_dice_7: 0.17195/1.10748, loss_spatial_bce_7: 0.13399/0.10940, loss_spatial_dice_7: 0.17910/0.22713, loss_spatial_ce_7: 0.05177/0.16853, loss_grounding_bce_7: 0.06679/0.08481, loss_grounding_dice_7: 0.06576/0.16114, loss_grounding_ce_7: 0.00117/0.33501, loss_mask_ce_8: 0.20619/1.04176, loss_mask_bce_8: 0.21193/0.33431, loss_mask_dice_8: 0.23022/1.18705, loss_spatial_bce_8: 0.15227/0.12967, loss_spatial_dice_8: 0.20947/0.26658, loss_spatial_ce_8: 0.12463/0.22290, loss_grounding_bce_8: 0.06642/0.08881, loss_grounding_dice_8: 0.06369/0.17072, loss_grounding_ce_8: 0.00223/0.43557, loss_mask_ce_9: 2.25445/3.49874, loss_mask_bce_9: 0.22637/0.36043, loss_mask_dice_9: 0.34403/1.77373, loss_spatial_bce_9: 0.49973/0.35774, loss_spatial_dice_9: 0.71353/0.79575, loss_spatial_ce_9: 1.26841/1.40730, loss_grounding_bce_9: 0.07491/0.10071, loss_grounding_dice_9: 0.04195/0.24441, loss_grounding_ce_9: 0.14911/0.70049] items per batch[64] items per second[0.37] total items[1619200] mini batches[ 25300] memory[4967] epoch remaining[0:08:12] INFO:trainer.default_trainer:epochs[ 13] optim steps[25400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16843/0.78061, loss_mask_bce_0: 0.29217/0.30187, loss_mask_dice_0: 0.16711/1.02997, loss_spatial_bce_0: 0.13710/0.08868, loss_spatial_dice_0: 0.06839/0.18726, loss_spatial_ce_0: 0.00041/0.06893, loss_grounding_bce_0: 0.15002/0.08048, loss_grounding_dice_0: 0.08628/0.15142, loss_grounding_ce_0: 0.00320/0.25314, loss_mask_ce_1: 0.20864/0.78246, loss_mask_bce_1: 0.28597/0.30261, loss_mask_dice_1: 0.16787/1.03411, loss_spatial_bce_1: 0.14543/0.08908, loss_spatial_dice_1: 0.07479/0.18984, loss_spatial_ce_1: 0.00032/0.07357, loss_grounding_bce_1: 0.14536/0.08076, loss_grounding_dice_1: 0.08639/0.15233, loss_grounding_ce_1: 0.00752/0.25513, loss_mask_ce_2: 0.22550/0.79037, loss_mask_bce_2: 0.29094/0.30261, loss_mask_dice_2: 0.17367/1.03630, loss_spatial_bce_2: 0.13733/0.08876, loss_spatial_dice_2: 0.07104/0.18992, loss_spatial_ce_2: 0.00043/0.07579, loss_grounding_bce_2: 0.14748/0.08063, loss_grounding_dice_2: 0.08878/0.15205, loss_grounding_ce_2: 0.01491/0.25747, loss_mask_ce_3: 0.21330/0.79099, loss_mask_bce_3: 0.28536/0.30419, loss_mask_dice_3: 0.17114/1.03190, loss_spatial_bce_3: 0.13425/0.09043, loss_spatial_dice_3: 0.06870/0.19054, loss_spatial_ce_3: 0.00073/0.08135, loss_grounding_bce_3: 0.14285/0.08111, loss_grounding_dice_3: 0.08675/0.15160, loss_grounding_ce_3: 0.00968/0.25700, loss_mask_ce_4: 0.20986/0.79638, loss_mask_bce_4: 0.30166/0.30628, loss_mask_dice_4: 0.18298/1.05078, loss_spatial_bce_4: 0.14705/0.09238, loss_spatial_dice_4: 0.07469/0.19790, loss_spatial_ce_4: 0.00047/0.09346, loss_grounding_bce_4: 0.15558/0.08188, loss_grounding_dice_4: 0.09923/0.15414, loss_grounding_ce_4: 0.01439/0.26336, loss_mask_ce_5: 0.36744/0.81877, loss_mask_bce_5: 0.29308/0.30833, loss_mask_dice_5: 0.16874/1.05781, loss_spatial_bce_5: 0.14652/0.09417, loss_spatial_dice_5: 0.07964/0.19998, loss_spatial_ce_5: 0.00031/0.10474, loss_grounding_bce_5: 0.15112/0.08228, loss_grounding_dice_5: 0.08848/0.15485, loss_grounding_ce_5: 0.05226/0.28222, loss_mask_ce_6: 0.30266/0.84461, loss_mask_bce_6: 0.29096/0.30995, loss_mask_dice_6: 0.16667/1.06104, loss_spatial_bce_6: 0.13767/0.09897, loss_spatial_dice_6: 0.06734/0.20244, loss_spatial_ce_6: 0.01369/0.12612, loss_grounding_bce_6: 0.14940/0.08328, loss_grounding_dice_6: 0.08437/0.15553, loss_grounding_ce_6: 0.03222/0.29278, loss_mask_ce_7: 0.23169/0.90586, loss_mask_bce_7: 0.28558/0.31695, loss_mask_dice_7: 0.15668/1.10735, loss_spatial_bce_7: 0.14833/0.10939, loss_spatial_dice_7: 0.07117/0.22710, loss_spatial_ce_7: 0.02806/0.16846, loss_grounding_bce_7: 0.14387/0.08484, loss_grounding_dice_7: 0.07881/0.16115, loss_grounding_ce_7: 0.03130/0.33467, loss_mask_ce_8: 0.25540/1.04194, loss_mask_bce_8: 0.31198/0.33430, loss_mask_dice_8: 0.17722/1.18685, loss_spatial_bce_8: 0.18182/0.12966, loss_spatial_dice_8: 0.11121/0.26655, loss_spatial_ce_8: 0.07659/0.22286, loss_grounding_bce_8: 0.16164/0.08884, loss_grounding_dice_8: 0.09262/0.17070, loss_grounding_ce_8: 0.00918/0.43535, loss_mask_ce_9: 1.73455/3.49823, loss_mask_bce_9: 0.31164/0.36048, loss_mask_dice_9: 0.18499/1.77397, loss_spatial_bce_9: 0.65545/0.35786, loss_spatial_dice_9: 0.55977/0.79576, loss_spatial_ce_9: 0.62163/1.40757, loss_grounding_bce_9: 0.15551/0.10075, loss_grounding_dice_9: 0.09365/0.24436, loss_grounding_ce_9: 0.04967/0.70014] items per batch[64] items per second[0.37] total items[1625600] mini batches[ 25400] memory[4967] epoch remaining[0:05:15] INFO:trainer.default_trainer:epochs[ 13] optim steps[25500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.00530/0.78036, loss_mask_bce_0: 0.08998/0.30187, loss_mask_dice_0: 0.10614/1.02897, loss_spatial_bce_0: 0.24428/0.08871, loss_spatial_dice_0: 0.11825/0.18725, loss_spatial_ce_0: 0.02861/0.06885, loss_grounding_bce_0: 0.05697/0.08054, loss_grounding_dice_0: 0.07219/0.15149, loss_grounding_ce_0: 0.42425/0.25320, loss_mask_ce_1: 0.98458/0.78224, loss_mask_bce_1: 0.13647/0.30260, loss_mask_dice_1: 0.11467/1.03317, loss_spatial_bce_1: 0.20119/0.08912, loss_spatial_dice_1: 0.10542/0.18983, loss_spatial_ce_1: 0.06867/0.07349, loss_grounding_bce_1: 0.09710/0.08081, loss_grounding_dice_1: 0.07366/0.15238, loss_grounding_ce_1: 0.42258/0.25518, loss_mask_ce_2: 1.11023/0.79009, loss_mask_bce_2: 0.10779/0.30260, loss_mask_dice_2: 0.10648/1.03527, loss_spatial_bce_2: 0.13812/0.08879, loss_spatial_dice_2: 0.08968/0.18993, loss_spatial_ce_2: 0.04818/0.07573, loss_grounding_bce_2: 0.07512/0.08068, loss_grounding_dice_2: 0.06404/0.15210, loss_grounding_ce_2: 0.45862/0.25759, loss_mask_ce_3: 1.00849/0.79079, loss_mask_bce_3: 0.10606/0.30416, loss_mask_dice_3: 0.10228/1.03092, loss_spatial_bce_3: 0.19805/0.09048, loss_spatial_dice_3: 0.11416/0.19053, loss_spatial_ce_3: 0.03278/0.08127, loss_grounding_bce_3: 0.07621/0.08116, loss_grounding_dice_3: 0.07204/0.15163, loss_grounding_ce_3: 0.44182/0.25723, loss_mask_ce_4: 0.36728/0.79620, loss_mask_bce_4: 0.31731/0.30626, loss_mask_dice_4: 0.19780/1.04975, loss_spatial_bce_4: 0.16143/0.09242, loss_spatial_dice_4: 0.11400/0.19789, loss_spatial_ce_4: 0.04789/0.09339, loss_grounding_bce_4: 0.08788/0.08193, loss_grounding_dice_4: 0.07128/0.15418, loss_grounding_ce_4: 0.43289/0.26340, loss_mask_ce_5: 0.36690/0.81853, loss_mask_bce_5: 0.29583/0.30829, loss_mask_dice_5: 0.19394/1.05675, loss_spatial_bce_5: 0.13724/0.09419, loss_spatial_dice_5: 0.10315/0.19996, loss_spatial_ce_5: 0.01635/0.10468, loss_grounding_bce_5: 0.06224/0.08233, loss_grounding_dice_5: 0.06557/0.15490, loss_grounding_ce_5: 0.44601/0.28226, loss_mask_ce_6: 1.09999/0.84437, loss_mask_bce_6: 0.11843/0.30991, loss_mask_dice_6: 0.10871/1.05992, loss_spatial_bce_6: 0.21774/0.09901, loss_spatial_dice_6: 0.13636/0.20244, loss_spatial_ce_6: 0.01194/0.12607, loss_grounding_bce_6: 0.06644/0.08335, loss_grounding_dice_6: 0.06828/0.15560, loss_grounding_ce_6: 0.47028/0.29275, loss_mask_ce_7: 0.47113/0.90556, loss_mask_bce_7: 0.22232/0.31694, loss_mask_dice_7: 0.17695/1.10629, loss_spatial_bce_7: 0.14389/0.10943, loss_spatial_dice_7: 0.11044/0.22708, loss_spatial_ce_7: 0.09626/0.16844, loss_grounding_bce_7: 0.12788/0.08490, loss_grounding_dice_7: 0.10756/0.16119, loss_grounding_ce_7: 0.04660/0.33452, loss_mask_ce_8: 0.37100/1.04151, loss_mask_bce_8: 0.15895/0.33428, loss_mask_dice_8: 0.14343/1.18565, loss_spatial_bce_8: 0.19993/0.12968, loss_spatial_dice_8: 0.13491/0.26651, loss_spatial_ce_8: 0.15281/0.22278, loss_grounding_bce_8: 0.10698/0.08890, loss_grounding_dice_8: 0.09634/0.17072, loss_grounding_ce_8: 0.03472/0.43521, loss_mask_ce_9: 1.28670/3.49715, loss_mask_bce_9: 0.35369/0.36044, loss_mask_dice_9: 0.43146/1.77211, loss_spatial_bce_9: 0.50797/0.35795, loss_spatial_dice_9: 0.65522/0.79565, loss_spatial_ce_9: 0.55726/1.40750, loss_grounding_bce_9: 0.23209/0.10079, loss_grounding_dice_9: 0.28613/0.24443, loss_grounding_ce_9: 0.04935/0.69995] items per batch[64] items per second[0.36] total items[1632000] mini batches[ 25500] memory[4967] epoch remaining[0:02:18] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00025578. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3737 s/iter. Eval: 0.0968 s/iter. Total: 0.4731 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0027 s/iter. Inference: 0.3723 s/iter. Eval: 0.0848 s/iter. Total: 0.4600 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3753 s/iter. Eval: 0.0807 s/iter. Total: 0.4590 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3779 s/iter. Eval: 0.0790 s/iter. Total: 0.4598 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3789 s/iter. Eval: 0.0777 s/iter. Total: 0.4595 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0029 s/iter. Inference: 0.3787 s/iter. Eval: 0.0746 s/iter. Total: 0.4564 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalmfjo9a83 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.266 | 83.022 | 65.760 | 133 | | Things | 61.326 | 84.083 | 72.435 | 80 | | Stuff | 46.119 | 81.421 | 55.683 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.34s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.33 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.43 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.489 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.546 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.562 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.377 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 18.33 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.46 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.330 | 68.792 | 48.913 | 26.161 | 49.095 | 67.430 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.605 | bicycle | 22.612 | car | 44.482 | | motorcycle | 42.155 | airplane | 62.390 | bus | 70.684 | | train | 75.251 | truck | 42.480 | boat | 31.783 | | traffic light | 28.827 | fire hydrant | 71.577 | stop sign | 67.918 | | parking meter | 52.281 | bench | 26.765 | bird | 34.918 | | cat | 77.508 | dog | 71.281 | horse | 51.458 | | sheep | 53.027 | cow | 56.760 | elephant | 65.424 | | bear | 80.056 | zebra | 65.579 | giraffe | 62.223 | | backpack | 24.516 | umbrella | 55.708 | handbag | 24.387 | | tie | 39.586 | suitcase | 49.387 | frisbee | 70.834 | | skis | 8.356 | snowboard | 35.118 | sports ball | 48.945 | | kite | 37.546 | baseball bat | 38.721 | baseball glove | 49.735 | | skateboard | 43.963 | surfboard | 44.658 | tennis racket | 62.685 | | bottle | 41.635 | wine glass | 37.456 | cup | 50.820 | | fork | 26.212 | knife | 24.332 | spoon | 22.265 | | bowl | 39.560 | banana | 22.817 | apple | 26.076 | | sandwich | 48.411 | orange | 30.758 | broccoli | 24.440 | | carrot | 23.215 | hot dog | 34.683 | pizza | 52.615 | | donut | 55.205 | cake | 46.002 | chair | 28.518 | | couch | 40.692 | potted plant | 21.622 | bed | 44.215 | | dining table | 16.065 | toilet | 68.874 | tv | 66.157 | | laptop | 70.304 | mouse | 62.856 | remote | 43.501 | | keyboard | 57.871 | cell phone | 45.948 | microwave | 63.959 | | oven | 34.038 | toaster | 41.724 | sink | 42.937 | | refrigerator | 68.416 | book | 14.265 | clock | 54.089 | | vase | 39.346 | scissors | 35.504 | teddy bear | 56.405 | | hair drier | 34.450 | toothbrush | 27.973 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.8116860141943, 'fwIoU': 71.15628399252896, 'IoU-person': 88.73449543089936, 'IoU-bicycle': 78.6553654448758, 'IoU-car': 71.20490867052735, 'IoU-motorcycle': 88.56928285832959, 'IoU-airplane': 85.13410496597238, 'IoU-bus': 87.81934863108182, 'IoU-train': 87.99536515236346, 'IoU-truck': 67.01737444487993, 'IoU-boat': 72.31228388106052, 'IoU-traffic light': 80.29164000293738, 'IoU-fire hydrant': 93.2949488497337, 'IoU-stop sign': 90.01757283211172, 'IoU-parking meter': 84.91272459525038, 'IoU-bench': 60.33507529379707, 'IoU-bird': 77.60217571654803, 'IoU-cat': 88.9788759841788, 'IoU-dog': 85.6286200491661, 'IoU-horse': 88.35466404462635, 'IoU-sheep': 86.24514163061603, 'IoU-cow': 89.5062303197527, 'IoU-elephant': 90.35728745458685, 'IoU-bear': 72.77827498305668, 'IoU-zebra': 84.2833104183826, 'IoU-giraffe': 89.63513177284709, 'IoU-backpack': 53.928613944705575, 'IoU-umbrella': 83.15280735541728, 'IoU-handbag': 49.46478897921555, 'IoU-tie': 76.44764457795372, 'IoU-suitcase': 87.66834629624915, 'IoU-frisbee': 83.91708479108925, 'IoU-skis': 57.9958232614737, 'IoU-snowboard': 69.81359612770613, 'IoU-sports ball': 80.46724458842999, 'IoU-kite': 79.56564176510737, 'IoU-baseball bat': 69.1090651641991, 'IoU-baseball glove': 82.34967964514539, 'IoU-skateboard': 86.33295166452378, 'IoU-surfboard': 86.69898706574456, 'IoU-tennis racket': 91.32035841603238, 'IoU-bottle': 68.52740481639056, 'IoU-wine glass': 82.069203873867, 'IoU-cup': 70.43715664712238, 'IoU-fork': 68.9294990301956, 'IoU-knife': 63.29376580645258, 'IoU-spoon': 60.83112361271187, 'IoU-bowl': 58.53537893024996, 'IoU-banana': 81.21979293633667, 'IoU-apple': 58.49550222102804, 'IoU-sandwich': 71.17219943728999, 'IoU-orange': 73.861907014654, 'IoU-broccoli': 69.19408670441624, 'IoU-carrot': 63.98388375720734, 'IoU-hot dog': 63.386694457312, 'IoU-pizza': 76.33448511174927, 'IoU-donut': 58.90462833672211, 'IoU-cake': 78.14767797864323, 'IoU-chair': 59.77711663600499, 'IoU-couch': 69.41542862818352, 'IoU-potted plant': 43.81005710838913, 'IoU-bed': 73.3654547995558, 'IoU-dining table': 53.39239318376157, 'IoU-toilet': 84.77180716851593, 'IoU-tv': 75.65706836928081, 'IoU-laptop': 78.77149985712923, 'IoU-mouse': 74.53997844891038, 'IoU-remote': 54.286665306016225, 'IoU-keyboard': 71.0648104614099, 'IoU-cell phone': 66.63345185791943, 'IoU-microwave': 70.53306241078617, 'IoU-oven': 69.6029727707803, 'IoU-toaster': 84.92016863689481, 'IoU-sink': 70.65767632753064, 'IoU-refrigerator': 78.96501850508253, 'IoU-book': 54.66660318667799, 'IoU-clock': 78.10452387853857, 'IoU-vase': 61.91762391827125, 'IoU-scissors': 72.95522911699163, 'IoU-teddy bear': 78.59225852114504, 'IoU-hair drier': 46.08441188178464, 'IoU-toothbrush': 72.66903258515002, 'IoU-banner': 30.297112218282855, 'IoU-blanket': 16.306681188699255, 'IoU-bridge': 36.80521080283212, 'IoU-cardboard': 43.00415666247838, 'IoU-counter': 30.63720536002439, 'IoU-curtain': 70.6084574356673, 'IoU-door-stuff': 48.88459519809392, 'IoU-floor-wood': 63.99774732584147, 'IoU-flower': 42.8231293199174, 'IoU-fruit': 46.47986656749751, 'IoU-gravel': 30.130887946859445, 'IoU-house': 24.315603532479475, 'IoU-light': 44.15628943407404, 'IoU-mirror-stuff': 60.12655744100015, 'IoU-net': 40.266046219062865, 'IoU-pillow': 16.479451086507435, 'IoU-platform': 28.96440173854421, 'IoU-playingfield': 68.26511780284243, 'IoU-railroad': 63.62417829015371, 'IoU-river': 52.912349578859555, 'IoU-road': 68.68652190597331, 'IoU-roof': 19.003842547073813, 'IoU-sand': 65.69341625476575, 'IoU-sea': 85.22873841175944, 'IoU-shelf': 37.652386337379376, 'IoU-snow': 92.61178191016847, 'IoU-stairs': 36.857578058006325, 'IoU-tent': 11.43021694983345, 'IoU-towel': 63.10731074694916, 'IoU-wall-brick': 50.317818999215824, 'IoU-wall-stone': 27.9034254449319, 'IoU-wall-tile': 69.8849010785676, 'IoU-wall-wood': 44.62745935289162, 'IoU-water-other': 20.960785511157056, 'IoU-window-blind': 49.96498700736089, 'IoU-window-other': 51.26498894761794, 'IoU-tree-merged': 81.89537334195632, 'IoU-fence-merged': 54.16848630065814, 'IoU-ceiling-merged': 68.39643311977068, 'IoU-sky-other-merged': 93.9547847798979, 'IoU-cabinet-merged': 63.856296208254925, 'IoU-table-merged': 38.76107439833367, 'IoU-floor-other-merged': 54.62047847805642, 'IoU-pavement-merged': 58.82324617805514, 'IoU-mountain-merged': 58.145640478016894, 'IoU-grass-merged': 71.76374345083323, 'IoU-dirt-merged': 47.16176369740877, 'IoU-paper-merged': 33.11844305548193, 'IoU-food-other-merged': 44.88826267479491, 'IoU-building-other-merged': 59.53962489228199, 'IoU-rock-merged': 61.90534458282696, 'IoU-wall-other-merged': 67.24268572919463, 'IoU-rug-merged': 67.05581260101431, 'mACC': 76.2290481975864, 'pACC': 81.89304919923728, 'ACC-person': 93.0778541599631, 'ACC-bicycle': 89.09220306132161, 'ACC-car': 87.48428973461503, 'ACC-motorcycle': 93.03979788990004, 'ACC-airplane': 91.64099008373681, 'ACC-bus': 93.69191820040757, 'ACC-train': 95.89018363032464, 'ACC-truck': 76.38939612442547, 'ACC-boat': 82.42555875759308, 'ACC-traffic light': 91.41195959721428, 'ACC-fire hydrant': 96.20116616208415, 'ACC-stop sign': 93.53145010451834, 'ACC-parking meter': 88.02942392404522, 'ACC-bench': 80.62172835991241, 'ACC-bird': 82.92048644993409, 'ACC-cat': 92.5498419279806, 'ACC-dog': 88.0437476809157, 'ACC-horse': 92.883825443668, 'ACC-sheep': 91.08322353280326, 'ACC-cow': 92.92951969410127, 'ACC-elephant': 92.49185398255962, 'ACC-bear': 74.14185994508826, 'ACC-zebra': 86.28294305898446, 'ACC-giraffe': 93.80887879251897, 'ACC-backpack': 71.96080665375413, 'ACC-umbrella': 87.16759402807817, 'ACC-handbag': 70.89272163309826, 'ACC-tie': 84.08359578957733, 'ACC-suitcase': 94.00489416251928, 'ACC-frisbee': 94.02436363636365, 'ACC-skis': 72.10090733465759, 'ACC-snowboard': 82.5172050647792, 'ACC-sports ball': 88.70644297660765, 'ACC-kite': 85.96976080986863, 'ACC-baseball bat': 87.39744809656175, 'ACC-baseball glove': 91.50003963021408, 'ACC-skateboard': 90.86890852862099, 'ACC-surfboard': 92.88698547305468, 'ACC-tennis racket': 95.14819629672388, 'ACC-bottle': 83.32418582814418, 'ACC-wine glass': 90.63244546789723, 'ACC-cup': 88.02399223730852, 'ACC-fork': 79.71635328146233, 'ACC-knife': 76.61006081922767, 'ACC-spoon': 77.55438825797276, 'ACC-bowl': 71.37556805157395, 'ACC-banana': 87.06795974213057, 'ACC-apple': 69.37193834351145, 'ACC-sandwich': 81.1584340753995, 'ACC-orange': 83.65490097981268, 'ACC-broccoli': 75.49600586876646, 'ACC-carrot': 75.28035594471038, 'ACC-hot dog': 70.13844404063117, 'ACC-pizza': 83.28683570112675, 'ACC-donut': 65.5027727404634, 'ACC-cake': 83.87285626395969, 'ACC-chair': 78.05679406624793, 'ACC-couch': 75.58288710760094, 'ACC-potted plant': 55.52947582957298, 'ACC-bed': 83.02341983429538, 'ACC-dining table': 79.18858607150642, 'ACC-toilet': 88.77535251413147, 'ACC-tv': 82.4630320313738, 'ACC-laptop': 90.29921745476432, 'ACC-mouse': 91.33217175488531, 'ACC-remote': 57.426365215778496, 'ACC-keyboard': 76.26942784714007, 'ACC-cell phone': 73.13030837346432, 'ACC-microwave': 74.76356136810611, 'ACC-oven': 89.83860334279223, 'ACC-toaster': 90.00533742079404, 'ACC-sink': 79.70202660596249, 'ACC-refrigerator': 89.11672147797903, 'ACC-book': 73.18914432209802, 'ACC-clock': 82.86872958793649, 'ACC-vase': 68.50158691931225, 'ACC-scissors': 77.32866429771644, 'ACC-teddy bear': 83.11866936341235, 'ACC-hair drier': 60.96120456550821, 'ACC-toothbrush': 82.17685892981237, 'ACC-banner': 78.93156668057092, 'ACC-blanket': 23.06890159122509, 'ACC-bridge': 52.81133498572779, 'ACC-cardboard': 64.34067180456594, 'ACC-counter': 53.700935678293284, 'ACC-curtain': 81.80412041918423, 'ACC-door-stuff': 71.59796607908771, 'ACC-floor-wood': 78.86914503056168, 'ACC-flower': 64.32529052763182, 'ACC-fruit': 66.29817484677652, 'ACC-gravel': 40.965725875185655, 'ACC-house': 28.435266768040083, 'ACC-light': 63.39677917926731, 'ACC-mirror-stuff': 68.62158449227124, 'ACC-net': 65.99385216371468, 'ACC-pillow': 43.69906518863865, 'ACC-platform': 45.807286006251715, 'ACC-playingfield': 86.17343402791985, 'ACC-railroad': 77.1541827856727, 'ACC-river': 77.37107668284712, 'ACC-road': 85.71648668310976, 'ACC-roof': 25.217152147074284, 'ACC-sand': 70.19385591652416, 'ACC-sea': 91.1867191798714, 'ACC-shelf': 52.54518737147305, 'ACC-snow': 95.54483202034106, 'ACC-stairs': 57.11698877702521, 'ACC-tent': 14.33842441376599, 'ACC-towel': 82.53232815035862, 'ACC-wall-brick': 68.20584494157296, 'ACC-wall-stone': 32.94060931436583, 'ACC-wall-tile': 88.13058864224116, 'ACC-wall-wood': 61.64156000013817, 'ACC-water-other': 32.73482637575878, 'ACC-window-blind': 61.80859586127917, 'ACC-window-other': 73.18215523671441, 'ACC-tree-merged': 89.37845378033342, 'ACC-fence-merged': 72.71582039989279, 'ACC-ceiling-merged': 82.07910373794253, 'ACC-sky-other-merged': 96.7908543627393, 'ACC-cabinet-merged': 76.97901715910668, 'ACC-table-merged': 52.776534795482235, 'ACC-floor-other-merged': 65.9362927936762, 'ACC-pavement-merged': 71.14167778791197, 'ACC-mountain-merged': 68.68408501588186, 'ACC-grass-merged': 83.62922866313069, 'ACC-dirt-merged': 72.23146421316345, 'ACC-paper-merged': 41.80627056798707, 'ACC-food-other-merged': 59.864534693520675, 'ACC-building-other-merged': 76.4782589747796, 'ACC-rock-merged': 84.32666530740902, 'ACC-wall-other-merged': 81.97931475898753, 'ACC-rug-merged': 81.65370306261124})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3069 s/iter. Inference: 0.1722 s/iter. Eval: 0.0000 s/iter. Total: 0.4791 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3130 s/iter. Inference: 0.3363 s/iter. Eval: 0.0000 s/iter. Total: 0.6494 s/iter. ETA=0:00:04 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 24/25. Dataloading: 0.3323 s/iter. Inference: 0.4017 s/iter. Eval: 0.0000 s/iter. Total: 0.7341 s/iter. ETA=0:00:00 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4000585308750366, 'noc@0.8': 2.4837576821773486, 'noc@0.85': 2.880889669300556, 'noc@0.9': 3.784606379865379, 'miou@iter1': 0.8688400593423551} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1459 s/iter. Eval: 0.0010 s/iter. Total: 0.1485 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.55382537841797, 'precision@0.6': 73.1053237915039, 'precision@0.7': 68.63583374023438, 'precision@0.8': 59.385929107666016, 'precision@0.9': 32.219200134277344, 'cIoU': 61.87982177734375, 'mIoU': 66.90727996826172} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.26598563410492, 'SQ': 83.02219216941592, 'RQ': 65.7597831563097, 'PQ_th': 61.3260342643301, 'SQ_th': 84.08306552328776, 'RQ_th': 72.43549926323371, 'PQ_st': 46.11874241867066, 'SQ_st': 81.42087389942073, 'RQ_st': 55.683230542084885}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.33029852149683, 'AP50': 68.79224863071803, 'AP75': 48.912993566777246, 'APs': 26.161347955434078, 'APm': 49.094892930536275, 'APl': 67.43039594090001, 'AP-person': 48.604921662147184, 'AP-bicycle': 22.612041543761606, 'AP-car': 44.48225257701998, 'AP-motorcycle': 42.155341869335565, 'AP-airplane': 62.39006971219101, 'AP-bus': 70.68428170737737, 'AP-train': 75.25107307202033, 'AP-truck': 42.48049018842918, 'AP-boat': 31.78253842268861, 'AP-traffic light': 28.82705266248146, 'AP-fire hydrant': 71.57660976946319, 'AP-stop sign': 67.9180226857199, 'AP-parking meter': 52.281164244041, 'AP-bench': 26.764966459772392, 'AP-bird': 34.91844816237772, 'AP-cat': 77.50780977393596, 'AP-dog': 71.28116861928547, 'AP-horse': 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INFO:trainer.default_trainer:This epoch takes 0:57:19.213983 INFO:trainer.default_trainer:PROGRESS: 28.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 14 training. INFO:trainer.default_trainer:epochs[ 14] optim steps[25600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.73047/0.78032, loss_mask_bce_0: 0.39620/0.30191, loss_mask_dice_0: 1.60051/1.02876, loss_spatial_bce_0: 0.09140/0.08872, loss_spatial_dice_0: 0.21004/0.18719, loss_spatial_ce_0: 0.00314/0.06885, loss_grounding_bce_0: 0.18680/0.08052, loss_grounding_dice_0: 0.35191/0.15147, loss_grounding_ce_0: 0.14247/0.25323, loss_mask_ce_1: 1.58271/0.78215, loss_mask_bce_1: 0.40257/0.30265, loss_mask_dice_1: 1.55490/1.03300, loss_spatial_bce_1: 0.09463/0.08912, loss_spatial_dice_1: 0.20210/0.18977, loss_spatial_ce_1: 0.00258/0.07350, loss_grounding_bce_1: 0.18396/0.08079, loss_grounding_dice_1: 0.34269/0.15235, loss_grounding_ce_1: 0.18441/0.25518, loss_mask_ce_2: 1.75180/0.79001, loss_mask_bce_2: 0.40833/0.30265, loss_mask_dice_2: 1.42850/1.03500, loss_spatial_bce_2: 0.09518/0.08880, loss_spatial_dice_2: 0.17919/0.18987, loss_spatial_ce_2: 0.00208/0.07572, loss_grounding_bce_2: 0.19664/0.08066, loss_grounding_dice_2: 0.37971/0.15207, loss_grounding_ce_2: 0.20485/0.25768, loss_mask_ce_3: 1.47655/0.79074, loss_mask_bce_3: 0.43579/0.30420, loss_mask_dice_3: 1.64500/1.03083, loss_spatial_bce_3: 0.09265/0.09047, loss_spatial_dice_3: 0.20104/0.19047, loss_spatial_ce_3: 0.00511/0.08128, loss_grounding_bce_3: 0.18581/0.08113, loss_grounding_dice_3: 0.37598/0.15160, loss_grounding_ce_3: 0.15769/0.25730, loss_mask_ce_4: 1.52746/0.79609, loss_mask_bce_4: 0.41456/0.30631, loss_mask_dice_4: 1.61963/1.04956, loss_spatial_bce_4: 0.08475/0.09243, loss_spatial_dice_4: 0.17960/0.19783, loss_spatial_ce_4: 0.00951/0.09334, loss_grounding_bce_4: 0.18764/0.08192, loss_grounding_dice_4: 0.34280/0.15415, loss_grounding_ce_4: 0.08930/0.26343, loss_mask_ce_5: 1.35885/0.81848, loss_mask_bce_5: 0.42469/0.30837, loss_mask_dice_5: 1.65296/1.05660, loss_spatial_bce_5: 0.09482/0.09419, loss_spatial_dice_5: 0.17637/0.19991, loss_spatial_ce_5: 0.01398/0.10461, loss_grounding_bce_5: 0.17846/0.08231, loss_grounding_dice_5: 0.33585/0.15487, loss_grounding_ce_5: 0.10924/0.28248, loss_mask_ce_6: 1.93857/0.84436, loss_mask_bce_6: 0.44289/0.30997, loss_mask_dice_6: 1.55543/1.05969, loss_spatial_bce_6: 0.10797/0.09902, loss_spatial_dice_6: 0.19623/0.20238, loss_spatial_ce_6: 0.05882/0.12598, loss_grounding_bce_6: 0.20848/0.08333, loss_grounding_dice_6: 0.34338/0.15556, loss_grounding_ce_6: 0.12179/0.29286, loss_mask_ce_7: 1.56798/0.90545, loss_mask_bce_7: 0.45913/0.31699, loss_mask_dice_7: 1.75287/1.10605, loss_spatial_bce_7: 0.09836/0.10943, loss_spatial_dice_7: 0.20418/0.22703, loss_spatial_ce_7: 0.05930/0.16835, loss_grounding_bce_7: 0.22679/0.08488, loss_grounding_dice_7: 0.30343/0.16114, loss_grounding_ce_7: 0.09062/0.33458, loss_mask_ce_8: 2.39161/1.04146, loss_mask_bce_8: 0.44145/0.33430, loss_mask_dice_8: 1.64331/1.18535, loss_spatial_bce_8: 0.13961/0.12967, loss_spatial_dice_8: 0.26958/0.26646, loss_spatial_ce_8: 0.26515/0.22259, loss_grounding_bce_8: 0.23876/0.08890, loss_grounding_dice_8: 0.32729/0.17072, loss_grounding_ce_8: 0.26100/0.43512, loss_mask_ce_9: 4.64093/3.49742, loss_mask_bce_9: 0.52712/0.36047, loss_mask_dice_9: 2.99005/1.77153, loss_spatial_bce_9: 0.26600/0.35787, loss_spatial_dice_9: 0.93809/0.79564, loss_spatial_ce_9: 1.28171/1.40715, loss_grounding_bce_9: 0.18410/0.10082, loss_grounding_dice_9: 0.41885/0.24444, loss_grounding_ce_9: 0.15293/0.69987] items per batch[64] items per second[0.16] total items[1638400] mini batches[ 25600] memory[4967] epoch remaining[1:03:14] INFO:trainer.default_trainer:epochs[ 14] optim steps[25700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73145/0.78003, loss_mask_bce_0: 0.53022/0.30187, loss_mask_dice_0: 3.25736/1.02882, loss_spatial_bce_0: 0.13434/0.08867, loss_spatial_dice_0: 0.40607/0.18711, loss_spatial_ce_0: 0.01109/0.06875, loss_grounding_bce_0: 0.09125/0.08052, loss_grounding_dice_0: 0.18560/0.15154, loss_grounding_ce_0: 0.01511/0.25316, loss_mask_ce_1: 0.79372/0.78184, loss_mask_bce_1: 0.51994/0.30261, loss_mask_dice_1: 3.25194/1.03313, loss_spatial_bce_1: 0.12150/0.08907, loss_spatial_dice_1: 0.33261/0.18968, loss_spatial_ce_1: 0.07542/0.07348, loss_grounding_bce_1: 0.11403/0.08080, loss_grounding_dice_1: 0.20103/0.15245, loss_grounding_ce_1: 0.01839/0.25504, loss_mask_ce_2: 0.76265/0.78963, loss_mask_bce_2: 0.55309/0.30260, loss_mask_dice_2: 3.73546/1.03507, loss_spatial_bce_2: 0.10888/0.08874, loss_spatial_dice_2: 0.34762/0.18978, loss_spatial_ce_2: 0.03223/0.07568, loss_grounding_bce_2: 0.11055/0.08065, loss_grounding_dice_2: 0.20685/0.15212, loss_grounding_ce_2: 0.01421/0.25754, loss_mask_ce_3: 0.75687/0.79045, loss_mask_bce_3: 0.54135/0.30416, loss_mask_dice_3: 3.65430/1.03089, loss_spatial_bce_3: 0.09713/0.09042, loss_spatial_dice_3: 0.30646/0.19038, loss_spatial_ce_3: 0.01592/0.08119, loss_grounding_bce_3: 0.12150/0.08113, loss_grounding_dice_3: 0.20253/0.15166, loss_grounding_ce_3: 0.01627/0.25716, loss_mask_ce_4: 0.65714/0.79581, loss_mask_bce_4: 0.57096/0.30627, loss_mask_dice_4: 3.65260/1.04968, loss_spatial_bce_4: 0.09302/0.09237, loss_spatial_dice_4: 0.33159/0.19775, loss_spatial_ce_4: 0.14569/0.09330, loss_grounding_bce_4: 0.11386/0.08191, loss_grounding_dice_4: 0.19640/0.15421, loss_grounding_ce_4: 0.00894/0.26337, loss_mask_ce_5: 0.83260/0.81821, loss_mask_bce_5: 0.56830/0.30833, loss_mask_dice_5: 3.76065/1.05665, loss_spatial_bce_5: 0.08577/0.09413, loss_spatial_dice_5: 0.31248/0.19982, loss_spatial_ce_5: 0.07672/0.10457, loss_grounding_bce_5: 0.14120/0.08231, loss_grounding_dice_5: 0.19968/0.15495, loss_grounding_ce_5: 0.01015/0.28236, loss_mask_ce_6: 1.08144/0.84408, loss_mask_bce_6: 0.53262/0.30995, loss_mask_dice_6: 3.44639/1.05983, loss_spatial_bce_6: 0.10337/0.09895, loss_spatial_dice_6: 0.30369/0.20227, loss_spatial_ce_6: 0.07575/0.12593, loss_grounding_bce_6: 0.09928/0.08333, loss_grounding_dice_6: 0.16766/0.15564, loss_grounding_ce_6: 0.00718/0.29267, loss_mask_ce_7: 0.83802/0.90514, loss_mask_bce_7: 0.56645/0.31696, loss_mask_dice_7: 4.14841/1.10617, loss_spatial_bce_7: 0.09315/0.10936, loss_spatial_dice_7: 0.39072/0.22694, loss_spatial_ce_7: 0.06910/0.16826, loss_grounding_bce_7: 0.09961/0.08487, loss_grounding_dice_7: 0.21476/0.16120, loss_grounding_ce_7: 0.02144/0.33435, loss_mask_ce_8: 1.40794/1.04110, loss_mask_bce_8: 0.68669/0.33427, loss_mask_dice_8: 4.68043/1.18545, loss_spatial_bce_8: 0.10083/0.12957, loss_spatial_dice_8: 0.50007/0.26638, loss_spatial_ce_8: 0.17068/0.22250, loss_grounding_bce_8: 0.07681/0.08890, loss_grounding_dice_8: 0.19254/0.17076, loss_grounding_ce_8: 0.06550/0.43482, loss_mask_ce_9: 5.78723/3.49676, loss_mask_bce_9: 0.59217/0.36041, loss_mask_dice_9: 6.57140/1.77152, loss_spatial_bce_9: 0.13219/0.35786, loss_spatial_dice_9: 0.91001/0.79562, loss_spatial_ce_9: 1.33307/1.40694, loss_grounding_bce_9: 0.09037/0.10078, loss_grounding_dice_9: 0.55759/0.24450, loss_grounding_ce_9: 0.67063/0.69960] items per batch[64] items per second[0.36] total items[1644800] mini batches[ 25700] memory[4967] epoch remaining[0:52:09] INFO:trainer.default_trainer:epochs[ 14] optim steps[25800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74220/0.77982, loss_mask_bce_0: 0.38773/0.30178, loss_mask_dice_0: 1.94863/1.02898, loss_spatial_bce_0: 0.07200/0.08859, loss_spatial_dice_0: 0.25629/0.18704, loss_spatial_ce_0: 0.06339/0.06867, loss_grounding_bce_0: 0.03184/0.08045, loss_grounding_dice_0: 0.22537/0.15152, loss_grounding_ce_0: 0.38563/0.25322, loss_mask_ce_1: 0.34276/0.78165, loss_mask_bce_1: 0.41147/0.30253, loss_mask_dice_1: 2.25225/1.03327, loss_spatial_bce_1: 0.06726/0.08899, loss_spatial_dice_1: 0.25058/0.18960, loss_spatial_ce_1: 0.24553/0.07340, loss_grounding_bce_1: 0.03480/0.08073, loss_grounding_dice_1: 0.23272/0.15243, loss_grounding_ce_1: 0.42707/0.25508, loss_mask_ce_2: 0.45338/0.78945, loss_mask_bce_2: 0.39895/0.30251, loss_mask_dice_2: 1.79682/1.03516, loss_spatial_bce_2: 0.05304/0.08866, loss_spatial_dice_2: 0.22891/0.18970, loss_spatial_ce_2: 0.09171/0.07561, loss_grounding_bce_2: 0.03215/0.08058, loss_grounding_dice_2: 0.21058/0.15210, loss_grounding_ce_2: 0.44922/0.25758, loss_mask_ce_3: 0.36670/0.79031, loss_mask_bce_3: 0.43159/0.30410, loss_mask_dice_3: 1.97848/1.03102, loss_spatial_bce_3: 0.04945/0.09034, loss_spatial_dice_3: 0.29976/0.19030, loss_spatial_ce_3: 0.09242/0.08114, loss_grounding_bce_3: 0.03410/0.08106, loss_grounding_dice_3: 0.24032/0.15163, loss_grounding_ce_3: 0.39231/0.25719, loss_mask_ce_4: 0.47635/0.79563, loss_mask_bce_4: 0.41906/0.30620, loss_mask_dice_4: 2.08535/1.04981, loss_spatial_bce_4: 0.06821/0.09228, loss_spatial_dice_4: 0.27173/0.19767, loss_spatial_ce_4: 0.05092/0.09319, loss_grounding_bce_4: 0.03306/0.08184, loss_grounding_dice_4: 0.24103/0.15417, loss_grounding_ce_4: 0.44705/0.26346, loss_mask_ce_5: 0.81688/0.81800, loss_mask_bce_5: 0.41444/0.30825, loss_mask_dice_5: 1.59197/1.05686, loss_spatial_bce_5: 0.04569/0.09405, loss_spatial_dice_5: 0.29866/0.19977, loss_spatial_ce_5: 0.22927/0.10445, loss_grounding_bce_5: 0.03389/0.08224, loss_grounding_dice_5: 0.22973/0.15493, loss_grounding_ce_5: 0.43968/0.28236, loss_mask_ce_6: 0.31253/0.84390, loss_mask_bce_6: 0.40944/0.30988, loss_mask_dice_6: 2.18212/1.06009, loss_spatial_bce_6: 0.05294/0.09887, loss_spatial_dice_6: 0.24851/0.20221, loss_spatial_ce_6: 0.12641/0.12584, loss_grounding_bce_6: 0.03668/0.08325, loss_grounding_dice_6: 0.22491/0.15563, loss_grounding_ce_6: 0.38295/0.29269, loss_mask_ce_7: 0.45081/0.90495, loss_mask_bce_7: 0.41051/0.31688, loss_mask_dice_7: 1.86962/1.10643, loss_spatial_bce_7: 0.08675/0.10926, loss_spatial_dice_7: 0.30549/0.22686, loss_spatial_ce_7: 0.15722/0.16824, loss_grounding_bce_7: 0.03370/0.08479, loss_grounding_dice_7: 0.19565/0.16121, loss_grounding_ce_7: 0.55599/0.33428, loss_mask_ce_8: 0.48352/1.04091, loss_mask_bce_8: 0.37514/0.33419, loss_mask_dice_8: 1.28304/1.18569, loss_spatial_bce_8: 0.08825/0.12945, loss_spatial_dice_8: 0.30533/0.26630, loss_spatial_ce_8: 0.23630/0.22236, loss_grounding_bce_8: 0.03636/0.08882, loss_grounding_dice_8: 0.21686/0.17078, loss_grounding_ce_8: 0.47439/0.43481, loss_mask_ce_9: 3.35013/3.49638, loss_mask_bce_9: 0.39541/0.36033, loss_mask_dice_9: 2.40991/1.77138, loss_spatial_bce_9: 0.45444/0.35774, loss_spatial_dice_9: 0.86068/0.79565, loss_spatial_ce_9: 2.13039/1.40683, loss_grounding_bce_9: 0.02920/0.10069, loss_grounding_dice_9: 0.32600/0.24446, loss_grounding_ce_9: 0.59081/0.69989] items per batch[64] items per second[0.37] total items[1651200] mini batches[ 25800] memory[4967] epoch remaining[0:48:05] INFO:trainer.default_trainer:epochs[ 14] optim steps[25900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52438/0.77925, loss_mask_bce_0: 0.05771/0.30178, loss_mask_dice_0: 2.60566/1.02871, loss_spatial_bce_0: 0.01069/0.08860, loss_spatial_dice_0: 0.45468/0.18699, loss_spatial_ce_0: 0.11920/0.06863, loss_grounding_bce_0: 0.00295/0.08047, loss_grounding_dice_0: 0.31024/0.15155, loss_grounding_ce_0: 0.55303/0.25333, loss_mask_ce_1: 0.52814/0.78115, loss_mask_bce_1: 0.06172/0.30252, loss_mask_dice_1: 2.55055/1.03299, loss_spatial_bce_1: 0.01158/0.08898, loss_spatial_dice_1: 0.40316/0.18957, loss_spatial_ce_1: 0.36648/0.07344, loss_grounding_bce_1: 0.00327/0.08074, loss_grounding_dice_1: 0.34797/0.15248, loss_grounding_ce_1: 0.52608/0.25521, loss_mask_ce_2: 0.57619/0.78901, loss_mask_bce_2: 0.06295/0.30250, loss_mask_dice_2: 2.77970/1.03478, loss_spatial_bce_2: 0.01084/0.08866, loss_spatial_dice_2: 0.44208/0.18967, loss_spatial_ce_2: 0.08947/0.07561, loss_grounding_bce_2: 0.00313/0.08059, loss_grounding_dice_2: 0.36201/0.15211, loss_grounding_ce_2: 0.51282/0.25776, loss_mask_ce_3: 0.55001/0.78979, loss_mask_bce_3: 0.05792/0.30411, loss_mask_dice_3: 2.56833/1.03079, loss_spatial_bce_3: 0.01250/0.09034, loss_spatial_dice_3: 0.48930/0.19026, loss_spatial_ce_3: 0.29596/0.08112, loss_grounding_bce_3: 0.00313/0.08106, loss_grounding_dice_3: 0.36391/0.15165, loss_grounding_ce_3: 0.53207/0.25734, loss_mask_ce_4: 0.57692/0.79511, loss_mask_bce_4: 0.06089/0.30620, loss_mask_dice_4: 3.12932/1.04962, loss_spatial_bce_4: 0.00912/0.09229, loss_spatial_dice_4: 0.46560/0.19763, loss_spatial_ce_4: 0.19052/0.09319, loss_grounding_bce_4: 0.00251/0.08185, loss_grounding_dice_4: 0.21604/0.15421, loss_grounding_ce_4: 0.52826/0.26351, loss_mask_ce_5: 0.54493/0.81743, loss_mask_bce_5: 0.07026/0.30826, loss_mask_dice_5: 2.81938/1.05654, loss_spatial_bce_5: 0.01382/0.09405, loss_spatial_dice_5: 0.45125/0.19973, loss_spatial_ce_5: 0.25372/0.10453, loss_grounding_bce_5: 0.00402/0.08225, loss_grounding_dice_5: 0.44042/0.15496, loss_grounding_ce_5: 0.56278/0.28245, loss_mask_ce_6: 0.54552/0.84336, loss_mask_bce_6: 0.06758/0.30988, loss_mask_dice_6: 2.85327/1.05990, loss_spatial_bce_6: 0.01407/0.09887, loss_spatial_dice_6: 0.49846/0.20218, loss_spatial_ce_6: 0.30100/0.12584, loss_grounding_bce_6: 0.00306/0.08326, loss_grounding_dice_6: 0.29024/0.15566, loss_grounding_ce_6: 0.54974/0.29284, loss_mask_ce_7: 0.77337/0.90436, loss_mask_bce_7: 0.07208/0.31690, loss_mask_dice_7: 2.59416/1.10617, loss_spatial_bce_7: 0.01993/0.10925, loss_spatial_dice_7: 0.51608/0.22683, loss_spatial_ce_7: 0.31569/0.16827, loss_grounding_bce_7: 0.00283/0.08480, loss_grounding_dice_7: 0.32272/0.16126, loss_grounding_ce_7: 0.60878/0.33448, loss_mask_ce_8: 0.88893/1.04029, loss_mask_bce_8: 0.08336/0.33422, loss_mask_dice_8: 3.17845/1.18547, loss_spatial_bce_8: 0.00789/0.12944, loss_spatial_dice_8: 0.51021/0.26626, loss_spatial_ce_8: 0.35440/0.22230, loss_grounding_bce_8: 0.00452/0.08887, loss_grounding_dice_8: 0.37453/0.17083, loss_grounding_ce_8: 0.69407/0.43495, loss_mask_ce_9: 3.11140/3.49582, loss_mask_bce_9: 0.04581/0.36035, loss_mask_dice_9: 2.94752/1.77092, loss_spatial_bce_9: 0.01756/0.35771, loss_spatial_dice_9: 0.83079/0.79566, loss_spatial_ce_9: 1.35238/1.40667, loss_grounding_bce_9: 0.00403/0.10073, loss_grounding_dice_9: 0.47004/0.24450, loss_grounding_ce_9: 0.58097/0.69989] items per batch[64] items per second[0.36] total items[1657600] mini batches[ 25900] memory[4967] epoch remaining[0:45:00] INFO:trainer.default_trainer:epochs[ 14] optim steps[26000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78966/0.77931, loss_mask_bce_0: 0.02065/0.30184, loss_mask_dice_0: 0.22888/1.02851, loss_spatial_bce_0: 0.02722/0.08859, loss_spatial_dice_0: 0.11207/0.18695, loss_spatial_ce_0: 0.00918/0.06859, loss_grounding_bce_0: 0.00818/0.08056, loss_grounding_dice_0: 0.02551/0.15152, loss_grounding_ce_0: 0.02200/0.25329, loss_mask_ce_1: 0.79593/0.78117, loss_mask_bce_1: 0.02246/0.30260, loss_mask_dice_1: 0.28262/1.03292, loss_spatial_bce_1: 0.02706/0.08897, loss_spatial_dice_1: 0.13062/0.18952, loss_spatial_ce_1: 0.00966/0.07343, loss_grounding_bce_1: 0.01008/0.08083, loss_grounding_dice_1: 0.03010/0.15245, loss_grounding_ce_1: 0.03667/0.25514, loss_mask_ce_2: 0.82278/0.78902, loss_mask_bce_2: 0.02449/0.30259, loss_mask_dice_2: 0.30378/1.03470, loss_spatial_bce_2: 0.02455/0.08865, loss_spatial_dice_2: 0.11997/0.18962, loss_spatial_ce_2: 0.02617/0.07557, loss_grounding_bce_2: 0.01071/0.08069, loss_grounding_dice_2: 0.03024/0.15208, loss_grounding_ce_2: 0.02925/0.25771, loss_mask_ce_3: 0.57501/0.78986, loss_mask_bce_3: 0.02912/0.30417, loss_mask_dice_3: 0.44975/1.03066, loss_spatial_bce_3: 0.02404/0.09035, loss_spatial_dice_3: 0.11340/0.19022, loss_spatial_ce_3: 0.01596/0.08106, loss_grounding_bce_3: 0.01076/0.08115, loss_grounding_dice_3: 0.03242/0.15163, loss_grounding_ce_3: 0.03099/0.25727, loss_mask_ce_4: 0.75657/0.79514, loss_mask_bce_4: 0.02295/0.30626, loss_mask_dice_4: 0.32173/1.04949, loss_spatial_bce_4: 0.02289/0.09229, loss_spatial_dice_4: 0.13739/0.19759, loss_spatial_ce_4: 0.02800/0.09316, loss_grounding_bce_4: 0.00954/0.08189, loss_grounding_dice_4: 0.02720/0.15417, loss_grounding_ce_4: 0.03299/0.26346, loss_mask_ce_5: 0.70222/0.81745, loss_mask_bce_5: 0.02158/0.30831, loss_mask_dice_5: 0.25150/1.05643, loss_spatial_bce_5: 0.02835/0.09406, loss_spatial_dice_5: 0.11528/0.19968, loss_spatial_ce_5: 0.02102/0.10446, loss_grounding_bce_5: 0.01178/0.08228, loss_grounding_dice_5: 0.03080/0.15491, loss_grounding_ce_5: 0.02502/0.28238, loss_mask_ce_6: 0.65890/0.84337, loss_mask_bce_6: 0.02547/0.30993, loss_mask_dice_6: 0.26784/1.05973, loss_spatial_bce_6: 0.03873/0.09885, loss_spatial_dice_6: 0.15774/0.20213, loss_spatial_ce_6: 0.04649/0.12586, loss_grounding_bce_6: 0.01343/0.08329, loss_grounding_dice_6: 0.03913/0.15562, loss_grounding_ce_6: 0.01209/0.29280, loss_mask_ce_7: 0.73086/0.90429, loss_mask_bce_7: 0.02609/0.31700, loss_mask_dice_7: 0.27780/1.10606, loss_spatial_bce_7: 0.11001/0.10926, loss_spatial_dice_7: 0.19875/0.22679, loss_spatial_ce_7: 0.06603/0.16830, loss_grounding_bce_7: 0.01275/0.08484, loss_grounding_dice_7: 0.03648/0.16121, loss_grounding_ce_7: 0.01645/0.33442, loss_mask_ce_8: 0.62590/1.04034, loss_mask_bce_8: 0.03513/0.33432, loss_mask_dice_8: 0.44008/1.18539, loss_spatial_bce_8: 0.10520/0.12944, loss_spatial_dice_8: 0.27295/0.26622, loss_spatial_ce_8: 0.13714/0.22227, loss_grounding_bce_8: 0.02326/0.08890, loss_grounding_dice_8: 0.04746/0.17079, loss_grounding_ce_8: 0.08235/0.43488, loss_mask_ce_9: 3.75905/3.49593, loss_mask_bce_9: 0.05235/0.36038, loss_mask_dice_9: 0.47955/1.77152, loss_spatial_bce_9: 0.44206/0.35776, loss_spatial_dice_9: 0.79266/0.79566, loss_spatial_ce_9: 1.36538/1.40639, loss_grounding_bce_9: 0.01675/0.10077, loss_grounding_dice_9: 0.04556/0.24450, loss_grounding_ce_9: 0.50676/0.69976] items per batch[64] items per second[0.36] total items[1664000] mini batches[ 26000] memory[4967] epoch remaining[0:41:55] INFO:trainer.default_trainer:epochs[ 14] optim steps[26100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63478/0.77929, loss_mask_bce_0: 0.39196/0.30184, loss_mask_dice_0: 2.97580/1.02855, loss_spatial_bce_0: 0.06438/0.08853, loss_spatial_dice_0: 0.29304/0.18700, loss_spatial_ce_0: 0.00850/0.06858, loss_grounding_bce_0: 0.05668/0.08055, loss_grounding_dice_0: 0.11383/0.15159, loss_grounding_ce_0: 0.03631/0.25341, loss_mask_ce_1: 0.95376/0.78120, loss_mask_bce_1: 0.38976/0.30261, loss_mask_dice_1: 3.25637/1.03299, loss_spatial_bce_1: 0.06024/0.08891, loss_spatial_dice_1: 0.31433/0.18955, loss_spatial_ce_1: 0.03295/0.07335, loss_grounding_bce_1: 0.05914/0.08082, loss_grounding_dice_1: 0.11729/0.15253, loss_grounding_ce_1: 0.03449/0.25519, loss_mask_ce_2: 0.90261/0.78905, loss_mask_bce_2: 0.41356/0.30259, loss_mask_dice_2: 3.35014/1.03472, loss_spatial_bce_2: 0.05808/0.08860, loss_spatial_dice_2: 0.29664/0.18966, loss_spatial_ce_2: 0.03729/0.07550, loss_grounding_bce_2: 0.05680/0.08068, loss_grounding_dice_2: 0.11405/0.15215, loss_grounding_ce_2: 0.04124/0.25772, loss_mask_ce_3: 0.89827/0.78981, loss_mask_bce_3: 0.40535/0.30418, loss_mask_dice_3: 2.60043/1.03074, loss_spatial_bce_3: 0.06377/0.09029, loss_spatial_dice_3: 0.26670/0.19026, loss_spatial_ce_3: 0.02314/0.08099, loss_grounding_bce_3: 0.05702/0.08114, loss_grounding_dice_3: 0.11094/0.15173, loss_grounding_ce_3: 0.04165/0.25732, loss_mask_ce_4: 0.89391/0.79513, loss_mask_bce_4: 0.39890/0.30624, loss_mask_dice_4: 3.44732/1.04956, loss_spatial_bce_4: 0.05910/0.09224, loss_spatial_dice_4: 0.31323/0.19763, loss_spatial_ce_4: 0.42326/0.09312, loss_grounding_bce_4: 0.06164/0.08188, loss_grounding_dice_4: 0.11805/0.15427, loss_grounding_ce_4: 0.02705/0.26360, loss_mask_ce_5: 0.78706/0.81756, loss_mask_bce_5: 0.42403/0.30829, loss_mask_dice_5: 3.30695/1.05649, loss_spatial_bce_5: 0.06094/0.09401, loss_spatial_dice_5: 0.31166/0.19971, loss_spatial_ce_5: 0.23512/0.10448, loss_grounding_bce_5: 0.06283/0.08224, loss_grounding_dice_5: 0.11668/0.15502, loss_grounding_ce_5: 0.04138/0.28251, loss_mask_ce_6: 0.81299/0.84348, loss_mask_bce_6: 0.40423/0.30992, loss_mask_dice_6: 3.06959/1.05984, loss_spatial_bce_6: 0.06008/0.09879, loss_spatial_dice_6: 0.32371/0.20218, loss_spatial_ce_6: 0.10458/0.12585, loss_grounding_bce_6: 0.06313/0.08327, loss_grounding_dice_6: 0.11648/0.15570, loss_grounding_ce_6: 0.05537/0.29282, loss_mask_ce_7: 0.66447/0.90443, loss_mask_bce_7: 0.38821/0.31698, loss_mask_dice_7: 3.47969/1.10613, loss_spatial_bce_7: 0.05678/0.10920, loss_spatial_dice_7: 0.41247/0.22684, loss_spatial_ce_7: 0.17783/0.16837, loss_grounding_bce_7: 0.06765/0.08481, loss_grounding_dice_7: 0.11925/0.16130, loss_grounding_ce_7: 0.02354/0.33437, loss_mask_ce_8: 0.88066/1.04050, loss_mask_bce_8: 0.45050/0.33431, loss_mask_dice_8: 3.20711/1.18541, loss_spatial_bce_8: 0.07885/0.12941, loss_spatial_dice_8: 0.41877/0.26630, loss_spatial_ce_8: 0.64630/0.22238, loss_grounding_bce_8: 0.05396/0.08887, loss_grounding_dice_8: 0.12229/0.17089, loss_grounding_ce_8: 0.01737/0.43494, loss_mask_ce_9: 5.90362/3.49639, loss_mask_bce_9: 0.44024/0.36037, loss_mask_dice_9: 3.99184/1.77145, loss_spatial_bce_9: 0.24666/0.35771, loss_spatial_dice_9: 0.92425/0.79569, loss_spatial_ce_9: 1.48510/1.40672, loss_grounding_bce_9: 0.07803/0.10075, loss_grounding_dice_9: 0.20205/0.24466, loss_grounding_ce_9: 0.25330/0.69933] items per batch[64] items per second[0.36] total items[1670400] mini batches[ 26100] memory[4967] epoch remaining[0:38:58] INFO:trainer.default_trainer:epochs[ 14] optim steps[26200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15812/0.77941, loss_mask_bce_0: 0.10845/0.30186, loss_mask_dice_0: 0.04724/1.02810, loss_spatial_bce_0: 0.25207/0.08851, loss_spatial_dice_0: 0.14943/0.18693, loss_spatial_ce_0: 0.00055/0.06854, loss_grounding_bce_0: 0.28346/0.08054, loss_grounding_dice_0: 0.12342/0.15159, loss_grounding_ce_0: 0.04815/0.25375, loss_mask_ce_1: 0.15919/0.78142, loss_mask_bce_1: 0.10875/0.30260, loss_mask_dice_1: 0.04415/1.03251, loss_spatial_bce_1: 0.25754/0.08888, loss_spatial_dice_1: 0.14618/0.18949, loss_spatial_ce_1: 0.00088/0.07331, loss_grounding_bce_1: 0.31146/0.08081, loss_grounding_dice_1: 0.11996/0.15256, loss_grounding_ce_1: 0.05198/0.25553, loss_mask_ce_2: 0.14041/0.78925, loss_mask_bce_2: 0.11729/0.30263, loss_mask_dice_2: 0.04685/1.03437, loss_spatial_bce_2: 0.24517/0.08858, loss_spatial_dice_2: 0.14819/0.18960, loss_spatial_ce_2: 0.00126/0.07542, loss_grounding_bce_2: 0.33146/0.08067, loss_grounding_dice_2: 0.12375/0.15217, loss_grounding_ce_2: 0.02840/0.25782, loss_mask_ce_3: 0.17472/0.79000, loss_mask_bce_3: 0.10750/0.30420, loss_mask_dice_3: 0.04688/1.03038, loss_spatial_bce_3: 0.23767/0.09027, loss_spatial_dice_3: 0.14124/0.19020, loss_spatial_ce_3: 0.00187/0.08095, loss_grounding_bce_3: 0.30261/0.08113, loss_grounding_dice_3: 0.12686/0.15175, loss_grounding_ce_3: 0.03046/0.25737, loss_mask_ce_4: 0.19554/0.79528, loss_mask_bce_4: 0.10597/0.30630, loss_mask_dice_4: 0.04385/1.04912, loss_spatial_bce_4: 0.27091/0.09221, loss_spatial_dice_4: 0.15627/0.19757, loss_spatial_ce_4: 0.00513/0.09313, loss_grounding_bce_4: 0.33519/0.08187, loss_grounding_dice_4: 0.12467/0.15429, loss_grounding_ce_4: 0.15303/0.26369, loss_mask_ce_5: 0.26917/0.81776, loss_mask_bce_5: 0.09902/0.30832, loss_mask_dice_5: 0.04940/1.05609, loss_spatial_bce_5: 0.31647/0.09397, loss_spatial_dice_5: 0.15760/0.19966, loss_spatial_ce_5: 0.05298/0.10452, loss_grounding_bce_5: 0.31163/0.08225, loss_grounding_dice_5: 0.14161/0.15504, loss_grounding_ce_5: 0.23369/0.28260, loss_mask_ce_6: 0.39748/0.84365, loss_mask_bce_6: 0.09063/0.30995, loss_mask_dice_6: 0.05436/1.05937, loss_spatial_bce_6: 0.36194/0.09877, loss_spatial_dice_6: 0.19539/0.20213, loss_spatial_ce_6: 0.31204/0.12589, loss_grounding_bce_6: 0.27217/0.08328, loss_grounding_dice_6: 0.13876/0.15572, loss_grounding_ce_6: 0.34710/0.29289, loss_mask_ce_7: 0.35937/0.90460, loss_mask_bce_7: 0.10671/0.31699, loss_mask_dice_7: 0.04753/1.10571, loss_spatial_bce_7: 0.45332/0.10920, loss_spatial_dice_7: 0.24887/0.22679, loss_spatial_ce_7: 0.21336/0.16835, loss_grounding_bce_7: 0.23750/0.08481, loss_grounding_dice_7: 0.09373/0.16131, loss_grounding_ce_7: 0.73462/0.33447, loss_mask_ce_8: 0.48249/1.04055, loss_mask_bce_8: 0.12616/0.33435, loss_mask_dice_8: 0.04742/1.18495, loss_spatial_bce_8: 0.25627/0.12943, loss_spatial_dice_8: 0.14463/0.26621, loss_spatial_ce_8: 0.21390/0.22226, loss_grounding_bce_8: 0.27132/0.08886, loss_grounding_dice_8: 0.15308/0.17089, loss_grounding_ce_8: 0.79000/0.43519, loss_mask_ce_9: 1.84153/3.49648, loss_mask_bce_9: 0.17967/0.36042, loss_mask_dice_9: 0.09957/1.77146, loss_spatial_bce_9: 0.65629/0.35776, loss_spatial_dice_9: 0.40722/0.79569, loss_spatial_ce_9: 0.25834/1.40666, loss_grounding_bce_9: 0.47288/0.10076, loss_grounding_dice_9: 0.26240/0.24465, loss_grounding_ce_9: 0.42040/0.69936] items per batch[64] items per second[0.36] total items[1676800] mini batches[ 26200] memory[4967] epoch remaining[0:35:54] INFO:trainer.default_trainer:epochs[ 14] optim steps[26300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92398/0.77905, loss_mask_bce_0: 0.09326/0.30185, loss_mask_dice_0: 0.44866/1.02778, loss_spatial_bce_0: 0.07812/0.08847, loss_spatial_dice_0: 0.35189/0.18692, loss_spatial_ce_0: 0.09265/0.06848, loss_grounding_bce_0: 0.01269/0.08058, loss_grounding_dice_0: 0.90244/0.15164, loss_grounding_ce_0: 1.92077/0.25370, loss_mask_ce_1: 0.99728/0.78105, loss_mask_bce_1: 0.09392/0.30261, loss_mask_dice_1: 0.41829/1.03224, loss_spatial_bce_1: 0.07508/0.08884, loss_spatial_dice_1: 0.32014/0.18947, loss_spatial_ce_1: 0.12249/0.07325, loss_grounding_bce_1: 0.00847/0.08085, loss_grounding_dice_1: 0.62816/0.15260, loss_grounding_ce_1: 3.42154/0.25552, loss_mask_ce_2: 0.86252/0.78880, loss_mask_bce_2: 0.09595/0.30264, loss_mask_dice_2: 0.45786/1.03408, loss_spatial_bce_2: 0.08669/0.08854, loss_spatial_dice_2: 0.28865/0.18958, loss_spatial_ce_2: 0.10242/0.07535, loss_grounding_bce_2: 0.01339/0.08071, loss_grounding_dice_2: 0.92217/0.15221, loss_grounding_ce_2: 3.69097/0.25781, loss_mask_ce_3: 0.88797/0.78952, loss_mask_bce_3: 0.09034/0.30423, loss_mask_dice_3: 0.39428/1.03013, loss_spatial_bce_3: 0.08909/0.09023, loss_spatial_dice_3: 0.31915/0.19020, loss_spatial_ce_3: 0.09329/0.08085, loss_grounding_bce_3: 0.01149/0.08117, loss_grounding_dice_3: 0.87200/0.15180, loss_grounding_ce_3: 1.90376/0.25731, loss_mask_ce_4: 0.82946/0.79484, loss_mask_bce_4: 0.08524/0.30628, loss_mask_dice_4: 0.43205/1.04889, loss_spatial_bce_4: 0.07620/0.09217, loss_spatial_dice_4: 0.30929/0.19755, loss_spatial_ce_4: 0.00688/0.09308, loss_grounding_bce_4: 0.00957/0.08190, loss_grounding_dice_4: 0.86087/0.15433, loss_grounding_ce_4: 1.41170/0.26364, loss_mask_ce_5: 0.83723/0.81731, loss_mask_bce_5: 0.09112/0.30827, loss_mask_dice_5: 0.45169/1.05592, loss_spatial_bce_5: 0.08443/0.09393, loss_spatial_dice_5: 0.34244/0.19965, loss_spatial_ce_5: 0.03383/0.10445, loss_grounding_bce_5: 0.00946/0.08228, loss_grounding_dice_5: 0.88882/0.15507, loss_grounding_ce_5: 3.47388/0.28256, loss_mask_ce_6: 0.86527/0.84315, loss_mask_bce_6: 0.08555/0.30993, loss_mask_dice_6: 0.41807/1.05905, loss_spatial_bce_6: 0.09340/0.09873, loss_spatial_dice_6: 0.36842/0.20210, loss_spatial_ce_6: 0.03633/0.12579, loss_grounding_bce_6: 0.01098/0.08331, loss_grounding_dice_6: 0.88235/0.15574, loss_grounding_ce_6: 2.87799/0.29292, loss_mask_ce_7: 0.99784/0.90403, loss_mask_bce_7: 0.07896/0.31692, loss_mask_dice_7: 0.39492/1.10533, loss_spatial_bce_7: 0.15368/0.10916, loss_spatial_dice_7: 0.46638/0.22680, loss_spatial_ce_7: 0.09534/0.16828, loss_grounding_bce_7: 0.00571/0.08482, loss_grounding_dice_7: 0.80953/0.16135, loss_grounding_ce_7: 2.96193/0.33442, loss_mask_ce_8: 1.49950/1.03995, loss_mask_bce_8: 0.08373/0.33429, loss_mask_dice_8: 0.42218/1.18452, loss_spatial_bce_8: 0.19759/0.12935, loss_spatial_dice_8: 0.53669/0.26616, loss_spatial_ce_8: 0.06800/0.22217, loss_grounding_bce_8: 0.00786/0.08884, loss_grounding_dice_8: 0.82609/0.17089, loss_grounding_ce_8: 3.00207/0.43501, loss_mask_ce_9: 3.70245/3.49611, loss_mask_bce_9: 0.10447/0.36031, loss_mask_dice_9: 0.49484/1.77048, loss_spatial_bce_9: 0.38311/0.35761, loss_spatial_dice_9: 0.63435/0.79570, loss_spatial_ce_9: 1.49097/1.40671, loss_grounding_bce_9: 0.01136/0.10071, loss_grounding_dice_9: 0.91114/0.24467, loss_grounding_ce_9: 3.55121/0.69889] items per batch[64] items per second[0.36] total items[1683200] mini batches[ 26300] memory[4967] epoch remaining[0:32:56] INFO:trainer.default_trainer:epochs[ 14] optim steps[26400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.92702/0.77908, loss_mask_bce_0: 0.63221/0.30185, loss_mask_dice_0: 0.98186/1.02782, loss_spatial_bce_0: 0.14869/0.08843, loss_spatial_dice_0: 0.32814/0.18688, loss_spatial_ce_0: 0.04946/0.06841, loss_grounding_bce_0: 0.19713/0.08056, loss_grounding_dice_0: 0.40286/0.15160, loss_grounding_ce_0: 0.40480/0.25367, loss_mask_ce_1: 0.95592/0.78106, loss_mask_bce_1: 0.70514/0.30262, loss_mask_dice_1: 1.03813/1.03228, loss_spatial_bce_1: 0.16607/0.08880, loss_spatial_dice_1: 0.31412/0.18942, loss_spatial_ce_1: 0.05227/0.07317, loss_grounding_bce_1: 0.22767/0.08083, loss_grounding_dice_1: 0.45715/0.15257, loss_grounding_ce_1: 0.44097/0.25546, loss_mask_ce_2: 1.03879/0.78881, loss_mask_bce_2: 0.64690/0.30266, loss_mask_dice_2: 1.05518/1.03416, loss_spatial_bce_2: 0.18319/0.08850, loss_spatial_dice_2: 0.35228/0.18953, loss_spatial_ce_2: 0.03878/0.07527, loss_grounding_bce_2: 0.35961/0.08068, loss_grounding_dice_2: 0.40172/0.15219, loss_grounding_ce_2: 0.38721/0.25768, loss_mask_ce_3: 0.78970/0.78947, loss_mask_bce_3: 0.85185/0.30425, loss_mask_dice_3: 1.01001/1.03008, loss_spatial_bce_3: 0.16213/0.09018, loss_spatial_dice_3: 0.32426/0.19016, loss_spatial_ce_3: 0.07711/0.08077, loss_grounding_bce_3: 0.24660/0.08114, loss_grounding_dice_3: 0.41841/0.15178, loss_grounding_ce_3: 0.38888/0.25728, loss_mask_ce_4: 0.93513/0.79489, loss_mask_bce_4: 0.75874/0.30627, loss_mask_dice_4: 1.06867/1.04891, loss_spatial_bce_4: 0.17833/0.09214, loss_spatial_dice_4: 0.33617/0.19751, loss_spatial_ce_4: 0.15702/0.09300, loss_grounding_bce_4: 0.29148/0.08187, loss_grounding_dice_4: 0.37136/0.15431, loss_grounding_ce_4: 0.36580/0.26348, loss_mask_ce_5: 0.75567/0.81736, loss_mask_bce_5: 0.68707/0.30826, loss_mask_dice_5: 1.02763/1.05588, loss_spatial_bce_5: 0.18411/0.09389, loss_spatial_dice_5: 0.35039/0.19962, loss_spatial_ce_5: 0.27287/0.10437, loss_grounding_bce_5: 0.26428/0.08223, loss_grounding_dice_5: 0.46168/0.15503, loss_grounding_ce_5: 0.36804/0.28264, loss_mask_ce_6: 0.55502/0.84328, loss_mask_bce_6: 0.77845/0.30989, loss_mask_dice_6: 1.11777/1.05901, loss_spatial_bce_6: 0.21326/0.09868, loss_spatial_dice_6: 0.38651/0.20208, loss_spatial_ce_6: 0.45405/0.12572, loss_grounding_bce_6: 0.27575/0.08326, loss_grounding_dice_6: 0.41423/0.15570, loss_grounding_ce_6: 0.33761/0.29293, loss_mask_ce_7: 1.03881/0.90408, loss_mask_bce_7: 0.86966/0.31690, loss_mask_dice_7: 1.01949/1.10535, loss_spatial_bce_7: 0.15898/0.10912, loss_spatial_dice_7: 0.38146/0.22676, loss_spatial_ce_7: 0.55582/0.16820, loss_grounding_bce_7: 0.25843/0.08477, loss_grounding_dice_7: 0.45632/0.16134, loss_grounding_ce_7: 0.28082/0.33446, loss_mask_ce_8: 1.27054/1.04005, loss_mask_bce_8: 0.67969/0.33426, loss_mask_dice_8: 0.98548/1.18456, loss_spatial_bce_8: 0.18219/0.12933, loss_spatial_dice_8: 0.43518/0.26612, loss_spatial_ce_8: 0.88872/0.22208, loss_grounding_bce_8: 0.26029/0.08879, loss_grounding_dice_8: 0.45078/0.17086, loss_grounding_ce_8: 0.47252/0.43504, loss_mask_ce_9: 4.04531/3.49649, loss_mask_bce_9: 0.41648/0.36036, loss_mask_dice_9: 1.68892/1.77062, loss_spatial_bce_9: 0.16898/0.35758, loss_spatial_dice_9: 0.82717/0.79567, loss_spatial_ce_9: 0.86977/1.40650, loss_grounding_bce_9: 0.15164/0.10068, loss_grounding_dice_9: 0.61787/0.24462, loss_grounding_ce_9: 0.80002/0.69882] items per batch[64] items per second[0.37] total items[1689600] mini batches[ 26400] memory[4967] epoch remaining[0:29:50] INFO:trainer.default_trainer:epochs[ 14] optim steps[26500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04120/0.77901, loss_mask_bce_0: 0.18775/0.30196, loss_mask_dice_0: 0.08399/1.02812, loss_spatial_bce_0: 0.09650/0.08848, loss_spatial_dice_0: 0.06114/0.18685, loss_spatial_ce_0: 0.00594/0.06834, loss_grounding_bce_0: 0.15364/0.08063, loss_grounding_dice_0: 0.07122/0.15160, loss_grounding_ce_0: 0.00135/0.25383, loss_mask_ce_1: 0.03925/0.78093, loss_mask_bce_1: 0.18073/0.30275, loss_mask_dice_1: 0.08502/1.03257, loss_spatial_bce_1: 0.08160/0.08883, loss_spatial_dice_1: 0.05666/0.18938, loss_spatial_ce_1: 0.01963/0.07309, loss_grounding_bce_1: 0.14130/0.08090, loss_grounding_dice_1: 0.06726/0.15259, loss_grounding_ce_1: 0.00136/0.25560, loss_mask_ce_2: 0.03882/0.78868, loss_mask_bce_2: 0.18020/0.30279, loss_mask_dice_2: 0.08374/1.03456, loss_spatial_bce_2: 0.07527/0.08854, loss_spatial_dice_2: 0.05527/0.18949, loss_spatial_ce_2: 0.01057/0.07517, loss_grounding_bce_2: 0.14885/0.08077, loss_grounding_dice_2: 0.06875/0.15219, loss_grounding_ce_2: 0.00102/0.25784, loss_mask_ce_3: 0.04812/0.78940, loss_mask_bce_3: 0.18053/0.30437, loss_mask_dice_3: 0.08551/1.03043, loss_spatial_bce_3: 0.11386/0.09023, loss_spatial_dice_3: 0.06525/0.19013, loss_spatial_ce_3: 0.00178/0.08071, loss_grounding_bce_3: 0.15258/0.08122, loss_grounding_dice_3: 0.07031/0.15178, loss_grounding_ce_3: 0.00116/0.25741, loss_mask_ce_4: 0.05985/0.79483, loss_mask_bce_4: 0.18777/0.30643, loss_mask_dice_4: 0.08833/1.04929, loss_spatial_bce_4: 0.10333/0.09217, loss_spatial_dice_4: 0.09994/0.19748, loss_spatial_ce_4: 0.00665/0.09300, loss_grounding_bce_4: 0.14909/0.08194, loss_grounding_dice_4: 0.07086/0.15431, loss_grounding_ce_4: 0.00122/0.26367, loss_mask_ce_5: 0.04228/0.81733, loss_mask_bce_5: 0.17978/0.30839, loss_mask_dice_5: 0.08536/1.05632, loss_spatial_bce_5: 0.10172/0.09391, loss_spatial_dice_5: 0.06921/0.19960, loss_spatial_ce_5: 0.00168/0.10432, loss_grounding_bce_5: 0.14602/0.08230, loss_grounding_dice_5: 0.07007/0.15502, loss_grounding_ce_5: 0.00137/0.28274, loss_mask_ce_6: 0.03651/0.84324, loss_mask_bce_6: 0.19028/0.31004, loss_mask_dice_6: 0.08868/1.05944, loss_spatial_bce_6: 0.10973/0.09871, loss_spatial_dice_6: 0.08906/0.20206, loss_spatial_ce_6: 0.07331/0.12567, loss_grounding_bce_6: 0.14406/0.08333, loss_grounding_dice_6: 0.07075/0.15568, loss_grounding_ce_6: 0.00226/0.29294, loss_mask_ce_7: 0.03736/0.90393, loss_mask_bce_7: 0.16998/0.31704, loss_mask_dice_7: 0.08878/1.10579, loss_spatial_bce_7: 0.11659/0.10917, loss_spatial_dice_7: 0.09464/0.22675, loss_spatial_ce_7: 0.03704/0.16819, loss_grounding_bce_7: 0.13273/0.08485, loss_grounding_dice_7: 0.07175/0.16133, loss_grounding_ce_7: 0.00445/0.33464, loss_mask_ce_8: 0.07202/1.03989, loss_mask_bce_8: 0.16491/0.33440, loss_mask_dice_8: 0.08719/1.18504, loss_spatial_bce_8: 0.12536/0.12936, loss_spatial_dice_8: 0.09286/0.26605, loss_spatial_ce_8: 0.16020/0.22210, loss_grounding_bce_8: 0.12142/0.08887, loss_grounding_dice_8: 0.06747/0.17084, loss_grounding_ce_8: 0.00258/0.43536, loss_mask_ce_9: 1.58453/3.49662, loss_mask_bce_9: 0.14665/0.36056, loss_mask_dice_9: 0.10125/1.77154, loss_spatial_bce_9: 0.57201/0.35762, loss_spatial_dice_9: 0.65853/0.79572, loss_spatial_ce_9: 0.76066/1.40621, loss_grounding_bce_9: 0.12388/0.10080, loss_grounding_dice_9: 0.08567/0.24462, loss_grounding_ce_9: 0.07399/0.69871] items per batch[64] items per second[0.35] total items[1696000] mini batches[ 26500] memory[4967] epoch remaining[0:26:55] INFO:trainer.default_trainer:epochs[ 14] optim steps[26600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32686/0.77894, loss_mask_bce_0: 0.43734/0.30186, loss_mask_dice_0: 0.58672/1.02797, loss_spatial_bce_0: 0.09224/0.08844, loss_spatial_dice_0: 0.14946/0.18679, loss_spatial_ce_0: 0.00197/0.06826, loss_grounding_bce_0: 0.14149/0.08060, loss_grounding_dice_0: 0.13443/0.15156, loss_grounding_ce_0: 0.00378/0.25368, loss_mask_ce_1: 0.30820/0.78083, loss_mask_bce_1: 0.43362/0.30263, loss_mask_dice_1: 0.61997/1.03227, loss_spatial_bce_1: 0.08830/0.08880, loss_spatial_dice_1: 0.14620/0.18933, loss_spatial_ce_1: 0.00251/0.07300, loss_grounding_bce_1: 0.13419/0.08087, loss_grounding_dice_1: 0.12963/0.15253, loss_grounding_ce_1: 0.00343/0.25548, loss_mask_ce_2: 0.30292/0.78851, loss_mask_bce_2: 0.43820/0.30269, loss_mask_dice_2: 0.60765/1.03435, loss_spatial_bce_2: 0.08948/0.08851, loss_spatial_dice_2: 0.14239/0.18944, loss_spatial_ce_2: 0.00399/0.07504, loss_grounding_bce_2: 0.12863/0.08074, loss_grounding_dice_2: 0.12503/0.15212, loss_grounding_ce_2: 0.00470/0.25774, loss_mask_ce_3: 0.33394/0.78924, loss_mask_bce_3: 0.45090/0.30424, loss_mask_dice_3: 0.62208/1.03024, loss_spatial_bce_3: 0.08723/0.09019, loss_spatial_dice_3: 0.13909/0.19007, loss_spatial_ce_3: 0.00773/0.08059, loss_grounding_bce_3: 0.13366/0.08119, loss_grounding_dice_3: 0.13057/0.15174, loss_grounding_ce_3: 0.00587/0.25728, loss_mask_ce_4: 0.36743/0.79475, loss_mask_bce_4: 0.45410/0.30628, loss_mask_dice_4: 0.63039/1.04900, loss_spatial_bce_4: 0.09242/0.09213, loss_spatial_dice_4: 0.14156/0.19744, loss_spatial_ce_4: 0.01713/0.09294, loss_grounding_bce_4: 0.13527/0.08191, loss_grounding_dice_4: 0.12818/0.15426, loss_grounding_ce_4: 0.00342/0.26357, loss_mask_ce_5: 0.39261/0.81730, loss_mask_bce_5: 0.44169/0.30825, loss_mask_dice_5: 0.62181/1.05604, loss_spatial_bce_5: 0.10107/0.09388, loss_spatial_dice_5: 0.14854/0.19956, loss_spatial_ce_5: 0.04120/0.10423, loss_grounding_bce_5: 0.13154/0.08226, loss_grounding_dice_5: 0.12609/0.15498, loss_grounding_ce_5: 0.00308/0.28252, loss_mask_ce_6: 0.42131/0.84319, loss_mask_bce_6: 0.46004/0.30992, loss_mask_dice_6: 0.65699/1.05912, loss_spatial_bce_6: 0.10089/0.09867, loss_spatial_dice_6: 0.14977/0.20199, loss_spatial_ce_6: 0.09120/0.12558, loss_grounding_bce_6: 0.13193/0.08329, loss_grounding_dice_6: 0.12832/0.15564, loss_grounding_ce_6: 0.00264/0.29275, loss_mask_ce_7: 0.62269/0.90377, loss_mask_bce_7: 0.44510/0.31689, loss_mask_dice_7: 0.64294/1.10555, loss_spatial_bce_7: 0.15372/0.10916, loss_spatial_dice_7: 0.17784/0.22670, loss_spatial_ce_7: 0.11523/0.16808, loss_grounding_bce_7: 0.12740/0.08480, loss_grounding_dice_7: 0.13255/0.16127, loss_grounding_ce_7: 0.00539/0.33441, loss_mask_ce_8: 1.03580/1.03981, loss_mask_bce_8: 0.43297/0.33425, loss_mask_dice_8: 0.64071/1.18483, loss_spatial_bce_8: 0.15537/0.12928, loss_spatial_dice_8: 0.24910/0.26596, loss_spatial_ce_8: 0.11534/0.22194, loss_grounding_bce_8: 0.12408/0.08883, loss_grounding_dice_8: 0.12888/0.17080, loss_grounding_ce_8: 0.00792/0.43517, loss_mask_ce_9: 2.71624/3.49638, loss_mask_bce_9: 0.43363/0.36033, loss_mask_dice_9: 0.89264/1.77119, loss_spatial_bce_9: 0.34282/0.35763, loss_spatial_dice_9: 0.77594/0.79569, loss_spatial_ce_9: 1.36567/1.40625, loss_grounding_bce_9: 0.12506/0.10072, loss_grounding_dice_9: 0.14948/0.24453, loss_grounding_ce_9: 0.04439/0.69848] items per batch[64] items per second[0.36] total items[1702400] mini batches[ 26600] memory[4967] epoch remaining[0:23:58] INFO:trainer.default_trainer:epochs[ 14] optim steps[26700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27752/0.77881, loss_mask_bce_0: 0.11930/0.30186, loss_mask_dice_0: 0.22523/1.02793, loss_spatial_bce_0: 0.11940/0.08841, loss_spatial_dice_0: 0.21104/0.18676, loss_spatial_ce_0: 0.01695/0.06822, loss_grounding_bce_0: 0.12656/0.08059, loss_grounding_dice_0: 0.05420/0.15149, loss_grounding_ce_0: 0.00740/0.25348, loss_mask_ce_1: 0.17760/0.78067, loss_mask_bce_1: 0.12715/0.30266, loss_mask_dice_1: 0.18466/1.03230, loss_spatial_bce_1: 0.08470/0.08876, loss_spatial_dice_1: 0.13424/0.18929, loss_spatial_ce_1: 0.12863/0.07300, loss_grounding_bce_1: 0.12680/0.08086, loss_grounding_dice_1: 0.05349/0.15246, loss_grounding_ce_1: 0.00541/0.25525, loss_mask_ce_2: 0.19157/0.78836, loss_mask_bce_2: 0.14203/0.30271, loss_mask_dice_2: 0.25414/1.03437, loss_spatial_bce_2: 0.08617/0.08849, loss_spatial_dice_2: 0.14980/0.18939, loss_spatial_ce_2: 0.15047/0.07501, loss_grounding_bce_2: 0.11753/0.08072, loss_grounding_dice_2: 0.04870/0.15206, loss_grounding_ce_2: 0.00628/0.25751, loss_mask_ce_3: 0.35918/0.78915, loss_mask_bce_3: 0.14023/0.30426, loss_mask_dice_3: 0.25078/1.03017, loss_spatial_bce_3: 0.09110/0.09017, loss_spatial_dice_3: 0.15522/0.19004, loss_spatial_ce_3: 0.18008/0.08050, loss_grounding_bce_3: 0.12269/0.08118, loss_grounding_dice_3: 0.04696/0.15166, loss_grounding_ce_3: 0.00612/0.25703, loss_mask_ce_4: 0.47200/0.79462, loss_mask_bce_4: 0.12705/0.30632, loss_mask_dice_4: 0.28971/1.04891, loss_spatial_bce_4: 0.10253/0.09211, loss_spatial_dice_4: 0.13011/0.19740, loss_spatial_ce_4: 0.04181/0.09285, loss_grounding_bce_4: 0.11073/0.08190, loss_grounding_dice_4: 0.04512/0.15420, loss_grounding_ce_4: 0.01204/0.26322, loss_mask_ce_5: 0.40071/0.81702, loss_mask_bce_5: 0.13007/0.30828, loss_mask_dice_5: 0.26410/1.05610, loss_spatial_bce_5: 0.11896/0.09385, loss_spatial_dice_5: 0.19459/0.19953, loss_spatial_ce_5: 0.00663/0.10417, loss_grounding_bce_5: 0.11053/0.08225, loss_grounding_dice_5: 0.05107/0.15492, loss_grounding_ce_5: 0.02222/0.28217, loss_mask_ce_6: 0.48577/0.84287, loss_mask_bce_6: 0.13853/0.30995, loss_mask_dice_6: 0.40391/1.05922, loss_spatial_bce_6: 0.13105/0.09864, loss_spatial_dice_6: 0.22517/0.20195, loss_spatial_ce_6: 0.04990/0.12553, loss_grounding_bce_6: 0.11528/0.08328, loss_grounding_dice_6: 0.05024/0.15558, loss_grounding_ce_6: 0.01737/0.29234, loss_mask_ce_7: 0.50061/0.90350, loss_mask_bce_7: 0.16043/0.31691, loss_mask_dice_7: 0.26394/1.10558, loss_spatial_bce_7: 0.16150/0.10913, loss_spatial_dice_7: 0.22690/0.22668, loss_spatial_ce_7: 0.03693/0.16795, loss_grounding_bce_7: 0.10566/0.08479, loss_grounding_dice_7: 0.04372/0.16120, loss_grounding_ce_7: 0.01110/0.33392, loss_mask_ce_8: 1.24122/1.03952, loss_mask_bce_8: 0.12721/0.33424, loss_mask_dice_8: 0.22475/1.18480, loss_spatial_bce_8: 0.07803/0.12925, loss_spatial_dice_8: 0.10825/0.26591, loss_spatial_ce_8: 0.26346/0.22184, loss_grounding_bce_8: 0.11249/0.08882, loss_grounding_dice_8: 0.04102/0.17072, loss_grounding_ce_8: 0.03790/0.43456, loss_mask_ce_9: 3.65603/3.49570, loss_mask_bce_9: 0.15709/0.36033, loss_mask_dice_9: 0.36455/1.77110, loss_spatial_bce_9: 0.37984/0.35772, loss_spatial_dice_9: 0.73885/0.79566, loss_spatial_ce_9: 0.78887/1.40623, loss_grounding_bce_9: 0.14667/0.10071, loss_grounding_dice_9: 0.09853/0.24442, loss_grounding_ce_9: 2.02981/0.69803] items per batch[64] items per second[0.36] total items[1708800] mini batches[ 26700] memory[4967] epoch remaining[0:20:58] INFO:trainer.default_trainer:epochs[ 14] optim steps[26800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42418/0.77864, loss_mask_bce_0: 0.09165/0.30187, loss_mask_dice_0: 1.67903/1.02812, loss_spatial_bce_0: 0.01063/0.08839, loss_spatial_dice_0: 0.15428/0.18666, loss_spatial_ce_0: 0.00073/0.06816, loss_grounding_bce_0: 0.00735/0.08052, loss_grounding_dice_0: 0.19558/0.15141, loss_grounding_ce_0: 0.66376/0.25349, loss_mask_ce_1: 0.32279/0.78058, loss_mask_bce_1: 0.08514/0.30265, loss_mask_dice_1: 1.49020/1.03253, loss_spatial_bce_1: 0.01042/0.08875, loss_spatial_dice_1: 0.14161/0.18919, loss_spatial_ce_1: 0.24992/0.07293, loss_grounding_bce_1: 0.00682/0.08080, loss_grounding_dice_1: 0.20232/0.15239, loss_grounding_ce_1: 0.72123/0.25522, loss_mask_ce_2: 0.31590/0.78822, loss_mask_bce_2: 0.09978/0.30271, loss_mask_dice_2: 1.87346/1.03459, loss_spatial_bce_2: 0.01107/0.08847, loss_spatial_dice_2: 0.19454/0.18930, loss_spatial_ce_2: 0.00163/0.07495, loss_grounding_bce_2: 0.00793/0.08066, loss_grounding_dice_2: 0.24676/0.15200, loss_grounding_ce_2: 0.68973/0.25753, loss_mask_ce_3: 0.52137/0.78909, loss_mask_bce_3: 0.09456/0.30428, loss_mask_dice_3: 1.75230/1.03033, loss_spatial_bce_3: 0.01090/0.09016, loss_spatial_dice_3: 0.16540/0.18994, loss_spatial_ce_3: 0.00550/0.08039, loss_grounding_bce_3: 0.00750/0.08111, loss_grounding_dice_3: 0.20954/0.15159, loss_grounding_ce_3: 0.66803/0.25706, loss_mask_ce_4: 0.46934/0.79456, loss_mask_bce_4: 0.08187/0.30631, loss_mask_dice_4: 1.16285/1.04896, loss_spatial_bce_4: 0.01199/0.09208, loss_spatial_dice_4: 0.15900/0.19731, loss_spatial_ce_4: 0.00404/0.09281, loss_grounding_bce_4: 0.00895/0.08184, loss_grounding_dice_4: 0.22790/0.15412, loss_grounding_ce_4: 0.65441/0.26325, loss_mask_ce_5: 0.58878/0.81701, loss_mask_bce_5: 0.08801/0.30826, loss_mask_dice_5: 1.26890/1.05622, loss_spatial_bce_5: 0.01210/0.09385, loss_spatial_dice_5: 0.19532/0.19946, loss_spatial_ce_5: 0.01646/0.10409, loss_grounding_bce_5: 0.00826/0.08219, loss_grounding_dice_5: 0.20617/0.15485, loss_grounding_ce_5: 0.69485/0.28216, loss_mask_ce_6: 0.54177/0.84288, loss_mask_bce_6: 0.08993/0.30995, loss_mask_dice_6: 1.52953/1.05939, loss_spatial_bce_6: 0.01241/0.09862, loss_spatial_dice_6: 0.19175/0.20186, loss_spatial_ce_6: 0.04095/0.12550, loss_grounding_bce_6: 0.00779/0.08321, loss_grounding_dice_6: 0.22608/0.15552, loss_grounding_ce_6: 0.65636/0.29230, loss_mask_ce_7: 0.68654/0.90342, loss_mask_bce_7: 0.09430/0.31690, loss_mask_dice_7: 1.80483/1.10578, loss_spatial_bce_7: 0.01142/0.10911, loss_spatial_dice_7: 0.19335/0.22660, loss_spatial_ce_7: 0.07053/0.16798, loss_grounding_bce_7: 0.00739/0.08472, loss_grounding_dice_7: 0.22189/0.16117, loss_grounding_ce_7: 0.68720/0.33384, loss_mask_ce_8: 0.63868/1.03947, loss_mask_bce_8: 0.11350/0.33419, loss_mask_dice_8: 1.88350/1.18492, loss_spatial_bce_8: 0.01102/0.12934, loss_spatial_dice_8: 0.19696/0.26584, loss_spatial_ce_8: 0.10988/0.22181, loss_grounding_bce_8: 0.00986/0.08874, loss_grounding_dice_8: 0.22157/0.17066, loss_grounding_ce_8: 0.69006/0.43426, loss_mask_ce_9: 6.94825/3.49633, loss_mask_bce_9: 0.10415/0.36032, loss_mask_dice_9: 2.32182/1.77123, loss_spatial_bce_9: 0.12072/0.35781, loss_spatial_dice_9: 0.80174/0.79566, loss_spatial_ce_9: 1.61426/1.40621, loss_grounding_bce_9: 0.00916/0.10063, loss_grounding_dice_9: 0.26955/0.24432, loss_grounding_ce_9: 0.73843/0.69767] items per batch[64] items per second[0.36] total items[1715200] mini batches[ 26800] memory[4967] epoch remaining[0:17:59] INFO:trainer.default_trainer:epochs[ 14] optim steps[26900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81604/0.77845, loss_mask_bce_0: 0.02928/0.30174, loss_mask_dice_0: 1.58038/1.02794, loss_spatial_bce_0: 0.00655/0.08837, loss_spatial_dice_0: 0.20767/0.18662, loss_spatial_ce_0: 0.01177/0.06807, loss_grounding_bce_0: 0.00059/0.08050, loss_grounding_dice_0: 0.18705/0.15136, loss_grounding_ce_0: 0.40888/0.25334, loss_mask_ce_1: 0.89616/0.78046, loss_mask_bce_1: 0.03382/0.30251, loss_mask_dice_1: 1.18287/1.03231, loss_spatial_bce_1: 0.00642/0.08873, loss_spatial_dice_1: 0.24203/0.18915, loss_spatial_ce_1: 0.01150/0.07282, loss_grounding_bce_1: 0.00051/0.08078, loss_grounding_dice_1: 0.15321/0.15236, loss_grounding_ce_1: 0.40663/0.25502, loss_mask_ce_2: 0.77770/0.78811, loss_mask_bce_2: 0.03321/0.30257, loss_mask_dice_2: 1.50985/1.03433, loss_spatial_bce_2: 0.00530/0.08845, loss_spatial_dice_2: 0.30469/0.18926, loss_spatial_ce_2: 0.62353/0.07490, loss_grounding_bce_2: 0.00078/0.08065, loss_grounding_dice_2: 0.16667/0.15194, loss_grounding_ce_2: 0.40629/0.25734, loss_mask_ce_3: 0.86183/0.78888, loss_mask_bce_3: 0.04057/0.30413, loss_mask_dice_3: 1.63443/1.03015, loss_spatial_bce_3: 0.00513/0.09015, loss_spatial_dice_3: 0.23149/0.18991, loss_spatial_ce_3: 0.05255/0.08029, loss_grounding_bce_3: 0.00051/0.08110, loss_grounding_dice_3: 0.14205/0.15156, loss_grounding_ce_3: 0.42242/0.25684, loss_mask_ce_4: 0.65489/0.79440, loss_mask_bce_4: 0.03377/0.30617, loss_mask_dice_4: 1.31209/1.04872, loss_spatial_bce_4: 0.00637/0.09207, loss_spatial_dice_4: 0.16243/0.19727, loss_spatial_ce_4: 0.04013/0.09271, loss_grounding_bce_4: 0.00045/0.08183, loss_grounding_dice_4: 0.15000/0.15407, loss_grounding_ce_4: 0.41231/0.26302, loss_mask_ce_5: 0.96385/0.81693, loss_mask_bce_5: 0.03823/0.30812, loss_mask_dice_5: 2.37010/1.05595, loss_spatial_bce_5: 0.00824/0.09383, loss_spatial_dice_5: 0.33201/0.19942, loss_spatial_ce_5: 0.05077/0.10401, loss_grounding_bce_5: 0.00080/0.08218, loss_grounding_dice_5: 0.19802/0.15483, loss_grounding_ce_5: 0.43990/0.28189, loss_mask_ce_6: 1.00354/0.84273, loss_mask_bce_6: 0.03041/0.30981, loss_mask_dice_6: 1.47436/1.05915, loss_spatial_bce_6: 0.00759/0.09859, loss_spatial_dice_6: 0.22882/0.20183, loss_spatial_ce_6: 0.06278/0.12538, loss_grounding_bce_6: 0.00063/0.08320, loss_grounding_dice_6: 0.18418/0.15548, loss_grounding_ce_6: 0.45094/0.29197, loss_mask_ce_7: 1.05808/0.90324, loss_mask_bce_7: 0.03669/0.31679, loss_mask_dice_7: 1.06959/1.10546, loss_spatial_bce_7: 0.00722/0.10908, loss_spatial_dice_7: 0.36595/0.22656, loss_spatial_ce_7: 0.07193/0.16785, loss_grounding_bce_7: 0.00050/0.08470, loss_grounding_dice_7: 0.12500/0.16112, loss_grounding_ce_7: 0.43364/0.33347, loss_mask_ce_8: 1.82225/1.03944, loss_mask_bce_8: 0.04322/0.33407, loss_mask_dice_8: 1.86360/1.18460, loss_spatial_bce_8: 0.01172/0.12930, loss_spatial_dice_8: 0.53941/0.26577, loss_spatial_ce_8: 0.31147/0.22165, loss_grounding_bce_8: 0.00046/0.08871, loss_grounding_dice_8: 0.06985/0.17057, loss_grounding_ce_8: 0.47509/0.43400, loss_mask_ce_9: 3.29184/3.49601, loss_mask_bce_9: 0.03061/0.36021, loss_mask_dice_9: 2.43947/1.77057, loss_spatial_bce_9: 0.01304/0.35778, loss_spatial_dice_9: 0.80688/0.79564, loss_spatial_ce_9: 1.25268/1.40608, loss_grounding_bce_9: 0.00080/0.10061, loss_grounding_dice_9: 0.17099/0.24427, loss_grounding_ce_9: 0.57073/0.69755] items per batch[64] items per second[0.37] total items[1721600] mini batches[ 26900] memory[4967] epoch remaining[0:14:59] INFO:trainer.default_trainer:epochs[ 14] optim steps[27000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72687/0.77827, loss_mask_bce_0: 0.06864/0.30183, loss_mask_dice_0: 2.96842/1.02916, loss_spatial_bce_0: 0.01336/0.08832, loss_spatial_dice_0: 0.29549/0.18660, loss_spatial_ce_0: 0.01241/0.06802, loss_grounding_bce_0: 0.00503/0.08049, loss_grounding_dice_0: 0.40831/0.15136, loss_grounding_ce_0: 0.55464/0.25317, loss_mask_ce_1: 1.22503/0.78030, loss_mask_bce_1: 0.07411/0.30259, loss_mask_dice_1: 2.82718/1.03345, loss_spatial_bce_1: 0.01265/0.08869, loss_spatial_dice_1: 0.27138/0.18915, loss_spatial_ce_1: 0.03066/0.07274, loss_grounding_bce_1: 0.00618/0.08076, loss_grounding_dice_1: 0.42939/0.15234, loss_grounding_ce_1: 0.70929/0.25488, loss_mask_ce_2: 0.76976/0.78794, loss_mask_bce_2: 0.08194/0.30266, loss_mask_dice_2: 3.16467/1.03558, loss_spatial_bce_2: 0.01412/0.08841, loss_spatial_dice_2: 0.36569/0.18925, loss_spatial_ce_2: 0.25114/0.07486, loss_grounding_bce_2: 0.00672/0.08064, loss_grounding_dice_2: 0.43070/0.15193, loss_grounding_ce_2: 0.64119/0.25724, loss_mask_ce_3: 0.83262/0.78872, loss_mask_bce_3: 0.07150/0.30422, loss_mask_dice_3: 2.93280/1.03129, loss_spatial_bce_3: 0.01407/0.09013, loss_spatial_dice_3: 0.19721/0.18990, loss_spatial_ce_3: 0.00596/0.08021, loss_grounding_bce_3: 0.00411/0.08108, loss_grounding_dice_3: 0.37563/0.15155, loss_grounding_ce_3: 0.65920/0.25671, loss_mask_ce_4: 1.23178/0.79417, loss_mask_bce_4: 0.09387/0.30625, loss_mask_dice_4: 3.11228/1.04991, loss_spatial_bce_4: 0.01474/0.09205, loss_spatial_dice_4: 0.31856/0.19727, loss_spatial_ce_4: 0.04180/0.09268, loss_grounding_bce_4: 0.00598/0.08182, loss_grounding_dice_4: 0.38127/0.15409, loss_grounding_ce_4: 0.61589/0.26297, loss_mask_ce_5: 1.08191/0.81676, loss_mask_bce_5: 0.06130/0.30819, loss_mask_dice_5: 2.30763/1.05724, loss_spatial_bce_5: 0.01370/0.09381, loss_spatial_dice_5: 0.34857/0.19944, loss_spatial_ce_5: 0.03250/0.10394, loss_grounding_bce_5: 0.00604/0.08217, loss_grounding_dice_5: 0.45553/0.15482, loss_grounding_ce_5: 0.57014/0.28179, loss_mask_ce_6: 1.27544/0.84259, loss_mask_bce_6: 0.07122/0.30988, loss_mask_dice_6: 2.94222/1.06034, loss_spatial_bce_6: 0.01445/0.09857, loss_spatial_dice_6: 0.26391/0.20184, loss_spatial_ce_6: 0.15282/0.12531, loss_grounding_bce_6: 0.00951/0.08319, loss_grounding_dice_6: 0.55538/0.15549, loss_grounding_ce_6: 0.57519/0.29186, loss_mask_ce_7: 1.46315/0.90310, loss_mask_bce_7: 0.08268/0.31688, loss_mask_dice_7: 3.45496/1.10672, loss_spatial_bce_7: 0.02053/0.10906, loss_spatial_dice_7: 0.40368/0.22658, loss_spatial_ce_7: 0.08255/0.16771, loss_grounding_bce_7: 0.01091/0.08469, loss_grounding_dice_7: 0.53264/0.16112, loss_grounding_ce_7: 0.54228/0.33351, loss_mask_ce_8: 1.44604/1.03930, loss_mask_bce_8: 0.08894/0.33416, loss_mask_dice_8: 3.59835/1.18596, loss_spatial_bce_8: 0.02297/0.12925, loss_spatial_dice_8: 0.44296/0.26576, loss_spatial_ce_8: 0.13232/0.22150, loss_grounding_bce_8: 0.00933/0.08874, loss_grounding_dice_8: 0.44865/0.17057, loss_grounding_ce_8: 0.70222/0.43374, loss_mask_ce_9: 4.19217/3.49601, loss_mask_bce_9: 0.05640/0.36034, loss_mask_dice_9: 3.82407/1.77270, loss_spatial_bce_9: 0.03407/0.35765, loss_spatial_dice_9: 0.93264/0.79572, loss_spatial_ce_9: 1.29217/1.40604, loss_grounding_bce_9: 0.00493/0.10061, loss_grounding_dice_9: 0.51024/0.24427, loss_grounding_ce_9: 0.44160/0.69744] items per batch[64] items per second[0.36] total items[1728000] mini batches[ 27000] memory[4967] epoch remaining[0:12:01] INFO:trainer.default_trainer:epochs[ 14] optim steps[27100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19963/0.77839, loss_mask_bce_0: 0.18298/0.30189, loss_mask_dice_0: 0.16342/1.02892, loss_spatial_bce_0: 0.10100/0.08832, loss_spatial_dice_0: 0.07623/0.18654, loss_spatial_ce_0: 0.00027/0.06800, loss_grounding_bce_0: 0.08471/0.08051, loss_grounding_dice_0: 0.08106/0.15133, loss_grounding_ce_0: 0.28041/0.25325, loss_mask_ce_1: 0.20385/0.78043, loss_mask_bce_1: 0.17675/0.30264, loss_mask_dice_1: 0.16189/1.03322, loss_spatial_bce_1: 0.10297/0.08869, loss_spatial_dice_1: 0.08214/0.18909, loss_spatial_ce_1: 0.00033/0.07270, loss_grounding_bce_1: 0.08775/0.08078, loss_grounding_dice_1: 0.07610/0.15230, loss_grounding_ce_1: 0.30440/0.25496, loss_mask_ce_2: 0.20163/0.78810, loss_mask_bce_2: 0.17550/0.30270, loss_mask_dice_2: 0.15696/1.03533, loss_spatial_bce_2: 0.10190/0.08841, loss_spatial_dice_2: 0.07516/0.18919, loss_spatial_ce_2: 0.00217/0.07481, loss_grounding_bce_2: 0.08931/0.08068, loss_grounding_dice_2: 0.07602/0.15192, loss_grounding_ce_2: 0.28306/0.25727, loss_mask_ce_3: 0.21792/0.78881, loss_mask_bce_3: 0.18210/0.30425, loss_mask_dice_3: 0.16287/1.03104, loss_spatial_bce_3: 0.10221/0.09013, loss_spatial_dice_3: 0.07678/0.18984, loss_spatial_ce_3: 0.00336/0.08016, loss_grounding_bce_3: 0.09217/0.08111, loss_grounding_dice_3: 0.08072/0.15152, loss_grounding_ce_3: 0.23692/0.25677, loss_mask_ce_4: 0.19640/0.79445, loss_mask_bce_4: 0.17584/0.30627, loss_mask_dice_4: 0.15389/1.04964, loss_spatial_bce_4: 0.13263/0.09205, loss_spatial_dice_4: 0.08607/0.19721, loss_spatial_ce_4: 0.00672/0.09263, loss_grounding_bce_4: 0.09348/0.08184, loss_grounding_dice_4: 0.07457/0.15406, loss_grounding_ce_4: 0.21726/0.26303, loss_mask_ce_5: 0.20252/0.81689, loss_mask_bce_5: 0.17415/0.30826, loss_mask_dice_5: 0.14741/1.05693, loss_spatial_bce_5: 0.13072/0.09382, loss_spatial_dice_5: 0.07488/0.19939, loss_spatial_ce_5: 0.01963/0.10392, loss_grounding_bce_5: 0.09709/0.08220, loss_grounding_dice_5: 0.07711/0.15478, loss_grounding_ce_5: 0.37959/0.28179, loss_mask_ce_6: 0.19042/0.84285, loss_mask_bce_6: 0.18083/0.30992, loss_mask_dice_6: 0.15966/1.06000, loss_spatial_bce_6: 0.13012/0.09859, loss_spatial_dice_6: 0.07993/0.20178, loss_spatial_ce_6: 0.06054/0.12531, loss_grounding_bce_6: 0.10214/0.08320, loss_grounding_dice_6: 0.08872/0.15546, loss_grounding_ce_6: 0.31210/0.29200, loss_mask_ce_7: 0.17739/0.90327, loss_mask_bce_7: 0.18674/0.31699, loss_mask_dice_7: 0.16479/1.10648, loss_spatial_bce_7: 0.22524/0.10909, loss_spatial_dice_7: 0.13669/0.22655, loss_spatial_ce_7: 0.11616/0.16771, loss_grounding_bce_7: 0.09651/0.08474, loss_grounding_dice_7: 0.08890/0.16110, loss_grounding_ce_7: 0.28221/0.33357, loss_mask_ce_8: 0.23182/1.03929, loss_mask_bce_8: 0.17046/0.33428, loss_mask_dice_8: 0.15709/1.18577, loss_spatial_bce_8: 0.11079/0.12925, loss_spatial_dice_8: 0.11022/0.26570, loss_spatial_ce_8: 0.07744/0.22146, loss_grounding_bce_8: 0.11562/0.08880, loss_grounding_dice_8: 0.10444/0.17056, loss_grounding_ce_8: 0.35445/0.43423, loss_mask_ce_9: 3.35277/3.49695, loss_mask_bce_9: 0.29686/0.36050, loss_mask_dice_9: 0.37101/1.77235, loss_spatial_bce_9: 0.49549/0.35776, loss_spatial_dice_9: 0.49029/0.79575, loss_spatial_ce_9: 0.56207/1.40583, loss_grounding_bce_9: 0.11854/0.10069, loss_grounding_dice_9: 0.24820/0.24431, loss_grounding_ce_9: 1.94912/0.69815] items per batch[64] items per second[0.36] total items[1734400] mini batches[ 27100] memory[4967] epoch remaining[0:09:03] INFO:trainer.default_trainer:epochs[ 14] optim steps[27200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26770/0.77871, loss_mask_bce_0: 0.19893/0.30199, loss_mask_dice_0: 0.57601/1.02953, loss_spatial_bce_0: 0.07494/0.08838, loss_spatial_dice_0: 0.23031/0.18656, loss_spatial_ce_0: 0.00435/0.06791, loss_grounding_bce_0: 0.09302/0.08051, loss_grounding_dice_0: 0.10885/0.15143, loss_grounding_ce_0: 1.15873/0.25311, loss_mask_ce_1: 1.28982/0.78082, loss_mask_bce_1: 0.21686/0.30275, loss_mask_dice_1: 0.64959/1.03379, loss_spatial_bce_1: 0.06818/0.08873, loss_spatial_dice_1: 0.21936/0.18911, loss_spatial_ce_1: 0.01124/0.07264, loss_grounding_bce_1: 0.09296/0.08078, loss_grounding_dice_1: 0.10737/0.15240, loss_grounding_ce_1: 1.28935/0.25480, loss_mask_ce_2: 1.34414/0.78838, loss_mask_bce_2: 0.21450/0.30282, loss_mask_dice_2: 0.65818/1.03590, loss_spatial_bce_2: 0.07787/0.08846, loss_spatial_dice_2: 0.22355/0.18921, loss_spatial_ce_2: 0.01247/0.07478, loss_grounding_bce_2: 0.09337/0.08069, loss_grounding_dice_2: 0.11325/0.15201, loss_grounding_ce_2: 1.06873/0.25715, loss_mask_ce_3: 1.28806/0.78915, loss_mask_bce_3: 0.20007/0.30437, loss_mask_dice_3: 0.58527/1.03159, loss_spatial_bce_3: 0.08384/0.09018, loss_spatial_dice_3: 0.22599/0.18986, loss_spatial_ce_3: 0.02226/0.08010, loss_grounding_bce_3: 0.08665/0.08113, loss_grounding_dice_3: 0.11546/0.15163, loss_grounding_ce_3: 1.07777/0.25660, loss_mask_ce_4: 1.22875/0.79482, loss_mask_bce_4: 0.20334/0.30640, loss_mask_dice_4: 0.72896/1.05030, loss_spatial_bce_4: 0.08548/0.09211, loss_spatial_dice_4: 0.21642/0.19724, loss_spatial_ce_4: 0.02549/0.09256, loss_grounding_bce_4: 0.09997/0.08185, loss_grounding_dice_4: 0.12517/0.15417, loss_grounding_ce_4: 1.16057/0.26284, loss_mask_ce_5: 1.14502/0.81723, loss_mask_bce_5: 0.19706/0.30836, loss_mask_dice_5: 0.64621/1.05743, loss_spatial_bce_5: 0.07864/0.09386, loss_spatial_dice_5: 0.22212/0.19941, loss_spatial_ce_5: 0.03718/0.10388, loss_grounding_bce_5: 0.09131/0.08222, loss_grounding_dice_5: 0.11989/0.15489, loss_grounding_ce_5: 1.01586/0.28166, loss_mask_ce_6: 1.38304/0.84323, loss_mask_bce_6: 0.20105/0.31002, loss_mask_dice_6: 0.73545/1.06063, loss_spatial_bce_6: 0.07951/0.09865, loss_spatial_dice_6: 0.21906/0.20180, loss_spatial_ce_6: 0.02589/0.12528, loss_grounding_bce_6: 0.07932/0.08321, loss_grounding_dice_6: 0.12469/0.15556, loss_grounding_ce_6: 1.09217/0.29183, loss_mask_ce_7: 1.67567/0.90358, loss_mask_bce_7: 0.23455/0.31714, loss_mask_dice_7: 0.80153/1.10707, loss_spatial_bce_7: 0.05289/0.10915, loss_spatial_dice_7: 0.21586/0.22656, loss_spatial_ce_7: 0.25812/0.16771, loss_grounding_bce_7: 0.08272/0.08475, loss_grounding_dice_7: 0.12108/0.16126, loss_grounding_ce_7: 1.68871/0.33347, loss_mask_ce_8: 2.06293/1.03961, loss_mask_bce_8: 0.29612/0.33440, loss_mask_dice_8: 0.95248/1.18639, loss_spatial_bce_8: 0.10821/0.12927, loss_spatial_dice_8: 0.22499/0.26572, loss_spatial_ce_8: 0.18793/0.22141, loss_grounding_bce_8: 0.10887/0.08881, loss_grounding_dice_8: 0.11955/0.17067, loss_grounding_ce_8: 0.62010/0.43402, loss_mask_ce_9: 4.35473/3.49734, loss_mask_bce_9: 0.45956/0.36065, loss_mask_dice_9: 1.92414/1.77309, loss_spatial_bce_9: 0.28839/0.35783, loss_spatial_dice_9: 0.86480/0.79573, loss_spatial_ce_9: 1.04528/1.40582, loss_grounding_bce_9: 0.15746/0.10070, loss_grounding_dice_9: 0.21665/0.24440, loss_grounding_ce_9: 2.55670/0.69774] items per batch[64] items per second[0.36] total items[1740800] mini batches[ 27200] memory[4967] epoch remaining[0:06:05] INFO:trainer.default_trainer:epochs[ 14] optim steps[27300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05388/0.77821, loss_mask_bce_0: 0.13705/0.30191, loss_mask_dice_0: 0.34255/1.02856, loss_spatial_bce_0: 0.03555/0.08842, loss_spatial_dice_0: 0.10165/0.18652, loss_spatial_ce_0: 0.00038/0.06788, loss_grounding_bce_0: 0.01192/0.08052, loss_grounding_dice_0: 0.07496/0.15140, loss_grounding_ce_0: 0.03282/0.25287, loss_mask_ce_1: 0.06303/0.78031, loss_mask_bce_1: 0.13649/0.30266, loss_mask_dice_1: 0.35296/1.03287, loss_spatial_bce_1: 0.03908/0.08877, loss_spatial_dice_1: 0.10881/0.18906, loss_spatial_ce_1: 0.00112/0.07259, loss_grounding_bce_1: 0.01021/0.08078, loss_grounding_dice_1: 0.05612/0.15238, loss_grounding_ce_1: 0.07458/0.25457, loss_mask_ce_2: 0.07663/0.78788, loss_mask_bce_2: 0.13504/0.30273, loss_mask_dice_2: 0.33480/1.03495, loss_spatial_bce_2: 0.04265/0.08850, loss_spatial_dice_2: 0.11554/0.18915, loss_spatial_ce_2: 0.00064/0.07475, loss_grounding_bce_2: 0.01442/0.08068, loss_grounding_dice_2: 0.06748/0.15200, loss_grounding_ce_2: 0.11066/0.25695, loss_mask_ce_3: 0.06857/0.78859, loss_mask_bce_3: 0.12805/0.30429, loss_mask_dice_3: 0.32327/1.03062, loss_spatial_bce_3: 0.04187/0.09022, loss_spatial_dice_3: 0.12029/0.18981, loss_spatial_ce_3: 0.00219/0.08010, loss_grounding_bce_3: 0.02019/0.08113, loss_grounding_dice_3: 0.09260/0.15163, loss_grounding_ce_3: 0.08409/0.25640, loss_mask_ce_4: 0.05331/0.79431, loss_mask_bce_4: 0.12525/0.30632, loss_mask_dice_4: 0.31977/1.04937, loss_spatial_bce_4: 0.04793/0.09214, loss_spatial_dice_4: 0.11247/0.19720, loss_spatial_ce_4: 0.00245/0.09249, loss_grounding_bce_4: 0.01502/0.08185, loss_grounding_dice_4: 0.06701/0.15417, loss_grounding_ce_4: 0.17571/0.26262, loss_mask_ce_5: 0.07613/0.81670, loss_mask_bce_5: 0.12746/0.30825, loss_mask_dice_5: 0.33053/1.05643, loss_spatial_bce_5: 0.05338/0.09389, loss_spatial_dice_5: 0.12049/0.19936, loss_spatial_ce_5: 0.00582/0.10384, loss_grounding_bce_5: 0.01278/0.08222, loss_grounding_dice_5: 0.06393/0.15487, loss_grounding_ce_5: 0.29505/0.28148, loss_mask_ce_6: 0.07347/0.84274, loss_mask_bce_6: 0.11947/0.30992, loss_mask_dice_6: 0.31298/1.05958, loss_spatial_bce_6: 0.06272/0.09867, loss_spatial_dice_6: 0.12797/0.20174, loss_spatial_ce_6: 0.03229/0.12523, loss_grounding_bce_6: 0.01386/0.08321, loss_grounding_dice_6: 0.07199/0.15555, loss_grounding_ce_6: 0.13123/0.29158, loss_mask_ce_7: 0.09899/0.90305, loss_mask_bce_7: 0.13343/0.31703, loss_mask_dice_7: 0.35339/1.10606, loss_spatial_bce_7: 0.06900/0.10915, loss_spatial_dice_7: 0.12310/0.22650, loss_spatial_ce_7: 0.03510/0.16768, loss_grounding_bce_7: 0.01449/0.08475, loss_grounding_dice_7: 0.06635/0.16124, loss_grounding_ce_7: 0.18938/0.33317, loss_mask_ce_8: 0.25910/1.03898, loss_mask_bce_8: 0.12636/0.33432, loss_mask_dice_8: 0.34752/1.18523, loss_spatial_bce_8: 0.05829/0.12934, loss_spatial_dice_8: 0.13172/0.26561, loss_spatial_ce_8: 0.06086/0.22132, loss_grounding_bce_8: 0.01976/0.08882, loss_grounding_dice_8: 0.08381/0.17065, loss_grounding_ce_8: 1.30613/0.43375, loss_mask_ce_9: 3.49628/3.49608, loss_mask_bce_9: 0.12156/0.36054, loss_mask_dice_9: 0.46705/1.77123, loss_spatial_bce_9: 0.18947/0.35795, loss_spatial_dice_9: 0.74996/0.79563, loss_spatial_ce_9: 1.01293/1.40605, loss_grounding_bce_9: 0.00776/0.10070, loss_grounding_dice_9: 0.08053/0.24434, loss_grounding_ce_9: 3.24494/0.69750] items per batch[64] items per second[0.36] total items[1747200] mini batches[ 27300] memory[4967] epoch remaining[0:03:06] INFO:trainer.default_trainer:epochs[ 14] optim steps[27400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59037/0.77781, loss_mask_bce_0: 0.42865/0.30190, loss_mask_dice_0: 3.35937/1.02811, loss_spatial_bce_0: 0.06598/0.08839, loss_spatial_dice_0: 0.29304/0.18648, loss_spatial_ce_0: 0.01718/0.06779, loss_grounding_bce_0: 0.19177/0.08053, loss_grounding_dice_0: 0.26108/0.15139, loss_grounding_ce_0: 0.00644/0.25268, loss_mask_ce_1: 0.57875/0.77989, loss_mask_bce_1: 0.42622/0.30265, loss_mask_dice_1: 3.23586/1.03237, loss_spatial_bce_1: 0.07109/0.08874, loss_spatial_dice_1: 0.28877/0.18902, loss_spatial_ce_1: 0.02437/0.07249, loss_grounding_bce_1: 0.18726/0.08078, loss_grounding_dice_1: 0.25013/0.15236, loss_grounding_ce_1: 0.00806/0.25435, loss_mask_ce_2: 0.62732/0.78753, loss_mask_bce_2: 0.42599/0.30270, loss_mask_dice_2: 3.34171/1.03432, loss_spatial_bce_2: 0.06975/0.08846, loss_spatial_dice_2: 0.28830/0.18911, loss_spatial_ce_2: 0.02521/0.07468, loss_grounding_bce_2: 0.19277/0.08069, loss_grounding_dice_2: 0.24675/0.15198, loss_grounding_ce_2: 0.00791/0.25676, loss_mask_ce_3: 0.67959/0.78827, loss_mask_bce_3: 0.41677/0.30426, loss_mask_dice_3: 2.94448/1.03002, loss_spatial_bce_3: 0.07063/0.09018, loss_spatial_dice_3: 0.33029/0.18975, loss_spatial_ce_3: 0.03402/0.08004, loss_grounding_bce_3: 0.19302/0.08114, loss_grounding_dice_3: 0.23070/0.15160, loss_grounding_ce_3: 0.00750/0.25618, loss_mask_ce_4: 0.71686/0.79391, loss_mask_bce_4: 0.44073/0.30629, loss_mask_dice_4: 2.78160/1.04888, loss_spatial_bce_4: 0.06655/0.09211, loss_spatial_dice_4: 0.30465/0.19715, loss_spatial_ce_4: 0.03288/0.09246, loss_grounding_bce_4: 0.19162/0.08186, loss_grounding_dice_4: 0.24970/0.15415, loss_grounding_ce_4: 0.00748/0.26240, loss_mask_ce_5: 0.68130/0.81645, loss_mask_bce_5: 0.42871/0.30817, loss_mask_dice_5: 3.14045/1.05586, loss_spatial_bce_5: 0.06406/0.09387, loss_spatial_dice_5: 0.29566/0.19933, loss_spatial_ce_5: 0.06376/0.10380, loss_grounding_bce_5: 0.20413/0.08223, loss_grounding_dice_5: 0.25222/0.15487, loss_grounding_ce_5: 0.01315/0.28122, loss_mask_ce_6: 0.64031/0.84250, loss_mask_bce_6: 0.44659/0.30985, loss_mask_dice_6: 3.43538/1.05904, loss_spatial_bce_6: 0.07152/0.09865, loss_spatial_dice_6: 0.30040/0.20169, loss_spatial_ce_6: 0.09829/0.12522, loss_grounding_bce_6: 0.20352/0.08322, loss_grounding_dice_6: 0.24127/0.15553, loss_grounding_ce_6: 0.01239/0.29136, loss_mask_ce_7: 0.70811/0.90258, loss_mask_bce_7: 0.43388/0.31700, loss_mask_dice_7: 3.42711/1.10543, loss_spatial_bce_7: 0.09379/0.10915, loss_spatial_dice_7: 0.29819/0.22647, loss_spatial_ce_7: 0.13147/0.16756, loss_grounding_bce_7: 0.20046/0.08475, loss_grounding_dice_7: 0.25775/0.16121, loss_grounding_ce_7: 0.00956/0.33283, loss_mask_ce_8: 0.91908/1.03858, loss_mask_bce_8: 0.49221/0.33425, loss_mask_dice_8: 3.52002/1.18459, loss_spatial_bce_8: 0.09871/0.12933, loss_spatial_dice_8: 0.39484/0.26557, loss_spatial_ce_8: 0.23579/0.22126, loss_grounding_bce_8: 0.24482/0.08882, loss_grounding_dice_8: 0.26294/0.17063, loss_grounding_ce_8: 0.00772/0.43342, loss_mask_ce_9: 5.20594/3.49540, loss_mask_bce_9: 0.48251/0.36047, loss_mask_dice_9: 4.03380/1.76996, loss_spatial_bce_9: 0.25692/0.35795, loss_spatial_dice_9: 0.93390/0.79560, loss_spatial_ce_9: 1.12631/1.40594, loss_grounding_bce_9: 0.22583/0.10073, loss_grounding_dice_9: 0.27320/0.24433, loss_grounding_ce_9: 0.04874/0.69725] items per batch[64] items per second[0.36] total items[1753600] mini batches[ 27400] memory[4967] epoch remaining[0:00:08] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00027405. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0029 s/iter. Inference: 0.3704 s/iter. Eval: 0.0937 s/iter. Total: 0.4670 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0026 s/iter. Inference: 0.3706 s/iter. Eval: 0.0789 s/iter. Total: 0.4521 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0027 s/iter. Inference: 0.3728 s/iter. Eval: 0.0742 s/iter. Total: 0.4498 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3767 s/iter. Eval: 0.0737 s/iter. Total: 0.4532 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3776 s/iter. Eval: 0.0746 s/iter. Total: 0.4551 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3748 s/iter. Eval: 0.0737 s/iter. Total: 0.4515 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaljuwob06u ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.372 | 83.026 | 65.904 | 133 | | Things | 61.747 | 83.937 | 73.058 | 80 | | Stuff | 45.749 | 81.651 | 55.104 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.57s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.16 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.81s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.11 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.695 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.380 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.763 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.395 | 69.471 | 48.799 | 26.230 | 49.525 | 67.665 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.018 | bicycle | 22.974 | car | 42.007 | | motorcycle | 41.165 | airplane | 60.848 | bus | 70.603 | | train | 74.041 | truck | 42.561 | boat | 29.165 | | traffic light | 28.430 | fire hydrant | 70.980 | stop sign | 67.800 | | parking meter | 51.641 | bench | 25.845 | bird | 33.507 | | cat | 77.203 | dog | 70.285 | horse | 49.174 | | sheep | 52.872 | cow | 56.348 | elephant | 65.353 | | bear | 79.567 | zebra | 66.356 | giraffe | 62.383 | | backpack | 23.805 | umbrella | 54.972 | handbag | 24.778 | | tie | 39.517 | suitcase | 52.478 | frisbee | 70.223 | | skis | 8.580 | snowboard | 34.999 | sports ball | 49.581 | | kite | 37.198 | baseball bat | 38.285 | baseball glove | 50.543 | | skateboard | 44.247 | surfboard | 45.333 | tennis racket | 62.674 | | bottle | 41.875 | wine glass | 36.710 | cup | 50.331 | | fork | 26.838 | knife | 24.547 | spoon | 23.294 | | bowl | 37.073 | banana | 22.460 | apple | 25.751 | | sandwich | 51.024 | orange | 30.522 | broccoli | 24.142 | | carrot | 23.530 | hot dog | 33.719 | pizza | 52.915 | | donut | 55.772 | cake | 46.717 | chair | 26.925 | | couch | 41.904 | potted plant | 22.267 | bed | 44.780 | | dining table | 14.307 | toilet | 68.440 | tv | 67.551 | | laptop | 70.143 | mouse | 64.228 | remote | 42.793 | | keyboard | 59.313 | cell phone | 44.401 | microwave | 62.874 | | oven | 32.058 | toaster | 52.083 | sink | 45.391 | | refrigerator | 68.633 | book | 14.368 | clock | 52.745 | | vase | 41.621 | scissors | 37.243 | teddy bear | 57.624 | | hair drier | 36.984 | toothbrush | 29.379 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.88667431042988, 'fwIoU': 71.60050115053899, 'IoU-person': 87.93376647042209, 'IoU-bicycle': 78.31446312230969, 'IoU-car': 71.44195806344055, 'IoU-motorcycle': 88.50439232467036, 'IoU-airplane': 84.85528797921825, 'IoU-bus': 87.88518959713792, 'IoU-train': 87.1482903389276, 'IoU-truck': 68.09886774813972, 'IoU-boat': 72.25152671981317, 'IoU-traffic light': 79.46256325987979, 'IoU-fire hydrant': 93.42952332801825, 'IoU-stop sign': 94.25651051124765, 'IoU-parking meter': 84.6090874934373, 'IoU-bench': 60.938325287227954, 'IoU-bird': 77.07192153741016, 'IoU-cat': 91.97818993962133, 'IoU-dog': 82.79884676372316, 'IoU-horse': 88.3138136949134, 'IoU-sheep': 90.26254368029174, 'IoU-cow': 90.1809938096359, 'IoU-elephant': 91.56758261207924, 'IoU-bear': 80.30127907462831, 'IoU-zebra': 79.93673057201653, 'IoU-giraffe': 89.59756567599041, 'IoU-backpack': 54.850817381826545, 'IoU-umbrella': 84.5401569608007, 'IoU-handbag': 49.33292375664609, 'IoU-tie': 72.8982217783202, 'IoU-suitcase': 86.94483543677808, 'IoU-frisbee': 84.45820554394328, 'IoU-skis': 59.18973902968082, 'IoU-snowboard': 75.36628005005014, 'IoU-sports ball': 77.05093662668509, 'IoU-kite': 79.22825512242827, 'IoU-baseball bat': 67.893374832735, 'IoU-baseball glove': 77.6672873499465, 'IoU-skateboard': 86.40592749742797, 'IoU-surfboard': 86.93366904448025, 'IoU-tennis racket': 91.05374319401767, 'IoU-bottle': 70.26125199499326, 'IoU-wine glass': 81.97728534570038, 'IoU-cup': 69.67027373834303, 'IoU-fork': 69.29943800583389, 'IoU-knife': 65.4038300158779, 'IoU-spoon': 61.03692449007839, 'IoU-bowl': 61.754888697319544, 'IoU-banana': 82.9686945378518, 'IoU-apple': 58.030692551721174, 'IoU-sandwich': 70.03364970894447, 'IoU-orange': 79.34709033451377, 'IoU-broccoli': 70.2623651651508, 'IoU-carrot': 64.73575847377808, 'IoU-hot dog': 63.784756609474215, 'IoU-pizza': 82.67041649188629, 'IoU-donut': 64.96862207235974, 'IoU-cake': 80.10009674993316, 'IoU-chair': 61.18617564205273, 'IoU-couch': 65.58640132389532, 'IoU-potted plant': 43.11377529978803, 'IoU-bed': 71.2214619931945, 'IoU-dining table': 55.176055547026735, 'IoU-toilet': 88.13802545311931, 'IoU-tv': 81.53744178931983, 'IoU-laptop': 78.43935731487225, 'IoU-mouse': 76.30249209147328, 'IoU-remote': 67.91039282880908, 'IoU-keyboard': 66.67337118809277, 'IoU-cell phone': 76.47443527989961, 'IoU-microwave': 70.88693565778269, 'IoU-oven': 72.45401423691189, 'IoU-toaster': 86.1890737076827, 'IoU-sink': 74.81196487313049, 'IoU-refrigerator': 82.00573920631878, 'IoU-book': 56.284059555094366, 'IoU-clock': 69.10898019945844, 'IoU-vase': 61.68241283443056, 'IoU-scissors': 87.61260167733207, 'IoU-teddy bear': 82.06579660232329, 'IoU-hair drier': 50.18288308419678, 'IoU-toothbrush': 76.58150851581509, 'IoU-banner': 31.76748318507888, 'IoU-blanket': 16.47348499524393, 'IoU-bridge': 36.81398838288795, 'IoU-cardboard': 56.19545424308012, 'IoU-counter': 33.334233502170164, 'IoU-curtain': 71.55844434921875, 'IoU-door-stuff': 47.625666939183425, 'IoU-floor-wood': 66.87702219382393, 'IoU-flower': 45.3480874907132, 'IoU-fruit': 47.3529666354548, 'IoU-gravel': 29.052157059076716, 'IoU-house': 25.16210979286629, 'IoU-light': 43.76538318227857, 'IoU-mirror-stuff': 63.937452240988215, 'IoU-net': 48.33696528796459, 'IoU-pillow': 21.376059234470617, 'IoU-platform': 28.446522258442798, 'IoU-playingfield': 69.82798613958863, 'IoU-railroad': 63.961013082300944, 'IoU-river': 53.89440223877079, 'IoU-road': 68.36405443603266, 'IoU-roof': 19.73127222531363, 'IoU-sand': 64.67330023372101, 'IoU-sea': 86.48148176812056, 'IoU-shelf': 37.540129556871406, 'IoU-snow': 92.27748203086816, 'IoU-stairs': 36.514939989729484, 'IoU-tent': 10.138007843879604, 'IoU-towel': 46.86603558223968, 'IoU-wall-brick': 52.17362317771054, 'IoU-wall-stone': 29.186196987734142, 'IoU-wall-tile': 72.59156052080078, 'IoU-wall-wood': 43.57450585816916, 'IoU-water-other': 28.10835786363454, 'IoU-window-blind': 51.30551456581382, 'IoU-window-other': 50.02125893836487, 'IoU-tree-merged': 81.63821828034727, 'IoU-fence-merged': 54.48357508755279, 'IoU-ceiling-merged': 67.96930565093326, 'IoU-sky-other-merged': 93.88149342042816, 'IoU-cabinet-merged': 63.83567694969023, 'IoU-table-merged': 40.443883043981025, 'IoU-floor-other-merged': 54.79378961747865, 'IoU-pavement-merged': 58.150917593022925, 'IoU-mountain-merged': 59.12942548879763, 'IoU-grass-merged': 71.79346419377435, 'IoU-dirt-merged': 47.379723682436186, 'IoU-paper-merged': 38.27411958467199, 'IoU-food-other-merged': 42.862689329745216, 'IoU-building-other-merged': 60.47769312013522, 'IoU-rock-merged': 67.1472587821387, 'IoU-wall-other-merged': 67.20291965147777, 'IoU-rug-merged': 69.92394170220815, 'mACC': 77.22017658760807, 'pACC': 82.32994962552799, 'ACC-person': 93.1021287327858, 'ACC-bicycle': 87.97669830666864, 'ACC-car': 86.20748393669494, 'ACC-motorcycle': 93.06733638621569, 'ACC-airplane': 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'ACC-mouse': 91.18787629550266, 'ACC-remote': 72.18824306760041, 'ACC-keyboard': 70.52367836633428, 'ACC-cell phone': 86.00903279060313, 'ACC-microwave': 74.81362569991815, 'ACC-oven': 86.19246708994874, 'ACC-toaster': 91.51418522218725, 'ACC-sink': 84.49688677508914, 'ACC-refrigerator': 91.8093056548653, 'ACC-book': 74.8082149561514, 'ACC-clock': 74.0682990277626, 'ACC-vase': 69.60348972859627, 'ACC-scissors': 93.19925350521996, 'ACC-teddy bear': 87.2327901410809, 'ACC-hair drier': 60.724959439842884, 'ACC-toothbrush': 85.30403057678943, 'ACC-banner': 75.13556660489328, 'ACC-blanket': 21.175002236008684, 'ACC-bridge': 53.927399611958194, 'ACC-cardboard': 71.00063981387233, 'ACC-counter': 59.82915631349882, 'ACC-curtain': 81.55407547960496, 'ACC-door-stuff': 69.8675581969393, 'ACC-floor-wood': 83.80970067707537, 'ACC-flower': 62.61252689373303, 'ACC-fruit': 69.26435958912226, 'ACC-gravel': 35.12491533262619, 'ACC-house': 30.321541174704265, 'ACC-light': 59.85709432301037, 'ACC-mirror-stuff': 75.39479717367263, 'ACC-net': 61.98733851675967, 'ACC-pillow': 45.0583828106644, 'ACC-platform': 45.42739436795456, 'ACC-playingfield': 88.26600202805105, 'ACC-railroad': 80.13126811386783, 'ACC-river': 83.93197894098518, 'ACC-road': 85.93067919958341, 'ACC-roof': 26.930818532598288, 'ACC-sand': 69.12876207515612, 'ACC-sea': 91.42065179274654, 'ACC-shelf': 50.77168942184146, 'ACC-snow': 95.99024076695727, 'ACC-stairs': 59.92260543083231, 'ACC-tent': 13.214678538419394, 'ACC-towel': 53.29545844323009, 'ACC-wall-brick': 71.39045855200084, 'ACC-wall-stone': 36.88722851441942, 'ACC-wall-tile': 85.7080445093336, 'ACC-wall-wood': 63.58209058335929, 'ACC-water-other': 38.90279492680993, 'ACC-window-blind': 63.825651039635446, 'ACC-window-other': 72.87461430381897, 'ACC-tree-merged': 89.64234382251846, 'ACC-fence-merged': 74.93872957058477, 'ACC-ceiling-merged': 82.4276176067903, 'ACC-sky-other-merged': 97.20949929327418, 'ACC-cabinet-merged': 79.04875857344922, 'ACC-table-merged': 52.80314664346797, 'ACC-floor-other-merged': 64.30493355867465, 'ACC-pavement-merged': 71.08545408551026, 'ACC-mountain-merged': 71.65867959144984, 'ACC-grass-merged': 83.997198842178, 'ACC-dirt-merged': 71.41695110303512, 'ACC-paper-merged': 50.28137242034726, 'ACC-food-other-merged': 56.96190879448747, 'ACC-building-other-merged': 74.02419046061674, 'ACC-rock-merged': 84.21236363812366, 'ACC-wall-other-merged': 82.41834567561665, 'ACC-rug-merged': 83.87075127891445})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3148 s/iter. Inference: 0.1852 s/iter. Eval: 0.0000 s/iter. Total: 0.5001 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3352 s/iter. Inference: 0.3386 s/iter. Eval: 0.0000 s/iter. Total: 0.6739 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3582 s/iter. Inference: 0.5433 s/iter. Eval: 0.0000 s/iter. Total: 0.9017 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4187884108867428, 'noc@0.8': 2.5320456540825287, 'noc@0.85': 2.974246414983904, 'noc@0.9': 3.810652619256658, 'miou@iter1': 0.8667617186071024} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1411 s/iter. Eval: 0.0010 s/iter. Total: 0.1434 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.12631225585938, 'precision@0.6': 72.56121063232422, 'precision@0.7': 68.20832061767578, 'precision@0.8': 58.336570739746094, 'precision@0.9': 32.219200134277344, 'cIoU': 62.21886444091797, 'mIoU': 66.66082763671875} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.371860257175165, 'SQ': 83.02570827480295, 'RQ': 65.90373989364728, 'PQ_th': 61.746745418509676, 'SQ_th': 83.93662885927583, 'RQ_th': 73.05847883954905, 'PQ_st': 45.74939208912309, 'SQ_st': 81.65073380767404, 'RQ_st': 55.104133937569266}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-mouse': 91.18787629550266, 'ACC-remote': 72.18824306760041, 'ACC-keyboard': 70.52367836633428, 'ACC-cell phone': 86.00903279060313, 'ACC-microwave': 74.81362569991815, 'ACC-oven': 86.19246708994874, 'ACC-toaster': 91.51418522218725, 'ACC-sink': 84.49688677508914, 'ACC-refrigerator': 91.8093056548653, 'ACC-book': 74.8082149561514, 'ACC-clock': 74.0682990277626, 'ACC-vase': 69.60348972859627, 'ACC-scissors': 93.19925350521996, 'ACC-teddy bear': 87.2327901410809, 'ACC-hair drier': 60.724959439842884, 'ACC-toothbrush': 85.30403057678943, 'ACC-banner': 75.13556660489328, 'ACC-blanket': 21.175002236008684, 'ACC-bridge': 53.927399611958194, 'ACC-cardboard': 71.00063981387233, 'ACC-counter': 59.82915631349882, 'ACC-curtain': 81.55407547960496, 'ACC-door-stuff': 69.8675581969393, 'ACC-floor-wood': 83.80970067707537, 'ACC-flower': 62.61252689373303, 'ACC-fruit': 69.26435958912226, 'ACC-gravel': 35.12491533262619, 'ACC-house': 30.321541174704265, 'ACC-light': 59.85709432301037, 'ACC-mirror-stuff': 75.39479717367263, 'ACC-net': 61.98733851675967, 'ACC-pillow': 45.0583828106644, 'ACC-platform': 45.42739436795456, 'ACC-playingfield': 88.26600202805105, 'ACC-railroad': 80.13126811386783, 'ACC-river': 83.93197894098518, 'ACC-road': 85.93067919958341, 'ACC-roof': 26.930818532598288, 'ACC-sand': 69.12876207515612, 'ACC-sea': 91.42065179274654, 'ACC-shelf': 50.77168942184146, 'ACC-snow': 95.99024076695727, 'ACC-stairs': 59.92260543083231, 'ACC-tent': 13.214678538419394, 'ACC-towel': 53.29545844323009, 'ACC-wall-brick': 71.39045855200084, 'ACC-wall-stone': 36.88722851441942, 'ACC-wall-tile': 85.7080445093336, 'ACC-wall-wood': 63.58209058335929, 'ACC-water-other': 38.90279492680993, 'ACC-window-blind': 63.825651039635446, 'ACC-window-other': 72.87461430381897, 'ACC-tree-merged': 89.64234382251846, 'ACC-fence-merged': 74.93872957058477, 'ACC-ceiling-merged': 82.4276176067903, 'ACC-sky-other-merged': 97.20949929327418, 'ACC-cabinet-merged': 79.04875857344922, 'ACC-table-merged': 52.80314664346797, 'ACC-floor-other-merged': 64.30493355867465, 'ACC-pavement-merged': 71.08545408551026, 'ACC-mountain-merged': 71.65867959144984, 'ACC-grass-merged': 83.997198842178, 'ACC-dirt-merged': 71.41695110303512, 'ACC-paper-merged': 50.28137242034726, 'ACC-food-other-merged': 56.96190879448747, 'ACC-building-other-merged': 74.02419046061674, 'ACC-rock-merged': 84.21236363812366, 'ACC-wall-other-merged': 82.41834567561665, 'ACC-rug-merged': 83.87075127891445})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4187884108867428, 'noc@0.8': 2.5320456540825287, 'noc@0.85': 2.974246414983904, 'noc@0.9': 3.810652619256658, 'miou@iter1': 0.8667617186071024}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.12631225585938, 'precision@0.6': 72.56121063232422, 'precision@0.7': 68.20832061767578, 'precision@0.8': 58.336570739746094, 'precision@0.9': 32.219200134277344, 'cIoU': 62.21886444091797, 'mIoU': 66.66082763671875}}} INFO:trainer.default_trainer:This epoch takes 0:57:38.891682 INFO:trainer.default_trainer:PROGRESS: 30.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 15 training. INFO:trainer.default_trainer:epochs[ 15] optim steps[27500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27643/0.77768, loss_mask_bce_0: 0.64896/0.30198, loss_mask_dice_0: 0.44955/1.02779, loss_spatial_bce_0: 0.16097/0.08841, loss_spatial_dice_0: 0.23370/0.18643, loss_spatial_ce_0: 0.28883/0.06777, loss_grounding_bce_0: 0.03070/0.08065, loss_grounding_dice_0: 0.04556/0.15142, loss_grounding_ce_0: 0.00248/0.25246, loss_mask_ce_1: 0.28434/0.77982, loss_mask_bce_1: 0.63491/0.30274, loss_mask_dice_1: 0.43905/1.03208, loss_spatial_bce_1: 0.15613/0.08876, loss_spatial_dice_1: 0.23031/0.18896, loss_spatial_ce_1: 0.31294/0.07247, loss_grounding_bce_1: 0.03195/0.08090, loss_grounding_dice_1: 0.04766/0.15237, loss_grounding_ce_1: 0.00204/0.25410, loss_mask_ce_2: 0.26949/0.78747, loss_mask_bce_2: 0.64240/0.30279, loss_mask_dice_2: 0.44168/1.03405, loss_spatial_bce_2: 0.16713/0.08848, loss_spatial_dice_2: 0.24279/0.18906, loss_spatial_ce_2: 0.33448/0.07466, loss_grounding_bce_2: 0.03142/0.08081, loss_grounding_dice_2: 0.04966/0.15200, loss_grounding_ce_2: 0.00215/0.25647, loss_mask_ce_3: 0.26531/0.78820, loss_mask_bce_3: 0.63189/0.30435, loss_mask_dice_3: 0.43548/1.02963, loss_spatial_bce_3: 0.16226/0.09020, loss_spatial_dice_3: 0.25455/0.18971, loss_spatial_ce_3: 0.34572/0.08002, loss_grounding_bce_3: 0.03201/0.08127, loss_grounding_dice_3: 0.04302/0.15162, loss_grounding_ce_3: 0.00173/0.25594, loss_mask_ce_4: 0.25352/0.79387, loss_mask_bce_4: 0.64211/0.30638, loss_mask_dice_4: 0.41703/1.04847, loss_spatial_bce_4: 0.19343/0.09212, loss_spatial_dice_4: 0.24976/0.19710, loss_spatial_ce_4: 0.29699/0.09243, loss_grounding_bce_4: 0.03219/0.08199, loss_grounding_dice_4: 0.04285/0.15418, loss_grounding_ce_4: 0.00100/0.26210, loss_mask_ce_5: 0.25973/0.81638, loss_mask_bce_5: 0.64817/0.30827, loss_mask_dice_5: 0.44324/1.05551, loss_spatial_bce_5: 0.20971/0.09388, loss_spatial_dice_5: 0.26698/0.19929, loss_spatial_ce_5: 0.44028/0.10376, loss_grounding_bce_5: 0.03203/0.08234, loss_grounding_dice_5: 0.04382/0.15488, loss_grounding_ce_5: 0.00092/0.28099, loss_mask_ce_6: 0.78951/0.84246, loss_mask_bce_6: 0.49295/0.30993, loss_mask_dice_6: 0.34534/1.05863, loss_spatial_bce_6: 0.19315/0.09865, loss_spatial_dice_6: 0.25030/0.20164, loss_spatial_ce_6: 0.45711/0.12519, loss_grounding_bce_6: 0.03090/0.08335, loss_grounding_dice_6: 0.04690/0.15553, loss_grounding_ce_6: 0.00065/0.29113, loss_mask_ce_7: 0.29463/0.90250, loss_mask_bce_7: 0.68779/0.31705, loss_mask_dice_7: 0.45864/1.10497, loss_spatial_bce_7: 0.20020/0.10915, loss_spatial_dice_7: 0.28217/0.22640, loss_spatial_ce_7: 0.48188/0.16756, loss_grounding_bce_7: 0.03414/0.08487, loss_grounding_dice_7: 0.03976/0.16122, loss_grounding_ce_7: 0.00244/0.33258, loss_mask_ce_8: 0.39554/1.03843, loss_mask_bce_8: 0.61612/0.33429, loss_mask_dice_8: 0.44404/1.18425, loss_spatial_bce_8: 0.23877/0.12935, loss_spatial_dice_8: 0.28657/0.26549, loss_spatial_ce_8: 0.28195/0.22118, loss_grounding_bce_8: 0.03677/0.08891, loss_grounding_dice_8: 0.04650/0.17067, loss_grounding_ce_8: 0.00629/0.43306, loss_mask_ce_9: 2.11316/3.49501, loss_mask_bce_9: 0.67882/0.36054, loss_mask_dice_9: 0.56175/1.76946, loss_spatial_bce_9: 0.53525/0.35815, loss_spatial_dice_9: 0.60798/0.79552, loss_spatial_ce_9: 1.01661/1.40561, loss_grounding_bce_9: 0.06244/0.10080, loss_grounding_dice_9: 0.18396/0.24434, loss_grounding_ce_9: 0.04050/0.69658] items per batch[64] items per second[0.16] total items[1760000] mini batches[ 27500] memory[4967] epoch remaining[0:53:46] INFO:trainer.default_trainer:epochs[ 15] optim steps[27600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66979/0.77758, loss_mask_bce_0: 0.32588/0.30207, loss_mask_dice_0: 0.62260/1.02817, loss_spatial_bce_0: 0.30522/0.08838, loss_spatial_dice_0: 0.21414/0.18640, loss_spatial_ce_0: 0.09598/0.06771, loss_grounding_bce_0: 0.04300/0.08060, loss_grounding_dice_0: 0.13750/0.15142, loss_grounding_ce_0: 0.31944/0.25252, loss_mask_ce_1: 0.73024/0.77975, loss_mask_bce_1: 0.32162/0.30283, loss_mask_dice_1: 0.58936/1.03248, loss_spatial_bce_1: 0.25011/0.08872, loss_spatial_dice_1: 0.23377/0.18893, loss_spatial_ce_1: 0.08614/0.07243, loss_grounding_bce_1: 0.04196/0.08086, loss_grounding_dice_1: 0.14225/0.15238, loss_grounding_ce_1: 0.31753/0.25419, loss_mask_ce_2: 1.07544/0.78742, loss_mask_bce_2: 0.30767/0.30287, loss_mask_dice_2: 0.47039/1.03439, loss_spatial_bce_2: 0.27686/0.08845, loss_spatial_dice_2: 0.20583/0.18902, loss_spatial_ce_2: 0.05885/0.07460, loss_grounding_bce_2: 0.04058/0.08077, loss_grounding_dice_2: 0.11704/0.15201, loss_grounding_ce_2: 0.34571/0.25652, loss_mask_ce_3: 0.74256/0.78817, loss_mask_bce_3: 0.32296/0.30443, loss_mask_dice_3: 0.73016/1.02996, loss_spatial_bce_3: 0.31340/0.09017, loss_spatial_dice_3: 0.20999/0.18969, loss_spatial_ce_3: 0.06287/0.07996, loss_grounding_bce_3: 0.04010/0.08122, loss_grounding_dice_3: 0.11299/0.15163, loss_grounding_ce_3: 0.34882/0.25597, loss_mask_ce_4: 0.91383/0.79379, loss_mask_bce_4: 0.31536/0.30649, loss_mask_dice_4: 0.41219/1.04882, loss_spatial_bce_4: 0.29149/0.09210, loss_spatial_dice_4: 0.23373/0.19709, loss_spatial_ce_4: 0.12349/0.09237, loss_grounding_bce_4: 0.03978/0.08194, loss_grounding_dice_4: 0.12044/0.15419, loss_grounding_ce_4: 0.33876/0.26220, loss_mask_ce_5: 0.69666/0.81638, loss_mask_bce_5: 0.30296/0.30837, loss_mask_dice_5: 0.63084/1.05599, loss_spatial_bce_5: 0.29114/0.09387, loss_spatial_dice_5: 0.23164/0.19930, loss_spatial_ce_5: 0.11265/0.10378, loss_grounding_bce_5: 0.04043/0.08229, loss_grounding_dice_5: 0.11687/0.15486, loss_grounding_ce_5: 0.32969/0.28115, loss_mask_ce_6: 0.99958/0.84254, loss_mask_bce_6: 0.31047/0.31002, loss_mask_dice_6: 0.43560/1.05901, loss_spatial_bce_6: 0.35534/0.09864, loss_spatial_dice_6: 0.23712/0.20164, loss_spatial_ce_6: 0.18338/0.12519, loss_grounding_bce_6: 0.04323/0.08329, loss_grounding_dice_6: 0.09440/0.15552, loss_grounding_ce_6: 0.34678/0.29123, loss_mask_ce_7: 0.73686/0.90246, loss_mask_bce_7: 0.32221/0.31715, loss_mask_dice_7: 0.61188/1.10533, loss_spatial_bce_7: 0.18972/0.10911, loss_spatial_dice_7: 0.20917/0.22640, loss_spatial_ce_7: 0.29035/0.16749, loss_grounding_bce_7: 0.03913/0.08482, loss_grounding_dice_7: 0.09918/0.16121, loss_grounding_ce_7: 0.38003/0.33260, loss_mask_ce_8: 0.78950/1.03842, loss_mask_bce_8: 0.29968/0.33433, loss_mask_dice_8: 0.62056/1.18448, loss_spatial_bce_8: 0.16806/0.12931, loss_spatial_dice_8: 0.20949/0.26548, loss_spatial_ce_8: 0.41593/0.22110, loss_grounding_bce_8: 0.03972/0.08885, loss_grounding_dice_8: 0.11985/0.17067, loss_grounding_ce_8: 0.38140/0.43296, loss_mask_ce_9: 2.08141/3.49517, loss_mask_bce_9: 0.68316/0.36068, loss_mask_dice_9: 0.93016/1.76977, loss_spatial_bce_9: 0.39259/0.35807, loss_spatial_dice_9: 0.71248/0.79552, loss_spatial_ce_9: 1.09823/1.40576, loss_grounding_bce_9: 0.04034/0.10077, loss_grounding_dice_9: 0.16920/0.24437, loss_grounding_ce_9: 0.42367/0.69665] items per batch[64] items per second[0.36] total items[1766400] mini batches[ 27600] memory[4967] epoch remaining[0:49:33] INFO:trainer.default_trainer:epochs[ 15] optim steps[27700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66465/0.77705, loss_mask_bce_0: 0.14969/0.30203, loss_mask_dice_0: 0.76544/1.02769, loss_spatial_bce_0: 0.01282/0.08838, loss_spatial_dice_0: 0.11573/0.18634, loss_spatial_ce_0: 0.01453/0.06773, loss_grounding_bce_0: 0.01452/0.08059, loss_grounding_dice_0: 0.07807/0.15141, loss_grounding_ce_0: 0.24268/0.25226, loss_mask_ce_1: 0.38190/0.77919, loss_mask_bce_1: 0.17476/0.30279, loss_mask_dice_1: 0.92803/1.03197, loss_spatial_bce_1: 0.01357/0.08873, loss_spatial_dice_1: 0.12270/0.18888, loss_spatial_ce_1: 0.01815/0.07242, loss_grounding_bce_1: 0.01761/0.08085, loss_grounding_dice_1: 0.08120/0.15236, loss_grounding_ce_1: 0.24893/0.25395, loss_mask_ce_2: 0.50794/0.78682, loss_mask_bce_2: 0.15590/0.30285, loss_mask_dice_2: 0.69491/1.03389, loss_spatial_bce_2: 0.01372/0.08846, loss_spatial_dice_2: 0.14185/0.18898, loss_spatial_ce_2: 0.02271/0.07462, loss_grounding_bce_2: 0.01961/0.08076, loss_grounding_dice_2: 0.07984/0.15202, loss_grounding_ce_2: 0.22929/0.25626, loss_mask_ce_3: 0.43466/0.78758, loss_mask_bce_3: 0.15354/0.30441, loss_mask_dice_3: 0.74442/1.02951, loss_spatial_bce_3: 0.01299/0.09016, loss_spatial_dice_3: 0.13017/0.18965, loss_spatial_ce_3: 0.04905/0.07998, loss_grounding_bce_3: 0.01385/0.08121, loss_grounding_dice_3: 0.06753/0.15164, loss_grounding_ce_3: 0.23808/0.25574, loss_mask_ce_4: 0.62640/0.79324, loss_mask_bce_4: 0.17256/0.30646, loss_mask_dice_4: 1.29601/1.04836, loss_spatial_bce_4: 0.01231/0.09211, loss_spatial_dice_4: 0.13131/0.19705, loss_spatial_ce_4: 0.04921/0.09245, loss_grounding_bce_4: 0.01671/0.08193, loss_grounding_dice_4: 0.07868/0.15419, loss_grounding_ce_4: 0.25705/0.26200, loss_mask_ce_5: 0.61579/0.81575, loss_mask_bce_5: 0.15015/0.30830, loss_mask_dice_5: 0.81845/1.05552, loss_spatial_bce_5: 0.01709/0.09388, loss_spatial_dice_5: 0.17063/0.19928, loss_spatial_ce_5: 0.04976/0.10383, loss_grounding_bce_5: 0.01511/0.08228, loss_grounding_dice_5: 0.08528/0.15485, loss_grounding_ce_5: 0.25093/0.28100, loss_mask_ce_6: 0.52404/0.84188, loss_mask_bce_6: 0.16709/0.30999, loss_mask_dice_6: 0.88767/1.05859, loss_spatial_bce_6: 0.01525/0.09865, loss_spatial_dice_6: 0.15157/0.20160, loss_spatial_ce_6: 0.06851/0.12520, loss_grounding_bce_6: 0.01971/0.08327, loss_grounding_dice_6: 0.10019/0.15553, loss_grounding_ce_6: 0.19958/0.29116, loss_mask_ce_7: 0.73751/0.90176, loss_mask_bce_7: 0.14834/0.31712, loss_mask_dice_7: 0.73765/1.10484, loss_spatial_bce_7: 0.03358/0.10911, loss_spatial_dice_7: 0.23525/0.22638, loss_spatial_ce_7: 0.06222/0.16751, loss_grounding_bce_7: 0.01444/0.08480, loss_grounding_dice_7: 0.07331/0.16121, loss_grounding_ce_7: 0.19742/0.33250, loss_mask_ce_8: 0.50771/1.03764, loss_mask_bce_8: 0.15728/0.33424, loss_mask_dice_8: 1.17337/1.18396, loss_spatial_bce_8: 0.03533/0.12931, loss_spatial_dice_8: 0.25533/0.26543, loss_spatial_ce_8: 0.15858/0.22108, loss_grounding_bce_8: 0.01200/0.08884, loss_grounding_dice_8: 0.11365/0.17067, loss_grounding_ce_8: 0.32138/0.43261, loss_mask_ce_9: 4.43162/3.49404, loss_mask_bce_9: 0.14818/0.36055, loss_mask_dice_9: 1.29815/1.76880, loss_spatial_bce_9: 0.09423/0.35799, loss_spatial_dice_9: 0.79878/0.79544, loss_spatial_ce_9: 1.25142/1.40527, loss_grounding_bce_9: 0.02135/0.10074, loss_grounding_dice_9: 0.32309/0.24432, loss_grounding_ce_9: 0.60720/0.69654] items per batch[64] items per second[0.36] total items[1772800] mini batches[ 27700] memory[4967] epoch remaining[0:46:00] INFO:trainer.default_trainer:epochs[ 15] optim steps[27800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25604/0.77699, loss_mask_bce_0: 0.32386/0.30211, loss_mask_dice_0: 0.21079/1.02795, loss_spatial_bce_0: 0.16582/0.08835, loss_spatial_dice_0: 0.10483/0.18631, loss_spatial_ce_0: 0.07780/0.06775, loss_grounding_bce_0: 0.15743/0.08065, loss_grounding_dice_0: 0.09303/0.15148, loss_grounding_ce_0: 0.05980/0.25232, loss_mask_ce_1: 0.25419/0.77923, loss_mask_bce_1: 0.30461/0.30284, loss_mask_dice_1: 0.20180/1.03222, loss_spatial_bce_1: 0.16271/0.08872, loss_spatial_dice_1: 0.10167/0.18884, loss_spatial_ce_1: 0.09291/0.07237, loss_grounding_bce_1: 0.15102/0.08091, loss_grounding_dice_1: 0.09080/0.15241, loss_grounding_ce_1: 0.04967/0.25394, loss_mask_ce_2: 0.23438/0.78671, loss_mask_bce_2: 0.31279/0.30292, loss_mask_dice_2: 0.20432/1.03417, loss_spatial_bce_2: 0.16573/0.08844, loss_spatial_dice_2: 0.10202/0.18894, loss_spatial_ce_2: 0.09233/0.07462, loss_grounding_bce_2: 0.15388/0.08082, loss_grounding_dice_2: 0.08968/0.15206, loss_grounding_ce_2: 0.04331/0.25630, loss_mask_ce_3: 0.22752/0.78756, loss_mask_bce_3: 0.30784/0.30449, loss_mask_dice_3: 0.20853/1.02975, loss_spatial_bce_3: 0.16528/0.09014, loss_spatial_dice_3: 0.10326/0.18961, loss_spatial_ce_3: 0.07876/0.07997, loss_grounding_bce_3: 0.15066/0.08128, loss_grounding_dice_3: 0.08942/0.15170, loss_grounding_ce_3: 0.04517/0.25579, loss_mask_ce_4: 0.21839/0.79323, loss_mask_bce_4: 0.31075/0.30655, loss_mask_dice_4: 0.21516/1.04857, loss_spatial_bce_4: 0.16138/0.09210, loss_spatial_dice_4: 0.10363/0.19702, loss_spatial_ce_4: 0.12398/0.09242, loss_grounding_bce_4: 0.15497/0.08199, loss_grounding_dice_4: 0.09429/0.15425, loss_grounding_ce_4: 0.04936/0.26198, loss_mask_ce_5: 0.21171/0.81584, loss_mask_bce_5: 0.31614/0.30837, loss_mask_dice_5: 0.21155/1.05573, loss_spatial_bce_5: 0.17896/0.09388, loss_spatial_dice_5: 0.11239/0.19926, loss_spatial_ce_5: 0.08883/0.10379, loss_grounding_bce_5: 0.15809/0.08234, loss_grounding_dice_5: 0.09658/0.15491, loss_grounding_ce_5: 0.05757/0.28098, loss_mask_ce_6: 0.23037/0.84195, loss_mask_bce_6: 0.31965/0.31007, loss_mask_dice_6: 0.22313/1.05889, loss_spatial_bce_6: 0.18437/0.09863, loss_spatial_dice_6: 0.12061/0.20160, loss_spatial_ce_6: 0.08451/0.12531, loss_grounding_bce_6: 0.15776/0.08333, loss_grounding_dice_6: 0.10271/0.15558, loss_grounding_ce_6: 0.06831/0.29122, loss_mask_ce_7: 0.23518/0.90187, loss_mask_bce_7: 0.32858/0.31719, loss_mask_dice_7: 0.23017/1.10516, loss_spatial_bce_7: 0.19477/0.10909, loss_spatial_dice_7: 0.11814/0.22635, loss_spatial_ce_7: 0.12748/0.16763, loss_grounding_bce_7: 0.16201/0.08487, loss_grounding_dice_7: 0.10197/0.16125, loss_grounding_ce_7: 0.03728/0.33246, loss_mask_ce_8: 0.22513/1.03764, loss_mask_bce_8: 0.31160/0.33433, loss_mask_dice_8: 0.21586/1.18428, loss_spatial_bce_8: 0.20954/0.12930, loss_spatial_dice_8: 0.14121/0.26540, loss_spatial_ce_8: 0.19542/0.22110, loss_grounding_bce_8: 0.14642/0.08888, loss_grounding_dice_8: 0.09677/0.17071, loss_grounding_ce_8: 0.02719/0.43244, loss_mask_ce_9: 1.89930/3.49417, loss_mask_bce_9: 0.37478/0.36066, loss_mask_dice_9: 0.30171/1.76965, loss_spatial_bce_9: 0.51247/0.35787, loss_spatial_dice_9: 0.61099/0.79543, loss_spatial_ce_9: 1.02189/1.40516, loss_grounding_bce_9: 0.18935/0.10077, loss_grounding_dice_9: 0.12541/0.24440, loss_grounding_ce_9: 0.04924/0.69637] items per batch[64] items per second[0.36] total items[1779200] mini batches[ 27800] memory[4967] epoch remaining[0:42:49] INFO:trainer.default_trainer:epochs[ 15] optim steps[27900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60583/0.77696, loss_mask_bce_0: 0.03078/0.30206, loss_mask_dice_0: 1.03414/1.02815, loss_spatial_bce_0: 0.01124/0.08830, loss_spatial_dice_0: 0.27164/0.18629, loss_spatial_ce_0: 0.11458/0.06769, loss_grounding_bce_0: 0.00646/0.08058, loss_grounding_dice_0: 0.28911/0.15142, loss_grounding_ce_0: 0.14015/0.25253, loss_mask_ce_1: 0.59647/0.77919, loss_mask_bce_1: 0.02498/0.30279, loss_mask_dice_1: 0.56391/1.03254, loss_spatial_bce_1: 0.01209/0.08866, loss_spatial_dice_1: 0.21907/0.18882, loss_spatial_ce_1: 0.10158/0.07230, loss_grounding_bce_1: 0.00691/0.08084, loss_grounding_dice_1: 0.27107/0.15236, loss_grounding_ce_1: 0.14323/0.25415, loss_mask_ce_2: 0.52323/0.78663, loss_mask_bce_2: 0.02787/0.30285, loss_mask_dice_2: 0.86563/1.03435, loss_spatial_bce_2: 0.01223/0.08839, loss_spatial_dice_2: 0.18091/0.18891, loss_spatial_ce_2: 0.08862/0.07458, loss_grounding_bce_2: 0.00728/0.08075, loss_grounding_dice_2: 0.29715/0.15202, loss_grounding_ce_2: 0.29502/0.25649, loss_mask_ce_3: 0.63646/0.78745, loss_mask_bce_3: 0.02474/0.30444, loss_mask_dice_3: 0.76018/1.03000, loss_spatial_bce_3: 0.00931/0.09010, loss_spatial_dice_3: 0.24747/0.18959, loss_spatial_ce_3: 0.09927/0.07990, loss_grounding_bce_3: 0.00636/0.08120, loss_grounding_dice_3: 0.29517/0.15162, loss_grounding_ce_3: 0.14579/0.25599, loss_mask_ce_4: 0.64749/0.79317, loss_mask_bce_4: 0.03524/0.30650, loss_mask_dice_4: 0.67031/1.04882, loss_spatial_bce_4: 0.01081/0.09205, loss_spatial_dice_4: 0.20724/0.19702, loss_spatial_ce_4: 0.18154/0.09238, loss_grounding_bce_4: 0.00723/0.08191, loss_grounding_dice_4: 0.22467/0.15418, loss_grounding_ce_4: 0.15362/0.26222, loss_mask_ce_5: 0.46434/0.81581, loss_mask_bce_5: 0.03247/0.30833, loss_mask_dice_5: 0.73960/1.05597, loss_spatial_bce_5: 0.01027/0.09382, loss_spatial_dice_5: 0.20380/0.19926, loss_spatial_ce_5: 0.17954/0.10380, loss_grounding_bce_5: 0.00891/0.08226, loss_grounding_dice_5: 0.28653/0.15484, loss_grounding_ce_5: 0.15011/0.28125, loss_mask_ce_6: 0.62675/0.84193, loss_mask_bce_6: 0.03023/0.31002, loss_mask_dice_6: 1.10093/1.05912, loss_spatial_bce_6: 0.00800/0.09859, loss_spatial_dice_6: 0.33151/0.20160, loss_spatial_ce_6: 0.17621/0.12533, loss_grounding_bce_6: 0.00705/0.08326, loss_grounding_dice_6: 0.32224/0.15553, loss_grounding_ce_6: 0.16082/0.29139, loss_mask_ce_7: 1.09701/0.90174, loss_mask_bce_7: 0.03584/0.31718, loss_mask_dice_7: 0.72994/1.10543, loss_spatial_bce_7: 0.01208/0.10903, loss_spatial_dice_7: 0.24239/0.22635, loss_spatial_ce_7: 0.16665/0.16763, loss_grounding_bce_7: 0.00740/0.08480, loss_grounding_dice_7: 0.28841/0.16120, loss_grounding_ce_7: 0.25394/0.33259, loss_mask_ce_8: 1.14863/1.03771, loss_mask_bce_8: 0.02832/0.33427, loss_mask_dice_8: 0.83833/1.18458, loss_spatial_bce_8: 0.01623/0.12922, loss_spatial_dice_8: 0.28857/0.26539, loss_spatial_ce_8: 0.90580/0.22110, loss_grounding_bce_8: 0.00875/0.08880, loss_grounding_dice_8: 0.26799/0.17067, loss_grounding_ce_8: 0.18199/0.43267, loss_mask_ce_9: 1.92999/3.49469, loss_mask_bce_9: 0.02289/0.36064, loss_mask_dice_9: 0.49410/1.77001, loss_spatial_bce_9: 0.10144/0.35782, loss_spatial_dice_9: 0.67761/0.79547, loss_spatial_ce_9: 4.87475/1.40510, loss_grounding_bce_9: 0.00473/0.10072, loss_grounding_dice_9: 0.35265/0.24442, loss_grounding_ce_9: 0.17569/0.69655] items per batch[64] items per second[0.36] total items[1785600] mini batches[ 27900] memory[4967] epoch remaining[0:39:44] INFO:trainer.default_trainer:epochs[ 15] optim steps[28000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34144/0.77720, loss_mask_bce_0: 0.24575/0.30199, loss_mask_dice_0: 0.15043/1.02831, loss_spatial_bce_0: 0.24210/0.08824, loss_spatial_dice_0: 0.12245/0.18623, loss_spatial_ce_0: 0.00061/0.06759, loss_grounding_bce_0: 0.00000/0.08056, loss_grounding_dice_0: 0.00084/0.15140, loss_grounding_ce_0: 0.32755/0.25248, loss_mask_ce_1: 1.39692/0.77950, loss_mask_bce_1: 0.23980/0.30271, loss_mask_dice_1: 0.14724/1.03264, loss_spatial_bce_1: 0.25412/0.08860, loss_spatial_dice_1: 0.12646/0.18878, loss_spatial_ce_1: 0.00098/0.07222, loss_grounding_bce_1: 0.00000/0.08082, loss_grounding_dice_1: 0.00024/0.15236, loss_grounding_ce_1: 0.39333/0.25411, loss_mask_ce_2: 1.38014/0.78698, loss_mask_bce_2: 0.23201/0.30277, loss_mask_dice_2: 0.14676/1.03450, loss_spatial_bce_2: 0.25707/0.08833, loss_spatial_dice_2: 0.12117/0.18886, loss_spatial_ce_2: 0.00070/0.07447, loss_grounding_bce_2: 0.00000/0.08073, loss_grounding_dice_2: 0.00022/0.15200, loss_grounding_ce_2: 0.34437/0.25646, loss_mask_ce_3: 1.38223/0.78778, loss_mask_bce_3: 0.22391/0.30436, loss_mask_dice_3: 0.13471/1.03012, loss_spatial_bce_3: 0.24019/0.09003, loss_spatial_dice_3: 0.12527/0.18955, loss_spatial_ce_3: 0.00208/0.07979, loss_grounding_bce_3: 0.00000/0.08117, loss_grounding_dice_3: 0.00009/0.15159, loss_grounding_ce_3: 0.30571/0.25601, loss_mask_ce_4: 1.43330/0.79345, loss_mask_bce_4: 0.24877/0.30644, loss_mask_dice_4: 0.13627/1.04892, loss_spatial_bce_4: 0.24676/0.09199, loss_spatial_dice_4: 0.12709/0.19698, loss_spatial_ce_4: 0.00139/0.09232, loss_grounding_bce_4: 0.00000/0.08188, loss_grounding_dice_4: 0.00004/0.15417, loss_grounding_ce_4: 0.36819/0.26215, loss_mask_ce_5: 1.70598/0.81620, loss_mask_bce_5: 0.26745/0.30828, loss_mask_dice_5: 0.13554/1.05609, loss_spatial_bce_5: 0.26465/0.09376, loss_spatial_dice_5: 0.13418/0.19923, loss_spatial_ce_5: 0.00860/0.10375, loss_grounding_bce_5: 0.00000/0.08222, loss_grounding_dice_5: 0.00001/0.15482, loss_grounding_ce_5: 0.49218/0.28149, loss_mask_ce_6: 1.59380/0.84229, loss_mask_bce_6: 0.24536/0.30997, loss_mask_dice_6: 0.12923/1.05930, loss_spatial_bce_6: 0.27107/0.09852, loss_spatial_dice_6: 0.12620/0.20156, loss_spatial_ce_6: 0.04871/0.12540, loss_grounding_bce_6: 0.00000/0.08322, loss_grounding_dice_6: 0.00003/0.15548, loss_grounding_ce_6: 0.30038/0.29141, loss_mask_ce_7: 1.71221/0.90212, loss_mask_bce_7: 0.29224/0.31716, loss_mask_dice_7: 0.14618/1.10562, loss_spatial_bce_7: 0.22122/0.10897, loss_spatial_dice_7: 0.12541/0.22631, loss_spatial_ce_7: 0.23193/0.16754, loss_grounding_bce_7: 0.00000/0.08477, loss_grounding_dice_7: 0.00006/0.16115, loss_grounding_ce_7: 0.35183/0.33263, loss_mask_ce_8: 1.93908/1.03794, loss_mask_bce_8: 0.29923/0.33425, loss_mask_dice_8: 0.15100/1.18477, loss_spatial_bce_8: 0.24401/0.12910, loss_spatial_dice_8: 0.14363/0.26537, loss_spatial_ce_8: 0.12523/0.22099, loss_grounding_bce_8: 0.00000/0.08877, loss_grounding_dice_8: 0.00004/0.17067, loss_grounding_ce_8: 0.40849/0.43284, loss_mask_ce_9: 3.74480/3.49507, loss_mask_bce_9: 0.52558/0.36060, loss_mask_dice_9: 0.42186/1.77055, loss_spatial_bce_9: 0.47911/0.35766, loss_spatial_dice_9: 0.60446/0.79546, loss_spatial_ce_9: 0.75220/1.40502, loss_grounding_bce_9: 0.00000/0.10067, loss_grounding_dice_9: 0.00055/0.24442, loss_grounding_ce_9: 0.33807/0.69657] items per batch[64] items per second[0.36] total items[1792000] mini batches[ 28000] memory[4967] epoch remaining[0:36:44] INFO:trainer.default_trainer:epochs[ 15] optim steps[28100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88867/0.77707, loss_mask_bce_0: 0.14619/0.30197, loss_mask_dice_0: 4.93511/1.02768, loss_spatial_bce_0: 0.01658/0.08823, loss_spatial_dice_0: 0.32658/0.18615, loss_spatial_ce_0: 0.01405/0.06750, loss_grounding_bce_0: 0.01015/0.08057, loss_grounding_dice_0: 0.50032/0.15135, loss_grounding_ce_0: 0.61420/0.25222, loss_mask_ce_1: 1.39856/0.77943, loss_mask_bce_1: 0.14605/0.30269, loss_mask_dice_1: 4.84903/1.03202, loss_spatial_bce_1: 0.01484/0.08859, loss_spatial_dice_1: 0.28801/0.18868, loss_spatial_ce_1: 0.00721/0.07211, loss_grounding_bce_1: 0.00929/0.08083, loss_grounding_dice_1: 0.50462/0.15231, loss_grounding_ce_1: 0.62909/0.25383, loss_mask_ce_2: 0.76068/0.78683, loss_mask_bce_2: 0.15566/0.30275, loss_mask_dice_2: 4.79258/1.03392, loss_spatial_bce_2: 0.01585/0.08832, loss_spatial_dice_2: 0.31215/0.18876, loss_spatial_ce_2: 0.01225/0.07434, loss_grounding_bce_2: 0.01255/0.08073, loss_grounding_dice_2: 0.45357/0.15194, loss_grounding_ce_2: 0.78944/0.25622, loss_mask_ce_3: 0.71665/0.78770, loss_mask_bce_3: 0.15501/0.30434, loss_mask_dice_3: 4.72932/1.02955, loss_spatial_bce_3: 0.01373/0.09002, loss_spatial_dice_3: 0.29944/0.18945, loss_spatial_ce_3: 0.01464/0.07968, loss_grounding_bce_3: 0.01036/0.08119, loss_grounding_dice_3: 0.47454/0.15154, loss_grounding_ce_3: 0.72934/0.25577, loss_mask_ce_4: 0.81314/0.79344, loss_mask_bce_4: 0.14422/0.30640, loss_mask_dice_4: 5.09184/1.04832, loss_spatial_bce_4: 0.01765/0.09198, loss_spatial_dice_4: 0.33748/0.19691, loss_spatial_ce_4: 0.03606/0.09219, loss_grounding_bce_4: 0.00910/0.08189, loss_grounding_dice_4: 0.48680/0.15411, loss_grounding_ce_4: 0.72211/0.26184, loss_mask_ce_5: 0.73674/0.81607, loss_mask_bce_5: 0.15781/0.30824, loss_mask_dice_5: 5.23055/1.05555, loss_spatial_bce_5: 0.01513/0.09375, loss_spatial_dice_5: 0.33759/0.19914, loss_spatial_ce_5: 0.03473/0.10364, loss_grounding_bce_5: 0.01126/0.08223, loss_grounding_dice_5: 0.50266/0.15476, loss_grounding_ce_5: 0.70629/0.28112, loss_mask_ce_6: 0.68886/0.84217, loss_mask_bce_6: 0.14787/0.30994, loss_mask_dice_6: 5.17838/1.05871, loss_spatial_bce_6: 0.01659/0.09851, loss_spatial_dice_6: 0.37717/0.20148, loss_spatial_ce_6: 0.11800/0.12534, loss_grounding_bce_6: 0.01187/0.08322, loss_grounding_dice_6: 0.50004/0.15541, loss_grounding_ce_6: 0.72681/0.29118, loss_mask_ce_7: 0.87564/0.90205, loss_mask_bce_7: 0.16682/0.31708, loss_mask_dice_7: 5.07497/1.10494, loss_spatial_bce_7: 0.02123/0.10897, loss_spatial_dice_7: 0.43505/0.22623, loss_spatial_ce_7: 0.04395/0.16738, loss_grounding_bce_7: 0.01267/0.08477, loss_grounding_dice_7: 0.49114/0.16109, loss_grounding_ce_7: 0.67874/0.33235, loss_mask_ce_8: 1.16904/1.03780, loss_mask_bce_8: 0.13827/0.33412, loss_mask_dice_8: 5.06983/1.18403, loss_spatial_bce_8: 0.02616/0.12912, loss_spatial_dice_8: 0.42017/0.26525, loss_spatial_ce_8: 0.09185/0.22083, loss_grounding_bce_8: 0.00994/0.08876, loss_grounding_dice_8: 0.52541/0.17060, loss_grounding_ce_8: 0.78018/0.43264, loss_mask_ce_9: 5.13764/3.49464, loss_mask_bce_9: 0.13876/0.36048, loss_mask_dice_9: 6.20268/1.76977, loss_spatial_bce_9: 0.04056/0.35775, loss_spatial_dice_9: 0.95432/0.79544, loss_spatial_ce_9: 1.11269/1.40467, loss_grounding_bce_9: 0.00638/0.10068, loss_grounding_dice_9: 0.51338/0.24435, loss_grounding_ce_9: 0.67317/0.69615] items per batch[64] items per second[0.36] total items[1798400] mini batches[ 28100] memory[4967] epoch remaining[0:33:42] INFO:trainer.default_trainer:epochs[ 15] optim steps[28200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.60229/0.77727, loss_mask_bce_0: 1.36630/0.30188, loss_mask_dice_0: 3.97356/1.02789, loss_spatial_bce_0: 0.13227/0.08820, loss_spatial_dice_0: 0.28314/0.18619, loss_spatial_ce_0: 0.01993/0.06748, loss_grounding_bce_0: 0.27093/0.08055, loss_grounding_dice_0: 0.52581/0.15137, loss_grounding_ce_0: 0.59340/0.25213, loss_mask_ce_1: 1.75672/0.77962, loss_mask_bce_1: 1.39327/0.30259, loss_mask_dice_1: 4.07036/1.03203, loss_spatial_bce_1: 0.14768/0.08856, loss_spatial_dice_1: 0.28866/0.18872, loss_spatial_ce_1: 0.00758/0.07208, loss_grounding_bce_1: 0.13205/0.08080, loss_grounding_dice_1: 0.55569/0.15231, loss_grounding_ce_1: 0.26081/0.25376, loss_mask_ce_2: 1.76722/0.78697, loss_mask_bce_2: 1.36039/0.30264, loss_mask_dice_2: 4.14933/1.03390, loss_spatial_bce_2: 0.16213/0.08829, loss_spatial_dice_2: 0.28715/0.18880, loss_spatial_ce_2: 0.00363/0.07432, loss_grounding_bce_2: 0.18627/0.08071, loss_grounding_dice_2: 0.52928/0.15194, loss_grounding_ce_2: 0.62506/0.25607, loss_mask_ce_3: 1.76531/0.78789, loss_mask_bce_3: 1.36794/0.30424, loss_mask_dice_3: 4.23730/1.02955, loss_spatial_bce_3: 0.16494/0.08999, loss_spatial_dice_3: 0.27170/0.18948, loss_spatial_ce_3: 0.00812/0.07966, loss_grounding_bce_3: 0.13913/0.08116, loss_grounding_dice_3: 0.55150/0.15156, loss_grounding_ce_3: 0.45986/0.25564, loss_mask_ce_4: 1.91198/0.79368, loss_mask_bce_4: 1.35556/0.30631, loss_mask_dice_4: 4.47554/1.04835, loss_spatial_bce_4: 0.21370/0.09196, loss_spatial_dice_4: 0.31348/0.19695, loss_spatial_ce_4: 0.05347/0.09216, loss_grounding_bce_4: 0.12653/0.08186, loss_grounding_dice_4: 0.56317/0.15411, loss_grounding_ce_4: 0.38473/0.26177, loss_mask_ce_5: 1.70942/0.81632, loss_mask_bce_5: 1.39590/0.30813, loss_mask_dice_5: 4.93162/1.05561, loss_spatial_bce_5: 0.30174/0.09374, loss_spatial_dice_5: 0.31327/0.19921, loss_spatial_ce_5: 0.04933/0.10368, loss_grounding_bce_5: 0.15738/0.08220, loss_grounding_dice_5: 0.56718/0.15477, loss_grounding_ce_5: 0.48827/0.28105, loss_mask_ce_6: 1.74557/0.84240, loss_mask_bce_6: 1.54939/0.30985, loss_mask_dice_6: 4.95965/1.05875, loss_spatial_bce_6: 0.25470/0.09850, loss_spatial_dice_6: 0.36632/0.20155, loss_spatial_ce_6: 0.13264/0.12533, loss_grounding_bce_6: 0.13214/0.08319, loss_grounding_dice_6: 0.57095/0.15541, loss_grounding_ce_6: 0.25147/0.29112, loss_mask_ce_7: 1.85256/0.90226, loss_mask_bce_7: 1.90532/0.31700, loss_mask_dice_7: 5.55873/1.10504, loss_spatial_bce_7: 0.29809/0.10896, loss_spatial_dice_7: 0.46346/0.22629, loss_spatial_ce_7: 0.14546/0.16741, loss_grounding_bce_7: 0.28608/0.08475, loss_grounding_dice_7: 0.55821/0.16110, loss_grounding_ce_7: 0.10978/0.33212, loss_mask_ce_8: 1.68059/1.03802, loss_mask_bce_8: 1.81467/0.33403, loss_mask_dice_8: 5.75084/1.18410, loss_spatial_bce_8: 0.26060/0.12909, loss_spatial_dice_8: 0.52003/0.26531, loss_spatial_ce_8: 0.14819/0.22077, loss_grounding_bce_8: 0.27596/0.08876, loss_grounding_dice_8: 0.55490/0.17062, loss_grounding_ce_8: 0.13843/0.43252, loss_mask_ce_9: 8.16375/3.49500, loss_mask_bce_9: 1.95112/0.36043, loss_mask_dice_9: 12.31759/1.76990, loss_spatial_bce_9: 0.17361/0.35763, loss_spatial_dice_9: 0.96285/0.79545, loss_spatial_ce_9: 1.22121/1.40445, loss_grounding_bce_9: 0.14808/0.10066, loss_grounding_dice_9: 0.56338/0.24436, loss_grounding_ce_9: 0.29458/0.69574] items per batch[64] items per second[0.36] total items[1804800] mini batches[ 28200] memory[4967] epoch remaining[0:30:40] INFO:trainer.default_trainer:epochs[ 15] optim steps[28300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89320/0.77710, loss_mask_bce_0: 0.44110/0.30204, loss_mask_dice_0: 0.28492/1.02766, loss_spatial_bce_0: 0.27023/0.08818, loss_spatial_dice_0: 0.11763/0.18617, loss_spatial_ce_0: 0.00282/0.06745, loss_grounding_bce_0: 0.14792/0.08056, loss_grounding_dice_0: 0.17361/0.15139, loss_grounding_ce_0: 0.00404/0.25221, loss_mask_ce_1: 0.80741/0.77949, loss_mask_bce_1: 0.22966/0.30274, loss_mask_dice_1: 0.22859/1.03175, loss_spatial_bce_1: 0.23341/0.08854, loss_spatial_dice_1: 0.12075/0.18872, loss_spatial_ce_1: 0.00727/0.07199, loss_grounding_bce_1: 0.14020/0.08081, loss_grounding_dice_1: 0.17952/0.15235, loss_grounding_ce_1: 0.00371/0.25388, loss_mask_ce_2: 0.77549/0.78678, loss_mask_bce_2: 0.48108/0.30281, loss_mask_dice_2: 0.25947/1.03359, loss_spatial_bce_2: 0.25404/0.08828, loss_spatial_dice_2: 0.11711/0.18879, loss_spatial_ce_2: 0.00568/0.07425, loss_grounding_bce_2: 0.14487/0.08073, loss_grounding_dice_2: 0.17562/0.15197, loss_grounding_ce_2: 0.00498/0.25618, loss_mask_ce_3: 0.83008/0.78773, loss_mask_bce_3: 0.45070/0.30440, loss_mask_dice_3: 0.22667/1.02925, loss_spatial_bce_3: 0.23420/0.08998, loss_spatial_dice_3: 0.11983/0.18949, loss_spatial_ce_3: 0.01275/0.07956, loss_grounding_bce_3: 0.14276/0.08117, loss_grounding_dice_3: 0.17378/0.15159, loss_grounding_ce_3: 0.00602/0.25571, loss_mask_ce_4: 0.79554/0.79354, loss_mask_bce_4: 0.73583/0.30652, loss_mask_dice_4: 0.36581/1.04802, loss_spatial_bce_4: 0.27125/0.09196, loss_spatial_dice_4: 0.12570/0.19694, loss_spatial_ce_4: 0.01245/0.09207, loss_grounding_bce_4: 0.13974/0.08188, loss_grounding_dice_4: 0.17669/0.15414, loss_grounding_ce_4: 0.00527/0.26185, loss_mask_ce_5: 0.13643/0.81614, loss_mask_bce_5: 0.42176/0.30831, loss_mask_dice_5: 0.22281/1.05532, loss_spatial_bce_5: 0.23756/0.09375, loss_spatial_dice_5: 0.12443/0.19921, loss_spatial_ce_5: 0.01436/0.10364, loss_grounding_bce_5: 0.11095/0.08221, loss_grounding_dice_5: 0.16435/0.15479, loss_grounding_ce_5: 0.00622/0.28107, loss_mask_ce_6: 0.95626/0.84216, loss_mask_bce_6: 0.66499/0.31005, loss_mask_dice_6: 0.33689/1.05846, loss_spatial_bce_6: 0.21424/0.09850, loss_spatial_dice_6: 0.12395/0.20153, loss_spatial_ce_6: 0.06214/0.12533, loss_grounding_bce_6: 0.12171/0.08321, loss_grounding_dice_6: 0.16708/0.15545, loss_grounding_ce_6: 0.00178/0.29117, loss_mask_ce_7: 1.06883/0.90210, loss_mask_bce_7: 0.56647/0.31718, loss_mask_dice_7: 0.32746/1.10477, loss_spatial_bce_7: 0.25063/0.10893, loss_spatial_dice_7: 0.14247/0.22628, loss_spatial_ce_7: 0.05520/0.16737, loss_grounding_bce_7: 0.12814/0.08477, loss_grounding_dice_7: 0.17527/0.16114, loss_grounding_ce_7: 0.00612/0.33209, loss_mask_ce_8: 0.97673/1.03794, loss_mask_bce_8: 0.41935/0.33418, loss_mask_dice_8: 0.35082/1.18381, loss_spatial_bce_8: 0.27423/0.12908, loss_spatial_dice_8: 0.13397/0.26530, loss_spatial_ce_8: 0.08778/0.22068, loss_grounding_bce_8: 0.15549/0.08879, loss_grounding_dice_8: 0.18583/0.17067, loss_grounding_ce_8: 0.23780/0.43253, loss_mask_ce_9: 3.52990/3.49490, loss_mask_bce_9: 0.35249/0.36059, loss_mask_dice_9: 0.36648/1.76968, loss_spatial_bce_9: 0.46628/0.35765, loss_spatial_dice_9: 0.81801/0.79552, loss_spatial_ce_9: 1.44311/1.40455, loss_grounding_bce_9: 0.24344/0.10067, loss_grounding_dice_9: 0.28514/0.24439, loss_grounding_ce_9: 1.07364/0.69594] items per batch[64] items per second[0.36] total items[1811200] mini batches[ 28300] memory[4967] epoch remaining[0:27:41] INFO:trainer.default_trainer:epochs[ 15] optim steps[28400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07128/0.77666, loss_mask_bce_0: 0.54688/0.30208, loss_mask_dice_0: 0.13231/1.02773, loss_spatial_bce_0: 0.36537/0.08816, loss_spatial_dice_0: 0.12767/0.18606, loss_spatial_ce_0: 0.00052/0.06733, loss_grounding_bce_0: 1.10175/0.08061, loss_grounding_dice_0: 0.26019/0.15133, loss_grounding_ce_0: 0.01225/0.25192, loss_mask_ce_1: 0.06316/0.77908, loss_mask_bce_1: 0.54128/0.30280, loss_mask_dice_1: 0.13174/1.03182, loss_spatial_bce_1: 0.38277/0.08851, loss_spatial_dice_1: 0.12793/0.18862, loss_spatial_ce_1: 0.00039/0.07188, loss_grounding_bce_1: 1.06843/0.08086, loss_grounding_dice_1: 0.25535/0.15228, loss_grounding_ce_1: 0.01429/0.25363, loss_mask_ce_2: 0.06632/0.78634, loss_mask_bce_2: 0.54468/0.30284, loss_mask_dice_2: 0.13737/1.03360, loss_spatial_bce_2: 0.32808/0.08825, loss_spatial_dice_2: 0.14243/0.18868, loss_spatial_ce_2: 0.00334/0.07412, loss_grounding_bce_2: 1.08569/0.08077, loss_grounding_dice_2: 0.26031/0.15190, loss_grounding_ce_2: 0.01416/0.25596, loss_mask_ce_3: 0.06874/0.78729, loss_mask_bce_3: 0.50446/0.30444, loss_mask_dice_3: 0.13502/1.02925, loss_spatial_bce_3: 0.38949/0.08994, loss_spatial_dice_3: 0.12471/0.18938, loss_spatial_ce_3: 0.00265/0.07944, loss_grounding_bce_3: 1.04376/0.08122, loss_grounding_dice_3: 0.26030/0.15154, loss_grounding_ce_3: 0.01026/0.25552, loss_mask_ce_4: 0.05226/0.79312, loss_mask_bce_4: 0.48234/0.30655, loss_mask_dice_4: 0.13140/1.04811, loss_spatial_bce_4: 0.36817/0.09192, loss_spatial_dice_4: 0.12998/0.19683, loss_spatial_ce_4: 0.00086/0.09194, loss_grounding_bce_4: 0.97553/0.08192, loss_grounding_dice_4: 0.25373/0.15408, loss_grounding_ce_4: 0.01130/0.26166, loss_mask_ce_5: 0.06796/0.81571, loss_mask_bce_5: 0.50443/0.30834, loss_mask_dice_5: 0.12657/1.05537, loss_spatial_bce_5: 0.35675/0.09371, loss_spatial_dice_5: 0.12390/0.19910, loss_spatial_ce_5: 0.00567/0.10351, loss_grounding_bce_5: 1.00146/0.08225, loss_grounding_dice_5: 0.24522/0.15473, loss_grounding_ce_5: 0.01156/0.28083, loss_mask_ce_6: 0.05190/0.84173, loss_mask_bce_6: 0.43661/0.31008, loss_mask_dice_6: 0.12838/1.05848, loss_spatial_bce_6: 0.46605/0.09847, loss_spatial_dice_6: 0.12878/0.20142, loss_spatial_ce_6: 0.08890/0.12526, loss_grounding_bce_6: 0.88318/0.08325, loss_grounding_dice_6: 0.24703/0.15537, loss_grounding_ce_6: 0.01304/0.29084, loss_mask_ce_7: 0.05133/0.90163, loss_mask_bce_7: 0.50564/0.31721, loss_mask_dice_7: 0.13221/1.10500, loss_spatial_bce_7: 0.34701/0.10890, loss_spatial_dice_7: 0.12005/0.22617, loss_spatial_ce_7: 0.13689/0.16722, loss_grounding_bce_7: 1.01167/0.08481, loss_grounding_dice_7: 0.26637/0.16110, loss_grounding_ce_7: 0.02486/0.33170, loss_mask_ce_8: 0.06360/1.03745, loss_mask_bce_8: 0.53985/0.33422, loss_mask_dice_8: 0.13403/1.18403, loss_spatial_bce_8: 0.39355/0.12908, loss_spatial_dice_8: 0.19604/0.26523, loss_spatial_ce_8: 0.02617/0.22052, loss_grounding_bce_8: 1.08561/0.08884, loss_grounding_dice_8: 0.26136/0.17062, loss_grounding_ce_8: 0.03435/0.43193, loss_mask_ce_9: 1.60349/3.49490, loss_mask_bce_9: 0.43730/0.36064, loss_mask_dice_9: 0.13155/1.77022, loss_spatial_bce_9: 0.64816/0.35760, loss_spatial_dice_9: 0.54556/0.79546, loss_spatial_ce_9: 0.51561/1.40442, loss_grounding_bce_9: 0.84682/0.10070, loss_grounding_dice_9: 0.24767/0.24430, loss_grounding_ce_9: 0.04014/0.69550] items per batch[64] items per second[0.36] total items[1817600] mini batches[ 28400] memory[4967] epoch remaining[0:24:41] INFO:trainer.default_trainer:epochs[ 15] optim steps[28500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87546/0.77678, loss_mask_bce_0: 0.21074/0.30204, loss_mask_dice_0: 1.18785/1.02807, loss_spatial_bce_0: 0.05385/0.08809, loss_spatial_dice_0: 0.25684/0.18604, loss_spatial_ce_0: 0.06230/0.06726, loss_grounding_bce_0: 0.04489/0.08059, loss_grounding_dice_0: 0.13689/0.15132, loss_grounding_ce_0: 0.10498/0.25189, loss_mask_ce_1: 1.07913/0.77906, loss_mask_bce_1: 0.20452/0.30276, loss_mask_dice_1: 1.22999/1.03217, loss_spatial_bce_1: 0.06370/0.08845, loss_spatial_dice_1: 0.26582/0.18861, loss_spatial_ce_1: 0.04404/0.07179, loss_grounding_bce_1: 0.05412/0.08082, loss_grounding_dice_1: 0.14341/0.15227, loss_grounding_ce_1: 0.11401/0.25354, loss_mask_ce_2: 0.83629/0.78628, loss_mask_bce_2: 0.20498/0.30281, loss_mask_dice_2: 1.30071/1.03394, loss_spatial_bce_2: 0.06621/0.08819, loss_spatial_dice_2: 0.24834/0.18866, loss_spatial_ce_2: 0.07876/0.07404, loss_grounding_bce_2: 0.05266/0.08074, loss_grounding_dice_2: 0.14475/0.15189, loss_grounding_ce_2: 0.10493/0.25587, loss_mask_ce_3: 0.76976/0.78726, loss_mask_bce_3: 0.20985/0.30441, loss_mask_dice_3: 1.08903/1.02963, loss_spatial_bce_3: 0.06074/0.08988, loss_spatial_dice_3: 0.27777/0.18936, loss_spatial_ce_3: 0.04067/0.07935, loss_grounding_bce_3: 0.05566/0.08118, loss_grounding_dice_3: 0.13954/0.15153, loss_grounding_ce_3: 0.08641/0.25539, loss_mask_ce_4: 0.86182/0.79308, loss_mask_bce_4: 0.19913/0.30651, loss_mask_dice_4: 1.14670/1.04853, loss_spatial_bce_4: 0.07868/0.09186, loss_spatial_dice_4: 0.28219/0.19683, loss_spatial_ce_4: 0.09801/0.09185, loss_grounding_bce_4: 0.06264/0.08187, loss_grounding_dice_4: 0.16143/0.15406, loss_grounding_ce_4: 0.04808/0.26150, loss_mask_ce_5: 1.03010/0.81569, loss_mask_bce_5: 0.22481/0.30829, loss_mask_dice_5: 1.14767/1.05574, loss_spatial_bce_5: 0.06982/0.09365, loss_spatial_dice_5: 0.33314/0.19910, loss_spatial_ce_5: 0.20109/0.10345, loss_grounding_bce_5: 0.05772/0.08221, loss_grounding_dice_5: 0.14448/0.15471, loss_grounding_ce_5: 0.07099/0.28063, loss_mask_ce_6: 0.86847/0.84172, loss_mask_bce_6: 0.26737/0.31004, loss_mask_dice_6: 1.33964/1.05885, loss_spatial_bce_6: 0.08256/0.09842, loss_spatial_dice_6: 0.37007/0.20143, loss_spatial_ce_6: 0.21053/0.12513, loss_grounding_bce_6: 0.06358/0.08320, loss_grounding_dice_6: 0.14040/0.15535, loss_grounding_ce_6: 0.12501/0.29076, loss_mask_ce_7: 1.16217/0.90149, loss_mask_bce_7: 0.22265/0.31719, loss_mask_dice_7: 1.14049/1.10531, loss_spatial_bce_7: 0.07994/0.10887, loss_spatial_dice_7: 0.36351/0.22619, loss_spatial_ce_7: 0.11726/0.16724, loss_grounding_bce_7: 0.06185/0.08479, loss_grounding_dice_7: 0.14142/0.16106, loss_grounding_ce_7: 0.07586/0.33148, loss_mask_ce_8: 1.68890/1.03733, loss_mask_bce_8: 0.25377/0.33416, loss_mask_dice_8: 1.30610/1.18431, loss_spatial_bce_8: 0.08658/0.12902, loss_spatial_dice_8: 0.38443/0.26524, loss_spatial_ce_8: 0.13601/0.22046, loss_grounding_bce_8: 0.06172/0.08878, loss_grounding_dice_8: 0.15161/0.17057, loss_grounding_ce_8: 0.03360/0.43156, loss_mask_ce_9: 3.07391/3.49451, loss_mask_bce_9: 0.29897/0.36058, loss_mask_dice_9: 1.80268/1.77067, loss_spatial_bce_9: 0.17373/0.35761, loss_spatial_dice_9: 0.86966/0.79548, loss_spatial_ce_9: 1.41362/1.40465, loss_grounding_bce_9: 0.06205/0.10066, loss_grounding_dice_9: 0.17954/0.24421, loss_grounding_ce_9: 0.02489/0.69532] items per batch[64] items per second[0.36] total items[1824000] mini batches[ 28500] memory[4967] epoch remaining[0:21:42] INFO:trainer.default_trainer:epochs[ 15] optim steps[28600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28976/0.77660, loss_mask_bce_0: 0.15025/0.30201, loss_mask_dice_0: 0.16024/1.02792, loss_spatial_bce_0: 0.13838/0.08806, loss_spatial_dice_0: 0.19762/0.18599, loss_spatial_ce_0: 0.09290/0.06725, loss_grounding_bce_0: 0.02780/0.08060, loss_grounding_dice_0: 0.02800/0.15128, loss_grounding_ce_0: 0.00194/0.25170, loss_mask_ce_1: 1.75712/0.77893, loss_mask_bce_1: 0.14860/0.30271, loss_mask_dice_1: 0.14668/1.03198, loss_spatial_bce_1: 0.13599/0.08842, loss_spatial_dice_1: 0.14652/0.18855, loss_spatial_ce_1: 0.05246/0.07176, loss_grounding_bce_1: 0.02793/0.08082, loss_grounding_dice_1: 0.02966/0.15221, loss_grounding_ce_1: 0.00097/0.25338, loss_mask_ce_2: 1.74140/0.78618, loss_mask_bce_2: 0.16683/0.30277, loss_mask_dice_2: 0.15028/1.03373, loss_spatial_bce_2: 0.13769/0.08815, loss_spatial_dice_2: 0.17632/0.18860, loss_spatial_ce_2: 0.05483/0.07403, loss_grounding_bce_2: 0.02853/0.08074, loss_grounding_dice_2: 0.03061/0.15183, loss_grounding_ce_2: 0.00097/0.25577, loss_mask_ce_3: 1.68552/0.78723, loss_mask_bce_3: 0.18101/0.30435, loss_mask_dice_3: 0.14148/1.02950, loss_spatial_bce_3: 0.19013/0.08986, loss_spatial_dice_3: 0.17268/0.18929, loss_spatial_ce_3: 0.03417/0.07931, loss_grounding_bce_3: 0.02631/0.08118, loss_grounding_dice_3: 0.02979/0.15147, loss_grounding_ce_3: 0.00192/0.25524, loss_mask_ce_4: 1.33922/0.79299, loss_mask_bce_4: 0.13429/0.30646, loss_mask_dice_4: 0.15045/1.04838, loss_spatial_bce_4: 0.23558/0.09183, loss_spatial_dice_4: 0.19947/0.19678, loss_spatial_ce_4: 0.08998/0.09178, loss_grounding_bce_4: 0.02797/0.08188, loss_grounding_dice_4: 0.03440/0.15400, loss_grounding_ce_4: 0.00184/0.26131, loss_mask_ce_5: 1.09104/0.81559, loss_mask_bce_5: 0.20629/0.30825, loss_mask_dice_5: 0.14976/1.05552, loss_spatial_bce_5: 0.18621/0.09362, loss_spatial_dice_5: 0.17709/0.19905, loss_spatial_ce_5: 0.04032/0.10346, loss_grounding_bce_5: 0.02577/0.08220, loss_grounding_dice_5: 0.03304/0.15464, loss_grounding_ce_5: 0.00330/0.28042, loss_mask_ce_6: 0.93334/0.84161, loss_mask_bce_6: 0.12788/0.31000, loss_mask_dice_6: 0.17344/1.05868, loss_spatial_bce_6: 0.30377/0.09841, loss_spatial_dice_6: 0.15844/0.20138, loss_spatial_ce_6: 0.01434/0.12513, loss_grounding_bce_6: 0.02422/0.08319, loss_grounding_dice_6: 0.02766/0.15528, loss_grounding_ce_6: 0.00069/0.29056, loss_mask_ce_7: 1.04267/0.90125, loss_mask_bce_7: 0.60411/0.31716, loss_mask_dice_7: 0.18736/1.10514, loss_spatial_bce_7: 0.13529/0.10883, loss_spatial_dice_7: 0.14780/0.22615, loss_spatial_ce_7: 0.30964/0.16720, loss_grounding_bce_7: 0.02794/0.08478, loss_grounding_dice_7: 0.02605/0.16099, loss_grounding_ce_7: 0.00425/0.33115, loss_mask_ce_8: 1.08141/1.03699, loss_mask_bce_8: 0.57516/0.33409, loss_mask_dice_8: 0.20130/1.18410, loss_spatial_bce_8: 0.22883/0.12898, loss_spatial_dice_8: 0.22501/0.26519, loss_spatial_ce_8: 0.30468/0.22039, loss_grounding_bce_8: 0.02605/0.08878, loss_grounding_dice_8: 0.03769/0.17050, loss_grounding_ce_8: 0.00277/0.43109, loss_mask_ce_9: 2.65978/3.49432, loss_mask_bce_9: 0.30384/0.36053, loss_mask_dice_9: 0.58560/1.77018, loss_spatial_bce_9: 0.50949/0.35760, loss_spatial_dice_9: 0.82322/0.79549, loss_spatial_ce_9: 1.75964/1.40448, loss_grounding_bce_9: 0.02759/0.10066, loss_grounding_dice_9: 0.02735/0.24411, loss_grounding_ce_9: 0.06714/0.69503] items per batch[64] items per second[0.36] total items[1830400] mini batches[ 28600] memory[4967] epoch remaining[0:18:43] INFO:trainer.default_trainer:epochs[ 15] optim steps[28700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82365/0.77654, loss_mask_bce_0: 0.43464/0.30202, loss_mask_dice_0: 9.02729/1.02811, loss_spatial_bce_0: 0.00875/0.08802, loss_spatial_dice_0: 0.30259/0.18597, loss_spatial_ce_0: 0.04587/0.06716, loss_grounding_bce_0: 0.02605/0.08058, loss_grounding_dice_0: 0.44770/0.15128, loss_grounding_ce_0: 0.58834/0.25168, loss_mask_ce_1: 0.57102/0.77891, loss_mask_bce_1: 0.42368/0.30271, loss_mask_dice_1: 8.84055/1.03223, loss_spatial_bce_1: 0.00878/0.08837, loss_spatial_dice_1: 0.28465/0.18854, loss_spatial_ce_1: 0.05338/0.07166, loss_grounding_bce_1: 0.02536/0.08080, loss_grounding_dice_1: 0.40615/0.15225, loss_grounding_ce_1: 0.58761/0.25326, loss_mask_ce_2: 0.99613/0.78618, loss_mask_bce_2: 0.41297/0.30279, loss_mask_dice_2: 7.72107/1.03395, loss_spatial_bce_2: 0.00917/0.08810, loss_spatial_dice_2: 0.33867/0.18858, loss_spatial_ce_2: 0.08179/0.07395, loss_grounding_bce_2: 0.02562/0.08071, loss_grounding_dice_2: 0.39468/0.15184, loss_grounding_ce_2: 0.58276/0.25579, loss_mask_ce_3: 1.06365/0.78723, loss_mask_bce_3: 0.45769/0.30439, loss_mask_dice_3: 8.18132/1.02970, loss_spatial_bce_3: 0.00750/0.08981, loss_spatial_dice_3: 0.26270/0.18927, loss_spatial_ce_3: 0.06690/0.07919, loss_grounding_bce_3: 0.02810/0.08116, loss_grounding_dice_3: 0.45554/0.15150, loss_grounding_ce_3: 0.68836/0.25519, loss_mask_ce_4: 0.66337/0.79294, loss_mask_bce_4: 0.39732/0.30648, loss_mask_dice_4: 8.66871/1.04861, loss_spatial_bce_4: 0.00815/0.09178, loss_spatial_dice_4: 0.27975/0.19677, loss_spatial_ce_4: 0.12000/0.09166, loss_grounding_bce_4: 0.02649/0.08185, loss_grounding_dice_4: 0.44934/0.15400, loss_grounding_ce_4: 0.57802/0.26129, loss_mask_ce_5: 0.57354/0.81555, loss_mask_bce_5: 0.41950/0.30828, loss_mask_dice_5: 8.99835/1.05570, loss_spatial_bce_5: 0.00685/0.09358, loss_spatial_dice_5: 0.26353/0.19905, loss_spatial_ce_5: 0.12475/0.10332, loss_grounding_bce_5: 0.02626/0.08217, loss_grounding_dice_5: 0.38197/0.15466, loss_grounding_ce_5: 0.63255/0.28039, loss_mask_ce_6: 0.68415/0.84156, loss_mask_bce_6: 0.45906/0.31003, loss_mask_dice_6: 8.96376/1.05884, loss_spatial_bce_6: 0.01087/0.09837, loss_spatial_dice_6: 0.28926/0.20137, loss_spatial_ce_6: 0.09053/0.12515, loss_grounding_bce_6: 0.02458/0.08319, loss_grounding_dice_6: 0.32730/0.15533, loss_grounding_ce_6: 0.61942/0.29050, loss_mask_ce_7: 0.96938/0.90125, loss_mask_bce_7: 0.45590/0.31718, loss_mask_dice_7: 8.64328/1.10540, loss_spatial_bce_7: 0.01582/0.10881, loss_spatial_dice_7: 0.38597/0.22616, loss_spatial_ce_7: 0.11564/0.16720, loss_grounding_bce_7: 0.02536/0.08477, loss_grounding_dice_7: 0.37368/0.16104, loss_grounding_ce_7: 0.65013/0.33109, loss_mask_ce_8: 1.02397/1.03703, loss_mask_bce_8: 0.57132/0.33416, loss_mask_dice_8: 10.86333/1.18437, loss_spatial_bce_8: 0.03483/0.12891, loss_spatial_dice_8: 0.50401/0.26516, loss_spatial_ce_8: 0.38808/0.22037, loss_grounding_bce_8: 0.02424/0.08879, loss_grounding_dice_8: 0.44515/0.17054, loss_grounding_ce_8: 0.68314/0.43131, loss_mask_ce_9: 7.42228/3.49455, loss_mask_bce_9: 0.46908/0.36057, loss_mask_dice_9: 12.14190/1.77054, loss_spatial_bce_9: 0.07791/0.35753, loss_spatial_dice_9: 0.99117/0.79554, loss_spatial_ce_9: 1.51998/1.40447, loss_grounding_bce_9: 0.02423/0.10066, loss_grounding_dice_9: 0.46936/0.24419, loss_grounding_ce_9: 0.64996/0.69507] items per batch[64] items per second[0.37] total items[1836800] mini batches[ 28700] memory[4967] epoch remaining[0:15:44] INFO:trainer.default_trainer:epochs[ 15] optim steps[28800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09398/0.77636, loss_mask_bce_0: 0.18754/0.30201, loss_mask_dice_0: 0.12674/1.02869, loss_spatial_bce_0: 0.08931/0.08798, loss_spatial_dice_0: 0.06382/0.18594, loss_spatial_ce_0: 0.00111/0.06710, loss_grounding_bce_0: 0.05889/0.08053, loss_grounding_dice_0: 0.04596/0.15128, loss_grounding_ce_0: 0.14125/0.25160, loss_mask_ce_1: 0.08294/0.77876, loss_mask_bce_1: 0.18990/0.30270, loss_mask_dice_1: 0.12683/1.03283, loss_spatial_bce_1: 0.08810/0.08833, loss_spatial_dice_1: 0.06666/0.18854, loss_spatial_ce_1: 0.00167/0.07157, loss_grounding_bce_1: 0.06379/0.08075, loss_grounding_dice_1: 0.04857/0.15225, loss_grounding_ce_1: 0.18097/0.25317, loss_mask_ce_2: 0.09302/0.78600, loss_mask_bce_2: 0.19300/0.30279, loss_mask_dice_2: 0.12686/1.03452, loss_spatial_bce_2: 0.08633/0.08806, loss_spatial_dice_2: 0.06700/0.18858, loss_spatial_ce_2: 0.00257/0.07389, loss_grounding_bce_2: 0.05643/0.08066, loss_grounding_dice_2: 0.04375/0.15185, loss_grounding_ce_2: 0.27814/0.25564, loss_mask_ce_3: 0.11695/0.78710, loss_mask_bce_3: 0.19021/0.30437, loss_mask_dice_3: 0.12406/1.03023, loss_spatial_bce_3: 0.08824/0.08977, loss_spatial_dice_3: 0.06255/0.18925, loss_spatial_ce_3: 0.00092/0.07915, loss_grounding_bce_3: 0.05722/0.08112, loss_grounding_dice_3: 0.04134/0.15150, loss_grounding_ce_3: 0.20215/0.25501, loss_mask_ce_4: 0.10612/0.79277, loss_mask_bce_4: 0.19332/0.30647, loss_mask_dice_4: 0.12700/1.04917, loss_spatial_bce_4: 0.09150/0.09175, loss_spatial_dice_4: 0.06934/0.19677, loss_spatial_ce_4: 0.00178/0.09156, loss_grounding_bce_4: 0.05703/0.08180, loss_grounding_dice_4: 0.04208/0.15403, loss_grounding_ce_4: 0.29552/0.26126, loss_mask_ce_5: 0.10390/0.81542, loss_mask_bce_5: 0.19501/0.30828, loss_mask_dice_5: 0.13006/1.05627, loss_spatial_bce_5: 0.08827/0.09354, loss_spatial_dice_5: 0.06816/0.19903, loss_spatial_ce_5: 0.00134/0.10328, loss_grounding_bce_5: 0.05718/0.08212, loss_grounding_dice_5: 0.04091/0.15466, loss_grounding_ce_5: 0.32060/0.28018, loss_mask_ce_6: 0.09538/0.84142, loss_mask_bce_6: 0.18957/0.31003, loss_mask_dice_6: 0.12832/1.05933, loss_spatial_bce_6: 0.08883/0.09833, loss_spatial_dice_6: 0.06209/0.20135, loss_spatial_ce_6: 0.00165/0.12510, loss_grounding_bce_6: 0.06363/0.08313, loss_grounding_dice_6: 0.04223/0.15532, loss_grounding_ce_6: 0.45643/0.29033, loss_mask_ce_7: 0.11759/0.90098, loss_mask_bce_7: 0.21691/0.31716, loss_mask_dice_7: 0.12884/1.10601, loss_spatial_bce_7: 0.09645/0.10875, loss_spatial_dice_7: 0.06933/0.22616, loss_spatial_ce_7: 0.02050/0.16715, loss_grounding_bce_7: 0.06320/0.08472, loss_grounding_dice_7: 0.04146/0.16104, loss_grounding_ce_7: 0.37605/0.33074, loss_mask_ce_8: 0.16683/1.03683, loss_mask_bce_8: 0.19584/0.33414, loss_mask_dice_8: 0.14192/1.18484, loss_spatial_bce_8: 0.13738/0.12884, loss_spatial_dice_8: 0.06986/0.26516, loss_spatial_ce_8: 0.07127/0.22036, loss_grounding_bce_8: 0.08310/0.08873, loss_grounding_dice_8: 0.06145/0.17054, loss_grounding_ce_8: 0.46831/0.43086, loss_mask_ce_9: 2.10136/3.49445, loss_mask_bce_9: 0.18124/0.36055, loss_mask_dice_9: 0.15162/1.77147, loss_spatial_bce_9: 0.57806/0.35753, loss_spatial_dice_9: 0.59002/0.79555, loss_spatial_ce_9: 1.72537/1.40458, loss_grounding_bce_9: 0.13876/0.10063, loss_grounding_dice_9: 0.05918/0.24419, loss_grounding_ce_9: 1.41180/0.69488] items per batch[64] items per second[0.36] total items[1843200] mini batches[ 28800] memory[4967] epoch remaining[0:12:47] INFO:trainer.default_trainer:epochs[ 15] optim steps[28900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81112/0.77643, loss_mask_bce_0: 0.36856/0.30219, loss_mask_dice_0: 1.09109/1.02884, loss_spatial_bce_0: 0.04232/0.08793, loss_spatial_dice_0: 0.17359/0.18590, loss_spatial_ce_0: 0.00818/0.06709, loss_grounding_bce_0: 0.03265/0.08056, loss_grounding_dice_0: 0.11549/0.15130, loss_grounding_ce_0: 0.01286/0.25166, loss_mask_ce_1: 0.81688/0.77882, loss_mask_bce_1: 0.37114/0.30290, loss_mask_dice_1: 1.08444/1.03304, loss_spatial_bce_1: 0.03976/0.08829, loss_spatial_dice_1: 0.14809/0.18850, loss_spatial_ce_1: 0.00512/0.07151, loss_grounding_bce_1: 0.03275/0.08078, loss_grounding_dice_1: 0.10484/0.15226, loss_grounding_ce_1: 0.01332/0.25322, loss_mask_ce_2: 0.83347/0.78603, loss_mask_bce_2: 0.37522/0.30298, loss_mask_dice_2: 1.09852/1.03466, loss_spatial_bce_2: 0.03879/0.08802, loss_spatial_dice_2: 0.15269/0.18855, loss_spatial_ce_2: 0.00639/0.07382, loss_grounding_bce_2: 0.02818/0.08070, loss_grounding_dice_2: 0.10312/0.15188, loss_grounding_ce_2: 0.02765/0.25565, loss_mask_ce_3: 0.87406/0.78716, loss_mask_bce_3: 0.38892/0.30457, loss_mask_dice_3: 1.11531/1.03031, loss_spatial_bce_3: 0.04811/0.08973, loss_spatial_dice_3: 0.18443/0.18922, loss_spatial_ce_3: 0.00299/0.07908, loss_grounding_bce_3: 0.03311/0.08115, loss_grounding_dice_3: 0.10843/0.15152, loss_grounding_ce_3: 0.01320/0.25503, loss_mask_ce_4: 0.89024/0.79285, loss_mask_bce_4: 0.41601/0.30666, loss_mask_dice_4: 1.12076/1.04921, loss_spatial_bce_4: 0.04591/0.09172, loss_spatial_dice_4: 0.18546/0.19676, loss_spatial_ce_4: 0.00668/0.09145, loss_grounding_bce_4: 0.03281/0.08184, loss_grounding_dice_4: 0.09653/0.15405, loss_grounding_ce_4: 0.01110/0.26130, loss_mask_ce_5: 0.85422/0.81557, loss_mask_bce_5: 0.48279/0.30846, loss_mask_dice_5: 1.20804/1.05630, loss_spatial_bce_5: 0.04175/0.09351, loss_spatial_dice_5: 0.17590/0.19901, loss_spatial_ce_5: 0.01980/0.10320, loss_grounding_bce_5: 0.03054/0.08217, loss_grounding_dice_5: 0.10119/0.15470, loss_grounding_ce_5: 0.00920/0.28022, loss_mask_ce_6: 1.03737/0.84149, loss_mask_bce_6: 0.48807/0.31025, loss_mask_dice_6: 1.22318/1.05946, loss_spatial_bce_6: 0.04509/0.09830, loss_spatial_dice_6: 0.19519/0.20133, loss_spatial_ce_6: 0.05601/0.12499, loss_grounding_bce_6: 0.03323/0.08318, loss_grounding_dice_6: 0.12510/0.15535, loss_grounding_ce_6: 0.01749/0.29032, loss_mask_ce_7: 0.80066/0.90104, loss_mask_bce_7: 0.49437/0.31736, loss_mask_dice_7: 1.41265/1.10612, loss_spatial_bce_7: 0.08425/0.10872, loss_spatial_dice_7: 0.27876/0.22617, loss_spatial_ce_7: 0.10615/0.16708, loss_grounding_bce_7: 0.04038/0.08476, loss_grounding_dice_7: 0.15902/0.16108, loss_grounding_ce_7: 0.02365/0.33073, loss_mask_ce_8: 0.90031/1.03698, loss_mask_bce_8: 0.50854/0.33436, loss_mask_dice_8: 1.21896/1.18494, loss_spatial_bce_8: 0.12048/0.12880, loss_spatial_dice_8: 0.28662/0.26515, loss_spatial_ce_8: 0.14601/0.22021, loss_grounding_bce_8: 0.04150/0.08879, loss_grounding_dice_8: 0.10004/0.17061, loss_grounding_ce_8: 0.16566/0.43128, loss_mask_ce_9: 3.94640/3.49524, loss_mask_bce_9: 0.57879/0.36080, loss_mask_dice_9: 1.92447/1.77187, loss_spatial_bce_9: 0.38001/0.35743, loss_spatial_dice_9: 0.91623/0.79561, loss_spatial_ce_9: 1.39244/1.40482, loss_grounding_bce_9: 0.09306/0.10069, loss_grounding_dice_9: 0.35014/0.24429, loss_grounding_ce_9: 0.33830/0.69554] items per batch[64] items per second[0.36] total items[1849600] mini batches[ 28900] memory[4967] epoch remaining[0:09:50] INFO:trainer.default_trainer:epochs[ 15] optim steps[29000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24206/0.77630, loss_mask_bce_0: 0.07192/0.30219, loss_mask_dice_0: 0.24566/1.02889, loss_spatial_bce_0: 0.04252/0.08791, loss_spatial_dice_0: 0.15698/0.18587, loss_spatial_ce_0: 0.00003/0.06706, loss_grounding_bce_0: 0.04905/0.08052, loss_grounding_dice_0: 0.14840/0.15131, loss_grounding_ce_0: 0.00054/0.25171, loss_mask_ce_1: 0.21998/0.77867, loss_mask_bce_1: 0.07030/0.30289, loss_mask_dice_1: 0.23336/1.03312, loss_spatial_bce_1: 0.03117/0.08827, loss_spatial_dice_1: 0.12467/0.18849, loss_spatial_ce_1: 0.00001/0.07141, loss_grounding_bce_1: 0.04018/0.08074, loss_grounding_dice_1: 0.12241/0.15228, loss_grounding_ce_1: 0.00078/0.25341, loss_mask_ce_2: 0.23093/0.78589, loss_mask_bce_2: 0.06766/0.30298, loss_mask_dice_2: 0.24556/1.03482, loss_spatial_bce_2: 0.03130/0.08800, loss_spatial_dice_2: 0.12938/0.18853, loss_spatial_ce_2: 0.00001/0.07377, loss_grounding_bce_2: 0.04579/0.08066, loss_grounding_dice_2: 0.14783/0.15191, loss_grounding_ce_2: 0.00074/0.25571, loss_mask_ce_3: 0.23832/0.78706, loss_mask_bce_3: 0.06229/0.30457, loss_mask_dice_3: 0.22953/1.03047, loss_spatial_bce_3: 0.02983/0.08971, loss_spatial_dice_3: 0.12154/0.18920, loss_spatial_ce_3: 0.00008/0.07904, loss_grounding_bce_3: 0.04580/0.08112, loss_grounding_dice_3: 0.14955/0.15154, loss_grounding_ce_3: 0.00080/0.25503, loss_mask_ce_4: 0.27129/0.79274, loss_mask_bce_4: 0.06237/0.30669, loss_mask_dice_4: 0.22514/1.04930, loss_spatial_bce_4: 0.03150/0.09169, loss_spatial_dice_4: 0.11410/0.19675, loss_spatial_ce_4: 0.00036/0.09132, loss_grounding_bce_4: 0.04980/0.08182, loss_grounding_dice_4: 0.15098/0.15412, loss_grounding_ce_4: 0.00071/0.26134, loss_mask_ce_5: 0.29925/0.81549, loss_mask_bce_5: 0.05735/0.30848, loss_mask_dice_5: 0.21417/1.05648, loss_spatial_bce_5: 0.03261/0.09348, loss_spatial_dice_5: 0.12320/0.19900, loss_spatial_ce_5: 0.02915/0.10311, loss_grounding_bce_5: 0.04476/0.08213, loss_grounding_dice_5: 0.14575/0.15474, loss_grounding_ce_5: 0.00187/0.28025, loss_mask_ce_6: 0.38103/0.84140, loss_mask_bce_6: 0.06133/0.31026, loss_mask_dice_6: 0.22314/1.05968, loss_spatial_bce_6: 0.05695/0.09827, loss_spatial_dice_6: 0.18399/0.20130, loss_spatial_ce_6: 0.00966/0.12495, loss_grounding_bce_6: 0.04397/0.08315, loss_grounding_dice_6: 0.12725/0.15539, loss_grounding_ce_6: 0.00132/0.29035, loss_mask_ce_7: 0.17211/0.90090, loss_mask_bce_7: 0.14774/0.31738, loss_mask_dice_7: 0.39973/1.10635, loss_spatial_bce_7: 0.05961/0.10869, loss_spatial_dice_7: 0.16336/0.22613, loss_spatial_ce_7: 0.03283/0.16703, loss_grounding_bce_7: 0.04580/0.08473, loss_grounding_dice_7: 0.13436/0.16113, loss_grounding_ce_7: 0.00145/0.33059, loss_mask_ce_8: 0.21941/1.03680, loss_mask_bce_8: 0.13820/0.33438, loss_mask_dice_8: 0.40492/1.18509, loss_spatial_bce_8: 0.05684/0.12874, loss_spatial_dice_8: 0.16322/0.26512, loss_spatial_ce_8: 0.07781/0.22016, loss_grounding_bce_8: 0.04726/0.08876, loss_grounding_dice_8: 0.14815/0.17066, loss_grounding_ce_8: 0.00293/0.43095, loss_mask_ce_9: 1.85023/3.49571, loss_mask_bce_9: 0.12630/0.36083, loss_mask_dice_9: 0.42552/1.77196, loss_spatial_bce_9: 0.15764/0.35744, loss_spatial_dice_9: 0.60286/0.79561, loss_spatial_ce_9: 0.51291/1.40471, loss_grounding_bce_9: 0.03711/0.10066, loss_grounding_dice_9: 0.15936/0.24434, loss_grounding_ce_9: 0.09001/0.69555] items per batch[64] items per second[0.36] total items[1856000] mini batches[ 29000] memory[4967] epoch remaining[0:06:52] INFO:trainer.default_trainer:epochs[ 15] optim steps[29100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70950/0.77601, loss_mask_bce_0: 0.42775/0.30220, loss_mask_dice_0: 0.55190/1.02848, loss_spatial_bce_0: 0.14448/0.08794, loss_spatial_dice_0: 0.16537/0.18584, loss_spatial_ce_0: 0.00213/0.06700, loss_grounding_bce_0: 0.14896/0.08052, loss_grounding_dice_0: 0.13946/0.15127, loss_grounding_ce_0: 0.03953/0.25166, loss_mask_ce_1: 0.66075/0.77836, loss_mask_bce_1: 0.44186/0.30289, loss_mask_dice_1: 0.56059/1.03270, loss_spatial_bce_1: 0.13180/0.08830, loss_spatial_dice_1: 0.15885/0.18845, loss_spatial_ce_1: 0.00530/0.07137, loss_grounding_bce_1: 0.16548/0.08074, loss_grounding_dice_1: 0.14595/0.15223, loss_grounding_ce_1: 0.02359/0.25333, loss_mask_ce_2: 0.65758/0.78563, loss_mask_bce_2: 0.40361/0.30298, loss_mask_dice_2: 0.54680/1.03439, loss_spatial_bce_2: 0.12835/0.08803, loss_spatial_dice_2: 0.15211/0.18849, loss_spatial_ce_2: 0.00912/0.07380, loss_grounding_bce_2: 0.14628/0.08066, loss_grounding_dice_2: 0.12990/0.15186, loss_grounding_ce_2: 0.02306/0.25562, loss_mask_ce_3: 0.73429/0.78679, loss_mask_bce_3: 0.41359/0.30455, loss_mask_dice_3: 0.51762/1.03016, loss_spatial_bce_3: 0.12675/0.08974, loss_spatial_dice_3: 0.16603/0.18916, loss_spatial_ce_3: 0.01422/0.07904, loss_grounding_bce_3: 0.15559/0.08112, loss_grounding_dice_3: 0.14021/0.15150, loss_grounding_ce_3: 0.02128/0.25496, loss_mask_ce_4: 0.77427/0.79251, loss_mask_bce_4: 0.43097/0.30669, loss_mask_dice_4: 0.50482/1.04891, loss_spatial_bce_4: 0.13032/0.09172, loss_spatial_dice_4: 0.18289/0.19672, loss_spatial_ce_4: 0.00737/0.09127, loss_grounding_bce_4: 0.15534/0.08180, loss_grounding_dice_4: 0.13013/0.15406, loss_grounding_ce_4: 0.01513/0.26135, loss_mask_ce_5: 0.71415/0.81524, loss_mask_bce_5: 0.45915/0.30850, loss_mask_dice_5: 0.55977/1.05611, loss_spatial_bce_5: 0.12025/0.09351, loss_spatial_dice_5: 0.17627/0.19898, loss_spatial_ce_5: 0.02273/0.10308, loss_grounding_bce_5: 0.16870/0.08213, loss_grounding_dice_5: 0.14300/0.15469, loss_grounding_ce_5: 0.01391/0.28017, loss_mask_ce_6: 0.73099/0.84117, loss_mask_bce_6: 0.42674/0.31025, loss_mask_dice_6: 0.50777/1.05927, loss_spatial_bce_6: 0.12235/0.09829, loss_spatial_dice_6: 0.20866/0.20128, loss_spatial_ce_6: 0.03878/0.12492, loss_grounding_bce_6: 0.15521/0.08315, loss_grounding_dice_6: 0.13546/0.15536, loss_grounding_ce_6: 0.01345/0.29024, loss_mask_ce_7: 0.87252/0.90069, loss_mask_bce_7: 0.43549/0.31735, loss_mask_dice_7: 0.55817/1.10589, loss_spatial_bce_7: 0.11695/0.10871, loss_spatial_dice_7: 0.22202/0.22609, loss_spatial_ce_7: 0.11927/0.16698, loss_grounding_bce_7: 0.17114/0.08473, loss_grounding_dice_7: 0.13803/0.16110, loss_grounding_ce_7: 0.00631/0.33061, loss_mask_ce_8: 0.86789/1.03653, loss_mask_bce_8: 0.45184/0.33434, loss_mask_dice_8: 0.68391/1.18452, loss_spatial_bce_8: 0.12214/0.12872, loss_spatial_dice_8: 0.24479/0.26505, loss_spatial_ce_8: 0.19279/0.22009, loss_grounding_bce_8: 0.21206/0.08875, loss_grounding_dice_8: 0.15306/0.17062, loss_grounding_ce_8: 0.00629/0.43085, loss_mask_ce_9: 3.51100/3.49500, loss_mask_bce_9: 0.51154/0.36084, loss_mask_dice_9: 1.11591/1.77099, loss_spatial_bce_9: 0.40733/0.35746, loss_spatial_dice_9: 0.86701/0.79550, loss_spatial_ce_9: 1.07606/1.40440, loss_grounding_bce_9: 0.13538/0.10069, loss_grounding_dice_9: 0.12495/0.24430, loss_grounding_ce_9: 0.07305/0.69548] items per batch[64] items per second[0.37] total items[1862400] mini batches[ 29100] memory[4967] epoch remaining[0:03:54] INFO:trainer.default_trainer:epochs[ 15] optim steps[29200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58333/0.77552, loss_mask_bce_0: 0.22456/0.30222, loss_mask_dice_0: 1.15169/1.02759, loss_spatial_bce_0: 0.10121/0.08800, loss_spatial_dice_0: 0.20633/0.18584, loss_spatial_ce_0: 0.03518/0.06692, loss_grounding_bce_0: 0.02091/0.08058, loss_grounding_dice_0: 0.04346/0.15129, loss_grounding_ce_0: 0.00022/0.25156, loss_mask_ce_1: 0.59547/0.77791, loss_mask_bce_1: 0.23007/0.30292, loss_mask_dice_1: 1.15803/1.03178, loss_spatial_bce_1: 0.10778/0.08836, loss_spatial_dice_1: 0.21148/0.18845, loss_spatial_ce_1: 0.03558/0.07128, loss_grounding_bce_1: 0.01835/0.08080, loss_grounding_dice_1: 0.03940/0.15223, loss_grounding_ce_1: 0.00025/0.25323, loss_mask_ce_2: 0.63850/0.78524, loss_mask_bce_2: 0.22025/0.30299, loss_mask_dice_2: 1.17679/1.03345, loss_spatial_bce_2: 0.10188/0.08808, loss_spatial_dice_2: 0.22423/0.18848, loss_spatial_ce_2: 0.03661/0.07373, loss_grounding_bce_2: 0.01811/0.08072, loss_grounding_dice_2: 0.03968/0.15188, loss_grounding_ce_2: 0.00009/0.25557, loss_mask_ce_3: 0.66795/0.78643, loss_mask_bce_3: 0.21374/0.30454, loss_mask_dice_3: 1.06164/1.02918, loss_spatial_bce_3: 0.09457/0.08980, loss_spatial_dice_3: 0.25669/0.18916, loss_spatial_ce_3: 0.04228/0.07897, loss_grounding_bce_3: 0.02059/0.08118, loss_grounding_dice_3: 0.04026/0.15153, loss_grounding_ce_3: 0.00005/0.25483, loss_mask_ce_4: 0.37000/0.79212, loss_mask_bce_4: 0.23885/0.30671, loss_mask_dice_4: 1.43720/1.04796, loss_spatial_bce_4: 0.10494/0.09178, loss_spatial_dice_4: 0.24811/0.19672, loss_spatial_ce_4: 0.04269/0.09123, loss_grounding_bce_4: 0.01851/0.08186, loss_grounding_dice_4: 0.03895/0.15410, loss_grounding_ce_4: 0.00005/0.26119, loss_mask_ce_5: 0.46077/0.81485, loss_mask_bce_5: 0.22246/0.30854, loss_mask_dice_5: 1.46259/1.05521, loss_spatial_bce_5: 0.08028/0.09358, loss_spatial_dice_5: 0.26926/0.19897, loss_spatial_ce_5: 0.06618/0.10302, loss_grounding_bce_5: 0.01646/0.08218, loss_grounding_dice_5: 0.03993/0.15471, loss_grounding_ce_5: 0.00004/0.28007, loss_mask_ce_6: 0.54954/0.84070, loss_mask_bce_6: 0.21902/0.31029, loss_mask_dice_6: 1.48485/1.05833, loss_spatial_bce_6: 0.07147/0.09836, loss_spatial_dice_6: 0.24894/0.20128, loss_spatial_ce_6: 0.08294/0.12486, loss_grounding_bce_6: 0.01352/0.08320, loss_grounding_dice_6: 0.03418/0.15535, loss_grounding_ce_6: 0.00089/0.29016, loss_mask_ce_7: 0.59237/0.90017, loss_mask_bce_7: 0.21899/0.31737, loss_mask_dice_7: 1.33770/1.10494, loss_spatial_bce_7: 0.10211/0.10882, loss_spatial_dice_7: 0.29475/0.22613, loss_spatial_ce_7: 0.11328/0.16693, loss_grounding_bce_7: 0.01518/0.08479, loss_grounding_dice_7: 0.03584/0.16113, loss_grounding_ce_7: 0.00568/0.33029, loss_mask_ce_8: 0.62185/1.03604, loss_mask_bce_8: 0.21998/0.33433, loss_mask_dice_8: 1.17081/1.18350, loss_spatial_bce_8: 0.07555/0.12877, loss_spatial_dice_8: 0.29389/0.26504, loss_spatial_ce_8: 0.21241/0.21999, loss_grounding_bce_8: 0.01788/0.08881, loss_grounding_dice_8: 0.04576/0.17065, loss_grounding_ce_8: 0.81697/0.43071, loss_mask_ce_9: 3.57587/3.49381, loss_mask_bce_9: 0.24198/0.36082, loss_mask_dice_9: 2.19438/1.76967, loss_spatial_bce_9: 0.45722/0.35745, loss_spatial_dice_9: 0.92113/0.79543, loss_spatial_ce_9: 1.47779/1.40386, loss_grounding_bce_9: 0.10467/0.10074, loss_grounding_dice_9: 0.32238/0.24428, loss_grounding_ce_9: 1.76733/0.69536] items per batch[64] items per second[0.37] total items[1868800] mini batches[ 29200] memory[4967] epoch remaining[0:00:56] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00029232. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0025 s/iter. Inference: 0.3774 s/iter. Eval: 0.0950 s/iter. Total: 0.4748 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3744 s/iter. Eval: 0.0818 s/iter. Total: 0.4588 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0026 s/iter. Inference: 0.3750 s/iter. Eval: 0.0749 s/iter. Total: 0.4526 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 47/79. Dataloading: 0.0027 s/iter. Inference: 0.3797 s/iter. Eval: 0.0712 s/iter. Total: 0.4538 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 59/79. Dataloading: 0.0027 s/iter. Inference: 0.3788 s/iter. Eval: 0.0691 s/iter. Total: 0.4507 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 71/79. Dataloading: 0.0027 s/iter. Inference: 0.3797 s/iter. Eval: 0.0676 s/iter. Total: 0.4502 s/iter. ETA=0:00:03 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval0lbwd_no ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.244 | 83.135 | 65.593 | 133 | | Things | 61.752 | 84.225 | 72.830 | 80 | | Stuff | 45.422 | 81.490 | 54.669 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.51s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.45 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.36 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.42s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 18.94 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.45 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.760 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.728 | 69.353 | 49.588 | 25.416 | 49.745 | 67.289 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.849 | bicycle | 23.376 | car | 44.184 | | motorcycle | 42.452 | airplane | 61.990 | bus | 71.731 | | train | 74.537 | truck | 42.844 | boat | 31.039 | | traffic light | 29.978 | fire hydrant | 72.156 | stop sign | 67.110 | | parking meter | 51.911 | bench | 27.789 | bird | 34.557 | | cat | 75.568 | dog | 70.591 | horse | 50.690 | | sheep | 54.474 | cow | 55.906 | elephant | 64.932 | | bear | 79.310 | zebra | 65.557 | giraffe | 62.046 | | backpack | 24.810 | umbrella | 55.379 | handbag | 23.701 | | tie | 39.789 | suitcase | 50.113 | frisbee | 70.261 | | skis | 8.869 | snowboard | 34.666 | sports ball | 50.574 | | kite | 37.626 | baseball bat | 38.599 | baseball glove | 48.908 | | skateboard | 44.317 | surfboard | 44.830 | tennis racket | 63.467 | | bottle | 42.419 | wine glass | 37.514 | cup | 51.100 | | fork | 27.542 | knife | 24.174 | spoon | 20.883 | | bowl | 37.787 | banana | 23.342 | apple | 26.220 | | sandwich | 48.881 | orange | 31.159 | broccoli | 24.059 | | carrot | 22.997 | hot dog | 38.406 | pizza | 52.248 | | donut | 55.426 | cake | 47.711 | chair | 29.649 | | couch | 42.793 | potted plant | 22.918 | bed | 43.280 | | dining table | 14.739 | toilet | 69.456 | tv | 67.935 | | laptop | 68.875 | mouse | 64.541 | remote | 43.719 | | keyboard | 58.613 | cell phone | 46.302 | microwave | 65.685 | | oven | 34.696 | toaster | 54.334 | sink | 44.304 | | refrigerator | 70.214 | book | 14.463 | clock | 53.571 | | vase | 41.407 | scissors | 37.259 | teddy bear | 56.303 | | hair drier | 31.862 | toothbrush | 29.002 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.73285262255983, 'fwIoU': 71.45077260413626, 'IoU-person': 88.68484969079647, 'IoU-bicycle': 73.83588228832282, 'IoU-car': 68.61757531123746, 'IoU-motorcycle': 88.62173342901004, 'IoU-airplane': 84.96045437695203, 'IoU-bus': 87.74754741586597, 'IoU-train': 87.96080348467815, 'IoU-truck': 62.64406564158984, 'IoU-boat': 71.79178246646855, 'IoU-traffic light': 79.50020238320606, 'IoU-fire hydrant': 93.24330136338223, 'IoU-stop sign': 94.59472462443318, 'IoU-parking meter': 89.08708293642641, 'IoU-bench': 60.58905757154874, 'IoU-bird': 78.48031859927306, 'IoU-cat': 87.67476599400656, 'IoU-dog': 85.71777218884394, 'IoU-horse': 87.73049660814254, 'IoU-sheep': 85.57340231983656, 'IoU-cow': 86.75188470426646, 'IoU-elephant': 89.01396052682479, 'IoU-bear': 87.56865946798615, 'IoU-zebra': 84.8328424573691, 'IoU-giraffe': 85.48591972103365, 'IoU-backpack': 52.04229036422557, 'IoU-umbrella': 86.25373706022934, 'IoU-handbag': 47.92325782604917, 'IoU-tie': 75.78789252569074, 'IoU-suitcase': 87.45653565853175, 'IoU-frisbee': 84.38352774141445, 'IoU-skis': 61.41344082557628, 'IoU-snowboard': 76.22685508231555, 'IoU-sports ball': 79.1762865516364, 'IoU-kite': 79.48914694991161, 'IoU-baseball bat': 69.14283298710082, 'IoU-baseball glove': 55.48966534633518, 'IoU-skateboard': 86.25929142710991, 'IoU-surfboard': 86.73135482102126, 'IoU-tennis racket': 91.16025774507762, 'IoU-bottle': 71.10292628409081, 'IoU-wine glass': 82.12263533079212, 'IoU-cup': 72.16749400106241, 'IoU-fork': 69.71032818051526, 'IoU-knife': 62.726842044912765, 'IoU-spoon': 59.957669115719334, 'IoU-bowl': 56.634958204751776, 'IoU-banana': 83.57192652147364, 'IoU-apple': 59.54097857271159, 'IoU-sandwich': 70.12882997043248, 'IoU-orange': 81.4629847870699, 'IoU-broccoli': 71.51195489130696, 'IoU-carrot': 64.61033035225407, 'IoU-hot dog': 66.34557966345137, 'IoU-pizza': 88.12171873750788, 'IoU-donut': 70.61926103788414, 'IoU-cake': 79.82322459716613, 'IoU-chair': 62.08397672983007, 'IoU-couch': 69.4781686672162, 'IoU-potted plant': 43.87862297644801, 'IoU-bed': 69.93907420387266, 'IoU-dining table': 55.28054274591666, 'IoU-toilet': 84.75373851654663, 'IoU-tv': 77.89806825851407, 'IoU-laptop': 80.44039953569774, 'IoU-mouse': 76.43865068652408, 'IoU-remote': 71.71718831293299, 'IoU-keyboard': 71.59844775811183, 'IoU-cell phone': 79.97205613429493, 'IoU-microwave': 77.5388540379942, 'IoU-oven': 75.21827213627368, 'IoU-toaster': 85.84692160389949, 'IoU-sink': 74.16817013952375, 'IoU-refrigerator': 79.50159471912174, 'IoU-book': 55.63814653534158, 'IoU-clock': 76.34563092962229, 'IoU-vase': 66.29353543215474, 'IoU-scissors': 88.5443019554539, 'IoU-teddy bear': 76.73810580435617, 'IoU-hair drier': 49.29526772860261, 'IoU-toothbrush': 76.46233848093287, 'IoU-banner': 28.239651946867557, 'IoU-blanket': 14.473571566580933, 'IoU-bridge': 39.34267926007144, 'IoU-cardboard': 52.26803487828432, 'IoU-counter': 31.945651028789573, 'IoU-curtain': 71.66834289377981, 'IoU-door-stuff': 49.602976049074705, 'IoU-floor-wood': 62.55967848433668, 'IoU-flower': 50.1073103998787, 'IoU-fruit': 47.818022536892826, 'IoU-gravel': 33.09419817154176, 'IoU-house': 23.706581526344213, 'IoU-light': 43.48471858875532, 'IoU-mirror-stuff': 64.33111878371419, 'IoU-net': 47.707441978223315, 'IoU-pillow': 19.732027477385568, 'IoU-platform': 27.48534031305625, 'IoU-playingfield': 70.5296610339446, 'IoU-railroad': 63.430433004388846, 'IoU-river': 52.42772270050284, 'IoU-road': 68.37580067674175, 'IoU-roof': 19.9846738026069, 'IoU-sand': 62.48558305036384, 'IoU-sea': 84.09600537525095, 'IoU-shelf': 38.88326831128225, 'IoU-snow': 92.09219743380311, 'IoU-stairs': 35.77359700531602, 'IoU-tent': 9.177977396871045, 'IoU-towel': 45.64159357881661, 'IoU-wall-brick': 49.5583615826385, 'IoU-wall-stone': 33.8961706088448, 'IoU-wall-tile': 71.73382554615891, 'IoU-wall-wood': 42.18736012860575, 'IoU-water-other': 17.709741048352658, 'IoU-window-blind': 50.62482517112715, 'IoU-window-other': 48.87480803519185, 'IoU-tree-merged': 82.24402775562724, 'IoU-fence-merged': 54.95517159447423, 'IoU-ceiling-merged': 68.38718491926232, 'IoU-sky-other-merged': 94.07252574921752, 'IoU-cabinet-merged': 64.80769775010613, 'IoU-table-merged': 42.081259836219466, 'IoU-floor-other-merged': 53.57695258959046, 'IoU-pavement-merged': 57.67875921799139, 'IoU-mountain-merged': 57.25122012022721, 'IoU-grass-merged': 71.59699853360823, 'IoU-dirt-merged': 47.871561348583704, 'IoU-paper-merged': 32.455295615357514, 'IoU-food-other-merged': 43.84486027427896, 'IoU-building-other-merged': 58.8627839984113, 'IoU-rock-merged': 66.98315821168976, 'IoU-wall-other-merged': 67.40440086357602, 'IoU-rug-merged': 68.46941424183892, 'mACC': 77.04198560286216, 'pACC': 82.1691874631212, 'ACC-person': 93.15851000880855, 'ACC-bicycle': 83.74834517309706, 'ACC-car': 86.65101524122697, 'ACC-motorcycle': 92.90116708062082, 'ACC-airplane': 91.3003098683159, 'ACC-bus': 94.11417951555808, 'ACC-train': 95.24031308482024, 'ACC-truck': 69.20075978684065, 'ACC-boat': 79.53585372716556, 'ACC-traffic light': 90.78408492372225, 'ACC-fire hydrant': 96.04717597475522, 'ACC-stop sign': 97.82714439375103, 'ACC-parking meter': 92.72022259748034, 'ACC-bench': 77.92101942484551, 'ACC-bird': 82.31638291133822, 'ACC-cat': 93.22111649817326, 'ACC-dog': 88.67718414495913, 'ACC-horse': 92.410236804501, 'ACC-sheep': 89.18011555713208, 'ACC-cow': 89.86601236040181, 'ACC-elephant': 90.97503795668811, 'ACC-bear': 89.22942019931442, 'ACC-zebra': 86.67515925442663, 'ACC-giraffe': 89.12045820904689, 'ACC-backpack': 75.73860001226983, 'ACC-umbrella': 90.11147771604735, 'ACC-handbag': 66.27859832549662, 'ACC-tie': 83.9032155277456, 'ACC-suitcase': 92.77143439395144, 'ACC-frisbee': 94.2290909090909, 'ACC-skis': 74.69854887784201, 'ACC-snowboard': 82.93943327373809, 'ACC-sports ball': 86.84993952925853, 'ACC-kite': 85.39462722030511, 'ACC-baseball bat': 85.78396481579993, 'ACC-baseball glove': 61.524105978397934, 'ACC-skateboard': 90.82806307256577, 'ACC-surfboard': 92.32825680709877, 'ACC-tennis racket': 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82.05116813926428, 'ACC-oven': 87.6902039456882, 'ACC-toaster': 91.10977295433084, 'ACC-sink': 82.44175431050138, 'ACC-refrigerator': 88.03253367669075, 'ACC-book': 75.36532319072035, 'ACC-clock': 81.86598275898376, 'ACC-vase': 74.80195763054455, 'ACC-scissors': 94.28020181986795, 'ACC-teddy bear': 81.18121864555155, 'ACC-hair drier': 60.42609512424216, 'ACC-toothbrush': 84.29986101459347, 'ACC-banner': 73.31380729766813, 'ACC-blanket': 21.73679941782465, 'ACC-bridge': 52.32806040043477, 'ACC-cardboard': 64.56832921331977, 'ACC-counter': 55.09138201578914, 'ACC-curtain': 82.87964500373522, 'ACC-door-stuff': 70.30721900408253, 'ACC-floor-wood': 78.5333409747933, 'ACC-flower': 74.00402182126746, 'ACC-fruit': 71.31996232792169, 'ACC-gravel': 48.23235750633759, 'ACC-house': 28.13328123527592, 'ACC-light': 63.61976275695746, 'ACC-mirror-stuff': 74.34461363267499, 'ACC-net': 66.215278470601, 'ACC-pillow': 53.6134511461405, 'ACC-platform': 48.19459247544032, 'ACC-playingfield': 90.98878155654909, 'ACC-railroad': 80.75898462221338, 'ACC-river': 77.79252588981045, 'ACC-road': 86.24078935652237, 'ACC-roof': 27.000696744856924, 'ACC-sand': 66.376686027349, 'ACC-sea': 92.64268100112844, 'ACC-shelf': 56.95095328321671, 'ACC-snow': 95.6049191123643, 'ACC-stairs': 59.531059038555846, 'ACC-tent': 10.731103964267641, 'ACC-towel': 52.60060404853329, 'ACC-wall-brick': 70.89034657664268, 'ACC-wall-stone': 41.55909521553766, 'ACC-wall-tile': 86.31010933199641, 'ACC-wall-wood': 62.23228671094362, 'ACC-water-other': 22.989069913187386, 'ACC-window-blind': 63.68924448294436, 'ACC-window-other': 71.36404807365398, 'ACC-tree-merged': 90.33984404008423, 'ACC-fence-merged': 73.88943914863252, 'ACC-ceiling-merged': 84.41739634095148, 'ACC-sky-other-merged': 96.92377021141034, 'ACC-cabinet-merged': 78.01848724958204, 'ACC-table-merged': 56.71646754276275, 'ACC-floor-other-merged': 65.71564079145318, 'ACC-pavement-merged': 70.37262664352346, 'ACC-mountain-merged': 66.98562820485638, 'ACC-grass-merged': 83.2180227824542, 'ACC-dirt-merged': 68.79727990324255, 'ACC-paper-merged': 39.98899542829699, 'ACC-food-other-merged': 64.32995808032035, 'ACC-building-other-merged': 73.04473101228788, 'ACC-rock-merged': 82.63780785219343, 'ACC-wall-other-merged': 83.31469571897256, 'ACC-rug-merged': 82.48701495400118})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3231 s/iter. Inference: 0.1757 s/iter. Eval: 0.0000 s/iter. Total: 0.4989 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3597 s/iter. Inference: 0.3371 s/iter. Eval: 0.0000 s/iter. Total: 0.6969 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3738 s/iter. Inference: 0.5402 s/iter. Eval: 0.0000 s/iter. Total: 0.9141 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.414983904009365, 'noc@0.8': 2.5218027509511267, 'noc@0.85': 2.951126719344454, 'noc@0.9': 3.862452443664033, 'miou@iter1': 0.8680662445970401} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1432 s/iter. Eval: 0.0010 s/iter. Total: 0.1458 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.05907440185547, 'precision@0.6': 73.1053237915039, 'precision@0.7': 68.83016204833984, 'precision@0.8': 59.0361442565918, 'precision@0.9': 32.024871826171875, 'cIoU': 62.005088806152344, 'mIoU': 67.1116943359375} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.24440700030625, 'SQ': 83.1354641977878, 'RQ': 65.59325382445785, 'PQ_th': 61.751913116276654, 'SQ_th': 84.22537027776066, 'RQ_th': 72.83039142037782, 'PQ_st': 45.421756259218824, 'SQ_st': 81.49032294499857, 'RQ_st': 54.66927254759747}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.72848668402651, 'AP50': 69.35327065734964, 'AP75': 49.58780662822415, 'APs': 25.416391141927065, 'APm': 49.74472553262739, 'APl': 67.28886690786862, 'AP-person': 49.8490841082097, 'AP-bicycle': 23.375791286541762, 'AP-car': 44.183527241079474, 'AP-motorcycle': 42.45169741173038, 'AP-airplane': 61.990468349691675, 'AP-bus': 71.73120554842086, 'AP-train': 74.53717800865024, 'AP-truck': 42.844072871239774, 'AP-boat': 31.03908493435028, 'AP-traffic light': 29.977696463848876, 'AP-fire hydrant': 72.15584386700459, 'AP-stop sign': 67.11041123522806, 'AP-parking meter': 51.91144795333773, 'AP-bench': 27.78851809698562, 'AP-bird': 34.55688891185389, 'AP-cat': 75.56829252447093, 'AP-dog': 70.59087747829722, 'AP-horse': 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'ACC-cabinet-merged': 78.01848724958204, 'ACC-table-merged': 56.71646754276275, 'ACC-floor-other-merged': 65.71564079145318, 'ACC-pavement-merged': 70.37262664352346, 'ACC-mountain-merged': 66.98562820485638, 'ACC-grass-merged': 83.2180227824542, 'ACC-dirt-merged': 68.79727990324255, 'ACC-paper-merged': 39.98899542829699, 'ACC-food-other-merged': 64.32995808032035, 'ACC-building-other-merged': 73.04473101228788, 'ACC-rock-merged': 82.63780785219343, 'ACC-wall-other-merged': 83.31469571897256, 'ACC-rug-merged': 82.48701495400118})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.414983904009365, 'noc@0.8': 2.5218027509511267, 'noc@0.85': 2.951126719344454, 'noc@0.9': 3.862452443664033, 'miou@iter1': 0.8680662445970401}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 76.05907440185547, 'precision@0.6': 73.1053237915039, 'precision@0.7': 68.83016204833984, 'precision@0.8': 59.0361442565918, 'precision@0.9': 32.024871826171875, 'cIoU': 62.005088806152344, 'mIoU': 67.1116943359375}}} INFO:trainer.default_trainer:This epoch takes 0:57:20.661143 INFO:trainer.default_trainer:PROGRESS: 32.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 16 training. INFO:trainer.default_trainer:epochs[ 16] optim steps[29300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02062/0.77559, loss_mask_bce_0: 0.05584/0.30232, loss_mask_dice_0: 0.04933/1.02742, loss_spatial_bce_0: 0.04059/0.08804, loss_spatial_dice_0: 0.04510/0.18585, loss_spatial_ce_0: 0.00004/0.06690, loss_grounding_bce_0: 0.04648/0.08061, loss_grounding_dice_0: 0.04485/0.15131, loss_grounding_ce_0: 0.00304/0.25156, loss_mask_ce_1: 0.02012/0.77802, loss_mask_bce_1: 0.05465/0.30300, loss_mask_dice_1: 0.05402/1.03161, loss_spatial_bce_1: 0.04169/0.08841, loss_spatial_dice_1: 0.04679/0.18847, loss_spatial_ce_1: 0.00003/0.07124, loss_grounding_bce_1: 0.04372/0.08083, loss_grounding_dice_1: 0.04587/0.15222, loss_grounding_ce_1: 0.00216/0.25321, loss_mask_ce_2: 0.01934/0.78534, loss_mask_bce_2: 0.05348/0.30309, loss_mask_dice_2: 0.05215/1.03326, loss_spatial_bce_2: 0.04087/0.08812, loss_spatial_dice_2: 0.04456/0.18851, loss_spatial_ce_2: 0.00004/0.07370, loss_grounding_bce_2: 0.04470/0.08073, loss_grounding_dice_2: 0.04649/0.15187, loss_grounding_ce_2: 0.00169/0.25560, loss_mask_ce_3: 0.03165/0.78645, loss_mask_bce_3: 0.05454/0.30464, loss_mask_dice_3: 0.05674/1.02902, loss_spatial_bce_3: 0.04206/0.08985, loss_spatial_dice_3: 0.04316/0.18917, loss_spatial_ce_3: 0.00010/0.07894, loss_grounding_bce_3: 0.04420/0.08122, loss_grounding_dice_3: 0.04543/0.15151, loss_grounding_ce_3: 0.00311/0.25488, loss_mask_ce_4: 0.03850/0.79223, loss_mask_bce_4: 0.05233/0.30683, loss_mask_dice_4: 0.05290/1.04788, loss_spatial_bce_4: 0.04049/0.09184, loss_spatial_dice_4: 0.04666/0.19673, loss_spatial_ce_4: 0.00008/0.09121, loss_grounding_bce_4: 0.04462/0.08190, loss_grounding_dice_4: 0.04645/0.15411, loss_grounding_ce_4: 0.00709/0.26113, loss_mask_ce_5: 0.03304/0.81502, loss_mask_bce_5: 0.05516/0.30864, loss_mask_dice_5: 0.04945/1.05517, loss_spatial_bce_5: 0.04193/0.09363, loss_spatial_dice_5: 0.04914/0.19898, loss_spatial_ce_5: 0.00090/0.10305, loss_grounding_bce_5: 0.04355/0.08218, loss_grounding_dice_5: 0.03910/0.15472, loss_grounding_ce_5: 0.00361/0.28009, loss_mask_ce_6: 0.04254/0.84077, loss_mask_bce_6: 0.05275/0.31039, loss_mask_dice_6: 0.04913/1.05816, loss_spatial_bce_6: 0.04186/0.09841, loss_spatial_dice_6: 0.05155/0.20128, loss_spatial_ce_6: 0.00888/0.12489, loss_grounding_bce_6: 0.04244/0.08322, loss_grounding_dice_6: 0.04001/0.15535, loss_grounding_ce_6: 0.01069/0.29015, loss_mask_ce_7: 0.05088/0.90028, loss_mask_bce_7: 0.05353/0.31747, loss_mask_dice_7: 0.05422/1.10473, loss_spatial_bce_7: 0.03990/0.10885, loss_spatial_dice_7: 0.05396/0.22612, loss_spatial_ce_7: 0.03679/0.16690, loss_grounding_bce_7: 0.04494/0.08482, loss_grounding_dice_7: 0.04565/0.16112, loss_grounding_ce_7: 0.00909/0.33021, loss_mask_ce_8: 0.09648/1.03618, loss_mask_bce_8: 0.05712/0.33442, loss_mask_dice_8: 0.08353/1.18330, loss_spatial_bce_8: 0.05863/0.12879, loss_spatial_dice_8: 0.05941/0.26500, loss_spatial_ce_8: 0.11173/0.21996, loss_grounding_bce_8: 0.04820/0.08883, loss_grounding_dice_8: 0.06850/0.17064, loss_grounding_ce_8: 0.00859/0.43051, loss_mask_ce_9: 1.77469/3.49424, loss_mask_bce_9: 0.10117/0.36095, loss_mask_dice_9: 0.16768/1.76925, loss_spatial_bce_9: 0.61895/0.35754, loss_spatial_dice_9: 0.59479/0.79541, loss_spatial_ce_9: 0.73770/1.40364, loss_grounding_bce_9: 0.08530/0.10076, loss_grounding_dice_9: 0.13829/0.24429, loss_grounding_ce_9: 0.06010/0.69500] items per batch[64] items per second[0.17] total items[1875200] mini batches[ 29300] memory[4967] epoch remaining[0:56:11] INFO:trainer.default_trainer:epochs[ 16] optim steps[29400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.83508/0.77524, loss_mask_bce_0: 1.20176/0.30227, loss_mask_dice_0: 1.07478/1.02707, loss_spatial_bce_0: 0.22133/0.08799, loss_spatial_dice_0: 0.25298/0.18578, loss_spatial_ce_0: 0.18479/0.06682, loss_grounding_bce_0: 0.08185/0.08061, loss_grounding_dice_0: 0.15803/0.15124, loss_grounding_ce_0: 4.74113/0.25144, loss_mask_ce_1: 1.64675/0.77761, loss_mask_bce_1: 1.15091/0.30295, loss_mask_dice_1: 1.04735/1.03133, loss_spatial_bce_1: 0.17788/0.08837, loss_spatial_dice_1: 0.25849/0.18841, loss_spatial_ce_1: 0.23066/0.07117, loss_grounding_bce_1: 0.07041/0.08083, loss_grounding_dice_1: 0.14307/0.15214, loss_grounding_ce_1: 5.09338/0.25311, loss_mask_ce_2: 1.62645/0.78488, loss_mask_bce_2: 1.18465/0.30303, loss_mask_dice_2: 1.09401/1.03293, loss_spatial_bce_2: 0.16878/0.08809, loss_spatial_dice_2: 0.25407/0.18846, loss_spatial_ce_2: 0.22493/0.07360, loss_grounding_bce_2: 0.06911/0.08072, loss_grounding_dice_2: 0.13655/0.15179, loss_grounding_ce_2: 5.37520/0.25547, loss_mask_ce_3: 1.35746/0.78601, loss_mask_bce_3: 1.02806/0.30457, loss_mask_dice_3: 1.13100/1.02871, loss_spatial_bce_3: 0.16424/0.08981, loss_spatial_dice_3: 0.24292/0.18910, loss_spatial_ce_3: 0.21823/0.07882, loss_grounding_bce_3: 0.09154/0.08121, loss_grounding_dice_3: 0.14696/0.15141, loss_grounding_ce_3: 4.37589/0.25473, loss_mask_ce_4: 1.60119/0.79170, loss_mask_bce_4: 1.06664/0.30675, loss_mask_dice_4: 1.02172/1.04756, loss_spatial_bce_4: 0.22854/0.09179, loss_spatial_dice_4: 0.21962/0.19667, loss_spatial_ce_4: 0.22694/0.09110, loss_grounding_bce_4: 0.09945/0.08190, loss_grounding_dice_4: 0.14399/0.15404, loss_grounding_ce_4: 4.34214/0.26096, loss_mask_ce_5: 1.67538/0.81453, loss_mask_bce_5: 1.07959/0.30856, loss_mask_dice_5: 1.09719/1.05489, loss_spatial_bce_5: 0.22067/0.09359, loss_spatial_dice_5: 0.26458/0.19893, loss_spatial_ce_5: 0.21004/0.10299, loss_grounding_bce_5: 0.12037/0.08218, loss_grounding_dice_5: 0.16803/0.15465, loss_grounding_ce_5: 4.39821/0.27989, loss_mask_ce_6: 1.60374/0.84036, loss_mask_bce_6: 1.04186/0.31031, loss_mask_dice_6: 1.08790/1.05778, loss_spatial_bce_6: 0.19361/0.09837, loss_spatial_dice_6: 0.21693/0.20122, loss_spatial_ce_6: 0.21951/0.12481, loss_grounding_bce_6: 0.08060/0.08321, loss_grounding_dice_6: 0.23226/0.15528, loss_grounding_ce_6: 5.65057/0.28996, loss_mask_ce_7: 1.57771/0.89995, loss_mask_bce_7: 1.05053/0.31737, loss_mask_dice_7: 1.35482/1.10440, loss_spatial_bce_7: 0.23384/0.10881, loss_spatial_dice_7: 0.23998/0.22606, loss_spatial_ce_7: 0.15335/0.16682, loss_grounding_bce_7: 0.28362/0.08482, loss_grounding_dice_7: 0.35681/0.16108, loss_grounding_ce_7: 5.41252/0.32996, loss_mask_ce_8: 1.41060/1.03579, loss_mask_bce_8: 1.15946/0.33436, loss_mask_dice_8: 1.40255/1.18299, loss_spatial_bce_8: 0.30435/0.12873, loss_spatial_dice_8: 0.29113/0.26493, loss_spatial_ce_8: 0.42901/0.21986, loss_grounding_bce_8: 0.23360/0.08882, loss_grounding_dice_8: 0.31237/0.17061, loss_grounding_ce_8: 5.04466/0.43043, loss_mask_ce_9: 5.31534/3.49349, loss_mask_bce_9: 1.22316/0.36088, loss_mask_dice_9: 1.54109/1.76880, loss_spatial_bce_9: 0.46967/0.35761, loss_spatial_dice_9: 0.78183/0.79539, loss_spatial_ce_9: 1.12178/1.40356, loss_grounding_bce_9: 0.30921/0.10078, loss_grounding_dice_9: 0.45491/0.24422, loss_grounding_ce_9: 3.38611/0.69454] items per batch[64] items per second[0.36] total items[1881600] mini batches[ 29400] memory[4967] epoch remaining[0:50:31] INFO:trainer.default_trainer:epochs[ 16] optim steps[29500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20856/0.77484, loss_mask_bce_0: 0.11695/0.30214, loss_mask_dice_0: 6.14412/1.02685, loss_spatial_bce_0: 0.00662/0.08794, loss_spatial_dice_0: 0.44035/0.18575, loss_spatial_ce_0: 0.02910/0.06675, loss_grounding_bce_0: 0.01807/0.08058, loss_grounding_dice_0: 0.13672/0.15121, loss_grounding_ce_0: 0.01033/0.25168, loss_mask_ce_1: 1.22144/0.77727, loss_mask_bce_1: 0.11931/0.30283, loss_mask_dice_1: 6.23259/1.03119, loss_spatial_bce_1: 0.00626/0.08831, loss_spatial_dice_1: 0.45845/0.18838, loss_spatial_ce_1: 0.03970/0.07107, loss_grounding_bce_1: 0.01901/0.08080, loss_grounding_dice_1: 0.14582/0.15211, loss_grounding_ce_1: 0.01072/0.25335, loss_mask_ce_2: 1.33120/0.78446, loss_mask_bce_2: 0.12820/0.30290, loss_mask_dice_2: 5.91511/1.03279, loss_spatial_bce_2: 0.00639/0.08804, loss_spatial_dice_2: 0.45899/0.18843, loss_spatial_ce_2: 0.22820/0.07351, loss_grounding_bce_2: 0.01723/0.08070, loss_grounding_dice_2: 0.12802/0.15179, loss_grounding_ce_2: 0.01233/0.25571, loss_mask_ce_3: 1.22838/0.78563, loss_mask_bce_3: 0.11620/0.30444, loss_mask_dice_3: 6.01735/1.02865, loss_spatial_bce_3: 0.00590/0.08976, loss_spatial_dice_3: 0.43268/0.18908, loss_spatial_ce_3: 0.11036/0.07874, loss_grounding_bce_3: 0.01814/0.08118, loss_grounding_dice_3: 0.12674/0.15138, loss_grounding_ce_3: 0.01135/0.25495, loss_mask_ce_4: 0.89703/0.79131, loss_mask_bce_4: 0.10709/0.30665, loss_mask_dice_4: 5.81204/1.04750, loss_spatial_bce_4: 0.00564/0.09173, loss_spatial_dice_4: 0.42549/0.19663, loss_spatial_ce_4: 0.32103/0.09102, loss_grounding_bce_4: 0.01636/0.08187, loss_grounding_dice_4: 0.13652/0.15402, loss_grounding_ce_4: 0.00252/0.26121, loss_mask_ce_5: 1.56677/0.81421, loss_mask_bce_5: 0.11738/0.30844, loss_mask_dice_5: 6.63016/1.05468, loss_spatial_bce_5: 0.00878/0.09353, loss_spatial_dice_5: 0.46348/0.19890, loss_spatial_ce_5: 0.11111/0.10295, loss_grounding_bce_5: 0.01388/0.08215, loss_grounding_dice_5: 0.11991/0.15461, loss_grounding_ce_5: 0.00375/0.28035, loss_mask_ce_6: 0.95630/0.83992, loss_mask_bce_6: 0.11899/0.31018, loss_mask_dice_6: 6.30825/1.05766, loss_spatial_bce_6: 0.00765/0.09830, loss_spatial_dice_6: 0.38766/0.20120, loss_spatial_ce_6: 0.12066/0.12474, loss_grounding_bce_6: 0.01734/0.08319, loss_grounding_dice_6: 0.12916/0.15524, loss_grounding_ce_6: 0.00340/0.29016, loss_mask_ce_7: 1.23925/0.89956, loss_mask_bce_7: 0.10916/0.31726, loss_mask_dice_7: 6.30324/1.10425, loss_spatial_bce_7: 0.00893/0.10873, loss_spatial_dice_7: 0.46579/0.22602, loss_spatial_ce_7: 0.20010/0.16674, loss_grounding_bce_7: 0.01641/0.08480, loss_grounding_dice_7: 0.12688/0.16105, loss_grounding_ce_7: 0.00367/0.33028, loss_mask_ce_8: 1.32943/1.03534, loss_mask_bce_8: 0.12710/0.33422, loss_mask_dice_8: 6.79474/1.18285, loss_spatial_bce_8: 0.00798/0.12865, loss_spatial_dice_8: 0.45773/0.26489, loss_spatial_ce_8: 0.33729/0.21984, loss_grounding_bce_8: 0.01890/0.08879, loss_grounding_dice_8: 0.12182/0.17056, loss_grounding_ce_8: 0.00582/0.43085, loss_mask_ce_9: 4.08305/3.49296, loss_mask_bce_9: 0.10877/0.36075, loss_mask_dice_9: 7.92755/1.76801, loss_spatial_bce_9: 0.07786/0.35755, loss_spatial_dice_9: 0.89960/0.79535, loss_spatial_ce_9: 3.61879/1.40384, loss_grounding_bce_9: 0.02174/0.10078, loss_grounding_dice_9: 0.18104/0.24414, loss_grounding_ce_9: 0.04728/0.69479] items per batch[64] items per second[0.36] total items[1888000] mini batches[ 29500] memory[4967] epoch remaining[0:46:53] INFO:trainer.default_trainer:epochs[ 16] optim steps[29600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25184/0.77492, loss_mask_bce_0: 0.21867/0.30205, loss_mask_dice_0: 0.76699/1.02686, loss_spatial_bce_0: 0.04480/0.08797, loss_spatial_dice_0: 0.16719/0.18573, loss_spatial_ce_0: 0.00398/0.06676, loss_grounding_bce_0: 0.01125/0.08063, loss_grounding_dice_0: 0.04540/0.15124, loss_grounding_ce_0: 0.05522/0.25175, loss_mask_ce_1: 0.23768/0.77731, loss_mask_bce_1: 0.23168/0.30275, loss_mask_dice_1: 0.86066/1.03127, loss_spatial_bce_1: 0.04489/0.08833, loss_spatial_dice_1: 0.15855/0.18835, loss_spatial_ce_1: 0.00123/0.07110, loss_grounding_bce_1: 0.01183/0.08085, loss_grounding_dice_1: 0.04770/0.15215, loss_grounding_ce_1: 0.05239/0.25345, loss_mask_ce_2: 0.26153/0.78456, loss_mask_bce_2: 0.22093/0.30282, loss_mask_dice_2: 0.84064/1.03280, loss_spatial_bce_2: 0.04481/0.08806, loss_spatial_dice_2: 0.15815/0.18839, loss_spatial_ce_2: 0.00077/0.07355, loss_grounding_bce_2: 0.01152/0.08075, loss_grounding_dice_2: 0.05087/0.15183, loss_grounding_ce_2: 0.07359/0.25582, loss_mask_ce_3: 0.31471/0.78581, loss_mask_bce_3: 0.21322/0.30436, loss_mask_dice_3: 0.81260/1.02866, loss_spatial_bce_3: 0.04661/0.08979, loss_spatial_dice_3: 0.16129/0.18905, loss_spatial_ce_3: 0.00125/0.07875, loss_grounding_bce_3: 0.00968/0.08123, loss_grounding_dice_3: 0.04132/0.15141, loss_grounding_ce_3: 0.06339/0.25503, loss_mask_ce_4: 0.41124/0.79141, loss_mask_bce_4: 0.19339/0.30658, loss_mask_dice_4: 0.68972/1.04752, loss_spatial_bce_4: 0.05262/0.09175, loss_spatial_dice_4: 0.18974/0.19661, loss_spatial_ce_4: 0.00187/0.09105, loss_grounding_bce_4: 0.01059/0.08192, loss_grounding_dice_4: 0.04526/0.15405, loss_grounding_ce_4: 0.10754/0.26130, loss_mask_ce_5: 0.36689/0.81425, loss_mask_bce_5: 0.19043/0.30838, loss_mask_dice_5: 0.72173/1.05462, loss_spatial_bce_5: 0.04952/0.09355, loss_spatial_dice_5: 0.17760/0.19886, loss_spatial_ce_5: 0.01165/0.10294, loss_grounding_bce_5: 0.00964/0.08219, loss_grounding_dice_5: 0.04422/0.15464, loss_grounding_ce_5: 0.16574/0.28035, loss_mask_ce_6: 0.40799/0.83996, loss_mask_bce_6: 0.18160/0.31013, loss_mask_dice_6: 0.75638/1.05769, loss_spatial_bce_6: 0.05393/0.09835, loss_spatial_dice_6: 0.17175/0.20118, loss_spatial_ce_6: 0.01819/0.12475, loss_grounding_bce_6: 0.01298/0.08323, loss_grounding_dice_6: 0.04505/0.15527, loss_grounding_ce_6: 0.13247/0.29018, loss_mask_ce_7: 0.19693/0.89955, loss_mask_bce_7: 0.19928/0.31718, loss_mask_dice_7: 0.80334/1.10421, loss_spatial_bce_7: 0.05835/0.10877, loss_spatial_dice_7: 0.19750/0.22604, loss_spatial_ce_7: 0.01764/0.16671, loss_grounding_bce_7: 0.01481/0.08483, loss_grounding_dice_7: 0.04933/0.16106, loss_grounding_ce_7: 0.27773/0.33027, loss_mask_ce_8: 0.24804/1.03542, loss_mask_bce_8: 0.18335/0.33416, loss_mask_dice_8: 0.79845/1.18291, loss_spatial_bce_8: 0.07355/0.12865, loss_spatial_dice_8: 0.22918/0.26490, loss_spatial_ce_8: 0.08874/0.21984, loss_grounding_bce_8: 0.01658/0.08883, loss_grounding_dice_8: 0.05321/0.17061, loss_grounding_ce_8: 0.63188/0.43077, loss_mask_ce_9: 2.87749/3.49263, loss_mask_bce_9: 0.21525/0.36065, loss_mask_dice_9: 1.05203/1.76794, loss_spatial_bce_9: 0.29252/0.35756, loss_spatial_dice_9: 0.84999/0.79532, loss_spatial_ce_9: 1.32870/1.40398, loss_grounding_bce_9: 0.02061/0.10082, loss_grounding_dice_9: 0.06850/0.24415, loss_grounding_ce_9: 1.34813/0.69451] items per batch[64] items per second[0.36] total items[1894400] mini batches[ 29600] memory[4967] epoch remaining[0:43:41] INFO:trainer.default_trainer:epochs[ 16] optim steps[29700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.41148/0.77513, loss_mask_bce_0: 0.72149/0.30213, loss_mask_dice_0: 1.88727/1.02738, loss_spatial_bce_0: 0.13218/0.08796, loss_spatial_dice_0: 0.20008/0.18572, loss_spatial_ce_0: 0.06536/0.06675, loss_grounding_bce_0: 0.05206/0.08064, loss_grounding_dice_0: 0.22540/0.15122, loss_grounding_ce_0: 0.41914/0.25192, loss_mask_ce_1: 2.41307/0.77749, loss_mask_bce_1: 0.68252/0.30284, loss_mask_dice_1: 1.96301/1.03178, loss_spatial_bce_1: 0.12658/0.08832, loss_spatial_dice_1: 0.20766/0.18834, loss_spatial_ce_1: 0.07198/0.07110, loss_grounding_bce_1: 0.05158/0.08087, loss_grounding_dice_1: 0.22445/0.15216, loss_grounding_ce_1: 0.45923/0.25341, loss_mask_ce_2: 2.33002/0.78480, loss_mask_bce_2: 0.68166/0.30291, loss_mask_dice_2: 2.18874/1.03333, loss_spatial_bce_2: 0.13020/0.08805, loss_spatial_dice_2: 0.20496/0.18838, loss_spatial_ce_2: 0.07591/0.07356, loss_grounding_bce_2: 0.05431/0.08078, loss_grounding_dice_2: 0.22549/0.15180, loss_grounding_ce_2: 0.45798/0.25581, loss_mask_ce_3: 2.34232/0.78604, loss_mask_bce_3: 0.68932/0.30444, loss_mask_dice_3: 1.94640/1.02925, loss_spatial_bce_3: 0.12954/0.08978, loss_spatial_dice_3: 0.21208/0.18904, loss_spatial_ce_3: 0.07538/0.07870, loss_grounding_bce_3: 0.05821/0.08123, loss_grounding_dice_3: 0.31775/0.15139, loss_grounding_ce_3: 0.47113/0.25511, loss_mask_ce_4: 2.63723/0.79166, loss_mask_bce_4: 0.73029/0.30666, loss_mask_dice_4: 1.88652/1.04808, loss_spatial_bce_4: 0.12704/0.09174, loss_spatial_dice_4: 0.20190/0.19660, loss_spatial_ce_4: 0.08567/0.09099, loss_grounding_bce_4: 0.12075/0.08192, loss_grounding_dice_4: 0.27782/0.15404, loss_grounding_ce_4: 0.32649/0.26127, loss_mask_ce_5: 2.10161/0.81446, loss_mask_bce_5: 0.97026/0.30847, loss_mask_dice_5: 2.48817/1.05521, loss_spatial_bce_5: 0.11641/0.09354, loss_spatial_dice_5: 0.19511/0.19886, loss_spatial_ce_5: 0.11538/0.10289, loss_grounding_bce_5: 0.12329/0.08220, loss_grounding_dice_5: 0.26994/0.15462, loss_grounding_ce_5: 0.31009/0.28025, loss_mask_ce_6: 1.64563/0.84007, loss_mask_bce_6: 1.03487/0.31025, loss_mask_dice_6: 2.60741/1.05822, loss_spatial_bce_6: 0.13251/0.09834, loss_spatial_dice_6: 0.19751/0.20118, loss_spatial_ce_6: 0.22328/0.12475, loss_grounding_bce_6: 0.12638/0.08324, loss_grounding_dice_6: 0.27893/0.15524, loss_grounding_ce_6: 0.30368/0.29019, loss_mask_ce_7: 1.91418/0.89955, loss_mask_bce_7: 0.95167/0.31730, loss_mask_dice_7: 2.43654/1.10473, loss_spatial_bce_7: 0.13686/0.10878, loss_spatial_dice_7: 0.19718/0.22604, loss_spatial_ce_7: 0.18340/0.16667, loss_grounding_bce_7: 0.10863/0.08484, loss_grounding_dice_7: 0.31770/0.16104, loss_grounding_ce_7: 0.35406/0.33018, loss_mask_ce_8: 2.36226/1.03555, loss_mask_bce_8: 0.84953/0.33431, loss_mask_dice_8: 2.67425/1.18355, loss_spatial_bce_8: 0.19715/0.12865, loss_spatial_dice_8: 0.23096/0.26490, loss_spatial_ce_8: 0.14805/0.21972, loss_grounding_bce_8: 0.12350/0.08885, loss_grounding_dice_8: 0.36387/0.17057, loss_grounding_ce_8: 0.32283/0.43092, loss_mask_ce_9: 3.93345/3.49323, loss_mask_bce_9: 1.07945/0.36084, loss_mask_dice_9: 3.25791/1.76920, loss_spatial_bce_9: 0.50629/0.35755, loss_spatial_dice_9: 0.83692/0.79537, loss_spatial_ce_9: 1.41444/1.40370, loss_grounding_bce_9: 0.18233/0.10083, loss_grounding_dice_9: 0.48938/0.24416, loss_grounding_ce_9: 0.31253/0.69470] items per batch[64] items per second[0.36] total items[1900800] mini batches[ 29700] memory[4967] epoch remaining[0:40:30] INFO:trainer.default_trainer:epochs[ 16] optim steps[29800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.48495/0.77516, loss_mask_bce_0: 0.66369/0.30214, loss_mask_dice_0: 1.03174/1.02766, loss_spatial_bce_0: 0.13617/0.08793, loss_spatial_dice_0: 0.23326/0.18575, loss_spatial_ce_0: 0.05968/0.06675, loss_grounding_bce_0: 0.26672/0.08064, loss_grounding_dice_0: 0.26354/0.15131, loss_grounding_ce_0: 0.08303/0.25205, loss_mask_ce_1: 1.46463/0.77756, loss_mask_bce_1: 0.65423/0.30285, loss_mask_dice_1: 1.00810/1.03208, loss_spatial_bce_1: 0.21213/0.08830, loss_spatial_dice_1: 0.25404/0.18836, loss_spatial_ce_1: 0.05014/0.07110, loss_grounding_bce_1: 0.27348/0.08087, loss_grounding_dice_1: 0.26101/0.15222, loss_grounding_ce_1: 0.08403/0.25353, loss_mask_ce_2: 1.45264/0.78491, loss_mask_bce_2: 0.66877/0.30291, loss_mask_dice_2: 1.00040/1.03362, loss_spatial_bce_2: 0.21827/0.08803, loss_spatial_dice_2: 0.26066/0.18841, loss_spatial_ce_2: 0.05304/0.07355, loss_grounding_bce_2: 0.32598/0.08076, loss_grounding_dice_2: 0.27983/0.15185, loss_grounding_ce_2: 0.08636/0.25595, loss_mask_ce_3: 1.44881/0.78606, loss_mask_bce_3: 0.71346/0.30444, loss_mask_dice_3: 1.00465/1.02962, loss_spatial_bce_3: 0.14591/0.08976, loss_spatial_dice_3: 0.24733/0.18907, loss_spatial_ce_3: 0.06292/0.07868, loss_grounding_bce_3: 0.32919/0.08122, loss_grounding_dice_3: 0.26719/0.15144, loss_grounding_ce_3: 0.09984/0.25523, loss_mask_ce_4: 1.58297/0.79181, loss_mask_bce_4: 0.67157/0.30667, loss_mask_dice_4: 1.00787/1.04841, loss_spatial_bce_4: 0.10952/0.09172, loss_spatial_dice_4: 0.23269/0.19662, loss_spatial_ce_4: 0.08180/0.09098, loss_grounding_bce_4: 0.32014/0.08189, loss_grounding_dice_4: 0.30278/0.15411, loss_grounding_ce_4: 0.13675/0.26137, loss_mask_ce_5: 1.48231/0.81444, loss_mask_bce_5: 0.68664/0.30850, loss_mask_dice_5: 1.15105/1.05559, loss_spatial_bce_5: 0.15358/0.09351, loss_spatial_dice_5: 0.26534/0.19888, loss_spatial_ce_5: 0.19769/0.10287, loss_grounding_bce_5: 0.26750/0.08219, loss_grounding_dice_5: 0.33306/0.15470, loss_grounding_ce_5: 0.14900/0.28031, loss_mask_ce_6: 1.62118/0.84014, loss_mask_bce_6: 0.82287/0.31027, loss_mask_dice_6: 1.25110/1.05849, loss_spatial_bce_6: 0.10882/0.09831, loss_spatial_dice_6: 0.24249/0.20122, loss_spatial_ce_6: 0.25015/0.12470, loss_grounding_bce_6: 0.36885/0.08322, loss_grounding_dice_6: 0.47218/0.15529, loss_grounding_ce_6: 0.04782/0.29027, loss_mask_ce_7: 1.73055/0.89977, loss_mask_bce_7: 0.72964/0.31733, loss_mask_dice_7: 1.19325/1.10502, loss_spatial_bce_7: 0.18653/0.10874, loss_spatial_dice_7: 0.26640/0.22608, loss_spatial_ce_7: 0.27498/0.16659, loss_grounding_bce_7: 0.35053/0.08481, loss_grounding_dice_7: 0.42681/0.16109, loss_grounding_ce_7: 0.05372/0.33028, loss_mask_ce_8: 1.85166/1.03573, loss_mask_bce_8: 0.88204/0.33435, loss_mask_dice_8: 1.21064/1.18395, loss_spatial_bce_8: 0.12113/0.12861, loss_spatial_dice_8: 0.27773/0.26497, loss_spatial_ce_8: 0.29177/0.21974, loss_grounding_bce_8: 0.48310/0.08884, loss_grounding_dice_8: 0.48271/0.17066, loss_grounding_ce_8: 0.07222/0.43104, loss_mask_ce_9: 4.50325/3.49351, loss_mask_bce_9: 0.71650/0.36082, loss_mask_dice_9: 1.58431/1.76951, loss_spatial_bce_9: 0.39660/0.35754, loss_spatial_dice_9: 0.85695/0.79543, loss_spatial_ce_9: 1.01990/1.40418, loss_grounding_bce_9: 0.27560/0.10079, loss_grounding_dice_9: 0.32331/0.24427, loss_grounding_ce_9: 0.59009/0.69437] items per batch[64] items per second[0.36] total items[1907200] mini batches[ 29800] memory[4967] epoch remaining[0:37:30] INFO:trainer.default_trainer:epochs[ 16] optim steps[29900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10712/0.77481, loss_mask_bce_0: 0.06155/0.30210, loss_mask_dice_0: 0.19649/1.02726, loss_spatial_bce_0: 0.02980/0.08790, loss_spatial_dice_0: 0.09468/0.18570, loss_spatial_ce_0: 0.00016/0.06667, loss_grounding_bce_0: 0.03523/0.08064, loss_grounding_dice_0: 0.11853/0.15121, loss_grounding_ce_0: 0.00668/0.25202, loss_mask_ce_1: 0.17715/0.77723, loss_mask_bce_1: 0.05651/0.30281, loss_mask_dice_1: 0.19168/1.03165, loss_spatial_bce_1: 0.02612/0.08826, loss_spatial_dice_1: 0.09410/0.18831, loss_spatial_ce_1: 0.00036/0.07100, loss_grounding_bce_1: 0.03317/0.08087, loss_grounding_dice_1: 0.10626/0.15214, loss_grounding_ce_1: 0.00591/0.25346, loss_mask_ce_2: 0.15910/0.78459, loss_mask_bce_2: 0.05872/0.30286, loss_mask_dice_2: 0.20562/1.03323, loss_spatial_bce_2: 0.02792/0.08799, loss_spatial_dice_2: 0.09458/0.18835, loss_spatial_ce_2: 0.00056/0.07345, loss_grounding_bce_2: 0.03237/0.08076, loss_grounding_dice_2: 0.11846/0.15178, loss_grounding_ce_2: 0.00570/0.25582, loss_mask_ce_3: 0.12066/0.78576, loss_mask_bce_3: 0.05881/0.30439, loss_mask_dice_3: 0.20594/1.02918, loss_spatial_bce_3: 0.03072/0.08973, loss_spatial_dice_3: 0.09706/0.18903, loss_spatial_ce_3: 0.00103/0.07857, loss_grounding_bce_3: 0.02986/0.08121, loss_grounding_dice_3: 0.10386/0.15137, loss_grounding_ce_3: 0.00392/0.25525, loss_mask_ce_4: 0.19001/0.79159, loss_mask_bce_4: 0.06034/0.30663, loss_mask_dice_4: 0.19913/1.04801, loss_spatial_bce_4: 0.02817/0.09168, loss_spatial_dice_4: 0.09469/0.19657, loss_spatial_ce_4: 0.00034/0.09089, loss_grounding_bce_4: 0.03240/0.08190, loss_grounding_dice_4: 0.10419/0.15402, loss_grounding_ce_4: 0.00676/0.26127, loss_mask_ce_5: 0.10218/0.81409, loss_mask_bce_5: 0.06557/0.30846, loss_mask_dice_5: 0.21842/1.05516, loss_spatial_bce_5: 0.03040/0.09349, loss_spatial_dice_5: 0.09686/0.19882, loss_spatial_ce_5: 0.00121/0.10280, loss_grounding_bce_5: 0.03415/0.08219, loss_grounding_dice_5: 0.11225/0.15461, loss_grounding_ce_5: 0.00343/0.28023, loss_mask_ce_6: 0.14062/0.83977, loss_mask_bce_6: 0.06199/0.31024, loss_mask_dice_6: 0.20440/1.05813, loss_spatial_bce_6: 0.03904/0.09827, loss_spatial_dice_6: 0.09755/0.20117, loss_spatial_ce_6: 0.00081/0.12459, loss_grounding_bce_6: 0.03361/0.08322, loss_grounding_dice_6: 0.11329/0.15523, loss_grounding_ce_6: 0.00418/0.29025, loss_mask_ce_7: 0.11432/0.89948, loss_mask_bce_7: 0.07332/0.31729, loss_mask_dice_7: 0.22364/1.10454, loss_spatial_bce_7: 0.03433/0.10871, loss_spatial_dice_7: 0.11411/0.22603, loss_spatial_ce_7: 0.01360/0.16646, loss_grounding_bce_7: 0.03768/0.08479, loss_grounding_dice_7: 0.11340/0.16101, loss_grounding_ce_7: 0.00276/0.33020, loss_mask_ce_8: 0.12206/1.03542, loss_mask_bce_8: 0.06445/0.33429, loss_mask_dice_8: 0.19246/1.18356, loss_spatial_bce_8: 0.03800/0.12856, loss_spatial_dice_8: 0.13111/0.26493, loss_spatial_ce_8: 0.05225/0.21965, loss_grounding_bce_8: 0.03730/0.08884, loss_grounding_dice_8: 0.11118/0.17059, loss_grounding_ce_8: 0.00724/0.43129, loss_mask_ce_9: 1.40638/3.49282, loss_mask_bce_9: 0.06853/0.36073, loss_mask_dice_9: 0.23121/1.76894, loss_spatial_bce_9: 0.16602/0.35751, loss_spatial_dice_9: 0.54018/0.79544, loss_spatial_ce_9: 0.52822/1.40387, loss_grounding_bce_9: 0.03623/0.10077, loss_grounding_dice_9: 0.12566/0.24417, loss_grounding_ce_9: 0.09892/0.69455] items per batch[64] items per second[0.36] total items[1913600] mini batches[ 29900] memory[4967] epoch remaining[0:34:31] INFO:trainer.default_trainer:epochs[ 16] optim steps[30000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00278/0.77501, loss_mask_bce_0: 0.01610/0.30210, loss_mask_dice_0: 0.04927/1.02715, loss_spatial_bce_0: 0.02293/0.08787, loss_spatial_dice_0: 0.06904/0.18569, loss_spatial_ce_0: 0.01200/0.06662, loss_grounding_bce_0: 0.01840/0.08068, loss_grounding_dice_0: 0.06029/0.15123, loss_grounding_ce_0: 0.00012/0.25188, loss_mask_ce_1: 0.00310/0.77741, loss_mask_bce_1: 0.01811/0.30280, loss_mask_dice_1: 0.05582/1.03139, loss_spatial_bce_1: 0.02144/0.08823, loss_spatial_dice_1: 0.06197/0.18830, loss_spatial_ce_1: 0.01795/0.07097, loss_grounding_bce_1: 0.01927/0.08090, loss_grounding_dice_1: 0.05912/0.15215, loss_grounding_ce_1: 0.00013/0.25333, loss_mask_ce_2: 0.00579/0.78470, loss_mask_bce_2: 0.01739/0.30286, loss_mask_dice_2: 0.05201/1.03299, loss_spatial_bce_2: 0.02048/0.08797, loss_spatial_dice_2: 0.05802/0.18834, loss_spatial_ce_2: 0.01322/0.07338, loss_grounding_bce_2: 0.01964/0.08081, loss_grounding_dice_2: 0.06514/0.15181, loss_grounding_ce_2: 0.00015/0.25568, loss_mask_ce_3: 0.00676/0.78596, loss_mask_bce_3: 0.01549/0.30439, loss_mask_dice_3: 0.04721/1.02908, loss_spatial_bce_3: 0.02062/0.08971, loss_spatial_dice_3: 0.05939/0.18901, loss_spatial_ce_3: 0.01613/0.07847, loss_grounding_bce_3: 0.01867/0.08125, loss_grounding_dice_3: 0.05686/0.15139, loss_grounding_ce_3: 0.00018/0.25510, loss_mask_ce_4: 0.00651/0.79169, loss_mask_bce_4: 0.01683/0.30662, loss_mask_dice_4: 0.05289/1.04781, loss_spatial_bce_4: 0.02103/0.09165, loss_spatial_dice_4: 0.06279/0.19656, loss_spatial_ce_4: 0.01863/0.09081, loss_grounding_bce_4: 0.01834/0.08194, loss_grounding_dice_4: 0.05747/0.15406, loss_grounding_ce_4: 0.00023/0.26108, loss_mask_ce_5: 0.00705/0.81431, loss_mask_bce_5: 0.01522/0.30846, loss_mask_dice_5: 0.04930/1.05495, loss_spatial_bce_5: 0.02076/0.09346, loss_spatial_dice_5: 0.06285/0.19881, loss_spatial_ce_5: 0.01770/0.10274, loss_grounding_bce_5: 0.01923/0.08224, loss_grounding_dice_5: 0.06458/0.15464, loss_grounding_ce_5: 0.00118/0.28000, loss_mask_ce_6: 0.01044/0.83995, loss_mask_bce_6: 0.01721/0.31026, loss_mask_dice_6: 0.05556/1.05796, loss_spatial_bce_6: 0.02071/0.09823, loss_spatial_dice_6: 0.07048/0.20115, loss_spatial_ce_6: 0.05829/0.12452, loss_grounding_bce_6: 0.01851/0.08327, loss_grounding_dice_6: 0.05400/0.15527, loss_grounding_ce_6: 0.00087/0.29007, loss_mask_ce_7: 0.00855/0.89946, loss_mask_bce_7: 0.01719/0.31729, loss_mask_dice_7: 0.05916/1.10437, loss_spatial_bce_7: 0.01874/0.10870, loss_spatial_dice_7: 0.05227/0.22602, loss_spatial_ce_7: 0.07940/0.16636, loss_grounding_bce_7: 0.01909/0.08484, loss_grounding_dice_7: 0.06192/0.16105, loss_grounding_ce_7: 0.00068/0.32996, loss_mask_ce_8: 0.00943/1.03530, loss_mask_bce_8: 0.01658/0.33431, loss_mask_dice_8: 0.04846/1.18341, loss_spatial_bce_8: 0.02323/0.12853, loss_spatial_dice_8: 0.06219/0.26490, loss_spatial_ce_8: 0.08616/0.21949, loss_grounding_bce_8: 0.01868/0.08887, loss_grounding_dice_8: 0.05979/0.17063, loss_grounding_ce_8: 0.00362/0.43087, loss_mask_ce_9: 1.49369/3.49314, loss_mask_bce_9: 0.02006/0.36079, loss_mask_dice_9: 0.05509/1.76850, loss_spatial_bce_9: 0.38096/0.35760, loss_spatial_dice_9: 0.57849/0.79550, loss_spatial_ce_9: 0.36225/1.40413, loss_grounding_bce_9: 0.02311/0.10081, loss_grounding_dice_9: 0.05631/0.24422, loss_grounding_ce_9: 0.04319/0.69407] items per batch[64] items per second[0.37] total items[1920000] mini batches[ 30000] memory[4967] epoch remaining[0:31:27] INFO:trainer.default_trainer:epochs[ 16] optim steps[30100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97036/0.77491, loss_mask_bce_0: 0.14581/0.30199, loss_mask_dice_0: 2.05118/1.02719, loss_spatial_bce_0: 0.01609/0.08785, loss_spatial_dice_0: 0.18285/0.18568, loss_spatial_ce_0: 0.08778/0.06659, loss_grounding_bce_0: 0.00744/0.08066, loss_grounding_dice_0: 0.28885/0.15125, loss_grounding_ce_0: 0.64441/0.25180, loss_mask_ce_1: 0.40299/0.77727, loss_mask_bce_1: 0.15947/0.30270, loss_mask_dice_1: 2.16251/1.03141, loss_spatial_bce_1: 0.01947/0.08820, loss_spatial_dice_1: 0.22571/0.18828, loss_spatial_ce_1: 0.00706/0.07093, loss_grounding_bce_1: 0.00734/0.08088, loss_grounding_dice_1: 0.27922/0.15215, loss_grounding_ce_1: 0.59522/0.25331, loss_mask_ce_2: 0.62328/0.78459, loss_mask_bce_2: 0.14198/0.30277, loss_mask_dice_2: 2.13842/1.03301, loss_spatial_bce_2: 0.01539/0.08794, loss_spatial_dice_2: 0.20899/0.18833, loss_spatial_ce_2: 0.01573/0.07338, loss_grounding_bce_2: 0.00666/0.08079, loss_grounding_dice_2: 0.26648/0.15183, loss_grounding_ce_2: 0.66766/0.25561, loss_mask_ce_3: 0.84801/0.78582, loss_mask_bce_3: 0.12003/0.30429, loss_mask_dice_3: 1.72532/1.02915, loss_spatial_bce_3: 0.01432/0.08969, loss_spatial_dice_3: 0.17116/0.18900, loss_spatial_ce_3: 0.01839/0.07850, loss_grounding_bce_3: 0.00794/0.08124, loss_grounding_dice_3: 0.29246/0.15143, loss_grounding_ce_3: 0.62055/0.25502, loss_mask_ce_4: 0.68916/0.79161, loss_mask_bce_4: 0.15327/0.30652, loss_mask_dice_4: 1.97185/1.04787, loss_spatial_bce_4: 0.02507/0.09164, loss_spatial_dice_4: 0.17406/0.19656, loss_spatial_ce_4: 0.19593/0.09078, loss_grounding_bce_4: 0.00683/0.08192, loss_grounding_dice_4: 0.28146/0.15406, loss_grounding_ce_4: 0.61673/0.26100, loss_mask_ce_5: 0.39777/0.81427, loss_mask_bce_5: 0.14212/0.30837, loss_mask_dice_5: 2.21948/1.05499, loss_spatial_bce_5: 0.01996/0.09344, loss_spatial_dice_5: 0.22445/0.19880, loss_spatial_ce_5: 0.03103/0.10271, loss_grounding_bce_5: 0.00689/0.08222, loss_grounding_dice_5: 0.27399/0.15464, loss_grounding_ce_5: 0.66859/0.27990, loss_mask_ce_6: 0.43677/0.83988, loss_mask_bce_6: 0.15804/0.31014, loss_mask_dice_6: 2.21628/1.05805, loss_spatial_bce_6: 0.02954/0.09822, loss_spatial_dice_6: 0.17284/0.20115, loss_spatial_ce_6: 0.03481/0.12450, loss_grounding_bce_6: 0.00722/0.08325, loss_grounding_dice_6: 0.25924/0.15530, loss_grounding_ce_6: 0.69145/0.28995, loss_mask_ce_7: 0.85747/0.89935, loss_mask_bce_7: 0.18566/0.31718, loss_mask_dice_7: 2.28867/1.10444, loss_spatial_bce_7: 0.04326/0.10868, loss_spatial_dice_7: 0.27755/0.22601, loss_spatial_ce_7: 0.08554/0.16630, loss_grounding_bce_7: 0.00675/0.08481, loss_grounding_dice_7: 0.29549/0.16105, loss_grounding_ce_7: 0.77538/0.32975, loss_mask_ce_8: 1.36819/1.03528, loss_mask_bce_8: 0.19578/0.33421, loss_mask_dice_8: 2.72164/1.18346, loss_spatial_bce_8: 0.01599/0.12853, loss_spatial_dice_8: 0.24081/0.26488, loss_spatial_ce_8: 0.09102/0.21943, loss_grounding_bce_8: 0.01072/0.08885, loss_grounding_dice_8: 0.36553/0.17069, loss_grounding_ce_8: 0.82142/0.43086, loss_mask_ce_9: 3.19764/3.49328, loss_mask_bce_9: 0.16716/0.36070, loss_mask_dice_9: 3.25369/1.76881, loss_spatial_bce_9: 0.05636/0.35759, loss_spatial_dice_9: 0.89395/0.79547, loss_spatial_ce_9: 2.56062/1.40407, loss_grounding_bce_9: 0.00937/0.10080, loss_grounding_dice_9: 0.47055/0.24422, loss_grounding_ce_9: 0.67370/0.69399] items per batch[64] items per second[0.36] total items[1926400] mini batches[ 30100] memory[4967] epoch remaining[0:28:31] INFO:trainer.default_trainer:epochs[ 16] optim steps[30200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14189/0.77460, loss_mask_bce_0: 0.20612/0.30190, loss_mask_dice_0: 0.09986/1.02699, loss_spatial_bce_0: 0.12775/0.08784, loss_spatial_dice_0: 0.06009/0.18560, loss_spatial_ce_0: 0.00007/0.06651, loss_grounding_bce_0: 0.13181/0.08065, loss_grounding_dice_0: 0.07775/0.15120, loss_grounding_ce_0: 0.02347/0.25199, loss_mask_ce_1: 0.13562/0.77696, loss_mask_bce_1: 0.20832/0.30262, loss_mask_dice_1: 0.11437/1.03116, loss_spatial_bce_1: 0.13258/0.08820, loss_spatial_dice_1: 0.05761/0.18820, loss_spatial_ce_1: 0.00003/0.07089, loss_grounding_bce_1: 0.13623/0.08087, loss_grounding_dice_1: 0.06727/0.15209, loss_grounding_ce_1: 0.03170/0.25354, loss_mask_ce_2: 0.14737/0.78431, loss_mask_bce_2: 0.21429/0.30270, loss_mask_dice_2: 0.13364/1.03283, loss_spatial_bce_2: 0.14219/0.08794, loss_spatial_dice_2: 0.08108/0.18826, loss_spatial_ce_2: 0.00003/0.07330, loss_grounding_bce_2: 0.13160/0.08078, loss_grounding_dice_2: 0.08445/0.15177, loss_grounding_ce_2: 0.03393/0.25581, loss_mask_ce_3: 0.14788/0.78556, loss_mask_bce_3: 0.21532/0.30422, loss_mask_dice_3: 0.09542/1.02902, loss_spatial_bce_3: 0.14953/0.08968, loss_spatial_dice_3: 0.05880/0.18892, loss_spatial_ce_3: 0.00003/0.07846, loss_grounding_bce_3: 0.14052/0.08122, loss_grounding_dice_3: 0.05790/0.15138, loss_grounding_ce_3: 0.03202/0.25526, loss_mask_ce_4: 0.10433/0.79136, loss_mask_bce_4: 0.20660/0.30645, loss_mask_dice_4: 0.10132/1.04776, loss_spatial_bce_4: 0.13917/0.09163, loss_spatial_dice_4: 0.05529/0.19648, loss_spatial_ce_4: 0.00003/0.09072, loss_grounding_bce_4: 0.13106/0.08191, loss_grounding_dice_4: 0.08718/0.15401, loss_grounding_ce_4: 0.02631/0.26110, loss_mask_ce_5: 0.14982/0.81407, loss_mask_bce_5: 0.20412/0.30829, loss_mask_dice_5: 0.08929/1.05480, loss_spatial_bce_5: 0.13331/0.09344, loss_spatial_dice_5: 0.06930/0.19872, loss_spatial_ce_5: 0.00108/0.10266, loss_grounding_bce_5: 0.13545/0.08220, loss_grounding_dice_5: 0.06510/0.15461, loss_grounding_ce_5: 0.03815/0.28008, loss_mask_ce_6: 0.13332/0.83977, loss_mask_bce_6: 0.22552/0.31008, loss_mask_dice_6: 0.12028/1.05789, loss_spatial_bce_6: 0.13982/0.09822, loss_spatial_dice_6: 0.06443/0.20108, loss_spatial_ce_6: 0.00051/0.12449, loss_grounding_bce_6: 0.13619/0.08325, loss_grounding_dice_6: 0.07463/0.15526, loss_grounding_ce_6: 0.02104/0.29015, loss_mask_ce_7: 0.13661/0.89911, loss_mask_bce_7: 0.21337/0.31714, loss_mask_dice_7: 0.09776/1.10435, loss_spatial_bce_7: 0.13649/0.10868, loss_spatial_dice_7: 0.06348/0.22592, loss_spatial_ce_7: 0.00228/0.16625, loss_grounding_bce_7: 0.13870/0.08480, loss_grounding_dice_7: 0.08154/0.16103, loss_grounding_ce_7: 0.02154/0.32991, loss_mask_ce_8: 0.13395/1.03525, loss_mask_bce_8: 0.24753/0.33415, loss_mask_dice_8: 0.12525/1.18347, loss_spatial_bce_8: 0.17499/0.12850, loss_spatial_dice_8: 0.07968/0.26480, loss_spatial_ce_8: 0.07366/0.21935, loss_grounding_bce_8: 0.15431/0.08885, loss_grounding_dice_8: 0.07819/0.17068, loss_grounding_ce_8: 0.01954/0.43104, loss_mask_ce_9: 1.58013/3.49315, loss_mask_bce_9: 0.19200/0.36070, loss_mask_dice_9: 0.13270/1.76912, loss_spatial_bce_9: 0.48437/0.35760, loss_spatial_dice_9: 0.72852/0.79545, loss_spatial_ce_9: 1.68493/1.40387, loss_grounding_bce_9: 0.12282/0.10077, loss_grounding_dice_9: 0.09047/0.24421, loss_grounding_ce_9: 0.26706/0.69394] items per batch[64] items per second[0.36] total items[1932800] mini batches[ 30200] memory[4967] epoch remaining[0:25:32] INFO:trainer.default_trainer:epochs[ 16] optim steps[30300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07687/0.77445, loss_mask_bce_0: 0.73710/0.30190, loss_mask_dice_0: 4.67783/1.02731, loss_spatial_bce_0: 0.03276/0.08780, loss_spatial_dice_0: 0.18230/0.18562, loss_spatial_ce_0: 0.00102/0.06645, loss_grounding_bce_0: 0.06673/0.08065, loss_grounding_dice_0: 0.11129/0.15119, loss_grounding_ce_0: 0.08506/0.25180, loss_mask_ce_1: 1.42859/0.77682, loss_mask_bce_1: 0.73566/0.30261, loss_mask_dice_1: 4.45204/1.03158, loss_spatial_bce_1: 0.03413/0.08816, loss_spatial_dice_1: 0.18394/0.18819, loss_spatial_ce_1: 0.00104/0.07088, loss_grounding_bce_1: 0.07091/0.08086, loss_grounding_dice_1: 0.11788/0.15207, loss_grounding_ce_1: 0.08761/0.25337, loss_mask_ce_2: 1.01075/0.78422, loss_mask_bce_2: 0.75726/0.30270, loss_mask_dice_2: 4.62986/1.03316, loss_spatial_bce_2: 0.03438/0.08791, loss_spatial_dice_2: 0.18330/0.18827, loss_spatial_ce_2: 0.00092/0.07326, loss_grounding_bce_2: 0.07348/0.08077, loss_grounding_dice_2: 0.11876/0.15175, loss_grounding_ce_2: 0.09593/0.25564, loss_mask_ce_3: 1.48548/0.78550, loss_mask_bce_3: 0.72975/0.30420, loss_mask_dice_3: 4.40355/1.02925, loss_spatial_bce_3: 0.03450/0.08964, loss_spatial_dice_3: 0.19915/0.18892, loss_spatial_ce_3: 0.00426/0.07843, loss_grounding_bce_3: 0.06998/0.08121, loss_grounding_dice_3: 0.11718/0.15135, loss_grounding_ce_3: 0.06897/0.25510, loss_mask_ce_4: 1.44264/0.79128, loss_mask_bce_4: 0.80156/0.30643, loss_mask_dice_4: 4.74108/1.04820, loss_spatial_bce_4: 0.03954/0.09160, loss_spatial_dice_4: 0.21850/0.19647, loss_spatial_ce_4: 0.03008/0.09072, loss_grounding_bce_4: 0.06263/0.08190, loss_grounding_dice_4: 0.11382/0.15399, loss_grounding_ce_4: 0.06492/0.26089, loss_mask_ce_5: 1.37476/0.81393, loss_mask_bce_5: 0.79131/0.30829, loss_mask_dice_5: 4.64343/1.05504, loss_spatial_bce_5: 0.03442/0.09339, loss_spatial_dice_5: 0.20734/0.19872, loss_spatial_ce_5: 0.00611/0.10266, loss_grounding_bce_5: 0.06688/0.08219, loss_grounding_dice_5: 0.12235/0.15458, loss_grounding_ce_5: 0.18670/0.27989, loss_mask_ce_6: 1.49455/0.83969, loss_mask_bce_6: 0.78654/0.31005, loss_mask_dice_6: 4.60232/1.05810, loss_spatial_bce_6: 0.03985/0.09817, loss_spatial_dice_6: 0.22031/0.20109, loss_spatial_ce_6: 0.03633/0.12448, loss_grounding_bce_6: 0.06384/0.08323, loss_grounding_dice_6: 0.11273/0.15523, loss_grounding_ce_6: 0.06415/0.28994, loss_mask_ce_7: 1.36498/0.89897, loss_mask_bce_7: 0.79245/0.31711, loss_mask_dice_7: 5.00136/1.10470, loss_spatial_bce_7: 0.04239/0.10864, loss_spatial_dice_7: 0.24034/0.22595, loss_spatial_ce_7: 0.06593/0.16618, loss_grounding_bce_7: 0.06344/0.08478, loss_grounding_dice_7: 0.12404/0.16100, loss_grounding_ce_7: 0.11394/0.32970, loss_mask_ce_8: 1.68584/1.03515, loss_mask_bce_8: 0.90038/0.33415, loss_mask_dice_8: 5.27666/1.18386, loss_spatial_bce_8: 0.04130/0.12847, loss_spatial_dice_8: 0.22745/0.26480, loss_spatial_ce_8: 0.09310/0.21924, loss_grounding_bce_8: 0.06017/0.08884, loss_grounding_dice_8: 0.14922/0.17065, loss_grounding_ce_8: 0.15898/0.43076, loss_mask_ce_9: 5.74943/3.49342, loss_mask_bce_9: 1.12189/0.36070, loss_mask_dice_9: 9.62512/1.76974, loss_spatial_bce_9: 0.20254/0.35757, loss_spatial_dice_9: 0.96377/0.79540, loss_spatial_ce_9: 1.65421/1.40360, loss_grounding_bce_9: 0.16040/0.10075, loss_grounding_dice_9: 0.29640/0.24416, loss_grounding_ce_9: 1.63635/0.69397] items per batch[64] items per second[0.37] total items[1939200] mini batches[ 30300] memory[4967] epoch remaining[0:22:31] INFO:trainer.default_trainer:epochs[ 16] optim steps[30400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54696/0.77443, loss_mask_bce_0: 0.57850/0.30189, loss_mask_dice_0: 0.67336/1.02704, loss_spatial_bce_0: 0.15720/0.08778, loss_spatial_dice_0: 0.22328/0.18555, loss_spatial_ce_0: 0.10205/0.06639, loss_grounding_bce_0: 0.23397/0.08067, loss_grounding_dice_0: 0.24581/0.15121, loss_grounding_ce_0: 0.08832/0.25153, loss_mask_ce_1: 0.48622/0.77686, loss_mask_bce_1: 0.57442/0.30258, loss_mask_dice_1: 0.68672/1.03138, loss_spatial_bce_1: 0.15580/0.08813, loss_spatial_dice_1: 0.22426/0.18813, loss_spatial_ce_1: 0.09204/0.07087, loss_grounding_bce_1: 0.22836/0.08089, loss_grounding_dice_1: 0.23707/0.15209, loss_grounding_ce_1: 0.08695/0.25311, loss_mask_ce_2: 0.53056/0.78425, loss_mask_bce_2: 0.59339/0.30267, loss_mask_dice_2: 0.64492/1.03287, loss_spatial_bce_2: 0.15126/0.08788, loss_spatial_dice_2: 0.20913/0.18821, loss_spatial_ce_2: 0.09628/0.07323, loss_grounding_bce_2: 0.23188/0.08080, loss_grounding_dice_2: 0.22984/0.15176, loss_grounding_ce_2: 0.08650/0.25534, loss_mask_ce_3: 0.55510/0.78557, loss_mask_bce_3: 0.63009/0.30416, loss_mask_dice_3: 0.68889/1.02902, loss_spatial_bce_3: 0.15809/0.08963, loss_spatial_dice_3: 0.23307/0.18886, loss_spatial_ce_3: 0.09370/0.07837, loss_grounding_bce_3: 0.25285/0.08124, loss_grounding_dice_3: 0.25010/0.15137, loss_grounding_ce_3: 0.09273/0.25482, loss_mask_ce_4: 0.56383/0.79132, loss_mask_bce_4: 0.64570/0.30642, loss_mask_dice_4: 0.77395/1.04800, loss_spatial_bce_4: 0.15596/0.09158, loss_spatial_dice_4: 0.22224/0.19641, loss_spatial_ce_4: 0.12992/0.09069, loss_grounding_bce_4: 0.25813/0.08194, loss_grounding_dice_4: 0.24975/0.15401, loss_grounding_ce_4: 0.08723/0.26060, loss_mask_ce_5: 0.88979/0.81392, loss_mask_bce_5: 0.62425/0.30826, loss_mask_dice_5: 0.70128/1.05485, loss_spatial_bce_5: 0.15514/0.09337, loss_spatial_dice_5: 0.20830/0.19865, loss_spatial_ce_5: 0.11262/0.10262, loss_grounding_bce_5: 0.24552/0.08221, loss_grounding_dice_5: 0.25272/0.15461, loss_grounding_ce_5: 0.09442/0.27962, loss_mask_ce_6: 0.74351/0.83978, loss_mask_bce_6: 0.59972/0.31000, loss_mask_dice_6: 0.73278/1.05793, loss_spatial_bce_6: 0.15675/0.09815, loss_spatial_dice_6: 0.19983/0.20103, loss_spatial_ce_6: 0.10633/0.12441, loss_grounding_bce_6: 0.23393/0.08325, loss_grounding_dice_6: 0.23006/0.15523, loss_grounding_ce_6: 0.09080/0.28967, loss_mask_ce_7: 0.59735/0.89893, loss_mask_bce_7: 0.64627/0.31708, loss_mask_dice_7: 0.66006/1.10438, loss_spatial_bce_7: 0.16405/0.10860, loss_spatial_dice_7: 0.21644/0.22587, loss_spatial_ce_7: 0.17291/0.16607, loss_grounding_bce_7: 0.24920/0.08479, loss_grounding_dice_7: 0.25889/0.16097, loss_grounding_ce_7: 0.09358/0.32933, loss_mask_ce_8: 0.62207/1.03522, loss_mask_bce_8: 0.74605/0.33412, loss_mask_dice_8: 0.72432/1.18361, loss_spatial_bce_8: 0.14837/0.12841, loss_spatial_dice_8: 0.19449/0.26471, loss_spatial_ce_8: 0.41871/0.21924, loss_grounding_bce_8: 0.28912/0.08884, loss_grounding_dice_8: 0.24166/0.17062, loss_grounding_ce_8: 0.13386/0.43028, loss_mask_ce_9: 2.68940/3.49320, loss_mask_bce_9: 0.60571/0.36063, loss_mask_dice_9: 0.96677/1.76931, loss_spatial_bce_9: 0.55762/0.35761, loss_spatial_dice_9: 0.88295/0.79538, loss_spatial_ce_9: 1.91387/1.40353, loss_grounding_bce_9: 0.22638/0.10075, loss_grounding_dice_9: 0.29648/0.24413, loss_grounding_ce_9: 0.21056/0.69345] items per batch[64] items per second[0.36] total items[1945600] mini batches[ 30400] memory[4967] epoch remaining[0:19:33] INFO:trainer.default_trainer:epochs[ 16] optim steps[30500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97699/0.77450, loss_mask_bce_0: 0.27516/0.30189, loss_mask_dice_0: 1.43937/1.02717, loss_spatial_bce_0: 0.02696/0.08775, loss_spatial_dice_0: 0.17869/0.18552, loss_spatial_ce_0: 0.04171/0.06630, loss_grounding_bce_0: 0.05848/0.08064, loss_grounding_dice_0: 0.33665/0.15116, loss_grounding_ce_0: 0.48570/0.25154, loss_mask_ce_1: 0.91319/0.77690, loss_mask_bce_1: 0.24737/0.30258, loss_mask_dice_1: 1.65915/1.03160, loss_spatial_bce_1: 0.02995/0.08811, loss_spatial_dice_1: 0.18080/0.18811, loss_spatial_ce_1: 0.04310/0.07081, loss_grounding_bce_1: 0.05755/0.08086, loss_grounding_dice_1: 0.29506/0.15203, loss_grounding_ce_1: 0.39966/0.25309, loss_mask_ce_2: 0.92842/0.78420, loss_mask_bce_2: 0.27353/0.30269, loss_mask_dice_2: 1.67323/1.03299, loss_spatial_bce_2: 0.03499/0.08785, loss_spatial_dice_2: 0.19183/0.18819, loss_spatial_ce_2: 0.06966/0.07314, loss_grounding_bce_2: 0.06026/0.08077, loss_grounding_dice_2: 0.35901/0.15171, loss_grounding_ce_2: 0.39705/0.25534, loss_mask_ce_3: 1.04139/0.78565, loss_mask_bce_3: 0.25799/0.30417, loss_mask_dice_3: 1.35734/1.02919, loss_spatial_bce_3: 0.03352/0.08960, loss_spatial_dice_3: 0.18610/0.18884, loss_spatial_ce_3: 0.04357/0.07828, loss_grounding_bce_3: 0.06166/0.08121, loss_grounding_dice_3: 0.42215/0.15132, loss_grounding_ce_3: 0.36089/0.25479, loss_mask_ce_4: 1.27463/0.79135, loss_mask_bce_4: 0.23704/0.30642, loss_mask_dice_4: 1.41467/1.04822, loss_spatial_bce_4: 0.04506/0.09156, loss_spatial_dice_4: 0.20289/0.19639, loss_spatial_ce_4: 0.04692/0.09063, loss_grounding_bce_4: 0.05171/0.08191, loss_grounding_dice_4: 0.28768/0.15397, loss_grounding_ce_4: 0.44573/0.26054, loss_mask_ce_5: 1.28401/0.81402, loss_mask_bce_5: 0.26454/0.30826, loss_mask_dice_5: 2.00050/1.05504, loss_spatial_bce_5: 0.07469/0.09334, loss_spatial_dice_5: 0.24871/0.19864, loss_spatial_ce_5: 0.04005/0.10254, loss_grounding_bce_5: 0.05330/0.08218, loss_grounding_dice_5: 0.34144/0.15454, loss_grounding_ce_5: 0.48289/0.27956, loss_mask_ce_6: 1.11175/0.83985, loss_mask_bce_6: 0.30213/0.31000, loss_mask_dice_6: 1.84127/1.05815, loss_spatial_bce_6: 0.05762/0.09811, loss_spatial_dice_6: 0.24727/0.20102, loss_spatial_ce_6: 0.05336/0.12438, loss_grounding_bce_6: 0.05731/0.08323, loss_grounding_dice_6: 0.29524/0.15519, loss_grounding_ce_6: 0.48159/0.28964, loss_mask_ce_7: 1.64438/0.89897, loss_mask_bce_7: 0.28662/0.31708, loss_mask_dice_7: 1.87768/1.10471, loss_spatial_bce_7: 0.12657/0.10857, loss_spatial_dice_7: 0.29867/0.22586, loss_spatial_ce_7: 0.10498/0.16601, loss_grounding_bce_7: 0.05645/0.08475, loss_grounding_dice_7: 0.35643/0.16092, loss_grounding_ce_7: 0.46058/0.32917, loss_mask_ce_8: 1.39491/1.03532, loss_mask_bce_8: 0.27169/0.33413, loss_mask_dice_8: 1.89947/1.18395, loss_spatial_bce_8: 0.09633/0.12834, loss_spatial_dice_8: 0.32358/0.26470, loss_spatial_ce_8: 0.14450/0.21913, loss_grounding_bce_8: 0.06034/0.08880, loss_grounding_dice_8: 0.46867/0.17057, loss_grounding_ce_8: 0.63546/0.43027, loss_mask_ce_9: 4.72924/3.49379, loss_mask_bce_9: 0.27397/0.36068, loss_mask_dice_9: 3.38573/1.77003, loss_spatial_bce_9: 0.16938/0.35749, loss_spatial_dice_9: 0.95277/0.79542, loss_spatial_ce_9: 1.49622/1.40353, loss_grounding_bce_9: 0.05900/0.10070, loss_grounding_dice_9: 0.72276/0.24412, loss_grounding_ce_9: 0.40509/0.69337] items per batch[64] items per second[0.37] total items[1952000] mini batches[ 30500] memory[4967] epoch remaining[0:16:33] INFO:trainer.default_trainer:epochs[ 16] optim steps[30600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42144/0.77427, loss_mask_bce_0: 0.50372/0.30194, loss_mask_dice_0: 0.58398/1.02731, loss_spatial_bce_0: 0.07545/0.08774, loss_spatial_dice_0: 0.11509/0.18550, loss_spatial_ce_0: 0.01771/0.06625, loss_grounding_bce_0: 0.02135/0.08062, loss_grounding_dice_0: 0.02748/0.15118, loss_grounding_ce_0: 0.05173/0.25159, loss_mask_ce_1: 0.40920/0.77667, loss_mask_bce_1: 0.49711/0.30265, loss_mask_dice_1: 0.57417/1.03172, loss_spatial_bce_1: 0.07316/0.08808, loss_spatial_dice_1: 0.11648/0.18805, loss_spatial_ce_1: 0.01755/0.07074, loss_grounding_bce_1: 0.02053/0.08083, loss_grounding_dice_1: 0.02592/0.15205, loss_grounding_ce_1: 0.03582/0.25318, loss_mask_ce_2: 0.38760/0.78400, loss_mask_bce_2: 0.49036/0.30273, loss_mask_dice_2: 0.56037/1.03306, loss_spatial_bce_2: 0.07627/0.08782, loss_spatial_dice_2: 0.10257/0.18812, loss_spatial_ce_2: 0.04023/0.07309, loss_grounding_bce_2: 0.02314/0.08075, loss_grounding_dice_2: 0.02700/0.15175, loss_grounding_ce_2: 0.04392/0.25539, loss_mask_ce_3: 0.39219/0.78546, loss_mask_bce_3: 0.48933/0.30421, loss_mask_dice_3: 0.56455/1.02922, loss_spatial_bce_3: 0.07977/0.08957, loss_spatial_dice_3: 0.11699/0.18878, loss_spatial_ce_3: 0.05032/0.07826, loss_grounding_bce_3: 0.02570/0.08119, loss_grounding_dice_3: 0.02783/0.15136, loss_grounding_ce_3: 0.02870/0.25485, loss_mask_ce_4: 0.43016/0.79119, loss_mask_bce_4: 0.47661/0.30647, loss_mask_dice_4: 0.59688/1.04832, loss_spatial_bce_4: 0.08876/0.09153, loss_spatial_dice_4: 0.12257/0.19635, loss_spatial_ce_4: 0.03583/0.09059, loss_grounding_bce_4: 0.02228/0.08188, loss_grounding_dice_4: 0.02826/0.15399, loss_grounding_ce_4: 0.01799/0.26069, loss_mask_ce_5: 0.48831/0.81384, loss_mask_bce_5: 0.46101/0.30831, loss_mask_dice_5: 0.56764/1.05518, loss_spatial_bce_5: 0.08283/0.09333, loss_spatial_dice_5: 0.11187/0.19861, loss_spatial_ce_5: 0.04463/0.10251, loss_grounding_bce_5: 0.02331/0.08216, loss_grounding_dice_5: 0.02780/0.15459, loss_grounding_ce_5: 0.03155/0.27959, loss_mask_ce_6: 0.45575/0.83964, loss_mask_bce_6: 0.47259/0.31008, loss_mask_dice_6: 0.55019/1.05833, loss_spatial_bce_6: 0.10224/0.09811, loss_spatial_dice_6: 0.12740/0.20099, loss_spatial_ce_6: 0.08901/0.12438, loss_grounding_bce_6: 0.02569/0.08321, loss_grounding_dice_6: 0.03085/0.15523, loss_grounding_ce_6: 0.04546/0.28960, loss_mask_ce_7: 0.50036/0.89877, loss_mask_bce_7: 0.47449/0.31713, loss_mask_dice_7: 0.60021/1.10478, loss_spatial_bce_7: 0.11368/0.10857, loss_spatial_dice_7: 0.16430/0.22583, loss_spatial_ce_7: 0.06669/0.16598, loss_grounding_bce_7: 0.02173/0.08473, loss_grounding_dice_7: 0.02689/0.16096, loss_grounding_ce_7: 0.07389/0.32923, loss_mask_ce_8: 0.78346/1.03506, loss_mask_bce_8: 0.48402/0.33416, loss_mask_dice_8: 0.61127/1.18400, loss_spatial_bce_8: 0.10598/0.12831, loss_spatial_dice_8: 0.14243/0.26463, loss_spatial_ce_8: 0.08940/0.21904, loss_grounding_bce_8: 0.03570/0.08878, loss_grounding_dice_8: 0.04969/0.17060, loss_grounding_ce_8: 0.75307/0.43036, loss_mask_ce_9: 3.34123/3.49329, loss_mask_bce_9: 0.58286/0.36071, loss_mask_dice_9: 1.74880/1.77042, loss_spatial_bce_9: 0.30635/0.35750, loss_spatial_dice_9: 0.95228/0.79544, loss_spatial_ce_9: 1.49919/1.40343, loss_grounding_bce_9: 0.04907/0.10069, loss_grounding_dice_9: 0.05229/0.24418, loss_grounding_ce_9: 2.38139/0.69327] items per batch[64] items per second[0.37] total items[1958400] mini batches[ 30600] memory[4967] epoch remaining[0:13:34] INFO:trainer.default_trainer:epochs[ 16] optim steps[30700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.40771/0.77430, loss_mask_bce_0: 0.51628/0.30199, loss_mask_dice_0: 1.22785/1.02744, loss_spatial_bce_0: 0.06316/0.08769, loss_spatial_dice_0: 0.17519/0.18549, loss_spatial_ce_0: 0.00394/0.06622, loss_grounding_bce_0: 0.04541/0.08057, loss_grounding_dice_0: 0.08244/0.15116, loss_grounding_ce_0: 0.48863/0.25146, loss_mask_ce_1: 0.32766/0.77664, loss_mask_bce_1: 0.69016/0.30270, loss_mask_dice_1: 1.15294/1.03176, loss_spatial_bce_1: 0.07230/0.08803, loss_spatial_dice_1: 0.16959/0.18805, loss_spatial_ce_1: 0.02007/0.07071, loss_grounding_bce_1: 0.03136/0.08078, loss_grounding_dice_1: 0.06303/0.15208, loss_grounding_ce_1: 0.68207/0.25311, loss_mask_ce_2: 0.33347/0.78402, loss_mask_bce_2: 0.68345/0.30277, loss_mask_dice_2: 1.17866/1.03318, loss_spatial_bce_2: 0.06633/0.08777, loss_spatial_dice_2: 0.16867/0.18812, loss_spatial_ce_2: 0.04065/0.07306, loss_grounding_bce_2: 0.04841/0.08070, loss_grounding_dice_2: 0.13416/0.15175, loss_grounding_ce_2: 0.40932/0.25533, loss_mask_ce_3: 0.33771/0.78550, loss_mask_bce_3: 0.67823/0.30425, loss_mask_dice_3: 1.20273/1.02935, loss_spatial_bce_3: 0.07180/0.08953, loss_spatial_dice_3: 0.18004/0.18876, loss_spatial_ce_3: 0.04732/0.07826, loss_grounding_bce_3: 0.04038/0.08113, loss_grounding_dice_3: 0.08892/0.15136, loss_grounding_ce_3: 0.56324/0.25480, loss_mask_ce_4: 0.40270/0.79127, loss_mask_bce_4: 0.70006/0.30652, loss_mask_dice_4: 1.15343/1.04847, loss_spatial_bce_4: 0.06035/0.09149, loss_spatial_dice_4: 0.16965/0.19636, loss_spatial_ce_4: 0.06230/0.09052, loss_grounding_bce_4: 0.04345/0.08183, loss_grounding_dice_4: 0.07467/0.15399, loss_grounding_ce_4: 0.97236/0.26066, loss_mask_ce_5: 0.43493/0.81392, loss_mask_bce_5: 0.68078/0.30838, loss_mask_dice_5: 1.20736/1.05539, loss_spatial_bce_5: 0.05858/0.09327, loss_spatial_dice_5: 0.20021/0.19861, loss_spatial_ce_5: 0.09402/0.10245, loss_grounding_bce_5: 0.04197/0.08211, loss_grounding_dice_5: 0.07799/0.15458, loss_grounding_ce_5: 0.75679/0.27945, loss_mask_ce_6: 0.53781/0.83973, loss_mask_bce_6: 0.70845/0.31013, loss_mask_dice_6: 1.23879/1.05844, loss_spatial_bce_6: 0.06510/0.09806, loss_spatial_dice_6: 0.18629/0.20099, loss_spatial_ce_6: 0.24022/0.12431, loss_grounding_bce_6: 0.10134/0.08315, loss_grounding_dice_6: 0.14571/0.15525, loss_grounding_ce_6: 2.46595/0.28951, loss_mask_ce_7: 0.47621/0.89892, loss_mask_bce_7: 0.78226/0.31719, loss_mask_dice_7: 1.42177/1.10496, loss_spatial_bce_7: 0.13032/0.10851, loss_spatial_dice_7: 0.23891/0.22585, loss_spatial_ce_7: 0.18604/0.16588, loss_grounding_bce_7: 0.13592/0.08469, loss_grounding_dice_7: 0.15537/0.16097, loss_grounding_ce_7: 2.56962/0.32924, loss_mask_ce_8: 0.65303/1.03543, loss_mask_bce_8: 0.81768/0.33420, loss_mask_dice_8: 1.48118/1.18410, loss_spatial_bce_8: 0.15033/0.12823, loss_spatial_dice_8: 0.23771/0.26466, loss_spatial_ce_8: 0.19663/0.21897, loss_grounding_bce_8: 0.22903/0.08874, loss_grounding_dice_8: 0.21745/0.17061, loss_grounding_ce_8: 2.30712/0.43034, loss_mask_ce_9: 5.13441/3.49387, loss_mask_bce_9: 0.79213/0.36073, loss_mask_dice_9: 2.46588/1.77058, loss_spatial_bce_9: 0.32450/0.35729, loss_spatial_dice_9: 0.91538/0.79546, loss_spatial_ce_9: 1.81005/1.40367, loss_grounding_bce_9: 0.29458/0.10064, loss_grounding_dice_9: 0.40208/0.24416, loss_grounding_ce_9: 2.18131/0.69360] items per batch[64] items per second[0.36] total items[1964800] mini batches[ 30700] memory[4967] epoch remaining[0:10:36] INFO:trainer.default_trainer:epochs[ 16] optim steps[30800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04070/0.77446, loss_mask_bce_0: 0.96250/0.30186, loss_mask_dice_0: 6.81983/1.02789, loss_spatial_bce_0: 0.00983/0.08767, loss_spatial_dice_0: 0.22191/0.18549, loss_spatial_ce_0: 0.25896/0.06619, loss_grounding_bce_0: 0.05551/0.08056, loss_grounding_dice_0: 0.07267/0.15110, loss_grounding_ce_0: 0.02002/0.25150, loss_mask_ce_1: 0.99050/0.77679, loss_mask_bce_1: 1.00390/0.30258, loss_mask_dice_1: 6.92432/1.03229, loss_spatial_bce_1: 0.01244/0.08801, loss_spatial_dice_1: 0.30195/0.18807, loss_spatial_ce_1: 0.04746/0.07068, loss_grounding_bce_1: 0.05398/0.08077, loss_grounding_dice_1: 0.07451/0.15204, loss_grounding_ce_1: 0.02302/0.25310, loss_mask_ce_2: 1.05337/0.78427, loss_mask_bce_2: 1.00251/0.30265, loss_mask_dice_2: 6.91980/1.03376, loss_spatial_bce_2: 0.00990/0.08775, loss_spatial_dice_2: 0.27390/0.18813, loss_spatial_ce_2: 0.03890/0.07301, loss_grounding_bce_2: 0.04900/0.08069, loss_grounding_dice_2: 0.06541/0.15170, loss_grounding_ce_2: 0.02769/0.25537, loss_mask_ce_3: 1.06614/0.78572, loss_mask_bce_3: 1.08148/0.30413, loss_mask_dice_3: 6.93182/1.02992, loss_spatial_bce_3: 0.01362/0.08951, loss_spatial_dice_3: 0.25238/0.18876, loss_spatial_ce_3: 0.03326/0.07821, loss_grounding_bce_3: 0.04977/0.08113, loss_grounding_dice_3: 0.06151/0.15132, loss_grounding_ce_3: 0.01738/0.25482, loss_mask_ce_4: 1.16663/0.79156, loss_mask_bce_4: 0.98834/0.30640, loss_mask_dice_4: 6.83316/1.04906, loss_spatial_bce_4: 0.01643/0.09146, loss_spatial_dice_4: 0.31882/0.19636, loss_spatial_ce_4: 0.06296/0.09054, loss_grounding_bce_4: 0.05146/0.08182, loss_grounding_dice_4: 0.07091/0.15395, loss_grounding_ce_4: 0.00995/0.26061, loss_mask_ce_5: 1.22872/0.81420, loss_mask_bce_5: 0.92182/0.30825, loss_mask_dice_5: 7.11238/1.05592, loss_spatial_bce_5: 0.01266/0.09325, loss_spatial_dice_5: 0.26693/0.19862, loss_spatial_ce_5: 0.08637/0.10242, loss_grounding_bce_5: 0.05116/0.08210, loss_grounding_dice_5: 0.07048/0.15454, loss_grounding_ce_5: 0.02653/0.27949, loss_mask_ce_6: 1.23158/0.84002, loss_mask_bce_6: 0.96779/0.31002, loss_mask_dice_6: 6.81994/1.05902, loss_spatial_bce_6: 0.01733/0.09804, loss_spatial_dice_6: 0.31182/0.20099, loss_spatial_ce_6: 0.07621/0.12427, loss_grounding_bce_6: 0.05702/0.08315, loss_grounding_dice_6: 0.07112/0.15520, loss_grounding_ce_6: 0.03223/0.28958, loss_mask_ce_7: 1.37659/0.89910, loss_mask_bce_7: 0.95736/0.31708, loss_mask_dice_7: 6.94219/1.10555, loss_spatial_bce_7: 0.02249/0.10850, loss_spatial_dice_7: 0.38300/0.22587, loss_spatial_ce_7: 0.09830/0.16580, loss_grounding_bce_7: 0.06627/0.08469, loss_grounding_dice_7: 0.07405/0.16093, loss_grounding_ce_7: 0.06241/0.32936, loss_mask_ce_8: 1.87216/1.03569, loss_mask_bce_8: 1.08068/0.33410, loss_mask_dice_8: 7.49242/1.18461, loss_spatial_bce_8: 0.02328/0.12822, loss_spatial_dice_8: 0.45110/0.26466, loss_spatial_ce_8: 0.12761/0.21890, loss_grounding_bce_8: 0.06820/0.08873, loss_grounding_dice_8: 0.07794/0.17060, loss_grounding_ce_8: 0.02425/0.43076, loss_mask_ce_9: 8.09028/3.49477, loss_mask_bce_9: 0.92420/0.36064, loss_mask_dice_9: 9.73387/1.77080, loss_spatial_bce_9: 0.11461/0.35731, loss_spatial_dice_9: 0.97516/0.79546, loss_spatial_ce_9: 1.20961/1.40393, loss_grounding_bce_9: 0.14513/0.10067, loss_grounding_dice_9: 0.18507/0.24414, loss_grounding_ce_9: 1.69958/0.69382] items per batch[64] items per second[0.36] total items[1971200] mini batches[ 30800] memory[4967] epoch remaining[0:07:39] INFO:trainer.default_trainer:epochs[ 16] optim steps[30900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.35287/0.77456, loss_mask_bce_0: 0.07751/0.30190, loss_mask_dice_0: 2.39576/1.02801, loss_spatial_bce_0: 0.00805/0.08767, loss_spatial_dice_0: 0.37453/0.18545, loss_spatial_ce_0: 0.11694/0.06613, loss_grounding_bce_0: 0.00168/0.08053, loss_grounding_dice_0: 0.05320/0.15109, loss_grounding_ce_0: 0.04638/0.25144, loss_mask_ce_1: 1.26976/0.77693, loss_mask_bce_1: 0.06802/0.30260, loss_mask_dice_1: 2.23978/1.03233, loss_spatial_bce_1: 0.00734/0.08802, loss_spatial_dice_1: 0.35695/0.18804, loss_spatial_ce_1: 0.10775/0.07061, loss_grounding_bce_1: 0.00283/0.08074, loss_grounding_dice_1: 0.11280/0.15201, loss_grounding_ce_1: 0.05152/0.25304, loss_mask_ce_2: 1.57789/0.78433, loss_mask_bce_2: 0.07536/0.30268, loss_mask_dice_2: 2.06641/1.03386, loss_spatial_bce_2: 0.00766/0.08775, loss_spatial_dice_2: 0.37837/0.18809, loss_spatial_ce_2: 0.12132/0.07294, loss_grounding_bce_2: 0.00208/0.08066, loss_grounding_dice_2: 0.08645/0.15168, loss_grounding_ce_2: 0.10106/0.25531, loss_mask_ce_3: 1.34246/0.78585, loss_mask_bce_3: 0.07979/0.30415, loss_mask_dice_3: 2.04049/1.03008, loss_spatial_bce_3: 0.00861/0.08950, loss_spatial_dice_3: 0.40281/0.18874, loss_spatial_ce_3: 0.11903/0.07815, loss_grounding_bce_3: 0.00339/0.08110, loss_grounding_dice_3: 0.13897/0.15130, loss_grounding_ce_3: 0.09836/0.25474, loss_mask_ce_4: 1.35536/0.79166, loss_mask_bce_4: 0.06582/0.30642, loss_mask_dice_4: 1.95124/1.04911, loss_spatial_bce_4: 0.00879/0.09147, loss_spatial_dice_4: 0.31856/0.19634, loss_spatial_ce_4: 0.27917/0.09047, loss_grounding_bce_4: 0.00364/0.08179, loss_grounding_dice_4: 0.13428/0.15394, loss_grounding_ce_4: 0.09410/0.26054, loss_mask_ce_5: 1.81145/0.81427, loss_mask_bce_5: 0.08572/0.30824, loss_mask_dice_5: 2.00938/1.05603, loss_spatial_bce_5: 0.01043/0.09324, loss_spatial_dice_5: 0.40576/0.19860, loss_spatial_ce_5: 0.13773/0.10236, loss_grounding_bce_5: 0.00445/0.08208, loss_grounding_dice_5: 0.10098/0.15450, loss_grounding_ce_5: 0.10068/0.27938, loss_mask_ce_6: 1.61582/0.84014, loss_mask_bce_6: 0.08774/0.31001, loss_mask_dice_6: 2.32186/1.05905, loss_spatial_bce_6: 0.01299/0.09804, loss_spatial_dice_6: 0.39096/0.20096, loss_spatial_ce_6: 0.22287/0.12423, loss_grounding_bce_6: 0.00483/0.08312, loss_grounding_dice_6: 0.10034/0.15521, loss_grounding_ce_6: 0.07995/0.28944, loss_mask_ce_7: 2.10444/0.89918, loss_mask_bce_7: 0.08999/0.31707, loss_mask_dice_7: 2.71259/1.10560, loss_spatial_bce_7: 0.01156/0.10849, loss_spatial_dice_7: 0.47795/0.22584, loss_spatial_ce_7: 0.20769/0.16583, loss_grounding_bce_7: 0.00199/0.08467, loss_grounding_dice_7: 0.07523/0.16091, loss_grounding_ce_7: 0.26966/0.32919, loss_mask_ce_8: 1.43722/1.03578, loss_mask_bce_8: 0.09547/0.33409, loss_mask_dice_8: 2.61588/1.18467, loss_spatial_bce_8: 0.01392/0.12820, loss_spatial_dice_8: 0.53526/0.26461, loss_spatial_ce_8: 0.23321/0.21886, loss_grounding_bce_8: 0.00247/0.08871, loss_grounding_dice_8: 0.08773/0.17058, loss_grounding_ce_8: 0.50635/0.43047, loss_mask_ce_9: 5.08821/3.49425, loss_mask_bce_9: 0.05435/0.36063, loss_mask_dice_9: 2.80398/1.77086, loss_spatial_bce_9: 0.03959/0.35738, loss_spatial_dice_9: 0.77835/0.79546, loss_spatial_ce_9: 1.30937/1.40384, loss_grounding_bce_9: 0.00515/0.10068, loss_grounding_dice_9: 0.47550/0.24414, loss_grounding_ce_9: 2.70083/0.69356] items per batch[64] items per second[0.36] total items[1977600] mini batches[ 30900] memory[4967] epoch remaining[0:04:41] INFO:trainer.default_trainer:epochs[ 16] optim steps[31000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72346/0.77402, loss_mask_bce_0: 0.53758/0.30177, loss_mask_dice_0: 0.83883/1.02695, loss_spatial_bce_0: 0.09406/0.08768, loss_spatial_dice_0: 0.18058/0.18543, loss_spatial_ce_0: 0.01506/0.06610, loss_grounding_bce_0: 0.10835/0.08053, loss_grounding_dice_0: 0.11881/0.15108, loss_grounding_ce_0: 0.09703/0.25133, loss_mask_ce_1: 0.67720/0.77641, loss_mask_bce_1: 0.53352/0.30247, loss_mask_dice_1: 0.85931/1.03133, loss_spatial_bce_1: 0.08538/0.08804, loss_spatial_dice_1: 0.16044/0.18800, loss_spatial_ce_1: 0.01595/0.07055, loss_grounding_bce_1: 0.10664/0.08074, loss_grounding_dice_1: 0.11714/0.15197, loss_grounding_ce_1: 0.10450/0.25299, loss_mask_ce_2: 0.69517/0.78380, loss_mask_bce_2: 0.50838/0.30255, loss_mask_dice_2: 0.84848/1.03279, loss_spatial_bce_2: 0.08630/0.08777, loss_spatial_dice_2: 0.16981/0.18806, loss_spatial_ce_2: 0.02430/0.07291, loss_grounding_bce_2: 0.10301/0.08066, loss_grounding_dice_2: 0.11931/0.15165, loss_grounding_ce_2: 0.09847/0.25538, loss_mask_ce_3: 0.78203/0.78537, loss_mask_bce_3: 0.52037/0.30403, loss_mask_dice_3: 0.87210/1.02907, loss_spatial_bce_3: 0.09575/0.08953, loss_spatial_dice_3: 0.18059/0.18871, loss_spatial_ce_3: 0.03977/0.07810, loss_grounding_bce_3: 0.10248/0.08110, loss_grounding_dice_3: 0.11359/0.15127, loss_grounding_ce_3: 0.09294/0.25470, loss_mask_ce_4: 0.83587/0.79125, loss_mask_bce_4: 0.52672/0.30628, loss_mask_dice_4: 0.88741/1.04803, loss_spatial_bce_4: 0.09941/0.09149, loss_spatial_dice_4: 0.17703/0.19632, loss_spatial_ce_4: 0.07532/0.09051, loss_grounding_bce_4: 0.10719/0.08179, loss_grounding_dice_4: 0.11511/0.15394, loss_grounding_ce_4: 0.11206/0.26045, loss_mask_ce_5: 0.75195/0.81379, loss_mask_bce_5: 0.50062/0.30808, loss_mask_dice_5: 0.89349/1.05497, loss_spatial_bce_5: 0.09483/0.09326, loss_spatial_dice_5: 0.19347/0.19857, loss_spatial_ce_5: 0.07836/0.10231, loss_grounding_bce_5: 0.10343/0.08207, loss_grounding_dice_5: 0.12028/0.15447, loss_grounding_ce_5: 0.09503/0.27941, loss_mask_ce_6: 0.86480/0.83978, loss_mask_bce_6: 0.50631/0.30987, loss_mask_dice_6: 0.91547/1.05791, loss_spatial_bce_6: 0.09939/0.09806, loss_spatial_dice_6: 0.18370/0.20093, loss_spatial_ce_6: 0.19270/0.12420, loss_grounding_bce_6: 0.10987/0.08312, loss_grounding_dice_6: 0.12655/0.15517, loss_grounding_ce_6: 0.09598/0.28938, loss_mask_ce_7: 0.93863/0.89872, loss_mask_bce_7: 0.49355/0.31692, loss_mask_dice_7: 0.96740/1.10452, loss_spatial_bce_7: 0.10665/0.10854, loss_spatial_dice_7: 0.20290/0.22581, loss_spatial_ce_7: 0.28304/0.16569, loss_grounding_bce_7: 0.11451/0.08466, loss_grounding_dice_7: 0.12419/0.16088, loss_grounding_ce_7: 0.15293/0.32899, loss_mask_ce_8: 0.92990/1.03533, loss_mask_bce_8: 0.50570/0.33395, loss_mask_dice_8: 1.02001/1.18349, loss_spatial_bce_8: 0.10881/0.12823, loss_spatial_dice_8: 0.19864/0.26455, loss_spatial_ce_8: 0.25225/0.21878, loss_grounding_bce_8: 0.13242/0.08870, loss_grounding_dice_8: 0.19580/0.17055, loss_grounding_ce_8: 0.11409/0.43028, loss_mask_ce_9: 3.45591/3.49310, loss_mask_bce_9: 0.59185/0.36050, loss_mask_dice_9: 1.86801/1.76900, loss_spatial_bce_9: 0.45783/0.35737, loss_spatial_dice_9: 0.79396/0.79535, loss_spatial_ce_9: 1.26733/1.40353, loss_grounding_bce_9: 0.14137/0.10066, loss_grounding_dice_9: 0.24440/0.24411, loss_grounding_ce_9: 0.45167/0.69352] items per batch[64] items per second[0.36] total items[1984000] mini batches[ 31000] memory[4967] epoch remaining[0:01:44] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00031059. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0020 s/iter. Inference: 0.3752 s/iter. Eval: 0.0993 s/iter. Total: 0.4765 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3740 s/iter. Eval: 0.0839 s/iter. Total: 0.4603 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0025 s/iter. Inference: 0.3791 s/iter. Eval: 0.0802 s/iter. Total: 0.4619 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0026 s/iter. Inference: 0.3831 s/iter. Eval: 0.0760 s/iter. Total: 0.4618 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0026 s/iter. Inference: 0.3845 s/iter. Eval: 0.0729 s/iter. Total: 0.4602 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3825 s/iter. Eval: 0.0708 s/iter. Total: 0.4561 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9jcbbo6p ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.286 | 83.040 | 65.783 | 133 | | Things | 61.370 | 84.066 | 72.498 | 80 | | Stuff | 46.102 | 81.490 | 55.645 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... DONE (t=0.52s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.04 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.34 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.63s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.42 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.692 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.374 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.769 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.46 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.633 | 69.183 | 49.165 | 25.929 | 49.598 | 67.984 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.978 | bicycle | 22.584 | car | 42.782 | | motorcycle | 42.936 | airplane | 60.891 | bus | 71.096 | | train | 75.236 | truck | 44.353 | boat | 31.493 | | traffic light | 29.178 | fire hydrant | 71.721 | stop sign | 68.250 | | parking meter | 52.279 | bench | 27.308 | bird | 34.003 | | cat | 76.515 | dog | 70.722 | horse | 50.174 | | sheep | 53.386 | cow | 56.935 | elephant | 66.051 | | bear | 80.165 | zebra | 66.050 | giraffe | 62.740 | | backpack | 23.222 | umbrella | 55.078 | handbag | 23.582 | | tie | 39.851 | suitcase | 51.885 | frisbee | 69.605 | | skis | 8.574 | snowboard | 34.244 | sports ball | 47.746 | | kite | 35.711 | baseball bat | 38.639 | baseball glove | 50.013 | | skateboard | 43.466 | surfboard | 44.419 | tennis racket | 62.913 | | bottle | 41.132 | wine glass | 36.741 | cup | 51.352 | | fork | 26.414 | knife | 24.322 | spoon | 21.904 | | bowl | 40.016 | banana | 22.275 | apple | 27.323 | | sandwich | 50.879 | orange | 29.466 | broccoli | 24.435 | | carrot | 23.137 | hot dog | 36.235 | pizza | 55.098 | | donut | 55.929 | cake | 46.688 | chair | 29.098 | | couch | 42.529 | potted plant | 22.569 | bed | 43.251 | | dining table | 15.376 | toilet | 69.749 | tv | 67.421 | | laptop | 70.478 | mouse | 64.028 | remote | 43.000 | | keyboard | 57.119 | cell phone | 46.075 | microwave | 65.445 | | oven | 36.120 | toaster | 46.443 | sink | 42.601 | | refrigerator | 69.607 | book | 13.497 | clock | 54.624 | | vase | 41.243 | scissors | 36.826 | teddy bear | 57.353 | | hair drier | 39.247 | toothbrush | 28.846 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.77379069188555, 'fwIoU': 71.50969935615288, 'IoU-person': 88.69590644972828, 'IoU-bicycle': 78.02051464265884, 'IoU-car': 71.72856634519853, 'IoU-motorcycle': 85.83706121682219, 'IoU-airplane': 89.41794588183278, 'IoU-bus': 86.52485526776972, 'IoU-train': 87.07171858951767, 'IoU-truck': 68.33970192484523, 'IoU-boat': 74.84703963464362, 'IoU-traffic light': 79.17879734066658, 'IoU-fire hydrant': 93.3345129125003, 'IoU-stop sign': 94.41265737445617, 'IoU-parking meter': 88.65976306356916, 'IoU-bench': 61.365086464604055, 'IoU-bird': 76.91369614498782, 'IoU-cat': 90.64614033992767, 'IoU-dog': 78.00066269227317, 'IoU-horse': 89.43527476694331, 'IoU-sheep': 82.57804745346333, 'IoU-cow': 86.47107786076087, 'IoU-elephant': 91.89828306389452, 'IoU-bear': 75.79397246831007, 'IoU-zebra': 83.60201502752453, 'IoU-giraffe': 87.06712645845224, 'IoU-backpack': 52.73140342518787, 'IoU-umbrella': 88.79778874636838, 'IoU-handbag': 51.19192441754746, 'IoU-tie': 76.67783469201459, 'IoU-suitcase': 85.03952051221131, 'IoU-frisbee': 84.34210226031618, 'IoU-skis': 58.98320698889054, 'IoU-snowboard': 75.13990786178384, 'IoU-sports ball': 80.33996257206292, 'IoU-kite': 79.3714954796936, 'IoU-baseball bat': 69.60379738072233, 'IoU-baseball glove': 79.81756201071023, 'IoU-skateboard': 85.71902179648583, 'IoU-surfboard': 86.83016929032715, 'IoU-tennis racket': 90.87474599254016, 'IoU-bottle': 70.67759019333793, 'IoU-wine glass': 83.00627127725207, 'IoU-cup': 71.43222894147982, 'IoU-fork': 69.06650372787489, 'IoU-knife': 64.86580674082785, 'IoU-spoon': 59.55855004120877, 'IoU-bowl': 60.714303642754, 'IoU-banana': 83.37541980557016, 'IoU-apple': 60.008942704615286, 'IoU-sandwich': 69.13362187725053, 'IoU-orange': 79.25588453183212, 'IoU-broccoli': 70.64878300723174, 'IoU-carrot': 65.25500209502458, 'IoU-hot dog': 63.105494992761066, 'IoU-pizza': 86.21413920445956, 'IoU-donut': 61.65768582541787, 'IoU-cake': 77.44822771835287, 'IoU-chair': 63.59086939743535, 'IoU-couch': 68.32974441682717, 'IoU-potted plant': 45.522296207666265, 'IoU-bed': 74.05395312407698, 'IoU-dining table': 53.2078959579832, 'IoU-toilet': 87.5842500284495, 'IoU-tv': 77.65944579353395, 'IoU-laptop': 79.23142096088883, 'IoU-mouse': 69.70599624676059, 'IoU-remote': 67.86643899655903, 'IoU-keyboard': 62.36997809682148, 'IoU-cell phone': 81.31821159612564, 'IoU-microwave': 78.15078353079379, 'IoU-oven': 72.84812337092164, 'IoU-toaster': 82.76219693958664, 'IoU-sink': 75.02385639329387, 'IoU-refrigerator': 82.9172610371466, 'IoU-book': 52.34661782022766, 'IoU-clock': 69.12827668767194, 'IoU-vase': 70.83703185670328, 'IoU-scissors': 86.7014234502376, 'IoU-teddy bear': 84.71141126871926, 'IoU-hair drier': 49.11461797830126, 'IoU-toothbrush': 72.67248591460661, 'IoU-banner': 28.27677660117377, 'IoU-blanket': 15.032703530699967, 'IoU-bridge': 37.16956867603402, 'IoU-cardboard': 53.90700960759821, 'IoU-counter': 33.61516845595019, 'IoU-curtain': 72.45822324544933, 'IoU-door-stuff': 48.088113038763034, 'IoU-floor-wood': 65.65026014901989, 'IoU-flower': 49.4876766615416, 'IoU-fruit': 50.121800123562906, 'IoU-gravel': 32.92939610482908, 'IoU-house': 23.77572874270433, 'IoU-light': 44.55859956342722, 'IoU-mirror-stuff': 66.51677687900781, 'IoU-net': 43.66234003277426, 'IoU-pillow': 16.605006999015785, 'IoU-platform': 30.164486736834977, 'IoU-playingfield': 67.68480525159346, 'IoU-railroad': 62.3715234038534, 'IoU-river': 52.85079690867295, 'IoU-road': 67.36802052942534, 'IoU-roof': 19.804374965371142, 'IoU-sand': 64.1625909684414, 'IoU-sea': 86.66012337953637, 'IoU-shelf': 38.53445785631223, 'IoU-snow': 91.93414464320043, 'IoU-stairs': 35.01909197717355, 'IoU-tent': 11.315401813424257, 'IoU-towel': 45.027308780337975, 'IoU-wall-brick': 49.69299113838493, 'IoU-wall-stone': 30.665198995596015, 'IoU-wall-tile': 70.35301475927118, 'IoU-wall-wood': 43.42585146960349, 'IoU-water-other': 25.20093855759168, 'IoU-window-blind': 49.113059522948916, 'IoU-window-other': 49.40784095632744, 'IoU-tree-merged': 82.15491513998305, 'IoU-fence-merged': 54.3765285909231, 'IoU-ceiling-merged': 68.77401756308397, 'IoU-sky-other-merged': 93.6401593176168, 'IoU-cabinet-merged': 64.85909782348355, 'IoU-table-merged': 42.512839369492, 'IoU-floor-other-merged': 54.30211452713765, 'IoU-pavement-merged': 57.144561832370144, 'IoU-mountain-merged': 58.044115132398964, 'IoU-grass-merged': 71.66093856904956, 'IoU-dirt-merged': 45.764275720604246, 'IoU-paper-merged': 41.35363960299256, 'IoU-food-other-merged': 42.54668429488643, 'IoU-building-other-merged': 58.973413612881885, 'IoU-rock-merged': 64.3831360165623, 'IoU-wall-other-merged': 68.17085035175457, 'IoU-rug-merged': 68.26179331930472, 'mACC': 77.53431133495654, 'pACC': 82.151854392311, 'ACC-person': 92.98569497444562, 'ACC-bicycle': 87.77768644383106, 'ACC-car': 86.7957236808925, 'ACC-motorcycle': 90.42722344568149, 'ACC-airplane': 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bat': 86.62835564812804, 'ACC-baseball glove': 91.60992340560443, 'ACC-skateboard': 90.23243757602417, 'ACC-surfboard': 92.70916517694876, 'ACC-tennis racket': 94.99480497317158, 'ACC-bottle': 84.24076233657833, 'ACC-wine glass': 90.87396420735799, 'ACC-cup': 90.37353023604948, 'ACC-fork': 82.12939101290695, 'ACC-knife': 77.40144354070492, 'ACC-spoon': 74.70087999441012, 'ACC-bowl': 72.38426482059226, 'ACC-banana': 90.35951138034419, 'ACC-apple': 74.67735111983377, 'ACC-sandwich': 83.15682430349668, 'ACC-orange': 89.34955528164824, 'ACC-broccoli': 84.69337944582145, 'ACC-carrot': 79.56075991925167, 'ACC-hot dog': 69.30487103395082, 'ACC-pizza': 92.03753720089438, 'ACC-donut': 68.79184791224391, 'ACC-cake': 85.1831573614917, 'ACC-chair': 80.72963731869731, 'ACC-couch': 75.80627109719605, 'ACC-potted plant': 60.4832621554782, 'ACC-bed': 87.72971902936067, 'ACC-dining table': 70.26572807203445, 'ACC-toilet': 92.83787183115037, 'ACC-tv': 88.10044419098634, 'ACC-laptop': 90.7464766097689, 'ACC-mouse': 84.41645112532422, 'ACC-remote': 72.04682023215949, 'ACC-keyboard': 67.82021341651351, 'ACC-cell phone': 90.85693007708826, 'ACC-microwave': 84.45929667302865, 'ACC-oven': 91.38764757724547, 'ACC-toaster': 91.19257140511023, 'ACC-sink': 83.67843327063488, 'ACC-refrigerator': 91.88477254039422, 'ACC-book': 70.06058950435684, 'ACC-clock': 73.07270208340782, 'ACC-vase': 80.3814129217921, 'ACC-scissors': 92.07652719242671, 'ACC-teddy bear': 90.17024221079073, 'ACC-hair drier': 60.946972931431986, 'ACC-toothbrush': 85.26580958999304, 'ACC-banner': 79.86996957578171, 'ACC-blanket': 20.98616113898217, 'ACC-bridge': 54.55453917495403, 'ACC-cardboard': 73.51001890359169, 'ACC-counter': 52.05941785437541, 'ACC-curtain': 82.589966796734, 'ACC-door-stuff': 68.05099387606106, 'ACC-floor-wood': 82.18633504684767, 'ACC-flower': 70.18700715743597, 'ACC-fruit': 69.04223539498734, 'ACC-gravel': 51.30376498845693, 'ACC-house': 28.064958556451362, 'ACC-light': 61.38992698005589, 'ACC-mirror-stuff': 78.3672168018724, 'ACC-net': 66.55858624152816, 'ACC-pillow': 31.2223298741001, 'ACC-platform': 51.55025372522739, 'ACC-playingfield': 83.58787990333244, 'ACC-railroad': 79.40617751675423, 'ACC-river': 85.20933397368273, 'ACC-road': 87.17894210303018, 'ACC-roof': 27.380320828596687, 'ACC-sand': 68.09228146670435, 'ACC-sea': 91.89997684702544, 'ACC-shelf': 56.07522972614299, 'ACC-snow': 95.38274621114209, 'ACC-stairs': 62.202433634876854, 'ACC-tent': 14.628666184324434, 'ACC-towel': 54.12191815196532, 'ACC-wall-brick': 69.2771746790725, 'ACC-wall-stone': 37.749086076300664, 'ACC-wall-tile': 84.00780637859485, 'ACC-wall-wood': 64.04795096144214, 'ACC-water-other': 32.085762137729354, 'ACC-window-blind': 65.45954740368725, 'ACC-window-other': 73.72532696901223, 'ACC-tree-merged': 89.78131665548533, 'ACC-fence-merged': 72.15713735955234, 'ACC-ceiling-merged': 82.85928053238918, 'ACC-sky-other-merged': 96.83048933436011, 'ACC-cabinet-merged': 77.08712159425603, 'ACC-table-merged': 60.730406402019675, 'ACC-floor-other-merged': 63.76707238657357, 'ACC-pavement-merged': 69.34009270678558, 'ACC-mountain-merged': 69.59319332811451, 'ACC-grass-merged': 83.9764069960193, 'ACC-dirt-merged': 72.54231149547118, 'ACC-paper-merged': 57.800950170855245, 'ACC-food-other-merged': 59.021788998712054, 'ACC-building-other-merged': 72.681515833602, 'ACC-rock-merged': 82.80485133976622, 'ACC-wall-other-merged': 81.4188710801781, 'ACC-rug-merged': 82.4146337755052})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3038 s/iter. Inference: 0.1740 s/iter. Eval: 0.0000 s/iter. Total: 0.4779 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3207 s/iter. Inference: 0.3405 s/iter. Eval: 0.0000 s/iter. Total: 0.6614 s/iter. ETA=0:00:04 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3471 s/iter. Inference: 0.4168 s/iter. Eval: 0.0000 s/iter. Total: 0.7641 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4073748902546093, 'noc@0.8': 2.5323383084577116, 'noc@0.85': 2.9499561018437226, 'noc@0.9': 3.7951419373719637, 'miou@iter1': 0.8773924677142422} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1452 s/iter. Eval: 0.0011 s/iter. Total: 0.1476 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.35950469970703, 'precision@0.6': 72.98873138427734, 'precision@0.7': 68.90789031982422, 'precision@0.8': 59.7357177734375, 'precision@0.9': 33.03536605834961, 'cIoU': 62.03396987915039, 'mIoU': 66.82454681396484} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.28550392275305, 'SQ': 83.03953720861209, 'RQ': 65.78252484721757, 'PQ_th': 61.369699759001186, 'SQ_th': 84.06603284649483, 'RQ_th': 72.4983352267935, 'PQ_st': 46.10181209445401, 'SQ_st': 81.49010983067593, 'RQ_st': 55.64545257615955}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.63331333757098, 'AP50': 69.18308604841104, 'AP75': 49.16471846530548, 'APs': 25.92906138543339, 'APm': 49.59810869377218, 'APl': 67.98395684917308, 'AP-person': 48.97755178398581, 'AP-bicycle': 22.583693134842004, 'AP-car': 42.78209416554078, 'AP-motorcycle': 42.936051601854714, 'AP-airplane': 60.89087912423719, 'AP-bus': 71.09645223508716, 'AP-train': 75.23616003313984, 'AP-truck': 44.35255308729628, 'AP-boat': 31.493067090818926, 'AP-traffic light': 29.177503992995323, 'AP-fire hydrant': 71.72067282172826, 'AP-stop sign': 68.25023067560973, 'AP-parking meter': 52.27910800344878, 'AP-bench': 27.30760132303037, 'AP-bird': 34.00330642855452, 'AP-cat': 76.51498248807039, 'AP-dog': 70.72220806760272, 'AP-horse': 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INFO:trainer.default_trainer:This epoch takes 0:57:19.731400 INFO:trainer.default_trainer:PROGRESS: 34.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 17 training. INFO:trainer.default_trainer:epochs[ 17] optim steps[31100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.41255/0.77412, loss_mask_bce_0: 0.56485/0.30179, loss_mask_dice_0: 1.75371/1.02685, loss_spatial_bce_0: 0.09622/0.08770, loss_spatial_dice_0: 0.27366/0.18540, loss_spatial_ce_0: 0.13702/0.06605, loss_grounding_bce_0: 0.08697/0.08052, loss_grounding_dice_0: 0.34846/0.15102, loss_grounding_ce_0: 0.60800/0.25121, loss_mask_ce_1: 1.46917/0.77644, loss_mask_bce_1: 0.56542/0.30249, loss_mask_dice_1: 1.71694/1.03117, loss_spatial_bce_1: 0.09773/0.08804, loss_spatial_dice_1: 0.28395/0.18798, loss_spatial_ce_1: 0.20174/0.07050, loss_grounding_bce_1: 0.09012/0.08074, loss_grounding_dice_1: 0.33739/0.15191, loss_grounding_ce_1: 0.63059/0.25287, loss_mask_ce_2: 1.41125/0.78390, loss_mask_bce_2: 0.55316/0.30259, loss_mask_dice_2: 1.70895/1.03264, loss_spatial_bce_2: 0.09718/0.08778, loss_spatial_dice_2: 0.28476/0.18805, loss_spatial_ce_2: 0.18621/0.07282, loss_grounding_bce_2: 0.12387/0.08066, loss_grounding_dice_2: 0.40706/0.15160, loss_grounding_ce_2: 0.17001/0.25513, loss_mask_ce_3: 1.48502/0.78542, loss_mask_bce_3: 0.56750/0.30407, loss_mask_dice_3: 1.70197/1.02900, loss_spatial_bce_3: 0.10480/0.08954, loss_spatial_dice_3: 0.29432/0.18870, loss_spatial_ce_3: 0.14383/0.07801, loss_grounding_bce_3: 0.11391/0.08111, loss_grounding_dice_3: 0.38019/0.15122, loss_grounding_ce_3: 0.14250/0.25453, loss_mask_ce_4: 1.47425/0.79135, loss_mask_bce_4: 0.57096/0.30631, loss_mask_dice_4: 1.78056/1.04794, loss_spatial_bce_4: 0.11142/0.09150, loss_spatial_dice_4: 0.26290/0.19631, loss_spatial_ce_4: 0.28267/0.09043, loss_grounding_bce_4: 0.10976/0.08179, loss_grounding_dice_4: 0.36097/0.15388, loss_grounding_ce_4: 0.14167/0.26028, loss_mask_ce_5: 1.53733/0.81395, loss_mask_bce_5: 0.57887/0.30810, loss_mask_dice_5: 1.94518/1.05491, loss_spatial_bce_5: 0.09242/0.09326, loss_spatial_dice_5: 0.28617/0.19857, loss_spatial_ce_5: 0.16095/0.10226, loss_grounding_bce_5: 0.12322/0.08206, loss_grounding_dice_5: 0.39320/0.15443, loss_grounding_ce_5: 0.15626/0.27922, loss_mask_ce_6: 1.32584/0.83998, loss_mask_bce_6: 0.58228/0.30989, loss_mask_dice_6: 2.06965/1.05780, loss_spatial_bce_6: 0.10535/0.09807, loss_spatial_dice_6: 0.26208/0.20091, loss_spatial_ce_6: 0.17428/0.12411, loss_grounding_bce_6: 0.11958/0.08311, loss_grounding_dice_6: 0.37909/0.15514, loss_grounding_ce_6: 0.13906/0.28913, loss_mask_ce_7: 1.46843/0.89888, loss_mask_bce_7: 0.54523/0.31699, loss_mask_dice_7: 2.03102/1.10446, loss_spatial_bce_7: 0.10688/0.10855, loss_spatial_dice_7: 0.31894/0.22582, loss_spatial_ce_7: 0.18223/0.16563, loss_grounding_bce_7: 0.11412/0.08466, loss_grounding_dice_7: 0.41248/0.16083, loss_grounding_ce_7: 0.18393/0.32877, loss_mask_ce_8: 1.64314/1.03563, loss_mask_bce_8: 0.53370/0.33397, loss_mask_dice_8: 1.98231/1.18343, loss_spatial_bce_8: 0.12073/0.12821, loss_spatial_dice_8: 0.32576/0.26457, loss_spatial_ce_8: 0.35895/0.21874, loss_grounding_bce_8: 0.09824/0.08869, loss_grounding_dice_8: 0.39555/0.17048, loss_grounding_ce_8: 0.15881/0.43013, loss_mask_ce_9: 5.20705/3.49370, loss_mask_bce_9: 0.66833/0.36055, loss_mask_dice_9: 2.89335/1.76924, loss_spatial_bce_9: 0.37250/0.35734, loss_spatial_dice_9: 0.92833/0.79536, loss_spatial_ce_9: 1.61035/1.40349, loss_grounding_bce_9: 0.19385/0.10067, loss_grounding_dice_9: 0.72329/0.24410, loss_grounding_ce_9: 0.29709/0.69340] items per batch[64] items per second[0.16] total items[1990400] mini batches[ 31100] memory[4967] epoch remaining[1:01:05] INFO:trainer.default_trainer:epochs[ 17] optim steps[31200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09204/0.77387, loss_mask_bce_0: 0.16876/0.30177, loss_mask_dice_0: 0.17198/1.02633, loss_spatial_bce_0: 0.07463/0.08769, loss_spatial_dice_0: 0.05932/0.18536, loss_spatial_ce_0: 0.00004/0.06601, loss_grounding_bce_0: 0.06147/0.08049, loss_grounding_dice_0: 0.11053/0.15097, loss_grounding_ce_0: 0.00085/0.25121, loss_mask_ce_1: 0.07854/0.77616, loss_mask_bce_1: 0.16300/0.30247, loss_mask_dice_1: 0.15859/1.03079, loss_spatial_bce_1: 0.07697/0.08804, loss_spatial_dice_1: 0.05880/0.18795, loss_spatial_ce_1: 0.00002/0.07046, loss_grounding_bce_1: 0.05864/0.08070, loss_grounding_dice_1: 0.10375/0.15186, loss_grounding_ce_1: 0.00060/0.25287, loss_mask_ce_2: 0.07805/0.78363, loss_mask_bce_2: 0.16010/0.30255, loss_mask_dice_2: 0.15546/1.03219, loss_spatial_bce_2: 0.07520/0.08778, loss_spatial_dice_2: 0.06207/0.18801, loss_spatial_ce_2: 0.00004/0.07280, loss_grounding_bce_2: 0.06500/0.08061, loss_grounding_dice_2: 0.11134/0.15155, loss_grounding_ce_2: 0.00065/0.25511, loss_mask_ce_3: 0.08554/0.78516, loss_mask_bce_3: 0.15744/0.30404, loss_mask_dice_3: 0.15067/1.02855, loss_spatial_bce_3: 0.07171/0.08954, loss_spatial_dice_3: 0.05982/0.18867, loss_spatial_ce_3: 0.00017/0.07798, loss_grounding_bce_3: 0.06138/0.08106, loss_grounding_dice_3: 0.10712/0.15116, loss_grounding_ce_3: 0.00073/0.25457, loss_mask_ce_4: 0.08122/0.79111, loss_mask_bce_4: 0.16377/0.30630, loss_mask_dice_4: 0.15474/1.04744, loss_spatial_bce_4: 0.07006/0.09152, loss_spatial_dice_4: 0.06044/0.19628, loss_spatial_ce_4: 0.00034/0.09042, loss_grounding_bce_4: 0.05936/0.08174, loss_grounding_dice_4: 0.10384/0.15381, loss_grounding_ce_4: 0.00189/0.26050, loss_mask_ce_5: 0.09713/0.81372, loss_mask_bce_5: 0.16215/0.30809, loss_mask_dice_5: 0.14984/1.05446, loss_spatial_bce_5: 0.06721/0.09327, loss_spatial_dice_5: 0.05789/0.19854, loss_spatial_ce_5: 0.00083/0.10225, loss_grounding_bce_5: 0.06490/0.08203, loss_grounding_dice_5: 0.10895/0.15438, loss_grounding_ce_5: 0.00155/0.27946, loss_mask_ce_6: 0.11842/0.83966, loss_mask_bce_6: 0.16549/0.30988, loss_mask_dice_6: 0.16015/1.05731, loss_spatial_bce_6: 0.07465/0.09807, loss_spatial_dice_6: 0.05888/0.20087, loss_spatial_ce_6: 0.00248/0.12412, loss_grounding_bce_6: 0.06382/0.08308, loss_grounding_dice_6: 0.11477/0.15508, loss_grounding_ce_6: 0.00404/0.28920, loss_mask_ce_7: 0.11694/0.89869, loss_mask_bce_7: 0.17391/0.31695, loss_mask_dice_7: 0.16490/1.10395, loss_spatial_bce_7: 0.06998/0.10855, loss_spatial_dice_7: 0.06186/0.22579, loss_spatial_ce_7: 0.01756/0.16562, loss_grounding_bce_7: 0.06228/0.08463, loss_grounding_dice_7: 0.11095/0.16079, loss_grounding_ce_7: 0.00228/0.32882, loss_mask_ce_8: 0.17810/1.03526, loss_mask_bce_8: 0.18882/0.33394, loss_mask_dice_8: 0.15438/1.18286, loss_spatial_bce_8: 0.08617/0.12822, loss_spatial_dice_8: 0.06837/0.26450, loss_spatial_ce_8: 0.08235/0.21867, loss_grounding_bce_8: 0.07042/0.08867, loss_grounding_dice_8: 0.09601/0.17045, loss_grounding_ce_8: 0.00580/0.42990, loss_mask_ce_9: 3.24158/3.49255, loss_mask_bce_9: 0.36706/0.36053, loss_mask_dice_9: 0.37691/1.76860, loss_spatial_bce_9: 0.36377/0.35753, loss_spatial_dice_9: 0.65986/0.79532, loss_spatial_ce_9: 0.55162/1.40334, loss_grounding_bce_9: 0.08732/0.10066, loss_grounding_dice_9: 0.18546/0.24404, loss_grounding_ce_9: 0.01697/0.69297] items per batch[64] items per second[0.36] total items[1996800] mini batches[ 31200] memory[4967] epoch remaining[0:51:46] INFO:trainer.default_trainer:epochs[ 17] optim steps[31300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36648/0.77381, loss_mask_bce_0: 0.27573/0.30176, loss_mask_dice_0: 3.33953/1.02642, loss_spatial_bce_0: 0.00870/0.08766, loss_spatial_dice_0: 0.23204/0.18532, loss_spatial_ce_0: 0.01801/0.06593, loss_grounding_bce_0: 0.03281/0.08044, loss_grounding_dice_0: 0.18820/0.15095, loss_grounding_ce_0: 0.34157/0.25106, loss_mask_ce_1: 0.55359/0.77602, loss_mask_bce_1: 0.26390/0.30248, loss_mask_dice_1: 3.06251/1.03094, loss_spatial_bce_1: 0.00773/0.08801, loss_spatial_dice_1: 0.20433/0.18790, loss_spatial_ce_1: 0.07257/0.07041, loss_grounding_bce_1: 0.03347/0.08066, loss_grounding_dice_1: 0.21464/0.15184, loss_grounding_ce_1: 0.30434/0.25270, loss_mask_ce_2: 0.69960/0.78344, loss_mask_bce_2: 0.25730/0.30257, loss_mask_dice_2: 2.98357/1.03227, loss_spatial_bce_2: 0.00771/0.08776, loss_spatial_dice_2: 0.25199/0.18797, loss_spatial_ce_2: 0.08510/0.07272, loss_grounding_bce_2: 0.03316/0.08057, loss_grounding_dice_2: 0.24162/0.15152, loss_grounding_ce_2: 0.39485/0.25493, loss_mask_ce_3: 0.62740/0.78493, loss_mask_bce_3: 0.26617/0.30405, loss_mask_dice_3: 2.57288/1.02863, loss_spatial_bce_3: 0.00824/0.08952, loss_spatial_dice_3: 0.25684/0.18863, loss_spatial_ce_3: 0.04914/0.07788, loss_grounding_bce_3: 0.03490/0.08102, loss_grounding_dice_3: 0.11313/0.15111, loss_grounding_ce_3: 0.38392/0.25443, loss_mask_ce_4: 0.61882/0.79087, loss_mask_bce_4: 0.23435/0.30631, loss_mask_dice_4: 2.78417/1.04751, loss_spatial_bce_4: 0.00767/0.09149, loss_spatial_dice_4: 0.28096/0.19625, loss_spatial_ce_4: 0.08398/0.09034, loss_grounding_bce_4: 0.03179/0.08170, loss_grounding_dice_4: 0.20490/0.15378, loss_grounding_ce_4: 0.32282/0.26031, loss_mask_ce_5: 0.62871/0.81353, loss_mask_bce_5: 0.24324/0.30810, loss_mask_dice_5: 3.08958/1.05455, loss_spatial_bce_5: 0.00728/0.09325, loss_spatial_dice_5: 0.21422/0.19851, loss_spatial_ce_5: 0.08380/0.10221, loss_grounding_bce_5: 0.03190/0.08198, loss_grounding_dice_5: 0.20795/0.15435, loss_grounding_ce_5: 0.31163/0.27932, loss_mask_ce_6: 1.01410/0.83962, loss_mask_bce_6: 0.25711/0.30989, loss_mask_dice_6: 3.46677/1.05744, loss_spatial_bce_6: 0.00838/0.09803, loss_spatial_dice_6: 0.22776/0.20083, loss_spatial_ce_6: 0.12057/0.12409, loss_grounding_bce_6: 0.03127/0.08302, loss_grounding_dice_6: 0.27611/0.15502, loss_grounding_ce_6: 0.56082/0.28905, loss_mask_ce_7: 0.87758/0.89855, loss_mask_bce_7: 0.26346/0.31696, loss_mask_dice_7: 3.69977/1.10414, loss_spatial_bce_7: 0.00952/0.10850, loss_spatial_dice_7: 0.34038/0.22577, loss_spatial_ce_7: 0.11656/0.16548, loss_grounding_bce_7: 0.03455/0.08458, loss_grounding_dice_7: 0.34870/0.16074, loss_grounding_ce_7: 0.40940/0.32868, loss_mask_ce_8: 0.85762/1.03504, loss_mask_bce_8: 0.29419/0.33395, loss_mask_dice_8: 3.64479/1.18299, loss_spatial_bce_8: 0.01125/0.12820, loss_spatial_dice_8: 0.43012/0.26448, loss_spatial_ce_8: 0.15843/0.21855, loss_grounding_bce_8: 0.03461/0.08861, loss_grounding_dice_8: 0.30321/0.17040, loss_grounding_ce_8: 0.47987/0.42984, loss_mask_ce_9: 5.92136/3.49277, loss_mask_bce_9: 0.28735/0.36055, loss_mask_dice_9: 4.52228/1.76860, loss_spatial_bce_9: 0.18711/0.35757, loss_spatial_dice_9: 0.93954/0.79537, loss_spatial_ce_9: 2.11567/1.40338, loss_grounding_bce_9: 0.03482/0.10061, loss_grounding_dice_9: 0.32955/0.24392, loss_grounding_ce_9: 0.54075/0.69302] items per batch[64] items per second[0.36] total items[2003200] mini batches[ 31300] memory[4967] epoch remaining[0:47:48] INFO:trainer.default_trainer:epochs[ 17] optim steps[31400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87552/0.77343, loss_mask_bce_0: 0.48336/0.30167, loss_mask_dice_0: 1.58214/1.02629, loss_spatial_bce_0: 0.08350/0.08765, loss_spatial_dice_0: 0.24490/0.18530, loss_spatial_ce_0: 0.08593/0.06590, loss_grounding_bce_0: 0.13849/0.08042, loss_grounding_dice_0: 0.29140/0.15101, loss_grounding_ce_0: 0.10892/0.25110, loss_mask_ce_1: 1.04985/0.77564, loss_mask_bce_1: 0.46889/0.30239, loss_mask_dice_1: 1.49513/1.03086, loss_spatial_bce_1: 0.08151/0.08799, loss_spatial_dice_1: 0.27204/0.18788, loss_spatial_ce_1: 0.11282/0.07042, loss_grounding_bce_1: 0.14280/0.08065, loss_grounding_dice_1: 0.28341/0.15191, loss_grounding_ce_1: 0.10993/0.25278, loss_mask_ce_2: 0.94480/0.78304, loss_mask_bce_2: 0.48014/0.30248, loss_mask_dice_2: 1.66896/1.03217, loss_spatial_bce_2: 0.08760/0.08774, loss_spatial_dice_2: 0.25026/0.18795, loss_spatial_ce_2: 0.09859/0.07269, loss_grounding_bce_2: 0.14026/0.08056, loss_grounding_dice_2: 0.27120/0.15160, loss_grounding_ce_2: 0.12896/0.25512, loss_mask_ce_3: 0.47129/0.78457, loss_mask_bce_3: 0.48288/0.30396, loss_mask_dice_3: 1.64129/1.02844, loss_spatial_bce_3: 0.09243/0.08950, loss_spatial_dice_3: 0.28765/0.18860, loss_spatial_ce_3: 0.12808/0.07784, loss_grounding_bce_3: 0.14383/0.08100, loss_grounding_dice_3: 0.27982/0.15119, loss_grounding_ce_3: 0.15558/0.25460, loss_mask_ce_4: 1.14143/0.79053, loss_mask_bce_4: 0.45075/0.30621, loss_mask_dice_4: 1.54076/1.04736, loss_spatial_bce_4: 0.09160/0.09148, loss_spatial_dice_4: 0.28365/0.19624, loss_spatial_ce_4: 0.11996/0.09031, loss_grounding_bce_4: 0.15231/0.08169, loss_grounding_dice_4: 0.28284/0.15385, loss_grounding_ce_4: 0.14621/0.26032, loss_mask_ce_5: 0.76834/0.81312, loss_mask_bce_5: 0.49999/0.30801, loss_mask_dice_5: 1.74188/1.05445, loss_spatial_bce_5: 0.08760/0.09324, loss_spatial_dice_5: 0.26856/0.19851, loss_spatial_ce_5: 0.24797/0.10222, loss_grounding_bce_5: 0.13669/0.08197, loss_grounding_dice_5: 0.25901/0.15441, loss_grounding_ce_5: 0.12577/0.27935, loss_mask_ce_6: 1.15337/0.83920, loss_mask_bce_6: 0.50626/0.30981, loss_mask_dice_6: 1.94060/1.05733, loss_spatial_bce_6: 0.10155/0.09802, loss_spatial_dice_6: 0.28931/0.20082, loss_spatial_ce_6: 0.36984/0.12412, loss_grounding_bce_6: 0.13526/0.08301, loss_grounding_dice_6: 0.26227/0.15509, loss_grounding_ce_6: 0.17350/0.28907, loss_mask_ce_7: 0.99169/0.89812, loss_mask_bce_7: 0.53513/0.31687, loss_mask_dice_7: 1.88809/1.10389, loss_spatial_bce_7: 0.14654/0.10850, loss_spatial_dice_7: 0.32638/0.22576, loss_spatial_ce_7: 0.12987/0.16543, loss_grounding_bce_7: 0.14312/0.08456, loss_grounding_dice_7: 0.30660/0.16079, loss_grounding_ce_7: 0.19418/0.32877, loss_mask_ce_8: 1.77276/1.03456, loss_mask_bce_8: 0.54724/0.33387, loss_mask_dice_8: 2.02062/1.18282, loss_spatial_bce_8: 0.10825/0.12818, loss_spatial_dice_8: 0.29597/0.26447, loss_spatial_ce_8: 0.22143/0.21852, loss_grounding_bce_8: 0.18298/0.08860, loss_grounding_dice_8: 0.39637/0.17044, loss_grounding_ce_8: 0.05060/0.42998, loss_mask_ce_9: 3.73878/3.49219, loss_mask_bce_9: 0.47925/0.36041, loss_mask_dice_9: 2.49546/1.76827, loss_spatial_bce_9: 0.32414/0.35752, loss_spatial_dice_9: 0.91817/0.79537, loss_spatial_ce_9: 1.85041/1.40312, loss_grounding_bce_9: 0.12789/0.10058, loss_grounding_dice_9: 0.30434/0.24394, loss_grounding_ce_9: 0.05878/0.69275] items per batch[64] items per second[0.37] total items[2009600] mini batches[ 31400] memory[4967] epoch remaining[0:44:20] INFO:trainer.default_trainer:epochs[ 17] optim steps[31500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.91531/0.77355, loss_mask_bce_0: 0.30661/0.30169, loss_mask_dice_0: 0.74570/1.02634, loss_spatial_bce_0: 0.03798/0.08767, loss_spatial_dice_0: 0.07516/0.18526, loss_spatial_ce_0: 0.00018/0.06594, loss_grounding_bce_0: 0.13862/0.08048, loss_grounding_dice_0: 0.15307/0.15098, loss_grounding_ce_0: 0.31914/0.25120, loss_mask_ce_1: 1.97064/0.77575, loss_mask_bce_1: 0.29550/0.30242, loss_mask_dice_1: 0.73493/1.03087, loss_spatial_bce_1: 0.03885/0.08802, loss_spatial_dice_1: 0.08994/0.18786, loss_spatial_ce_1: 0.00015/0.07046, loss_grounding_bce_1: 0.13655/0.08068, loss_grounding_dice_1: 0.14030/0.15186, loss_grounding_ce_1: 0.30794/0.25292, loss_mask_ce_2: 1.95123/0.78310, loss_mask_bce_2: 0.31380/0.30253, loss_mask_dice_2: 0.78831/1.03223, loss_spatial_bce_2: 0.03856/0.08778, loss_spatial_dice_2: 0.07393/0.18793, loss_spatial_ce_2: 0.00015/0.07270, loss_grounding_bce_2: 0.13441/0.08060, loss_grounding_dice_2: 0.14945/0.15156, loss_grounding_ce_2: 0.32453/0.25525, loss_mask_ce_3: 1.95874/0.78470, loss_mask_bce_3: 0.31201/0.30401, loss_mask_dice_3: 0.80930/1.02850, loss_spatial_bce_3: 0.03964/0.08952, loss_spatial_dice_3: 0.06695/0.18857, loss_spatial_ce_3: 0.00040/0.07788, loss_grounding_bce_3: 0.14001/0.08103, loss_grounding_dice_3: 0.13800/0.15114, loss_grounding_ce_3: 0.35827/0.25476, loss_mask_ce_4: 2.10420/0.79055, loss_mask_bce_4: 0.29148/0.30626, loss_mask_dice_4: 0.71775/1.04746, loss_spatial_bce_4: 0.03813/0.09150, loss_spatial_dice_4: 0.08659/0.19622, loss_spatial_ce_4: 0.00065/0.09035, loss_grounding_bce_4: 0.13759/0.08171, loss_grounding_dice_4: 0.15872/0.15378, loss_grounding_ce_4: 0.36841/0.26050, loss_mask_ce_5: 2.06295/0.81335, loss_mask_bce_5: 0.29070/0.30806, loss_mask_dice_5: 0.99924/1.05452, loss_spatial_bce_5: 0.04027/0.09327, loss_spatial_dice_5: 0.09055/0.19849, loss_spatial_ce_5: 0.00081/0.10226, loss_grounding_bce_5: 0.13623/0.08199, loss_grounding_dice_5: 0.14684/0.15435, loss_grounding_ce_5: 0.41506/0.27967, loss_mask_ce_6: 2.12220/0.83944, loss_mask_bce_6: 0.30250/0.30985, loss_mask_dice_6: 1.06564/1.05739, loss_spatial_bce_6: 0.04449/0.09806, loss_spatial_dice_6: 0.07450/0.20079, loss_spatial_ce_6: 0.02883/0.12417, loss_grounding_bce_6: 0.13860/0.08307, loss_grounding_dice_6: 0.17064/0.15505, loss_grounding_ce_6: 0.40145/0.28926, loss_mask_ce_7: 2.33204/0.89827, loss_mask_bce_7: 0.30550/0.31694, loss_mask_dice_7: 1.07321/1.10401, loss_spatial_bce_7: 0.04827/0.10852, loss_spatial_dice_7: 0.15043/0.22573, loss_spatial_ce_7: 0.05671/0.16537, loss_grounding_bce_7: 0.13552/0.08459, loss_grounding_dice_7: 0.17657/0.16074, loss_grounding_ce_7: 0.45098/0.32911, loss_mask_ce_8: 2.29261/1.03469, loss_mask_bce_8: 0.28500/0.33399, loss_mask_dice_8: 1.01139/1.18293, loss_spatial_bce_8: 0.05009/0.12821, loss_spatial_dice_8: 0.21454/0.26441, loss_spatial_ce_8: 0.11901/0.21851, loss_grounding_bce_8: 0.14249/0.08865, loss_grounding_dice_8: 0.17234/0.17039, loss_grounding_ce_8: 0.57307/0.43013, loss_mask_ce_9: 6.67592/3.49276, loss_mask_bce_9: 0.33395/0.36052, loss_mask_dice_9: 1.79272/1.76872, loss_spatial_bce_9: 0.35500/0.35759, loss_spatial_dice_9: 0.91795/0.79538, loss_spatial_ce_9: 2.32808/1.40301, loss_grounding_bce_9: 0.14657/0.10064, loss_grounding_dice_9: 0.27994/0.24390, loss_grounding_ce_9: 0.54296/0.69292] items per batch[64] items per second[0.36] total items[2016000] mini batches[ 31500] memory[4967] epoch remaining[0:41:10] INFO:trainer.default_trainer:epochs[ 17] optim steps[31600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43419/0.77355, loss_mask_bce_0: 0.07732/0.30180, loss_mask_dice_0: 0.49142/1.02651, loss_spatial_bce_0: 0.02218/0.08765, loss_spatial_dice_0: 0.12055/0.18525, loss_spatial_ce_0: 0.01536/0.06587, loss_grounding_bce_0: 0.04856/0.08050, loss_grounding_dice_0: 0.13858/0.15107, loss_grounding_ce_0: 0.07175/0.25126, loss_mask_ce_1: 0.42180/0.77570, loss_mask_bce_1: 0.06763/0.30254, loss_mask_dice_1: 0.43083/1.03102, loss_spatial_bce_1: 0.02310/0.08799, loss_spatial_dice_1: 0.14550/0.18786, loss_spatial_ce_1: 0.01540/0.07038, loss_grounding_bce_1: 0.04438/0.08068, loss_grounding_dice_1: 0.12805/0.15194, loss_grounding_ce_1: 0.06466/0.25303, loss_mask_ce_2: 0.45667/0.78314, loss_mask_bce_2: 0.07216/0.30264, loss_mask_dice_2: 0.48957/1.03234, loss_spatial_bce_2: 0.02289/0.08775, loss_spatial_dice_2: 0.14227/0.18793, loss_spatial_ce_2: 0.01227/0.07263, loss_grounding_bce_2: 0.04254/0.08061, loss_grounding_dice_2: 0.12317/0.15165, loss_grounding_ce_2: 0.09415/0.25531, loss_mask_ce_3: 0.42117/0.78474, loss_mask_bce_3: 0.06403/0.30412, loss_mask_dice_3: 0.42271/1.02857, loss_spatial_bce_3: 0.02581/0.08950, loss_spatial_dice_3: 0.13333/0.18858, loss_spatial_ce_3: 0.01470/0.07779, loss_grounding_bce_3: 0.03726/0.08104, loss_grounding_dice_3: 0.11533/0.15122, loss_grounding_ce_3: 0.06306/0.25483, loss_mask_ce_4: 0.40157/0.79057, loss_mask_bce_4: 0.06982/0.30638, loss_mask_dice_4: 0.48457/1.04759, loss_spatial_bce_4: 0.02051/0.09147, loss_spatial_dice_4: 0.12794/0.19623, loss_spatial_ce_4: 0.01499/0.09028, loss_grounding_bce_4: 0.04355/0.08172, loss_grounding_dice_4: 0.12511/0.15386, loss_grounding_ce_4: 0.05340/0.26067, loss_mask_ce_5: 0.53516/0.81327, loss_mask_bce_5: 0.06618/0.30817, loss_mask_dice_5: 0.40625/1.05468, loss_spatial_bce_5: 0.01937/0.09324, loss_spatial_dice_5: 0.11056/0.19850, loss_spatial_ce_5: 0.01569/0.10220, loss_grounding_bce_5: 0.03429/0.08200, loss_grounding_dice_5: 0.10329/0.15441, loss_grounding_ce_5: 0.09182/0.27967, loss_mask_ce_6: 0.58240/0.83943, loss_mask_bce_6: 0.07473/0.30996, loss_mask_dice_6: 0.48918/1.05752, loss_spatial_bce_6: 0.02577/0.09804, loss_spatial_dice_6: 0.12941/0.20080, loss_spatial_ce_6: 0.03247/0.12408, loss_grounding_bce_6: 0.03070/0.08307, loss_grounding_dice_6: 0.09528/0.15513, loss_grounding_ce_6: 0.11060/0.28935, loss_mask_ce_7: 0.58103/0.89819, loss_mask_bce_7: 0.07057/0.31704, loss_mask_dice_7: 0.46129/1.10411, loss_spatial_bce_7: 0.02426/0.10848, loss_spatial_dice_7: 0.13315/0.22574, loss_spatial_ce_7: 0.05998/0.16531, loss_grounding_bce_7: 0.04586/0.08459, loss_grounding_dice_7: 0.12157/0.16082, loss_grounding_ce_7: 0.03075/0.32907, loss_mask_ce_8: 0.80960/1.03473, loss_mask_bce_8: 0.07039/0.33407, loss_mask_dice_8: 0.44003/1.18297, loss_spatial_bce_8: 0.03882/0.12814, loss_spatial_dice_8: 0.17076/0.26437, loss_spatial_ce_8: 0.10239/0.21843, loss_grounding_bce_8: 0.05410/0.08865, loss_grounding_dice_8: 0.13372/0.17049, loss_grounding_ce_8: 0.05504/0.43011, loss_mask_ce_9: 2.74921/3.49282, loss_mask_bce_9: 0.06694/0.36058, loss_mask_dice_9: 0.75247/1.76869, loss_spatial_bce_9: 0.17897/0.35750, loss_spatial_dice_9: 0.79156/0.79543, loss_spatial_ce_9: 1.62828/1.40299, loss_grounding_bce_9: 0.04510/0.10064, loss_grounding_dice_9: 0.14577/0.24404, loss_grounding_ce_9: 0.04801/0.69268] items per batch[64] items per second[0.37] total items[2022400] mini batches[ 31600] memory[4967] epoch remaining[0:38:02] INFO:trainer.default_trainer:epochs[ 17] optim steps[31700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.66170/0.77341, loss_mask_bce_0: 0.07679/0.30171, loss_mask_dice_0: 0.15150/1.02665, loss_spatial_bce_0: 0.04526/0.08763, loss_spatial_dice_0: 0.09641/0.18524, loss_spatial_ce_0: 0.17585/0.06583, loss_grounding_bce_0: 0.04511/0.08049, loss_grounding_dice_0: 0.10857/0.15110, loss_grounding_ce_0: 0.60863/0.25129, loss_mask_ce_1: 0.71580/0.77553, loss_mask_bce_1: 0.07286/0.30244, loss_mask_dice_1: 0.17075/1.03108, loss_spatial_bce_1: 0.04607/0.08798, loss_spatial_dice_1: 0.09284/0.18786, loss_spatial_ce_1: 0.46567/0.07037, loss_grounding_bce_1: 0.05515/0.08067, loss_grounding_dice_1: 0.10782/0.15197, loss_grounding_ce_1: 0.18652/0.25307, loss_mask_ce_2: 0.72548/0.78299, loss_mask_bce_2: 0.08130/0.30255, loss_mask_dice_2: 0.17951/1.03248, loss_spatial_bce_2: 0.05809/0.08773, loss_spatial_dice_2: 0.12020/0.18794, loss_spatial_ce_2: 0.21966/0.07259, loss_grounding_bce_2: 0.05701/0.08061, loss_grounding_dice_2: 0.09556/0.15171, loss_grounding_ce_2: 0.15630/0.25538, loss_mask_ce_3: 0.80315/0.78461, loss_mask_bce_3: 0.08266/0.30404, loss_mask_dice_3: 0.15782/1.02874, loss_spatial_bce_3: 0.04623/0.08948, loss_spatial_dice_3: 0.10989/0.18858, loss_spatial_ce_3: 0.15775/0.07774, loss_grounding_bce_3: 0.05330/0.08105, loss_grounding_dice_3: 0.10097/0.15124, loss_grounding_ce_3: 0.54364/0.25491, loss_mask_ce_4: 0.76194/0.79043, loss_mask_bce_4: 0.07143/0.30628, loss_mask_dice_4: 0.15416/1.04771, loss_spatial_bce_4: 0.04106/0.09145, loss_spatial_dice_4: 0.09324/0.19622, loss_spatial_ce_4: 0.43063/0.09021, loss_grounding_bce_4: 0.05878/0.08172, loss_grounding_dice_4: 0.10752/0.15392, loss_grounding_ce_4: 0.23035/0.26076, loss_mask_ce_5: 0.64326/0.81311, loss_mask_bce_5: 0.07525/0.30810, loss_mask_dice_5: 0.16217/1.05478, loss_spatial_bce_5: 0.03115/0.09322, loss_spatial_dice_5: 0.09898/0.19851, loss_spatial_ce_5: 0.28404/0.10217, loss_grounding_bce_5: 0.05254/0.08199, loss_grounding_dice_5: 0.11108/0.15446, loss_grounding_ce_5: 0.21766/0.27973, loss_mask_ce_6: 0.73366/0.83922, loss_mask_bce_6: 0.07919/0.30987, loss_mask_dice_6: 0.16318/1.05765, loss_spatial_bce_6: 0.04208/0.09802, loss_spatial_dice_6: 0.12301/0.20081, loss_spatial_ce_6: 0.60617/0.12403, loss_grounding_bce_6: 0.05585/0.08306, loss_grounding_dice_6: 0.10825/0.15516, loss_grounding_ce_6: 0.54082/0.28941, loss_mask_ce_7: 1.47310/0.89808, loss_mask_bce_7: 0.07509/0.31694, loss_mask_dice_7: 0.14873/1.10425, loss_spatial_bce_7: 0.06018/0.10847, loss_spatial_dice_7: 0.10242/0.22575, loss_spatial_ce_7: 0.75364/0.16523, loss_grounding_bce_7: 0.04765/0.08458, loss_grounding_dice_7: 0.09701/0.16085, loss_grounding_ce_7: 0.56780/0.32910, loss_mask_ce_8: 0.87768/1.03440, loss_mask_bce_8: 0.07540/0.33398, loss_mask_dice_8: 0.13918/1.18305, loss_spatial_bce_8: 0.06885/0.12814, loss_spatial_dice_8: 0.17403/0.26439, loss_spatial_ce_8: 0.74042/0.21837, loss_grounding_bce_8: 0.05784/0.08863, loss_grounding_dice_8: 0.10654/0.17054, loss_grounding_ce_8: 0.54411/0.43035, loss_mask_ce_9: 2.87962/3.49228, loss_mask_bce_9: 0.05362/0.36045, loss_mask_dice_9: 0.19194/1.76850, loss_spatial_bce_9: 0.10485/0.35748, loss_spatial_dice_9: 0.44534/0.79542, loss_spatial_ce_9: 0.64904/1.40317, loss_grounding_bce_9: 0.04364/0.10063, loss_grounding_dice_9: 0.14486/0.24412, loss_grounding_ce_9: 0.61116/0.69263] items per batch[64] items per second[0.35] total items[2028800] mini batches[ 31700] memory[4967] epoch remaining[0:35:14] INFO:trainer.default_trainer:epochs[ 17] optim steps[31800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33857/0.77336, loss_mask_bce_0: 0.29130/0.30183, loss_mask_dice_0: 0.37630/1.02648, loss_spatial_bce_0: 0.23963/0.08762, loss_spatial_dice_0: 0.22585/0.18522, loss_spatial_ce_0: 0.01162/0.06579, loss_grounding_bce_0: 0.13412/0.08051, loss_grounding_dice_0: 0.18987/0.15108, loss_grounding_ce_0: 0.06884/0.25142, loss_mask_ce_1: 0.32974/0.77550, loss_mask_bce_1: 0.30474/0.30252, loss_mask_dice_1: 0.38099/1.03084, loss_spatial_bce_1: 0.24336/0.08796, loss_spatial_dice_1: 0.22857/0.18783, loss_spatial_ce_1: 0.00759/0.07034, loss_grounding_bce_1: 0.13657/0.08068, loss_grounding_dice_1: 0.18942/0.15197, loss_grounding_ce_1: 0.06872/0.25313, loss_mask_ce_2: 0.36504/0.78298, loss_mask_bce_2: 0.28802/0.30265, loss_mask_dice_2: 0.36998/1.03227, loss_spatial_bce_2: 0.25791/0.08772, loss_spatial_dice_2: 0.22011/0.18791, loss_spatial_ce_2: 0.01016/0.07254, loss_grounding_bce_2: 0.13319/0.08062, loss_grounding_dice_2: 0.18179/0.15169, loss_grounding_ce_2: 0.08102/0.25550, loss_mask_ce_3: 0.38203/0.78464, loss_mask_bce_3: 0.28488/0.30414, loss_mask_dice_3: 0.37503/1.02853, loss_spatial_bce_3: 0.27475/0.08947, loss_spatial_dice_3: 0.23147/0.18856, loss_spatial_ce_3: 0.01837/0.07769, loss_grounding_bce_3: 0.12826/0.08106, loss_grounding_dice_3: 0.18810/0.15124, loss_grounding_ce_3: 0.08556/0.25499, loss_mask_ce_4: 0.46659/0.79035, loss_mask_bce_4: 0.28914/0.30639, loss_mask_dice_4: 0.39015/1.04747, loss_spatial_bce_4: 0.24123/0.09143, loss_spatial_dice_4: 0.23043/0.19621, loss_spatial_ce_4: 0.17771/0.09016, loss_grounding_bce_4: 0.13892/0.08173, loss_grounding_dice_4: 0.19753/0.15392, loss_grounding_ce_4: 0.12573/0.26078, loss_mask_ce_5: 0.45713/0.81303, loss_mask_bce_5: 0.29883/0.30821, loss_mask_dice_5: 0.41077/1.05455, loss_spatial_bce_5: 0.23304/0.09322, loss_spatial_dice_5: 0.22439/0.19850, loss_spatial_ce_5: 0.17162/0.10214, loss_grounding_bce_5: 0.14484/0.08201, loss_grounding_dice_5: 0.21117/0.15448, loss_grounding_ce_5: 0.11591/0.27960, loss_mask_ce_6: 0.47966/0.83912, loss_mask_bce_6: 0.29878/0.30998, loss_mask_dice_6: 0.40762/1.05741, loss_spatial_bce_6: 0.23804/0.09804, loss_spatial_dice_6: 0.23424/0.20080, loss_spatial_ce_6: 0.17992/0.12397, loss_grounding_bce_6: 0.14653/0.08308, loss_grounding_dice_6: 0.21359/0.15517, loss_grounding_ce_6: 0.10358/0.28932, loss_mask_ce_7: 0.40481/0.89796, loss_mask_bce_7: 0.34262/0.31705, loss_mask_dice_7: 0.42322/1.10399, loss_spatial_bce_7: 0.29267/0.10846, loss_spatial_dice_7: 0.25220/0.22572, loss_spatial_ce_7: 0.16958/0.16512, loss_grounding_bce_7: 0.18045/0.08460, loss_grounding_dice_7: 0.22512/0.16085, loss_grounding_ce_7: 0.09837/0.32900, loss_mask_ce_8: 0.53625/1.03448, loss_mask_bce_8: 0.35097/0.33406, loss_mask_dice_8: 0.44520/1.18267, loss_spatial_bce_8: 0.28309/0.12813, loss_spatial_dice_8: 0.27597/0.26436, loss_spatial_ce_8: 0.14078/0.21828, loss_grounding_bce_8: 0.17896/0.08867, loss_grounding_dice_8: 0.24569/0.17055, loss_grounding_ce_8: 0.08902/0.43034, loss_mask_ce_9: 2.85231/3.49274, loss_mask_bce_9: 0.40715/0.36051, loss_mask_dice_9: 0.54974/1.76807, loss_spatial_bce_9: 0.47578/0.35743, loss_spatial_dice_9: 0.74071/0.79541, loss_spatial_ce_9: 1.10716/1.40314, loss_grounding_bce_9: 0.20741/0.10066, loss_grounding_dice_9: 0.31769/0.24412, loss_grounding_ce_9: 0.29046/0.69275] items per batch[64] items per second[0.36] total items[2035200] mini batches[ 31800] memory[4967] epoch remaining[0:32:13] INFO:trainer.default_trainer:epochs[ 17] optim steps[31900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.40025/0.77333, loss_mask_bce_0: 0.08354/0.30185, loss_mask_dice_0: 0.36070/1.02651, loss_spatial_bce_0: 0.02509/0.08760, loss_spatial_dice_0: 0.11008/0.18522, loss_spatial_ce_0: 0.09844/0.06578, loss_grounding_bce_0: 0.04777/0.08050, loss_grounding_dice_0: 0.17581/0.15109, loss_grounding_ce_0: 0.00003/0.25121, loss_mask_ce_1: 0.37434/0.77547, loss_mask_bce_1: 0.08264/0.30256, loss_mask_dice_1: 0.30966/1.03089, loss_spatial_bce_1: 0.02591/0.08794, loss_spatial_dice_1: 0.12130/0.18784, loss_spatial_ce_1: 0.11010/0.07035, loss_grounding_bce_1: 0.04271/0.08068, loss_grounding_dice_1: 0.17484/0.15198, loss_grounding_ce_1: 0.00007/0.25292, loss_mask_ce_2: 0.42264/0.78295, loss_mask_bce_2: 0.09102/0.30269, loss_mask_dice_2: 0.40961/1.03234, loss_spatial_bce_2: 0.02645/0.08771, loss_spatial_dice_2: 0.12718/0.18792, loss_spatial_ce_2: 0.10732/0.07254, loss_grounding_bce_2: 0.04797/0.08061, loss_grounding_dice_2: 0.17672/0.15170, loss_grounding_ce_2: 0.00006/0.25529, loss_mask_ce_3: 0.42326/0.78463, loss_mask_bce_3: 0.08022/0.30417, loss_mask_dice_3: 0.39973/1.02861, loss_spatial_bce_3: 0.02494/0.08945, loss_spatial_dice_3: 0.12778/0.18858, loss_spatial_ce_3: 0.10745/0.07767, loss_grounding_bce_3: 0.05815/0.08104, loss_grounding_dice_3: 0.20902/0.15125, loss_grounding_ce_3: 0.00014/0.25484, loss_mask_ce_4: 0.40677/0.79035, loss_mask_bce_4: 0.08652/0.30640, loss_mask_dice_4: 0.33498/1.04758, loss_spatial_bce_4: 0.02499/0.09141, loss_spatial_dice_4: 0.11357/0.19623, loss_spatial_ce_4: 0.14242/0.09010, loss_grounding_bce_4: 0.04790/0.08171, loss_grounding_dice_4: 0.17911/0.15395, loss_grounding_ce_4: 0.00005/0.26060, loss_mask_ce_5: 0.52775/0.81308, loss_mask_bce_5: 0.09529/0.30822, loss_mask_dice_5: 0.41102/1.05463, loss_spatial_bce_5: 0.02936/0.09320, loss_spatial_dice_5: 0.13455/0.19852, loss_spatial_ce_5: 0.20805/0.10208, loss_grounding_bce_5: 0.04432/0.08199, loss_grounding_dice_5: 0.15527/0.15449, loss_grounding_ce_5: 0.00057/0.27947, loss_mask_ce_6: 0.55640/0.83909, loss_mask_bce_6: 0.09703/0.31002, loss_mask_dice_6: 0.49792/1.05739, loss_spatial_bce_6: 0.02610/0.09802, loss_spatial_dice_6: 0.12359/0.20082, loss_spatial_ce_6: 0.15494/0.12392, loss_grounding_bce_6: 0.04663/0.08306, loss_grounding_dice_6: 0.18130/0.15517, loss_grounding_ce_6: 0.00037/0.28913, loss_mask_ce_7: 0.83308/0.89799, loss_mask_bce_7: 0.09563/0.31711, loss_mask_dice_7: 0.41246/1.10408, loss_spatial_bce_7: 0.04479/0.10843, loss_spatial_dice_7: 0.16028/0.22570, loss_spatial_ce_7: 0.11822/0.16502, loss_grounding_bce_7: 0.05310/0.08459, loss_grounding_dice_7: 0.21761/0.16088, loss_grounding_ce_7: 0.00669/0.32874, loss_mask_ce_8: 1.34416/1.03467, loss_mask_bce_8: 0.09348/0.33414, loss_mask_dice_8: 0.44170/1.18283, loss_spatial_bce_8: 0.03567/0.12809, loss_spatial_dice_8: 0.17937/0.26438, loss_spatial_ce_8: 0.24102/0.21825, loss_grounding_bce_8: 0.05887/0.08866, loss_grounding_dice_8: 0.22088/0.17056, loss_grounding_ce_8: 0.19531/0.43009, loss_mask_ce_9: 2.21668/3.49308, loss_mask_bce_9: 0.17967/0.36064, loss_mask_dice_9: 0.73878/1.76845, loss_spatial_bce_9: 0.34434/0.35732, loss_spatial_dice_9: 0.86027/0.79541, loss_spatial_ce_9: 1.17750/1.40304, loss_grounding_bce_9: 0.06094/0.10064, loss_grounding_dice_9: 0.22564/0.24414, loss_grounding_ce_9: 1.05993/0.69269] items per batch[64] items per second[0.37] total items[2041600] mini batches[ 31900] memory[4967] epoch remaining[0:29:11] INFO:trainer.default_trainer:epochs[ 17] optim steps[32000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10606/0.77320, loss_mask_bce_0: 0.21676/0.30181, loss_mask_dice_0: 0.16600/1.02654, loss_spatial_bce_0: 0.10897/0.08760, loss_spatial_dice_0: 0.08891/0.18519, loss_spatial_ce_0: 0.00042/0.06574, loss_grounding_bce_0: 0.10877/0.08051, loss_grounding_dice_0: 0.07703/0.15109, loss_grounding_ce_0: 0.00015/0.25106, loss_mask_ce_1: 0.12424/0.77530, loss_mask_bce_1: 0.21631/0.30253, loss_mask_dice_1: 0.16549/1.03081, loss_spatial_bce_1: 0.10919/0.08794, loss_spatial_dice_1: 0.09033/0.18781, loss_spatial_ce_1: 0.00032/0.07028, loss_grounding_bce_1: 0.10932/0.08069, loss_grounding_dice_1: 0.07213/0.15197, loss_grounding_ce_1: 0.00020/0.25275, loss_mask_ce_2: 0.11679/0.78277, loss_mask_bce_2: 0.21027/0.30267, loss_mask_dice_2: 0.16267/1.03231, loss_spatial_bce_2: 0.11115/0.08770, loss_spatial_dice_2: 0.08129/0.18788, loss_spatial_ce_2: 0.00052/0.07248, loss_grounding_bce_2: 0.10975/0.08062, loss_grounding_dice_2: 0.06861/0.15171, loss_grounding_ce_2: 0.00011/0.25512, loss_mask_ce_3: 0.11412/0.78449, loss_mask_bce_3: 0.21444/0.30414, loss_mask_dice_3: 0.15447/1.02859, loss_spatial_bce_3: 0.11063/0.08946, loss_spatial_dice_3: 0.08077/0.18855, loss_spatial_ce_3: 0.00117/0.07759, loss_grounding_bce_3: 0.10664/0.08105, loss_grounding_dice_3: 0.06347/0.15125, loss_grounding_ce_3: 0.00017/0.25466, loss_mask_ce_4: 0.10510/0.79024, loss_mask_bce_4: 0.21493/0.30640, loss_mask_dice_4: 0.15400/1.04756, loss_spatial_bce_4: 0.11306/0.09143, loss_spatial_dice_4: 0.09175/0.19619, loss_spatial_ce_4: 0.00230/0.09006, loss_grounding_bce_4: 0.11159/0.08172, loss_grounding_dice_4: 0.07030/0.15394, loss_grounding_ce_4: 0.00037/0.26042, loss_mask_ce_5: 0.10859/0.81288, loss_mask_bce_5: 0.20983/0.30822, loss_mask_dice_5: 0.15604/1.05469, loss_spatial_bce_5: 0.11277/0.09321, loss_spatial_dice_5: 0.08261/0.19849, loss_spatial_ce_5: 0.00539/0.10206, loss_grounding_bce_5: 0.10883/0.08199, loss_grounding_dice_5: 0.07037/0.15447, loss_grounding_ce_5: 0.00041/0.27926, loss_mask_ce_6: 0.12358/0.83894, loss_mask_bce_6: 0.20752/0.30999, loss_mask_dice_6: 0.15160/1.05746, loss_spatial_bce_6: 0.11414/0.09804, loss_spatial_dice_6: 0.08952/0.20079, loss_spatial_ce_6: 0.03446/0.12388, loss_grounding_bce_6: 0.10420/0.08307, loss_grounding_dice_6: 0.07139/0.15516, loss_grounding_ce_6: 0.00192/0.28890, loss_mask_ce_7: 0.14978/0.89773, loss_mask_bce_7: 0.20718/0.31712, loss_mask_dice_7: 0.16337/1.10418, loss_spatial_bce_7: 0.10829/0.10845, loss_spatial_dice_7: 0.09242/0.22568, loss_spatial_ce_7: 0.00577/0.16488, loss_grounding_bce_7: 0.10740/0.08461, loss_grounding_dice_7: 0.07526/0.16085, loss_grounding_ce_7: 0.00204/0.32849, loss_mask_ce_8: 0.12082/1.03434, loss_mask_bce_8: 0.21188/0.33414, loss_mask_dice_8: 0.17199/1.18289, loss_spatial_bce_8: 0.12877/0.12807, loss_spatial_dice_8: 0.10637/0.26434, loss_spatial_ce_8: 0.04078/0.21814, loss_grounding_bce_8: 0.10144/0.08867, loss_grounding_dice_8: 0.07311/0.17054, loss_grounding_ce_8: 0.00174/0.42983, loss_mask_ce_9: 2.09723/3.49260, loss_mask_bce_9: 0.23303/0.36066, loss_mask_dice_9: 0.23129/1.76884, loss_spatial_bce_9: 0.48948/0.35734, loss_spatial_dice_9: 0.61118/0.79537, loss_spatial_ce_9: 1.03865/1.40310, loss_grounding_bce_9: 0.10741/0.10064, loss_grounding_dice_9: 0.08925/0.24408, loss_grounding_ce_9: 0.40325/0.69249] items per batch[64] items per second[0.36] total items[2048000] mini batches[ 32000] memory[4967] epoch remaining[0:26:16] INFO:trainer.default_trainer:epochs[ 17] optim steps[32100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.29647/0.77302, loss_mask_bce_0: 1.14426/0.30182, loss_mask_dice_0: 1.46100/1.02694, loss_spatial_bce_0: 0.24176/0.08757, loss_spatial_dice_0: 0.31705/0.18516, loss_spatial_ce_0: 0.08452/0.06569, loss_grounding_bce_0: 0.35149/0.08051, loss_grounding_dice_0: 0.12579/0.15106, loss_grounding_ce_0: 0.00317/0.25099, loss_mask_ce_1: 1.24520/0.77510, loss_mask_bce_1: 1.13497/0.30255, loss_mask_dice_1: 1.51468/1.03128, loss_spatial_bce_1: 0.23825/0.08791, loss_spatial_dice_1: 0.29827/0.18779, loss_spatial_ce_1: 0.05701/0.07019, loss_grounding_bce_1: 0.32115/0.08069, loss_grounding_dice_1: 0.12171/0.15194, loss_grounding_ce_1: 0.00119/0.25270, loss_mask_ce_2: 1.26740/0.78252, loss_mask_bce_2: 1.13281/0.30268, loss_mask_dice_2: 1.43768/1.03272, loss_spatial_bce_2: 0.23712/0.08767, loss_spatial_dice_2: 0.29439/0.18786, loss_spatial_ce_2: 0.04993/0.07240, loss_grounding_bce_2: 0.35016/0.08062, loss_grounding_dice_2: 0.13327/0.15169, loss_grounding_ce_2: 0.00052/0.25507, loss_mask_ce_3: 1.28547/0.78430, loss_mask_bce_3: 1.12243/0.30414, loss_mask_dice_3: 1.47232/1.02892, loss_spatial_bce_3: 0.24030/0.08943, loss_spatial_dice_3: 0.29054/0.18853, loss_spatial_ce_3: 0.04401/0.07751, loss_grounding_bce_3: 0.33118/0.08105, loss_grounding_dice_3: 0.13130/0.15122, loss_grounding_ce_3: 0.00043/0.25460, loss_mask_ce_4: 1.25793/0.79004, loss_mask_bce_4: 1.10478/0.30641, loss_mask_dice_4: 1.50489/1.04810, loss_spatial_bce_4: 0.23501/0.09140, loss_spatial_dice_4: 0.30624/0.19620, loss_spatial_ce_4: 0.10521/0.08995, loss_grounding_bce_4: 0.30461/0.08172, loss_grounding_dice_4: 0.13269/0.15391, loss_grounding_ce_4: 0.00042/0.26035, loss_mask_ce_5: 1.01702/0.81267, loss_mask_bce_5: 1.10054/0.30822, loss_mask_dice_5: 1.64953/1.05517, loss_spatial_bce_5: 0.26579/0.09318, loss_spatial_dice_5: 0.32633/0.19848, loss_spatial_ce_5: 0.18517/0.10201, loss_grounding_bce_5: 0.30818/0.08198, loss_grounding_dice_5: 0.14369/0.15444, loss_grounding_ce_5: 0.00515/0.27919, loss_mask_ce_6: 1.35428/0.83873, loss_mask_bce_6: 1.10992/0.31000, loss_mask_dice_6: 1.55311/1.05796, loss_spatial_bce_6: 0.30600/0.09801, loss_spatial_dice_6: 0.31713/0.20077, loss_spatial_ce_6: 0.15131/0.12385, loss_grounding_bce_6: 0.30471/0.08305, loss_grounding_dice_6: 0.14791/0.15509, loss_grounding_ce_6: 0.00267/0.28898, loss_mask_ce_7: 1.33629/0.89751, loss_mask_bce_7: 1.13048/0.31712, loss_mask_dice_7: 1.59455/1.10471, loss_spatial_bce_7: 0.30342/0.10841, loss_spatial_dice_7: 0.33399/0.22569, loss_spatial_ce_7: 0.20369/0.16478, loss_grounding_bce_7: 0.31630/0.08459, loss_grounding_dice_7: 0.16041/0.16082, loss_grounding_ce_7: 0.00454/0.32842, loss_mask_ce_8: 1.56414/1.03418, loss_mask_bce_8: 1.12554/0.33415, loss_mask_dice_8: 1.61214/1.18344, loss_spatial_bce_8: 0.26339/0.12801, loss_spatial_dice_8: 0.33666/0.26433, loss_spatial_ce_8: 0.46665/0.21802, loss_grounding_bce_8: 0.32628/0.08866, loss_grounding_dice_8: 0.12801/0.17051, loss_grounding_ce_8: 0.08016/0.42977, loss_mask_ce_9: 3.89455/3.49296, loss_mask_bce_9: 1.28680/0.36068, loss_mask_dice_9: 2.16733/1.76930, loss_spatial_bce_9: 0.46343/0.35729, loss_spatial_dice_9: 0.89701/0.79540, loss_spatial_ce_9: 1.25890/1.40322, loss_grounding_bce_9: 0.36809/0.10064, loss_grounding_dice_9: 0.11114/0.24404, loss_grounding_ce_9: 0.46319/0.69241] items per batch[64] items per second[0.36] total items[2054400] mini batches[ 32100] memory[4967] epoch remaining[0:23:17] INFO:trainer.default_trainer:epochs[ 17] optim steps[32200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.12225/0.77286, loss_mask_bce_0: 0.22451/0.30173, loss_mask_dice_0: 0.33533/1.02661, loss_spatial_bce_0: 0.11335/0.08754, loss_spatial_dice_0: 0.16762/0.18511, loss_spatial_ce_0: 0.00997/0.06563, loss_grounding_bce_0: 0.12304/0.08052, loss_grounding_dice_0: 0.16138/0.15102, loss_grounding_ce_0: 0.00570/0.25059, loss_mask_ce_1: 0.15634/0.77495, loss_mask_bce_1: 0.22203/0.30244, loss_mask_dice_1: 0.31851/1.03103, loss_spatial_bce_1: 0.11939/0.08788, loss_spatial_dice_1: 0.17347/0.18774, loss_spatial_ce_1: 0.00784/0.07013, loss_grounding_bce_1: 0.12152/0.08069, loss_grounding_dice_1: 0.14269/0.15189, loss_grounding_ce_1: 0.00370/0.25230, loss_mask_ce_2: 0.16507/0.78235, loss_mask_bce_2: 0.22218/0.30258, loss_mask_dice_2: 0.31670/1.03245, loss_spatial_bce_2: 0.10553/0.08765, loss_spatial_dice_2: 0.14587/0.18782, loss_spatial_ce_2: 0.00621/0.07235, loss_grounding_bce_2: 0.12704/0.08063, loss_grounding_dice_2: 0.18770/0.15164, loss_grounding_ce_2: 0.00369/0.25469, loss_mask_ce_3: 0.19940/0.78402, loss_mask_bce_3: 0.23686/0.30404, loss_mask_dice_3: 0.34593/1.02863, loss_spatial_bce_3: 0.10935/0.08941, loss_spatial_dice_3: 0.15589/0.18848, loss_spatial_ce_3: 0.00884/0.07745, loss_grounding_bce_3: 0.13450/0.08106, loss_grounding_dice_3: 0.15316/0.15116, loss_grounding_ce_3: 0.00313/0.25421, loss_mask_ce_4: 0.20447/0.78987, loss_mask_bce_4: 0.22876/0.30633, loss_mask_dice_4: 0.32554/1.04785, loss_spatial_bce_4: 0.10952/0.09138, loss_spatial_dice_4: 0.15071/0.19616, loss_spatial_ce_4: 0.00200/0.08991, loss_grounding_bce_4: 0.11978/0.08172, loss_grounding_dice_4: 0.15680/0.15385, loss_grounding_ce_4: 0.00380/0.25998, loss_mask_ce_5: 0.25710/0.81246, loss_mask_bce_5: 0.21411/0.30812, loss_mask_dice_5: 0.28913/1.05492, loss_spatial_bce_5: 0.10043/0.09315, loss_spatial_dice_5: 0.13640/0.19844, loss_spatial_ce_5: 0.00237/0.10193, loss_grounding_bce_5: 0.12202/0.08198, loss_grounding_dice_5: 0.13246/0.15438, loss_grounding_ce_5: 0.00377/0.27881, loss_mask_ce_6: 0.37697/0.83855, loss_mask_bce_6: 0.22123/0.30989, loss_mask_dice_6: 0.30517/1.05773, loss_spatial_bce_6: 0.10119/0.09799, loss_spatial_dice_6: 0.14146/0.20073, loss_spatial_ce_6: 0.02448/0.12378, loss_grounding_bce_6: 0.11934/0.08303, loss_grounding_dice_6: 0.16613/0.15504, loss_grounding_ce_6: 0.00600/0.28860, loss_mask_ce_7: 0.28535/0.89722, loss_mask_bce_7: 0.23724/0.31702, loss_mask_dice_7: 0.38690/1.10448, loss_spatial_bce_7: 0.09499/0.10838, loss_spatial_dice_7: 0.14418/0.22564, loss_spatial_ce_7: 0.07818/0.16471, loss_grounding_bce_7: 0.12208/0.08458, loss_grounding_dice_7: 0.15520/0.16078, loss_grounding_ce_7: 0.00436/0.32790, loss_mask_ce_8: 0.58140/1.03387, loss_mask_bce_8: 0.22256/0.33401, loss_mask_dice_8: 0.34832/1.18319, loss_spatial_bce_8: 0.10347/0.12796, loss_spatial_dice_8: 0.13195/0.26428, loss_spatial_ce_8: 0.11074/0.21795, loss_grounding_bce_8: 0.12006/0.08866, loss_grounding_dice_8: 0.15679/0.17050, loss_grounding_ce_8: 0.01389/0.42912, loss_mask_ce_9: 2.40826/3.49230, loss_mask_bce_9: 0.23419/0.36056, loss_mask_dice_9: 0.41907/1.76895, loss_spatial_bce_9: 0.39179/0.35738, loss_spatial_dice_9: 0.58092/0.79532, loss_spatial_ce_9: 1.82452/1.40308, loss_grounding_bce_9: 0.12536/0.10062, loss_grounding_dice_9: 0.19216/0.24396, loss_grounding_ce_9: 0.10631/0.69199] items per batch[64] items per second[0.37] total items[2060800] mini batches[ 32200] memory[4967] epoch remaining[0:20:17] INFO:trainer.default_trainer:epochs[ 17] optim steps[32300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03974/0.77310, loss_mask_bce_0: 0.10362/0.30165, loss_mask_dice_0: 1.20635/1.02675, loss_spatial_bce_0: 0.02710/0.08749, loss_spatial_dice_0: 0.23904/0.18507, loss_spatial_ce_0: 0.00859/0.06557, loss_grounding_bce_0: 0.00115/0.08047, loss_grounding_dice_0: 0.06756/0.15101, loss_grounding_ce_0: 0.01997/0.25064, loss_mask_ce_1: 0.99839/0.77513, loss_mask_bce_1: 0.09823/0.30237, loss_mask_dice_1: 1.15493/1.03118, loss_spatial_bce_1: 0.02145/0.08783, loss_spatial_dice_1: 0.22348/0.18771, loss_spatial_ce_1: 0.05641/0.07007, loss_grounding_bce_1: 0.00417/0.08064, loss_grounding_dice_1: 0.11104/0.15186, loss_grounding_ce_1: 0.02700/0.25239, loss_mask_ce_2: 1.01223/0.78252, loss_mask_bce_2: 0.10455/0.30253, loss_mask_dice_2: 1.09854/1.03261, loss_spatial_bce_2: 0.02704/0.08760, loss_spatial_dice_2: 0.23004/0.18779, loss_spatial_ce_2: 0.04776/0.07232, loss_grounding_bce_2: 0.00141/0.08057, loss_grounding_dice_2: 0.06375/0.15162, loss_grounding_ce_2: 0.01983/0.25475, loss_mask_ce_3: 0.90068/0.78424, loss_mask_bce_3: 0.10357/0.30399, loss_mask_dice_3: 1.07172/1.02879, loss_spatial_bce_3: 0.02489/0.08936, loss_spatial_dice_3: 0.22128/0.18846, loss_spatial_ce_3: 0.03178/0.07741, loss_grounding_bce_3: 0.00154/0.08100, loss_grounding_dice_3: 0.04539/0.15115, loss_grounding_ce_3: 0.01990/0.25426, loss_mask_ce_4: 0.88736/0.79008, loss_mask_bce_4: 0.08875/0.30626, loss_mask_dice_4: 1.04974/1.04792, loss_spatial_bce_4: 0.01995/0.09134, loss_spatial_dice_4: 0.23643/0.19614, loss_spatial_ce_4: 0.04099/0.08985, loss_grounding_bce_4: 0.00319/0.08168, loss_grounding_dice_4: 0.07714/0.15384, loss_grounding_ce_4: 0.01670/0.26001, loss_mask_ce_5: 1.04647/0.81267, loss_mask_bce_5: 0.08130/0.30807, loss_mask_dice_5: 0.99423/1.05515, loss_spatial_bce_5: 0.02373/0.09310, loss_spatial_dice_5: 0.23053/0.19842, loss_spatial_ce_5: 0.04846/0.10187, loss_grounding_bce_5: 0.00168/0.08194, loss_grounding_dice_5: 0.05015/0.15437, loss_grounding_ce_5: 0.03424/0.27894, loss_mask_ce_6: 1.31704/0.83870, loss_mask_bce_6: 0.09751/0.30985, loss_mask_dice_6: 1.03357/1.05793, loss_spatial_bce_6: 0.02557/0.09793, loss_spatial_dice_6: 0.21613/0.20070, loss_spatial_ce_6: 0.02133/0.12370, loss_grounding_bce_6: 0.00334/0.08299, loss_grounding_dice_6: 0.07143/0.15504, loss_grounding_ce_6: 0.04263/0.28868, loss_mask_ce_7: 1.77684/0.89733, loss_mask_bce_7: 0.09252/0.31700, loss_mask_dice_7: 1.32056/1.10457, loss_spatial_bce_7: 0.02941/0.10832, loss_spatial_dice_7: 0.24840/0.22561, loss_spatial_ce_7: 0.13294/0.16469, loss_grounding_bce_7: 0.00255/0.08454, loss_grounding_dice_7: 0.08753/0.16077, loss_grounding_ce_7: 0.39335/0.32783, loss_mask_ce_8: 1.85956/1.03405, loss_mask_bce_8: 0.09981/0.33396, loss_mask_dice_8: 1.26153/1.18327, loss_spatial_bce_8: 0.02714/0.12792, loss_spatial_dice_8: 0.26896/0.26426, loss_spatial_ce_8: 0.04153/0.21783, loss_grounding_bce_8: 0.00281/0.08860, loss_grounding_dice_8: 0.10371/0.17048, loss_grounding_ce_8: 0.23310/0.42911, loss_mask_ce_9: 5.03235/3.49304, loss_mask_bce_9: 0.09229/0.36056, loss_mask_dice_9: 2.42314/1.76956, loss_spatial_bce_9: 0.29797/0.35734, loss_spatial_dice_9: 0.94912/0.79538, loss_spatial_ce_9: 2.24523/1.40322, loss_grounding_bce_9: 0.01073/0.10055, loss_grounding_dice_9: 0.36148/0.24392, loss_grounding_ce_9: 0.67906/0.69191] items per batch[64] items per second[0.35] total items[2067200] mini batches[ 32300] memory[4967] epoch remaining[0:17:21] INFO:trainer.default_trainer:epochs[ 17] optim steps[32400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31923/0.77295, loss_mask_bce_0: 0.02455/0.30158, loss_mask_dice_0: 0.28277/1.02648, loss_spatial_bce_0: 0.00892/0.08748, loss_spatial_dice_0: 0.12305/0.18504, loss_spatial_ce_0: 0.05198/0.06557, loss_grounding_bce_0: 0.01126/0.08049, loss_grounding_dice_0: 0.17508/0.15103, loss_grounding_ce_0: 0.27236/0.25051, loss_mask_ce_1: 1.41454/0.77497, loss_mask_bce_1: 0.02806/0.30229, loss_mask_dice_1: 0.32957/1.03095, loss_spatial_bce_1: 0.00791/0.08783, loss_spatial_dice_1: 0.11630/0.18768, loss_spatial_ce_1: 0.05896/0.07008, loss_grounding_bce_1: 0.01131/0.08065, loss_grounding_dice_1: 0.15350/0.15187, loss_grounding_ce_1: 0.43228/0.25227, loss_mask_ce_2: 1.52145/0.78232, loss_mask_bce_2: 0.02760/0.30245, loss_mask_dice_2: 0.21280/1.03237, loss_spatial_bce_2: 0.00859/0.08760, loss_spatial_dice_2: 0.09551/0.18776, loss_spatial_ce_2: 0.04833/0.07234, loss_grounding_bce_2: 0.01134/0.08058, loss_grounding_dice_2: 0.12754/0.15163, loss_grounding_ce_2: 0.31999/0.25461, loss_mask_ce_3: 1.68285/0.78399, loss_mask_bce_3: 0.02508/0.30390, loss_mask_dice_3: 0.25068/1.02860, loss_spatial_bce_3: 0.01129/0.08936, loss_spatial_dice_3: 0.14330/0.18842, loss_spatial_ce_3: 0.04532/0.07737, loss_grounding_bce_3: 0.01258/0.08101, loss_grounding_dice_3: 0.25167/0.15115, loss_grounding_ce_3: 0.30877/0.25414, loss_mask_ce_4: 1.71076/0.78992, loss_mask_bce_4: 0.03159/0.30617, loss_mask_dice_4: 0.35950/1.04771, loss_spatial_bce_4: 0.01788/0.09133, loss_spatial_dice_4: 0.14713/0.19612, loss_spatial_ce_4: 0.03926/0.08986, loss_grounding_bce_4: 0.00973/0.08169, loss_grounding_dice_4: 0.25896/0.15385, loss_grounding_ce_4: 0.30218/0.25984, loss_mask_ce_5: 1.40657/0.81253, loss_mask_bce_5: 0.02202/0.30796, loss_mask_dice_5: 0.29845/1.05491, loss_spatial_bce_5: 0.01289/0.09310, loss_spatial_dice_5: 0.12988/0.19840, loss_spatial_ce_5: 0.05983/0.10188, loss_grounding_bce_5: 0.01264/0.08195, loss_grounding_dice_5: 0.21614/0.15438, loss_grounding_ce_5: 0.35046/0.27880, loss_mask_ce_6: 1.67280/0.83855, loss_mask_bce_6: 0.02282/0.30976, loss_mask_dice_6: 0.35247/1.05767, loss_spatial_bce_6: 0.01165/0.09791, loss_spatial_dice_6: 0.13935/0.20065, loss_spatial_ce_6: 0.08026/0.12367, loss_grounding_bce_6: 0.01111/0.08300, loss_grounding_dice_6: 0.25560/0.15505, loss_grounding_ce_6: 0.39172/0.28857, loss_mask_ce_7: 1.51026/0.89709, loss_mask_bce_7: 0.02526/0.31690, loss_mask_dice_7: 0.30951/1.10431, loss_spatial_bce_7: 0.06581/0.10830, loss_spatial_dice_7: 0.25008/0.22557, loss_spatial_ce_7: 0.06342/0.16463, loss_grounding_bce_7: 0.01326/0.08455, loss_grounding_dice_7: 0.20598/0.16077, loss_grounding_ce_7: 0.43570/0.32760, loss_mask_ce_8: 1.24609/1.03378, loss_mask_bce_8: 0.02837/0.33386, loss_mask_dice_8: 0.56597/1.18299, loss_spatial_bce_8: 0.05247/0.12790, loss_spatial_dice_8: 0.29042/0.26419, loss_spatial_ce_8: 0.13409/0.21770, loss_grounding_bce_8: 0.01535/0.08860, loss_grounding_dice_8: 0.25457/0.17047, loss_grounding_ce_8: 0.41344/0.42882, loss_mask_ce_9: 4.87427/3.49223, loss_mask_bce_9: 0.02532/0.36046, loss_mask_dice_9: 0.61003/1.76907, loss_spatial_bce_9: 0.06623/0.35745, loss_spatial_dice_9: 0.77053/0.79528, loss_spatial_ce_9: 0.97364/1.40327, loss_grounding_bce_9: 0.00923/0.10057, loss_grounding_dice_9: 0.29753/0.24396, loss_grounding_ce_9: 0.46832/0.69149] items per batch[64] items per second[0.36] total items[2073600] mini batches[ 32400] memory[4967] epoch remaining[0:14:22] INFO:trainer.default_trainer:epochs[ 17] optim steps[32500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21626/0.77292, loss_mask_bce_0: 0.61461/0.30167, loss_mask_dice_0: 1.26278/1.02735, loss_spatial_bce_0: 0.14432/0.08743, loss_spatial_dice_0: 0.25160/0.18506, loss_spatial_ce_0: 0.06805/0.06549, loss_grounding_bce_0: 0.01996/0.08048, loss_grounding_dice_0: 0.07242/0.15102, loss_grounding_ce_0: 0.09841/0.25060, loss_mask_ce_1: 1.29597/0.77495, loss_mask_bce_1: 0.62036/0.30238, loss_mask_dice_1: 1.23920/1.03182, loss_spatial_bce_1: 0.13359/0.08778, loss_spatial_dice_1: 0.26417/0.18771, loss_spatial_ce_1: 0.07114/0.07004, loss_grounding_bce_1: 0.02836/0.08063, loss_grounding_dice_1: 0.09529/0.15186, loss_grounding_ce_1: 0.16040/0.25243, loss_mask_ce_2: 1.22188/0.78225, loss_mask_bce_2: 0.63183/0.30252, loss_mask_dice_2: 1.24774/1.03318, loss_spatial_bce_2: 0.12245/0.08756, loss_spatial_dice_2: 0.23722/0.18778, loss_spatial_ce_2: 0.04484/0.07226, loss_grounding_bce_2: 0.02831/0.08056, loss_grounding_dice_2: 0.08581/0.15162, loss_grounding_ce_2: 0.10893/0.25479, loss_mask_ce_3: 1.26310/0.78392, loss_mask_bce_3: 0.63252/0.30399, loss_mask_dice_3: 1.22218/1.02942, loss_spatial_bce_3: 0.15925/0.08932, loss_spatial_dice_3: 0.23565/0.18846, loss_spatial_ce_3: 0.05149/0.07727, loss_grounding_bce_3: 0.03288/0.08100, loss_grounding_dice_3: 0.09834/0.15118, loss_grounding_ce_3: 0.15037/0.25416, loss_mask_ce_4: 1.29500/0.78992, loss_mask_bce_4: 0.66554/0.30626, loss_mask_dice_4: 1.29300/1.04859, loss_spatial_bce_4: 0.14901/0.09129, loss_spatial_dice_4: 0.26299/0.19614, loss_spatial_ce_4: 0.08457/0.08979, loss_grounding_bce_4: 0.05419/0.08168, loss_grounding_dice_4: 0.16568/0.15386, loss_grounding_ce_4: 0.01739/0.25998, loss_mask_ce_5: 1.20325/0.81260, loss_mask_bce_5: 0.64750/0.30803, loss_mask_dice_5: 1.14899/1.05573, loss_spatial_bce_5: 0.11670/0.09305, loss_spatial_dice_5: 0.24872/0.19844, loss_spatial_ce_5: 0.05241/0.10178, loss_grounding_bce_5: 0.02740/0.08194, loss_grounding_dice_5: 0.07950/0.15441, loss_grounding_ce_5: 0.00702/0.27900, loss_mask_ce_6: 1.25638/0.83864, loss_mask_bce_6: 0.61446/0.30985, loss_mask_dice_6: 1.17992/1.05851, loss_spatial_bce_6: 0.13668/0.09787, loss_spatial_dice_6: 0.24468/0.20069, loss_spatial_ce_6: 0.07589/0.12359, loss_grounding_bce_6: 0.02462/0.08299, loss_grounding_dice_6: 0.08141/0.15508, loss_grounding_ce_6: 0.00332/0.28883, loss_mask_ce_7: 1.37743/0.89715, loss_mask_bce_7: 0.67216/0.31699, loss_mask_dice_7: 1.16356/1.10517, loss_spatial_bce_7: 0.13142/0.10825, loss_spatial_dice_7: 0.27470/0.22564, loss_spatial_ce_7: 0.13482/0.16453, loss_grounding_bce_7: 0.04506/0.08455, loss_grounding_dice_7: 0.16166/0.16079, loss_grounding_ce_7: 0.12017/0.32779, loss_mask_ce_8: 1.70555/1.03395, loss_mask_bce_8: 0.63306/0.33394, loss_mask_dice_8: 1.83393/1.18394, loss_spatial_bce_8: 0.13916/0.12784, loss_spatial_dice_8: 0.33109/0.26425, loss_spatial_ce_8: 0.21621/0.21760, loss_grounding_bce_8: 0.13366/0.08858, loss_grounding_dice_8: 0.49237/0.17052, loss_grounding_ce_8: 1.54977/0.42886, loss_mask_ce_9: 4.44899/3.49257, loss_mask_bce_9: 0.81499/0.36054, loss_mask_dice_9: 2.96139/1.77034, loss_spatial_bce_9: 0.29341/0.35732, loss_spatial_dice_9: 0.87772/0.79539, loss_spatial_ce_9: 1.11427/1.40341, loss_grounding_bce_9: 0.20672/0.10055, loss_grounding_dice_9: 0.73455/0.24399, loss_grounding_ce_9: 2.13341/0.69163] items per batch[64] items per second[0.36] total items[2080000] mini batches[ 32500] memory[4967] epoch remaining[0:11:24] INFO:trainer.default_trainer:epochs[ 17] optim steps[32600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26389/0.77318, loss_mask_bce_0: 0.03541/0.30166, loss_mask_dice_0: 0.35635/1.02738, loss_spatial_bce_0: 0.01007/0.08741, loss_spatial_dice_0: 0.12392/0.18502, loss_spatial_ce_0: 0.00000/0.06545, loss_grounding_bce_0: 0.00296/0.08045, loss_grounding_dice_0: 0.06406/0.15107, loss_grounding_ce_0: 0.09364/0.25068, loss_mask_ce_1: 0.27494/0.77516, loss_mask_bce_1: 0.02708/0.30236, loss_mask_dice_1: 0.37462/1.03196, loss_spatial_bce_1: 0.00976/0.08777, loss_spatial_dice_1: 0.11381/0.18767, loss_spatial_ce_1: 0.00000/0.06998, loss_grounding_bce_1: 0.00319/0.08060, loss_grounding_dice_1: 0.08710/0.15187, loss_grounding_ce_1: 0.07766/0.25250, loss_mask_ce_2: 0.23516/0.78248, loss_mask_bce_2: 0.02991/0.30250, loss_mask_dice_2: 0.39091/1.03331, loss_spatial_bce_2: 0.01085/0.08755, loss_spatial_dice_2: 0.12483/0.18775, loss_spatial_ce_2: 0.00001/0.07219, loss_grounding_bce_2: 0.00337/0.08054, loss_grounding_dice_2: 0.09309/0.15163, loss_grounding_ce_2: 0.06880/0.25490, loss_mask_ce_3: 0.26188/0.78425, loss_mask_bce_3: 0.03166/0.30397, loss_mask_dice_3: 0.35946/1.02948, loss_spatial_bce_3: 0.01237/0.08931, loss_spatial_dice_3: 0.14930/0.18843, loss_spatial_ce_3: 0.00026/0.07719, loss_grounding_bce_3: 0.00387/0.08097, loss_grounding_dice_3: 0.06940/0.15120, loss_grounding_ce_3: 0.09392/0.25428, loss_mask_ce_4: 0.21044/0.79015, loss_mask_bce_4: 0.02278/0.30624, loss_mask_dice_4: 0.35254/1.04870, loss_spatial_bce_4: 0.01206/0.09128, loss_spatial_dice_4: 0.14576/0.19610, loss_spatial_ce_4: 0.00005/0.08972, loss_grounding_bce_4: 0.00382/0.08165, loss_grounding_dice_4: 0.07884/0.15389, loss_grounding_ce_4: 0.09239/0.26011, loss_mask_ce_5: 0.22541/0.81290, loss_mask_bce_5: 0.02792/0.30800, loss_mask_dice_5: 0.32241/1.05581, loss_spatial_bce_5: 0.01237/0.09304, loss_spatial_dice_5: 0.13879/0.19841, loss_spatial_ce_5: 0.00160/0.10169, loss_grounding_bce_5: 0.00589/0.08191, loss_grounding_dice_5: 0.11574/0.15443, loss_grounding_ce_5: 0.11312/0.27905, loss_mask_ce_6: 0.22982/0.83891, loss_mask_bce_6: 0.02527/0.30985, loss_mask_dice_6: 0.33099/1.05859, loss_spatial_bce_6: 0.01910/0.09785, loss_spatial_dice_6: 0.13188/0.20066, loss_spatial_ce_6: 0.04555/0.12348, loss_grounding_bce_6: 0.00351/0.08298, loss_grounding_dice_6: 0.06763/0.15512, loss_grounding_ce_6: 0.22970/0.28891, loss_mask_ce_7: 0.24853/0.89742, loss_mask_bce_7: 0.02311/0.31701, loss_mask_dice_7: 0.34517/1.10521, loss_spatial_bce_7: 0.02543/0.10821, loss_spatial_dice_7: 0.21390/0.22562, loss_spatial_ce_7: 0.02269/0.16442, loss_grounding_bce_7: 0.00783/0.08455, loss_grounding_dice_7: 0.12000/0.16084, loss_grounding_ce_7: 0.23533/0.32792, loss_mask_ce_8: 0.39205/1.03435, loss_mask_bce_8: 0.01979/0.33392, loss_mask_dice_8: 0.35077/1.18396, loss_spatial_bce_8: 0.02821/0.12780, loss_spatial_dice_8: 0.21611/0.26418, loss_spatial_ce_8: 0.12478/0.21753, loss_grounding_bce_8: 0.00619/0.08858, loss_grounding_dice_8: 0.09580/0.17057, loss_grounding_ce_8: 0.43759/0.42889, loss_mask_ce_9: 3.68704/3.49329, loss_mask_bce_9: 0.02838/0.36058, loss_mask_dice_9: 0.49449/1.77032, loss_spatial_bce_9: 0.05960/0.35726, loss_spatial_dice_9: 0.68474/0.79540, loss_spatial_ce_9: 1.07767/1.40351, loss_grounding_bce_9: 0.00540/0.10055, loss_grounding_dice_9: 0.11733/0.24406, loss_grounding_ce_9: 2.66694/0.69209] items per batch[64] items per second[0.36] total items[2086400] mini batches[ 32600] memory[4967] epoch remaining[0:08:27] INFO:trainer.default_trainer:epochs[ 17] optim steps[32700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61309/0.77363, loss_mask_bce_0: 0.29011/0.30170, loss_mask_dice_0: 0.44093/1.02717, loss_spatial_bce_0: 0.20618/0.08743, loss_spatial_dice_0: 0.15804/0.18499, loss_spatial_ce_0: 0.00234/0.06543, loss_grounding_bce_0: 0.11579/0.08048, loss_grounding_dice_0: 0.08220/0.15105, loss_grounding_ce_0: 0.18807/0.25044, loss_mask_ce_1: 0.61971/0.77557, loss_mask_bce_1: 0.27379/0.30240, loss_mask_dice_1: 0.25357/1.03169, loss_spatial_bce_1: 0.19050/0.08778, loss_spatial_dice_1: 0.16397/0.18764, loss_spatial_ce_1: 0.01277/0.06997, loss_grounding_bce_1: 0.10427/0.08064, loss_grounding_dice_1: 0.13133/0.15186, loss_grounding_ce_1: 0.18010/0.25225, loss_mask_ce_2: 0.61872/0.78281, loss_mask_bce_2: 0.26819/0.30255, loss_mask_dice_2: 0.31888/1.03321, loss_spatial_bce_2: 0.19602/0.08756, loss_spatial_dice_2: 0.17667/0.18771, loss_spatial_ce_2: 0.02186/0.07218, loss_grounding_bce_2: 0.10448/0.08057, loss_grounding_dice_2: 0.08107/0.15162, loss_grounding_ce_2: 0.20549/0.25463, loss_mask_ce_3: 0.62342/0.78469, loss_mask_bce_3: 0.27067/0.30401, loss_mask_dice_3: 0.24996/1.02922, loss_spatial_bce_3: 0.20254/0.08933, loss_spatial_dice_3: 0.16711/0.18840, loss_spatial_ce_3: 0.01792/0.07720, loss_grounding_bce_3: 0.10480/0.08101, loss_grounding_dice_3: 0.08216/0.15117, loss_grounding_ce_3: 0.20968/0.25406, loss_mask_ce_4: 0.71187/0.79045, loss_mask_bce_4: 0.27367/0.30628, loss_mask_dice_4: 0.31161/1.04847, loss_spatial_bce_4: 0.22079/0.09130, loss_spatial_dice_4: 0.16478/0.19608, loss_spatial_ce_4: 0.00746/0.08975, loss_grounding_bce_4: 0.11378/0.08169, loss_grounding_dice_4: 0.13941/0.15387, loss_grounding_ce_4: 0.26802/0.25981, loss_mask_ce_5: 0.67436/0.81322, loss_mask_bce_5: 0.26920/0.30804, loss_mask_dice_5: 0.37930/1.05550, loss_spatial_bce_5: 0.17318/0.09307, loss_spatial_dice_5: 0.16846/0.19840, loss_spatial_ce_5: 0.02281/0.10177, loss_grounding_bce_5: 0.10768/0.08195, loss_grounding_dice_5: 0.21057/0.15442, loss_grounding_ce_5: 0.28023/0.27875, loss_mask_ce_6: 0.69089/0.83928, loss_mask_bce_6: 0.28760/0.30988, loss_mask_dice_6: 0.24462/1.05828, loss_spatial_bce_6: 0.20056/0.09791, loss_spatial_dice_6: 0.16630/0.20064, loss_spatial_ce_6: 0.06049/0.12351, loss_grounding_bce_6: 0.11294/0.08301, loss_grounding_dice_6: 0.08779/0.15508, loss_grounding_ce_6: 0.21646/0.28864, loss_mask_ce_7: 0.81713/0.89764, loss_mask_bce_7: 0.28807/0.31703, loss_mask_dice_7: 0.24190/1.10484, loss_spatial_bce_7: 0.23654/0.10825, loss_spatial_dice_7: 0.17003/0.22558, loss_spatial_ce_7: 0.03837/0.16444, loss_grounding_bce_7: 0.11303/0.08457, loss_grounding_dice_7: 0.20821/0.16081, loss_grounding_ce_7: 0.27288/0.32754, loss_mask_ce_8: 0.74618/1.03452, loss_mask_bce_8: 0.28539/0.33396, loss_mask_dice_8: 0.24627/1.18357, loss_spatial_bce_8: 0.28666/0.12780, loss_spatial_dice_8: 0.20018/0.26415, loss_spatial_ce_8: 0.20166/0.21757, loss_grounding_bce_8: 0.11506/0.08860, loss_grounding_dice_8: 0.22504/0.17054, loss_grounding_ce_8: 0.21912/0.42854, loss_mask_ce_9: 2.66934/3.49317, loss_mask_bce_9: 0.23209/0.36061, loss_mask_dice_9: 0.39461/1.77060, loss_spatial_bce_9: 0.50597/0.35732, loss_spatial_dice_9: 0.63644/0.79532, loss_spatial_ce_9: 0.71650/1.40323, loss_grounding_bce_9: 0.10391/0.10055, loss_grounding_dice_9: 0.09424/0.24397, loss_grounding_ce_9: 0.20533/0.69181] items per batch[64] items per second[0.36] total items[2092800] mini batches[ 32700] memory[4967] epoch remaining[0:05:30] INFO:trainer.default_trainer:epochs[ 17] optim steps[32800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.96946/0.77342, loss_mask_bce_0: 0.64103/0.30166, loss_mask_dice_0: 0.65137/1.02620, loss_spatial_bce_0: 0.16543/0.08744, loss_spatial_dice_0: 0.19874/0.18498, loss_spatial_ce_0: 0.12567/0.06538, loss_grounding_bce_0: 0.19340/0.08047, loss_grounding_dice_0: 0.11846/0.15098, loss_grounding_ce_0: 0.02525/0.25046, loss_mask_ce_1: 0.92139/0.77535, loss_mask_bce_1: 0.63858/0.30238, loss_mask_dice_1: 0.66163/1.03075, loss_spatial_bce_1: 0.17027/0.08779, loss_spatial_dice_1: 0.19510/0.18764, loss_spatial_ce_1: 0.07549/0.06992, loss_grounding_bce_1: 0.19926/0.08064, loss_grounding_dice_1: 0.12240/0.15180, loss_grounding_ce_1: 0.01346/0.25224, loss_mask_ce_2: 0.89464/0.78259, loss_mask_bce_2: 0.64546/0.30252, loss_mask_dice_2: 0.65167/1.03228, loss_spatial_bce_2: 0.17482/0.08757, loss_spatial_dice_2: 0.20137/0.18771, loss_spatial_ce_2: 0.07168/0.07212, loss_grounding_bce_2: 0.21642/0.08056, loss_grounding_dice_2: 0.12313/0.15157, loss_grounding_ce_2: 0.01820/0.25465, loss_mask_ce_3: 0.87746/0.78451, loss_mask_bce_3: 0.64668/0.30396, loss_mask_dice_3: 0.65431/1.02825, loss_spatial_bce_3: 0.17970/0.08934, loss_spatial_dice_3: 0.21351/0.18839, loss_spatial_ce_3: 0.07136/0.07716, loss_grounding_bce_3: 0.19376/0.08100, loss_grounding_dice_3: 0.11862/0.15111, loss_grounding_ce_3: 0.01864/0.25411, loss_mask_ce_4: 0.87485/0.79024, loss_mask_bce_4: 0.64060/0.30623, loss_mask_dice_4: 0.68818/1.04751, loss_spatial_bce_4: 0.19648/0.09130, loss_spatial_dice_4: 0.21200/0.19607, loss_spatial_ce_4: 0.12732/0.08973, loss_grounding_bce_4: 0.19748/0.08169, loss_grounding_dice_4: 0.13533/0.15379, loss_grounding_ce_4: 0.03379/0.25979, loss_mask_ce_5: 0.93800/0.81310, loss_mask_bce_5: 0.63991/0.30798, loss_mask_dice_5: 0.68985/1.05449, loss_spatial_bce_5: 0.20382/0.09308, loss_spatial_dice_5: 0.23070/0.19838, loss_spatial_ce_5: 0.17158/0.10180, loss_grounding_bce_5: 0.20798/0.08194, loss_grounding_dice_5: 0.13536/0.15436, loss_grounding_ce_5: 0.03973/0.27877, loss_mask_ce_6: 0.97955/0.83908, loss_mask_bce_6: 0.65599/0.30982, loss_mask_dice_6: 0.67388/1.05726, loss_spatial_bce_6: 0.23170/0.09791, loss_spatial_dice_6: 0.22081/0.20062, loss_spatial_ce_6: 0.17734/0.12352, loss_grounding_bce_6: 0.20889/0.08299, loss_grounding_dice_6: 0.12615/0.15500, loss_grounding_ce_6: 0.04353/0.28869, loss_mask_ce_7: 1.25570/0.89750, loss_mask_bce_7: 0.63663/0.31698, loss_mask_dice_7: 0.71369/1.10381, loss_spatial_bce_7: 0.34295/0.10826, loss_spatial_dice_7: 0.26480/0.22557, loss_spatial_ce_7: 0.16389/0.16446, loss_grounding_bce_7: 0.19558/0.08455, loss_grounding_dice_7: 0.14110/0.16072, loss_grounding_ce_7: 0.07889/0.32768, loss_mask_ce_8: 1.17857/1.03422, loss_mask_bce_8: 0.61572/0.33390, loss_mask_dice_8: 0.66654/1.18246, loss_spatial_bce_8: 0.44583/0.12784, loss_spatial_dice_8: 0.27318/0.26410, loss_spatial_ce_8: 0.10960/0.21762, loss_grounding_bce_8: 0.18648/0.08858, loss_grounding_dice_8: 0.11979/0.17046, loss_grounding_ce_8: 0.05683/0.42855, loss_mask_ce_9: 2.78249/3.49213, loss_mask_bce_9: 0.62339/0.36059, loss_mask_dice_9: 1.05394/1.76901, loss_spatial_bce_9: 0.50303/0.35739, loss_spatial_dice_9: 0.83771/0.79526, loss_spatial_ce_9: 1.59381/1.40307, loss_grounding_bce_9: 0.13729/0.10054, loss_grounding_dice_9: 0.14796/0.24389, loss_grounding_ce_9: 0.19758/0.69150] items per batch[64] items per second[0.36] total items[2099200] mini batches[ 32800] memory[4967] epoch remaining[0:02:32] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00032886. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3584 s/iter. Eval: 0.1030 s/iter. Total: 0.4640 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3675 s/iter. Eval: 0.0843 s/iter. Total: 0.4544 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3728 s/iter. Eval: 0.0836 s/iter. Total: 0.4592 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3762 s/iter. Eval: 0.0807 s/iter. Total: 0.4597 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3741 s/iter. Eval: 0.0778 s/iter. Total: 0.4546 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3729 s/iter. Eval: 0.0756 s/iter. Total: 0.4515 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalhb6td4gu ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.489 | 83.279 | 65.910 | 133 | | Things | 61.595 | 84.235 | 72.639 | 80 | | Stuff | 46.272 | 81.834 | 55.753 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.53s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.20 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.42 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.24s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.76 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.697 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.679 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.894 | 69.654 | 49.478 | 26.296 | 49.895 | 67.906 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.610 | bicycle | 23.184 | car | 42.980 | | motorcycle | 42.112 | airplane | 62.342 | bus | 71.051 | | train | 74.681 | truck | 44.633 | boat | 31.486 | | traffic light | 29.447 | fire hydrant | 71.474 | stop sign | 68.595 | | parking meter | 53.918 | bench | 27.076 | bird | 34.569 | | cat | 77.275 | dog | 71.617 | horse | 51.088 | | sheep | 53.567 | cow | 56.763 | elephant | 66.936 | | bear | 80.370 | zebra | 66.644 | giraffe | 62.693 | | backpack | 24.380 | umbrella | 55.475 | handbag | 24.462 | | tie | 40.424 | suitcase | 50.973 | frisbee | 69.379 | | skis | 8.438 | snowboard | 34.660 | sports ball | 50.319 | | kite | 36.152 | baseball bat | 38.434 | baseball glove | 49.666 | | skateboard | 44.140 | surfboard | 44.277 | tennis racket | 63.720 | | bottle | 42.163 | wine glass | 37.059 | cup | 51.264 | | fork | 26.852 | knife | 24.611 | spoon | 22.277 | | bowl | 39.459 | banana | 22.265 | apple | 26.048 | | sandwich | 50.325 | orange | 31.209 | broccoli | 25.761 | | carrot | 22.681 | hot dog | 36.496 | pizza | 51.507 | | donut | 55.264 | cake | 47.105 | chair | 28.018 | | couch | 45.018 | potted plant | 23.670 | bed | 44.365 | | dining table | 15.501 | toilet | 69.568 | tv | 67.287 | | laptop | 71.362 | mouse | 63.205 | remote | 45.131 | | keyboard | 57.335 | cell phone | 46.242 | microwave | 62.476 | | oven | 34.995 | toaster | 51.321 | sink | 44.970 | | refrigerator | 71.044 | book | 13.591 | clock | 54.760 | | vase | 41.309 | scissors | 36.539 | teddy bear | 57.962 | | hair drier | 34.732 | toothbrush | 29.786 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.17309317253105, 'fwIoU': 71.35971430189349, 'IoU-person': 88.60525400454998, 'IoU-bicycle': 71.79505617835467, 'IoU-car': 73.78091659901659, 'IoU-motorcycle': 88.59426227458313, 'IoU-airplane': 84.71764236177857, 'IoU-bus': 85.55330002360823, 'IoU-train': 88.22702050120856, 'IoU-truck': 70.27088872476013, 'IoU-boat': 72.10155499668954, 'IoU-traffic light': 79.10890554486001, 'IoU-fire hydrant': 93.42320293257599, 'IoU-stop sign': 95.77728767742208, 'IoU-parking meter': 84.33589586367553, 'IoU-bench': 63.330254500774515, 'IoU-bird': 78.5650874025059, 'IoU-cat': 90.36093858246502, 'IoU-dog': 78.69725967669152, 'IoU-horse': 88.06415985525618, 'IoU-sheep': 88.23315705478436, 'IoU-cow': 88.19577386113035, 'IoU-elephant': 91.79101363660854, 'IoU-bear': 92.97906654085472, 'IoU-zebra': 90.07378074272376, 'IoU-giraffe': 86.99478556167426, 'IoU-backpack': 53.80713407452049, 'IoU-umbrella': 85.83516173132023, 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33.019874142283214, 'IoU-curtain': 71.2173632869525, 'IoU-door-stuff': 48.926845762656505, 'IoU-floor-wood': 62.04462554160498, 'IoU-flower': 48.737529306808966, 'IoU-fruit': 45.98851927787489, 'IoU-gravel': 28.2295966363968, 'IoU-house': 24.843597683214902, 'IoU-light': 45.275961031774465, 'IoU-mirror-stuff': 62.10715070487696, 'IoU-net': 38.33296156127949, 'IoU-pillow': 17.119429900261554, 'IoU-platform': 28.53189739151636, 'IoU-playingfield': 66.65386905753563, 'IoU-railroad': 63.175919694009885, 'IoU-river': 51.350217184951994, 'IoU-road': 66.66351622121482, 'IoU-roof': 17.03045506014597, 'IoU-sand': 65.96201135308192, 'IoU-sea': 86.31544603146583, 'IoU-shelf': 37.85417063155416, 'IoU-snow': 92.34683568407996, 'IoU-stairs': 32.639129877405395, 'IoU-tent': 10.908083328473987, 'IoU-towel': 45.93384672306899, 'IoU-wall-brick': 51.95850856038322, 'IoU-wall-stone': 31.79169991001991, 'IoU-wall-tile': 70.39511215946716, 'IoU-wall-wood': 44.51100285126435, 'IoU-water-other': 29.677293029433798, 'IoU-window-blind': 50.183581763364984, 'IoU-window-other': 49.95972086100323, 'IoU-tree-merged': 81.93003889293658, 'IoU-fence-merged': 52.61877785399848, 'IoU-ceiling-merged': 69.15721791556044, 'IoU-sky-other-merged': 94.05975463466312, 'IoU-cabinet-merged': 64.3664793978492, 'IoU-table-merged': 42.9508777101285, 'IoU-floor-other-merged': 53.5767565294915, 'IoU-pavement-merged': 55.68090985803066, 'IoU-mountain-merged': 57.99134229093522, 'IoU-grass-merged': 73.04273271024321, 'IoU-dirt-merged': 44.81115073258468, 'IoU-paper-merged': 33.19245833898668, 'IoU-food-other-merged': 43.95900885944569, 'IoU-building-other-merged': 60.101683598864206, 'IoU-rock-merged': 63.48767480399674, 'IoU-wall-other-merged': 66.60299017778127, 'IoU-rug-merged': 67.33818190371301, 'mACC': 76.61175317725673, 'pACC': 81.99978200020499, 'ACC-person': 92.78738023423202, 'ACC-bicycle': 78.6140986925786, 'ACC-car': 84.30644095663664, 'ACC-motorcycle': 93.17488864467094, 'ACC-airplane': 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bat': 87.06740891143262, 'ACC-baseball glove': 92.01090911711088, 'ACC-skateboard': 90.66569116346712, 'ACC-surfboard': 92.7319332011842, 'ACC-tennis racket': 94.57388231632945, 'ACC-bottle': 83.08368193931324, 'ACC-wine glass': 90.5895631992167, 'ACC-cup': 87.99140357968237, 'ACC-fork': 81.57857715954432, 'ACC-knife': 77.55092694365062, 'ACC-spoon': 76.41976678709561, 'ACC-bowl': 73.49671825145421, 'ACC-banana': 90.06088048179853, 'ACC-apple': 67.64784515509174, 'ACC-sandwich': 82.79497183854241, 'ACC-orange': 79.57051241963498, 'ACC-broccoli': 79.41030577804366, 'ACC-carrot': 73.13254923421528, 'ACC-hot dog': 73.07553196588795, 'ACC-pizza': 88.07662985634191, 'ACC-donut': 75.66367378936404, 'ACC-cake': 82.71195854934716, 'ACC-chair': 76.02828510313027, 'ACC-couch': 81.84215464757125, 'ACC-potted plant': 60.451810340536994, 'ACC-bed': 88.13191314466778, 'ACC-dining table': 73.5759060234394, 'ACC-toilet': 87.06598951214654, 'ACC-tv': 83.91488841137607, 'ACC-laptop': 90.02130523770676, 'ACC-mouse': 91.39169363188064, 'ACC-remote': 71.61168419237562, 'ACC-keyboard': 72.0013703345482, 'ACC-cell phone': 85.08889769715941, 'ACC-microwave': 60.85729615017249, 'ACC-oven': 89.48758411691686, 'ACC-toaster': 90.87164187275042, 'ACC-sink': 84.64792623756658, 'ACC-refrigerator': 92.68647176129102, 'ACC-book': 69.96536241629543, 'ACC-clock': 82.64360616672649, 'ACC-vase': 72.73115355068386, 'ACC-scissors': 72.08001634392015, 'ACC-teddy bear': 86.76076308401568, 'ACC-hair drier': 63.60971166709361, 'ACC-toothbrush': 83.9141765114663, 'ACC-banner': 81.53109405561047, 'ACC-blanket': 26.268579199427585, 'ACC-bridge': 51.195171317412104, 'ACC-cardboard': 69.94328922495274, 'ACC-counter': 48.210634874076504, 'ACC-curtain': 83.71133535458729, 'ACC-door-stuff': 69.04055940415964, 'ACC-floor-wood': 79.8968432220128, 'ACC-flower': 71.97679293645673, 'ACC-fruit': 71.26006626724397, 'ACC-gravel': 37.833081287754574, 'ACC-house': 30.327685852016124, 'ACC-light': 63.57131407189067, 'ACC-mirror-stuff': 78.46053935067549, 'ACC-net': 67.72147619015946, 'ACC-pillow': 37.392454454015784, 'ACC-platform': 46.97853416945178, 'ACC-playingfield': 84.71949990662176, 'ACC-railroad': 80.48171027065291, 'ACC-river': 70.21121408099916, 'ACC-road': 88.03405467350304, 'ACC-roof': 21.712281044872814, 'ACC-sand': 71.58199842818551, 'ACC-sea': 91.28031888987896, 'ACC-shelf': 52.70322584411811, 'ACC-snow': 95.51309303445217, 'ACC-stairs': 58.86857236747937, 'ACC-tent': 13.984143347650255, 'ACC-towel': 55.245888913561124, 'ACC-wall-brick': 69.49909817284137, 'ACC-wall-stone': 37.29912220260396, 'ACC-wall-tile': 84.84163088367886, 'ACC-wall-wood': 64.50572838611845, 'ACC-water-other': 50.28038544666109, 'ACC-window-blind': 64.37265372004951, 'ACC-window-other': 69.86285266424778, 'ACC-tree-merged': 89.56622073607501, 'ACC-fence-merged': 67.65798704938575, 'ACC-ceiling-merged': 83.90668423351451, 'ACC-sky-other-merged': 96.85435299512956, 'ACC-cabinet-merged': 79.4616147393768, 'ACC-table-merged': 63.08684045902536, 'ACC-floor-other-merged': 65.85194192893931, 'ACC-pavement-merged': 66.31527796959222, 'ACC-mountain-merged': 70.37473006183336, 'ACC-grass-merged': 83.97735848116395, 'ACC-dirt-merged': 67.2511761394016, 'ACC-paper-merged': 42.82252056095332, 'ACC-food-other-merged': 61.963632934112326, 'ACC-building-other-merged': 75.50632339299955, 'ACC-rock-merged': 81.76719728132338, 'ACC-wall-other-merged': 82.09744794177298, 'ACC-rug-merged': 85.38214513778593})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2793 s/iter. Inference: 0.1765 s/iter. Eval: 0.0000 s/iter. Total: 0.4558 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.2972 s/iter. Inference: 0.4603 s/iter. Eval: 0.0000 s/iter. Total: 0.7575 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3230 s/iter. Inference: 0.5173 s/iter. Eval: 0.0000 s/iter. Total: 0.8404 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4000585308750366, 'noc@0.8': 2.5118525021949076, 'noc@0.85': 2.940883816213052, 'noc@0.9': 3.7699736611062336, 'miou@iter1': 0.8771276705649501} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1450 s/iter. Eval: 0.0010 s/iter. Total: 0.1474 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.59269714355469, 'precision@0.6': 72.91099548339844, 'precision@0.7': 68.63583374023438, 'precision@0.8': 59.77458190917969, 'precision@0.9': 32.80217742919922, 'cIoU': 61.500152587890625, 'mIoU': 66.99813079833984} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.48891495760453, 'SQ': 83.2785534449653, 'RQ': 65.90985761024041, 'PQ_th': 61.59516290906291, 'SQ_th': 84.23549125838736, 'RQ_th': 72.63887639820533, 'PQ_st': 46.27193691766736, 'SQ_st': 81.8341190096112, 'RQ_st': 55.75284811897261}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.89430996032303, 'AP50': 69.65388996276153, 'AP75': 49.47765815178013, 'APs': 26.295748659892553, 'APm': 49.89470230416794, 'APl': 67.90589275324666, 'AP-person': 49.610479999734494, 'AP-bicycle': 23.18403070574068, 'AP-car': 42.98033361338674, 'AP-motorcycle': 42.11230573801225, 'AP-airplane': 62.3420777543607, 'AP-bus': 71.05074040057748, 'AP-train': 74.68062079466745, 'AP-truck': 44.63262943559248, 'AP-boat': 31.485566408213717, 'AP-traffic light': 29.447260395322633, 'AP-fire hydrant': 71.47354362476031, 'AP-stop sign': 68.59514928130326, 'AP-parking meter': 53.91803498513321, 'AP-bench': 27.076230567816808, 'AP-bird': 34.5685870236774, 'AP-cat': 77.27534891807227, 'AP-dog': 71.61740673715833, 'AP-horse': 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57.961520983131045, 'AP-hair drier': 34.73211565662061, 'AP-toothbrush': 29.786465997289902}), ('sem_seg', {'mIoU': 65.17309317253105, 'fwIoU': 71.35971430189349, 'IoU-person': 88.60525400454998, 'IoU-bicycle': 71.79505617835467, 'IoU-car': 73.78091659901659, 'IoU-motorcycle': 88.59426227458313, 'IoU-airplane': 84.71764236177857, 'IoU-bus': 85.55330002360823, 'IoU-train': 88.22702050120856, 'IoU-truck': 70.27088872476013, 'IoU-boat': 72.10155499668954, 'IoU-traffic light': 79.10890554486001, 'IoU-fire hydrant': 93.42320293257599, 'IoU-stop sign': 95.77728767742208, 'IoU-parking meter': 84.33589586367553, 'IoU-bench': 63.330254500774515, 'IoU-bird': 78.5650874025059, 'IoU-cat': 90.36093858246502, 'IoU-dog': 78.69725967669152, 'IoU-horse': 88.06415985525618, 'IoU-sheep': 88.23315705478436, 'IoU-cow': 88.19577386113035, 'IoU-elephant': 91.79101363660854, 'IoU-bear': 92.97906654085472, 'IoU-zebra': 90.07378074272376, 'IoU-giraffe': 86.99478556167426, 'IoU-backpack': 53.80713407452049, 'IoU-umbrella': 85.83516173132023, 'IoU-handbag': 51.95230538916312, 'IoU-tie': 62.818093783381755, 'IoU-suitcase': 79.45490155602157, 'IoU-frisbee': 84.15397977745366, 'IoU-skis': 58.71222502502951, 'IoU-snowboard': 70.93326990626102, 'IoU-sports ball': 76.95887683145163, 'IoU-kite': 79.71705084031805, 'IoU-baseball bat': 69.53379067701026, 'IoU-baseball glove': 78.13969569115069, 'IoU-skateboard': 86.21902094171003, 'IoU-surfboard': 86.5773035517959, 'IoU-tennis racket': 90.59205003418502, 'IoU-bottle': 68.47529410538971, 'IoU-wine glass': 82.3234124940784, 'IoU-cup': 72.51511073857674, 'IoU-fork': 70.1542143047062, 'IoU-knife': 65.61441196802163, 'IoU-spoon': 59.70738317967097, 'IoU-bowl': 62.12133341480565, 'IoU-banana': 82.74849145802901, 'IoU-apple': 55.73064321263256, 'IoU-sandwich': 71.2206442400868, 'IoU-orange': 73.21443155679829, 'IoU-broccoli': 69.10928254247358, 'IoU-carrot': 62.680441692322276, 'IoU-hot dog': 65.13449844925259, 'IoU-pizza': 83.0320543516716, 'IoU-donut': 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'IoU-counter': 33.019874142283214, 'IoU-curtain': 71.2173632869525, 'IoU-door-stuff': 48.926845762656505, 'IoU-floor-wood': 62.04462554160498, 'IoU-flower': 48.737529306808966, 'IoU-fruit': 45.98851927787489, 'IoU-gravel': 28.2295966363968, 'IoU-house': 24.843597683214902, 'IoU-light': 45.275961031774465, 'IoU-mirror-stuff': 62.10715070487696, 'IoU-net': 38.33296156127949, 'IoU-pillow': 17.119429900261554, 'IoU-platform': 28.53189739151636, 'IoU-playingfield': 66.65386905753563, 'IoU-railroad': 63.175919694009885, 'IoU-river': 51.350217184951994, 'IoU-road': 66.66351622121482, 'IoU-roof': 17.03045506014597, 'IoU-sand': 65.96201135308192, 'IoU-sea': 86.31544603146583, 'IoU-shelf': 37.85417063155416, 'IoU-snow': 92.34683568407996, 'IoU-stairs': 32.639129877405395, 'IoU-tent': 10.908083328473987, 'IoU-towel': 45.93384672306899, 'IoU-wall-brick': 51.95850856038322, 'IoU-wall-stone': 31.79169991001991, 'IoU-wall-tile': 70.39511215946716, 'IoU-wall-wood': 44.51100285126435, 'IoU-water-other': 29.677293029433798, 'IoU-window-blind': 50.183581763364984, 'IoU-window-other': 49.95972086100323, 'IoU-tree-merged': 81.93003889293658, 'IoU-fence-merged': 52.61877785399848, 'IoU-ceiling-merged': 69.15721791556044, 'IoU-sky-other-merged': 94.05975463466312, 'IoU-cabinet-merged': 64.3664793978492, 'IoU-table-merged': 42.9508777101285, 'IoU-floor-other-merged': 53.5767565294915, 'IoU-pavement-merged': 55.68090985803066, 'IoU-mountain-merged': 57.99134229093522, 'IoU-grass-merged': 73.04273271024321, 'IoU-dirt-merged': 44.81115073258468, 'IoU-paper-merged': 33.19245833898668, 'IoU-food-other-merged': 43.95900885944569, 'IoU-building-other-merged': 60.101683598864206, 'IoU-rock-merged': 63.48767480399674, 'IoU-wall-other-merged': 66.60299017778127, 'IoU-rug-merged': 67.33818190371301, 'mACC': 76.61175317725673, 'pACC': 81.99978200020499, 'ACC-person': 92.78738023423202, 'ACC-bicycle': 78.6140986925786, 'ACC-car': 84.30644095663664, 'ACC-motorcycle': 93.17488864467094, 'ACC-airplane': 91.11098565477474, 'ACC-bus': 90.75093094333086, 'ACC-train': 93.20292938688236, 'ACC-truck': 83.76889328436594, 'ACC-boat': 81.07140952384455, 'ACC-traffic light': 90.99023133544894, 'ACC-fire hydrant': 96.02534513017375, 'ACC-stop sign': 98.25674000954511, 'ACC-parking meter': 87.87779429431959, 'ACC-bench': 76.66010984358665, 'ACC-bird': 82.1433157002722, 'ACC-cat': 93.68352414758016, 'ACC-dog': 81.22367606148771, 'ACC-horse': 92.8929632659544, 'ACC-sheep': 92.26542561257443, 'ACC-cow': 91.34917519893989, 'ACC-elephant': 93.96570565018635, 'ACC-bear': 94.8947006249611, 'ACC-zebra': 92.39977816933748, 'ACC-giraffe': 90.95700589414777, 'ACC-backpack': 72.96638504482871, 'ACC-umbrella': 90.79742778674972, 'ACC-handbag': 71.62672962349687, 'ACC-tie': 68.84043799992615, 'ACC-suitcase': 85.3044580988011, 'ACC-frisbee': 93.36654545454546, 'ACC-skis': 71.53122519139889, 'ACC-snowboard': 81.80353163037888, 'ACC-sports ball': 87.08975511933926, 'ACC-kite': 86.21592302019387, 'ACC-baseball bat': 87.06740891143262, 'ACC-baseball glove': 92.01090911711088, 'ACC-skateboard': 90.66569116346712, 'ACC-surfboard': 92.7319332011842, 'ACC-tennis racket': 94.57388231632945, 'ACC-bottle': 83.08368193931324, 'ACC-wine glass': 90.5895631992167, 'ACC-cup': 87.99140357968237, 'ACC-fork': 81.57857715954432, 'ACC-knife': 77.55092694365062, 'ACC-spoon': 76.41976678709561, 'ACC-bowl': 73.49671825145421, 'ACC-banana': 90.06088048179853, 'ACC-apple': 67.64784515509174, 'ACC-sandwich': 82.79497183854241, 'ACC-orange': 79.57051241963498, 'ACC-broccoli': 79.41030577804366, 'ACC-carrot': 73.13254923421528, 'ACC-hot dog': 73.07553196588795, 'ACC-pizza': 88.07662985634191, 'ACC-donut': 75.66367378936404, 'ACC-cake': 82.71195854934716, 'ACC-chair': 76.02828510313027, 'ACC-couch': 81.84215464757125, 'ACC-potted plant': 60.451810340536994, 'ACC-bed': 88.13191314466778, 'ACC-dining table': 73.5759060234394, 'ACC-toilet': 87.06598951214654, 'ACC-tv': 83.91488841137607, 'ACC-laptop': 90.02130523770676, 'ACC-mouse': 91.39169363188064, 'ACC-remote': 71.61168419237562, 'ACC-keyboard': 72.0013703345482, 'ACC-cell phone': 85.08889769715941, 'ACC-microwave': 60.85729615017249, 'ACC-oven': 89.48758411691686, 'ACC-toaster': 90.87164187275042, 'ACC-sink': 84.64792623756658, 'ACC-refrigerator': 92.68647176129102, 'ACC-book': 69.96536241629543, 'ACC-clock': 82.64360616672649, 'ACC-vase': 72.73115355068386, 'ACC-scissors': 72.08001634392015, 'ACC-teddy bear': 86.76076308401568, 'ACC-hair drier': 63.60971166709361, 'ACC-toothbrush': 83.9141765114663, 'ACC-banner': 81.53109405561047, 'ACC-blanket': 26.268579199427585, 'ACC-bridge': 51.195171317412104, 'ACC-cardboard': 69.94328922495274, 'ACC-counter': 48.210634874076504, 'ACC-curtain': 83.71133535458729, 'ACC-door-stuff': 69.04055940415964, 'ACC-floor-wood': 79.8968432220128, 'ACC-flower': 71.97679293645673, 'ACC-fruit': 71.26006626724397, 'ACC-gravel': 37.833081287754574, 'ACC-house': 30.327685852016124, 'ACC-light': 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79.4616147393768, 'ACC-table-merged': 63.08684045902536, 'ACC-floor-other-merged': 65.85194192893931, 'ACC-pavement-merged': 66.31527796959222, 'ACC-mountain-merged': 70.37473006183336, 'ACC-grass-merged': 83.97735848116395, 'ACC-dirt-merged': 67.2511761394016, 'ACC-paper-merged': 42.82252056095332, 'ACC-food-other-merged': 61.963632934112326, 'ACC-building-other-merged': 75.50632339299955, 'ACC-rock-merged': 81.76719728132338, 'ACC-wall-other-merged': 82.09744794177298, 'ACC-rug-merged': 85.38214513778593})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4000585308750366, 'noc@0.8': 2.5118525021949076, 'noc@0.85': 2.940883816213052, 'noc@0.9': 3.7699736611062336, 'miou@iter1': 0.8771276705649501}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.59269714355469, 'precision@0.6': 72.91099548339844, 'precision@0.7': 68.63583374023438, 'precision@0.8': 59.77458190917969, 'precision@0.9': 32.80217742919922, 'cIoU': 61.500152587890625, 'mIoU': 66.99813079833984}}} INFO:trainer.default_trainer:This epoch takes 0:57:28.828583 INFO:trainer.default_trainer:PROGRESS: 36.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 18 training. INFO:trainer.default_trainer:epochs[ 18] optim steps[32900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.29034/0.77330, loss_mask_bce_0: 0.01749/0.30161, loss_mask_dice_0: 0.11371/1.02594, loss_spatial_bce_0: 0.02476/0.08744, loss_spatial_dice_0: 0.13415/0.18499, loss_spatial_ce_0: 0.00000/0.06537, loss_grounding_bce_0: 0.02973/0.08046, loss_grounding_dice_0: 0.16181/0.15094, loss_grounding_ce_0: 0.00511/0.25037, loss_mask_ce_1: 0.19470/0.77515, loss_mask_bce_1: 0.01319/0.30234, loss_mask_dice_1: 0.10417/1.03046, loss_spatial_bce_1: 0.01981/0.08779, loss_spatial_dice_1: 0.11152/0.18764, loss_spatial_ce_1: 0.00000/0.06993, loss_grounding_bce_1: 0.03114/0.08064, loss_grounding_dice_1: 0.17295/0.15177, loss_grounding_ce_1: 0.00315/0.25212, loss_mask_ce_2: 0.18887/0.78243, loss_mask_bce_2: 0.01163/0.30247, loss_mask_dice_2: 0.09782/1.03190, loss_spatial_bce_2: 0.02713/0.08759, loss_spatial_dice_2: 0.12581/0.18772, loss_spatial_ce_2: 0.00001/0.07211, loss_grounding_bce_2: 0.02421/0.08056, loss_grounding_dice_2: 0.17202/0.15154, loss_grounding_ce_2: 0.00296/0.25454, loss_mask_ce_3: 0.23621/0.78434, loss_mask_bce_3: 0.01190/0.30392, loss_mask_dice_3: 0.09279/1.02794, loss_spatial_bce_3: 0.01895/0.08935, loss_spatial_dice_3: 0.10785/0.18840, loss_spatial_ce_3: 0.00000/0.07714, loss_grounding_bce_3: 0.02786/0.08100, loss_grounding_dice_3: 0.16075/0.15108, loss_grounding_ce_3: 0.00489/0.25403, loss_mask_ce_4: 0.21247/0.79014, loss_mask_bce_4: 0.01457/0.30618, loss_mask_dice_4: 0.11872/1.04719, loss_spatial_bce_4: 0.02398/0.09133, loss_spatial_dice_4: 0.11470/0.19606, loss_spatial_ce_4: 0.00000/0.08976, loss_grounding_bce_4: 0.02908/0.08168, loss_grounding_dice_4: 0.18601/0.15378, loss_grounding_ce_4: 0.00668/0.25978, loss_mask_ce_5: 0.22781/0.81301, loss_mask_bce_5: 0.01737/0.30794, loss_mask_dice_5: 0.11511/1.05413, loss_spatial_bce_5: 0.02943/0.09310, loss_spatial_dice_5: 0.13266/0.19839, loss_spatial_ce_5: 0.00020/0.10180, loss_grounding_bce_5: 0.03434/0.08193, loss_grounding_dice_5: 0.19091/0.15431, loss_grounding_ce_5: 0.00355/0.27870, loss_mask_ce_6: 0.29873/0.83895, loss_mask_bce_6: 0.01410/0.30977, loss_mask_dice_6: 0.09862/1.05693, loss_spatial_bce_6: 0.03282/0.09792, loss_spatial_dice_6: 0.12171/0.20062, loss_spatial_ce_6: 0.00023/0.12353, loss_grounding_bce_6: 0.03804/0.08298, loss_grounding_dice_6: 0.18510/0.15497, loss_grounding_ce_6: 0.00178/0.28860, loss_mask_ce_7: 0.20484/0.89735, loss_mask_bce_7: 0.01408/0.31692, loss_mask_dice_7: 0.10646/1.10349, loss_spatial_bce_7: 0.03617/0.10826, loss_spatial_dice_7: 0.13142/0.22556, loss_spatial_ce_7: 0.00279/0.16447, loss_grounding_bce_7: 0.03617/0.08455, loss_grounding_dice_7: 0.17374/0.16067, loss_grounding_ce_7: 0.00237/0.32747, loss_mask_ce_8: 0.20131/1.03402, loss_mask_bce_8: 0.01864/0.33385, loss_mask_dice_8: 0.09904/1.18215, loss_spatial_bce_8: 0.00904/0.12787, loss_spatial_dice_8: 0.09949/0.26412, loss_spatial_ce_8: 0.01289/0.21764, loss_grounding_bce_8: 0.04023/0.08857, loss_grounding_dice_8: 0.20101/0.17042, loss_grounding_ce_8: 0.00828/0.42847, loss_mask_ce_9: 1.46079/3.49191, loss_mask_bce_9: 0.01465/0.36058, loss_mask_dice_9: 0.14096/1.76822, loss_spatial_bce_9: 0.02398/0.35732, loss_spatial_dice_9: 0.38373/0.79524, loss_spatial_ce_9: 0.25135/1.40281, loss_grounding_bce_9: 0.03528/0.10057, loss_grounding_dice_9: 0.21269/0.24390, loss_grounding_ce_9: 0.01233/0.69111] items per batch[64] items per second[0.16] total items[2105600] mini batches[ 32900] memory[4967] epoch remaining[1:10:16] INFO:trainer.default_trainer:epochs[ 18] optim steps[33000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19386/0.77314, loss_mask_bce_0: 0.10329/0.30147, loss_mask_dice_0: 0.03247/1.02622, loss_spatial_bce_0: 0.09483/0.08737, loss_spatial_dice_0: 0.03061/0.18498, loss_spatial_ce_0: 0.00005/0.06537, loss_grounding_bce_0: 0.00246/0.08044, loss_grounding_dice_0: 0.01103/0.15100, loss_grounding_ce_0: 0.00248/0.25016, loss_mask_ce_1: 0.19702/0.77498, loss_mask_bce_1: 0.10149/0.30221, loss_mask_dice_1: 0.03307/1.03075, loss_spatial_bce_1: 0.08540/0.08772, loss_spatial_dice_1: 0.02733/0.18762, loss_spatial_ce_1: 0.00001/0.06985, loss_grounding_bce_1: 0.00249/0.08062, loss_grounding_dice_1: 0.01012/0.15180, loss_grounding_ce_1: 0.00243/0.25193, loss_mask_ce_2: 0.22207/0.78229, loss_mask_bce_2: 0.11045/0.30232, loss_mask_dice_2: 0.03270/1.03214, loss_spatial_bce_2: 0.09724/0.08752, loss_spatial_dice_2: 0.02922/0.18770, loss_spatial_ce_2: 0.00001/0.07204, loss_grounding_bce_2: 0.00175/0.08054, loss_grounding_dice_2: 0.00894/0.15157, loss_grounding_ce_2: 0.00454/0.25437, loss_mask_ce_3: 0.23604/0.78424, loss_mask_bce_3: 0.10911/0.30380, loss_mask_dice_3: 0.03300/1.02823, loss_spatial_bce_3: 0.09385/0.08928, loss_spatial_dice_3: 0.02853/0.18839, loss_spatial_ce_3: 0.00003/0.07711, loss_grounding_bce_3: 0.00266/0.08098, loss_grounding_dice_3: 0.01326/0.15109, loss_grounding_ce_3: 0.00402/0.25387, loss_mask_ce_4: 0.24091/0.79008, loss_mask_bce_4: 0.09743/0.30604, loss_mask_dice_4: 0.03276/1.04741, loss_spatial_bce_4: 0.11146/0.09125, loss_spatial_dice_4: 0.03029/0.19605, loss_spatial_ce_4: 0.00000/0.08970, loss_grounding_bce_4: 0.00261/0.08166, loss_grounding_dice_4: 0.01163/0.15384, loss_grounding_ce_4: 0.00767/0.25958, loss_mask_ce_5: 0.26097/0.81289, loss_mask_bce_5: 0.10217/0.30780, loss_mask_dice_5: 0.03320/1.05439, loss_spatial_bce_5: 0.09604/0.09302, loss_spatial_dice_5: 0.03104/0.19837, loss_spatial_ce_5: 0.00001/0.10174, loss_grounding_bce_5: 0.00246/0.08190, loss_grounding_dice_5: 0.01121/0.15433, loss_grounding_ce_5: 0.06379/0.27851, loss_mask_ce_6: 0.24906/0.83885, loss_mask_bce_6: 0.11142/0.30963, loss_mask_dice_6: 0.03720/1.05714, loss_spatial_bce_6: 0.10504/0.09786, loss_spatial_dice_6: 0.02978/0.20063, loss_spatial_ce_6: 0.00001/0.12350, loss_grounding_bce_6: 0.00278/0.08295, loss_grounding_dice_6: 0.01260/0.15501, loss_grounding_ce_6: 0.01022/0.28841, loss_mask_ce_7: 0.25047/0.89724, loss_mask_bce_7: 0.10982/0.31677, loss_mask_dice_7: 0.03651/1.10368, loss_spatial_bce_7: 0.11546/0.10819, loss_spatial_dice_7: 0.03331/0.22558, loss_spatial_ce_7: 0.00004/0.16439, loss_grounding_bce_7: 0.00223/0.08452, loss_grounding_dice_7: 0.01030/0.16071, loss_grounding_ce_7: 0.01223/0.32726, loss_mask_ce_8: 0.19851/1.03395, loss_mask_bce_8: 0.11122/0.33369, loss_mask_dice_8: 0.03781/1.18231, loss_spatial_bce_8: 0.11427/0.12778, loss_spatial_dice_8: 0.02908/0.26409, loss_spatial_ce_8: 0.01410/0.21757, loss_grounding_bce_8: 0.00344/0.08853, loss_grounding_dice_8: 0.01339/0.17045, loss_grounding_ce_8: 0.00895/0.42829, loss_mask_ce_9: 1.94895/3.49154, loss_mask_bce_9: 0.10128/0.36042, loss_mask_dice_9: 0.04410/1.76839, loss_spatial_bce_9: 0.77056/0.35732, loss_spatial_dice_9: 0.66746/0.79527, loss_spatial_ce_9: 3.20528/1.40277, loss_grounding_bce_9: 0.00270/0.10053, loss_grounding_dice_9: 0.01610/0.24397, loss_grounding_ce_9: 0.31986/0.69069] items per batch[64] items per second[0.36] total items[2112000] mini batches[ 33000] memory[4967] epoch remaining[0:52:39] INFO:trainer.default_trainer:epochs[ 18] optim steps[33100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25369/0.77275, loss_mask_bce_0: 0.45417/0.30154, loss_mask_dice_0: 0.52600/1.02610, loss_spatial_bce_0: 0.15246/0.08735, loss_spatial_dice_0: 0.19182/0.18492, loss_spatial_ce_0: 0.00125/0.06531, loss_grounding_bce_0: 0.12071/0.08043, loss_grounding_dice_0: 0.18854/0.15096, loss_grounding_ce_0: 0.02957/0.25027, loss_mask_ce_1: 0.25209/0.77456, loss_mask_bce_1: 0.45304/0.30227, loss_mask_dice_1: 0.55837/1.03064, loss_spatial_bce_1: 0.13370/0.08769, loss_spatial_dice_1: 0.17676/0.18755, loss_spatial_ce_1: 0.00218/0.06977, loss_grounding_bce_1: 0.11876/0.08061, loss_grounding_dice_1: 0.19249/0.15179, loss_grounding_ce_1: 0.03869/0.25203, loss_mask_ce_2: 0.26477/0.78188, loss_mask_bce_2: 0.45641/0.30237, loss_mask_dice_2: 0.56254/1.03201, loss_spatial_bce_2: 0.14667/0.08749, loss_spatial_dice_2: 0.18812/0.18763, loss_spatial_ce_2: 0.00247/0.07197, loss_grounding_bce_2: 0.12988/0.08053, loss_grounding_dice_2: 0.18535/0.15154, loss_grounding_ce_2: 0.03587/0.25449, loss_mask_ce_3: 0.27278/0.78388, loss_mask_bce_3: 0.45567/0.30386, loss_mask_dice_3: 0.51085/1.02809, loss_spatial_bce_3: 0.16347/0.08925, loss_spatial_dice_3: 0.18762/0.18832, loss_spatial_ce_3: 0.00658/0.07702, loss_grounding_bce_3: 0.11777/0.08096, loss_grounding_dice_3: 0.18856/0.15107, loss_grounding_ce_3: 0.03974/0.25408, loss_mask_ce_4: 0.30076/0.78972, loss_mask_bce_4: 0.44947/0.30610, loss_mask_dice_4: 0.54168/1.04731, loss_spatial_bce_4: 0.15801/0.09121, loss_spatial_dice_4: 0.18717/0.19597, loss_spatial_ce_4: 0.02884/0.08962, loss_grounding_bce_4: 0.11441/0.08165, loss_grounding_dice_4: 0.19993/0.15382, loss_grounding_ce_4: 0.04292/0.25974, loss_mask_ce_5: 0.31043/0.81258, loss_mask_bce_5: 0.46158/0.30785, loss_mask_dice_5: 0.53718/1.05428, loss_spatial_bce_5: 0.13941/0.09298, loss_spatial_dice_5: 0.19416/0.19830, loss_spatial_ce_5: 0.04645/0.10164, loss_grounding_bce_5: 0.12903/0.08188, loss_grounding_dice_5: 0.19143/0.15428, loss_grounding_ce_5: 0.03751/0.27872, loss_mask_ce_6: 0.27885/0.83855, loss_mask_bce_6: 0.47827/0.30968, loss_mask_dice_6: 0.56219/1.05704, loss_spatial_bce_6: 0.16680/0.09782, loss_spatial_dice_6: 0.20731/0.20056, loss_spatial_ce_6: 0.05406/0.12339, loss_grounding_bce_6: 0.12703/0.08293, loss_grounding_dice_6: 0.19775/0.15497, loss_grounding_ce_6: 0.04824/0.28853, loss_mask_ce_7: 0.46328/0.89678, loss_mask_bce_7: 0.47376/0.31685, loss_mask_dice_7: 0.55355/1.10362, loss_spatial_bce_7: 0.19764/0.10816, loss_spatial_dice_7: 0.18988/0.22550, loss_spatial_ce_7: 0.09059/0.16428, loss_grounding_bce_7: 0.11864/0.08451, loss_grounding_dice_7: 0.19708/0.16067, loss_grounding_ce_7: 0.06594/0.32729, loss_mask_ce_8: 0.78573/1.03339, loss_mask_bce_8: 0.41166/0.33373, loss_mask_dice_8: 0.55956/1.18221, loss_spatial_bce_8: 0.16652/0.12774, loss_spatial_dice_8: 0.20077/0.26399, loss_spatial_ce_8: 0.17616/0.21750, loss_grounding_bce_8: 0.12252/0.08851, loss_grounding_dice_8: 0.20950/0.17042, loss_grounding_ce_8: 0.19376/0.42837, loss_mask_ce_9: 2.33254/3.49132, loss_mask_bce_9: 0.43850/0.36047, loss_mask_dice_9: 0.60082/1.76808, loss_spatial_bce_9: 0.43272/0.35729, loss_spatial_dice_9: 0.85061/0.79529, loss_spatial_ce_9: 1.20652/1.40267, loss_grounding_bce_9: 0.10245/0.10049, loss_grounding_dice_9: 0.24669/0.24387, loss_grounding_ce_9: 0.28403/0.69089] items per batch[64] items per second[0.37] total items[2118400] mini batches[ 33100] memory[4967] epoch remaining[0:48:22] INFO:trainer.default_trainer:epochs[ 18] optim steps[33200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91854/0.77255, loss_mask_bce_0: 0.11784/0.30160, loss_mask_dice_0: 0.66652/1.02575, loss_spatial_bce_0: 0.06565/0.08752, loss_spatial_dice_0: 0.17765/0.18492, loss_spatial_ce_0: 0.04376/0.06534, loss_grounding_bce_0: 0.02047/0.08045, loss_grounding_dice_0: 0.12029/0.15100, loss_grounding_ce_0: 0.69761/0.25043, loss_mask_ce_1: 0.87136/0.77430, loss_mask_bce_1: 0.11033/0.30234, loss_mask_dice_1: 0.68875/1.03027, loss_spatial_bce_1: 0.07152/0.08784, loss_spatial_dice_1: 0.17095/0.18754, loss_spatial_ce_1: 0.06807/0.06985, loss_grounding_bce_1: 0.02001/0.08063, loss_grounding_dice_1: 0.11927/0.15184, loss_grounding_ce_1: 0.70211/0.25217, loss_mask_ce_2: 0.86387/0.78173, loss_mask_bce_2: 0.10748/0.30242, loss_mask_dice_2: 0.71549/1.03164, loss_spatial_bce_2: 0.06375/0.08763, loss_spatial_dice_2: 0.16588/0.18763, loss_spatial_ce_2: 0.08316/0.07201, loss_grounding_bce_2: 0.01971/0.08055, loss_grounding_dice_2: 0.12565/0.15160, loss_grounding_ce_2: 0.69541/0.25465, loss_mask_ce_3: 0.87294/0.78369, loss_mask_bce_3: 0.10866/0.30392, loss_mask_dice_3: 0.66585/1.02776, loss_spatial_bce_3: 0.05664/0.08941, loss_spatial_dice_3: 0.16339/0.18833, loss_spatial_ce_3: 0.07774/0.07702, loss_grounding_bce_3: 0.02010/0.08098, loss_grounding_dice_3: 0.11799/0.15112, loss_grounding_ce_3: 0.70246/0.25426, loss_mask_ce_4: 0.69050/0.78943, loss_mask_bce_4: 0.13119/0.30616, loss_mask_dice_4: 0.71862/1.04689, loss_spatial_bce_4: 0.06034/0.09139, loss_spatial_dice_4: 0.17957/0.19599, loss_spatial_ce_4: 0.07548/0.08962, loss_grounding_bce_4: 0.02084/0.08166, loss_grounding_dice_4: 0.12701/0.15387, loss_grounding_ce_4: 0.66475/0.25986, loss_mask_ce_5: 0.63174/0.81227, loss_mask_bce_5: 0.12768/0.30789, loss_mask_dice_5: 0.84927/1.05387, loss_spatial_bce_5: 0.05994/0.09317, loss_spatial_dice_5: 0.17356/0.19830, loss_spatial_ce_5: 0.09988/0.10162, loss_grounding_bce_5: 0.02123/0.08190, loss_grounding_dice_5: 0.13032/0.15435, loss_grounding_ce_5: 0.70499/0.27892, loss_mask_ce_6: 0.59894/0.83831, loss_mask_bce_6: 0.13171/0.30972, loss_mask_dice_6: 0.83654/1.05667, loss_spatial_bce_6: 0.05819/0.09803, loss_spatial_dice_6: 0.16913/0.20054, loss_spatial_ce_6: 0.13741/0.12341, loss_grounding_bce_6: 0.02383/0.08296, loss_grounding_dice_6: 0.14903/0.15503, loss_grounding_ce_6: 0.73819/0.28874, loss_mask_ce_7: 0.78005/0.89642, loss_mask_bce_7: 0.11881/0.31689, loss_mask_dice_7: 0.81123/1.10315, loss_spatial_bce_7: 0.05202/0.10832, loss_spatial_dice_7: 0.18700/0.22550, loss_spatial_ce_7: 0.13434/0.16421, loss_grounding_bce_7: 0.02544/0.08452, loss_grounding_dice_7: 0.14223/0.16071, loss_grounding_ce_7: 0.79934/0.32736, loss_mask_ce_8: 1.24621/1.03289, loss_mask_bce_8: 0.14791/0.33376, loss_mask_dice_8: 0.91647/1.18173, loss_spatial_bce_8: 0.05816/0.12792, loss_spatial_dice_8: 0.22564/0.26394, loss_spatial_ce_8: 0.12009/0.21743, loss_grounding_bce_8: 0.02719/0.08853, loss_grounding_dice_8: 0.16173/0.17048, loss_grounding_ce_8: 0.88060/0.42834, loss_mask_ce_9: 6.49719/3.49075, loss_mask_bce_9: 0.24052/0.36055, loss_mask_dice_9: 2.06122/1.76716, loss_spatial_bce_9: 0.26256/0.35736, loss_spatial_dice_9: 0.91845/0.79522, loss_spatial_ce_9: 1.13733/1.40276, loss_grounding_bce_9: 0.04792/0.10049, loss_grounding_dice_9: 0.38144/0.24388, loss_grounding_ce_9: 0.70303/0.69105] items per batch[64] items per second[0.37] total items[2124800] mini batches[ 33200] memory[4967] epoch remaining[0:44:57] INFO:trainer.default_trainer:epochs[ 18] optim steps[33300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45223/0.77243, loss_mask_bce_0: 0.27388/0.30176, loss_mask_dice_0: 0.44159/1.02602, loss_spatial_bce_0: 0.12520/0.08754, loss_spatial_dice_0: 0.24918/0.18492, loss_spatial_ce_0: 0.00663/0.06532, loss_grounding_bce_0: 0.02708/0.08047, loss_grounding_dice_0: 0.03435/0.15103, loss_grounding_ce_0: 0.20805/0.25037, loss_mask_ce_1: 0.50093/0.77420, loss_mask_bce_1: 0.29749/0.30249, loss_mask_dice_1: 0.45138/1.03058, loss_spatial_bce_1: 0.12520/0.08786, loss_spatial_dice_1: 0.23442/0.18754, loss_spatial_ce_1: 0.01410/0.06982, loss_grounding_bce_1: 0.02674/0.08065, loss_grounding_dice_1: 0.03133/0.15185, loss_grounding_ce_1: 0.23092/0.25215, loss_mask_ce_2: 0.47075/0.78154, loss_mask_bce_2: 0.30935/0.30256, loss_mask_dice_2: 0.45774/1.03191, loss_spatial_bce_2: 0.11330/0.08765, loss_spatial_dice_2: 0.25487/0.18764, loss_spatial_ce_2: 0.01268/0.07197, loss_grounding_bce_2: 0.02610/0.08059, loss_grounding_dice_2: 0.03236/0.15162, loss_grounding_ce_2: 0.25131/0.25455, loss_mask_ce_3: 0.43611/0.78365, loss_mask_bce_3: 0.28105/0.30407, loss_mask_dice_3: 0.44608/1.02812, loss_spatial_bce_3: 0.13750/0.08944, loss_spatial_dice_3: 0.27241/0.18834, loss_spatial_ce_3: 0.03311/0.07700, loss_grounding_bce_3: 0.02326/0.08101, loss_grounding_dice_3: 0.03040/0.15114, loss_grounding_ce_3: 0.23270/0.25419, loss_mask_ce_4: 0.47866/0.78928, loss_mask_bce_4: 0.30312/0.30631, loss_mask_dice_4: 0.48263/1.04716, loss_spatial_bce_4: 0.17361/0.09141, loss_spatial_dice_4: 0.29884/0.19601, loss_spatial_ce_4: 0.09156/0.08964, loss_grounding_bce_4: 0.02437/0.08169, loss_grounding_dice_4: 0.03061/0.15389, loss_grounding_ce_4: 0.25268/0.25977, loss_mask_ce_5: 0.30807/0.81222, loss_mask_bce_5: 0.61047/0.30804, loss_mask_dice_5: 0.50746/1.05420, loss_spatial_bce_5: 0.12631/0.09321, loss_spatial_dice_5: 0.26422/0.19832, loss_spatial_ce_5: 0.05722/0.10158, loss_grounding_bce_5: 0.02750/0.08193, loss_grounding_dice_5: 0.03202/0.15437, loss_grounding_ce_5: 0.23542/0.27881, loss_mask_ce_6: 0.37186/0.83818, loss_mask_bce_6: 0.56709/0.30988, loss_mask_dice_6: 0.50818/1.05698, loss_spatial_bce_6: 0.14938/0.09804, loss_spatial_dice_6: 0.26660/0.20054, loss_spatial_ce_6: 0.08190/0.12349, loss_grounding_bce_6: 0.02690/0.08298, loss_grounding_dice_6: 0.03279/0.15503, loss_grounding_ce_6: 0.18699/0.28885, loss_mask_ce_7: 0.33740/0.89636, loss_mask_bce_7: 0.58869/0.31704, loss_mask_dice_7: 0.51597/1.10345, loss_spatial_bce_7: 0.14050/0.10833, loss_spatial_dice_7: 0.29346/0.22551, loss_spatial_ce_7: 0.15993/0.16425, loss_grounding_bce_7: 0.02435/0.08457, loss_grounding_dice_7: 0.03168/0.16073, loss_grounding_ce_7: 0.36009/0.32716, loss_mask_ce_8: 0.37489/1.03272, loss_mask_bce_8: 0.66069/0.33391, loss_mask_dice_8: 0.53547/1.18203, loss_spatial_bce_8: 0.20525/0.12793, loss_spatial_dice_8: 0.33748/0.26392, loss_spatial_ce_8: 0.36176/0.21744, loss_grounding_bce_8: 0.02466/0.08854, loss_grounding_dice_8: 0.03076/0.17045, loss_grounding_ce_8: 0.41316/0.42822, loss_mask_ce_9: 3.32774/3.49085, loss_mask_bce_9: 0.43215/0.36075, loss_mask_dice_9: 0.68529/1.76782, loss_spatial_bce_9: 0.43497/0.35730, loss_spatial_dice_9: 0.85426/0.79522, loss_spatial_ce_9: 1.51531/1.40265, loss_grounding_bce_9: 0.03358/0.10058, loss_grounding_dice_9: 0.04800/0.24391, loss_grounding_ce_9: 1.71706/0.69074] items per batch[64] items per second[0.36] total items[2131200] mini batches[ 33300] memory[4967] epoch remaining[0:41:51] INFO:trainer.default_trainer:epochs[ 18] optim steps[33400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.20057/0.77256, loss_mask_bce_0: 0.23888/0.30179, loss_mask_dice_0: 1.74130/1.02619, loss_spatial_bce_0: 0.04629/0.08753, loss_spatial_dice_0: 0.26111/0.18493, loss_spatial_ce_0: 0.02332/0.06529, loss_grounding_bce_0: 0.02978/0.08047, loss_grounding_dice_0: 0.11365/0.15111, loss_grounding_ce_0: 0.33508/0.25046, loss_mask_ce_1: 2.37130/0.77439, loss_mask_bce_1: 0.26618/0.30253, loss_mask_dice_1: 1.63550/1.03078, loss_spatial_bce_1: 0.04870/0.08784, loss_spatial_dice_1: 0.28405/0.18754, loss_spatial_ce_1: 0.02212/0.06980, loss_grounding_bce_1: 0.03008/0.08065, loss_grounding_dice_1: 0.13410/0.15192, loss_grounding_ce_1: 0.27266/0.25224, loss_mask_ce_2: 2.24761/0.78168, loss_mask_bce_2: 0.26716/0.30259, loss_mask_dice_2: 1.69320/1.03210, loss_spatial_bce_2: 0.04854/0.08764, loss_spatial_dice_2: 0.28119/0.18765, loss_spatial_ce_2: 0.01727/0.07196, loss_grounding_bce_2: 0.03542/0.08059, loss_grounding_dice_2: 0.22368/0.15172, loss_grounding_ce_2: 0.32587/0.25462, loss_mask_ce_3: 2.61848/0.78382, loss_mask_bce_3: 0.26384/0.30411, loss_mask_dice_3: 1.77809/1.02833, loss_spatial_bce_3: 0.05032/0.08943, loss_spatial_dice_3: 0.28179/0.18835, loss_spatial_ce_3: 0.02486/0.07696, loss_grounding_bce_3: 0.03017/0.08101, loss_grounding_dice_3: 0.12456/0.15123, loss_grounding_ce_3: 0.29025/0.25424, loss_mask_ce_4: 2.64162/0.78940, loss_mask_bce_4: 0.25864/0.30636, loss_mask_dice_4: 1.78058/1.04739, loss_spatial_bce_4: 0.04643/0.09139, loss_spatial_dice_4: 0.27990/0.19602, loss_spatial_ce_4: 0.12143/0.08959, loss_grounding_bce_4: 0.02872/0.08169, loss_grounding_dice_4: 0.12372/0.15397, loss_grounding_ce_4: 0.29367/0.25987, loss_mask_ce_5: 2.58444/0.81235, loss_mask_bce_5: 0.25687/0.30810, loss_mask_dice_5: 1.96126/1.05436, loss_spatial_bce_5: 0.05369/0.09319, loss_spatial_dice_5: 0.29414/0.19833, loss_spatial_ce_5: 0.03371/0.10156, loss_grounding_bce_5: 0.03084/0.08192, loss_grounding_dice_5: 0.13548/0.15447, loss_grounding_ce_5: 0.28434/0.27892, loss_mask_ce_6: 2.99785/0.83838, loss_mask_bce_6: 0.24392/0.30994, loss_mask_dice_6: 1.94650/1.05721, loss_spatial_bce_6: 0.05156/0.09802, loss_spatial_dice_6: 0.26799/0.20055, loss_spatial_ce_6: 0.10642/0.12347, loss_grounding_bce_6: 0.03129/0.08298, loss_grounding_dice_6: 0.12512/0.15511, loss_grounding_ce_6: 0.35409/0.28889, loss_mask_ce_7: 2.99496/0.89649, loss_mask_bce_7: 0.28061/0.31711, loss_mask_dice_7: 2.01918/1.10360, loss_spatial_bce_7: 0.08611/0.10832, loss_spatial_dice_7: 0.30935/0.22553, loss_spatial_ce_7: 0.07927/0.16420, loss_grounding_bce_7: 0.02817/0.08457, loss_grounding_dice_7: 0.12898/0.16081, loss_grounding_ce_7: 0.37684/0.32736, loss_mask_ce_8: 3.58525/1.03304, loss_mask_bce_8: 0.31135/0.33398, loss_mask_dice_8: 2.54275/1.18226, loss_spatial_bce_8: 0.05508/0.12791, loss_spatial_dice_8: 0.31471/0.26395, loss_spatial_ce_8: 0.22051/0.21739, loss_grounding_bce_8: 0.03198/0.08857, loss_grounding_dice_8: 0.13545/0.17055, loss_grounding_ce_8: 0.31571/0.42835, loss_mask_ce_9: 3.25222/3.49129, loss_mask_bce_9: 0.28045/0.36090, loss_mask_dice_9: 3.06398/1.76817, loss_spatial_bce_9: 0.34830/0.35724, loss_spatial_dice_9: 0.94812/0.79526, loss_spatial_ce_9: 1.93899/1.40291, loss_grounding_bce_9: 0.03269/0.10062, loss_grounding_dice_9: 0.34825/0.24405, loss_grounding_ce_9: 0.43503/0.69082] items per batch[64] items per second[0.36] total items[2137600] mini batches[ 33400] memory[4967] epoch remaining[0:38:49] INFO:trainer.default_trainer:epochs[ 18] optim steps[33500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34869/0.77263, loss_mask_bce_0: 0.09969/0.30177, loss_mask_dice_0: 0.40260/1.02654, loss_spatial_bce_0: 0.03241/0.08748, loss_spatial_dice_0: 0.19056/0.18491, loss_spatial_ce_0: 0.39480/0.06528, loss_grounding_bce_0: 0.03692/0.08040, loss_grounding_dice_0: 0.10338/0.15110, loss_grounding_ce_0: 0.00971/0.25055, loss_mask_ce_1: 0.40582/0.77448, loss_mask_bce_1: 0.09667/0.30252, loss_mask_dice_1: 0.52250/1.03116, loss_spatial_bce_1: 0.03945/0.08779, loss_spatial_dice_1: 0.16987/0.18753, loss_spatial_ce_1: 0.29181/0.06978, loss_grounding_bce_1: 0.04213/0.08058, loss_grounding_dice_1: 0.12811/0.15190, loss_grounding_ce_1: 0.00881/0.25231, loss_mask_ce_2: 1.16886/0.78177, loss_mask_bce_2: 0.09455/0.30259, loss_mask_dice_2: 0.44540/1.03242, loss_spatial_bce_2: 0.04507/0.08759, loss_spatial_dice_2: 0.16868/0.18764, loss_spatial_ce_2: 0.23407/0.07195, loss_grounding_bce_2: 0.03630/0.08052, loss_grounding_dice_2: 0.11558/0.15170, loss_grounding_ce_2: 0.00949/0.25471, loss_mask_ce_3: 0.18708/0.78388, loss_mask_bce_3: 0.09405/0.30409, loss_mask_dice_3: 0.42633/1.02866, loss_spatial_bce_3: 0.04387/0.08938, loss_spatial_dice_3: 0.18856/0.18835, loss_spatial_ce_3: 0.22052/0.07694, loss_grounding_bce_3: 0.03835/0.08095, loss_grounding_dice_3: 0.13455/0.15121, loss_grounding_ce_3: 0.00513/0.25436, loss_mask_ce_4: 0.28132/0.78948, loss_mask_bce_4: 0.09111/0.30635, loss_mask_dice_4: 0.42163/1.04780, loss_spatial_bce_4: 0.10944/0.09134, loss_spatial_dice_4: 0.21452/0.19602, loss_spatial_ce_4: 0.08230/0.08956, loss_grounding_bce_4: 0.04038/0.08163, loss_grounding_dice_4: 0.17570/0.15395, loss_grounding_ce_4: 0.00513/0.26007, loss_mask_ce_5: 1.22243/0.81243, loss_mask_bce_5: 0.09761/0.30811, loss_mask_dice_5: 0.53380/1.05474, loss_spatial_bce_5: 0.20626/0.09315, loss_spatial_dice_5: 0.23423/0.19835, loss_spatial_ce_5: 0.08371/0.10154, loss_grounding_bce_5: 0.04064/0.08186, loss_grounding_dice_5: 0.18366/0.15446, loss_grounding_ce_5: 0.00962/0.27909, loss_mask_ce_6: 0.21711/0.83843, loss_mask_bce_6: 0.09220/0.30995, loss_mask_dice_6: 0.42094/1.05765, loss_spatial_bce_6: 0.12967/0.09798, loss_spatial_dice_6: 0.20738/0.20056, loss_spatial_ce_6: 0.12442/0.12349, loss_grounding_bce_6: 0.04213/0.08291, loss_grounding_dice_6: 0.14398/0.15510, loss_grounding_ce_6: 0.00273/0.28911, loss_mask_ce_7: 0.39385/0.89655, loss_mask_bce_7: 0.09812/0.31711, loss_mask_dice_7: 0.40485/1.10396, loss_spatial_bce_7: 0.05001/0.10826, loss_spatial_dice_7: 0.17064/0.22554, loss_spatial_ce_7: 0.26121/0.16420, loss_grounding_bce_7: 0.04318/0.08451, loss_grounding_dice_7: 0.16331/0.16079, loss_grounding_ce_7: 0.01198/0.32750, loss_mask_ce_8: 1.00785/1.03308, loss_mask_bce_8: 0.10053/0.33397, loss_mask_dice_8: 0.73633/1.18269, loss_spatial_bce_8: 0.04778/0.12788, loss_spatial_dice_8: 0.23579/0.26399, loss_spatial_ce_8: 0.60904/0.21733, loss_grounding_bce_8: 0.04168/0.08851, loss_grounding_dice_8: 0.12791/0.17055, loss_grounding_ce_8: 0.08445/0.42847, loss_mask_ce_9: 2.63014/3.49186, loss_mask_bce_9: 0.08309/0.36088, loss_mask_dice_9: 0.55128/1.76866, loss_spatial_bce_9: 0.36318/0.35708, loss_spatial_dice_9: 0.79121/0.79529, loss_spatial_ce_9: 0.94613/1.40249, loss_grounding_bce_9: 0.03309/0.10056, loss_grounding_dice_9: 0.15770/0.24408, loss_grounding_ce_9: 0.20725/0.69110] items per batch[64] items per second[0.36] total items[2144000] mini batches[ 33500] memory[4967] epoch remaining[0:35:50] INFO:trainer.default_trainer:epochs[ 18] optim steps[33600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42604/0.77249, loss_mask_bce_0: 1.58624/0.30177, loss_mask_dice_0: 5.08534/1.02628, loss_spatial_bce_0: 0.07050/0.08746, loss_spatial_dice_0: 0.46546/0.18489, loss_spatial_ce_0: 0.05277/0.06525, loss_grounding_bce_0: 0.08937/0.08041, loss_grounding_dice_0: 0.45986/0.15108, loss_grounding_ce_0: 0.87180/0.25043, loss_mask_ce_1: 1.25612/0.77420, loss_mask_bce_1: 1.36478/0.30251, loss_mask_dice_1: 4.80814/1.03088, loss_spatial_bce_1: 0.06717/0.08778, loss_spatial_dice_1: 0.47663/0.18751, loss_spatial_ce_1: 0.03348/0.06976, loss_grounding_bce_1: 0.08303/0.08058, loss_grounding_dice_1: 0.44934/0.15188, loss_grounding_ce_1: 0.82469/0.25219, loss_mask_ce_2: 1.55058/0.78154, loss_mask_bce_2: 1.41109/0.30258, loss_mask_dice_2: 4.95909/1.03213, loss_spatial_bce_2: 0.06421/0.08758, loss_spatial_dice_2: 0.47745/0.18761, loss_spatial_ce_2: 0.04059/0.07194, loss_grounding_bce_2: 0.08121/0.08053, loss_grounding_dice_2: 0.45546/0.15168, loss_grounding_ce_2: 0.83601/0.25456, loss_mask_ce_3: 1.20299/0.78359, loss_mask_bce_3: 1.71756/0.30410, loss_mask_dice_3: 5.00952/1.02838, loss_spatial_bce_3: 0.05725/0.08936, loss_spatial_dice_3: 0.45275/0.18834, loss_spatial_ce_3: 0.07121/0.07694, loss_grounding_bce_3: 0.08830/0.08096, loss_grounding_dice_3: 0.44890/0.15119, loss_grounding_ce_3: 0.86895/0.25424, loss_mask_ce_4: 1.27623/0.78924, loss_mask_bce_4: 1.44291/0.30634, loss_mask_dice_4: 4.98774/1.04751, loss_spatial_bce_4: 0.08123/0.09133, loss_spatial_dice_4: 0.50673/0.19599, loss_spatial_ce_4: 0.13649/0.08958, loss_grounding_bce_4: 0.08328/0.08164, loss_grounding_dice_4: 0.46350/0.15393, loss_grounding_ce_4: 0.83478/0.25991, loss_mask_ce_5: 1.63100/0.81223, loss_mask_bce_5: 1.27004/0.30808, loss_mask_dice_5: 5.07944/1.05445, loss_spatial_bce_5: 0.07686/0.09313, loss_spatial_dice_5: 0.48707/0.19834, loss_spatial_ce_5: 0.22556/0.10152, loss_grounding_bce_5: 0.08627/0.08187, loss_grounding_dice_5: 0.49324/0.15444, loss_grounding_ce_5: 0.85375/0.27892, loss_mask_ce_6: 1.67499/0.83828, loss_mask_bce_6: 1.34969/0.30992, loss_mask_dice_6: 5.01678/1.05737, loss_spatial_bce_6: 0.07801/0.09798, loss_spatial_dice_6: 0.49297/0.20057, loss_spatial_ce_6: 0.32234/0.12348, loss_grounding_bce_6: 0.08238/0.08291, loss_grounding_dice_6: 0.47786/0.15508, loss_grounding_ce_6: 0.84981/0.28896, loss_mask_ce_7: 1.34277/0.89637, loss_mask_bce_7: 1.48072/0.31708, loss_mask_dice_7: 4.86213/1.10370, loss_spatial_bce_7: 0.09345/0.10825, loss_spatial_dice_7: 0.53123/0.22552, loss_spatial_ce_7: 0.34338/0.16419, loss_grounding_bce_7: 0.09297/0.08450, loss_grounding_dice_7: 0.46259/0.16077, loss_grounding_ce_7: 0.80006/0.32733, loss_mask_ce_8: 1.58860/1.03271, loss_mask_bce_8: 1.36319/0.33392, loss_mask_dice_8: 5.10923/1.18240, loss_spatial_bce_8: 0.18926/0.12785, loss_spatial_dice_8: 0.53891/0.26394, loss_spatial_ce_8: 0.18887/0.21724, loss_grounding_bce_8: 0.09936/0.08850, loss_grounding_dice_8: 0.50469/0.17051, loss_grounding_ce_8: 0.87700/0.42813, loss_mask_ce_9: 6.38023/3.49133, loss_mask_bce_9: 1.27043/0.36078, loss_mask_dice_9: 6.69387/1.76783, loss_spatial_bce_9: 0.27684/0.35706, loss_spatial_dice_9: 0.97067/0.79524, loss_spatial_ce_9: 1.18292/1.40263, loss_grounding_bce_9: 0.09645/0.10056, loss_grounding_dice_9: 0.71366/0.24400, loss_grounding_ce_9: 0.69416/0.69054] items per batch[64] items per second[0.36] total items[2150400] mini batches[ 33600] memory[4967] epoch remaining[0:32:52] INFO:trainer.default_trainer:epochs[ 18] optim steps[33700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74673/0.77224, loss_mask_bce_0: 0.41774/0.30175, loss_mask_dice_0: 0.81965/1.02633, loss_spatial_bce_0: 0.04820/0.08745, loss_spatial_dice_0: 0.13247/0.18488, loss_spatial_ce_0: 0.00295/0.06519, loss_grounding_bce_0: 0.15178/0.08038, loss_grounding_dice_0: 0.23740/0.15110, loss_grounding_ce_0: 0.00270/0.25040, loss_mask_ce_1: 0.77071/0.77393, loss_mask_bce_1: 0.41684/0.30248, loss_mask_dice_1: 0.86430/1.03093, loss_spatial_bce_1: 0.05366/0.08776, loss_spatial_dice_1: 0.13930/0.18749, loss_spatial_ce_1: 0.00565/0.06969, loss_grounding_bce_1: 0.15213/0.08055, loss_grounding_dice_1: 0.24273/0.15191, loss_grounding_ce_1: 0.00405/0.25216, loss_mask_ce_2: 0.72915/0.78124, loss_mask_bce_2: 0.41163/0.30255, loss_mask_dice_2: 0.83273/1.03220, loss_spatial_bce_2: 0.05541/0.08756, loss_spatial_dice_2: 0.14795/0.18760, loss_spatial_ce_2: 0.00885/0.07189, loss_grounding_bce_2: 0.15207/0.08050, loss_grounding_dice_2: 0.23237/0.15172, loss_grounding_ce_2: 0.00470/0.25457, loss_mask_ce_3: 0.73333/0.78339, loss_mask_bce_3: 0.41423/0.30408, loss_mask_dice_3: 0.84042/1.02846, loss_spatial_bce_3: 0.05376/0.08935, loss_spatial_dice_3: 0.14345/0.18833, loss_spatial_ce_3: 0.00572/0.07688, loss_grounding_bce_3: 0.15923/0.08092, loss_grounding_dice_3: 0.24938/0.15121, loss_grounding_ce_3: 0.00453/0.25425, loss_mask_ce_4: 0.53417/0.78905, loss_mask_bce_4: 0.44099/0.30632, loss_mask_dice_4: 0.94818/1.04762, loss_spatial_bce_4: 0.04902/0.09132, loss_spatial_dice_4: 0.14384/0.19598, loss_spatial_ce_4: 0.00174/0.08957, loss_grounding_bce_4: 0.14772/0.08160, loss_grounding_dice_4: 0.24697/0.15395, loss_grounding_ce_4: 0.00458/0.25997, loss_mask_ce_5: 0.57473/0.81200, loss_mask_bce_5: 0.42637/0.30808, loss_mask_dice_5: 0.93412/1.05446, loss_spatial_bce_5: 0.05206/0.09312, loss_spatial_dice_5: 0.13714/0.19834, loss_spatial_ce_5: 0.00435/0.10145, loss_grounding_bce_5: 0.15218/0.08185, loss_grounding_dice_5: 0.25218/0.15446, loss_grounding_ce_5: 0.01162/0.27894, loss_mask_ce_6: 0.74824/0.83808, loss_mask_bce_6: 0.45207/0.30992, loss_mask_dice_6: 0.97006/1.05747, loss_spatial_bce_6: 0.05731/0.09796, loss_spatial_dice_6: 0.13584/0.20057, loss_spatial_ce_6: 0.06800/0.12345, loss_grounding_bce_6: 0.15762/0.08288, loss_grounding_dice_6: 0.24809/0.15512, loss_grounding_ce_6: 0.05692/0.28890, loss_mask_ce_7: 0.80894/0.89618, loss_mask_bce_7: 0.43634/0.31707, loss_mask_dice_7: 1.13113/1.10381, loss_spatial_bce_7: 0.07449/0.10825, loss_spatial_dice_7: 0.17271/0.22553, loss_spatial_ce_7: 0.07425/0.16419, loss_grounding_bce_7: 0.15860/0.08447, loss_grounding_dice_7: 0.25950/0.16080, loss_grounding_ce_7: 0.04263/0.32724, loss_mask_ce_8: 0.92068/1.03249, loss_mask_bce_8: 0.55506/0.33394, loss_mask_dice_8: 1.31265/1.18244, loss_spatial_bce_8: 0.10266/0.12784, loss_spatial_dice_8: 0.28879/0.26396, loss_spatial_ce_8: 0.14798/0.21724, loss_grounding_bce_8: 0.15450/0.08846, loss_grounding_dice_8: 0.25074/0.17053, loss_grounding_ce_8: 0.03307/0.42818, loss_mask_ce_9: 5.01230/3.49154, loss_mask_bce_9: 0.78288/0.36080, loss_mask_dice_9: 2.54311/1.76813, loss_spatial_bce_9: 0.24675/0.35708, loss_spatial_dice_9: 0.92214/0.79525, loss_spatial_ce_9: 1.27163/1.40270, loss_grounding_bce_9: 0.14775/0.10053, loss_grounding_dice_9: 0.36310/0.24403, loss_grounding_ce_9: 0.35717/0.69046] items per batch[64] items per second[0.36] total items[2156800] mini batches[ 33700] memory[4967] epoch remaining[0:29:53] INFO:trainer.default_trainer:epochs[ 18] optim steps[33800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74118/0.77226, loss_mask_bce_0: 0.37736/0.30172, loss_mask_dice_0: 3.66658/1.02607, loss_spatial_bce_0: 0.04450/0.08744, loss_spatial_dice_0: 0.22243/0.18489, loss_spatial_ce_0: 0.53566/0.06516, loss_grounding_bce_0: 0.04383/0.08036, loss_grounding_dice_0: 0.20757/0.15107, loss_grounding_ce_0: 0.36489/0.25042, loss_mask_ce_1: 0.79152/0.77397, loss_mask_bce_1: 0.39061/0.30245, loss_mask_dice_1: 3.91466/1.03070, loss_spatial_bce_1: 0.04616/0.08775, loss_spatial_dice_1: 0.23388/0.18749, loss_spatial_ce_1: 0.07819/0.06964, loss_grounding_bce_1: 0.04772/0.08053, loss_grounding_dice_1: 0.25972/0.15188, loss_grounding_ce_1: 0.40021/0.25217, loss_mask_ce_2: 0.66115/0.78127, loss_mask_bce_2: 0.37808/0.30252, loss_mask_dice_2: 3.90891/1.03194, loss_spatial_bce_2: 0.04287/0.08755, loss_spatial_dice_2: 0.21108/0.18760, loss_spatial_ce_2: 0.23762/0.07187, loss_grounding_bce_2: 0.04395/0.08047, loss_grounding_dice_2: 0.21466/0.15170, loss_grounding_ce_2: 0.36798/0.25453, loss_mask_ce_3: 0.72359/0.78343, loss_mask_bce_3: 0.37627/0.30405, loss_mask_dice_3: 4.16949/1.02822, loss_spatial_bce_3: 0.04508/0.08933, loss_spatial_dice_3: 0.26265/0.18832, loss_spatial_ce_3: 0.10716/0.07686, loss_grounding_bce_3: 0.04396/0.08090, loss_grounding_dice_3: 0.21330/0.15119, loss_grounding_ce_3: 0.37421/0.25425, loss_mask_ce_4: 0.82035/0.78908, loss_mask_bce_4: 0.37718/0.30628, loss_mask_dice_4: 3.99242/1.04735, loss_spatial_bce_4: 0.05000/0.09129, loss_spatial_dice_4: 0.22994/0.19598, loss_spatial_ce_4: 0.08364/0.08955, loss_grounding_bce_4: 0.04431/0.08157, loss_grounding_dice_4: 0.26481/0.15393, loss_grounding_ce_4: 0.33252/0.25999, loss_mask_ce_5: 0.88972/0.81208, loss_mask_bce_5: 0.38225/0.30804, loss_mask_dice_5: 3.94616/1.05420, loss_spatial_bce_5: 0.04238/0.09309, loss_spatial_dice_5: 0.22098/0.19835, loss_spatial_ce_5: 0.13542/0.10145, loss_grounding_bce_5: 0.04730/0.08181, loss_grounding_dice_5: 0.31515/0.15443, loss_grounding_ce_5: 0.39545/0.27897, loss_mask_ce_6: 0.92565/0.83815, loss_mask_bce_6: 0.40139/0.30989, loss_mask_dice_6: 3.73509/1.05728, loss_spatial_bce_6: 0.05185/0.09794, loss_spatial_dice_6: 0.26541/0.20059, loss_spatial_ce_6: 0.22387/0.12347, loss_grounding_bce_6: 0.04858/0.08285, loss_grounding_dice_6: 0.22217/0.15512, loss_grounding_ce_6: 0.35912/0.28892, loss_mask_ce_7: 1.11609/0.89640, loss_mask_bce_7: 0.41657/0.31702, loss_mask_dice_7: 3.90625/1.10353, loss_spatial_bce_7: 0.04328/0.10823, loss_spatial_dice_7: 0.24264/0.22552, loss_spatial_ce_7: 0.34558/0.16413, loss_grounding_bce_7: 0.05238/0.08443, loss_grounding_dice_7: 0.22218/0.16076, loss_grounding_ce_7: 0.34933/0.32744, loss_mask_ce_8: 1.70052/1.03259, loss_mask_bce_8: 0.51486/0.33387, loss_mask_dice_8: 5.04024/1.18219, loss_spatial_bce_8: 0.05653/0.12778, loss_spatial_dice_8: 0.32309/0.26393, loss_spatial_ce_8: 0.28985/0.21722, loss_grounding_bce_8: 0.04727/0.08843, loss_grounding_dice_8: 0.36992/0.17051, loss_grounding_ce_8: 0.48284/0.42857, loss_mask_ce_9: 6.16887/3.49164, loss_mask_bce_9: 0.47645/0.36079, loss_mask_dice_9: 7.20563/1.76777, loss_spatial_bce_9: 0.21267/0.35702, loss_spatial_dice_9: 0.95520/0.79525, loss_spatial_ce_9: 1.42248/1.40292, loss_grounding_bce_9: 0.06321/0.10050, loss_grounding_dice_9: 0.53193/0.24400, loss_grounding_ce_9: 0.45695/0.69067] items per batch[64] items per second[0.37] total items[2163200] mini batches[ 33800] memory[4967] epoch remaining[0:26:54] INFO:trainer.default_trainer:epochs[ 18] optim steps[33900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57709/0.77198, loss_mask_bce_0: 0.31485/0.30173, loss_mask_dice_0: 0.85105/1.02620, loss_spatial_bce_0: 0.06006/0.08744, loss_spatial_dice_0: 0.15856/0.18486, loss_spatial_ce_0: 0.20292/0.06514, loss_grounding_bce_0: 0.07766/0.08036, loss_grounding_dice_0: 0.05757/0.15106, loss_grounding_ce_0: 0.23403/0.25024, loss_mask_ce_1: 0.59373/0.77373, loss_mask_bce_1: 0.32274/0.30245, loss_mask_dice_1: 0.87160/1.03075, loss_spatial_bce_1: 0.06116/0.08775, loss_spatial_dice_1: 0.16992/0.18746, loss_spatial_ce_1: 0.17226/0.06960, loss_grounding_bce_1: 0.08028/0.08053, loss_grounding_dice_1: 0.05983/0.15187, loss_grounding_ce_1: 0.27622/0.25199, loss_mask_ce_2: 0.57537/0.78099, loss_mask_bce_2: 0.32211/0.30254, loss_mask_dice_2: 0.86225/1.03211, loss_spatial_bce_2: 0.06129/0.08754, loss_spatial_dice_2: 0.14234/0.18757, loss_spatial_ce_2: 0.23514/0.07180, loss_grounding_bce_2: 0.09114/0.08047, loss_grounding_dice_2: 0.06581/0.15169, loss_grounding_ce_2: 0.27074/0.25436, loss_mask_ce_3: 0.64751/0.78310, loss_mask_bce_3: 0.34559/0.30406, loss_mask_dice_3: 0.90822/1.02837, loss_spatial_bce_3: 0.06116/0.08932, loss_spatial_dice_3: 0.14679/0.18829, loss_spatial_ce_3: 0.20944/0.07679, loss_grounding_bce_3: 0.09704/0.08090, loss_grounding_dice_3: 0.06230/0.15116, loss_grounding_ce_3: 0.31036/0.25407, loss_mask_ce_4: 0.62830/0.78880, loss_mask_bce_4: 0.33787/0.30629, loss_mask_dice_4: 0.90795/1.04749, loss_spatial_bce_4: 0.06457/0.09128, loss_spatial_dice_4: 0.16361/0.19596, loss_spatial_ce_4: 0.20109/0.08951, loss_grounding_bce_4: 0.10286/0.08156, loss_grounding_dice_4: 0.06888/0.15391, loss_grounding_ce_4: 0.33210/0.25980, loss_mask_ce_5: 0.73366/0.81188, loss_mask_bce_5: 0.33069/0.30804, loss_mask_dice_5: 0.88010/1.05434, loss_spatial_bce_5: 0.06148/0.09308, loss_spatial_dice_5: 0.15827/0.19830, loss_spatial_ce_5: 0.23692/0.10139, loss_grounding_bce_5: 0.09294/0.08181, loss_grounding_dice_5: 0.06722/0.15442, loss_grounding_ce_5: 0.32353/0.27879, loss_mask_ce_6: 0.85661/0.83800, loss_mask_bce_6: 0.32067/0.30989, loss_mask_dice_6: 0.86106/1.05738, loss_spatial_bce_6: 0.06434/0.09794, loss_spatial_dice_6: 0.16400/0.20057, loss_spatial_ce_6: 0.20840/0.12340, loss_grounding_bce_6: 0.08292/0.08285, loss_grounding_dice_6: 0.05996/0.15513, loss_grounding_ce_6: 0.35243/0.28871, loss_mask_ce_7: 1.31015/0.89621, loss_mask_bce_7: 0.30439/0.31700, loss_mask_dice_7: 0.84234/1.10372, loss_spatial_bce_7: 0.07184/0.10821, loss_spatial_dice_7: 0.19829/0.22548, loss_spatial_ce_7: 0.36525/0.16404, loss_grounding_bce_7: 0.09160/0.08443, loss_grounding_dice_7: 0.06950/0.16077, loss_grounding_ce_7: 0.33540/0.32726, loss_mask_ce_8: 1.02293/1.03255, loss_mask_bce_8: 0.31971/0.33388, loss_mask_dice_8: 0.92112/1.18242, loss_spatial_bce_8: 0.06960/0.12773, loss_spatial_dice_8: 0.19284/0.26390, loss_spatial_ce_8: 0.53240/0.21720, loss_grounding_bce_8: 0.08614/0.08844, loss_grounding_dice_8: 0.06646/0.17050, loss_grounding_ce_8: 0.27469/0.42895, loss_mask_ce_9: 4.10567/3.49166, loss_mask_bce_9: 0.35947/0.36081, loss_mask_dice_9: 1.47195/1.76838, loss_spatial_bce_9: 0.34912/0.35693, loss_spatial_dice_9: 0.72063/0.79517, loss_spatial_ce_9: 1.05561/1.40292, loss_grounding_bce_9: 0.14980/0.10050, loss_grounding_dice_9: 0.15576/0.24398, loss_grounding_ce_9: 0.40726/0.69057] items per batch[64] items per second[0.37] total items[2169600] mini batches[ 33900] memory[4967] epoch remaining[0:23:56] INFO:trainer.default_trainer:epochs[ 18] optim steps[34000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01114/0.77222, loss_mask_bce_0: 0.03408/0.30169, loss_mask_dice_0: 0.22161/1.02681, loss_spatial_bce_0: 0.02794/0.08741, loss_spatial_dice_0: 0.13586/0.18487, loss_spatial_ce_0: 0.00007/0.06511, loss_grounding_bce_0: 0.02376/0.08031, loss_grounding_dice_0: 0.20085/0.15108, loss_grounding_ce_0: 0.00324/0.25020, loss_mask_ce_1: 0.00833/0.77392, loss_mask_bce_1: 0.03553/0.30242, loss_mask_dice_1: 0.19749/1.03136, loss_spatial_bce_1: 0.02749/0.08772, loss_spatial_dice_1: 0.10811/0.18748, loss_spatial_ce_1: 0.00004/0.06954, loss_grounding_bce_1: 0.02286/0.08048, loss_grounding_dice_1: 0.18688/0.15188, loss_grounding_ce_1: 0.00225/0.25196, loss_mask_ce_2: 0.00894/0.78130, loss_mask_bce_2: 0.03662/0.30251, loss_mask_dice_2: 0.19181/1.03265, loss_spatial_bce_2: 0.02812/0.08752, loss_spatial_dice_2: 0.11191/0.18758, loss_spatial_ce_2: 0.00003/0.07173, loss_grounding_bce_2: 0.02326/0.08042, loss_grounding_dice_2: 0.22869/0.15170, loss_grounding_ce_2: 0.00293/0.25434, loss_mask_ce_3: 0.00737/0.78331, loss_mask_bce_3: 0.03602/0.30402, loss_mask_dice_3: 0.19943/1.02895, loss_spatial_bce_3: 0.02782/0.08930, loss_spatial_dice_3: 0.09991/0.18830, loss_spatial_ce_3: 0.00009/0.07673, loss_grounding_bce_3: 0.02126/0.08085, loss_grounding_dice_3: 0.18640/0.15117, loss_grounding_ce_3: 0.00182/0.25404, loss_mask_ce_4: 0.00837/0.78905, loss_mask_bce_4: 0.03461/0.30626, loss_mask_dice_4: 0.16034/1.04804, loss_spatial_bce_4: 0.02634/0.09125, loss_spatial_dice_4: 0.13705/0.19597, loss_spatial_ce_4: 0.00010/0.08947, loss_grounding_bce_4: 0.02186/0.08151, loss_grounding_dice_4: 0.17120/0.15391, loss_grounding_ce_4: 0.00136/0.25974, loss_mask_ce_5: 0.01202/0.81211, loss_mask_bce_5: 0.03447/0.30802, loss_mask_dice_5: 0.18139/1.05493, loss_spatial_bce_5: 0.02832/0.09304, loss_spatial_dice_5: 0.13595/0.19831, loss_spatial_ce_5: 0.00047/0.10142, loss_grounding_bce_5: 0.02215/0.08176, loss_grounding_dice_5: 0.18774/0.15444, loss_grounding_ce_5: 0.00282/0.27865, loss_mask_ce_6: 0.00960/0.83820, loss_mask_bce_6: 0.03248/0.30986, loss_mask_dice_6: 0.14705/1.05792, loss_spatial_bce_6: 0.03461/0.09792, loss_spatial_dice_6: 0.12886/0.20058, loss_spatial_ce_6: 0.03475/0.12342, loss_grounding_bce_6: 0.02114/0.08279, loss_grounding_dice_6: 0.13465/0.15516, loss_grounding_ce_6: 0.00177/0.28862, loss_mask_ce_7: 0.00918/0.89637, loss_mask_bce_7: 0.03605/0.31697, loss_mask_dice_7: 0.17453/1.10433, loss_spatial_bce_7: 0.04022/0.10819, loss_spatial_dice_7: 0.13150/0.22552, loss_spatial_ce_7: 0.03674/0.16400, loss_grounding_bce_7: 0.02081/0.08437, loss_grounding_dice_7: 0.16481/0.16078, loss_grounding_ce_7: 0.00273/0.32721, loss_mask_ce_8: 0.04396/1.03283, loss_mask_bce_8: 0.03376/0.33385, loss_mask_dice_8: 0.16276/1.18295, loss_spatial_bce_8: 0.03564/0.12770, loss_spatial_dice_8: 0.13895/0.26391, loss_spatial_ce_8: 0.03217/0.21719, loss_grounding_bce_8: 0.02108/0.08839, loss_grounding_dice_8: 0.20317/0.17055, loss_grounding_ce_8: 0.00320/0.42879, loss_mask_ce_9: 1.61740/3.49141, loss_mask_bce_9: 0.03943/0.36076, loss_mask_dice_9: 0.20747/1.76882, loss_spatial_bce_9: 0.43150/0.35687, loss_spatial_dice_9: 0.67201/0.79511, loss_spatial_ce_9: 0.63193/1.40313, loss_grounding_bce_9: 0.02553/0.10046, loss_grounding_dice_9: 0.21942/0.24398, loss_grounding_ce_9: 0.05741/0.69034] items per batch[64] items per second[0.37] total items[2176000] mini batches[ 34000] memory[4967] epoch remaining[0:20:58] INFO:trainer.default_trainer:epochs[ 18] optim steps[34100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61043/0.77212, loss_mask_bce_0: 1.78643/0.30174, loss_mask_dice_0: 2.98659/1.02652, loss_spatial_bce_0: 0.12347/0.08739, loss_spatial_dice_0: 0.19740/0.18483, loss_spatial_ce_0: 0.00027/0.06507, loss_grounding_bce_0: 0.11709/0.08032, loss_grounding_dice_0: 0.20969/0.15106, loss_grounding_ce_0: 0.18067/0.24992, loss_mask_ce_1: 0.60843/0.77374, loss_mask_bce_1: 1.78079/0.30249, loss_mask_dice_1: 3.08927/1.03102, loss_spatial_bce_1: 0.12473/0.08770, loss_spatial_dice_1: 0.18582/0.18744, loss_spatial_ce_1: 0.00040/0.06948, loss_grounding_bce_1: 0.11839/0.08049, loss_grounding_dice_1: 0.21288/0.15186, loss_grounding_ce_1: 0.17778/0.25168, loss_mask_ce_2: 0.59040/0.78110, loss_mask_bce_2: 1.79616/0.30258, loss_mask_dice_2: 3.20448/1.03239, loss_spatial_bce_2: 0.13843/0.08750, loss_spatial_dice_2: 0.21143/0.18754, loss_spatial_ce_2: 0.00136/0.07167, loss_grounding_bce_2: 0.11814/0.08043, loss_grounding_dice_2: 0.21823/0.15168, loss_grounding_ce_2: 0.18416/0.25406, loss_mask_ce_3: 0.61155/0.78316, loss_mask_bce_3: 1.74087/0.30409, loss_mask_dice_3: 3.13030/1.02865, loss_spatial_bce_3: 0.13461/0.08928, loss_spatial_dice_3: 0.19632/0.18826, loss_spatial_ce_3: 0.00565/0.07668, loss_grounding_bce_3: 0.10900/0.08086, loss_grounding_dice_3: 0.21235/0.15116, loss_grounding_ce_3: 0.18040/0.25377, loss_mask_ce_4: 0.54694/0.78884, loss_mask_bce_4: 1.64076/0.30633, loss_mask_dice_4: 3.21010/1.04778, loss_spatial_bce_4: 0.14416/0.09123, loss_spatial_dice_4: 0.21871/0.19593, loss_spatial_ce_4: 0.02114/0.08940, loss_grounding_bce_4: 0.11898/0.08152, loss_grounding_dice_4: 0.23327/0.15389, loss_grounding_ce_4: 0.19273/0.25945, loss_mask_ce_5: 0.61846/0.81192, loss_mask_bce_5: 1.61464/0.30807, loss_mask_dice_5: 3.14399/1.05462, loss_spatial_bce_5: 0.16977/0.09304, loss_spatial_dice_5: 0.20778/0.19826, loss_spatial_ce_5: 0.03983/0.10136, loss_grounding_bce_5: 0.07260/0.08176, loss_grounding_dice_5: 0.18594/0.15441, loss_grounding_ce_5: 0.39011/0.27836, loss_mask_ce_6: 0.64545/0.83802, loss_mask_bce_6: 1.53955/0.30991, loss_mask_dice_6: 3.16922/1.05758, loss_spatial_bce_6: 0.14000/0.09790, loss_spatial_dice_6: 0.19660/0.20053, loss_spatial_ce_6: 0.06314/0.12335, loss_grounding_bce_6: 0.07286/0.08279, loss_grounding_dice_6: 0.20380/0.15513, loss_grounding_ce_6: 0.27361/0.28835, loss_mask_ce_7: 0.81921/0.89618, loss_mask_bce_7: 1.57634/0.31701, loss_mask_dice_7: 3.18629/1.10401, loss_spatial_bce_7: 0.14252/0.10821, loss_spatial_dice_7: 0.20708/0.22545, loss_spatial_ce_7: 0.27839/0.16393, loss_grounding_bce_7: 0.07755/0.08437, loss_grounding_dice_7: 0.22399/0.16075, loss_grounding_ce_7: 0.42005/0.32698, loss_mask_ce_8: 1.17275/1.03278, loss_mask_bce_8: 1.66070/0.33383, loss_mask_dice_8: 3.53363/1.18256, loss_spatial_bce_8: 0.20398/0.12770, loss_spatial_dice_8: 0.33091/0.26386, loss_spatial_ce_8: 0.10401/0.21712, loss_grounding_bce_8: 0.11271/0.08837, loss_grounding_dice_8: 0.30363/0.17052, loss_grounding_ce_8: 0.39281/0.42854, loss_mask_ce_9: 5.11373/3.49134, loss_mask_bce_9: 1.70802/0.36081, loss_mask_dice_9: 5.82002/1.76836, loss_spatial_bce_9: 0.23607/0.35692, loss_spatial_dice_9: 0.95206/0.79504, loss_spatial_ce_9: 1.73734/1.40300, loss_grounding_bce_9: 0.11611/0.10046, loss_grounding_dice_9: 0.35691/0.24395, loss_grounding_ce_9: 0.33283/0.69000] items per batch[64] items per second[0.37] total items[2182400] mini batches[ 34100] memory[4967] epoch remaining[0:18:00] INFO:trainer.default_trainer:epochs[ 18] optim steps[34200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30159/0.77189, loss_mask_bce_0: 0.22375/0.30180, loss_mask_dice_0: 0.33441/1.02648, loss_spatial_bce_0: 0.07153/0.08738, loss_spatial_dice_0: 0.10283/0.18477, loss_spatial_ce_0: 0.04438/0.06497, loss_grounding_bce_0: 0.06948/0.08034, loss_grounding_dice_0: 0.04611/0.15107, loss_grounding_ce_0: 0.02389/0.25003, loss_mask_ce_1: 0.30878/0.77354, loss_mask_bce_1: 0.22556/0.30257, loss_mask_dice_1: 0.32113/1.03098, loss_spatial_bce_1: 0.08424/0.08769, loss_spatial_dice_1: 0.11429/0.18738, loss_spatial_ce_1: 0.05370/0.06936, loss_grounding_bce_1: 0.06795/0.08052, loss_grounding_dice_1: 0.04388/0.15186, loss_grounding_ce_1: 0.02297/0.25178, loss_mask_ce_2: 0.31472/0.78085, loss_mask_bce_2: 0.22007/0.30265, loss_mask_dice_2: 0.32795/1.03239, loss_spatial_bce_2: 0.08948/0.08749, loss_spatial_dice_2: 0.11801/0.18748, loss_spatial_ce_2: 0.04256/0.07155, loss_grounding_bce_2: 0.06436/0.08045, loss_grounding_dice_2: 0.04321/0.15168, loss_grounding_ce_2: 0.01675/0.25415, loss_mask_ce_3: 0.28338/0.78296, loss_mask_bce_3: 0.23728/0.30417, loss_mask_dice_3: 0.33184/1.02863, loss_spatial_bce_3: 0.10014/0.08928, loss_spatial_dice_3: 0.12942/0.18821, loss_spatial_ce_3: 0.04732/0.07658, loss_grounding_bce_3: 0.07146/0.08088, loss_grounding_dice_3: 0.04718/0.15116, loss_grounding_ce_3: 0.01522/0.25387, loss_mask_ce_4: 0.32806/0.78864, loss_mask_bce_4: 0.18037/0.30641, loss_mask_dice_4: 0.29549/1.04769, loss_spatial_bce_4: 0.11290/0.09124, loss_spatial_dice_4: 0.13366/0.19588, loss_spatial_ce_4: 0.05564/0.08928, loss_grounding_bce_4: 0.05661/0.08155, loss_grounding_dice_4: 0.04666/0.15390, loss_grounding_ce_4: 0.00651/0.25958, loss_mask_ce_5: 0.40976/0.81184, loss_mask_bce_5: 0.19166/0.30814, loss_mask_dice_5: 0.29347/1.05456, loss_spatial_bce_5: 0.09817/0.09303, loss_spatial_dice_5: 0.11996/0.19821, loss_spatial_ce_5: 0.07653/0.10123, loss_grounding_bce_5: 0.06352/0.08178, loss_grounding_dice_5: 0.04202/0.15440, loss_grounding_ce_5: 0.00611/0.27846, loss_mask_ce_6: 0.37883/0.83783, loss_mask_bce_6: 0.23452/0.30999, loss_mask_dice_6: 0.30043/1.05754, loss_spatial_bce_6: 0.07395/0.09790, loss_spatial_dice_6: 0.10722/0.20047, loss_spatial_ce_6: 0.05654/0.12327, loss_grounding_bce_6: 0.07976/0.08281, loss_grounding_dice_6: 0.04389/0.15513, loss_grounding_ce_6: 0.00951/0.28843, loss_mask_ce_7: 0.47310/0.89603, loss_mask_bce_7: 0.20259/0.31709, loss_mask_dice_7: 0.31918/1.10398, loss_spatial_bce_7: 0.07516/0.10821, loss_spatial_dice_7: 0.11643/0.22539, loss_spatial_ce_7: 0.14187/0.16378, loss_grounding_bce_7: 0.06092/0.08440, loss_grounding_dice_7: 0.04781/0.16074, loss_grounding_ce_7: 0.00693/0.32706, loss_mask_ce_8: 0.54719/1.03266, loss_mask_bce_8: 0.19619/0.33392, loss_mask_dice_8: 0.35527/1.18253, loss_spatial_bce_8: 0.08508/0.12770, loss_spatial_dice_8: 0.16322/0.26379, loss_spatial_ce_8: 0.27296/0.21699, loss_grounding_bce_8: 0.05297/0.08839, loss_grounding_dice_8: 0.05135/0.17052, loss_grounding_ce_8: 0.00227/0.42861, loss_mask_ce_9: 2.13593/3.49135, loss_mask_bce_9: 0.28482/0.36092, loss_mask_dice_9: 0.51127/1.76843, loss_spatial_bce_9: 0.38567/0.35692, loss_spatial_dice_9: 0.79334/0.79501, loss_spatial_ce_9: 2.12306/1.40281, loss_grounding_bce_9: 0.08712/0.10049, loss_grounding_dice_9: 0.10141/0.24395, loss_grounding_ce_9: 0.09591/0.69004] items per batch[64] items per second[0.37] total items[2188800] mini batches[ 34200] memory[4967] epoch remaining[0:15:03] INFO:trainer.default_trainer:epochs[ 18] optim steps[34300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16823/0.77181, loss_mask_bce_0: 0.41258/0.30180, loss_mask_dice_0: 0.77951/1.02717, loss_spatial_bce_0: 0.09973/0.08736, loss_spatial_dice_0: 0.18407/0.18480, loss_spatial_ce_0: 0.00145/0.06492, loss_grounding_bce_0: 0.13498/0.08036, loss_grounding_dice_0: 0.20133/0.15107, loss_grounding_ce_0: 0.00006/0.25005, loss_mask_ce_1: 0.17134/0.77347, loss_mask_bce_1: 0.41532/0.30257, loss_mask_dice_1: 0.71774/1.03159, loss_spatial_bce_1: 0.10913/0.08766, loss_spatial_dice_1: 0.18438/0.18740, loss_spatial_ce_1: 0.00175/0.06931, loss_grounding_bce_1: 0.15519/0.08054, loss_grounding_dice_1: 0.21330/0.15189, loss_grounding_ce_1: 0.00013/0.25182, loss_mask_ce_2: 0.16096/0.78074, loss_mask_bce_2: 0.41512/0.30265, loss_mask_dice_2: 0.70708/1.03309, loss_spatial_bce_2: 0.09657/0.08747, loss_spatial_dice_2: 0.17405/0.18748, loss_spatial_ce_2: 0.00257/0.07149, loss_grounding_bce_2: 0.12758/0.08047, loss_grounding_dice_2: 0.20627/0.15169, loss_grounding_ce_2: 0.00023/0.25405, loss_mask_ce_3: 0.16428/0.78288, loss_mask_bce_3: 0.45393/0.30417, loss_mask_dice_3: 0.68798/1.02933, loss_spatial_bce_3: 0.11481/0.08926, loss_spatial_dice_3: 0.20471/0.18824, loss_spatial_ce_3: 0.00615/0.07654, loss_grounding_bce_3: 0.12744/0.08089, loss_grounding_dice_3: 0.20312/0.15118, loss_grounding_ce_3: 0.00035/0.25378, loss_mask_ce_4: 0.16366/0.78851, loss_mask_bce_4: 0.43927/0.30641, loss_mask_dice_4: 0.75403/1.04835, loss_spatial_bce_4: 0.12220/0.09121, loss_spatial_dice_4: 0.19067/0.19592, loss_spatial_ce_4: 0.02147/0.08920, loss_grounding_bce_4: 0.14553/0.08156, loss_grounding_dice_4: 0.20409/0.15390, loss_grounding_ce_4: 0.00023/0.25948, loss_mask_ce_5: 0.15582/0.81167, loss_mask_bce_5: 0.44415/0.30814, loss_mask_dice_5: 0.74968/1.05527, loss_spatial_bce_5: 0.10796/0.09301, loss_spatial_dice_5: 0.19812/0.19824, loss_spatial_ce_5: 0.03506/0.10113, loss_grounding_bce_5: 0.13715/0.08180, loss_grounding_dice_5: 0.21014/0.15441, loss_grounding_ce_5: 0.00023/0.27856, loss_mask_ce_6: 0.14133/0.83768, loss_mask_bce_6: 0.42864/0.30998, loss_mask_dice_6: 0.70472/1.05821, loss_spatial_bce_6: 0.12733/0.09788, loss_spatial_dice_6: 0.20940/0.20052, loss_spatial_ce_6: 0.06613/0.12319, loss_grounding_bce_6: 0.12951/0.08282, loss_grounding_dice_6: 0.19329/0.15513, loss_grounding_ce_6: 0.00005/0.28850, loss_mask_ce_7: 0.16607/0.89597, loss_mask_bce_7: 0.43898/0.31710, loss_mask_dice_7: 0.82242/1.10472, loss_spatial_bce_7: 0.13482/0.10819, loss_spatial_dice_7: 0.24004/0.22543, loss_spatial_ce_7: 0.03996/0.16367, loss_grounding_bce_7: 0.11636/0.08440, loss_grounding_dice_7: 0.19888/0.16074, loss_grounding_ce_7: 0.00036/0.32713, loss_mask_ce_8: 0.23654/1.03250, loss_mask_bce_8: 0.46517/0.33392, loss_mask_dice_8: 0.82571/1.18329, loss_spatial_bce_8: 0.19242/0.12767, loss_spatial_dice_8: 0.31591/0.26380, loss_spatial_ce_8: 0.09342/0.21683, loss_grounding_bce_8: 0.11682/0.08839, loss_grounding_dice_8: 0.25178/0.17051, loss_grounding_ce_8: 0.01891/0.42879, loss_mask_ce_9: 2.26882/3.49112, loss_mask_bce_9: 0.55469/0.36092, loss_mask_dice_9: 1.05165/1.76972, loss_spatial_bce_9: 0.28905/0.35682, loss_spatial_dice_9: 0.80288/0.79506, loss_spatial_ce_9: 1.01466/1.40277, loss_grounding_bce_9: 0.15029/0.10049, loss_grounding_dice_9: 0.23151/0.24394, loss_grounding_ce_9: 0.12590/0.68995] items per batch[64] items per second[0.37] total items[2195200] mini batches[ 34300] memory[4967] epoch remaining[0:12:06] INFO:trainer.default_trainer:epochs[ 18] optim steps[34400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15856/0.77207, loss_mask_bce_0: 0.17440/0.30178, loss_mask_dice_0: 0.29878/1.02702, loss_spatial_bce_0: 0.05465/0.08735, loss_spatial_dice_0: 0.07914/0.18474, loss_spatial_ce_0: 0.00002/0.06487, loss_grounding_bce_0: 0.06198/0.08037, loss_grounding_dice_0: 0.14599/0.15104, loss_grounding_ce_0: 1.56477/0.25002, loss_mask_ce_1: 0.15855/0.77372, loss_mask_bce_1: 0.16018/0.30256, loss_mask_dice_1: 0.29310/1.03147, loss_spatial_bce_1: 0.04728/0.08765, loss_spatial_dice_1: 0.08389/0.18734, loss_spatial_ce_1: 0.00001/0.06925, loss_grounding_bce_1: 0.04577/0.08056, loss_grounding_dice_1: 0.11595/0.15187, loss_grounding_ce_1: 2.30049/0.25183, loss_mask_ce_2: 0.16627/0.78095, loss_mask_bce_2: 0.14954/0.30263, loss_mask_dice_2: 0.27958/1.03294, loss_spatial_bce_2: 0.05056/0.08746, loss_spatial_dice_2: 0.07154/0.18743, loss_spatial_ce_2: 0.00000/0.07141, loss_grounding_bce_2: 0.05569/0.08049, loss_grounding_dice_2: 0.13440/0.15166, loss_grounding_ce_2: 1.20206/0.25395, loss_mask_ce_3: 0.16749/0.78312, loss_mask_bce_3: 0.16861/0.30415, loss_mask_dice_3: 0.29448/1.02925, loss_spatial_bce_3: 0.05287/0.08925, loss_spatial_dice_3: 0.07278/0.18818, loss_spatial_ce_3: 0.00002/0.07646, loss_grounding_bce_3: 0.06057/0.08090, loss_grounding_dice_3: 0.13833/0.15115, loss_grounding_ce_3: 1.21338/0.25376, loss_mask_ce_4: 0.16388/0.78875, loss_mask_bce_4: 0.20261/0.30638, loss_mask_dice_4: 0.30679/1.04829, loss_spatial_bce_4: 0.05530/0.09121, loss_spatial_dice_4: 0.08034/0.19586, loss_spatial_ce_4: 0.00008/0.08911, loss_grounding_bce_4: 0.06616/0.08158, loss_grounding_dice_4: 0.14213/0.15387, loss_grounding_ce_4: 0.86173/0.25977, loss_mask_ce_5: 0.20183/0.81188, loss_mask_bce_5: 0.18272/0.30812, loss_mask_dice_5: 0.28900/1.05508, loss_spatial_bce_5: 0.05222/0.09300, loss_spatial_dice_5: 0.08138/0.19819, loss_spatial_ce_5: 0.00011/0.10101, loss_grounding_bce_5: 0.05327/0.08183, loss_grounding_dice_5: 0.13628/0.15438, loss_grounding_ce_5: 1.64873/0.27862, loss_mask_ce_6: 0.19528/0.83800, loss_mask_bce_6: 0.21165/0.30995, loss_mask_dice_6: 0.31738/1.05806, loss_spatial_bce_6: 0.05626/0.09787, loss_spatial_dice_6: 0.09237/0.20047, loss_spatial_ce_6: 0.00168/0.12310, loss_grounding_bce_6: 0.08126/0.08287, loss_grounding_dice_6: 0.15735/0.15512, loss_grounding_ce_6: 0.89735/0.28858, loss_mask_ce_7: 1.19955/0.89634, loss_mask_bce_7: 0.14675/0.31709, loss_mask_dice_7: 0.26008/1.10460, loss_spatial_bce_7: 0.06357/0.10817, loss_spatial_dice_7: 0.12038/0.22537, loss_spatial_ce_7: 0.04757/0.16353, loss_grounding_bce_7: 0.09656/0.08446, loss_grounding_dice_7: 0.21607/0.16073, loss_grounding_ce_7: 1.72821/0.32733, loss_mask_ce_8: 0.72771/1.03280, loss_mask_bce_8: 0.11195/0.33392, loss_mask_dice_8: 0.27575/1.18323, loss_spatial_bce_8: 0.09115/0.12762, loss_spatial_dice_8: 0.12891/0.26374, loss_spatial_ce_8: 0.05562/0.21671, loss_grounding_bce_8: 0.07895/0.08843, loss_grounding_dice_8: 0.14861/0.17050, loss_grounding_ce_8: 2.03733/0.42899, loss_mask_ce_9: 2.66358/3.49168, loss_mask_bce_9: 0.19279/0.36091, loss_mask_dice_9: 0.60091/1.76950, loss_spatial_bce_9: 0.28047/0.35681, loss_spatial_dice_9: 0.83348/0.79506, loss_spatial_ce_9: 0.87713/1.40266, loss_grounding_bce_9: 0.13985/0.10052, loss_grounding_dice_9: 0.30608/0.24392, loss_grounding_ce_9: 2.95965/0.69018] items per batch[64] items per second[0.36] total items[2201600] mini batches[ 34400] memory[4967] epoch remaining[0:09:10] INFO:trainer.default_trainer:epochs[ 18] optim steps[34500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07341/0.77191, loss_mask_bce_0: 0.04530/0.30185, loss_mask_dice_0: 0.32559/1.02730, loss_spatial_bce_0: 0.02118/0.08734, loss_spatial_dice_0: 0.06410/0.18472, loss_spatial_ce_0: 0.01623/0.06479, loss_grounding_bce_0: 0.00636/0.08042, loss_grounding_dice_0: 0.07693/0.15103, loss_grounding_ce_0: 0.00157/0.25006, loss_mask_ce_1: 0.07640/0.77353, loss_mask_bce_1: 0.04650/0.30262, loss_mask_dice_1: 0.19979/1.03170, loss_spatial_bce_1: 0.02358/0.08765, loss_spatial_dice_1: 0.05500/0.18731, loss_spatial_ce_1: 0.01347/0.06918, loss_grounding_bce_1: 0.00755/0.08061, loss_grounding_dice_1: 0.10717/0.15186, loss_grounding_ce_1: 0.00189/0.25182, loss_mask_ce_2: 0.08049/0.78077, loss_mask_bce_2: 0.04327/0.30270, loss_mask_dice_2: 0.38957/1.03320, loss_spatial_bce_2: 0.02080/0.08746, loss_spatial_dice_2: 0.05319/0.18741, loss_spatial_ce_2: 0.01600/0.07136, loss_grounding_bce_2: 0.00756/0.08054, loss_grounding_dice_2: 0.08365/0.15165, loss_grounding_ce_2: 0.00193/0.25395, loss_mask_ce_3: 0.08502/0.78296, loss_mask_bce_3: 0.04770/0.30423, loss_mask_dice_3: 0.14360/1.02952, loss_spatial_bce_3: 0.02245/0.08925, loss_spatial_dice_3: 0.06137/0.18816, loss_spatial_ce_3: 0.01627/0.07638, loss_grounding_bce_3: 0.00757/0.08096, loss_grounding_dice_3: 0.07034/0.15114, loss_grounding_ce_3: 0.00132/0.25384, loss_mask_ce_4: 0.08428/0.78853, loss_mask_bce_4: 0.04853/0.30644, loss_mask_dice_4: 0.17850/1.04852, loss_spatial_bce_4: 0.03036/0.09121, loss_spatial_dice_4: 0.06633/0.19585, loss_spatial_ce_4: 0.01246/0.08905, loss_grounding_bce_4: 0.00672/0.08163, loss_grounding_dice_4: 0.09863/0.15387, loss_grounding_ce_4: 0.00234/0.25980, loss_mask_ce_5: 0.10121/0.81168, loss_mask_bce_5: 0.05154/0.30818, loss_mask_dice_5: 0.23103/1.05545, loss_spatial_bce_5: 0.02143/0.09301, loss_spatial_dice_5: 0.06171/0.19816, loss_spatial_ce_5: 0.02645/0.10091, loss_grounding_bce_5: 0.00768/0.08188, loss_grounding_dice_5: 0.06527/0.15438, loss_grounding_ce_5: 0.00163/0.27871, loss_mask_ce_6: 0.07482/0.83776, loss_mask_bce_6: 0.04643/0.31000, loss_mask_dice_6: 0.13975/1.05832, loss_spatial_bce_6: 0.02012/0.09788, loss_spatial_dice_6: 0.08489/0.20044, loss_spatial_ce_6: 0.01956/0.12301, loss_grounding_bce_6: 0.00769/0.08292, loss_grounding_dice_6: 0.06416/0.15511, loss_grounding_ce_6: 0.00545/0.28861, loss_mask_ce_7: 0.10309/0.89615, loss_mask_bce_7: 0.04944/0.31713, loss_mask_dice_7: 0.19757/1.10476, loss_spatial_bce_7: 0.02184/0.10817, loss_spatial_dice_7: 0.08540/0.22532, loss_spatial_ce_7: 0.04344/0.16343, loss_grounding_bce_7: 0.00717/0.08452, loss_grounding_dice_7: 0.05709/0.16071, loss_grounding_ce_7: 0.01054/0.32751, loss_mask_ce_8: 0.36045/1.03259, loss_mask_bce_8: 0.05439/0.33398, loss_mask_dice_8: 0.27059/1.18352, loss_spatial_bce_8: 0.02160/0.12759, loss_spatial_dice_8: 0.08651/0.26371, loss_spatial_ce_8: 0.08977/0.21663, loss_grounding_bce_8: 0.00616/0.08847, loss_grounding_dice_8: 0.05483/0.17047, loss_grounding_ce_8: 0.14229/0.42899, loss_mask_ce_9: 2.21094/3.49182, loss_mask_bce_9: 0.06269/0.36095, loss_mask_dice_9: 0.30120/1.76964, loss_spatial_bce_9: 0.42004/0.35683, loss_spatial_dice_9: 0.85661/0.79507, loss_spatial_ce_9: 1.33663/1.40249, loss_grounding_bce_9: 0.00835/0.10058, loss_grounding_dice_9: 0.28935/0.24388, loss_grounding_ce_9: 0.15861/0.69027] items per batch[64] items per second[0.35] total items[2208000] mini batches[ 34500] memory[4967] epoch remaining[0:06:15] INFO:trainer.default_trainer:epochs[ 18] optim steps[34600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.37846/0.77187, loss_mask_bce_0: 0.42001/0.30180, loss_mask_dice_0: 1.38056/1.02715, loss_spatial_bce_0: 0.09462/0.08734, loss_spatial_dice_0: 0.14126/0.18468, loss_spatial_ce_0: 0.00014/0.06474, loss_grounding_bce_0: 0.01035/0.08044, loss_grounding_dice_0: 0.36060/0.15106, loss_grounding_ce_0: 0.43702/0.25007, loss_mask_ce_1: 1.61551/0.77348, loss_mask_bce_1: 0.45958/0.30257, loss_mask_dice_1: 1.03268/1.03155, loss_spatial_bce_1: 0.09399/0.08765, loss_spatial_dice_1: 0.15425/0.18728, loss_spatial_ce_1: 0.00011/0.06911, loss_grounding_bce_1: 0.01271/0.08063, loss_grounding_dice_1: 0.44356/0.15188, loss_grounding_ce_1: 0.21416/0.25173, loss_mask_ce_2: 1.57651/0.78076, loss_mask_bce_2: 0.44132/0.30264, loss_mask_dice_2: 1.11168/1.03307, loss_spatial_bce_2: 0.09064/0.08746, loss_spatial_dice_2: 0.16074/0.18739, loss_spatial_ce_2: 0.00008/0.07129, loss_grounding_bce_2: 0.00650/0.08055, loss_grounding_dice_2: 0.23973/0.15166, loss_grounding_ce_2: 0.26372/0.25390, loss_mask_ce_3: 1.38776/0.78291, loss_mask_bce_3: 0.44365/0.30418, loss_mask_dice_3: 1.03890/1.02936, loss_spatial_bce_3: 0.09863/0.08925, loss_spatial_dice_3: 0.15672/0.18812, loss_spatial_ce_3: 0.00066/0.07633, loss_grounding_bce_3: 0.01058/0.08097, loss_grounding_dice_3: 0.31675/0.15117, loss_grounding_ce_3: 0.16871/0.25375, loss_mask_ce_4: 1.48779/0.78851, loss_mask_bce_4: 0.43361/0.30637, loss_mask_dice_4: 1.06444/1.04827, loss_spatial_bce_4: 0.08974/0.09120, loss_spatial_dice_4: 0.15165/0.19582, loss_spatial_ce_4: 0.00651/0.08900, loss_grounding_bce_4: 0.00895/0.08163, loss_grounding_dice_4: 0.22781/0.15390, loss_grounding_ce_4: 0.18771/0.25973, loss_mask_ce_5: 1.45637/0.81166, loss_mask_bce_5: 0.47465/0.30811, loss_mask_dice_5: 1.08259/1.05525, loss_spatial_bce_5: 0.09185/0.09299, loss_spatial_dice_5: 0.14174/0.19812, loss_spatial_ce_5: 0.03999/0.10089, loss_grounding_bce_5: 0.01206/0.08188, loss_grounding_dice_5: 0.27501/0.15440, loss_grounding_ce_5: 0.44007/0.27864, loss_mask_ce_6: 1.40751/0.83771, loss_mask_bce_6: 0.45308/0.30993, loss_mask_dice_6: 1.03766/1.05811, loss_spatial_bce_6: 0.10549/0.09787, loss_spatial_dice_6: 0.15495/0.20040, loss_spatial_ce_6: 0.07683/0.12299, loss_grounding_bce_6: 0.00977/0.08291, loss_grounding_dice_6: 0.39637/0.15512, loss_grounding_ce_6: 0.34406/0.28864, loss_mask_ce_7: 2.08224/0.89605, loss_mask_bce_7: 0.49545/0.31706, loss_mask_dice_7: 1.08686/1.10451, loss_spatial_bce_7: 0.10377/0.10815, loss_spatial_dice_7: 0.17295/0.22529, loss_spatial_ce_7: 0.11680/0.16336, loss_grounding_bce_7: 0.01316/0.08452, loss_grounding_dice_7: 0.52346/0.16073, loss_grounding_ce_7: 0.07808/0.32737, loss_mask_ce_8: 1.90905/1.03252, loss_mask_bce_8: 0.50160/0.33387, loss_mask_dice_8: 1.26062/1.18318, loss_spatial_bce_8: 0.15304/0.12760, loss_spatial_dice_8: 0.25518/0.26367, loss_spatial_ce_8: 0.16615/0.21649, loss_grounding_bce_8: 0.02343/0.08847, loss_grounding_dice_8: 0.55614/0.17050, loss_grounding_ce_8: 0.05837/0.42885, loss_mask_ce_9: 2.66387/3.49104, loss_mask_bce_9: 0.48692/0.36088, loss_mask_dice_9: 1.91078/1.76908, loss_spatial_bce_9: 0.35341/0.35678, loss_spatial_dice_9: 0.88817/0.79504, loss_spatial_ce_9: 1.58297/1.40243, loss_grounding_bce_9: 0.01361/0.10059, loss_grounding_dice_9: 0.73598/0.24389, loss_grounding_ce_9: 0.09547/0.69002] items per batch[64] items per second[0.37] total items[2214400] mini batches[ 34600] memory[4967] epoch remaining[0:03:19] INFO:trainer.default_trainer:epochs[ 18] optim steps[34700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25220/0.77145, loss_mask_bce_0: 0.16364/0.30175, loss_mask_dice_0: 0.20909/1.02663, loss_spatial_bce_0: 0.08332/0.08739, loss_spatial_dice_0: 0.10144/0.18468, loss_spatial_ce_0: 0.00101/0.06470, loss_grounding_bce_0: 0.10301/0.08046, loss_grounding_dice_0: 0.24025/0.15108, loss_grounding_ce_0: 0.00044/0.25020, loss_mask_ce_1: 0.25092/0.77306, loss_mask_bce_1: 0.16606/0.30254, loss_mask_dice_1: 0.21115/1.03100, loss_spatial_bce_1: 0.08412/0.08770, loss_spatial_dice_1: 0.09078/0.18727, loss_spatial_ce_1: 0.00154/0.06909, loss_grounding_bce_1: 0.08555/0.08065, loss_grounding_dice_1: 0.22747/0.15189, loss_grounding_ce_1: 0.00044/0.25193, loss_mask_ce_2: 0.26767/0.78033, loss_mask_bce_2: 0.16809/0.30260, loss_mask_dice_2: 0.20507/1.03251, loss_spatial_bce_2: 0.08522/0.08751, loss_spatial_dice_2: 0.10115/0.18740, loss_spatial_ce_2: 0.00322/0.07127, loss_grounding_bce_2: 0.09831/0.08058, loss_grounding_dice_2: 0.21744/0.15169, loss_grounding_ce_2: 0.00059/0.25414, loss_mask_ce_3: 0.31210/0.78247, loss_mask_bce_3: 0.16221/0.30413, loss_mask_dice_3: 0.20508/1.02877, loss_spatial_bce_3: 0.08656/0.08930, loss_spatial_dice_3: 0.09984/0.18812, loss_spatial_ce_3: 0.00366/0.07628, loss_grounding_bce_3: 0.10520/0.08100, loss_grounding_dice_3: 0.22400/0.15120, loss_grounding_ce_3: 0.00034/0.25389, loss_mask_ce_4: 0.33959/0.78807, loss_mask_bce_4: 0.16917/0.30633, loss_mask_dice_4: 0.20011/1.04771, loss_spatial_bce_4: 0.08571/0.09126, loss_spatial_dice_4: 0.09646/0.19582, loss_spatial_ce_4: 0.05570/0.08900, loss_grounding_bce_4: 0.08684/0.08166, loss_grounding_dice_4: 0.22119/0.15392, loss_grounding_ce_4: 0.00084/0.25985, loss_mask_ce_5: 0.35453/0.81131, loss_mask_bce_5: 0.17093/0.30807, loss_mask_dice_5: 0.19874/1.05473, loss_spatial_bce_5: 0.09161/0.09304, loss_spatial_dice_5: 0.09307/0.19812, loss_spatial_ce_5: 0.08682/0.10090, loss_grounding_bce_5: 0.10842/0.08191, loss_grounding_dice_5: 0.21974/0.15445, loss_grounding_ce_5: 0.00051/0.27873, loss_mask_ce_6: 0.38240/0.83730, loss_mask_bce_6: 0.16183/0.30989, loss_mask_dice_6: 0.19613/1.05751, loss_spatial_bce_6: 0.08120/0.09791, loss_spatial_dice_6: 0.10478/0.20039, loss_spatial_ce_6: 0.08964/0.12300, loss_grounding_bce_6: 0.09565/0.08296, loss_grounding_dice_6: 0.22486/0.15516, loss_grounding_ce_6: 0.00059/0.28880, loss_mask_ce_7: 0.31149/0.89559, loss_mask_bce_7: 0.17438/0.31701, loss_mask_dice_7: 0.20858/1.10393, loss_spatial_bce_7: 0.09780/0.10817, loss_spatial_dice_7: 0.12814/0.22527, loss_spatial_ce_7: 0.14158/0.16338, loss_grounding_bce_7: 0.10873/0.08456, loss_grounding_dice_7: 0.23021/0.16078, loss_grounding_ce_7: 0.00282/0.32736, loss_mask_ce_8: 0.42502/1.03212, loss_mask_bce_8: 0.19691/0.33380, loss_mask_dice_8: 0.20145/1.18253, loss_spatial_bce_8: 0.12159/0.12762, loss_spatial_dice_8: 0.11827/0.26364, loss_spatial_ce_8: 0.18730/0.21650, loss_grounding_bce_8: 0.12285/0.08852, loss_grounding_dice_8: 0.19670/0.17054, loss_grounding_ce_8: 0.00489/0.42873, loss_mask_ce_9: 2.61172/3.49047, loss_mask_bce_9: 0.27308/0.36082, loss_mask_dice_9: 0.27621/1.76789, loss_spatial_bce_9: 0.45264/0.35681, loss_spatial_dice_9: 0.76893/0.79502, loss_spatial_ce_9: 0.70081/1.40229, loss_grounding_bce_9: 0.23134/0.10063, loss_grounding_dice_9: 0.22544/0.24392, loss_grounding_ce_9: 0.03749/0.68990] items per batch[64] items per second[0.36] total items[2220800] mini batches[ 34700] memory[4967] epoch remaining[0:00:22] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00034713. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3738 s/iter. Eval: 0.1149 s/iter. Total: 0.4915 s/iter. ETA=0:00:33 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0023 s/iter. Inference: 0.3747 s/iter. Eval: 0.0941 s/iter. Total: 0.4712 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0025 s/iter. Inference: 0.3793 s/iter. Eval: 0.0859 s/iter. Total: 0.4678 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3795 s/iter. Eval: 0.0805 s/iter. Total: 0.4628 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0027 s/iter. Inference: 0.3815 s/iter. Eval: 0.0775 s/iter. Total: 0.4618 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0027 s/iter. Inference: 0.3810 s/iter. Eval: 0.0754 s/iter. Total: 0.4593 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalafng0d4r ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.463 | 82.996 | 66.058 | 133 | | Things | 61.619 | 83.921 | 72.906 | 80 | | Stuff | 46.171 | 81.599 | 55.722 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.58s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.20 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.83s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.31 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.765 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.478 | 69.126 | 49.039 | 25.370 | 49.799 | 67.590 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.793 | bicycle | 22.375 | car | 42.565 | | motorcycle | 41.827 | airplane | 63.200 | bus | 71.047 | | train | 74.720 | truck | 44.252 | boat | 30.786 | | traffic light | 28.298 | fire hydrant | 70.124 | stop sign | 68.989 | | parking meter | 51.416 | bench | 26.944 | bird | 33.834 | | cat | 76.417 | dog | 70.163 | horse | 50.462 | | sheep | 53.725 | cow | 56.800 | elephant | 65.890 | | bear | 80.047 | zebra | 66.036 | giraffe | 61.526 | | backpack | 23.335 | umbrella | 55.011 | handbag | 23.840 | | tie | 40.320 | suitcase | 51.145 | frisbee | 70.376 | | skis | 7.304 | snowboard | 33.631 | sports ball | 48.837 | | kite | 37.896 | baseball bat | 38.045 | baseball glove | 50.964 | | skateboard | 44.364 | surfboard | 45.399 | tennis racket | 63.035 | | bottle | 41.254 | wine glass | 37.595 | cup | 50.586 | | fork | 26.583 | knife | 24.052 | spoon | 20.213 | | bowl | 38.670 | banana | 22.474 | apple | 25.862 | | sandwich | 47.667 | orange | 31.351 | broccoli | 23.717 | | carrot | 21.779 | hot dog | 33.262 | pizza | 51.994 | | donut | 55.593 | cake | 48.128 | chair | 29.160 | | couch | 45.204 | potted plant | 22.483 | bed | 43.601 | | dining table | 15.363 | toilet | 69.884 | tv | 67.262 | | laptop | 70.409 | mouse | 65.107 | remote | 43.202 | | keyboard | 58.416 | cell phone | 45.462 | microwave | 64.922 | | oven | 34.389 | toaster | 50.319 | sink | 43.943 | | refrigerator | 70.158 | book | 14.177 | clock | 54.937 | | vase | 41.844 | scissors | 32.640 | teddy bear | 57.225 | | hair drier | 34.983 | toothbrush | 28.621 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.48720026357202, 'fwIoU': 71.08652325775495, 'IoU-person': 87.93042389034079, 'IoU-bicycle': 72.5152228099504, 'IoU-car': 72.02235453727045, 'IoU-motorcycle': 87.98838437885028, 'IoU-airplane': 87.16226610652461, 'IoU-bus': 87.7859199252434, 'IoU-train': 88.31192541672202, 'IoU-truck': 68.68887179562587, 'IoU-boat': 72.22285599735177, 'IoU-traffic light': 79.56264827389813, 'IoU-fire hydrant': 92.96176283328761, 'IoU-stop sign': 94.03019929178541, 'IoU-parking meter': 83.17059689349571, 'IoU-bench': 61.27642259889136, 'IoU-bird': 77.30539090711683, 'IoU-cat': 90.56855273514883, 'IoU-dog': 86.65516290263665, 'IoU-horse': 88.26390637118563, 'IoU-sheep': 88.49090326753229, 'IoU-cow': 88.87405927405926, 'IoU-elephant': 91.76654842568051, 'IoU-bear': 85.71907755154636, 'IoU-zebra': 87.625416256977, 'IoU-giraffe': 89.50794902216715, 'IoU-backpack': 53.14561324807926, 'IoU-umbrella': 85.02968642021045, 'IoU-handbag': 49.139198577615275, 'IoU-tie': 75.02875115872126, 'IoU-suitcase': 77.27104284218083, 'IoU-frisbee': 84.0777819321068, 'IoU-skis': 60.837547853986486, 'IoU-snowboard': 74.50779668018122, 'IoU-sports ball': 78.94213542692593, 'IoU-kite': 79.3081319914437, 'IoU-baseball bat': 69.45676125779995, 'IoU-baseball glove': 77.1106122018473, 'IoU-skateboard': 85.60782975765642, 'IoU-surfboard': 86.12760651055677, 'IoU-tennis racket': 90.79187843639593, 'IoU-bottle': 70.81847513288223, 'IoU-wine glass': 82.66160046511551, 'IoU-cup': 73.20949175061597, 'IoU-fork': 69.28718456568966, 'IoU-knife': 63.83218600995942, 'IoU-spoon': 60.12203272477245, 'IoU-bowl': 57.59494965542863, 'IoU-banana': 79.5316895721875, 'IoU-apple': 57.52278912777362, 'IoU-sandwich': 70.8740145010074, 'IoU-orange': 73.97037527366443, 'IoU-broccoli': 68.70145744981923, 'IoU-carrot': 58.49249845009299, 'IoU-hot dog': 57.47511285602953, 'IoU-pizza': 65.51206784558384, 'IoU-donut': 60.0522774001432, 'IoU-cake': 75.40542078704534, 'IoU-chair': 64.0720574417181, 'IoU-couch': 68.38062561967747, 'IoU-potted plant': 38.23560261298015, 'IoU-bed': 68.31418371417845, 'IoU-dining table': 52.8790274187349, 'IoU-toilet': 80.4295045311194, 'IoU-tv': 80.91030711785653, 'IoU-laptop': 76.25965116952048, 'IoU-mouse': 73.26186517870224, 'IoU-remote': 46.18297113020694, 'IoU-keyboard': 50.7285898488794, 'IoU-cell phone': 67.10496077656752, 'IoU-microwave': 71.19670436491515, 'IoU-oven': 70.815722129535, 'IoU-toaster': 82.61556373705906, 'IoU-sink': 73.62527837837149, 'IoU-refrigerator': 83.35021557685454, 'IoU-book': 51.386956742552236, 'IoU-clock': 73.22103471114426, 'IoU-vase': 66.4663099883342, 'IoU-scissors': 55.12979480820768, 'IoU-teddy bear': 84.47393969331229, 'IoU-hair drier': 48.06172374858863, 'IoU-toothbrush': 74.9886179882883, 'IoU-banner': 33.65822156011623, 'IoU-blanket': 16.168478440137587, 'IoU-bridge': 40.59557193506003, 'IoU-cardboard': 47.5271525284896, 'IoU-counter': 29.755134361372544, 'IoU-curtain': 71.69996715504074, 'IoU-door-stuff': 46.64386108398075, 'IoU-floor-wood': 61.25325484043187, 'IoU-flower': 48.43003431317865, 'IoU-fruit': 47.68331173174122, 'IoU-gravel': 36.36004742614882, 'IoU-house': 24.186552253987447, 'IoU-light': 45.46276586330455, 'IoU-mirror-stuff': 61.82018309563887, 'IoU-net': 42.778688132690625, 'IoU-pillow': 22.166493647498047, 'IoU-platform': 27.47480450855475, 'IoU-playingfield': 70.90289126623148, 'IoU-railroad': 64.28498666595534, 'IoU-river': 52.4130667773921, 'IoU-road': 67.46978605160356, 'IoU-roof': 18.664020783321654, 'IoU-sand': 65.48056056585044, 'IoU-sea': 85.85568041214711, 'IoU-shelf': 39.723678833143225, 'IoU-snow': 92.33578540608805, 'IoU-stairs': 30.810289225306896, 'IoU-tent': 10.80116548879995, 'IoU-towel': 43.70435032510687, 'IoU-wall-brick': 49.55112383670334, 'IoU-wall-stone': 26.736388832993406, 'IoU-wall-tile': 70.53155971205855, 'IoU-wall-wood': 42.27830784389668, 'IoU-water-other': 27.808612735611515, 'IoU-window-blind': 50.0711539776283, 'IoU-window-other': 50.87110266548852, 'IoU-tree-merged': 81.96657371721085, 'IoU-fence-merged': 54.1097839048306, 'IoU-ceiling-merged': 67.16677682398543, 'IoU-sky-other-merged': 94.13311572302028, 'IoU-cabinet-merged': 64.62201246132945, 'IoU-table-merged': 35.93120844287596, 'IoU-floor-other-merged': 54.880512896819866, 'IoU-pavement-merged': 56.879117759124874, 'IoU-mountain-merged': 57.89027629921397, 'IoU-grass-merged': 72.49463983965306, 'IoU-dirt-merged': 46.82121333541264, 'IoU-paper-merged': 36.87588912458431, 'IoU-food-other-merged': 41.18483800830287, 'IoU-building-other-merged': 59.11881191316045, 'IoU-rock-merged': 66.64657656201885, 'IoU-wall-other-merged': 68.73055482016203, 'IoU-rug-merged': 69.44267138656224, 'mACC': 75.82631334584873, 'pACC': 81.76924617221653, 'ACC-person': 92.9264072568931, 'ACC-bicycle': 82.55423073262324, 'ACC-car': 86.42689809257709, 'ACC-motorcycle': 92.37172278387612, 'ACC-airplane': 93.48258749983103, 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87.0250769841686, 'ACC-baseball glove': 92.18708343240887, 'ACC-skateboard': 90.66266141810037, 'ACC-surfboard': 92.57897531729978, 'ACC-tennis racket': 94.70338153582058, 'ACC-bottle': 86.43219556628675, 'ACC-wine glass': 91.00270167358705, 'ACC-cup': 83.68448996779159, 'ACC-fork': 82.63506379268499, 'ACC-knife': 77.37378178354217, 'ACC-spoon': 77.09096289719972, 'ACC-bowl': 67.67168701853304, 'ACC-banana': 85.33499141436167, 'ACC-apple': 70.1035071488189, 'ACC-sandwich': 80.67352073255843, 'ACC-orange': 82.86794416863187, 'ACC-broccoli': 76.82823795338214, 'ACC-carrot': 68.65966207375345, 'ACC-hot dog': 63.873655105695946, 'ACC-pizza': 70.85458665202678, 'ACC-donut': 66.80108193063099, 'ACC-cake': 83.1895693638206, 'ACC-chair': 79.10782233166488, 'ACC-couch': 74.46022801954899, 'ACC-potted plant': 49.55091241631496, 'ACC-bed': 74.34484405659298, 'ACC-dining table': 76.70633329243725, 'ACC-toilet': 84.29094950445408, 'ACC-tv': 88.67259441622181, 'ACC-laptop': 87.35097180822268, 'ACC-mouse': 82.98143278176394, 'ACC-remote': 49.240649211958925, 'ACC-keyboard': 56.03659098732277, 'ACC-cell phone': 75.91809193791259, 'ACC-microwave': 74.96330609127429, 'ACC-oven': 85.28892267754158, 'ACC-toaster': 89.8274233943259, 'ACC-sink': 82.42196124395139, 'ACC-refrigerator': 91.17037439311487, 'ACC-book': 68.74691799052982, 'ACC-clock': 77.25724550206147, 'ACC-vase': 74.79801919151544, 'ACC-scissors': 58.53177882452323, 'ACC-teddy bear': 89.69871726907957, 'ACC-hair drier': 59.97352916061822, 'ACC-toothbrush': 82.98384294649061, 'ACC-banner': 58.99874861595483, 'ACC-blanket': 37.36461983786904, 'ACC-bridge': 58.0035528173664, 'ACC-cardboard': 61.45662352770104, 'ACC-counter': 54.087127340681086, 'ACC-curtain': 83.26903065127415, 'ACC-door-stuff': 68.15318603962058, 'ACC-floor-wood': 81.10787782243602, 'ACC-flower': 72.3693849525641, 'ACC-fruit': 71.02059765399386, 'ACC-gravel': 53.793452132459976, 'ACC-house': 28.327670048047604, 'ACC-light': 67.60254373540559, 'ACC-mirror-stuff': 76.23620781860704, 'ACC-net': 65.4604891221209, 'ACC-pillow': 49.154815426895425, 'ACC-platform': 42.90478947731988, 'ACC-playingfield': 90.69338234111729, 'ACC-railroad': 80.00845912889271, 'ACC-river': 75.40242530066988, 'ACC-road': 86.73236597168102, 'ACC-roof': 25.89433925362735, 'ACC-sand': 71.06946560834527, 'ACC-sea': 91.22135622373985, 'ACC-shelf': 56.83546708474931, 'ACC-snow': 95.83138771358645, 'ACC-stairs': 57.385642282606206, 'ACC-tent': 14.369581491441213, 'ACC-towel': 54.90766867233887, 'ACC-wall-brick': 69.50699669084608, 'ACC-wall-stone': 31.62956543167344, 'ACC-wall-tile': 85.87940424285667, 'ACC-wall-wood': 63.46546511277891, 'ACC-water-other': 42.51241956997405, 'ACC-window-blind': 65.52662657821881, 'ACC-window-other': 75.12367846027003, 'ACC-tree-merged': 89.28566228647557, 'ACC-fence-merged': 72.45853600065611, 'ACC-ceiling-merged': 80.871233219506, 'ACC-sky-other-merged': 97.18820802362487, 'ACC-cabinet-merged': 77.2631568387814, 'ACC-table-merged': 55.93728763136029, 'ACC-floor-other-merged': 65.99853101501932, 'ACC-pavement-merged': 68.23599881809447, 'ACC-mountain-merged': 67.771755251122, 'ACC-grass-merged': 84.15602103139653, 'ACC-dirt-merged': 67.71651739167237, 'ACC-paper-merged': 48.80396760738316, 'ACC-food-other-merged': 57.99467334185415, 'ACC-building-other-merged': 74.02280656937342, 'ACC-rock-merged': 82.65712282264168, 'ACC-wall-other-merged': 83.34195608394944, 'ACC-rug-merged': 83.77164564186653})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3280 s/iter. Inference: 0.1763 s/iter. Eval: 0.0000 s/iter. Total: 0.5043 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3511 s/iter. Inference: 0.3537 s/iter. Eval: 0.0000 s/iter. Total: 0.7050 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3579 s/iter. Inference: 0.5514 s/iter. Eval: 0.0000 s/iter. Total: 0.9094 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4105940883816213, 'noc@0.8': 2.472929470295581, 'noc@0.85': 2.903131401814457, 'noc@0.9': 3.703833772314896, 'miou@iter1': 0.870404317429933} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1477 s/iter. Eval: 0.0011 s/iter. Total: 0.1504 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.55382537841797, 'precision@0.6': 72.94986724853516, 'precision@0.7': 68.71356201171875, 'precision@0.8': 59.96890640258789, 'precision@0.9': 32.72444534301758, 'cIoU': 62.211944580078125, 'mIoU': 66.95598602294922} INFO:trainer.default_trainer:This epoch takes 0:57:07.022714 INFO:trainer.default_trainer:PROGRESS: 38.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 19 training. INFO:trainer.default_trainer:epochs[ 19] optim steps[34800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75389/0.77142, loss_mask_bce_0: 0.39801/0.30171, loss_mask_dice_0: 0.30869/1.02661, loss_spatial_bce_0: 0.14789/0.08736, loss_spatial_dice_0: 0.13601/0.18466, loss_spatial_ce_0: 0.00119/0.06465, loss_grounding_bce_0: 0.01051/0.08044, loss_grounding_dice_0: 0.03061/0.15108, loss_grounding_ce_0: 0.05155/0.25005, loss_mask_ce_1: 0.75186/0.77304, loss_mask_bce_1: 0.40318/0.30249, loss_mask_dice_1: 0.29550/1.03094, loss_spatial_bce_1: 0.15247/0.08767, loss_spatial_dice_1: 0.15542/0.18725, loss_spatial_ce_1: 0.00116/0.06903, loss_grounding_bce_1: 0.01172/0.08063, loss_grounding_dice_1: 0.02884/0.15190, loss_grounding_ce_1: 0.04824/0.25176, loss_mask_ce_2: 0.66123/0.78031, loss_mask_bce_2: 0.40666/0.30256, loss_mask_dice_2: 0.31590/1.03248, loss_spatial_bce_2: 0.13885/0.08748, loss_spatial_dice_2: 0.13558/0.18737, loss_spatial_ce_2: 0.00106/0.07120, loss_grounding_bce_2: 0.01149/0.08056, loss_grounding_dice_2: 0.03400/0.15169, loss_grounding_ce_2: 0.03724/0.25398, loss_mask_ce_3: 0.74703/0.78244, loss_mask_bce_3: 0.40644/0.30409, loss_mask_dice_3: 0.32108/1.02876, loss_spatial_bce_3: 0.14358/0.08928, loss_spatial_dice_3: 0.14079/0.18810, loss_spatial_ce_3: 0.00219/0.07618, loss_grounding_bce_3: 0.01333/0.08097, loss_grounding_dice_3: 0.03279/0.15120, loss_grounding_ce_3: 0.05782/0.25376, loss_mask_ce_4: 0.79203/0.78807, loss_mask_bce_4: 0.40768/0.30628, loss_mask_dice_4: 0.29612/1.04764, loss_spatial_bce_4: 0.14429/0.09123, loss_spatial_dice_4: 0.15701/0.19580, loss_spatial_ce_4: 0.00144/0.08891, loss_grounding_bce_4: 0.01306/0.08164, loss_grounding_dice_4: 0.03371/0.15393, loss_grounding_ce_4: 0.06526/0.25966, loss_mask_ce_5: 0.78522/0.81124, loss_mask_bce_5: 0.41568/0.30804, loss_mask_dice_5: 0.30314/1.05470, loss_spatial_bce_5: 0.13914/0.09302, loss_spatial_dice_5: 0.12904/0.19811, loss_spatial_ce_5: 0.03188/0.10082, loss_grounding_bce_5: 0.01171/0.08189, loss_grounding_dice_5: 0.03205/0.15446, loss_grounding_ce_5: 0.06744/0.27858, loss_mask_ce_6: 1.07195/0.83723, loss_mask_bce_6: 0.39055/0.30983, loss_mask_dice_6: 0.29856/1.05743, loss_spatial_bce_6: 0.14155/0.09789, loss_spatial_dice_6: 0.12975/0.20035, loss_spatial_ce_6: 0.07267/0.12292, loss_grounding_bce_6: 0.01138/0.08294, loss_grounding_dice_6: 0.03164/0.15518, loss_grounding_ce_6: 0.07962/0.28861, loss_mask_ce_7: 0.82899/0.89547, loss_mask_bce_7: 0.39489/0.31694, loss_mask_dice_7: 0.30228/1.10391, loss_spatial_bce_7: 0.18178/0.10815, loss_spatial_dice_7: 0.24238/0.22523, loss_spatial_ce_7: 0.17058/0.16328, loss_grounding_bce_7: 0.01044/0.08454, loss_grounding_dice_7: 0.03026/0.16078, loss_grounding_ce_7: 0.06900/0.32715, loss_mask_ce_8: 0.78177/1.03205, loss_mask_bce_8: 0.41183/0.33374, loss_mask_dice_8: 0.32119/1.18242, loss_spatial_bce_8: 0.43622/0.12757, loss_spatial_dice_8: 0.27397/0.26360, loss_spatial_ce_8: 0.16345/0.21636, loss_grounding_bce_8: 0.01386/0.08848, loss_grounding_dice_8: 0.03614/0.17055, loss_grounding_ce_8: 0.08398/0.42855, loss_mask_ce_9: 2.04267/3.49039, loss_mask_bce_9: 0.41524/0.36074, loss_mask_dice_9: 0.58573/1.76780, loss_spatial_bce_9: 0.55149/0.35677, loss_spatial_dice_9: 0.70319/0.79502, loss_spatial_ce_9: 1.98242/1.40244, loss_grounding_bce_9: 0.04235/0.10059, loss_grounding_dice_9: 0.18210/0.24395, loss_grounding_ce_9: 0.16186/0.68956] items per batch[64] items per second[0.16] total items[2227200] mini batches[ 34800] memory[4967] epoch remaining[0:53:35] INFO:trainer.default_trainer:epochs[ 19] optim steps[34900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07368/0.77119, loss_mask_bce_0: 0.05758/0.30177, loss_mask_dice_0: 0.06583/1.02672, loss_spatial_bce_0: 0.03438/0.08735, loss_spatial_dice_0: 0.03614/0.18462, loss_spatial_ce_0: 0.08856/0.06458, loss_grounding_bce_0: 0.02155/0.08048, loss_grounding_dice_0: 0.06065/0.15113, loss_grounding_ce_0: 0.31418/0.25013, loss_mask_ce_1: 0.08107/0.77291, loss_mask_bce_1: 0.06338/0.30254, loss_mask_dice_1: 0.06767/1.03108, loss_spatial_bce_1: 0.03264/0.08766, loss_spatial_dice_1: 0.03473/0.18721, loss_spatial_ce_1: 0.12776/0.06899, loss_grounding_bce_1: 0.01995/0.08066, loss_grounding_dice_1: 0.05466/0.15196, loss_grounding_ce_1: 0.48721/0.25184, loss_mask_ce_2: 0.09310/0.78019, loss_mask_bce_2: 0.06160/0.30261, loss_mask_dice_2: 0.07463/1.03262, loss_spatial_bce_2: 0.03303/0.08747, loss_spatial_dice_2: 0.03635/0.18733, loss_spatial_ce_2: 0.10704/0.07116, loss_grounding_bce_2: 0.01913/0.08060, loss_grounding_dice_2: 0.06065/0.15173, loss_grounding_ce_2: 0.37527/0.25404, loss_mask_ce_3: 0.10718/0.78235, loss_mask_bce_3: 0.06169/0.30414, loss_mask_dice_3: 0.06408/1.02890, loss_spatial_bce_3: 0.03256/0.08927, loss_spatial_dice_3: 0.03277/0.18807, loss_spatial_ce_3: 0.12005/0.07614, loss_grounding_bce_3: 0.01893/0.08101, loss_grounding_dice_3: 0.05336/0.15126, loss_grounding_ce_3: 0.26432/0.25389, loss_mask_ce_4: 0.11370/0.78791, loss_mask_bce_4: 0.06928/0.30632, loss_mask_dice_4: 0.08020/1.04779, loss_spatial_bce_4: 0.03394/0.09122, loss_spatial_dice_4: 0.03516/0.19576, loss_spatial_ce_4: 0.14941/0.08888, loss_grounding_bce_4: 0.01837/0.08168, loss_grounding_dice_4: 0.05461/0.15399, loss_grounding_ce_4: 0.32314/0.25998, loss_mask_ce_5: 0.09012/0.81107, loss_mask_bce_5: 0.07861/0.30807, loss_mask_dice_5: 0.08767/1.05483, loss_spatial_bce_5: 0.05830/0.09301, loss_spatial_dice_5: 0.04717/0.19807, loss_spatial_ce_5: 0.10075/0.10080, loss_grounding_bce_5: 0.02195/0.08193, loss_grounding_dice_5: 0.06112/0.15451, loss_grounding_ce_5: 0.11589/0.27871, loss_mask_ce_6: 0.10279/0.83705, loss_mask_bce_6: 0.09567/0.30987, loss_mask_dice_6: 0.09554/1.05763, loss_spatial_bce_6: 0.12213/0.09790, loss_spatial_dice_6: 0.05546/0.20032, loss_spatial_ce_6: 0.18371/0.12285, loss_grounding_bce_6: 0.01867/0.08298, loss_grounding_dice_6: 0.05351/0.15522, loss_grounding_ce_6: 0.12786/0.28865, loss_mask_ce_7: 0.09683/0.89526, loss_mask_bce_7: 0.09282/0.31699, loss_mask_dice_7: 0.09805/1.10409, loss_spatial_bce_7: 0.04730/0.10815, loss_spatial_dice_7: 0.04489/0.22518, loss_spatial_ce_7: 0.15922/0.16319, loss_grounding_bce_7: 0.01924/0.08458, loss_grounding_dice_7: 0.05469/0.16085, loss_grounding_ce_7: 0.20281/0.32708, loss_mask_ce_8: 0.24516/1.03185, loss_mask_bce_8: 0.22995/0.33378, loss_mask_dice_8: 0.12780/1.18248, loss_spatial_bce_8: 0.30397/0.12756, loss_spatial_dice_8: 0.09842/0.26352, loss_spatial_ce_8: 0.28356/0.21634, loss_grounding_bce_8: 0.02584/0.08852, loss_grounding_dice_8: 0.06751/0.17061, loss_grounding_ce_8: 0.51185/0.42864, loss_mask_ce_9: 2.92523/3.49023, loss_mask_bce_9: 0.43004/0.36079, loss_mask_dice_9: 0.22031/1.76785, loss_spatial_bce_9: 0.41541/0.35682, loss_spatial_dice_9: 0.51016/0.79500, loss_spatial_ce_9: 0.78191/1.40261, loss_grounding_bce_9: 0.01126/0.10062, loss_grounding_dice_9: 0.05235/0.24394, loss_grounding_ce_9: 2.01448/0.68961] items per batch[64] items per second[0.36] total items[2233600] mini batches[ 34900] memory[4967] epoch remaining[0:49:16] INFO:trainer.default_trainer:epochs[ 19] optim steps[35000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45524/0.77075, loss_mask_bce_0: 0.21522/0.30170, loss_mask_dice_0: 0.83226/1.02633, loss_spatial_bce_0: 0.03071/0.08734, loss_spatial_dice_0: 0.14864/0.18460, loss_spatial_ce_0: 0.00877/0.06452, loss_grounding_bce_0: 0.01064/0.08046, loss_grounding_dice_0: 0.05646/0.15114, loss_grounding_ce_0: 0.01897/0.25015, loss_mask_ce_1: 0.76719/0.77251, loss_mask_bce_1: 0.25085/0.30246, loss_mask_dice_1: 0.85852/1.03071, loss_spatial_bce_1: 0.02815/0.08764, loss_spatial_dice_1: 0.15001/0.18718, loss_spatial_ce_1: 0.03416/0.06894, loss_grounding_bce_1: 0.01124/0.08064, loss_grounding_dice_1: 0.05260/0.15195, loss_grounding_ce_1: 0.00923/0.25186, loss_mask_ce_2: 0.48285/0.77990, loss_mask_bce_2: 0.20223/0.30252, loss_mask_dice_2: 0.86572/1.03221, loss_spatial_bce_2: 0.02753/0.08746, loss_spatial_dice_2: 0.15736/0.18731, loss_spatial_ce_2: 0.03956/0.07111, loss_grounding_bce_2: 0.01263/0.08058, loss_grounding_dice_2: 0.06529/0.15175, loss_grounding_ce_2: 0.02181/0.25409, loss_mask_ce_3: 0.58972/0.78200, loss_mask_bce_3: 0.21479/0.30405, loss_mask_dice_3: 0.89083/1.02850, loss_spatial_bce_3: 0.03414/0.08926, loss_spatial_dice_3: 0.16051/0.18805, loss_spatial_ce_3: 0.06612/0.07609, loss_grounding_bce_3: 0.01256/0.08100, loss_grounding_dice_3: 0.06571/0.15126, loss_grounding_ce_3: 0.06035/0.25389, loss_mask_ce_4: 0.77996/0.78758, loss_mask_bce_4: 0.20230/0.30624, loss_mask_dice_4: 0.98599/1.04742, loss_spatial_bce_4: 0.04264/0.09122, loss_spatial_dice_4: 0.16841/0.19575, loss_spatial_ce_4: 0.14912/0.08888, loss_grounding_bce_4: 0.01259/0.08167, loss_grounding_dice_4: 0.05492/0.15398, loss_grounding_ce_4: 0.06079/0.26001, loss_mask_ce_5: 0.99275/0.81061, loss_mask_bce_5: 0.19724/0.30799, loss_mask_dice_5: 0.90499/1.05442, loss_spatial_bce_5: 0.03850/0.09301, loss_spatial_dice_5: 0.18280/0.19807, loss_spatial_ce_5: 0.20888/0.10075, loss_grounding_bce_5: 0.01137/0.08192, loss_grounding_dice_5: 0.05604/0.15453, loss_grounding_ce_5: 0.06914/0.27869, loss_mask_ce_6: 0.83680/0.83668, loss_mask_bce_6: 0.21775/0.30978, loss_mask_dice_6: 0.82622/1.05711, loss_spatial_bce_6: 0.11156/0.09790, loss_spatial_dice_6: 0.21153/0.20031, loss_spatial_ce_6: 0.02672/0.12283, loss_grounding_bce_6: 0.01743/0.08297, loss_grounding_dice_6: 0.07892/0.15522, loss_grounding_ce_6: 0.04573/0.28860, loss_mask_ce_7: 1.07793/0.89490, loss_mask_bce_7: 0.27037/0.31688, loss_mask_dice_7: 1.01959/1.10356, loss_spatial_bce_7: 0.18891/0.10813, loss_spatial_dice_7: 0.30354/0.22518, loss_spatial_ce_7: 0.06172/0.16310, loss_grounding_bce_7: 0.01456/0.08456, loss_grounding_dice_7: 0.07715/0.16084, loss_grounding_ce_7: 0.08411/0.32711, loss_mask_ce_8: 0.19980/1.03126, loss_mask_bce_8: 0.43345/0.33369, loss_mask_dice_8: 1.30326/1.18205, loss_spatial_bce_8: 0.16443/0.12752, loss_spatial_dice_8: 0.29385/0.26348, loss_spatial_ce_8: 0.53588/0.21631, loss_grounding_bce_8: 0.01390/0.08850, loss_grounding_dice_8: 0.07240/0.17060, loss_grounding_ce_8: 0.63772/0.42875, loss_mask_ce_9: 2.93163/3.48959, loss_mask_bce_9: 0.30256/0.36069, loss_mask_dice_9: 1.74045/1.76693, loss_spatial_bce_9: 0.17466/0.35678, loss_spatial_dice_9: 0.87880/0.79493, loss_spatial_ce_9: 1.35303/1.40267, loss_grounding_bce_9: 0.02092/0.10058, loss_grounding_dice_9: 0.13497/0.24389, loss_grounding_ce_9: 3.61639/0.68953] items per batch[64] items per second[0.37] total items[2240000] mini batches[ 35000] memory[4967] epoch remaining[0:45:42] INFO:trainer.default_trainer:epochs[ 19] optim steps[35100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.51976/0.77068, loss_mask_bce_0: 0.68736/0.30174, loss_mask_dice_0: 1.67790/1.02667, loss_spatial_bce_0: 0.17212/0.08735, loss_spatial_dice_0: 0.34367/0.18462, loss_spatial_ce_0: 0.71965/0.06450, loss_grounding_bce_0: 0.08136/0.08052, loss_grounding_dice_0: 0.17961/0.15113, loss_grounding_ce_0: 0.01823/0.25011, loss_mask_ce_1: 1.17245/0.77243, loss_mask_bce_1: 0.56583/0.30249, loss_mask_dice_1: 1.59532/1.03094, loss_spatial_bce_1: 0.20285/0.08766, loss_spatial_dice_1: 0.35421/0.18720, loss_spatial_ce_1: 0.77552/0.06892, loss_grounding_bce_1: 0.08311/0.08069, loss_grounding_dice_1: 0.18899/0.15194, loss_grounding_ce_1: 0.01762/0.25183, loss_mask_ce_2: 0.65635/0.77982, loss_mask_bce_2: 0.73739/0.30256, loss_mask_dice_2: 1.66491/1.03246, loss_spatial_bce_2: 0.16660/0.08748, loss_spatial_dice_2: 0.32182/0.18734, loss_spatial_ce_2: 0.75746/0.07110, loss_grounding_bce_2: 0.08543/0.08063, loss_grounding_dice_2: 0.22621/0.15174, loss_grounding_ce_2: 0.01503/0.25409, loss_mask_ce_3: 1.21515/0.78192, loss_mask_bce_3: 0.57157/0.30408, loss_mask_dice_3: 1.54349/1.02872, loss_spatial_bce_3: 0.18963/0.08928, loss_spatial_dice_3: 0.33108/0.18809, loss_spatial_ce_3: 0.69894/0.07607, loss_grounding_bce_3: 0.08821/0.08106, loss_grounding_dice_3: 0.14678/0.15124, loss_grounding_ce_3: 0.02084/0.25382, loss_mask_ce_4: 1.19498/0.78751, loss_mask_bce_4: 0.55006/0.30628, loss_mask_dice_4: 1.69888/1.04770, loss_spatial_bce_4: 0.17758/0.09123, loss_spatial_dice_4: 0.36987/0.19579, loss_spatial_ce_4: 0.65104/0.08886, loss_grounding_bce_4: 0.08100/0.08171, loss_grounding_dice_4: 0.18986/0.15396, loss_grounding_ce_4: 0.01265/0.25999, loss_mask_ce_5: 0.68915/0.81057, loss_mask_bce_5: 0.83164/0.30804, loss_mask_dice_5: 1.69165/1.05460, loss_spatial_bce_5: 0.24825/0.09302, loss_spatial_dice_5: 0.38924/0.19811, loss_spatial_ce_5: 0.65463/0.10076, loss_grounding_bce_5: 0.07886/0.08198, loss_grounding_dice_5: 0.20045/0.15452, loss_grounding_ce_5: 0.00802/0.27875, loss_mask_ce_6: 0.70984/0.83656, loss_mask_bce_6: 0.85853/0.30982, loss_mask_dice_6: 1.69490/1.05740, loss_spatial_bce_6: 0.40489/0.09790, loss_spatial_dice_6: 0.37992/0.20035, loss_spatial_ce_6: 0.66323/0.12283, loss_grounding_bce_6: 0.08466/0.08301, loss_grounding_dice_6: 0.21416/0.15521, loss_grounding_ce_6: 0.00770/0.28883, loss_mask_ce_7: 0.66885/0.89480, loss_mask_bce_7: 0.70153/0.31690, loss_mask_dice_7: 1.79991/1.10381, loss_spatial_bce_7: 0.28344/0.10814, loss_spatial_dice_7: 0.54781/0.22522, loss_spatial_ce_7: 1.08071/0.16315, loss_grounding_bce_7: 0.08352/0.08461, loss_grounding_dice_7: 0.18225/0.16082, loss_grounding_ce_7: 0.03497/0.32702, loss_mask_ce_8: 0.72756/1.03122, loss_mask_bce_8: 0.58599/0.33368, loss_mask_dice_8: 1.75010/1.18214, loss_spatial_bce_8: 0.37007/0.12754, loss_spatial_dice_8: 0.49307/0.26350, loss_spatial_ce_8: 1.33864/0.21632, loss_grounding_bce_8: 0.08556/0.08854, loss_grounding_dice_8: 0.14811/0.17057, loss_grounding_ce_8: 0.02840/0.42871, loss_mask_ce_9: 3.85973/3.48931, loss_mask_bce_9: 0.47199/0.36065, loss_mask_dice_9: 2.22242/1.76682, loss_spatial_bce_9: 0.39909/0.35672, loss_spatial_dice_9: 0.83146/0.79495, loss_spatial_ce_9: 1.62389/1.40263, loss_grounding_bce_9: 0.08557/0.10063, loss_grounding_dice_9: 0.30454/0.24384, loss_grounding_ce_9: 0.37261/0.68941] items per batch[64] items per second[0.37] total items[2246400] mini batches[ 35100] memory[4967] epoch remaining[0:42:24] INFO:trainer.default_trainer:epochs[ 19] optim steps[35200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26497/0.77086, loss_mask_bce_0: 0.11432/0.30179, loss_mask_dice_0: 0.60862/1.02708, loss_spatial_bce_0: 0.07106/0.08733, loss_spatial_dice_0: 0.16176/0.18460, loss_spatial_ce_0: 0.00107/0.06446, loss_grounding_bce_0: 0.02952/0.08048, loss_grounding_dice_0: 0.16157/0.15116, loss_grounding_ce_0: 0.61089/0.25032, loss_mask_ce_1: 1.68855/0.77263, loss_mask_bce_1: 0.10949/0.30253, loss_mask_dice_1: 0.65315/1.03131, loss_spatial_bce_1: 0.07923/0.08765, loss_spatial_dice_1: 0.17094/0.18718, loss_spatial_ce_1: 0.00061/0.06887, loss_grounding_bce_1: 0.02716/0.08066, loss_grounding_dice_1: 0.12828/0.15197, loss_grounding_ce_1: 0.55751/0.25199, loss_mask_ce_2: 1.93790/0.78005, loss_mask_bce_2: 0.10590/0.30258, loss_mask_dice_2: 0.46129/1.03279, loss_spatial_bce_2: 0.06355/0.08747, loss_spatial_dice_2: 0.17399/0.18731, loss_spatial_ce_2: 0.00046/0.07104, loss_grounding_bce_2: 0.02793/0.08060, loss_grounding_dice_2: 0.12013/0.15177, loss_grounding_ce_2: 0.59545/0.25429, loss_mask_ce_3: 1.80845/0.78212, loss_mask_bce_3: 0.12244/0.30410, loss_mask_dice_3: 0.45520/1.02915, loss_spatial_bce_3: 0.06975/0.08928, loss_spatial_dice_3: 0.17946/0.18807, loss_spatial_ce_3: 0.00263/0.07601, loss_grounding_bce_3: 0.02976/0.08103, loss_grounding_dice_3: 0.10408/0.15127, loss_grounding_ce_3: 0.61703/0.25407, loss_mask_ce_4: 1.25868/0.78778, loss_mask_bce_4: 0.13016/0.30630, loss_mask_dice_4: 0.52023/1.04815, loss_spatial_bce_4: 0.07776/0.09122, loss_spatial_dice_4: 0.15806/0.19576, loss_spatial_ce_4: 0.00087/0.08882, loss_grounding_bce_4: 0.03186/0.08168, loss_grounding_dice_4: 0.11196/0.15400, loss_grounding_ce_4: 0.61186/0.26028, loss_mask_ce_5: 1.37987/0.81080, loss_mask_bce_5: 0.17769/0.30806, loss_mask_dice_5: 0.43602/1.05508, loss_spatial_bce_5: 0.07621/0.09301, loss_spatial_dice_5: 0.15819/0.19807, loss_spatial_ce_5: 0.01246/0.10072, loss_grounding_bce_5: 0.04900/0.08195, loss_grounding_dice_5: 0.10821/0.15455, loss_grounding_ce_5: 0.79926/0.27899, loss_mask_ce_6: 1.46838/0.83680, loss_mask_bce_6: 0.17264/0.30986, loss_mask_dice_6: 0.52732/1.05784, loss_spatial_bce_6: 0.09119/0.09789, loss_spatial_dice_6: 0.15753/0.20033, loss_spatial_ce_6: 0.00727/0.12278, loss_grounding_bce_6: 0.04390/0.08299, loss_grounding_dice_6: 0.12501/0.15522, loss_grounding_ce_6: 0.70603/0.28904, loss_mask_ce_7: 1.42967/0.89501, loss_mask_bce_7: 0.15631/0.31696, loss_mask_dice_7: 0.54391/1.10432, loss_spatial_bce_7: 0.07418/0.10813, loss_spatial_dice_7: 0.11944/0.22520, loss_spatial_ce_7: 0.01807/0.16306, loss_grounding_bce_7: 0.03793/0.08458, loss_grounding_dice_7: 0.13215/0.16086, loss_grounding_ce_7: 0.78431/0.32717, loss_mask_ce_8: 1.94255/1.03154, loss_mask_bce_8: 0.20296/0.33371, loss_mask_dice_8: 0.71029/1.18270, loss_spatial_bce_8: 0.07184/0.12754, loss_spatial_dice_8: 0.15576/0.26348, loss_spatial_ce_8: 0.04243/0.21628, loss_grounding_bce_8: 0.04685/0.08851, loss_grounding_dice_8: 0.14157/0.17060, loss_grounding_ce_8: 0.79199/0.42906, loss_mask_ce_9: 5.85957/3.49010, loss_mask_bce_9: 0.15397/0.36071, loss_mask_dice_9: 1.09609/1.76784, loss_spatial_bce_9: 0.09395/0.35668, loss_spatial_dice_9: 0.86812/0.79498, loss_spatial_ce_9: 1.57933/1.40253, loss_grounding_bce_9: 0.03779/0.10060, loss_grounding_dice_9: 0.26480/0.24393, loss_grounding_ce_9: 0.82498/0.68962] items per batch[64] items per second[0.37] total items[2252800] mini batches[ 35200] memory[4967] epoch remaining[0:39:22] INFO:trainer.default_trainer:epochs[ 19] optim steps[35300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34127/0.77081, loss_mask_bce_0: 0.20077/0.30182, loss_mask_dice_0: 2.66871/1.02727, loss_spatial_bce_0: 0.00846/0.08731, loss_spatial_dice_0: 0.22072/0.18460, loss_spatial_ce_0: 0.00754/0.06444, loss_grounding_bce_0: 0.02192/0.08047, loss_grounding_dice_0: 0.09455/0.15117, loss_grounding_ce_0: 0.32690/0.25034, loss_mask_ce_1: 1.03207/0.77259, loss_mask_bce_1: 0.21753/0.30256, loss_mask_dice_1: 2.42111/1.03156, loss_spatial_bce_1: 0.00964/0.08762, loss_spatial_dice_1: 0.19846/0.18718, loss_spatial_ce_1: 0.00396/0.06886, loss_grounding_bce_1: 0.01964/0.08065, loss_grounding_dice_1: 0.08594/0.15197, loss_grounding_ce_1: 0.37960/0.25201, loss_mask_ce_2: 1.23446/0.78001, loss_mask_bce_2: 0.19940/0.30262, loss_mask_dice_2: 2.79127/1.03302, loss_spatial_bce_2: 0.00870/0.08745, loss_spatial_dice_2: 0.22239/0.18731, loss_spatial_ce_2: 0.01392/0.07103, loss_grounding_bce_2: 0.02100/0.08059, loss_grounding_dice_2: 0.08334/0.15176, loss_grounding_ce_2: 0.24519/0.25433, loss_mask_ce_3: 1.06971/0.78207, loss_mask_bce_3: 0.20013/0.30412, loss_mask_dice_3: 2.44286/1.02930, loss_spatial_bce_3: 0.00781/0.08926, loss_spatial_dice_3: 0.22291/0.18808, loss_spatial_ce_3: 0.03569/0.07598, loss_grounding_bce_3: 0.02236/0.08102, loss_grounding_dice_3: 0.08842/0.15128, loss_grounding_ce_3: 0.30521/0.25404, loss_mask_ce_4: 1.01936/0.78778, loss_mask_bce_4: 0.21541/0.30632, loss_mask_dice_4: 2.24758/1.04832, loss_spatial_bce_4: 0.00875/0.09120, loss_spatial_dice_4: 0.21565/0.19578, loss_spatial_ce_4: 0.00766/0.08880, loss_grounding_bce_4: 0.02200/0.08167, loss_grounding_dice_4: 0.09374/0.15400, loss_grounding_ce_4: 0.42786/0.26023, loss_mask_ce_5: 1.08935/0.81077, loss_mask_bce_5: 0.26074/0.30806, loss_mask_dice_5: 2.49899/1.05528, loss_spatial_bce_5: 0.00815/0.09301, loss_spatial_dice_5: 0.23219/0.19810, loss_spatial_ce_5: 0.00461/0.10070, loss_grounding_bce_5: 0.01817/0.08194, loss_grounding_dice_5: 0.07979/0.15453, loss_grounding_ce_5: 0.36038/0.27900, loss_mask_ce_6: 1.11265/0.83673, loss_mask_bce_6: 0.21761/0.30988, loss_mask_dice_6: 2.44730/1.05803, loss_spatial_bce_6: 0.00832/0.09788, loss_spatial_dice_6: 0.19328/0.20036, loss_spatial_ce_6: 0.08921/0.12280, loss_grounding_bce_6: 0.01870/0.08298, loss_grounding_dice_6: 0.08453/0.15521, loss_grounding_ce_6: 0.29142/0.28897, loss_mask_ce_7: 1.11271/0.89494, loss_mask_bce_7: 0.22641/0.31703, loss_mask_dice_7: 2.62772/1.10450, loss_spatial_bce_7: 0.00914/0.10813, loss_spatial_dice_7: 0.23842/0.22522, loss_spatial_ce_7: 0.09045/0.16304, loss_grounding_bce_7: 0.02006/0.08458, loss_grounding_dice_7: 0.08686/0.16084, loss_grounding_ce_7: 0.19965/0.32705, loss_mask_ce_8: 1.32849/1.03152, loss_mask_bce_8: 0.27011/0.33374, loss_mask_dice_8: 3.20690/1.18286, loss_spatial_bce_8: 0.01264/0.12750, loss_spatial_dice_8: 0.23343/0.26349, loss_spatial_ce_8: 0.04323/0.21627, loss_grounding_bce_8: 0.01694/0.08851, loss_grounding_dice_8: 0.08970/0.17058, loss_grounding_ce_8: 1.04028/0.42913, loss_mask_ce_9: 4.27483/3.49031, loss_mask_bce_9: 0.33070/0.36075, loss_mask_dice_9: 5.50962/1.76840, loss_spatial_bce_9: 0.14456/0.35657, loss_spatial_dice_9: 0.92502/0.79498, loss_spatial_ce_9: 1.23838/1.40240, loss_grounding_bce_9: 0.07413/0.10062, loss_grounding_dice_9: 0.26749/0.24397, loss_grounding_ce_9: 1.81379/0.68941] items per batch[64] items per second[0.36] total items[2259200] mini batches[ 35300] memory[4999] epoch remaining[0:36:29] INFO:trainer.default_trainer:epochs[ 19] optim steps[35400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50055/0.77057, loss_mask_bce_0: 0.27453/0.30174, loss_mask_dice_0: 0.15937/1.02703, loss_spatial_bce_0: 0.27121/0.08729, loss_spatial_dice_0: 0.12192/0.18459, loss_spatial_ce_0: 0.00033/0.06440, loss_grounding_bce_0: 0.14722/0.08046, loss_grounding_dice_0: 0.08501/0.15115, loss_grounding_ce_0: 0.04132/0.25010, loss_mask_ce_1: 0.49810/0.77240, loss_mask_bce_1: 0.26676/0.30247, loss_mask_dice_1: 0.15233/1.03126, loss_spatial_bce_1: 0.22270/0.08760, loss_spatial_dice_1: 0.11605/0.18716, loss_spatial_ce_1: 0.00048/0.06882, loss_grounding_bce_1: 0.14311/0.08063, loss_grounding_dice_1: 0.08308/0.15195, loss_grounding_ce_1: 0.05074/0.25182, loss_mask_ce_2: 0.47869/0.77976, loss_mask_bce_2: 0.26880/0.30256, loss_mask_dice_2: 0.15631/1.03272, loss_spatial_bce_2: 0.22166/0.08743, loss_spatial_dice_2: 0.11460/0.18729, loss_spatial_ce_2: 0.00363/0.07101, loss_grounding_bce_2: 0.14356/0.08057, loss_grounding_dice_2: 0.08365/0.15172, loss_grounding_ce_2: 0.05190/0.25409, loss_mask_ce_3: 0.48486/0.78186, loss_mask_bce_3: 0.28007/0.30404, loss_mask_dice_3: 0.15821/1.02904, loss_spatial_bce_3: 0.25340/0.08924, loss_spatial_dice_3: 0.11807/0.18807, loss_spatial_ce_3: 0.03204/0.07591, loss_grounding_bce_3: 0.14185/0.08099, loss_grounding_dice_3: 0.07964/0.15125, loss_grounding_ce_3: 0.04707/0.25384, loss_mask_ce_4: 0.49785/0.78759, loss_mask_bce_4: 0.29088/0.30626, loss_mask_dice_4: 0.16865/1.04804, loss_spatial_bce_4: 0.25165/0.09119, loss_spatial_dice_4: 0.12744/0.19576, loss_spatial_ce_4: 0.07103/0.08874, loss_grounding_bce_4: 0.14299/0.08165, loss_grounding_dice_4: 0.08568/0.15397, loss_grounding_ce_4: 0.05498/0.25998, loss_mask_ce_5: 0.48585/0.81063, loss_mask_bce_5: 0.30612/0.30798, loss_mask_dice_5: 0.17364/1.05502, loss_spatial_bce_5: 0.27359/0.09299, loss_spatial_dice_5: 0.13283/0.19811, loss_spatial_ce_5: 0.09580/0.10063, loss_grounding_bce_5: 0.15517/0.08191, loss_grounding_dice_5: 0.08889/0.15450, loss_grounding_ce_5: 0.04243/0.27892, loss_mask_ce_6: 0.52247/0.83652, loss_mask_bce_6: 0.27117/0.30979, loss_mask_dice_6: 0.15807/1.05776, loss_spatial_bce_6: 0.25362/0.09787, loss_spatial_dice_6: 0.13813/0.20035, loss_spatial_ce_6: 0.03461/0.12274, loss_grounding_bce_6: 0.14378/0.08295, loss_grounding_dice_6: 0.08419/0.15517, loss_grounding_ce_6: 0.03800/0.28880, loss_mask_ce_7: 0.57004/0.89472, loss_mask_bce_7: 0.26400/0.31693, loss_mask_dice_7: 0.16767/1.10417, loss_spatial_bce_7: 0.25035/0.10813, loss_spatial_dice_7: 0.14507/0.22522, loss_spatial_ce_7: 0.02278/0.16300, loss_grounding_bce_7: 0.13985/0.08455, loss_grounding_dice_7: 0.08430/0.16081, loss_grounding_ce_7: 0.06501/0.32697, loss_mask_ce_8: 0.62519/1.03126, loss_mask_bce_8: 0.26536/0.33360, loss_mask_dice_8: 0.18201/1.18245, loss_spatial_bce_8: 0.35809/0.12747, loss_spatial_dice_8: 0.15149/0.26345, loss_spatial_ce_8: 0.11090/0.21625, loss_grounding_bce_8: 0.14120/0.08848, loss_grounding_dice_8: 0.09680/0.17053, loss_grounding_ce_8: 0.08726/0.42900, loss_mask_ce_9: 1.77683/3.48965, loss_mask_bce_9: 0.49223/0.36059, loss_mask_dice_9: 0.29900/1.76761, loss_spatial_bce_9: 0.59490/0.35649, loss_spatial_dice_9: 0.73732/0.79499, loss_spatial_ce_9: 0.84458/1.40216, loss_grounding_bce_9: 0.25414/0.10061, loss_grounding_dice_9: 0.15591/0.24390, loss_grounding_ce_9: 0.09353/0.68912] items per batch[64] items per second[0.36] total items[2265600] mini batches[ 35400] memory[4999] epoch remaining[0:33:33] INFO:trainer.default_trainer:epochs[ 19] optim steps[35500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45550/0.77064, loss_mask_bce_0: 0.06522/0.30165, loss_mask_dice_0: 2.26066/1.02713, loss_spatial_bce_0: 0.02180/0.08728, loss_spatial_dice_0: 0.28343/0.18460, loss_spatial_ce_0: 0.01056/0.06436, loss_grounding_bce_0: 0.01530/0.08046, loss_grounding_dice_0: 0.45175/0.15119, loss_grounding_ce_0: 0.85305/0.25002, loss_mask_ce_1: 1.36330/0.77240, loss_mask_bce_1: 0.06775/0.30238, loss_mask_dice_1: 2.25865/1.03136, loss_spatial_bce_1: 0.02144/0.08758, loss_spatial_dice_1: 0.29982/0.18718, loss_spatial_ce_1: 0.01652/0.06877, loss_grounding_bce_1: 0.01798/0.08062, loss_grounding_dice_1: 0.48692/0.15201, loss_grounding_ce_1: 0.84662/0.25174, loss_mask_ce_2: 1.59013/0.77976, loss_mask_bce_2: 0.07781/0.30247, loss_mask_dice_2: 2.37171/1.03282, loss_spatial_bce_2: 0.02170/0.08741, loss_spatial_dice_2: 0.31551/0.18729, loss_spatial_ce_2: 0.02290/0.07095, loss_grounding_bce_2: 0.01604/0.08056, loss_grounding_dice_2: 0.50529/0.15174, loss_grounding_ce_2: 0.77510/0.25410, loss_mask_ce_3: 1.45741/0.78178, loss_mask_bce_3: 0.10114/0.30395, loss_mask_dice_3: 2.37174/1.02917, loss_spatial_bce_3: 0.01818/0.08923, loss_spatial_dice_3: 0.31013/0.18808, loss_spatial_ce_3: 0.00988/0.07585, loss_grounding_bce_3: 0.01757/0.08099, loss_grounding_dice_3: 0.54615/0.15129, loss_grounding_ce_3: 0.85588/0.25375, loss_mask_ce_4: 1.89549/0.78753, loss_mask_bce_4: 0.09186/0.30616, loss_mask_dice_4: 2.06824/1.04818, loss_spatial_bce_4: 0.02513/0.09118, loss_spatial_dice_4: 0.29804/0.19579, loss_spatial_ce_4: 0.06285/0.08868, loss_grounding_bce_4: 0.01738/0.08165, loss_grounding_dice_4: 0.44598/0.15402, loss_grounding_ce_4: 1.02715/0.25989, loss_mask_ce_5: 1.67517/0.81053, loss_mask_bce_5: 0.08094/0.30788, loss_mask_dice_5: 2.25686/1.05519, loss_spatial_bce_5: 0.01157/0.09297, loss_spatial_dice_5: 0.35341/0.19812, loss_spatial_ce_5: 0.03833/0.10062, loss_grounding_bce_5: 0.01474/0.08191, loss_grounding_dice_5: 0.43293/0.15455, loss_grounding_ce_5: 1.08853/0.27892, loss_mask_ce_6: 1.85557/0.83651, loss_mask_bce_6: 0.07583/0.30969, loss_mask_dice_6: 2.25540/1.05786, loss_spatial_bce_6: 0.01160/0.09784, loss_spatial_dice_6: 0.28893/0.20034, loss_spatial_ce_6: 0.05377/0.12277, loss_grounding_bce_6: 0.01863/0.08294, loss_grounding_dice_6: 0.51607/0.15520, loss_grounding_ce_6: 0.96322/0.28876, loss_mask_ce_7: 2.30990/0.89464, loss_mask_bce_7: 0.14885/0.31683, loss_mask_dice_7: 2.79516/1.10437, loss_spatial_bce_7: 0.02425/0.10813, loss_spatial_dice_7: 0.35969/0.22524, loss_spatial_ce_7: 0.08741/0.16295, loss_grounding_bce_7: 0.01291/0.08453, loss_grounding_dice_7: 0.57855/0.16084, loss_grounding_ce_7: 1.05468/0.32674, loss_mask_ce_8: 2.18515/1.03122, loss_mask_bce_8: 0.11626/0.33347, loss_mask_dice_8: 3.25597/1.18253, loss_spatial_bce_8: 0.03325/0.12745, loss_spatial_dice_8: 0.46734/0.26345, loss_spatial_ce_8: 0.10896/0.21616, loss_grounding_bce_8: 0.00831/0.08846, loss_grounding_dice_8: 0.60133/0.17055, loss_grounding_ce_8: 0.94384/0.42890, loss_mask_ce_9: 7.63415/3.48996, loss_mask_bce_9: 0.10099/0.36049, loss_mask_dice_9: 5.32777/1.76768, loss_spatial_bce_9: 0.02092/0.35650, loss_spatial_dice_9: 0.87220/0.79500, loss_spatial_ce_9: 1.06264/1.40193, loss_grounding_bce_9: 0.00917/0.10061, loss_grounding_dice_9: 0.85807/0.24398, loss_grounding_ce_9: 1.00089/0.68884] items per batch[64] items per second[0.36] total items[2272000] mini batches[ 35500] memory[4999] epoch remaining[0:30:40] INFO:trainer.default_trainer:epochs[ 19] optim steps[35600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01961/0.77064, loss_mask_bce_0: 0.02960/0.30175, loss_mask_dice_0: 0.00947/1.02713, loss_spatial_bce_0: 0.07231/0.08728, loss_spatial_dice_0: 0.02901/0.18462, loss_spatial_ce_0: 0.01448/0.06433, loss_grounding_bce_0: 0.07680/0.08046, loss_grounding_dice_0: 0.02488/0.15118, loss_grounding_ce_0: 0.00032/0.25018, loss_mask_ce_1: 0.01827/0.77238, loss_mask_bce_1: 0.02556/0.30248, loss_mask_dice_1: 0.00892/1.03124, loss_spatial_bce_1: 0.06845/0.08757, loss_spatial_dice_1: 0.02736/0.18719, loss_spatial_ce_1: 0.02135/0.06871, loss_grounding_bce_1: 0.06439/0.08063, loss_grounding_dice_1: 0.02349/0.15200, loss_grounding_ce_1: 0.00050/0.25178, loss_mask_ce_2: 0.01509/0.77979, loss_mask_bce_2: 0.02629/0.30256, loss_mask_dice_2: 0.00894/1.03269, loss_spatial_bce_2: 0.07444/0.08741, loss_spatial_dice_2: 0.02927/0.18730, loss_spatial_ce_2: 0.00925/0.07094, loss_grounding_bce_2: 0.06597/0.08056, loss_grounding_dice_2: 0.02412/0.15172, loss_grounding_ce_2: 0.00051/0.25434, loss_mask_ce_3: 0.01557/0.78181, loss_mask_bce_3: 0.02556/0.30404, loss_mask_dice_3: 0.00898/1.02907, loss_spatial_bce_3: 0.07566/0.08924, loss_spatial_dice_3: 0.02927/0.18810, loss_spatial_ce_3: 0.01974/0.07583, loss_grounding_bce_3: 0.06564/0.08099, loss_grounding_dice_3: 0.02411/0.15125, loss_grounding_ce_3: 0.00070/0.25397, loss_mask_ce_4: 0.01370/0.78757, loss_mask_bce_4: 0.02886/0.30627, loss_mask_dice_4: 0.00979/1.04813, loss_spatial_bce_4: 0.07652/0.09119, loss_spatial_dice_4: 0.02930/0.19581, loss_spatial_ce_4: 0.02455/0.08865, loss_grounding_bce_4: 0.07499/0.08166, loss_grounding_dice_4: 0.02684/0.15402, loss_grounding_ce_4: 0.00041/0.26001, loss_mask_ce_5: 0.01574/0.81059, loss_mask_bce_5: 0.02909/0.30797, loss_mask_dice_5: 0.01000/1.05512, loss_spatial_bce_5: 0.08445/0.09297, loss_spatial_dice_5: 0.03135/0.19816, loss_spatial_ce_5: 0.01738/0.10064, loss_grounding_bce_5: 0.08230/0.08192, loss_grounding_dice_5: 0.03021/0.15451, loss_grounding_ce_5: 0.00043/0.27904, loss_mask_ce_6: 0.02572/0.83650, loss_mask_bce_6: 0.02770/0.30978, loss_mask_dice_6: 0.00943/1.05784, loss_spatial_bce_6: 0.06861/0.09785, loss_spatial_dice_6: 0.02549/0.20037, loss_spatial_ce_6: 0.02055/0.12277, loss_grounding_bce_6: 0.07168/0.08294, loss_grounding_dice_6: 0.02546/0.15517, loss_grounding_ce_6: 0.00145/0.28887, loss_mask_ce_7: 0.04376/0.89467, loss_mask_bce_7: 0.02798/0.31692, loss_mask_dice_7: 0.01015/1.10429, loss_spatial_bce_7: 0.06522/0.10813, loss_spatial_dice_7: 0.02637/0.22526, loss_spatial_ce_7: 0.01479/0.16297, loss_grounding_bce_7: 0.07375/0.08454, loss_grounding_dice_7: 0.02657/0.16079, loss_grounding_ce_7: 0.01281/0.32679, loss_mask_ce_8: 0.03675/1.03131, loss_mask_bce_8: 0.02516/0.33357, loss_mask_dice_8: 0.00934/1.18241, loss_spatial_bce_8: 0.07944/0.12747, loss_spatial_dice_8: 0.03036/0.26347, loss_spatial_ce_8: 0.24660/0.21617, loss_grounding_bce_8: 0.06693/0.08845, loss_grounding_dice_8: 0.02518/0.17053, loss_grounding_ce_8: 0.00682/0.42903, loss_mask_ce_9: 2.00876/3.48990, loss_mask_bce_9: 0.02723/0.36064, loss_mask_dice_9: 0.01078/1.76772, loss_spatial_bce_9: 0.43850/0.35654, loss_spatial_dice_9: 0.21417/0.79508, loss_spatial_ce_9: 0.29675/1.40196, loss_grounding_bce_9: 0.07196/0.10065, loss_grounding_dice_9: 0.02805/0.24396, loss_grounding_ce_9: 0.38579/0.68916] items per batch[64] items per second[0.37] total items[2278400] mini batches[ 35600] memory[4999] epoch remaining[0:27:40] INFO:trainer.default_trainer:epochs[ 19] optim steps[35700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94080/0.77052, loss_mask_bce_0: 0.49385/0.30172, loss_mask_dice_0: 0.97713/1.02727, loss_spatial_bce_0: 0.09937/0.08728, loss_spatial_dice_0: 0.20284/0.18456, loss_spatial_ce_0: 0.00103/0.06427, loss_grounding_bce_0: 0.17787/0.08051, loss_grounding_dice_0: 0.27272/0.15114, loss_grounding_ce_0: 0.00763/0.24999, loss_mask_ce_1: 0.95384/0.77233, loss_mask_bce_1: 0.48990/0.30245, loss_mask_dice_1: 0.98908/1.03138, loss_spatial_bce_1: 0.10543/0.08757, loss_spatial_dice_1: 0.16599/0.18712, loss_spatial_ce_1: 0.00073/0.06863, loss_grounding_bce_1: 0.17757/0.08068, loss_grounding_dice_1: 0.26490/0.15196, loss_grounding_ce_1: 0.01033/0.25162, loss_mask_ce_2: 1.15415/0.77977, loss_mask_bce_2: 0.45807/0.30252, loss_mask_dice_2: 0.85235/1.03280, loss_spatial_bce_2: 0.11010/0.08741, loss_spatial_dice_2: 0.18837/0.18725, loss_spatial_ce_2: 0.00134/0.07088, loss_grounding_bce_2: 0.18262/0.08062, loss_grounding_dice_2: 0.28485/0.15168, loss_grounding_ce_2: 0.01021/0.25417, loss_mask_ce_3: 1.08914/0.78185, loss_mask_bce_3: 0.45778/0.30397, loss_mask_dice_3: 0.79434/1.02919, loss_spatial_bce_3: 0.11418/0.08923, loss_spatial_dice_3: 0.19888/0.18805, loss_spatial_ce_3: 0.00711/0.07576, loss_grounding_bce_3: 0.18486/0.08101, loss_grounding_dice_3: 0.28178/0.15121, loss_grounding_ce_3: 0.01122/0.25384, loss_mask_ce_4: 0.94875/0.78751, loss_mask_bce_4: 0.48242/0.30622, loss_mask_dice_4: 1.00522/1.04829, loss_spatial_bce_4: 0.11251/0.09119, loss_spatial_dice_4: 0.18336/0.19576, loss_spatial_ce_4: 0.01279/0.08854, loss_grounding_bce_4: 0.17314/0.08171, loss_grounding_dice_4: 0.26048/0.15396, loss_grounding_ce_4: 0.01620/0.25988, loss_mask_ce_5: 0.96752/0.81046, loss_mask_bce_5: 0.46431/0.30791, loss_mask_dice_5: 0.92061/1.05527, loss_spatial_bce_5: 0.10920/0.09297, loss_spatial_dice_5: 0.19205/0.19811, loss_spatial_ce_5: 0.05277/0.10058, loss_grounding_bce_5: 0.17155/0.08196, loss_grounding_dice_5: 0.27089/0.15447, loss_grounding_ce_5: 0.02329/0.27885, loss_mask_ce_6: 1.03896/0.83637, loss_mask_bce_6: 0.47879/0.30973, loss_mask_dice_6: 0.91404/1.05802, loss_spatial_bce_6: 0.12418/0.09785, loss_spatial_dice_6: 0.19603/0.20033, loss_spatial_ce_6: 0.21224/0.12272, loss_grounding_bce_6: 0.16716/0.08299, loss_grounding_dice_6: 0.25628/0.15514, loss_grounding_ce_6: 0.02453/0.28868, loss_mask_ce_7: 1.13909/0.89458, loss_mask_bce_7: 0.47335/0.31686, loss_mask_dice_7: 0.94870/1.10449, loss_spatial_bce_7: 0.12244/0.10812, loss_spatial_dice_7: 0.22402/0.22523, loss_spatial_ce_7: 0.09393/0.16291, loss_grounding_bce_7: 0.17378/0.08458, loss_grounding_dice_7: 0.25912/0.16074, loss_grounding_ce_7: 0.01861/0.32647, loss_mask_ce_8: 0.95993/1.03125, loss_mask_bce_8: 0.49426/0.33351, loss_mask_dice_8: 0.92754/1.18267, loss_spatial_bce_8: 0.12724/0.12747, loss_spatial_dice_8: 0.26263/0.26341, loss_spatial_ce_8: 0.13802/0.21611, loss_grounding_bce_8: 0.19602/0.08850, loss_grounding_dice_8: 0.25561/0.17047, loss_grounding_ce_8: 0.01703/0.42881, loss_mask_ce_9: 4.61790/3.48991, loss_mask_bce_9: 0.52386/0.36059, loss_mask_dice_9: 1.49719/1.76807, loss_spatial_bce_9: 0.31692/0.35654, loss_spatial_dice_9: 0.93512/0.79505, loss_spatial_ce_9: 1.41763/1.40185, loss_grounding_bce_9: 0.17120/0.10067, loss_grounding_dice_9: 0.32693/0.24392, loss_grounding_ce_9: 0.03381/0.68886] items per batch[64] items per second[0.37] total items[2284800] mini batches[ 35700] memory[4999] epoch remaining[0:24:39] INFO:trainer.default_trainer:epochs[ 19] optim steps[35800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78910/0.77054, loss_mask_bce_0: 0.06769/0.30174, loss_mask_dice_0: 3.00468/1.02712, loss_spatial_bce_0: 0.00233/0.08726, loss_spatial_dice_0: 0.20944/0.18452, loss_spatial_ce_0: 0.03752/0.06422, loss_grounding_bce_0: 0.00864/0.08053, loss_grounding_dice_0: 0.37837/0.15115, loss_grounding_ce_0: 0.71245/0.25012, loss_mask_ce_1: 0.65018/0.77237, loss_mask_bce_1: 0.05689/0.30246, loss_mask_dice_1: 2.92799/1.03129, loss_spatial_bce_1: 0.00148/0.08755, loss_spatial_dice_1: 0.20141/0.18709, loss_spatial_ce_1: 0.62802/0.06862, loss_grounding_bce_1: 0.00941/0.08070, loss_grounding_dice_1: 0.38422/0.15199, loss_grounding_ce_1: 0.65970/0.25169, loss_mask_ce_2: 0.68942/0.77983, loss_mask_bce_2: 0.09410/0.30255, loss_mask_dice_2: 3.65075/1.03267, loss_spatial_bce_2: 0.00182/0.08739, loss_spatial_dice_2: 0.19437/0.18721, loss_spatial_ce_2: 0.04020/0.07083, loss_grounding_bce_2: 0.00813/0.08064, loss_grounding_dice_2: 0.41026/0.15170, loss_grounding_ce_2: 0.73420/0.25425, loss_mask_ce_3: 0.66754/0.78190, loss_mask_bce_3: 0.07714/0.30399, loss_mask_dice_3: 3.32041/1.02902, loss_spatial_bce_3: 0.00248/0.08922, loss_spatial_dice_3: 0.21878/0.18801, loss_spatial_ce_3: 0.06417/0.07570, loss_grounding_bce_3: 0.00644/0.08103, loss_grounding_dice_3: 0.25931/0.15123, loss_grounding_ce_3: 0.73609/0.25393, loss_mask_ce_4: 0.62235/0.78753, loss_mask_bce_4: 0.12168/0.30625, loss_mask_dice_4: 3.65002/1.04818, loss_spatial_bce_4: 0.00303/0.09118, loss_spatial_dice_4: 0.25607/0.19574, loss_spatial_ce_4: 0.05594/0.08846, loss_grounding_bce_4: 0.01360/0.08173, loss_grounding_dice_4: 0.33230/0.15397, loss_grounding_ce_4: 0.78509/0.25987, loss_mask_ce_5: 0.67898/0.81051, loss_mask_bce_5: 0.06654/0.30793, loss_mask_dice_5: 2.89594/1.05516, loss_spatial_bce_5: 0.00360/0.09296, loss_spatial_dice_5: 0.29455/0.19809, loss_spatial_ce_5: 0.04157/0.10050, loss_grounding_bce_5: 0.00571/0.08197, loss_grounding_dice_5: 0.35584/0.15448, loss_grounding_ce_5: 0.73200/0.27888, loss_mask_ce_6: 0.74798/0.83645, loss_mask_bce_6: 0.05795/0.30975, loss_mask_dice_6: 3.10811/1.05793, loss_spatial_bce_6: 0.00436/0.09785, loss_spatial_dice_6: 0.24169/0.20031, loss_spatial_ce_6: 0.08070/0.12266, loss_grounding_bce_6: 0.00916/0.08300, loss_grounding_dice_6: 0.39243/0.15515, loss_grounding_ce_6: 0.70561/0.28865, loss_mask_ce_7: 1.03524/0.89451, loss_mask_bce_7: 0.10307/0.31689, loss_mask_dice_7: 3.69484/1.10441, loss_spatial_bce_7: 0.00236/0.10811, loss_spatial_dice_7: 0.23552/0.22521, loss_spatial_ce_7: 0.26651/0.16286, loss_grounding_bce_7: 0.00766/0.08459, loss_grounding_dice_7: 0.36037/0.16076, loss_grounding_ce_7: 0.70462/0.32628, loss_mask_ce_8: 0.98049/1.03120, loss_mask_bce_8: 0.10496/0.33350, loss_mask_dice_8: 5.03683/1.18259, loss_spatial_bce_8: 0.00395/0.12746, loss_spatial_dice_8: 0.35281/0.26335, loss_spatial_ce_8: 0.24214/0.21606, loss_grounding_bce_8: 0.00740/0.08852, loss_grounding_dice_8: 0.39404/0.17049, loss_grounding_ce_8: 0.73656/0.42865, loss_mask_ce_9: 4.41313/3.48923, loss_mask_bce_9: 0.08504/0.36060, loss_mask_dice_9: 6.14420/1.76827, loss_spatial_bce_9: 0.01399/0.35658, loss_spatial_dice_9: 0.95984/0.79506, loss_spatial_ce_9: 1.81112/1.40165, loss_grounding_bce_9: 0.00554/0.10068, loss_grounding_dice_9: 0.50423/0.24392, loss_grounding_ce_9: 0.69318/0.68874] items per batch[64] items per second[0.36] total items[2291200] mini batches[ 35800] memory[4999] epoch remaining[0:21:43] INFO:trainer.default_trainer:epochs[ 19] optim steps[35900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.12113/0.77051, loss_mask_bce_0: 0.09966/0.30181, loss_mask_dice_0: 0.06950/1.02717, loss_spatial_bce_0: 0.08345/0.08728, loss_spatial_dice_0: 0.05314/0.18450, loss_spatial_ce_0: 0.00000/0.06415, loss_grounding_bce_0: 0.08306/0.08052, loss_grounding_dice_0: 0.05958/0.15109, loss_grounding_ce_0: 0.00906/0.24989, loss_mask_ce_1: 0.14231/0.77230, loss_mask_bce_1: 0.09760/0.30254, loss_mask_dice_1: 0.06676/1.03138, loss_spatial_bce_1: 0.08493/0.08756, loss_spatial_dice_1: 0.05244/0.18707, loss_spatial_ce_1: 0.00001/0.06854, loss_grounding_bce_1: 0.08512/0.08069, loss_grounding_dice_1: 0.06054/0.15192, loss_grounding_ce_1: 0.01276/0.25148, loss_mask_ce_2: 0.14489/0.77983, loss_mask_bce_2: 0.10211/0.30262, loss_mask_dice_2: 0.06911/1.03279, loss_spatial_bce_2: 0.07666/0.08740, loss_spatial_dice_2: 0.05575/0.18719, loss_spatial_ce_2: 0.00006/0.07080, loss_grounding_bce_2: 0.08471/0.08063, loss_grounding_dice_2: 0.06072/0.15164, loss_grounding_ce_2: 0.01563/0.25406, loss_mask_ce_3: 0.12849/0.78180, loss_mask_bce_3: 0.10203/0.30407, loss_mask_dice_3: 0.06916/1.02915, loss_spatial_bce_3: 0.08267/0.08923, loss_spatial_dice_3: 0.05766/0.18799, loss_spatial_ce_3: 0.00025/0.07562, loss_grounding_bce_3: 0.09280/0.08102, loss_grounding_dice_3: 0.06197/0.15119, loss_grounding_ce_3: 0.01218/0.25372, loss_mask_ce_4: 0.12388/0.78750, loss_mask_bce_4: 0.09701/0.30633, loss_mask_dice_4: 0.06779/1.04830, loss_spatial_bce_4: 0.07746/0.09120, loss_spatial_dice_4: 0.05280/0.19571, loss_spatial_ce_4: 0.00031/0.08840, loss_grounding_bce_4: 0.08224/0.08172, loss_grounding_dice_4: 0.06153/0.15390, loss_grounding_ce_4: 0.01390/0.25967, loss_mask_ce_5: 0.12788/0.81050, loss_mask_bce_5: 0.10597/0.30800, loss_mask_dice_5: 0.06894/1.05527, loss_spatial_bce_5: 0.09009/0.09298, loss_spatial_dice_5: 0.06395/0.19806, loss_spatial_ce_5: 0.00026/0.10042, loss_grounding_bce_5: 0.09046/0.08197, loss_grounding_dice_5: 0.06517/0.15443, loss_grounding_ce_5: 0.01805/0.27864, loss_mask_ce_6: 0.10090/0.83647, loss_mask_bce_6: 0.10217/0.30981, loss_mask_dice_6: 0.07097/1.05807, loss_spatial_bce_6: 0.07907/0.09785, loss_spatial_dice_6: 0.06086/0.20027, loss_spatial_ce_6: 0.00210/0.12265, loss_grounding_bce_6: 0.09086/0.08299, loss_grounding_dice_6: 0.06375/0.15510, loss_grounding_ce_6: 0.01355/0.28842, loss_mask_ce_7: 0.08899/0.89440, loss_mask_bce_7: 0.10135/0.31697, loss_mask_dice_7: 0.07269/1.10459, loss_spatial_bce_7: 0.07844/0.10812, loss_spatial_dice_7: 0.06586/0.22518, loss_spatial_ce_7: 0.07402/0.16284, loss_grounding_bce_7: 0.08520/0.08459, loss_grounding_dice_7: 0.06425/0.16069, loss_grounding_ce_7: 0.02451/0.32592, loss_mask_ce_8: 0.15187/1.03125, loss_mask_bce_8: 0.10947/0.33356, loss_mask_dice_8: 0.08404/1.18275, loss_spatial_bce_8: 0.08109/0.12745, loss_spatial_dice_8: 0.06640/0.26328, loss_spatial_ce_8: 0.05645/0.21601, loss_grounding_bce_8: 0.08974/0.08852, loss_grounding_dice_8: 0.07191/0.17044, loss_grounding_ce_8: 0.04280/0.42836, loss_mask_ce_9: 1.66792/3.48916, loss_mask_bce_9: 0.10403/0.36065, loss_mask_dice_9: 0.07299/1.76843, loss_spatial_bce_9: 0.55322/0.35667, loss_spatial_dice_9: 0.64318/0.79505, loss_spatial_ce_9: 0.65671/1.40143, loss_grounding_bce_9: 0.08859/0.10069, loss_grounding_dice_9: 0.06011/0.24390, loss_grounding_ce_9: 0.11549/0.68834] items per batch[64] items per second[0.36] total items[2297600] mini batches[ 35900] memory[4999] epoch remaining[0:18:47] INFO:trainer.default_trainer:epochs[ 19] optim steps[36000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.76425/0.77045, loss_mask_bce_0: 0.35662/0.30175, loss_mask_dice_0: 0.35131/1.02693, loss_spatial_bce_0: 0.10782/0.08724, loss_spatial_dice_0: 0.12260/0.18446, loss_spatial_ce_0: 0.00704/0.06405, loss_grounding_bce_0: 0.07735/0.08049, loss_grounding_dice_0: 0.08480/0.15108, loss_grounding_ce_0: 0.27200/0.24992, loss_mask_ce_1: 0.76159/0.77222, loss_mask_bce_1: 0.35484/0.30250, loss_mask_dice_1: 0.34797/1.03117, loss_spatial_bce_1: 0.11159/0.08753, loss_spatial_dice_1: 0.12543/0.18704, loss_spatial_ce_1: 0.00531/0.06844, loss_grounding_bce_1: 0.07853/0.08067, loss_grounding_dice_1: 0.09214/0.15190, loss_grounding_ce_1: 0.25199/0.25148, loss_mask_ce_2: 0.71038/0.77972, loss_mask_bce_2: 0.35358/0.30259, loss_mask_dice_2: 0.33898/1.03262, loss_spatial_bce_2: 0.12223/0.08737, loss_spatial_dice_2: 0.12982/0.18717, loss_spatial_ce_2: 0.01223/0.07071, loss_grounding_bce_2: 0.07418/0.08062, loss_grounding_dice_2: 0.08673/0.15161, loss_grounding_ce_2: 0.23532/0.25408, loss_mask_ce_3: 0.69622/0.78170, loss_mask_bce_3: 0.35616/0.30404, loss_mask_dice_3: 0.33221/1.02900, loss_spatial_bce_3: 0.11800/0.08921, loss_spatial_dice_3: 0.12340/0.18797, loss_spatial_ce_3: 0.02816/0.07551, loss_grounding_bce_3: 0.06990/0.08100, loss_grounding_dice_3: 0.08681/0.15116, loss_grounding_ce_3: 0.21645/0.25371, loss_mask_ce_4: 0.76205/0.78751, loss_mask_bce_4: 0.36624/0.30627, loss_mask_dice_4: 0.35968/1.04801, loss_spatial_bce_4: 0.10993/0.09118, loss_spatial_dice_4: 0.13081/0.19570, loss_spatial_ce_4: 0.03063/0.08831, loss_grounding_bce_4: 0.06824/0.08171, loss_grounding_dice_4: 0.08504/0.15389, loss_grounding_ce_4: 0.23166/0.25969, loss_mask_ce_5: 0.71296/0.81042, loss_mask_bce_5: 0.35170/0.30795, loss_mask_dice_5: 0.34764/1.05502, loss_spatial_bce_5: 0.11756/0.09295, loss_spatial_dice_5: 0.12931/0.19804, loss_spatial_ce_5: 0.11451/0.10032, loss_grounding_bce_5: 0.06284/0.08194, loss_grounding_dice_5: 0.08300/0.15440, loss_grounding_ce_5: 0.22686/0.27855, loss_mask_ce_6: 0.77596/0.83641, loss_mask_bce_6: 0.35439/0.30977, loss_mask_dice_6: 0.34745/1.05788, loss_spatial_bce_6: 0.12476/0.09782, loss_spatial_dice_6: 0.13520/0.20024, loss_spatial_ce_6: 0.06256/0.12258, loss_grounding_bce_6: 0.06462/0.08297, loss_grounding_dice_6: 0.08714/0.15508, loss_grounding_ce_6: 0.23040/0.28838, loss_mask_ce_7: 0.84979/0.89434, loss_mask_bce_7: 0.36751/0.31693, loss_mask_dice_7: 0.33965/1.10431, loss_spatial_bce_7: 0.15116/0.10808, loss_spatial_dice_7: 0.15889/0.22516, loss_spatial_ce_7: 0.11258/0.16271, loss_grounding_bce_7: 0.09513/0.08457, loss_grounding_dice_7: 0.09274/0.16070, loss_grounding_ce_7: 0.27260/0.32580, loss_mask_ce_8: 0.95532/1.03114, loss_mask_bce_8: 0.39479/0.33354, loss_mask_dice_8: 0.34776/1.18249, loss_spatial_bce_8: 0.13480/0.12741, loss_spatial_dice_8: 0.19582/0.26324, loss_spatial_ce_8: 0.24666/0.21588, loss_grounding_bce_8: 0.07196/0.08849, loss_grounding_dice_8: 0.09506/0.17041, loss_grounding_ce_8: 0.55184/0.42833, loss_mask_ce_9: 2.14512/3.48940, loss_mask_bce_9: 0.37205/0.36061, loss_mask_dice_9: 0.45623/1.76819, loss_spatial_bce_9: 0.44509/0.35660, loss_spatial_dice_9: 0.83697/0.79507, loss_spatial_ce_9: 1.08189/1.40120, loss_grounding_bce_9: 0.05903/0.10068, loss_grounding_dice_9: 0.13960/0.24388, loss_grounding_ce_9: 0.66715/0.68822] items per batch[64] items per second[0.37] total items[2304000] mini batches[ 36000] memory[4999] epoch remaining[0:15:50] INFO:trainer.default_trainer:epochs[ 19] optim steps[36100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36851/0.77024, loss_mask_bce_0: 0.57172/0.30167, loss_mask_dice_0: 1.57202/1.02783, loss_spatial_bce_0: 0.07027/0.08721, loss_spatial_dice_0: 0.15826/0.18444, loss_spatial_ce_0: 0.00158/0.06399, loss_grounding_bce_0: 0.11447/0.08047, loss_grounding_dice_0: 0.19706/0.15105, loss_grounding_ce_0: 0.44344/0.24983, loss_mask_ce_1: 0.30924/0.77199, loss_mask_bce_1: 0.56005/0.30242, loss_mask_dice_1: 1.56941/1.03195, loss_spatial_bce_1: 0.06884/0.08750, loss_spatial_dice_1: 0.17285/0.18703, loss_spatial_ce_1: 0.00505/0.06837, loss_grounding_bce_1: 0.10830/0.08065, loss_grounding_dice_1: 0.19692/0.15185, loss_grounding_ce_1: 0.44731/0.25138, loss_mask_ce_2: 0.33261/0.77954, loss_mask_bce_2: 0.58438/0.30250, loss_mask_dice_2: 1.56025/1.03336, loss_spatial_bce_2: 0.06870/0.08734, loss_spatial_dice_2: 0.16895/0.18716, loss_spatial_ce_2: 0.01457/0.07065, loss_grounding_bce_2: 0.11455/0.08060, loss_grounding_dice_2: 0.19644/0.15157, loss_grounding_ce_2: 0.45410/0.25398, loss_mask_ce_3: 0.38312/0.78150, loss_mask_bce_3: 0.60912/0.30396, loss_mask_dice_3: 1.61843/1.02974, loss_spatial_bce_3: 0.07512/0.08918, loss_spatial_dice_3: 0.17180/0.18796, loss_spatial_ce_3: 0.02813/0.07547, loss_grounding_bce_3: 0.11501/0.08098, loss_grounding_dice_3: 0.18466/0.15111, loss_grounding_ce_3: 0.46359/0.25360, loss_mask_ce_4: 0.43294/0.78734, loss_mask_bce_4: 0.57458/0.30619, loss_mask_dice_4: 1.55097/1.04877, loss_spatial_bce_4: 0.07387/0.09115, loss_spatial_dice_4: 0.23827/0.19568, loss_spatial_ce_4: 0.02088/0.08827, loss_grounding_bce_4: 0.10835/0.08168, loss_grounding_dice_4: 0.19377/0.15385, loss_grounding_ce_4: 0.47730/0.25958, loss_mask_ce_5: 0.53680/0.81024, loss_mask_bce_5: 0.58321/0.30789, loss_mask_dice_5: 1.57947/1.05567, loss_spatial_bce_5: 0.07591/0.09293, loss_spatial_dice_5: 0.22976/0.19804, loss_spatial_ce_5: 0.04133/0.10027, loss_grounding_bce_5: 0.10810/0.08192, loss_grounding_dice_5: 0.19506/0.15438, loss_grounding_ce_5: 0.44539/0.27843, loss_mask_ce_6: 0.66329/0.83621, loss_mask_bce_6: 0.59953/0.30971, loss_mask_dice_6: 1.49203/1.05865, loss_spatial_bce_6: 0.07785/0.09780, loss_spatial_dice_6: 0.20330/0.20024, loss_spatial_ce_6: 0.06488/0.12253, loss_grounding_bce_6: 0.10740/0.08295, loss_grounding_dice_6: 0.17751/0.15504, loss_grounding_ce_6: 0.40955/0.28823, loss_mask_ce_7: 0.65399/0.89413, loss_mask_bce_7: 0.58454/0.31687, loss_mask_dice_7: 1.65752/1.10504, loss_spatial_bce_7: 0.07412/0.10805, loss_spatial_dice_7: 0.26162/0.22518, loss_spatial_ce_7: 0.06721/0.16266, loss_grounding_bce_7: 0.11111/0.08456, loss_grounding_dice_7: 0.18964/0.16066, loss_grounding_ce_7: 0.49406/0.32564, loss_mask_ce_8: 0.72061/1.03101, loss_mask_bce_8: 0.59863/0.33348, loss_mask_dice_8: 1.54051/1.18313, loss_spatial_bce_8: 0.09609/0.12736, loss_spatial_dice_8: 0.27892/0.26323, loss_spatial_ce_8: 0.11501/0.21582, loss_grounding_bce_8: 0.10923/0.08849, loss_grounding_dice_8: 0.17670/0.17037, loss_grounding_ce_8: 0.49553/0.42827, loss_mask_ce_9: 2.88031/3.48930, loss_mask_bce_9: 0.60871/0.36052, loss_mask_dice_9: 2.22654/1.76895, loss_spatial_bce_9: 0.36160/0.35657, loss_spatial_dice_9: 0.92396/0.79505, loss_spatial_ce_9: 1.53636/1.40105, loss_grounding_bce_9: 0.12139/0.10067, loss_grounding_dice_9: 0.27370/0.24381, loss_grounding_ce_9: 0.55272/0.68806] items per batch[64] items per second[0.36] total items[2310400] mini batches[ 36100] memory[4999] epoch remaining[0:12:55] INFO:trainer.default_trainer:epochs[ 19] optim steps[36200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05841/0.77065, loss_mask_bce_0: 0.00678/0.30181, loss_mask_dice_0: 0.08347/1.02815, loss_spatial_bce_0: 0.00304/0.08716, loss_spatial_dice_0: 0.04199/0.18443, loss_spatial_ce_0: 0.00003/0.06389, loss_grounding_bce_0: 0.00300/0.08045, loss_grounding_dice_0: 0.04854/0.15104, loss_grounding_ce_0: 0.00665/0.24984, loss_mask_ce_1: 0.06112/0.77234, loss_mask_bce_1: 0.00563/0.30258, loss_mask_dice_1: 0.08891/1.03232, loss_spatial_bce_1: 0.00271/0.08745, loss_spatial_dice_1: 0.03454/0.18702, loss_spatial_ce_1: 0.00004/0.06825, loss_grounding_bce_1: 0.00350/0.08063, loss_grounding_dice_1: 0.04118/0.15184, loss_grounding_ce_1: 0.00635/0.25139, loss_mask_ce_2: 0.06903/0.77993, loss_mask_bce_2: 0.00633/0.30265, loss_mask_dice_2: 0.06969/1.03375, loss_spatial_bce_2: 0.00323/0.08730, loss_spatial_dice_2: 0.04111/0.18714, loss_spatial_ce_2: 0.00007/0.07053, loss_grounding_bce_2: 0.00413/0.08058, loss_grounding_dice_2: 0.04760/0.15154, loss_grounding_ce_2: 0.00855/0.25399, loss_mask_ce_3: 0.06282/0.78197, loss_mask_bce_3: 0.00569/0.30411, loss_mask_dice_3: 0.08481/1.03011, loss_spatial_bce_3: 0.00403/0.08915, loss_spatial_dice_3: 0.04987/0.18796, loss_spatial_ce_3: 0.00002/0.07535, loss_grounding_bce_3: 0.00308/0.08096, loss_grounding_dice_3: 0.04707/0.15109, loss_grounding_ce_3: 0.00718/0.25362, loss_mask_ce_4: 0.06642/0.78771, loss_mask_bce_4: 0.00689/0.30636, loss_mask_dice_4: 0.08173/1.04915, loss_spatial_bce_4: 0.00491/0.09112, loss_spatial_dice_4: 0.06768/0.19568, loss_spatial_ce_4: 0.00064/0.08818, loss_grounding_bce_4: 0.00391/0.08166, loss_grounding_dice_4: 0.04336/0.15384, loss_grounding_ce_4: 0.00688/0.25964, loss_mask_ce_5: 0.08903/0.81070, loss_mask_bce_5: 0.00642/0.30804, loss_mask_dice_5: 0.07469/1.05607, loss_spatial_bce_5: 0.00387/0.09290, loss_spatial_dice_5: 0.05459/0.19803, loss_spatial_ce_5: 0.00227/0.10018, loss_grounding_bce_5: 0.00350/0.08189, loss_grounding_dice_5: 0.05120/0.15436, loss_grounding_ce_5: 0.01340/0.27851, loss_mask_ce_6: 0.08523/0.83669, loss_mask_bce_6: 0.00865/0.30985, loss_mask_dice_6: 0.06436/1.05916, loss_spatial_bce_6: 0.00387/0.09778, loss_spatial_dice_6: 0.04415/0.20022, loss_spatial_ce_6: 0.01469/0.12250, loss_grounding_bce_6: 0.00365/0.08292, loss_grounding_dice_6: 0.04241/0.15503, loss_grounding_ce_6: 0.00759/0.28824, loss_mask_ce_7: 0.09478/0.89460, loss_mask_bce_7: 0.00810/0.31706, loss_mask_dice_7: 0.07795/1.10547, loss_spatial_bce_7: 0.00490/0.10803, loss_spatial_dice_7: 0.05450/0.22518, loss_spatial_ce_7: 0.02541/0.16261, loss_grounding_bce_7: 0.00463/0.08454, loss_grounding_dice_7: 0.04350/0.16064, loss_grounding_ce_7: 0.01485/0.32564, loss_mask_ce_8: 0.12110/1.03145, loss_mask_bce_8: 0.01359/0.33365, loss_mask_dice_8: 0.09225/1.18350, loss_spatial_bce_8: 0.00610/0.12735, loss_spatial_dice_8: 0.09220/0.26322, loss_spatial_ce_8: 0.06901/0.21583, loss_grounding_bce_8: 0.00922/0.08847, loss_grounding_dice_8: 0.06655/0.17033, loss_grounding_ce_8: 0.01044/0.42833, loss_mask_ce_9: 1.77474/3.49000, loss_mask_bce_9: 0.00682/0.36069, loss_mask_dice_9: 0.11186/1.76951, loss_spatial_bce_9: 0.03916/0.35647, loss_spatial_dice_9: 0.65328/0.79513, loss_spatial_ce_9: 0.89241/1.40120, loss_grounding_bce_9: 0.00364/0.10065, loss_grounding_dice_9: 0.06038/0.24384, loss_grounding_ce_9: 0.14302/0.68854] items per batch[64] items per second[0.37] total items[2316800] mini batches[ 36200] memory[4999] epoch remaining[0:09:58] INFO:trainer.default_trainer:epochs[ 19] optim steps[36300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59943/0.77069, loss_mask_bce_0: 0.10158/0.30171, loss_mask_dice_0: 0.09019/1.02854, loss_spatial_bce_0: 0.30241/0.08714, loss_spatial_dice_0: 0.32864/0.18444, loss_spatial_ce_0: 0.26188/0.06387, loss_grounding_bce_0: 0.07434/0.08043, loss_grounding_dice_0: 0.05940/0.15102, loss_grounding_ce_0: 0.29856/0.24980, loss_mask_ce_1: 0.44240/0.77243, loss_mask_bce_1: 0.30255/0.30248, loss_mask_dice_1: 0.24952/1.03268, loss_spatial_bce_1: 0.39288/0.08743, loss_spatial_dice_1: 0.32253/0.18702, loss_spatial_ce_1: 0.41504/0.06828, loss_grounding_bce_1: 0.22343/0.08062, loss_grounding_dice_1: 0.18634/0.15182, loss_grounding_ce_1: 0.05164/0.25126, loss_mask_ce_2: 0.55035/0.78000, loss_mask_bce_2: 0.09728/0.30254, loss_mask_dice_2: 0.07289/1.03407, loss_spatial_bce_2: 0.33389/0.08728, loss_spatial_dice_2: 0.35347/0.18716, loss_spatial_ce_2: 0.30872/0.07054, loss_grounding_bce_2: 0.21984/0.08057, loss_grounding_dice_2: 0.20525/0.15155, loss_grounding_ce_2: 0.04570/0.25394, loss_mask_ce_3: 0.78019/0.78214, loss_mask_bce_3: 0.10451/0.30402, loss_mask_dice_3: 0.07841/1.03050, loss_spatial_bce_3: 0.37590/0.08914, loss_spatial_dice_3: 0.38119/0.18799, loss_spatial_ce_3: 0.25391/0.07532, loss_grounding_bce_3: 0.20749/0.08094, loss_grounding_dice_3: 0.17296/0.15107, loss_grounding_ce_3: 0.05656/0.25354, loss_mask_ce_4: 0.47146/0.78781, loss_mask_bce_4: 0.25211/0.30628, loss_mask_dice_4: 0.27222/1.04951, loss_spatial_bce_4: 0.28723/0.09110, loss_spatial_dice_4: 0.34118/0.19568, loss_spatial_ce_4: 0.42626/0.08815, loss_grounding_bce_4: 0.06711/0.08164, loss_grounding_dice_4: 0.04277/0.15383, loss_grounding_ce_4: 0.44973/0.25961, loss_mask_ce_5: 0.38584/0.81073, loss_mask_bce_5: 0.23000/0.30796, loss_mask_dice_5: 0.24437/1.05640, loss_spatial_bce_5: 0.39550/0.09288, loss_spatial_dice_5: 0.35649/0.19804, loss_spatial_ce_5: 0.50442/0.10016, loss_grounding_bce_5: 0.08563/0.08187, loss_grounding_dice_5: 0.04265/0.15434, loss_grounding_ce_5: 0.46311/0.27846, loss_mask_ce_6: 0.49563/0.83678, loss_mask_bce_6: 0.22878/0.30975, loss_mask_dice_6: 0.25287/1.05954, loss_spatial_bce_6: 0.33494/0.09777, loss_spatial_dice_6: 0.36775/0.20024, loss_spatial_ce_6: 0.71753/0.12249, loss_grounding_bce_6: 0.09611/0.08290, loss_grounding_dice_6: 0.04754/0.15504, loss_grounding_ce_6: 0.48584/0.28819, loss_mask_ce_7: 0.47631/0.89459, loss_mask_bce_7: 0.17610/0.31697, loss_mask_dice_7: 0.12769/1.10589, loss_spatial_bce_7: 0.29790/0.10803, loss_spatial_dice_7: 0.39753/0.22521, loss_spatial_ce_7: 0.62710/0.16253, loss_grounding_bce_7: 0.11036/0.08453, loss_grounding_dice_7: 0.10154/0.16061, loss_grounding_ce_7: 0.25348/0.32549, loss_mask_ce_8: 0.61932/1.03138, loss_mask_bce_8: 0.17026/0.33358, loss_mask_dice_8: 0.29228/1.18386, loss_spatial_bce_8: 0.63322/0.12733, loss_spatial_dice_8: 0.32607/0.26322, loss_spatial_ce_8: 0.58030/0.21580, loss_grounding_bce_8: 0.11119/0.08846, loss_grounding_dice_8: 0.18640/0.17034, loss_grounding_ce_8: 0.21939/0.42837, loss_mask_ce_9: 3.16939/3.49061, loss_mask_bce_9: 0.23247/0.36059, loss_mask_dice_9: 0.30014/1.76983, loss_spatial_bce_9: 0.59356/0.35645, loss_spatial_dice_9: 0.60524/0.79520, loss_spatial_ce_9: 0.61613/1.40138, loss_grounding_bce_9: 0.16399/0.10066, loss_grounding_dice_9: 0.20694/0.24380, loss_grounding_ce_9: 0.43693/0.68869] items per batch[64] items per second[0.36] total items[2323200] mini batches[ 36300] memory[4999] epoch remaining[0:07:02] INFO:trainer.default_trainer:epochs[ 19] optim steps[36400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05187/0.77052, loss_mask_bce_0: 0.11825/0.30160, loss_mask_dice_0: 0.17612/1.02837, loss_spatial_bce_0: 0.02524/0.08712, loss_spatial_dice_0: 0.05248/0.18441, loss_spatial_ce_0: 0.00011/0.06383, loss_grounding_bce_0: 0.01311/0.08039, loss_grounding_dice_0: 0.08074/0.15099, loss_grounding_ce_0: 0.40052/0.24971, loss_mask_ce_1: 0.06311/0.77220, loss_mask_bce_1: 0.11183/0.30238, loss_mask_dice_1: 0.15707/1.03252, loss_spatial_bce_1: 0.02625/0.08741, loss_spatial_dice_1: 0.05855/0.18698, loss_spatial_ce_1: 0.00010/0.06822, loss_grounding_bce_1: 0.01325/0.08059, loss_grounding_dice_1: 0.07443/0.15180, loss_grounding_ce_1: 0.45700/0.25109, loss_mask_ce_2: 0.05587/0.77980, loss_mask_bce_2: 0.12205/0.30245, loss_mask_dice_2: 0.16963/1.03388, loss_spatial_bce_2: 0.02504/0.08727, loss_spatial_dice_2: 0.05980/0.18712, loss_spatial_ce_2: 0.00021/0.07053, loss_grounding_bce_2: 0.01253/0.08054, loss_grounding_dice_2: 0.07239/0.15152, loss_grounding_ce_2: 0.42322/0.25374, loss_mask_ce_3: 0.05845/0.78193, loss_mask_bce_3: 0.12252/0.30391, loss_mask_dice_3: 0.17133/1.03039, loss_spatial_bce_3: 0.02881/0.08911, loss_spatial_dice_3: 0.05255/0.18795, loss_spatial_ce_3: 0.00053/0.07530, loss_grounding_bce_3: 0.01469/0.08091, loss_grounding_dice_3: 0.07396/0.15105, loss_grounding_ce_3: 0.36404/0.25339, loss_mask_ce_4: 0.05993/0.78763, loss_mask_bce_4: 0.12817/0.30617, loss_mask_dice_4: 0.17073/1.04938, loss_spatial_bce_4: 0.02770/0.09109, loss_spatial_dice_4: 0.05978/0.19566, loss_spatial_ce_4: 0.00018/0.08811, loss_grounding_bce_4: 0.01331/0.08160, loss_grounding_dice_4: 0.07069/0.15379, loss_grounding_ce_4: 0.44671/0.25948, loss_mask_ce_5: 0.05545/0.81051, loss_mask_bce_5: 0.11485/0.30786, loss_mask_dice_5: 0.15164/1.05623, loss_spatial_bce_5: 0.02769/0.09286, loss_spatial_dice_5: 0.05251/0.19801, loss_spatial_ce_5: 0.00014/0.10014, loss_grounding_bce_5: 0.01224/0.08183, loss_grounding_dice_5: 0.06798/0.15432, loss_grounding_ce_5: 0.47627/0.27830, loss_mask_ce_6: 0.06382/0.83660, loss_mask_bce_6: 0.11299/0.30964, loss_mask_dice_6: 0.16241/1.05934, loss_spatial_bce_6: 0.03487/0.09776, loss_spatial_dice_6: 0.05464/0.20022, loss_spatial_ce_6: 0.00378/0.12247, loss_grounding_bce_6: 0.01395/0.08286, loss_grounding_dice_6: 0.07312/0.15500, loss_grounding_ce_6: 0.54237/0.28805, loss_mask_ce_7: 0.06378/0.89440, loss_mask_bce_7: 0.11101/0.31687, loss_mask_dice_7: 0.16267/1.10563, loss_spatial_bce_7: 0.03264/0.10802, loss_spatial_dice_7: 0.05619/0.22518, loss_spatial_ce_7: 0.00179/0.16248, loss_grounding_bce_7: 0.01443/0.08449, loss_grounding_dice_7: 0.06877/0.16060, loss_grounding_ce_7: 0.46869/0.32532, loss_mask_ce_8: 0.08829/1.03122, loss_mask_bce_8: 0.11405/0.33347, loss_mask_dice_8: 0.15886/1.18353, loss_spatial_bce_8: 0.03255/0.12732, loss_spatial_dice_8: 0.06041/0.26319, loss_spatial_ce_8: 0.08360/0.21572, loss_grounding_bce_8: 0.01740/0.08843, loss_grounding_dice_8: 0.06634/0.17032, loss_grounding_ce_8: 0.83308/0.42812, loss_mask_ce_9: 3.26440/3.48985, loss_mask_bce_9: 0.10449/0.36048, loss_mask_dice_9: 0.37908/1.76914, loss_spatial_bce_9: 0.29552/0.35640, loss_spatial_dice_9: 0.77867/0.79515, loss_spatial_ce_9: 0.89551/1.40134, loss_grounding_bce_9: 0.01810/0.10063, loss_grounding_dice_9: 0.16880/0.24377, loss_grounding_ce_9: 2.40988/0.68853] items per batch[64] items per second[0.36] total items[2329600] mini batches[ 36400] memory[4999] epoch remaining[0:04:06] INFO:trainer.default_trainer:epochs[ 19] optim steps[36500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56871/0.77037, loss_mask_bce_0: 0.41122/0.30173, loss_mask_dice_0: 2.44966/1.02748, loss_spatial_bce_0: 0.06098/0.08721, loss_spatial_dice_0: 0.20331/0.18439, loss_spatial_ce_0: 0.01074/0.06377, loss_grounding_bce_0: 0.02030/0.08044, loss_grounding_dice_0: 0.23983/0.15103, loss_grounding_ce_0: 0.71251/0.24974, loss_mask_ce_1: 0.64510/0.77207, loss_mask_bce_1: 0.41101/0.30251, loss_mask_dice_1: 2.46076/1.03159, loss_spatial_bce_1: 0.05835/0.08749, loss_spatial_dice_1: 0.16507/0.18696, loss_spatial_ce_1: 0.06548/0.06817, loss_grounding_bce_1: 0.02277/0.08064, loss_grounding_dice_1: 0.25886/0.15184, loss_grounding_ce_1: 0.68888/0.25118, loss_mask_ce_2: 0.63880/0.77972, loss_mask_bce_2: 0.40409/0.30258, loss_mask_dice_2: 2.54129/1.03293, loss_spatial_bce_2: 0.06459/0.08735, loss_spatial_dice_2: 0.19074/0.18711, loss_spatial_ce_2: 0.00824/0.07047, loss_grounding_bce_2: 0.02158/0.08059, loss_grounding_dice_2: 0.25836/0.15155, loss_grounding_ce_2: 0.66177/0.25375, loss_mask_ce_3: 0.64286/0.78183, loss_mask_bce_3: 0.43210/0.30405, loss_mask_dice_3: 2.41312/1.02947, loss_spatial_bce_3: 0.06367/0.08920, loss_spatial_dice_3: 0.20026/0.18794, loss_spatial_ce_3: 0.00511/0.07528, loss_grounding_bce_3: 0.02095/0.08096, loss_grounding_dice_3: 0.25795/0.15108, loss_grounding_ce_3: 0.71767/0.25352, loss_mask_ce_4: 0.78260/0.78748, loss_mask_bce_4: 0.43377/0.30630, loss_mask_dice_4: 2.57387/1.04847, loss_spatial_bce_4: 0.06090/0.09118, loss_spatial_dice_4: 0.17690/0.19565, loss_spatial_ce_4: 0.12921/0.08806, loss_grounding_bce_4: 0.02228/0.08165, loss_grounding_dice_4: 0.25464/0.15382, loss_grounding_ce_4: 0.69479/0.25959, loss_mask_ce_5: 0.73467/0.81046, loss_mask_bce_5: 0.45954/0.30800, loss_mask_dice_5: 2.69216/1.05528, loss_spatial_bce_5: 0.05690/0.09296, loss_spatial_dice_5: 0.15751/0.19800, loss_spatial_ce_5: 0.05759/0.10010, loss_grounding_bce_5: 0.02099/0.08190, loss_grounding_dice_5: 0.25644/0.15437, loss_grounding_ce_5: 0.68860/0.27826, loss_mask_ce_6: 0.88715/0.83651, loss_mask_bce_6: 0.44513/0.30979, loss_mask_dice_6: 2.60581/1.05843, loss_spatial_bce_6: 0.09199/0.09787, loss_spatial_dice_6: 0.22032/0.20020, loss_spatial_ce_6: 0.02462/0.12242, loss_grounding_bce_6: 0.02017/0.08292, loss_grounding_dice_6: 0.24961/0.15505, loss_grounding_ce_6: 0.68460/0.28810, loss_mask_ce_7: 0.95289/0.89417, loss_mask_bce_7: 0.45224/0.31702, loss_mask_dice_7: 2.58935/1.10466, loss_spatial_bce_7: 0.09112/0.10813, loss_spatial_dice_7: 0.22751/0.22516, loss_spatial_ce_7: 0.11089/0.16245, loss_grounding_bce_7: 0.02813/0.08453, loss_grounding_dice_7: 0.29508/0.16064, loss_grounding_ce_7: 0.74390/0.32542, loss_mask_ce_8: 1.69157/1.03094, loss_mask_bce_8: 0.48131/0.33363, loss_mask_dice_8: 3.11958/1.18252, loss_spatial_bce_8: 0.09897/0.12743, loss_spatial_dice_8: 0.28726/0.26315, loss_spatial_ce_8: 0.15885/0.21574, loss_grounding_bce_8: 0.03202/0.08848, loss_grounding_dice_8: 0.32995/0.17035, loss_grounding_ce_8: 0.73086/0.42825, loss_mask_ce_9: 4.78969/3.48921, loss_mask_bce_9: 0.49393/0.36062, loss_mask_dice_9: 4.51323/1.76794, loss_spatial_bce_9: 0.27086/0.35651, loss_spatial_dice_9: 0.90466/0.79510, loss_spatial_ce_9: 1.25294/1.40120, loss_grounding_bce_9: 0.02176/0.10067, loss_grounding_dice_9: 0.49068/0.24382, loss_grounding_ce_9: 0.65758/0.68864] items per batch[64] items per second[0.36] total items[2336000] mini batches[ 36500] memory[4999] epoch remaining[0:01:10] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00036540. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0023 s/iter. Inference: 0.3770 s/iter. Eval: 0.0899 s/iter. Total: 0.4692 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0023 s/iter. Inference: 0.3747 s/iter. Eval: 0.0755 s/iter. Total: 0.4526 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0025 s/iter. Inference: 0.3768 s/iter. Eval: 0.0750 s/iter. Total: 0.4545 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0026 s/iter. Inference: 0.3790 s/iter. Eval: 0.0738 s/iter. Total: 0.4555 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0026 s/iter. Inference: 0.3800 s/iter. Eval: 0.0732 s/iter. Total: 0.4559 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3784 s/iter. Eval: 0.0727 s/iter. Total: 0.4539 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaltghs15d2 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.513 | 83.052 | 66.046 | 133 | | Things | 61.600 | 83.980 | 72.811 | 80 | | Stuff | 46.325 | 81.651 | 55.834 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.92 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.41 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.85s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.47 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.52 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.769 | 69.448 | 49.250 | 25.679 | 49.713 | 67.708 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.125 | bicycle | 23.082 | car | 44.463 | | motorcycle | 41.565 | airplane | 63.069 | bus | 70.719 | | train | 74.785 | truck | 42.000 | boat | 31.215 | | traffic light | 29.112 | fire hydrant | 70.863 | stop sign | 67.707 | | parking meter | 52.151 | bench | 26.897 | bird | 34.681 | | cat | 77.659 | dog | 71.609 | horse | 51.857 | | sheep | 54.153 | cow | 56.529 | elephant | 65.916 | | bear | 81.050 | zebra | 66.529 | giraffe | 62.462 | | backpack | 23.482 | umbrella | 55.786 | handbag | 24.618 | | tie | 40.582 | suitcase | 51.205 | frisbee | 70.312 | | skis | 9.558 | snowboard | 34.257 | sports ball | 47.873 | | kite | 38.687 | baseball bat | 38.519 | baseball glove | 51.102 | | skateboard | 44.099 | surfboard | 45.240 | tennis racket | 62.968 | | bottle | 41.589 | wine glass | 36.704 | cup | 50.580 | | fork | 26.554 | knife | 24.003 | spoon | 21.292 | | bowl | 40.326 | banana | 23.429 | apple | 27.068 | | sandwich | 47.497 | orange | 31.659 | broccoli | 23.111 | | carrot | 23.082 | hot dog | 32.443 | pizza | 53.803 | | donut | 56.789 | cake | 47.955 | chair | 28.121 | | couch | 44.348 | potted plant | 22.096 | bed | 44.206 | | dining table | 13.863 | toilet | 68.548 | tv | 67.537 | | laptop | 70.989 | mouse | 62.896 | remote | 43.902 | | keyboard | 58.290 | cell phone | 47.209 | microwave | 66.363 | | oven | 35.454 | toaster | 52.714 | sink | 44.494 | | refrigerator | 70.017 | book | 14.160 | clock | 55.225 | | vase | 40.533 | scissors | 35.639 | teddy bear | 57.503 | | hair drier | 35.564 | toothbrush | 26.499 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.67041526151162, 'fwIoU': 71.62234334452143, 'IoU-person': 88.86206962402744, 'IoU-bicycle': 75.40980186245123, 'IoU-car': 70.8313505992012, 'IoU-motorcycle': 88.18476974860822, 'IoU-airplane': 83.43199064680049, 'IoU-bus': 88.08558641423751, 'IoU-train': 88.40580910003807, 'IoU-truck': 67.72374541349865, 'IoU-boat': 75.2071766665335, 'IoU-traffic light': 78.81303368528754, 'IoU-fire hydrant': 93.01300374307067, 'IoU-stop sign': 95.04086098900194, 'IoU-parking meter': 88.20611343694443, 'IoU-bench': 60.97987937480448, 'IoU-bird': 77.06454771461854, 'IoU-cat': 90.9949969962097, 'IoU-dog': 86.1142383213863, 'IoU-horse': 88.64894745865372, 'IoU-sheep': 89.9846040354328, 'IoU-cow': 89.84692825599389, 'IoU-elephant': 92.05053652632475, 'IoU-bear': 80.56307928908757, 'IoU-zebra': 90.64265789926573, 'IoU-giraffe': 89.59251063183592, 'IoU-backpack': 53.37748375391514, 'IoU-umbrella': 87.93838485086074, 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74.17822882814123, 'IoU-chair': 62.799557320277245, 'IoU-couch': 71.8458850361727, 'IoU-potted plant': 42.78811166419236, 'IoU-bed': 74.63486149057627, 'IoU-dining table': 52.86615248238525, 'IoU-toilet': 87.90909393845787, 'IoU-tv': 80.1864849975054, 'IoU-laptop': 77.81484292682302, 'IoU-mouse': 75.53423448562287, 'IoU-remote': 73.8070287470186, 'IoU-keyboard': 52.64168430158019, 'IoU-cell phone': 71.61372218843749, 'IoU-microwave': 71.00839516924383, 'IoU-oven': 70.61555278311545, 'IoU-toaster': 84.43418902028651, 'IoU-sink': 72.49405074760955, 'IoU-refrigerator': 82.44869208517956, 'IoU-book': 56.960249316858004, 'IoU-clock': 80.47079551070925, 'IoU-vase': 62.127527389595336, 'IoU-scissors': 86.3828614653123, 'IoU-teddy bear': 85.10574280813047, 'IoU-hair drier': 47.11189116072128, 'IoU-toothbrush': 73.55101657657376, 'IoU-banner': 35.59774402906374, 'IoU-blanket': 15.857206979683799, 'IoU-bridge': 39.40952015004518, 'IoU-cardboard': 44.78404204764713, 'IoU-counter': 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26.764444624212015, 'IoU-window-blind': 49.95292221974161, 'IoU-window-other': 49.564748852749915, 'IoU-tree-merged': 82.19898698705114, 'IoU-fence-merged': 54.440689843139744, 'IoU-ceiling-merged': 68.03858473381929, 'IoU-sky-other-merged': 94.10566258576868, 'IoU-cabinet-merged': 62.96786588723188, 'IoU-table-merged': 40.70731792001293, 'IoU-floor-other-merged': 55.175802057952176, 'IoU-pavement-merged': 57.814074978240484, 'IoU-mountain-merged': 58.52101024869745, 'IoU-grass-merged': 72.6890466317938, 'IoU-dirt-merged': 46.98668295105336, 'IoU-paper-merged': 34.165619134941515, 'IoU-food-other-merged': 40.99203801636127, 'IoU-building-other-merged': 59.98494209139125, 'IoU-rock-merged': 64.79818923288349, 'IoU-wall-other-merged': 68.43192940963546, 'IoU-rug-merged': 67.16656181310658, 'mACC': 77.38209903438754, 'pACC': 82.16617386488319, 'ACC-person': 92.82894083870343, 'ACC-bicycle': 84.43978160611226, 'ACC-car': 85.61908028122131, 'ACC-motorcycle': 92.95463754249131, 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77.82752766869753, 'ACC-table-merged': 58.350431438224874, 'ACC-floor-other-merged': 67.34327161200191, 'ACC-pavement-merged': 71.80788015692447, 'ACC-mountain-merged': 69.00926646187972, 'ACC-grass-merged': 83.72539185435193, 'ACC-dirt-merged': 73.5504859125318, 'ACC-paper-merged': 44.29846539757209, 'ACC-food-other-merged': 53.043612381975024, 'ACC-building-other-merged': 73.27498008624268, 'ACC-rock-merged': 83.28427703929485, 'ACC-wall-other-merged': 82.62559117977165, 'ACC-rug-merged': 82.55150159878927})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3303 s/iter. Inference: 0.2676 s/iter. Eval: 0.0000 s/iter. Total: 0.5979 s/iter. ETA=0:00:08 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3490 s/iter. Inference: 0.3932 s/iter. Eval: 0.0000 s/iter. Total: 0.7423 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3594 s/iter. Inference: 0.5815 s/iter. Eval: 0.0000 s/iter. Total: 0.9411 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4009364940005853, 'noc@0.8': 2.5346795434591747, 'noc@0.85': 3.0043898156277438, 'noc@0.9': 3.8446005267778753, 'miou@iter1': 0.8724452686616821} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1539 s/iter. Eval: 0.0011 s/iter. Total: 0.1563 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.35950469970703, 'precision@0.6': 72.67781066894531, 'precision@0.7': 68.01399230957031, 'precision@0.8': 58.88068389892578, 'precision@0.9': 32.06373977661133, 'cIoU': 61.14328384399414, 'mIoU': 66.61537170410156} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.51296020863025, 'SQ': 83.05175616918456, 'RQ': 66.04565020417749, 'PQ_th': 61.599758734464835, 'SQ_th': 83.979525829018, 'RQ_th': 72.8105922593013, 'PQ_st': 46.325339792276104, 'SQ_st': 81.65134913547365, 'RQ_st': 55.83441691342459}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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77.82752766869753, 'ACC-table-merged': 58.350431438224874, 'ACC-floor-other-merged': 67.34327161200191, 'ACC-pavement-merged': 71.80788015692447, 'ACC-mountain-merged': 69.00926646187972, 'ACC-grass-merged': 83.72539185435193, 'ACC-dirt-merged': 73.5504859125318, 'ACC-paper-merged': 44.29846539757209, 'ACC-food-other-merged': 53.043612381975024, 'ACC-building-other-merged': 73.27498008624268, 'ACC-rock-merged': 83.28427703929485, 'ACC-wall-other-merged': 82.62559117977165, 'ACC-rug-merged': 82.55150159878927})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4009364940005853, 'noc@0.8': 2.5346795434591747, 'noc@0.85': 3.0043898156277438, 'noc@0.9': 3.8446005267778753, 'miou@iter1': 0.8724452686616821}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.35950469970703, 'precision@0.6': 72.67781066894531, 'precision@0.7': 68.01399230957031, 'precision@0.8': 58.88068389892578, 'precision@0.9': 32.06373977661133, 'cIoU': 61.14328384399414, 'mIoU': 66.61537170410156}}} INFO:trainer.default_trainer:This epoch takes 0:57:06.952856 INFO:trainer.default_trainer:PROGRESS: 40.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 20 training. INFO:trainer.default_trainer:epochs[ 20] optim steps[36600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60477/0.77028, loss_mask_bce_0: 0.32629/0.30170, loss_mask_dice_0: 0.79336/1.02750, loss_spatial_bce_0: 0.04621/0.08719, loss_spatial_dice_0: 0.11356/0.18440, loss_spatial_ce_0: 0.00032/0.06370, loss_grounding_bce_0: 0.04332/0.08043, loss_grounding_dice_0: 0.14319/0.15102, loss_grounding_ce_0: 0.16172/0.24955, loss_mask_ce_1: 0.62468/0.77196, loss_mask_bce_1: 0.32067/0.30248, loss_mask_dice_1: 0.75625/1.03161, loss_spatial_bce_1: 0.04583/0.08747, loss_spatial_dice_1: 0.12536/0.18696, loss_spatial_ce_1: 0.00039/0.06812, loss_grounding_bce_1: 0.04413/0.08063, loss_grounding_dice_1: 0.12632/0.15183, loss_grounding_ce_1: 0.17164/0.25098, loss_mask_ce_2: 0.53646/0.77972, loss_mask_bce_2: 0.31320/0.30255, loss_mask_dice_2: 0.78252/1.03291, loss_spatial_bce_2: 0.04711/0.08733, loss_spatial_dice_2: 0.12425/0.18711, loss_spatial_ce_2: 0.00044/0.07040, loss_grounding_bce_2: 0.04445/0.08058, loss_grounding_dice_2: 0.16010/0.15154, loss_grounding_ce_2: 0.16271/0.25357, loss_mask_ce_3: 0.62220/0.78171, loss_mask_bce_3: 0.33029/0.30403, loss_mask_dice_3: 0.79879/1.02951, loss_spatial_bce_3: 0.04517/0.08918, loss_spatial_dice_3: 0.12789/0.18794, loss_spatial_ce_3: 0.00093/0.07523, loss_grounding_bce_3: 0.04625/0.08096, loss_grounding_dice_3: 0.12763/0.15108, loss_grounding_ce_3: 0.17109/0.25330, loss_mask_ce_4: 0.65827/0.78739, loss_mask_bce_4: 0.33448/0.30629, loss_mask_dice_4: 0.81707/1.04847, loss_spatial_bce_4: 0.04980/0.09116, loss_spatial_dice_4: 0.14002/0.19565, loss_spatial_ce_4: 0.00270/0.08802, loss_grounding_bce_4: 0.05378/0.08165, loss_grounding_dice_4: 0.16664/0.15381, loss_grounding_ce_4: 0.17135/0.25939, loss_mask_ce_5: 0.74346/0.81025, loss_mask_bce_5: 0.32759/0.30798, loss_mask_dice_5: 0.84655/1.05533, loss_spatial_bce_5: 0.04975/0.09293, loss_spatial_dice_5: 0.15579/0.19800, loss_spatial_ce_5: 0.01320/0.10003, loss_grounding_bce_5: 0.05278/0.08189, loss_grounding_dice_5: 0.15471/0.15437, loss_grounding_ce_5: 0.16502/0.27804, loss_mask_ce_6: 0.71380/0.83638, loss_mask_bce_6: 0.32599/0.30977, loss_mask_dice_6: 0.86339/1.05849, loss_spatial_bce_6: 0.05370/0.09785, loss_spatial_dice_6: 0.13851/0.20020, loss_spatial_ce_6: 0.07098/0.12234, loss_grounding_bce_6: 0.04901/0.08291, loss_grounding_dice_6: 0.14624/0.15505, loss_grounding_ce_6: 0.18312/0.28788, loss_mask_ce_7: 0.81368/0.89394, loss_mask_bce_7: 0.32553/0.31702, loss_mask_dice_7: 0.84515/1.10470, loss_spatial_bce_7: 0.05026/0.10810, loss_spatial_dice_7: 0.16090/0.22517, loss_spatial_ce_7: 0.12036/0.16239, loss_grounding_bce_7: 0.04827/0.08453, loss_grounding_dice_7: 0.16263/0.16064, loss_grounding_ce_7: 0.23885/0.32510, loss_mask_ce_8: 1.05683/1.03079, loss_mask_bce_8: 0.36783/0.33361, loss_mask_dice_8: 0.96346/1.18247, loss_spatial_bce_8: 0.05860/0.12739, loss_spatial_dice_8: 0.21149/0.26314, loss_spatial_ce_8: 0.06668/0.21572, loss_grounding_bce_8: 0.05134/0.08847, loss_grounding_dice_8: 0.17656/0.17035, loss_grounding_ce_8: 0.37554/0.42796, loss_mask_ce_9: 4.27986/3.48917, loss_mask_bce_9: 0.35011/0.36061, loss_mask_dice_9: 1.49869/1.76784, loss_spatial_bce_9: 0.32918/0.35644, loss_spatial_dice_9: 0.92813/0.79511, loss_spatial_ce_9: 1.39242/1.40107, loss_grounding_bce_9: 0.05447/0.10067, loss_grounding_dice_9: 0.31068/0.24380, loss_grounding_ce_9: 0.71095/0.68824] items per batch[64] items per second[0.16] total items[2342400] mini batches[ 36600] memory[4999] epoch remaining[0:56:11] INFO:trainer.default_trainer:epochs[ 20] optim steps[36700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09811/0.76980, loss_mask_bce_0: 0.11459/0.30167, loss_mask_dice_0: 0.06990/1.02734, loss_spatial_bce_0: 0.11702/0.08717, loss_spatial_dice_0: 0.08227/0.18438, loss_spatial_ce_0: 0.00097/0.06367, loss_grounding_bce_0: 0.07690/0.08045, loss_grounding_dice_0: 0.08604/0.15104, loss_grounding_ce_0: 0.01169/0.24936, loss_mask_ce_1: 0.07669/0.77148, loss_mask_bce_1: 0.11646/0.30247, loss_mask_dice_1: 0.07384/1.03141, loss_spatial_bce_1: 0.11280/0.08744, loss_spatial_dice_1: 0.07607/0.18693, loss_spatial_ce_1: 0.00117/0.06813, loss_grounding_bce_1: 0.08537/0.08066, loss_grounding_dice_1: 0.10139/0.15185, loss_grounding_ce_1: 0.00605/0.25079, loss_mask_ce_2: 0.06157/0.77924, loss_mask_bce_2: 0.10981/0.30252, loss_mask_dice_2: 0.07253/1.03270, loss_spatial_bce_2: 0.11219/0.08730, loss_spatial_dice_2: 0.08506/0.18708, loss_spatial_ce_2: 0.00169/0.07038, loss_grounding_bce_2: 0.07078/0.08061, loss_grounding_dice_2: 0.09174/0.15156, loss_grounding_ce_2: 0.01362/0.25339, loss_mask_ce_3: 0.06374/0.78127, loss_mask_bce_3: 0.12102/0.30401, loss_mask_dice_3: 0.08222/1.02932, loss_spatial_bce_3: 0.10752/0.08916, loss_spatial_dice_3: 0.07939/0.18791, loss_spatial_ce_3: 0.00076/0.07520, loss_grounding_bce_3: 0.07380/0.08098, loss_grounding_dice_3: 0.09030/0.15110, loss_grounding_ce_3: 0.01023/0.25318, loss_mask_ce_4: 0.06067/0.78692, loss_mask_bce_4: 0.11392/0.30627, loss_mask_dice_4: 0.07652/1.04824, loss_spatial_bce_4: 0.11370/0.09115, loss_spatial_dice_4: 0.08216/0.19563, loss_spatial_ce_4: 0.00064/0.08796, loss_grounding_bce_4: 0.07755/0.08168, loss_grounding_dice_4: 0.09231/0.15380, loss_grounding_ce_4: 0.01076/0.25920, loss_mask_ce_5: 0.17995/0.80982, loss_mask_bce_5: 0.11080/0.30796, loss_mask_dice_5: 0.06477/1.05517, loss_spatial_bce_5: 0.11277/0.09292, loss_spatial_dice_5: 0.08108/0.19798, loss_spatial_ce_5: 0.00060/0.09995, loss_grounding_bce_5: 0.07192/0.08191, loss_grounding_dice_5: 0.07509/0.15439, loss_grounding_ce_5: 0.09911/0.27780, loss_mask_ce_6: 0.08447/0.83602, loss_mask_bce_6: 0.11686/0.30976, loss_mask_dice_6: 0.07033/1.05826, loss_spatial_bce_6: 0.11925/0.09784, loss_spatial_dice_6: 0.08542/0.20017, loss_spatial_ce_6: 0.00093/0.12222, loss_grounding_bce_6: 0.07212/0.08293, loss_grounding_dice_6: 0.08328/0.15506, loss_grounding_ce_6: 0.07461/0.28765, loss_mask_ce_7: 0.06219/0.89351, loss_mask_bce_7: 0.11735/0.31699, loss_mask_dice_7: 0.07809/1.10444, loss_spatial_bce_7: 0.11719/0.10807, loss_spatial_dice_7: 0.07531/0.22514, loss_spatial_ce_7: 0.00168/0.16236, loss_grounding_bce_7: 0.07039/0.08455, loss_grounding_dice_7: 0.07964/0.16065, loss_grounding_ce_7: 0.00494/0.32485, loss_mask_ce_8: 0.08139/1.03043, loss_mask_bce_8: 0.11416/0.33358, loss_mask_dice_8: 0.05629/1.18216, loss_spatial_bce_8: 0.11274/0.12735, loss_spatial_dice_8: 0.04698/0.26311, loss_spatial_ce_8: 0.04483/0.21558, loss_grounding_bce_8: 0.05729/0.08848, loss_grounding_dice_8: 0.04757/0.17035, loss_grounding_ce_8: 0.00503/0.42761, loss_mask_ce_9: 1.96534/3.48840, loss_mask_bce_9: 0.12767/0.36057, loss_mask_dice_9: 0.09120/1.76732, loss_spatial_bce_9: 0.50805/0.35639, loss_spatial_dice_9: 0.43575/0.79504, loss_spatial_ce_9: 0.54500/1.40083, loss_grounding_bce_9: 0.05516/0.10068, loss_grounding_dice_9: 0.08696/0.24376, loss_grounding_ce_9: 0.45584/0.68792] items per batch[64] items per second[0.36] total items[2348800] mini batches[ 36700] memory[4999] epoch remaining[0:50:45] INFO:trainer.default_trainer:epochs[ 20] optim steps[36800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34284/0.76977, loss_mask_bce_0: 0.03015/0.30165, loss_mask_dice_0: 0.35793/1.02725, loss_spatial_bce_0: 0.00675/0.08713, loss_spatial_dice_0: 0.12251/0.18436, loss_spatial_ce_0: 0.01782/0.06363, loss_grounding_bce_0: 0.00468/0.08043, loss_grounding_dice_0: 0.16508/0.15106, loss_grounding_ce_0: 0.23603/0.24952, loss_mask_ce_1: 0.20232/0.77140, loss_mask_bce_1: 0.01033/0.30246, loss_mask_dice_1: 0.22352/1.03139, loss_spatial_bce_1: 0.00759/0.08740, loss_spatial_dice_1: 0.15622/0.18691, loss_spatial_ce_1: 0.05413/0.06809, loss_grounding_bce_1: 0.00445/0.08064, loss_grounding_dice_1: 0.13728/0.15186, loss_grounding_ce_1: 0.19931/0.25095, loss_mask_ce_2: 0.27361/0.77925, loss_mask_bce_2: 0.01209/0.30250, loss_mask_dice_2: 0.28670/1.03268, loss_spatial_bce_2: 0.00922/0.08726, loss_spatial_dice_2: 0.23002/0.18705, loss_spatial_ce_2: 0.05697/0.07035, loss_grounding_bce_2: 0.00391/0.08059, loss_grounding_dice_2: 0.16812/0.15157, loss_grounding_ce_2: 0.30726/0.25355, loss_mask_ce_3: 0.28578/0.78127, loss_mask_bce_3: 0.01031/0.30401, loss_mask_dice_3: 0.25498/1.02921, loss_spatial_bce_3: 0.00507/0.08911, loss_spatial_dice_3: 0.13584/0.18789, loss_spatial_ce_3: 0.04729/0.07516, loss_grounding_bce_3: 0.00604/0.08094, loss_grounding_dice_3: 0.15006/0.15111, loss_grounding_ce_3: 0.27355/0.25339, loss_mask_ce_4: 0.27192/0.78688, loss_mask_bce_4: 0.01276/0.30626, loss_mask_dice_4: 0.27255/1.04816, loss_spatial_bce_4: 0.00868/0.09109, loss_spatial_dice_4: 0.24842/0.19561, loss_spatial_ce_4: 0.01103/0.08791, loss_grounding_bce_4: 0.00409/0.08165, loss_grounding_dice_4: 0.14993/0.15381, loss_grounding_ce_4: 0.60087/0.25942, loss_mask_ce_5: 0.31446/0.80978, loss_mask_bce_5: 0.00952/0.30794, loss_mask_dice_5: 0.25660/1.05510, loss_spatial_bce_5: 0.00731/0.09287, loss_spatial_dice_5: 0.30968/0.19797, loss_spatial_ce_5: 0.05467/0.09996, loss_grounding_bce_5: 0.00752/0.08189, loss_grounding_dice_5: 0.18973/0.15440, loss_grounding_ce_5: 1.17602/0.27804, loss_mask_ce_6: 0.22795/0.83604, loss_mask_bce_6: 0.01244/0.30974, loss_mask_dice_6: 0.25274/1.05812, loss_spatial_bce_6: 0.00620/0.09779, loss_spatial_dice_6: 0.15947/0.20016, loss_spatial_ce_6: 0.11878/0.12217, loss_grounding_bce_6: 0.00700/0.08291, loss_grounding_dice_6: 0.15412/0.15507, loss_grounding_ce_6: 4.91857/0.28800, loss_mask_ce_7: 0.50505/0.89358, loss_mask_bce_7: 0.01468/0.31694, loss_mask_dice_7: 0.19802/1.10429, loss_spatial_bce_7: 0.00916/0.10804, loss_spatial_dice_7: 0.15272/0.22512, loss_spatial_ce_7: 0.23454/0.16237, loss_grounding_bce_7: 0.00678/0.08452, loss_grounding_dice_7: 0.19665/0.16066, loss_grounding_ce_7: 0.50416/0.32494, loss_mask_ce_8: 0.46253/1.03041, loss_mask_bce_8: 0.01246/0.33353, loss_mask_dice_8: 0.33977/1.18201, loss_spatial_bce_8: 0.01460/0.12730, loss_spatial_dice_8: 0.21611/0.26311, loss_spatial_ce_8: 0.38449/0.21555, loss_grounding_bce_8: 0.00493/0.08846, loss_grounding_dice_8: 0.16721/0.17037, loss_grounding_ce_8: 1.99760/0.42782, loss_mask_ce_9: 2.62914/3.48847, loss_mask_bce_9: 0.01144/0.36057, loss_mask_dice_9: 0.31973/1.76703, loss_spatial_bce_9: 0.02622/0.35632, loss_spatial_dice_9: 0.70768/0.79505, loss_spatial_ce_9: 0.78545/1.40089, loss_grounding_bce_9: 0.00309/0.10067, loss_grounding_dice_9: 0.16677/0.24375, loss_grounding_ce_9: 2.55995/0.68794] items per batch[64] items per second[0.37] total items[2355200] mini batches[ 36800] memory[4999] epoch remaining[0:46:48] INFO:trainer.default_trainer:epochs[ 20] optim steps[36900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.17053/0.76945, loss_mask_bce_0: 0.19435/0.30163, loss_mask_dice_0: 2.95489/1.02715, loss_spatial_bce_0: 0.01732/0.08714, loss_spatial_dice_0: 0.15917/0.18433, loss_spatial_ce_0: 0.01868/0.06358, loss_grounding_bce_0: 0.02216/0.08047, loss_grounding_dice_0: 0.13305/0.15106, loss_grounding_ce_0: 0.00624/0.24946, loss_mask_ce_1: 1.11402/0.77105, loss_mask_bce_1: 0.18751/0.30243, loss_mask_dice_1: 2.98220/1.03128, loss_spatial_bce_1: 0.01922/0.08740, loss_spatial_dice_1: 0.18580/0.18687, loss_spatial_ce_1: 0.01490/0.06802, loss_grounding_bce_1: 0.01894/0.08067, loss_grounding_dice_1: 0.10881/0.15186, loss_grounding_ce_1: 0.00945/0.25083, loss_mask_ce_2: 1.13826/0.77890, loss_mask_bce_2: 0.22373/0.30247, loss_mask_dice_2: 2.93748/1.03258, loss_spatial_bce_2: 0.01653/0.08727, loss_spatial_dice_2: 0.13731/0.18701, loss_spatial_ce_2: 0.01843/0.07029, loss_grounding_bce_2: 0.01364/0.08063, loss_grounding_dice_2: 0.08860/0.15157, loss_grounding_ce_2: 0.03090/0.25344, loss_mask_ce_3: 1.46875/0.78099, loss_mask_bce_3: 0.22400/0.30398, loss_mask_dice_3: 2.57839/1.02905, loss_spatial_bce_3: 0.01858/0.08913, loss_spatial_dice_3: 0.22979/0.18786, loss_spatial_ce_3: 0.01895/0.07508, loss_grounding_bce_3: 0.02046/0.08098, loss_grounding_dice_3: 0.12532/0.15112, loss_grounding_ce_3: 0.03330/0.25328, loss_mask_ce_4: 1.28436/0.78656, loss_mask_bce_4: 0.22242/0.30623, loss_mask_dice_4: 2.90591/1.04802, loss_spatial_bce_4: 0.01671/0.09111, loss_spatial_dice_4: 0.20632/0.19556, loss_spatial_ce_4: 0.00822/0.08782, loss_grounding_bce_4: 0.02434/0.08169, loss_grounding_dice_4: 0.13520/0.15380, loss_grounding_ce_4: 0.04007/0.25932, loss_mask_ce_5: 1.30030/0.80948, loss_mask_bce_5: 0.20239/0.30791, loss_mask_dice_5: 3.36098/1.05503, loss_spatial_bce_5: 0.01845/0.09289, loss_spatial_dice_5: 0.19719/0.19793, loss_spatial_ce_5: 0.00772/0.09986, loss_grounding_bce_5: 0.02135/0.08194, loss_grounding_dice_5: 0.13521/0.15441, loss_grounding_ce_5: 0.21110/0.27793, loss_mask_ce_6: 1.59978/0.83577, loss_mask_bce_6: 0.19917/0.30972, loss_mask_dice_6: 3.10188/1.05808, loss_spatial_bce_6: 0.02533/0.09781, loss_spatial_dice_6: 0.22516/0.20012, loss_spatial_ce_6: 0.07623/0.12210, loss_grounding_bce_6: 0.01950/0.08295, loss_grounding_dice_6: 0.10291/0.15506, loss_grounding_ce_6: 0.32328/0.28785, loss_mask_ce_7: 1.69898/0.89330, loss_mask_bce_7: 0.21302/0.31692, loss_mask_dice_7: 3.18687/1.10420, loss_spatial_bce_7: 0.04171/0.10805, loss_spatial_dice_7: 0.29068/0.22508, loss_spatial_ce_7: 0.33551/0.16230, loss_grounding_bce_7: 0.01599/0.08455, loss_grounding_dice_7: 0.13183/0.16067, loss_grounding_ce_7: 5.14779/0.32496, loss_mask_ce_8: 1.67016/1.03007, loss_mask_bce_8: 0.25222/0.33352, loss_mask_dice_8: 3.13362/1.18191, loss_spatial_bce_8: 0.03409/0.12730, loss_spatial_dice_8: 0.32961/0.26305, loss_spatial_ce_8: 0.10473/0.21552, loss_grounding_bce_8: 0.03855/0.08850, loss_grounding_dice_8: 0.19801/0.17037, loss_grounding_ce_8: 1.04189/0.42767, loss_mask_ce_9: 5.46936/3.48791, loss_mask_bce_9: 0.35870/0.36048, loss_mask_dice_9: 5.24992/1.76679, loss_spatial_bce_9: 0.17131/0.35639, loss_spatial_dice_9: 0.95001/0.79506, loss_spatial_ce_9: 1.76600/1.40095, loss_grounding_bce_9: 0.03625/0.10068, loss_grounding_dice_9: 0.23610/0.24371, loss_grounding_ce_9: 1.11018/0.68775] items per batch[64] items per second[0.37] total items[2361600] mini batches[ 36900] memory[4999] epoch remaining[0:43:29] INFO:trainer.default_trainer:epochs[ 20] optim steps[37000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85472/0.76940, loss_mask_bce_0: 0.62475/0.30174, loss_mask_dice_0: 1.42123/1.02759, loss_spatial_bce_0: 0.05329/0.08715, loss_spatial_dice_0: 0.21984/0.18430, loss_spatial_ce_0: 0.03086/0.06357, loss_grounding_bce_0: 0.15760/0.08047, loss_grounding_dice_0: 0.34662/0.15107, loss_grounding_ce_0: 0.13938/0.24969, loss_mask_ce_1: 0.91252/0.77107, loss_mask_bce_1: 0.61855/0.30254, loss_mask_dice_1: 1.42707/1.03166, loss_spatial_bce_1: 0.05297/0.08740, loss_spatial_dice_1: 0.22408/0.18683, loss_spatial_ce_1: 0.02436/0.06803, loss_grounding_bce_1: 0.15255/0.08068, loss_grounding_dice_1: 0.36172/0.15187, loss_grounding_ce_1: 0.13116/0.25100, loss_mask_ce_2: 1.05053/0.77893, loss_mask_bce_2: 0.51789/0.30259, loss_mask_dice_2: 1.36683/1.03301, loss_spatial_bce_2: 0.05679/0.08727, loss_spatial_dice_2: 0.23908/0.18698, loss_spatial_ce_2: 0.02643/0.07029, loss_grounding_bce_2: 0.08001/0.08063, loss_grounding_dice_2: 0.30956/0.15156, loss_grounding_ce_2: 0.27480/0.25359, loss_mask_ce_3: 0.99085/0.78100, loss_mask_bce_3: 0.54339/0.30410, loss_mask_dice_3: 1.38609/1.02951, loss_spatial_bce_3: 0.06531/0.08913, loss_spatial_dice_3: 0.25567/0.18785, loss_spatial_ce_3: 0.01376/0.07505, loss_grounding_bce_3: 0.08701/0.08099, loss_grounding_dice_3: 0.31699/0.15112, loss_grounding_ce_3: 0.23043/0.25338, loss_mask_ce_4: 1.01503/0.78659, loss_mask_bce_4: 0.56151/0.30635, loss_mask_dice_4: 1.35216/1.04847, loss_spatial_bce_4: 0.05755/0.09111, loss_spatial_dice_4: 0.24670/0.19555, loss_spatial_ce_4: 0.01459/0.08780, loss_grounding_bce_4: 0.05663/0.08170, loss_grounding_dice_4: 0.31371/0.15381, loss_grounding_ce_4: 0.23912/0.25942, loss_mask_ce_5: 1.09415/0.80950, loss_mask_bce_5: 0.55192/0.30804, loss_mask_dice_5: 1.35974/1.05545, loss_spatial_bce_5: 0.06632/0.09291, loss_spatial_dice_5: 0.24786/0.19793, loss_spatial_ce_5: 0.06362/0.09982, loss_grounding_bce_5: 0.05705/0.08195, loss_grounding_dice_5: 0.30224/0.15441, loss_grounding_ce_5: 0.22911/0.27806, loss_mask_ce_6: 1.14799/0.83580, loss_mask_bce_6: 0.55606/0.30986, loss_mask_dice_6: 1.36211/1.05857, loss_spatial_bce_6: 0.07248/0.09783, loss_spatial_dice_6: 0.25376/0.20011, loss_spatial_ce_6: 0.10277/0.12208, loss_grounding_bce_6: 0.05564/0.08296, loss_grounding_dice_6: 0.29334/0.15507, loss_grounding_ce_6: 0.25583/0.28788, loss_mask_ce_7: 1.32154/0.89329, loss_mask_bce_7: 0.47160/0.31708, loss_mask_dice_7: 1.36237/1.10469, loss_spatial_bce_7: 0.06237/0.10807, loss_spatial_dice_7: 0.26007/0.22509, loss_spatial_ce_7: 0.07213/0.16230, loss_grounding_bce_7: 0.04177/0.08457, loss_grounding_dice_7: 0.29669/0.16067, loss_grounding_ce_7: 0.29208/0.32496, loss_mask_ce_8: 1.57337/1.03008, loss_mask_bce_8: 0.67419/0.33370, loss_mask_dice_8: 1.48733/1.18255, loss_spatial_bce_8: 0.10151/0.12732, loss_spatial_dice_8: 0.34037/0.26306, loss_spatial_ce_8: 0.20240/0.21549, loss_grounding_bce_8: 0.03440/0.08851, loss_grounding_dice_8: 0.30337/0.17039, loss_grounding_ce_8: 0.57172/0.42781, loss_mask_ce_9: 3.45393/3.48843, loss_mask_bce_9: 0.54771/0.36068, loss_mask_dice_9: 1.97461/1.76795, loss_spatial_bce_9: 0.30905/0.35635, loss_spatial_dice_9: 0.92711/0.79513, loss_spatial_ce_9: 1.39644/1.40084, loss_grounding_bce_9: 0.10082/0.10071, loss_grounding_dice_9: 0.57643/0.24377, loss_grounding_ce_9: 0.45263/0.68817] items per batch[64] items per second[0.36] total items[2368000] mini batches[ 37000] memory[4999] epoch remaining[0:40:27] INFO:trainer.default_trainer:epochs[ 20] optim steps[37100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26078/0.76944, loss_mask_bce_0: 0.60891/0.30185, loss_mask_dice_0: 1.17543/1.02753, loss_spatial_bce_0: 0.11581/0.08716, loss_spatial_dice_0: 0.17534/0.18430, loss_spatial_ce_0: 0.00144/0.06354, loss_grounding_bce_0: 0.06270/0.08046, loss_grounding_dice_0: 0.26395/0.15109, loss_grounding_ce_0: 0.53993/0.24981, loss_mask_ce_1: 1.70241/0.77107, loss_mask_bce_1: 0.51589/0.30265, loss_mask_dice_1: 1.13931/1.03155, loss_spatial_bce_1: 0.11902/0.08742, loss_spatial_dice_1: 0.19696/0.18683, loss_spatial_ce_1: 0.00912/0.06800, loss_grounding_bce_1: 0.06779/0.08067, loss_grounding_dice_1: 0.26005/0.15189, loss_grounding_ce_1: 0.54952/0.25113, loss_mask_ce_2: 1.64497/0.77891, loss_mask_bce_2: 0.53506/0.30271, loss_mask_dice_2: 1.10355/1.03293, loss_spatial_bce_2: 0.12349/0.08728, loss_spatial_dice_2: 0.18549/0.18698, loss_spatial_ce_2: 0.01436/0.07024, loss_grounding_bce_2: 0.05110/0.08063, loss_grounding_dice_2: 0.27672/0.15158, loss_grounding_ce_2: 0.44541/0.25383, loss_mask_ce_3: 1.68224/0.78103, loss_mask_bce_3: 0.50184/0.30422, loss_mask_dice_3: 1.08560/1.02944, loss_spatial_bce_3: 0.11512/0.08914, loss_spatial_dice_3: 0.15696/0.18785, loss_spatial_ce_3: 0.06025/0.07499, loss_grounding_bce_3: 0.06331/0.08099, loss_grounding_dice_3: 0.25484/0.15113, loss_grounding_ce_3: 0.57751/0.25362, loss_mask_ce_4: 1.57908/0.78654, loss_mask_bce_4: 0.51937/0.30646, loss_mask_dice_4: 1.08779/1.04842, loss_spatial_bce_4: 0.13049/0.09110, loss_spatial_dice_4: 0.19416/0.19554, loss_spatial_ce_4: 0.01301/0.08777, loss_grounding_bce_4: 0.05851/0.08170, loss_grounding_dice_4: 0.24635/0.15381, loss_grounding_ce_4: 0.49926/0.25956, loss_mask_ce_5: 1.57420/0.80951, loss_mask_bce_5: 0.53145/0.30816, loss_mask_dice_5: 1.12449/1.05543, loss_spatial_bce_5: 0.12153/0.09291, loss_spatial_dice_5: 0.16154/0.19793, loss_spatial_ce_5: 0.02910/0.09979, loss_grounding_bce_5: 0.04660/0.08195, loss_grounding_dice_5: 0.26928/0.15442, loss_grounding_ce_5: 0.40342/0.27820, loss_mask_ce_6: 1.26117/0.83580, loss_mask_bce_6: 0.73741/0.30995, loss_mask_dice_6: 1.29939/1.05856, loss_spatial_bce_6: 0.13801/0.09784, loss_spatial_dice_6: 0.16057/0.20012, loss_spatial_ce_6: 0.07158/0.12210, loss_grounding_bce_6: 0.05484/0.08296, loss_grounding_dice_6: 0.27267/0.15509, loss_grounding_ce_6: 0.43184/0.28811, loss_mask_ce_7: 1.53663/0.89314, loss_mask_bce_7: 0.59298/0.31720, loss_mask_dice_7: 1.32592/1.10469, loss_spatial_bce_7: 0.11552/0.10808, loss_spatial_dice_7: 0.17468/0.22511, loss_spatial_ce_7: 0.21616/0.16228, loss_grounding_bce_7: 0.05064/0.08456, loss_grounding_dice_7: 0.26020/0.16069, loss_grounding_ce_7: 0.36281/0.32522, loss_mask_ce_8: 1.28392/1.03007, loss_mask_bce_8: 0.58907/0.33378, loss_mask_dice_8: 1.09822/1.18251, loss_spatial_bce_8: 0.15382/0.12732, loss_spatial_dice_8: 0.18892/0.26305, loss_spatial_ce_8: 0.15494/0.21546, loss_grounding_bce_8: 0.04735/0.08849, loss_grounding_dice_8: 0.22276/0.17041, loss_grounding_ce_8: 0.37293/0.42816, loss_mask_ce_9: 3.48595/3.48832, loss_mask_bce_9: 0.57172/0.36077, loss_mask_dice_9: 1.55853/1.76803, loss_spatial_bce_9: 0.39365/0.35635, loss_spatial_dice_9: 0.76246/0.79518, loss_spatial_ce_9: 1.51494/1.40073, loss_grounding_bce_9: 0.20102/0.10070, loss_grounding_dice_9: 0.32949/0.24388, loss_grounding_ce_9: 0.30332/0.68806] items per batch[64] items per second[0.37] total items[2374400] mini batches[ 37100] memory[4999] epoch remaining[0:37:22] INFO:trainer.default_trainer:epochs[ 20] optim steps[37200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.35913/0.76932, loss_mask_bce_0: 0.30478/0.30180, loss_mask_dice_0: 0.91146/1.02699, loss_spatial_bce_0: 0.04222/0.08716, loss_spatial_dice_0: 0.20074/0.18425, loss_spatial_ce_0: 0.02610/0.06351, loss_grounding_bce_0: 0.03649/0.08045, loss_grounding_dice_0: 0.17997/0.15105, loss_grounding_ce_0: 0.11714/0.24981, loss_mask_ce_1: 0.27509/0.77105, loss_mask_bce_1: 0.31485/0.30260, loss_mask_dice_1: 0.96456/1.03102, loss_spatial_bce_1: 0.03977/0.08742, loss_spatial_dice_1: 0.15990/0.18677, loss_spatial_ce_1: 0.12011/0.06796, loss_grounding_bce_1: 0.03826/0.08066, loss_grounding_dice_1: 0.08730/0.15184, loss_grounding_ce_1: 0.51125/0.25118, loss_mask_ce_2: 0.34685/0.77885, loss_mask_bce_2: 0.30559/0.30266, loss_mask_dice_2: 0.97052/1.03241, loss_spatial_bce_2: 0.03874/0.08728, loss_spatial_dice_2: 0.17419/0.18692, loss_spatial_ce_2: 0.09197/0.07025, loss_grounding_bce_2: 0.03478/0.08061, loss_grounding_dice_2: 0.09898/0.15154, loss_grounding_ce_2: 0.14456/0.25383, loss_mask_ce_3: 0.35505/0.78098, loss_mask_bce_3: 0.28929/0.30418, loss_mask_dice_3: 0.91129/1.02892, loss_spatial_bce_3: 0.03241/0.08914, loss_spatial_dice_3: 0.18753/0.18779, loss_spatial_ce_3: 0.13363/0.07495, loss_grounding_bce_3: 0.04228/0.08098, loss_grounding_dice_3: 0.18170/0.15110, loss_grounding_ce_3: 0.08832/0.25364, loss_mask_ce_4: 0.38073/0.78648, loss_mask_bce_4: 0.32757/0.30642, loss_mask_dice_4: 0.97741/1.04798, loss_spatial_bce_4: 0.04957/0.09109, loss_spatial_dice_4: 0.18516/0.19548, loss_spatial_ce_4: 0.17460/0.08774, loss_grounding_bce_4: 0.03622/0.08168, loss_grounding_dice_4: 0.09522/0.15376, loss_grounding_ce_4: 0.60683/0.25958, loss_mask_ce_5: 0.55652/0.80949, loss_mask_bce_5: 0.29290/0.30810, loss_mask_dice_5: 0.94990/1.05490, loss_spatial_bce_5: 0.04149/0.09290, loss_spatial_dice_5: 0.18774/0.19788, loss_spatial_ce_5: 0.13432/0.09974, loss_grounding_bce_5: 0.03556/0.08192, loss_grounding_dice_5: 0.05679/0.15437, loss_grounding_ce_5: 0.11421/0.27823, loss_mask_ce_6: 0.55045/0.83575, loss_mask_bce_6: 0.33341/0.30989, loss_mask_dice_6: 1.07082/1.05808, loss_spatial_bce_6: 0.04854/0.09782, loss_spatial_dice_6: 0.19467/0.20006, loss_spatial_ce_6: 0.12196/0.12206, loss_grounding_bce_6: 0.03950/0.08294, loss_grounding_dice_6: 0.05694/0.15503, loss_grounding_ce_6: 0.08007/0.28816, loss_mask_ce_7: 0.55956/0.89297, loss_mask_bce_7: 0.29878/0.31716, loss_mask_dice_7: 0.97614/1.10420, loss_spatial_bce_7: 0.06255/0.10808, loss_spatial_dice_7: 0.22784/0.22507, loss_spatial_ce_7: 0.20774/0.16222, loss_grounding_bce_7: 0.04094/0.08453, loss_grounding_dice_7: 0.33310/0.16064, loss_grounding_ce_7: 0.00569/0.32526, loss_mask_ce_8: 1.02545/1.02979, loss_mask_bce_8: 0.33241/0.33372, loss_mask_dice_8: 1.08203/1.18196, loss_spatial_bce_8: 0.05755/0.12731, loss_spatial_dice_8: 0.30658/0.26301, loss_spatial_ce_8: 0.45383/0.21542, loss_grounding_bce_8: 0.03598/0.08846, loss_grounding_dice_8: 0.12989/0.17034, loss_grounding_ce_8: 0.28839/0.42827, loss_mask_ce_9: 3.60537/3.48790, loss_mask_bce_9: 0.31168/0.36068, loss_mask_dice_9: 1.46340/1.76718, loss_spatial_bce_9: 0.26000/0.35634, loss_spatial_dice_9: 0.92775/0.79514, loss_spatial_ce_9: 1.64814/1.40068, loss_grounding_bce_9: 0.03090/0.10066, loss_grounding_dice_9: 0.13932/0.24377, loss_grounding_ce_9: 0.17733/0.68812] items per batch[64] items per second[0.36] total items[2380800] mini batches[ 37200] memory[4999] epoch remaining[0:34:30] INFO:trainer.default_trainer:epochs[ 20] optim steps[37300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26848/0.76940, loss_mask_bce_0: 0.05884/0.30177, loss_mask_dice_0: 0.63306/1.02669, loss_spatial_bce_0: 0.01351/0.08713, loss_spatial_dice_0: 0.10398/0.18421, loss_spatial_ce_0: 0.05401/0.06345, loss_grounding_bce_0: 0.02282/0.08041, loss_grounding_dice_0: 0.07422/0.15104, loss_grounding_ce_0: 0.00007/0.24974, loss_mask_ce_1: 0.26051/0.77106, loss_mask_bce_1: 0.05293/0.30258, loss_mask_dice_1: 0.60701/1.03078, loss_spatial_bce_1: 0.01375/0.08739, loss_spatial_dice_1: 0.10253/0.18676, loss_spatial_ce_1: 0.03459/0.06788, loss_grounding_bce_1: 0.01727/0.08063, loss_grounding_dice_1: 0.06564/0.15183, loss_grounding_ce_1: 0.00010/0.25120, loss_mask_ce_2: 0.29309/0.77896, loss_mask_bce_2: 0.05587/0.30264, loss_mask_dice_2: 0.53794/1.03209, loss_spatial_bce_2: 0.01538/0.08724, loss_spatial_dice_2: 0.12198/0.18690, loss_spatial_ce_2: 0.04893/0.07018, loss_grounding_bce_2: 0.01534/0.08058, loss_grounding_dice_2: 0.06147/0.15155, loss_grounding_ce_2: 0.00047/0.25381, loss_mask_ce_3: 0.28626/0.78112, loss_mask_bce_3: 0.05106/0.30414, loss_mask_dice_3: 0.53496/1.02863, loss_spatial_bce_3: 0.01628/0.08910, loss_spatial_dice_3: 0.11390/0.18777, loss_spatial_ce_3: 0.09084/0.07488, loss_grounding_bce_3: 0.01661/0.08094, loss_grounding_dice_3: 0.06007/0.15110, loss_grounding_ce_3: 0.00057/0.25362, loss_mask_ce_4: 0.24281/0.78657, loss_mask_bce_4: 0.06136/0.30641, loss_mask_dice_4: 0.56780/1.04774, loss_spatial_bce_4: 0.01678/0.09105, loss_spatial_dice_4: 0.12916/0.19546, loss_spatial_ce_4: 0.05819/0.08770, loss_grounding_bce_4: 0.02352/0.08164, loss_grounding_dice_4: 0.08601/0.15374, loss_grounding_ce_4: 0.00024/0.25952, loss_mask_ce_5: 0.26996/0.80953, loss_mask_bce_5: 0.06492/0.30810, loss_mask_dice_5: 0.56501/1.05463, loss_spatial_bce_5: 0.01760/0.09287, loss_spatial_dice_5: 0.12026/0.19785, loss_spatial_ce_5: 0.09115/0.09970, loss_grounding_bce_5: 0.02216/0.08188, loss_grounding_dice_5: 0.07071/0.15436, loss_grounding_ce_5: 0.00022/0.27816, loss_mask_ce_6: 0.34111/0.83572, loss_mask_bce_6: 0.05733/0.30991, loss_mask_dice_6: 0.55131/1.05784, loss_spatial_bce_6: 0.02095/0.09778, loss_spatial_dice_6: 0.11330/0.20003, loss_spatial_ce_6: 0.03390/0.12200, loss_grounding_bce_6: 0.02119/0.08290, loss_grounding_dice_6: 0.07567/0.15502, loss_grounding_ce_6: 0.00012/0.28817, loss_mask_ce_7: 0.28725/0.89308, loss_mask_bce_7: 0.06646/0.31717, loss_mask_dice_7: 0.63001/1.10398, loss_spatial_bce_7: 0.04458/0.10805, loss_spatial_dice_7: 0.21035/0.22505, loss_spatial_ce_7: 0.02661/0.16219, loss_grounding_bce_7: 0.01733/0.08449, loss_grounding_dice_7: 0.06601/0.16063, loss_grounding_ce_7: 0.00063/0.32524, loss_mask_ce_8: 0.65288/1.02986, loss_mask_bce_8: 0.07395/0.33369, loss_mask_dice_8: 0.59960/1.18165, loss_spatial_bce_8: 0.03000/0.12728, loss_spatial_dice_8: 0.19785/0.26300, loss_spatial_ce_8: 0.04523/0.21538, loss_grounding_bce_8: 0.04209/0.08842, loss_grounding_dice_8: 0.07858/0.17034, loss_grounding_ce_8: 0.95126/0.42839, loss_mask_ce_9: 3.61030/3.48816, loss_mask_bce_9: 0.05933/0.36065, loss_mask_dice_9: 0.81076/1.76662, loss_spatial_bce_9: 0.08747/0.35631, loss_spatial_dice_9: 0.77641/0.79516, loss_spatial_ce_9: 1.28769/1.40068, loss_grounding_bce_9: 0.05870/0.10060, loss_grounding_dice_9: 0.09702/0.24376, loss_grounding_ce_9: 0.89638/0.68813] items per batch[64] items per second[0.36] total items[2387200] mini batches[ 37300] memory[4999] epoch remaining[0:31:33] INFO:trainer.default_trainer:epochs[ 20] optim steps[37400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82405/0.76934, loss_mask_bce_0: 0.14878/0.30180, loss_mask_dice_0: 0.70927/1.02715, loss_spatial_bce_0: 0.02411/0.08714, loss_spatial_dice_0: 0.15375/0.18425, loss_spatial_ce_0: 0.00015/0.06341, loss_grounding_bce_0: 0.03591/0.08041, loss_grounding_dice_0: 0.08045/0.15105, loss_grounding_ce_0: 0.00042/0.24984, loss_mask_ce_1: 0.77435/0.77101, loss_mask_bce_1: 0.14653/0.30261, loss_mask_dice_1: 0.74719/1.03127, loss_spatial_bce_1: 0.02390/0.08739, loss_spatial_dice_1: 0.16789/0.18679, loss_spatial_ce_1: 0.00009/0.06784, loss_grounding_bce_1: 0.03752/0.08063, loss_grounding_dice_1: 0.08038/0.15184, loss_grounding_ce_1: 0.00064/0.25126, loss_mask_ce_2: 0.73686/0.77888, loss_mask_bce_2: 0.13885/0.30268, loss_mask_dice_2: 0.70294/1.03257, loss_spatial_bce_2: 0.02646/0.08725, loss_spatial_dice_2: 0.17902/0.18694, loss_spatial_ce_2: 0.00021/0.07014, loss_grounding_bce_2: 0.04222/0.08058, loss_grounding_dice_2: 0.08898/0.15154, loss_grounding_ce_2: 0.00029/0.25395, loss_mask_ce_3: 0.83600/0.78104, loss_mask_bce_3: 0.13346/0.30417, loss_mask_dice_3: 0.65961/1.02916, loss_spatial_bce_3: 0.02524/0.08911, loss_spatial_dice_3: 0.15608/0.18781, loss_spatial_ce_3: 0.00258/0.07484, loss_grounding_bce_3: 0.04570/0.08095, loss_grounding_dice_3: 0.09278/0.15110, loss_grounding_ce_3: 0.00011/0.25370, loss_mask_ce_4: 0.73523/0.78649, loss_mask_bce_4: 0.15163/0.30646, loss_mask_dice_4: 0.75641/1.04824, loss_spatial_bce_4: 0.02570/0.09105, loss_spatial_dice_4: 0.15902/0.19550, loss_spatial_ce_4: 0.00151/0.08765, loss_grounding_bce_4: 0.04301/0.08165, loss_grounding_dice_4: 0.08144/0.15374, loss_grounding_ce_4: 0.00042/0.25965, loss_mask_ce_5: 0.75225/0.80954, loss_mask_bce_5: 0.13625/0.30816, loss_mask_dice_5: 0.67754/1.05508, loss_spatial_bce_5: 0.02841/0.09287, loss_spatial_dice_5: 0.16954/0.19789, loss_spatial_ce_5: 0.00621/0.09968, loss_grounding_bce_5: 0.04700/0.08189, loss_grounding_dice_5: 0.09458/0.15436, loss_grounding_ce_5: 0.00025/0.27831, loss_mask_ce_6: 0.81549/0.83566, loss_mask_bce_6: 0.13574/0.30997, loss_mask_dice_6: 0.76215/1.05837, loss_spatial_bce_6: 0.02905/0.09779, loss_spatial_dice_6: 0.14679/0.20007, loss_spatial_ce_6: 0.03561/0.12199, loss_grounding_bce_6: 0.03975/0.08291, loss_grounding_dice_6: 0.07715/0.15502, loss_grounding_ce_6: 0.00020/0.28827, loss_mask_ce_7: 0.87568/0.89309, loss_mask_bce_7: 0.12402/0.31725, loss_mask_dice_7: 0.66120/1.10456, loss_spatial_bce_7: 0.03766/0.10807, loss_spatial_dice_7: 0.18739/0.22511, loss_spatial_ce_7: 0.03872/0.16215, loss_grounding_bce_7: 0.04483/0.08450, loss_grounding_dice_7: 0.07920/0.16063, loss_grounding_ce_7: 0.00118/0.32521, loss_mask_ce_8: 1.17905/1.02994, loss_mask_bce_8: 0.13555/0.33373, loss_mask_dice_8: 0.82139/1.18217, loss_spatial_bce_8: 0.04275/0.12729, loss_spatial_dice_8: 0.21507/0.26304, loss_spatial_ce_8: 0.16488/0.21540, loss_grounding_bce_8: 0.03955/0.08843, loss_grounding_dice_8: 0.09158/0.17034, loss_grounding_ce_8: 0.22879/0.42850, loss_mask_ce_9: 3.53175/3.48859, loss_mask_bce_9: 0.15599/0.36072, loss_mask_dice_9: 1.16662/1.76717, loss_spatial_bce_9: 0.23390/0.35629, loss_spatial_dice_9: 0.85819/0.79514, loss_spatial_ce_9: 1.24190/1.40054, loss_grounding_bce_9: 0.05047/0.10062, loss_grounding_dice_9: 0.09766/0.24375, loss_grounding_ce_9: 0.12411/0.68834] items per batch[64] items per second[0.37] total items[2393600] mini batches[ 37400] memory[4999] epoch remaining[0:28:32] INFO:trainer.default_trainer:epochs[ 20] optim steps[37500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.41929/0.76895, loss_mask_bce_0: 0.68442/0.30174, loss_mask_dice_0: 1.27266/1.02701, loss_spatial_bce_0: 0.03857/0.08713, loss_spatial_dice_0: 0.08049/0.18418, loss_spatial_ce_0: 0.00258/0.06338, loss_grounding_bce_0: 0.03427/0.08042, loss_grounding_dice_0: 0.13310/0.15103, loss_grounding_ce_0: 0.52256/0.24969, loss_mask_ce_1: 0.56882/0.77063, loss_mask_bce_1: 0.55977/0.30255, loss_mask_dice_1: 1.28916/1.03114, loss_spatial_bce_1: 0.03457/0.08739, loss_spatial_dice_1: 0.08291/0.18672, loss_spatial_ce_1: 0.00078/0.06778, loss_grounding_bce_1: 0.03327/0.08064, loss_grounding_dice_1: 0.12929/0.15183, loss_grounding_ce_1: 0.57032/0.25115, loss_mask_ce_2: 0.43138/0.77850, loss_mask_bce_2: 0.67185/0.30263, loss_mask_dice_2: 1.31247/1.03241, loss_spatial_bce_2: 0.03209/0.08724, loss_spatial_dice_2: 0.09326/0.18686, loss_spatial_ce_2: 0.00344/0.07010, loss_grounding_bce_2: 0.03403/0.08058, loss_grounding_dice_2: 0.13089/0.15154, loss_grounding_ce_2: 0.56434/0.25386, loss_mask_ce_3: 0.44117/0.78066, loss_mask_bce_3: 0.68542/0.30411, loss_mask_dice_3: 1.37441/1.02897, loss_spatial_bce_3: 0.04015/0.08910, loss_spatial_dice_3: 0.09149/0.18773, loss_spatial_ce_3: 0.00394/0.07478, loss_grounding_bce_3: 0.03489/0.08095, loss_grounding_dice_3: 0.12958/0.15107, loss_grounding_ce_3: 0.51933/0.25357, loss_mask_ce_4: 0.61435/0.78612, loss_mask_bce_4: 0.62668/0.30641, loss_mask_dice_4: 1.29241/1.04808, loss_spatial_bce_4: 0.06374/0.09105, loss_spatial_dice_4: 0.11771/0.19543, loss_spatial_ce_4: 0.00443/0.08758, loss_grounding_bce_4: 0.03429/0.08164, loss_grounding_dice_4: 0.12763/0.15372, loss_grounding_ce_4: 0.55277/0.25950, loss_mask_ce_5: 0.55481/0.80912, loss_mask_bce_5: 0.64793/0.30810, loss_mask_dice_5: 1.33125/1.05500, loss_spatial_bce_5: 0.05993/0.09286, loss_spatial_dice_5: 0.10676/0.19782, loss_spatial_ce_5: 0.05480/0.09962, loss_grounding_bce_5: 0.03630/0.08188, loss_grounding_dice_5: 0.13701/0.15435, loss_grounding_ce_5: 0.56057/0.27814, loss_mask_ce_6: 0.63926/0.83526, loss_mask_bce_6: 0.58845/0.30993, loss_mask_dice_6: 1.46648/1.05826, loss_spatial_bce_6: 0.05404/0.09779, loss_spatial_dice_6: 0.10596/0.19999, loss_spatial_ce_6: 0.06262/0.12192, loss_grounding_bce_6: 0.03859/0.08290, loss_grounding_dice_6: 0.14361/0.15500, loss_grounding_ce_6: 0.56913/0.28810, loss_mask_ce_7: 1.36458/0.89271, loss_mask_bce_7: 0.49426/0.31718, loss_mask_dice_7: 1.40625/1.10447, loss_spatial_bce_7: 0.06579/0.10806, loss_spatial_dice_7: 0.19728/0.22503, loss_spatial_ce_7: 0.04581/0.16205, loss_grounding_bce_7: 0.03621/0.08450, loss_grounding_dice_7: 0.14375/0.16061, loss_grounding_ce_7: 0.67963/0.32517, loss_mask_ce_8: 1.04626/1.02957, loss_mask_bce_8: 0.87922/0.33368, loss_mask_dice_8: 1.72592/1.18207, loss_spatial_bce_8: 0.07595/0.12726, loss_spatial_dice_8: 0.24178/0.26295, loss_spatial_ce_8: 0.09343/0.21524, loss_grounding_bce_8: 0.04049/0.08843, loss_grounding_dice_8: 0.18821/0.17032, loss_grounding_ce_8: 0.68052/0.42839, loss_mask_ce_9: 6.74170/3.48805, loss_mask_bce_9: 0.88163/0.36068, loss_mask_dice_9: 3.52293/1.76730, loss_spatial_bce_9: 0.21031/0.35625, loss_spatial_dice_9: 0.85949/0.79512, loss_spatial_ce_9: 1.01368/1.40056, loss_grounding_bce_9: 0.07458/0.10060, loss_grounding_dice_9: 0.43338/0.24375, loss_grounding_ce_9: 0.71106/0.68822] items per batch[64] items per second[0.36] total items[2400000] mini batches[ 37500] memory[4999] epoch remaining[0:25:34] INFO:trainer.default_trainer:epochs[ 20] optim steps[37600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97986/0.76897, loss_mask_bce_0: 0.13806/0.30175, loss_mask_dice_0: 1.84875/1.02730, loss_spatial_bce_0: 0.01716/0.08709, loss_spatial_dice_0: 0.21472/0.18416, loss_spatial_ce_0: 0.07596/0.06333, loss_grounding_bce_0: 0.00531/0.08041, loss_grounding_dice_0: 0.03514/0.15103, loss_grounding_ce_0: 0.01655/0.24968, loss_mask_ce_1: 0.94333/0.77065, loss_mask_bce_1: 0.13648/0.30256, loss_mask_dice_1: 1.81187/1.03143, loss_spatial_bce_1: 0.01900/0.08735, loss_spatial_dice_1: 0.32955/0.18670, loss_spatial_ce_1: 0.10742/0.06772, loss_grounding_bce_1: 0.00608/0.08063, loss_grounding_dice_1: 0.04532/0.15182, loss_grounding_ce_1: 0.00753/0.25110, loss_mask_ce_2: 0.88454/0.77857, loss_mask_bce_2: 0.14169/0.30264, loss_mask_dice_2: 1.81721/1.03275, loss_spatial_bce_2: 0.01727/0.08720, loss_spatial_dice_2: 0.29084/0.18685, loss_spatial_ce_2: 0.08460/0.07005, loss_grounding_bce_2: 0.00522/0.08057, loss_grounding_dice_2: 0.04023/0.15152, loss_grounding_ce_2: 0.01065/0.25379, loss_mask_ce_3: 0.96217/0.78065, loss_mask_bce_3: 0.13619/0.30413, loss_mask_dice_3: 1.91934/1.02930, loss_spatial_bce_3: 0.01506/0.08905, loss_spatial_dice_3: 0.23391/0.18772, loss_spatial_ce_3: 0.04284/0.07472, loss_grounding_bce_3: 0.00593/0.08094, loss_grounding_dice_3: 0.04646/0.15106, loss_grounding_ce_3: 0.00780/0.25352, loss_mask_ce_4: 0.93357/0.78618, loss_mask_bce_4: 0.13508/0.30645, loss_mask_dice_4: 1.90920/1.04840, loss_spatial_bce_4: 0.02228/0.09101, loss_spatial_dice_4: 0.31085/0.19541, loss_spatial_ce_4: 0.06567/0.08752, loss_grounding_bce_4: 0.00487/0.08163, loss_grounding_dice_4: 0.03798/0.15371, loss_grounding_ce_4: 0.00761/0.25948, loss_mask_ce_5: 1.34657/0.80915, loss_mask_bce_5: 0.14975/0.30812, loss_mask_dice_5: 2.26311/1.05535, loss_spatial_bce_5: 0.01769/0.09283, loss_spatial_dice_5: 0.29392/0.19781, loss_spatial_ce_5: 0.14175/0.09956, loss_grounding_bce_5: 0.00515/0.08187, loss_grounding_dice_5: 0.04709/0.15436, loss_grounding_ce_5: 0.02179/0.27804, loss_mask_ce_6: 1.08281/0.83536, loss_mask_bce_6: 0.13338/0.30995, loss_mask_dice_6: 1.84567/1.05863, loss_spatial_bce_6: 0.01850/0.09776, loss_spatial_dice_6: 0.31380/0.19999, loss_spatial_ce_6: 0.22585/0.12188, loss_grounding_bce_6: 0.00436/0.08289, loss_grounding_dice_6: 0.04371/0.15500, loss_grounding_ce_6: 0.01231/0.28806, loss_mask_ce_7: 0.76100/0.89275, loss_mask_bce_7: 0.16452/0.31720, loss_mask_dice_7: 2.10522/1.10479, loss_spatial_bce_7: 0.01879/0.10803, loss_spatial_dice_7: 0.39139/0.22504, loss_spatial_ce_7: 0.43480/0.16194, loss_grounding_bce_7: 0.00494/0.08448, loss_grounding_dice_7: 0.03422/0.16060, loss_grounding_ce_7: 0.01810/0.32513, loss_mask_ce_8: 1.19373/1.02970, loss_mask_bce_8: 0.14835/0.33369, loss_mask_dice_8: 2.16119/1.18235, loss_spatial_bce_8: 0.02462/0.12722, loss_spatial_dice_8: 0.48066/0.26293, loss_spatial_ce_8: 0.40774/0.21512, loss_grounding_bce_8: 0.00663/0.08841, loss_grounding_dice_8: 0.06042/0.17030, loss_grounding_ce_8: 0.05001/0.42825, loss_mask_ce_9: 5.25965/3.48826, loss_mask_bce_9: 0.12509/0.36066, loss_mask_dice_9: 3.09197/1.76786, loss_spatial_bce_9: 0.22365/0.35617, loss_spatial_dice_9: 0.92729/0.79511, loss_spatial_ce_9: 1.34525/1.40044, loss_grounding_bce_9: 0.00957/0.10056, loss_grounding_dice_9: 0.14717/0.24370, loss_grounding_ce_9: 0.30922/0.68847] items per batch[64] items per second[0.36] total items[2406400] mini batches[ 37600] memory[4999] epoch remaining[0:22:37] INFO:trainer.default_trainer:epochs[ 20] optim steps[37700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44033/0.76890, loss_mask_bce_0: 0.42236/0.30171, loss_mask_dice_0: 1.27190/1.02722, loss_spatial_bce_0: 0.04365/0.08710, loss_spatial_dice_0: 0.19501/0.18414, loss_spatial_ce_0: 0.00363/0.06331, loss_grounding_bce_0: 0.07352/0.08041, loss_grounding_dice_0: 0.09092/0.15104, loss_grounding_ce_0: 0.32110/0.24960, loss_mask_ce_1: 0.61702/0.77049, loss_mask_bce_1: 0.44951/0.30253, loss_mask_dice_1: 1.05303/1.03137, loss_spatial_bce_1: 0.04242/0.08736, loss_spatial_dice_1: 0.20318/0.18668, loss_spatial_ce_1: 0.00531/0.06770, loss_grounding_bce_1: 0.07671/0.08063, loss_grounding_dice_1: 0.09141/0.15184, loss_grounding_ce_1: 0.31617/0.25100, loss_mask_ce_2: 0.52240/0.77845, loss_mask_bce_2: 0.41819/0.30261, loss_mask_dice_2: 1.18890/1.03273, loss_spatial_bce_2: 0.03827/0.08721, loss_spatial_dice_2: 0.18547/0.18682, loss_spatial_ce_2: 0.00350/0.07000, loss_grounding_bce_2: 0.07595/0.08058, loss_grounding_dice_2: 0.09636/0.15155, loss_grounding_ce_2: 0.32354/0.25369, loss_mask_ce_3: 0.70061/0.78052, loss_mask_bce_3: 0.42246/0.30410, loss_mask_dice_3: 0.97858/1.02923, loss_spatial_bce_3: 0.04043/0.08907, loss_spatial_dice_3: 0.20848/0.18770, loss_spatial_ce_3: 0.00543/0.07469, loss_grounding_bce_3: 0.07732/0.08095, loss_grounding_dice_3: 0.09645/0.15108, loss_grounding_ce_3: 0.33360/0.25349, loss_mask_ce_4: 0.67564/0.78606, loss_mask_bce_4: 0.42940/0.30642, loss_mask_dice_4: 0.92195/1.04833, loss_spatial_bce_4: 0.04570/0.09103, loss_spatial_dice_4: 0.20232/0.19539, loss_spatial_ce_4: 0.01208/0.08751, loss_grounding_bce_4: 0.07770/0.08163, loss_grounding_dice_4: 0.09130/0.15375, loss_grounding_ce_4: 0.32482/0.25940, loss_mask_ce_5: 0.58301/0.80907, loss_mask_bce_5: 0.42321/0.30809, loss_mask_dice_5: 1.08923/1.05534, loss_spatial_bce_5: 0.04102/0.09284, loss_spatial_dice_5: 0.21437/0.19779, loss_spatial_ce_5: 0.13442/0.09958, loss_grounding_bce_5: 0.07236/0.08187, loss_grounding_dice_5: 0.09539/0.15439, loss_grounding_ce_5: 0.30816/0.27795, loss_mask_ce_6: 0.41646/0.83516, loss_mask_bce_6: 0.42889/0.30992, loss_mask_dice_6: 1.34533/1.05862, loss_spatial_bce_6: 0.04777/0.09778, loss_spatial_dice_6: 0.23214/0.19997, loss_spatial_ce_6: 0.10683/0.12191, loss_grounding_bce_6: 0.07031/0.08289, loss_grounding_dice_6: 0.09851/0.15502, loss_grounding_ce_6: 0.31923/0.28794, loss_mask_ce_7: 0.72836/0.89266, loss_mask_bce_7: 0.40612/0.31717, loss_mask_dice_7: 1.40887/1.10478, loss_spatial_bce_7: 0.04656/0.10804, loss_spatial_dice_7: 0.27916/0.22502, loss_spatial_ce_7: 0.42150/0.16193, loss_grounding_bce_7: 0.06542/0.08447, loss_grounding_dice_7: 0.09749/0.16062, loss_grounding_ce_7: 0.32377/0.32495, loss_mask_ce_8: 0.74774/1.02947, loss_mask_bce_8: 0.68356/0.33368, loss_mask_dice_8: 1.71359/1.18241, loss_spatial_bce_8: 0.11280/0.12724, loss_spatial_dice_8: 0.39852/0.26289, loss_spatial_ce_8: 0.25397/0.21504, loss_grounding_bce_8: 0.06860/0.08841, loss_grounding_dice_8: 0.11502/0.17033, loss_grounding_ce_8: 0.46928/0.42806, loss_mask_ce_9: 3.54410/3.48786, loss_mask_bce_9: 0.58744/0.36062, loss_mask_dice_9: 2.63856/1.76755, loss_spatial_bce_9: 0.33075/0.35624, loss_spatial_dice_9: 0.93238/0.79505, loss_spatial_ce_9: 1.57738/1.40018, loss_grounding_bce_9: 0.09655/0.10055, loss_grounding_dice_9: 0.21290/0.24373, loss_grounding_ce_9: 0.58499/0.68803] items per batch[64] items per second[0.36] total items[2412800] mini batches[ 37700] memory[4999] epoch remaining[0:19:41] INFO:trainer.default_trainer:epochs[ 20] optim steps[37800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83917/0.76884, loss_mask_bce_0: 0.06023/0.30173, loss_mask_dice_0: 0.66793/1.02734, loss_spatial_bce_0: 0.01058/0.08709, loss_spatial_dice_0: 0.15972/0.18411, loss_spatial_ce_0: 0.00089/0.06324, loss_grounding_bce_0: 0.01813/0.08042, loss_grounding_dice_0: 0.06602/0.15104, loss_grounding_ce_0: 0.00220/0.24956, loss_mask_ce_1: 0.72253/0.77049, loss_mask_bce_1: 0.05496/0.30253, loss_mask_dice_1: 0.70002/1.03146, loss_spatial_bce_1: 0.00931/0.08735, loss_spatial_dice_1: 0.12482/0.18665, loss_spatial_ce_1: 0.00049/0.06765, loss_grounding_bce_1: 0.01346/0.08063, loss_grounding_dice_1: 0.05412/0.15183, loss_grounding_ce_1: 0.00210/0.25097, loss_mask_ce_2: 0.35137/0.77845, loss_mask_bce_2: 0.08122/0.30262, loss_mask_dice_2: 0.87488/1.03278, loss_spatial_bce_2: 0.01085/0.08720, loss_spatial_dice_2: 0.13669/0.18681, loss_spatial_ce_2: 0.00144/0.06995, loss_grounding_bce_2: 0.01356/0.08059, loss_grounding_dice_2: 0.05865/0.15154, loss_grounding_ce_2: 0.00269/0.25368, loss_mask_ce_3: 0.75647/0.78050, loss_mask_bce_3: 0.06452/0.30412, loss_mask_dice_3: 0.70568/1.02930, loss_spatial_bce_3: 0.01026/0.08906, loss_spatial_dice_3: 0.14819/0.18769, loss_spatial_ce_3: 0.00203/0.07463, loss_grounding_bce_3: 0.01441/0.08095, loss_grounding_dice_3: 0.05619/0.15106, loss_grounding_ce_3: 0.00102/0.25350, loss_mask_ce_4: 0.84279/0.78604, loss_mask_bce_4: 0.06767/0.30642, loss_mask_dice_4: 0.72476/1.04842, loss_spatial_bce_4: 0.00961/0.09103, loss_spatial_dice_4: 0.13662/0.19538, loss_spatial_ce_4: 0.06510/0.08743, loss_grounding_bce_4: 0.01492/0.08164, loss_grounding_dice_4: 0.05913/0.15374, loss_grounding_ce_4: 0.00190/0.25939, loss_mask_ce_5: 0.83050/0.80907, loss_mask_bce_5: 0.06736/0.30813, loss_mask_dice_5: 0.72415/1.05545, loss_spatial_bce_5: 0.00962/0.09284, loss_spatial_dice_5: 0.16155/0.19779, loss_spatial_ce_5: 0.03412/0.09952, loss_grounding_bce_5: 0.01396/0.08188, loss_grounding_dice_5: 0.05731/0.15437, loss_grounding_ce_5: 0.00372/0.27790, loss_mask_ce_6: 0.89942/0.83515, loss_mask_bce_6: 0.06708/0.30993, loss_mask_dice_6: 0.62259/1.05869, loss_spatial_bce_6: 0.01044/0.09778, loss_spatial_dice_6: 0.15046/0.19997, loss_spatial_ce_6: 0.00781/0.12189, loss_grounding_bce_6: 0.01677/0.08290, loss_grounding_dice_6: 0.06304/0.15502, loss_grounding_ce_6: 0.00304/0.28785, loss_mask_ce_7: 0.91970/0.89247, loss_mask_bce_7: 0.06554/0.31724, loss_mask_dice_7: 0.78544/1.10490, loss_spatial_bce_7: 0.01430/0.10804, loss_spatial_dice_7: 0.18639/0.22505, loss_spatial_ce_7: 0.06093/0.16191, loss_grounding_bce_7: 0.01678/0.08451, loss_grounding_dice_7: 0.06572/0.16062, loss_grounding_ce_7: 0.02715/0.32481, loss_mask_ce_8: 1.16303/1.02943, loss_mask_bce_8: 0.07732/0.33370, loss_mask_dice_8: 0.79806/1.18247, loss_spatial_bce_8: 0.02662/0.12722, loss_spatial_dice_8: 0.25654/0.26286, loss_spatial_ce_8: 0.03798/0.21496, loss_grounding_bce_8: 0.01733/0.08846, loss_grounding_dice_8: 0.05642/0.17033, loss_grounding_ce_8: 0.72776/0.42771, loss_mask_ce_9: 3.22582/3.48829, loss_mask_bce_9: 0.10465/0.36064, loss_mask_dice_9: 1.40202/1.76789, loss_spatial_bce_9: 0.15819/0.35617, loss_spatial_dice_9: 0.85323/0.79504, loss_spatial_ce_9: 1.52196/1.40013, loss_grounding_bce_9: 0.02793/0.10057, loss_grounding_dice_9: 0.12080/0.24367, loss_grounding_ce_9: 1.20312/0.68781] items per batch[64] items per second[0.36] total items[2419200] mini batches[ 37800] memory[4999] epoch remaining[0:16:44] INFO:trainer.default_trainer:epochs[ 20] optim steps[37900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01920/0.76877, loss_mask_bce_0: 0.14276/0.30176, loss_mask_dice_0: 0.08902/1.02775, loss_spatial_bce_0: 0.11365/0.08708, loss_spatial_dice_0: 0.07142/0.18409, loss_spatial_ce_0: 0.00099/0.06319, loss_grounding_bce_0: 0.11205/0.08046, loss_grounding_dice_0: 0.06969/0.15104, loss_grounding_ce_0: 0.00147/0.24945, loss_mask_ce_1: 0.01951/0.77042, loss_mask_bce_1: 0.14031/0.30257, loss_mask_dice_1: 0.08878/1.03189, loss_spatial_bce_1: 0.11227/0.08734, loss_spatial_dice_1: 0.07169/0.18663, loss_spatial_ce_1: 0.00171/0.06759, loss_grounding_bce_1: 0.10875/0.08066, loss_grounding_dice_1: 0.07220/0.15182, loss_grounding_ce_1: 0.00142/0.25084, loss_mask_ce_2: 0.01966/0.77834, loss_mask_bce_2: 0.13384/0.30265, loss_mask_dice_2: 0.08561/1.03317, loss_spatial_bce_2: 0.10381/0.08719, loss_spatial_dice_2: 0.06922/0.18679, loss_spatial_ce_2: 0.00140/0.06992, loss_grounding_bce_2: 0.10935/0.08062, loss_grounding_dice_2: 0.07046/0.15154, loss_grounding_ce_2: 0.00165/0.25356, loss_mask_ce_3: 0.02023/0.78036, loss_mask_bce_3: 0.13567/0.30413, loss_mask_dice_3: 0.08285/1.02969, loss_spatial_bce_3: 0.11257/0.08905, loss_spatial_dice_3: 0.06903/0.18768, loss_spatial_ce_3: 0.00135/0.07456, loss_grounding_bce_3: 0.11028/0.08100, loss_grounding_dice_3: 0.06919/0.15106, loss_grounding_ce_3: 0.00249/0.25336, loss_mask_ce_4: 0.02317/0.78598, loss_mask_bce_4: 0.14032/0.30645, loss_mask_dice_4: 0.09104/1.04892, loss_spatial_bce_4: 0.11432/0.09102, loss_spatial_dice_4: 0.06985/0.19537, loss_spatial_ce_4: 0.00718/0.08738, loss_grounding_bce_4: 0.10661/0.08168, loss_grounding_dice_4: 0.07271/0.15374, loss_grounding_ce_4: 0.00298/0.25928, loss_mask_ce_5: 0.02598/0.80896, loss_mask_bce_5: 0.13268/0.30814, loss_mask_dice_5: 0.08620/1.05598, loss_spatial_bce_5: 0.11120/0.09282, loss_spatial_dice_5: 0.06860/0.19778, loss_spatial_ce_5: 0.00933/0.09948, loss_grounding_bce_5: 0.10134/0.08191, loss_grounding_dice_5: 0.06636/0.15438, loss_grounding_ce_5: 0.00276/0.27771, loss_mask_ce_6: 0.02201/0.83503, loss_mask_bce_6: 0.13049/0.30995, loss_mask_dice_6: 0.08530/1.05918, loss_spatial_bce_6: 0.10937/0.09776, loss_spatial_dice_6: 0.06459/0.19998, loss_spatial_ce_6: 0.01454/0.12184, loss_grounding_bce_6: 0.10478/0.08293, loss_grounding_dice_6: 0.06941/0.15501, loss_grounding_ce_6: 0.00426/0.28769, loss_mask_ce_7: 0.02841/0.89237, loss_mask_bce_7: 0.13256/0.31725, loss_mask_dice_7: 0.08643/1.10531, loss_spatial_bce_7: 0.11856/0.10803, loss_spatial_dice_7: 0.07418/0.22507, loss_spatial_ce_7: 0.00468/0.16188, loss_grounding_bce_7: 0.10706/0.08454, loss_grounding_dice_7: 0.06842/0.16062, loss_grounding_ce_7: 0.00240/0.32462, loss_mask_ce_8: 0.03449/1.02924, loss_mask_bce_8: 0.13126/0.33369, loss_mask_dice_8: 0.08463/1.18288, loss_spatial_bce_8: 0.16836/0.12720, loss_spatial_dice_8: 0.17164/0.26286, loss_spatial_ce_8: 0.13730/0.21486, loss_grounding_bce_8: 0.10550/0.08850, loss_grounding_dice_8: 0.06751/0.17033, loss_grounding_ce_8: 0.00230/0.42748, loss_mask_ce_9: 1.65649/3.48810, loss_mask_bce_9: 0.13941/0.36067, loss_mask_dice_9: 0.09849/1.76855, loss_spatial_bce_9: 1.03523/0.35616, loss_spatial_dice_9: 0.66783/0.79505, loss_spatial_ce_9: 0.82130/1.39994, loss_grounding_bce_9: 0.11302/0.10062, loss_grounding_dice_9: 0.08077/0.24368, loss_grounding_ce_9: 0.11095/0.68743] items per batch[64] items per second[0.36] total items[2425600] mini batches[ 37900] memory[4999] epoch remaining[0:13:48] INFO:trainer.default_trainer:epochs[ 20] optim steps[38000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75811/0.76875, loss_mask_bce_0: 1.16243/0.30188, loss_mask_dice_0: 2.89643/1.02770, loss_spatial_bce_0: 0.09241/0.08707, loss_spatial_dice_0: 0.20849/0.18405, loss_spatial_ce_0: 0.00950/0.06314, loss_grounding_bce_0: 0.31568/0.08053, loss_grounding_dice_0: 0.19103/0.15106, loss_grounding_ce_0: 0.00049/0.24945, loss_mask_ce_1: 1.00586/0.77043, loss_mask_bce_1: 1.15454/0.30268, loss_mask_dice_1: 2.78495/1.03187, loss_spatial_bce_1: 0.10305/0.08732, loss_spatial_dice_1: 0.20772/0.18659, loss_spatial_ce_1: 0.01609/0.06750, loss_grounding_bce_1: 0.33232/0.08073, loss_grounding_dice_1: 0.19244/0.15183, loss_grounding_ce_1: 0.00084/0.25083, loss_mask_ce_2: 0.83024/0.77830, loss_mask_bce_2: 1.16820/0.30277, loss_mask_dice_2: 2.79681/1.03313, loss_spatial_bce_2: 0.09979/0.08717, loss_spatial_dice_2: 0.21488/0.18674, loss_spatial_ce_2: 0.01042/0.06984, loss_grounding_bce_2: 0.32658/0.08069, loss_grounding_dice_2: 0.19527/0.15155, loss_grounding_ce_2: 0.00098/0.25351, loss_mask_ce_3: 0.74660/0.78035, loss_mask_bce_3: 1.29135/0.30426, loss_mask_dice_3: 2.78590/1.02969, loss_spatial_bce_3: 0.10027/0.08903, loss_spatial_dice_3: 0.21782/0.18764, loss_spatial_ce_3: 0.01734/0.07450, loss_grounding_bce_3: 0.33407/0.08106, loss_grounding_dice_3: 0.18307/0.15108, loss_grounding_ce_3: 0.00067/0.25335, loss_mask_ce_4: 0.86101/0.78600, loss_mask_bce_4: 1.18016/0.30657, loss_mask_dice_4: 3.00316/1.04885, loss_spatial_bce_4: 0.09777/0.09100, loss_spatial_dice_4: 0.20923/0.19532, loss_spatial_ce_4: 0.01967/0.08731, loss_grounding_bce_4: 0.37305/0.08175, loss_grounding_dice_4: 0.19743/0.15374, loss_grounding_ce_4: 0.00322/0.25922, loss_mask_ce_5: 1.03546/0.80897, loss_mask_bce_5: 1.02764/0.30826, loss_mask_dice_5: 2.95002/1.05598, loss_spatial_bce_5: 0.10461/0.09280, loss_spatial_dice_5: 0.21860/0.19775, loss_spatial_ce_5: 0.03402/0.09939, loss_grounding_bce_5: 0.38536/0.08198, loss_grounding_dice_5: 0.20557/0.15438, loss_grounding_ce_5: 0.01241/0.27762, loss_mask_ce_6: 1.11561/0.83502, loss_mask_bce_6: 1.04849/0.31008, loss_mask_dice_6: 2.86389/1.05917, loss_spatial_bce_6: 0.11208/0.09774, loss_spatial_dice_6: 0.20448/0.19994, loss_spatial_ce_6: 0.08134/0.12178, loss_grounding_bce_6: 0.28554/0.08300, loss_grounding_dice_6: 0.17258/0.15500, loss_grounding_ce_6: 0.01309/0.28760, loss_mask_ce_7: 0.99623/0.89236, loss_mask_bce_7: 0.90752/0.31739, loss_mask_dice_7: 2.90932/1.10527, loss_spatial_bce_7: 0.11987/0.10800, loss_spatial_dice_7: 0.24489/0.22503, loss_spatial_ce_7: 0.12412/0.16179, loss_grounding_bce_7: 0.29588/0.08461, loss_grounding_dice_7: 0.17555/0.16062, loss_grounding_ce_7: 0.10279/0.32448, loss_mask_ce_8: 1.21222/1.02926, loss_mask_bce_8: 1.11450/0.33382, loss_mask_dice_8: 3.50175/1.18284, loss_spatial_bce_8: 0.14483/0.12714, loss_spatial_dice_8: 0.29730/0.26280, loss_spatial_ce_8: 0.26439/0.21475, loss_grounding_bce_8: 0.33309/0.08857, loss_grounding_dice_8: 0.18326/0.17032, loss_grounding_ce_8: 0.30435/0.42732, loss_mask_ce_9: 7.55967/3.48849, loss_mask_bce_9: 1.13493/0.36078, loss_mask_dice_9: 5.63316/1.76865, loss_spatial_bce_9: 0.27333/0.35612, loss_spatial_dice_9: 0.94350/0.79509, loss_spatial_ce_9: 1.25089/1.39989, loss_grounding_bce_9: 0.31538/0.10068, loss_grounding_dice_9: 0.21122/0.24367, loss_grounding_ce_9: 0.36191/0.68772] items per batch[64] items per second[0.36] total items[2432000] mini batches[ 38000] memory[4999] epoch remaining[0:10:51] INFO:trainer.default_trainer:epochs[ 20] optim steps[38100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36450/0.76866, loss_mask_bce_0: 0.13790/0.30182, loss_mask_dice_0: 0.08991/1.02764, loss_spatial_bce_0: 0.08339/0.08705, loss_spatial_dice_0: 0.05488/0.18401, loss_spatial_ce_0: 0.00608/0.06311, loss_grounding_bce_0: 0.09728/0.08055, loss_grounding_dice_0: 0.05511/0.15101, loss_grounding_ce_0: 0.04376/0.24939, loss_mask_ce_1: 0.40407/0.77038, loss_mask_bce_1: 0.14770/0.30262, loss_mask_dice_1: 0.09027/1.03176, loss_spatial_bce_1: 0.08143/0.08730, loss_spatial_dice_1: 0.04653/0.18654, loss_spatial_ce_1: 0.00190/0.06747, loss_grounding_bce_1: 0.09974/0.08075, loss_grounding_dice_1: 0.05169/0.15179, loss_grounding_ce_1: 0.04987/0.25078, loss_mask_ce_2: 0.32645/0.77820, loss_mask_bce_2: 0.12659/0.30272, loss_mask_dice_2: 0.08040/1.03302, loss_spatial_bce_2: 0.08378/0.08716, loss_spatial_dice_2: 0.04656/0.18670, loss_spatial_ce_2: 0.00211/0.06979, loss_grounding_bce_2: 0.09634/0.08072, loss_grounding_dice_2: 0.05508/0.15152, loss_grounding_ce_2: 0.08313/0.25346, loss_mask_ce_3: 0.29692/0.78029, loss_mask_bce_3: 0.13394/0.30421, loss_mask_dice_3: 0.08050/1.02961, loss_spatial_bce_3: 0.08896/0.08902, loss_spatial_dice_3: 0.04881/0.18760, loss_spatial_ce_3: 0.00143/0.07447, loss_grounding_bce_3: 0.09871/0.08108, loss_grounding_dice_3: 0.05613/0.15104, loss_grounding_ce_3: 0.05905/0.25331, loss_mask_ce_4: 0.33716/0.78596, loss_mask_bce_4: 0.12913/0.30650, loss_mask_dice_4: 0.08053/1.04882, loss_spatial_bce_4: 0.08096/0.09098, loss_spatial_dice_4: 0.04642/0.19529, loss_spatial_ce_4: 0.00520/0.08729, loss_grounding_bce_4: 0.09486/0.08177, loss_grounding_dice_4: 0.05292/0.15373, loss_grounding_ce_4: 0.13560/0.25918, loss_mask_ce_5: 0.32801/0.80892, loss_mask_bce_5: 0.15621/0.30821, loss_mask_dice_5: 0.09298/1.05590, loss_spatial_bce_5: 0.07473/0.09279, loss_spatial_dice_5: 0.05747/0.19772, loss_spatial_ce_5: 0.00235/0.09940, loss_grounding_bce_5: 0.08560/0.08201, loss_grounding_dice_5: 0.05342/0.15435, loss_grounding_ce_5: 0.16431/0.27768, loss_mask_ce_6: 0.34686/0.83499, loss_mask_bce_6: 0.13062/0.31003, loss_mask_dice_6: 0.09475/1.05911, loss_spatial_bce_6: 0.09019/0.09773, loss_spatial_dice_6: 0.06039/0.19991, loss_spatial_ce_6: 0.00853/0.12178, loss_grounding_bce_6: 0.09370/0.08302, loss_grounding_dice_6: 0.06837/0.15497, loss_grounding_ce_6: 0.06966/0.28766, loss_mask_ce_7: 0.50946/0.89228, loss_mask_bce_7: 0.13004/0.31735, loss_mask_dice_7: 0.09248/1.10527, loss_spatial_bce_7: 0.11429/0.10801, loss_spatial_dice_7: 0.08046/0.22502, loss_spatial_ce_7: 0.06109/0.16179, loss_grounding_bce_7: 0.09043/0.08466, loss_grounding_dice_7: 0.06898/0.16063, loss_grounding_ce_7: 0.43948/0.32466, loss_mask_ce_8: 0.75890/1.02920, loss_mask_bce_8: 0.12183/0.33375, loss_mask_dice_8: 0.10657/1.18275, loss_spatial_bce_8: 0.16432/0.12716, loss_spatial_dice_8: 0.13378/0.26277, loss_spatial_ce_8: 0.08238/0.21470, loss_grounding_bce_8: 0.07512/0.08860, loss_grounding_dice_8: 0.04628/0.17030, loss_grounding_ce_8: 1.46490/0.42741, loss_mask_ce_9: 4.34669/3.48888, loss_mask_bce_9: 0.30837/0.36076, loss_mask_dice_9: 0.42143/1.76850, loss_spatial_bce_9: 0.59593/0.35615, loss_spatial_dice_9: 0.65704/0.79508, loss_spatial_ce_9: 0.96599/1.40004, loss_grounding_bce_9: 0.14188/0.10071, loss_grounding_dice_9: 0.21060/0.24365, loss_grounding_ce_9: 1.80222/0.68804] items per batch[64] items per second[0.37] total items[2438400] mini batches[ 38100] memory[4999] epoch remaining[0:07:52] INFO:trainer.default_trainer:epochs[ 20] optim steps[38200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67778/0.76862, loss_mask_bce_0: 0.57634/0.30184, loss_mask_dice_0: 1.59694/1.02798, loss_spatial_bce_0: 0.07354/0.08704, loss_spatial_dice_0: 0.18967/0.18401, loss_spatial_ce_0: 0.03952/0.06311, loss_grounding_bce_0: 0.04466/0.08060, loss_grounding_dice_0: 0.09713/0.15105, loss_grounding_ce_0: 0.00315/0.24937, loss_mask_ce_1: 0.87704/0.77040, loss_mask_bce_1: 0.62391/0.30262, loss_mask_dice_1: 1.38875/1.03202, loss_spatial_bce_1: 0.07384/0.08730, loss_spatial_dice_1: 0.18374/0.18656, loss_spatial_ce_1: 0.02245/0.06742, loss_grounding_bce_1: 0.04476/0.08080, loss_grounding_dice_1: 0.10286/0.15183, loss_grounding_ce_1: 0.00218/0.25070, loss_mask_ce_2: 0.92109/0.77824, loss_mask_bce_2: 0.61749/0.30273, loss_mask_dice_2: 1.49521/1.03331, loss_spatial_bce_2: 0.07582/0.08716, loss_spatial_dice_2: 0.18087/0.18672, loss_spatial_ce_2: 0.03291/0.06978, loss_grounding_bce_2: 0.04246/0.08077, loss_grounding_dice_2: 0.09444/0.15157, loss_grounding_ce_2: 0.00135/0.25336, loss_mask_ce_3: 0.87116/0.78034, loss_mask_bce_3: 0.60451/0.30423, loss_mask_dice_3: 1.36592/1.02987, loss_spatial_bce_3: 0.07616/0.08902, loss_spatial_dice_3: 0.19078/0.18762, loss_spatial_ce_3: 0.04927/0.07447, loss_grounding_bce_3: 0.03769/0.08113, loss_grounding_dice_3: 0.08468/0.15109, loss_grounding_ce_3: 0.00176/0.25323, loss_mask_ce_4: 0.67882/0.78606, loss_mask_bce_4: 0.66121/0.30652, loss_mask_dice_4: 1.58310/1.04916, loss_spatial_bce_4: 0.08921/0.09097, loss_spatial_dice_4: 0.21217/0.19529, loss_spatial_ce_4: 0.11609/0.08728, loss_grounding_bce_4: 0.03374/0.08182, loss_grounding_dice_4: 0.08673/0.15377, loss_grounding_ce_4: 0.00184/0.25911, loss_mask_ce_5: 0.67504/0.80888, loss_mask_bce_5: 0.68965/0.30822, loss_mask_dice_5: 1.68555/1.05620, loss_spatial_bce_5: 0.09771/0.09278, loss_spatial_dice_5: 0.20718/0.19773, loss_spatial_ce_5: 0.12829/0.09944, loss_grounding_bce_5: 0.03758/0.08205, loss_grounding_dice_5: 0.08636/0.15439, loss_grounding_ce_5: 0.00132/0.27766, loss_mask_ce_6: 0.67016/0.83503, loss_mask_bce_6: 0.63663/0.31003, loss_mask_dice_6: 1.72545/1.05943, loss_spatial_bce_6: 0.09536/0.09774, loss_spatial_dice_6: 0.19849/0.19993, loss_spatial_ce_6: 0.16529/0.12181, loss_grounding_bce_6: 0.04551/0.08307, loss_grounding_dice_6: 0.10259/0.15502, loss_grounding_ce_6: 0.00372/0.28758, loss_mask_ce_7: 0.84784/0.89230, loss_mask_bce_7: 0.60392/0.31735, loss_mask_dice_7: 1.63236/1.10557, loss_spatial_bce_7: 0.18915/0.10802, loss_spatial_dice_7: 0.32712/0.22504, loss_spatial_ce_7: 0.11062/0.16181, loss_grounding_bce_7: 0.04612/0.08469, loss_grounding_dice_7: 0.09970/0.16066, loss_grounding_ce_7: 0.00507/0.32455, loss_mask_ce_8: 1.09420/1.02909, loss_mask_bce_8: 0.71017/0.33373, loss_mask_dice_8: 1.59313/1.18297, loss_spatial_bce_8: 0.16175/0.12711, loss_spatial_dice_8: 0.37643/0.26277, loss_spatial_ce_8: 0.23570/0.21467, loss_grounding_bce_8: 0.03603/0.08865, loss_grounding_dice_8: 0.10652/0.17036, loss_grounding_ce_8: 0.00214/0.42721, loss_mask_ce_9: 4.86507/3.48845, loss_mask_bce_9: 0.91913/0.36072, loss_mask_dice_9: 2.49373/1.76868, loss_spatial_bce_9: 0.35633/0.35610, loss_spatial_dice_9: 0.78742/0.79506, loss_spatial_ce_9: 1.13530/1.40005, loss_grounding_bce_9: 0.05952/0.10076, loss_grounding_dice_9: 0.24254/0.24369, loss_grounding_ce_9: 0.31673/0.68747] items per batch[64] items per second[0.36] total items[2444800] mini batches[ 38200] memory[4999] epoch remaining[0:04:55] INFO:trainer.default_trainer:epochs[ 20] optim steps[38300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00986/0.76848, loss_mask_bce_0: 0.20798/0.30187, loss_mask_dice_0: 0.19194/1.02728, loss_spatial_bce_0: 0.16731/0.08710, loss_spatial_dice_0: 0.15642/0.18403, loss_spatial_ce_0: 0.03773/0.06308, loss_grounding_bce_0: 0.12114/0.08064, loss_grounding_dice_0: 0.11184/0.15106, loss_grounding_ce_0: 0.00146/0.24922, loss_mask_ce_1: 0.01292/0.77023, loss_mask_bce_1: 0.20918/0.30265, loss_mask_dice_1: 0.19115/1.03129, loss_spatial_bce_1: 0.15813/0.08735, loss_spatial_dice_1: 0.15232/0.18657, loss_spatial_ce_1: 0.03672/0.06742, loss_grounding_bce_1: 0.12770/0.08084, loss_grounding_dice_1: 0.11385/0.15183, loss_grounding_ce_1: 0.00160/0.25062, loss_mask_ce_2: 0.01015/0.77808, loss_mask_bce_2: 0.21828/0.30276, loss_mask_dice_2: 0.19611/1.03254, loss_spatial_bce_2: 0.15049/0.08721, loss_spatial_dice_2: 0.14095/0.18673, loss_spatial_ce_2: 0.04136/0.06978, loss_grounding_bce_2: 0.12517/0.08080, loss_grounding_dice_2: 0.11554/0.15157, loss_grounding_ce_2: 0.00133/0.25325, loss_mask_ce_3: 0.01013/0.78016, loss_mask_bce_3: 0.21273/0.30426, loss_mask_dice_3: 0.19251/1.02910, loss_spatial_bce_3: 0.14842/0.08907, loss_spatial_dice_3: 0.14146/0.18763, loss_spatial_ce_3: 0.03273/0.07447, loss_grounding_bce_3: 0.12341/0.08118, loss_grounding_dice_3: 0.11412/0.15109, loss_grounding_ce_3: 0.00204/0.25310, loss_mask_ce_4: 0.01277/0.78583, loss_mask_bce_4: 0.20772/0.30656, loss_mask_dice_4: 0.19042/1.04841, loss_spatial_bce_4: 0.14339/0.09104, loss_spatial_dice_4: 0.13516/0.19532, loss_spatial_ce_4: 0.02216/0.08728, loss_grounding_bce_4: 0.12200/0.08186, loss_grounding_dice_4: 0.11281/0.15378, loss_grounding_ce_4: 0.00239/0.25903, loss_mask_ce_5: 0.01120/0.80861, loss_mask_bce_5: 0.20829/0.30826, loss_mask_dice_5: 0.19355/1.05547, loss_spatial_bce_5: 0.13985/0.09285, loss_spatial_dice_5: 0.13515/0.19776, loss_spatial_ce_5: 0.02916/0.09943, loss_grounding_bce_5: 0.12581/0.08207, loss_grounding_dice_5: 0.11452/0.15439, loss_grounding_ce_5: 0.00206/0.27760, loss_mask_ce_6: 0.01164/0.83479, loss_mask_bce_6: 0.21765/0.31009, loss_mask_dice_6: 0.20736/1.05864, loss_spatial_bce_6: 0.15134/0.09783, loss_spatial_dice_6: 0.14316/0.19996, loss_spatial_ce_6: 0.04030/0.12184, loss_grounding_bce_6: 0.12943/0.08309, loss_grounding_dice_6: 0.12303/0.15502, loss_grounding_ce_6: 0.00325/0.28752, loss_mask_ce_7: 0.01013/0.89206, loss_mask_bce_7: 0.21398/0.31741, loss_mask_dice_7: 0.20153/1.10476, loss_spatial_bce_7: 0.14476/0.10811, loss_spatial_dice_7: 0.13373/0.22506, loss_spatial_ce_7: 0.07425/0.16181, loss_grounding_bce_7: 0.12774/0.08473, loss_grounding_dice_7: 0.11817/0.16068, loss_grounding_ce_7: 0.00319/0.32431, loss_mask_ce_8: 0.01476/1.02881, loss_mask_bce_8: 0.21092/0.33374, loss_mask_dice_8: 0.20211/1.18203, loss_spatial_bce_8: 0.16561/0.12721, loss_spatial_dice_8: 0.14150/0.26276, loss_spatial_ce_8: 0.14276/0.21470, loss_grounding_bce_8: 0.12399/0.08866, loss_grounding_dice_8: 0.11838/0.17035, loss_grounding_ce_8: 0.00238/0.42700, loss_mask_ce_9: 1.42528/3.48761, loss_mask_bce_9: 0.22104/0.36073, loss_mask_dice_9: 0.19855/1.76708, loss_spatial_bce_9: 0.45164/0.35619, loss_spatial_dice_9: 0.43834/0.79498, loss_spatial_ce_9: 0.66971/1.39988, loss_grounding_bce_9: 0.13317/0.10078, loss_grounding_dice_9: 0.12233/0.24367, loss_grounding_ce_9: 0.06399/0.68707] items per batch[64] items per second[0.37] total items[2451200] mini batches[ 38300] memory[4999] epoch remaining[0:01:58] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00038367. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3696 s/iter. Eval: 0.0879 s/iter. Total: 0.4601 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3654 s/iter. Eval: 0.0791 s/iter. Total: 0.4470 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0025 s/iter. Inference: 0.3698 s/iter. Eval: 0.0800 s/iter. Total: 0.4524 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0026 s/iter. Inference: 0.3751 s/iter. Eval: 0.0758 s/iter. Total: 0.4536 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0026 s/iter. Inference: 0.3774 s/iter. Eval: 0.0738 s/iter. Total: 0.4539 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3760 s/iter. Eval: 0.0722 s/iter. Total: 0.4510 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evald2n2hf2n ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.610 | 83.112 | 66.132 | 133 | | Things | 61.821 | 84.085 | 73.022 | 80 | | Stuff | 46.235 | 81.645 | 55.731 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.54s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.52 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.76s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.00 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.452 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.690 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.487 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.670 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.368 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.242 | 68.976 | 48.720 | 25.934 | 49.716 | 67.006 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.170 | bicycle | 22.046 | car | 42.644 | | motorcycle | 40.931 | airplane | 61.481 | bus | 70.734 | | train | 73.749 | truck | 44.014 | boat | 29.428 | | traffic light | 27.925 | fire hydrant | 68.689 | stop sign | 68.427 | | parking meter | 51.752 | bench | 26.058 | bird | 33.637 | | cat | 77.165 | dog | 71.133 | horse | 51.078 | | sheep | 54.319 | cow | 56.738 | elephant | 65.487 | | bear | 80.292 | zebra | 65.935 | giraffe | 61.438 | | backpack | 23.491 | umbrella | 55.645 | handbag | 24.270 | | tie | 40.445 | suitcase | 51.645 | frisbee | 69.989 | | skis | 7.570 | snowboard | 34.037 | sports ball | 48.869 | | kite | 37.375 | baseball bat | 37.007 | baseball glove | 49.469 | | skateboard | 43.127 | surfboard | 45.037 | tennis racket | 62.360 | | bottle | 41.106 | wine glass | 36.084 | cup | 49.701 | | fork | 25.531 | knife | 24.553 | spoon | 21.928 | | bowl | 37.974 | banana | 22.161 | apple | 25.362 | | sandwich | 49.898 | orange | 31.121 | broccoli | 23.414 | | carrot | 22.274 | hot dog | 35.902 | pizza | 52.955 | | donut | 55.504 | cake | 47.564 | chair | 28.158 | | couch | 43.277 | potted plant | 22.937 | bed | 44.022 | | dining table | 15.336 | toilet | 68.200 | tv | 66.864 | | laptop | 70.825 | mouse | 63.769 | remote | 44.696 | | keyboard | 56.662 | cell phone | 45.375 | microwave | 65.278 | | oven | 34.566 | toaster | 48.406 | sink | 43.329 | | refrigerator | 71.394 | book | 13.208 | clock | 53.650 | | vase | 39.903 | scissors | 35.180 | teddy bear | 55.828 | | hair drier | 32.456 | toothbrush | 29.434 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.65135185809449, 'fwIoU': 71.69040155836777, 'IoU-person': 88.71169097651097, 'IoU-bicycle': 73.31489382442757, 'IoU-car': 72.56426865326316, 'IoU-motorcycle': 88.70986322262424, 'IoU-airplane': 84.58320914242553, 'IoU-bus': 88.17636437813862, 'IoU-train': 88.45333349715857, 'IoU-truck': 69.7457871936783, 'IoU-boat': 71.48710222619822, 'IoU-traffic light': 78.04130498637684, 'IoU-fire hydrant': 93.00391290690014, 'IoU-stop sign': 94.60168948558928, 'IoU-parking meter': 84.71343077454252, 'IoU-bench': 62.500417626656656, 'IoU-bird': 77.28952870845403, 'IoU-cat': 90.90680492091889, 'IoU-dog': 85.21044030379686, 'IoU-horse': 87.91226171868492, 'IoU-sheep': 86.16028898517767, 'IoU-cow': 87.77439794268665, 'IoU-elephant': 91.71576580077328, 'IoU-bear': 77.88126181464466, 'IoU-zebra': 83.45970948658868, 'IoU-giraffe': 89.70142346892183, 'IoU-backpack': 51.62863852088936, 'IoU-umbrella': 88.40823661957262, 'IoU-handbag': 51.92852961621484, 'IoU-tie': 75.8115028877387, 'IoU-suitcase': 86.78802769430428, 'IoU-frisbee': 84.6691043359417, 'IoU-skis': 57.72539628357477, 'IoU-snowboard': 71.85129276147919, 'IoU-sports ball': 80.91095864590794, 'IoU-kite': 79.29678026500994, 'IoU-baseball bat': 68.4290859351839, 'IoU-baseball glove': 77.2355347749854, 'IoU-skateboard': 86.21825133544282, 'IoU-surfboard': 86.10212258033648, 'IoU-tennis racket': 91.4709650180592, 'IoU-bottle': 72.60971926871157, 'IoU-wine glass': 81.80718913821931, 'IoU-cup': 69.6058587560214, 'IoU-fork': 68.68277000269212, 'IoU-knife': 63.202873475756164, 'IoU-spoon': 61.330522797383814, 'IoU-bowl': 63.16447368845994, 'IoU-banana': 82.49171887669993, 'IoU-apple': 58.81996612533625, 'IoU-sandwich': 69.65957005848817, 'IoU-orange': 69.69355263767578, 'IoU-broccoli': 69.99371941323152, 'IoU-carrot': 63.77026900657711, 'IoU-hot dog': 67.74580382913503, 'IoU-pizza': 83.65997336195518, 'IoU-donut': 64.9300631075966, 'IoU-cake': 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34.53037825738092, 'IoU-curtain': 71.25888328166796, 'IoU-door-stuff': 48.450575347435134, 'IoU-floor-wood': 66.46920802278552, 'IoU-flower': 45.18707230592739, 'IoU-fruit': 45.721635140941785, 'IoU-gravel': 32.578077768538186, 'IoU-house': 24.549365962284934, 'IoU-light': 44.79707347424343, 'IoU-mirror-stuff': 64.99862969080841, 'IoU-net': 51.852521036616615, 'IoU-pillow': 19.662586450589643, 'IoU-platform': 26.95613078133111, 'IoU-playingfield': 69.44446493824236, 'IoU-railroad': 64.20713698923217, 'IoU-river': 51.329138915552306, 'IoU-road': 66.94381252314284, 'IoU-roof': 18.601233811362285, 'IoU-sand': 63.41380729737137, 'IoU-sea': 85.73610026188538, 'IoU-shelf': 38.16384723313512, 'IoU-snow': 91.97283890871951, 'IoU-stairs': 32.431125033446115, 'IoU-tent': 9.983471739844585, 'IoU-towel': 46.40549720340391, 'IoU-wall-brick': 49.374892912060744, 'IoU-wall-stone': 28.670833207929224, 'IoU-wall-tile': 69.77146867712045, 'IoU-wall-wood': 43.79082017771403, 'IoU-water-other': 31.615694719713737, 'IoU-window-blind': 49.35824758491223, 'IoU-window-other': 49.40804442373725, 'IoU-tree-merged': 82.08444530963355, 'IoU-fence-merged': 55.766379848301085, 'IoU-ceiling-merged': 67.89294409749924, 'IoU-sky-other-merged': 94.11175155866, 'IoU-cabinet-merged': 64.41438436994146, 'IoU-table-merged': 42.294770622803576, 'IoU-floor-other-merged': 53.001637459120474, 'IoU-pavement-merged': 57.19163832304132, 'IoU-mountain-merged': 57.3417181321274, 'IoU-grass-merged': 72.15555695442039, 'IoU-dirt-merged': 46.61693496536129, 'IoU-paper-merged': 34.75297061534574, 'IoU-food-other-merged': 43.71321615945181, 'IoU-building-other-merged': 59.3619887851586, 'IoU-rock-merged': 63.1050193787019, 'IoU-wall-other-merged': 68.86177874971251, 'IoU-rug-merged': 67.56921988002368, 'mACC': 77.29850903088055, 'pACC': 82.25737752652333, 'ACC-person': 93.32481531853341, 'ACC-bicycle': 82.64703082953336, 'ACC-car': 86.04830087049497, 'ACC-motorcycle': 93.27753600832553, 'ACC-airplane': 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'ACC-mouse': 92.10162729204319, 'ACC-remote': 77.24381963372066, 'ACC-keyboard': 76.30630468450393, 'ACC-cell phone': 89.28993194248925, 'ACC-microwave': 75.12853547305667, 'ACC-oven': 91.78010562101345, 'ACC-toaster': 90.32079267541639, 'ACC-sink': 84.0159764735938, 'ACC-refrigerator': 91.76602852690401, 'ACC-book': 74.60221064133815, 'ACC-clock': 85.51173829855273, 'ACC-vase': 76.29752326508365, 'ACC-scissors': 76.496547576417, 'ACC-teddy bear': 90.87316508220304, 'ACC-hair drier': 58.31412062733043, 'ACC-toothbrush': 85.61848505906879, 'ACC-banner': 78.67246081203923, 'ACC-blanket': 28.60591973135372, 'ACC-bridge': 57.475455339638174, 'ACC-cardboard': 58.64164606659881, 'ACC-counter': 59.776527927442245, 'ACC-curtain': 84.37996266213264, 'ACC-door-stuff': 72.77863190190203, 'ACC-floor-wood': 79.4829145212315, 'ACC-flower': 64.51332699013625, 'ACC-fruit': 67.73140201299329, 'ACC-gravel': 45.23476260651365, 'ACC-house': 30.749887691574468, 'ACC-light': 66.06061947326111, 'ACC-mirror-stuff': 79.24585447447143, 'ACC-net': 65.0572659975657, 'ACC-pillow': 38.488303638134816, 'ACC-platform': 43.47596509076495, 'ACC-playingfield': 86.91971955642713, 'ACC-railroad': 83.70566832215688, 'ACC-river': 62.39064747451507, 'ACC-road': 86.19688468049691, 'ACC-roof': 25.453556454668803, 'ACC-sand': 67.90658786655966, 'ACC-sea': 89.4193330337375, 'ACC-shelf': 54.30442637962557, 'ACC-snow': 95.44734861972965, 'ACC-stairs': 57.17957860698245, 'ACC-tent': 12.185397158641802, 'ACC-towel': 54.46022865746332, 'ACC-wall-brick': 69.2198769552418, 'ACC-wall-stone': 34.75631492566594, 'ACC-wall-tile': 87.30145903861094, 'ACC-wall-wood': 61.96215421071045, 'ACC-water-other': 64.49368682920901, 'ACC-window-blind': 61.161937936622564, 'ACC-window-other': 69.04514779153014, 'ACC-tree-merged': 89.85689458096036, 'ACC-fence-merged': 72.60729305618935, 'ACC-ceiling-merged': 83.8450309352713, 'ACC-sky-other-merged': 96.90963161335638, 'ACC-cabinet-merged': 78.05244319051839, 'ACC-table-merged': 56.10694374174099, 'ACC-floor-other-merged': 62.38749199976629, 'ACC-pavement-merged': 71.11292335642266, 'ACC-mountain-merged': 70.66740880150233, 'ACC-grass-merged': 83.83688894184147, 'ACC-dirt-merged': 69.83044219639007, 'ACC-paper-merged': 45.504329818389586, 'ACC-food-other-merged': 58.715697726395256, 'ACC-building-other-merged': 76.07137202333382, 'ACC-rock-merged': 83.82554996996122, 'ACC-wall-other-merged': 80.74256145558849, 'ACC-rug-merged': 83.6823058354054})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3253 s/iter. Inference: 0.1921 s/iter. Eval: 0.0000 s/iter. Total: 0.5174 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3358 s/iter. Inference: 0.3519 s/iter. Eval: 0.0000 s/iter. Total: 0.6878 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3488 s/iter. Inference: 0.5671 s/iter. Eval: 0.0000 s/iter. Total: 0.9159 s/iter. ETA=0:00:03 /tmp/amlt_code/datasets/evaluation/interactive_evaluation.py:92: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`. plt.figure() INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4003511852502195, 'noc@0.8': 2.5051214515657008, 'noc@0.85': 2.9461515949663446, 'noc@0.9': 3.7641205736025753, 'miou@iter1': 0.8711856030427052} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1447 s/iter. Eval: 0.0010 s/iter. Total: 0.1472 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.51496124267578, 'precision@0.6': 72.40575408935547, 'precision@0.7': 68.4415054321289, 'precision@0.8': 59.54138946533203, 'precision@0.9': 32.06373977661133, 'cIoU': 61.870765686035156, 'mIoU': 66.80838012695312} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.61017257975297, 'SQ': 83.11249552291375, 'RQ': 66.13184987682547, 'PQ_th': 61.82132839213962, 'SQ_th': 84.08500484694748, 'RQ_th': 73.02246872097014, 'PQ_st': 46.234843051622256, 'SQ_st': 81.64455692059867, 'RQ_st': 55.7309157724561}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.24244003479315, 'AP50': 68.9755565911113, 'AP75': 48.720138369043106, 'APs': 25.933587562271754, 'APm': 49.71581421112137, 'APl': 67.00613799758696, 'AP-person': 48.17002174950738, 'AP-bicycle': 22.04638033940782, 'AP-car': 42.644017593785534, 'AP-motorcycle': 40.93122087938087, 'AP-airplane': 61.48141068124038, 'AP-bus': 70.73424931698669, 'AP-train': 73.74922106196242, 'AP-truck': 44.01364074073747, 'AP-boat': 29.42784647216105, 'AP-traffic light': 27.924659179524646, 'AP-fire hydrant': 68.68882952922995, 'AP-stop sign': 68.42707182695035, 'AP-parking meter': 51.75180363043324, 'AP-bench': 26.058094918499947, 'AP-bird': 33.63699900536096, 'AP-cat': 77.16535273736463, 'AP-dog': 71.13326851856804, 'AP-horse': 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55.82836990310397, 'AP-hair drier': 32.45646103071846, 'AP-toothbrush': 29.43401217718215}), ('sem_seg', {'mIoU': 65.65135185809449, 'fwIoU': 71.69040155836777, 'IoU-person': 88.71169097651097, 'IoU-bicycle': 73.31489382442757, 'IoU-car': 72.56426865326316, 'IoU-motorcycle': 88.70986322262424, 'IoU-airplane': 84.58320914242553, 'IoU-bus': 88.17636437813862, 'IoU-train': 88.45333349715857, 'IoU-truck': 69.7457871936783, 'IoU-boat': 71.48710222619822, 'IoU-traffic light': 78.04130498637684, 'IoU-fire hydrant': 93.00391290690014, 'IoU-stop sign': 94.60168948558928, 'IoU-parking meter': 84.71343077454252, 'IoU-bench': 62.500417626656656, 'IoU-bird': 77.28952870845403, 'IoU-cat': 90.90680492091889, 'IoU-dog': 85.21044030379686, 'IoU-horse': 87.91226171868492, 'IoU-sheep': 86.16028898517767, 'IoU-cow': 87.77439794268665, 'IoU-elephant': 91.71576580077328, 'IoU-bear': 77.88126181464466, 'IoU-zebra': 83.45970948658868, 'IoU-giraffe': 89.70142346892183, 'IoU-backpack': 51.62863852088936, 'IoU-umbrella': 88.40823661957262, 'IoU-handbag': 51.92852961621484, 'IoU-tie': 75.8115028877387, 'IoU-suitcase': 86.78802769430428, 'IoU-frisbee': 84.6691043359417, 'IoU-skis': 57.72539628357477, 'IoU-snowboard': 71.85129276147919, 'IoU-sports ball': 80.91095864590794, 'IoU-kite': 79.29678026500994, 'IoU-baseball bat': 68.4290859351839, 'IoU-baseball glove': 77.2355347749854, 'IoU-skateboard': 86.21825133544282, 'IoU-surfboard': 86.10212258033648, 'IoU-tennis racket': 91.4709650180592, 'IoU-bottle': 72.60971926871157, 'IoU-wine glass': 81.80718913821931, 'IoU-cup': 69.6058587560214, 'IoU-fork': 68.68277000269212, 'IoU-knife': 63.202873475756164, 'IoU-spoon': 61.330522797383814, 'IoU-bowl': 63.16447368845994, 'IoU-banana': 82.49171887669993, 'IoU-apple': 58.81996612533625, 'IoU-sandwich': 69.65957005848817, 'IoU-orange': 69.69355263767578, 'IoU-broccoli': 69.99371941323152, 'IoU-carrot': 63.77026900657711, 'IoU-hot dog': 67.74580382913503, 'IoU-pizza': 83.65997336195518, 'IoU-donut': 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'IoU-counter': 34.53037825738092, 'IoU-curtain': 71.25888328166796, 'IoU-door-stuff': 48.450575347435134, 'IoU-floor-wood': 66.46920802278552, 'IoU-flower': 45.18707230592739, 'IoU-fruit': 45.721635140941785, 'IoU-gravel': 32.578077768538186, 'IoU-house': 24.549365962284934, 'IoU-light': 44.79707347424343, 'IoU-mirror-stuff': 64.99862969080841, 'IoU-net': 51.852521036616615, 'IoU-pillow': 19.662586450589643, 'IoU-platform': 26.95613078133111, 'IoU-playingfield': 69.44446493824236, 'IoU-railroad': 64.20713698923217, 'IoU-river': 51.329138915552306, 'IoU-road': 66.94381252314284, 'IoU-roof': 18.601233811362285, 'IoU-sand': 63.41380729737137, 'IoU-sea': 85.73610026188538, 'IoU-shelf': 38.16384723313512, 'IoU-snow': 91.97283890871951, 'IoU-stairs': 32.431125033446115, 'IoU-tent': 9.983471739844585, 'IoU-towel': 46.40549720340391, 'IoU-wall-brick': 49.374892912060744, 'IoU-wall-stone': 28.670833207929224, 'IoU-wall-tile': 69.77146867712045, 'IoU-wall-wood': 43.79082017771403, 'IoU-water-other': 31.615694719713737, 'IoU-window-blind': 49.35824758491223, 'IoU-window-other': 49.40804442373725, 'IoU-tree-merged': 82.08444530963355, 'IoU-fence-merged': 55.766379848301085, 'IoU-ceiling-merged': 67.89294409749924, 'IoU-sky-other-merged': 94.11175155866, 'IoU-cabinet-merged': 64.41438436994146, 'IoU-table-merged': 42.294770622803576, 'IoU-floor-other-merged': 53.001637459120474, 'IoU-pavement-merged': 57.19163832304132, 'IoU-mountain-merged': 57.3417181321274, 'IoU-grass-merged': 72.15555695442039, 'IoU-dirt-merged': 46.61693496536129, 'IoU-paper-merged': 34.75297061534574, 'IoU-food-other-merged': 43.71321615945181, 'IoU-building-other-merged': 59.3619887851586, 'IoU-rock-merged': 63.1050193787019, 'IoU-wall-other-merged': 68.86177874971251, 'IoU-rug-merged': 67.56921988002368, 'mACC': 77.29850903088055, 'pACC': 82.25737752652333, 'ACC-person': 93.32481531853341, 'ACC-bicycle': 82.64703082953336, 'ACC-car': 86.04830087049497, 'ACC-motorcycle': 93.27753600832553, 'ACC-airplane': 90.82298384820349, 'ACC-bus': 93.28711234586181, 'ACC-train': 95.3127053119463, 'ACC-truck': 79.69530464094761, 'ACC-boat': 80.94565343328104, 'ACC-traffic light': 92.0246659078504, 'ACC-fire hydrant': 95.92419732948456, 'ACC-stop sign': 98.40456489532126, 'ACC-parking meter': 87.76232249937469, 'ACC-bench': 77.73597877350242, 'ACC-bird': 82.52240511529968, 'ACC-cat': 95.94974255526684, 'ACC-dog': 88.51580640369127, 'ACC-horse': 92.66833574039013, 'ACC-sheep': 90.64587895703875, 'ACC-cow': 91.12671207335181, 'ACC-elephant': 93.96790363376452, 'ACC-bear': 79.43590402663058, 'ACC-zebra': 85.54459961424435, 'ACC-giraffe': 93.7766713605669, 'ACC-backpack': 71.9940009290015, 'ACC-umbrella': 92.86307528076654, 'ACC-handbag': 71.29672651990693, 'ACC-tie': 83.68809941564453, 'ACC-suitcase': 93.08398186121963, 'ACC-frisbee': 94.39127272727272, 'ACC-skis': 73.75237225593447, 'ACC-snowboard': 82.06754671695525, 'ACC-sports ball': 88.7452062724181, 'ACC-kite': 86.0010974102628, 'ACC-baseball bat': 87.33451213390376, 'ACC-baseball glove': 92.36397829705368, 'ACC-skateboard': 90.99256702470028, 'ACC-surfboard': 92.40196575202717, 'ACC-tennis racket': 95.26113166255917, 'ACC-bottle': 87.75470359098397, 'ACC-wine glass': 90.53117803847618, 'ACC-cup': 86.63341140152353, 'ACC-fork': 81.64165098838853, 'ACC-knife': 78.36227742360958, 'ACC-spoon': 80.24389943155896, 'ACC-bowl': 74.31961820740369, 'ACC-banana': 90.6637622699695, 'ACC-apple': 71.25897789179999, 'ACC-sandwich': 82.82078262802546, 'ACC-orange': 77.67640965328309, 'ACC-broccoli': 79.95215645203234, 'ACC-carrot': 74.43947004175683, 'ACC-hot dog': 75.02331368011887, 'ACC-pizza': 91.45173199333017, 'ACC-donut': 73.14896825754155, 'ACC-cake': 83.82243369367721, 'ACC-chair': 78.51511083037994, 'ACC-couch': 76.45943989636753, 'ACC-potted plant': 59.988063367042265, 'ACC-bed': 83.31741060071455, 'ACC-dining table': 78.08519985967632, 'ACC-toilet': 84.41024282780552, 'ACC-tv': 89.73908475929233, 'ACC-laptop': 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78.05244319051839, 'ACC-table-merged': 56.10694374174099, 'ACC-floor-other-merged': 62.38749199976629, 'ACC-pavement-merged': 71.11292335642266, 'ACC-mountain-merged': 70.66740880150233, 'ACC-grass-merged': 83.83688894184147, 'ACC-dirt-merged': 69.83044219639007, 'ACC-paper-merged': 45.504329818389586, 'ACC-food-other-merged': 58.715697726395256, 'ACC-building-other-merged': 76.07137202333382, 'ACC-rock-merged': 83.82554996996122, 'ACC-wall-other-merged': 80.74256145558849, 'ACC-rug-merged': 83.6823058354054})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4003511852502195, 'noc@0.8': 2.5051214515657008, 'noc@0.85': 2.9461515949663446, 'noc@0.9': 3.7641205736025753, 'miou@iter1': 0.8711856030427052}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.51496124267578, 'precision@0.6': 72.40575408935547, 'precision@0.7': 68.4415054321289, 'precision@0.8': 59.54138946533203, 'precision@0.9': 32.06373977661133, 'cIoU': 61.870765686035156, 'mIoU': 66.80838012695312}}} INFO:trainer.default_trainer:This epoch takes 0:57:17.185985 INFO:trainer.default_trainer:PROGRESS: 42.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 21 training. INFO:trainer.default_trainer:epochs[ 21] optim steps[38400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.04230/0.76854, loss_mask_bce_0: 0.27981/0.30189, loss_mask_dice_0: 1.74650/1.02731, loss_spatial_bce_0: 0.05106/0.08711, loss_spatial_dice_0: 0.28875/0.18403, loss_spatial_ce_0: 0.06592/0.06309, loss_grounding_bce_0: 0.06397/0.08068, loss_grounding_dice_0: 0.22338/0.15107, loss_grounding_ce_0: 0.23816/0.24919, loss_mask_ce_1: 1.99377/0.77028, loss_mask_bce_1: 0.26646/0.30268, loss_mask_dice_1: 1.79017/1.03138, loss_spatial_bce_1: 0.04610/0.08737, loss_spatial_dice_1: 0.27796/0.18657, loss_spatial_ce_1: 0.06178/0.06740, loss_grounding_bce_1: 0.04873/0.08088, loss_grounding_dice_1: 0.18618/0.15185, loss_grounding_ce_1: 0.29569/0.25058, loss_mask_ce_2: 2.43383/0.77808, loss_mask_bce_2: 0.28560/0.30279, loss_mask_dice_2: 1.81957/1.03262, loss_spatial_bce_2: 0.04925/0.08722, loss_spatial_dice_2: 0.30006/0.18673, loss_spatial_ce_2: 0.05770/0.06974, loss_grounding_bce_2: 0.05219/0.08086, loss_grounding_dice_2: 0.20157/0.15159, loss_grounding_ce_2: 0.28864/0.25323, loss_mask_ce_3: 1.73191/0.78019, loss_mask_bce_3: 0.29002/0.30430, loss_mask_dice_3: 2.23274/1.02919, loss_spatial_bce_3: 0.04918/0.08908, loss_spatial_dice_3: 0.29840/0.18764, loss_spatial_ce_3: 0.05947/0.07446, loss_grounding_bce_3: 0.04922/0.08125, loss_grounding_dice_3: 0.21031/0.15110, loss_grounding_ce_3: 0.28966/0.25306, loss_mask_ce_4: 1.78241/0.78589, loss_mask_bce_4: 0.27830/0.30660, loss_mask_dice_4: 2.06998/1.04852, loss_spatial_bce_4: 0.05501/0.09106, loss_spatial_dice_4: 0.30790/0.19533, loss_spatial_ce_4: 0.06011/0.08725, loss_grounding_bce_4: 0.06000/0.08191, loss_grounding_dice_4: 0.23848/0.15381, loss_grounding_ce_4: 0.32678/0.25908, loss_mask_ce_5: 2.07354/0.80866, loss_mask_bce_5: 0.29082/0.30831, loss_mask_dice_5: 2.25334/1.05557, loss_spatial_bce_5: 0.04258/0.09286, loss_spatial_dice_5: 0.29921/0.19779, loss_spatial_ce_5: 0.12847/0.09939, loss_grounding_bce_5: 0.06278/0.08212, loss_grounding_dice_5: 0.23129/0.15443, loss_grounding_ce_5: 0.31265/0.27752, loss_mask_ce_6: 2.18622/0.83482, loss_mask_bce_6: 0.33648/0.31014, loss_mask_dice_6: 2.21875/1.05874, loss_spatial_bce_6: 0.04602/0.09786, loss_spatial_dice_6: 0.29635/0.19999, loss_spatial_ce_6: 0.25387/0.12184, loss_grounding_bce_6: 0.10988/0.08314, loss_grounding_dice_6: 0.27583/0.15505, loss_grounding_ce_6: 0.24757/0.28742, loss_mask_ce_7: 2.12879/0.89210, loss_mask_bce_7: 0.40495/0.31745, loss_mask_dice_7: 2.26503/1.10482, loss_spatial_bce_7: 0.06035/0.10813, loss_spatial_dice_7: 0.37248/0.22510, loss_spatial_ce_7: 0.13825/0.16174, loss_grounding_bce_7: 0.17362/0.08477, loss_grounding_dice_7: 0.29455/0.16073, loss_grounding_ce_7: 0.14586/0.32423, loss_mask_ce_8: 2.75052/1.02887, loss_mask_bce_8: 0.36147/0.33379, loss_mask_dice_8: 2.47282/1.18217, loss_spatial_bce_8: 0.08350/0.12719, loss_spatial_dice_8: 0.38792/0.26276, loss_spatial_ce_8: 0.19213/0.21465, loss_grounding_bce_8: 0.18555/0.08870, loss_grounding_dice_8: 0.31934/0.17039, loss_grounding_ce_8: 0.32140/0.42687, loss_mask_ce_9: 4.21524/3.48772, loss_mask_bce_9: 0.42056/0.36077, loss_mask_dice_9: 3.53611/1.76720, loss_spatial_bce_9: 0.20989/0.35616, loss_spatial_dice_9: 0.94219/0.79497, loss_spatial_ce_9: 1.53571/1.39999, loss_grounding_bce_9: 0.13341/0.10083, loss_grounding_dice_9: 0.31870/0.24372, loss_grounding_ce_9: 0.43159/0.68672] items per batch[64] items per second[0.16] total items[2457600] mini batches[ 38400] memory[4999] epoch remaining[0:59:07] INFO:trainer.default_trainer:epochs[ 21] optim steps[38500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65413/0.76828, loss_mask_bce_0: 1.03837/0.30189, loss_mask_dice_0: 1.49287/1.02701, loss_spatial_bce_0: 0.17628/0.08710, loss_spatial_dice_0: 0.26700/0.18397, loss_spatial_ce_0: 0.06006/0.06307, loss_grounding_bce_0: 0.14376/0.08073, loss_grounding_dice_0: 0.13883/0.15108, loss_grounding_ce_0: 0.05222/0.24892, loss_mask_ce_1: 0.67944/0.77006, loss_mask_bce_1: 0.98867/0.30267, loss_mask_dice_1: 1.59470/1.03110, loss_spatial_bce_1: 0.16805/0.08736, loss_spatial_dice_1: 0.29270/0.18651, loss_spatial_ce_1: 0.26134/0.06738, loss_grounding_bce_1: 0.15662/0.08093, loss_grounding_dice_1: 0.15719/0.15183, loss_grounding_ce_1: 0.04956/0.25033, loss_mask_ce_2: 0.70179/0.77786, loss_mask_bce_2: 1.04604/0.30278, loss_mask_dice_2: 1.71645/1.03235, loss_spatial_bce_2: 0.15393/0.08721, loss_spatial_dice_2: 0.30082/0.18667, loss_spatial_ce_2: 0.21458/0.06971, loss_grounding_bce_2: 0.15580/0.08091, loss_grounding_dice_2: 0.15196/0.15159, loss_grounding_ce_2: 0.04739/0.25296, loss_mask_ce_3: 0.63721/0.78001, loss_mask_bce_3: 1.04102/0.30429, loss_mask_dice_3: 1.56032/1.02890, loss_spatial_bce_3: 0.17084/0.08908, loss_spatial_dice_3: 0.28991/0.18758, loss_spatial_ce_3: 0.26173/0.07443, loss_grounding_bce_3: 0.15447/0.08130, loss_grounding_dice_3: 0.16850/0.15111, loss_grounding_ce_3: 0.06046/0.25281, loss_mask_ce_4: 0.66873/0.78569, loss_mask_bce_4: 1.08430/0.30659, loss_mask_dice_4: 1.54602/1.04823, loss_spatial_bce_4: 0.16965/0.09105, loss_spatial_dice_4: 0.26374/0.19527, loss_spatial_ce_4: 0.03065/0.08722, loss_grounding_bce_4: 0.16017/0.08196, loss_grounding_dice_4: 0.15910/0.15378, loss_grounding_ce_4: 0.03669/0.25882, loss_mask_ce_5: 0.64117/0.80847, loss_mask_bce_5: 1.09486/0.30829, loss_mask_dice_5: 1.53656/1.05529, loss_spatial_bce_5: 0.15068/0.09285, loss_spatial_dice_5: 0.28820/0.19773, loss_spatial_ce_5: 0.02002/0.09937, loss_grounding_bce_5: 0.15136/0.08216, loss_grounding_dice_5: 0.17073/0.15442, loss_grounding_ce_5: 0.03012/0.27726, loss_mask_ce_6: 1.04127/0.83461, loss_mask_bce_6: 0.60671/0.31010, loss_mask_dice_6: 1.40800/1.05843, loss_spatial_bce_6: 0.12466/0.09785, loss_spatial_dice_6: 0.32033/0.19994, loss_spatial_ce_6: 0.21811/0.12187, loss_grounding_bce_6: 0.15217/0.08319, loss_grounding_dice_6: 0.16548/0.15504, loss_grounding_ce_6: 0.03664/0.28722, loss_mask_ce_7: 0.80766/0.89181, loss_mask_bce_7: 1.07338/0.31743, loss_mask_dice_7: 1.50078/1.10450, loss_spatial_bce_7: 0.15937/0.10812, loss_spatial_dice_7: 0.34432/0.22504, loss_spatial_ce_7: 0.19174/0.16172, loss_grounding_bce_7: 0.14937/0.08481, loss_grounding_dice_7: 0.20098/0.16071, loss_grounding_ce_7: 0.02850/0.32391, loss_mask_ce_8: 0.92098/1.02856, loss_mask_bce_8: 1.26445/0.33378, loss_mask_dice_8: 1.75939/1.18187, loss_spatial_bce_8: 0.21833/0.12720, loss_spatial_dice_8: 0.35287/0.26268, loss_spatial_ce_8: 0.11847/0.21461, loss_grounding_bce_8: 0.16020/0.08875, loss_grounding_dice_8: 0.22640/0.17038, loss_grounding_ce_8: 0.03285/0.42636, loss_mask_ce_9: 2.71127/3.48710, loss_mask_bce_9: 0.91018/0.36074, loss_mask_dice_9: 2.60619/1.76677, loss_spatial_bce_9: 0.25005/0.35620, loss_spatial_dice_9: 0.92177/0.79493, loss_spatial_ce_9: 1.74938/1.39999, loss_grounding_bce_9: 0.15885/0.10088, loss_grounding_dice_9: 0.43736/0.24368, loss_grounding_ce_9: 0.42752/0.68621] items per batch[64] items per second[0.36] total items[2464000] mini batches[ 38500] memory[4999] epoch remaining[0:51:31] INFO:trainer.default_trainer:epochs[ 21] optim steps[38600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09304/0.76806, loss_mask_bce_0: 0.40249/0.30191, loss_mask_dice_0: 0.27165/1.02691, loss_spatial_bce_0: 0.14743/0.08709, loss_spatial_dice_0: 0.10023/0.18395, loss_spatial_ce_0: 0.00026/0.06303, loss_grounding_bce_0: 0.43315/0.08073, loss_grounding_dice_0: 0.34394/0.15107, loss_grounding_ce_0: 0.01199/0.24904, loss_mask_ce_1: 0.08334/0.76991, loss_mask_bce_1: 0.37268/0.30269, loss_mask_dice_1: 0.25715/1.03096, loss_spatial_bce_1: 0.15114/0.08734, loss_spatial_dice_1: 0.10193/0.18650, loss_spatial_ce_1: 0.00004/0.06735, loss_grounding_bce_1: 0.42793/0.08092, loss_grounding_dice_1: 0.30723/0.15183, loss_grounding_ce_1: 0.02832/0.25042, loss_mask_ce_2: 0.10258/0.77769, loss_mask_bce_2: 0.37252/0.30279, loss_mask_dice_2: 0.26665/1.03223, loss_spatial_bce_2: 0.16232/0.08720, loss_spatial_dice_2: 0.11043/0.18665, loss_spatial_ce_2: 0.00009/0.06968, loss_grounding_bce_2: 0.42072/0.08090, loss_grounding_dice_2: 0.32638/0.15157, loss_grounding_ce_2: 0.02870/0.25305, loss_mask_ce_3: 0.07870/0.77983, loss_mask_bce_3: 0.37800/0.30430, loss_mask_dice_3: 0.26952/1.02877, loss_spatial_bce_3: 0.17656/0.08905, loss_spatial_dice_3: 0.11773/0.18756, loss_spatial_ce_3: 0.00024/0.07440, loss_grounding_bce_3: 0.43257/0.08129, loss_grounding_dice_3: 0.32809/0.15111, loss_grounding_ce_3: 0.11709/0.25292, loss_mask_ce_4: 0.14663/0.78550, loss_mask_bce_4: 0.37049/0.30661, loss_mask_dice_4: 0.25882/1.04814, loss_spatial_bce_4: 0.17833/0.09103, loss_spatial_dice_4: 0.11912/0.19524, loss_spatial_ce_4: 0.00081/0.08716, loss_grounding_bce_4: 0.38744/0.08195, loss_grounding_dice_4: 0.29284/0.15376, loss_grounding_ce_4: 0.11526/0.25891, loss_mask_ce_5: 0.11466/0.80834, loss_mask_bce_5: 0.34718/0.30830, loss_mask_dice_5: 0.25164/1.05520, loss_spatial_bce_5: 0.17770/0.09284, loss_spatial_dice_5: 0.12989/0.19771, loss_spatial_ce_5: 0.01256/0.09931, loss_grounding_bce_5: 0.40508/0.08215, loss_grounding_dice_5: 0.34205/0.15441, loss_grounding_ce_5: 0.24247/0.27738, loss_mask_ce_6: 0.12737/0.83446, loss_mask_bce_6: 0.35339/0.31012, loss_mask_dice_6: 0.26442/1.05832, loss_spatial_bce_6: 0.19612/0.09783, loss_spatial_dice_6: 0.14070/0.19993, loss_spatial_ce_6: 0.10413/0.12188, loss_grounding_bce_6: 0.33658/0.08317, loss_grounding_dice_6: 0.31731/0.15503, loss_grounding_ce_6: 0.05008/0.28729, loss_mask_ce_7: 0.13807/0.89164, loss_mask_bce_7: 0.38422/0.31745, loss_mask_dice_7: 0.28040/1.10438, loss_spatial_bce_7: 0.19253/0.10810, loss_spatial_dice_7: 0.12662/0.22503, loss_spatial_ce_7: 0.06612/0.16170, loss_grounding_bce_7: 0.41597/0.08480, loss_grounding_dice_7: 0.35045/0.16068, loss_grounding_ce_7: 0.01279/0.32389, loss_mask_ce_8: 0.19990/1.02834, loss_mask_bce_8: 0.37738/0.33380, loss_mask_dice_8: 0.24884/1.18175, loss_spatial_bce_8: 0.16605/0.12717, loss_spatial_dice_8: 0.12142/0.26266, loss_spatial_ce_8: 0.05955/0.21460, loss_grounding_bce_8: 0.23758/0.08873, loss_grounding_dice_8: 0.19941/0.17035, loss_grounding_ce_8: 0.01621/0.42645, loss_mask_ce_9: 2.40557/3.48684, loss_mask_bce_9: 0.33263/0.36080, loss_mask_dice_9: 0.29827/1.76657, loss_spatial_bce_9: 0.44888/0.35622, loss_spatial_dice_9: 0.52673/0.79490, loss_spatial_ce_9: 0.56103/1.40000, loss_grounding_bce_9: 0.20154/0.10086, loss_grounding_dice_9: 0.28507/0.24362, loss_grounding_ce_9: 1.24731/0.68648] items per batch[64] items per second[0.37] total items[2470400] mini batches[ 38600] memory[4999] epoch remaining[0:47:31] INFO:trainer.default_trainer:epochs[ 21] optim steps[38700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09359/0.76798, loss_mask_bce_0: 0.24499/0.30184, loss_mask_dice_0: 0.35765/1.02673, loss_spatial_bce_0: 0.05823/0.08707, loss_spatial_dice_0: 0.12918/0.18392, loss_spatial_ce_0: 0.11303/0.06301, loss_grounding_bce_0: 0.01048/0.08075, loss_grounding_dice_0: 0.08440/0.15109, loss_grounding_ce_0: 0.00184/0.24921, loss_mask_ce_1: 0.10844/0.76983, loss_mask_bce_1: 0.22200/0.30261, loss_mask_dice_1: 0.34348/1.03076, loss_spatial_bce_1: 0.05900/0.08733, loss_spatial_dice_1: 0.13789/0.18647, loss_spatial_ce_1: 0.06897/0.06730, loss_grounding_bce_1: 0.01445/0.08094, loss_grounding_dice_1: 0.08615/0.15184, loss_grounding_ce_1: 0.00376/0.25049, loss_mask_ce_2: 0.11738/0.77766, loss_mask_bce_2: 0.23865/0.30272, loss_mask_dice_2: 0.33890/1.03194, loss_spatial_bce_2: 0.05882/0.08719, loss_spatial_dice_2: 0.12615/0.18663, loss_spatial_ce_2: 0.08618/0.06963, loss_grounding_bce_2: 0.01748/0.08091, loss_grounding_dice_2: 0.10667/0.15159, loss_grounding_ce_2: 0.00921/0.25321, loss_mask_ce_3: 0.13108/0.77983, loss_mask_bce_3: 0.26721/0.30422, loss_mask_dice_3: 0.34031/1.02855, loss_spatial_bce_3: 0.06329/0.08905, loss_spatial_dice_3: 0.12257/0.18753, loss_spatial_ce_3: 0.08787/0.07433, loss_grounding_bce_3: 0.01554/0.08131, loss_grounding_dice_3: 0.08724/0.15115, loss_grounding_ce_3: 0.00597/0.25310, loss_mask_ce_4: 0.14214/0.78553, loss_mask_bce_4: 0.24845/0.30653, loss_mask_dice_4: 0.35618/1.04795, loss_spatial_bce_4: 0.06439/0.09102, loss_spatial_dice_4: 0.15492/0.19521, loss_spatial_ce_4: 0.05376/0.08711, loss_grounding_bce_4: 0.02411/0.08197, loss_grounding_dice_4: 0.11711/0.15378, loss_grounding_ce_4: 0.00634/0.25904, loss_mask_ce_5: 0.12464/0.80822, loss_mask_bce_5: 0.25354/0.30823, loss_mask_dice_5: 0.39560/1.05498, loss_spatial_bce_5: 0.06016/0.09281, loss_spatial_dice_5: 0.13326/0.19768, loss_spatial_ce_5: 0.04964/0.09931, loss_grounding_bce_5: 0.01950/0.08217, loss_grounding_dice_5: 0.11882/0.15443, loss_grounding_ce_5: 0.00239/0.27747, loss_mask_ce_6: 0.11078/0.83438, loss_mask_bce_6: 0.24388/0.31004, loss_mask_dice_6: 0.33664/1.05810, loss_spatial_bce_6: 0.06289/0.09781, loss_spatial_dice_6: 0.13962/0.19989, loss_spatial_ce_6: 0.10022/0.12185, loss_grounding_bce_6: 0.01717/0.08319, loss_grounding_dice_6: 0.11352/0.15507, loss_grounding_ce_6: 0.00223/0.28735, loss_mask_ce_7: 0.11030/0.89163, loss_mask_bce_7: 0.25686/0.31738, loss_mask_dice_7: 0.34971/1.10425, loss_spatial_bce_7: 0.13531/0.10809, loss_spatial_dice_7: 0.22366/0.22501, loss_spatial_ce_7: 0.12681/0.16163, loss_grounding_bce_7: 0.01578/0.08481, loss_grounding_dice_7: 0.10939/0.16072, loss_grounding_ce_7: 0.00893/0.32404, loss_mask_ce_8: 0.19412/1.02811, loss_mask_bce_8: 0.28501/0.33374, loss_mask_dice_8: 0.37768/1.18148, loss_spatial_bce_8: 0.11745/0.12715, loss_spatial_dice_8: 0.21591/0.26263, loss_spatial_ce_8: 0.24030/0.21452, loss_grounding_bce_8: 0.02291/0.08875, loss_grounding_dice_8: 0.15179/0.17036, loss_grounding_ce_8: 0.01017/0.42657, loss_mask_ce_9: 2.54014/3.48677, loss_mask_bce_9: 0.30973/0.36073, loss_mask_dice_9: 0.75503/1.76624, loss_spatial_bce_9: 0.48610/0.35618, loss_spatial_dice_9: 0.76238/0.79484, loss_spatial_ce_9: 1.95339/1.39993, loss_grounding_bce_9: 0.05252/0.10087, loss_grounding_dice_9: 0.26276/0.24359, loss_grounding_ce_9: 0.08221/0.68637] items per batch[64] items per second[0.37] total items[2476800] mini batches[ 38700] memory[4999] epoch remaining[0:43:56] INFO:trainer.default_trainer:epochs[ 21] optim steps[38800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84898/0.76812, loss_mask_bce_0: 0.34276/0.30198, loss_mask_dice_0: 0.18385/1.02702, loss_spatial_bce_0: 0.26077/0.08712, loss_spatial_dice_0: 0.14600/0.18392, loss_spatial_ce_0: 0.28939/0.06297, loss_grounding_bce_0: 0.61549/0.08079, loss_grounding_dice_0: 0.22891/0.15108, loss_grounding_ce_0: 0.01517/0.24927, loss_mask_ce_1: 0.86223/0.76992, loss_mask_bce_1: 0.34874/0.30274, loss_mask_dice_1: 0.18432/1.03105, loss_spatial_bce_1: 0.27932/0.08737, loss_spatial_dice_1: 0.15415/0.18647, loss_spatial_ce_1: 0.27912/0.06726, loss_grounding_bce_1: 0.59304/0.08098, loss_grounding_dice_1: 0.20631/0.15182, loss_grounding_ce_1: 0.00738/0.25054, loss_mask_ce_2: 0.90661/0.77780, loss_mask_bce_2: 0.36444/0.30284, loss_mask_dice_2: 0.19949/1.03219, loss_spatial_bce_2: 0.27866/0.08724, loss_spatial_dice_2: 0.17880/0.18661, loss_spatial_ce_2: 0.24203/0.06961, loss_grounding_bce_2: 0.59676/0.08094, loss_grounding_dice_2: 0.19626/0.15158, loss_grounding_ce_2: 0.02170/0.25324, loss_mask_ce_3: 1.01048/0.77999, loss_mask_bce_3: 0.38477/0.30437, loss_mask_dice_3: 0.20568/1.02883, loss_spatial_bce_3: 0.24916/0.08909, loss_spatial_dice_3: 0.17905/0.18753, loss_spatial_ce_3: 0.23468/0.07432, loss_grounding_bce_3: 0.57544/0.08134, loss_grounding_dice_3: 0.18407/0.15111, loss_grounding_ce_3: 0.03418/0.25312, loss_mask_ce_4: 1.03391/0.78568, loss_mask_bce_4: 0.33436/0.30665, loss_mask_dice_4: 0.20291/1.04825, loss_spatial_bce_4: 0.25973/0.09106, loss_spatial_dice_4: 0.18942/0.19522, loss_spatial_ce_4: 0.26060/0.08710, loss_grounding_bce_4: 0.57913/0.08200, loss_grounding_dice_4: 0.17610/0.15376, loss_grounding_ce_4: 0.07662/0.25904, loss_mask_ce_5: 0.88392/0.80833, loss_mask_bce_5: 0.33863/0.30836, loss_mask_dice_5: 0.19805/1.05527, loss_spatial_bce_5: 0.22593/0.09286, loss_spatial_dice_5: 0.16331/0.19768, loss_spatial_ce_5: 0.29104/0.09929, loss_grounding_bce_5: 0.58187/0.08221, loss_grounding_dice_5: 0.19702/0.15441, loss_grounding_ce_5: 0.06699/0.27746, loss_mask_ce_6: 0.83981/0.83451, loss_mask_bce_6: 0.38218/0.31018, loss_mask_dice_6: 0.21640/1.05843, loss_spatial_bce_6: 0.19552/0.09786, loss_spatial_dice_6: 0.14723/0.19990, loss_spatial_ce_6: 0.45622/0.12190, loss_grounding_bce_6: 0.61845/0.08323, loss_grounding_dice_6: 0.18521/0.15504, loss_grounding_ce_6: 0.04478/0.28733, loss_mask_ce_7: 0.87606/0.89165, loss_mask_bce_7: 0.33918/0.31754, loss_mask_dice_7: 0.23782/1.10458, loss_spatial_bce_7: 0.25243/0.10814, loss_spatial_dice_7: 0.18517/0.22502, loss_spatial_ce_7: 0.99168/0.16166, loss_grounding_bce_7: 0.67484/0.08486, loss_grounding_dice_7: 0.20273/0.16070, loss_grounding_ce_7: 0.01569/0.32398, loss_mask_ce_8: 0.91498/1.02818, loss_mask_bce_8: 0.54835/0.33394, loss_mask_dice_8: 0.38509/1.18178, loss_spatial_bce_8: 0.84279/0.12722, loss_spatial_dice_8: 0.28047/0.26261, loss_spatial_ce_8: 0.13830/0.21453, loss_grounding_bce_8: 0.57026/0.08880, loss_grounding_dice_8: 0.21093/0.17033, loss_grounding_ce_8: 0.10759/0.42656, loss_mask_ce_9: 1.75627/3.48693, loss_mask_bce_9: 0.50974/0.36089, loss_mask_dice_9: 0.35500/1.76693, loss_spatial_bce_9: 0.82291/0.35641, loss_spatial_dice_9: 0.73285/0.79487, loss_spatial_ce_9: 1.93093/1.39991, loss_grounding_bce_9: 0.43033/0.10093, loss_grounding_dice_9: 0.14566/0.24355, loss_grounding_ce_9: 0.49957/0.68633] items per batch[64] items per second[0.36] total items[2483200] mini batches[ 38800] memory[4999] epoch remaining[0:41:05] INFO:trainer.default_trainer:epochs[ 21] optim steps[38900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.91355/0.76824, loss_mask_bce_0: 0.07298/0.30199, loss_mask_dice_0: 1.80288/1.02715, loss_spatial_bce_0: 0.01273/0.08708, loss_spatial_dice_0: 0.24450/0.18394, loss_spatial_ce_0: 0.02120/0.06294, loss_grounding_bce_0: 0.00356/0.08081, loss_grounding_dice_0: 0.05698/0.15107, loss_grounding_ce_0: 0.04722/0.24945, loss_mask_ce_1: 1.03278/0.77011, loss_mask_bce_1: 0.07171/0.30277, loss_mask_dice_1: 2.20471/1.03116, loss_spatial_bce_1: 0.01236/0.08734, loss_spatial_dice_1: 0.23493/0.18650, loss_spatial_ce_1: 0.02309/0.06724, loss_grounding_bce_1: 0.00606/0.08099, loss_grounding_dice_1: 0.07717/0.15181, loss_grounding_ce_1: 0.06819/0.25065, loss_mask_ce_2: 0.91213/0.77799, loss_mask_bce_2: 0.07124/0.30286, loss_mask_dice_2: 2.32533/1.03229, loss_spatial_bce_2: 0.01257/0.08721, loss_spatial_dice_2: 0.23219/0.18663, loss_spatial_ce_2: 0.00772/0.06957, loss_grounding_bce_2: 0.00358/0.08096, loss_grounding_dice_2: 0.06210/0.15157, loss_grounding_ce_2: 0.03423/0.25345, loss_mask_ce_3: 0.88099/0.78015, loss_mask_bce_3: 0.07318/0.30439, loss_mask_dice_3: 2.31152/1.02889, loss_spatial_bce_3: 0.01280/0.08907, loss_spatial_dice_3: 0.24657/0.18758, loss_spatial_ce_3: 0.01960/0.07429, loss_grounding_bce_3: 0.00378/0.08136, loss_grounding_dice_3: 0.04921/0.15109, loss_grounding_ce_3: 0.03796/0.25326, loss_mask_ce_4: 1.22696/0.78591, loss_mask_bce_4: 0.07959/0.30668, loss_mask_dice_4: 2.50478/1.04842, loss_spatial_bce_4: 0.01268/0.09104, loss_spatial_dice_4: 0.24189/0.19525, loss_spatial_ce_4: 0.02865/0.08711, loss_grounding_bce_4: 0.00352/0.08201, loss_grounding_dice_4: 0.05075/0.15374, loss_grounding_ce_4: 0.01700/0.25928, loss_mask_ce_5: 0.92571/0.80849, loss_mask_bce_5: 0.07702/0.30839, loss_mask_dice_5: 2.18704/1.05541, loss_spatial_bce_5: 0.01337/0.09284, loss_spatial_dice_5: 0.21204/0.19771, loss_spatial_ce_5: 0.02490/0.09930, loss_grounding_bce_5: 0.00427/0.08222, loss_grounding_dice_5: 0.04641/0.15440, loss_grounding_ce_5: 0.04238/0.27769, loss_mask_ce_6: 1.01454/0.83463, loss_mask_bce_6: 0.06822/0.31022, loss_mask_dice_6: 1.88121/1.05860, loss_spatial_bce_6: 0.01557/0.09784, loss_spatial_dice_6: 0.21826/0.19994, loss_spatial_ce_6: 0.22539/0.12196, loss_grounding_bce_6: 0.00369/0.08325, loss_grounding_dice_6: 0.06598/0.15503, loss_grounding_ce_6: 0.46832/0.28761, loss_mask_ce_7: 1.58768/0.89178, loss_mask_bce_7: 0.08900/0.31758, loss_mask_dice_7: 2.62123/1.10478, loss_spatial_bce_7: 0.02367/0.10812, loss_spatial_dice_7: 0.30444/0.22509, loss_spatial_ce_7: 0.15470/0.16167, loss_grounding_bce_7: 0.00311/0.08488, loss_grounding_dice_7: 0.04470/0.16070, loss_grounding_ce_7: 0.55514/0.32418, loss_mask_ce_8: 1.65775/1.02843, loss_mask_bce_8: 0.11045/0.33397, loss_mask_dice_8: 2.26496/1.18197, loss_spatial_bce_8: 0.02158/0.12720, loss_spatial_dice_8: 0.38295/0.26266, loss_spatial_ce_8: 0.10685/0.21452, loss_grounding_bce_8: 0.00511/0.08883, loss_grounding_dice_8: 0.07204/0.17036, loss_grounding_ce_8: 0.26706/0.42675, loss_mask_ce_9: 4.75959/3.48718, loss_mask_bce_9: 0.10771/0.36093, loss_mask_dice_9: 3.69772/1.76711, loss_spatial_bce_9: 0.12015/0.35637, loss_spatial_dice_9: 0.95273/0.79491, loss_spatial_ce_9: 2.60878/1.39994, loss_grounding_bce_9: 0.01076/0.10095, loss_grounding_dice_9: 0.27904/0.24358, loss_grounding_ce_9: 1.47410/0.68637] items per batch[64] items per second[0.36] total items[2489600] mini batches[ 38900] memory[4999] epoch remaining[0:38:12] INFO:trainer.default_trainer:epochs[ 21] optim steps[39000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.13483/0.76818, loss_mask_bce_0: 0.05255/0.30197, loss_mask_dice_0: 2.17912/1.02715, loss_spatial_bce_0: 0.04634/0.08706, loss_spatial_dice_0: 0.22049/0.18394, loss_spatial_ce_0: 0.00091/0.06294, loss_grounding_bce_0: 0.00351/0.08077, loss_grounding_dice_0: 0.17727/0.15108, loss_grounding_ce_0: 0.16962/0.24952, loss_mask_ce_1: 0.92352/0.77000, loss_mask_bce_1: 0.06198/0.30275, loss_mask_dice_1: 2.48961/1.03120, loss_spatial_bce_1: 0.04492/0.08732, loss_spatial_dice_1: 0.24571/0.18650, loss_spatial_ce_1: 0.00139/0.06726, loss_grounding_bce_1: 0.00339/0.08096, loss_grounding_dice_1: 0.23669/0.15180, loss_grounding_ce_1: 0.16978/0.25069, loss_mask_ce_2: 0.93774/0.77786, loss_mask_bce_2: 0.07100/0.30285, loss_mask_dice_2: 2.63462/1.03233, loss_spatial_bce_2: 0.05726/0.08719, loss_spatial_dice_2: 0.25293/0.18663, loss_spatial_ce_2: 0.00113/0.06961, loss_grounding_bce_2: 0.00352/0.08092, loss_grounding_dice_2: 0.27272/0.15156, loss_grounding_ce_2: 0.20279/0.25349, loss_mask_ce_3: 1.11901/0.78003, loss_mask_bce_3: 0.07557/0.30437, loss_mask_dice_3: 2.49204/1.02889, loss_spatial_bce_3: 0.06637/0.08905, loss_spatial_dice_3: 0.19925/0.18758, loss_spatial_ce_3: 0.00232/0.07428, loss_grounding_bce_3: 0.00359/0.08133, loss_grounding_dice_3: 0.23543/0.15109, loss_grounding_ce_3: 0.21734/0.25330, loss_mask_ce_4: 0.94030/0.78585, loss_mask_bce_4: 0.06751/0.30667, loss_mask_dice_4: 2.23585/1.04841, loss_spatial_bce_4: 0.05616/0.09102, loss_spatial_dice_4: 0.23254/0.19526, loss_spatial_ce_4: 0.00628/0.08711, loss_grounding_bce_4: 0.00308/0.08197, loss_grounding_dice_4: 0.17730/0.15374, loss_grounding_ce_4: 0.20349/0.25940, loss_mask_ce_5: 1.14258/0.80837, loss_mask_bce_5: 0.06813/0.30838, loss_mask_dice_5: 2.43735/1.05546, loss_spatial_bce_5: 0.08091/0.09283, loss_spatial_dice_5: 0.25095/0.19773, loss_spatial_ce_5: 0.04391/0.09931, loss_grounding_bce_5: 0.00225/0.08218, loss_grounding_dice_5: 0.18981/0.15438, loss_grounding_ce_5: 0.19865/0.27788, loss_mask_ce_6: 0.84871/0.83457, loss_mask_bce_6: 0.06645/0.31019, loss_mask_dice_6: 2.81589/1.05864, loss_spatial_bce_6: 0.06778/0.09782, loss_spatial_dice_6: 0.21276/0.19995, loss_spatial_ce_6: 0.06265/0.12203, loss_grounding_bce_6: 0.00351/0.08321, loss_grounding_dice_6: 0.28127/0.15502, loss_grounding_ce_6: 0.19952/0.28772, loss_mask_ce_7: 1.28996/0.89158, loss_mask_bce_7: 0.07762/0.31756, loss_mask_dice_7: 2.27945/1.10478, loss_spatial_bce_7: 0.04556/0.10811, loss_spatial_dice_7: 0.37471/0.22509, loss_spatial_ce_7: 0.12628/0.16170, loss_grounding_bce_7: 0.00318/0.08484, loss_grounding_dice_7: 0.22989/0.16071, loss_grounding_ce_7: 0.25187/0.32436, loss_mask_ce_8: 1.37572/1.02829, loss_mask_bce_8: 0.06503/0.33394, loss_mask_dice_8: 2.53574/1.18194, loss_spatial_bce_8: 0.12111/0.12718, loss_spatial_dice_8: 0.33266/0.26265, loss_spatial_ce_8: 0.12263/0.21448, loss_grounding_bce_8: 0.00270/0.08879, loss_grounding_dice_8: 0.03353/0.17033, loss_grounding_ce_8: 0.29086/0.42688, loss_mask_ce_9: 5.22721/3.48705, loss_mask_bce_9: 0.06765/0.36092, loss_mask_dice_9: 3.96306/1.76706, loss_spatial_bce_9: 0.03664/0.35632, loss_spatial_dice_9: 0.89529/0.79492, loss_spatial_ce_9: 1.35358/1.39975, loss_grounding_bce_9: 0.00343/0.10091, loss_grounding_dice_9: 0.19115/0.24355, loss_grounding_ce_9: 0.54955/0.68650] items per batch[64] items per second[0.36] total items[2496000] mini batches[ 39000] memory[4999] epoch remaining[0:35:17] INFO:trainer.default_trainer:epochs[ 21] optim steps[39100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82250/0.76816, loss_mask_bce_0: 0.11847/0.30187, loss_mask_dice_0: 0.89832/1.02696, loss_spatial_bce_0: 0.02787/0.08704, loss_spatial_dice_0: 0.16358/0.18393, loss_spatial_ce_0: 0.00201/0.06289, loss_grounding_bce_0: 0.01859/0.08074, loss_grounding_dice_0: 0.82521/0.15115, loss_grounding_ce_0: 0.90300/0.24948, loss_mask_ce_1: 0.47833/0.76999, loss_mask_bce_1: 0.22571/0.30267, loss_mask_dice_1: 0.95927/1.03097, loss_spatial_bce_1: 0.02952/0.08729, loss_spatial_dice_1: 0.16364/0.18648, loss_spatial_ce_1: 0.00097/0.06723, loss_grounding_bce_1: 0.00580/0.08093, loss_grounding_dice_1: 0.63345/0.15186, loss_grounding_ce_1: 1.18115/0.25066, loss_mask_ce_2: 0.81709/0.77783, loss_mask_bce_2: 0.10848/0.30276, loss_mask_dice_2: 0.77084/1.03207, loss_spatial_bce_2: 0.02842/0.08716, loss_spatial_dice_2: 0.16448/0.18662, loss_spatial_ce_2: 0.00106/0.06955, loss_grounding_bce_2: 0.00980/0.08089, loss_grounding_dice_2: 0.64571/0.15161, loss_grounding_ce_2: 1.26949/0.25348, loss_mask_ce_3: 0.77067/0.78004, loss_mask_bce_3: 0.11448/0.30428, loss_mask_dice_3: 0.80679/1.02868, loss_spatial_bce_3: 0.02666/0.08903, loss_spatial_dice_3: 0.16562/0.18757, loss_spatial_ce_3: 0.01104/0.07424, loss_grounding_bce_3: 0.00482/0.08130, loss_grounding_dice_3: 0.58091/0.15116, loss_grounding_ce_3: 1.08141/0.25328, loss_mask_ce_4: 0.44123/0.78591, loss_mask_bce_4: 0.19899/0.30656, loss_mask_dice_4: 0.82911/1.04805, loss_spatial_bce_4: 0.03004/0.09100, loss_spatial_dice_4: 0.19622/0.19524, loss_spatial_ce_4: 0.03673/0.08706, loss_grounding_bce_4: 0.00864/0.08194, loss_grounding_dice_4: 0.95524/0.15382, loss_grounding_ce_4: 1.05440/0.25937, loss_mask_ce_5: 0.71913/0.80838, loss_mask_bce_5: 0.11737/0.30828, loss_mask_dice_5: 0.76896/1.05518, loss_spatial_bce_5: 0.03048/0.09281, loss_spatial_dice_5: 0.18278/0.19772, loss_spatial_ce_5: 0.03101/0.09926, loss_grounding_bce_5: 0.29875/0.08216, loss_grounding_dice_5: 0.98013/0.15444, loss_grounding_ce_5: 0.07070/0.27798, loss_mask_ce_6: 0.55858/0.83450, loss_mask_bce_6: 0.24063/0.31010, loss_mask_dice_6: 0.76146/1.05838, loss_spatial_bce_6: 0.03890/0.09780, loss_spatial_dice_6: 0.21827/0.19996, loss_spatial_ce_6: 0.05703/0.12198, loss_grounding_bce_6: 0.01092/0.08317, loss_grounding_dice_6: 0.92565/0.15509, loss_grounding_ce_6: 0.41522/0.28774, loss_mask_ce_7: 0.88298/0.89159, loss_mask_bce_7: 0.12061/0.31746, loss_mask_dice_7: 0.71644/1.10446, loss_spatial_bce_7: 0.04966/0.10808, loss_spatial_dice_7: 0.22979/0.22510, loss_spatial_ce_7: 0.04680/0.16165, loss_grounding_bce_7: 0.00408/0.08480, loss_grounding_dice_7: 0.51378/0.16078, loss_grounding_ce_7: 0.41681/0.32430, loss_mask_ce_8: 1.01400/1.02815, loss_mask_bce_8: 0.12834/0.33383, loss_mask_dice_8: 0.99053/1.18164, loss_spatial_bce_8: 0.07057/0.12715, loss_spatial_dice_8: 0.29454/0.26263, loss_spatial_ce_8: 0.06109/0.21441, loss_grounding_bce_8: 0.02081/0.08875, loss_grounding_dice_8: 0.94937/0.17041, loss_grounding_ce_8: 0.34032/0.42684, loss_mask_ce_9: 3.32741/3.48718, loss_mask_bce_9: 0.11952/0.36081, loss_mask_dice_9: 0.91977/1.76678, loss_spatial_bce_9: 0.46302/0.35624, loss_spatial_dice_9: 0.91911/0.79492, loss_spatial_ce_9: 2.45021/1.39960, loss_grounding_bce_9: 0.01620/0.10088, loss_grounding_dice_9: 0.92180/0.24362, loss_grounding_ce_9: 0.38008/0.68632] items per batch[64] items per second[0.37] total items[2502400] mini batches[ 39100] memory[4999] epoch remaining[0:32:11] INFO:trainer.default_trainer:epochs[ 21] optim steps[39200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78519/0.76806, loss_mask_bce_0: 0.60971/0.30191, loss_mask_dice_0: 0.63166/1.02703, loss_spatial_bce_0: 0.13857/0.08704, loss_spatial_dice_0: 0.12597/0.18391, loss_spatial_ce_0: 0.06299/0.06286, loss_grounding_bce_0: 0.06845/0.08074, loss_grounding_dice_0: 0.11226/0.15122, loss_grounding_ce_0: 0.55593/0.24950, loss_mask_ce_1: 1.14603/0.76985, loss_mask_bce_1: 0.60785/0.30270, loss_mask_dice_1: 0.61448/1.03101, loss_spatial_bce_1: 0.13750/0.08729, loss_spatial_dice_1: 0.14583/0.18646, loss_spatial_ce_1: 0.04568/0.06719, loss_grounding_bce_1: 0.05462/0.08093, loss_grounding_dice_1: 0.09639/0.15192, loss_grounding_ce_1: 0.55898/0.25071, loss_mask_ce_2: 1.19496/0.77776, loss_mask_bce_2: 0.60032/0.30278, loss_mask_dice_2: 0.63433/1.03206, loss_spatial_bce_2: 0.13992/0.08717, loss_spatial_dice_2: 0.14025/0.18661, loss_spatial_ce_2: 0.03430/0.06951, loss_grounding_bce_2: 0.05238/0.08089, loss_grounding_dice_2: 0.09336/0.15168, loss_grounding_ce_2: 0.52224/0.25352, loss_mask_ce_3: 1.10982/0.77992, loss_mask_bce_3: 0.61282/0.30431, loss_mask_dice_3: 0.63916/1.02868, loss_spatial_bce_3: 0.14886/0.08903, loss_spatial_dice_3: 0.15265/0.18756, loss_spatial_ce_3: 0.03619/0.07420, loss_grounding_bce_3: 0.05037/0.08130, loss_grounding_dice_3: 0.08987/0.15121, loss_grounding_ce_3: 0.48508/0.25333, loss_mask_ce_4: 0.81645/0.78584, loss_mask_bce_4: 0.56074/0.30659, loss_mask_dice_4: 0.54759/1.04817, loss_spatial_bce_4: 0.15542/0.09101, loss_spatial_dice_4: 0.17155/0.19523, loss_spatial_ce_4: 0.05946/0.08700, loss_grounding_bce_4: 0.08999/0.08194, loss_grounding_dice_4: 0.10675/0.15388, loss_grounding_ce_4: 0.48120/0.25945, loss_mask_ce_5: 0.87065/0.80833, loss_mask_bce_5: 0.59863/0.30831, loss_mask_dice_5: 0.57904/1.05522, loss_spatial_bce_5: 0.15614/0.09282, loss_spatial_dice_5: 0.14067/0.19771, loss_spatial_ce_5: 0.03815/0.09924, loss_grounding_bce_5: 0.05216/0.08217, loss_grounding_dice_5: 0.08581/0.15450, loss_grounding_ce_5: 0.49951/0.27797, loss_mask_ce_6: 0.89428/0.83444, loss_mask_bce_6: 0.59585/0.31014, loss_mask_dice_6: 0.59734/1.05844, loss_spatial_bce_6: 0.16601/0.09781, loss_spatial_dice_6: 0.17298/0.19994, loss_spatial_ce_6: 0.05033/0.12195, loss_grounding_bce_6: 0.09107/0.08317, loss_grounding_dice_6: 0.12250/0.15515, loss_grounding_ce_6: 0.62410/0.28775, loss_mask_ce_7: 1.03498/0.89160, loss_mask_bce_7: 0.53025/0.31749, loss_mask_dice_7: 0.59008/1.10448, loss_spatial_bce_7: 0.17123/0.10809, loss_spatial_dice_7: 0.23732/0.22509, loss_spatial_ce_7: 0.05244/0.16163, loss_grounding_bce_7: 0.04486/0.08480, loss_grounding_dice_7: 0.11378/0.16083, loss_grounding_ce_7: 0.66169/0.32427, loss_mask_ce_8: 1.29947/1.02820, loss_mask_bce_8: 0.61450/0.33388, loss_mask_dice_8: 0.65547/1.18175, loss_spatial_bce_8: 0.20214/0.12714, loss_spatial_dice_8: 0.27033/0.26261, loss_spatial_ce_8: 0.08288/0.21439, loss_grounding_bce_8: 0.06104/0.08874, loss_grounding_dice_8: 0.09919/0.17047, loss_grounding_ce_8: 0.49599/0.42697, loss_mask_ce_9: 4.61050/3.48729, loss_mask_bce_9: 0.58281/0.36088, loss_mask_dice_9: 1.18371/1.76693, loss_spatial_bce_9: 0.43664/0.35617, loss_spatial_dice_9: 0.74757/0.79488, loss_spatial_ce_9: 1.03543/1.39939, loss_grounding_bce_9: 0.06486/0.10090, loss_grounding_dice_9: 0.18764/0.24372, loss_grounding_ce_9: 0.54488/0.68616] items per batch[64] items per second[0.37] total items[2508800] mini batches[ 39200] memory[4999] epoch remaining[0:29:12] INFO:trainer.default_trainer:epochs[ 21] optim steps[39300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43120/0.76794, loss_mask_bce_0: 0.33043/0.30191, loss_mask_dice_0: 5.67175/1.02712, loss_spatial_bce_0: 0.01140/0.08700, loss_spatial_dice_0: 0.25384/0.18385, loss_spatial_ce_0: 0.00766/0.06283, loss_grounding_bce_0: 0.00492/0.08074, loss_grounding_dice_0: 0.28672/0.15119, loss_grounding_ce_0: 0.51233/0.24965, loss_mask_ce_1: 0.69960/0.76972, loss_mask_bce_1: 0.26978/0.30269, loss_mask_dice_1: 3.89854/1.03106, loss_spatial_bce_1: 0.00933/0.08725, loss_spatial_dice_1: 0.25476/0.18640, loss_spatial_ce_1: 0.09592/0.06716, loss_grounding_bce_1: 0.00410/0.08093, loss_grounding_dice_1: 0.29020/0.15192, loss_grounding_ce_1: 0.60632/0.25087, loss_mask_ce_2: 0.52370/0.77765, loss_mask_bce_2: 0.30216/0.30277, loss_mask_dice_2: 4.56081/1.03207, loss_spatial_bce_2: 0.00957/0.08713, loss_spatial_dice_2: 0.26016/0.18656, loss_spatial_ce_2: 0.02894/0.06948, loss_grounding_bce_2: 0.00547/0.08089, loss_grounding_dice_2: 0.34295/0.15169, loss_grounding_ce_2: 0.47426/0.25358, loss_mask_ce_3: 0.78510/0.77984, loss_mask_bce_3: 0.27529/0.30430, loss_mask_dice_3: 4.32692/1.02877, loss_spatial_bce_3: 0.00999/0.08901, loss_spatial_dice_3: 0.22483/0.18751, loss_spatial_ce_3: 0.01796/0.07416, loss_grounding_bce_3: 0.00370/0.08129, loss_grounding_dice_3: 0.24349/0.15122, loss_grounding_ce_3: 0.65208/0.25339, loss_mask_ce_4: 0.65636/0.78577, loss_mask_bce_4: 0.32357/0.30658, loss_mask_dice_4: 3.91991/1.04826, loss_spatial_bce_4: 0.01034/0.09098, loss_spatial_dice_4: 0.24889/0.19518, loss_spatial_ce_4: 0.02969/0.08695, loss_grounding_bce_4: 0.00434/0.08194, loss_grounding_dice_4: 0.17109/0.15388, loss_grounding_ce_4: 0.59961/0.25961, loss_mask_ce_5: 0.75705/0.80829, loss_mask_bce_5: 0.34509/0.30831, loss_mask_dice_5: 4.56717/1.05537, loss_spatial_bce_5: 0.01200/0.09279, loss_spatial_dice_5: 0.22405/0.19766, loss_spatial_ce_5: 0.04273/0.09921, loss_grounding_bce_5: 0.00536/0.08216, loss_grounding_dice_5: 0.32743/0.15449, loss_grounding_ce_5: 0.57306/0.27804, loss_mask_ce_6: 0.85838/0.83434, loss_mask_bce_6: 0.31200/0.31015, loss_mask_dice_6: 4.30482/1.05852, loss_spatial_bce_6: 0.01284/0.09777, loss_spatial_dice_6: 0.26967/0.19989, loss_spatial_ce_6: 0.12692/0.12192, loss_grounding_bce_6: 0.00493/0.08317, loss_grounding_dice_6: 0.18949/0.15516, loss_grounding_ce_6: 0.55038/0.28772, loss_mask_ce_7: 0.82982/0.89146, loss_mask_bce_7: 0.31117/0.31749, loss_mask_dice_7: 4.97491/1.10459, loss_spatial_bce_7: 0.01323/0.10807, loss_spatial_dice_7: 0.28830/0.22504, loss_spatial_ce_7: 0.13275/0.16158, loss_grounding_bce_7: 0.00666/0.08480, loss_grounding_dice_7: 0.38567/0.16084, loss_grounding_ce_7: 0.62052/0.32429, loss_mask_ce_8: 1.10902/1.02806, loss_mask_bce_8: 0.29441/0.33385, loss_mask_dice_8: 5.07488/1.18189, loss_spatial_bce_8: 0.01414/0.12708, loss_spatial_dice_8: 0.33528/0.26257, loss_spatial_ce_8: 0.32279/0.21433, loss_grounding_bce_8: 0.00673/0.08874, loss_grounding_dice_8: 0.39164/0.17047, loss_grounding_ce_8: 0.77841/0.42705, loss_mask_ce_9: 4.39854/3.48722, loss_mask_bce_9: 0.24555/0.36086, loss_mask_dice_9: 6.53697/1.76751, loss_spatial_bce_9: 0.09499/0.35609, loss_spatial_dice_9: 0.97425/0.79487, loss_spatial_ce_9: 1.49714/1.39938, loss_grounding_bce_9: 0.00480/0.10088, loss_grounding_dice_9: 0.59310/0.24370, loss_grounding_ce_9: 0.62357/0.68622] items per batch[64] items per second[0.36] total items[2515200] mini batches[ 39300] memory[4999] epoch remaining[0:26:19] INFO:trainer.default_trainer:epochs[ 21] optim steps[39400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44800/0.76795, loss_mask_bce_0: 0.00782/0.30190, loss_mask_dice_0: 0.52372/1.02718, loss_spatial_bce_0: 0.00339/0.08700, loss_spatial_dice_0: 0.24060/0.18384, loss_spatial_ce_0: 0.04625/0.06278, loss_grounding_bce_0: 0.00552/0.08074, loss_grounding_dice_0: 0.37852/0.15119, loss_grounding_ce_0: 0.16437/0.24967, loss_mask_ce_1: 0.41089/0.76980, loss_mask_bce_1: 0.00501/0.30268, loss_mask_dice_1: 0.29339/1.03112, loss_spatial_bce_1: 0.00378/0.08725, loss_spatial_dice_1: 0.32487/0.18638, loss_spatial_ce_1: 0.04178/0.06710, loss_grounding_bce_1: 0.00482/0.08094, loss_grounding_dice_1: 0.39203/0.15191, loss_grounding_ce_1: 0.02559/0.25088, loss_mask_ce_2: 0.44806/0.77767, loss_mask_bce_2: 0.01125/0.30277, loss_mask_dice_2: 0.58929/1.03214, loss_spatial_bce_2: 0.00388/0.08713, loss_spatial_dice_2: 0.36828/0.18655, loss_spatial_ce_2: 0.11734/0.06940, loss_grounding_bce_2: 0.00187/0.08088, loss_grounding_dice_2: 0.31120/0.15169, loss_grounding_ce_2: 0.15650/0.25367, loss_mask_ce_3: 0.69650/0.77985, loss_mask_bce_3: 0.01045/0.30430, loss_mask_dice_3: 0.54826/1.02885, loss_spatial_bce_3: 0.00266/0.08900, loss_spatial_dice_3: 0.31628/0.18751, loss_spatial_ce_3: 0.07839/0.07407, loss_grounding_bce_3: 0.00417/0.08129, loss_grounding_dice_3: 0.24616/0.15123, loss_grounding_ce_3: 0.03151/0.25352, loss_mask_ce_4: 0.54667/0.78579, loss_mask_bce_4: 0.00782/0.30658, loss_mask_dice_4: 0.72408/1.04837, loss_spatial_bce_4: 0.00192/0.09097, loss_spatial_dice_4: 0.35316/0.19518, loss_spatial_ce_4: 0.16952/0.08689, loss_grounding_bce_4: 0.00240/0.08194, loss_grounding_dice_4: 0.15074/0.15387, loss_grounding_ce_4: 0.15351/0.25969, loss_mask_ce_5: 0.45024/0.80838, loss_mask_bce_5: 0.00512/0.30831, loss_mask_dice_5: 0.49551/1.05541, loss_spatial_bce_5: 0.00154/0.09278, loss_spatial_dice_5: 0.23835/0.19765, loss_spatial_ce_5: 0.20828/0.09916, loss_grounding_bce_5: 0.00457/0.08216, loss_grounding_dice_5: 0.30962/0.15449, loss_grounding_ce_5: 0.02131/0.27830, loss_mask_ce_6: 0.47803/0.83447, loss_mask_bce_6: 0.00764/0.31014, loss_mask_dice_6: 0.79341/1.05863, loss_spatial_bce_6: 0.00260/0.09777, loss_spatial_dice_6: 0.25368/0.19987, loss_spatial_ce_6: 0.13423/0.12187, loss_grounding_bce_6: 0.00357/0.08317, loss_grounding_dice_6: 0.29759/0.15515, loss_grounding_ce_6: 0.02543/0.28785, loss_mask_ce_7: 0.32122/0.89150, loss_mask_bce_7: 0.00482/0.31749, loss_mask_dice_7: 0.51866/1.10471, loss_spatial_bce_7: 0.00279/0.10805, loss_spatial_dice_7: 0.31867/0.22502, loss_spatial_ce_7: 0.16119/0.16146, loss_grounding_bce_7: 0.00264/0.08479, loss_grounding_dice_7: 0.22303/0.16083, loss_grounding_ce_7: 0.02419/0.32434, loss_mask_ce_8: 0.31660/1.02818, loss_mask_bce_8: 0.00717/0.33383, loss_mask_dice_8: 0.77236/1.18208, loss_spatial_bce_8: 0.00269/0.12705, loss_spatial_dice_8: 0.35189/0.26256, loss_spatial_ce_8: 0.21056/0.21423, loss_grounding_bce_8: 0.00264/0.08874, loss_grounding_dice_8: 0.31264/0.17044, loss_grounding_ce_8: 0.10940/0.42711, loss_mask_ce_9: 1.54729/3.48732, loss_mask_bce_9: 0.00411/0.36083, loss_mask_dice_9: 0.72575/1.76767, loss_spatial_bce_9: 0.01068/0.35608, loss_spatial_dice_9: 0.47970/0.79482, loss_spatial_ce_9: 1.25070/1.39914, loss_grounding_bce_9: 0.00232/0.10086, loss_grounding_dice_9: 0.27941/0.24363, loss_grounding_ce_9: 0.26690/0.68636] items per batch[64] items per second[0.36] total items[2521600] mini batches[ 39400] memory[4999] epoch remaining[0:23:22] INFO:trainer.default_trainer:epochs[ 21] optim steps[39500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.31089/0.76791, loss_mask_bce_0: 0.17665/0.30182, loss_mask_dice_0: 1.56064/1.02685, loss_spatial_bce_0: 0.02842/0.08699, loss_spatial_dice_0: 0.25790/0.18379, loss_spatial_ce_0: 0.00484/0.06274, loss_grounding_bce_0: 0.04806/0.08073, loss_grounding_dice_0: 0.04701/0.15116, loss_grounding_ce_0: 0.31930/0.24948, loss_mask_ce_1: 2.61869/0.76976, loss_mask_bce_1: 0.16312/0.30260, loss_mask_dice_1: 1.60722/1.03087, loss_spatial_bce_1: 0.02714/0.08723, loss_spatial_dice_1: 0.25380/0.18634, loss_spatial_ce_1: 0.00551/0.06706, loss_grounding_bce_1: 0.04915/0.08093, loss_grounding_dice_1: 0.05157/0.15190, loss_grounding_ce_1: 0.35069/0.25074, loss_mask_ce_2: 2.56685/0.77766, loss_mask_bce_2: 0.17322/0.30268, loss_mask_dice_2: 1.97022/1.03192, loss_spatial_bce_2: 0.02700/0.08712, loss_spatial_dice_2: 0.25641/0.18651, loss_spatial_ce_2: 0.00621/0.06937, loss_grounding_bce_2: 0.04873/0.08087, loss_grounding_dice_2: 0.04927/0.15165, loss_grounding_ce_2: 0.24649/0.25350, loss_mask_ce_3: 1.93735/0.77984, loss_mask_bce_3: 0.20975/0.30422, loss_mask_dice_3: 1.70125/1.02855, loss_spatial_bce_3: 0.02716/0.08900, loss_spatial_dice_3: 0.25639/0.18746, loss_spatial_ce_3: 0.05601/0.07403, loss_grounding_bce_3: 0.05682/0.08128, loss_grounding_dice_3: 0.05207/0.15120, loss_grounding_ce_3: 0.28774/0.25338, loss_mask_ce_4: 2.76545/0.78577, loss_mask_bce_4: 0.17060/0.30650, loss_mask_dice_4: 1.82149/1.04813, loss_spatial_bce_4: 0.02984/0.09095, loss_spatial_dice_4: 0.29861/0.19513, loss_spatial_ce_4: 0.06000/0.08684, loss_grounding_bce_4: 0.05532/0.08193, loss_grounding_dice_4: 0.05097/0.15383, loss_grounding_ce_4: 0.34358/0.25953, loss_mask_ce_5: 2.37066/0.80836, loss_mask_bce_5: 0.16180/0.30822, loss_mask_dice_5: 1.65912/1.05515, loss_spatial_bce_5: 0.02786/0.09277, loss_spatial_dice_5: 0.27474/0.19762, loss_spatial_ce_5: 0.06115/0.09912, loss_grounding_bce_5: 0.04860/0.08215, loss_grounding_dice_5: 0.04949/0.15447, loss_grounding_ce_5: 0.33559/0.27805, loss_mask_ce_6: 3.29354/0.83450, loss_mask_bce_6: 0.10961/0.31004, loss_mask_dice_6: 1.34138/1.05830, loss_spatial_bce_6: 0.03811/0.09777, loss_spatial_dice_6: 0.31261/0.19982, loss_spatial_ce_6: 0.09940/0.12182, loss_grounding_bce_6: 0.05222/0.08315, loss_grounding_dice_6: 0.04706/0.15511, loss_grounding_ce_6: 0.30429/0.28766, loss_mask_ce_7: 2.52073/0.89141, loss_mask_bce_7: 0.15777/0.31740, loss_mask_dice_7: 1.58893/1.10449, loss_spatial_bce_7: 0.06413/0.10807, loss_spatial_dice_7: 0.36320/0.22499, loss_spatial_ce_7: 0.09595/0.16135, loss_grounding_bce_7: 0.04776/0.08477, loss_grounding_dice_7: 0.04443/0.16080, loss_grounding_ce_7: 0.35893/0.32415, loss_mask_ce_8: 2.37063/1.02808, loss_mask_bce_8: 0.17461/0.33375, loss_mask_dice_8: 1.95186/1.18181, loss_spatial_bce_8: 0.04886/0.12702, loss_spatial_dice_8: 0.37575/0.26249, loss_spatial_ce_8: 0.11470/0.21413, loss_grounding_bce_8: 0.04749/0.08873, loss_grounding_dice_8: 0.05353/0.17042, loss_grounding_ce_8: 0.23749/0.42688, loss_mask_ce_9: 4.59667/3.48669, loss_mask_bce_9: 0.14651/0.36072, loss_mask_dice_9: 2.56507/1.76725, loss_spatial_bce_9: 0.17445/0.35611, loss_spatial_dice_9: 0.83445/0.79476, loss_spatial_ce_9: 1.11762/1.39876, loss_grounding_bce_9: 0.09530/0.10084, loss_grounding_dice_9: 0.11572/0.24356, loss_grounding_ce_9: 0.02959/0.68603] items per batch[64] items per second[0.36] total items[2528000] mini batches[ 39500] memory[4999] epoch remaining[0:20:25] INFO:trainer.default_trainer:epochs[ 21] optim steps[39600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71326/0.76799, loss_mask_bce_0: 0.74665/0.30185, loss_mask_dice_0: 1.04176/1.02677, loss_spatial_bce_0: 0.09144/0.08695, loss_spatial_dice_0: 0.18757/0.18373, loss_spatial_ce_0: 0.00306/0.06270, loss_grounding_bce_0: 0.04415/0.08074, loss_grounding_dice_0: 0.23858/0.15116, loss_grounding_ce_0: 0.16531/0.24953, loss_mask_ce_1: 1.06903/0.76987, loss_mask_bce_1: 0.53592/0.30262, loss_mask_dice_1: 0.94490/1.03071, loss_spatial_bce_1: 0.11971/0.08719, loss_spatial_dice_1: 0.18404/0.18628, loss_spatial_ce_1: 0.01714/0.06701, loss_grounding_bce_1: 0.03483/0.08093, loss_grounding_dice_1: 0.22230/0.15187, loss_grounding_ce_1: 0.17445/0.25078, loss_mask_ce_2: 1.05706/0.77773, loss_mask_bce_2: 0.52004/0.30270, loss_mask_dice_2: 0.90730/1.03177, loss_spatial_bce_2: 0.07357/0.08709, loss_spatial_dice_2: 0.20644/0.18647, loss_spatial_ce_2: 0.01196/0.06934, loss_grounding_bce_2: 0.02858/0.08087, loss_grounding_dice_2: 0.20149/0.15166, loss_grounding_ce_2: 0.26780/0.25351, loss_mask_ce_3: 1.12375/0.78002, loss_mask_bce_3: 0.54384/0.30424, loss_mask_dice_3: 0.98196/1.02838, loss_spatial_bce_3: 0.07860/0.08896, loss_spatial_dice_3: 0.18291/0.18743, loss_spatial_ce_3: 0.00804/0.07398, loss_grounding_bce_3: 0.03711/0.08129, loss_grounding_dice_3: 0.24450/0.15120, loss_grounding_ce_3: 0.21397/0.25336, loss_mask_ce_4: 1.12819/0.78588, loss_mask_bce_4: 0.51349/0.30652, loss_mask_dice_4: 1.06512/1.04800, loss_spatial_bce_4: 0.11377/0.09091, loss_spatial_dice_4: 0.23474/0.19508, loss_spatial_ce_4: 0.06071/0.08681, loss_grounding_bce_4: 0.03783/0.08193, loss_grounding_dice_4: 0.25622/0.15383, loss_grounding_ce_4: 0.17173/0.25958, loss_mask_ce_5: 0.99467/0.80845, loss_mask_bce_5: 0.54116/0.30825, loss_mask_dice_5: 1.06935/1.05504, loss_spatial_bce_5: 0.11268/0.09275, loss_spatial_dice_5: 0.24709/0.19757, loss_spatial_ce_5: 0.16227/0.09908, loss_grounding_bce_5: 0.02463/0.08216, loss_grounding_dice_5: 0.20435/0.15447, loss_grounding_ce_5: 0.27090/0.27816, loss_mask_ce_6: 0.96971/0.83463, loss_mask_bce_6: 0.50017/0.31004, loss_mask_dice_6: 1.07776/1.05814, loss_spatial_bce_6: 0.09452/0.09774, loss_spatial_dice_6: 0.25316/0.19979, loss_spatial_ce_6: 0.18143/0.12179, loss_grounding_bce_6: 0.03353/0.08316, loss_grounding_dice_6: 0.23815/0.15510, loss_grounding_ce_6: 0.24532/0.28777, loss_mask_ce_7: 1.08262/0.89157, loss_mask_bce_7: 0.51853/0.31742, loss_mask_dice_7: 1.09457/1.10442, loss_spatial_bce_7: 0.12323/0.10805, loss_spatial_dice_7: 0.40810/0.22496, loss_spatial_ce_7: 0.16337/0.16129, loss_grounding_bce_7: 0.04626/0.08477, loss_grounding_dice_7: 0.26670/0.16080, loss_grounding_ce_7: 0.44398/0.32423, loss_mask_ce_8: 1.18468/1.02817, loss_mask_bce_8: 0.57455/0.33382, loss_mask_dice_8: 1.42102/1.18169, loss_spatial_bce_8: 0.10610/0.12697, loss_spatial_dice_8: 0.36657/0.26244, loss_spatial_ce_8: 0.05920/0.21404, loss_grounding_bce_8: 0.04525/0.08874, loss_grounding_dice_8: 0.24949/0.17042, loss_grounding_ce_8: 0.51794/0.42713, loss_mask_ce_9: 4.84076/3.48719, loss_mask_bce_9: 0.70657/0.36077, loss_mask_dice_9: 1.68171/1.76745, loss_spatial_bce_9: 0.37665/0.35617, loss_spatial_dice_9: 0.90616/0.79482, loss_spatial_ce_9: 1.21709/1.39883, loss_grounding_bce_9: 0.17623/0.10085, loss_grounding_dice_9: 0.65502/0.24356, loss_grounding_ce_9: 0.47048/0.68615] items per batch[64] items per second[0.36] total items[2534400] mini batches[ 39600] memory[4999] epoch remaining[0:17:30] INFO:trainer.default_trainer:epochs[ 21] optim steps[39700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38104/0.76756, loss_mask_bce_0: 0.39164/0.30183, loss_mask_dice_0: 3.36466/1.02650, loss_spatial_bce_0: 0.02908/0.08697, loss_spatial_dice_0: 0.24893/0.18368, loss_spatial_ce_0: 0.00340/0.06262, loss_grounding_bce_0: 0.00000/0.08077, loss_grounding_dice_0: 0.00009/0.15115, loss_grounding_ce_0: 0.08462/0.24950, loss_mask_ce_1: 1.36698/0.76942, loss_mask_bce_1: 0.40158/0.30261, loss_mask_dice_1: 3.15276/1.03044, loss_spatial_bce_1: 0.02681/0.08722, loss_spatial_dice_1: 0.23826/0.18623, loss_spatial_ce_1: 0.01480/0.06693, loss_grounding_bce_1: 0.00000/0.08096, loss_grounding_dice_1: 0.00020/0.15187, loss_grounding_ce_1: 0.09715/0.25077, loss_mask_ce_2: 1.52748/0.77728, loss_mask_bce_2: 0.41354/0.30268, loss_mask_dice_2: 3.06718/1.03149, loss_spatial_bce_2: 0.02753/0.08711, loss_spatial_dice_2: 0.27170/0.18641, loss_spatial_ce_2: 0.00639/0.06926, loss_grounding_bce_2: 0.00000/0.08090, loss_grounding_dice_2: 0.00018/0.15164, loss_grounding_ce_2: 0.09657/0.25348, loss_mask_ce_3: 1.47294/0.77954, loss_mask_bce_3: 0.42240/0.30422, loss_mask_dice_3: 2.53883/1.02809, loss_spatial_bce_3: 0.03391/0.08898, loss_spatial_dice_3: 0.36395/0.18737, loss_spatial_ce_3: 0.00446/0.07390, loss_grounding_bce_3: 0.00000/0.08131, loss_grounding_dice_3: 0.00016/0.15120, loss_grounding_ce_3: 0.07813/0.25332, loss_mask_ce_4: 1.50651/0.78546, loss_mask_bce_4: 0.39457/0.30650, loss_mask_dice_4: 2.90429/1.04770, loss_spatial_bce_4: 0.02669/0.09093, loss_spatial_dice_4: 0.26835/0.19503, loss_spatial_ce_4: 0.01562/0.08673, loss_grounding_bce_4: 0.00000/0.08196, loss_grounding_dice_4: 0.00017/0.15382, loss_grounding_ce_4: 0.11665/0.25951, loss_mask_ce_5: 1.90041/0.80807, loss_mask_bce_5: 0.40297/0.30823, loss_mask_dice_5: 2.69349/1.05471, loss_spatial_bce_5: 0.02774/0.09277, loss_spatial_dice_5: 0.30890/0.19752, loss_spatial_ce_5: 0.00441/0.09899, loss_grounding_bce_5: 0.00000/0.08218, loss_grounding_dice_5: 0.00012/0.15446, loss_grounding_ce_5: 0.11721/0.27815, loss_mask_ce_6: 1.55080/0.83428, loss_mask_bce_6: 0.40180/0.31003, loss_mask_dice_6: 2.90991/1.05782, loss_spatial_bce_6: 0.02581/0.09776, loss_spatial_dice_6: 0.27432/0.19973, loss_spatial_ce_6: 0.07713/0.12172, loss_grounding_bce_6: 0.00000/0.08319, loss_grounding_dice_6: 0.00013/0.15509, loss_grounding_ce_6: 0.17252/0.28780, loss_mask_ce_7: 1.78952/0.89121, loss_mask_bce_7: 0.40323/0.31740, loss_mask_dice_7: 3.47299/1.10411, loss_spatial_bce_7: 0.03652/0.10807, loss_spatial_dice_7: 0.42663/0.22489, loss_spatial_ce_7: 0.06104/0.16122, loss_grounding_bce_7: 0.00000/0.08481, loss_grounding_dice_7: 0.00041/0.16079, loss_grounding_ce_7: 0.13738/0.32419, loss_mask_ce_8: 1.83200/1.02779, loss_mask_bce_8: 0.41449/0.33379, loss_mask_dice_8: 3.47861/1.18136, loss_spatial_bce_8: 0.03675/0.12696, loss_spatial_dice_8: 0.53837/0.26236, loss_spatial_ce_8: 0.15265/0.21395, loss_grounding_bce_8: 0.00000/0.08877, loss_grounding_dice_8: 0.00013/0.17042, loss_grounding_ce_8: 0.08921/0.42697, loss_mask_ce_9: 4.30590/3.48641, loss_mask_bce_9: 0.48253/0.36076, loss_mask_dice_9: 4.55836/1.76685, loss_spatial_bce_9: 0.28337/0.35630, loss_spatial_dice_9: 0.82617/0.79477, loss_spatial_ce_9: 3.65668/1.39863, loss_grounding_bce_9: 0.00000/0.10088, loss_grounding_dice_9: 0.02465/0.24355, loss_grounding_ce_9: 0.66391/0.68595] items per batch[64] items per second[0.36] total items[2540800] mini batches[ 39700] memory[4999] epoch remaining[0:14:33] INFO:trainer.default_trainer:epochs[ 21] optim steps[39800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39092/0.76764, loss_mask_bce_0: 0.43391/0.30193, loss_mask_dice_0: 0.74119/1.02730, loss_spatial_bce_0: 0.13229/0.08692, loss_spatial_dice_0: 0.19145/0.18371, loss_spatial_ce_0: 0.00102/0.06255, loss_grounding_bce_0: 0.04611/0.08074, loss_grounding_dice_0: 0.18690/0.15121, loss_grounding_ce_0: 0.00136/0.24951, loss_mask_ce_1: 1.32742/0.76948, loss_mask_bce_1: 0.43757/0.30273, loss_mask_dice_1: 0.74544/1.03116, loss_spatial_bce_1: 0.14125/0.08716, loss_spatial_dice_1: 0.20172/0.18627, loss_spatial_ce_1: 0.00118/0.06687, loss_grounding_bce_1: 0.04422/0.08093, loss_grounding_dice_1: 0.18296/0.15197, loss_grounding_ce_1: 0.00133/0.25076, loss_mask_ce_2: 1.20473/0.77734, loss_mask_bce_2: 0.41868/0.30278, loss_mask_dice_2: 0.76833/1.03228, loss_spatial_bce_2: 0.14453/0.08705, loss_spatial_dice_2: 0.19966/0.18645, loss_spatial_ce_2: 0.00129/0.06923, loss_grounding_bce_2: 0.04473/0.08089, loss_grounding_dice_2: 0.20056/0.15174, loss_grounding_ce_2: 0.00193/0.25341, loss_mask_ce_3: 1.34718/0.77955, loss_mask_bce_3: 0.44251/0.30431, loss_mask_dice_3: 0.73543/1.02894, loss_spatial_bce_3: 0.14425/0.08893, loss_spatial_dice_3: 0.20107/0.18741, loss_spatial_ce_3: 0.01066/0.07384, loss_grounding_bce_3: 0.04424/0.08130, loss_grounding_dice_3: 0.18102/0.15129, loss_grounding_ce_3: 0.00127/0.25334, loss_mask_ce_4: 1.25163/0.78541, loss_mask_bce_4: 0.43734/0.30662, loss_mask_dice_4: 0.74969/1.04848, loss_spatial_bce_4: 0.14389/0.09089, loss_spatial_dice_4: 0.21978/0.19508, loss_spatial_ce_4: 0.01287/0.08665, loss_grounding_bce_4: 0.05252/0.08195, loss_grounding_dice_4: 0.20205/0.15391, loss_grounding_ce_4: 0.00089/0.25940, loss_mask_ce_5: 1.30122/0.80800, loss_mask_bce_5: 0.43732/0.30834, loss_mask_dice_5: 0.76031/1.05554, loss_spatial_bce_5: 0.12504/0.09273, loss_spatial_dice_5: 0.21431/0.19757, loss_spatial_ce_5: 0.02860/0.09893, loss_grounding_bce_5: 0.04290/0.08218, loss_grounding_dice_5: 0.17975/0.15453, loss_grounding_ce_5: 0.00292/0.27799, loss_mask_ce_6: 1.43964/0.83423, loss_mask_bce_6: 0.44391/0.31012, loss_mask_dice_6: 0.79474/1.05863, loss_spatial_bce_6: 0.13142/0.09771, loss_spatial_dice_6: 0.21814/0.19977, loss_spatial_ce_6: 0.02571/0.12166, loss_grounding_bce_6: 0.04486/0.08317, loss_grounding_dice_6: 0.17690/0.15516, loss_grounding_ce_6: 0.00034/0.28770, loss_mask_ce_7: 1.42159/0.89119, loss_mask_bce_7: 0.45735/0.31751, loss_mask_dice_7: 0.82598/1.10494, loss_spatial_bce_7: 0.13232/0.10802, loss_spatial_dice_7: 0.22629/0.22495, loss_spatial_ce_7: 0.03486/0.16116, loss_grounding_bce_7: 0.04982/0.08479, loss_grounding_dice_7: 0.20092/0.16087, loss_grounding_ce_7: 0.00031/0.32413, loss_mask_ce_8: 1.39094/1.02786, loss_mask_bce_8: 0.45361/0.33390, loss_mask_dice_8: 0.81839/1.18222, loss_spatial_bce_8: 0.16802/0.12689, loss_spatial_dice_8: 0.24173/0.26243, loss_spatial_ce_8: 0.10783/0.21389, loss_grounding_bce_8: 0.05890/0.08874, loss_grounding_dice_8: 0.17717/0.17048, loss_grounding_ce_8: 0.07348/0.42681, loss_mask_ce_9: 2.47654/3.48665, loss_mask_bce_9: 0.37114/0.36081, loss_mask_dice_9: 0.94612/1.76803, loss_spatial_bce_9: 0.57891/0.35617, loss_spatial_dice_9: 0.87840/0.79483, loss_spatial_ce_9: 1.89334/1.39895, loss_grounding_bce_9: 0.03452/0.10084, loss_grounding_dice_9: 0.19429/0.24358, loss_grounding_ce_9: 3.14980/0.68598] items per batch[64] items per second[0.36] total items[2547200] mini batches[ 39800] memory[4999] epoch remaining[0:11:36] INFO:trainer.default_trainer:epochs[ 21] optim steps[39900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.25801/0.76769, loss_mask_bce_0: 0.51337/0.30188, loss_mask_dice_0: 0.32212/1.02752, loss_spatial_bce_0: 0.20375/0.08692, loss_spatial_dice_0: 0.11736/0.18370, loss_spatial_ce_0: 0.16937/0.06253, loss_grounding_bce_0: 0.06326/0.08075, loss_grounding_dice_0: 0.12688/0.15123, loss_grounding_ce_0: 0.18226/0.24944, loss_mask_ce_1: 0.91860/0.76952, loss_mask_bce_1: 0.57645/0.30267, loss_mask_dice_1: 0.44246/1.03133, loss_spatial_bce_1: 0.20105/0.08716, loss_spatial_dice_1: 0.11846/0.18625, loss_spatial_ce_1: 0.17834/0.06685, loss_grounding_bce_1: 0.06076/0.08094, loss_grounding_dice_1: 0.12600/0.15199, loss_grounding_ce_1: 0.17427/0.25072, loss_mask_ce_2: 1.04528/0.77738, loss_mask_bce_2: 0.53328/0.30273, loss_mask_dice_2: 0.34688/1.03250, loss_spatial_bce_2: 0.20391/0.08706, loss_spatial_dice_2: 0.11746/0.18644, loss_spatial_ce_2: 0.19284/0.06920, loss_grounding_bce_2: 0.07099/0.08090, loss_grounding_dice_2: 0.13669/0.15175, loss_grounding_ce_2: 0.11576/0.25341, loss_mask_ce_3: 0.84845/0.77959, loss_mask_bce_3: 0.56559/0.30429, loss_mask_dice_3: 0.43027/1.02911, loss_spatial_bce_3: 0.20457/0.08893, loss_spatial_dice_3: 0.11155/0.18739, loss_spatial_ce_3: 0.20589/0.07381, loss_grounding_bce_3: 0.07005/0.08131, loss_grounding_dice_3: 0.13545/0.15131, loss_grounding_ce_3: 0.11484/0.25334, loss_mask_ce_4: 0.82661/0.78545, loss_mask_bce_4: 0.57931/0.30657, loss_mask_dice_4: 0.42330/1.04868, loss_spatial_bce_4: 0.21338/0.09087, loss_spatial_dice_4: 0.12684/0.19506, loss_spatial_ce_4: 0.19905/0.08664, loss_grounding_bce_4: 0.07933/0.08195, loss_grounding_dice_4: 0.17264/0.15395, loss_grounding_ce_4: 0.05297/0.25933, loss_mask_ce_5: 0.91854/0.80810, loss_mask_bce_5: 0.54136/0.30829, loss_mask_dice_5: 0.34310/1.05577, loss_spatial_bce_5: 0.22235/0.09272, loss_spatial_dice_5: 0.12529/0.19756, loss_spatial_ce_5: 0.23706/0.09888, loss_grounding_bce_5: 0.07264/0.08217, loss_grounding_dice_5: 0.12414/0.15455, loss_grounding_ce_5: 0.06473/0.27792, loss_mask_ce_6: 1.05915/0.83432, loss_mask_bce_6: 0.54472/0.31008, loss_mask_dice_6: 0.33665/1.05882, loss_spatial_bce_6: 0.22148/0.09771, loss_spatial_dice_6: 0.11600/0.19975, loss_spatial_ce_6: 0.29415/0.12160, loss_grounding_bce_6: 0.05824/0.08317, loss_grounding_dice_6: 0.11457/0.15519, loss_grounding_ce_6: 0.22752/0.28767, loss_mask_ce_7: 1.21455/0.89124, loss_mask_bce_7: 0.54514/0.31747, loss_mask_dice_7: 0.33029/1.10520, loss_spatial_bce_7: 0.20489/0.10800, loss_spatial_dice_7: 0.13642/0.22497, loss_spatial_ce_7: 0.32545/0.16109, loss_grounding_bce_7: 0.05475/0.08479, loss_grounding_dice_7: 0.11099/0.16091, loss_grounding_ce_7: 0.45841/0.32412, loss_mask_ce_8: 1.08212/1.02789, loss_mask_bce_8: 0.53911/0.33385, loss_mask_dice_8: 0.33180/1.18251, loss_spatial_bce_8: 0.23707/0.12689, loss_spatial_dice_8: 0.18549/0.26244, loss_spatial_ce_8: 0.71540/0.21379, loss_grounding_bce_8: 0.05847/0.08874, loss_grounding_dice_8: 0.12769/0.17053, loss_grounding_ce_8: 0.66250/0.42692, loss_mask_ce_9: 2.43959/3.48725, loss_mask_bce_9: 0.60279/0.36075, loss_mask_dice_9: 0.77910/1.76815, loss_spatial_bce_9: 0.55442/0.35613, loss_spatial_dice_9: 0.77760/0.79488, loss_spatial_ce_9: 1.51208/1.39901, loss_grounding_bce_9: 0.11050/0.10084, loss_grounding_dice_9: 0.26409/0.24363, loss_grounding_ce_9: 0.03705/0.68626] items per batch[64] items per second[0.36] total items[2553600] mini batches[ 39900] memory[4999] epoch remaining[0:08:39] INFO:trainer.default_trainer:epochs[ 21] optim steps[40000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99915/0.76784, loss_mask_bce_0: 0.61352/0.30189, loss_mask_dice_0: 0.77354/1.02754, loss_spatial_bce_0: 0.11067/0.08691, loss_spatial_dice_0: 0.19864/0.18368, loss_spatial_ce_0: 0.00139/0.06251, loss_grounding_bce_0: 0.10048/0.08075, loss_grounding_dice_0: 0.43731/0.15123, loss_grounding_ce_0: 0.00095/0.24924, loss_mask_ce_1: 0.98142/0.76963, loss_mask_bce_1: 0.55456/0.30269, loss_mask_dice_1: 0.75585/1.03134, loss_spatial_bce_1: 0.12516/0.08715, loss_spatial_dice_1: 0.20748/0.18623, loss_spatial_ce_1: 0.00111/0.06680, loss_grounding_bce_1: 0.10525/0.08094, loss_grounding_dice_1: 0.44450/0.15198, loss_grounding_ce_1: 0.00042/0.25050, loss_mask_ce_2: 1.04331/0.77744, loss_mask_bce_2: 0.43719/0.30275, loss_mask_dice_2: 0.74240/1.03249, loss_spatial_bce_2: 0.12354/0.08705, loss_spatial_dice_2: 0.21770/0.18642, loss_spatial_ce_2: 0.00131/0.06915, loss_grounding_bce_2: 0.11157/0.08090, loss_grounding_dice_2: 0.49799/0.15175, loss_grounding_ce_2: 0.00103/0.25323, loss_mask_ce_3: 1.13265/0.77967, loss_mask_bce_3: 0.43453/0.30431, loss_mask_dice_3: 0.74468/1.02914, loss_spatial_bce_3: 0.12526/0.08891, loss_spatial_dice_3: 0.21681/0.18738, loss_spatial_ce_3: 0.00633/0.07378, loss_grounding_bce_3: 0.10019/0.08131, loss_grounding_dice_3: 0.45177/0.15130, loss_grounding_ce_3: 0.00147/0.25316, loss_mask_ce_4: 1.39740/0.78551, loss_mask_bce_4: 0.44310/0.30660, loss_mask_dice_4: 0.76770/1.04871, loss_spatial_bce_4: 0.13237/0.09086, loss_spatial_dice_4: 0.22741/0.19504, loss_spatial_ce_4: 0.07048/0.08662, loss_grounding_bce_4: 0.08198/0.08196, loss_grounding_dice_4: 0.39860/0.15394, loss_grounding_ce_4: 0.00191/0.25914, loss_mask_ce_5: 1.14656/0.80818, loss_mask_bce_5: 0.45614/0.30834, loss_mask_dice_5: 0.82088/1.05584, loss_spatial_bce_5: 0.21017/0.09271, loss_spatial_dice_5: 0.28474/0.19754, loss_spatial_ce_5: 0.07345/0.09891, loss_grounding_bce_5: 0.10614/0.08218, loss_grounding_dice_5: 0.44821/0.15454, loss_grounding_ce_5: 0.00257/0.27779, loss_mask_ce_6: 0.80185/0.83440, loss_mask_bce_6: 0.78388/0.31010, loss_mask_dice_6: 0.87374/1.05881, loss_spatial_bce_6: 0.18848/0.09770, loss_spatial_dice_6: 0.30174/0.19973, loss_spatial_ce_6: 0.08417/0.12154, loss_grounding_bce_6: 0.11626/0.08318, loss_grounding_dice_6: 0.41482/0.15519, loss_grounding_ce_6: 0.00589/0.28752, loss_mask_ce_7: 0.73036/0.89128, loss_mask_bce_7: 0.83372/0.31749, loss_mask_dice_7: 0.91249/1.10525, loss_spatial_bce_7: 0.12324/0.10800, loss_spatial_dice_7: 0.22393/0.22496, loss_spatial_ce_7: 0.31643/0.16098, loss_grounding_bce_7: 0.10261/0.08479, loss_grounding_dice_7: 0.47762/0.16089, loss_grounding_ce_7: 0.00313/0.32387, loss_mask_ce_8: 0.98476/1.02797, loss_mask_bce_8: 0.59995/0.33386, loss_mask_dice_8: 0.80234/1.18250, loss_spatial_bce_8: 0.14797/0.12685, loss_spatial_dice_8: 0.21948/0.26239, loss_spatial_ce_8: 0.11201/0.21367, loss_grounding_bce_8: 0.09207/0.08874, loss_grounding_dice_8: 0.54434/0.17051, loss_grounding_ce_8: 0.00247/0.42666, loss_mask_ce_9: 3.11853/3.48733, loss_mask_bce_9: 0.57088/0.36078, loss_mask_dice_9: 1.15920/1.76848, loss_spatial_bce_9: 0.34979/0.35613, loss_spatial_dice_9: 0.65876/0.79488, loss_spatial_ce_9: 0.80130/1.39902, loss_grounding_bce_9: 0.02845/0.10085, loss_grounding_dice_9: 0.47210/0.24362, loss_grounding_ce_9: 0.69258/0.68613] items per batch[64] items per second[0.37] total items[2560000] mini batches[ 40000] memory[4999] epoch remaining[0:05:42] INFO:trainer.default_trainer:epochs[ 21] optim steps[40100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14946/0.76769, loss_mask_bce_0: 0.22490/0.30183, loss_mask_dice_0: 0.39978/1.02682, loss_spatial_bce_0: 0.12568/0.08693, loss_spatial_dice_0: 0.18451/0.18366, loss_spatial_ce_0: 0.02608/0.06246, loss_grounding_bce_0: 0.08987/0.08078, loss_grounding_dice_0: 0.09048/0.15124, loss_grounding_ce_0: 0.00427/0.24929, loss_mask_ce_1: 0.14483/0.76934, loss_mask_bce_1: 0.23425/0.30262, loss_mask_dice_1: 0.46209/1.03057, loss_spatial_bce_1: 0.13045/0.08716, loss_spatial_dice_1: 0.19168/0.18621, loss_spatial_ce_1: 0.02131/0.06677, loss_grounding_bce_1: 0.08671/0.08096, loss_grounding_dice_1: 0.08924/0.15198, loss_grounding_ce_1: 0.00502/0.25059, loss_mask_ce_2: 0.13487/0.77723, loss_mask_bce_2: 0.21948/0.30268, loss_mask_dice_2: 0.43184/1.03175, loss_spatial_bce_2: 0.13571/0.08707, loss_spatial_dice_2: 0.19187/0.18640, loss_spatial_ce_2: 0.00836/0.06911, loss_grounding_bce_2: 0.09224/0.08093, loss_grounding_dice_2: 0.09246/0.15174, loss_grounding_ce_2: 0.00429/0.25328, loss_mask_ce_3: 0.15468/0.77951, loss_mask_bce_3: 0.22298/0.30424, loss_mask_dice_3: 0.42102/1.02838, loss_spatial_bce_3: 0.13080/0.08893, loss_spatial_dice_3: 0.18579/0.18735, loss_spatial_ce_3: 0.00112/0.07375, loss_grounding_bce_3: 0.09371/0.08133, loss_grounding_dice_3: 0.09216/0.15130, loss_grounding_ce_3: 0.00895/0.25325, loss_mask_ce_4: 0.14000/0.78522, loss_mask_bce_4: 0.23305/0.30653, loss_mask_dice_4: 0.40189/1.04792, loss_spatial_bce_4: 0.14447/0.09088, loss_spatial_dice_4: 0.19259/0.19502, loss_spatial_ce_4: 0.00168/0.08661, loss_grounding_bce_4: 0.08655/0.08198, loss_grounding_dice_4: 0.07897/0.15395, loss_grounding_ce_4: 0.00876/0.25920, loss_mask_ce_5: 0.16139/0.80799, loss_mask_bce_5: 0.23585/0.30826, loss_mask_dice_5: 0.44964/1.05506, loss_spatial_bce_5: 0.14422/0.09273, loss_spatial_dice_5: 0.19489/0.19753, loss_spatial_ce_5: 0.00277/0.09889, loss_grounding_bce_5: 0.09056/0.08220, loss_grounding_dice_5: 0.08302/0.15452, loss_grounding_ce_5: 0.01288/0.27784, loss_mask_ce_6: 0.21053/0.83422, loss_mask_bce_6: 0.22094/0.31004, loss_mask_dice_6: 0.40263/1.05810, loss_spatial_bce_6: 0.13437/0.09772, loss_spatial_dice_6: 0.20040/0.19972, loss_spatial_ce_6: 0.03096/0.12149, loss_grounding_bce_6: 0.08271/0.08320, loss_grounding_dice_6: 0.07774/0.15519, loss_grounding_ce_6: 0.02286/0.28755, loss_mask_ce_7: 0.16427/0.89098, loss_mask_bce_7: 0.23370/0.31741, loss_mask_dice_7: 0.41636/1.10443, loss_spatial_bce_7: 0.12072/0.10800, loss_spatial_dice_7: 0.19250/0.22495, loss_spatial_ce_7: 0.06038/0.16096, loss_grounding_bce_7: 0.09007/0.08481, loss_grounding_dice_7: 0.08566/0.16090, loss_grounding_ce_7: 0.02296/0.32389, loss_mask_ce_8: 0.20815/1.02758, loss_mask_bce_8: 0.22513/0.33375, loss_mask_dice_8: 0.43868/1.18164, loss_spatial_bce_8: 0.13313/0.12686, loss_spatial_dice_8: 0.19718/0.26235, loss_spatial_ce_8: 0.15845/0.21358, loss_grounding_bce_8: 0.09113/0.08876, loss_grounding_dice_8: 0.09405/0.17049, loss_grounding_ce_8: 0.02322/0.42652, loss_mask_ce_9: 3.03286/3.48633, loss_mask_bce_9: 0.24130/0.36068, loss_mask_dice_9: 0.60714/1.76705, loss_spatial_bce_9: 0.52823/0.35616, loss_spatial_dice_9: 0.67233/0.79477, loss_spatial_ce_9: 0.91317/1.39893, loss_grounding_bce_9: 0.09775/0.10086, loss_grounding_dice_9: 0.10602/0.24358, loss_grounding_ce_9: 0.33227/0.68596] items per batch[64] items per second[0.37] total items[2566400] mini batches[ 40100] memory[4999] epoch remaining[0:02:45] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00040194. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0025 s/iter. Inference: 0.3699 s/iter. Eval: 0.0901 s/iter. Total: 0.4625 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0023 s/iter. Inference: 0.3726 s/iter. Eval: 0.0848 s/iter. Total: 0.4597 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0024 s/iter. Inference: 0.3770 s/iter. Eval: 0.0815 s/iter. Total: 0.4611 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0026 s/iter. Inference: 0.3781 s/iter. Eval: 0.0777 s/iter. Total: 0.4585 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0026 s/iter. Inference: 0.3792 s/iter. Eval: 0.0754 s/iter. Total: 0.4574 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3774 s/iter. Eval: 0.0735 s/iter. Total: 0.4537 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval3p3k7f96 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.650 | 82.910 | 66.287 | 133 | | Things | 61.747 | 83.928 | 73.048 | 80 | | Stuff | 46.447 | 81.374 | 56.080 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.57s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.67 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.41 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.88s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.32 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.53 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.692 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.255 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.767 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.415 | 69.153 | 48.828 | 25.519 | 49.584 | 67.411 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.746 | bicycle | 23.287 | car | 44.303 | | motorcycle | 42.582 | airplane | 61.102 | bus | 70.526 | | train | 74.667 | truck | 41.225 | boat | 30.038 | | traffic light | 27.136 | fire hydrant | 71.387 | stop sign | 67.837 | | parking meter | 52.009 | bench | 27.162 | bird | 33.886 | | cat | 76.707 | dog | 70.941 | horse | 50.452 | | sheep | 54.295 | cow | 56.125 | elephant | 66.397 | | bear | 80.415 | zebra | 66.706 | giraffe | 62.133 | | backpack | 24.326 | umbrella | 55.319 | handbag | 23.181 | | tie | 40.259 | suitcase | 51.562 | frisbee | 70.790 | | skis | 8.699 | snowboard | 34.984 | sports ball | 49.964 | | kite | 37.114 | baseball bat | 38.392 | baseball glove | 50.883 | | skateboard | 44.129 | surfboard | 45.201 | tennis racket | 63.493 | | bottle | 41.467 | wine glass | 36.681 | cup | 51.528 | | fork | 25.387 | knife | 23.769 | spoon | 21.633 | | bowl | 37.880 | banana | 22.202 | apple | 26.581 | | sandwich | 49.227 | orange | 30.826 | broccoli | 24.495 | | carrot | 21.658 | hot dog | 29.468 | pizza | 53.202 | | donut | 55.602 | cake | 47.614 | chair | 28.524 | | couch | 41.251 | potted plant | 22.580 | bed | 44.848 | | dining table | 15.290 | toilet | 68.762 | tv | 67.540 | | laptop | 69.503 | mouse | 64.260 | remote | 42.997 | | keyboard | 58.920 | cell phone | 45.931 | microwave | 64.633 | | oven | 33.972 | toaster | 44.394 | sink | 44.390 | | refrigerator | 69.602 | book | 13.904 | clock | 54.487 | | vase | 40.669 | scissors | 36.340 | teddy bear | 58.040 | | hair drier | 35.709 | toothbrush | 29.058 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.13138763332215, 'fwIoU': 71.44017369963423, 'IoU-person': 88.46840206852985, 'IoU-bicycle': 73.93246987638182, 'IoU-car': 72.09486962736636, 'IoU-motorcycle': 83.9378952696381, 'IoU-airplane': 87.27012678373205, 'IoU-bus': 88.06892833881486, 'IoU-train': 88.33598140619583, 'IoU-truck': 69.97432179178972, 'IoU-boat': 72.47700484841275, 'IoU-traffic light': 79.52768426360466, 'IoU-fire hydrant': 93.36304603047047, 'IoU-stop sign': 85.56480433006665, 'IoU-parking meter': 84.95610793752248, 'IoU-bench': 57.34201573701176, 'IoU-bird': 72.8170630962263, 'IoU-cat': 85.48749793055183, 'IoU-dog': 78.82943850072134, 'IoU-horse': 88.29890145449014, 'IoU-sheep': 80.34743210102675, 'IoU-cow': 88.17575661511029, 'IoU-elephant': 88.39484322139236, 'IoU-bear': 77.01344882543349, 'IoU-zebra': 85.41025857470504, 'IoU-giraffe': 89.4592842536627, 'IoU-backpack': 51.99413663670025, 'IoU-umbrella': 88.82407703495298, 'IoU-handbag': 50.66423506112909, 'IoU-tie': 75.952720854237, 'IoU-suitcase': 78.63273973088073, 'IoU-frisbee': 84.74005486295704, 'IoU-skis': 59.23501315280675, 'IoU-snowboard': 72.31582820615945, 'IoU-sports ball': 68.17426019183225, 'IoU-kite': 79.31833269115606, 'IoU-baseball bat': 69.33623471593096, 'IoU-baseball glove': 52.8712118355393, 'IoU-skateboard': 86.15911910735986, 'IoU-surfboard': 80.22328127709953, 'IoU-tennis racket': 91.30266383018083, 'IoU-bottle': 71.37038866774826, 'IoU-wine glass': 82.29145610898479, 'IoU-cup': 71.31393178514772, 'IoU-fork': 70.38818350868544, 'IoU-knife': 63.32577979401307, 'IoU-spoon': 62.71780912798298, 'IoU-bowl': 58.909919545406375, 'IoU-banana': 83.4738966343393, 'IoU-apple': 58.07110309588218, 'IoU-sandwich': 69.19758552176918, 'IoU-orange': 79.5355076920438, 'IoU-broccoli': 69.66771840299467, 'IoU-carrot': 62.941369430649154, 'IoU-hot dog': 66.148245216466, 'IoU-pizza': 86.70375740279404, 'IoU-donut': 65.60390683615506, 'IoU-cake': 75.7988930691716, 'IoU-chair': 63.8494229529478, 'IoU-couch': 69.7980881341788, 'IoU-potted plant': 43.23619748063735, 'IoU-bed': 71.29709404594409, 'IoU-dining table': 55.0020539111712, 'IoU-toilet': 80.00037003896185, 'IoU-tv': 77.6459915416638, 'IoU-laptop': 74.33125940600091, 'IoU-mouse': 74.74110331423529, 'IoU-remote': 52.14485118829187, 'IoU-keyboard': 68.73687141063279, 'IoU-cell phone': 80.18875103263092, 'IoU-microwave': 77.6735919147244, 'IoU-oven': 75.0160862210027, 'IoU-toaster': 85.33624150710315, 'IoU-sink': 72.90143829937021, 'IoU-refrigerator': 79.73837666213399, 'IoU-book': 54.67973999265475, 'IoU-clock': 71.80726513922046, 'IoU-vase': 66.14968126164995, 'IoU-scissors': 87.80125097177734, 'IoU-teddy bear': 79.61907399329786, 'IoU-hair drier': 51.15529653753856, 'IoU-toothbrush': 72.80211327324713, 'IoU-banner': 39.06699522037009, 'IoU-blanket': 17.010963818714036, 'IoU-bridge': 38.55047968635621, 'IoU-cardboard': 49.17447985418102, 'IoU-counter': 33.00689656234551, 'IoU-curtain': 71.68325493856645, 'IoU-door-stuff': 49.04042929600666, 'IoU-floor-wood': 60.3310787446419, 'IoU-flower': 48.39904603122548, 'IoU-fruit': 48.11349500372118, 'IoU-gravel': 31.29526248841915, 'IoU-house': 24.349000555094502, 'IoU-light': 44.49835144600319, 'IoU-mirror-stuff': 63.77555477151845, 'IoU-net': 47.64149272691925, 'IoU-pillow': 28.62786789774451, 'IoU-platform': 29.78164746972329, 'IoU-playingfield': 69.85412525686013, 'IoU-railroad': 64.75540427359337, 'IoU-river': 54.479504512514865, 'IoU-road': 67.93293733048225, 'IoU-roof': 18.34363598965043, 'IoU-sand': 61.53184869767549, 'IoU-sea': 86.42676737503344, 'IoU-shelf': 39.28538650816065, 'IoU-snow': 92.04331523819835, 'IoU-stairs': 33.66998170958325, 'IoU-tent': 10.995498326931445, 'IoU-towel': 45.633774790510515, 'IoU-wall-brick': 52.783364995271754, 'IoU-wall-stone': 26.846257789026605, 'IoU-wall-tile': 71.34746199857625, 'IoU-wall-wood': 45.295126994848076, 'IoU-water-other': 32.028267323367, 'IoU-window-blind': 48.13510473433102, 'IoU-window-other': 49.52130863941391, 'IoU-tree-merged': 81.97134755263245, 'IoU-fence-merged': 55.418218152130684, 'IoU-ceiling-merged': 67.14179905247428, 'IoU-sky-other-merged': 93.98784480868284, 'IoU-cabinet-merged': 64.82550732294986, 'IoU-table-merged': 41.24011581912017, 'IoU-floor-other-merged': 53.735110570883215, 'IoU-pavement-merged': 58.35643576824023, 'IoU-mountain-merged': 58.271295864170646, 'IoU-grass-merged': 70.95381421129439, 'IoU-dirt-merged': 46.19200946953759, 'IoU-paper-merged': 37.23752949525882, 'IoU-food-other-merged': 44.62038371294916, 'IoU-building-other-merged': 59.262001174868274, 'IoU-rock-merged': 64.4983138642632, 'IoU-wall-other-merged': 68.80610516692009, 'IoU-rug-merged': 68.29820008685972, 'mACC': 76.96023981994115, 'pACC': 82.20080394400931, 'ACC-person': 92.96795779867024, 'ACC-bicycle': 82.94109248860008, 'ACC-car': 86.85505956283602, 'ACC-motorcycle': 88.20065711373243, 'ACC-airplane': 94.05036135400606, 'ACC-bus': 93.83144825430534, 'ACC-train': 95.5867492933755, 'ACC-truck': 77.97616430687879, 'ACC-boat': 81.56218563276205, 'ACC-traffic light': 90.79447584945818, 'ACC-fire hydrant': 96.05779782823129, 'ACC-stop sign': 88.79764946647524, 'ACC-parking meter': 88.19738289851054, 'ACC-bench': 76.06115002096236, 'ACC-bird': 78.18449018198004, 'ACC-cat': 91.47582832105205, 'ACC-dog': 81.28440130486013, 'ACC-horse': 92.79407624761818, 'ACC-sheep': 84.61639651003101, 'ACC-cow': 91.21952432215794, 'ACC-elephant': 90.44304298799693, 'ACC-bear': 78.6034966469719, 'ACC-zebra': 87.53030799159134, 'ACC-giraffe': 93.48063652183333, 'ACC-backpack': 73.94808108605534, 'ACC-umbrella': 93.85375155799292, 'ACC-handbag': 68.15420715834946, 'ACC-tie': 84.3272937627268, 'ACC-suitcase': 85.77965781944357, 'ACC-frisbee': 94.24727272727273, 'ACC-skis': 74.1145565372048, 'ACC-snowboard': 82.78905733388846, 'ACC-sports ball': 88.79017169555824, 'ACC-kite': 86.01179464332908, 'ACC-baseball bat': 87.73460503937244, 'ACC-baseball glove': 60.6165740760756, 'ACC-skateboard': 90.85880937739856, 'ACC-surfboard': 86.25081579790101, 'ACC-tennis racket': 95.13012663819023, 'ACC-bottle': 85.02260188758154, 'ACC-wine glass': 90.42637486174321, 'ACC-cup': 89.49510113891303, 'ACC-fork': 84.32259789970334, 'ACC-knife': 78.99098703011651, 'ACC-spoon': 79.26669872624817, 'ACC-bowl': 66.5934043176337, 'ACC-banana': 91.29989567202834, 'ACC-apple': 72.87419877162586, 'ACC-sandwich': 82.62449169339075, 'ACC-orange': 89.79606904885345, 'ACC-broccoli': 82.78472810587458, 'ACC-carrot': 81.96436186547864, 'ACC-hot dog': 73.14013482715194, 'ACC-pizza': 92.6819918243167, 'ACC-donut': 73.70832139953303, 'ACC-cake': 86.27370348314315, 'ACC-chair': 78.25758185091712, 'ACC-couch': 77.58644563472676, 'ACC-potted plant': 55.631401458577926, 'ACC-bed': 85.72748400134451, 'ACC-dining table': 76.08527190803797, 'ACC-toilet': 84.45269734266503, 'ACC-tv': 91.09561604387423, 'ACC-laptop': 83.62349561204468, 'ACC-mouse': 90.44727985022132, 'ACC-remote': 55.59279497021571, 'ACC-keyboard': 77.57975570731791, 'ACC-cell phone': 89.89128369418864, 'ACC-microwave': 82.10097616903631, 'ACC-oven': 90.04417551600923, 'ACC-toaster': 90.75873489441487, 'ACC-sink': 81.98549351868894, 'ACC-refrigerator': 89.18050736879275, 'ACC-book': 69.82036787985767, 'ACC-clock': 76.72717460556878, 'ACC-vase': 75.36225716920394, 'ACC-scissors': 93.71918298763188, 'ACC-teddy bear': 84.28487089745201, 'ACC-hair drier': 63.70933310562719, 'ACC-toothbrush': 84.26945795691452, 'ACC-banner': 78.35857873764269, 'ACC-blanket': 21.47884532511566, 'ACC-bridge': 55.63568511727599, 'ACC-cardboard': 68.95823760360622, 'ACC-counter': 53.61447252473034, 'ACC-curtain': 83.07240098366057, 'ACC-door-stuff': 72.76792334516877, 'ACC-floor-wood': 77.15446297661347, 'ACC-flower': 70.76607912594426, 'ACC-fruit': 67.46395107023739, 'ACC-gravel': 46.607226364374924, 'ACC-house': 29.260717478941505, 'ACC-light': 64.39428377292042, 'ACC-mirror-stuff': 78.83464991516938, 'ACC-net': 67.09727016559029, 'ACC-pillow': 49.83275826110831, 'ACC-platform': 50.3537614299318, 'ACC-playingfield': 89.60492091349226, 'ACC-railroad': 81.67252348937498, 'ACC-river': 77.29162723023859, 'ACC-road': 85.12672155537993, 'ACC-roof': 24.163885799035967, 'ACC-sand': 67.39933293550615, 'ACC-sea': 90.40845077208246, 'ACC-shelf': 54.78759117301912, 'ACC-snow': 95.52472832003474, 'ACC-stairs': 62.458213454187884, 'ACC-tent': 14.027846899557108, 'ACC-towel': 55.89082716377008, 'ACC-wall-brick': 71.94863324082696, 'ACC-wall-stone': 33.70066725999933, 'ACC-wall-tile': 86.49513681593285, 'ACC-wall-wood': 61.035585227045615, 'ACC-water-other': 50.02480669361875, 'ACC-window-blind': 67.05114075890313, 'ACC-window-other': 71.1093985385718, 'ACC-tree-merged': 89.8986270963129, 'ACC-fence-merged': 73.05848268037012, 'ACC-ceiling-merged': 83.41561395708493, 'ACC-sky-other-merged': 97.0101702461767, 'ACC-cabinet-merged': 78.5858864800276, 'ACC-table-merged': 55.878770132686526, 'ACC-floor-other-merged': 66.7324253088486, 'ACC-pavement-merged': 71.98229717561227, 'ACC-mountain-merged': 69.41971862413307, 'ACC-grass-merged': 83.58603448741862, 'ACC-dirt-merged': 66.81535260584728, 'ACC-paper-merged': 48.396223588685736, 'ACC-food-other-merged': 64.2235722797645, 'ACC-building-other-merged': 75.650564876686, 'ACC-rock-merged': 83.69942033913277, 'ACC-wall-other-merged': 82.08392537048343, 'ACC-rug-merged': 84.58305751197045})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3063 s/iter. Inference: 0.1741 s/iter. Eval: 0.0000 s/iter. Total: 0.4804 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3307 s/iter. Inference: 0.3384 s/iter. Eval: 0.0000 s/iter. Total: 0.6692 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3443 s/iter. Inference: 0.5453 s/iter. Eval: 0.0000 s/iter. Total: 0.8897 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3874743927421715, 'noc@0.8': 2.4717588527948493, 'noc@0.85': 2.9373719637108575, 'noc@0.9': 3.7740708223587944, 'miou@iter1': 0.8751240105945742} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1423 s/iter. Eval: 0.0011 s/iter. Total: 0.1448 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.35950469970703, 'precision@0.6': 72.56121063232422, 'precision@0.7': 68.59696960449219, 'precision@0.8': 59.502525329589844, 'precision@0.9': 32.29692840576172, 'cIoU': 61.70115661621094, 'mIoU': 66.90166473388672} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.65030674788949, 'SQ': 82.91030529250901, 'RQ': 66.28663885158048, 'PQ_th': 61.74730410521517, 'SQ_th': 83.92818439528129, 'RQ_th': 73.04842662310168, 'PQ_st': 46.44729186890727, 'SQ_st': 81.37388400530548, 'RQ_st': 56.08016674362403}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.41480780995856, 'AP50': 69.15321429101915, 'AP75': 48.82767370831694, 'APs': 25.5191441758153, 'APm': 49.5842796930898, 'APl': 67.41124799780455, 'AP-person': 48.74551536689273, 'AP-bicycle': 23.28718368407923, 'AP-car': 44.30333640478235, 'AP-motorcycle': 42.58230493646654, 'AP-airplane': 61.10194624516356, 'AP-bus': 70.52592044632333, 'AP-train': 74.66746102461398, 'AP-truck': 41.22465372373083, 'AP-boat': 30.03841327495581, 'AP-traffic light': 27.135828390011913, 'AP-fire hydrant': 71.38739758324559, 'AP-stop sign': 67.83730055138305, 'AP-parking meter': 52.00853774761245, 'AP-bench': 27.16160651537572, 'AP-bird': 33.88634555057211, 'AP-cat': 76.70654414891287, 'AP-dog': 70.94091339399277, 'AP-horse': 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INFO:trainer.default_trainer:This epoch takes 0:57:05.604985 INFO:trainer.default_trainer:PROGRESS: 44.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 22 training. INFO:trainer.default_trainer:epochs[ 22] optim steps[40200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19207/0.76767, loss_mask_bce_0: 0.36343/0.30181, loss_mask_dice_0: 0.81373/1.02662, loss_spatial_bce_0: 0.09016/0.08694, loss_spatial_dice_0: 0.21082/0.18366, loss_spatial_ce_0: 0.03220/0.06244, loss_grounding_bce_0: 0.02116/0.08078, loss_grounding_dice_0: 0.15992/0.15124, loss_grounding_ce_0: 0.08239/0.24935, loss_mask_ce_1: 1.42223/0.76936, loss_mask_bce_1: 0.53739/0.30261, loss_mask_dice_1: 0.64281/1.03038, loss_spatial_bce_1: 0.09449/0.08718, loss_spatial_dice_1: 0.20166/0.18620, loss_spatial_ce_1: 0.03025/0.06673, loss_grounding_bce_1: 0.02152/0.08096, loss_grounding_dice_1: 0.16143/0.15199, loss_grounding_ce_1: 0.06956/0.25066, loss_mask_ce_2: 1.47756/0.77721, loss_mask_bce_2: 0.37528/0.30266, loss_mask_dice_2: 0.71747/1.03152, loss_spatial_bce_2: 0.08262/0.08708, loss_spatial_dice_2: 0.20371/0.18640, loss_spatial_ce_2: 0.03791/0.06907, loss_grounding_bce_2: 0.02066/0.08093, loss_grounding_dice_2: 0.16687/0.15174, loss_grounding_ce_2: 0.06092/0.25336, loss_mask_ce_3: 1.32510/0.77951, loss_mask_bce_3: 0.54502/0.30422, loss_mask_dice_3: 0.78203/1.02817, loss_spatial_bce_3: 0.08877/0.08897, loss_spatial_dice_3: 0.21050/0.18737, loss_spatial_ce_3: 0.07518/0.07371, loss_grounding_bce_3: 0.02096/0.08134, loss_grounding_dice_3: 0.14943/0.15131, loss_grounding_ce_3: 0.05656/0.25335, loss_mask_ce_4: 1.17461/0.78520, loss_mask_bce_4: 0.63712/0.30651, loss_mask_dice_4: 0.72283/1.04773, loss_spatial_bce_4: 0.09749/0.09090, loss_spatial_dice_4: 0.20395/0.19502, loss_spatial_ce_4: 0.08333/0.08660, loss_grounding_bce_4: 0.02152/0.08198, loss_grounding_dice_4: 0.16299/0.15398, loss_grounding_ce_4: 0.02316/0.25931, loss_mask_ce_5: 1.38511/0.80800, loss_mask_bce_5: 0.55878/0.30823, loss_mask_dice_5: 0.71297/1.05485, loss_spatial_bce_5: 0.17083/0.09275, loss_spatial_dice_5: 0.28393/0.19754, loss_spatial_ce_5: 0.15826/0.09888, loss_grounding_bce_5: 0.02280/0.08219, loss_grounding_dice_5: 0.15181/0.15452, loss_grounding_ce_5: 0.03709/0.27789, loss_mask_ce_6: 1.10584/0.83426, loss_mask_bce_6: 0.40343/0.31001, loss_mask_dice_6: 0.97507/1.05787, loss_spatial_bce_6: 0.22504/0.09776, loss_spatial_dice_6: 0.29489/0.19973, loss_spatial_ce_6: 0.49226/0.12150, loss_grounding_bce_6: 0.01968/0.08319, loss_grounding_dice_6: 0.13572/0.15518, loss_grounding_ce_6: 0.04887/0.28762, loss_mask_ce_7: 1.50144/0.89104, loss_mask_bce_7: 0.40287/0.31740, loss_mask_dice_7: 0.80831/1.10428, loss_spatial_bce_7: 0.19477/0.10802, loss_spatial_dice_7: 0.37236/0.22496, loss_spatial_ce_7: 0.25108/0.16094, loss_grounding_bce_7: 0.02643/0.08481, loss_grounding_dice_7: 0.18266/0.16092, loss_grounding_ce_7: 0.03924/0.32394, loss_mask_ce_8: 2.04598/1.02763, loss_mask_bce_8: 0.66538/0.33372, loss_mask_dice_8: 1.10754/1.18146, loss_spatial_bce_8: 0.19807/0.12686, loss_spatial_dice_8: 0.33592/0.26237, loss_spatial_ce_8: 0.35597/0.21358, loss_grounding_bce_8: 0.02758/0.08875, loss_grounding_dice_8: 0.24222/0.17052, loss_grounding_ce_8: 0.03304/0.42650, loss_mask_ce_9: 2.69175/3.48649, loss_mask_bce_9: 0.49529/0.36067, loss_mask_dice_9: 1.32834/1.76662, loss_spatial_bce_9: 0.34364/0.35616, loss_spatial_dice_9: 0.76098/0.79478, loss_spatial_ce_9: 0.97582/1.39883, loss_grounding_bce_9: 0.02755/0.10088, loss_grounding_dice_9: 0.28877/0.24360, loss_grounding_ce_9: 0.07279/0.68585] items per batch[64] items per second[0.17] total items[2572800] mini batches[ 40200] memory[4999] epoch remaining[1:26:00] INFO:trainer.default_trainer:epochs[ 22] optim steps[40300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.11904/0.76756, loss_mask_bce_0: 0.47532/0.30173, loss_mask_dice_0: 0.78601/1.02664, loss_spatial_bce_0: 0.08975/0.08692, loss_spatial_dice_0: 0.21518/0.18364, loss_spatial_ce_0: 0.03963/0.06239, loss_grounding_bce_0: 0.03866/0.08076, loss_grounding_dice_0: 0.06723/0.15117, loss_grounding_ce_0: 0.65740/0.24930, loss_mask_ce_1: 1.12034/0.76927, loss_mask_bce_1: 0.48011/0.30254, loss_mask_dice_1: 0.76710/1.03043, loss_spatial_bce_1: 0.09824/0.08715, loss_spatial_dice_1: 0.23706/0.18618, loss_spatial_ce_1: 0.05396/0.06669, loss_grounding_bce_1: 0.04100/0.08095, loss_grounding_dice_1: 0.06823/0.15192, loss_grounding_ce_1: 0.65923/0.25067, loss_mask_ce_2: 1.14227/0.77715, loss_mask_bce_2: 0.46523/0.30260, loss_mask_dice_2: 0.75551/1.03159, loss_spatial_bce_2: 0.08599/0.08706, loss_spatial_dice_2: 0.22340/0.18639, loss_spatial_ce_2: 0.04380/0.06904, loss_grounding_bce_2: 0.04111/0.08091, loss_grounding_dice_2: 0.06672/0.15167, loss_grounding_ce_2: 0.69000/0.25330, loss_mask_ce_3: 1.13509/0.77943, loss_mask_bce_3: 0.47891/0.30414, loss_mask_dice_3: 0.76720/1.02821, loss_spatial_bce_3: 0.09713/0.08893, loss_spatial_dice_3: 0.24301/0.18734, loss_spatial_ce_3: 0.06553/0.07368, loss_grounding_bce_3: 0.04112/0.08132, loss_grounding_dice_3: 0.06556/0.15125, loss_grounding_ce_3: 0.70751/0.25324, loss_mask_ce_4: 1.11037/0.78511, loss_mask_bce_4: 0.46308/0.30644, loss_mask_dice_4: 0.77477/1.04783, loss_spatial_bce_4: 0.09114/0.09087, loss_spatial_dice_4: 0.24962/0.19501, loss_spatial_ce_4: 0.06026/0.08658, loss_grounding_bce_4: 0.04055/0.08196, loss_grounding_dice_4: 0.06681/0.15391, loss_grounding_ce_4: 0.66652/0.25920, loss_mask_ce_5: 1.06833/0.80794, loss_mask_bce_5: 0.47027/0.30816, loss_mask_dice_5: 0.80476/1.05488, loss_spatial_bce_5: 0.09407/0.09271, loss_spatial_dice_5: 0.24708/0.19753, loss_spatial_ce_5: 0.15447/0.09885, loss_grounding_bce_5: 0.04038/0.08218, loss_grounding_dice_5: 0.07326/0.15446, loss_grounding_ce_5: 0.67811/0.27774, loss_mask_ce_6: 0.94297/0.83423, loss_mask_bce_6: 0.47575/0.30994, loss_mask_dice_6: 0.80806/1.05789, loss_spatial_bce_6: 0.09449/0.09772, loss_spatial_dice_6: 0.25699/0.19971, loss_spatial_ce_6: 0.16374/0.12152, loss_grounding_bce_6: 0.03806/0.08318, loss_grounding_dice_6: 0.07162/0.15512, loss_grounding_ce_6: 0.66137/0.28746, loss_mask_ce_7: 1.19993/0.89105, loss_mask_bce_7: 0.49561/0.31732, loss_mask_dice_7: 0.85868/1.10435, loss_spatial_bce_7: 0.20992/0.10799, loss_spatial_dice_7: 0.32759/0.22497, loss_spatial_ce_7: 0.19884/0.16092, loss_grounding_bce_7: 0.04297/0.08480, loss_grounding_dice_7: 0.07684/0.16084, loss_grounding_ce_7: 0.69035/0.32373, loss_mask_ce_8: 1.21628/1.02764, loss_mask_bce_8: 0.52859/0.33365, loss_mask_dice_8: 0.86073/1.18156, loss_spatial_bce_8: 0.25287/0.12682, loss_spatial_dice_8: 0.35724/0.26239, loss_spatial_ce_8: 0.49174/0.21351, loss_grounding_bce_8: 0.04771/0.08873, loss_grounding_dice_8: 0.07956/0.17046, loss_grounding_ce_8: 0.72707/0.42623, loss_mask_ce_9: 3.63283/3.48618, loss_mask_bce_9: 1.20262/0.36059, loss_mask_dice_9: 2.79393/1.76648, loss_spatial_bce_9: 0.28354/0.35614, loss_spatial_dice_9: 0.89988/0.79477, loss_spatial_ce_9: 1.30738/1.39872, loss_grounding_bce_9: 0.16518/0.10089, loss_grounding_dice_9: 0.37130/0.24356, loss_grounding_ce_9: 0.78732/0.68543] items per batch[64] items per second[0.36] total items[2579200] mini batches[ 40300] memory[4999] epoch remaining[0:53:21] INFO:trainer.default_trainer:epochs[ 22] optim steps[40400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99049/0.76737, loss_mask_bce_0: 0.15948/0.30160, loss_mask_dice_0: 0.21050/1.02674, loss_spatial_bce_0: 0.18909/0.08686, loss_spatial_dice_0: 0.14418/0.18362, loss_spatial_ce_0: 0.14805/0.06237, loss_grounding_bce_0: 0.09639/0.08074, loss_grounding_dice_0: 0.07650/0.15121, loss_grounding_ce_0: 0.47607/0.24932, loss_mask_ce_1: 1.04256/0.76908, loss_mask_bce_1: 0.17178/0.30242, loss_mask_dice_1: 0.19763/1.03052, loss_spatial_bce_1: 0.12108/0.08709, loss_spatial_dice_1: 0.11488/0.18616, loss_spatial_ce_1: 0.16730/0.06666, loss_grounding_bce_1: 0.10004/0.08092, loss_grounding_dice_1: 0.07381/0.15198, loss_grounding_ce_1: 0.50256/0.25070, loss_mask_ce_2: 1.10017/0.77697, loss_mask_bce_2: 0.21965/0.30246, loss_mask_dice_2: 0.20102/1.03166, loss_spatial_bce_2: 0.16156/0.08701, loss_spatial_dice_2: 0.14036/0.18639, loss_spatial_ce_2: 0.15320/0.06904, loss_grounding_bce_2: 0.15488/0.08088, loss_grounding_dice_2: 0.09623/0.15172, loss_grounding_ce_2: 0.59981/0.25328, loss_mask_ce_3: 1.20432/0.77928, loss_mask_bce_3: 0.18042/0.30401, loss_mask_dice_3: 0.17349/1.02831, loss_spatial_bce_3: 0.19027/0.08888, loss_spatial_dice_3: 0.14114/0.18734, loss_spatial_ce_3: 0.16145/0.07366, loss_grounding_bce_3: 0.12286/0.08129, loss_grounding_dice_3: 0.08008/0.15130, loss_grounding_ce_3: 0.62947/0.25322, loss_mask_ce_4: 1.03056/0.78496, loss_mask_bce_4: 0.15772/0.30629, loss_mask_dice_4: 0.17526/1.04793, loss_spatial_bce_4: 0.23162/0.09082, loss_spatial_dice_4: 0.17493/0.19501, loss_spatial_ce_4: 0.14427/0.08657, loss_grounding_bce_4: 0.11460/0.08194, loss_grounding_dice_4: 0.07447/0.15397, loss_grounding_ce_4: 0.54938/0.25916, loss_mask_ce_5: 1.03649/0.80784, loss_mask_bce_5: 0.17689/0.30801, loss_mask_dice_5: 0.19831/1.05503, loss_spatial_bce_5: 0.14219/0.09266, loss_spatial_dice_5: 0.14021/0.19752, loss_spatial_ce_5: 0.15052/0.09884, loss_grounding_bce_5: 0.14066/0.08215, loss_grounding_dice_5: 0.09202/0.15452, loss_grounding_ce_5: 0.52604/0.27769, loss_mask_ce_6: 1.10283/0.83414, loss_mask_bce_6: 0.21483/0.30979, loss_mask_dice_6: 0.24584/1.05804, loss_spatial_bce_6: 0.13922/0.09765, loss_spatial_dice_6: 0.15250/0.19970, loss_spatial_ce_6: 0.16248/0.12151, loss_grounding_bce_6: 0.16962/0.08316, loss_grounding_dice_6: 0.11997/0.15519, loss_grounding_ce_6: 0.55868/0.28739, loss_mask_ce_7: 0.50119/0.89087, loss_mask_bce_7: 0.45079/0.31718, loss_mask_dice_7: 0.23271/1.10447, loss_spatial_bce_7: 0.15839/0.10792, loss_spatial_dice_7: 0.19983/0.22495, loss_spatial_ce_7: 0.17948/0.16087, loss_grounding_bce_7: 0.30323/0.08478, loss_grounding_dice_7: 0.11147/0.16089, loss_grounding_ce_7: 0.26670/0.32363, loss_mask_ce_8: 0.64299/1.02752, loss_mask_bce_8: 0.54786/0.33350, loss_mask_dice_8: 0.24880/1.18171, loss_spatial_bce_8: 0.15059/0.12674, loss_spatial_dice_8: 0.18250/0.26237, loss_spatial_ce_8: 0.30593/0.21350, loss_grounding_bce_8: 0.41182/0.08871, loss_grounding_dice_8: 0.12271/0.17051, loss_grounding_ce_8: 0.24397/0.42620, loss_mask_ce_9: 3.17983/3.48619, loss_mask_bce_9: 0.31430/0.36039, loss_mask_dice_9: 0.39221/1.76641, loss_spatial_bce_9: 0.62975/0.35603, loss_spatial_dice_9: 0.82058/0.79475, loss_spatial_ce_9: 1.16356/1.39860, loss_grounding_bce_9: 0.22214/0.10083, loss_grounding_dice_9: 0.18892/0.24355, loss_grounding_ce_9: 0.34055/0.68536] items per batch[64] items per second[0.36] total items[2585600] mini batches[ 40400] memory[4999] epoch remaining[0:49:11] INFO:trainer.default_trainer:epochs[ 22] optim steps[40500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34492/0.76714, loss_mask_bce_0: 0.17904/0.30157, loss_mask_dice_0: 0.45838/1.02655, loss_spatial_bce_0: 0.05614/0.08687, loss_spatial_dice_0: 0.14662/0.18359, loss_spatial_ce_0: 0.00357/0.06234, loss_grounding_bce_0: 0.03771/0.08074, loss_grounding_dice_0: 0.36257/0.15119, loss_grounding_ce_0: 0.01127/0.24931, loss_mask_ce_1: 0.38728/0.76887, loss_mask_bce_1: 0.17615/0.30239, loss_mask_dice_1: 0.44660/1.03024, loss_spatial_bce_1: 0.05386/0.08710, loss_spatial_dice_1: 0.16488/0.18614, loss_spatial_ce_1: 0.00491/0.06660, loss_grounding_bce_1: 0.03662/0.08093, loss_grounding_dice_1: 0.34262/0.15197, loss_grounding_ce_1: 0.01664/0.25067, loss_mask_ce_2: 0.37715/0.77672, loss_mask_bce_2: 0.16613/0.30244, loss_mask_dice_2: 0.44300/1.03136, loss_spatial_bce_2: 0.05576/0.08700, loss_spatial_dice_2: 0.15917/0.18636, loss_spatial_ce_2: 0.00977/0.06907, loss_grounding_bce_2: 0.03975/0.08089, loss_grounding_dice_2: 0.34452/0.15171, loss_grounding_ce_2: 0.01593/0.25328, loss_mask_ce_3: 0.35281/0.77905, loss_mask_bce_3: 0.17401/0.30399, loss_mask_dice_3: 0.48393/1.02805, loss_spatial_bce_3: 0.05333/0.08887, loss_spatial_dice_3: 0.17067/0.18731, loss_spatial_ce_3: 0.03870/0.07363, loss_grounding_bce_3: 0.04290/0.08130, loss_grounding_dice_3: 0.38219/0.15127, loss_grounding_ce_3: 0.01492/0.25332, loss_mask_ce_4: 0.41044/0.78475, loss_mask_bce_4: 0.17043/0.30626, loss_mask_dice_4: 0.48014/1.04758, loss_spatial_bce_4: 0.05905/0.09081, loss_spatial_dice_4: 0.18430/0.19499, loss_spatial_ce_4: 0.03031/0.08655, loss_grounding_bce_4: 0.03935/0.08195, loss_grounding_dice_4: 0.39731/0.15394, loss_grounding_ce_4: 0.01933/0.25919, loss_mask_ce_5: 0.45618/0.80755, loss_mask_bce_5: 0.16979/0.30799, loss_mask_dice_5: 0.47691/1.05472, loss_spatial_bce_5: 0.05824/0.09265, loss_spatial_dice_5: 0.16023/0.19749, loss_spatial_ce_5: 0.01630/0.09880, loss_grounding_bce_5: 0.04041/0.08217, loss_grounding_dice_5: 0.38741/0.15451, loss_grounding_ce_5: 0.03295/0.27761, loss_mask_ce_6: 0.39325/0.83385, loss_mask_bce_6: 0.17346/0.30977, loss_mask_dice_6: 0.49666/1.05767, loss_spatial_bce_6: 0.06368/0.09764, loss_spatial_dice_6: 0.14575/0.19966, loss_spatial_ce_6: 0.07879/0.12150, loss_grounding_bce_6: 0.04175/0.08317, loss_grounding_dice_6: 0.38349/0.15516, loss_grounding_ce_6: 0.01302/0.28733, loss_mask_ce_7: 0.37488/0.89054, loss_mask_bce_7: 0.17686/0.31716, loss_mask_dice_7: 0.47536/1.10411, loss_spatial_bce_7: 0.07145/0.10792, loss_spatial_dice_7: 0.18299/0.22492, loss_spatial_ce_7: 0.07230/0.16082, loss_grounding_bce_7: 0.04288/0.08478, loss_grounding_dice_7: 0.38081/0.16087, loss_grounding_ce_7: 0.01717/0.32362, loss_mask_ce_8: 0.28562/1.02714, loss_mask_bce_8: 0.19066/0.33350, loss_mask_dice_8: 0.45659/1.18133, loss_spatial_bce_8: 0.08524/0.12673, loss_spatial_dice_8: 0.19622/0.26233, loss_spatial_ce_8: 0.13831/0.21341, loss_grounding_bce_8: 0.04483/0.08872, loss_grounding_dice_8: 0.40190/0.17047, loss_grounding_ce_8: 0.01856/0.42607, loss_mask_ce_9: 1.43187/3.48555, loss_mask_bce_9: 0.20813/0.36037, loss_mask_dice_9: 0.68253/1.76573, loss_spatial_bce_9: 0.51942/0.35610, loss_spatial_dice_9: 0.69799/0.79472, loss_spatial_ce_9: 0.90659/1.39857, loss_grounding_bce_9: 0.04905/0.10082, loss_grounding_dice_9: 0.46157/0.24350, loss_grounding_ce_9: 0.03021/0.68552] items per batch[64] items per second[0.37] total items[2592000] mini batches[ 40500] memory[4999] epoch remaining[0:45:23] INFO:trainer.default_trainer:epochs[ 22] optim steps[40600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30769/0.76713, loss_mask_bce_0: 0.37879/0.30172, loss_mask_dice_0: 0.18490/1.02684, loss_spatial_bce_0: 0.25464/0.08692, loss_spatial_dice_0: 0.13131/0.18359, loss_spatial_ce_0: 0.00466/0.06231, loss_grounding_bce_0: 0.11525/0.08079, loss_grounding_dice_0: 0.07295/0.15121, loss_grounding_ce_0: 0.04477/0.24931, loss_mask_ce_1: 0.29860/0.76889, loss_mask_bce_1: 0.37820/0.30251, loss_mask_dice_1: 0.18887/1.03053, loss_spatial_bce_1: 0.23739/0.08715, loss_spatial_dice_1: 0.12526/0.18614, loss_spatial_ce_1: 0.00288/0.06659, loss_grounding_bce_1: 0.11949/0.08097, loss_grounding_dice_1: 0.07666/0.15199, loss_grounding_ce_1: 0.03217/0.25067, loss_mask_ce_2: 0.31295/0.77675, loss_mask_bce_2: 0.37127/0.30258, loss_mask_dice_2: 0.19032/1.03167, loss_spatial_bce_2: 0.25312/0.08706, loss_spatial_dice_2: 0.13719/0.18636, loss_spatial_ce_2: 0.00324/0.06902, loss_grounding_bce_2: 0.11994/0.08093, loss_grounding_dice_2: 0.08247/0.15173, loss_grounding_ce_2: 0.04315/0.25326, loss_mask_ce_3: 0.36654/0.77909, loss_mask_bce_3: 0.39246/0.30413, loss_mask_dice_3: 0.18912/1.02836, loss_spatial_bce_3: 0.24781/0.08893, loss_spatial_dice_3: 0.12902/0.18731, loss_spatial_ce_3: 0.00242/0.07360, loss_grounding_bce_3: 0.12133/0.08135, loss_grounding_dice_3: 0.08032/0.15130, loss_grounding_ce_3: 0.12359/0.25330, loss_mask_ce_4: 0.35068/0.78476, loss_mask_bce_4: 0.37361/0.30643, loss_mask_dice_4: 0.20031/1.04788, loss_spatial_bce_4: 0.24513/0.09086, loss_spatial_dice_4: 0.12103/0.19499, loss_spatial_ce_4: 0.00790/0.08650, loss_grounding_bce_4: 0.11834/0.08202, loss_grounding_dice_4: 0.09129/0.15396, loss_grounding_ce_4: 0.10213/0.25912, loss_mask_ce_5: 0.38558/0.80758, loss_mask_bce_5: 0.36461/0.30815, loss_mask_dice_5: 0.19733/1.05503, loss_spatial_bce_5: 0.24472/0.09270, loss_spatial_dice_5: 0.11987/0.19749, loss_spatial_ce_5: 0.01764/0.09876, loss_grounding_bce_5: 0.11897/0.08224, loss_grounding_dice_5: 0.08471/0.15455, loss_grounding_ce_5: 0.08534/0.27754, loss_mask_ce_6: 0.32173/0.83390, loss_mask_bce_6: 0.34135/0.30994, loss_mask_dice_6: 0.18947/1.05806, loss_spatial_bce_6: 0.24144/0.09771, loss_spatial_dice_6: 0.12488/0.19968, loss_spatial_ce_6: 0.00819/0.12151, loss_grounding_bce_6: 0.11817/0.08322, loss_grounding_dice_6: 0.08345/0.15518, loss_grounding_ce_6: 0.03645/0.28731, loss_mask_ce_7: 0.31418/0.89054, loss_mask_bce_7: 0.37134/0.31735, loss_mask_dice_7: 0.20464/1.10449, loss_spatial_bce_7: 0.31010/0.10799, loss_spatial_dice_7: 0.13700/0.22492, loss_spatial_ce_7: 0.00917/0.16076, loss_grounding_bce_7: 0.11587/0.08486, loss_grounding_dice_7: 0.08136/0.16090, loss_grounding_ce_7: 0.03162/0.32349, loss_mask_ce_8: 0.26511/1.02729, loss_mask_bce_8: 0.39615/0.33370, loss_mask_dice_8: 0.23648/1.18178, loss_spatial_bce_8: 0.26593/0.12679, loss_spatial_dice_8: 0.13374/0.26232, loss_spatial_ce_8: 0.15062/0.21338, loss_grounding_bce_8: 0.11747/0.08881, loss_grounding_dice_8: 0.10564/0.17052, loss_grounding_ce_8: 0.02391/0.42588, loss_mask_ce_9: 1.50717/3.48564, loss_mask_bce_9: 0.42313/0.36054, loss_mask_dice_9: 0.26688/1.76653, loss_spatial_bce_9: 0.60292/0.35614, loss_spatial_dice_9: 0.56699/0.79474, loss_spatial_ce_9: 0.83164/1.39836, loss_grounding_bce_9: 0.13291/0.10091, loss_grounding_dice_9: 0.07503/0.24354, loss_grounding_ce_9: 0.01068/0.68519] items per batch[64] items per second[0.37] total items[2598400] mini batches[ 40600] memory[4999] epoch remaining[0:42:06] INFO:trainer.default_trainer:epochs[ 22] optim steps[40700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03187/0.76704, loss_mask_bce_0: 0.23044/0.30178, loss_mask_dice_0: 0.18568/1.02681, loss_spatial_bce_0: 0.02838/0.08691, loss_spatial_dice_0: 0.04337/0.18357, loss_spatial_ce_0: 0.00017/0.06227, loss_grounding_bce_0: 0.00170/0.08078, loss_grounding_dice_0: 0.02511/0.15119, loss_grounding_ce_0: 0.00002/0.24945, loss_mask_ce_1: 0.03260/0.76879, loss_mask_bce_1: 0.22959/0.30257, loss_mask_dice_1: 0.18232/1.03043, loss_spatial_bce_1: 0.02924/0.08715, loss_spatial_dice_1: 0.04822/0.18612, loss_spatial_ce_1: 0.00005/0.06653, loss_grounding_bce_1: 0.00216/0.08096, loss_grounding_dice_1: 0.02759/0.15196, loss_grounding_ce_1: 0.00003/0.25079, loss_mask_ce_2: 0.03480/0.77668, loss_mask_bce_2: 0.25363/0.30264, loss_mask_dice_2: 0.18432/1.03163, loss_spatial_bce_2: 0.03089/0.08706, loss_spatial_dice_2: 0.05257/0.18633, loss_spatial_ce_2: 0.00039/0.06897, loss_grounding_bce_2: 0.00068/0.08093, loss_grounding_dice_2: 0.01531/0.15171, loss_grounding_ce_2: 0.00005/0.25342, loss_mask_ce_3: 0.03986/0.77899, loss_mask_bce_3: 0.26517/0.30419, loss_mask_dice_3: 0.19308/1.02830, loss_spatial_bce_3: 0.03404/0.08893, loss_spatial_dice_3: 0.05471/0.18730, loss_spatial_ce_3: 0.00258/0.07356, loss_grounding_bce_3: 0.00261/0.08135, loss_grounding_dice_3: 0.03098/0.15129, loss_grounding_ce_3: 0.00005/0.25340, loss_mask_ce_4: 0.05520/0.78468, loss_mask_bce_4: 0.24851/0.30648, loss_mask_dice_4: 0.18795/1.04773, loss_spatial_bce_4: 0.04613/0.09086, loss_spatial_dice_4: 0.06461/0.19497, loss_spatial_ce_4: 0.00070/0.08645, loss_grounding_bce_4: 0.00213/0.08203, loss_grounding_dice_4: 0.02972/0.15394, loss_grounding_ce_4: 0.00003/0.25925, loss_mask_ce_5: 0.04883/0.80749, loss_mask_bce_5: 0.28125/0.30821, loss_mask_dice_5: 0.18433/1.05500, loss_spatial_bce_5: 0.04601/0.09272, loss_spatial_dice_5: 0.06650/0.19750, loss_spatial_ce_5: 0.00133/0.09871, loss_grounding_bce_5: 0.00156/0.08224, loss_grounding_dice_5: 0.02757/0.15453, loss_grounding_ce_5: 0.00001/0.27779, loss_mask_ce_6: 0.08510/0.83383, loss_mask_bce_6: 0.29456/0.30998, loss_mask_dice_6: 0.19125/1.05792, loss_spatial_bce_6: 0.07501/0.09771, loss_spatial_dice_6: 0.07450/0.19968, loss_spatial_ce_6: 0.00200/0.12150, loss_grounding_bce_6: 0.00152/0.08322, loss_grounding_dice_6: 0.02999/0.15516, loss_grounding_ce_6: 0.00001/0.28750, loss_mask_ce_7: 0.07163/0.89052, loss_mask_bce_7: 0.30867/0.31738, loss_mask_dice_7: 0.19282/1.10434, loss_spatial_bce_7: 0.12335/0.10797, loss_spatial_dice_7: 0.08632/0.22492, loss_spatial_ce_7: 0.06261/0.16072, loss_grounding_bce_7: 0.00169/0.08487, loss_grounding_dice_7: 0.02526/0.16088, loss_grounding_ce_7: 0.00002/0.32357, loss_mask_ce_8: 0.10274/1.02730, loss_mask_bce_8: 0.34461/0.33374, loss_mask_dice_8: 0.20557/1.18169, loss_spatial_bce_8: 0.15004/0.12677, loss_spatial_dice_8: 0.08514/0.26228, loss_spatial_ce_8: 0.08582/0.21334, loss_grounding_bce_8: 0.00147/0.08882, loss_grounding_dice_8: 0.02550/0.17050, loss_grounding_ce_8: 0.00033/0.42601, loss_mask_ce_9: 2.20794/3.48579, loss_mask_bce_9: 0.20956/0.36056, loss_mask_dice_9: 0.25712/1.76635, loss_spatial_bce_9: 0.32929/0.35615, loss_spatial_dice_9: 0.66682/0.79479, loss_spatial_ce_9: 0.81513/1.39844, loss_grounding_bce_9: 0.00200/0.10090, loss_grounding_dice_9: 0.06119/0.24351, loss_grounding_ce_9: 0.14237/0.68525] items per batch[64] items per second[0.36] total items[2604800] mini batches[ 40700] memory[4999] epoch remaining[0:39:11] INFO:trainer.default_trainer:epochs[ 22] optim steps[40800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18868/0.76704, loss_mask_bce_0: 0.07237/0.30186, loss_mask_dice_0: 0.04008/1.02691, loss_spatial_bce_0: 0.04784/0.08689, loss_spatial_dice_0: 0.02591/0.18356, loss_spatial_ce_0: 0.09249/0.06220, loss_grounding_bce_0: 0.04543/0.08078, loss_grounding_dice_0: 0.02641/0.15119, loss_grounding_ce_0: 0.03161/0.24960, loss_mask_ce_1: 0.20393/0.76872, loss_mask_bce_1: 0.07170/0.30265, loss_mask_dice_1: 0.04014/1.03064, loss_spatial_bce_1: 0.04471/0.08713, loss_spatial_dice_1: 0.02764/0.18610, loss_spatial_ce_1: 0.09244/0.06646, loss_grounding_bce_1: 0.04872/0.08097, loss_grounding_dice_1: 0.02673/0.15198, loss_grounding_ce_1: 0.03682/0.25088, loss_mask_ce_2: 0.25282/0.77666, loss_mask_bce_2: 0.07667/0.30272, loss_mask_dice_2: 0.04014/1.03181, loss_spatial_bce_2: 0.04508/0.08704, loss_spatial_dice_2: 0.02914/0.18633, loss_spatial_ce_2: 0.09244/0.06890, loss_grounding_bce_2: 0.04842/0.08094, loss_grounding_dice_2: 0.02697/0.15172, loss_grounding_ce_2: 0.06228/0.25366, loss_mask_ce_3: 0.20236/0.77898, loss_mask_bce_3: 0.07340/0.30427, loss_mask_dice_3: 0.03831/1.02848, loss_spatial_bce_3: 0.04919/0.08891, loss_spatial_dice_3: 0.02796/0.18729, loss_spatial_ce_3: 0.09253/0.07349, loss_grounding_bce_3: 0.04975/0.08135, loss_grounding_dice_3: 0.02577/0.15129, loss_grounding_ce_3: 0.06482/0.25356, loss_mask_ce_4: 0.21862/0.78466, loss_mask_bce_4: 0.07336/0.30656, loss_mask_dice_4: 0.03850/1.04793, loss_spatial_bce_4: 0.04398/0.09084, loss_spatial_dice_4: 0.02616/0.19495, loss_spatial_ce_4: 0.09263/0.08640, loss_grounding_bce_4: 0.04869/0.08202, loss_grounding_dice_4: 0.02587/0.15395, loss_grounding_ce_4: 0.08795/0.25942, loss_mask_ce_5: 0.21226/0.80752, loss_mask_bce_5: 0.07238/0.30831, loss_mask_dice_5: 0.03939/1.05521, loss_spatial_bce_5: 0.04709/0.09270, loss_spatial_dice_5: 0.02383/0.19749, loss_spatial_ce_5: 0.09282/0.09863, loss_grounding_bce_5: 0.04753/0.08226, loss_grounding_dice_5: 0.02753/0.15454, loss_grounding_ce_5: 0.09043/0.27794, loss_mask_ce_6: 0.17996/0.83388, loss_mask_bce_6: 0.07200/0.31007, loss_mask_dice_6: 0.03837/1.05815, loss_spatial_bce_6: 0.05001/0.09769, loss_spatial_dice_6: 0.02387/0.19966, loss_spatial_ce_6: 0.09350/0.12142, loss_grounding_bce_6: 0.04420/0.08324, loss_grounding_dice_6: 0.02381/0.15518, loss_grounding_ce_6: 0.05164/0.28768, loss_mask_ce_7: 0.17154/0.89050, loss_mask_bce_7: 0.07550/0.31748, loss_mask_dice_7: 0.04244/1.10451, loss_spatial_bce_7: 0.05068/0.10794, loss_spatial_dice_7: 0.03198/0.22491, loss_spatial_ce_7: 0.09432/0.16063, loss_grounding_bce_7: 0.04697/0.08489, loss_grounding_dice_7: 0.02609/0.16090, loss_grounding_ce_7: 0.05229/0.32365, loss_mask_ce_8: 0.17291/1.02746, loss_mask_bce_8: 0.10087/0.33385, loss_mask_dice_8: 0.06923/1.18192, loss_spatial_bce_8: 0.04905/0.12672, loss_spatial_dice_8: 0.02883/0.26225, loss_spatial_ce_8: 0.00549/0.21327, loss_grounding_bce_8: 0.06618/0.08884, loss_grounding_dice_8: 0.04498/0.17054, loss_grounding_ce_8: 0.03399/0.42625, loss_mask_ce_9: 1.65504/3.48620, loss_mask_bce_9: 0.13817/0.36067, loss_mask_dice_9: 0.09528/1.76681, loss_spatial_bce_9: 0.56266/0.35612, loss_spatial_dice_9: 0.44322/0.79483, loss_spatial_ce_9: 0.84138/1.39824, loss_grounding_bce_9: 0.09903/0.10095, loss_grounding_dice_9: 0.05918/0.24357, loss_grounding_ce_9: 0.11823/0.68554] items per batch[64] items per second[0.36] total items[2611200] mini batches[ 40800] memory[4999] epoch remaining[0:36:16] INFO:trainer.default_trainer:epochs[ 22] optim steps[40900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27741/0.76686, loss_mask_bce_0: 0.40571/0.30186, loss_mask_dice_0: 0.35014/1.02638, loss_spatial_bce_0: 0.16476/0.08690, loss_spatial_dice_0: 0.12797/0.18353, loss_spatial_ce_0: 0.00564/0.06220, loss_grounding_bce_0: 0.13252/0.08080, loss_grounding_dice_0: 0.08695/0.15122, loss_grounding_ce_0: 0.03080/0.24952, loss_mask_ce_1: 0.29871/0.76851, loss_mask_bce_1: 0.43028/0.30267, loss_mask_dice_1: 0.37074/1.03005, loss_spatial_bce_1: 0.16854/0.08714, loss_spatial_dice_1: 0.13686/0.18606, loss_spatial_ce_1: 0.00802/0.06646, loss_grounding_bce_1: 0.14117/0.08099, loss_grounding_dice_1: 0.08749/0.15200, loss_grounding_ce_1: 0.04207/0.25073, loss_mask_ce_2: 0.25429/0.77642, loss_mask_bce_2: 0.44328/0.30273, loss_mask_dice_2: 0.36598/1.03124, loss_spatial_bce_2: 0.16920/0.08704, loss_spatial_dice_2: 0.13280/0.18629, loss_spatial_ce_2: 0.02825/0.06890, loss_grounding_bce_2: 0.14580/0.08096, loss_grounding_dice_2: 0.08448/0.15174, loss_grounding_ce_2: 0.02611/0.25353, loss_mask_ce_3: 0.29309/0.77881, loss_mask_bce_3: 0.44325/0.30426, loss_mask_dice_3: 0.36163/1.02790, loss_spatial_bce_3: 0.17538/0.08891, loss_spatial_dice_3: 0.13736/0.18725, loss_spatial_ce_3: 0.02077/0.07350, loss_grounding_bce_3: 0.15740/0.08137, loss_grounding_dice_3: 0.08560/0.15133, loss_grounding_ce_3: 0.02350/0.25342, loss_mask_ce_4: 0.28172/0.78444, loss_mask_bce_4: 0.41305/0.30656, loss_mask_dice_4: 0.33214/1.04729, loss_spatial_bce_4: 0.18028/0.09084, loss_spatial_dice_4: 0.13124/0.19491, loss_spatial_ce_4: 0.00506/0.08641, loss_grounding_bce_4: 0.14057/0.08204, loss_grounding_dice_4: 0.08639/0.15396, loss_grounding_ce_4: 0.02313/0.25932, loss_mask_ce_5: 0.24138/0.80730, loss_mask_bce_5: 0.39660/0.30831, loss_mask_dice_5: 0.33734/1.05461, loss_spatial_bce_5: 0.18070/0.09273, loss_spatial_dice_5: 0.11891/0.19745, loss_spatial_ce_5: 0.00480/0.09863, loss_grounding_bce_5: 0.14102/0.08228, loss_grounding_dice_5: 0.08634/0.15455, loss_grounding_ce_5: 0.03832/0.27785, loss_mask_ce_6: 0.29910/0.83369, loss_mask_bce_6: 0.39948/0.31007, loss_mask_dice_6: 0.33418/1.05752, loss_spatial_bce_6: 0.18966/0.09772, loss_spatial_dice_6: 0.14995/0.19963, loss_spatial_ce_6: 0.00995/0.12140, loss_grounding_bce_6: 0.14189/0.08326, loss_grounding_dice_6: 0.08447/0.15520, loss_grounding_ce_6: 0.04029/0.28753, loss_mask_ce_7: 0.34172/0.89025, loss_mask_bce_7: 0.39725/0.31747, loss_mask_dice_7: 0.33891/1.10383, loss_spatial_bce_7: 0.17456/0.10797, loss_spatial_dice_7: 0.15714/0.22488, loss_spatial_ce_7: 0.05156/0.16067, loss_grounding_bce_7: 0.12909/0.08491, loss_grounding_dice_7: 0.08533/0.16089, loss_grounding_ce_7: 0.03661/0.32346, loss_mask_ce_8: 0.45583/1.02705, loss_mask_bce_8: 0.40786/0.33382, loss_mask_dice_8: 0.33148/1.18121, loss_spatial_bce_8: 0.18950/0.12672, loss_spatial_dice_8: 0.13510/0.26219, loss_spatial_ce_8: 0.08060/0.21329, loss_grounding_bce_8: 0.13593/0.08886, loss_grounding_dice_8: 0.09040/0.17054, loss_grounding_ce_8: 0.11388/0.42604, loss_mask_ce_9: 3.15210/3.48567, loss_mask_bce_9: 0.54022/0.36063, loss_mask_dice_9: 0.55033/1.76589, loss_spatial_bce_9: 0.57796/0.35612, loss_spatial_dice_9: 0.79120/0.79483, loss_spatial_ce_9: 1.71784/1.39825, loss_grounding_bce_9: 0.18496/0.10098, loss_grounding_dice_9: 0.17094/0.24360, loss_grounding_ce_9: 0.30312/0.68522] items per batch[64] items per second[0.36] total items[2617600] mini batches[ 40900] memory[4999] epoch remaining[0:33:17] INFO:trainer.default_trainer:epochs[ 22] optim steps[41000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53606/0.76699, loss_mask_bce_0: 0.33157/0.30186, loss_mask_dice_0: 0.68000/1.02651, loss_spatial_bce_0: 0.06148/0.08690, loss_spatial_dice_0: 0.12860/0.18353, loss_spatial_ce_0: 0.00034/0.06222, loss_grounding_bce_0: 0.08161/0.08081, loss_grounding_dice_0: 0.05389/0.15123, loss_grounding_ce_0: 0.07622/0.24955, loss_mask_ce_1: 0.57573/0.76866, loss_mask_bce_1: 0.33494/0.30267, loss_mask_dice_1: 0.69080/1.03017, loss_spatial_bce_1: 0.05968/0.08713, loss_spatial_dice_1: 0.12342/0.18607, loss_spatial_ce_1: 0.00054/0.06646, loss_grounding_bce_1: 0.07314/0.08100, loss_grounding_dice_1: 0.04994/0.15200, loss_grounding_ce_1: 0.04500/0.25076, loss_mask_ce_2: 0.58190/0.77649, loss_mask_bce_2: 0.33585/0.30274, loss_mask_dice_2: 0.66156/1.03134, loss_spatial_bce_2: 0.06188/0.08704, loss_spatial_dice_2: 0.12571/0.18630, loss_spatial_ce_2: 0.00074/0.06891, loss_grounding_bce_2: 0.07412/0.08096, loss_grounding_dice_2: 0.05142/0.15174, loss_grounding_ce_2: 0.03994/0.25354, loss_mask_ce_3: 0.57741/0.77890, loss_mask_bce_3: 0.32393/0.30429, loss_mask_dice_3: 0.63389/1.02810, loss_spatial_bce_3: 0.06473/0.08892, loss_spatial_dice_3: 0.12854/0.18726, loss_spatial_ce_3: 0.00105/0.07348, loss_grounding_bce_3: 0.07496/0.08138, loss_grounding_dice_3: 0.05142/0.15134, loss_grounding_ce_3: 0.03342/0.25343, loss_mask_ce_4: 0.58775/0.78457, loss_mask_bce_4: 0.31705/0.30657, loss_mask_dice_4: 0.65102/1.04746, loss_spatial_bce_4: 0.06514/0.09085, loss_spatial_dice_4: 0.12695/0.19492, loss_spatial_ce_4: 0.00823/0.08640, loss_grounding_bce_4: 0.07923/0.08205, loss_grounding_dice_4: 0.05243/0.15395, loss_grounding_ce_4: 0.01407/0.25935, loss_mask_ce_5: 0.62045/0.80738, loss_mask_bce_5: 0.32175/0.30832, loss_mask_dice_5: 0.59077/1.05474, loss_spatial_bce_5: 0.06404/0.09275, loss_spatial_dice_5: 0.12096/0.19746, loss_spatial_ce_5: 0.02028/0.09862, loss_grounding_bce_5: 0.07302/0.08228, loss_grounding_dice_5: 0.05387/0.15455, loss_grounding_ce_5: 0.03954/0.27792, loss_mask_ce_6: 0.66359/0.83378, loss_mask_bce_6: 0.31764/0.31008, loss_mask_dice_6: 0.63851/1.05767, loss_spatial_bce_6: 0.06651/0.09775, loss_spatial_dice_6: 0.13357/0.19966, loss_spatial_ce_6: 0.04082/0.12142, loss_grounding_bce_6: 0.07602/0.08326, loss_grounding_dice_6: 0.05349/0.15519, loss_grounding_ce_6: 0.03596/0.28756, loss_mask_ce_7: 0.63320/0.89037, loss_mask_bce_7: 0.31725/0.31747, loss_mask_dice_7: 0.61540/1.10399, loss_spatial_bce_7: 0.07778/0.10798, loss_spatial_dice_7: 0.15873/0.22491, loss_spatial_ce_7: 0.05049/0.16067, loss_grounding_bce_7: 0.07354/0.08491, loss_grounding_dice_7: 0.05219/0.16089, loss_grounding_ce_7: 0.02544/0.32345, loss_mask_ce_8: 0.67062/1.02719, loss_mask_bce_8: 0.31361/0.33384, loss_mask_dice_8: 0.65892/1.18132, loss_spatial_bce_8: 0.07306/0.12672, loss_spatial_dice_8: 0.16187/0.26220, loss_spatial_ce_8: 0.07882/0.21327, loss_grounding_bce_8: 0.07321/0.08886, loss_grounding_dice_8: 0.05567/0.17055, loss_grounding_ce_8: 0.01910/0.42611, loss_mask_ce_9: 2.07014/3.48593, loss_mask_bce_9: 0.30995/0.36067, loss_mask_dice_9: 0.84635/1.76611, loss_spatial_bce_9: 0.42193/0.35610, loss_spatial_dice_9: 0.84660/0.79480, loss_spatial_ce_9: 1.54777/1.39814, loss_grounding_bce_9: 0.08556/0.10099, loss_grounding_dice_9: 0.06602/0.24360, loss_grounding_ce_9: 0.58835/0.68524] items per batch[64] items per second[0.36] total items[2624000] mini batches[ 41000] memory[4999] epoch remaining[0:30:17] INFO:trainer.default_trainer:epochs[ 22] optim steps[41100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53178/0.76695, loss_mask_bce_0: 0.46025/0.30189, loss_mask_dice_0: 0.16710/1.02631, loss_spatial_bce_0: 0.26858/0.08690, loss_spatial_dice_0: 0.11475/0.18353, loss_spatial_ce_0: 0.16742/0.06221, loss_grounding_bce_0: 0.14467/0.08083, loss_grounding_dice_0: 0.06748/0.15124, loss_grounding_ce_0: 0.01966/0.24978, loss_mask_ce_1: 0.54164/0.76863, loss_mask_bce_1: 0.44356/0.30270, loss_mask_dice_1: 0.16403/1.02999, loss_spatial_bce_1: 0.24224/0.08714, loss_spatial_dice_1: 0.11865/0.18607, loss_spatial_ce_1: 0.17053/0.06642, loss_grounding_bce_1: 0.22410/0.08101, loss_grounding_dice_1: 0.07849/0.15202, loss_grounding_ce_1: 0.02171/0.25102, loss_mask_ce_2: 0.55346/0.77643, loss_mask_bce_2: 0.44633/0.30277, loss_mask_dice_2: 0.17089/1.03112, loss_spatial_bce_2: 0.22770/0.08704, loss_spatial_dice_2: 0.11267/0.18629, loss_spatial_ce_2: 0.18113/0.06887, loss_grounding_bce_2: 0.18764/0.08098, loss_grounding_dice_2: 0.07243/0.15175, loss_grounding_ce_2: 0.02244/0.25370, loss_mask_ce_3: 0.86832/0.77883, loss_mask_bce_3: 0.26233/0.30431, loss_mask_dice_3: 0.14707/1.02790, loss_spatial_bce_3: 0.24710/0.08892, loss_spatial_dice_3: 0.11651/0.18727, loss_spatial_ce_3: 0.16899/0.07344, loss_grounding_bce_3: 0.20566/0.08140, loss_grounding_dice_3: 0.07670/0.15136, loss_grounding_ce_3: 0.01491/0.25360, loss_mask_ce_4: 0.72862/0.78453, loss_mask_bce_4: 0.32074/0.30660, loss_mask_dice_4: 0.16905/1.04723, loss_spatial_bce_4: 0.22220/0.09085, loss_spatial_dice_4: 0.11364/0.19494, loss_spatial_ce_4: 0.16242/0.08634, loss_grounding_bce_4: 0.15188/0.08206, loss_grounding_dice_4: 0.06983/0.15396, loss_grounding_ce_4: 0.01509/0.25949, loss_mask_ce_5: 1.20228/0.80732, loss_mask_bce_5: 0.21974/0.30835, loss_mask_dice_5: 0.13354/1.05447, loss_spatial_bce_5: 0.22212/0.09275, loss_spatial_dice_5: 0.12265/0.19748, loss_spatial_ce_5: 0.17628/0.09860, loss_grounding_bce_5: 0.16427/0.08231, loss_grounding_dice_5: 0.06726/0.15457, loss_grounding_ce_5: 0.01994/0.27812, loss_mask_ce_6: 0.76451/0.83371, loss_mask_bce_6: 0.27852/0.31011, loss_mask_dice_6: 0.14255/1.05745, loss_spatial_bce_6: 0.16402/0.09775, loss_spatial_dice_6: 0.11269/0.19967, loss_spatial_ce_6: 0.21664/0.12141, loss_grounding_bce_6: 0.19003/0.08329, loss_grounding_dice_6: 0.06826/0.15521, loss_grounding_ce_6: 0.02191/0.28754, loss_mask_ce_7: 0.79514/0.89027, loss_mask_bce_7: 0.29454/0.31750, loss_mask_dice_7: 0.16594/1.10373, loss_spatial_bce_7: 0.15115/0.10798, loss_spatial_dice_7: 0.10184/0.22491, loss_spatial_ce_7: 0.29284/0.16066, loss_grounding_bce_7: 0.18427/0.08492, loss_grounding_dice_7: 0.07112/0.16092, loss_grounding_ce_7: 0.02866/0.32346, loss_mask_ce_8: 1.22374/1.02717, loss_mask_bce_8: 0.50181/0.33389, loss_mask_dice_8: 0.18204/1.18112, loss_spatial_bce_8: 0.18306/0.12669, loss_spatial_dice_8: 0.10530/0.26217, loss_spatial_ce_8: 0.24589/0.21325, loss_grounding_bce_8: 0.18775/0.08889, loss_grounding_dice_8: 0.08022/0.17056, loss_grounding_ce_8: 0.07685/0.42632, loss_mask_ce_9: 3.04727/3.48586, loss_mask_bce_9: 0.38417/0.36072, loss_mask_dice_9: 0.25475/1.76584, loss_spatial_bce_9: 0.54772/0.35606, loss_spatial_dice_9: 0.55016/0.79479, loss_spatial_ce_9: 0.58507/1.39812, loss_grounding_bce_9: 0.21220/0.10103, loss_grounding_dice_9: 0.07020/0.24363, loss_grounding_ce_9: 0.39129/0.68532] items per batch[64] items per second[0.36] total items[2630400] mini batches[ 41100] memory[4999] epoch remaining[0:27:17] INFO:trainer.default_trainer:epochs[ 22] optim steps[41200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.78441/0.76678, loss_mask_bce_0: 0.12866/0.30195, loss_mask_dice_0: 1.16046/1.02656, loss_spatial_bce_0: 0.03203/0.08689, loss_spatial_dice_0: 0.22660/0.18350, loss_spatial_ce_0: 0.00783/0.06216, loss_grounding_bce_0: 0.03221/0.08083, loss_grounding_dice_0: 0.35552/0.15123, loss_grounding_ce_0: 0.02068/0.24982, loss_mask_ce_1: 0.49260/0.76844, loss_mask_bce_1: 0.11193/0.30276, loss_mask_dice_1: 0.99367/1.03027, loss_spatial_bce_1: 0.03243/0.08713, loss_spatial_dice_1: 0.24358/0.18604, loss_spatial_ce_1: 0.00889/0.06637, loss_grounding_bce_1: 0.01894/0.08102, loss_grounding_dice_1: 0.28807/0.15201, loss_grounding_ce_1: 0.32708/0.25105, loss_mask_ce_2: 0.42585/0.77628, loss_mask_bce_2: 0.11651/0.30282, loss_mask_dice_2: 1.06021/1.03138, loss_spatial_bce_2: 0.03106/0.08702, loss_spatial_dice_2: 0.25347/0.18626, loss_spatial_ce_2: 0.01409/0.06888, loss_grounding_bce_2: 0.02586/0.08098, loss_grounding_dice_2: 0.28821/0.15174, loss_grounding_ce_2: 0.32534/0.25375, loss_mask_ce_3: 0.44650/0.77862, loss_mask_bce_3: 0.11393/0.30438, loss_mask_dice_3: 1.03807/1.02821, loss_spatial_bce_3: 0.03482/0.08892, loss_spatial_dice_3: 0.24091/0.18725, loss_spatial_ce_3: 0.01966/0.07340, loss_grounding_bce_3: 0.02818/0.08140, loss_grounding_dice_3: 0.31448/0.15136, loss_grounding_ce_3: 0.02880/0.25361, loss_mask_ce_4: 0.21261/0.78437, loss_mask_bce_4: 0.13551/0.30665, loss_mask_dice_4: 1.22247/1.04750, loss_spatial_bce_4: 0.03205/0.09085, loss_spatial_dice_4: 0.24300/0.19492, loss_spatial_ce_4: 0.03460/0.08628, loss_grounding_bce_4: 0.02410/0.08207, loss_grounding_dice_4: 0.31412/0.15397, loss_grounding_ce_4: 0.02621/0.25949, loss_mask_ce_5: 0.60719/0.80714, loss_mask_bce_5: 0.12540/0.30839, loss_mask_dice_5: 1.14334/1.05472, loss_spatial_bce_5: 0.03431/0.09276, loss_spatial_dice_5: 0.26908/0.19747, loss_spatial_ce_5: 0.03372/0.09853, loss_grounding_bce_5: 0.02020/0.08231, loss_grounding_dice_5: 0.26594/0.15457, loss_grounding_ce_5: 0.37594/0.27795, loss_mask_ce_6: 0.30770/0.83353, loss_mask_bce_6: 0.12623/0.31017, loss_mask_dice_6: 1.20343/1.05770, loss_spatial_bce_6: 0.03265/0.09775, loss_spatial_dice_6: 0.25227/0.19966, loss_spatial_ce_6: 0.07085/0.12137, loss_grounding_bce_6: 0.01602/0.08330, loss_grounding_dice_6: 0.27047/0.15521, loss_grounding_ce_6: 0.31697/0.28749, loss_mask_ce_7: 1.27517/0.89012, loss_mask_bce_7: 0.09521/0.31755, loss_mask_dice_7: 0.99085/1.10410, loss_spatial_bce_7: 0.03659/0.10795, loss_spatial_dice_7: 0.29127/0.22490, loss_spatial_ce_7: 0.21647/0.16062, loss_grounding_bce_7: 0.01897/0.08494, loss_grounding_dice_7: 0.24231/0.16093, loss_grounding_ce_7: 0.34635/0.32331, loss_mask_ce_8: 0.25776/1.02702, loss_mask_bce_8: 0.11492/0.33392, loss_mask_dice_8: 1.06989/1.18149, loss_spatial_bce_8: 0.03531/0.12665, loss_spatial_dice_8: 0.29858/0.26210, loss_spatial_ce_8: 0.25231/0.21319, loss_grounding_bce_8: 0.02156/0.08890, loss_grounding_dice_8: 0.26657/0.17057, loss_grounding_ce_8: 0.04767/0.42616, loss_mask_ce_9: 2.25504/3.48605, loss_mask_bce_9: 0.11783/0.36082, loss_mask_dice_9: 1.15286/1.76661, loss_spatial_bce_9: 0.08199/0.35595, loss_spatial_dice_9: 0.79105/0.79473, loss_spatial_ce_9: 1.17303/1.39803, loss_grounding_bce_9: 0.02253/0.10104, loss_grounding_dice_9: 0.30932/0.24360, loss_grounding_ce_9: 0.04441/0.68519] items per batch[64] items per second[0.36] total items[2636800] mini batches[ 41200] memory[4999] epoch remaining[0:24:21] INFO:trainer.default_trainer:epochs[ 22] optim steps[41300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10857/0.76673, loss_mask_bce_0: 0.08192/0.30193, loss_mask_dice_0: 0.25723/1.02679, loss_spatial_bce_0: 0.01507/0.08685, loss_spatial_dice_0: 0.09789/0.18348, loss_spatial_ce_0: 0.00002/0.06215, loss_grounding_bce_0: 0.04403/0.08083, loss_grounding_dice_0: 0.13784/0.15124, loss_grounding_ce_0: 0.01193/0.24970, loss_mask_ce_1: 0.12333/0.76837, loss_mask_bce_1: 0.07344/0.30274, loss_mask_dice_1: 0.26652/1.03045, loss_spatial_bce_1: 0.01729/0.08708, loss_spatial_dice_1: 0.09946/0.18602, loss_spatial_ce_1: 0.00001/0.06630, loss_grounding_bce_1: 0.04336/0.08102, loss_grounding_dice_1: 0.12794/0.15203, loss_grounding_ce_1: 0.01431/0.25103, loss_mask_ce_2: 0.15683/0.77623, loss_mask_bce_2: 0.08472/0.30281, loss_mask_dice_2: 0.21488/1.03159, loss_spatial_bce_2: 0.01455/0.08697, loss_spatial_dice_2: 0.11307/0.18624, loss_spatial_ce_2: 0.00004/0.06883, loss_grounding_bce_2: 0.05091/0.08098, loss_grounding_dice_2: 0.12423/0.15177, loss_grounding_ce_2: 0.02222/0.25380, loss_mask_ce_3: 0.16570/0.77858, loss_mask_bce_3: 0.08139/0.30437, loss_mask_dice_3: 0.21779/1.02837, loss_spatial_bce_3: 0.01901/0.08887, loss_spatial_dice_3: 0.10370/0.18722, loss_spatial_ce_3: 0.00159/0.07337, loss_grounding_bce_3: 0.04360/0.08140, loss_grounding_dice_3: 0.11454/0.15138, loss_grounding_ce_3: 0.03192/0.25362, loss_mask_ce_4: 0.13973/0.78434, loss_mask_bce_4: 0.08807/0.30663, loss_mask_dice_4: 0.25362/1.04769, loss_spatial_bce_4: 0.02090/0.09080, loss_spatial_dice_4: 0.10109/0.19490, loss_spatial_ce_4: 0.01702/0.08622, loss_grounding_bce_4: 0.04545/0.08206, loss_grounding_dice_4: 0.14383/0.15397, loss_grounding_ce_4: 0.05244/0.25937, loss_mask_ce_5: 0.15561/0.80698, loss_mask_bce_5: 0.09255/0.30839, loss_mask_dice_5: 0.24250/1.05492, loss_spatial_bce_5: 0.01183/0.09271, loss_spatial_dice_5: 0.12092/0.19745, loss_spatial_ce_5: 0.06349/0.09850, loss_grounding_bce_5: 0.04286/0.08230, loss_grounding_dice_5: 0.15301/0.15458, loss_grounding_ce_5: 0.40000/0.27780, loss_mask_ce_6: 0.16448/0.83352, loss_mask_bce_6: 0.10576/0.31017, loss_mask_dice_6: 0.23693/1.05790, loss_spatial_bce_6: 0.01401/0.09771, loss_spatial_dice_6: 0.10653/0.19964, loss_spatial_ce_6: 0.04270/0.12136, loss_grounding_bce_6: 0.05025/0.08328, loss_grounding_dice_6: 0.13552/0.15523, loss_grounding_ce_6: 0.05211/0.28732, loss_mask_ce_7: 0.22947/0.88999, loss_mask_bce_7: 0.09563/0.31753, loss_mask_dice_7: 0.25802/1.10427, loss_spatial_bce_7: 0.03209/0.10791, loss_spatial_dice_7: 0.12181/0.22489, loss_spatial_ce_7: 0.00630/0.16056, loss_grounding_bce_7: 0.05911/0.08494, loss_grounding_dice_7: 0.15628/0.16093, loss_grounding_ce_7: 0.05800/0.32307, loss_mask_ce_8: 0.79850/1.02698, loss_mask_bce_8: 0.08506/0.33388, loss_mask_dice_8: 0.23091/1.18165, loss_spatial_bce_8: 0.04393/0.12658, loss_spatial_dice_8: 0.13701/0.26206, loss_spatial_ce_8: 0.00759/0.21314, loss_grounding_bce_8: 0.05101/0.08890, loss_grounding_dice_8: 0.13906/0.17056, loss_grounding_ce_8: 0.15694/0.42584, loss_mask_ce_9: 1.82271/3.48581, loss_mask_bce_9: 0.08472/0.36079, loss_mask_dice_9: 0.35863/1.76689, loss_spatial_bce_9: 0.08167/0.35591, loss_spatial_dice_9: 0.84331/0.79472, loss_spatial_ce_9: 2.05276/1.39812, loss_grounding_bce_9: 0.03763/0.10104, loss_grounding_dice_9: 0.19118/0.24355, loss_grounding_ce_9: 0.09173/0.68497] items per batch[64] items per second[0.37] total items[2643200] mini batches[ 41300] memory[4999] epoch remaining[0:21:20] INFO:trainer.default_trainer:epochs[ 22] optim steps[41400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13928/0.76675, loss_mask_bce_0: 0.69558/0.30185, loss_mask_dice_0: 0.60215/1.02675, loss_spatial_bce_0: 0.34336/0.08682, loss_spatial_dice_0: 0.28373/0.18343, loss_spatial_ce_0: 0.01433/0.06214, loss_grounding_bce_0: 0.29717/0.08081, loss_grounding_dice_0: 0.28235/0.15125, loss_grounding_ce_0: 0.27856/0.24961, loss_mask_ce_1: 0.14209/0.76837, loss_mask_bce_1: 0.66743/0.30266, loss_mask_dice_1: 0.56722/1.03038, loss_spatial_bce_1: 0.28741/0.08705, loss_spatial_dice_1: 0.26515/0.18597, loss_spatial_ce_1: 0.03309/0.06626, loss_grounding_bce_1: 0.29152/0.08099, loss_grounding_dice_1: 0.27226/0.15203, loss_grounding_ce_1: 0.27149/0.25092, loss_mask_ce_2: 0.14650/0.77626, loss_mask_bce_2: 0.73326/0.30273, loss_mask_dice_2: 0.58302/1.03152, loss_spatial_bce_2: 0.32160/0.08694, loss_spatial_dice_2: 0.27385/0.18618, loss_spatial_ce_2: 0.02488/0.06881, loss_grounding_bce_2: 0.30247/0.08095, loss_grounding_dice_2: 0.27064/0.15178, loss_grounding_ce_2: 0.29955/0.25370, loss_mask_ce_3: 0.13600/0.77860, loss_mask_bce_3: 0.71560/0.30427, loss_mask_dice_3: 0.56123/1.02830, loss_spatial_bce_3: 0.32478/0.08884, loss_spatial_dice_3: 0.28525/0.18717, loss_spatial_ce_3: 0.03231/0.07332, loss_grounding_bce_3: 0.29686/0.08137, loss_grounding_dice_3: 0.25663/0.15137, loss_grounding_ce_3: 0.30341/0.25356, loss_mask_ce_4: 0.12061/0.78433, loss_mask_bce_4: 0.69512/0.30653, loss_mask_dice_4: 0.54582/1.04761, loss_spatial_bce_4: 0.42680/0.09078, loss_spatial_dice_4: 0.30376/0.19486, loss_spatial_ce_4: 0.01064/0.08617, loss_grounding_bce_4: 0.29174/0.08204, loss_grounding_dice_4: 0.24865/0.15395, loss_grounding_ce_4: 0.26092/0.25927, loss_mask_ce_5: 0.11625/0.80703, loss_mask_bce_5: 0.66781/0.30829, loss_mask_dice_5: 0.55906/1.05486, loss_spatial_bce_5: 0.40349/0.09269, loss_spatial_dice_5: 0.28019/0.19740, loss_spatial_ce_5: 0.00639/0.09843, loss_grounding_bce_5: 0.28610/0.08227, loss_grounding_dice_5: 0.25186/0.15457, loss_grounding_ce_5: 0.28480/0.27770, loss_mask_ce_6: 0.11271/0.83352, loss_mask_bce_6: 0.61932/0.31007, loss_mask_dice_6: 0.52394/1.05783, loss_spatial_bce_6: 0.55775/0.09769, loss_spatial_dice_6: 0.31158/0.19960, loss_spatial_ce_6: 0.01101/0.12128, loss_grounding_bce_6: 0.34151/0.08325, loss_grounding_dice_6: 0.28766/0.15523, loss_grounding_ce_6: 0.01335/0.28716, loss_mask_ce_7: 0.13326/0.88993, loss_mask_bce_7: 0.60828/0.31743, loss_mask_dice_7: 0.54036/1.10421, loss_spatial_bce_7: 0.29976/0.10788, loss_spatial_dice_7: 0.29564/0.22485, loss_spatial_ce_7: 0.24519/0.16046, loss_grounding_bce_7: 0.32890/0.08491, loss_grounding_dice_7: 0.29235/0.16093, loss_grounding_ce_7: 0.01098/0.32293, loss_mask_ce_8: 0.21464/1.02695, loss_mask_bce_8: 0.69185/0.33379, loss_mask_dice_8: 0.54561/1.18157, loss_spatial_bce_8: 0.28970/0.12652, loss_spatial_dice_8: 0.23851/0.26200, loss_spatial_ce_8: 0.13652/0.21307, loss_grounding_bce_8: 0.39936/0.08887, loss_grounding_dice_8: 0.30177/0.17054, loss_grounding_ce_8: 0.03220/0.42560, loss_mask_ce_9: 1.80185/3.48565, loss_mask_bce_9: 0.58711/0.36067, loss_mask_dice_9: 0.75040/1.76652, loss_spatial_bce_9: 0.56775/0.35590, loss_spatial_dice_9: 0.72113/0.79470, loss_spatial_ce_9: 1.31056/1.39797, loss_grounding_bce_9: 0.31397/0.10103, loss_grounding_dice_9: 0.40649/0.24356, loss_grounding_ce_9: 0.14704/0.68475] items per batch[64] items per second[0.36] total items[2649600] mini batches[ 41400] memory[4999] epoch remaining[0:18:23] INFO:trainer.default_trainer:epochs[ 22] optim steps[41500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72901/0.76659, loss_mask_bce_0: 0.20022/0.30185, loss_mask_dice_0: 0.06035/1.02682, loss_spatial_bce_0: 0.14778/0.08681, loss_spatial_dice_0: 0.05656/0.18340, loss_spatial_ce_0: 0.00161/0.06208, loss_grounding_bce_0: 0.00000/0.08080, loss_grounding_dice_0: 0.00002/0.15125, loss_grounding_ce_0: 0.02585/0.24980, loss_mask_ce_1: 1.64412/0.76819, loss_mask_bce_1: 0.17513/0.30266, loss_mask_dice_1: 0.06201/1.03051, loss_spatial_bce_1: 0.14959/0.08705, loss_spatial_dice_1: 0.06258/0.18594, loss_spatial_ce_1: 0.00182/0.06622, loss_grounding_bce_1: 0.00000/0.08099, loss_grounding_dice_1: 0.00001/0.15203, loss_grounding_ce_1: 0.02412/0.25113, loss_mask_ce_2: 1.62251/0.77613, loss_mask_bce_2: 0.22844/0.30274, loss_mask_dice_2: 0.05942/1.03164, loss_spatial_bce_2: 0.15653/0.08693, loss_spatial_dice_2: 0.06667/0.18615, loss_spatial_ce_2: 0.00169/0.06877, loss_grounding_bce_2: 0.00000/0.08095, loss_grounding_dice_2: 0.00005/0.15177, loss_grounding_ce_2: 0.02996/0.25391, loss_mask_ce_3: 1.61823/0.77840, loss_mask_bce_3: 0.23155/0.30428, loss_mask_dice_3: 0.06786/1.02835, loss_spatial_bce_3: 0.16121/0.08884, loss_spatial_dice_3: 0.07306/0.18715, loss_spatial_ce_3: 0.00214/0.07327, loss_grounding_bce_3: 0.00000/0.08140, loss_grounding_dice_3: 0.00001/0.15137, loss_grounding_ce_3: 0.01707/0.25367, loss_mask_ce_4: 1.55943/0.78420, loss_mask_bce_4: 0.26977/0.30655, loss_mask_dice_4: 0.06634/1.04777, loss_spatial_bce_4: 0.14921/0.09078, loss_spatial_dice_4: 0.08672/0.19485, loss_spatial_ce_4: 0.00390/0.08610, loss_grounding_bce_4: 0.00000/0.08204, loss_grounding_dice_4: 0.00003/0.15395, loss_grounding_ce_4: 0.02017/0.25946, loss_mask_ce_5: 1.71709/0.80691, loss_mask_bce_5: 0.25046/0.30830, loss_mask_dice_5: 0.06926/1.05499, loss_spatial_bce_5: 0.15002/0.09269, loss_spatial_dice_5: 0.07153/0.19740, loss_spatial_ce_5: 0.00507/0.09838, loss_grounding_bce_5: 0.00000/0.08230, loss_grounding_dice_5: 0.00001/0.15459, loss_grounding_ce_5: 0.01670/0.27782, loss_mask_ce_6: 1.74679/0.83337, loss_mask_bce_6: 0.23857/0.31008, loss_mask_dice_6: 0.07672/1.05795, loss_spatial_bce_6: 0.16876/0.09769, loss_spatial_dice_6: 0.10325/0.19959, loss_spatial_ce_6: 0.04114/0.12123, loss_grounding_bce_6: 0.00000/0.08327, loss_grounding_dice_6: 0.00001/0.15524, loss_grounding_ce_6: 0.01509/0.28737, loss_mask_ce_7: 1.55043/0.88979, loss_mask_bce_7: 0.25185/0.31744, loss_mask_dice_7: 0.06791/1.10434, loss_spatial_bce_7: 0.16604/0.10789, loss_spatial_dice_7: 0.08084/0.22486, loss_spatial_ce_7: 0.02056/0.16038, loss_grounding_bce_7: 0.00000/0.08491, loss_grounding_dice_7: 0.00003/0.16094, loss_grounding_ce_7: 0.03632/0.32316, loss_mask_ce_8: 1.59061/1.02685, loss_mask_bce_8: 0.22868/0.33380, loss_mask_dice_8: 0.06291/1.18169, loss_spatial_bce_8: 0.14679/0.12652, loss_spatial_dice_8: 0.09787/0.26198, loss_spatial_ce_8: 0.06462/0.21294, loss_grounding_bce_8: 0.00000/0.08890, loss_grounding_dice_8: 0.00002/0.17056, loss_grounding_ce_8: 0.35753/0.42557, loss_mask_ce_9: 4.40151/3.48582, loss_mask_bce_9: 0.22239/0.36069, loss_mask_dice_9: 0.22367/1.76649, loss_spatial_bce_9: 0.53456/0.35586, loss_spatial_dice_9: 0.59540/0.79466, loss_spatial_ce_9: 1.88427/1.39779, loss_grounding_bce_9: 0.00000/0.10103, loss_grounding_dice_9: 0.00265/0.24358, loss_grounding_ce_9: 0.54615/0.68470] items per batch[64] items per second[0.36] total items[2656000] mini batches[ 41500] memory[4999] epoch remaining[0:15:25] INFO:trainer.default_trainer:epochs[ 22] optim steps[41600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49871/0.76651, loss_mask_bce_0: 0.62970/0.30189, loss_mask_dice_0: 1.16143/1.02748, loss_spatial_bce_0: 0.18420/0.08680, loss_spatial_dice_0: 0.30268/0.18343, loss_spatial_ce_0: 0.07964/0.06204, loss_grounding_bce_0: 0.16844/0.08077, loss_grounding_dice_0: 0.20460/0.15127, loss_grounding_ce_0: 0.00194/0.24977, loss_mask_ce_1: 1.37248/0.76814, loss_mask_bce_1: 0.63845/0.30270, loss_mask_dice_1: 1.16649/1.03118, loss_spatial_bce_1: 0.24517/0.08704, loss_spatial_dice_1: 0.31432/0.18596, loss_spatial_ce_1: 0.05531/0.06617, loss_grounding_bce_1: 0.16430/0.08096, loss_grounding_dice_1: 0.19854/0.15205, loss_grounding_ce_1: 0.00225/0.25111, loss_mask_ce_2: 1.44635/0.77610, loss_mask_bce_2: 0.63409/0.30277, loss_mask_dice_2: 1.16132/1.03235, loss_spatial_bce_2: 0.24792/0.08691, loss_spatial_dice_2: 0.30903/0.18617, loss_spatial_ce_2: 0.04760/0.06871, loss_grounding_bce_2: 0.16203/0.08092, loss_grounding_dice_2: 0.19417/0.15180, loss_grounding_ce_2: 0.00154/0.25389, loss_mask_ce_3: 1.60025/0.77831, loss_mask_bce_3: 0.59781/0.30432, loss_mask_dice_3: 1.15723/1.02900, loss_spatial_bce_3: 0.24915/0.08881, loss_spatial_dice_3: 0.32285/0.18717, loss_spatial_ce_3: 0.02987/0.07321, loss_grounding_bce_3: 0.16211/0.08138, loss_grounding_dice_3: 0.18578/0.15141, loss_grounding_ce_3: 0.00324/0.25368, loss_mask_ce_4: 1.55430/0.78412, loss_mask_bce_4: 0.61380/0.30659, loss_mask_dice_4: 1.20627/1.04845, loss_spatial_bce_4: 0.22939/0.09077, loss_spatial_dice_4: 0.32196/0.19488, loss_spatial_ce_4: 0.11416/0.08610, loss_grounding_bce_4: 0.15427/0.08201, loss_grounding_dice_4: 0.18847/0.15397, loss_grounding_ce_4: 0.00262/0.25943, loss_mask_ce_5: 1.73069/0.80683, loss_mask_bce_5: 0.65499/0.30833, loss_mask_dice_5: 1.22406/1.05565, loss_spatial_bce_5: 0.24464/0.09266, loss_spatial_dice_5: 0.33315/0.19743, loss_spatial_ce_5: 0.08033/0.09834, loss_grounding_bce_5: 0.16077/0.08226, loss_grounding_dice_5: 0.19688/0.15460, loss_grounding_ce_5: 0.00127/0.27779, loss_mask_ce_6: 1.85202/0.83325, loss_mask_bce_6: 0.57383/0.31012, loss_mask_dice_6: 1.30064/1.05858, loss_spatial_bce_6: 0.30090/0.09767, loss_spatial_dice_6: 0.34242/0.19962, loss_spatial_ce_6: 0.11273/0.12117, loss_grounding_bce_6: 0.15949/0.08323, loss_grounding_dice_6: 0.19264/0.15524, loss_grounding_ce_6: 0.00144/0.28741, loss_mask_ce_7: 2.03004/0.88972, loss_mask_bce_7: 0.59435/0.31745, loss_mask_dice_7: 1.23649/1.10499, loss_spatial_bce_7: 0.19584/0.10785, loss_spatial_dice_7: 0.32026/0.22488, loss_spatial_ce_7: 0.21791/0.16033, loss_grounding_bce_7: 0.16420/0.08488, loss_grounding_dice_7: 0.19140/0.16095, loss_grounding_ce_7: 0.00048/0.32319, loss_mask_ce_8: 2.09410/1.02686, loss_mask_bce_8: 0.61083/0.33381, loss_mask_dice_8: 1.48814/1.18236, loss_spatial_bce_8: 0.19936/0.12647, loss_spatial_dice_8: 0.37885/0.26197, loss_spatial_ce_8: 0.21138/0.21288, loss_grounding_bce_8: 0.16197/0.08888, loss_grounding_dice_8: 0.19456/0.17057, loss_grounding_ce_8: 0.00225/0.42549, loss_mask_ce_9: 5.60274/3.48578, loss_mask_bce_9: 0.66882/0.36071, loss_mask_dice_9: 2.03813/1.76748, loss_spatial_bce_9: 0.34051/0.35580, loss_spatial_dice_9: 0.88190/0.79470, loss_spatial_ce_9: 1.58004/1.39792, loss_grounding_bce_9: 0.16306/0.10101, loss_grounding_dice_9: 0.13667/0.24358, loss_grounding_ce_9: 0.02018/0.68470] items per batch[64] items per second[0.36] total items[2662400] mini batches[ 41600] memory[4999] epoch remaining[0:12:27] INFO:trainer.default_trainer:epochs[ 22] optim steps[41700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58879/0.76682, loss_mask_bce_0: 0.67953/0.30190, loss_mask_dice_0: 1.12578/1.02751, loss_spatial_bce_0: 0.12972/0.08681, loss_spatial_dice_0: 0.18991/0.18342, loss_spatial_ce_0: 0.22522/0.06204, loss_grounding_bce_0: 0.06806/0.08077, loss_grounding_dice_0: 0.16093/0.15123, loss_grounding_ce_0: 0.77513/0.24969, loss_mask_ce_1: 0.73988/0.76842, loss_mask_bce_1: 0.64911/0.30272, loss_mask_dice_1: 1.06364/1.03118, loss_spatial_bce_1: 0.13727/0.08705, loss_spatial_dice_1: 0.21497/0.18596, loss_spatial_ce_1: 0.26395/0.06617, loss_grounding_bce_1: 0.06596/0.08096, loss_grounding_dice_1: 0.15577/0.15200, loss_grounding_ce_1: 0.76360/0.25106, loss_mask_ce_2: 0.54524/0.77640, loss_mask_bce_2: 0.65121/0.30281, loss_mask_dice_2: 1.16435/1.03236, loss_spatial_bce_2: 0.13374/0.08692, loss_spatial_dice_2: 0.19525/0.18616, loss_spatial_ce_2: 0.24272/0.06874, loss_grounding_bce_2: 0.06481/0.08092, loss_grounding_dice_2: 0.17432/0.15175, loss_grounding_ce_2: 0.75377/0.25384, loss_mask_ce_3: 0.49983/0.77864, loss_mask_bce_3: 0.66605/0.30435, loss_mask_dice_3: 1.18168/1.02897, loss_spatial_bce_3: 0.14851/0.08883, loss_spatial_dice_3: 0.20841/0.18717, loss_spatial_ce_3: 0.29142/0.07322, loss_grounding_bce_3: 0.06517/0.08137, loss_grounding_dice_3: 0.17886/0.15135, loss_grounding_ce_3: 0.74997/0.25361, loss_mask_ce_4: 0.47478/0.78433, loss_mask_bce_4: 0.64840/0.30663, loss_mask_dice_4: 1.12988/1.04851, loss_spatial_bce_4: 0.14365/0.09078, loss_spatial_dice_4: 0.21923/0.19487, loss_spatial_ce_4: 0.31133/0.08614, loss_grounding_bce_4: 0.06959/0.08201, loss_grounding_dice_4: 0.18298/0.15393, loss_grounding_ce_4: 0.76496/0.25930, loss_mask_ce_5: 0.49531/0.80716, loss_mask_bce_5: 0.66130/0.30836, loss_mask_dice_5: 1.10749/1.05567, loss_spatial_bce_5: 0.13838/0.09269, loss_spatial_dice_5: 0.19409/0.19744, loss_spatial_ce_5: 0.16825/0.09832, loss_grounding_bce_5: 0.05999/0.08226, loss_grounding_dice_5: 0.16080/0.15456, loss_grounding_ce_5: 0.76544/0.27769, loss_mask_ce_6: 0.51862/0.83362, loss_mask_bce_6: 0.66900/0.31014, loss_mask_dice_6: 1.16205/1.05860, loss_spatial_bce_6: 0.15004/0.09769, loss_spatial_dice_6: 0.19850/0.19962, loss_spatial_ce_6: 0.18518/0.12115, loss_grounding_bce_6: 0.06414/0.08323, loss_grounding_dice_6: 0.16698/0.15520, loss_grounding_ce_6: 0.75796/0.28740, loss_mask_ce_7: 0.65821/0.89011, loss_mask_bce_7: 0.64153/0.31748, loss_mask_dice_7: 1.17824/1.10509, loss_spatial_bce_7: 0.13968/0.10786, loss_spatial_dice_7: 0.17243/0.22487, loss_spatial_ce_7: 0.30119/0.16030, loss_grounding_bce_7: 0.07333/0.08488, loss_grounding_dice_7: 0.21871/0.16092, loss_grounding_ce_7: 0.78507/0.32314, loss_mask_ce_8: 0.67028/1.02726, loss_mask_bce_8: 0.74322/0.33384, loss_mask_dice_8: 1.30193/1.18244, loss_spatial_bce_8: 0.16857/0.12648, loss_spatial_dice_8: 0.18320/0.26195, loss_spatial_ce_8: 0.19084/0.21284, loss_grounding_bce_8: 0.08297/0.08887, loss_grounding_dice_8: 0.22449/0.17054, loss_grounding_ce_8: 0.81913/0.42546, loss_mask_ce_9: 6.09609/3.48659, loss_mask_bce_9: 0.90198/0.36078, loss_mask_dice_9: 1.87506/1.76753, loss_spatial_bce_9: 0.52223/0.35584, loss_spatial_dice_9: 0.79829/0.79472, loss_spatial_ce_9: 1.35875/1.39799, loss_grounding_bce_9: 0.14109/0.10102, loss_grounding_dice_9: 0.37791/0.24360, loss_grounding_ce_9: 0.72675/0.68513] items per batch[64] items per second[0.37] total items[2668800] mini batches[ 41700] memory[4999] epoch remaining[0:09:29] INFO:trainer.default_trainer:epochs[ 22] optim steps[41800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.16313/0.76656, loss_mask_bce_0: 0.07868/0.30192, loss_mask_dice_0: 0.73290/1.02763, loss_spatial_bce_0: 0.01362/0.08678, loss_spatial_dice_0: 0.20261/0.18341, loss_spatial_ce_0: 0.00928/0.06201, loss_grounding_bce_0: 0.01098/0.08077, loss_grounding_dice_0: 0.17540/0.15123, loss_grounding_ce_0: 0.04015/0.24958, loss_mask_ce_1: 1.12168/0.76819, loss_mask_bce_1: 0.07285/0.30273, loss_mask_dice_1: 0.60590/1.03126, loss_spatial_bce_1: 0.01408/0.08702, loss_spatial_dice_1: 0.25262/0.18596, loss_spatial_ce_1: 1.01931/0.06615, loss_grounding_bce_1: 0.01266/0.08096, loss_grounding_dice_1: 0.11660/0.15199, loss_grounding_ce_1: 0.02612/0.25099, loss_mask_ce_2: 1.25995/0.77615, loss_mask_bce_2: 0.06577/0.30282, loss_mask_dice_2: 0.58765/1.03247, loss_spatial_bce_2: 0.01553/0.08690, loss_spatial_dice_2: 0.26658/0.18616, loss_spatial_ce_2: 0.03769/0.06873, loss_grounding_bce_2: 0.00959/0.08092, loss_grounding_dice_2: 0.11388/0.15175, loss_grounding_ce_2: 0.02373/0.25371, loss_mask_ce_3: 1.14866/0.77840, loss_mask_bce_3: 0.07418/0.30436, loss_mask_dice_3: 0.56478/1.02905, loss_spatial_bce_3: 0.01672/0.08880, loss_spatial_dice_3: 0.27556/0.18716, loss_spatial_ce_3: 0.02573/0.07322, loss_grounding_bce_3: 0.01152/0.08138, loss_grounding_dice_3: 0.13706/0.15134, loss_grounding_ce_3: 0.02895/0.25349, loss_mask_ce_4: 1.03080/0.78420, loss_mask_bce_4: 0.07764/0.30663, loss_mask_dice_4: 0.56661/1.04867, loss_spatial_bce_4: 0.01599/0.09076, loss_spatial_dice_4: 0.18007/0.19488, loss_spatial_ce_4: 0.14240/0.08612, loss_grounding_bce_4: 0.01849/0.08202, loss_grounding_dice_4: 0.23489/0.15393, loss_grounding_ce_4: 0.03685/0.25923, loss_mask_ce_5: 1.31224/0.80702, loss_mask_bce_5: 0.05733/0.30838, loss_mask_dice_5: 0.53065/1.05578, loss_spatial_bce_5: 0.01336/0.09267, loss_spatial_dice_5: 0.20531/0.19746, loss_spatial_ce_5: 0.20787/0.09827, loss_grounding_bce_5: 0.01422/0.08227, loss_grounding_dice_5: 0.13842/0.15457, loss_grounding_ce_5: 0.03844/0.27752, loss_mask_ce_6: 1.10275/0.83352, loss_mask_bce_6: 0.07372/0.31015, loss_mask_dice_6: 0.88717/1.05866, loss_spatial_bce_6: 0.02090/0.09766, loss_spatial_dice_6: 0.22304/0.19963, loss_spatial_ce_6: 0.50014/0.12111, loss_grounding_bce_6: 0.01249/0.08324, loss_grounding_dice_6: 0.16086/0.15522, loss_grounding_ce_6: 0.06421/0.28723, loss_mask_ce_7: 0.97405/0.89001, loss_mask_bce_7: 0.08670/0.31749, loss_mask_dice_7: 0.68137/1.10523, loss_spatial_bce_7: 0.02899/0.10786, loss_spatial_dice_7: 0.28950/0.22489, loss_spatial_ce_7: 0.28656/0.16029, loss_grounding_bce_7: 0.01895/0.08489, loss_grounding_dice_7: 0.35888/0.16093, loss_grounding_ce_7: 0.02249/0.32296, loss_mask_ce_8: 2.59389/1.02706, loss_mask_bce_8: 0.10504/0.33388, loss_mask_dice_8: 1.16784/1.18261, loss_spatial_bce_8: 0.02956/0.12645, loss_spatial_dice_8: 0.39348/0.26198, loss_spatial_ce_8: 0.28258/0.21279, loss_grounding_bce_8: 0.01665/0.08888, loss_grounding_dice_8: 0.28426/0.17054, loss_grounding_ce_8: 0.04347/0.42515, loss_mask_ce_9: 3.26730/3.48689, loss_mask_bce_9: 0.06663/0.36084, loss_mask_dice_9: 1.20953/1.76777, loss_spatial_bce_9: 0.07929/0.35578, loss_spatial_dice_9: 0.86171/0.79473, loss_spatial_ce_9: 2.06814/1.39801, loss_grounding_bce_9: 0.01692/0.10101, loss_grounding_dice_9: 0.25283/0.24359, loss_grounding_ce_9: 0.06189/0.68530] items per batch[64] items per second[0.36] total items[2675200] mini batches[ 41800] memory[4999] epoch remaining[0:06:32] INFO:trainer.default_trainer:epochs[ 22] optim steps[41900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13392/0.76633, loss_mask_bce_0: 0.48475/0.30188, loss_mask_dice_0: 0.28347/1.02714, loss_spatial_bce_0: 0.37717/0.08678, loss_spatial_dice_0: 0.24572/0.18337, loss_spatial_ce_0: 0.09624/0.06199, loss_grounding_bce_0: 0.47596/0.08079, loss_grounding_dice_0: 0.26220/0.15125, loss_grounding_ce_0: 0.00217/0.24938, loss_mask_ce_1: 0.12963/0.76790, loss_mask_bce_1: 0.47041/0.30270, loss_mask_dice_1: 0.27260/1.03074, loss_spatial_bce_1: 0.38680/0.08702, loss_spatial_dice_1: 0.24579/0.18592, loss_spatial_ce_1: 0.13172/0.06614, loss_grounding_bce_1: 0.46589/0.08097, loss_grounding_dice_1: 0.25604/0.15199, loss_grounding_ce_1: 0.00393/0.25077, loss_mask_ce_2: 0.12948/0.77590, loss_mask_bce_2: 0.48412/0.30279, loss_mask_dice_2: 0.27620/1.03203, loss_spatial_bce_2: 0.41045/0.08689, loss_spatial_dice_2: 0.25296/0.18612, loss_spatial_ce_2: 0.16157/0.06869, loss_grounding_bce_2: 0.46372/0.08093, loss_grounding_dice_2: 0.26454/0.15175, loss_grounding_ce_2: 0.00519/0.25347, loss_mask_ce_3: 0.13691/0.77812, loss_mask_bce_3: 0.49613/0.30433, loss_mask_dice_3: 0.27292/1.02860, loss_spatial_bce_3: 0.39771/0.08880, loss_spatial_dice_3: 0.25105/0.18713, loss_spatial_ce_3: 0.23408/0.07323, loss_grounding_bce_3: 0.48321/0.08139, loss_grounding_dice_3: 0.26304/0.15136, loss_grounding_ce_3: 0.00604/0.25326, loss_mask_ce_4: 0.12810/0.78401, loss_mask_bce_4: 0.49631/0.30659, loss_mask_dice_4: 0.27630/1.04822, loss_spatial_bce_4: 0.44779/0.09075, loss_spatial_dice_4: 0.24751/0.19485, loss_spatial_ce_4: 0.22072/0.08605, loss_grounding_bce_4: 0.47849/0.08202, loss_grounding_dice_4: 0.25920/0.15394, loss_grounding_ce_4: 0.00444/0.25899, loss_mask_ce_5: 0.10139/0.80680, loss_mask_bce_5: 0.48151/0.30833, loss_mask_dice_5: 0.27836/1.05531, loss_spatial_bce_5: 0.42192/0.09267, loss_spatial_dice_5: 0.25507/0.19743, loss_spatial_ce_5: 0.22006/0.09820, loss_grounding_bce_5: 0.44757/0.08228, loss_grounding_dice_5: 0.25911/0.15460, loss_grounding_ce_5: 0.00335/0.27728, loss_mask_ce_6: 0.11054/0.83324, loss_mask_bce_6: 0.47351/0.31011, loss_mask_dice_6: 0.28313/1.05819, loss_spatial_bce_6: 0.41135/0.09767, loss_spatial_dice_6: 0.24909/0.19961, loss_spatial_ce_6: 0.22804/0.12106, loss_grounding_bce_6: 0.46107/0.08324, loss_grounding_dice_6: 0.26461/0.15522, loss_grounding_ce_6: 0.00093/0.28698, loss_mask_ce_7: 0.13395/0.88969, loss_mask_bce_7: 0.47253/0.31744, loss_mask_dice_7: 0.26884/1.10477, loss_spatial_bce_7: 0.57431/0.10786, loss_spatial_dice_7: 0.26270/0.22486, loss_spatial_ce_7: 0.39610/0.16024, loss_grounding_bce_7: 0.45576/0.08489, loss_grounding_dice_7: 0.25533/0.16093, loss_grounding_ce_7: 0.00108/0.32267, loss_mask_ce_8: 0.15642/1.02678, loss_mask_bce_8: 0.48311/0.33382, loss_mask_dice_8: 0.26854/1.18204, loss_spatial_bce_8: 0.43246/0.12646, loss_spatial_dice_8: 0.23818/0.26192, loss_spatial_ce_8: 0.35162/0.21275, loss_grounding_bce_8: 0.45873/0.08888, loss_grounding_dice_8: 0.25000/0.17052, loss_grounding_ce_8: 0.00084/0.42484, loss_mask_ce_9: 0.94511/3.48599, loss_mask_bce_9: 0.47041/0.36079, loss_mask_dice_9: 0.26820/1.76695, loss_spatial_bce_9: 0.48357/0.35582, loss_spatial_dice_9: 0.63280/0.79468, loss_spatial_ce_9: 0.23535/1.39789, loss_grounding_bce_9: 0.43788/0.10101, loss_grounding_dice_9: 0.25118/0.24355, loss_grounding_ce_9: 0.00870/0.68490] items per batch[64] items per second[0.36] total items[2681600] mini batches[ 41900] memory[4999] epoch remaining[0:03:34] INFO:trainer.default_trainer:epochs[ 22] optim steps[42000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.01417/0.76604, loss_mask_bce_0: 0.19542/0.30196, loss_mask_dice_0: 0.27424/1.02661, loss_spatial_bce_0: 0.15338/0.08681, loss_spatial_dice_0: 0.19431/0.18335, loss_spatial_ce_0: 0.00485/0.06196, loss_grounding_bce_0: 0.16819/0.08083, loss_grounding_dice_0: 0.06469/0.15123, loss_grounding_ce_0: 0.00139/0.24935, loss_mask_ce_1: 0.99440/0.76754, loss_mask_bce_1: 0.20949/0.30278, loss_mask_dice_1: 0.30304/1.03023, loss_spatial_bce_1: 0.18134/0.08706, loss_spatial_dice_1: 0.20537/0.18589, loss_spatial_ce_1: 0.00602/0.06612, loss_grounding_bce_1: 0.17694/0.08101, loss_grounding_dice_1: 0.06676/0.15196, loss_grounding_ce_1: 0.00107/0.25076, loss_mask_ce_2: 0.94888/0.77558, loss_mask_bce_2: 0.21815/0.30287, loss_mask_dice_2: 0.31529/1.03151, loss_spatial_bce_2: 0.16949/0.08694, loss_spatial_dice_2: 0.21677/0.18610, loss_spatial_ce_2: 0.00508/0.06864, loss_grounding_bce_2: 0.16518/0.08096, loss_grounding_dice_2: 0.06444/0.15173, loss_grounding_ce_2: 0.00179/0.25348, loss_mask_ce_3: 0.97144/0.77786, loss_mask_bce_3: 0.21974/0.30441, loss_mask_dice_3: 0.29277/1.02809, loss_spatial_bce_3: 0.18230/0.08886, loss_spatial_dice_3: 0.22107/0.18711, loss_spatial_ce_3: 0.00635/0.07317, loss_grounding_bce_3: 0.17927/0.08143, loss_grounding_dice_3: 0.07077/0.15133, loss_grounding_ce_3: 0.00251/0.25323, loss_mask_ce_4: 0.91640/0.78364, loss_mask_bce_4: 0.23026/0.30667, loss_mask_dice_4: 0.32786/1.04775, loss_spatial_bce_4: 0.20496/0.09082, loss_spatial_dice_4: 0.24707/0.19484, loss_spatial_ce_4: 0.00250/0.08599, loss_grounding_bce_4: 0.16435/0.08205, loss_grounding_dice_4: 0.06464/0.15391, loss_grounding_ce_4: 0.00571/0.25899, loss_mask_ce_5: 1.10082/0.80654, loss_mask_bce_5: 0.25374/0.30841, loss_mask_dice_5: 0.35367/1.05481, loss_spatial_bce_5: 0.22296/0.09276, loss_spatial_dice_5: 0.20327/0.19741, loss_spatial_ce_5: 0.00227/0.09814, loss_grounding_bce_5: 0.17764/0.08230, loss_grounding_dice_5: 0.07400/0.15457, loss_grounding_ce_5: 0.03521/0.27726, loss_mask_ce_6: 1.06662/0.83294, loss_mask_bce_6: 0.26406/0.31019, loss_mask_dice_6: 0.37504/1.05769, loss_spatial_bce_6: 0.21494/0.09773, loss_spatial_dice_6: 0.20778/0.19959, loss_spatial_ce_6: 0.03293/0.12103, loss_grounding_bce_6: 0.18107/0.08327, loss_grounding_dice_6: 0.07049/0.15518, loss_grounding_ce_6: 0.07183/0.28702, loss_mask_ce_7: 0.90775/0.88938, loss_mask_bce_7: 0.27683/0.31754, loss_mask_dice_7: 0.35457/1.10427, loss_spatial_bce_7: 0.24771/0.10793, loss_spatial_dice_7: 0.21996/0.22482, loss_spatial_ce_7: 0.08671/0.16023, loss_grounding_bce_7: 0.18155/0.08492, loss_grounding_dice_7: 0.07775/0.16090, loss_grounding_ce_7: 0.09229/0.32264, loss_mask_ce_8: 0.92644/1.02651, loss_mask_bce_8: 0.25326/0.33389, loss_mask_dice_8: 0.39137/1.18149, loss_spatial_bce_8: 0.24272/0.12654, loss_spatial_dice_8: 0.23901/0.26190, loss_spatial_ce_8: 0.11140/0.21272, loss_grounding_bce_8: 0.16291/0.08891, loss_grounding_dice_8: 0.08698/0.17051, loss_grounding_ce_8: 0.01414/0.42480, loss_mask_ce_9: 3.77376/3.48551, loss_mask_bce_9: 0.27478/0.36086, loss_mask_dice_9: 0.49805/1.76605, loss_spatial_bce_9: 0.41535/0.35587, loss_spatial_dice_9: 0.76652/0.79464, loss_spatial_ce_9: 0.78070/1.39774, loss_grounding_bce_9: 0.15739/0.10104, loss_grounding_dice_9: 0.07048/0.24352, loss_grounding_ce_9: 0.38574/0.68481] items per batch[64] items per second[0.37] total items[2688000] mini batches[ 42000] memory[4999] epoch remaining[0:00:37] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00042021. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3664 s/iter. Eval: 0.0898 s/iter. Total: 0.4589 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3659 s/iter. Eval: 0.0794 s/iter. Total: 0.4479 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3726 s/iter. Eval: 0.0778 s/iter. Total: 0.4531 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3762 s/iter. Eval: 0.0745 s/iter. Total: 0.4535 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0028 s/iter. Inference: 0.3783 s/iter. Eval: 0.0729 s/iter. Total: 0.4541 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0028 s/iter. Inference: 0.3768 s/iter. Eval: 0.0703 s/iter. Total: 0.4500 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalufv6eclg ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.512 | 82.909 | 66.169 | 133 | | Things | 61.779 | 83.820 | 73.205 | 80 | | Stuff | 46.053 | 81.533 | 55.548 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.92 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.32s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.71 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.49 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.486 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.565 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.601 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.759 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.370 | 69.359 | 48.625 | 25.583 | 49.563 | 67.253 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.952 | bicycle | 22.254 | car | 43.068 | | motorcycle | 42.117 | airplane | 62.635 | bus | 70.819 | | train | 74.961 | truck | 43.576 | boat | 30.867 | | traffic light | 28.945 | fire hydrant | 69.977 | stop sign | 68.427 | | parking meter | 47.076 | bench | 27.078 | bird | 33.358 | | cat | 76.563 | dog | 71.311 | horse | 49.585 | | sheep | 54.109 | cow | 56.931 | elephant | 65.775 | | bear | 80.923 | zebra | 66.379 | giraffe | 62.191 | | backpack | 23.631 | umbrella | 55.343 | handbag | 23.880 | | tie | 40.056 | suitcase | 52.015 | frisbee | 69.398 | | skis | 7.751 | snowboard | 33.928 | sports ball | 50.405 | | kite | 37.738 | baseball bat | 38.603 | baseball glove | 50.542 | | skateboard | 43.120 | surfboard | 44.508 | tennis racket | 63.399 | | bottle | 41.547 | wine glass | 36.030 | cup | 50.955 | | fork | 26.353 | knife | 23.823 | spoon | 22.600 | | bowl | 37.456 | banana | 22.851 | apple | 27.006 | | sandwich | 48.196 | orange | 31.112 | broccoli | 24.007 | | carrot | 22.984 | hot dog | 30.280 | pizza | 50.490 | | donut | 56.159 | cake | 47.748 | chair | 28.339 | | couch | 42.675 | potted plant | 23.377 | bed | 43.239 | | dining table | 15.361 | toilet | 69.386 | tv | 67.001 | | laptop | 69.301 | mouse | 64.099 | remote | 44.372 | | keyboard | 57.575 | cell phone | 44.748 | microwave | 64.799 | | oven | 33.973 | toaster | 56.480 | sink | 45.928 | | refrigerator | 68.397 | book | 14.565 | clock | 53.150 | | vase | 40.056 | scissors | 35.612 | teddy bear | 56.984 | | hair drier | 30.775 | toothbrush | 27.609 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.68732598554467, 'fwIoU': 70.94625408257431, 'IoU-person': 88.72958504029819, 'IoU-bicycle': 73.41445079832626, 'IoU-car': 73.69898022801257, 'IoU-motorcycle': 82.40931721628102, 'IoU-airplane': 80.81391370722005, 'IoU-bus': 88.20497555840576, 'IoU-train': 89.64822803284073, 'IoU-truck': 69.89034020538206, 'IoU-boat': 75.96067710721408, 'IoU-traffic light': 80.3132271135297, 'IoU-fire hydrant': 92.99021188350729, 'IoU-stop sign': 85.53136533002973, 'IoU-parking meter': 83.90654930134012, 'IoU-bench': 55.18499913312914, 'IoU-bird': 76.57422736208723, 'IoU-cat': 88.92994559027903, 'IoU-dog': 86.40789289918398, 'IoU-horse': 85.71620895870774, 'IoU-sheep': 80.75149646970185, 'IoU-cow': 88.05677703286806, 'IoU-elephant': 84.65275642516423, 'IoU-bear': 73.39594251964115, 'IoU-zebra': 78.53944188217127, 'IoU-giraffe': 89.52755097342305, 'IoU-backpack': 53.19548334062662, 'IoU-umbrella': 81.5234553252096, 'IoU-handbag': 51.2732723246207, 'IoU-tie': 76.3465192713588, 'IoU-suitcase': 79.87469144640616, 'IoU-frisbee': 84.64165944150895, 'IoU-skis': 57.43191282713109, 'IoU-snowboard': 72.58944945263076, 'IoU-sports ball': 80.39514978601997, 'IoU-kite': 79.2801996440327, 'IoU-baseball bat': 67.96354396624571, 'IoU-baseball glove': 54.790058724162485, 'IoU-skateboard': 86.19442310974568, 'IoU-surfboard': 86.4573490983907, 'IoU-tennis racket': 90.98076878617172, 'IoU-bottle': 71.67145990339941, 'IoU-wine glass': 82.17953410665493, 'IoU-cup': 67.84655573435036, 'IoU-fork': 69.49729238202522, 'IoU-knife': 63.69183191591616, 'IoU-spoon': 60.81932658217563, 'IoU-bowl': 59.233747858200324, 'IoU-banana': 82.1460886999655, 'IoU-apple': 59.07464072393627, 'IoU-sandwich': 70.0521050883478, 'IoU-orange': 76.67533764756372, 'IoU-broccoli': 69.3393588807592, 'IoU-carrot': 64.13047089265478, 'IoU-hot dog': 67.6457177354638, 'IoU-pizza': 78.9611130635326, 'IoU-donut': 66.67436475551185, 'IoU-cake': 75.2607137093023, 'IoU-chair': 61.18772584751697, 'IoU-couch': 70.09309050278844, 'IoU-potted plant': 42.18542903750206, 'IoU-bed': 73.6236432645249, 'IoU-dining table': 54.25403658114395, 'IoU-toilet': 77.8248018854795, 'IoU-tv': 72.93371051105775, 'IoU-laptop': 75.9000609799854, 'IoU-mouse': 73.82344224857779, 'IoU-remote': 71.6024027990808, 'IoU-keyboard': 67.439847448424, 'IoU-cell phone': 76.40545838973881, 'IoU-microwave': 69.26126862892568, 'IoU-oven': 71.855103148857, 'IoU-toaster': 84.40242009566347, 'IoU-sink': 73.67952884204011, 'IoU-refrigerator': 80.93916085993767, 'IoU-book': 52.47400622696914, 'IoU-clock': 76.41624584051159, 'IoU-vase': 58.34953826468335, 'IoU-scissors': 85.87945913944985, 'IoU-teddy bear': 79.54962643797214, 'IoU-hair drier': 49.18095458758109, 'IoU-toothbrush': 72.28467856651218, 'IoU-banner': 36.19675765773636, 'IoU-blanket': 17.056118621752887, 'IoU-bridge': 35.20630894603531, 'IoU-cardboard': 48.35731219250432, 'IoU-counter': 33.26195427268177, 'IoU-curtain': 71.40298388121433, 'IoU-door-stuff': 48.81331962542043, 'IoU-floor-wood': 63.41456209586999, 'IoU-flower': 40.38622221284945, 'IoU-fruit': 47.611589502107485, 'IoU-gravel': 30.396462242422377, 'IoU-house': 23.838134437241916, 'IoU-light': 45.43226389098337, 'IoU-mirror-stuff': 63.150692364147275, 'IoU-net': 41.28297748890001, 'IoU-pillow': 24.1951469430126, 'IoU-platform': 26.79167156126815, 'IoU-playingfield': 70.04808440878482, 'IoU-railroad': 63.37678015454171, 'IoU-river': 54.802168945483295, 'IoU-road': 67.46321641566088, 'IoU-roof': 18.50997120824395, 'IoU-sand': 65.25744647480639, 'IoU-sea': 85.31503484495278, 'IoU-shelf': 38.98211123132592, 'IoU-snow': 92.44911325632268, 'IoU-stairs': 35.32890483021413, 'IoU-tent': 10.923539163149126, 'IoU-towel': 46.31244655610555, 'IoU-wall-brick': 52.58113538108034, 'IoU-wall-stone': 28.842693567736276, 'IoU-wall-tile': 69.09018987130283, 'IoU-wall-wood': 44.923637592963566, 'IoU-water-other': 26.22371815348811, 'IoU-window-blind': 51.111303692148844, 'IoU-window-other': 50.91818361915033, 'IoU-tree-merged': 82.07746202806464, 'IoU-fence-merged': 53.38539696168794, 'IoU-ceiling-merged': 67.34474988548746, 'IoU-sky-other-merged': 93.76381122950951, 'IoU-cabinet-merged': 64.64450470487091, 'IoU-table-merged': 38.35960167160009, 'IoU-floor-other-merged': 54.94164980371309, 'IoU-pavement-merged': 57.83239588748852, 'IoU-mountain-merged': 58.861358953433495, 'IoU-grass-merged': 69.78633961096587, 'IoU-dirt-merged': 46.08472666237307, 'IoU-paper-merged': 34.85163141055882, 'IoU-food-other-merged': 43.03866682997943, 'IoU-building-other-merged': 59.43394289195728, 'IoU-rock-merged': 61.84315207513589, 'IoU-wall-other-merged': 66.51093779771979, 'IoU-rug-merged': 68.76757324159655, 'mACC': 75.73977839973782, 'pACC': 81.80735543493782, 'ACC-person': 93.45536032847855, 'ACC-bicycle': 82.81303268120895, 'ACC-car': 85.3294994906142, 'ACC-motorcycle': 86.55680650118926, 'ACC-airplane': 84.52197027585993, 'ACC-bus': 93.99639667547336, 'ACC-train': 94.95737310253787, 'ACC-truck': 77.4162198036458, 'ACC-boat': 85.61422415774868, 'ACC-traffic light': 90.40833949423663, 'ACC-fire hydrant': 95.90599606297532, 'ACC-stop sign': 88.31901893507161, 'ACC-parking meter': 86.6851041384521, 'ACC-bench': 74.05684810867677, 'ACC-bird': 82.35206632085324, 'ACC-cat': 92.4083659443562, 'ACC-dog': 89.309141821798, 'ACC-horse': 90.1059529221432, 'ACC-sheep': 84.30631159702571, 'ACC-cow': 91.44532728245686, 'ACC-elephant': 86.85014161787905, 'ACC-bear': 74.76549550816415, 'ACC-zebra': 80.29040453541735, 'ACC-giraffe': 93.33327612335151, 'ACC-backpack': 71.2482800326027, 'ACC-umbrella': 85.29512243722895, 'ACC-handbag': 70.17378309673693, 'ACC-tie': 84.34124280496094, 'ACC-suitcase': 85.34091836629892, 'ACC-frisbee': 94.07381818181818, 'ACC-skis': 73.53806001424206, 'ACC-snowboard': 82.78905733388846, 'ACC-sports ball': 87.3833224796105, 'ACC-kite': 84.47932161923387, 'ACC-baseball bat': 87.07490128793953, 'ACC-baseball glove': 61.81592846386085, 'ACC-skateboard': 90.6702918879129, 'ACC-surfboard': 92.2969085482108, 'ACC-tennis racket': 94.81300413092471, 'ACC-bottle': 86.18926872982414, 'ACC-wine glass': 90.17062247284727, 'ACC-cup': 85.42275826229503, 'ACC-fork': 79.16121083200255, 'ACC-knife': 78.17359126547959, 'ACC-spoon': 79.99214951273547, 'ACC-bowl': 69.0281519505958, 'ACC-banana': 90.38796947242884, 'ACC-apple': 72.10973894182634, 'ACC-sandwich': 82.89083707422643, 'ACC-orange': 85.85156150709996, 'ACC-broccoli': 80.69226346467947, 'ACC-carrot': 75.85940649683512, 'ACC-hot dog': 75.07353614056188, 'ACC-pizza': 88.47611318244782, 'ACC-donut': 74.60527654950543, 'ACC-cake': 83.03885880844588, 'ACC-chair': 75.44012954631229, 'ACC-couch': 75.72896839412614, 'ACC-potted plant': 56.53539019280548, 'ACC-bed': 82.22009194103643, 'ACC-dining table': 75.20799779309236, 'ACC-toilet': 80.89499611942553, 'ACC-tv': 76.18765685216279, 'ACC-laptop': 86.26876405599137, 'ACC-mouse': 91.65900097038696, 'ACC-remote': 76.03085799901179, 'ACC-keyboard': 74.12320682286953, 'ACC-cell phone': 86.19945488610429, 'ACC-microwave': 72.93382110459345, 'ACC-oven': 84.34903363488792, 'ACC-toaster': 91.16383144698847, 'ACC-sink': 82.58210143599717, 'ACC-refrigerator': 88.68335657404468, 'ACC-book': 68.5005323470903, 'ACC-clock': 81.52298418553265, 'ACC-vase': 65.89388758789623, 'ACC-scissors': 91.40566895297908, 'ACC-teddy bear': 83.43227902627474, 'ACC-hair drier': 60.41755614379644, 'ACC-toothbrush': 82.1038915913829, 'ACC-banner': 69.80655350455102, 'ACC-blanket': 25.951980697146855, 'ACC-bridge': 51.323862539743814, 'ACC-cardboard': 56.63492802093937, 'ACC-counter': 54.42563136864449, 'ACC-curtain': 83.47819377946584, 'ACC-door-stuff': 71.11674522771136, 'ACC-floor-wood': 83.69717480469939, 'ACC-flower': 55.770008058705535, 'ACC-fruit': 66.87400956140011, 'ACC-gravel': 41.70291899012809, 'ACC-house': 28.414509316852232, 'ACC-light': 63.430035313708224, 'ACC-mirror-stuff': 73.54694862537148, 'ACC-net': 65.89757744655284, 'ACC-pillow': 57.100580000158395, 'ACC-platform': 45.036942147176184, 'ACC-playingfield': 89.67719550695465, 'ACC-railroad': 83.63231753206745, 'ACC-river': 74.2706445074353, 'ACC-road': 83.8455258374935, 'ACC-roof': 24.49738618815371, 'ACC-sand': 69.87519706344332, 'ACC-sea': 91.0950284312528, 'ACC-shelf': 52.23263586525727, 'ACC-snow': 95.42908969856441, 'ACC-stairs': 53.77406511205612, 'ACC-tent': 12.61814596568121, 'ACC-towel': 55.53590803153473, 'ACC-wall-brick': 68.83191246070582, 'ACC-wall-stone': 33.39825899927747, 'ACC-wall-tile': 86.21019048791284, 'ACC-wall-wood': 64.39355396802446, 'ACC-water-other': 43.37627850827903, 'ACC-window-blind': 63.320905486268956, 'ACC-window-other': 73.9608066261443, 'ACC-tree-merged': 90.30456481229277, 'ACC-fence-merged': 69.73205738496475, 'ACC-ceiling-merged': 83.10486467309296, 'ACC-sky-other-merged': 96.98087694689168, 'ACC-cabinet-merged': 78.65971320668436, 'ACC-table-merged': 56.63036513624735, 'ACC-floor-other-merged': 63.86896691101791, 'ACC-pavement-merged': 72.55342529979274, 'ACC-mountain-merged': 69.98698244997948, 'ACC-grass-merged': 82.77268440337286, 'ACC-dirt-merged': 69.4217292442479, 'ACC-paper-merged': 45.55961121438247, 'ACC-food-other-merged': 57.855779404439936, 'ACC-building-other-merged': 73.77516923541405, 'ACC-rock-merged': 83.31316630597938, 'ACC-wall-other-merged': 84.63848883176246, 'ACC-rug-merged': 82.90302764139402})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3001 s/iter. Inference: 0.1748 s/iter. Eval: 0.0000 s/iter. Total: 0.4750 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3178 s/iter. Inference: 0.3421 s/iter. Eval: 0.0000 s/iter. Total: 0.6600 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3397 s/iter. Inference: 0.4428 s/iter. Eval: 0.0000 s/iter. Total: 0.7826 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.373134328358209, 'noc@0.8': 2.462686567164179, 'noc@0.85': 2.9215686274509802, 'noc@0.9': 3.759145449224466, 'miou@iter1': 0.8756917434929057} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1486 s/iter. Eval: 0.0011 s/iter. Total: 0.1510 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.28176879882812, 'precision@0.6': 72.36688995361328, 'precision@0.7': 68.20832061767578, 'precision@0.8': 59.42479705810547, 'precision@0.9': 32.452388763427734, 'cIoU': 61.79861831665039, 'mIoU': 66.5622329711914} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.51221165872293, 'SQ': 82.90850838859771, 'RQ': 66.16892847704756, 'PQ_th': 61.77873193095501, 'SQ_th': 83.81956674260083, 'RQ_th': 73.20543605924782, 'PQ_st': 46.05331313459901, 'SQ_st': 81.53332596746091, 'RQ_st': 55.5477849567453}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.36987313937577, 'AP50': 69.35917040787399, 'AP75': 48.62492766810158, 'APs': 25.58329959453388, 'APm': 49.562706100361304, 'APl': 67.25288871535373, 'AP-person': 48.951510984818256, 'AP-bicycle': 22.25404552276883, 'AP-car': 43.067894355227466, 'AP-motorcycle': 42.11654251094043, 'AP-airplane': 62.634676851545144, 'AP-bus': 70.81851314585539, 'AP-train': 74.96089809669182, 'AP-truck': 43.57598655533045, 'AP-boat': 30.86677425167354, 'AP-traffic light': 28.945350923396855, 'AP-fire hydrant': 69.97674428890299, 'AP-stop sign': 68.42703147466443, 'AP-parking meter': 47.07556403254763, 'AP-bench': 27.077788317889194, 'AP-bird': 33.35788875700229, 'AP-cat': 76.56281575066596, 'AP-dog': 71.31050558899616, 'AP-horse': 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'ACC-table-merged': 56.63036513624735, 'ACC-floor-other-merged': 63.86896691101791, 'ACC-pavement-merged': 72.55342529979274, 'ACC-mountain-merged': 69.98698244997948, 'ACC-grass-merged': 82.77268440337286, 'ACC-dirt-merged': 69.4217292442479, 'ACC-paper-merged': 45.55961121438247, 'ACC-food-other-merged': 57.855779404439936, 'ACC-building-other-merged': 73.77516923541405, 'ACC-rock-merged': 83.31316630597938, 'ACC-wall-other-merged': 84.63848883176246, 'ACC-rug-merged': 82.90302764139402})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.373134328358209, 'noc@0.8': 2.462686567164179, 'noc@0.85': 2.9215686274509802, 'noc@0.9': 3.759145449224466, 'miou@iter1': 0.8756917434929057}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.28176879882812, 'precision@0.6': 72.36688995361328, 'precision@0.7': 68.20832061767578, 'precision@0.8': 59.42479705810547, 'precision@0.9': 32.452388763427734, 'cIoU': 61.79861831665039, 'mIoU': 66.5622329711914}}} INFO:trainer.default_trainer:This epoch takes 0:57:20.118798 INFO:trainer.default_trainer:PROGRESS: 46.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 23 training. INFO:trainer.default_trainer:epochs[ 23] optim steps[42100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02155/0.76614, loss_mask_bce_0: 0.04154/0.30196, loss_mask_dice_0: 0.09107/1.02645, loss_spatial_bce_0: 0.01822/0.08680, loss_spatial_dice_0: 0.05276/0.18334, loss_spatial_ce_0: 0.00248/0.06192, loss_grounding_bce_0: 0.01065/0.08084, loss_grounding_dice_0: 0.04245/0.15123, loss_grounding_ce_0: 0.09406/0.24943, loss_mask_ce_1: 0.02376/0.76760, loss_mask_bce_1: 0.05242/0.30278, loss_mask_dice_1: 0.11277/1.03000, loss_spatial_bce_1: 0.01813/0.08706, loss_spatial_dice_1: 0.06438/0.18589, loss_spatial_ce_1: 0.00574/0.06609, loss_grounding_bce_1: 0.01163/0.08101, loss_grounding_dice_1: 0.04679/0.15195, loss_grounding_ce_1: 0.09369/0.25088, loss_mask_ce_2: 0.02272/0.77570, loss_mask_bce_2: 0.04651/0.30288, loss_mask_dice_2: 0.10581/1.03131, loss_spatial_bce_2: 0.01767/0.08694, loss_spatial_dice_2: 0.05942/0.18610, loss_spatial_ce_2: 0.00642/0.06859, loss_grounding_bce_2: 0.01039/0.08096, loss_grounding_dice_2: 0.04322/0.15172, loss_grounding_ce_2: 0.09323/0.25360, loss_mask_ce_3: 0.01245/0.77793, loss_mask_bce_3: 0.04650/0.30441, loss_mask_dice_3: 0.10338/1.02799, loss_spatial_bce_3: 0.01748/0.08885, loss_spatial_dice_3: 0.05439/0.18711, loss_spatial_ce_3: 0.01098/0.07311, loss_grounding_bce_3: 0.01313/0.08143, loss_grounding_dice_3: 0.04163/0.15134, loss_grounding_ce_3: 0.09295/0.25336, loss_mask_ce_4: 0.02471/0.78374, loss_mask_bce_4: 0.04395/0.30668, loss_mask_dice_4: 0.10652/1.04762, loss_spatial_bce_4: 0.02040/0.09081, loss_spatial_dice_4: 0.09152/0.19484, loss_spatial_ce_4: 0.01115/0.08595, loss_grounding_bce_4: 0.01248/0.08205, loss_grounding_dice_4: 0.04443/0.15392, loss_grounding_ce_4: 0.09669/0.25911, loss_mask_ce_5: 0.02063/0.80670, loss_mask_bce_5: 0.04570/0.30842, loss_mask_dice_5: 0.10629/1.05467, loss_spatial_bce_5: 0.01872/0.09275, loss_spatial_dice_5: 0.06668/0.19742, loss_spatial_ce_5: 0.00403/0.09812, loss_grounding_bce_5: 0.01085/0.08231, loss_grounding_dice_5: 0.03954/0.15459, loss_grounding_ce_5: 0.09389/0.27736, loss_mask_ce_6: 0.01501/0.83314, loss_mask_bce_6: 0.04798/0.31022, loss_mask_dice_6: 0.10294/1.05758, loss_spatial_bce_6: 0.01920/0.09773, loss_spatial_dice_6: 0.06955/0.19959, loss_spatial_ce_6: 0.02639/0.12100, loss_grounding_bce_6: 0.01017/0.08328, loss_grounding_dice_6: 0.04080/0.15519, loss_grounding_ce_6: 0.09403/0.28707, loss_mask_ce_7: 0.03432/0.88945, loss_mask_bce_7: 0.04884/0.31754, loss_mask_dice_7: 0.10248/1.10415, loss_spatial_bce_7: 0.02766/0.10792, loss_spatial_dice_7: 0.10201/0.22482, loss_spatial_ce_7: 0.06525/0.16023, loss_grounding_bce_7: 0.01009/0.08493, loss_grounding_dice_7: 0.03972/0.16089, loss_grounding_ce_7: 0.10267/0.32261, loss_mask_ce_8: 0.06035/1.02668, loss_mask_bce_8: 0.05122/0.33390, loss_mask_dice_8: 0.10752/1.18138, loss_spatial_bce_8: 0.03175/0.12654, loss_spatial_dice_8: 0.11443/0.26189, loss_spatial_ce_8: 0.11258/0.21268, loss_grounding_bce_8: 0.01487/0.08891, loss_grounding_dice_8: 0.04409/0.17051, loss_grounding_ce_8: 0.12299/0.42473, loss_mask_ce_9: 1.18563/3.48569, loss_mask_bce_9: 0.04298/0.36086, loss_mask_dice_9: 0.11868/1.76568, loss_spatial_bce_9: 0.31718/0.35590, loss_spatial_dice_9: 0.78608/0.79465, loss_spatial_ce_9: 2.21107/1.39778, loss_grounding_bce_9: 0.01116/0.10106, loss_grounding_dice_9: 0.05693/0.24357, loss_grounding_ce_9: 0.26225/0.68457] items per batch[64] items per second[0.16] total items[2694400] mini batches[ 42100] memory[4999] epoch remaining[0:54:26] INFO:trainer.default_trainer:epochs[ 23] optim steps[42200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53011/0.76589, loss_mask_bce_0: 0.24097/0.30194, loss_mask_dice_0: 0.23375/1.02656, loss_spatial_bce_0: 0.07092/0.08676, loss_spatial_dice_0: 0.08074/0.18329, loss_spatial_ce_0: 0.06272/0.06185, loss_grounding_bce_0: 0.02263/0.08083, loss_grounding_dice_0: 0.04122/0.15121, loss_grounding_ce_0: 0.15493/0.24946, loss_mask_ce_1: 0.49553/0.76731, loss_mask_bce_1: 0.24127/0.30275, loss_mask_dice_1: 0.23282/1.03004, loss_spatial_bce_1: 0.08307/0.08702, loss_spatial_dice_1: 0.09520/0.18584, loss_spatial_ce_1: 0.10399/0.06602, loss_grounding_bce_1: 0.02263/0.08101, loss_grounding_dice_1: 0.04335/0.15193, loss_grounding_ce_1: 0.16087/0.25089, loss_mask_ce_2: 0.45801/0.77546, loss_mask_bce_2: 0.23698/0.30285, loss_mask_dice_2: 0.23179/1.03140, loss_spatial_bce_2: 0.07073/0.08690, loss_spatial_dice_2: 0.08358/0.18605, loss_spatial_ce_2: 0.10598/0.06851, loss_grounding_bce_2: 0.02006/0.08095, loss_grounding_dice_2: 0.03959/0.15169, loss_grounding_ce_2: 0.15531/0.25365, loss_mask_ce_3: 0.47619/0.77763, loss_mask_bce_3: 0.23135/0.30438, loss_mask_dice_3: 0.23411/1.02815, loss_spatial_bce_3: 0.08069/0.08881, loss_spatial_dice_3: 0.09580/0.18707, loss_spatial_ce_3: 0.10118/0.07306, loss_grounding_bce_3: 0.02190/0.08143, loss_grounding_dice_3: 0.03878/0.15131, loss_grounding_ce_3: 0.15933/0.25339, loss_mask_ce_4: 0.54798/0.78350, loss_mask_bce_4: 0.24877/0.30664, loss_mask_dice_4: 0.24987/1.04768, loss_spatial_bce_4: 0.10326/0.09077, loss_spatial_dice_4: 0.10004/0.19480, loss_spatial_ce_4: 0.28692/0.08586, loss_grounding_bce_4: 0.01938/0.08204, loss_grounding_dice_4: 0.03945/0.15388, loss_grounding_ce_4: 0.14883/0.25913, loss_mask_ce_5: 0.64312/0.80645, loss_mask_bce_5: 0.26755/0.30841, loss_mask_dice_5: 0.39454/1.05477, loss_spatial_bce_5: 0.13354/0.09271, loss_spatial_dice_5: 0.14541/0.19738, loss_spatial_ce_5: 0.31114/0.09805, loss_grounding_bce_5: 0.02286/0.08230, loss_grounding_dice_5: 0.04397/0.15456, loss_grounding_ce_5: 0.14497/0.27732, loss_mask_ce_6: 0.72650/0.83282, loss_mask_bce_6: 0.27065/0.31021, loss_mask_dice_6: 0.39169/1.05774, loss_spatial_bce_6: 0.32096/0.09770, loss_spatial_dice_6: 0.20424/0.19956, loss_spatial_ce_6: 0.07575/0.12088, loss_grounding_bce_6: 0.02308/0.08327, loss_grounding_dice_6: 0.04402/0.15515, loss_grounding_ce_6: 0.16465/0.28706, loss_mask_ce_7: 0.75720/0.88915, loss_mask_bce_7: 0.30311/0.31751, loss_mask_dice_7: 0.27116/1.10427, loss_spatial_bce_7: 0.18766/0.10788, loss_spatial_dice_7: 0.19331/0.22480, loss_spatial_ce_7: 0.08552/0.16016, loss_grounding_bce_7: 0.02244/0.08493, loss_grounding_dice_7: 0.04627/0.16087, loss_grounding_ce_7: 0.23313/0.32259, loss_mask_ce_8: 1.27806/1.02648, loss_mask_bce_8: 0.37375/0.33385, loss_mask_dice_8: 0.39468/1.18152, loss_spatial_bce_8: 0.17130/0.12650, loss_spatial_dice_8: 0.16034/0.26186, loss_spatial_ce_8: 0.08600/0.21258, loss_grounding_bce_8: 0.03195/0.08890, loss_grounding_dice_8: 0.05381/0.17048, loss_grounding_ce_8: 0.20272/0.42470, loss_mask_ce_9: 4.28400/3.48543, loss_mask_bce_9: 0.80797/0.36081, loss_mask_dice_9: 0.79235/1.76585, loss_spatial_bce_9: 0.50647/0.35586, loss_spatial_dice_9: 0.81524/0.79466, loss_spatial_ce_9: 1.83022/1.39784, loss_grounding_bce_9: 0.02732/0.10106, loss_grounding_dice_9: 0.07096/0.24354, loss_grounding_ce_9: 0.65926/0.68444] items per batch[64] items per second[0.37] total items[2700800] mini batches[ 42200] memory[4999] epoch remaining[0:49:18] INFO:trainer.default_trainer:epochs[ 23] optim steps[42300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19568/0.76560, loss_mask_bce_0: 0.09090/0.30186, loss_mask_dice_0: 0.10269/1.02634, loss_spatial_bce_0: 0.04360/0.08675, loss_spatial_dice_0: 0.05374/0.18326, loss_spatial_ce_0: 0.00019/0.06182, loss_grounding_bce_0: 0.02381/0.08082, loss_grounding_dice_0: 0.06575/0.15121, loss_grounding_ce_0: 0.00002/0.24957, loss_mask_ce_1: 0.18734/0.76700, loss_mask_bce_1: 0.09942/0.30268, loss_mask_dice_1: 0.10482/1.02983, loss_spatial_bce_1: 0.04570/0.08700, loss_spatial_dice_1: 0.05638/0.18581, loss_spatial_ce_1: 0.00008/0.06596, loss_grounding_bce_1: 0.01954/0.08100, loss_grounding_dice_1: 0.05802/0.15194, loss_grounding_ce_1: 0.00001/0.25094, loss_mask_ce_2: 0.19104/0.77513, loss_mask_bce_2: 0.09292/0.30278, loss_mask_dice_2: 0.10171/1.03119, loss_spatial_bce_2: 0.04715/0.08688, loss_spatial_dice_2: 0.05779/0.18600, loss_spatial_ce_2: 0.00012/0.06846, loss_grounding_bce_2: 0.02437/0.08093, loss_grounding_dice_2: 0.06794/0.15169, loss_grounding_ce_2: 0.00003/0.25370, loss_mask_ce_3: 0.20591/0.77733, loss_mask_bce_3: 0.09255/0.30432, loss_mask_dice_3: 0.10536/1.02793, loss_spatial_bce_3: 0.04245/0.08880, loss_spatial_dice_3: 0.05155/0.18704, loss_spatial_ce_3: 0.00047/0.07301, loss_grounding_bce_3: 0.02743/0.08141, loss_grounding_dice_3: 0.06827/0.15132, loss_grounding_ce_3: 0.00003/0.25341, loss_mask_ce_4: 0.10499/0.78314, loss_mask_bce_4: 0.09512/0.30659, loss_mask_dice_4: 0.10688/1.04746, loss_spatial_bce_4: 0.04078/0.09075, loss_spatial_dice_4: 0.05355/0.19477, loss_spatial_ce_4: 0.00068/0.08582, loss_grounding_bce_4: 0.02790/0.08203, loss_grounding_dice_4: 0.07468/0.15389, loss_grounding_ce_4: 0.00007/0.25920, loss_mask_ce_5: 0.07771/0.80610, loss_mask_bce_5: 0.09468/0.30833, loss_mask_dice_5: 0.11221/1.05454, loss_spatial_bce_5: 0.04597/0.09269, loss_spatial_dice_5: 0.06030/0.19735, loss_spatial_ce_5: 0.00417/0.09801, loss_grounding_bce_5: 0.02703/0.08228, loss_grounding_dice_5: 0.07249/0.15456, loss_grounding_ce_5: 0.00005/0.27742, loss_mask_ce_6: 0.08306/0.83248, loss_mask_bce_6: 0.08541/0.31014, loss_mask_dice_6: 0.09431/1.05753, loss_spatial_bce_6: 0.06513/0.09768, loss_spatial_dice_6: 0.06145/0.19953, loss_spatial_ce_6: 0.00070/0.12086, loss_grounding_bce_6: 0.02460/0.08324, loss_grounding_dice_6: 0.06884/0.15515, loss_grounding_ce_6: 0.00003/0.28724, loss_mask_ce_7: 0.06116/0.88882, loss_mask_bce_7: 0.09354/0.31744, loss_mask_dice_7: 0.10650/1.10404, loss_spatial_bce_7: 0.07613/0.10787, loss_spatial_dice_7: 0.06532/0.22475, loss_spatial_ce_7: 0.00101/0.16011, loss_grounding_bce_7: 0.02353/0.08492, loss_grounding_dice_7: 0.06149/0.16087, loss_grounding_ce_7: 0.00001/0.32267, loss_mask_ce_8: 0.05798/1.02600, loss_mask_bce_8: 0.09527/0.33376, loss_mask_dice_8: 0.09956/1.18122, loss_spatial_bce_8: 0.06069/0.12649, loss_spatial_dice_8: 0.05725/0.26182, loss_spatial_ce_8: 0.08925/0.21258, loss_grounding_bce_8: 0.02491/0.08888, loss_grounding_dice_8: 0.05857/0.17048, loss_grounding_ce_8: 0.00020/0.42479, loss_mask_ce_9: 1.40977/3.48480, loss_mask_bce_9: 0.10142/0.36072, loss_mask_dice_9: 0.13480/1.76527, loss_spatial_bce_9: 0.36852/0.35588, loss_spatial_dice_9: 0.65346/0.79459, loss_spatial_ce_9: 1.08211/1.39775, loss_grounding_bce_9: 0.01899/0.10103, loss_grounding_dice_9: 0.06668/0.24353, loss_grounding_ce_9: 0.17149/0.68438] items per batch[64] items per second[0.37] total items[2707200] mini batches[ 42300] memory[4999] epoch remaining[0:45:54] INFO:trainer.default_trainer:epochs[ 23] optim steps[42400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37752/0.76567, loss_mask_bce_0: 0.44028/0.30184, loss_mask_dice_0: 0.40236/1.02635, loss_spatial_bce_0: 0.14730/0.08673, loss_spatial_dice_0: 0.15017/0.18325, loss_spatial_ce_0: 0.03885/0.06176, loss_grounding_bce_0: 0.38758/0.08084, loss_grounding_dice_0: 0.15453/0.15119, loss_grounding_ce_0: 0.08294/0.24952, loss_mask_ce_1: 0.30830/0.76704, loss_mask_bce_1: 0.44791/0.30264, loss_mask_dice_1: 0.40818/1.02978, loss_spatial_bce_1: 0.15465/0.08699, loss_spatial_dice_1: 0.15148/0.18580, loss_spatial_ce_1: 0.03488/0.06593, loss_grounding_bce_1: 0.35960/0.08103, loss_grounding_dice_1: 0.14459/0.15191, loss_grounding_ce_1: 0.05869/0.25091, loss_mask_ce_2: 0.31347/0.77512, loss_mask_bce_2: 0.47852/0.30274, loss_mask_dice_2: 0.42507/1.03112, loss_spatial_bce_2: 0.16697/0.08687, loss_spatial_dice_2: 0.14805/0.18600, loss_spatial_ce_2: 0.07784/0.06844, loss_grounding_bce_2: 0.34642/0.08096, loss_grounding_dice_2: 0.14822/0.15167, loss_grounding_ce_2: 0.04521/0.25366, loss_mask_ce_3: 0.30178/0.77744, loss_mask_bce_3: 0.49634/0.30429, loss_mask_dice_3: 0.41057/1.02790, loss_spatial_bce_3: 0.17053/0.08880, loss_spatial_dice_3: 0.14710/0.18704, loss_spatial_ce_3: 0.08868/0.07301, loss_grounding_bce_3: 0.36254/0.08144, loss_grounding_dice_3: 0.15092/0.15129, loss_grounding_ce_3: 0.01655/0.25336, loss_mask_ce_4: 0.28458/0.78321, loss_mask_bce_4: 0.63182/0.30655, loss_mask_dice_4: 0.48495/1.04742, loss_spatial_bce_4: 0.20517/0.09074, loss_spatial_dice_4: 0.20431/0.19478, loss_spatial_ce_4: 0.08166/0.08582, loss_grounding_bce_4: 0.35367/0.08205, loss_grounding_dice_4: 0.14196/0.15386, loss_grounding_ce_4: 0.01172/0.25913, loss_mask_ce_5: 0.36750/0.80615, loss_mask_bce_5: 0.70591/0.30832, loss_mask_dice_5: 0.49214/1.05450, loss_spatial_bce_5: 0.24984/0.09269, loss_spatial_dice_5: 0.23057/0.19736, loss_spatial_ce_5: 0.10898/0.09801, loss_grounding_bce_5: 0.36604/0.08231, loss_grounding_dice_5: 0.16394/0.15453, loss_grounding_ce_5: 0.04069/0.27734, loss_mask_ce_6: 0.40853/0.83255, loss_mask_bce_6: 0.42983/0.31014, loss_mask_dice_6: 0.41549/1.05745, loss_spatial_bce_6: 0.24161/0.09768, loss_spatial_dice_6: 0.22526/0.19955, loss_spatial_ce_6: 0.19731/0.12089, loss_grounding_bce_6: 0.39232/0.08327, loss_grounding_dice_6: 0.17406/0.15513, loss_grounding_ce_6: 0.07110/0.28715, loss_mask_ce_7: 0.67954/0.88889, loss_mask_bce_7: 0.47179/0.31741, loss_mask_dice_7: 0.43009/1.10398, loss_spatial_bce_7: 0.27146/0.10787, loss_spatial_dice_7: 0.22983/0.22475, loss_spatial_ce_7: 0.21164/0.16013, loss_grounding_bce_7: 0.43483/0.08495, loss_grounding_dice_7: 0.17314/0.16086, loss_grounding_ce_7: 0.09229/0.32254, loss_mask_ce_8: 1.18324/1.02606, loss_mask_bce_8: 0.43306/0.33372, loss_mask_dice_8: 0.42702/1.18112, loss_spatial_bce_8: 0.31990/0.12648, loss_spatial_dice_8: 0.23443/0.26182, loss_spatial_ce_8: 0.39109/0.21259, loss_grounding_bce_8: 0.40152/0.08892, loss_grounding_dice_8: 0.18559/0.17046, loss_grounding_ce_8: 0.69283/0.42459, loss_mask_ce_9: 2.13309/3.48455, loss_mask_bce_9: 0.51463/0.36067, loss_mask_dice_9: 0.66814/1.76517, loss_spatial_bce_9: 0.42870/0.35585, loss_spatial_dice_9: 0.88161/0.79463, loss_spatial_ce_9: 1.96199/1.39751, loss_grounding_bce_9: 0.29565/0.10103, loss_grounding_dice_9: 0.17315/0.24344, loss_grounding_ce_9: 1.04239/0.68410] items per batch[64] items per second[0.36] total items[2713600] mini batches[ 42400] memory[4999] epoch remaining[0:42:47] INFO:trainer.default_trainer:epochs[ 23] optim steps[42500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08986/0.76579, loss_mask_bce_0: 0.22588/0.30197, loss_mask_dice_0: 0.67992/1.02661, loss_spatial_bce_0: 0.03931/0.08677, loss_spatial_dice_0: 0.16702/0.18323, loss_spatial_ce_0: 0.02035/0.06173, loss_grounding_bce_0: 0.03086/0.08083, loss_grounding_dice_0: 0.02552/0.15118, loss_grounding_ce_0: 0.00079/0.24962, loss_mask_ce_1: 0.87407/0.76718, loss_mask_bce_1: 0.21865/0.30277, loss_mask_dice_1: 0.97337/1.03011, loss_spatial_bce_1: 0.03938/0.08702, loss_spatial_dice_1: 0.15764/0.18578, loss_spatial_ce_1: 0.02483/0.06590, loss_grounding_bce_1: 0.02961/0.08102, loss_grounding_dice_1: 0.02467/0.15190, loss_grounding_ce_1: 0.00085/0.25102, loss_mask_ce_2: 1.03319/0.77519, loss_mask_bce_2: 0.22451/0.30288, loss_mask_dice_2: 0.69160/1.03137, loss_spatial_bce_2: 0.04015/0.08691, loss_spatial_dice_2: 0.16044/0.18598, loss_spatial_ce_2: 0.01332/0.06840, loss_grounding_bce_2: 0.02726/0.08095, loss_grounding_dice_2: 0.02413/0.15165, loss_grounding_ce_2: 0.00090/0.25375, loss_mask_ce_3: 0.92996/0.77753, loss_mask_bce_3: 0.23279/0.30441, loss_mask_dice_3: 0.68449/1.02814, loss_spatial_bce_3: 0.04413/0.08884, loss_spatial_dice_3: 0.17673/0.18703, loss_spatial_ce_3: 0.00892/0.07297, loss_grounding_bce_3: 0.02760/0.08143, loss_grounding_dice_3: 0.02233/0.15128, loss_grounding_ce_3: 0.00080/0.25340, loss_mask_ce_4: 0.93465/0.78332, loss_mask_bce_4: 0.21384/0.30669, loss_mask_dice_4: 0.68629/1.04769, loss_spatial_bce_4: 0.03920/0.09077, loss_spatial_dice_4: 0.16105/0.19476, loss_spatial_ce_4: 0.00027/0.08579, loss_grounding_bce_4: 0.03533/0.08204, loss_grounding_dice_4: 0.02630/0.15385, loss_grounding_ce_4: 0.00051/0.25924, loss_mask_ce_5: 0.97686/0.80630, loss_mask_bce_5: 0.22922/0.30847, loss_mask_dice_5: 0.87529/1.05477, loss_spatial_bce_5: 0.04385/0.09272, loss_spatial_dice_5: 0.17679/0.19734, loss_spatial_ce_5: 0.00094/0.09797, loss_grounding_bce_5: 0.02947/0.08230, loss_grounding_dice_5: 0.02305/0.15453, loss_grounding_ce_5: 0.00065/0.27741, loss_mask_ce_6: 0.97124/0.83266, loss_mask_bce_6: 0.22683/0.31031, loss_mask_dice_6: 0.59750/1.05779, loss_spatial_bce_6: 0.04189/0.09773, loss_spatial_dice_6: 0.17352/0.19953, loss_spatial_ce_6: 0.04100/0.12087, loss_grounding_bce_6: 0.03169/0.08326, loss_grounding_dice_6: 0.02517/0.15513, loss_grounding_ce_6: 0.00275/0.28728, loss_mask_ce_7: 1.19908/0.88897, loss_mask_bce_7: 0.22756/0.31760, loss_mask_dice_7: 0.61738/1.10435, loss_spatial_bce_7: 0.05301/0.10792, loss_spatial_dice_7: 0.18753/0.22473, loss_spatial_ce_7: 0.04716/0.16007, loss_grounding_bce_7: 0.11658/0.08493, loss_grounding_dice_7: 0.10034/0.16087, loss_grounding_ce_7: 0.10497/0.32262, loss_mask_ce_8: 1.18929/1.02618, loss_mask_bce_8: 0.20861/0.33391, loss_mask_dice_8: 0.89799/1.18154, loss_spatial_bce_8: 0.05045/0.12650, loss_spatial_dice_8: 0.17717/0.26177, loss_spatial_ce_8: 0.11902/0.21254, loss_grounding_bce_8: 0.03405/0.08891, loss_grounding_dice_8: 0.03127/0.17047, loss_grounding_ce_8: 0.02394/0.42482, loss_mask_ce_9: 3.10106/3.48527, loss_mask_bce_9: 0.19733/0.36090, loss_mask_dice_9: 1.21836/1.76600, loss_spatial_bce_9: 0.28386/0.35584, loss_spatial_dice_9: 0.84425/0.79465, loss_spatial_ce_9: 1.40314/1.39739, loss_grounding_bce_9: 0.08181/0.10104, loss_grounding_dice_9: 0.06790/0.24349, loss_grounding_ce_9: 0.04031/0.68447] items per batch[64] items per second[0.36] total items[2720000] mini batches[ 42500] memory[4999] epoch remaining[0:39:52] INFO:trainer.default_trainer:epochs[ 23] optim steps[42600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.38371/0.76566, loss_mask_bce_0: 0.85308/0.30198, loss_mask_dice_0: 1.30821/1.02664, loss_spatial_bce_0: 0.11875/0.08671, loss_spatial_dice_0: 0.24831/0.18321, loss_spatial_ce_0: 0.02923/0.06169, loss_grounding_bce_0: 0.12675/0.08082, loss_grounding_dice_0: 0.06741/0.15116, loss_grounding_ce_0: 0.05079/0.24959, loss_mask_ce_1: 1.41135/0.76705, loss_mask_bce_1: 0.83298/0.30279, loss_mask_dice_1: 1.28664/1.03010, loss_spatial_bce_1: 0.11719/0.08697, loss_spatial_dice_1: 0.24426/0.18577, loss_spatial_ce_1: 0.03034/0.06585, loss_grounding_bce_1: 0.13432/0.08101, loss_grounding_dice_1: 0.06544/0.15191, loss_grounding_ce_1: 0.03576/0.25100, loss_mask_ce_2: 1.36283/0.77509, loss_mask_bce_2: 0.86182/0.30290, loss_mask_dice_2: 1.33231/1.03137, loss_spatial_bce_2: 0.12034/0.08685, loss_spatial_dice_2: 0.24200/0.18595, loss_spatial_ce_2: 0.03277/0.06837, loss_grounding_bce_2: 0.12612/0.08095, loss_grounding_dice_2: 0.06229/0.15166, loss_grounding_ce_2: 0.05220/0.25374, loss_mask_ce_3: 1.30444/0.77736, loss_mask_bce_3: 0.84291/0.30443, loss_mask_dice_3: 1.33139/1.02816, loss_spatial_bce_3: 0.12988/0.08879, loss_spatial_dice_3: 0.26191/0.18701, loss_spatial_ce_3: 0.05215/0.07294, loss_grounding_bce_3: 0.12307/0.08142, loss_grounding_dice_3: 0.06089/0.15129, loss_grounding_ce_3: 0.06308/0.25347, loss_mask_ce_4: 1.36132/0.78318, loss_mask_bce_4: 0.82824/0.30671, loss_mask_dice_4: 1.32037/1.04769, loss_spatial_bce_4: 0.12216/0.09072, loss_spatial_dice_4: 0.26879/0.19476, loss_spatial_ce_4: 0.12191/0.08578, loss_grounding_bce_4: 0.12869/0.08203, loss_grounding_dice_4: 0.06394/0.15386, loss_grounding_ce_4: 0.02973/0.25928, loss_mask_ce_5: 1.47543/0.80620, loss_mask_bce_5: 0.83259/0.30850, loss_mask_dice_5: 1.33235/1.05483, loss_spatial_bce_5: 0.13470/0.09267, loss_spatial_dice_5: 0.27570/0.19734, loss_spatial_ce_5: 0.17261/0.09792, loss_grounding_bce_5: 0.12752/0.08230, loss_grounding_dice_5: 0.06227/0.15454, loss_grounding_ce_5: 0.06850/0.27737, loss_mask_ce_6: 1.51708/0.83258, loss_mask_bce_6: 0.77605/0.31032, loss_mask_dice_6: 1.27142/1.05781, loss_spatial_bce_6: 0.14989/0.09768, loss_spatial_dice_6: 0.28787/0.19954, loss_spatial_ce_6: 0.19072/0.12082, loss_grounding_bce_6: 0.12308/0.08326, loss_grounding_dice_6: 0.06434/0.15514, loss_grounding_ce_6: 0.05701/0.28729, loss_mask_ce_7: 1.68335/0.88886, loss_mask_bce_7: 0.85559/0.31762, loss_mask_dice_7: 1.37392/1.10436, loss_spatial_bce_7: 0.19138/0.10787, loss_spatial_dice_7: 0.33423/0.22475, loss_spatial_ce_7: 0.23174/0.16003, loss_grounding_bce_7: 0.12334/0.08493, loss_grounding_dice_7: 0.06070/0.16089, loss_grounding_ce_7: 0.07544/0.32253, loss_mask_ce_8: 1.31950/1.02612, loss_mask_bce_8: 0.75527/0.33391, loss_mask_dice_8: 1.46920/1.18156, loss_spatial_bce_8: 0.15946/0.12643, loss_spatial_dice_8: 0.32157/0.26177, loss_spatial_ce_8: 0.25907/0.21245, loss_grounding_bce_8: 0.13541/0.08890, loss_grounding_dice_8: 0.05960/0.17049, loss_grounding_ce_8: 1.39902/0.42487, loss_mask_ce_9: 4.34464/3.48541, loss_mask_bce_9: 0.87627/0.36091, loss_mask_dice_9: 2.03547/1.76638, loss_spatial_bce_9: 0.37831/0.35574, loss_spatial_dice_9: 0.82364/0.79470, loss_spatial_ce_9: 1.13034/1.39747, loss_grounding_bce_9: 0.15196/0.10102, loss_grounding_dice_9: 0.08477/0.24349, loss_grounding_ce_9: 1.14161/0.68446] items per batch[64] items per second[0.36] total items[2726400] mini batches[ 42600] memory[4999] epoch remaining[0:36:54] INFO:trainer.default_trainer:epochs[ 23] optim steps[42700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.65276/0.76573, loss_mask_bce_0: 0.22744/0.30200, loss_mask_dice_0: 0.54449/1.02616, loss_spatial_bce_0: 0.05563/0.08674, loss_spatial_dice_0: 0.12540/0.18318, loss_spatial_ce_0: 0.13919/0.06167, loss_grounding_bce_0: 0.00878/0.08085, loss_grounding_dice_0: 0.03667/0.15110, loss_grounding_ce_0: 0.11723/0.24968, loss_mask_ce_1: 1.50521/0.76709, loss_mask_bce_1: 0.26356/0.30282, loss_mask_dice_1: 0.56346/1.02965, loss_spatial_bce_1: 0.05849/0.08699, loss_spatial_dice_1: 0.13783/0.18574, loss_spatial_ce_1: 0.14569/0.06584, loss_grounding_bce_1: 0.00840/0.08105, loss_grounding_dice_1: 0.03863/0.15185, loss_grounding_ce_1: 0.18735/0.25108, loss_mask_ce_2: 1.47033/0.77513, loss_mask_bce_2: 0.31263/0.30292, loss_mask_dice_2: 0.58881/1.03091, loss_spatial_bce_2: 0.06068/0.08688, loss_spatial_dice_2: 0.15297/0.18592, loss_spatial_ce_2: 0.13668/0.06834, loss_grounding_bce_2: 0.00833/0.08098, loss_grounding_dice_2: 0.03615/0.15160, loss_grounding_ce_2: 0.14434/0.25380, loss_mask_ce_3: 1.43741/0.77738, loss_mask_bce_3: 0.27134/0.30447, loss_mask_dice_3: 0.60053/1.02768, loss_spatial_bce_3: 0.06089/0.08882, loss_spatial_dice_3: 0.15148/0.18698, loss_spatial_ce_3: 0.09528/0.07291, loss_grounding_bce_3: 0.00615/0.08145, loss_grounding_dice_3: 0.02779/0.15123, loss_grounding_ce_3: 0.09051/0.25355, loss_mask_ce_4: 1.31207/0.78322, loss_mask_bce_4: 0.32554/0.30674, loss_mask_dice_4: 0.61309/1.04720, loss_spatial_bce_4: 0.05974/0.09075, loss_spatial_dice_4: 0.14470/0.19472, loss_spatial_ce_4: 0.11105/0.08576, loss_grounding_bce_4: 0.01052/0.08207, loss_grounding_dice_4: 0.04556/0.15381, loss_grounding_ce_4: 0.03302/0.25938, loss_mask_ce_5: 1.44842/0.80620, loss_mask_bce_5: 0.30968/0.30852, loss_mask_dice_5: 0.62608/1.05437, loss_spatial_bce_5: 0.07553/0.09270, loss_spatial_dice_5: 0.16412/0.19732, loss_spatial_ce_5: 0.12902/0.09789, loss_grounding_bce_5: 0.01059/0.08233, loss_grounding_dice_5: 0.05508/0.15448, loss_grounding_ce_5: 0.26075/0.27749, loss_mask_ce_6: 1.44798/0.83256, loss_mask_bce_6: 0.30498/0.31034, loss_mask_dice_6: 0.59719/1.05731, loss_spatial_bce_6: 0.06831/0.09772, loss_spatial_dice_6: 0.15341/0.19952, loss_spatial_ce_6: 0.10645/0.12078, loss_grounding_bce_6: 0.00741/0.08328, loss_grounding_dice_6: 0.03572/0.15507, loss_grounding_ce_6: 0.14216/0.28735, loss_mask_ce_7: 1.55021/0.88879, loss_mask_bce_7: 0.25462/0.31764, loss_mask_dice_7: 0.61464/1.10389, loss_spatial_bce_7: 0.13986/0.10792, loss_spatial_dice_7: 0.19125/0.22475, loss_spatial_ce_7: 0.30284/0.16005, loss_grounding_bce_7: 0.00485/0.08495, loss_grounding_dice_7: 0.02553/0.16084, loss_grounding_ce_7: 0.77898/0.32253, loss_mask_ce_8: 1.23498/1.02607, loss_mask_bce_8: 0.36908/0.33392, loss_mask_dice_8: 0.86482/1.18107, loss_spatial_bce_8: 0.16031/0.12647, loss_spatial_dice_8: 0.22671/0.26173, loss_spatial_ce_8: 0.23654/0.21246, loss_grounding_bce_8: 0.00513/0.08891, loss_grounding_dice_8: 0.02368/0.17042, loss_grounding_ce_8: 0.23721/0.42493, loss_mask_ce_9: 5.77269/3.48499, loss_mask_bce_9: 0.42697/0.36089, loss_mask_dice_9: 1.54865/1.76578, loss_spatial_bce_9: 0.27358/0.35581, loss_spatial_dice_9: 0.84312/0.79465, loss_spatial_ce_9: 1.11977/1.39746, loss_grounding_bce_9: 0.01091/0.10103, loss_grounding_dice_9: 0.06664/0.24344, loss_grounding_ce_9: 2.52355/0.68456] items per batch[64] items per second[0.36] total items[2732800] mini batches[ 42700] memory[4999] epoch remaining[0:33:57] INFO:trainer.default_trainer:epochs[ 23] optim steps[42800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.48596/0.76577, loss_mask_bce_0: 0.58383/0.30203, loss_mask_dice_0: 0.86288/1.02605, loss_spatial_bce_0: 0.18806/0.08674, loss_spatial_dice_0: 0.27948/0.18319, loss_spatial_ce_0: 0.00302/0.06163, loss_grounding_bce_0: 0.27006/0.08082, loss_grounding_dice_0: 0.11038/0.15108, loss_grounding_ce_0: 0.60575/0.24961, loss_mask_ce_1: 1.37869/0.76712, loss_mask_bce_1: 0.55231/0.30284, loss_mask_dice_1: 0.82722/1.02949, loss_spatial_bce_1: 0.20935/0.08700, loss_spatial_dice_1: 0.31140/0.18576, loss_spatial_ce_1: 0.00412/0.06580, loss_grounding_bce_1: 0.23727/0.08103, loss_grounding_dice_1: 0.10640/0.15184, loss_grounding_ce_1: 0.37734/0.25102, loss_mask_ce_2: 1.36613/0.77522, loss_mask_bce_2: 0.55508/0.30296, loss_mask_dice_2: 0.72461/1.03075, loss_spatial_bce_2: 0.21706/0.08689, loss_spatial_dice_2: 0.31415/0.18593, loss_spatial_ce_2: 0.00499/0.06828, loss_grounding_bce_2: 0.23644/0.08095, loss_grounding_dice_2: 0.10812/0.15160, loss_grounding_ce_2: 0.32067/0.25373, loss_mask_ce_3: 1.32485/0.77745, loss_mask_bce_3: 0.58500/0.30449, loss_mask_dice_3: 0.85280/1.02756, loss_spatial_bce_3: 0.21072/0.08883, loss_spatial_dice_3: 0.28755/0.18700, loss_spatial_ce_3: 0.01037/0.07286, loss_grounding_bce_3: 0.24605/0.08143, loss_grounding_dice_3: 0.11295/0.15123, loss_grounding_ce_3: 0.19918/0.25345, loss_mask_ce_4: 1.39712/0.78330, loss_mask_bce_4: 0.60453/0.30678, loss_mask_dice_4: 0.86582/1.04707, loss_spatial_bce_4: 0.22234/0.09077, loss_spatial_dice_4: 0.31593/0.19474, loss_spatial_ce_4: 0.01274/0.08571, loss_grounding_bce_4: 0.22963/0.08204, loss_grounding_dice_4: 0.10921/0.15381, loss_grounding_ce_4: 0.19999/0.25932, loss_mask_ce_5: 1.73406/0.80633, loss_mask_bce_5: 0.56258/0.30857, loss_mask_dice_5: 0.64952/1.05421, loss_spatial_bce_5: 0.20032/0.09271, loss_spatial_dice_5: 0.21994/0.19732, loss_spatial_ce_5: 0.03958/0.09786, loss_grounding_bce_5: 0.24557/0.08232, loss_grounding_dice_5: 0.11485/0.15447, loss_grounding_ce_5: 0.14375/0.27735, loss_mask_ce_6: 1.80203/0.83268, loss_mask_bce_6: 0.56724/0.31037, loss_mask_dice_6: 0.61547/1.05715, loss_spatial_bce_6: 0.21407/0.09773, loss_spatial_dice_6: 0.28450/0.19953, loss_spatial_ce_6: 0.05939/0.12071, loss_grounding_bce_6: 0.28564/0.08327, loss_grounding_dice_6: 0.11212/0.15508, loss_grounding_ce_6: 0.34809/0.28722, loss_mask_ce_7: 1.65900/0.88887, loss_mask_bce_7: 0.59198/0.31769, loss_mask_dice_7: 0.89505/1.10372, loss_spatial_bce_7: 0.24357/0.10793, loss_spatial_dice_7: 0.28336/0.22476, loss_spatial_ce_7: 0.10829/0.15997, loss_grounding_bce_7: 0.26192/0.08494, loss_grounding_dice_7: 0.10155/0.16084, loss_grounding_ce_7: 0.34514/0.32238, loss_mask_ce_8: 1.96162/1.02621, loss_mask_bce_8: 0.84733/0.33396, loss_mask_dice_8: 0.90132/1.18083, loss_spatial_bce_8: 0.35637/0.12646, loss_spatial_dice_8: 0.37773/0.26173, loss_spatial_ce_8: 0.24596/0.21240, loss_grounding_bce_8: 0.35020/0.08888, loss_grounding_dice_8: 0.18002/0.17040, loss_grounding_ce_8: 1.86231/0.42479, loss_mask_ce_9: 3.42226/3.48536, loss_mask_bce_9: 0.75335/0.36094, loss_mask_dice_9: 1.43936/1.76563, loss_spatial_bce_9: 0.45402/0.35575, loss_spatial_dice_9: 0.75229/0.79464, loss_spatial_ce_9: 1.74322/1.39745, loss_grounding_bce_9: 0.28243/0.10101, loss_grounding_dice_9: 0.11592/0.24341, loss_grounding_ce_9: 0.72144/0.68446] items per batch[64] items per second[0.37] total items[2739200] mini batches[ 42800] memory[4999] epoch remaining[0:30:54] INFO:trainer.default_trainer:epochs[ 23] optim steps[42900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.62142/0.76571, loss_mask_bce_0: 0.44015/0.30209, loss_mask_dice_0: 0.45942/1.02590, loss_spatial_bce_0: 0.09834/0.08675, loss_spatial_dice_0: 0.10433/0.18317, loss_spatial_ce_0: 0.00039/0.06162, loss_grounding_bce_0: 0.24079/0.08081, loss_grounding_dice_0: 0.15371/0.15107, loss_grounding_ce_0: 0.16424/0.24977, loss_mask_ce_1: 0.65816/0.76700, loss_mask_bce_1: 0.45483/0.30291, loss_mask_dice_1: 0.46686/1.02935, loss_spatial_bce_1: 0.10544/0.08701, loss_spatial_dice_1: 0.11614/0.18575, loss_spatial_ce_1: 0.00036/0.06579, loss_grounding_bce_1: 0.23378/0.08102, loss_grounding_dice_1: 0.15465/0.15184, loss_grounding_ce_1: 0.21922/0.25114, loss_mask_ce_2: 0.63577/0.77512, loss_mask_bce_2: 0.43762/0.30303, loss_mask_dice_2: 0.46705/1.03064, loss_spatial_bce_2: 0.10870/0.08690, loss_spatial_dice_2: 0.11329/0.18593, loss_spatial_ce_2: 0.00045/0.06827, loss_grounding_bce_2: 0.22623/0.08095, loss_grounding_dice_2: 0.15600/0.15159, loss_grounding_ce_2: 0.16493/0.25386, loss_mask_ce_3: 0.64187/0.77732, loss_mask_bce_3: 0.44670/0.30457, loss_mask_dice_3: 0.44049/1.02746, loss_spatial_bce_3: 0.10902/0.08884, loss_spatial_dice_3: 0.10860/0.18699, loss_spatial_ce_3: 0.00054/0.07284, loss_grounding_bce_3: 0.22895/0.08142, loss_grounding_dice_3: 0.15036/0.15120, loss_grounding_ce_3: 0.21935/0.25358, loss_mask_ce_4: 0.64509/0.78324, loss_mask_bce_4: 0.43157/0.30684, loss_mask_dice_4: 0.45402/1.04693, loss_spatial_bce_4: 0.10995/0.09078, loss_spatial_dice_4: 0.11956/0.19472, loss_spatial_ce_4: 0.01153/0.08568, loss_grounding_bce_4: 0.43078/0.08204, loss_grounding_dice_4: 0.25031/0.15380, loss_grounding_ce_4: 0.02270/0.25951, loss_mask_ce_5: 0.57012/0.80627, loss_mask_bce_5: 0.61371/0.30864, loss_mask_dice_5: 0.55965/1.05410, loss_spatial_bce_5: 0.13002/0.09273, loss_spatial_dice_5: 0.13647/0.19732, loss_spatial_ce_5: 0.00494/0.09784, loss_grounding_bce_5: 0.38234/0.08233, loss_grounding_dice_5: 0.23908/0.15449, loss_grounding_ce_5: 0.01984/0.27749, loss_mask_ce_6: 0.65469/0.83261, loss_mask_bce_6: 0.67897/0.31046, loss_mask_dice_6: 0.62372/1.05700, loss_spatial_bce_6: 0.16694/0.09775, loss_spatial_dice_6: 0.15340/0.19954, loss_spatial_ce_6: 0.00923/0.12066, loss_grounding_bce_6: 0.47340/0.08329, loss_grounding_dice_6: 0.25671/0.15510, loss_grounding_ce_6: 0.01971/0.28737, loss_mask_ce_7: 0.72183/0.88876, loss_mask_bce_7: 0.54804/0.31778, loss_mask_dice_7: 0.55963/1.10358, loss_spatial_bce_7: 0.14643/0.10795, loss_spatial_dice_7: 0.17356/0.22477, loss_spatial_ce_7: 0.09106/0.15994, loss_grounding_bce_7: 0.36180/0.08497, loss_grounding_dice_7: 0.22719/0.16084, loss_grounding_ce_7: 0.01836/0.32245, loss_mask_ce_8: 0.79384/1.02611, loss_mask_bce_8: 0.56205/0.33403, loss_mask_dice_8: 0.59711/1.18071, loss_spatial_bce_8: 0.14969/0.12647, loss_spatial_dice_8: 0.19211/0.26171, loss_spatial_ce_8: 0.10629/0.21234, loss_grounding_bce_8: 0.32999/0.08890, loss_grounding_dice_8: 0.21891/0.17041, loss_grounding_ce_8: 0.02090/0.42483, loss_mask_ce_9: 2.30043/3.48534, loss_mask_bce_9: 0.65784/0.36109, loss_mask_dice_9: 1.08666/1.76555, loss_spatial_bce_9: 0.44509/0.35577, loss_spatial_dice_9: 0.80413/0.79464, loss_spatial_ce_9: 1.13264/1.39733, loss_grounding_bce_9: 0.35829/0.10105, loss_grounding_dice_9: 0.24821/0.24344, loss_grounding_ce_9: 0.04610/0.68465] items per batch[64] items per second[0.37] total items[2745600] mini batches[ 42900] memory[4999] epoch remaining[0:27:54] INFO:trainer.default_trainer:epochs[ 23] optim steps[43000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13070/0.76549, loss_mask_bce_0: 0.19063/0.30202, loss_mask_dice_0: 0.31134/1.02594, loss_spatial_bce_0: 0.05055/0.08673, loss_spatial_dice_0: 0.08086/0.18312, loss_spatial_ce_0: 0.00005/0.06163, loss_grounding_bce_0: 0.07816/0.08083, loss_grounding_dice_0: 0.11442/0.15105, loss_grounding_ce_0: 0.01490/0.24967, loss_mask_ce_1: 0.13701/0.76674, loss_mask_bce_1: 0.19043/0.30284, loss_mask_dice_1: 0.31074/1.02940, loss_spatial_bce_1: 0.05111/0.08699, loss_spatial_dice_1: 0.07863/0.18570, loss_spatial_ce_1: 0.00005/0.06576, loss_grounding_bce_1: 0.07705/0.08104, loss_grounding_dice_1: 0.11685/0.15182, loss_grounding_ce_1: 0.01414/0.25105, loss_mask_ce_2: 0.13551/0.77486, loss_mask_bce_2: 0.19594/0.30296, loss_mask_dice_2: 0.31930/1.03067, loss_spatial_bce_2: 0.05207/0.08688, loss_spatial_dice_2: 0.08350/0.18588, loss_spatial_ce_2: 0.00005/0.06824, loss_grounding_bce_2: 0.07637/0.08097, loss_grounding_dice_2: 0.11473/0.15157, loss_grounding_ce_2: 0.01187/0.25383, loss_mask_ce_3: 0.13575/0.77708, loss_mask_bce_3: 0.19030/0.30449, loss_mask_dice_3: 0.31209/1.02743, loss_spatial_bce_3: 0.05601/0.08883, loss_spatial_dice_3: 0.08586/0.18695, loss_spatial_ce_3: 0.00027/0.07281, loss_grounding_bce_3: 0.07435/0.08145, loss_grounding_dice_3: 0.11008/0.15117, loss_grounding_ce_3: 0.00856/0.25354, loss_mask_ce_4: 0.12436/0.78302, loss_mask_bce_4: 0.18971/0.30676, loss_mask_dice_4: 0.32092/1.04694, loss_spatial_bce_4: 0.05115/0.09077, loss_spatial_dice_4: 0.09317/0.19469, loss_spatial_ce_4: 0.00089/0.08567, loss_grounding_bce_4: 0.07698/0.08207, loss_grounding_dice_4: 0.11737/0.15378, loss_grounding_ce_4: 0.00841/0.25943, loss_mask_ce_5: 0.12665/0.80605, loss_mask_bce_5: 0.18367/0.30857, loss_mask_dice_5: 0.33186/1.05414, loss_spatial_bce_5: 0.05323/0.09272, loss_spatial_dice_5: 0.10165/0.19729, loss_spatial_ce_5: 0.02598/0.09780, loss_grounding_bce_5: 0.07314/0.08235, loss_grounding_dice_5: 0.11457/0.15446, loss_grounding_ce_5: 0.01386/0.27737, loss_mask_ce_6: 0.12867/0.83240, loss_mask_bce_6: 0.17839/0.31038, loss_mask_dice_6: 0.30977/1.05701, loss_spatial_bce_6: 0.05813/0.09774, loss_spatial_dice_6: 0.08990/0.19951, loss_spatial_ce_6: 0.06814/0.12066, loss_grounding_bce_6: 0.07574/0.08332, loss_grounding_dice_6: 0.11209/0.15506, loss_grounding_ce_6: 0.00523/0.28721, loss_mask_ce_7: 0.16597/0.88857, loss_mask_bce_7: 0.18258/0.31774, loss_mask_dice_7: 0.30625/1.10361, loss_spatial_bce_7: 0.07225/0.10795, loss_spatial_dice_7: 0.11155/0.22475, loss_spatial_ce_7: 0.02475/0.15991, loss_grounding_bce_7: 0.07667/0.08501, loss_grounding_dice_7: 0.11205/0.16082, loss_grounding_ce_7: 0.00890/0.32229, loss_mask_ce_8: 0.29290/1.02593, loss_mask_bce_8: 0.18758/0.33398, loss_mask_dice_8: 0.31939/1.18088, loss_spatial_bce_8: 0.08666/0.12646, loss_spatial_dice_8: 0.15976/0.26165, loss_spatial_ce_8: 0.09099/0.21226, loss_grounding_bce_8: 0.08278/0.08894, loss_grounding_dice_8: 0.12401/0.17039, loss_grounding_ce_8: 0.02869/0.42477, loss_mask_ce_9: 3.36672/3.48521, loss_mask_bce_9: 0.31637/0.36105, loss_mask_dice_9: 0.60738/1.76582, loss_spatial_bce_9: 0.41759/0.35578, loss_spatial_dice_9: 0.79358/0.79460, loss_spatial_ce_9: 1.76581/1.39727, loss_grounding_bce_9: 0.12768/0.10106, loss_grounding_dice_9: 0.21433/0.24341, loss_grounding_ce_9: 0.18313/0.68460] items per batch[64] items per second[0.36] total items[2752000] mini batches[ 43000] memory[4999] epoch remaining[0:24:58] INFO:trainer.default_trainer:epochs[ 23] optim steps[43100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.03956/0.76535, loss_mask_bce_0: 0.06389/0.30198, loss_mask_dice_0: 2.49889/1.02618, loss_spatial_bce_0: 0.00330/0.08670, loss_spatial_dice_0: 0.32269/0.18311, loss_spatial_ce_0: 0.04279/0.06162, loss_grounding_bce_0: 0.00242/0.08082, loss_grounding_dice_0: 0.25691/0.15109, loss_grounding_ce_0: 0.65216/0.24970, loss_mask_ce_1: 1.27256/0.76659, loss_mask_bce_1: 0.05049/0.30280, loss_mask_dice_1: 2.67563/1.02970, loss_spatial_bce_1: 0.00380/0.08695, loss_spatial_dice_1: 0.31894/0.18570, loss_spatial_ce_1: 0.02907/0.06573, loss_grounding_bce_1: 0.00592/0.08103, loss_grounding_dice_1: 0.80000/0.15187, loss_grounding_ce_1: 0.15954/0.25109, loss_mask_ce_2: 0.84319/0.77472, loss_mask_bce_2: 0.05965/0.30292, loss_mask_dice_2: 2.15771/1.03102, loss_spatial_bce_2: 0.00386/0.08685, loss_spatial_dice_2: 0.21072/0.18587, loss_spatial_ce_2: 0.03221/0.06820, loss_grounding_bce_2: 0.00333/0.08095, loss_grounding_dice_2: 0.71429/0.15160, loss_grounding_ce_2: 0.24464/0.25382, loss_mask_ce_3: 0.93000/0.77696, loss_mask_bce_3: 0.05543/0.30445, loss_mask_dice_3: 2.05430/1.02766, loss_spatial_bce_3: 0.00443/0.08880, loss_spatial_dice_3: 0.33266/0.18695, loss_spatial_ce_3: 0.00849/0.07278, loss_grounding_bce_3: 0.00330/0.08143, loss_grounding_dice_3: 0.69232/0.15123, loss_grounding_ce_3: 0.23578/0.25356, loss_mask_ce_4: 0.97312/0.78289, loss_mask_bce_4: 0.06721/0.30673, loss_mask_dice_4: 3.24156/1.04719, loss_spatial_bce_4: 0.00306/0.09074, loss_spatial_dice_4: 0.25513/0.19469, loss_spatial_ce_4: 0.00235/0.08558, loss_grounding_bce_4: 0.00262/0.08205, loss_grounding_dice_4: 0.58443/0.15381, loss_grounding_ce_4: 0.79086/0.25956, loss_mask_ce_5: 0.93084/0.80592, loss_mask_bce_5: 0.06070/0.30854, loss_mask_dice_5: 2.40702/1.05448, loss_spatial_bce_5: 0.00509/0.09269, loss_spatial_dice_5: 0.29177/0.19729, loss_spatial_ce_5: 0.01915/0.09777, loss_grounding_bce_5: 0.00142/0.08233, loss_grounding_dice_5: 0.50001/0.15450, loss_grounding_ce_5: 0.19400/0.27732, loss_mask_ce_6: 1.46572/0.83231, loss_mask_bce_6: 0.04637/0.31035, loss_mask_dice_6: 2.37579/1.05726, loss_spatial_bce_6: 0.00391/0.09771, loss_spatial_dice_6: 0.28839/0.19952, loss_spatial_ce_6: 0.03914/0.12064, loss_grounding_bce_6: 0.00019/0.08330, loss_grounding_dice_6: 0.52281/0.15511, loss_grounding_ce_6: 0.75210/0.28729, loss_mask_ce_7: 1.30229/0.88848, loss_mask_bce_7: 0.05718/0.31770, loss_mask_dice_7: 2.26409/1.10388, loss_spatial_bce_7: 0.00460/0.10792, loss_spatial_dice_7: 0.39905/0.22476, loss_spatial_ce_7: 0.19814/0.15985, loss_grounding_bce_7: 0.00080/0.08498, loss_grounding_dice_7: 0.66635/0.16086, loss_grounding_ce_7: 1.35092/0.32231, loss_mask_ce_8: 1.27994/1.02590, loss_mask_bce_8: 0.07087/0.33394, loss_mask_dice_8: 2.31615/1.18114, loss_spatial_bce_8: 0.00549/0.12643, loss_spatial_dice_8: 0.42552/0.26166, loss_spatial_ce_8: 0.16747/0.21219, loss_grounding_bce_8: 0.00651/0.08892, loss_grounding_dice_8: 0.75000/0.17042, loss_grounding_ce_8: 0.16748/0.42477, loss_mask_ce_9: 4.32209/3.48493, loss_mask_bce_9: 0.04272/0.36099, loss_mask_dice_9: 2.64246/1.76599, loss_spatial_bce_9: 0.01617/0.35576, loss_spatial_dice_9: 0.89573/0.79460, loss_spatial_ce_9: 3.39110/1.39724, loss_grounding_bce_9: 0.00485/0.10105, loss_grounding_dice_9: 0.85207/0.24343, loss_grounding_ce_9: 0.41918/0.68441] items per batch[64] items per second[0.36] total items[2758400] mini batches[ 43100] memory[4999] epoch remaining[0:22:01] INFO:trainer.default_trainer:epochs[ 23] optim steps[43200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.89365/0.76533, loss_mask_bce_0: 0.19772/0.30193, loss_mask_dice_0: 3.50084/1.02603, loss_spatial_bce_0: 0.01039/0.08666, loss_spatial_dice_0: 0.23456/0.18307, loss_spatial_ce_0: 0.01210/0.06160, loss_grounding_bce_0: 0.03626/0.08079, loss_grounding_dice_0: 0.17929/0.15108, loss_grounding_ce_0: 0.34324/0.24951, loss_mask_ce_1: 1.77809/0.76654, loss_mask_bce_1: 0.19069/0.30275, loss_mask_dice_1: 3.17262/1.02957, loss_spatial_bce_1: 0.01391/0.08692, loss_spatial_dice_1: 0.26527/0.18566, loss_spatial_ce_1: 0.01732/0.06570, loss_grounding_bce_1: 0.03729/0.08101, loss_grounding_dice_1: 0.37238/0.15186, loss_grounding_ce_1: 0.33970/0.25091, loss_mask_ce_2: 1.50723/0.77468, loss_mask_bce_2: 0.22361/0.30287, loss_mask_dice_2: 3.74631/1.03090, loss_spatial_bce_2: 0.01459/0.08682, loss_spatial_dice_2: 0.29444/0.18584, loss_spatial_ce_2: 0.00518/0.06818, loss_grounding_bce_2: 0.02992/0.08093, loss_grounding_dice_2: 0.17747/0.15159, loss_grounding_ce_2: 0.31677/0.25363, loss_mask_ce_3: 1.84691/0.77693, loss_mask_bce_3: 0.21323/0.30440, loss_mask_dice_3: 3.07499/1.02756, loss_spatial_bce_3: 0.01359/0.08877, loss_spatial_dice_3: 0.25630/0.18692, loss_spatial_ce_3: 0.02821/0.07276, loss_grounding_bce_3: 0.02811/0.08141, loss_grounding_dice_3: 0.17078/0.15121, loss_grounding_ce_3: 0.29539/0.25337, loss_mask_ce_4: 1.62721/0.78291, loss_mask_bce_4: 0.18997/0.30668, loss_mask_dice_4: 3.46422/1.04707, loss_spatial_bce_4: 0.01315/0.09072, loss_spatial_dice_4: 0.25029/0.19466, loss_spatial_ce_4: 0.10884/0.08557, loss_grounding_bce_4: 0.02962/0.08203, loss_grounding_dice_4: 0.16200/0.15381, loss_grounding_ce_4: 0.26664/0.25935, loss_mask_ce_5: 1.49958/0.80596, loss_mask_bce_5: 0.20363/0.30849, loss_mask_dice_5: 4.05186/1.05440, loss_spatial_bce_5: 0.01229/0.09267, loss_spatial_dice_5: 0.25223/0.19726, loss_spatial_ce_5: 0.09056/0.09773, loss_grounding_bce_5: 0.03300/0.08231, loss_grounding_dice_5: 0.36239/0.15450, loss_grounding_ce_5: 0.31273/0.27723, loss_mask_ce_6: 1.49120/0.83231, loss_mask_bce_6: 0.19016/0.31029, loss_mask_dice_6: 3.78230/1.05718, loss_spatial_bce_6: 0.02415/0.09768, loss_spatial_dice_6: 0.29292/0.19948, loss_spatial_ce_6: 0.06542/0.12063, loss_grounding_bce_6: 0.03159/0.08328, loss_grounding_dice_6: 0.46340/0.15510, loss_grounding_ce_6: 0.20123/0.28717, loss_mask_ce_7: 1.67623/0.88841, loss_mask_bce_7: 0.21860/0.31764, loss_mask_dice_7: 4.21880/1.10382, loss_spatial_bce_7: 0.01310/0.10789, loss_spatial_dice_7: 0.28339/0.22471, loss_spatial_ce_7: 0.07380/0.15985, loss_grounding_bce_7: 0.02943/0.08496, loss_grounding_dice_7: 0.18147/0.16086, loss_grounding_ce_7: 0.27956/0.32200, loss_mask_ce_8: 2.50037/1.02583, loss_mask_bce_8: 0.20472/0.33389, loss_mask_dice_8: 4.72769/1.18106, loss_spatial_bce_8: 0.02629/0.12640, loss_spatial_dice_8: 0.37395/0.26161, loss_spatial_ce_8: 0.17170/0.21211, loss_grounding_bce_8: 0.03032/0.08889, loss_grounding_dice_8: 0.18849/0.17043, loss_grounding_ce_8: 0.56175/0.42447, loss_mask_ce_9: 6.09644/3.48478, loss_mask_bce_9: 0.27019/0.36095, loss_mask_dice_9: 5.27378/1.76599, loss_spatial_bce_9: 0.07637/0.35571, loss_spatial_dice_9: 0.95279/0.79458, loss_spatial_ce_9: 1.20232/1.39711, loss_grounding_bce_9: 0.04393/0.10103, loss_grounding_dice_9: 0.49425/0.24344, loss_grounding_ce_9: 0.61871/0.68409] items per batch[64] items per second[0.37] total items[2764800] mini batches[ 43200] memory[4999] epoch remaining[0:19:02] INFO:trainer.default_trainer:epochs[ 23] optim steps[43300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07079/0.76541, loss_mask_bce_0: 0.25141/0.30195, loss_mask_dice_0: 0.15754/1.02589, loss_spatial_bce_0: 0.13807/0.08667, loss_spatial_dice_0: 0.08745/0.18305, loss_spatial_ce_0: 0.00226/0.06155, loss_grounding_bce_0: 0.08495/0.08083, loss_grounding_dice_0: 0.07309/0.15108, loss_grounding_ce_0: 0.00955/0.24954, loss_mask_ce_1: 0.08582/0.76654, loss_mask_bce_1: 0.25664/0.30278, loss_mask_dice_1: 0.15965/1.02947, loss_spatial_bce_1: 0.14138/0.08693, loss_spatial_dice_1: 0.08859/0.18564, loss_spatial_ce_1: 0.00206/0.06566, loss_grounding_bce_1: 0.08822/0.08104, loss_grounding_dice_1: 0.07488/0.15184, loss_grounding_ce_1: 0.01879/0.25096, loss_mask_ce_2: 0.08031/0.77468, loss_mask_bce_2: 0.25040/0.30289, loss_mask_dice_2: 0.15569/1.03077, loss_spatial_bce_2: 0.14107/0.08683, loss_spatial_dice_2: 0.08923/0.18582, loss_spatial_ce_2: 0.00136/0.06813, loss_grounding_bce_2: 0.08811/0.08096, loss_grounding_dice_2: 0.07908/0.15158, loss_grounding_ce_2: 0.01256/0.25366, loss_mask_ce_3: 0.08950/0.77689, loss_mask_bce_3: 0.24097/0.30443, loss_mask_dice_3: 0.15256/1.02746, loss_spatial_bce_3: 0.13570/0.08878, loss_spatial_dice_3: 0.08598/0.18691, loss_spatial_ce_3: 0.00125/0.07273, loss_grounding_bce_3: 0.08195/0.08144, loss_grounding_dice_3: 0.06740/0.15121, loss_grounding_ce_3: 0.03196/0.25339, loss_mask_ce_4: 0.07101/0.78288, loss_mask_bce_4: 0.24217/0.30671, loss_mask_dice_4: 0.15179/1.04691, loss_spatial_bce_4: 0.13103/0.09072, loss_spatial_dice_4: 0.08484/0.19464, loss_spatial_ce_4: 0.00230/0.08551, loss_grounding_bce_4: 0.08511/0.08206, loss_grounding_dice_4: 0.07817/0.15380, loss_grounding_ce_4: 0.00989/0.25942, loss_mask_ce_5: 0.07793/0.80596, loss_mask_bce_5: 0.24973/0.30851, loss_mask_dice_5: 0.15837/1.05424, loss_spatial_bce_5: 0.14794/0.09268, loss_spatial_dice_5: 0.14501/0.19725, loss_spatial_ce_5: 0.00500/0.09765, loss_grounding_bce_5: 0.08646/0.08234, loss_grounding_dice_5: 0.07550/0.15448, loss_grounding_ce_5: 0.00444/0.27735, loss_mask_ce_6: 0.07680/0.83232, loss_mask_bce_6: 0.24020/0.31033, loss_mask_dice_6: 0.14755/1.05702, loss_spatial_bce_6: 0.20686/0.09771, loss_spatial_dice_6: 0.29200/0.19949, loss_spatial_ce_6: 0.00283/0.12057, loss_grounding_bce_6: 0.09049/0.08332, loss_grounding_dice_6: 0.07806/0.15508, loss_grounding_ce_6: 0.01749/0.28717, loss_mask_ce_7: 0.08935/0.88845, loss_mask_bce_7: 0.25796/0.31768, loss_mask_dice_7: 0.16237/1.10369, loss_spatial_bce_7: 0.22937/0.10792, loss_spatial_dice_7: 0.31892/0.22472, loss_spatial_ce_7: 0.04497/0.15977, loss_grounding_bce_7: 0.08980/0.08499, loss_grounding_dice_7: 0.07807/0.16082, loss_grounding_ce_7: 0.01562/0.32218, loss_mask_ce_8: 0.16707/1.02589, loss_mask_bce_8: 0.25957/0.33390, loss_mask_dice_8: 0.15989/1.18096, loss_spatial_bce_8: 0.28123/0.12639, loss_spatial_dice_8: 0.32222/0.26160, loss_spatial_ce_8: 0.01669/0.21204, loss_grounding_bce_8: 0.09296/0.08895, loss_grounding_dice_8: 0.08101/0.17041, loss_grounding_ce_8: 0.04465/0.42460, loss_mask_ce_9: 2.66956/3.48508, loss_mask_bce_9: 0.24785/0.36096, loss_mask_dice_9: 0.22531/1.76598, loss_spatial_bce_9: 0.55853/0.35571, loss_spatial_dice_9: 0.63687/0.79457, loss_spatial_ce_9: 0.93908/1.39700, loss_grounding_bce_9: 0.10067/0.10105, loss_grounding_dice_9: 0.08451/0.24341, loss_grounding_ce_9: 0.44617/0.68408] items per batch[64] items per second[0.37] total items[2771200] mini batches[ 43300] memory[4999] epoch remaining[0:16:05] INFO:trainer.default_trainer:epochs[ 23] optim steps[43400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27934/0.76517, loss_mask_bce_0: 0.12422/0.30192, loss_mask_dice_0: 0.06338/1.02629, loss_spatial_bce_0: 0.12478/0.08666, loss_spatial_dice_0: 0.06140/0.18302, loss_spatial_ce_0: 0.00185/0.06152, loss_grounding_bce_0: 0.12143/0.08083, loss_grounding_dice_0: 0.05919/0.15105, loss_grounding_ce_0: 0.02373/0.24943, loss_mask_ce_1: 0.24562/0.76638, loss_mask_bce_1: 0.12242/0.30274, loss_mask_dice_1: 0.06048/1.02979, loss_spatial_bce_1: 0.12606/0.08691, loss_spatial_dice_1: 0.06297/0.18562, loss_spatial_ce_1: 0.00206/0.06563, loss_grounding_bce_1: 0.12711/0.08104, loss_grounding_dice_1: 0.06091/0.15181, loss_grounding_ce_1: 0.02277/0.25087, loss_mask_ce_2: 0.23352/0.77450, loss_mask_bce_2: 0.12430/0.30285, loss_mask_dice_2: 0.06307/1.03115, loss_spatial_bce_2: 0.12770/0.08681, loss_spatial_dice_2: 0.06201/0.18581, loss_spatial_ce_2: 0.00271/0.06809, loss_grounding_bce_2: 0.11594/0.08096, loss_grounding_dice_2: 0.05795/0.15155, loss_grounding_ce_2: 0.02397/0.25358, loss_mask_ce_3: 0.26922/0.77667, loss_mask_bce_3: 0.13175/0.30439, loss_mask_dice_3: 0.05942/1.02789, loss_spatial_bce_3: 0.12209/0.08877, loss_spatial_dice_3: 0.06495/0.18688, loss_spatial_ce_3: 0.00907/0.07268, loss_grounding_bce_3: 0.13084/0.08144, loss_grounding_dice_3: 0.05960/0.15118, loss_grounding_ce_3: 0.01660/0.25330, loss_mask_ce_4: 0.22950/0.78271, loss_mask_bce_4: 0.12289/0.30668, loss_mask_dice_4: 0.06305/1.04731, loss_spatial_bce_4: 0.13452/0.09071, loss_spatial_dice_4: 0.07694/0.19461, loss_spatial_ce_4: 0.03899/0.08545, loss_grounding_bce_4: 0.11843/0.08206, loss_grounding_dice_4: 0.06116/0.15376, loss_grounding_ce_4: 0.01139/0.25932, loss_mask_ce_5: 0.29392/0.80579, loss_mask_bce_5: 0.12498/0.30848, loss_mask_dice_5: 0.06207/1.05472, loss_spatial_bce_5: 0.13771/0.09266, loss_spatial_dice_5: 0.07693/0.19723, loss_spatial_ce_5: 0.01740/0.09758, loss_grounding_bce_5: 0.12462/0.08233, loss_grounding_dice_5: 0.06279/0.15446, loss_grounding_ce_5: 0.02009/0.27726, loss_mask_ce_6: 0.48307/0.83215, loss_mask_bce_6: 0.12696/0.31028, loss_mask_dice_6: 0.05961/1.05739, loss_spatial_bce_6: 0.13726/0.09769, loss_spatial_dice_6: 0.07362/0.19948, loss_spatial_ce_6: 0.05160/0.12051, loss_grounding_bce_6: 0.12610/0.08331, loss_grounding_dice_6: 0.05926/0.15505, loss_grounding_ce_6: 0.03015/0.28707, loss_mask_ce_7: 0.30248/0.88823, loss_mask_bce_7: 0.12298/0.31764, loss_mask_dice_7: 0.06356/1.10410, loss_spatial_bce_7: 0.15294/0.10790, loss_spatial_dice_7: 0.08339/0.22470, loss_spatial_ce_7: 0.06399/0.15973, loss_grounding_bce_7: 0.12437/0.08498, loss_grounding_dice_7: 0.06223/0.16078, loss_grounding_ce_7: 0.04131/0.32201, loss_mask_ce_8: 0.34706/1.02583, loss_mask_bce_8: 0.12870/0.33384, loss_mask_dice_8: 0.07061/1.18138, loss_spatial_bce_8: 0.15540/0.12635, loss_spatial_dice_8: 0.07688/0.26157, loss_spatial_ce_8: 0.11211/0.21193, loss_grounding_bce_8: 0.13021/0.08894, loss_grounding_dice_8: 0.07314/0.17038, loss_grounding_ce_8: 0.04455/0.42441, loss_mask_ce_9: 2.66414/3.48469, loss_mask_bce_9: 0.13484/0.36090, loss_mask_dice_9: 0.09791/1.76646, loss_spatial_bce_9: 0.72048/0.35569, loss_spatial_dice_9: 0.59293/0.79453, loss_spatial_ce_9: 0.57670/1.39680, loss_grounding_bce_9: 0.13262/0.10103, loss_grounding_dice_9: 0.09504/0.24337, loss_grounding_ce_9: 0.77683/0.68393] items per batch[64] items per second[0.36] total items[2777600] mini batches[ 43400] memory[4999] epoch remaining[0:13:09] INFO:trainer.default_trainer:epochs[ 23] optim steps[43500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00103/0.76538, loss_mask_bce_0: 0.05467/0.30196, loss_mask_dice_0: 0.02704/1.02677, loss_spatial_bce_0: 0.04705/0.08664, loss_spatial_dice_0: 0.02846/0.18304, loss_spatial_ce_0: 0.00012/0.06147, loss_grounding_bce_0: 0.06019/0.08085, loss_grounding_dice_0: 0.03072/0.15110, loss_grounding_ce_0: 0.00033/0.24949, loss_mask_ce_1: 0.00095/0.76661, loss_mask_bce_1: 0.05185/0.30278, loss_mask_dice_1: 0.02675/1.03025, loss_spatial_bce_1: 0.05130/0.08689, loss_spatial_dice_1: 0.03115/0.18564, loss_spatial_ce_1: 0.00004/0.06559, loss_grounding_bce_1: 0.06551/0.08106, loss_grounding_dice_1: 0.03366/0.15185, loss_grounding_ce_1: 0.00041/0.25092, loss_mask_ce_2: 0.00106/0.77468, loss_mask_bce_2: 0.05300/0.30288, loss_mask_dice_2: 0.02576/1.03157, loss_spatial_bce_2: 0.04867/0.08680, loss_spatial_dice_2: 0.02759/0.18583, loss_spatial_ce_2: 0.00001/0.06806, loss_grounding_bce_2: 0.05946/0.08099, loss_grounding_dice_2: 0.03021/0.15159, loss_grounding_ce_2: 0.00049/0.25364, loss_mask_ce_3: 0.00132/0.77691, loss_mask_bce_3: 0.04769/0.30442, loss_mask_dice_3: 0.02485/1.02830, loss_spatial_bce_3: 0.04904/0.08875, loss_spatial_dice_3: 0.02861/0.18691, loss_spatial_ce_3: 0.00006/0.07263, loss_grounding_bce_3: 0.06346/0.08146, loss_grounding_dice_3: 0.03195/0.15122, loss_grounding_ce_3: 0.00054/0.25335, loss_mask_ce_4: 0.00232/0.78289, loss_mask_bce_4: 0.04619/0.30674, loss_mask_dice_4: 0.02478/1.04783, loss_spatial_bce_4: 0.05609/0.09070, loss_spatial_dice_4: 0.02987/0.19464, loss_spatial_ce_4: 0.00006/0.08541, loss_grounding_bce_4: 0.05946/0.08207, loss_grounding_dice_4: 0.03044/0.15380, loss_grounding_ce_4: 0.00184/0.25938, loss_mask_ce_5: 0.00751/0.80607, loss_mask_bce_5: 0.04470/0.30853, loss_mask_dice_5: 0.02404/1.05523, loss_spatial_bce_5: 0.05419/0.09265, loss_spatial_dice_5: 0.02906/0.19725, loss_spatial_ce_5: 0.00016/0.09756, loss_grounding_bce_5: 0.05721/0.08235, loss_grounding_dice_5: 0.03055/0.15451, loss_grounding_ce_5: 0.00396/0.27738, loss_mask_ce_6: 0.00727/0.83239, loss_mask_bce_6: 0.04580/0.31035, loss_mask_dice_6: 0.02397/1.05790, loss_spatial_bce_6: 0.05010/0.09768, loss_spatial_dice_6: 0.02800/0.19950, loss_spatial_ce_6: 0.00030/0.12046, loss_grounding_bce_6: 0.05708/0.08333, loss_grounding_dice_6: 0.02836/0.15509, loss_grounding_ce_6: 0.02237/0.28720, loss_mask_ce_7: 0.01632/0.88851, loss_mask_bce_7: 0.04633/0.31769, loss_mask_dice_7: 0.02533/1.10459, loss_spatial_bce_7: 0.05524/0.10790, loss_spatial_dice_7: 0.03136/0.22473, loss_spatial_ce_7: 0.00202/0.15969, loss_grounding_bce_7: 0.05473/0.08501, loss_grounding_dice_7: 0.02916/0.16083, loss_grounding_ce_7: 0.01622/0.32209, loss_mask_ce_8: 0.03128/1.02623, loss_mask_bce_8: 0.04598/0.33393, loss_mask_dice_8: 0.02659/1.18197, loss_spatial_bce_8: 0.06977/0.12632, loss_spatial_dice_8: 0.03378/0.26161, loss_spatial_ce_8: 0.01720/0.21189, loss_grounding_bce_8: 0.05660/0.08896, loss_grounding_dice_8: 0.03291/0.17043, loss_grounding_ce_8: 0.03989/0.42458, loss_mask_ce_9: 1.40577/3.48535, loss_mask_bce_9: 0.04673/0.36089, loss_mask_dice_9: 0.02994/1.76689, loss_spatial_bce_9: 0.47104/0.35562, loss_spatial_dice_9: 0.40909/0.79458, loss_spatial_ce_9: 0.83130/1.39700, loss_grounding_bce_9: 0.05925/0.10101, loss_grounding_dice_9: 0.03812/0.24344, loss_grounding_ce_9: 0.03718/0.68413] items per batch[64] items per second[0.37] total items[2784000] mini batches[ 43500] memory[4999] epoch remaining[0:10:12] INFO:trainer.default_trainer:epochs[ 23] optim steps[43600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50260/0.76539, loss_mask_bce_0: 0.39497/0.30189, loss_mask_dice_0: 0.12901/1.02703, loss_spatial_bce_0: 0.20800/0.08661, loss_spatial_dice_0: 0.07483/0.18303, loss_spatial_ce_0: 0.13812/0.06148, loss_grounding_bce_0: 0.33834/0.08084, loss_grounding_dice_0: 0.09961/0.15113, loss_grounding_ce_0: 0.00123/0.24965, loss_mask_ce_1: 0.52755/0.76664, loss_mask_bce_1: 0.41946/0.30270, loss_mask_dice_1: 0.13135/1.03049, loss_spatial_bce_1: 0.20827/0.08686, loss_spatial_dice_1: 0.07453/0.18563, loss_spatial_ce_1: 0.13869/0.06557, loss_grounding_bce_1: 0.32050/0.08105, loss_grounding_dice_1: 0.09242/0.15186, loss_grounding_ce_1: 0.00142/0.25102, loss_mask_ce_2: 0.58129/0.77466, loss_mask_bce_2: 0.41893/0.30279, loss_mask_dice_2: 0.13896/1.03177, loss_spatial_bce_2: 0.20399/0.08677, loss_spatial_dice_2: 0.07803/0.18583, loss_spatial_ce_2: 0.14015/0.06805, loss_grounding_bce_2: 0.33851/0.08097, loss_grounding_dice_2: 0.09939/0.15161, loss_grounding_ce_2: 0.00112/0.25370, loss_mask_ce_3: 0.75090/0.77692, loss_mask_bce_3: 0.39125/0.30434, loss_mask_dice_3: 0.13714/1.02867, loss_spatial_bce_3: 0.19173/0.08873, loss_spatial_dice_3: 0.07436/0.18691, loss_spatial_ce_3: 0.14003/0.07260, loss_grounding_bce_3: 0.30051/0.08144, loss_grounding_dice_3: 0.09906/0.15125, loss_grounding_ce_3: 0.00144/0.25340, loss_mask_ce_4: 1.19168/0.78301, loss_mask_bce_4: 0.35667/0.30665, loss_mask_dice_4: 0.13944/1.04802, loss_spatial_bce_4: 0.18919/0.09067, loss_spatial_dice_4: 0.07862/0.19464, loss_spatial_ce_4: 0.14547/0.08537, loss_grounding_bce_4: 0.30831/0.08206, loss_grounding_dice_4: 0.10095/0.15382, loss_grounding_ce_4: 0.00090/0.25951, loss_mask_ce_5: 1.10431/0.80612, loss_mask_bce_5: 0.36364/0.30844, loss_mask_dice_5: 0.14481/1.05547, loss_spatial_bce_5: 0.20508/0.09262, loss_spatial_dice_5: 0.07367/0.19725, loss_spatial_ce_5: 0.14976/0.09750, loss_grounding_bce_5: 0.31877/0.08233, loss_grounding_dice_5: 0.10056/0.15454, loss_grounding_ce_5: 0.00066/0.27749, loss_mask_ce_6: 0.74089/0.83246, loss_mask_bce_6: 0.38211/0.31027, loss_mask_dice_6: 0.14587/1.05813, loss_spatial_bce_6: 0.19412/0.09766, loss_spatial_dice_6: 0.07677/0.19950, loss_spatial_ce_6: 0.15580/0.12045, loss_grounding_bce_6: 0.30877/0.08331, loss_grounding_dice_6: 0.10053/0.15512, loss_grounding_ce_6: 0.00117/0.28731, loss_mask_ce_7: 0.75161/0.88861, loss_mask_bce_7: 0.41573/0.31762, loss_mask_dice_7: 0.14830/1.10482, loss_spatial_bce_7: 0.18318/0.10786, loss_spatial_dice_7: 0.07273/0.22474, loss_spatial_ce_7: 0.20801/0.15964, loss_grounding_bce_7: 0.31206/0.08499, loss_grounding_dice_7: 0.09576/0.16086, loss_grounding_ce_7: 0.00062/0.32222, loss_mask_ce_8: 0.73555/1.02634, loss_mask_bce_8: 0.36915/0.33386, loss_mask_dice_8: 0.15325/1.18230, loss_spatial_bce_8: 0.16217/0.12627, loss_spatial_dice_8: 0.08222/0.26163, loss_spatial_ce_8: 0.26692/0.21179, loss_grounding_bce_8: 0.29297/0.08895, loss_grounding_dice_8: 0.09500/0.17048, loss_grounding_ce_8: 0.00021/0.42469, loss_mask_ce_9: 2.99287/3.48587, loss_mask_bce_9: 0.41555/0.36083, loss_mask_dice_9: 0.20278/1.76726, loss_spatial_bce_9: 0.67986/0.35556, loss_spatial_dice_9: 0.75752/0.79461, loss_spatial_ce_9: 1.86376/1.39704, loss_grounding_bce_9: 0.36435/0.10101, loss_grounding_dice_9: 0.10435/0.24352, loss_grounding_ce_9: 0.22044/0.68412] items per batch[64] items per second[0.36] total items[2790400] mini batches[ 43600] memory[4999] epoch remaining[0:07:16] INFO:trainer.default_trainer:epochs[ 23] optim steps[43700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.16522/0.76531, loss_mask_bce_0: 0.02456/0.30191, loss_mask_dice_0: 0.81865/1.02668, loss_spatial_bce_0: 0.00684/0.08663, loss_spatial_dice_0: 0.22915/0.18301, loss_spatial_ce_0: 0.09520/0.06150, loss_grounding_bce_0: 0.00365/0.08088, loss_grounding_dice_0: 0.05376/0.15112, loss_grounding_ce_0: 0.88843/0.24969, loss_mask_ce_1: 1.44299/0.76661, loss_mask_bce_1: 0.02383/0.30273, loss_mask_dice_1: 0.65183/1.03024, loss_spatial_bce_1: 0.00638/0.08688, loss_spatial_dice_1: 0.17568/0.18560, loss_spatial_ce_1: 0.23385/0.06558, loss_grounding_bce_1: 0.00295/0.08108, loss_grounding_dice_1: 0.03679/0.15186, loss_grounding_ce_1: 0.73145/0.25103, loss_mask_ce_2: 1.31156/0.77461, loss_mask_bce_2: 0.02122/0.30281, loss_mask_dice_2: 0.95303/1.03147, loss_spatial_bce_2: 0.00760/0.08681, loss_spatial_dice_2: 0.22107/0.18582, loss_spatial_ce_2: 0.33829/0.06805, loss_grounding_bce_2: 0.00311/0.08100, loss_grounding_dice_2: 0.05256/0.15159, loss_grounding_ce_2: 0.92459/0.25373, loss_mask_ce_3: 0.94637/0.77686, loss_mask_bce_3: 0.01944/0.30436, loss_mask_dice_3: 0.79040/1.02839, loss_spatial_bce_3: 0.00779/0.08877, loss_spatial_dice_3: 0.25815/0.18691, loss_spatial_ce_3: 0.07059/0.07257, loss_grounding_bce_3: 0.00295/0.08148, loss_grounding_dice_3: 0.04826/0.15124, loss_grounding_ce_3: 0.59774/0.25342, loss_mask_ce_4: 1.25646/0.78304, loss_mask_bce_4: 0.02226/0.30668, loss_mask_dice_4: 0.45933/1.04770, loss_spatial_bce_4: 0.00697/0.09071, loss_spatial_dice_4: 0.22056/0.19462, loss_spatial_ce_4: 0.09326/0.08533, loss_grounding_bce_4: 0.00343/0.08207, loss_grounding_dice_4: 0.05809/0.15381, loss_grounding_ce_4: 0.57740/0.25949, loss_mask_ce_5: 1.22758/0.80602, loss_mask_bce_5: 0.02432/0.30846, loss_mask_dice_5: 0.72601/1.05521, loss_spatial_bce_5: 0.00815/0.09267, loss_spatial_dice_5: 0.23881/0.19724, loss_spatial_ce_5: 0.02202/0.09748, loss_grounding_bce_5: 0.00236/0.08235, loss_grounding_dice_5: 0.04558/0.15452, loss_grounding_ce_5: 0.65420/0.27745, loss_mask_ce_6: 0.86686/0.83238, loss_mask_bce_6: 0.02285/0.31031, loss_mask_dice_6: 0.84986/1.05780, loss_spatial_bce_6: 0.00864/0.09771, loss_spatial_dice_6: 0.23601/0.19948, loss_spatial_ce_6: 0.11684/0.12040, loss_grounding_bce_6: 0.00399/0.08334, loss_grounding_dice_6: 0.05762/0.15512, loss_grounding_ce_6: 0.37003/0.28722, loss_mask_ce_7: 1.25742/0.88851, loss_mask_bce_7: 0.02484/0.31766, loss_mask_dice_7: 0.79745/1.10448, loss_spatial_bce_7: 0.00830/0.10790, loss_spatial_dice_7: 0.24526/0.22470, loss_spatial_ce_7: 0.24934/0.15961, loss_grounding_bce_7: 0.00257/0.08503, loss_grounding_dice_7: 0.03687/0.16085, loss_grounding_ce_7: 0.26928/0.32208, loss_mask_ce_8: 1.37531/1.02618, loss_mask_bce_8: 0.02778/0.33389, loss_mask_dice_8: 0.71296/1.18188, loss_spatial_bce_8: 0.01179/0.12630, loss_spatial_dice_8: 0.33504/0.26160, loss_spatial_ce_8: 0.14811/0.21176, loss_grounding_bce_8: 0.00351/0.08899, loss_grounding_dice_8: 0.05821/0.17047, loss_grounding_ce_8: 0.71090/0.42445, loss_mask_ce_9: 4.26918/3.48522, loss_mask_bce_9: 0.02397/0.36083, loss_mask_dice_9: 0.81419/1.76662, loss_spatial_bce_9: 0.10872/0.35559, loss_spatial_dice_9: 0.75889/0.79455, loss_spatial_ce_9: 2.03945/1.39696, loss_grounding_bce_9: 0.00219/0.10104, loss_grounding_dice_9: 0.07505/0.24348, loss_grounding_ce_9: 0.88603/0.68399] items per batch[64] items per second[0.36] total items[2796800] mini batches[ 43700] memory[4999] epoch remaining[0:04:20] INFO:trainer.default_trainer:epochs[ 23] optim steps[43800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39827/0.76493, loss_mask_bce_0: 0.31390/0.30193, loss_mask_dice_0: 0.80947/1.02604, loss_spatial_bce_0: 0.05366/0.08667, loss_spatial_dice_0: 0.13747/0.18298, loss_spatial_ce_0: 0.00017/0.06146, loss_grounding_bce_0: 0.05662/0.08093, loss_grounding_dice_0: 0.02441/0.15114, loss_grounding_ce_0: 0.02155/0.24967, loss_mask_ce_1: 0.40247/0.76626, loss_mask_bce_1: 0.31693/0.30275, loss_mask_dice_1: 0.78349/1.02959, loss_spatial_bce_1: 0.06358/0.08692, loss_spatial_dice_1: 0.14377/0.18558, loss_spatial_ce_1: 0.00024/0.06553, loss_grounding_bce_1: 0.06340/0.08113, loss_grounding_dice_1: 0.02339/0.15188, loss_grounding_ce_1: 0.01576/0.25101, loss_mask_ce_2: 0.37117/0.77424, loss_mask_bce_2: 0.32944/0.30285, loss_mask_dice_2: 0.74883/1.03081, loss_spatial_bce_2: 0.06114/0.08685, loss_spatial_dice_2: 0.14911/0.18579, loss_spatial_ce_2: 0.00032/0.06800, loss_grounding_bce_2: 0.05787/0.08105, loss_grounding_dice_2: 0.02119/0.15162, loss_grounding_ce_2: 0.06387/0.25373, loss_mask_ce_3: 0.37451/0.77657, loss_mask_bce_3: 0.31558/0.30439, loss_mask_dice_3: 0.79574/1.02779, loss_spatial_bce_3: 0.06297/0.08881, loss_spatial_dice_3: 0.14370/0.18688, loss_spatial_ce_3: 0.00539/0.07254, loss_grounding_bce_3: 0.06263/0.08153, loss_grounding_dice_3: 0.02177/0.15126, loss_grounding_ce_3: 0.01545/0.25342, loss_mask_ce_4: 0.37514/0.78273, loss_mask_bce_4: 0.33517/0.30669, loss_mask_dice_4: 0.80077/1.04703, loss_spatial_bce_4: 0.05778/0.09076, loss_spatial_dice_4: 0.14429/0.19459, loss_spatial_ce_4: 0.00594/0.08530, loss_grounding_bce_4: 0.06373/0.08212, loss_grounding_dice_4: 0.02364/0.15383, loss_grounding_ce_4: 0.00302/0.25958, loss_mask_ce_5: 0.36463/0.80569, loss_mask_bce_5: 0.34072/0.30847, loss_mask_dice_5: 0.77802/1.05450, loss_spatial_bce_5: 0.05967/0.09270, loss_spatial_dice_5: 0.14926/0.19722, loss_spatial_ce_5: 0.00436/0.09743, loss_grounding_bce_5: 0.05470/0.08240, loss_grounding_dice_5: 0.02978/0.15455, loss_grounding_ce_5: 0.04242/0.27747, loss_mask_ce_6: 0.31375/0.83201, loss_mask_bce_6: 0.34608/0.31032, loss_mask_dice_6: 0.79029/1.05716, loss_spatial_bce_6: 0.06466/0.09775, loss_spatial_dice_6: 0.14057/0.19946, loss_spatial_ce_6: 0.01868/0.12038, loss_grounding_bce_6: 0.05714/0.08338, loss_grounding_dice_6: 0.03263/0.15514, loss_grounding_ce_6: 0.00459/0.28719, loss_mask_ce_7: 0.65723/0.88820, loss_mask_bce_7: 0.37634/0.31766, loss_mask_dice_7: 0.84241/1.10379, loss_spatial_bce_7: 0.07789/0.10796, loss_spatial_dice_7: 0.16372/0.22466, loss_spatial_ce_7: 0.01735/0.15958, loss_grounding_bce_7: 0.09121/0.08508, loss_grounding_dice_7: 0.04021/0.16087, loss_grounding_ce_7: 0.11164/0.32203, loss_mask_ce_8: 0.72577/1.02584, loss_mask_bce_8: 0.38266/0.33385, loss_mask_dice_8: 0.98091/1.18113, loss_spatial_bce_8: 0.10203/0.12635, loss_spatial_dice_8: 0.23531/0.26155, loss_spatial_ce_8: 0.11794/0.21170, loss_grounding_bce_8: 0.05295/0.08903, loss_grounding_dice_8: 0.02572/0.17049, loss_grounding_ce_8: 0.31844/0.42444, loss_mask_ce_9: 3.55368/3.48467, loss_mask_bce_9: 0.42288/0.36082, loss_mask_dice_9: 1.78880/1.76535, loss_spatial_bce_9: 0.30929/0.35571, loss_spatial_dice_9: 0.83072/0.79452, loss_spatial_ce_9: 1.21591/1.39667, loss_grounding_bce_9: 0.06751/0.10109, loss_grounding_dice_9: 0.04470/0.24349, loss_grounding_ce_9: 1.70366/0.68385] items per batch[64] items per second[0.36] total items[2803200] mini batches[ 43800] memory[4999] epoch remaining[0:01:24] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00043848. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0021 s/iter. Inference: 0.3659 s/iter. Eval: 0.0979 s/iter. Total: 0.4658 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3683 s/iter. Eval: 0.0850 s/iter. Total: 0.4558 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3734 s/iter. Eval: 0.0812 s/iter. Total: 0.4573 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3771 s/iter. Eval: 0.0773 s/iter. Total: 0.4572 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3782 s/iter. Eval: 0.0767 s/iter. Total: 0.4578 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3763 s/iter. Eval: 0.0751 s/iter. Total: 0.4543 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalvqkhxg2z ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.545 | 83.054 | 66.022 | 133 | | Things | 61.671 | 84.137 | 72.781 | 80 | | Stuff | 46.299 | 81.420 | 55.818 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.52s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.52 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.63s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.63 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.696 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.370 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.767 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.658 | 69.600 | 49.208 | 26.166 | 49.922 | 67.591 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.734 | bicycle | 22.645 | car | 42.301 | | motorcycle | 42.578 | airplane | 62.468 | bus | 69.797 | | train | 74.730 | truck | 44.215 | boat | 30.080 | | traffic light | 27.498 | fire hydrant | 69.396 | stop sign | 68.468 | | parking meter | 49.800 | bench | 25.361 | bird | 33.556 | | cat | 75.735 | dog | 71.379 | horse | 49.074 | | sheep | 55.187 | cow | 57.141 | elephant | 64.915 | | bear | 79.637 | zebra | 65.529 | giraffe | 61.337 | | backpack | 23.454 | umbrella | 55.517 | handbag | 24.529 | | tie | 40.353 | suitcase | 50.913 | frisbee | 69.850 | | skis | 8.752 | snowboard | 34.810 | sports ball | 50.078 | | kite | 37.125 | baseball bat | 38.054 | baseball glove | 49.906 | | skateboard | 43.578 | surfboard | 44.631 | tennis racket | 62.723 | | bottle | 41.862 | wine glass | 37.039 | cup | 52.003 | | fork | 26.940 | knife | 23.339 | spoon | 22.270 | | bowl | 42.857 | banana | 22.836 | apple | 27.792 | | sandwich | 49.106 | orange | 31.434 | broccoli | 24.151 | | carrot | 22.845 | hot dog | 36.294 | pizza | 52.212 | | donut | 55.948 | cake | 46.841 | chair | 29.756 | | couch | 44.985 | potted plant | 23.069 | bed | 41.835 | | dining table | 15.034 | toilet | 70.722 | tv | 66.563 | | laptop | 70.337 | mouse | 65.059 | remote | 43.611 | | keyboard | 59.941 | cell phone | 46.197 | microwave | 65.490 | | oven | 32.870 | toaster | 50.548 | sink | 43.806 | | refrigerator | 70.169 | book | 13.678 | clock | 54.615 | | vase | 40.483 | scissors | 37.086 | teddy bear | 58.059 | | hair drier | 39.175 | toothbrush | 27.929 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.72047514722483, 'fwIoU': 71.12487813412662, 'IoU-person': 88.93921111393037, 'IoU-bicycle': 70.66567250735264, 'IoU-car': 72.71940416795375, 'IoU-motorcycle': 88.50849233949035, 'IoU-airplane': 81.19972838243872, 'IoU-bus': 86.54596503120744, 'IoU-train': 87.39183967618604, 'IoU-truck': 70.66916758482924, 'IoU-boat': 72.14328269190221, 'IoU-traffic light': 79.70907919359749, 'IoU-fire hydrant': 92.7027664174904, 'IoU-stop sign': 85.07858202152582, 'IoU-parking meter': 88.25178122380554, 'IoU-bench': 63.74522395105051, 'IoU-bird': 77.54114951413705, 'IoU-cat': 84.72268800328268, 'IoU-dog': 84.20883849119669, 'IoU-horse': 87.3659248379147, 'IoU-sheep': 82.13220892120638, 'IoU-cow': 87.97859679010111, 'IoU-elephant': 87.02064154939251, 'IoU-bear': 84.99785575825732, 'IoU-zebra': 77.12659274038568, 'IoU-giraffe': 88.80547792652848, 'IoU-backpack': 50.01367726704417, 'IoU-umbrella': 82.43721863089115, 'IoU-handbag': 48.8366924995495, 'IoU-tie': 71.7052074183378, 'IoU-suitcase': 84.56817958020086, 'IoU-frisbee': 84.4625189483464, 'IoU-skis': 57.11005389946101, 'IoU-snowboard': 70.8222642008264, 'IoU-sports ball': 69.65150025854479, 'IoU-kite': 77.57134271444262, 'IoU-baseball bat': 68.04052868153867, 'IoU-baseball glove': 80.29427550037192, 'IoU-skateboard': 86.29862011954044, 'IoU-surfboard': 86.63480264238457, 'IoU-tennis racket': 89.66166592241403, 'IoU-bottle': 69.30227154917876, 'IoU-wine glass': 82.10606516125578, 'IoU-cup': 71.65911427264166, 'IoU-fork': 68.86298969140861, 'IoU-knife': 60.33373316530268, 'IoU-spoon': 60.37721657947841, 'IoU-bowl': 60.00747768225306, 'IoU-banana': 83.22623811748072, 'IoU-apple': 60.71462574529792, 'IoU-sandwich': 69.74648389655871, 'IoU-orange': 74.32238481719484, 'IoU-broccoli': 68.41689343521705, 'IoU-carrot': 63.33348580629226, 'IoU-hot dog': 65.33341305848089, 'IoU-pizza': 78.5310202675695, 'IoU-donut': 72.52277233537482, 'IoU-cake': 74.74073429647254, 'IoU-chair': 62.334520332674096, 'IoU-couch': 69.14359743327205, 'IoU-potted plant': 40.55254050387514, 'IoU-bed': 73.58582649313489, 'IoU-dining table': 52.78988797536317, 'IoU-toilet': 81.99482420311364, 'IoU-tv': 77.23694760175447, 'IoU-laptop': 76.70946270668144, 'IoU-mouse': 75.75153764967062, 'IoU-remote': 68.33121714566953, 'IoU-keyboard': 65.5068296613957, 'IoU-cell phone': 77.39605020535788, 'IoU-microwave': 70.80446904718454, 'IoU-oven': 71.58267612400195, 'IoU-toaster': 78.21338840738322, 'IoU-sink': 73.34930542480679, 'IoU-refrigerator': 82.21516410856687, 'IoU-book': 56.09580903697315, 'IoU-clock': 64.02733975378206, 'IoU-vase': 54.35376691095814, 'IoU-scissors': 62.70741199649312, 'IoU-teddy bear': 83.8712394238749, 'IoU-hair drier': 48.90750930386539, 'IoU-toothbrush': 75.66604112848135, 'IoU-banner': 33.83720508223026, 'IoU-blanket': 14.169972503079082, 'IoU-bridge': 36.76214821519073, 'IoU-cardboard': 54.41318737689447, 'IoU-counter': 33.75437017227067, 'IoU-curtain': 71.92761229304546, 'IoU-door-stuff': 47.874042711220405, 'IoU-floor-wood': 65.0000301808972, 'IoU-flower': 40.35553642810169, 'IoU-fruit': 49.55543423805489, 'IoU-gravel': 31.28630841887417, 'IoU-house': 23.196935071451986, 'IoU-light': 43.62223206855933, 'IoU-mirror-stuff': 64.65445645247097, 'IoU-net': 48.63341666124204, 'IoU-pillow': 26.39488882096643, 'IoU-platform': 28.858063435790232, 'IoU-playingfield': 66.54956359982067, 'IoU-railroad': 63.8014574351344, 'IoU-river': 55.344466526972056, 'IoU-road': 66.53099864699118, 'IoU-roof': 14.191663676027394, 'IoU-sand': 64.10170570071658, 'IoU-sea': 86.53743827343068, 'IoU-shelf': 39.083173639360304, 'IoU-snow': 91.81769369578272, 'IoU-stairs': 34.494832180807705, 'IoU-tent': 10.786813691261534, 'IoU-towel': 45.5483439629204, 'IoU-wall-brick': 48.98507483359562, 'IoU-wall-stone': 29.601177176692506, 'IoU-wall-tile': 71.19434994763427, 'IoU-wall-wood': 44.40042614917559, 'IoU-water-other': 29.509196591521746, 'IoU-window-blind': 49.34564126267385, 'IoU-window-other': 49.30194215060935, 'IoU-tree-merged': 82.11176215631151, 'IoU-fence-merged': 55.25910340598258, 'IoU-ceiling-merged': 69.80018330132638, 'IoU-sky-other-merged': 93.54670927581985, 'IoU-cabinet-merged': 63.72870852747666, 'IoU-table-merged': 41.75223990434533, 'IoU-floor-other-merged': 52.47125795709873, 'IoU-pavement-merged': 57.807487605005946, 'IoU-mountain-merged': 58.53376279133984, 'IoU-grass-merged': 71.02094869307871, 'IoU-dirt-merged': 44.511873827494014, 'IoU-paper-merged': 35.57686763388175, 'IoU-food-other-merged': 39.09538579192841, 'IoU-building-other-merged': 59.78082897630933, 'IoU-rock-merged': 65.97670214912192, 'IoU-wall-other-merged': 67.97105738356437, 'IoU-rug-merged': 68.51351435449816, 'mACC': 76.32161679499833, 'pACC': 81.87746592582987, 'ACC-person': 93.27095810165797, 'ACC-bicycle': 81.1853319604399, 'ACC-car': 88.15675465760195, 'ACC-motorcycle': 93.12357917993003, 'ACC-airplane': 86.95352611378058, 'ACC-bus': 92.84724697579993, 'ACC-train': 94.9896109037947, 'ACC-truck': 80.49223635807255, 'ACC-boat': 80.79518014679513, 'ACC-traffic light': 91.0123269821288, 'ACC-fire hydrant': 95.29142309351074, 'ACC-stop sign': 88.11030957464011, 'ACC-parking meter': 91.69199227077682, 'ACC-bench': 76.56239521391687, 'ACC-bird': 82.43065541917039, 'ACC-cat': 87.74959789824247, 'ACC-dog': 87.28146679410466, 'ACC-horse': 92.29287051324016, 'ACC-sheep': 85.75200815747834, 'ACC-cow': 91.01273079701765, 'ACC-elephant': 88.97473466260192, 'ACC-bear': 86.64096664953075, 'ACC-zebra': 78.8562358031229, 'ACC-giraffe': 92.46895844490398, 'ACC-backpack': 73.71024355614763, 'ACC-umbrella': 85.88722414188337, 'ACC-handbag': 68.79654263668802, 'ACC-tie': 78.81509723986746, 'ACC-suitcase': 93.7750242032391, 'ACC-frisbee': 94.21563636363636, 'ACC-skis': 70.75881525194465, 'ACC-snowboard': 81.35289363473831, 'ACC-sports ball': 77.27643915196246, 'ACC-kite': 84.79117742556613, 'ACC-baseball bat': 85.68394158943275, 'ACC-baseball glove': 92.16834914939149, 'ACC-skateboard': 90.5514585418621, 'ACC-surfboard': 92.29846244897463, 'ACC-tennis racket': 94.73500343825447, 'ACC-bottle': 82.9557222122144, 'ACC-wine glass': 90.37197874925205, 'ACC-cup': 89.0214248594843, 'ACC-fork': 79.87960319141206, 'ACC-knife': 72.79328790210302, 'ACC-spoon': 77.53753642667192, 'ACC-bowl': 72.9260281119565, 'ACC-banana': 90.809689195209, 'ACC-apple': 75.81652722682797, 'ACC-sandwich': 83.67327886086791, 'ACC-orange': 84.03177683591315, 'ACC-broccoli': 81.03869905327073, 'ACC-carrot': 75.56969928959514, 'ACC-hot dog': 71.42118668810654, 'ACC-pizza': 83.9210643230259, 'ACC-donut': 82.23028444756878, 'ACC-cake': 87.63144851208416, 'ACC-chair': 83.29926874278993, 'ACC-couch': 74.24327290795331, 'ACC-potted plant': 55.30989866409253, 'ACC-bed': 84.94302195656874, 'ACC-dining table': 75.80191065945209, 'ACC-toilet': 85.84191295150312, 'ACC-tv': 83.22675811002934, 'ACC-laptop': 91.07232142175451, 'ACC-mouse': 90.10205296364838, 'ACC-remote': 71.81961633259868, 'ACC-keyboard': 72.67955501222228, 'ACC-cell phone': 87.0232551962553, 'ACC-microwave': 74.5809888816175, 'ACC-oven': 91.86823735168032, 'ACC-toaster': 90.9592303165501, 'ACC-sink': 83.78723392099016, 'ACC-refrigerator': 91.88902365650368, 'ACC-book': 74.86067916281417, 'ACC-clock': 68.1336989242362, 'ACC-vase': 61.910722377622065, 'ACC-scissors': 66.74138500950335, 'ACC-teddy bear': 89.42803846898849, 'ACC-hair drier': 60.97116670936157, 'ACC-toothbrush': 83.70917303683113, 'ACC-banner': 78.95688265729464, 'ACC-blanket': 20.112542382528233, 'ACC-bridge': 54.61821815669982, 'ACC-cardboard': 72.18644757888613, 'ACC-counter': 53.187472755507216, 'ACC-curtain': 82.69449233642113, 'ACC-door-stuff': 73.1124271038722, 'ACC-floor-wood': 80.49750887916639, 'ACC-flower': 55.368277821990475, 'ACC-fruit': 68.85375937393586, 'ACC-gravel': 53.225915388588675, 'ACC-house': 28.39421183191229, 'ACC-light': 60.35892699919611, 'ACC-mirror-stuff': 76.25780348563822, 'ACC-net': 63.77005584709558, 'ACC-pillow': 56.31043868286901, 'ACC-platform': 45.436833792706615, 'ACC-playingfield': 80.71079145097994, 'ACC-railroad': 78.14988185712907, 'ACC-river': 78.66214917189922, 'ACC-road': 82.22016032701661, 'ACC-roof': 17.70775016603715, 'ACC-sand': 67.72743567166911, 'ACC-sea': 91.53727418312437, 'ACC-shelf': 55.963493517009454, 'ACC-snow': 95.51788946021894, 'ACC-stairs': 65.76827534104682, 'ACC-tent': 14.544447976044598, 'ACC-towel': 54.73038963477269, 'ACC-wall-brick': 68.98923257960817, 'ACC-wall-stone': 35.10385115963041, 'ACC-wall-tile': 88.06952156702634, 'ACC-wall-wood': 59.611485294502266, 'ACC-water-other': 44.26888686082943, 'ACC-window-blind': 61.13087596592634, 'ACC-window-other': 76.0488454871731, 'ACC-tree-merged': 90.01480067707219, 'ACC-fence-merged': 77.56411694292977, 'ACC-ceiling-merged': 81.20162433450537, 'ACC-sky-other-merged': 96.89691295435759, 'ACC-cabinet-merged': 78.32734624857831, 'ACC-table-merged': 56.67070248449409, 'ACC-floor-other-merged': 63.96782919798195, 'ACC-pavement-merged': 75.87672618139354, 'ACC-mountain-merged': 70.25180025676933, 'ACC-grass-merged': 82.57323903435488, 'ACC-dirt-merged': 72.0422274036833, 'ACC-paper-merged': 45.885003442350474, 'ACC-food-other-merged': 49.117721895787305, 'ACC-building-other-merged': 75.98396178426015, 'ACC-rock-merged': 83.4951081850099, 'ACC-wall-other-merged': 80.42583607553048, 'ACC-rug-merged': 82.08415982278818})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3069 s/iter. Inference: 0.1776 s/iter. Eval: 0.0000 s/iter. Total: 0.4845 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3350 s/iter. Inference: 0.3367 s/iter. Eval: 0.0000 s/iter. Total: 0.6718 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3453 s/iter. Inference: 0.4795 s/iter. Eval: 0.0000 s/iter. Total: 0.8249 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.417617793386011, 'noc@0.8': 2.4969271290605795, 'noc@0.85': 2.9663447468539657, 'noc@0.9': 3.733099209833187, 'miou@iter1': 0.8695446809272442} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0012 s/iter. Inference: 0.1442 s/iter. Eval: 0.0010 s/iter. Total: 0.1465 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.67042541503906, 'precision@0.6': 72.63894653320312, 'precision@0.7': 68.4803695678711, 'precision@0.8': 60.124366760253906, 'precision@0.9': 33.501747131347656, 'cIoU': 62.09733581542969, 'mIoU': 67.09038543701172} INFO:trainer.default_trainer:This epoch takes 0:57:02.648323 INFO:trainer.default_trainer:PROGRESS: 48.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 24 training. INFO:trainer.default_trainer:epochs[ 24] optim steps[43900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83045/0.76499, loss_mask_bce_0: 0.15082/0.30195, loss_mask_dice_0: 0.08640/1.02622, loss_spatial_bce_0: 0.11265/0.08666, loss_spatial_dice_0: 0.05819/0.18299, loss_spatial_ce_0: 0.00526/0.06143, loss_grounding_bce_0: 0.08785/0.08094, loss_grounding_dice_0: 0.05906/0.15116, loss_grounding_ce_0: 0.00259/0.24950, loss_mask_ce_1: 0.86196/0.76629, loss_mask_bce_1: 0.15708/0.30276, loss_mask_dice_1: 0.08478/1.02973, loss_spatial_bce_1: 0.10801/0.08691, loss_spatial_dice_1: 0.05915/0.18559, loss_spatial_ce_1: 0.00363/0.06549, loss_grounding_bce_1: 0.08847/0.08114, loss_grounding_dice_1: 0.05499/0.15188, loss_grounding_ce_1: 0.00186/0.25085, loss_mask_ce_2: 0.92808/0.77431, loss_mask_bce_2: 0.16772/0.30287, loss_mask_dice_2: 0.08862/1.03096, loss_spatial_bce_2: 0.11444/0.08684, loss_spatial_dice_2: 0.05886/0.18580, loss_spatial_ce_2: 0.00306/0.06796, loss_grounding_bce_2: 0.08573/0.08106, loss_grounding_dice_2: 0.05417/0.15163, loss_grounding_ce_2: 0.00136/0.25357, loss_mask_ce_3: 0.93025/0.77661, loss_mask_bce_3: 0.15335/0.30440, loss_mask_dice_3: 0.08502/1.02792, loss_spatial_bce_3: 0.11200/0.08881, loss_spatial_dice_3: 0.05885/0.18689, loss_spatial_ce_3: 0.00176/0.07251, loss_grounding_bce_3: 0.08113/0.08154, loss_grounding_dice_3: 0.05646/0.15127, loss_grounding_ce_3: 0.00133/0.25324, loss_mask_ce_4: 0.88548/0.78277, loss_mask_bce_4: 0.15138/0.30673, loss_mask_dice_4: 0.09138/1.04720, loss_spatial_bce_4: 0.11586/0.09076, loss_spatial_dice_4: 0.06136/0.19461, loss_spatial_ce_4: 0.00810/0.08526, loss_grounding_bce_4: 0.08044/0.08213, loss_grounding_dice_4: 0.05903/0.15384, loss_grounding_ce_4: 0.00359/0.25941, loss_mask_ce_5: 0.75219/0.80573, loss_mask_bce_5: 0.15206/0.30850, loss_mask_dice_5: 0.08639/1.05471, loss_spatial_bce_5: 0.10870/0.09270, loss_spatial_dice_5: 0.05764/0.19723, loss_spatial_ce_5: 0.00996/0.09739, loss_grounding_bce_5: 0.08690/0.08241, loss_grounding_dice_5: 0.05903/0.15457, loss_grounding_ce_5: 0.00269/0.27725, loss_mask_ce_6: 0.73274/0.83197, loss_mask_bce_6: 0.15374/0.31035, loss_mask_dice_6: 0.08744/1.05735, loss_spatial_bce_6: 0.11051/0.09774, loss_spatial_dice_6: 0.06178/0.19946, loss_spatial_ce_6: 0.05143/0.12035, loss_grounding_bce_6: 0.08078/0.08338, loss_grounding_dice_6: 0.05817/0.15514, loss_grounding_ce_6: 0.00787/0.28699, loss_mask_ce_7: 0.66847/0.88815, loss_mask_bce_7: 0.14758/0.31768, loss_mask_dice_7: 0.08808/1.10399, loss_spatial_bce_7: 0.12439/0.10795, loss_spatial_dice_7: 0.06588/0.22468, loss_spatial_ce_7: 0.01406/0.15954, loss_grounding_bce_7: 0.08062/0.08508, loss_grounding_dice_7: 0.05874/0.16089, loss_grounding_ce_7: 0.00496/0.32178, loss_mask_ce_8: 0.48365/1.02589, loss_mask_bce_8: 0.14803/0.33386, loss_mask_dice_8: 0.09004/1.18129, loss_spatial_bce_8: 0.11786/0.12633, loss_spatial_dice_8: 0.06939/0.26156, loss_spatial_ce_8: 0.17036/0.21165, loss_grounding_bce_8: 0.08216/0.08902, loss_grounding_dice_8: 0.05939/0.17051, loss_grounding_ce_8: 0.00310/0.42429, loss_mask_ce_9: 2.68685/3.48487, loss_mask_bce_9: 0.15034/0.36087, loss_mask_dice_9: 0.10647/1.76546, loss_spatial_bce_9: 0.55060/0.35567, loss_spatial_dice_9: 0.49503/0.79454, loss_spatial_ce_9: 0.70813/1.39663, loss_grounding_bce_9: 0.08370/0.10109, loss_grounding_dice_9: 0.07851/0.24350, loss_grounding_ce_9: 0.01537/0.68370] items per batch[64] items per second[0.16] total items[2809600] mini batches[ 43900] memory[4999] epoch remaining[0:56:19] INFO:trainer.default_trainer:epochs[ 24] optim steps[44000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.28137/0.76480, loss_mask_bce_0: 0.06355/0.30190, loss_mask_dice_0: 0.82254/1.02599, loss_spatial_bce_0: 0.01967/0.08664, loss_spatial_dice_0: 0.10176/0.18295, loss_spatial_ce_0: 0.00234/0.06137, loss_grounding_bce_0: 0.00775/0.08095, loss_grounding_dice_0: 0.11262/0.15117, loss_grounding_ce_0: 0.40920/0.24932, loss_mask_ce_1: 0.21614/0.76605, loss_mask_bce_1: 0.05681/0.30271, loss_mask_dice_1: 0.55683/1.02946, loss_spatial_bce_1: 0.01946/0.08689, loss_spatial_dice_1: 0.12863/0.18555, loss_spatial_ce_1: 0.00636/0.06542, loss_grounding_bce_1: 0.00861/0.08114, loss_grounding_dice_1: 0.10330/0.15189, loss_grounding_ce_1: 0.37327/0.25068, loss_mask_ce_2: 0.22269/0.77419, loss_mask_bce_2: 0.05949/0.30281, loss_mask_dice_2: 0.37953/1.03073, loss_spatial_bce_2: 0.02036/0.08682, loss_spatial_dice_2: 0.15619/0.18576, loss_spatial_ce_2: 0.00319/0.06789, loss_grounding_bce_2: 0.00904/0.08107, loss_grounding_dice_2: 0.17788/0.15166, loss_grounding_ce_2: 0.35364/0.25337, loss_mask_ce_3: 0.31399/0.77640, loss_mask_bce_3: 0.05983/0.30435, loss_mask_dice_3: 0.86689/1.02770, loss_spatial_bce_3: 0.01969/0.08879, loss_spatial_dice_3: 0.14178/0.18685, loss_spatial_ce_3: 0.00308/0.07245, loss_grounding_bce_3: 0.00796/0.08154, loss_grounding_dice_3: 0.06245/0.15127, loss_grounding_ce_3: 0.44621/0.25304, loss_mask_ce_4: 0.21053/0.78257, loss_mask_bce_4: 0.06044/0.30667, loss_mask_dice_4: 0.49411/1.04699, loss_spatial_bce_4: 0.02299/0.09074, loss_spatial_dice_4: 0.13496/0.19458, loss_spatial_ce_4: 0.00984/0.08524, loss_grounding_bce_4: 0.00867/0.08214, loss_grounding_dice_4: 0.23751/0.15385, loss_grounding_ce_4: 0.34228/0.25921, loss_mask_ce_5: 0.21814/0.80556, loss_mask_bce_5: 0.06455/0.30845, loss_mask_dice_5: 0.57459/1.05444, loss_spatial_bce_5: 0.01879/0.09268, loss_spatial_dice_5: 0.14757/0.19719, loss_spatial_ce_5: 0.01778/0.09734, loss_grounding_bce_5: 0.00821/0.08242, loss_grounding_dice_5: 0.10516/0.15459, loss_grounding_ce_5: 0.43505/0.27704, loss_mask_ce_6: 0.24333/0.83170, loss_mask_bce_6: 0.05905/0.31030, loss_mask_dice_6: 0.64296/1.05708, loss_spatial_bce_6: 0.02118/0.09771, loss_spatial_dice_6: 0.11679/0.19942, loss_spatial_ce_6: 0.00463/0.12031, loss_grounding_bce_6: 0.00920/0.08339, loss_grounding_dice_6: 0.11670/0.15515, loss_grounding_ce_6: 0.39283/0.28677, loss_mask_ce_7: 0.23317/0.88786, loss_mask_bce_7: 0.05968/0.31763, loss_mask_dice_7: 0.72816/1.10372, loss_spatial_bce_7: 0.02330/0.10792, loss_spatial_dice_7: 0.12839/0.22462, loss_spatial_ce_7: 0.03894/0.15950, loss_grounding_bce_7: 0.00794/0.08508, loss_grounding_dice_7: 0.10853/0.16089, loss_grounding_ce_7: 0.39205/0.32154, loss_mask_ce_8: 0.43047/1.02561, loss_mask_bce_8: 0.06071/0.33379, loss_mask_dice_8: 0.34550/1.18099, loss_spatial_bce_8: 0.04182/0.12629, loss_spatial_dice_8: 0.20986/0.26150, loss_spatial_ce_8: 0.06587/0.21158, loss_grounding_bce_8: 0.00884/0.08902, loss_grounding_dice_8: 0.06403/0.17050, loss_grounding_ce_8: 0.46134/0.42423, loss_mask_ce_9: 2.62767/3.48422, loss_mask_bce_9: 0.06974/0.36078, loss_mask_dice_9: 1.00821/1.76487, loss_spatial_bce_9: 0.31369/0.35573, loss_spatial_dice_9: 0.86917/0.79451, loss_spatial_ce_9: 1.52612/1.39645, loss_grounding_bce_9: 0.01058/0.10106, loss_grounding_dice_9: 0.14313/0.24345, loss_grounding_ce_9: 0.55447/0.68343] items per batch[64] items per second[0.37] total items[2816000] mini batches[ 44000] memory[4999] epoch remaining[0:50:11] INFO:trainer.default_trainer:epochs[ 24] optim steps[44100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81853/0.76477, loss_mask_bce_0: 0.13667/0.30188, loss_mask_dice_0: 0.74870/1.02617, loss_spatial_bce_0: 0.08088/0.08661, loss_spatial_dice_0: 0.27060/0.18294, loss_spatial_ce_0: 0.21136/0.06136, loss_grounding_bce_0: 0.01818/0.08094, loss_grounding_dice_0: 0.19969/0.15118, loss_grounding_ce_0: 0.02301/0.24944, loss_mask_ce_1: 1.04664/0.76601, loss_mask_bce_1: 0.13033/0.30268, loss_mask_dice_1: 0.82254/1.02963, loss_spatial_bce_1: 0.03681/0.08686, loss_spatial_dice_1: 0.25960/0.18555, loss_spatial_ce_1: 0.38198/0.06541, loss_grounding_bce_1: 0.01844/0.08114, loss_grounding_dice_1: 0.25373/0.15190, loss_grounding_ce_1: 0.01061/0.25079, loss_mask_ce_2: 0.58318/0.77408, loss_mask_bce_2: 0.13084/0.30279, loss_mask_dice_2: 0.76688/1.03088, loss_spatial_bce_2: 0.07098/0.08679, loss_spatial_dice_2: 0.28023/0.18575, loss_spatial_ce_2: 0.21361/0.06786, loss_grounding_bce_2: 0.02005/0.08106, loss_grounding_dice_2: 0.21514/0.15167, loss_grounding_ce_2: 0.00692/0.25351, loss_mask_ce_3: 0.62343/0.77630, loss_mask_bce_3: 0.13079/0.30432, loss_mask_dice_3: 0.74934/1.02789, loss_spatial_bce_3: 0.17985/0.08876, loss_spatial_dice_3: 0.31464/0.18685, loss_spatial_ce_3: 0.04972/0.07242, loss_grounding_bce_3: 0.01981/0.08153, loss_grounding_dice_3: 0.26100/0.15127, loss_grounding_ce_3: 0.00745/0.25311, loss_mask_ce_4: 0.63063/0.78245, loss_mask_bce_4: 0.12992/0.30665, loss_mask_dice_4: 0.77903/1.04720, loss_spatial_bce_4: 0.05387/0.09070, loss_spatial_dice_4: 0.29608/0.19457, loss_spatial_ce_4: 0.13707/0.08520, loss_grounding_bce_4: 0.01186/0.08213, loss_grounding_dice_4: 0.19744/0.15385, loss_grounding_ce_4: 0.00565/0.25935, loss_mask_ce_5: 0.59726/0.80551, loss_mask_bce_5: 0.13325/0.30841, loss_mask_dice_5: 0.72758/1.05461, loss_spatial_bce_5: 0.03487/0.09264, loss_spatial_dice_5: 0.26158/0.19718, loss_spatial_ce_5: 0.40700/0.09733, loss_grounding_bce_5: 0.01741/0.08241, loss_grounding_dice_5: 0.23586/0.15460, loss_grounding_ce_5: 0.01483/0.27713, loss_mask_ce_6: 0.54877/0.83169, loss_mask_bce_6: 0.12605/0.31027, loss_mask_dice_6: 0.72301/1.05727, loss_spatial_bce_6: 0.03682/0.09767, loss_spatial_dice_6: 0.24781/0.19941, loss_spatial_ce_6: 0.37831/0.12028, loss_grounding_bce_6: 0.01573/0.08337, loss_grounding_dice_6: 0.23455/0.15515, loss_grounding_ce_6: 0.01123/0.28684, loss_mask_ce_7: 0.81031/0.88782, loss_mask_bce_7: 0.13948/0.31760, loss_mask_dice_7: 0.78416/1.10389, loss_spatial_bce_7: 0.04085/0.10786, loss_spatial_dice_7: 0.27045/0.22461, loss_spatial_ce_7: 0.33137/0.15944, loss_grounding_bce_7: 0.01662/0.08506, loss_grounding_dice_7: 0.26189/0.16089, loss_grounding_ce_7: 0.00937/0.32164, loss_mask_ce_8: 0.70872/1.02551, loss_mask_bce_8: 0.13479/0.33377, loss_mask_dice_8: 0.78872/1.18120, loss_spatial_bce_8: 0.04231/0.12623, loss_spatial_dice_8: 0.32190/0.26149, loss_spatial_ce_8: 0.30915/0.21152, loss_grounding_bce_8: 0.01790/0.08903, loss_grounding_dice_8: 0.23060/0.17052, loss_grounding_ce_8: 0.00408/0.42429, loss_mask_ce_9: 3.31927/3.48426, loss_mask_bce_9: 0.17215/0.36077, loss_mask_dice_9: 1.83227/1.76507, loss_spatial_bce_9: 0.26977/0.35564, loss_spatial_dice_9: 0.82969/0.79449, loss_spatial_ce_9: 1.57918/1.39648, loss_grounding_bce_9: 0.03825/0.10107, loss_grounding_dice_9: 0.53029/0.24345, loss_grounding_ce_9: 0.04156/0.68345] items per batch[64] items per second[0.37] total items[2822400] mini batches[ 44100] memory[4999] epoch remaining[0:46:27] INFO:trainer.default_trainer:epochs[ 24] optim steps[44200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.77630/0.76451, loss_mask_bce_0: 0.02116/0.30189, loss_mask_dice_0: 1.14962/1.02595, loss_spatial_bce_0: 0.00441/0.08661, loss_spatial_dice_0: 0.27543/0.18290, loss_spatial_ce_0: 0.08717/0.06132, loss_grounding_bce_0: 0.00178/0.08093, loss_grounding_dice_0: 0.23059/0.15114, loss_grounding_ce_0: 0.02580/0.24929, loss_mask_ce_1: 1.54199/0.76570, loss_mask_bce_1: 0.02678/0.30269, loss_mask_dice_1: 1.18498/1.02943, loss_spatial_bce_1: 0.00420/0.08686, loss_spatial_dice_1: 0.32165/0.18550, loss_spatial_ce_1: 0.09144/0.06538, loss_grounding_bce_1: 0.00197/0.08113, loss_grounding_dice_1: 0.14993/0.15187, loss_grounding_ce_1: 0.02007/0.25067, loss_mask_ce_2: 2.02444/0.77382, loss_mask_bce_2: 0.02372/0.30280, loss_mask_dice_2: 0.89049/1.03069, loss_spatial_bce_2: 0.00477/0.08679, loss_spatial_dice_2: 0.31015/0.18570, loss_spatial_ce_2: 0.13702/0.06782, loss_grounding_bce_2: 0.00132/0.08105, loss_grounding_dice_2: 0.09921/0.15162, loss_grounding_ce_2: 0.02024/0.25337, loss_mask_ce_3: 1.58601/0.77600, loss_mask_bce_3: 0.02725/0.30434, loss_mask_dice_3: 1.08753/1.02772, loss_spatial_bce_3: 0.00476/0.08876, loss_spatial_dice_3: 0.32519/0.18681, loss_spatial_ce_3: 0.18450/0.07241, loss_grounding_bce_3: 0.00476/0.08152, loss_grounding_dice_3: 0.34147/0.15123, loss_grounding_ce_3: 2.96917/0.25308, loss_mask_ce_4: 1.54453/0.78220, loss_mask_bce_4: 0.02601/0.30665, loss_mask_dice_4: 1.34921/1.04701, loss_spatial_bce_4: 0.00517/0.09070, loss_spatial_dice_4: 0.33785/0.19453, loss_spatial_ce_4: 0.87775/0.08517, loss_grounding_bce_4: 0.00216/0.08212, loss_grounding_dice_4: 0.15664/0.15382, loss_grounding_ce_4: 0.00821/0.25923, loss_mask_ce_5: 2.31767/0.80529, loss_mask_bce_5: 0.02956/0.30841, loss_mask_dice_5: 1.42755/1.05443, loss_spatial_bce_5: 0.00465/0.09264, loss_spatial_dice_5: 0.37180/0.19714, loss_spatial_ce_5: 0.67389/0.09736, loss_grounding_bce_5: 0.00180/0.08240, loss_grounding_dice_5: 0.22450/0.15455, loss_grounding_ce_5: 0.01807/0.27702, loss_mask_ce_6: 3.22100/0.83142, loss_mask_bce_6: 0.02831/0.31027, loss_mask_dice_6: 1.45077/1.05705, loss_spatial_bce_6: 0.00637/0.09768, loss_spatial_dice_6: 0.36440/0.19937, loss_spatial_ce_6: 0.43776/0.12029, loss_grounding_bce_6: 0.00208/0.08336, loss_grounding_dice_6: 0.14922/0.15511, loss_grounding_ce_6: 0.02475/0.28673, loss_mask_ce_7: 1.60835/0.88759, loss_mask_bce_7: 0.02644/0.31760, loss_mask_dice_7: 1.16516/1.10373, loss_spatial_bce_7: 0.00485/0.10787, loss_spatial_dice_7: 0.40345/0.22457, loss_spatial_ce_7: 0.15462/0.15945, loss_grounding_bce_7: 0.00113/0.08504, loss_grounding_dice_7: 0.20537/0.16083, loss_grounding_ce_7: 0.23491/0.32157, loss_mask_ce_8: 2.20447/1.02531, loss_mask_bce_8: 0.02295/0.33372, loss_mask_dice_8: 1.06878/1.18102, loss_spatial_bce_8: 0.00965/0.12621, loss_spatial_dice_8: 0.44401/0.26143, loss_spatial_ce_8: 0.42338/0.21149, loss_grounding_bce_8: 0.00089/0.08902, loss_grounding_dice_8: 0.16267/0.17047, loss_grounding_ce_8: 0.40715/0.42424, loss_mask_ce_9: 2.60653/3.48375, loss_mask_bce_9: 0.01251/0.36074, loss_mask_dice_9: 1.53043/1.76469, loss_spatial_bce_9: 0.01144/0.35558, loss_spatial_dice_9: 0.80765/0.79446, loss_spatial_ce_9: 1.26349/1.39634, loss_grounding_bce_9: 0.00048/0.10107, loss_grounding_dice_9: 0.10889/0.24338, loss_grounding_ce_9: 1.23038/0.68356] items per batch[64] items per second[0.37] total items[2828800] mini batches[ 44200] memory[4999] epoch remaining[0:43:22] INFO:trainer.default_trainer:epochs[ 24] optim steps[44300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.40884/0.76465, loss_mask_bce_0: 0.05250/0.30191, loss_mask_dice_0: 4.58958/1.02646, loss_spatial_bce_0: 0.00186/0.08662, loss_spatial_dice_0: 0.41363/0.18291, loss_spatial_ce_0: 0.09755/0.06133, loss_grounding_bce_0: 0.00118/0.08093, loss_grounding_dice_0: 0.04439/0.15117, loss_grounding_ce_0: 0.00406/0.24965, loss_mask_ce_1: 1.43441/0.76587, loss_mask_bce_1: 0.04769/0.30271, loss_mask_dice_1: 4.16177/1.02994, loss_spatial_bce_1: 0.00161/0.08687, loss_spatial_dice_1: 0.42982/0.18552, loss_spatial_ce_1: 0.14890/0.06535, loss_grounding_bce_1: 0.00213/0.08112, loss_grounding_dice_1: 0.05854/0.15188, loss_grounding_ce_1: 0.00184/0.25098, loss_mask_ce_2: 1.38482/0.77398, loss_mask_bce_2: 0.05601/0.30283, loss_mask_dice_2: 4.65180/1.03126, loss_spatial_bce_2: 0.00204/0.08680, loss_spatial_dice_2: 0.45598/0.18572, loss_spatial_ce_2: 0.20623/0.06781, loss_grounding_bce_2: 0.00246/0.08105, loss_grounding_dice_2: 0.06034/0.15164, loss_grounding_ce_2: 0.00130/0.25370, loss_mask_ce_3: 1.53384/0.77621, loss_mask_bce_3: 0.05792/0.30437, loss_mask_dice_3: 3.99108/1.02826, loss_spatial_bce_3: 0.00369/0.08877, loss_spatial_dice_3: 0.52088/0.18683, loss_spatial_ce_3: 0.09586/0.07239, loss_grounding_bce_3: 0.00137/0.08152, loss_grounding_dice_3: 0.04983/0.15124, loss_grounding_ce_3: 0.00090/0.25343, loss_mask_ce_4: 1.30408/0.78241, loss_mask_bce_4: 0.04745/0.30667, loss_mask_dice_4: 4.87754/1.04751, loss_spatial_bce_4: 0.00363/0.09072, loss_spatial_dice_4: 0.56666/0.19455, loss_spatial_ce_4: 0.02387/0.08514, loss_grounding_bce_4: 0.00162/0.08212, loss_grounding_dice_4: 0.05960/0.15384, loss_grounding_ce_4: 0.00184/0.25945, loss_mask_ce_5: 1.59384/0.80548, loss_mask_bce_5: 0.06008/0.30844, loss_mask_dice_5: 4.44030/1.05504, loss_spatial_bce_5: 0.00332/0.09266, loss_spatial_dice_5: 0.50992/0.19716, loss_spatial_ce_5: 0.21911/0.09736, loss_grounding_bce_5: 0.00258/0.08240, loss_grounding_dice_5: 0.09344/0.15458, loss_grounding_ce_5: 0.00803/0.27735, loss_mask_ce_6: 1.44144/0.83169, loss_mask_bce_6: 0.05148/0.31031, loss_mask_dice_6: 4.33518/1.05762, loss_spatial_bce_6: 0.00692/0.09770, loss_spatial_dice_6: 0.66362/0.19940, loss_spatial_ce_6: 0.09179/0.12028, loss_grounding_bce_6: 0.00185/0.08336, loss_grounding_dice_6: 0.07039/0.15512, loss_grounding_ce_6: 0.00677/0.28693, loss_mask_ce_7: 1.18150/0.88784, loss_mask_bce_7: 0.06763/0.31765, loss_mask_dice_7: 5.05321/1.10444, loss_spatial_bce_7: 0.00317/0.10789, loss_spatial_dice_7: 0.64370/0.22460, loss_spatial_ce_7: 0.16330/0.15944, loss_grounding_bce_7: 0.00221/0.08505, loss_grounding_dice_7: 0.06547/0.16087, loss_grounding_ce_7: 0.00623/0.32175, loss_mask_ce_8: 1.89711/1.02557, loss_mask_bce_8: 0.09108/0.33379, loss_mask_dice_8: 5.10680/1.18167, loss_spatial_bce_8: 0.00278/0.12622, loss_spatial_dice_8: 0.53285/0.26145, loss_spatial_ce_8: 0.76591/0.21147, loss_grounding_bce_8: 0.00373/0.08903, loss_grounding_dice_8: 0.12532/0.17048, loss_grounding_ce_8: 0.06338/0.42460, loss_mask_ce_9: 7.02931/3.48431, loss_mask_bce_9: 0.04511/0.36080, loss_mask_dice_9: 5.46403/1.76553, loss_spatial_bce_9: 0.00777/0.35551, loss_spatial_dice_9: 0.91705/0.79450, loss_spatial_ce_9: 2.21498/1.39636, loss_grounding_bce_9: 0.00364/0.10106, loss_grounding_dice_9: 0.26078/0.24342, loss_grounding_ce_9: 0.87680/0.68388] items per batch[64] items per second[0.37] total items[2835200] mini batches[ 44300] memory[4999] epoch remaining[0:40:21] INFO:trainer.default_trainer:epochs[ 24] optim steps[44400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58579/0.76481, loss_mask_bce_0: 0.06530/0.30190, loss_mask_dice_0: 0.66947/1.02674, loss_spatial_bce_0: 0.01460/0.08660, loss_spatial_dice_0: 0.15248/0.18293, loss_spatial_ce_0: 0.00055/0.06129, loss_grounding_bce_0: 0.00738/0.08089, loss_grounding_dice_0: 0.17625/0.15124, loss_grounding_ce_0: 0.05785/0.24972, loss_mask_ce_1: 0.66244/0.76601, loss_mask_bce_1: 0.06535/0.30270, loss_mask_dice_1: 0.67211/1.03020, loss_spatial_bce_1: 0.01576/0.08685, loss_spatial_dice_1: 0.15944/0.18553, loss_spatial_ce_1: 0.00019/0.06531, loss_grounding_bce_1: 0.00762/0.08109, loss_grounding_dice_1: 0.16570/0.15197, loss_grounding_ce_1: 0.05114/0.25106, loss_mask_ce_2: 0.80714/0.77408, loss_mask_bce_2: 0.04725/0.30282, loss_mask_dice_2: 0.58743/1.03149, loss_spatial_bce_2: 0.01563/0.08679, loss_spatial_dice_2: 0.18974/0.18573, loss_spatial_ce_2: 0.59818/0.06781, loss_grounding_bce_2: 0.00769/0.08101, loss_grounding_dice_2: 0.17421/0.15172, loss_grounding_ce_2: 0.06278/0.25380, loss_mask_ce_3: 0.78664/0.77632, loss_mask_bce_3: 0.07111/0.30437, loss_mask_dice_3: 0.76907/1.02852, loss_spatial_bce_3: 0.01568/0.08875, loss_spatial_dice_3: 0.15350/0.18685, loss_spatial_ce_3: 0.00044/0.07236, loss_grounding_bce_3: 0.00707/0.08149, loss_grounding_dice_3: 0.16722/0.15134, loss_grounding_ce_3: 0.05956/0.25350, loss_mask_ce_4: 0.73756/0.78254, loss_mask_bce_4: 0.07491/0.30666, loss_mask_dice_4: 0.77121/1.04778, loss_spatial_bce_4: 0.01787/0.09070, loss_spatial_dice_4: 0.18309/0.19456, loss_spatial_ce_4: 0.00015/0.08510, loss_grounding_bce_4: 0.00831/0.08209, loss_grounding_dice_4: 0.17682/0.15393, loss_grounding_ce_4: 0.06033/0.25947, loss_mask_ce_5: 0.93276/0.80558, loss_mask_bce_5: 0.06737/0.30845, loss_mask_dice_5: 0.70213/1.05533, loss_spatial_bce_5: 0.01561/0.09264, loss_spatial_dice_5: 0.15332/0.19718, loss_spatial_ce_5: 0.00150/0.09732, loss_grounding_bce_5: 0.00863/0.08237, loss_grounding_dice_5: 0.20368/0.15465, loss_grounding_ce_5: 0.07211/0.27736, loss_mask_ce_6: 1.60109/0.83187, loss_mask_bce_6: 0.06540/0.31032, loss_mask_dice_6: 0.95934/1.05792, loss_spatial_bce_6: 0.01614/0.09769, loss_spatial_dice_6: 0.17836/0.19943, loss_spatial_ce_6: 0.06223/0.12026, loss_grounding_bce_6: 0.00967/0.08334, loss_grounding_dice_6: 0.20631/0.15520, loss_grounding_ce_6: 0.07123/0.28693, loss_mask_ce_7: 1.05458/0.88808, loss_mask_bce_7: 0.06848/0.31765, loss_mask_dice_7: 0.74205/1.10474, loss_spatial_bce_7: 0.02348/0.10786, loss_spatial_dice_7: 0.24574/0.22462, loss_spatial_ce_7: 0.08015/0.15940, loss_grounding_bce_7: 0.00470/0.08503, loss_grounding_dice_7: 0.12126/0.16097, loss_grounding_ce_7: 0.22942/0.32175, loss_mask_ce_8: 1.54474/1.02593, loss_mask_bce_8: 0.06801/0.33381, loss_mask_dice_8: 0.68563/1.18204, loss_spatial_bce_8: 0.02603/0.12619, loss_spatial_dice_8: 0.25303/0.26147, loss_spatial_ce_8: 0.02963/0.21138, loss_grounding_bce_8: 0.00617/0.08901, loss_grounding_dice_8: 0.15446/0.17059, loss_grounding_ce_8: 0.39510/0.42464, loss_mask_ce_9: 4.04581/3.48462, loss_mask_bce_9: 0.06788/0.36081, loss_mask_dice_9: 1.29698/1.76576, loss_spatial_bce_9: 0.12263/0.35548, loss_spatial_dice_9: 0.86295/0.79454, loss_spatial_ce_9: 1.17703/1.39655, loss_grounding_bce_9: 0.01841/0.10103, loss_grounding_dice_9: 0.33186/0.24356, loss_grounding_ce_9: 0.51830/0.68369] items per batch[64] items per second[0.36] total items[2841600] mini batches[ 44400] memory[4999] epoch remaining[0:37:25] INFO:trainer.default_trainer:epochs[ 24] optim steps[44500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22916/0.76450, loss_mask_bce_0: 0.45438/0.30186, loss_mask_dice_0: 0.45784/1.02646, loss_spatial_bce_0: 0.10645/0.08659, loss_spatial_dice_0: 0.09638/0.18289, loss_spatial_ce_0: 0.03706/0.06129, loss_grounding_bce_0: 0.07522/0.08088, loss_grounding_dice_0: 0.07460/0.15121, loss_grounding_ce_0: 0.83888/0.24965, loss_mask_ce_1: 0.23163/0.76567, loss_mask_bce_1: 0.45756/0.30265, loss_mask_dice_1: 0.45873/1.02992, loss_spatial_bce_1: 0.11180/0.08684, loss_spatial_dice_1: 0.10358/0.18550, loss_spatial_ce_1: 0.03654/0.06529, loss_grounding_bce_1: 0.07819/0.08107, loss_grounding_dice_1: 0.07830/0.15196, loss_grounding_ce_1: 0.84525/0.25098, loss_mask_ce_2: 0.23363/0.77373, loss_mask_bce_2: 0.46367/0.30277, loss_mask_dice_2: 0.46800/1.03130, loss_spatial_bce_2: 0.11805/0.08677, loss_spatial_dice_2: 0.10253/0.18570, loss_spatial_ce_2: 0.04937/0.06779, loss_grounding_bce_2: 0.08447/0.08100, loss_grounding_dice_2: 0.08416/0.15171, loss_grounding_ce_2: 0.90085/0.25371, loss_mask_ce_3: 0.22274/0.77605, loss_mask_bce_3: 0.46677/0.30431, loss_mask_dice_3: 0.44740/1.02830, loss_spatial_bce_3: 0.11104/0.08874, loss_spatial_dice_3: 0.10769/0.18682, loss_spatial_ce_3: 0.06404/0.07234, loss_grounding_bce_3: 0.08085/0.08147, loss_grounding_dice_3: 0.07553/0.15130, loss_grounding_ce_3: 0.78661/0.25336, loss_mask_ce_4: 0.21820/0.78223, loss_mask_bce_4: 0.46880/0.30660, loss_mask_dice_4: 0.44956/1.04751, loss_spatial_bce_4: 0.11602/0.09068, loss_spatial_dice_4: 0.11472/0.19454, loss_spatial_ce_4: 0.16093/0.08506, loss_grounding_bce_4: 0.08171/0.08208, loss_grounding_dice_4: 0.07107/0.15389, loss_grounding_ce_4: 0.87920/0.25937, loss_mask_ce_5: 0.19045/0.80527, loss_mask_bce_5: 0.48630/0.30838, loss_mask_dice_5: 0.43029/1.05513, loss_spatial_bce_5: 0.11666/0.09263, loss_spatial_dice_5: 0.10902/0.19714, loss_spatial_ce_5: 0.14956/0.09730, loss_grounding_bce_5: 0.07915/0.08236, loss_grounding_dice_5: 0.07108/0.15461, loss_grounding_ce_5: 0.97919/0.27723, loss_mask_ce_6: 0.16123/0.83152, loss_mask_bce_6: 0.48118/0.31026, loss_mask_dice_6: 0.42669/1.05768, loss_spatial_bce_6: 0.12463/0.09768, loss_spatial_dice_6: 0.10756/0.19941, loss_spatial_ce_6: 0.21321/0.12021, loss_grounding_bce_6: 0.08829/0.08333, loss_grounding_dice_6: 0.07366/0.15517, loss_grounding_ce_6: 1.11749/0.28681, loss_mask_ce_7: 0.22785/0.88774, loss_mask_bce_7: 0.48200/0.31759, loss_mask_dice_7: 0.45811/1.10452, loss_spatial_bce_7: 0.12077/0.10786, loss_spatial_dice_7: 0.11333/0.22459, loss_spatial_ce_7: 0.09942/0.15935, loss_grounding_bce_7: 0.08337/0.08501, loss_grounding_dice_7: 0.08336/0.16094, loss_grounding_ce_7: 0.93598/0.32164, loss_mask_ce_8: 0.35004/1.02550, loss_mask_bce_8: 0.46781/0.33373, loss_mask_dice_8: 0.52621/1.18170, loss_spatial_bce_8: 0.19784/0.12620, loss_spatial_dice_8: 0.17175/0.26143, loss_spatial_ce_8: 0.13176/0.21134, loss_grounding_bce_8: 0.07695/0.08899, loss_grounding_dice_8: 0.06080/0.17055, loss_grounding_ce_8: 1.06927/0.42462, loss_mask_ce_9: 2.04284/3.48401, loss_mask_bce_9: 0.66899/0.36071, loss_mask_dice_9: 0.99214/1.76516, loss_spatial_bce_9: 0.40722/0.35547, loss_spatial_dice_9: 0.90435/0.79445, loss_spatial_ce_9: 1.55428/1.39640, loss_grounding_bce_9: 0.13993/0.10100, loss_grounding_dice_9: 0.14518/0.24349, loss_grounding_ce_9: 2.52511/0.68369] items per batch[64] items per second[0.36] total items[2848000] mini batches[ 44500] memory[4999] epoch remaining[0:34:30] INFO:trainer.default_trainer:epochs[ 24] optim steps[44600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53234/0.76453, loss_mask_bce_0: 0.38414/0.30180, loss_mask_dice_0: 0.34211/1.02615, loss_spatial_bce_0: 0.13545/0.08656, loss_spatial_dice_0: 0.09418/0.18285, loss_spatial_ce_0: 0.04731/0.06127, loss_grounding_bce_0: 0.01990/0.08088, loss_grounding_dice_0: 0.01113/0.15122, loss_grounding_ce_0: 0.03996/0.24955, loss_mask_ce_1: 1.53056/0.76569, loss_mask_bce_1: 0.37859/0.30259, loss_mask_dice_1: 0.33305/1.02961, loss_spatial_bce_1: 0.13455/0.08681, loss_spatial_dice_1: 0.09303/0.18545, loss_spatial_ce_1: 0.04788/0.06529, loss_grounding_bce_1: 0.02463/0.08107, loss_grounding_dice_1: 0.00901/0.15197, loss_grounding_ce_1: 0.03631/0.25090, loss_mask_ce_2: 1.57457/0.77373, loss_mask_bce_2: 0.37785/0.30271, loss_mask_dice_2: 0.34231/1.03098, loss_spatial_bce_2: 0.13685/0.08675, loss_spatial_dice_2: 0.08950/0.18566, loss_spatial_ce_2: 0.04717/0.06777, loss_grounding_bce_2: 0.02254/0.08100, loss_grounding_dice_2: 0.00830/0.15171, loss_grounding_ce_2: 0.05188/0.25364, loss_mask_ce_3: 1.68833/0.77609, loss_mask_bce_3: 0.37165/0.30426, loss_mask_dice_3: 0.32831/1.02801, loss_spatial_bce_3: 0.13551/0.08872, loss_spatial_dice_3: 0.08597/0.18678, loss_spatial_ce_3: 0.04764/0.07233, loss_grounding_bce_3: 0.02121/0.08147, loss_grounding_dice_3: 0.00958/0.15132, loss_grounding_ce_3: 0.06173/0.25332, loss_mask_ce_4: 1.65713/0.78227, loss_mask_bce_4: 0.39680/0.30654, loss_mask_dice_4: 0.34657/1.04722, loss_spatial_bce_4: 0.14166/0.09067, loss_spatial_dice_4: 0.09582/0.19450, loss_spatial_ce_4: 0.04675/0.08503, loss_grounding_bce_4: 0.02371/0.08207, loss_grounding_dice_4: 0.01004/0.15390, loss_grounding_ce_4: 0.06243/0.25939, loss_mask_ce_5: 1.60409/0.80527, loss_mask_bce_5: 0.43575/0.30832, loss_mask_dice_5: 0.37630/1.05484, loss_spatial_bce_5: 0.15434/0.09261, loss_spatial_dice_5: 0.10918/0.19711, loss_spatial_ce_5: 0.04736/0.09724, loss_grounding_bce_5: 0.02242/0.08236, loss_grounding_dice_5: 0.00992/0.15462, loss_grounding_ce_5: 0.06600/0.27720, loss_mask_ce_6: 1.49649/0.83155, loss_mask_bce_6: 0.44193/0.31021, loss_mask_dice_6: 0.37916/1.05733, loss_spatial_bce_6: 0.14771/0.09767, loss_spatial_dice_6: 0.10448/0.19940, loss_spatial_ce_6: 0.04805/0.12017, loss_grounding_bce_6: 0.02266/0.08332, loss_grounding_dice_6: 0.00956/0.15519, loss_grounding_ce_6: 0.07977/0.28668, loss_mask_ce_7: 1.52763/0.88782, loss_mask_bce_7: 0.46581/0.31752, loss_mask_dice_7: 0.39086/1.10415, loss_spatial_bce_7: 0.20067/0.10787, loss_spatial_dice_7: 0.14074/0.22458, loss_spatial_ce_7: 0.09919/0.15931, loss_grounding_bce_7: 0.02191/0.08499, loss_grounding_dice_7: 0.00980/0.16095, loss_grounding_ce_7: 0.13848/0.32152, loss_mask_ce_8: 1.40694/1.02554, loss_mask_bce_8: 0.54954/0.33367, loss_mask_dice_8: 0.40560/1.18136, loss_spatial_bce_8: 0.17336/0.12618, loss_spatial_dice_8: 0.14007/0.26142, loss_spatial_ce_8: 0.11243/0.21126, loss_grounding_bce_8: 0.02266/0.08898, loss_grounding_dice_8: 0.01562/0.17056, loss_grounding_ce_8: 0.05827/0.42462, loss_mask_ce_9: 4.81732/3.48424, loss_mask_bce_9: 0.71104/0.36062, loss_mask_dice_9: 1.53076/1.76468, loss_spatial_bce_9: 0.51993/0.35546, loss_spatial_dice_9: 0.56239/0.79446, loss_spatial_ce_9: 1.09672/1.39640, loss_grounding_bce_9: 0.01961/0.10097, loss_grounding_dice_9: 0.03124/0.24351, loss_grounding_ce_9: 0.35860/0.68353] items per batch[64] items per second[0.36] total items[2854400] mini batches[ 44600] memory[4999] epoch remaining[0:31:34] INFO:trainer.default_trainer:epochs[ 24] optim steps[44700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08658/0.76456, loss_mask_bce_0: 0.08846/0.30175, loss_mask_dice_0: 0.52619/1.02611, loss_spatial_bce_0: 0.02629/0.08654, loss_spatial_dice_0: 0.13146/0.18283, loss_spatial_ce_0: 0.00764/0.06122, loss_grounding_bce_0: 0.04047/0.08086, loss_grounding_dice_0: 0.06115/0.15118, loss_grounding_ce_0: 0.00045/0.24958, loss_mask_ce_1: 0.07329/0.76570, loss_mask_bce_1: 0.09073/0.30254, loss_mask_dice_1: 0.40828/1.02957, loss_spatial_bce_1: 0.02382/0.08678, loss_spatial_dice_1: 0.13621/0.18543, loss_spatial_ce_1: 0.00346/0.06526, loss_grounding_bce_1: 0.03496/0.08105, loss_grounding_dice_1: 0.05493/0.15194, loss_grounding_ce_1: 0.00027/0.25087, loss_mask_ce_2: 0.07166/0.77370, loss_mask_bce_2: 0.08759/0.30267, loss_mask_dice_2: 0.40382/1.03099, loss_spatial_bce_2: 0.02110/0.08672, loss_spatial_dice_2: 0.12034/0.18565, loss_spatial_ce_2: 0.02671/0.06773, loss_grounding_bce_2: 0.04011/0.08098, loss_grounding_dice_2: 0.06082/0.15169, loss_grounding_ce_2: 0.00069/0.25366, loss_mask_ce_3: 0.09311/0.77609, loss_mask_bce_3: 0.09206/0.30421, loss_mask_dice_3: 0.52463/1.02797, loss_spatial_bce_3: 0.02321/0.08869, loss_spatial_dice_3: 0.13597/0.18677, loss_spatial_ce_3: 0.12369/0.07231, loss_grounding_bce_3: 0.03727/0.08145, loss_grounding_dice_3: 0.05432/0.15127, loss_grounding_ce_3: 0.00047/0.25336, loss_mask_ce_4: 0.09808/0.78232, loss_mask_bce_4: 0.09041/0.30648, loss_mask_dice_4: 0.53334/1.04720, loss_spatial_bce_4: 0.02276/0.09064, loss_spatial_dice_4: 0.11867/0.19450, loss_spatial_ce_4: 0.14233/0.08500, loss_grounding_bce_4: 0.04037/0.08204, loss_grounding_dice_4: 0.05918/0.15386, loss_grounding_ce_4: 0.00035/0.25938, loss_mask_ce_5: 0.08324/0.80528, loss_mask_bce_5: 0.09399/0.30827, loss_mask_dice_5: 0.51177/1.05483, loss_spatial_bce_5: 0.02007/0.09259, loss_spatial_dice_5: 0.11950/0.19710, loss_spatial_ce_5: 0.09842/0.09724, loss_grounding_bce_5: 0.03758/0.08233, loss_grounding_dice_5: 0.05531/0.15459, loss_grounding_ce_5: 0.00031/0.27731, loss_mask_ce_6: 0.16282/0.83165, loss_mask_bce_6: 0.08794/0.31014, loss_mask_dice_6: 0.50220/1.05729, loss_spatial_bce_6: 0.02344/0.09764, loss_spatial_dice_6: 0.14051/0.19939, loss_spatial_ce_6: 0.16283/0.12014, loss_grounding_bce_6: 0.03528/0.08329, loss_grounding_dice_6: 0.05780/0.15515, loss_grounding_ce_6: 0.00030/0.28673, loss_mask_ce_7: 0.18748/0.88790, loss_mask_bce_7: 0.09608/0.31745, loss_mask_dice_7: 0.54098/1.10411, loss_spatial_bce_7: 0.02369/0.10784, loss_spatial_dice_7: 0.12199/0.22456, loss_spatial_ce_7: 0.21152/0.15927, loss_grounding_bce_7: 0.03899/0.08496, loss_grounding_dice_7: 0.06253/0.16091, loss_grounding_ce_7: 0.00083/0.32155, loss_mask_ce_8: 0.34648/1.02557, loss_mask_bce_8: 0.09913/0.33361, loss_mask_dice_8: 0.59859/1.18133, loss_spatial_bce_8: 0.02535/0.12613, loss_spatial_dice_8: 0.14071/0.26139, loss_spatial_ce_8: 0.10562/0.21121, loss_grounding_bce_8: 0.03667/0.08894, loss_grounding_dice_8: 0.05438/0.17051, loss_grounding_ce_8: 0.00049/0.42461, loss_mask_ce_9: 1.45732/3.48428, loss_mask_bce_9: 0.10832/0.36059, loss_mask_dice_9: 0.75579/1.76494, loss_spatial_bce_9: 0.34993/0.35543, loss_spatial_dice_9: 0.81480/0.79450, loss_spatial_ce_9: 1.32859/1.39659, loss_grounding_bce_9: 0.04389/0.10094, loss_grounding_dice_9: 0.07062/0.24347, loss_grounding_ce_9: 0.01613/0.68355] items per batch[64] items per second[0.37] total items[2860800] mini batches[ 44700] memory[4999] epoch remaining[0:28:36] INFO:trainer.default_trainer:epochs[ 24] optim steps[44800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53443/0.76446, loss_mask_bce_0: 0.53564/0.30172, loss_mask_dice_0: 1.60782/1.02601, loss_spatial_bce_0: 0.04658/0.08652, loss_spatial_dice_0: 0.25998/0.18280, loss_spatial_ce_0: 0.00228/0.06120, loss_grounding_bce_0: 0.13091/0.08084, loss_grounding_dice_0: 0.10251/0.15116, loss_grounding_ce_0: 0.15837/0.24957, loss_mask_ce_1: 1.20804/0.76556, loss_mask_bce_1: 0.50773/0.30251, loss_mask_dice_1: 1.72506/1.02945, loss_spatial_bce_1: 0.04245/0.08676, loss_spatial_dice_1: 0.25247/0.18539, loss_spatial_ce_1: 0.00381/0.06524, loss_grounding_bce_1: 0.12899/0.08103, loss_grounding_dice_1: 0.09485/0.15192, loss_grounding_ce_1: 0.15309/0.25091, loss_mask_ce_2: 1.38037/0.77356, loss_mask_bce_2: 0.51388/0.30264, loss_mask_dice_2: 1.76407/1.03089, loss_spatial_bce_2: 0.04403/0.08671, loss_spatial_dice_2: 0.23887/0.18561, loss_spatial_ce_2: 0.04524/0.06770, loss_grounding_bce_2: 0.12967/0.08096, loss_grounding_dice_2: 0.09107/0.15169, loss_grounding_ce_2: 0.23039/0.25361, loss_mask_ce_3: 1.28972/0.77600, loss_mask_bce_3: 0.52123/0.30417, loss_mask_dice_3: 1.65276/1.02789, loss_spatial_bce_3: 0.06158/0.08868, loss_spatial_dice_3: 0.25729/0.18673, loss_spatial_ce_3: 0.03562/0.07228, loss_grounding_bce_3: 0.13987/0.08144, loss_grounding_dice_3: 0.09579/0.15126, loss_grounding_ce_3: 0.18585/0.25331, loss_mask_ce_4: 1.58619/0.78221, loss_mask_bce_4: 0.51966/0.30645, loss_mask_dice_4: 1.52531/1.04710, loss_spatial_bce_4: 0.04017/0.09064, loss_spatial_dice_4: 0.26102/0.19446, loss_spatial_ce_4: 0.03945/0.08499, loss_grounding_bce_4: 0.13486/0.08204, loss_grounding_dice_4: 0.09248/0.15384, loss_grounding_ce_4: 0.19173/0.25941, loss_mask_ce_5: 1.58339/0.80515, loss_mask_bce_5: 0.50751/0.30824, loss_mask_dice_5: 1.60268/1.05478, loss_spatial_bce_5: 0.05187/0.09259, loss_spatial_dice_5: 0.27888/0.19707, loss_spatial_ce_5: 0.03464/0.09724, loss_grounding_bce_5: 0.14577/0.08234, loss_grounding_dice_5: 0.10119/0.15457, loss_grounding_ce_5: 0.38351/0.27733, loss_mask_ce_6: 1.62681/0.83154, loss_mask_bce_6: 0.49784/0.31011, loss_mask_dice_6: 1.42923/1.05719, loss_spatial_bce_6: 0.09108/0.09763, loss_spatial_dice_6: 0.32056/0.19935, loss_spatial_ce_6: 0.11263/0.12018, loss_grounding_bce_6: 0.13966/0.08330, loss_grounding_dice_6: 0.09469/0.15515, loss_grounding_ce_6: 0.41212/0.28674, loss_mask_ce_7: 1.35234/0.88776, loss_mask_bce_7: 0.50166/0.31742, loss_mask_dice_7: 1.55501/1.10409, loss_spatial_bce_7: 0.20946/0.10785, loss_spatial_dice_7: 0.40574/0.22453, loss_spatial_ce_7: 0.24428/0.15930, loss_grounding_bce_7: 0.14253/0.08496, loss_grounding_dice_7: 0.09404/0.16093, loss_grounding_ce_7: 0.64725/0.32150, loss_mask_ce_8: 1.66874/1.02535, loss_mask_bce_8: 0.51990/0.33359, loss_mask_dice_8: 1.55796/1.18132, loss_spatial_bce_8: 0.13432/0.12612, loss_spatial_dice_8: 0.43448/0.26137, loss_spatial_ce_8: 0.41181/0.21121, loss_grounding_bce_8: 0.13843/0.08894, loss_grounding_dice_8: 0.09196/0.17052, loss_grounding_ce_8: 0.53653/0.42456, loss_mask_ce_9: 5.25213/3.48396, loss_mask_bce_9: 0.62598/0.36061, loss_mask_dice_9: 2.95902/1.76517, loss_spatial_bce_9: 0.40517/0.35558, loss_spatial_dice_9: 0.96031/0.79452, loss_spatial_ce_9: 2.07235/1.39654, loss_grounding_bce_9: 0.17356/0.10095, loss_grounding_dice_9: 0.13703/0.24349, loss_grounding_ce_9: 1.52489/0.68337] items per batch[64] items per second[0.37] total items[2867200] mini batches[ 44800] memory[4999] epoch remaining[0:25:39] INFO:trainer.default_trainer:epochs[ 24] optim steps[44900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01361/0.76451, loss_mask_bce_0: 0.22024/0.30174, loss_mask_dice_0: 0.19483/1.02635, loss_spatial_bce_0: 0.20866/0.08650, loss_spatial_dice_0: 0.16955/0.18278, loss_spatial_ce_0: 0.00001/0.06115, loss_grounding_bce_0: 0.23187/0.08085, loss_grounding_dice_0: 0.20601/0.15117, loss_grounding_ce_0: 0.00074/0.24959, loss_mask_ce_1: 0.01688/0.76559, loss_mask_bce_1: 0.21695/0.30254, loss_mask_dice_1: 0.18935/1.02984, loss_spatial_bce_1: 0.20824/0.08674, loss_spatial_dice_1: 0.17312/0.18537, loss_spatial_ce_1: 0.00002/0.06521, loss_grounding_bce_1: 0.23688/0.08103, loss_grounding_dice_1: 0.20822/0.15195, loss_grounding_ce_1: 0.00077/0.25091, loss_mask_ce_2: 0.02348/0.77356, loss_mask_bce_2: 0.22228/0.30267, loss_mask_dice_2: 0.19557/1.03129, loss_spatial_bce_2: 0.20972/0.08669, loss_spatial_dice_2: 0.17063/0.18560, loss_spatial_ce_2: 0.00005/0.06768, loss_grounding_bce_2: 0.23719/0.08097, loss_grounding_dice_2: 0.21074/0.15173, loss_grounding_ce_2: 0.00161/0.25365, loss_mask_ce_3: 0.01339/0.77598, loss_mask_bce_3: 0.21726/0.30421, loss_mask_dice_3: 0.19229/1.02823, loss_spatial_bce_3: 0.21543/0.08866, loss_spatial_dice_3: 0.18264/0.18671, loss_spatial_ce_3: 0.00007/0.07224, loss_grounding_bce_3: 0.23051/0.08144, loss_grounding_dice_3: 0.20475/0.15129, loss_grounding_ce_3: 0.00100/0.25337, loss_mask_ce_4: 0.01998/0.78222, loss_mask_bce_4: 0.21190/0.30649, loss_mask_dice_4: 0.18747/1.04750, loss_spatial_bce_4: 0.20977/0.09062, loss_spatial_dice_4: 0.18040/0.19446, loss_spatial_ce_4: 0.00010/0.08494, loss_grounding_bce_4: 0.22538/0.08204, loss_grounding_dice_4: 0.20223/0.15387, loss_grounding_ce_4: 0.00208/0.25940, loss_mask_ce_5: 0.01576/0.80515, loss_mask_bce_5: 0.21640/0.30828, loss_mask_dice_5: 0.18983/1.05519, loss_spatial_bce_5: 0.21950/0.09257, loss_spatial_dice_5: 0.20558/0.19707, loss_spatial_ce_5: 0.00058/0.09727, loss_grounding_bce_5: 0.22789/0.08235, loss_grounding_dice_5: 0.19792/0.15461, loss_grounding_ce_5: 0.00206/0.27728, loss_mask_ce_6: 0.01870/0.83160, loss_mask_bce_6: 0.21371/0.31014, loss_mask_dice_6: 0.18545/1.05754, loss_spatial_bce_6: 0.21170/0.09761, loss_spatial_dice_6: 0.20377/0.19933, loss_spatial_ce_6: 0.01404/0.12015, loss_grounding_bce_6: 0.23093/0.08331, loss_grounding_dice_6: 0.20028/0.15518, loss_grounding_ce_6: 0.00187/0.28668, loss_mask_ce_7: 0.02556/0.88782, loss_mask_bce_7: 0.21518/0.31743, loss_mask_dice_7: 0.19008/1.10443, loss_spatial_bce_7: 0.20734/0.10784, loss_spatial_dice_7: 0.17826/0.22451, loss_spatial_ce_7: 0.93194/0.15925, loss_grounding_bce_7: 0.22892/0.08497, loss_grounding_dice_7: 0.20878/0.16097, loss_grounding_ce_7: 0.00418/0.32140, loss_mask_ce_8: 0.03285/1.02541, loss_mask_bce_8: 0.20858/0.33362, loss_mask_dice_8: 0.16784/1.18183, loss_spatial_bce_8: 0.21032/0.12610, loss_spatial_dice_8: 0.16831/0.26137, loss_spatial_ce_8: 0.01555/0.21111, loss_grounding_bce_8: 0.22279/0.08895, loss_grounding_dice_8: 0.18037/0.17053, loss_grounding_ce_8: 0.00468/0.42453, loss_mask_ce_9: 0.99901/3.48440, loss_mask_bce_9: 0.21026/0.36060, loss_mask_dice_9: 0.18171/1.76571, loss_spatial_bce_9: 0.75060/0.35560, loss_spatial_dice_9: 0.55808/0.79453, loss_spatial_ce_9: 1.15978/1.39657, loss_grounding_bce_9: 0.22401/0.10095, loss_grounding_dice_9: 0.19144/0.24349, loss_grounding_ce_9: 0.01714/0.68333] items per batch[64] items per second[0.37] total items[2873600] mini batches[ 44900] memory[4999] epoch remaining[0:22:41] INFO:trainer.default_trainer:epochs[ 24] optim steps[45000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01197/0.76434, loss_mask_bce_0: 0.04522/0.30166, loss_mask_dice_0: 0.04178/1.02603, loss_spatial_bce_0: 0.04272/0.08648, loss_spatial_dice_0: 0.03351/0.18275, loss_spatial_ce_0: 0.00001/0.06112, loss_grounding_bce_0: 0.03036/0.08084, loss_grounding_dice_0: 0.04539/0.15120, loss_grounding_ce_0: 0.00087/0.24949, loss_mask_ce_1: 0.01355/0.76543, loss_mask_bce_1: 0.04747/0.30245, loss_mask_dice_1: 0.04324/1.02955, loss_spatial_bce_1: 0.04432/0.08673, loss_spatial_dice_1: 0.03711/0.18534, loss_spatial_ce_1: 0.00001/0.06516, loss_grounding_bce_1: 0.02658/0.08103, loss_grounding_dice_1: 0.04114/0.15195, loss_grounding_ce_1: 0.00067/0.25092, loss_mask_ce_2: 0.01096/0.77334, loss_mask_bce_2: 0.04386/0.30258, loss_mask_dice_2: 0.04168/1.03102, loss_spatial_bce_2: 0.04517/0.08667, loss_spatial_dice_2: 0.03836/0.18556, loss_spatial_ce_2: 0.00003/0.06763, loss_grounding_bce_2: 0.02828/0.08096, loss_grounding_dice_2: 0.04249/0.15175, loss_grounding_ce_2: 0.00029/0.25356, loss_mask_ce_3: 0.01062/0.77583, loss_mask_bce_3: 0.04791/0.30412, loss_mask_dice_3: 0.04623/1.02791, loss_spatial_bce_3: 0.04345/0.08865, loss_spatial_dice_3: 0.03356/0.18666, loss_spatial_ce_3: 0.00007/0.07220, loss_grounding_bce_3: 0.02720/0.08144, loss_grounding_dice_3: 0.04332/0.15133, loss_grounding_ce_3: 0.00057/0.25331, loss_mask_ce_4: 0.00948/0.78203, loss_mask_bce_4: 0.04691/0.30641, loss_mask_dice_4: 0.04442/1.04727, loss_spatial_bce_4: 0.04584/0.09060, loss_spatial_dice_4: 0.04016/0.19442, loss_spatial_ce_4: 0.00014/0.08489, loss_grounding_bce_4: 0.02595/0.08204, loss_grounding_dice_4: 0.04056/0.15391, loss_grounding_ce_4: 0.00050/0.25926, loss_mask_ce_5: 0.01221/0.80505, loss_mask_bce_5: 0.04927/0.30819, loss_mask_dice_5: 0.04469/1.05485, loss_spatial_bce_5: 0.04332/0.09255, loss_spatial_dice_5: 0.03838/0.19704, loss_spatial_ce_5: 0.00036/0.09724, loss_grounding_bce_5: 0.02635/0.08236, loss_grounding_dice_5: 0.04102/0.15465, loss_grounding_ce_5: 0.00072/0.27711, loss_mask_ce_6: 0.01819/0.83149, loss_mask_bce_6: 0.04443/0.31005, loss_mask_dice_6: 0.04302/1.05723, loss_spatial_bce_6: 0.04599/0.09759, loss_spatial_dice_6: 0.03942/0.19929, loss_spatial_ce_6: 0.00005/0.12014, loss_grounding_bce_6: 0.02872/0.08332, loss_grounding_dice_6: 0.04545/0.15522, loss_grounding_ce_6: 0.00440/0.28649, loss_mask_ce_7: 0.07219/0.88766, loss_mask_bce_7: 0.04968/0.31735, loss_mask_dice_7: 0.05382/1.10416, loss_spatial_bce_7: 0.05483/0.10781, loss_spatial_dice_7: 0.03716/0.22449, loss_spatial_ce_7: 0.00319/0.15924, loss_grounding_bce_7: 0.03595/0.08496, loss_grounding_dice_7: 0.06052/0.16102, loss_grounding_ce_7: 0.02988/0.32125, loss_mask_ce_8: 0.04679/1.02532, loss_mask_bce_8: 0.04743/0.33351, loss_mask_dice_8: 0.04978/1.18146, loss_spatial_bce_8: 0.05623/0.12606, loss_spatial_dice_8: 0.04790/0.26132, loss_spatial_ce_8: 0.00535/0.21109, loss_grounding_bce_8: 0.03234/0.08894, loss_grounding_dice_8: 0.05718/0.17055, loss_grounding_ce_8: 0.00200/0.42433, loss_mask_ce_9: 1.39485/3.48389, loss_mask_bce_9: 0.04838/0.36051, loss_mask_dice_9: 0.05366/1.76539, loss_spatial_bce_9: 0.42008/0.35557, loss_spatial_dice_9: 0.63748/0.79449, loss_spatial_ce_9: 1.40801/1.39632, loss_grounding_bce_9: 0.02938/0.10096, loss_grounding_dice_9: 0.04851/0.24352, loss_grounding_ce_9: 0.12824/0.68296] items per batch[64] items per second[0.36] total items[2880000] mini batches[ 45000] memory[4999] epoch remaining[0:19:46] INFO:trainer.default_trainer:epochs[ 24] optim steps[45100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19308/0.76440, loss_mask_bce_0: 0.39507/0.30169, loss_mask_dice_0: 0.37541/1.02595, loss_spatial_bce_0: 0.16306/0.08647, loss_spatial_dice_0: 0.15871/0.18272, loss_spatial_ce_0: 0.01637/0.06106, loss_grounding_bce_0: 0.14803/0.08083, loss_grounding_dice_0: 0.09089/0.15116, loss_grounding_ce_0: 0.00019/0.24951, loss_mask_ce_1: 0.19927/0.76549, loss_mask_bce_1: 0.41492/0.30249, loss_mask_dice_1: 0.38001/1.02945, loss_spatial_bce_1: 0.15683/0.08671, loss_spatial_dice_1: 0.16247/0.18532, loss_spatial_ce_1: 0.02248/0.06511, loss_grounding_bce_1: 0.14212/0.08101, loss_grounding_dice_1: 0.08348/0.15190, loss_grounding_ce_1: 0.00015/0.25096, loss_mask_ce_2: 0.19375/0.77343, loss_mask_bce_2: 0.39439/0.30262, loss_mask_dice_2: 0.38575/1.03092, loss_spatial_bce_2: 0.14913/0.08666, loss_spatial_dice_2: 0.15152/0.18553, loss_spatial_ce_2: 0.05015/0.06758, loss_grounding_bce_2: 0.13961/0.08095, loss_grounding_dice_2: 0.07897/0.15171, loss_grounding_ce_2: 0.00018/0.25362, loss_mask_ce_3: 0.22194/0.77592, loss_mask_bce_3: 0.38827/0.30414, loss_mask_dice_3: 0.40189/1.02780, loss_spatial_bce_3: 0.14979/0.08864, loss_spatial_dice_3: 0.16642/0.18664, loss_spatial_ce_3: 0.04732/0.07213, loss_grounding_bce_3: 0.14703/0.08143, loss_grounding_dice_3: 0.09375/0.15129, loss_grounding_ce_3: 0.00029/0.25337, loss_mask_ce_4: 0.22335/0.78215, loss_mask_bce_4: 0.37676/0.30644, loss_mask_dice_4: 0.38763/1.04710, loss_spatial_bce_4: 0.15812/0.09059, loss_spatial_dice_4: 0.18679/0.19441, loss_spatial_ce_4: 0.09887/0.08485, loss_grounding_bce_4: 0.14487/0.08203, loss_grounding_dice_4: 0.10324/0.15386, loss_grounding_ce_4: 0.00100/0.25930, loss_mask_ce_5: 0.24275/0.80519, loss_mask_bce_5: 0.41277/0.30823, loss_mask_dice_5: 0.40157/1.05471, loss_spatial_bce_5: 0.16450/0.09253, loss_spatial_dice_5: 0.18356/0.19701, loss_spatial_ce_5: 0.13358/0.09717, loss_grounding_bce_5: 0.17222/0.08235, loss_grounding_dice_5: 0.09811/0.15462, loss_grounding_ce_5: 0.00057/0.27721, loss_mask_ce_6: 0.24217/0.83163, loss_mask_bce_6: 0.37197/0.31010, loss_mask_dice_6: 0.38695/1.05711, loss_spatial_bce_6: 0.14577/0.09758, loss_spatial_dice_6: 0.17139/0.19928, loss_spatial_ce_6: 0.07750/0.12007, loss_grounding_bce_6: 0.17862/0.08329, loss_grounding_dice_6: 0.09120/0.15518, loss_grounding_ce_6: 0.00241/0.28648, loss_mask_ce_7: 0.26191/0.88784, loss_mask_bce_7: 0.38295/0.31741, loss_mask_dice_7: 0.38943/1.10399, loss_spatial_bce_7: 0.18558/0.10780, loss_spatial_dice_7: 0.17078/0.22447, loss_spatial_ce_7: 0.08816/0.15919, loss_grounding_bce_7: 0.16739/0.08493, loss_grounding_dice_7: 0.08339/0.16097, loss_grounding_ce_7: 0.00382/0.32123, loss_mask_ce_8: 0.27252/1.02551, loss_mask_bce_8: 0.40367/0.33354, loss_mask_dice_8: 0.44039/1.18130, loss_spatial_bce_8: 0.21813/0.12603, loss_spatial_dice_8: 0.20323/0.26129, loss_spatial_ce_8: 0.34935/0.21099, loss_grounding_bce_8: 0.15602/0.08891, loss_grounding_dice_8: 0.06196/0.17050, loss_grounding_ce_8: 0.00054/0.42438, loss_mask_ce_9: 2.02452/3.48415, loss_mask_bce_9: 0.34635/0.36061, loss_mask_dice_9: 0.49100/1.76570, loss_spatial_bce_9: 0.55355/0.35554, loss_spatial_dice_9: 0.78308/0.79450, loss_spatial_ce_9: 1.20899/1.39623, loss_grounding_bce_9: 0.17321/0.10093, loss_grounding_dice_9: 0.07864/0.24345, loss_grounding_ce_9: 0.19169/0.68300] items per batch[64] items per second[0.37] total items[2886400] mini batches[ 45100] memory[4999] epoch remaining[0:16:50] INFO:trainer.default_trainer:epochs[ 24] optim steps[45200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.64449/0.76436, loss_mask_bce_0: 0.36567/0.30167, loss_mask_dice_0: 0.27692/1.02615, loss_spatial_bce_0: 0.13845/0.08646, loss_spatial_dice_0: 0.13812/0.18270, loss_spatial_ce_0: 0.00121/0.06102, loss_grounding_bce_0: 0.19431/0.08083, loss_grounding_dice_0: 0.09055/0.15116, loss_grounding_ce_0: 0.00660/0.24948, loss_mask_ce_1: 0.99113/0.76545, loss_mask_bce_1: 0.37756/0.30247, loss_mask_dice_1: 0.31837/1.02966, loss_spatial_bce_1: 0.13926/0.08671, loss_spatial_dice_1: 0.14829/0.18530, loss_spatial_ce_1: 0.00227/0.06506, loss_grounding_bce_1: 0.19423/0.08102, loss_grounding_dice_1: 0.09254/0.15190, loss_grounding_ce_1: 0.00495/0.25093, loss_mask_ce_2: 0.43481/0.77336, loss_mask_bce_2: 0.36897/0.30260, loss_mask_dice_2: 0.73806/1.03113, loss_spatial_bce_2: 0.14132/0.08666, loss_spatial_dice_2: 0.15596/0.18550, loss_spatial_ce_2: 0.00339/0.06753, loss_grounding_bce_2: 0.17165/0.08095, loss_grounding_dice_2: 0.09583/0.15173, loss_grounding_ce_2: 0.00654/0.25356, loss_mask_ce_3: 0.89964/0.77582, loss_mask_bce_3: 0.38354/0.30412, loss_mask_dice_3: 0.35936/1.02803, loss_spatial_bce_3: 0.13790/0.08863, loss_spatial_dice_3: 0.16477/0.18662, loss_spatial_ce_3: 0.00118/0.07210, loss_grounding_bce_3: 0.15958/0.08143, loss_grounding_dice_3: 0.09648/0.15129, loss_grounding_ce_3: 0.00307/0.25326, loss_mask_ce_4: 0.72732/0.78208, loss_mask_bce_4: 0.42665/0.30641, loss_mask_dice_4: 0.30273/1.04733, loss_spatial_bce_4: 0.13443/0.09059, loss_spatial_dice_4: 0.13801/0.19439, loss_spatial_ce_4: 0.00177/0.08482, loss_grounding_bce_4: 0.20923/0.08203, loss_grounding_dice_4: 0.09051/0.15386, loss_grounding_ce_4: 0.00313/0.25925, loss_mask_ce_5: 0.57607/0.80514, loss_mask_bce_5: 0.41799/0.30821, loss_mask_dice_5: 0.36778/1.05490, loss_spatial_bce_5: 0.14560/0.09253, loss_spatial_dice_5: 0.15932/0.19699, loss_spatial_ce_5: 0.02260/0.09713, loss_grounding_bce_5: 0.16545/0.08235, loss_grounding_dice_5: 0.10684/0.15462, loss_grounding_ce_5: 0.00361/0.27716, loss_mask_ce_6: 0.86252/0.83158, loss_mask_bce_6: 0.38688/0.31008, loss_mask_dice_6: 0.33771/1.05732, loss_spatial_bce_6: 0.15583/0.09758, loss_spatial_dice_6: 0.15699/0.19925, loss_spatial_ce_6: 0.09224/0.12005, loss_grounding_bce_6: 0.15310/0.08329, loss_grounding_dice_6: 0.11455/0.15519, loss_grounding_ce_6: 0.00794/0.28636, loss_mask_ce_7: 0.49137/0.88776, loss_mask_bce_7: 0.41921/0.31739, loss_mask_dice_7: 0.48541/1.10420, loss_spatial_bce_7: 0.16275/0.10778, loss_spatial_dice_7: 0.17963/0.22442, loss_spatial_ce_7: 0.05467/0.15918, loss_grounding_bce_7: 0.16185/0.08492, loss_grounding_dice_7: 0.12364/0.16096, loss_grounding_ce_7: 0.00652/0.32114, loss_mask_ce_8: 1.30101/1.02543, loss_mask_bce_8: 0.37495/0.33352, loss_mask_dice_8: 0.40462/1.18162, loss_spatial_bce_8: 0.15376/0.12602, loss_spatial_dice_8: 0.15815/0.26125, loss_spatial_ce_8: 0.08689/0.21098, loss_grounding_bce_8: 0.16350/0.08891, loss_grounding_dice_8: 0.14308/0.17050, loss_grounding_ce_8: 0.01293/0.42416, loss_mask_ce_9: 3.33464/3.48392, loss_mask_bce_9: 0.49134/0.36062, loss_mask_dice_9: 0.56081/1.76583, loss_spatial_bce_9: 0.55941/0.35555, loss_spatial_dice_9: 0.76150/0.79445, loss_spatial_ce_9: 1.32553/1.39624, loss_grounding_bce_9: 0.25613/0.10094, loss_grounding_dice_9: 0.16132/0.24342, loss_grounding_ce_9: 0.04232/0.68276] items per batch[64] items per second[0.36] total items[2892800] mini batches[ 45200] memory[4999] epoch remaining[0:13:55] INFO:trainer.default_trainer:epochs[ 24] optim steps[45300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77558/0.76437, loss_mask_bce_0: 0.08275/0.30178, loss_mask_dice_0: 1.20196/1.02671, loss_spatial_bce_0: 0.01470/0.08643, loss_spatial_dice_0: 0.22915/0.18272, loss_spatial_ce_0: 0.00169/0.06098, loss_grounding_bce_0: 0.02007/0.08082, loss_grounding_dice_0: 0.26588/0.15116, loss_grounding_ce_0: 0.00489/0.24951, loss_mask_ce_1: 0.57358/0.76544, loss_mask_bce_1: 0.07502/0.30257, loss_mask_dice_1: 1.12826/1.03021, loss_spatial_bce_1: 0.01509/0.08668, loss_spatial_dice_1: 0.22871/0.18531, loss_spatial_ce_1: 0.00156/0.06503, loss_grounding_bce_1: 0.00974/0.08100, loss_grounding_dice_1: 0.30024/0.15191, loss_grounding_ce_1: 0.00459/0.25097, loss_mask_ce_2: 0.76379/0.77332, loss_mask_bce_2: 0.08584/0.30271, loss_mask_dice_2: 1.38878/1.03162, loss_spatial_bce_2: 0.01508/0.08663, loss_spatial_dice_2: 0.18844/0.18553, loss_spatial_ce_2: 0.00336/0.06750, loss_grounding_bce_2: 0.01119/0.08095, loss_grounding_dice_2: 0.23174/0.15173, loss_grounding_ce_2: 0.00333/0.25358, loss_mask_ce_3: 0.73611/0.77584, loss_mask_bce_3: 0.07611/0.30423, loss_mask_dice_3: 1.02317/1.02857, loss_spatial_bce_3: 0.01491/0.08860, loss_spatial_dice_3: 0.22306/0.18664, loss_spatial_ce_3: 0.00362/0.07206, loss_grounding_bce_3: 0.01555/0.08143, loss_grounding_dice_3: 0.30779/0.15131, loss_grounding_ce_3: 0.00768/0.25324, loss_mask_ce_4: 1.02130/0.78211, loss_mask_bce_4: 0.05974/0.30651, loss_mask_dice_4: 0.76044/1.04785, loss_spatial_bce_4: 0.01476/0.09056, loss_spatial_dice_4: 0.23386/0.19441, loss_spatial_ce_4: 0.32037/0.08480, loss_grounding_bce_4: 0.01206/0.08202, loss_grounding_dice_4: 0.23559/0.15387, loss_grounding_ce_4: 0.01043/0.25932, loss_mask_ce_5: 0.74463/0.80520, loss_mask_bce_5: 0.06322/0.30832, loss_mask_dice_5: 1.12566/1.05548, loss_spatial_bce_5: 0.01483/0.09250, loss_spatial_dice_5: 0.20764/0.19701, loss_spatial_ce_5: 0.00434/0.09709, loss_grounding_bce_5: 0.01342/0.08234, loss_grounding_dice_5: 0.30111/0.15464, loss_grounding_ce_5: 0.00724/0.27721, loss_mask_ce_6: 1.15384/0.83165, loss_mask_bce_6: 0.06219/0.31019, loss_mask_dice_6: 0.81296/1.05786, loss_spatial_bce_6: 0.01820/0.09754, loss_spatial_dice_6: 0.22989/0.19927, loss_spatial_ce_6: 0.06153/0.12007, loss_grounding_bce_6: 0.02194/0.08328, loss_grounding_dice_6: 0.41008/0.15520, loss_grounding_ce_6: 0.01884/0.28632, loss_mask_ce_7: 0.66941/0.88780, loss_mask_bce_7: 0.07185/0.31749, loss_mask_dice_7: 1.04462/1.10482, loss_spatial_bce_7: 0.02068/0.10775, loss_spatial_dice_7: 0.29906/0.22445, loss_spatial_ce_7: 0.03854/0.15915, loss_grounding_bce_7: 0.01910/0.08491, loss_grounding_dice_7: 0.44229/0.16096, loss_grounding_ce_7: 0.02072/0.32107, loss_mask_ce_8: 1.31165/1.02548, loss_mask_bce_8: 0.07439/0.33362, loss_mask_dice_8: 1.19391/1.18218, loss_spatial_bce_8: 0.04376/0.12598, loss_spatial_dice_8: 0.46103/0.26129, loss_spatial_ce_8: 0.06725/0.21097, loss_grounding_bce_8: 0.01395/0.08890, loss_grounding_dice_8: 0.33701/0.17051, loss_grounding_ce_8: 0.11759/0.42410, loss_mask_ce_9: 3.27566/3.48426, loss_mask_bce_9: 0.06234/0.36068, loss_mask_dice_9: 1.11439/1.76657, loss_spatial_bce_9: 0.09344/0.35548, loss_spatial_dice_9: 0.86947/0.79450, loss_spatial_ce_9: 1.67695/1.39639, loss_grounding_bce_9: 0.00390/0.10092, loss_grounding_dice_9: 0.29742/0.24343, loss_grounding_ce_9: 0.65839/0.68282] items per batch[64] items per second[0.36] total items[2899200] mini batches[ 45300] memory[4999] epoch remaining[0:10:59] INFO:trainer.default_trainer:epochs[ 24] optim steps[45400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11538/0.76441, loss_mask_bce_0: 0.04909/0.30174, loss_mask_dice_0: 0.14183/1.02670, loss_spatial_bce_0: 0.03642/0.08642, loss_spatial_dice_0: 0.11892/0.18270, loss_spatial_ce_0: 0.00938/0.06094, loss_grounding_bce_0: 0.03127/0.08081, loss_grounding_dice_0: 0.10515/0.15116, loss_grounding_ce_0: 0.00270/0.24957, loss_mask_ce_1: 0.12580/0.76547, loss_mask_bce_1: 0.04399/0.30252, loss_mask_dice_1: 0.15593/1.03018, loss_spatial_bce_1: 0.01934/0.08667, loss_spatial_dice_1: 0.07512/0.18528, loss_spatial_ce_1: 0.12031/0.06504, loss_grounding_bce_1: 0.03293/0.08099, loss_grounding_dice_1: 0.12424/0.15190, loss_grounding_ce_1: 0.00416/0.25105, loss_mask_ce_2: 0.13600/0.77333, loss_mask_bce_2: 0.04280/0.30266, loss_mask_dice_2: 0.13686/1.03157, loss_spatial_bce_2: 0.04291/0.08662, loss_spatial_dice_2: 0.13121/0.18550, loss_spatial_ce_2: 0.02334/0.06746, loss_grounding_bce_2: 0.03342/0.08093, loss_grounding_dice_2: 0.11798/0.15172, loss_grounding_ce_2: 0.00465/0.25364, loss_mask_ce_3: 0.09996/0.77590, loss_mask_bce_3: 0.05038/0.30420, loss_mask_dice_3: 0.16356/1.02855, loss_spatial_bce_3: 0.05108/0.08858, loss_spatial_dice_3: 0.13687/0.18661, loss_spatial_ce_3: 0.03714/0.07202, loss_grounding_bce_3: 0.03389/0.08141, loss_grounding_dice_3: 0.10831/0.15130, loss_grounding_ce_3: 0.00348/0.25337, loss_mask_ce_4: 0.11906/0.78214, loss_mask_bce_4: 0.05501/0.30646, loss_mask_dice_4: 0.17361/1.04780, loss_spatial_bce_4: 0.04634/0.09055, loss_spatial_dice_4: 0.14205/0.19439, loss_spatial_ce_4: 0.01190/0.08474, loss_grounding_bce_4: 0.03481/0.08200, loss_grounding_dice_4: 0.11376/0.15387, loss_grounding_ce_4: 0.00340/0.25955, loss_mask_ce_5: 0.10114/0.80526, loss_mask_bce_5: 0.05423/0.30826, loss_mask_dice_5: 0.18572/1.05536, loss_spatial_bce_5: 0.04160/0.09248, loss_spatial_dice_5: 0.11575/0.19699, loss_spatial_ce_5: 0.00533/0.09705, loss_grounding_bce_5: 0.03728/0.08232, loss_grounding_dice_5: 0.12627/0.15463, loss_grounding_ce_5: 0.00543/0.27736, loss_mask_ce_6: 0.12035/0.83175, loss_mask_bce_6: 0.04623/0.31014, loss_mask_dice_6: 0.17051/1.05782, loss_spatial_bce_6: 0.01630/0.09752, loss_spatial_dice_6: 0.06191/0.19923, loss_spatial_ce_6: 0.01047/0.12003, loss_grounding_bce_6: 0.03118/0.08327, loss_grounding_dice_6: 0.13051/0.15519, loss_grounding_ce_6: 0.00574/0.28651, loss_mask_ce_7: 0.10042/0.88789, loss_mask_bce_7: 0.03884/0.31745, loss_mask_dice_7: 0.14715/1.10476, loss_spatial_bce_7: 0.02156/0.10771, loss_spatial_dice_7: 0.09018/0.22440, loss_spatial_ce_7: 0.07502/0.15912, loss_grounding_bce_7: 0.02889/0.08489, loss_grounding_dice_7: 0.09773/0.16097, loss_grounding_ce_7: 0.00313/0.32121, loss_mask_ce_8: 0.15637/1.02558, loss_mask_bce_8: 0.03656/0.33356, loss_mask_dice_8: 0.12529/1.18202, loss_spatial_bce_8: 0.02122/0.12593, loss_spatial_dice_8: 0.08022/0.26124, loss_spatial_ce_8: 0.11941/0.21091, loss_grounding_bce_8: 0.02608/0.08889, loss_grounding_dice_8: 0.09983/0.17052, loss_grounding_ce_8: 0.00338/0.42424, loss_mask_ce_9: 1.49833/3.48474, loss_mask_bce_9: 0.02809/0.36064, loss_mask_dice_9: 0.10591/1.76651, loss_spatial_bce_9: 0.14770/0.35550, loss_spatial_dice_9: 0.50451/0.79446, loss_spatial_ce_9: 1.30856/1.39632, loss_grounding_bce_9: 0.01500/0.10090, loss_grounding_dice_9: 0.08099/0.24344, loss_grounding_ce_9: 0.08034/0.68315] items per batch[64] items per second[0.37] total items[2905600] mini batches[ 45400] memory[4999] epoch remaining[0:08:03] INFO:trainer.default_trainer:epochs[ 24] optim steps[45500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88495/0.76428, loss_mask_bce_0: 0.40870/0.30182, loss_mask_dice_0: 0.30583/1.02678, loss_spatial_bce_0: 0.15220/0.08641, loss_spatial_dice_0: 0.12219/0.18266, loss_spatial_ce_0: 0.22703/0.06092, loss_grounding_bce_0: 0.19082/0.08083, loss_grounding_dice_0: 0.07117/0.15114, loss_grounding_ce_0: 0.11145/0.24947, loss_mask_ce_1: 0.88982/0.76534, loss_mask_bce_1: 0.40644/0.30260, loss_mask_dice_1: 0.30780/1.03034, loss_spatial_bce_1: 0.15233/0.08666, loss_spatial_dice_1: 0.10983/0.18523, loss_spatial_ce_1: 0.26444/0.06502, loss_grounding_bce_1: 0.16886/0.08102, loss_grounding_dice_1: 0.06724/0.15187, loss_grounding_ce_1: 0.08973/0.25092, loss_mask_ce_2: 0.87652/0.77317, loss_mask_bce_2: 0.38576/0.30275, loss_mask_dice_2: 0.29593/1.03167, loss_spatial_bce_2: 0.15178/0.08661, loss_spatial_dice_2: 0.13673/0.18546, loss_spatial_ce_2: 0.23076/0.06745, loss_grounding_bce_2: 0.14815/0.08095, loss_grounding_dice_2: 0.06839/0.15169, loss_grounding_ce_2: 0.07715/0.25353, loss_mask_ce_3: 0.89807/0.77578, loss_mask_bce_3: 0.38502/0.30429, loss_mask_dice_3: 0.29598/1.02872, loss_spatial_bce_3: 0.14444/0.08858, loss_spatial_dice_3: 0.11282/0.18658, loss_spatial_ce_3: 0.30085/0.07199, loss_grounding_bce_3: 0.14447/0.08142, loss_grounding_dice_3: 0.07108/0.15127, loss_grounding_ce_3: 0.10642/0.25333, loss_mask_ce_4: 0.92062/0.78203, loss_mask_bce_4: 0.39769/0.30654, loss_mask_dice_4: 0.30116/1.04798, loss_spatial_bce_4: 0.16339/0.09054, loss_spatial_dice_4: 0.09893/0.19435, loss_spatial_ce_4: 0.68436/0.08474, loss_grounding_bce_4: 0.15791/0.08202, loss_grounding_dice_4: 0.07059/0.15384, loss_grounding_ce_4: 0.12836/0.25944, loss_mask_ce_5: 0.87770/0.80513, loss_mask_bce_5: 0.45222/0.30836, loss_mask_dice_5: 0.30697/1.05552, loss_spatial_bce_5: 0.17075/0.09247, loss_spatial_dice_5: 0.10382/0.19696, loss_spatial_ce_5: 0.65866/0.09703, loss_grounding_bce_5: 0.19152/0.08234, loss_grounding_dice_5: 0.07354/0.15460, loss_grounding_ce_5: 0.08670/0.27724, loss_mask_ce_6: 0.97376/0.83161, loss_mask_bce_6: 0.42285/0.31025, loss_mask_dice_6: 0.29257/1.05800, loss_spatial_bce_6: 0.16407/0.09752, loss_spatial_dice_6: 0.10969/0.19920, loss_spatial_ce_6: 0.65554/0.12003, loss_grounding_bce_6: 0.18259/0.08328, loss_grounding_dice_6: 0.07257/0.15516, loss_grounding_ce_6: 0.15046/0.28638, loss_mask_ce_7: 1.02694/0.88772, loss_mask_bce_7: 0.43942/0.31756, loss_mask_dice_7: 0.31571/1.10497, loss_spatial_bce_7: 0.16931/0.10770, loss_spatial_dice_7: 0.15967/0.22436, loss_spatial_ce_7: 0.33039/0.15909, loss_grounding_bce_7: 0.17695/0.08491, loss_grounding_dice_7: 0.06587/0.16094, loss_grounding_ce_7: 0.13860/0.32103, loss_mask_ce_8: 1.06343/1.02535, loss_mask_bce_8: 0.43928/0.33367, loss_mask_dice_8: 0.29789/1.18217, loss_spatial_bce_8: 0.26963/0.12592, loss_spatial_dice_8: 0.25834/0.26118, loss_spatial_ce_8: 0.15093/0.21088, loss_grounding_bce_8: 0.16916/0.08890, loss_grounding_dice_8: 0.06675/0.17049, loss_grounding_ce_8: 0.21415/0.42417, loss_mask_ce_9: 4.11055/3.48444, loss_mask_bce_9: 0.43174/0.36073, loss_mask_dice_9: 0.40141/1.76649, loss_spatial_bce_9: 0.70522/0.35555, loss_spatial_dice_9: 0.77894/0.79445, loss_spatial_ce_9: 1.48855/1.39618, loss_grounding_bce_9: 0.22512/0.10092, loss_grounding_dice_9: 0.08399/0.24337, loss_grounding_ce_9: 0.51923/0.68290] items per batch[64] items per second[0.37] total items[2912000] mini batches[ 45500] memory[4999] epoch remaining[0:05:07] INFO:trainer.default_trainer:epochs[ 24] optim steps[45600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86452/0.76410, loss_mask_bce_0: 0.09917/0.30171, loss_mask_dice_0: 0.37362/1.02625, loss_spatial_bce_0: 0.07619/0.08640, loss_spatial_dice_0: 0.05576/0.18262, loss_spatial_ce_0: 0.00042/0.06090, loss_grounding_bce_0: 0.00383/0.08078, loss_grounding_dice_0: 0.73331/0.15113, loss_grounding_ce_0: 0.00600/0.24931, loss_mask_ce_1: 0.82447/0.76515, loss_mask_bce_1: 0.10174/0.30250, loss_mask_dice_1: 0.42338/1.02985, loss_spatial_bce_1: 0.10369/0.08666, loss_spatial_dice_1: 0.08849/0.18520, loss_spatial_ce_1: 0.00075/0.06499, loss_grounding_bce_1: 0.00024/0.08096, loss_grounding_dice_1: 0.18290/0.15184, loss_grounding_ce_1: 1.08511/0.25077, loss_mask_ce_2: 1.30904/0.77299, loss_mask_bce_2: 0.09816/0.30265, loss_mask_dice_2: 0.17620/1.03113, loss_spatial_bce_2: 0.07875/0.08661, loss_spatial_dice_2: 0.10144/0.18544, loss_spatial_ce_2: 0.00046/0.06742, loss_grounding_bce_2: 0.00054/0.08089, loss_grounding_dice_2: 0.16403/0.15166, loss_grounding_ce_2: 1.25917/0.25338, loss_mask_ce_3: 1.29795/0.77558, loss_mask_bce_3: 0.10270/0.30418, loss_mask_dice_3: 0.11806/1.02824, loss_spatial_bce_3: 0.07208/0.08857, loss_spatial_dice_3: 0.07497/0.18655, loss_spatial_ce_3: 0.00044/0.07196, loss_grounding_bce_3: 0.00043/0.08137, loss_grounding_dice_3: 0.30442/0.15125, loss_grounding_ce_3: 1.29111/0.25322, loss_mask_ce_4: 1.33151/0.78185, loss_mask_bce_4: 0.10411/0.30643, loss_mask_dice_4: 0.17750/1.04749, loss_spatial_bce_4: 0.08709/0.09054, loss_spatial_dice_4: 0.13126/0.19433, loss_spatial_ce_4: 0.00184/0.08470, loss_grounding_bce_4: 0.00042/0.08196, loss_grounding_dice_4: 0.15544/0.15380, loss_grounding_ce_4: 1.23119/0.25932, loss_mask_ce_5: 1.01562/0.80494, loss_mask_bce_5: 0.10520/0.30824, loss_mask_dice_5: 0.46678/1.05500, loss_spatial_bce_5: 0.06389/0.09248, loss_spatial_dice_5: 0.13997/0.19695, loss_spatial_ce_5: 0.03582/0.09700, loss_grounding_bce_5: 0.00028/0.08229, loss_grounding_dice_5: 0.10610/0.15457, loss_grounding_ce_5: 1.25420/0.27712, loss_mask_ce_6: 1.10423/0.83140, loss_mask_bce_6: 0.11085/0.31016, loss_mask_dice_6: 0.47125/1.05754, loss_spatial_bce_6: 0.07663/0.09753, loss_spatial_dice_6: 0.13214/0.19918, loss_spatial_ce_6: 0.02087/0.12006, loss_grounding_bce_6: 0.00007/0.08323, loss_grounding_dice_6: 0.07694/0.15514, loss_grounding_ce_6: 1.18432/0.28623, loss_mask_ce_7: 1.65445/0.88754, loss_mask_bce_7: 0.11201/0.31746, loss_mask_dice_7: 0.11032/1.10442, loss_spatial_bce_7: 0.16135/0.10772, loss_spatial_dice_7: 0.13154/0.22434, loss_spatial_ce_7: 0.04022/0.15908, loss_grounding_bce_7: 0.00837/0.08486, loss_grounding_dice_7: 0.84615/0.16093, loss_grounding_ce_7: 0.00771/0.32088, loss_mask_ce_8: 1.56942/1.02516, loss_mask_bce_8: 0.11914/0.33357, loss_mask_dice_8: 0.42924/1.18160, loss_spatial_bce_8: 0.26287/0.12592, loss_spatial_dice_8: 0.29081/0.26115, loss_spatial_ce_8: 0.20722/0.21090, loss_grounding_bce_8: 0.00128/0.08886, loss_grounding_dice_8: 0.17028/0.17045, loss_grounding_ce_8: 1.39741/0.42399, loss_mask_ce_9: 3.66527/3.48381, loss_mask_bce_9: 0.12065/0.36062, loss_mask_dice_9: 0.53715/1.76540, loss_spatial_bce_9: 0.49272/0.35556, loss_spatial_dice_9: 0.63898/0.79442, loss_spatial_ce_9: 3.39180/1.39632, loss_grounding_bce_9: 0.00298/0.10088, loss_grounding_dice_9: 0.92144/0.24332, loss_grounding_ce_9: 0.01008/0.68273] items per batch[64] items per second[0.37] total items[2918400] mini batches[ 45600] memory[4999] epoch remaining[0:02:11] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00045675. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0025 s/iter. Inference: 0.3674 s/iter. Eval: 0.0896 s/iter. Total: 0.4595 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0023 s/iter. Inference: 0.3673 s/iter. Eval: 0.0821 s/iter. Total: 0.4518 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0025 s/iter. Inference: 0.3736 s/iter. Eval: 0.0797 s/iter. Total: 0.4559 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0026 s/iter. Inference: 0.3772 s/iter. Eval: 0.0751 s/iter. Total: 0.4550 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0026 s/iter. Inference: 0.3791 s/iter. Eval: 0.0732 s/iter. Total: 0.4550 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0027 s/iter. Inference: 0.3762 s/iter. Eval: 0.0720 s/iter. Total: 0.4511 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalw98asd2l ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.729 | 83.241 | 66.176 | 133 | | Things | 61.945 | 84.191 | 73.075 | 80 | | Stuff | 46.346 | 81.807 | 55.763 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.15 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=5.04s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.84 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.383 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.545 | 69.115 | 49.175 | 25.926 | 49.863 | 67.552 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.815 | bicycle | 22.699 | car | 43.962 | | motorcycle | 42.037 | airplane | 62.633 | bus | 71.214 | | train | 75.139 | truck | 42.180 | boat | 31.643 | | traffic light | 28.807 | fire hydrant | 71.717 | stop sign | 68.357 | | parking meter | 50.289 | bench | 27.104 | bird | 34.726 | | cat | 77.212 | dog | 71.378 | horse | 50.424 | | sheep | 52.617 | cow | 55.969 | elephant | 65.996 | | bear | 80.164 | zebra | 66.252 | giraffe | 62.482 | | backpack | 23.608 | umbrella | 55.857 | handbag | 23.543 | | tie | 40.114 | suitcase | 52.088 | frisbee | 70.118 | | skis | 8.561 | snowboard | 34.647 | sports ball | 48.953 | | kite | 36.953 | baseball bat | 39.385 | baseball glove | 49.932 | | skateboard | 44.349 | surfboard | 45.004 | tennis racket | 63.977 | | bottle | 42.035 | wine glass | 37.639 | cup | 51.277 | | fork | 26.524 | knife | 25.324 | spoon | 23.257 | | bowl | 38.937 | banana | 21.727 | apple | 27.145 | | sandwich | 47.547 | orange | 31.827 | broccoli | 23.175 | | carrot | 23.480 | hot dog | 32.748 | pizza | 51.142 | | donut | 56.768 | cake | 47.486 | chair | 29.374 | | couch | 41.784 | potted plant | 22.567 | bed | 42.670 | | dining table | 14.806 | toilet | 69.064 | tv | 67.443 | | laptop | 69.798 | mouse | 63.801 | remote | 44.242 | | keyboard | 59.310 | cell phone | 45.892 | microwave | 64.768 | | oven | 31.030 | toaster | 44.263 | sink | 45.513 | | refrigerator | 70.003 | book | 13.677 | clock | 55.012 | | vase | 41.029 | scissors | 35.670 | teddy bear | 57.943 | | hair drier | 35.916 | toothbrush | 29.063 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.5358396019897, 'fwIoU': 71.34636675190701, 'IoU-person': 88.66273074266086, 'IoU-bicycle': 77.32197800692099, 'IoU-car': 71.20725840938113, 'IoU-motorcycle': 85.42987907813601, 'IoU-airplane': 81.17521453607833, 'IoU-bus': 87.30575650377219, 'IoU-train': 87.30785906375966, 'IoU-truck': 66.71797735604335, 'IoU-boat': 71.84222801445682, 'IoU-traffic light': 78.3038734293044, 'IoU-fire hydrant': 93.34719960476974, 'IoU-stop sign': 92.74793589241197, 'IoU-parking meter': 84.78355369275896, 'IoU-bench': 63.976795191007675, 'IoU-bird': 76.64772242367228, 'IoU-cat': 90.72472624711932, 'IoU-dog': 87.43030102783528, 'IoU-horse': 88.71066701516145, 'IoU-sheep': 85.99495980864359, 'IoU-cow': 87.85957025695198, 'IoU-elephant': 91.82642694431148, 'IoU-bear': 86.07193390007228, 'IoU-zebra': 86.0209785517738, 'IoU-giraffe': 89.55794066218928, 'IoU-backpack': 53.3384386575147, 'IoU-umbrella': 85.87492761604582, 'IoU-handbag': 50.63262423995268, 'IoU-tie': 74.47251710981384, 'IoU-suitcase': 86.23814952598103, 'IoU-frisbee': 83.36094316028311, 'IoU-skis': 62.109402563003094, 'IoU-snowboard': 75.43472433205254, 'IoU-sports ball': 77.78755099749223, 'IoU-kite': 79.75475711746365, 'IoU-baseball bat': 68.4775334190755, 'IoU-baseball glove': 81.13319565515766, 'IoU-skateboard': 86.22372492105515, 'IoU-surfboard': 87.03201470381751, 'IoU-tennis racket': 87.6253884471255, 'IoU-bottle': 71.64521922819104, 'IoU-wine glass': 82.66665680154752, 'IoU-cup': 71.98038360054535, 'IoU-fork': 71.59677057636242, 'IoU-knife': 64.91999717203504, 'IoU-spoon': 60.89364975082924, 'IoU-bowl': 63.694712833014144, 'IoU-banana': 82.39695067305134, 'IoU-apple': 58.67471254304846, 'IoU-sandwich': 69.68472567352738, 'IoU-orange': 76.10140300735188, 'IoU-broccoli': 69.3812535709836, 'IoU-carrot': 64.20942281012756, 'IoU-hot dog': 64.64333398782642, 'IoU-pizza': 83.47362953862425, 'IoU-donut': 67.43866306696175, 'IoU-cake': 76.24272426259117, 'IoU-chair': 62.13213302988685, 'IoU-couch': 66.79346882693639, 'IoU-potted plant': 42.42069577735263, 'IoU-bed': 73.69174604291544, 'IoU-dining table': 53.51765198564787, 'IoU-toilet': 85.33564066117607, 'IoU-tv': 75.30166450070209, 'IoU-laptop': 74.85506852518904, 'IoU-mouse': 73.90379821088136, 'IoU-remote': 71.62845730856564, 'IoU-keyboard': 66.19049310869993, 'IoU-cell phone': 84.19419867276352, 'IoU-microwave': 66.67442292314864, 'IoU-oven': 70.27095855143915, 'IoU-toaster': 85.549499871762, 'IoU-sink': 74.06053881273891, 'IoU-refrigerator': 83.8534438086188, 'IoU-book': 53.896538715457886, 'IoU-clock': 73.64051274312082, 'IoU-vase': 65.19585901502005, 'IoU-scissors': 83.87819372094779, 'IoU-teddy bear': 85.49655130703702, 'IoU-hair drier': 48.038605582647854, 'IoU-toothbrush': 75.84646368913636, 'IoU-banner': 28.92359543441928, 'IoU-blanket': 16.222242732124016, 'IoU-bridge': 37.200437652207675, 'IoU-cardboard': 52.77030467003284, 'IoU-counter': 33.882167288919, 'IoU-curtain': 71.73360185201571, 'IoU-door-stuff': 48.251736068860666, 'IoU-floor-wood': 63.72305514690208, 'IoU-flower': 41.58095278716726, 'IoU-fruit': 47.20794929641525, 'IoU-gravel': 23.11379447664441, 'IoU-house': 25.719650415830507, 'IoU-light': 46.25483103529367, 'IoU-mirror-stuff': 64.1409052136684, 'IoU-net': 47.138723460144554, 'IoU-pillow': 23.46829422296895, 'IoU-platform': 27.615566274391202, 'IoU-playingfield': 68.2653732307702, 'IoU-railroad': 64.19512171279531, 'IoU-river': 49.59092543105502, 'IoU-road': 66.04603408044221, 'IoU-roof': 20.0051605944862, 'IoU-sand': 63.2998276290068, 'IoU-sea': 83.20931953541373, 'IoU-shelf': 39.826914322373305, 'IoU-snow': 92.03772800777523, 'IoU-stairs': 33.971341296700366, 'IoU-tent': 11.249244709193135, 'IoU-towel': 44.51220890712911, 'IoU-wall-brick': 49.48655086414829, 'IoU-wall-stone': 31.982140392787635, 'IoU-wall-tile': 68.9561035307126, 'IoU-wall-wood': 44.72326882528412, 'IoU-water-other': 21.75267771537208, 'IoU-window-blind': 49.07780161665338, 'IoU-window-other': 50.04037572542801, 'IoU-tree-merged': 81.96577104534818, 'IoU-fence-merged': 54.23926362486424, 'IoU-ceiling-merged': 67.53863219329695, 'IoU-sky-other-merged': 94.06710987396355, 'IoU-cabinet-merged': 64.85135091197478, 'IoU-table-merged': 42.753664754045815, 'IoU-floor-other-merged': 54.93991839142942, 'IoU-pavement-merged': 56.98172912640436, 'IoU-mountain-merged': 58.26086297239307, 'IoU-grass-merged': 72.31507544433484, 'IoU-dirt-merged': 46.75810604824845, 'IoU-paper-merged': 33.27771070282948, 'IoU-food-other-merged': 37.94632710729941, 'IoU-building-other-merged': 60.274814780844245, 'IoU-rock-merged': 67.07209554961192, 'IoU-wall-other-merged': 67.41156002856982, 'IoU-rug-merged': 67.9545750402024, 'mACC': 77.35139642826728, 'pACC': 82.00586857474771, 'ACC-person': 93.07786757688879, 'ACC-bicycle': 87.89125299599381, 'ACC-car': 87.52877273323314, 'ACC-motorcycle': 89.86148292741181, 'ACC-airplane': 84.9112736565813, 'ACC-bus': 93.71586810629493, 'ACC-train': 93.16753581773881, 'ACC-truck': 75.23376968406397, 'ACC-boat': 80.55632081596171, 'ACC-traffic light': 91.57618399821324, 'ACC-fire hydrant': 96.0119477169954, 'ACC-stop sign': 98.08299012165766, 'ACC-parking meter': 88.21591540881033, 'ACC-bench': 76.1318748413423, 'ACC-bird': 82.64154597435503, 'ACC-cat': 94.48332561636413, 'ACC-dog': 89.96482792818522, 'ACC-horse': 93.992989075848, 'ACC-sheep': 90.61798899726351, 'ACC-cow': 91.22863981087997, 'ACC-elephant': 94.1135373255627, 'ACC-bear': 87.94419841996188, 'ACC-zebra': 88.25504930725774, 'ACC-giraffe': 93.42737760438385, 'ACC-backpack': 72.48862849580635, 'ACC-umbrella': 90.76923581251776, 'ACC-handbag': 69.27158923538667, 'ACC-tie': 82.18830386484198, 'ACC-suitcase': 93.22876885379621, 'ACC-frisbee': 94.05418181818182, 'ACC-skis': 77.9759247965056, 'ACC-snowboard': 82.70088903039358, 'ACC-sports ball': 85.93047415263435, 'ACC-kite': 86.11096428634355, 'ACC-baseball bat': 88.16054664378994, 'ACC-baseball glove': 91.57569731163039, 'ACC-skateboard': 91.00008528172143, 'ACC-surfboard': 92.96724782555235, 'ACC-tennis racket': 92.3922722093671, 'ACC-bottle': 87.56034105270403, 'ACC-wine glass': 91.16453010824827, 'ACC-cup': 89.67007152886434, 'ACC-fork': 83.90133274381976, 'ACC-knife': 79.06151535135926, 'ACC-spoon': 78.09775706235588, 'ACC-bowl': 74.81922144884466, 'ACC-banana': 90.34763777173966, 'ACC-apple': 71.99567648991815, 'ACC-sandwich': 82.33727801467514, 'ACC-orange': 87.96276832887169, 'ACC-broccoli': 80.64842134838777, 'ACC-carrot': 77.60085172900114, 'ACC-hot dog': 71.73390593474784, 'ACC-pizza': 88.99211780983653, 'ACC-donut': 77.5922011401209, 'ACC-cake': 87.71152889676145, 'ACC-chair': 82.53011282430872, 'ACC-couch': 71.10824486685325, 'ACC-potted plant': 61.69758661691815, 'ACC-bed': 82.51368304183708, 'ACC-dining table': 77.92520935795541, 'ACC-toilet': 89.95499993131727, 'ACC-tv': 87.53150655940838, 'ACC-laptop': 84.63283613663285, 'ACC-mouse': 87.2612361070528, 'ACC-remote': 75.8766839243098, 'ACC-keyboard': 76.22365910964258, 'ACC-cell phone': 92.94912134096384, 'ACC-microwave': 75.15450741310934, 'ACC-oven': 88.75258193075652, 'ACC-toaster': 91.29931982099112, 'ACC-sink': 83.13430758751902, 'ACC-refrigerator': 92.46388178986543, 'ACC-book': 72.0515676220896, 'ACC-clock': 78.8580850748118, 'ACC-vase': 73.98072518885269, 'ACC-scissors': 89.26339880083373, 'ACC-teddy bear': 91.45646862657739, 'ACC-hair drier': 60.42324879742692, 'ACC-toothbrush': 83.41990965948575, 'ACC-banner': 78.95503576219559, 'ACC-blanket': 25.72456845032402, 'ACC-bridge': 57.54853062178113, 'ACC-cardboard': 72.52688672386215, 'ACC-counter': 58.73496745045333, 'ACC-curtain': 83.04938295516591, 'ACC-door-stuff': 72.833679305565, 'ACC-floor-wood': 78.64404931321401, 'ACC-flower': 58.738222616091804, 'ACC-fruit': 66.24822291972482, 'ACC-gravel': 27.572038828956146, 'ACC-house': 31.123026464688802, 'ACC-light': 66.58865032729778, 'ACC-mirror-stuff': 76.49282819870852, 'ACC-net': 63.65670996534776, 'ACC-pillow': 56.714089225513675, 'ACC-platform': 42.40200448535425, 'ACC-playingfield': 83.16034610924824, 'ACC-railroad': 82.39980990486617, 'ACC-river': 89.94826192425396, 'ACC-road': 87.7908025751345, 'ACC-roof': 27.388551381877303, 'ACC-sand': 66.75786367523729, 'ACC-sea': 86.38333296113636, 'ACC-shelf': 57.89395209804044, 'ACC-snow': 95.60117245377543, 'ACC-stairs': 59.878055700713894, 'ACC-tent': 14.034433798528717, 'ACC-towel': 53.31630949092754, 'ACC-wall-brick': 69.29346895834612, 'ACC-wall-stone': 40.197659924685034, 'ACC-wall-tile': 86.87037035183909, 'ACC-wall-wood': 60.90177731216633, 'ACC-water-other': 31.252304426800197, 'ACC-window-blind': 65.19714945625492, 'ACC-window-other': 73.88247294616338, 'ACC-tree-merged': 89.95162294752713, 'ACC-fence-merged': 73.15599314640392, 'ACC-ceiling-merged': 83.33650760567632, 'ACC-sky-other-merged': 96.92895581147056, 'ACC-cabinet-merged': 78.33608129135541, 'ACC-table-merged': 56.0586656894408, 'ACC-floor-other-merged': 65.36247279172794, 'ACC-pavement-merged': 68.91217569145579, 'ACC-mountain-merged': 67.22547718151516, 'ACC-grass-merged': 83.20544404051363, 'ACC-dirt-merged': 75.00924272250494, 'ACC-paper-merged': 44.12234054282833, 'ACC-food-other-merged': 48.71480372807125, 'ACC-building-other-merged': 77.57581811784164, 'ACC-rock-merged': 83.36572662102061, 'ACC-wall-other-merged': 79.54964178053878, 'ACC-rug-merged': 82.11638121592193})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2884 s/iter. Inference: 0.1734 s/iter. Eval: 0.0000 s/iter. Total: 0.4618 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3198 s/iter. Inference: 0.3406 s/iter. Eval: 0.0000 s/iter. Total: 0.6605 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3232 s/iter. Inference: 0.5550 s/iter. Eval: 0.0000 s/iter. Total: 0.8783 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3962540239976589, 'noc@0.8': 2.5542873865964295, 'noc@0.85': 3.004097161252561, 'noc@0.9': 3.8337723148961076, 'miou@iter1': 0.8710874390595135} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1458 s/iter. Eval: 0.0011 s/iter. Total: 0.1481 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.59269714355469, 'precision@0.6': 72.75553894042969, 'precision@0.7': 68.55810546875, 'precision@0.8': 59.65798568725586, 'precision@0.9': 32.452388763427734, 'cIoU': 62.065086364746094, 'mIoU': 67.02537536621094} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.728845293189075, 'SQ': 83.24087568898226, 'RQ': 66.17617482330319, 'PQ_th': 61.94481616371298, 'SQ_th': 84.19101950971267, 'RQ_th': 73.07513656530975, 'PQ_st': 46.34624775277572, 'SQ_st': 81.80669633693638, 'RQ_st': 55.76264766555741}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.54476469539429, 'AP50': 69.11468648576046, 'AP75': 49.17457480971639, 'APs': 25.926280025708444, 'APm': 49.863301083911296, 'APl': 67.55192521406894, 'AP-person': 48.814672127576365, 'AP-bicycle': 22.69919066722707, 'AP-car': 43.961524047756946, 'AP-motorcycle': 42.03663451437405, 'AP-airplane': 62.633183482234855, 'AP-bus': 71.21437685363009, 'AP-train': 75.13941826728696, 'AP-truck': 42.1801042746504, 'AP-boat': 31.64338915664105, 'AP-traffic light': 28.80732642390444, 'AP-fire hydrant': 71.71748614870704, 'AP-stop sign': 68.35722542751635, 'AP-parking meter': 50.28873834143748, 'AP-bench': 27.10372891472037, 'AP-bird': 34.72607193027556, 'AP-cat': 77.21185672295137, 'AP-dog': 71.37804918834853, 'AP-horse': 50.42413540512204, 'AP-sheep': 52.61711379074041, 'AP-cow': 55.96892542257531, 'AP-elephant': 65.99628491065947, 'AP-bear': 80.1640613693983, 'AP-zebra': 66.25196936526207, 'AP-giraffe': 62.48171177730677, 'AP-backpack': 23.607705196055655, 'AP-umbrella': 55.856763205930235, 'AP-handbag': 23.542699839260326, 'AP-tie': 40.11369112054732, 'AP-suitcase': 52.08775847690723, 'AP-frisbee': 70.11777662639503, 'AP-skis': 8.560872540218693, 'AP-snowboard': 34.64701428080561, 'AP-sports ball': 48.95314183997016, 'AP-kite': 36.95306300505008, 'AP-baseball bat': 39.385346863648756, 'AP-baseball glove': 49.932240268248215, 'AP-skateboard': 44.3488713330708, 'AP-surfboard': 45.00397161044313, 'AP-tennis racket': 63.976630611016425, 'AP-bottle': 42.03472747283868, 'AP-wine glass': 37.63897638443587, 'AP-cup': 51.27700052244285, 'AP-fork': 26.52392238897828, 'AP-knife': 25.323696083531495, 'AP-spoon': 23.257256748620854, 'AP-bowl': 38.936618638014146, 'AP-banana': 21.72722667725719, 'AP-apple': 27.145216081751045, 'AP-sandwich': 47.547190155246625, 'AP-orange': 31.82708438934708, 'AP-broccoli': 23.175489142751466, 'AP-carrot': 23.480104393860735, 'AP-hot dog': 32.74757469951125, 'AP-pizza': 51.14230412521555, 'AP-donut': 56.76779235876263, 'AP-cake': 47.4859259926254, 'AP-chair': 29.373508857457143, 'AP-couch': 41.784268165173174, 'AP-potted plant': 22.566799325379748, 'AP-bed': 42.67039513368416, 'AP-dining table': 14.80649096922096, 'AP-toilet': 69.0636960709026, 'AP-tv': 67.44289162990302, 'AP-laptop': 69.79769212979252, 'AP-mouse': 63.801446125728475, 'AP-remote': 44.24227700647522, 'AP-keyboard': 59.31038015827641, 'AP-cell phone': 45.89218592096093, 'AP-microwave': 64.76778899654812, 'AP-oven': 31.030317476317286, 'AP-toaster': 44.26278280001913, 'AP-sink': 45.51262672428216, 'AP-refrigerator': 70.00336467899683, 'AP-book': 13.676707965041167, 'AP-clock': 55.01165862879076, 'AP-vase': 41.02905588458927, 'AP-scissors': 35.66991752745158, 'AP-teddy bear': 57.94284207050199, 'AP-hair drier': 35.915932252565916, 'AP-toothbrush': 29.063317530423056}), ('sem_seg', {'mIoU': 65.5358396019897, 'fwIoU': 71.34636675190701, 'IoU-person': 88.66273074266086, 'IoU-bicycle': 77.32197800692099, 'IoU-car': 71.20725840938113, 'IoU-motorcycle': 85.42987907813601, 'IoU-airplane': 81.17521453607833, 'IoU-bus': 87.30575650377219, 'IoU-train': 87.30785906375966, 'IoU-truck': 66.71797735604335, 'IoU-boat': 71.84222801445682, 'IoU-traffic light': 78.3038734293044, 'IoU-fire hydrant': 93.34719960476974, 'IoU-stop sign': 92.74793589241197, 'IoU-parking meter': 84.78355369275896, 'IoU-bench': 63.976795191007675, 'IoU-bird': 76.64772242367228, 'IoU-cat': 90.72472624711932, 'IoU-dog': 87.43030102783528, 'IoU-horse': 88.71066701516145, 'IoU-sheep': 85.99495980864359, 'IoU-cow': 87.85957025695198, 'IoU-elephant': 91.82642694431148, 'IoU-bear': 86.07193390007228, 'IoU-zebra': 86.0209785517738, 'IoU-giraffe': 89.55794066218928, 'IoU-backpack': 53.3384386575147, 'IoU-umbrella': 85.87492761604582, 'IoU-handbag': 50.63262423995268, 'IoU-tie': 74.47251710981384, 'IoU-suitcase': 86.23814952598103, 'IoU-frisbee': 83.36094316028311, 'IoU-skis': 62.109402563003094, 'IoU-snowboard': 75.43472433205254, 'IoU-sports ball': 77.78755099749223, 'IoU-kite': 79.75475711746365, 'IoU-baseball bat': 68.4775334190755, 'IoU-baseball glove': 81.13319565515766, 'IoU-skateboard': 86.22372492105515, 'IoU-surfboard': 87.03201470381751, 'IoU-tennis racket': 87.6253884471255, 'IoU-bottle': 71.64521922819104, 'IoU-wine glass': 82.66665680154752, 'IoU-cup': 71.98038360054535, 'IoU-fork': 71.59677057636242, 'IoU-knife': 64.91999717203504, 'IoU-spoon': 60.89364975082924, 'IoU-bowl': 63.694712833014144, 'IoU-banana': 82.39695067305134, 'IoU-apple': 58.67471254304846, 'IoU-sandwich': 69.68472567352738, 'IoU-orange': 76.10140300735188, 'IoU-broccoli': 69.3812535709836, 'IoU-carrot': 64.20942281012756, 'IoU-hot dog': 64.64333398782642, 'IoU-pizza': 83.47362953862425, 'IoU-donut': 67.43866306696175, 'IoU-cake': 76.24272426259117, 'IoU-chair': 62.13213302988685, 'IoU-couch': 66.79346882693639, 'IoU-potted plant': 42.42069577735263, 'IoU-bed': 73.69174604291544, 'IoU-dining table': 53.51765198564787, 'IoU-toilet': 85.33564066117607, 'IoU-tv': 75.30166450070209, 'IoU-laptop': 74.85506852518904, 'IoU-mouse': 73.90379821088136, 'IoU-remote': 71.62845730856564, 'IoU-keyboard': 66.19049310869993, 'IoU-cell phone': 84.19419867276352, 'IoU-microwave': 66.67442292314864, 'IoU-oven': 70.27095855143915, 'IoU-toaster': 85.549499871762, 'IoU-sink': 74.06053881273891, 'IoU-refrigerator': 83.8534438086188, 'IoU-book': 53.896538715457886, 'IoU-clock': 73.64051274312082, 'IoU-vase': 65.19585901502005, 'IoU-scissors': 83.87819372094779, 'IoU-teddy bear': 85.49655130703702, 'IoU-hair drier': 48.038605582647854, 'IoU-toothbrush': 75.84646368913636, 'IoU-banner': 28.92359543441928, 'IoU-blanket': 16.222242732124016, 'IoU-bridge': 37.200437652207675, 'IoU-cardboard': 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'IoU-water-other': 21.75267771537208, 'IoU-window-blind': 49.07780161665338, 'IoU-window-other': 50.04037572542801, 'IoU-tree-merged': 81.96577104534818, 'IoU-fence-merged': 54.23926362486424, 'IoU-ceiling-merged': 67.53863219329695, 'IoU-sky-other-merged': 94.06710987396355, 'IoU-cabinet-merged': 64.85135091197478, 'IoU-table-merged': 42.753664754045815, 'IoU-floor-other-merged': 54.93991839142942, 'IoU-pavement-merged': 56.98172912640436, 'IoU-mountain-merged': 58.26086297239307, 'IoU-grass-merged': 72.31507544433484, 'IoU-dirt-merged': 46.75810604824845, 'IoU-paper-merged': 33.27771070282948, 'IoU-food-other-merged': 37.94632710729941, 'IoU-building-other-merged': 60.274814780844245, 'IoU-rock-merged': 67.07209554961192, 'IoU-wall-other-merged': 67.41156002856982, 'IoU-rug-merged': 67.9545750402024, 'mACC': 77.35139642826728, 'pACC': 82.00586857474771, 'ACC-person': 93.07786757688879, 'ACC-bicycle': 87.89125299599381, 'ACC-car': 87.52877273323314, 'ACC-motorcycle': 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'ACC-laptop': 84.63283613663285, 'ACC-mouse': 87.2612361070528, 'ACC-remote': 75.8766839243098, 'ACC-keyboard': 76.22365910964258, 'ACC-cell phone': 92.94912134096384, 'ACC-microwave': 75.15450741310934, 'ACC-oven': 88.75258193075652, 'ACC-toaster': 91.29931982099112, 'ACC-sink': 83.13430758751902, 'ACC-refrigerator': 92.46388178986543, 'ACC-book': 72.0515676220896, 'ACC-clock': 78.8580850748118, 'ACC-vase': 73.98072518885269, 'ACC-scissors': 89.26339880083373, 'ACC-teddy bear': 91.45646862657739, 'ACC-hair drier': 60.42324879742692, 'ACC-toothbrush': 83.41990965948575, 'ACC-banner': 78.95503576219559, 'ACC-blanket': 25.72456845032402, 'ACC-bridge': 57.54853062178113, 'ACC-cardboard': 72.52688672386215, 'ACC-counter': 58.73496745045333, 'ACC-curtain': 83.04938295516591, 'ACC-door-stuff': 72.833679305565, 'ACC-floor-wood': 78.64404931321401, 'ACC-flower': 58.738222616091804, 'ACC-fruit': 66.24822291972482, 'ACC-gravel': 27.572038828956146, 'ACC-house': 31.123026464688802, 'ACC-light': 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67.02537536621094}}} INFO:trainer.default_trainer:This epoch takes 0:56:50.346131 INFO:trainer.default_trainer:PROGRESS: 50.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 25 training. INFO:trainer.default_trainer:epochs[ 25] optim steps[45700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.97956/0.76395, loss_mask_bce_0: 0.03876/0.30173, loss_mask_dice_0: 1.51667/1.02622, loss_spatial_bce_0: 0.00600/0.08642, loss_spatial_dice_0: 0.29103/0.18261, loss_spatial_ce_0: 0.04572/0.06087, loss_grounding_bce_0: 0.00479/0.08079, loss_grounding_dice_0: 0.09840/0.15114, loss_grounding_ce_0: 0.01894/0.24924, loss_mask_ce_1: 1.78333/0.76502, loss_mask_bce_1: 0.03708/0.30253, loss_mask_dice_1: 1.44077/1.02976, loss_spatial_bce_1: 0.00538/0.08667, loss_spatial_dice_1: 0.36088/0.18518, loss_spatial_ce_1: 0.04399/0.06498, loss_grounding_bce_1: 0.00421/0.08097, loss_grounding_dice_1: 0.09316/0.15187, loss_grounding_ce_1: 0.01922/0.25071, loss_mask_ce_2: 1.56341/0.77286, loss_mask_bce_2: 0.06157/0.30267, loss_mask_dice_2: 1.72362/1.03108, loss_spatial_bce_2: 0.00424/0.08663, loss_spatial_dice_2: 0.23402/0.18542, loss_spatial_ce_2: 0.11083/0.06737, loss_grounding_bce_2: 0.00447/0.08090, loss_grounding_dice_2: 0.08114/0.15169, loss_grounding_ce_2: 0.01903/0.25330, loss_mask_ce_3: 1.74547/0.77544, loss_mask_bce_3: 0.04225/0.30420, loss_mask_dice_3: 0.97270/1.02818, loss_spatial_bce_3: 0.00536/0.08859, loss_spatial_dice_3: 0.30010/0.18654, loss_spatial_ce_3: 0.21892/0.07192, loss_grounding_bce_3: 0.00550/0.08138, loss_grounding_dice_3: 0.08075/0.15128, loss_grounding_ce_3: 0.01923/0.25319, loss_mask_ce_4: 1.66950/0.78168, loss_mask_bce_4: 0.05360/0.30646, loss_mask_dice_4: 1.96929/1.04742, loss_spatial_bce_4: 0.00681/0.09056, loss_spatial_dice_4: 0.35945/0.19433, loss_spatial_ce_4: 0.16880/0.08467, loss_grounding_bce_4: 0.00661/0.08197, loss_grounding_dice_4: 0.09438/0.15383, loss_grounding_ce_4: 0.02674/0.25920, loss_mask_ce_5: 1.63724/0.80478, loss_mask_bce_5: 0.05130/0.30826, loss_mask_dice_5: 2.10549/1.05493, loss_spatial_bce_5: 0.00633/0.09249, loss_spatial_dice_5: 0.34262/0.19694, loss_spatial_ce_5: 0.33306/0.09696, loss_grounding_bce_5: 0.00530/0.08229, loss_grounding_dice_5: 0.08983/0.15459, loss_grounding_ce_5: 0.01519/0.27705, loss_mask_ce_6: 1.63846/0.83125, loss_mask_bce_6: 0.03529/0.31017, loss_mask_dice_6: 1.53841/1.05749, loss_spatial_bce_6: 0.00869/0.09754, loss_spatial_dice_6: 0.31631/0.19917, loss_spatial_ce_6: 0.14974/0.12003, loss_grounding_bce_6: 0.00537/0.08323, loss_grounding_dice_6: 0.10159/0.15516, loss_grounding_ce_6: 0.00617/0.28618, loss_mask_ce_7: 1.70263/0.88739, loss_mask_bce_7: 0.04443/0.31747, loss_mask_dice_7: 1.89758/1.10437, loss_spatial_bce_7: 0.01983/0.10773, loss_spatial_dice_7: 0.54396/0.22434, loss_spatial_ce_7: 0.47731/0.15908, loss_grounding_bce_7: 0.00482/0.08486, loss_grounding_dice_7: 0.08844/0.16095, loss_grounding_ce_7: 0.01143/0.32080, loss_mask_ce_8: 1.61202/1.02503, loss_mask_bce_8: 0.07155/0.33359, loss_mask_dice_8: 1.91171/1.18156, loss_spatial_bce_8: 0.01466/0.12592, loss_spatial_dice_8: 0.49770/0.26112, loss_spatial_ce_8: 0.50710/0.21084, loss_grounding_bce_8: 0.00532/0.08886, loss_grounding_dice_8: 0.07203/0.17047, loss_grounding_ce_8: 0.11223/0.42377, loss_mask_ce_9: 2.74412/3.48379, loss_mask_bce_9: 0.04165/0.36063, loss_mask_dice_9: 1.96543/1.76503, loss_spatial_bce_9: 0.02431/0.35559, loss_spatial_dice_9: 0.85891/0.79442, loss_spatial_ce_9: 1.67680/1.39629, loss_grounding_bce_9: 0.00486/0.10089, loss_grounding_dice_9: 0.15256/0.24334, loss_grounding_ce_9: 1.33174/0.68253] items per batch[64] items per second[0.16] total items[2924800] mini batches[ 45700] memory[4999] epoch remaining[1:04:18] INFO:trainer.default_trainer:epochs[ 25] optim steps[45800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10368/0.76383, loss_mask_bce_0: 0.10165/0.30169, loss_mask_dice_0: 0.16284/1.02625, loss_spatial_bce_0: 0.04374/0.08639, loss_spatial_dice_0: 0.06077/0.18258, loss_spatial_ce_0: 0.00013/0.06090, loss_grounding_bce_0: 0.11085/0.08080, loss_grounding_dice_0: 0.04546/0.15113, loss_grounding_ce_0: 0.00077/0.24915, loss_mask_ce_1: 0.09335/0.76495, loss_mask_bce_1: 0.10169/0.30249, loss_mask_dice_1: 0.14539/1.02977, loss_spatial_bce_1: 0.04417/0.08665, loss_spatial_dice_1: 0.06113/0.18514, loss_spatial_ce_1: 0.00018/0.06499, loss_grounding_bce_1: 0.10777/0.08098, loss_grounding_dice_1: 0.04657/0.15187, loss_grounding_ce_1: 0.00080/0.25062, loss_mask_ce_2: 0.09950/0.77282, loss_mask_bce_2: 0.10634/0.30262, loss_mask_dice_2: 0.13866/1.03104, loss_spatial_bce_2: 0.04556/0.08660, loss_spatial_dice_2: 0.05723/0.18539, loss_spatial_ce_2: 0.00014/0.06739, loss_grounding_bce_2: 0.11167/0.08092, loss_grounding_dice_2: 0.04464/0.15169, loss_grounding_ce_2: 0.00051/0.25317, loss_mask_ce_3: 0.07671/0.77532, loss_mask_bce_3: 0.10242/0.30417, loss_mask_dice_3: 0.12814/1.02819, loss_spatial_bce_3: 0.04373/0.08856, loss_spatial_dice_3: 0.05017/0.18651, loss_spatial_ce_3: 0.00011/0.07193, loss_grounding_bce_3: 0.11022/0.08139, loss_grounding_dice_3: 0.04470/0.15128, loss_grounding_ce_3: 0.00038/0.25307, loss_mask_ce_4: 0.06833/0.78156, loss_mask_bce_4: 0.11666/0.30646, loss_mask_dice_4: 0.14747/1.04745, loss_spatial_bce_4: 0.04624/0.09053, loss_spatial_dice_4: 0.07162/0.19429, loss_spatial_ce_4: 0.00042/0.08471, loss_grounding_bce_4: 0.11629/0.08198, loss_grounding_dice_4: 0.04449/0.15382, loss_grounding_ce_4: 0.00031/0.25908, loss_mask_ce_5: 0.10710/0.80467, loss_mask_bce_5: 0.11411/0.30827, loss_mask_dice_5: 0.13447/1.05490, loss_spatial_bce_5: 0.04271/0.09247, loss_spatial_dice_5: 0.06687/0.19691, loss_spatial_ce_5: 0.00140/0.09700, loss_grounding_bce_5: 0.10788/0.08230, loss_grounding_dice_5: 0.04157/0.15459, loss_grounding_ce_5: 0.00028/0.27687, loss_mask_ce_6: 0.09570/0.83108, loss_mask_bce_6: 0.10875/0.31016, loss_mask_dice_6: 0.14373/1.05749, loss_spatial_bce_6: 0.04464/0.09751, loss_spatial_dice_6: 0.06876/0.19914, loss_spatial_ce_6: 0.00020/0.12007, loss_grounding_bce_6: 0.10865/0.08327, loss_grounding_dice_6: 0.04214/0.15517, loss_grounding_ce_6: 0.00041/0.28600, loss_mask_ce_7: 0.07164/0.88717, loss_mask_bce_7: 0.12297/0.31743, loss_mask_dice_7: 0.17010/1.10435, loss_spatial_bce_7: 0.04594/0.10773, loss_spatial_dice_7: 0.07600/0.22432, loss_spatial_ce_7: 0.01901/0.15909, loss_grounding_bce_7: 0.12718/0.08488, loss_grounding_dice_7: 0.05096/0.16094, loss_grounding_ce_7: 0.00087/0.32065, loss_mask_ce_8: 0.18380/1.02495, loss_mask_bce_8: 0.11336/0.33354, loss_mask_dice_8: 0.14866/1.18155, loss_spatial_bce_8: 0.05132/0.12590, loss_spatial_dice_8: 0.07735/0.26108, loss_spatial_ce_8: 0.03453/0.21081, loss_grounding_bce_8: 0.11952/0.08887, loss_grounding_dice_8: 0.04570/0.17045, loss_grounding_ce_8: 0.00221/0.42364, loss_mask_ce_9: 2.11438/3.48341, loss_mask_bce_9: 0.13724/0.36060, loss_mask_dice_9: 0.35446/1.76500, loss_spatial_bce_9: 0.25971/0.35561, loss_spatial_dice_9: 0.45566/0.79439, loss_spatial_ce_9: 0.75140/1.39610, loss_grounding_bce_9: 0.13678/0.10092, loss_grounding_dice_9: 0.05490/0.24333, loss_grounding_ce_9: 0.00935/0.68234] items per batch[64] items per second[0.37] total items[2931200] mini batches[ 45800] memory[4999] epoch remaining[0:51:14] INFO:trainer.default_trainer:epochs[ 25] optim steps[45900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39105/0.76359, loss_mask_bce_0: 0.31147/0.30165, loss_mask_dice_0: 1.99867/1.02618, loss_spatial_bce_0: 0.01239/0.08635, loss_spatial_dice_0: 0.23410/0.18254, loss_spatial_ce_0: 0.00280/0.06084, loss_grounding_bce_0: 0.04189/0.08078, loss_grounding_dice_0: 0.20563/0.15113, loss_grounding_ce_0: 0.32849/0.24928, loss_mask_ce_1: 1.41578/0.76475, loss_mask_bce_1: 0.27517/0.30245, loss_mask_dice_1: 2.00999/1.02966, loss_spatial_bce_1: 0.01645/0.08661, loss_spatial_dice_1: 0.24028/0.18511, loss_spatial_ce_1: 0.01604/0.06492, loss_grounding_bce_1: 0.05270/0.08097, loss_grounding_dice_1: 0.21922/0.15187, loss_grounding_ce_1: 0.44593/0.25081, loss_mask_ce_2: 1.40487/0.77264, loss_mask_bce_2: 0.28977/0.30258, loss_mask_dice_2: 1.93769/1.03095, loss_spatial_bce_2: 0.01481/0.08657, loss_spatial_dice_2: 0.24666/0.18535, loss_spatial_ce_2: 0.02234/0.06732, loss_grounding_bce_2: 0.06699/0.08090, loss_grounding_dice_2: 0.23182/0.15169, loss_grounding_ce_2: 0.34989/0.25334, loss_mask_ce_3: 1.34379/0.77507, loss_mask_bce_3: 0.32977/0.30414, loss_mask_dice_3: 2.10436/1.02813, loss_spatial_bce_3: 0.01605/0.08853, loss_spatial_dice_3: 0.26974/0.18647, loss_spatial_ce_3: 0.00821/0.07188, loss_grounding_bce_3: 0.06122/0.08136, loss_grounding_dice_3: 0.20264/0.15128, loss_grounding_ce_3: 0.27035/0.25326, loss_mask_ce_4: 1.51494/0.78127, loss_mask_bce_4: 0.32890/0.30643, loss_mask_dice_4: 2.11103/1.04737, loss_spatial_bce_4: 0.01343/0.09050, loss_spatial_dice_4: 0.24497/0.19426, loss_spatial_ce_4: 0.04248/0.08464, loss_grounding_bce_4: 0.08764/0.08197, loss_grounding_dice_4: 0.22993/0.15382, loss_grounding_ce_4: 0.44126/0.25932, loss_mask_ce_5: 1.43845/0.80439, loss_mask_bce_5: 0.31082/0.30824, loss_mask_dice_5: 2.15600/1.05487, loss_spatial_bce_5: 0.01595/0.09243, loss_spatial_dice_5: 0.28962/0.19687, loss_spatial_ce_5: 0.01428/0.09694, loss_grounding_bce_5: 0.01659/0.08228, loss_grounding_dice_5: 0.10239/0.15457, loss_grounding_ce_5: 4.09237/0.27711, loss_mask_ce_6: 1.37705/0.83080, loss_mask_bce_6: 0.37456/0.31014, loss_mask_dice_6: 2.27820/1.05743, loss_spatial_bce_6: 0.02135/0.09747, loss_spatial_dice_6: 0.27367/0.19911, loss_spatial_ce_6: 0.07195/0.12003, loss_grounding_bce_6: 0.02226/0.08324, loss_grounding_dice_6: 0.13350/0.15516, loss_grounding_ce_6: 3.33618/0.28618, loss_mask_ce_7: 1.79132/0.88686, loss_mask_bce_7: 0.40746/0.31740, loss_mask_dice_7: 2.71052/1.10425, loss_spatial_bce_7: 0.03445/0.10769, loss_spatial_dice_7: 0.38990/0.22430, loss_spatial_ce_7: 0.10190/0.15905, loss_grounding_bce_7: 0.03378/0.08485, loss_grounding_dice_7: 0.12957/0.16095, loss_grounding_ce_7: 4.91774/0.32096, loss_mask_ce_8: 2.13194/1.02466, loss_mask_bce_8: 0.49153/0.33350, loss_mask_dice_8: 3.33369/1.18147, loss_spatial_bce_8: 0.03073/0.12586, loss_spatial_dice_8: 0.49630/0.26104, loss_spatial_ce_8: 0.19026/0.21072, loss_grounding_bce_8: 0.12813/0.08884, loss_grounding_dice_8: 0.25997/0.17045, loss_grounding_ce_8: 0.79568/0.42380, loss_mask_ce_9: 3.15794/3.48321, loss_mask_bce_9: 0.35784/0.36058, loss_mask_dice_9: 4.63360/1.76491, loss_spatial_bce_9: 0.06683/0.35560, loss_spatial_dice_9: 0.96295/0.79442, loss_spatial_ce_9: 1.38367/1.39620, loss_grounding_bce_9: 0.05484/0.10090, loss_grounding_dice_9: 0.26110/0.24335, loss_grounding_ce_9: 2.62836/0.68247] items per batch[64] items per second[0.37] total items[2937600] mini batches[ 45900] memory[4999] epoch remaining[0:47:27] INFO:trainer.default_trainer:epochs[ 25] optim steps[46000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94651/0.76343, loss_mask_bce_0: 0.33339/0.30161, loss_mask_dice_0: 0.43715/1.02614, loss_spatial_bce_0: 0.08815/0.08633, loss_spatial_dice_0: 0.11942/0.18252, loss_spatial_ce_0: 0.00121/0.06079, loss_grounding_bce_0: 0.10747/0.08077, loss_grounding_dice_0: 0.15551/0.15115, loss_grounding_ce_0: 0.11919/0.24917, loss_mask_ce_1: 0.98532/0.76454, loss_mask_bce_1: 0.34261/0.30241, loss_mask_dice_1: 0.47446/1.02965, loss_spatial_bce_1: 0.09491/0.08659, loss_spatial_dice_1: 0.12162/0.18509, loss_spatial_ce_1: 0.00131/0.06489, loss_grounding_bce_1: 0.11455/0.08095, loss_grounding_dice_1: 0.18791/0.15186, loss_grounding_ce_1: 0.11275/0.25072, loss_mask_ce_2: 0.96820/0.77247, loss_mask_bce_2: 0.33434/0.30254, loss_mask_dice_2: 0.48206/1.03091, loss_spatial_bce_2: 0.09426/0.08655, loss_spatial_dice_2: 0.12435/0.18533, loss_spatial_ce_2: 0.00148/0.06727, loss_grounding_bce_2: 0.10579/0.08089, loss_grounding_dice_2: 0.17349/0.15169, loss_grounding_ce_2: 0.14403/0.25326, loss_mask_ce_3: 1.24616/0.77490, loss_mask_bce_3: 0.35524/0.30411, loss_mask_dice_3: 0.45332/1.02815, loss_spatial_bce_3: 0.08843/0.08851, loss_spatial_dice_3: 0.12283/0.18645, loss_spatial_ce_3: 0.00665/0.07183, loss_grounding_bce_3: 0.11705/0.08135, loss_grounding_dice_3: 0.16361/0.15129, loss_grounding_ce_3: 0.16489/0.25315, loss_mask_ce_4: 1.22798/0.78106, loss_mask_bce_4: 0.37452/0.30639, loss_mask_dice_4: 0.41914/1.04734, loss_spatial_bce_4: 0.10291/0.09048, loss_spatial_dice_4: 0.14363/0.19424, loss_spatial_ce_4: 0.02416/0.08458, loss_grounding_bce_4: 0.11902/0.08195, loss_grounding_dice_4: 0.16946/0.15383, loss_grounding_ce_4: 0.18456/0.25926, loss_mask_ce_5: 1.20734/0.80420, loss_mask_bce_5: 0.36022/0.30820, loss_mask_dice_5: 0.45808/1.05482, loss_spatial_bce_5: 0.09431/0.09241, loss_spatial_dice_5: 0.13769/0.19685, loss_spatial_ce_5: 0.02204/0.09689, loss_grounding_bce_5: 0.11932/0.08227, loss_grounding_dice_5: 0.18895/0.15457, loss_grounding_ce_5: 0.24761/0.27708, loss_mask_ce_6: 1.27005/0.83063, loss_mask_bce_6: 0.37386/0.31008, loss_mask_dice_6: 0.48156/1.05738, loss_spatial_bce_6: 0.10908/0.09745, loss_spatial_dice_6: 0.16476/0.19909, loss_spatial_ce_6: 0.04153/0.12001, loss_grounding_bce_6: 0.12950/0.08322, loss_grounding_dice_6: 0.19816/0.15515, loss_grounding_ce_6: 0.22433/0.28619, loss_mask_ce_7: 1.19194/0.88665, loss_mask_bce_7: 0.37677/0.31736, loss_mask_dice_7: 0.52976/1.10427, loss_spatial_bce_7: 0.15953/0.10766, loss_spatial_dice_7: 0.18627/0.22427, loss_spatial_ce_7: 0.08902/0.15904, loss_grounding_bce_7: 0.14898/0.08484, loss_grounding_dice_7: 0.22020/0.16094, loss_grounding_ce_7: 0.22328/0.32101, loss_mask_ce_8: 1.56225/1.02454, loss_mask_bce_8: 0.35389/0.33344, loss_mask_dice_8: 0.57862/1.18146, loss_spatial_bce_8: 0.14347/0.12581, loss_spatial_dice_8: 0.21050/0.26102, loss_spatial_ce_8: 0.14081/0.21066, loss_grounding_bce_8: 0.12887/0.08882, loss_grounding_dice_8: 0.23957/0.17045, loss_grounding_ce_8: 0.32284/0.42384, loss_mask_ce_9: 3.46850/3.48306, loss_mask_bce_9: 0.50163/0.36052, loss_mask_dice_9: 0.91799/1.76466, loss_spatial_bce_9: 0.56642/0.35551, loss_spatial_dice_9: 0.84456/0.79438, loss_spatial_ce_9: 1.10859/1.39602, loss_grounding_bce_9: 0.30921/0.10089, loss_grounding_dice_9: 0.35038/0.24332, loss_grounding_ce_9: 0.05074/0.68228] items per batch[64] items per second[0.36] total items[2944000] mini batches[ 46000] memory[4999] epoch remaining[0:44:37] INFO:trainer.default_trainer:epochs[ 25] optim steps[46100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75064/0.76341, loss_mask_bce_0: 0.37249/0.30165, loss_mask_dice_0: 0.47993/1.02616, loss_spatial_bce_0: 0.14306/0.08636, loss_spatial_dice_0: 0.17268/0.18252, loss_spatial_ce_0: 0.00601/0.06077, loss_grounding_bce_0: 0.00538/0.08080, loss_grounding_dice_0: 0.05435/0.15116, loss_grounding_ce_0: 0.20662/0.24916, loss_mask_ce_1: 0.71961/0.76454, loss_mask_bce_1: 0.38517/0.30244, loss_mask_dice_1: 0.48995/1.02965, loss_spatial_bce_1: 0.14346/0.08661, loss_spatial_dice_1: 0.16646/0.18508, loss_spatial_ce_1: 0.01140/0.06490, loss_grounding_bce_1: 0.00708/0.08099, loss_grounding_dice_1: 0.05028/0.15188, loss_grounding_ce_1: 0.21449/0.25073, loss_mask_ce_2: 0.80886/0.77241, loss_mask_bce_2: 0.37465/0.30258, loss_mask_dice_2: 0.47166/1.03091, loss_spatial_bce_2: 0.14576/0.08657, loss_spatial_dice_2: 0.17988/0.18532, loss_spatial_ce_2: 0.03079/0.06726, loss_grounding_bce_2: 0.00699/0.08093, loss_grounding_dice_2: 0.07723/0.15171, loss_grounding_ce_2: 0.20374/0.25325, loss_mask_ce_3: 0.80449/0.77485, loss_mask_bce_3: 0.37908/0.30415, loss_mask_dice_3: 0.47753/1.02821, loss_spatial_bce_3: 0.14900/0.08854, loss_spatial_dice_3: 0.18425/0.18645, loss_spatial_ce_3: 0.05978/0.07181, loss_grounding_bce_3: 0.00606/0.08138, loss_grounding_dice_3: 0.05998/0.15131, loss_grounding_ce_3: 0.20343/0.25312, loss_mask_ce_4: 0.82221/0.78109, loss_mask_bce_4: 0.37842/0.30646, loss_mask_dice_4: 0.50327/1.04737, loss_spatial_bce_4: 0.13954/0.09050, loss_spatial_dice_4: 0.18958/0.19424, loss_spatial_ce_4: 0.08477/0.08458, loss_grounding_bce_4: 0.01060/0.08199, loss_grounding_dice_4: 0.05722/0.15385, loss_grounding_ce_4: 0.18411/0.25925, loss_mask_ce_5: 0.95787/0.80420, loss_mask_bce_5: 0.40068/0.30826, loss_mask_dice_5: 0.44664/1.05491, loss_spatial_bce_5: 0.14372/0.09244, loss_spatial_dice_5: 0.17598/0.19686, loss_spatial_ce_5: 0.09922/0.09689, loss_grounding_bce_5: 0.00511/0.08231, loss_grounding_dice_5: 0.03674/0.15459, loss_grounding_ce_5: 0.19090/0.27712, loss_mask_ce_6: 0.95124/0.83061, loss_mask_bce_6: 0.37960/0.31014, loss_mask_dice_6: 0.48256/1.05746, loss_spatial_bce_6: 0.15474/0.09749, loss_spatial_dice_6: 0.17910/0.19910, loss_spatial_ce_6: 0.23720/0.12001, loss_grounding_bce_6: 0.00772/0.08326, loss_grounding_dice_6: 0.05043/0.15518, loss_grounding_ce_6: 0.19940/0.28625, loss_mask_ce_7: 1.07249/0.88662, loss_mask_bce_7: 0.40715/0.31747, loss_mask_dice_7: 0.48292/1.10442, loss_spatial_bce_7: 0.19604/0.10771, loss_spatial_dice_7: 0.23480/0.22430, loss_spatial_ce_7: 0.16342/0.15905, loss_grounding_bce_7: 0.00895/0.08489, loss_grounding_dice_7: 0.05598/0.16096, loss_grounding_ce_7: 0.20589/0.32097, loss_mask_ce_8: 0.96664/1.02456, loss_mask_bce_8: 0.42515/0.33353, loss_mask_dice_8: 0.56153/1.18160, loss_spatial_bce_8: 0.22253/0.12584, loss_spatial_dice_8: 0.24899/0.26101, loss_spatial_ce_8: 0.19061/0.21067, loss_grounding_bce_8: 0.00880/0.08886, loss_grounding_dice_8: 0.05359/0.17048, loss_grounding_ce_8: 0.26721/0.42380, loss_mask_ce_9: 4.77334/3.48315, loss_mask_bce_9: 0.41307/0.36061, loss_mask_dice_9: 0.73408/1.76503, loss_spatial_bce_9: 0.48949/0.35553, loss_spatial_dice_9: 0.85072/0.79436, loss_spatial_ce_9: 1.83013/1.39581, loss_grounding_bce_9: 0.00815/0.10094, loss_grounding_dice_9: 0.11676/0.24335, loss_grounding_ce_9: 0.59893/0.68232] items per batch[64] items per second[0.37] total items[2950400] mini batches[ 46100] memory[4999] epoch remaining[0:41:27] INFO:trainer.default_trainer:epochs[ 25] optim steps[46200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45521/0.76355, loss_mask_bce_0: 0.41634/0.30172, loss_mask_dice_0: 0.42770/1.02619, loss_spatial_bce_0: 0.18686/0.08637, loss_spatial_dice_0: 0.18908/0.18253, loss_spatial_ce_0: 0.01594/0.06074, loss_grounding_bce_0: 0.23886/0.08081, loss_grounding_dice_0: 0.13422/0.15121, loss_grounding_ce_0: 0.01095/0.24929, loss_mask_ce_1: 0.43852/0.76464, loss_mask_bce_1: 0.39817/0.30252, loss_mask_dice_1: 0.41012/1.02962, loss_spatial_bce_1: 0.19503/0.08662, loss_spatial_dice_1: 0.19704/0.18509, loss_spatial_ce_1: 0.06470/0.06486, loss_grounding_bce_1: 0.23258/0.08100, loss_grounding_dice_1: 0.13780/0.15194, loss_grounding_ce_1: 0.01029/0.25089, loss_mask_ce_2: 0.44756/0.77254, loss_mask_bce_2: 0.38891/0.30265, loss_mask_dice_2: 0.38859/1.03091, loss_spatial_bce_2: 0.17976/0.08658, loss_spatial_dice_2: 0.21120/0.18534, loss_spatial_ce_2: 0.05647/0.06725, loss_grounding_bce_2: 0.22761/0.08095, loss_grounding_dice_2: 0.13373/0.15178, loss_grounding_ce_2: 0.01075/0.25337, loss_mask_ce_3: 0.41016/0.77496, loss_mask_bce_3: 0.40399/0.30421, loss_mask_dice_3: 0.39727/1.02819, loss_spatial_bce_3: 0.19698/0.08854, loss_spatial_dice_3: 0.21176/0.18648, loss_spatial_ce_3: 0.07021/0.07178, loss_grounding_bce_3: 0.24059/0.08139, loss_grounding_dice_3: 0.13557/0.15138, loss_grounding_ce_3: 0.01085/0.25327, loss_mask_ce_4: 0.38667/0.78122, loss_mask_bce_4: 0.42457/0.30651, loss_mask_dice_4: 0.38863/1.04734, loss_spatial_bce_4: 0.21434/0.09050, loss_spatial_dice_4: 0.19272/0.19425, loss_spatial_ce_4: 0.05248/0.08454, loss_grounding_bce_4: 0.24195/0.08200, loss_grounding_dice_4: 0.14045/0.15392, loss_grounding_ce_4: 0.00550/0.25940, loss_mask_ce_5: 0.43716/0.80436, loss_mask_bce_5: 0.43009/0.30832, loss_mask_dice_5: 0.37056/1.05493, loss_spatial_bce_5: 0.21786/0.09244, loss_spatial_dice_5: 0.23103/0.19688, loss_spatial_ce_5: 0.04711/0.09684, loss_grounding_bce_5: 0.24910/0.08232, loss_grounding_dice_5: 0.14208/0.15465, loss_grounding_ce_5: 0.00643/0.27727, loss_mask_ce_6: 0.40109/0.83078, loss_mask_bce_6: 0.43606/0.31020, loss_mask_dice_6: 0.40919/1.05743, loss_spatial_bce_6: 0.21018/0.09749, loss_spatial_dice_6: 0.25133/0.19913, loss_spatial_ce_6: 0.05623/0.12002, loss_grounding_bce_6: 0.25184/0.08328, loss_grounding_dice_6: 0.13886/0.15524, loss_grounding_ce_6: 0.00992/0.28644, loss_mask_ce_7: 0.45225/0.88682, loss_mask_bce_7: 0.44315/0.31753, loss_mask_dice_7: 0.33088/1.10442, loss_spatial_bce_7: 0.21631/0.10770, loss_spatial_dice_7: 0.32493/0.22433, loss_spatial_ce_7: 0.16690/0.15903, loss_grounding_bce_7: 0.24731/0.08491, loss_grounding_dice_7: 0.13775/0.16103, loss_grounding_ce_7: 0.01881/0.32114, loss_mask_ce_8: 0.77290/1.02481, loss_mask_bce_8: 0.49811/0.33357, loss_mask_dice_8: 0.41963/1.18158, loss_spatial_bce_8: 0.17464/0.12582, loss_spatial_dice_8: 0.20045/0.26101, loss_spatial_ce_8: 0.28801/0.21067, loss_grounding_bce_8: 0.27416/0.08889, loss_grounding_dice_8: 0.13126/0.17055, loss_grounding_ce_8: 0.01611/0.42387, loss_mask_ce_9: 3.33534/3.48344, loss_mask_bce_9: 0.42045/0.36069, loss_mask_dice_9: 0.61554/1.76493, loss_spatial_bce_9: 0.41263/0.35552, loss_spatial_dice_9: 0.82735/0.79439, loss_spatial_ce_9: 1.39891/1.39605, loss_grounding_bce_9: 0.24067/0.10095, loss_grounding_dice_9: 0.20349/0.24343, loss_grounding_ce_9: 0.35901/0.68225] items per batch[64] items per second[0.37] total items[2956800] mini batches[ 46200] memory[4999] epoch remaining[0:38:15] INFO:trainer.default_trainer:epochs[ 25] optim steps[46300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44873/0.76358, loss_mask_bce_0: 0.43701/0.30175, loss_mask_dice_0: 2.78017/1.02595, loss_spatial_bce_0: 0.01095/0.08635, loss_spatial_dice_0: 0.22222/0.18250, loss_spatial_ce_0: 0.00523/0.06069, loss_grounding_bce_0: 0.02139/0.08078, loss_grounding_dice_0: 0.15137/0.15116, loss_grounding_ce_0: 0.57418/0.24931, loss_mask_ce_1: 0.38025/0.76462, loss_mask_bce_1: 0.42911/0.30255, loss_mask_dice_1: 2.91476/1.02942, loss_spatial_bce_1: 0.00943/0.08660, loss_spatial_dice_1: 0.22211/0.18505, loss_spatial_ce_1: 0.00048/0.06481, loss_grounding_bce_1: 0.02121/0.08098, loss_grounding_dice_1: 0.15814/0.15189, loss_grounding_ce_1: 0.56277/0.25108, loss_mask_ce_2: 0.43644/0.77254, loss_mask_bce_2: 0.43570/0.30268, loss_mask_dice_2: 3.22825/1.03071, loss_spatial_bce_2: 0.00838/0.08656, loss_spatial_dice_2: 0.20666/0.18531, loss_spatial_ce_2: 0.00087/0.06718, loss_grounding_bce_2: 0.02078/0.08092, loss_grounding_dice_2: 0.15847/0.15173, loss_grounding_ce_2: 0.62122/0.25349, loss_mask_ce_3: 0.36683/0.77496, loss_mask_bce_3: 0.43737/0.30424, loss_mask_dice_3: 3.45634/1.02802, loss_spatial_bce_3: 0.01024/0.08852, loss_spatial_dice_3: 0.22038/0.18644, loss_spatial_ce_3: 0.00827/0.07172, loss_grounding_bce_3: 0.02043/0.08137, loss_grounding_dice_3: 0.17081/0.15134, loss_grounding_ce_3: 0.63012/0.25339, loss_mask_ce_4: 0.44479/0.78117, loss_mask_bce_4: 0.44863/0.30656, loss_mask_dice_4: 3.26115/1.04716, loss_spatial_bce_4: 0.01007/0.09048, loss_spatial_dice_4: 0.23939/0.19422, loss_spatial_ce_4: 0.03273/0.08447, loss_grounding_bce_4: 0.01956/0.08198, loss_grounding_dice_4: 0.15170/0.15387, loss_grounding_ce_4: 0.59307/0.25941, loss_mask_ce_5: 0.54396/0.80435, loss_mask_bce_5: 0.46636/0.30836, loss_mask_dice_5: 3.43788/1.05472, loss_spatial_bce_5: 0.00851/0.09242, loss_spatial_dice_5: 0.22741/0.19686, loss_spatial_ce_5: 0.03394/0.09677, loss_grounding_bce_5: 0.02152/0.08230, loss_grounding_dice_5: 0.18325/0.15460, loss_grounding_ce_5: 0.63516/0.27729, loss_mask_ce_6: 0.51598/0.83076, loss_mask_bce_6: 0.44153/0.31025, loss_mask_dice_6: 3.31256/1.05726, loss_spatial_bce_6: 0.00789/0.09747, loss_spatial_dice_6: 0.25661/0.19912, loss_spatial_ce_6: 0.06650/0.11998, loss_grounding_bce_6: 0.02107/0.08326, loss_grounding_dice_6: 0.16049/0.15519, loss_grounding_ce_6: 0.58400/0.28654, loss_mask_ce_7: 0.55315/0.88685, loss_mask_bce_7: 0.44541/0.31759, loss_mask_dice_7: 3.14757/1.10426, loss_spatial_bce_7: 0.01057/0.10768, loss_spatial_dice_7: 0.29913/0.22433, loss_spatial_ce_7: 0.05169/0.15899, loss_grounding_bce_7: 0.02185/0.08489, loss_grounding_dice_7: 0.16453/0.16100, loss_grounding_ce_7: 0.67535/0.32121, loss_mask_ce_8: 0.91252/1.02481, loss_mask_bce_8: 0.42907/0.33361, loss_mask_dice_8: 3.39073/1.18141, loss_spatial_bce_8: 0.01460/0.12580, loss_spatial_dice_8: 0.41155/0.26098, loss_spatial_ce_8: 0.06223/0.21063, loss_grounding_bce_8: 0.02250/0.08889, loss_grounding_dice_8: 0.18217/0.17051, loss_grounding_ce_8: 0.70728/0.42385, loss_mask_ce_9: 3.62742/3.48344, loss_mask_bce_9: 0.43855/0.36074, loss_mask_dice_9: 6.52403/1.76484, loss_spatial_bce_9: 0.13654/0.35558, loss_spatial_dice_9: 0.95753/0.79440, loss_spatial_ce_9: 1.24728/1.39595, loss_grounding_bce_9: 0.03413/0.10097, loss_grounding_dice_9: 0.45832/0.24338, loss_grounding_ce_9: 0.55348/0.68222] items per batch[64] items per second[0.37] total items[2963200] mini batches[ 46300] memory[4999] epoch remaining[0:35:12] INFO:trainer.default_trainer:epochs[ 25] optim steps[46400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23338/0.76360, loss_mask_bce_0: 0.18137/0.30178, loss_mask_dice_0: 4.78601/1.02571, loss_spatial_bce_0: 0.01730/0.08635, loss_spatial_dice_0: 0.25231/0.18248, loss_spatial_ce_0: 0.13341/0.06067, loss_grounding_bce_0: 0.02512/0.08077, loss_grounding_dice_0: 0.63322/0.15116, loss_grounding_ce_0: 0.43963/0.24938, loss_mask_ce_1: 0.99393/0.76461, loss_mask_bce_1: 0.22123/0.30258, loss_mask_dice_1: 4.69547/1.02917, loss_spatial_bce_1: 0.01876/0.08661, loss_spatial_dice_1: 0.22977/0.18504, loss_spatial_ce_1: 0.15049/0.06481, loss_grounding_bce_1: 0.03499/0.08097, loss_grounding_dice_1: 0.58996/0.15190, loss_grounding_ce_1: 0.42847/0.25121, loss_mask_ce_2: 0.91727/0.77257, loss_mask_bce_2: 0.18810/0.30271, loss_mask_dice_2: 4.91272/1.03046, loss_spatial_bce_2: 0.01669/0.08657, loss_spatial_dice_2: 0.22722/0.18530, loss_spatial_ce_2: 0.00824/0.06717, loss_grounding_bce_2: 0.03248/0.08092, loss_grounding_dice_2: 0.67791/0.15175, loss_grounding_ce_2: 0.17358/0.25355, loss_mask_ce_3: 0.97225/0.77497, loss_mask_bce_3: 0.20725/0.30428, loss_mask_dice_3: 4.97670/1.02780, loss_spatial_bce_3: 0.01539/0.08853, loss_spatial_dice_3: 0.26640/0.18643, loss_spatial_ce_3: 0.02688/0.07170, loss_grounding_bce_3: 0.02757/0.08136, loss_grounding_dice_3: 0.63826/0.15133, loss_grounding_ce_3: 0.45351/0.25347, loss_mask_ce_4: 0.87650/0.78115, loss_mask_bce_4: 0.19394/0.30660, loss_mask_dice_4: 4.74814/1.04694, loss_spatial_bce_4: 0.01803/0.09049, loss_spatial_dice_4: 0.23395/0.19420, loss_spatial_ce_4: 0.08223/0.08445, loss_grounding_bce_4: 0.02868/0.08197, loss_grounding_dice_4: 0.66911/0.15387, loss_grounding_ce_4: 0.13912/0.25949, loss_mask_ce_5: 1.10827/0.80437, loss_mask_bce_5: 0.21440/0.30840, loss_mask_dice_5: 5.04248/1.05452, loss_spatial_bce_5: 0.01494/0.09244, loss_spatial_dice_5: 0.18307/0.19685, loss_spatial_ce_5: 0.13786/0.09677, loss_grounding_bce_5: 0.03388/0.08229, loss_grounding_dice_5: 0.60753/0.15459, loss_grounding_ce_5: 0.44191/0.27740, loss_mask_ce_6: 1.14754/0.83076, loss_mask_bce_6: 0.20711/0.31029, loss_mask_dice_6: 5.27436/1.05708, loss_spatial_bce_6: 0.01764/0.09750, loss_spatial_dice_6: 0.20650/0.19912, loss_spatial_ce_6: 0.12494/0.11994, loss_grounding_bce_6: 0.03332/0.08325, loss_grounding_dice_6: 0.55029/0.15517, loss_grounding_ce_6: 0.64868/0.28659, loss_mask_ce_7: 1.05865/0.88684, loss_mask_bce_7: 0.23420/0.31763, loss_mask_dice_7: 5.34810/1.10401, loss_spatial_bce_7: 0.01870/0.10771, loss_spatial_dice_7: 0.24773/0.22434, loss_spatial_ce_7: 0.23458/0.15898, loss_grounding_bce_7: 0.04020/0.08487, loss_grounding_dice_7: 0.66693/0.16097, loss_grounding_ce_7: 0.30770/0.32126, loss_mask_ce_8: 1.29215/1.02490, loss_mask_bce_8: 0.21733/0.33363, loss_mask_dice_8: 4.99002/1.18108, loss_spatial_bce_8: 0.01911/0.12581, loss_spatial_dice_8: 0.27154/0.26097, loss_spatial_ce_8: 0.08485/0.21061, loss_grounding_bce_8: 0.02749/0.08887, loss_grounding_dice_8: 0.67378/0.17049, loss_grounding_ce_8: 0.23746/0.42389, loss_mask_ce_9: 4.89482/3.48343, loss_mask_bce_9: 0.17265/0.36076, loss_mask_dice_9: 5.84304/1.76447, loss_spatial_bce_9: 0.10436/0.35563, loss_spatial_dice_9: 0.96961/0.79443, loss_spatial_ce_9: 1.24510/1.39597, loss_grounding_bce_9: 0.02088/0.10096, loss_grounding_dice_9: 0.63512/0.24337, loss_grounding_ce_9: 0.32524/0.68202] items per batch[64] items per second[0.37] total items[2969600] mini batches[ 46400] memory[4999] epoch remaining[0:32:15] INFO:trainer.default_trainer:epochs[ 25] optim steps[46500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.67707/0.76341, loss_mask_bce_0: 0.16532/0.30176, loss_mask_dice_0: 0.57963/1.02560, loss_spatial_bce_0: 0.04601/0.08633, loss_spatial_dice_0: 0.14357/0.18245, loss_spatial_ce_0: 0.00045/0.06062, loss_grounding_bce_0: 0.09515/0.08078, loss_grounding_dice_0: 0.27773/0.15113, loss_grounding_ce_0: 0.14074/0.24928, loss_mask_ce_1: 0.68644/0.76440, loss_mask_bce_1: 0.15314/0.30256, loss_mask_dice_1: 0.57792/1.02906, loss_spatial_bce_1: 0.04265/0.08659, loss_spatial_dice_1: 0.15420/0.18500, loss_spatial_ce_1: 0.00192/0.06475, loss_grounding_bce_1: 0.09850/0.08098, loss_grounding_dice_1: 0.26043/0.15186, loss_grounding_ce_1: 0.14460/0.25113, loss_mask_ce_2: 0.71952/0.77232, loss_mask_bce_2: 0.15039/0.30269, loss_mask_dice_2: 0.55387/1.03033, loss_spatial_bce_2: 0.03960/0.08654, loss_spatial_dice_2: 0.14463/0.18527, loss_spatial_ce_2: 0.00239/0.06710, loss_grounding_bce_2: 0.11046/0.08093, loss_grounding_dice_2: 0.29379/0.15171, loss_grounding_ce_2: 0.12179/0.25347, loss_mask_ce_3: 0.76458/0.77476, loss_mask_bce_3: 0.16167/0.30426, loss_mask_dice_3: 0.59692/1.02772, loss_spatial_bce_3: 0.04264/0.08850, loss_spatial_dice_3: 0.14132/0.18640, loss_spatial_ce_3: 0.01065/0.07165, loss_grounding_bce_3: 0.11034/0.08137, loss_grounding_dice_3: 0.26954/0.15129, loss_grounding_ce_3: 0.12412/0.25335, loss_mask_ce_4: 0.81474/0.78095, loss_mask_bce_4: 0.16386/0.30659, loss_mask_dice_4: 0.56831/1.04681, loss_spatial_bce_4: 0.04567/0.09047, loss_spatial_dice_4: 0.17573/0.19418, loss_spatial_ce_4: 0.01617/0.08438, loss_grounding_bce_4: 0.09536/0.08197, loss_grounding_dice_4: 0.27419/0.15384, loss_grounding_ce_4: 0.14937/0.25936, loss_mask_ce_5: 0.95807/0.80424, loss_mask_bce_5: 0.16115/0.30839, loss_mask_dice_5: 0.56141/1.05442, loss_spatial_bce_5: 0.05071/0.09242, loss_spatial_dice_5: 0.18936/0.19683, loss_spatial_ce_5: 0.03518/0.09671, loss_grounding_bce_5: 0.10738/0.08230, loss_grounding_dice_5: 0.29609/0.15458, loss_grounding_ce_5: 0.14445/0.27735, loss_mask_ce_6: 0.95362/0.83056, loss_mask_bce_6: 0.16303/0.31028, loss_mask_dice_6: 0.54823/1.05697, loss_spatial_bce_6: 0.06526/0.09748, loss_spatial_dice_6: 0.19493/0.19909, loss_spatial_ce_6: 0.33812/0.11989, loss_grounding_bce_6: 0.09398/0.08326, loss_grounding_dice_6: 0.26404/0.15514, loss_grounding_ce_6: 0.17805/0.28652, loss_mask_ce_7: 0.70601/0.88668, loss_mask_bce_7: 0.15575/0.31762, loss_mask_dice_7: 0.56423/1.10386, loss_spatial_bce_7: 0.04600/0.10769, loss_spatial_dice_7: 0.21990/0.22432, loss_spatial_ce_7: 0.17327/0.15889, loss_grounding_bce_7: 0.07143/0.08489, loss_grounding_dice_7: 0.25047/0.16094, loss_grounding_ce_7: 0.21268/0.32111, loss_mask_ce_8: 0.97888/1.02478, loss_mask_bce_8: 0.14017/0.33361, loss_mask_dice_8: 0.53501/1.18091, loss_spatial_bce_8: 0.04772/0.12578, loss_spatial_dice_8: 0.26441/0.26095, loss_spatial_ce_8: 0.05890/0.21050, loss_grounding_bce_8: 0.06478/0.08886, loss_grounding_dice_8: 0.21408/0.17044, loss_grounding_ce_8: 0.26370/0.42375, loss_mask_ce_9: 3.57188/3.48341, loss_mask_bce_9: 0.16732/0.36077, loss_mask_dice_9: 0.94152/1.76459, loss_spatial_bce_9: 0.20925/0.35559, loss_spatial_dice_9: 0.78173/0.79443, loss_spatial_ce_9: 1.46381/1.39577, loss_grounding_bce_9: 0.07544/0.10097, loss_grounding_dice_9: 0.30968/0.24334, loss_grounding_ce_9: 0.46128/0.68204] items per batch[64] items per second[0.37] total items[2976000] mini batches[ 46500] memory[4999] epoch remaining[0:29:16] INFO:trainer.default_trainer:epochs[ 25] optim steps[46600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86025/0.76335, loss_mask_bce_0: 0.25494/0.30177, loss_mask_dice_0: 0.67543/1.02572, loss_spatial_bce_0: 0.06756/0.08632, loss_spatial_dice_0: 0.15716/0.18243, loss_spatial_ce_0: 0.00186/0.06059, loss_grounding_bce_0: 0.08019/0.08080, loss_grounding_dice_0: 0.18143/0.15115, loss_grounding_ce_0: 0.02764/0.24928, loss_mask_ce_1: 0.87042/0.76429, loss_mask_bce_1: 0.25187/0.30259, loss_mask_dice_1: 0.68816/1.02924, loss_spatial_bce_1: 0.06526/0.08658, loss_spatial_dice_1: 0.16185/0.18499, loss_spatial_ce_1: 0.00194/0.06472, loss_grounding_bce_1: 0.07948/0.08099, loss_grounding_dice_1: 0.18842/0.15187, loss_grounding_ce_1: 0.02147/0.25115, loss_mask_ce_2: 0.93735/0.77227, loss_mask_bce_2: 0.25254/0.30270, loss_mask_dice_2: 0.67936/1.03043, loss_spatial_bce_2: 0.06657/0.08653, loss_spatial_dice_2: 0.16388/0.18526, loss_spatial_ce_2: 0.00124/0.06708, loss_grounding_bce_2: 0.08266/0.08093, loss_grounding_dice_2: 0.19315/0.15172, loss_grounding_ce_2: 0.02147/0.25351, loss_mask_ce_3: 0.92203/0.77473, loss_mask_bce_3: 0.25617/0.30427, loss_mask_dice_3: 0.69230/1.02786, loss_spatial_bce_3: 0.06764/0.08849, loss_spatial_dice_3: 0.16663/0.18639, loss_spatial_ce_3: 0.00319/0.07162, loss_grounding_bce_3: 0.07657/0.08138, loss_grounding_dice_3: 0.17989/0.15131, loss_grounding_ce_3: 0.02643/0.25340, loss_mask_ce_4: 1.66547/0.78090, loss_mask_bce_4: 0.24964/0.30660, loss_mask_dice_4: 0.66418/1.04696, loss_spatial_bce_4: 0.06576/0.09046, loss_spatial_dice_4: 0.16760/0.19417, loss_spatial_ce_4: 0.03498/0.08436, loss_grounding_bce_4: 0.07355/0.08197, loss_grounding_dice_4: 0.18487/0.15385, loss_grounding_ce_4: 0.03181/0.25942, loss_mask_ce_5: 0.83645/0.80417, loss_mask_bce_5: 0.23909/0.30841, loss_mask_dice_5: 0.67702/1.05462, loss_spatial_bce_5: 0.06668/0.09241, loss_spatial_dice_5: 0.17654/0.19682, loss_spatial_ce_5: 0.04905/0.09670, loss_grounding_bce_5: 0.06901/0.08231, loss_grounding_dice_5: 0.17569/0.15459, loss_grounding_ce_5: 0.00966/0.27738, loss_mask_ce_6: 0.73397/0.83049, loss_mask_bce_6: 0.24513/0.31029, loss_mask_dice_6: 0.64869/1.05713, loss_spatial_bce_6: 0.07361/0.09747, loss_spatial_dice_6: 0.16796/0.19907, loss_spatial_ce_6: 0.05469/0.11988, loss_grounding_bce_6: 0.07171/0.08326, loss_grounding_dice_6: 0.17033/0.15514, loss_grounding_ce_6: 0.00847/0.28658, loss_mask_ce_7: 0.84679/0.88656, loss_mask_bce_7: 0.25241/0.31764, loss_mask_dice_7: 0.65651/1.10407, loss_spatial_bce_7: 0.07606/0.10768, loss_spatial_dice_7: 0.19247/0.22432, loss_spatial_ce_7: 0.09984/0.15886, loss_grounding_bce_7: 0.07391/0.08490, loss_grounding_dice_7: 0.17074/0.16096, loss_grounding_ce_7: 0.01843/0.32108, loss_mask_ce_8: 1.27122/1.02469, loss_mask_bce_8: 0.26618/0.33363, loss_mask_dice_8: 0.69608/1.18111, loss_spatial_bce_8: 0.09841/0.12577, loss_spatial_dice_8: 0.21748/0.26095, loss_spatial_ce_8: 0.09279/0.21046, loss_grounding_bce_8: 0.08116/0.08890, loss_grounding_dice_8: 0.17467/0.17046, loss_grounding_ce_8: 0.03617/0.42373, loss_mask_ce_9: 2.77846/3.48348, loss_mask_bce_9: 0.25441/0.36080, loss_mask_dice_9: 1.12741/1.76496, loss_spatial_bce_9: 0.28096/0.35561, loss_spatial_dice_9: 0.84386/0.79441, loss_spatial_ce_9: 1.40781/1.39581, loss_grounding_bce_9: 0.07390/0.10098, loss_grounding_dice_9: 0.25054/0.24332, loss_grounding_ce_9: 0.04502/0.68214] items per batch[64] items per second[0.36] total items[2982400] mini batches[ 46600] memory[4999] epoch remaining[0:26:22] INFO:trainer.default_trainer:epochs[ 25] optim steps[46700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53544/0.76312, loss_mask_bce_0: 0.10081/0.30174, loss_mask_dice_0: 0.71041/1.02564, loss_spatial_bce_0: 0.02069/0.08631, loss_spatial_dice_0: 0.11199/0.18240, loss_spatial_ce_0: 0.00009/0.06056, loss_grounding_bce_0: 0.02330/0.08080, loss_grounding_dice_0: 0.11462/0.15111, loss_grounding_ce_0: 0.30953/0.24920, loss_mask_ce_1: 0.51033/0.76409, loss_mask_bce_1: 0.10423/0.30256, loss_mask_dice_1: 0.66865/1.02919, loss_spatial_bce_1: 0.02201/0.08656, loss_spatial_dice_1: 0.13021/0.18496, loss_spatial_ce_1: 0.00004/0.06467, loss_grounding_bce_1: 0.03159/0.08099, loss_grounding_dice_1: 0.18059/0.15183, loss_grounding_ce_1: 0.25219/0.25108, loss_mask_ce_2: 0.54658/0.77204, loss_mask_bce_2: 0.09393/0.30268, loss_mask_dice_2: 0.63120/1.03037, loss_spatial_bce_2: 0.02056/0.08652, loss_spatial_dice_2: 0.11551/0.18523, loss_spatial_ce_2: 0.00004/0.06703, loss_grounding_bce_2: 0.02286/0.08093, loss_grounding_dice_2: 0.11460/0.15169, loss_grounding_ce_2: 0.34237/0.25340, loss_mask_ce_3: 0.50954/0.77452, loss_mask_bce_3: 0.10204/0.30426, loss_mask_dice_3: 0.64948/1.02777, loss_spatial_bce_3: 0.02231/0.08848, loss_spatial_dice_3: 0.12973/0.18636, loss_spatial_ce_3: 0.00006/0.07159, loss_grounding_bce_3: 0.02423/0.08137, loss_grounding_dice_3: 0.13199/0.15127, loss_grounding_ce_3: 0.34933/0.25339, loss_mask_ce_4: 0.56549/0.78077, loss_mask_bce_4: 0.10172/0.30657, loss_mask_dice_4: 0.62117/1.04688, loss_spatial_bce_4: 0.02398/0.09046, loss_spatial_dice_4: 0.14722/0.19414, loss_spatial_ce_4: 0.00063/0.08432, loss_grounding_bce_4: 0.02379/0.08197, loss_grounding_dice_4: 0.13530/0.15382, loss_grounding_ce_4: 0.32408/0.25931, loss_mask_ce_5: 0.75056/0.80395, loss_mask_bce_5: 0.10133/0.30838, loss_mask_dice_5: 0.67596/1.05458, loss_spatial_bce_5: 0.02310/0.09241, loss_spatial_dice_5: 0.18675/0.19679, loss_spatial_ce_5: 0.00426/0.09666, loss_grounding_bce_5: 0.02570/0.08230, loss_grounding_dice_5: 0.13683/0.15455, loss_grounding_ce_5: 0.38666/0.27734, loss_mask_ce_6: 0.58826/0.83029, loss_mask_bce_6: 0.10508/0.31027, loss_mask_dice_6: 0.65770/1.05710, loss_spatial_bce_6: 0.02288/0.09746, loss_spatial_dice_6: 0.15303/0.19905, loss_spatial_ce_6: 0.00341/0.11985, loss_grounding_bce_6: 0.02252/0.08326, loss_grounding_dice_6: 0.14569/0.15512, loss_grounding_ce_6: 0.47679/0.28650, loss_mask_ce_7: 0.58580/0.88640, loss_mask_bce_7: 0.09808/0.31763, loss_mask_dice_7: 0.62247/1.10400, loss_spatial_bce_7: 0.02532/0.10767, loss_spatial_dice_7: 0.17705/0.22430, loss_spatial_ce_7: 0.02678/0.15881, loss_grounding_bce_7: 0.02302/0.08489, loss_grounding_dice_7: 0.13446/0.16092, loss_grounding_ce_7: 0.36332/0.32105, loss_mask_ce_8: 0.69086/1.02447, loss_mask_bce_8: 0.12907/0.33362, loss_mask_dice_8: 0.81964/1.18108, loss_spatial_bce_8: 0.02207/0.12575, loss_spatial_dice_8: 0.16380/0.26094, loss_spatial_ce_8: 0.08978/0.21043, loss_grounding_bce_8: 0.02008/0.08889, loss_grounding_dice_8: 0.13252/0.17041, loss_grounding_ce_8: 0.41616/0.42364, loss_mask_ce_9: 3.39328/3.48343, loss_mask_bce_9: 0.11809/0.36078, loss_mask_dice_9: 0.86077/1.76487, loss_spatial_bce_9: 0.30658/0.35561, loss_spatial_dice_9: 0.79977/0.79440, loss_spatial_ce_9: 1.77844/1.39588, loss_grounding_bce_9: 0.03155/0.10097, loss_grounding_dice_9: 0.18923/0.24323, loss_grounding_ce_9: 0.42661/0.68197] items per batch[64] items per second[0.37] total items[2988800] mini batches[ 46700] memory[4999] epoch remaining[0:23:26] INFO:trainer.default_trainer:epochs[ 25] optim steps[46800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.26331/0.76315, loss_mask_bce_0: 0.61676/0.30178, loss_mask_dice_0: 0.64457/1.02554, loss_spatial_bce_0: 0.19603/0.08630, loss_spatial_dice_0: 0.22544/0.18238, loss_spatial_ce_0: 0.05665/0.06053, loss_grounding_bce_0: 0.23286/0.08081, loss_grounding_dice_0: 0.34865/0.15108, loss_grounding_ce_0: 0.45512/0.24903, loss_mask_ce_1: 1.21616/0.76408, loss_mask_bce_1: 0.52343/0.30259, loss_mask_dice_1: 0.62289/1.02912, loss_spatial_bce_1: 0.19629/0.08656, loss_spatial_dice_1: 0.21754/0.18493, loss_spatial_ce_1: 0.04438/0.06464, loss_grounding_bce_1: 0.24421/0.08100, loss_grounding_dice_1: 0.35091/0.15180, loss_grounding_ce_1: 0.43936/0.25086, loss_mask_ce_2: 1.30655/0.77203, loss_mask_bce_2: 0.49345/0.30272, loss_mask_dice_2: 0.57835/1.03031, loss_spatial_bce_2: 0.20654/0.08652, loss_spatial_dice_2: 0.22786/0.18520, loss_spatial_ce_2: 0.03881/0.06698, loss_grounding_bce_2: 0.22799/0.08094, loss_grounding_dice_2: 0.33706/0.15165, loss_grounding_ce_2: 0.39856/0.25318, loss_mask_ce_3: 1.27328/0.77450, loss_mask_bce_3: 0.50216/0.30429, loss_mask_dice_3: 0.57053/1.02766, loss_spatial_bce_3: 0.18015/0.08849, loss_spatial_dice_3: 0.22381/0.18634, loss_spatial_ce_3: 0.06766/0.07154, loss_grounding_bce_3: 0.25796/0.08138, loss_grounding_dice_3: 0.33867/0.15124, loss_grounding_ce_3: 0.34126/0.25323, loss_mask_ce_4: 1.50759/0.78073, loss_mask_bce_4: 0.37657/0.30662, loss_mask_dice_4: 0.56168/1.04683, loss_spatial_bce_4: 0.24239/0.09047, loss_spatial_dice_4: 0.26180/0.19413, loss_spatial_ce_4: 0.02662/0.08428, loss_grounding_bce_4: 0.15328/0.08197, loss_grounding_dice_4: 0.33583/0.15378, loss_grounding_ce_4: 0.53576/0.25909, loss_mask_ce_5: 1.57933/0.80397, loss_mask_bce_5: 0.44761/0.30842, loss_mask_dice_5: 0.56941/1.05447, loss_spatial_bce_5: 0.20837/0.09241, loss_spatial_dice_5: 0.23757/0.19678, loss_spatial_ce_5: 0.14739/0.09663, loss_grounding_bce_5: 0.20633/0.08230, loss_grounding_dice_5: 0.38365/0.15450, loss_grounding_ce_5: 0.55504/0.27718, loss_mask_ce_6: 1.54691/0.83030, loss_mask_bce_6: 0.55586/0.31030, loss_mask_dice_6: 0.62252/1.05705, loss_spatial_bce_6: 0.24488/0.09747, loss_spatial_dice_6: 0.29004/0.19903, loss_spatial_ce_6: 0.30917/0.11984, loss_grounding_bce_6: 0.24619/0.08327, loss_grounding_dice_6: 0.36840/0.15509, loss_grounding_ce_6: 0.63152/0.28637, loss_mask_ce_7: 1.46954/0.88638, loss_mask_bce_7: 0.30689/0.31766, loss_mask_dice_7: 0.59921/1.10393, loss_spatial_bce_7: 0.40950/0.10769, loss_spatial_dice_7: 0.37959/0.22430, loss_spatial_ce_7: 0.55602/0.15877, loss_grounding_bce_7: 0.11927/0.08490, loss_grounding_dice_7: 0.36671/0.16088, loss_grounding_ce_7: 0.59354/0.32086, loss_mask_ce_8: 1.38330/1.02444, loss_mask_bce_8: 0.34480/0.33368, loss_mask_dice_8: 0.57299/1.18098, loss_spatial_bce_8: 0.40439/0.12577, loss_spatial_dice_8: 0.52266/0.26093, loss_spatial_ce_8: 0.65312/0.21033, loss_grounding_bce_8: 0.14652/0.08890, loss_grounding_dice_8: 0.34410/0.17037, loss_grounding_ce_8: 0.60212/0.42343, loss_mask_ce_9: 2.97069/3.48316, loss_mask_bce_9: 0.29788/0.36084, loss_mask_dice_9: 0.59655/1.76487, loss_spatial_bce_9: 0.64922/0.35560, loss_spatial_dice_9: 0.68830/0.79438, loss_spatial_ce_9: 0.89306/1.39569, loss_grounding_bce_9: 0.11589/0.10098, loss_grounding_dice_9: 0.35310/0.24319, loss_grounding_ce_9: 0.48170/0.68185] items per batch[64] items per second[0.37] total items[2995200] mini batches[ 46800] memory[4999] epoch remaining[0:20:29] INFO:trainer.default_trainer:epochs[ 25] optim steps[46900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31848/0.76312, loss_mask_bce_0: 0.53107/0.30179, loss_mask_dice_0: 0.69885/1.02544, loss_spatial_bce_0: 0.11952/0.08628, loss_spatial_dice_0: 0.14525/0.18235, loss_spatial_ce_0: 0.00209/0.06047, loss_grounding_bce_0: 0.03320/0.08078, loss_grounding_dice_0: 0.42010/0.15105, loss_grounding_ce_0: 0.08264/0.24921, loss_mask_ce_1: 0.34427/0.76401, loss_mask_bce_1: 0.56085/0.30260, loss_mask_dice_1: 0.66472/1.02907, loss_spatial_bce_1: 0.13121/0.08654, loss_spatial_dice_1: 0.16473/0.18491, loss_spatial_ce_1: 0.00105/0.06458, loss_grounding_bce_1: 0.03434/0.08096, loss_grounding_dice_1: 0.36671/0.15176, loss_grounding_ce_1: 0.10052/0.25101, loss_mask_ce_2: 0.31055/0.77198, loss_mask_bce_2: 0.54402/0.30272, loss_mask_dice_2: 0.66821/1.03020, loss_spatial_bce_2: 0.12460/0.08650, loss_spatial_dice_2: 0.15855/0.18518, loss_spatial_ce_2: 0.00056/0.06696, loss_grounding_bce_2: 0.03265/0.08089, loss_grounding_dice_2: 0.37120/0.15163, loss_grounding_ce_2: 0.11210/0.25346, loss_mask_ce_3: 0.33596/0.77448, loss_mask_bce_3: 0.59397/0.30430, loss_mask_dice_3: 0.68087/1.02755, loss_spatial_bce_3: 0.15003/0.08847, loss_spatial_dice_3: 0.17142/0.18632, loss_spatial_ce_3: 0.00057/0.07148, loss_grounding_bce_3: 0.02716/0.08133, loss_grounding_dice_3: 0.33128/0.15122, loss_grounding_ce_3: 0.07714/0.25339, loss_mask_ce_4: 0.30776/0.78070, loss_mask_bce_4: 0.56748/0.30663, loss_mask_dice_4: 0.61270/1.04674, loss_spatial_bce_4: 0.13844/0.09045, loss_spatial_dice_4: 0.17613/0.19411, loss_spatial_ce_4: 0.00441/0.08423, loss_grounding_bce_4: 0.02986/0.08194, loss_grounding_dice_4: 0.42798/0.15377, loss_grounding_ce_4: 0.04300/0.25926, loss_mask_ce_5: 0.31992/0.80397, loss_mask_bce_5: 0.56758/0.30843, loss_mask_dice_5: 0.66513/1.05438, loss_spatial_bce_5: 0.17418/0.09240, loss_spatial_dice_5: 0.20242/0.19676, loss_spatial_ce_5: 0.00156/0.09656, loss_grounding_bce_5: 0.03399/0.08226, loss_grounding_dice_5: 0.46626/0.15447, loss_grounding_ce_5: 0.05892/0.27734, loss_mask_ce_6: 0.29848/0.83020, loss_mask_bce_6: 0.55515/0.31032, loss_mask_dice_6: 0.68233/1.05702, loss_spatial_bce_6: 0.18231/0.09745, loss_spatial_dice_6: 0.19000/0.19901, loss_spatial_ce_6: 0.01324/0.11983, loss_grounding_bce_6: 0.03695/0.08322, loss_grounding_dice_6: 0.46086/0.15506, loss_grounding_ce_6: 0.01507/0.28645, loss_mask_ce_7: 0.29629/0.88631, loss_mask_bce_7: 0.49514/0.31768, loss_mask_dice_7: 0.69725/1.10386, loss_spatial_bce_7: 0.15041/0.10767, loss_spatial_dice_7: 0.19951/0.22429, loss_spatial_ce_7: 0.08631/0.15868, loss_grounding_bce_7: 0.03480/0.08487, loss_grounding_dice_7: 0.42323/0.16085, loss_grounding_ce_7: 0.01110/0.32091, loss_mask_ce_8: 0.42890/1.02438, loss_mask_bce_8: 0.50132/0.33370, loss_mask_dice_8: 0.71955/1.18094, loss_spatial_bce_8: 0.24944/0.12576, loss_spatial_dice_8: 0.22501/0.26091, loss_spatial_ce_8: 0.08261/0.21021, loss_grounding_bce_8: 0.04166/0.08889, loss_grounding_dice_8: 0.46029/0.17036, loss_grounding_ce_8: 0.04879/0.42364, loss_mask_ce_9: 2.89889/3.48334, loss_mask_bce_9: 0.71618/0.36085, loss_mask_dice_9: 0.96219/1.76512, loss_spatial_bce_9: 0.45021/0.35558, loss_spatial_dice_9: 0.75869/0.79436, loss_spatial_ce_9: 1.80125/1.39560, loss_grounding_bce_9: 0.02802/0.10095, loss_grounding_dice_9: 0.46809/0.24319, loss_grounding_ce_9: 0.07386/0.68172] items per batch[64] items per second[0.36] total items[3001600] mini batches[ 46900] memory[4999] epoch remaining[0:17:37] INFO:trainer.default_trainer:epochs[ 25] optim steps[47000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.22314/0.76305, loss_mask_bce_0: 0.40943/0.30180, loss_mask_dice_0: 0.50711/1.02530, loss_spatial_bce_0: 0.19496/0.08629, loss_spatial_dice_0: 0.21826/0.18235, loss_spatial_ce_0: 0.30206/0.06043, loss_grounding_bce_0: 0.03961/0.08082, loss_grounding_dice_0: 0.08143/0.15104, loss_grounding_ce_0: 0.37616/0.24918, loss_mask_ce_1: 1.22834/0.76390, loss_mask_bce_1: 0.43673/0.30264, loss_mask_dice_1: 0.51853/1.02896, loss_spatial_bce_1: 0.20830/0.08656, loss_spatial_dice_1: 0.21808/0.18491, loss_spatial_ce_1: 0.30231/0.06453, loss_grounding_bce_1: 0.03938/0.08099, loss_grounding_dice_1: 0.08651/0.15174, loss_grounding_ce_1: 0.37403/0.25100, loss_mask_ce_2: 1.26184/0.77191, loss_mask_bce_2: 0.42521/0.30275, loss_mask_dice_2: 0.50124/1.03009, loss_spatial_bce_2: 0.20418/0.08652, loss_spatial_dice_2: 0.21179/0.18519, loss_spatial_ce_2: 0.32474/0.06693, loss_grounding_bce_2: 0.04096/0.08093, loss_grounding_dice_2: 0.08427/0.15161, loss_grounding_ce_2: 0.38963/0.25344, loss_mask_ce_3: 1.21674/0.77433, loss_mask_bce_3: 0.42062/0.30434, loss_mask_dice_3: 0.51155/1.02749, loss_spatial_bce_3: 0.18842/0.08848, loss_spatial_dice_3: 0.22499/0.18633, loss_spatial_ce_3: 0.40284/0.07143, loss_grounding_bce_3: 0.03925/0.08137, loss_grounding_dice_3: 0.08805/0.15119, loss_grounding_ce_3: 0.40158/0.25338, loss_mask_ce_4: 1.12581/0.78060, loss_mask_bce_4: 0.45057/0.30669, loss_mask_dice_4: 0.64691/1.04663, loss_spatial_bce_4: 0.20338/0.09046, loss_spatial_dice_4: 0.21951/0.19412, loss_spatial_ce_4: 0.36294/0.08419, loss_grounding_bce_4: 0.04249/0.08197, loss_grounding_dice_4: 0.08698/0.15374, loss_grounding_ce_4: 0.38617/0.25927, loss_mask_ce_5: 1.20879/0.80384, loss_mask_bce_5: 0.41685/0.30848, loss_mask_dice_5: 0.61886/1.05430, loss_spatial_bce_5: 0.19068/0.09242, loss_spatial_dice_5: 0.24437/0.19677, loss_spatial_ce_5: 0.32638/0.09652, loss_grounding_bce_5: 0.03219/0.08229, loss_grounding_dice_5: 0.08067/0.15446, loss_grounding_ce_5: 0.42331/0.27730, loss_mask_ce_6: 1.05045/0.83006, loss_mask_bce_6: 0.44432/0.31037, loss_mask_dice_6: 0.61850/1.05693, loss_spatial_bce_6: 0.20729/0.09746, loss_spatial_dice_6: 0.25960/0.19902, loss_spatial_ce_6: 0.27787/0.11982, loss_grounding_bce_6: 0.03705/0.08326, loss_grounding_dice_6: 0.08002/0.15504, loss_grounding_ce_6: 0.41069/0.28642, loss_mask_ce_7: 1.00977/0.88610, loss_mask_bce_7: 0.42115/0.31772, loss_mask_dice_7: 0.65889/1.10375, loss_spatial_bce_7: 0.21004/0.10768, loss_spatial_dice_7: 0.29521/0.22430, loss_spatial_ce_7: 0.23886/0.15866, loss_grounding_bce_7: 0.03699/0.08490, loss_grounding_dice_7: 0.08485/0.16083, loss_grounding_ce_7: 0.40503/0.32081, loss_mask_ce_8: 1.47763/1.02415, loss_mask_bce_8: 0.44168/0.33377, loss_mask_dice_8: 0.64720/1.18080, loss_spatial_bce_8: 0.18284/0.12577, loss_spatial_dice_8: 0.28862/0.26091, loss_spatial_ce_8: 0.36930/0.21014, loss_grounding_bce_8: 0.04337/0.08893, loss_grounding_dice_8: 0.09412/0.17033, loss_grounding_ce_8: 0.38698/0.42353, loss_mask_ce_9: 4.17210/3.48296, loss_mask_bce_9: 0.40786/0.36092, loss_mask_dice_9: 1.16590/1.76478, loss_spatial_bce_9: 0.43494/0.35554, loss_spatial_dice_9: 0.83097/0.79433, loss_spatial_ce_9: 1.71785/1.39563, loss_grounding_bce_9: 0.03338/0.10100, loss_grounding_dice_9: 0.09127/0.24316, loss_grounding_ce_9: 0.65339/0.68161] items per batch[64] items per second[0.36] total items[3008000] mini batches[ 47000] memory[4999] epoch remaining[0:14:42] INFO:trainer.default_trainer:epochs[ 25] optim steps[47100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44661/0.76302, loss_mask_bce_0: 0.15143/0.30184, loss_mask_dice_0: 0.47320/1.02579, loss_spatial_bce_0: 0.03577/0.08628, loss_spatial_dice_0: 0.11836/0.18236, loss_spatial_ce_0: 0.01041/0.06038, loss_grounding_bce_0: 0.01728/0.08082, loss_grounding_dice_0: 0.06808/0.15107, loss_grounding_ce_0: 0.24630/0.24909, loss_mask_ce_1: 0.45290/0.76392, loss_mask_bce_1: 0.14843/0.30268, loss_mask_dice_1: 0.50654/1.02949, loss_spatial_bce_1: 0.03654/0.08654, loss_spatial_dice_1: 0.11078/0.18492, loss_spatial_ce_1: 0.00281/0.06448, loss_grounding_bce_1: 0.01475/0.08099, loss_grounding_dice_1: 0.07756/0.15177, loss_grounding_ce_1: 0.23904/0.25095, loss_mask_ce_2: 0.41484/0.77188, loss_mask_bce_2: 0.15405/0.30279, loss_mask_dice_2: 0.52903/1.03063, loss_spatial_bce_2: 0.03885/0.08650, loss_spatial_dice_2: 0.13487/0.18520, loss_spatial_ce_2: 0.00456/0.06689, loss_grounding_bce_2: 0.01698/0.08093, loss_grounding_dice_2: 0.05835/0.15164, loss_grounding_ce_2: 0.23143/0.25335, loss_mask_ce_3: 0.47843/0.77432, loss_mask_bce_3: 0.15299/0.30438, loss_mask_dice_3: 0.52786/1.02809, loss_spatial_bce_3: 0.03788/0.08848, loss_spatial_dice_3: 0.13671/0.18635, loss_spatial_ce_3: 0.00404/0.07141, loss_grounding_bce_3: 0.01812/0.08137, loss_grounding_dice_3: 0.06769/0.15123, loss_grounding_ce_3: 0.22889/0.25327, loss_mask_ce_4: 0.49648/0.78056, loss_mask_bce_4: 0.15323/0.30674, loss_mask_dice_4: 0.49706/1.04721, loss_spatial_bce_4: 0.04256/0.09045, loss_spatial_dice_4: 0.13786/0.19415, loss_spatial_ce_4: 0.00591/0.08416, loss_grounding_bce_4: 0.01770/0.08200, loss_grounding_dice_4: 0.08927/0.15380, loss_grounding_ce_4: 0.25729/0.25912, loss_mask_ce_5: 0.47644/0.80382, loss_mask_bce_5: 0.15356/0.30853, loss_mask_dice_5: 0.52928/1.05487, loss_spatial_bce_5: 0.04017/0.09242, loss_spatial_dice_5: 0.14794/0.19681, loss_spatial_ce_5: 0.00978/0.09647, loss_grounding_bce_5: 0.01649/0.08231, loss_grounding_dice_5: 0.06177/0.15450, loss_grounding_ce_5: 0.23820/0.27718, loss_mask_ce_6: 0.42596/0.83009, loss_mask_bce_6: 0.14836/0.31041, loss_mask_dice_6: 0.51873/1.05748, loss_spatial_bce_6: 0.04677/0.09745, loss_spatial_dice_6: 0.12462/0.19904, loss_spatial_ce_6: 0.04296/0.11981, loss_grounding_bce_6: 0.01659/0.08327, loss_grounding_dice_6: 0.05959/0.15509, loss_grounding_ce_6: 0.21313/0.28632, loss_mask_ce_7: 0.43862/0.88608, loss_mask_bce_7: 0.15070/0.31776, loss_mask_dice_7: 0.52061/1.10427, loss_spatial_bce_7: 0.04458/0.10765, loss_spatial_dice_7: 0.17037/0.22431, loss_spatial_ce_7: 0.15248/0.15866, loss_grounding_bce_7: 0.01613/0.08491, loss_grounding_dice_7: 0.05897/0.16086, loss_grounding_ce_7: 0.23195/0.32065, loss_mask_ce_8: 0.60900/1.02414, loss_mask_bce_8: 0.15252/0.33381, loss_mask_dice_8: 0.61880/1.18137, loss_spatial_bce_8: 0.10285/0.12574, loss_spatial_dice_8: 0.23903/0.26094, loss_spatial_ce_8: 0.12354/0.21008, loss_grounding_bce_8: 0.01678/0.08893, loss_grounding_dice_8: 0.11700/0.17036, loss_grounding_ce_8: 0.39715/0.42353, loss_mask_ce_9: 2.64959/3.48302, loss_mask_bce_9: 0.19680/0.36095, loss_mask_dice_9: 0.96435/1.76577, loss_spatial_bce_9: 0.31671/0.35554, loss_spatial_dice_9: 0.89269/0.79439, loss_spatial_ce_9: 2.10991/1.39571, loss_grounding_bce_9: 0.02176/0.10103, loss_grounding_dice_9: 0.20924/0.24319, loss_grounding_ce_9: 0.46855/0.68154] items per batch[64] items per second[0.36] total items[3014400] mini batches[ 47100] memory[4999] epoch remaining[0:11:46] INFO:trainer.default_trainer:epochs[ 25] optim steps[47200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32622/0.76312, loss_mask_bce_0: 0.87525/0.30179, loss_mask_dice_0: 0.61318/1.02583, loss_spatial_bce_0: 0.15012/0.08624, loss_spatial_dice_0: 0.11465/0.18235, loss_spatial_ce_0: 0.00770/0.06036, loss_grounding_bce_0: 0.17659/0.08081, loss_grounding_dice_0: 0.12864/0.15103, loss_grounding_ce_0: 0.30743/0.24926, loss_mask_ce_1: 0.31233/0.76400, loss_mask_bce_1: 0.83852/0.30263, loss_mask_dice_1: 0.61113/1.02963, loss_spatial_bce_1: 0.14805/0.08650, loss_spatial_dice_1: 0.11334/0.18490, loss_spatial_ce_1: 0.00738/0.06447, loss_grounding_bce_1: 0.16311/0.08098, loss_grounding_dice_1: 0.12340/0.15174, loss_grounding_ce_1: 0.29646/0.25110, loss_mask_ce_2: 0.31995/0.77197, loss_mask_bce_2: 0.81956/0.30275, loss_mask_dice_2: 0.59640/1.03071, loss_spatial_bce_2: 0.15477/0.08647, loss_spatial_dice_2: 0.11664/0.18518, loss_spatial_ce_2: 0.00784/0.06685, loss_grounding_bce_2: 0.17696/0.08092, loss_grounding_dice_2: 0.13889/0.15163, loss_grounding_ce_2: 0.24730/0.25353, loss_mask_ce_3: 0.36783/0.77443, loss_mask_bce_3: 0.82931/0.30434, loss_mask_dice_3: 0.60672/1.02817, loss_spatial_bce_3: 0.17001/0.08844, loss_spatial_dice_3: 0.13371/0.18634, loss_spatial_ce_3: 0.02028/0.07141, loss_grounding_bce_3: 0.17288/0.08136, loss_grounding_dice_3: 0.12684/0.15120, loss_grounding_ce_3: 0.31003/0.25341, loss_mask_ce_4: 0.32617/0.78066, loss_mask_bce_4: 0.82739/0.30669, loss_mask_dice_4: 0.63283/1.04729, loss_spatial_bce_4: 0.14502/0.09041, loss_spatial_dice_4: 0.12669/0.19415, loss_spatial_ce_4: 0.02570/0.08413, loss_grounding_bce_4: 0.15990/0.08198, loss_grounding_dice_4: 0.12400/0.15376, loss_grounding_ce_4: 0.35710/0.25929, loss_mask_ce_5: 0.34630/0.80389, loss_mask_bce_5: 0.81708/0.30849, loss_mask_dice_5: 0.61096/1.05492, loss_spatial_bce_5: 0.17037/0.09238, loss_spatial_dice_5: 0.14865/0.19680, loss_spatial_ce_5: 0.05550/0.09645, loss_grounding_bce_5: 0.16589/0.08230, loss_grounding_dice_5: 0.12226/0.15447, loss_grounding_ce_5: 0.37108/0.27737, loss_mask_ce_6: 0.38610/0.83018, loss_mask_bce_6: 0.75614/0.31037, loss_mask_dice_6: 0.61453/1.05755, loss_spatial_bce_6: 0.19850/0.09742, loss_spatial_dice_6: 0.16876/0.19904, loss_spatial_ce_6: 0.05403/0.11984, loss_grounding_bce_6: 0.17020/0.08325, loss_grounding_dice_6: 0.14179/0.15506, loss_grounding_ce_6: 0.40810/0.28654, loss_mask_ce_7: 0.43446/0.88614, loss_mask_bce_7: 0.79701/0.31772, loss_mask_dice_7: 0.65298/1.10441, loss_spatial_bce_7: 0.19883/0.10762, loss_spatial_dice_7: 0.19187/0.22431, loss_spatial_ce_7: 0.10851/0.15862, loss_grounding_bce_7: 0.16032/0.08490, loss_grounding_dice_7: 0.13888/0.16084, loss_grounding_ce_7: 0.43780/0.32097, loss_mask_ce_8: 0.77971/1.02424, loss_mask_bce_8: 0.82435/0.33376, loss_mask_dice_8: 0.73034/1.18142, loss_spatial_bce_8: 0.16661/0.12571, loss_spatial_dice_8: 0.19727/0.26093, loss_spatial_ce_8: 0.15350/0.21003, loss_grounding_bce_8: 0.14135/0.08892, loss_grounding_dice_8: 0.16048/0.17033, loss_grounding_ce_8: 0.46279/0.42396, loss_mask_ce_9: 4.63364/3.48350, loss_mask_bce_9: 0.93734/0.36092, loss_mask_dice_9: 1.00112/1.76576, loss_spatial_bce_9: 0.40131/0.35555, loss_spatial_dice_9: 0.87196/0.79441, loss_spatial_ce_9: 2.09485/1.39584, loss_grounding_bce_9: 0.22402/0.10103, loss_grounding_dice_9: 0.23673/0.24319, loss_grounding_ce_9: 0.52387/0.68201] items per batch[64] items per second[0.36] total items[3020800] mini batches[ 47200] memory[4999] epoch remaining[0:08:51] INFO:trainer.default_trainer:epochs[ 25] optim steps[47300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02876/0.76307, loss_mask_bce_0: 0.00519/0.30188, loss_mask_dice_0: 0.24122/1.02618, loss_spatial_bce_0: 0.00371/0.08624, loss_spatial_dice_0: 0.17882/0.18235, loss_spatial_ce_0: 0.00035/0.06034, loss_grounding_bce_0: 0.00176/0.08081, loss_grounding_dice_0: 0.10987/0.15103, loss_grounding_ce_0: 0.57543/0.24929, loss_mask_ce_1: 0.03266/0.76398, loss_mask_bce_1: 0.00434/0.30271, loss_mask_dice_1: 0.11374/1.02997, loss_spatial_bce_1: 0.00267/0.08651, loss_spatial_dice_1: 0.07499/0.18489, loss_spatial_ce_1: 0.00024/0.06443, loss_grounding_bce_1: 0.00139/0.08097, loss_grounding_dice_1: 0.03579/0.15176, loss_grounding_ce_1: 0.48433/0.25111, loss_mask_ce_2: 0.03518/0.77193, loss_mask_bce_2: 0.00238/0.30282, loss_mask_dice_2: 0.05531/1.03108, loss_spatial_bce_2: 0.00264/0.08647, loss_spatial_dice_2: 0.10363/0.18517, loss_spatial_ce_2: 0.00015/0.06682, loss_grounding_bce_2: 0.00269/0.08091, loss_grounding_dice_2: 0.12751/0.15164, loss_grounding_ce_2: 0.56041/0.25356, loss_mask_ce_3: 0.03087/0.77440, loss_mask_bce_3: 0.00337/0.30440, loss_mask_dice_3: 0.12586/1.02853, loss_spatial_bce_3: 0.00241/0.08844, loss_spatial_dice_3: 0.04722/0.18633, loss_spatial_ce_3: 0.00031/0.07140, loss_grounding_bce_3: 0.00156/0.08135, loss_grounding_dice_3: 0.04429/0.15121, loss_grounding_ce_3: 0.56378/0.25343, loss_mask_ce_4: 0.02988/0.78064, loss_mask_bce_4: 0.00508/0.30675, loss_mask_dice_4: 0.14658/1.04758, loss_spatial_bce_4: 0.00489/0.09042, loss_spatial_dice_4: 0.07248/0.19416, loss_spatial_ce_4: 0.00159/0.08409, loss_grounding_bce_4: 0.01728/0.08198, loss_grounding_dice_4: 0.60084/0.15378, loss_grounding_ce_4: 0.15035/0.25924, loss_mask_ce_5: 0.04000/0.80386, loss_mask_bce_5: 0.00278/0.30857, loss_mask_dice_5: 0.09302/1.05519, loss_spatial_bce_5: 0.00350/0.09240, loss_spatial_dice_5: 0.10571/0.19680, loss_spatial_ce_5: 0.00347/0.09642, loss_grounding_bce_5: 0.00182/0.08229, loss_grounding_dice_5: 0.15376/0.15447, loss_grounding_ce_5: 0.13507/0.27735, loss_mask_ce_6: 0.09009/0.83014, loss_mask_bce_6: 0.00423/0.31044, loss_mask_dice_6: 0.25174/1.05787, loss_spatial_bce_6: 0.00348/0.09744, loss_spatial_dice_6: 0.10440/0.19904, loss_spatial_ce_6: 0.06692/0.11981, loss_grounding_bce_6: 0.00379/0.08325, loss_grounding_dice_6: 0.19753/0.15507, loss_grounding_ce_6: 0.15935/0.28652, loss_mask_ce_7: 0.08981/0.88604, loss_mask_bce_7: 0.00195/0.31779, loss_mask_dice_7: 0.10166/1.10478, loss_spatial_bce_7: 0.00466/0.10763, loss_spatial_dice_7: 0.09301/0.22431, loss_spatial_ce_7: 0.00805/0.15856, loss_grounding_bce_7: 0.00189/0.08490, loss_grounding_dice_7: 0.15542/0.16084, loss_grounding_ce_7: 0.15898/0.32092, loss_mask_ce_8: 0.07899/1.02419, loss_mask_bce_8: 0.00365/0.33384, loss_mask_dice_8: 0.17973/1.18178, loss_spatial_bce_8: 0.00593/0.12573, loss_spatial_dice_8: 0.24708/0.26093, loss_spatial_ce_8: 0.06102/0.20994, loss_grounding_bce_8: 0.00210/0.08893, loss_grounding_dice_8: 0.17331/0.17032, loss_grounding_ce_8: 0.17490/0.42371, loss_mask_ce_9: 2.51246/3.48344, loss_mask_bce_9: 0.00247/0.36101, loss_mask_dice_9: 0.16896/1.76608, loss_spatial_bce_9: 0.05866/0.35559, loss_spatial_dice_9: 0.54843/0.79441, loss_spatial_ce_9: 3.25293/1.39591, loss_grounding_bce_9: 0.00588/0.10106, loss_grounding_dice_9: 0.50723/0.24321, loss_grounding_ce_9: 0.38946/0.68193] items per batch[64] items per second[0.35] total items[3027200] mini batches[ 47300] memory[4999] epoch remaining[0:05:56] INFO:trainer.default_trainer:epochs[ 25] optim steps[47400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24932/0.76280, loss_mask_bce_0: 0.21642/0.30185, loss_mask_dice_0: 0.27911/1.02572, loss_spatial_bce_0: 0.07577/0.08625, loss_spatial_dice_0: 0.12605/0.18233, loss_spatial_ce_0: 0.07044/0.06030, loss_grounding_bce_0: 0.04123/0.08083, loss_grounding_dice_0: 0.05401/0.15102, loss_grounding_ce_0: 0.00218/0.24935, loss_mask_ce_1: 0.25211/0.76376, loss_mask_bce_1: 0.21427/0.30269, loss_mask_dice_1: 0.27503/1.02954, loss_spatial_bce_1: 0.07581/0.08651, loss_spatial_dice_1: 0.12999/0.18487, loss_spatial_ce_1: 0.06797/0.06438, loss_grounding_bce_1: 0.03668/0.08100, loss_grounding_dice_1: 0.03834/0.15174, loss_grounding_ce_1: 0.00282/0.25113, loss_mask_ce_2: 0.27624/0.77164, loss_mask_bce_2: 0.22255/0.30279, loss_mask_dice_2: 0.30324/1.03062, loss_spatial_bce_2: 0.07518/0.08647, loss_spatial_dice_2: 0.13164/0.18515, loss_spatial_ce_2: 0.08102/0.06678, loss_grounding_bce_2: 0.03618/0.08094, loss_grounding_dice_2: 0.04258/0.15163, loss_grounding_ce_2: 0.00386/0.25374, loss_mask_ce_3: 0.24152/0.77411, loss_mask_bce_3: 0.19363/0.30437, loss_mask_dice_3: 0.33196/1.02809, loss_spatial_bce_3: 0.07546/0.08845, loss_spatial_dice_3: 0.11934/0.18631, loss_spatial_ce_3: 0.12660/0.07135, loss_grounding_bce_3: 0.03641/0.08137, loss_grounding_dice_3: 0.05229/0.15120, loss_grounding_ce_3: 0.00285/0.25351, loss_mask_ce_4: 0.27476/0.78038, loss_mask_bce_4: 0.22847/0.30673, loss_mask_dice_4: 0.28309/1.04714, loss_spatial_bce_4: 0.07701/0.09043, loss_spatial_dice_4: 0.13805/0.19414, loss_spatial_ce_4: 0.11090/0.08404, loss_grounding_bce_4: 0.04437/0.08201, loss_grounding_dice_4: 0.04792/0.15378, loss_grounding_ce_4: 0.00298/0.25927, loss_mask_ce_5: 0.20380/0.80356, loss_mask_bce_5: 0.23047/0.30854, loss_mask_dice_5: 0.28840/1.05472, loss_spatial_bce_5: 0.08033/0.09241, loss_spatial_dice_5: 0.12872/0.19677, loss_spatial_ce_5: 0.25604/0.09638, loss_grounding_bce_5: 0.04449/0.08232, loss_grounding_dice_5: 0.04938/0.15447, loss_grounding_ce_5: 0.00330/0.27739, loss_mask_ce_6: 0.17424/0.82990, loss_mask_bce_6: 0.23632/0.31042, loss_mask_dice_6: 0.29700/1.05737, loss_spatial_bce_6: 0.10754/0.09745, loss_spatial_dice_6: 0.17566/0.19901, loss_spatial_ce_6: 0.23084/0.11981, loss_grounding_bce_6: 0.04434/0.08328, loss_grounding_dice_6: 0.04585/0.15505, loss_grounding_ce_6: 0.00235/0.28652, loss_mask_ce_7: 0.24305/0.88573, loss_mask_bce_7: 0.23042/0.31777, loss_mask_dice_7: 0.31300/1.10425, loss_spatial_bce_7: 0.35992/0.10766, loss_spatial_dice_7: 0.23272/0.22427, loss_spatial_ce_7: 0.23231/0.15850, loss_grounding_bce_7: 0.04660/0.08492, loss_grounding_dice_7: 0.05247/0.16084, loss_grounding_ce_7: 0.00271/0.32094, loss_mask_ce_8: 0.13653/1.02384, loss_mask_bce_8: 0.20283/0.33380, loss_mask_dice_8: 0.40256/1.18122, loss_spatial_bce_8: 0.26831/0.12574, loss_spatial_dice_8: 0.23932/0.26091, loss_spatial_ce_8: 0.25938/0.20985, loss_grounding_bce_8: 0.04501/0.08896, loss_grounding_dice_8: 0.06113/0.17033, loss_grounding_ce_8: 0.00977/0.42369, loss_mask_ce_9: 2.21867/3.48262, loss_mask_bce_9: 0.20902/0.36096, loss_mask_dice_9: 0.45389/1.76515, loss_spatial_bce_9: 0.82214/0.35562, loss_spatial_dice_9: 0.88625/0.79437, loss_spatial_ce_9: 1.37989/1.39590, loss_grounding_bce_9: 0.04341/0.10107, loss_grounding_dice_9: 0.09043/0.24318, loss_grounding_ce_9: 0.48302/0.68182] items per batch[64] items per second[0.37] total items[3033600] mini batches[ 47400] memory[4999] epoch remaining[0:02:59] INFO:trainer.default_trainer:epochs[ 25] optim steps[47500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.74797/0.76284, loss_mask_bce_0: 0.29515/0.30188, loss_mask_dice_0: 0.51592/1.02556, loss_spatial_bce_0: 0.10770/0.08628, loss_spatial_dice_0: 0.10913/0.18233, loss_spatial_ce_0: 0.15984/0.06029, loss_grounding_bce_0: 0.06645/0.08088, loss_grounding_dice_0: 0.04248/0.15103, loss_grounding_ce_0: 0.01220/0.24931, loss_mask_ce_1: 2.38668/0.76378, loss_mask_bce_1: 0.28903/0.30270, loss_mask_dice_1: 0.53807/1.02940, loss_spatial_bce_1: 0.10146/0.08654, loss_spatial_dice_1: 0.12251/0.18487, loss_spatial_ce_1: 0.17882/0.06439, loss_grounding_bce_1: 0.07480/0.08104, loss_grounding_dice_1: 0.05264/0.15172, loss_grounding_ce_1: 0.00795/0.25104, loss_mask_ce_2: 2.46209/0.77163, loss_mask_bce_2: 0.26679/0.30281, loss_mask_dice_2: 0.52209/1.03047, loss_spatial_bce_2: 0.12502/0.08650, loss_spatial_dice_2: 0.12196/0.18515, loss_spatial_ce_2: 0.19395/0.06678, loss_grounding_bce_2: 0.07102/0.08098, loss_grounding_dice_2: 0.04596/0.15164, loss_grounding_ce_2: 0.00555/0.25365, loss_mask_ce_3: 2.50983/0.77411, loss_mask_bce_3: 0.30523/0.30439, loss_mask_dice_3: 0.58265/1.02793, loss_spatial_bce_3: 0.11076/0.08848, loss_spatial_dice_3: 0.16341/0.18632, loss_spatial_ce_3: 0.10653/0.07135, loss_grounding_bce_3: 0.06819/0.08142, loss_grounding_dice_3: 0.03940/0.15121, loss_grounding_ce_3: 0.00381/0.25341, loss_mask_ce_4: 2.73785/0.78039, loss_mask_bce_4: 0.32733/0.30676, loss_mask_dice_4: 0.56579/1.04698, loss_spatial_bce_4: 0.11925/0.09046, loss_spatial_dice_4: 0.19486/0.19414, loss_spatial_ce_4: 0.11690/0.08401, loss_grounding_bce_4: 0.06313/0.08206, loss_grounding_dice_4: 0.03839/0.15379, loss_grounding_ce_4: 0.00314/0.25921, loss_mask_ce_5: 2.73602/0.80357, loss_mask_bce_5: 0.32531/0.30857, loss_mask_dice_5: 0.58729/1.05462, loss_spatial_bce_5: 0.12237/0.09245, loss_spatial_dice_5: 0.23609/0.19679, loss_spatial_ce_5: 0.12697/0.09634, loss_grounding_bce_5: 0.07067/0.08236, loss_grounding_dice_5: 0.03599/0.15448, loss_grounding_ce_5: 0.00225/0.27729, loss_mask_ce_6: 2.60325/0.82995, loss_mask_bce_6: 0.31764/0.31043, loss_mask_dice_6: 0.70331/1.05723, loss_spatial_bce_6: 0.12070/0.09747, loss_spatial_dice_6: 0.21666/0.19902, loss_spatial_ce_6: 0.12683/0.11983, loss_grounding_bce_6: 0.07015/0.08331, loss_grounding_dice_6: 0.04779/0.15507, loss_grounding_ce_6: 0.00707/0.28640, loss_mask_ce_7: 2.81772/0.88575, loss_mask_bce_7: 0.54103/0.31780, loss_mask_dice_7: 0.91114/1.10411, loss_spatial_bce_7: 0.07294/0.10768, loss_spatial_dice_7: 0.14839/0.22428, loss_spatial_ce_7: 0.10768/0.15855, loss_grounding_bce_7: 0.07499/0.08496, loss_grounding_dice_7: 0.05025/0.16085, loss_grounding_ce_7: 0.10249/0.32089, loss_mask_ce_8: 3.93331/1.02391, loss_mask_bce_8: 0.66812/0.33381, loss_mask_dice_8: 1.28845/1.18104, loss_spatial_bce_8: 0.09304/0.12579, loss_spatial_dice_8: 0.20280/0.26093, loss_spatial_ce_8: 0.17033/0.20989, loss_grounding_bce_8: 0.09191/0.08900, loss_grounding_dice_8: 0.04858/0.17033, loss_grounding_ce_8: 0.02155/0.42357, loss_mask_ce_9: 6.88367/3.48266, loss_mask_bce_9: 0.93621/0.36098, loss_mask_dice_9: 1.98835/1.76461, loss_spatial_bce_9: 0.41958/0.35562, loss_spatial_dice_9: 0.82717/0.79438, loss_spatial_ce_9: 1.31678/1.39592, loss_grounding_bce_9: 0.13520/0.10112, loss_grounding_dice_9: 0.11429/0.24316, loss_grounding_ce_9: 1.25621/0.68178] items per batch[64] items per second[0.37] total items[3040000] mini batches[ 47500] memory[4999] epoch remaining[0:00:03] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00047502. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0017 s/iter. Inference: 0.3722 s/iter. Eval: 0.0929 s/iter. Total: 0.4669 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0021 s/iter. Inference: 0.3730 s/iter. Eval: 0.0867 s/iter. Total: 0.4620 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0024 s/iter. Inference: 0.3789 s/iter. Eval: 0.0801 s/iter. Total: 0.4616 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0025 s/iter. Inference: 0.3814 s/iter. Eval: 0.0768 s/iter. Total: 0.4608 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0025 s/iter. Inference: 0.3810 s/iter. Eval: 0.0745 s/iter. Total: 0.4581 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0026 s/iter. Inference: 0.3788 s/iter. Eval: 0.0724 s/iter. Total: 0.4540 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaly7w00cmx ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.256 | 82.911 | 65.854 | 133 | | Things | 61.459 | 83.795 | 72.800 | 80 | | Stuff | 45.893 | 81.577 | 55.370 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.54s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.60 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.38 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.69s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.46 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.263 | 68.889 | 48.761 | 26.182 | 49.781 | 67.060 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.402 | bicycle | 21.322 | car | 43.474 | | motorcycle | 40.562 | airplane | 62.240 | bus | 70.645 | | train | 75.067 | truck | 41.960 | boat | 31.121 | | traffic light | 27.664 | fire hydrant | 70.807 | stop sign | 68.694 | | parking meter | 50.875 | bench | 26.683 | bird | 33.926 | | cat | 77.921 | dog | 71.043 | horse | 49.535 | | sheep | 52.955 | cow | 56.417 | elephant | 65.227 | | bear | 80.023 | zebra | 65.326 | giraffe | 61.715 | | backpack | 23.040 | umbrella | 55.640 | handbag | 24.660 | | tie | 39.665 | suitcase | 51.905 | frisbee | 70.080 | | skis | 7.658 | snowboard | 34.089 | sports ball | 49.888 | | kite | 36.863 | baseball bat | 37.787 | baseball glove | 50.230 | | skateboard | 43.802 | surfboard | 45.454 | tennis racket | 63.492 | | bottle | 41.820 | wine glass | 37.191 | cup | 50.353 | | fork | 25.218 | knife | 23.755 | spoon | 22.731 | | bowl | 39.985 | banana | 21.865 | apple | 26.804 | | sandwich | 47.898 | orange | 31.528 | broccoli | 23.409 | | carrot | 23.128 | hot dog | 33.764 | pizza | 53.379 | | donut | 55.714 | cake | 47.363 | chair | 28.447 | | couch | 41.586 | potted plant | 22.083 | bed | 41.880 | | dining table | 15.556 | toilet | 69.538 | tv | 65.805 | | laptop | 69.668 | mouse | 64.088 | remote | 44.308 | | keyboard | 57.943 | cell phone | 47.555 | microwave | 64.791 | | oven | 29.837 | toaster | 49.409 | sink | 44.123 | | refrigerator | 69.851 | book | 14.503 | clock | 54.769 | | vase | 39.623 | scissors | 34.165 | teddy bear | 55.150 | | hair drier | 35.103 | toothbrush | 27.536 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.570 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.609 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.82040847302683, 'fwIoU': 70.78789122262737, 'IoU-person': 87.91560210368424, 'IoU-bicycle': 76.43154083160508, 'IoU-car': 70.31823268396012, 'IoU-motorcycle': 85.71503995860708, 'IoU-airplane': 86.7802883606925, 'IoU-bus': 87.53007109761137, 'IoU-train': 87.53509590012241, 'IoU-truck': 66.32099873487944, 'IoU-boat': 77.49980895770274, 'IoU-traffic light': 77.91555572522458, 'IoU-fire hydrant': 93.30350982941053, 'IoU-stop sign': 94.25390274504197, 'IoU-parking meter': 88.77826703485334, 'IoU-bench': 60.85157586144941, 'IoU-bird': 76.22594023555253, 'IoU-cat': 88.321417652697, 'IoU-dog': 80.83684663401127, 'IoU-horse': 88.24752631966874, 'IoU-sheep': 80.07780566648135, 'IoU-cow': 89.17979498742118, 'IoU-elephant': 88.51025710198857, 'IoU-bear': 75.69512318520658, 'IoU-zebra': 86.66169323706453, 'IoU-giraffe': 89.51918810061636, 'IoU-backpack': 52.203939391142285, 'IoU-umbrella': 83.96150491308727, 'IoU-handbag': 49.84912148989501, 'IoU-tie': 61.3290439754813, 'IoU-suitcase': 77.53278609461785, 'IoU-frisbee': 84.67232375979113, 'IoU-skis': 59.725547958523215, 'IoU-snowboard': 71.36024512591561, 'IoU-sports ball': 79.12651687545318, 'IoU-kite': 79.71514356507797, 'IoU-baseball bat': 68.48288814079744, 'IoU-baseball glove': 72.1616147765999, 'IoU-skateboard': 86.22377488351492, 'IoU-surfboard': 86.09462572615357, 'IoU-tennis racket': 90.87675243857218, 'IoU-bottle': 70.76417337125687, 'IoU-wine glass': 82.99479192615695, 'IoU-cup': 70.97033798289291, 'IoU-fork': 71.49045281993095, 'IoU-knife': 63.437417633227554, 'IoU-spoon': 58.574271817864, 'IoU-bowl': 62.19945115880674, 'IoU-banana': 83.05534130111212, 'IoU-apple': 61.913951012747546, 'IoU-sandwich': 70.60276089060193, 'IoU-orange': 76.71150607019833, 'IoU-broccoli': 68.14197696416338, 'IoU-carrot': 64.03156437887746, 'IoU-hot dog': 63.83031340283883, 'IoU-pizza': 82.48865890906725, 'IoU-donut': 64.42119218061858, 'IoU-cake': 78.25256976170792, 'IoU-chair': 62.056953527430935, 'IoU-couch': 69.50220812627565, 'IoU-potted plant': 43.500299246846055, 'IoU-bed': 70.31969233773786, 'IoU-dining table': 53.86787127260354, 'IoU-toilet': 88.42192623481094, 'IoU-tv': 75.21704861885904, 'IoU-laptop': 80.18727595706193, 'IoU-mouse': 69.1034282773894, 'IoU-remote': 67.39215283623659, 'IoU-keyboard': 64.0843451605044, 'IoU-cell phone': 77.89587935227993, 'IoU-microwave': 70.20456614283954, 'IoU-oven': 70.74866582901053, 'IoU-toaster': 84.85454113141103, 'IoU-sink': 71.96979703178897, 'IoU-refrigerator': 84.57263866540265, 'IoU-book': 54.13876227637006, 'IoU-clock': 76.16716777899573, 'IoU-vase': 62.543091487896795, 'IoU-scissors': 83.53159547644177, 'IoU-teddy bear': 81.51327970533457, 'IoU-hair drier': 48.13062213535416, 'IoU-toothbrush': 70.92507656909504, 'IoU-banner': 26.82498875141548, 'IoU-blanket': 14.422385644594279, 'IoU-bridge': 38.03732169771257, 'IoU-cardboard': 52.12076462384117, 'IoU-counter': 31.02146988024332, 'IoU-curtain': 71.89321722728842, 'IoU-door-stuff': 47.56523943731127, 'IoU-floor-wood': 62.995762524110475, 'IoU-flower': 45.72132799821868, 'IoU-fruit': 50.20970961844034, 'IoU-gravel': 23.82976827951628, 'IoU-house': 24.783700035213535, 'IoU-light': 44.34941079794024, 'IoU-mirror-stuff': 60.81113432787756, 'IoU-net': 43.49578608168685, 'IoU-pillow': 20.13053472038425, 'IoU-platform': 27.672504396736187, 'IoU-playingfield': 68.57038191015286, 'IoU-railroad': 61.95249807624936, 'IoU-river': 39.61773341081026, 'IoU-road': 66.80655950057218, 'IoU-roof': 22.106545234142963, 'IoU-sand': 66.88213594364375, 'IoU-sea': 84.97681992788485, 'IoU-shelf': 39.84747443699488, 'IoU-snow': 92.09128936097567, 'IoU-stairs': 32.77139080900598, 'IoU-tent': 11.792718747833757, 'IoU-towel': 44.74410426376407, 'IoU-wall-brick': 48.94191289728696, 'IoU-wall-stone': 30.739021703682507, 'IoU-wall-tile': 69.55075489685208, 'IoU-wall-wood': 45.44204623511032, 'IoU-water-other': 23.59095752158262, 'IoU-window-blind': 50.39629986829387, 'IoU-window-other': 49.93492389114614, 'IoU-tree-merged': 82.01769843386907, 'IoU-fence-merged': 54.511148610156, 'IoU-ceiling-merged': 67.75764304166523, 'IoU-sky-other-merged': 93.6666000336949, 'IoU-cabinet-merged': 63.97732618576174, 'IoU-table-merged': 40.2339043314968, 'IoU-floor-other-merged': 53.603880612750245, 'IoU-pavement-merged': 57.329967111000826, 'IoU-mountain-merged': 57.511375528109056, 'IoU-grass-merged': 71.01434560059771, 'IoU-dirt-merged': 46.07983323223534, 'IoU-paper-merged': 34.34727016839952, 'IoU-food-other-merged': 44.17659044019444, 'IoU-building-other-merged': 57.57147248747071, 'IoU-rock-merged': 64.14095562630946, 'IoU-wall-other-merged': 65.6787603179318, 'IoU-rug-merged': 68.3844316521867, 'mACC': 76.72810536560304, 'pACC': 81.63166807913723, 'ACC-person': 92.47119612462835, 'ACC-bicycle': 88.31549437142536, 'ACC-car': 86.33601559426664, 'ACC-motorcycle': 90.01650603728572, 'ACC-airplane': 90.9580393613071, 'ACC-bus': 93.2629788823016, 'ACC-train': 95.28624401684999, 'ACC-truck': 75.99535572966249, 'ACC-boat': 86.91544475320505, 'ACC-traffic light': 92.63128098615857, 'ACC-fire hydrant': 96.00725061596074, 'ACC-stop sign': 98.4101296053704, 'ACC-parking meter': 92.49718448401214, 'ACC-bench': 75.45548388901757, 'ACC-bird': 82.50710458288395, 'ACC-cat': 91.58836433134363, 'ACC-dog': 83.06240549837503, 'ACC-horse': 92.94972466901484, 'ACC-sheep': 84.320966551874, 'ACC-cow': 92.64822023221645, 'ACC-elephant': 91.25282713909766, 'ACC-bear': 77.17203374006137, 'ACC-zebra': 88.73408721155066, 'ACC-giraffe': 93.37446731026108, 'ACC-backpack': 68.15431065459549, 'ACC-umbrella': 89.75003974740552, 'ACC-handbag': 73.8527032627162, 'ACC-tie': 67.23698191957966, 'ACC-suitcase': 85.81403695973803, 'ACC-frisbee': 94.34036363636363, 'ACC-skis': 76.40044020892887, 'ACC-snowboard': 82.36340035757145, 'ACC-sports ball': 89.67810959158992, 'ACC-kite': 86.03809725169205, 'ACC-baseball bat': 87.56377885501502, 'ACC-baseball glove': 91.01042634904852, 'ACC-skateboard': 90.8691329542037, 'ACC-surfboard': 92.68795105347716, 'ACC-tennis racket': 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71.82942686757278, 'ACC-grass-merged': 83.27871200759989, 'ACC-dirt-merged': 67.39799571318869, 'ACC-paper-merged': 44.379007425956765, 'ACC-food-other-merged': 62.23845391340398, 'ACC-building-other-merged': 68.87183414296875, 'ACC-rock-merged': 83.91804099916327, 'ACC-wall-other-merged': 83.36624774632827, 'ACC-rug-merged': 80.43105908385984})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3357 s/iter. Inference: 0.1760 s/iter. Eval: 0.0000 s/iter. Total: 0.5117 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3401 s/iter. Inference: 0.3402 s/iter. Eval: 0.0000 s/iter. Total: 0.6804 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3487 s/iter. Inference: 0.5303 s/iter. Eval: 0.0000 s/iter. Total: 0.8792 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4032777290020486, 'noc@0.8': 2.4717588527948493, 'noc@0.85': 2.9180567749487856, 'noc@0.9': 3.6994439566871526, 'miou@iter1': 0.8707428709749664} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0017 s/iter. Inference: 0.1504 s/iter. Eval: 0.0011 s/iter. Total: 0.1532 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 74.31014251708984, 'precision@0.6': 71.47299194335938, 'precision@0.7': 67.58647155761719, 'precision@0.8': 58.25884246826172, 'precision@0.9': 31.752817153930664, 'cIoU': 60.84974670410156, 'mIoU': 66.0033187866211} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.256020771930295, 'SQ': 82.91110635489841, 'RQ': 65.8543150401266, 'PQ_th': 61.458813751593766, 'SQ_th': 83.7950615307752, 'RQ_th': 72.80014513244726, 'PQ_st': 45.89331438753258, 'SQ_st': 81.5768343913108, 'RQ_st': 55.37004320266148}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-baseball bat': 87.56377885501502, 'ACC-baseball glove': 91.01042634904852, 'ACC-skateboard': 90.8691329542037, 'ACC-surfboard': 92.68795105347716, 'ACC-tennis racket': 94.96679700244442, 'ACC-bottle': 85.36448330992906, 'ACC-wine glass': 90.61422277021269, 'ACC-cup': 88.92394055175386, 'ACC-fork': 81.72142083075035, 'ACC-knife': 78.04041181211988, 'ACC-spoon': 78.50590019605667, 'ACC-bowl': 75.82696823927073, 'ACC-banana': 92.020053450523, 'ACC-apple': 77.04610838829755, 'ACC-sandwich': 82.05078063909042, 'ACC-orange': 87.55805344689504, 'ACC-broccoli': 81.91518622244725, 'ACC-carrot': 76.39370197926567, 'ACC-hot dog': 70.86007870001177, 'ACC-pizza': 87.8649841039316, 'ACC-donut': 72.84284499781124, 'ACC-cake': 86.85014510685706, 'ACC-chair': 78.66559198873726, 'ACC-couch': 77.05188573046759, 'ACC-potted plant': 61.40615543804808, 'ACC-bed': 84.11183981663015, 'ACC-dining table': 72.9232502413121, 'ACC-toilet': 92.40778913713882, 'ACC-tv': 86.44957035643635, 'ACC-laptop': 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63.01218035830494, 'ACC-mirror-stuff': 76.20082107879004, 'ACC-net': 66.6813545193137, 'ACC-pillow': 40.76131290704949, 'ACC-platform': 45.59361504289875, 'ACC-playingfield': 84.40566386351526, 'ACC-railroad': 84.45713488049651, 'ACC-river': 51.665575665595, 'ACC-road': 85.23515139101427, 'ACC-roof': 31.282499480497254, 'ACC-sand': 75.58071758349226, 'ACC-sea': 91.04306832030396, 'ACC-shelf': 58.83178625689538, 'ACC-snow': 95.34284627371963, 'ACC-stairs': 55.146891301199375, 'ACC-tent': 14.763018012554838, 'ACC-towel': 54.897107752076536, 'ACC-wall-brick': 67.16584152971919, 'ACC-wall-stone': 44.199156414930926, 'ACC-wall-tile': 87.00604716649771, 'ACC-wall-wood': 64.06992001429074, 'ACC-water-other': 47.388453944205295, 'ACC-window-blind': 63.60647373680555, 'ACC-window-other': 70.05927044129933, 'ACC-tree-merged': 89.35340186095351, 'ACC-fence-merged': 70.70549140531665, 'ACC-ceiling-merged': 82.8537432511322, 'ACC-sky-other-merged': 97.02171560996842, 'ACC-cabinet-merged': 77.64677001209657, 'ACC-table-merged': 53.40464939604451, 'ACC-floor-other-merged': 65.34012293247686, 'ACC-pavement-merged': 70.49189230885013, 'ACC-mountain-merged': 71.82942686757278, 'ACC-grass-merged': 83.27871200759989, 'ACC-dirt-merged': 67.39799571318869, 'ACC-paper-merged': 44.379007425956765, 'ACC-food-other-merged': 62.23845391340398, 'ACC-building-other-merged': 68.87183414296875, 'ACC-rock-merged': 83.91804099916327, 'ACC-wall-other-merged': 83.36624774632827, 'ACC-rug-merged': 80.43105908385984})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.4032777290020486, 'noc@0.8': 2.4717588527948493, 'noc@0.85': 2.9180567749487856, 'noc@0.9': 3.6994439566871526, 'miou@iter1': 0.8707428709749664}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 74.31014251708984, 'precision@0.6': 71.47299194335938, 'precision@0.7': 67.58647155761719, 'precision@0.8': 58.25884246826172, 'precision@0.9': 31.752817153930664, 'cIoU': 60.84974670410156, 'mIoU': 66.0033187866211}}} INFO:trainer.default_trainer:This epoch takes 0:57:04.228008 INFO:trainer.default_trainer:PROGRESS: 52.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 26 training. INFO:trainer.default_trainer:epochs[ 26] optim steps[47600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72405/0.76275, loss_mask_bce_0: 0.04880/0.30176, loss_mask_dice_0: 1.35468/1.02529, loss_spatial_bce_0: 0.00401/0.08626, loss_spatial_dice_0: 0.41425/0.18232, loss_spatial_ce_0: 0.26786/0.06027, loss_grounding_bce_0: 0.01660/0.08085, loss_grounding_dice_0: 0.30563/0.15102, loss_grounding_ce_0: 0.14507/0.24922, loss_mask_ce_1: 2.07084/0.76369, loss_mask_bce_1: 0.04363/0.30259, loss_mask_dice_1: 0.73996/1.02915, loss_spatial_bce_1: 0.00450/0.08651, loss_spatial_dice_1: 0.45137/0.18487, loss_spatial_ce_1: 0.00621/0.06440, loss_grounding_bce_1: 0.01987/0.08101, loss_grounding_dice_1: 0.42241/0.15171, loss_grounding_ce_1: 0.13187/0.25098, loss_mask_ce_2: 1.81750/0.77153, loss_mask_bce_2: 0.04841/0.30270, loss_mask_dice_2: 1.30219/1.03023, loss_spatial_bce_2: 0.00471/0.08647, loss_spatial_dice_2: 0.42968/0.18514, loss_spatial_ce_2: 0.26178/0.06676, loss_grounding_bce_2: 0.01304/0.08096, loss_grounding_dice_2: 0.29525/0.15162, loss_grounding_ce_2: 0.16603/0.25359, loss_mask_ce_3: 2.02140/0.77402, loss_mask_bce_3: 0.04447/0.30428, loss_mask_dice_3: 1.14293/1.02770, loss_spatial_bce_3: 0.00491/0.08845, loss_spatial_dice_3: 0.49712/0.18631, loss_spatial_ce_3: 0.01716/0.07132, loss_grounding_bce_3: 0.01410/0.08140, loss_grounding_dice_3: 0.25659/0.15120, loss_grounding_ce_3: 0.17361/0.25335, loss_mask_ce_4: 2.56884/0.78034, loss_mask_bce_4: 0.04861/0.30664, loss_mask_dice_4: 1.57901/1.04672, loss_spatial_bce_4: 0.00519/0.09043, loss_spatial_dice_4: 0.49442/0.19413, loss_spatial_ce_4: 0.05146/0.08400, loss_grounding_bce_4: 0.01988/0.08203, loss_grounding_dice_4: 0.30980/0.15379, loss_grounding_ce_4: 0.05081/0.25910, loss_mask_ce_5: 1.98859/0.80350, loss_mask_bce_5: 0.03749/0.30845, loss_mask_dice_5: 1.15647/1.05434, loss_spatial_bce_5: 0.00632/0.09242, loss_spatial_dice_5: 0.50773/0.19677, loss_spatial_ce_5: 0.07823/0.09633, loss_grounding_bce_5: 0.01618/0.08234, loss_grounding_dice_5: 0.23527/0.15448, loss_grounding_ce_5: 0.01321/0.27715, loss_mask_ce_6: 1.60307/0.82991, loss_mask_bce_6: 0.04473/0.31030, loss_mask_dice_6: 1.10818/1.05700, loss_spatial_bce_6: 0.00834/0.09745, loss_spatial_dice_6: 0.41657/0.19901, loss_spatial_ce_6: 0.48618/0.11987, loss_grounding_bce_6: 0.02237/0.08329, loss_grounding_dice_6: 0.36318/0.15507, loss_grounding_ce_6: 0.08439/0.28624, loss_mask_ce_7: 1.76163/0.88563, loss_mask_bce_7: 0.10479/0.31769, loss_mask_dice_7: 1.48272/1.10386, loss_spatial_bce_7: 0.00824/0.10766, loss_spatial_dice_7: 0.42083/0.22429, loss_spatial_ce_7: 0.60993/0.15855, loss_grounding_bce_7: 0.00856/0.08493, loss_grounding_dice_7: 0.28182/0.16084, loss_grounding_ce_7: 2.24687/0.32079, loss_mask_ce_8: 1.84121/1.02385, loss_mask_bce_8: 0.03431/0.33369, loss_mask_dice_8: 1.03331/1.18076, loss_spatial_bce_8: 0.01174/0.12577, loss_spatial_dice_8: 0.58229/0.26094, loss_spatial_ce_8: 0.34283/0.20984, loss_grounding_bce_8: 0.14177/0.08897, loss_grounding_dice_8: 0.54785/0.17033, loss_grounding_ce_8: 0.06197/0.42339, loss_mask_ce_9: 5.15059/3.48245, loss_mask_bce_9: 0.03344/0.36082, loss_mask_dice_9: 1.38927/1.76420, loss_spatial_bce_9: 0.00826/0.35561, loss_spatial_dice_9: 0.83701/0.79436, loss_spatial_ce_9: 1.04012/1.39588, loss_grounding_bce_9: 0.07480/0.10109, loss_grounding_dice_9: 0.51005/0.24316, loss_grounding_ce_9: 2.17572/0.68148] items per batch[64] items per second[0.16] total items[3046400] mini batches[ 47600] memory[4999] epoch remaining[0:53:12] INFO:trainer.default_trainer:epochs[ 26] optim steps[47700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07765/0.76260, loss_mask_bce_0: 0.01154/0.30180, loss_mask_dice_0: 0.03005/1.02543, loss_spatial_bce_0: 0.00995/0.08621, loss_spatial_dice_0: 0.03115/0.18230, loss_spatial_ce_0: 0.16249/0.06023, loss_grounding_bce_0: 0.01226/0.08084, loss_grounding_dice_0: 0.03146/0.15100, loss_grounding_ce_0: 0.02832/0.24916, loss_mask_ce_1: 0.07865/0.76355, loss_mask_bce_1: 0.00941/0.30262, loss_mask_dice_1: 0.02632/1.02930, loss_spatial_bce_1: 0.00938/0.08647, loss_spatial_dice_1: 0.03104/0.18484, loss_spatial_ce_1: 0.26897/0.06437, loss_grounding_bce_1: 0.01099/0.08100, loss_grounding_dice_1: 0.03046/0.15170, loss_grounding_ce_1: 0.03526/0.25089, loss_mask_ce_2: 0.06613/0.77136, loss_mask_bce_2: 0.01086/0.30273, loss_mask_dice_2: 0.02772/1.03032, loss_spatial_bce_2: 0.00950/0.08643, loss_spatial_dice_2: 0.03125/0.18512, loss_spatial_ce_2: 0.22495/0.06672, loss_grounding_bce_2: 0.01214/0.08094, loss_grounding_dice_2: 0.03109/0.15160, loss_grounding_ce_2: 0.01799/0.25355, loss_mask_ce_3: 0.07755/0.77388, loss_mask_bce_3: 0.01009/0.30432, loss_mask_dice_3: 0.02554/1.02785, loss_spatial_bce_3: 0.00985/0.08841, loss_spatial_dice_3: 0.02996/0.18629, loss_spatial_ce_3: 0.17949/0.07129, loss_grounding_bce_3: 0.01058/0.08138, loss_grounding_dice_3: 0.02597/0.15119, loss_grounding_ce_3: 0.01035/0.25331, loss_mask_ce_4: 0.09399/0.78019, loss_mask_bce_4: 0.00780/0.30666, loss_mask_dice_4: 0.02098/1.04678, loss_spatial_bce_4: 0.01069/0.09039, loss_spatial_dice_4: 0.03392/0.19411, loss_spatial_ce_4: 0.16573/0.08394, loss_grounding_bce_4: 0.00953/0.08202, loss_grounding_dice_4: 0.02594/0.15378, loss_grounding_ce_4: 0.03642/0.25903, loss_mask_ce_5: 0.12599/0.80332, loss_mask_bce_5: 0.00916/0.30847, loss_mask_dice_5: 0.02525/1.05443, loss_spatial_bce_5: 0.01061/0.09237, loss_spatial_dice_5: 0.03369/0.19676, loss_spatial_ce_5: 0.13067/0.09626, loss_grounding_bce_5: 0.01085/0.08232, loss_grounding_dice_5: 0.02731/0.15446, loss_grounding_ce_5: 0.13033/0.27703, loss_mask_ce_6: 0.17893/0.82973, loss_mask_bce_6: 0.00949/0.31032, loss_mask_dice_6: 0.02462/1.05712, loss_spatial_bce_6: 0.01224/0.09741, loss_spatial_dice_6: 0.03605/0.19900, loss_spatial_ce_6: 0.14005/0.11982, loss_grounding_bce_6: 0.01157/0.08327, loss_grounding_dice_6: 0.02810/0.15504, loss_grounding_ce_6: 0.21415/0.28614, loss_mask_ce_7: 0.16411/0.88544, loss_mask_bce_7: 0.00964/0.31770, loss_mask_dice_7: 0.02363/1.10398, loss_spatial_bce_7: 0.01328/0.10761, loss_spatial_dice_7: 0.03746/0.22427, loss_spatial_ce_7: 0.19342/0.15849, loss_grounding_bce_7: 0.01126/0.08491, loss_grounding_dice_7: 0.02687/0.16082, loss_grounding_ce_7: 0.17808/0.32070, loss_mask_ce_8: 0.29249/1.02373, loss_mask_bce_8: 0.00753/0.33372, loss_mask_dice_8: 0.02012/1.18088, loss_spatial_bce_8: 0.01757/0.12572, loss_spatial_dice_8: 0.04433/0.26093, loss_spatial_ce_8: 0.10824/0.20977, loss_grounding_bce_8: 0.00989/0.08896, loss_grounding_dice_8: 0.02472/0.17031, loss_grounding_ce_8: 0.37023/0.42342, loss_mask_ce_9: 2.43516/3.48249, loss_mask_bce_9: 0.01808/0.36086, loss_mask_dice_9: 0.07379/1.76443, loss_spatial_bce_9: 0.11559/0.35559, loss_spatial_dice_9: 0.44369/0.79440, loss_spatial_ce_9: 0.46382/1.39609, loss_grounding_bce_9: 0.02151/0.10107, loss_grounding_dice_9: 0.08465/0.24310, loss_grounding_ce_9: 0.71998/0.68136] items per batch[64] items per second[0.37] total items[3052800] mini batches[ 47700] memory[4999] epoch remaining[0:48:29] INFO:trainer.default_trainer:epochs[ 26] optim steps[47800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84676/0.76247, loss_mask_bce_0: 0.06850/0.30175, loss_mask_dice_0: 0.31754/1.02520, loss_spatial_bce_0: 0.01947/0.08619, loss_spatial_dice_0: 0.09997/0.18226, loss_spatial_ce_0: 0.01862/0.06020, loss_grounding_bce_0: 0.00332/0.08083, loss_grounding_dice_0: 0.04925/0.15099, loss_grounding_ce_0: 0.01021/0.24911, loss_mask_ce_1: 0.34963/0.76340, loss_mask_bce_1: 0.08634/0.30257, loss_mask_dice_1: 0.40345/1.02905, loss_spatial_bce_1: 0.01976/0.08645, loss_spatial_dice_1: 0.10447/0.18481, loss_spatial_ce_1: 0.00536/0.06433, loss_grounding_bce_1: 0.00372/0.08099, loss_grounding_dice_1: 0.06335/0.15171, loss_grounding_ce_1: 0.00934/0.25087, loss_mask_ce_2: 0.88390/0.77123, loss_mask_bce_2: 0.06897/0.30268, loss_mask_dice_2: 0.22709/1.02999, loss_spatial_bce_2: 0.01839/0.08641, loss_spatial_dice_2: 0.09851/0.18508, loss_spatial_ce_2: 0.01985/0.06669, loss_grounding_bce_2: 0.00264/0.08093, loss_grounding_dice_2: 0.04068/0.15159, loss_grounding_ce_2: 0.00855/0.25355, loss_mask_ce_3: 0.89323/0.77373, loss_mask_bce_3: 0.06273/0.30427, loss_mask_dice_3: 0.22476/1.02759, loss_spatial_bce_3: 0.02037/0.08838, loss_spatial_dice_3: 0.09849/0.18625, loss_spatial_ce_3: 0.05110/0.07126, loss_grounding_bce_3: 0.00278/0.08138, loss_grounding_dice_3: 0.05216/0.15117, loss_grounding_ce_3: 0.01186/0.25330, loss_mask_ce_4: 0.45894/0.78009, loss_mask_bce_4: 0.08611/0.30660, loss_mask_dice_4: 0.41494/1.04657, loss_spatial_bce_4: 0.02073/0.09036, loss_spatial_dice_4: 0.13165/0.19408, loss_spatial_ce_4: 0.06943/0.08392, loss_grounding_bce_4: 0.00259/0.08201, loss_grounding_dice_4: 0.08144/0.15377, loss_grounding_ce_4: 0.00893/0.25900, loss_mask_ce_5: 0.69193/0.80310, loss_mask_bce_5: 0.08938/0.30843, loss_mask_dice_5: 0.43803/1.05419, loss_spatial_bce_5: 0.01789/0.09235, loss_spatial_dice_5: 0.10607/0.19674, loss_spatial_ce_5: 0.08123/0.09623, loss_grounding_bce_5: 0.00336/0.08231, loss_grounding_dice_5: 0.09820/0.15446, loss_grounding_ce_5: 0.01056/0.27707, loss_mask_ce_6: 1.19732/0.82951, loss_mask_bce_6: 0.10001/0.31029, loss_mask_dice_6: 0.42347/1.05693, loss_spatial_bce_6: 0.02691/0.09738, loss_spatial_dice_6: 0.11568/0.19897, loss_spatial_ce_6: 0.02038/0.11979, loss_grounding_bce_6: 0.00387/0.08326, loss_grounding_dice_6: 0.05916/0.15504, loss_grounding_ce_6: 0.00652/0.28616, loss_mask_ce_7: 0.75547/0.88522, loss_mask_bce_7: 0.09573/0.31766, loss_mask_dice_7: 0.46133/1.10372, loss_spatial_bce_7: 0.05296/0.10758, loss_spatial_dice_7: 0.18675/0.22425, loss_spatial_ce_7: 0.04133/0.15845, loss_grounding_bce_7: 0.00355/0.08491, loss_grounding_dice_7: 0.06049/0.16081, loss_grounding_ce_7: 0.01065/0.32077, loss_mask_ce_8: 0.80500/1.02345, loss_mask_bce_8: 0.32274/0.33368, loss_mask_dice_8: 0.56138/1.18064, loss_spatial_bce_8: 0.02626/0.12569, loss_spatial_dice_8: 0.18081/0.26089, loss_spatial_ce_8: 0.08100/0.20978, loss_grounding_bce_8: 0.00382/0.08895, loss_grounding_dice_8: 0.07493/0.17031, loss_grounding_ce_8: 0.03511/0.42340, loss_mask_ce_9: 3.73220/3.48206, loss_mask_bce_9: 0.27408/0.36081, loss_mask_dice_9: 0.66365/1.76388, loss_spatial_bce_9: 0.16631/0.35555, loss_spatial_dice_9: 0.84025/0.79434, loss_spatial_ce_9: 1.03498/1.39587, loss_grounding_bce_9: 0.00263/0.10105, loss_grounding_dice_9: 0.06648/0.24304, loss_grounding_ce_9: 0.79669/0.68136] items per batch[64] items per second[0.37] total items[3059200] mini batches[ 47800] memory[4999] epoch remaining[0:45:07] INFO:trainer.default_trainer:epochs[ 26] optim steps[47900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44082/0.76246, loss_mask_bce_0: 0.04639/0.30185, loss_mask_dice_0: 0.12513/1.02515, loss_spatial_bce_0: 0.01508/0.08621, loss_spatial_dice_0: 0.03683/0.18224, loss_spatial_ce_0: 0.00008/0.06016, loss_grounding_bce_0: 0.01667/0.08086, loss_grounding_dice_0: 0.04549/0.15099, loss_grounding_ce_0: 0.16289/0.24917, loss_mask_ce_1: 0.47093/0.76336, loss_mask_bce_1: 0.04641/0.30268, loss_mask_dice_1: 0.12494/1.02898, loss_spatial_bce_1: 0.01574/0.08647, loss_spatial_dice_1: 0.03557/0.18478, loss_spatial_ce_1: 0.00030/0.06431, loss_grounding_bce_1: 0.01652/0.08102, loss_grounding_dice_1: 0.04251/0.15169, loss_grounding_ce_1: 0.16938/0.25095, loss_mask_ce_2: 0.44397/0.77121, loss_mask_bce_2: 0.04099/0.30278, loss_mask_dice_2: 0.11055/1.02992, loss_spatial_bce_2: 0.01562/0.08644, loss_spatial_dice_2: 0.02975/0.18506, loss_spatial_ce_2: 0.00018/0.06665, loss_grounding_bce_2: 0.01659/0.08096, loss_grounding_dice_2: 0.04090/0.15159, loss_grounding_ce_2: 0.15425/0.25363, loss_mask_ce_3: 0.41763/0.77374, loss_mask_bce_3: 0.04557/0.30437, loss_mask_dice_3: 0.12080/1.02752, loss_spatial_bce_3: 0.01557/0.08841, loss_spatial_dice_3: 0.03574/0.18623, loss_spatial_ce_3: 0.00025/0.07123, loss_grounding_bce_3: 0.01577/0.08140, loss_grounding_dice_3: 0.04057/0.15117, loss_grounding_ce_3: 0.14858/0.25339, loss_mask_ce_4: 0.52413/0.78008, loss_mask_bce_4: 0.04702/0.30671, loss_mask_dice_4: 0.11100/1.04653, loss_spatial_bce_4: 0.01591/0.09039, loss_spatial_dice_4: 0.03707/0.19406, loss_spatial_ce_4: 0.00069/0.08391, loss_grounding_bce_4: 0.01784/0.08205, loss_grounding_dice_4: 0.04460/0.15377, loss_grounding_ce_4: 0.15641/0.25907, loss_mask_ce_5: 0.62752/0.80309, loss_mask_bce_5: 0.04467/0.30856, loss_mask_dice_5: 0.11942/1.05413, loss_spatial_bce_5: 0.01490/0.09239, loss_spatial_dice_5: 0.03347/0.19672, loss_spatial_ce_5: 0.00098/0.09620, loss_grounding_bce_5: 0.01719/0.08235, loss_grounding_dice_5: 0.04513/0.15446, loss_grounding_ce_5: 0.20110/0.27713, loss_mask_ce_6: 0.59232/0.82946, loss_mask_bce_6: 0.04297/0.31044, loss_mask_dice_6: 0.11038/1.05693, loss_spatial_bce_6: 0.01621/0.09743, loss_spatial_dice_6: 0.03788/0.19895, loss_spatial_ce_6: 0.01294/0.11976, loss_grounding_bce_6: 0.01855/0.08331, loss_grounding_dice_6: 0.04468/0.15505, loss_grounding_ce_6: 0.19254/0.28617, loss_mask_ce_7: 0.35005/0.88515, loss_mask_bce_7: 0.04128/0.31781, loss_mask_dice_7: 0.11619/1.10376, loss_spatial_bce_7: 0.01591/0.10764, loss_spatial_dice_7: 0.03830/0.22424, loss_spatial_ce_7: 0.03868/0.15843, loss_grounding_bce_7: 0.01514/0.08496, loss_grounding_dice_7: 0.04114/0.16084, loss_grounding_ce_7: 0.16763/0.32085, loss_mask_ce_8: 0.54364/1.02340, loss_mask_bce_8: 0.04593/0.33384, loss_mask_dice_8: 0.13391/1.18064, loss_spatial_bce_8: 0.01711/0.12575, loss_spatial_dice_8: 0.04811/0.26085, loss_spatial_ce_8: 0.03998/0.20980, loss_grounding_bce_8: 0.01706/0.08901, loss_grounding_dice_8: 0.04742/0.17035, loss_grounding_ce_8: 0.29349/0.42346, loss_mask_ce_9: 3.61845/3.48195, loss_mask_bce_9: 0.08620/0.36096, loss_mask_dice_9: 0.29282/1.76405, loss_spatial_bce_9: 0.33831/0.35560, loss_spatial_dice_9: 0.72437/0.79436, loss_spatial_ce_9: 0.96650/1.39570, loss_grounding_bce_9: 0.06141/0.10109, loss_grounding_dice_9: 0.16420/0.24306, loss_grounding_ce_9: 0.34059/0.68114] items per batch[64] items per second[0.37] total items[3065600] mini batches[ 47900] memory[4999] epoch remaining[0:41:55] INFO:trainer.default_trainer:epochs[ 26] optim steps[48000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54120/0.76260, loss_mask_bce_0: 0.51708/0.30184, loss_mask_dice_0: 0.88241/1.02514, loss_spatial_bce_0: 0.11572/0.08621, loss_spatial_dice_0: 0.13952/0.18224, loss_spatial_ce_0: 0.00098/0.06017, loss_grounding_bce_0: 0.08350/0.08084, loss_grounding_dice_0: 0.14458/0.15098, loss_grounding_ce_0: 0.26614/0.24922, loss_mask_ce_1: 0.56504/0.76349, loss_mask_bce_1: 0.51190/0.30268, loss_mask_dice_1: 0.91333/1.02900, loss_spatial_bce_1: 0.12203/0.08647, loss_spatial_dice_1: 0.14033/0.18478, loss_spatial_ce_1: 0.00111/0.06430, loss_grounding_bce_1: 0.08114/0.08100, loss_grounding_dice_1: 0.13766/0.15168, loss_grounding_ce_1: 0.27094/0.25101, loss_mask_ce_2: 0.79777/0.77134, loss_mask_bce_2: 0.52160/0.30278, loss_mask_dice_2: 0.94814/1.02997, loss_spatial_bce_2: 0.12477/0.08643, loss_spatial_dice_2: 0.13528/0.18506, loss_spatial_ce_2: 0.00382/0.06667, loss_grounding_bce_2: 0.07728/0.08094, loss_grounding_dice_2: 0.13776/0.15157, loss_grounding_ce_2: 0.27100/0.25370, loss_mask_ce_3: 0.56054/0.77389, loss_mask_bce_3: 0.54269/0.30437, loss_mask_dice_3: 1.02453/1.02755, loss_spatial_bce_3: 0.11390/0.08841, loss_spatial_dice_3: 0.14841/0.18623, loss_spatial_ce_3: 0.00437/0.07122, loss_grounding_bce_3: 0.08144/0.08138, loss_grounding_dice_3: 0.15457/0.15116, loss_grounding_ce_3: 0.27449/0.25346, loss_mask_ce_4: 0.56925/0.78024, loss_mask_bce_4: 0.53826/0.30671, loss_mask_dice_4: 1.00126/1.04659, loss_spatial_bce_4: 0.12549/0.09040, loss_spatial_dice_4: 0.18193/0.19406, loss_spatial_ce_4: 0.01493/0.08390, loss_grounding_bce_4: 0.08164/0.08202, loss_grounding_dice_4: 0.15546/0.15375, loss_grounding_ce_4: 0.26703/0.25917, loss_mask_ce_5: 0.84865/0.80329, loss_mask_bce_5: 0.52958/0.30854, loss_mask_dice_5: 0.93404/1.05418, loss_spatial_bce_5: 0.11838/0.09240, loss_spatial_dice_5: 0.17677/0.19672, loss_spatial_ce_5: 0.06375/0.09620, loss_grounding_bce_5: 0.08468/0.08233, loss_grounding_dice_5: 0.15048/0.15445, loss_grounding_ce_5: 0.26860/0.27723, loss_mask_ce_6: 0.79567/0.82967, loss_mask_bce_6: 0.53487/0.31044, loss_mask_dice_6: 0.94948/1.05698, loss_spatial_bce_6: 0.11997/0.09744, loss_spatial_dice_6: 0.16684/0.19895, loss_spatial_ce_6: 0.06043/0.11975, loss_grounding_bce_6: 0.08071/0.08328, loss_grounding_dice_6: 0.13644/0.15502, loss_grounding_ce_6: 0.33451/0.28635, loss_mask_ce_7: 0.72783/0.88531, loss_mask_bce_7: 0.53011/0.31779, loss_mask_dice_7: 0.91881/1.10381, loss_spatial_bce_7: 0.15165/0.10764, loss_spatial_dice_7: 0.16855/0.22424, loss_spatial_ce_7: 0.12193/0.15842, loss_grounding_bce_7: 0.08549/0.08494, loss_grounding_dice_7: 0.15741/0.16082, loss_grounding_ce_7: 0.36673/0.32101, loss_mask_ce_8: 0.87040/1.02348, loss_mask_bce_8: 0.67863/0.33380, loss_mask_dice_8: 1.22757/1.18068, loss_spatial_bce_8: 0.14850/0.12575, loss_spatial_dice_8: 0.18051/0.26083, loss_spatial_ce_8: 0.12477/0.20977, loss_grounding_bce_8: 0.09178/0.08900, loss_grounding_dice_8: 0.16100/0.17032, loss_grounding_ce_8: 0.31639/0.42361, loss_mask_ce_9: 3.30830/3.48222, loss_mask_bce_9: 0.83539/0.36096, loss_mask_dice_9: 1.95146/1.76422, loss_spatial_bce_9: 0.42774/0.35556, loss_spatial_dice_9: 0.86507/0.79436, loss_spatial_ce_9: 1.20956/1.39572, loss_grounding_bce_9: 0.11565/0.10108, loss_grounding_dice_9: 0.31441/0.24306, loss_grounding_ce_9: 0.46462/0.68113] items per batch[64] items per second[0.36] total items[3072000] mini batches[ 48000] memory[4999] epoch remaining[0:38:59] INFO:trainer.default_trainer:epochs[ 26] optim steps[48100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24241/0.76262, loss_mask_bce_0: 0.65321/0.30194, loss_mask_dice_0: 0.67899/1.02513, loss_spatial_bce_0: 0.20497/0.08621, loss_spatial_dice_0: 0.21919/0.18224, loss_spatial_ce_0: 0.05717/0.06016, loss_grounding_bce_0: 0.16150/0.08086, loss_grounding_dice_0: 0.13753/0.15099, loss_grounding_ce_0: 0.26933/0.24926, loss_mask_ce_1: 1.34241/0.76348, loss_mask_bce_1: 0.64643/0.30278, loss_mask_dice_1: 0.69304/1.02903, loss_spatial_bce_1: 0.19918/0.08648, loss_spatial_dice_1: 0.16452/0.18477, loss_spatial_ce_1: 0.05931/0.06429, loss_grounding_bce_1: 0.15677/0.08102, loss_grounding_dice_1: 0.14862/0.15170, loss_grounding_ce_1: 0.27050/0.25102, loss_mask_ce_2: 1.30248/0.77138, loss_mask_bce_2: 0.64516/0.30289, loss_mask_dice_2: 0.64983/1.02996, loss_spatial_bce_2: 0.20879/0.08644, loss_spatial_dice_2: 0.22259/0.18505, loss_spatial_ce_2: 0.05909/0.06665, loss_grounding_bce_2: 0.16039/0.08097, loss_grounding_dice_2: 0.13228/0.15158, loss_grounding_ce_2: 0.27595/0.25374, loss_mask_ce_3: 1.34318/0.77389, loss_mask_bce_3: 0.64070/0.30448, loss_mask_dice_3: 0.66724/1.02758, loss_spatial_bce_3: 0.20531/0.08842, loss_spatial_dice_3: 0.19322/0.18624, loss_spatial_ce_3: 0.05922/0.07120, loss_grounding_bce_3: 0.15955/0.08141, loss_grounding_dice_3: 0.16453/0.15117, loss_grounding_ce_3: 0.30731/0.25352, loss_mask_ce_4: 1.32810/0.78024, loss_mask_bce_4: 0.65732/0.30683, loss_mask_dice_4: 0.65199/1.04663, loss_spatial_bce_4: 0.20382/0.09040, loss_spatial_dice_4: 0.24573/0.19407, loss_spatial_ce_4: 0.07653/0.08388, loss_grounding_bce_4: 0.15732/0.08205, loss_grounding_dice_4: 0.11234/0.15376, loss_grounding_ce_4: 0.37712/0.25919, loss_mask_ce_5: 1.23037/0.80332, loss_mask_bce_5: 0.63298/0.30865, loss_mask_dice_5: 0.74130/1.05423, loss_spatial_bce_5: 0.20176/0.09241, loss_spatial_dice_5: 0.21646/0.19673, loss_spatial_ce_5: 0.05911/0.09619, loss_grounding_bce_5: 0.15228/0.08235, loss_grounding_dice_5: 0.16396/0.15446, loss_grounding_ce_5: 0.27871/0.27730, loss_mask_ce_6: 1.59772/0.82969, loss_mask_bce_6: 0.64276/0.31055, loss_mask_dice_6: 0.67791/1.05701, loss_spatial_bce_6: 0.20283/0.09746, loss_spatial_dice_6: 0.22699/0.19896, loss_spatial_ce_6: 0.10626/0.11976, loss_grounding_bce_6: 0.15647/0.08331, loss_grounding_dice_6: 0.37189/0.15504, loss_grounding_ce_6: 0.26316/0.28636, loss_mask_ce_7: 1.48761/0.88530, loss_mask_bce_7: 0.64976/0.31793, loss_mask_dice_7: 0.67994/1.10388, loss_spatial_bce_7: 0.22011/0.10765, loss_spatial_dice_7: 0.30912/0.22425, loss_spatial_ce_7: 0.11346/0.15842, loss_grounding_bce_7: 0.16073/0.08496, loss_grounding_dice_7: 0.21121/0.16084, loss_grounding_ce_7: 0.27250/0.32099, loss_mask_ce_8: 1.40268/1.02341, loss_mask_bce_8: 0.68070/0.33393, loss_mask_dice_8: 0.73246/1.18078, loss_spatial_bce_8: 0.24423/0.12575, loss_spatial_dice_8: 0.26127/0.26083, loss_spatial_ce_8: 0.06976/0.20974, loss_grounding_bce_8: 0.16747/0.08903, loss_grounding_dice_8: 0.20871/0.17034, loss_grounding_ce_8: 0.30778/0.42369, loss_mask_ce_9: 2.81837/3.48240, loss_mask_bce_9: 0.67170/0.36109, loss_mask_dice_9: 0.90378/1.76446, loss_spatial_bce_9: 0.44989/0.35557, loss_spatial_dice_9: 0.84325/0.79439, loss_spatial_ce_9: 1.52147/1.39578, loss_grounding_bce_9: 0.16160/0.10110, loss_grounding_dice_9: 0.21951/0.24310, loss_grounding_ce_9: 0.29836/0.68102] items per batch[64] items per second[0.35] total items[3078400] mini batches[ 48100] memory[4999] epoch remaining[0:36:13] INFO:trainer.default_trainer:epochs[ 26] optim steps[48200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03670/0.76246, loss_mask_bce_0: 0.24799/0.30189, loss_mask_dice_0: 0.15617/1.02506, loss_spatial_bce_0: 0.14174/0.08620, loss_spatial_dice_0: 0.09564/0.18222, loss_spatial_ce_0: 0.00704/0.06012, loss_grounding_bce_0: 0.03244/0.08084, loss_grounding_dice_0: 0.11087/0.15098, loss_grounding_ce_0: 0.00241/0.24925, loss_mask_ce_1: 0.02897/0.76330, loss_mask_bce_1: 0.25156/0.30274, loss_mask_dice_1: 0.16205/1.02889, loss_spatial_bce_1: 0.12887/0.08646, loss_spatial_dice_1: 0.08669/0.18475, loss_spatial_ce_1: 0.01123/0.06423, loss_grounding_bce_1: 0.03344/0.08100, loss_grounding_dice_1: 0.12092/0.15171, loss_grounding_ce_1: 0.00329/0.25100, loss_mask_ce_2: 0.03050/0.77122, loss_mask_bce_2: 0.25181/0.30284, loss_mask_dice_2: 0.16153/1.02988, loss_spatial_bce_2: 0.13277/0.08642, loss_spatial_dice_2: 0.08846/0.18503, loss_spatial_ce_2: 0.01121/0.06659, loss_grounding_bce_2: 0.03095/0.08094, loss_grounding_dice_2: 0.11441/0.15158, loss_grounding_ce_2: 0.00448/0.25375, loss_mask_ce_3: 0.03009/0.77376, loss_mask_bce_3: 0.23789/0.30443, loss_mask_dice_3: 0.15305/1.02745, loss_spatial_bce_3: 0.14057/0.08840, loss_spatial_dice_3: 0.08975/0.18622, loss_spatial_ce_3: 0.01787/0.07115, loss_grounding_bce_3: 0.02980/0.08139, loss_grounding_dice_3: 0.11789/0.15117, loss_grounding_ce_3: 0.00577/0.25350, loss_mask_ce_4: 0.02565/0.78010, loss_mask_bce_4: 0.23545/0.30679, loss_mask_dice_4: 0.16016/1.04659, loss_spatial_bce_4: 0.14025/0.09039, loss_spatial_dice_4: 0.09301/0.19404, loss_spatial_ce_4: 0.03044/0.08382, loss_grounding_bce_4: 0.03356/0.08203, loss_grounding_dice_4: 0.11969/0.15376, loss_grounding_ce_4: 0.00350/0.25913, loss_mask_ce_5: 0.03252/0.80310, loss_mask_bce_5: 0.24897/0.30861, loss_mask_dice_5: 0.15595/1.05412, loss_spatial_bce_5: 0.13572/0.09240, loss_spatial_dice_5: 0.09301/0.19672, loss_spatial_ce_5: 0.05495/0.09612, loss_grounding_bce_5: 0.03457/0.08233, loss_grounding_dice_5: 0.12315/0.15445, loss_grounding_ce_5: 0.00455/0.27732, loss_mask_ce_6: 0.02595/0.82955, loss_mask_bce_6: 0.23561/0.31050, loss_mask_dice_6: 0.15155/1.05693, loss_spatial_bce_6: 0.15280/0.09744, loss_spatial_dice_6: 0.09117/0.19894, loss_spatial_ce_6: 0.06327/0.11972, loss_grounding_bce_6: 0.03782/0.08328, loss_grounding_dice_6: 0.12461/0.15503, loss_grounding_ce_6: 0.00610/0.28633, loss_mask_ce_7: 0.02197/0.88507, loss_mask_bce_7: 0.23110/0.31788, loss_mask_dice_7: 0.15621/1.10377, loss_spatial_bce_7: 0.16034/0.10764, loss_spatial_dice_7: 0.11596/0.22423, loss_spatial_ce_7: 0.23756/0.15833, loss_grounding_bce_7: 0.03021/0.08494, loss_grounding_dice_7: 0.12239/0.16083, loss_grounding_ce_7: 0.00901/0.32100, loss_mask_ce_8: 0.03398/1.02319, loss_mask_bce_8: 0.25340/0.33387, loss_mask_dice_8: 0.15809/1.18066, loss_spatial_bce_8: 0.17415/0.12574, loss_spatial_dice_8: 0.09571/0.26081, loss_spatial_ce_8: 0.30061/0.20965, loss_grounding_bce_8: 0.05066/0.08901, loss_grounding_dice_8: 0.13559/0.17033, loss_grounding_ce_8: 0.00392/0.42361, loss_mask_ce_9: 1.60323/3.48199, loss_mask_bce_9: 0.23854/0.36095, loss_mask_dice_9: 0.17467/1.76400, loss_spatial_bce_9: 0.50457/0.35555, loss_spatial_dice_9: 0.73089/0.79439, loss_spatial_ce_9: 1.09007/1.39566, loss_grounding_bce_9: 0.03693/0.10106, loss_grounding_dice_9: 0.14985/0.24307, loss_grounding_ce_9: 0.10744/0.68074] items per batch[64] items per second[0.36] total items[3084800] mini batches[ 48200] memory[4999] epoch remaining[0:33:17] INFO:trainer.default_trainer:epochs[ 26] optim steps[48300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72717/0.76251, loss_mask_bce_0: 0.05158/0.30191, loss_mask_dice_0: 0.64744/1.02513, loss_spatial_bce_0: 0.03286/0.08619, loss_spatial_dice_0: 0.21167/0.18221, loss_spatial_ce_0: 0.01095/0.06009, loss_grounding_bce_0: 0.01229/0.08084, loss_grounding_dice_0: 0.03954/0.15099, loss_grounding_ce_0: 0.20698/0.24923, loss_mask_ce_1: 0.89710/0.76337, loss_mask_bce_1: 0.04611/0.30276, loss_mask_dice_1: 0.64637/1.02891, loss_spatial_bce_1: 0.02606/0.08645, loss_spatial_dice_1: 0.20397/0.18475, loss_spatial_ce_1: 0.02026/0.06423, loss_grounding_bce_1: 0.01214/0.08101, loss_grounding_dice_1: 0.03838/0.15171, loss_grounding_ce_1: 0.23399/0.25106, loss_mask_ce_2: 0.96992/0.77134, loss_mask_bce_2: 0.04504/0.30285, loss_mask_dice_2: 0.65164/1.02992, loss_spatial_bce_2: 0.02546/0.08642, loss_spatial_dice_2: 0.20228/0.18502, loss_spatial_ce_2: 0.01894/0.06656, loss_grounding_bce_2: 0.01178/0.08094, loss_grounding_dice_2: 0.03974/0.15159, loss_grounding_ce_2: 0.22588/0.25377, loss_mask_ce_3: 0.82346/0.77386, loss_mask_bce_3: 0.05721/0.30445, loss_mask_dice_3: 0.58595/1.02746, loss_spatial_bce_3: 0.02706/0.08840, loss_spatial_dice_3: 0.20311/0.18621, loss_spatial_ce_3: 0.02645/0.07112, loss_grounding_bce_3: 0.01434/0.08139, loss_grounding_dice_3: 0.03987/0.15118, loss_grounding_ce_3: 0.16644/0.25355, loss_mask_ce_4: 1.11307/0.78010, loss_mask_bce_4: 0.04617/0.30681, loss_mask_dice_4: 0.55264/1.04666, loss_spatial_bce_4: 0.03308/0.09038, loss_spatial_dice_4: 0.20974/0.19404, loss_spatial_ce_4: 0.02633/0.08382, loss_grounding_bce_4: 0.01340/0.08204, loss_grounding_dice_4: 0.04277/0.15378, loss_grounding_ce_4: 0.16686/0.25915, loss_mask_ce_5: 0.81971/0.80319, loss_mask_bce_5: 0.04427/0.30863, loss_mask_dice_5: 0.51999/1.05413, loss_spatial_bce_5: 0.02034/0.09239, loss_spatial_dice_5: 0.20913/0.19671, loss_spatial_ce_5: 0.03280/0.09612, loss_grounding_bce_5: 0.01480/0.08234, loss_grounding_dice_5: 0.04074/0.15446, loss_grounding_ce_5: 0.19012/0.27730, loss_mask_ce_6: 1.00908/0.82962, loss_mask_bce_6: 0.04426/0.31051, loss_mask_dice_6: 0.59350/1.05697, loss_spatial_bce_6: 0.01916/0.09743, loss_spatial_dice_6: 0.19601/0.19894, loss_spatial_ce_6: 0.11678/0.11969, loss_grounding_bce_6: 0.01247/0.08328, loss_grounding_dice_6: 0.03797/0.15505, loss_grounding_ce_6: 0.21630/0.28633, loss_mask_ce_7: 0.91389/0.88508, loss_mask_bce_7: 0.04622/0.31790, loss_mask_dice_7: 0.66918/1.10377, loss_spatial_bce_7: 0.02677/0.10762, loss_spatial_dice_7: 0.24488/0.22424, loss_spatial_ce_7: 0.13912/0.15835, loss_grounding_bce_7: 0.01296/0.08495, loss_grounding_dice_7: 0.04456/0.16083, loss_grounding_ce_7: 0.22127/0.32095, loss_mask_ce_8: 1.25830/1.02324, loss_mask_bce_8: 0.05161/0.33389, loss_mask_dice_8: 0.64734/1.18071, loss_spatial_bce_8: 0.08976/0.12572, loss_spatial_dice_8: 0.32410/0.26079, loss_spatial_ce_8: 0.16751/0.20965, loss_grounding_bce_8: 0.01154/0.08902, loss_grounding_dice_8: 0.04396/0.17033, loss_grounding_ce_8: 0.23520/0.42356, loss_mask_ce_9: 4.59712/3.48225, loss_mask_bce_9: 0.06482/0.36099, loss_mask_dice_9: 0.91802/1.76405, loss_spatial_bce_9: 0.25833/0.35557, loss_spatial_dice_9: 0.85005/0.79437, loss_spatial_ce_9: 1.12501/1.39546, loss_grounding_bce_9: 0.02506/0.10107, loss_grounding_dice_9: 0.11094/0.24308, loss_grounding_ce_9: 0.61703/0.68075] items per batch[64] items per second[0.37] total items[3091200] mini batches[ 48300] memory[4999] epoch remaining[0:30:16] INFO:trainer.default_trainer:epochs[ 26] optim steps[48400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.45687/0.76245, loss_mask_bce_0: 0.12608/0.30192, loss_mask_dice_0: 0.09245/1.02516, loss_spatial_bce_0: 0.06532/0.08617, loss_spatial_dice_0: 0.04568/0.18223, loss_spatial_ce_0: 0.12965/0.06009, loss_grounding_bce_0: 0.06916/0.08086, loss_grounding_dice_0: 0.04952/0.15103, loss_grounding_ce_0: 0.19141/0.24934, loss_mask_ce_1: 0.47109/0.76331, loss_mask_bce_1: 0.12431/0.30278, loss_mask_dice_1: 0.09092/1.02896, loss_spatial_bce_1: 0.06342/0.08643, loss_spatial_dice_1: 0.04495/0.18476, loss_spatial_ce_1: 0.13703/0.06424, loss_grounding_bce_1: 0.07035/0.08103, loss_grounding_dice_1: 0.05151/0.15176, loss_grounding_ce_1: 0.24866/0.25120, loss_mask_ce_2: 0.53489/0.77131, loss_mask_bce_2: 0.11720/0.30287, loss_mask_dice_2: 0.08618/1.02995, loss_spatial_bce_2: 0.06266/0.08640, loss_spatial_dice_2: 0.04384/0.18503, loss_spatial_ce_2: 0.17615/0.06658, loss_grounding_bce_2: 0.06707/0.08096, loss_grounding_dice_2: 0.05123/0.15164, loss_grounding_ce_2: 0.27159/0.25389, loss_mask_ce_3: 0.60418/0.77381, loss_mask_bce_3: 0.11995/0.30446, loss_mask_dice_3: 0.08776/1.02747, loss_spatial_bce_3: 0.09650/0.08838, loss_spatial_dice_3: 0.05887/0.18622, loss_spatial_ce_3: 0.22520/0.07112, loss_grounding_bce_3: 0.06145/0.08142, loss_grounding_dice_3: 0.04877/0.15122, loss_grounding_ce_3: 0.23601/0.25363, loss_mask_ce_4: 0.78598/0.78006, loss_mask_bce_4: 0.13378/0.30683, loss_mask_dice_4: 0.09051/1.04665, loss_spatial_bce_4: 0.16247/0.09036, loss_spatial_dice_4: 0.10291/0.19406, loss_spatial_ce_4: 0.18854/0.08383, loss_grounding_bce_4: 0.06581/0.08206, loss_grounding_dice_4: 0.04328/0.15384, loss_grounding_ce_4: 0.22653/0.25927, loss_mask_ce_5: 0.92511/0.80322, loss_mask_bce_5: 0.11592/0.30864, loss_mask_dice_5: 0.09248/1.05412, loss_spatial_bce_5: 0.12208/0.09238, loss_spatial_dice_5: 0.07995/0.19673, loss_spatial_ce_5: 0.21520/0.09613, loss_grounding_bce_5: 0.05758/0.08236, loss_grounding_dice_5: 0.05046/0.15451, loss_grounding_ce_5: 0.35140/0.27751, loss_mask_ce_6: 0.83516/0.82960, loss_mask_bce_6: 0.11934/0.31054, loss_mask_dice_6: 0.08846/1.05697, loss_spatial_bce_6: 0.20539/0.09741, loss_spatial_dice_6: 0.09985/0.19896, loss_spatial_ce_6: 0.23709/0.11974, loss_grounding_bce_6: 0.06585/0.08330, loss_grounding_dice_6: 0.05026/0.15510, loss_grounding_ce_6: 0.51972/0.28643, loss_mask_ce_7: 1.02928/0.88504, loss_mask_bce_7: 0.16250/0.31794, loss_mask_dice_7: 0.09522/1.10382, loss_spatial_bce_7: 0.24025/0.10760, loss_spatial_dice_7: 0.11867/0.22428, loss_spatial_ce_7: 0.18777/0.15841, loss_grounding_bce_7: 0.06221/0.08497, loss_grounding_dice_7: 0.04450/0.16090, loss_grounding_ce_7: 0.71621/0.32104, loss_mask_ce_8: 1.05869/1.02336, loss_mask_bce_8: 0.16966/0.33392, loss_mask_dice_8: 0.09521/1.18068, loss_spatial_bce_8: 0.29784/0.12572, loss_spatial_dice_8: 0.24638/0.26082, loss_spatial_ce_8: 0.27045/0.20969, loss_grounding_bce_8: 0.06923/0.08905, loss_grounding_dice_8: 0.05016/0.17040, loss_grounding_ce_8: 0.41807/0.42383, loss_mask_ce_9: 2.95611/3.48226, loss_mask_bce_9: 0.36542/0.36101, loss_mask_dice_9: 0.19899/1.76402, loss_spatial_bce_9: 0.60024/0.35548, loss_spatial_dice_9: 0.66706/0.79436, loss_spatial_ce_9: 1.78913/1.39555, loss_grounding_bce_9: 0.13119/0.10110, loss_grounding_dice_9: 0.10477/0.24316, loss_grounding_ce_9: 0.46588/0.68087] items per batch[64] items per second[0.36] total items[3097600] mini batches[ 48400] memory[4999] epoch remaining[0:27:19] INFO:trainer.default_trainer:epochs[ 26] optim steps[48500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10052/0.76238, loss_mask_bce_0: 0.21630/0.30190, loss_mask_dice_0: 0.15995/1.02514, loss_spatial_bce_0: 0.10070/0.08616, loss_spatial_dice_0: 0.08527/0.18219, loss_spatial_ce_0: 0.00007/0.06009, loss_grounding_bce_0: 0.09208/0.08088, loss_grounding_dice_0: 0.07234/0.15103, loss_grounding_ce_0: 0.00039/0.24937, loss_mask_ce_1: 0.11050/0.76321, loss_mask_bce_1: 0.21361/0.30275, loss_mask_dice_1: 0.15593/1.02896, loss_spatial_bce_1: 0.10114/0.08642, loss_spatial_dice_1: 0.08536/0.18473, loss_spatial_ce_1: 0.00018/0.06422, loss_grounding_bce_1: 0.09212/0.08105, loss_grounding_dice_1: 0.06536/0.15175, loss_grounding_ce_1: 0.00059/0.25126, loss_mask_ce_2: 0.10264/0.77126, loss_mask_bce_2: 0.20874/0.30285, loss_mask_dice_2: 0.15890/1.02990, loss_spatial_bce_2: 0.10130/0.08639, loss_spatial_dice_2: 0.08104/0.18500, loss_spatial_ce_2: 0.00034/0.06657, loss_grounding_bce_2: 0.09299/0.08098, loss_grounding_dice_2: 0.07137/0.15164, loss_grounding_ce_2: 0.00079/0.25391, loss_mask_ce_3: 0.12289/0.77374, loss_mask_bce_3: 0.20662/0.30444, loss_mask_dice_3: 0.16021/1.02743, loss_spatial_bce_3: 0.09899/0.08840, loss_spatial_dice_3: 0.07867/0.18618, loss_spatial_ce_3: 0.00024/0.07109, loss_grounding_bce_3: 0.09264/0.08144, loss_grounding_dice_3: 0.06807/0.15121, loss_grounding_ce_3: 0.00066/0.25365, loss_mask_ce_4: 0.12217/0.77997, loss_mask_bce_4: 0.20889/0.30679, loss_mask_dice_4: 0.15780/1.04658, loss_spatial_bce_4: 0.09840/0.09035, loss_spatial_dice_4: 0.08314/0.19403, loss_spatial_ce_4: 0.00189/0.08382, loss_grounding_bce_4: 0.09002/0.08207, loss_grounding_dice_4: 0.06347/0.15383, loss_grounding_ce_4: 0.00029/0.25930, loss_mask_ce_5: 0.09580/0.80312, loss_mask_bce_5: 0.19110/0.30863, loss_mask_dice_5: 0.15629/1.05409, loss_spatial_bce_5: 0.10187/0.09237, loss_spatial_dice_5: 0.08363/0.19670, loss_spatial_ce_5: 0.00499/0.09609, loss_grounding_bce_5: 0.08010/0.08238, loss_grounding_dice_5: 0.04965/0.15451, loss_grounding_ce_5: 0.00056/0.27754, loss_mask_ce_6: 0.10568/0.82949, loss_mask_bce_6: 0.20417/0.31053, loss_mask_dice_6: 0.16040/1.05700, loss_spatial_bce_6: 0.10040/0.09741, loss_spatial_dice_6: 0.08221/0.19893, loss_spatial_ce_6: 0.02424/0.11971, loss_grounding_bce_6: 0.08164/0.08333, loss_grounding_dice_6: 0.06526/0.15510, loss_grounding_ce_6: 0.00226/0.28643, loss_mask_ce_7: 0.11499/0.88494, loss_mask_bce_7: 0.20508/0.31792, loss_mask_dice_7: 0.15957/1.10381, loss_spatial_bce_7: 0.10269/0.10760, loss_spatial_dice_7: 0.09673/0.22425, loss_spatial_ce_7: 0.03019/0.15842, loss_grounding_bce_7: 0.09642/0.08500, loss_grounding_dice_7: 0.07485/0.16089, loss_grounding_ce_7: 0.00075/0.32106, loss_mask_ce_8: 0.16864/1.02333, loss_mask_bce_8: 0.21038/0.33390, loss_mask_dice_8: 0.16551/1.18074, loss_spatial_bce_8: 0.17668/0.12572, loss_spatial_dice_8: 0.12487/0.26079, loss_spatial_ce_8: 0.01393/0.20958, loss_grounding_bce_8: 0.09764/0.08908, loss_grounding_dice_8: 0.07785/0.17040, loss_grounding_ce_8: 0.00054/0.42380, loss_mask_ce_9: 2.12512/3.48234, loss_mask_bce_9: 0.23808/0.36104, loss_mask_dice_9: 0.28435/1.76427, loss_spatial_bce_9: 0.53267/0.35554, loss_spatial_dice_9: 0.68863/0.79435, loss_spatial_ce_9: 0.96163/1.39556, loss_grounding_bce_9: 0.12425/0.10113, loss_grounding_dice_9: 0.07503/0.24314, loss_grounding_ce_9: 0.14999/0.68070] items per batch[64] items per second[0.36] total items[3104000] mini batches[ 48500] memory[4999] epoch remaining[0:24:23] INFO:trainer.default_trainer:epochs[ 26] optim steps[48600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18060/0.76245, loss_mask_bce_0: 0.27450/0.30192, loss_mask_dice_0: 1.65475/1.02526, loss_spatial_bce_0: 0.04460/0.08615, loss_spatial_dice_0: 0.22912/0.18219, loss_spatial_ce_0: 0.01206/0.06005, loss_grounding_bce_0: 0.10712/0.08087, loss_grounding_dice_0: 0.05899/0.15102, loss_grounding_ce_0: 0.00068/0.24926, loss_mask_ce_1: 1.16334/0.76323, loss_mask_bce_1: 0.26229/0.30277, loss_mask_dice_1: 1.62775/1.02897, loss_spatial_bce_1: 0.03944/0.08641, loss_spatial_dice_1: 0.21920/0.18473, loss_spatial_ce_1: 0.02336/0.06416, loss_grounding_bce_1: 0.12117/0.08105, loss_grounding_dice_1: 0.06589/0.15175, loss_grounding_ce_1: 0.00082/0.25115, loss_mask_ce_2: 1.28500/0.77124, loss_mask_bce_2: 0.26919/0.30286, loss_mask_dice_2: 1.54605/1.03000, loss_spatial_bce_2: 0.03895/0.08638, loss_spatial_dice_2: 0.21591/0.18501, loss_spatial_ce_2: 0.01735/0.06654, loss_grounding_bce_2: 0.11645/0.08097, loss_grounding_dice_2: 0.06080/0.15163, loss_grounding_ce_2: 0.00168/0.25379, loss_mask_ce_3: 1.50678/0.77377, loss_mask_bce_3: 0.25581/0.30445, loss_mask_dice_3: 1.49534/1.02751, loss_spatial_bce_3: 0.04464/0.08839, loss_spatial_dice_3: 0.21545/0.18619, loss_spatial_ce_3: 0.03001/0.07106, loss_grounding_bce_3: 0.10620/0.08143, loss_grounding_dice_3: 0.06042/0.15120, loss_grounding_ce_3: 0.00130/0.25358, loss_mask_ce_4: 1.59841/0.78002, loss_mask_bce_4: 0.25222/0.30680, loss_mask_dice_4: 1.50989/1.04663, loss_spatial_bce_4: 0.03721/0.09035, loss_spatial_dice_4: 0.19968/0.19402, loss_spatial_ce_4: 0.04508/0.08381, loss_grounding_bce_4: 0.10883/0.08207, loss_grounding_dice_4: 0.06070/0.15384, loss_grounding_ce_4: 0.00148/0.25915, loss_mask_ce_5: 1.66343/0.80319, loss_mask_bce_5: 0.29094/0.30864, loss_mask_dice_5: 1.67265/1.05419, loss_spatial_bce_5: 0.03993/0.09236, loss_spatial_dice_5: 0.21331/0.19670, loss_spatial_ce_5: 0.06645/0.09605, loss_grounding_bce_5: 0.11829/0.08237, loss_grounding_dice_5: 0.06351/0.15451, loss_grounding_ce_5: 0.00218/0.27748, loss_mask_ce_6: 1.72792/0.82957, loss_mask_bce_6: 0.26306/0.31055, loss_mask_dice_6: 1.48658/1.05703, loss_spatial_bce_6: 0.04787/0.09740, loss_spatial_dice_6: 0.21333/0.19893, loss_spatial_ce_6: 0.05669/0.11972, loss_grounding_bce_6: 0.11266/0.08333, loss_grounding_dice_6: 0.06399/0.15510, loss_grounding_ce_6: 0.00356/0.28642, loss_mask_ce_7: 2.04431/0.88504, loss_mask_bce_7: 0.26912/0.31794, loss_mask_dice_7: 1.57597/1.10391, loss_spatial_bce_7: 0.04645/0.10758, loss_spatial_dice_7: 0.22805/0.22426, loss_spatial_ce_7: 0.10428/0.15839, loss_grounding_bce_7: 0.11606/0.08499, loss_grounding_dice_7: 0.06905/0.16088, loss_grounding_ce_7: 0.00670/0.32101, loss_mask_ce_8: 2.36515/1.02337, loss_mask_bce_8: 0.28436/0.33392, loss_mask_dice_8: 1.64879/1.18079, loss_spatial_bce_8: 0.05227/0.12570, loss_spatial_dice_8: 0.27334/0.26079, loss_spatial_ce_8: 0.12708/0.20958, loss_grounding_bce_8: 0.11450/0.08908, loss_grounding_dice_8: 0.06040/0.17038, loss_grounding_ce_8: 0.00964/0.42366, loss_mask_ce_9: 4.60396/3.48213, loss_mask_bce_9: 0.26968/0.36105, loss_mask_dice_9: 2.55068/1.76447, loss_spatial_bce_9: 0.29421/0.35551, loss_spatial_dice_9: 0.91324/0.79432, loss_spatial_ce_9: 1.52654/1.39554, loss_grounding_bce_9: 0.11426/0.10114, loss_grounding_dice_9: 0.07361/0.24311, loss_grounding_ce_9: 0.01911/0.68053] items per batch[64] items per second[0.37] total items[3110400] mini batches[ 48600] memory[4999] epoch remaining[0:21:26] INFO:trainer.default_trainer:epochs[ 26] optim steps[48700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04093/0.76252, loss_mask_bce_0: 0.05560/0.30186, loss_mask_dice_0: 2.68495/1.02526, loss_spatial_bce_0: 0.00718/0.08614, loss_spatial_dice_0: 0.42081/0.18215, loss_spatial_ce_0: 0.10619/0.06004, loss_grounding_bce_0: 0.00387/0.08085, loss_grounding_dice_0: 0.12249/0.15103, loss_grounding_ce_0: 1.00152/0.24928, loss_mask_ce_1: 1.24285/0.76324, loss_mask_bce_1: 0.05608/0.30271, loss_mask_dice_1: 2.79513/1.02892, loss_spatial_bce_1: 0.00565/0.08640, loss_spatial_dice_1: 0.32315/0.18469, loss_spatial_ce_1: 0.09683/0.06416, loss_grounding_bce_1: 0.00723/0.08103, loss_grounding_dice_1: 0.15117/0.15176, loss_grounding_ce_1: 0.68602/0.25117, loss_mask_ce_2: 1.05186/0.77127, loss_mask_bce_2: 0.06775/0.30280, loss_mask_dice_2: 2.92599/1.02994, loss_spatial_bce_2: 0.00714/0.08637, loss_spatial_dice_2: 0.34917/0.18497, loss_spatial_ce_2: 0.12027/0.06650, loss_grounding_bce_2: 0.00257/0.08095, loss_grounding_dice_2: 0.07381/0.15164, loss_grounding_ce_2: 0.35917/0.25378, loss_mask_ce_3: 1.11701/0.77386, loss_mask_bce_3: 0.06462/0.30439, loss_mask_dice_3: 3.23544/1.02751, loss_spatial_bce_3: 0.00732/0.08838, loss_spatial_dice_3: 0.34530/0.18615, loss_spatial_ce_3: 0.16220/0.07102, loss_grounding_bce_3: 0.00473/0.08141, loss_grounding_dice_3: 0.09552/0.15121, loss_grounding_ce_3: 0.62962/0.25360, loss_mask_ce_4: 1.05200/0.78009, loss_mask_bce_4: 0.05371/0.30673, loss_mask_dice_4: 2.81530/1.04658, loss_spatial_bce_4: 0.00593/0.09033, loss_spatial_dice_4: 0.32074/0.19399, loss_spatial_ce_4: 0.16239/0.08377, loss_grounding_bce_4: 0.00623/0.08205, loss_grounding_dice_4: 0.09680/0.15384, loss_grounding_ce_4: 0.47074/0.25918, loss_mask_ce_5: 1.56487/0.80320, loss_mask_bce_5: 0.04750/0.30859, loss_mask_dice_5: 2.67138/1.05410, loss_spatial_bce_5: 0.00596/0.09235, loss_spatial_dice_5: 0.32448/0.19667, loss_spatial_ce_5: 0.02550/0.09600, loss_grounding_bce_5: 0.00551/0.08236, loss_grounding_dice_5: 0.14701/0.15452, loss_grounding_ce_5: 0.68754/0.27746, loss_mask_ce_6: 1.14619/0.82956, loss_mask_bce_6: 0.06600/0.31050, loss_mask_dice_6: 3.07342/1.05693, loss_spatial_bce_6: 0.00812/0.09739, loss_spatial_dice_6: 0.28027/0.19890, loss_spatial_ce_6: 0.35139/0.11974, loss_grounding_bce_6: 0.00576/0.08331, loss_grounding_dice_6: 0.12216/0.15510, loss_grounding_ce_6: 0.80824/0.28642, loss_mask_ce_7: 1.51266/0.88496, loss_mask_bce_7: 0.05775/0.31787, loss_mask_dice_7: 2.91202/1.10379, loss_spatial_bce_7: 0.00783/0.10756, loss_spatial_dice_7: 0.41136/0.22423, loss_spatial_ce_7: 0.42260/0.15837, loss_grounding_bce_7: 0.00293/0.08498, loss_grounding_dice_7: 0.10123/0.16089, loss_grounding_ce_7: 0.90864/0.32094, loss_mask_ce_8: 2.10366/1.02331, loss_mask_bce_8: 0.05837/0.33386, loss_mask_dice_8: 3.06006/1.18074, loss_spatial_bce_8: 0.01103/0.12567, loss_spatial_dice_8: 0.56931/0.26075, loss_spatial_ce_8: 0.53354/0.20952, loss_grounding_bce_8: 0.00560/0.08906, loss_grounding_dice_8: 0.12783/0.17040, loss_grounding_ce_8: 1.60782/0.42354, loss_mask_ce_9: 3.83444/3.48197, loss_mask_bce_9: 0.04692/0.36098, loss_mask_dice_9: 3.56027/1.76424, loss_spatial_bce_9: 0.01361/0.35561, loss_spatial_dice_9: 0.90720/0.79429, loss_spatial_ce_9: 2.15259/1.39546, loss_grounding_bce_9: 0.00572/0.10114, loss_grounding_dice_9: 0.31307/0.24311, loss_grounding_ce_9: 0.86465/0.68042] items per batch[64] items per second[0.37] total items[3116800] mini batches[ 48700] memory[4999] epoch remaining[0:18:28] INFO:trainer.default_trainer:epochs[ 26] optim steps[48800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.93594/0.76252, loss_mask_bce_0: 0.01805/0.30188, loss_mask_dice_0: 1.32100/1.02530, loss_spatial_bce_0: 0.00578/0.08614, loss_spatial_dice_0: 0.32471/0.18214, loss_spatial_ce_0: 0.18966/0.06000, loss_grounding_bce_0: 0.00356/0.08083, loss_grounding_dice_0: 0.17044/0.15102, loss_grounding_ce_0: 0.13981/0.24929, loss_mask_ce_1: 1.63742/0.76323, loss_mask_bce_1: 0.01930/0.30274, loss_mask_dice_1: 1.25709/1.02901, loss_spatial_bce_1: 0.00481/0.08640, loss_spatial_dice_1: 0.33155/0.18467, loss_spatial_ce_1: 0.10393/0.06413, loss_grounding_bce_1: 0.00359/0.08101, loss_grounding_dice_1: 0.20268/0.15176, loss_grounding_ce_1: 0.16090/0.25117, loss_mask_ce_2: 1.65606/0.77125, loss_mask_bce_2: 0.01628/0.30283, loss_mask_dice_2: 1.26516/1.03000, loss_spatial_bce_2: 0.00692/0.08637, loss_spatial_dice_2: 0.38805/0.18495, loss_spatial_ce_2: 0.02931/0.06648, loss_grounding_bce_2: 0.00615/0.08093, loss_grounding_dice_2: 0.22456/0.15163, loss_grounding_ce_2: 0.15385/0.25378, loss_mask_ce_3: 1.67814/0.77386, loss_mask_bce_3: 0.03393/0.30443, loss_mask_dice_3: 1.12487/1.02755, loss_spatial_bce_3: 0.00734/0.08839, loss_spatial_dice_3: 0.38253/0.18614, loss_spatial_ce_3: 0.05770/0.07099, loss_grounding_bce_3: 0.00344/0.08139, loss_grounding_dice_3: 0.21124/0.15121, loss_grounding_ce_3: 0.11885/0.25358, loss_mask_ce_4: 1.69704/0.78015, loss_mask_bce_4: 0.02000/0.30676, loss_mask_dice_4: 1.48831/1.04662, loss_spatial_bce_4: 0.00387/0.09035, loss_spatial_dice_4: 0.36322/0.19398, loss_spatial_ce_4: 0.06582/0.08378, loss_grounding_bce_4: 0.00379/0.08203, loss_grounding_dice_4: 0.06364/0.15383, loss_grounding_ce_4: 0.23066/0.25916, loss_mask_ce_5: 1.21992/0.80324, loss_mask_bce_5: 0.04167/0.30862, loss_mask_dice_5: 1.45232/1.05409, loss_spatial_bce_5: 0.00624/0.09237, loss_spatial_dice_5: 0.32426/0.19665, loss_spatial_ce_5: 0.40585/0.09597, loss_grounding_bce_5: 0.00031/0.08233, loss_grounding_dice_5: 0.04946/0.15451, loss_grounding_ce_5: 0.26941/0.27742, loss_mask_ce_6: 1.40572/0.82956, loss_mask_bce_6: 0.02694/0.31054, loss_mask_dice_6: 1.50221/1.05696, loss_spatial_bce_6: 0.00632/0.09740, loss_spatial_dice_6: 0.33399/0.19889, loss_spatial_ce_6: 0.05485/0.11971, loss_grounding_bce_6: 0.00130/0.08329, loss_grounding_dice_6: 0.10817/0.15510, loss_grounding_ce_6: 0.03460/0.28638, loss_mask_ce_7: 0.83891/0.88499, loss_mask_bce_7: 0.04356/0.31791, loss_mask_dice_7: 1.21597/1.10378, loss_spatial_bce_7: 0.00442/0.10758, loss_spatial_dice_7: 0.34157/0.22424, loss_spatial_ce_7: 0.53687/0.15834, loss_grounding_bce_7: 0.00123/0.08495, loss_grounding_dice_7: 0.14660/0.16088, loss_grounding_ce_7: 0.32285/0.32091, loss_mask_ce_8: 1.90808/1.02333, loss_mask_bce_8: 0.02378/0.33390, loss_mask_dice_8: 1.09440/1.18071, loss_spatial_bce_8: 0.00965/0.12569, loss_spatial_dice_8: 0.40257/0.26074, loss_spatial_ce_8: 0.26859/0.20942, loss_grounding_bce_8: 0.00132/0.08904, loss_grounding_dice_8: 0.11299/0.17039, loss_grounding_ce_8: 2.16020/0.42350, loss_mask_ce_9: 3.96810/3.48214, loss_mask_bce_9: 0.03259/0.36105, loss_mask_dice_9: 1.80403/1.76433, loss_spatial_bce_9: 0.01851/0.35564, loss_spatial_dice_9: 0.88804/0.79429, loss_spatial_ce_9: 2.18704/1.39531, loss_grounding_bce_9: 0.00049/0.10112, loss_grounding_dice_9: 0.09608/0.24312, loss_grounding_ce_9: 1.70769/0.68047] items per batch[64] items per second[0.37] total items[3123200] mini batches[ 48800] memory[4999] epoch remaining[0:15:30] INFO:trainer.default_trainer:epochs[ 26] optim steps[48900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26105/0.76235, loss_mask_bce_0: 0.30890/0.30185, loss_mask_dice_0: 0.41275/1.02561, loss_spatial_bce_0: 0.10286/0.08612, loss_spatial_dice_0: 0.14581/0.18213, loss_spatial_ce_0: 0.00544/0.05997, loss_grounding_bce_0: 0.09057/0.08081, loss_grounding_dice_0: 0.19725/0.15103, loss_grounding_ce_0: 0.06488/0.24919, loss_mask_ce_1: 0.33409/0.76307, loss_mask_bce_1: 0.30319/0.30272, loss_mask_dice_1: 0.40601/1.02947, loss_spatial_bce_1: 0.09366/0.08637, loss_spatial_dice_1: 0.14758/0.18466, loss_spatial_ce_1: 0.00373/0.06409, loss_grounding_bce_1: 0.09465/0.08099, loss_grounding_dice_1: 0.20021/0.15176, loss_grounding_ce_1: 0.07795/0.25111, loss_mask_ce_2: 0.26037/0.77108, loss_mask_bce_2: 0.29553/0.30281, loss_mask_dice_2: 0.38220/1.03039, loss_spatial_bce_2: 0.09655/0.08635, loss_spatial_dice_2: 0.15058/0.18494, loss_spatial_ce_2: 0.00517/0.06642, loss_grounding_bce_2: 0.09144/0.08091, loss_grounding_dice_2: 0.20128/0.15164, loss_grounding_ce_2: 0.05954/0.25375, loss_mask_ce_3: 0.30963/0.77374, loss_mask_bce_3: 0.30338/0.30441, loss_mask_dice_3: 0.39561/1.02796, loss_spatial_bce_3: 0.10106/0.08836, loss_spatial_dice_3: 0.14163/0.18613, loss_spatial_ce_3: 0.00416/0.07095, loss_grounding_bce_3: 0.09914/0.08137, loss_grounding_dice_3: 0.19676/0.15122, loss_grounding_ce_3: 0.07613/0.25355, loss_mask_ce_4: 0.33245/0.77997, loss_mask_bce_4: 0.29634/0.30673, loss_mask_dice_4: 0.40051/1.04704, loss_spatial_bce_4: 0.09957/0.09032, loss_spatial_dice_4: 0.14570/0.19397, loss_spatial_ce_4: 0.00924/0.08374, loss_grounding_bce_4: 0.09846/0.08200, loss_grounding_dice_4: 0.19354/0.15383, loss_grounding_ce_4: 0.10026/0.25909, loss_mask_ce_5: 0.29180/0.80307, loss_mask_bce_5: 0.29348/0.30860, loss_mask_dice_5: 0.40123/1.05447, loss_spatial_bce_5: 0.08718/0.09235, loss_spatial_dice_5: 0.14831/0.19664, loss_spatial_ce_5: 0.00534/0.09592, loss_grounding_bce_5: 0.09960/0.08231, loss_grounding_dice_5: 0.19839/0.15452, loss_grounding_ce_5: 0.08785/0.27735, loss_mask_ce_6: 0.68414/0.82939, loss_mask_bce_6: 0.26727/0.31052, loss_mask_dice_6: 0.39901/1.05733, loss_spatial_bce_6: 0.12228/0.09739, loss_spatial_dice_6: 0.14785/0.19889, loss_spatial_ce_6: 0.02240/0.11967, loss_grounding_bce_6: 0.09286/0.08328, loss_grounding_dice_6: 0.18708/0.15511, loss_grounding_ce_6: 0.08052/0.28633, loss_mask_ce_7: 0.53100/0.88489, loss_mask_bce_7: 0.29233/0.31789, loss_mask_dice_7: 0.42276/1.10419, loss_spatial_bce_7: 0.14042/0.10756, loss_spatial_dice_7: 0.17437/0.22424, loss_spatial_ce_7: 0.14405/0.15830, loss_grounding_bce_7: 0.09404/0.08495, loss_grounding_dice_7: 0.19761/0.16088, loss_grounding_ce_7: 0.14686/0.32089, loss_mask_ce_8: 0.79683/1.02323, loss_mask_bce_8: 0.30543/0.33387, loss_mask_dice_8: 0.41910/1.18123, loss_spatial_bce_8: 0.17419/0.12566, loss_spatial_dice_8: 0.17800/0.26073, loss_spatial_ce_8: 0.07213/0.20931, loss_grounding_bce_8: 0.09507/0.08904, loss_grounding_dice_8: 0.22264/0.17042, loss_grounding_ce_8: 0.16993/0.42335, loss_mask_ce_9: 2.71292/3.48198, loss_mask_bce_9: 0.28701/0.36100, loss_mask_dice_9: 0.51085/1.76487, loss_spatial_bce_9: 0.54660/0.35565, loss_spatial_dice_9: 0.83104/0.79430, loss_spatial_ce_9: 1.28711/1.39531, loss_grounding_bce_9: 0.12117/0.10109, loss_grounding_dice_9: 0.30169/0.24309, loss_grounding_ce_9: 0.25942/0.68031] items per batch[64] items per second[0.36] total items[3129600] mini batches[ 48900] memory[4999] epoch remaining[0:12:34] INFO:trainer.default_trainer:epochs[ 26] optim steps[49000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.70693/0.76251, loss_mask_bce_0: 0.59958/0.30187, loss_mask_dice_0: 0.67516/1.02569, loss_spatial_bce_0: 0.19923/0.08607, loss_spatial_dice_0: 0.21091/0.18209, loss_spatial_ce_0: 0.09139/0.05992, loss_grounding_bce_0: 0.01344/0.08078, loss_grounding_dice_0: 0.12532/0.15102, loss_grounding_ce_0: 0.84501/0.24911, loss_mask_ce_1: 1.28035/0.76316, loss_mask_bce_1: 0.59309/0.30276, loss_mask_dice_1: 0.86101/1.02956, loss_spatial_bce_1: 0.16226/0.08633, loss_spatial_dice_1: 0.18256/0.18463, loss_spatial_ce_1: 0.10879/0.06405, loss_grounding_bce_1: 0.00989/0.08096, loss_grounding_dice_1: 0.11854/0.15176, loss_grounding_ce_1: 0.77236/0.25101, loss_mask_ce_2: 1.57669/0.77120, loss_mask_bce_2: 0.61802/0.30284, loss_mask_dice_2: 0.86664/1.03051, loss_spatial_bce_2: 0.16723/0.08631, loss_spatial_dice_2: 0.20009/0.18491, loss_spatial_ce_2: 0.10961/0.06639, loss_grounding_bce_2: 0.00944/0.08089, loss_grounding_dice_2: 0.13186/0.15163, loss_grounding_ce_2: 0.80019/0.25364, loss_mask_ce_3: 1.21874/0.77387, loss_mask_bce_3: 0.63148/0.30444, loss_mask_dice_3: 0.67269/1.02808, loss_spatial_bce_3: 0.17532/0.08832, loss_spatial_dice_3: 0.19269/0.18609, loss_spatial_ce_3: 0.10823/0.07092, loss_grounding_bce_3: 0.01176/0.08134, loss_grounding_dice_3: 0.15965/0.15122, loss_grounding_ce_3: 0.76984/0.25345, loss_mask_ce_4: 1.29922/0.78012, loss_mask_bce_4: 0.60759/0.30675, loss_mask_dice_4: 0.70955/1.04711, loss_spatial_bce_4: 0.15934/0.09028, loss_spatial_dice_4: 0.17738/0.19394, loss_spatial_ce_4: 0.09179/0.08372, loss_grounding_bce_4: 0.01549/0.08198, loss_grounding_dice_4: 0.13872/0.15383, loss_grounding_ce_4: 0.59593/0.25896, loss_mask_ce_5: 1.59391/0.80330, loss_mask_bce_5: 0.59969/0.30863, loss_mask_dice_5: 0.67064/1.05460, loss_spatial_bce_5: 0.15968/0.09232, loss_spatial_dice_5: 0.15828/0.19661, loss_spatial_ce_5: 0.09222/0.09590, loss_grounding_bce_5: 0.03050/0.08229, loss_grounding_dice_5: 0.13468/0.15452, loss_grounding_ce_5: 0.86053/0.27726, loss_mask_ce_6: 1.48800/0.82959, loss_mask_bce_6: 0.61678/0.31054, loss_mask_dice_6: 0.63302/1.05742, loss_spatial_bce_6: 0.14401/0.09735, loss_spatial_dice_6: 0.16035/0.19886, loss_spatial_ce_6: 0.13328/0.11966, loss_grounding_bce_6: 0.01155/0.08326, loss_grounding_dice_6: 0.13024/0.15510, loss_grounding_ce_6: 0.61049/0.28623, loss_mask_ce_7: 1.40546/0.88507, loss_mask_bce_7: 0.62362/0.31792, loss_mask_dice_7: 0.71974/1.10428, loss_spatial_bce_7: 0.18501/0.10751, loss_spatial_dice_7: 0.18357/0.22423, loss_spatial_ce_7: 0.17465/0.15830, loss_grounding_bce_7: 0.02995/0.08492, loss_grounding_dice_7: 0.15556/0.16087, loss_grounding_ce_7: 1.01313/0.32086, loss_mask_ce_8: 1.23224/1.02337, loss_mask_bce_8: 0.61948/0.33390, loss_mask_dice_8: 0.77456/1.18133, loss_spatial_bce_8: 0.19801/0.12563, loss_spatial_dice_8: 0.19151/0.26070, loss_spatial_ce_8: 0.39995/0.20925, loss_grounding_bce_8: 0.07822/0.08902, loss_grounding_dice_8: 0.47691/0.17045, loss_grounding_ce_8: 0.05731/0.42350, loss_mask_ce_9: 4.38167/3.48248, loss_mask_bce_9: 0.66874/0.36103, loss_mask_dice_9: 1.14281/1.76505, loss_spatial_bce_9: 0.44595/0.35557, loss_spatial_dice_9: 0.77036/0.79433, loss_spatial_ce_9: 1.16520/1.39541, loss_grounding_bce_9: 0.12395/0.10105, loss_grounding_dice_9: 0.75128/0.24314, loss_grounding_ce_9: 0.03218/0.68071] items per batch[64] items per second[0.37] total items[3136000] mini batches[ 49000] memory[4999] epoch remaining[0:09:38] INFO:trainer.default_trainer:epochs[ 26] optim steps[49100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23751/0.76266, loss_mask_bce_0: 0.24284/0.30185, loss_mask_dice_0: 1.18726/1.02571, loss_spatial_bce_0: 0.07360/0.08606, loss_spatial_dice_0: 0.30163/0.18209, loss_spatial_ce_0: 0.07817/0.05991, loss_grounding_bce_0: 0.05508/0.08078, loss_grounding_dice_0: 0.08532/0.15103, loss_grounding_ce_0: 0.00220/0.24917, loss_mask_ce_1: 1.21215/0.76326, loss_mask_bce_1: 0.24231/0.30274, loss_mask_dice_1: 1.13202/1.02950, loss_spatial_bce_1: 0.06935/0.08632, loss_spatial_dice_1: 0.32141/0.18463, loss_spatial_ce_1: 0.07794/0.06402, loss_grounding_bce_1: 0.06310/0.08096, loss_grounding_dice_1: 0.08580/0.15178, loss_grounding_ce_1: 0.00459/0.25098, loss_mask_ce_2: 1.20443/0.77130, loss_mask_bce_2: 0.25606/0.30282, loss_mask_dice_2: 1.21123/1.03044, loss_spatial_bce_2: 0.05589/0.08630, loss_spatial_dice_2: 0.30852/0.18490, loss_spatial_ce_2: 0.08957/0.06635, loss_grounding_bce_2: 0.06374/0.08089, loss_grounding_dice_2: 0.08438/0.15165, loss_grounding_ce_2: 0.00874/0.25357, loss_mask_ce_3: 1.19976/0.77397, loss_mask_bce_3: 0.23131/0.30442, loss_mask_dice_3: 1.13418/1.02800, loss_spatial_bce_3: 0.08265/0.08831, loss_spatial_dice_3: 0.33124/0.18609, loss_spatial_ce_3: 0.05496/0.07089, loss_grounding_bce_3: 0.06717/0.08134, loss_grounding_dice_3: 0.08674/0.15123, loss_grounding_ce_3: 0.00462/0.25354, loss_mask_ce_4: 1.14356/0.78017, loss_mask_bce_4: 0.24453/0.30674, loss_mask_dice_4: 1.18945/1.04708, loss_spatial_bce_4: 0.08106/0.09027, loss_spatial_dice_4: 0.32856/0.19393, loss_spatial_ce_4: 0.09914/0.08369, loss_grounding_bce_4: 0.06562/0.08198, loss_grounding_dice_4: 0.08376/0.15385, loss_grounding_ce_4: 0.00503/0.25893, loss_mask_ce_5: 1.08360/0.80339, loss_mask_bce_5: 0.24925/0.30862, loss_mask_dice_5: 1.21995/1.05462, loss_spatial_bce_5: 0.06028/0.09230, loss_spatial_dice_5: 0.30654/0.19660, loss_spatial_ce_5: 0.09310/0.09586, loss_grounding_bce_5: 0.07293/0.08230, loss_grounding_dice_5: 0.08542/0.15453, loss_grounding_ce_5: 0.02867/0.27726, loss_mask_ce_6: 1.12536/0.82977, loss_mask_bce_6: 0.23770/0.31054, loss_mask_dice_6: 1.16508/1.05745, loss_spatial_bce_6: 0.07017/0.09734, loss_spatial_dice_6: 0.29478/0.19885, loss_spatial_ce_6: 0.13790/0.11966, loss_grounding_bce_6: 0.06812/0.08326, loss_grounding_dice_6: 0.08160/0.15512, loss_grounding_ce_6: 0.01278/0.28618, loss_mask_ce_7: 1.22043/0.88523, loss_mask_bce_7: 0.24011/0.31792, loss_mask_dice_7: 1.19813/1.10429, loss_spatial_bce_7: 0.14669/0.10750, loss_spatial_dice_7: 0.39088/0.22422, loss_spatial_ce_7: 0.13666/0.15828, loss_grounding_bce_7: 0.08350/0.08492, loss_grounding_dice_7: 0.08782/0.16089, loss_grounding_ce_7: 0.01176/0.32083, loss_mask_ce_8: 1.43312/1.02348, loss_mask_bce_8: 0.25835/0.33392, loss_mask_dice_8: 1.30147/1.18131, loss_spatial_bce_8: 0.10644/0.12560, loss_spatial_dice_8: 0.36061/0.26068, loss_spatial_ce_8: 0.24452/0.20919, loss_grounding_bce_8: 0.07509/0.08904, loss_grounding_dice_8: 0.08574/0.17046, loss_grounding_ce_8: 0.40112/0.42345, loss_mask_ce_9: 2.90060/3.48285, loss_mask_bce_9: 0.28934/0.36103, loss_mask_dice_9: 1.93291/1.76514, loss_spatial_bce_9: 0.27078/0.35563, loss_spatial_dice_9: 0.83402/0.79432, loss_spatial_ce_9: 1.31253/1.39553, loss_grounding_bce_9: 0.08149/0.10105, loss_grounding_dice_9: 0.12164/0.24314, loss_grounding_ce_9: 0.94673/0.68067] items per batch[64] items per second[0.36] total items[3142400] mini batches[ 49100] memory[4999] epoch remaining[0:06:43] INFO:trainer.default_trainer:epochs[ 26] optim steps[49200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27819/0.76254, loss_mask_bce_0: 0.35623/0.30189, loss_mask_dice_0: 0.30291/1.02530, loss_spatial_bce_0: 0.15626/0.08608, loss_spatial_dice_0: 0.22386/0.18206, loss_spatial_ce_0: 0.01339/0.05987, loss_grounding_bce_0: 0.22847/0.08080, loss_grounding_dice_0: 0.17735/0.15101, loss_grounding_ce_0: 0.03340/0.24915, loss_mask_ce_1: 0.28964/0.76318, loss_mask_bce_1: 0.37943/0.30278, loss_mask_dice_1: 0.30288/1.02913, loss_spatial_bce_1: 0.14847/0.08633, loss_spatial_dice_1: 0.22998/0.18458, loss_spatial_ce_1: 0.03829/0.06400, loss_grounding_bce_1: 0.25587/0.08099, loss_grounding_dice_1: 0.17876/0.15177, loss_grounding_ce_1: 0.03828/0.25097, loss_mask_ce_2: 0.31530/0.77127, loss_mask_bce_2: 0.36134/0.30286, loss_mask_dice_2: 0.31299/1.03005, loss_spatial_bce_2: 0.16622/0.08632, loss_spatial_dice_2: 0.25249/0.18486, loss_spatial_ce_2: 0.01669/0.06632, loss_grounding_bce_2: 0.24283/0.08092, loss_grounding_dice_2: 0.17963/0.15164, loss_grounding_ce_2: 0.04735/0.25361, loss_mask_ce_3: 0.29401/0.77399, loss_mask_bce_3: 0.37831/0.30446, loss_mask_dice_3: 0.30759/1.02758, loss_spatial_bce_3: 0.13650/0.08832, loss_spatial_dice_3: 0.20545/0.18605, loss_spatial_ce_3: 0.05477/0.07086, loss_grounding_bce_3: 0.24681/0.08137, loss_grounding_dice_3: 0.17559/0.15122, loss_grounding_ce_3: 0.03296/0.25358, loss_mask_ce_4: 0.32889/0.78015, loss_mask_bce_4: 0.37401/0.30677, loss_mask_dice_4: 0.30533/1.04665, loss_spatial_bce_4: 0.15125/0.09029, loss_spatial_dice_4: 0.20357/0.19389, loss_spatial_ce_4: 0.02031/0.08367, loss_grounding_bce_4: 0.24055/0.08199, loss_grounding_dice_4: 0.17711/0.15383, loss_grounding_ce_4: 0.02990/0.25895, loss_mask_ce_5: 0.34482/0.80329, loss_mask_bce_5: 0.33905/0.30865, loss_mask_dice_5: 0.29664/1.05421, loss_spatial_bce_5: 0.13764/0.09232, loss_spatial_dice_5: 0.18622/0.19657, loss_spatial_ce_5: 0.03505/0.09583, loss_grounding_bce_5: 0.22048/0.08230, loss_grounding_dice_5: 0.18475/0.15451, loss_grounding_ce_5: 0.06096/0.27730, loss_mask_ce_6: 0.36289/0.82970, loss_mask_bce_6: 0.32593/0.31057, loss_mask_dice_6: 0.34476/1.05705, loss_spatial_bce_6: 0.14879/0.09737, loss_spatial_dice_6: 0.17865/0.19882, loss_spatial_ce_6: 0.04986/0.11965, loss_grounding_bce_6: 0.20732/0.08327, loss_grounding_dice_6: 0.20659/0.15511, loss_grounding_ce_6: 0.03320/0.28621, loss_mask_ce_7: 0.35026/0.88510, loss_mask_bce_7: 0.32871/0.31794, loss_mask_dice_7: 0.30635/1.10384, loss_spatial_bce_7: 0.21605/0.10753, loss_spatial_dice_7: 0.39847/0.22419, loss_spatial_ce_7: 0.08630/0.15823, loss_grounding_bce_7: 0.20331/0.08492, loss_grounding_dice_7: 0.17425/0.16087, loss_grounding_ce_7: 0.07792/0.32088, loss_mask_ce_8: 0.68624/1.02330, loss_mask_bce_8: 0.29352/0.33395, loss_mask_dice_8: 0.31924/1.18087, loss_spatial_bce_8: 0.25639/0.12564, loss_spatial_dice_8: 0.38850/0.26064, loss_spatial_ce_8: 0.27720/0.20911, loss_grounding_bce_8: 0.19888/0.08905, loss_grounding_dice_8: 0.18201/0.17044, loss_grounding_ce_8: 0.24377/0.42338, loss_mask_ce_9: 3.09552/3.48229, loss_mask_bce_9: 0.34152/0.36109, loss_mask_dice_9: 0.57250/1.76461, loss_spatial_bce_9: 0.26953/0.35568, loss_spatial_dice_9: 0.66119/0.79427, loss_spatial_ce_9: 1.08681/1.39549, loss_grounding_bce_9: 0.23004/0.10108, loss_grounding_dice_9: 0.35890/0.24310, loss_grounding_ce_9: 0.48287/0.68050] items per batch[64] items per second[0.37] total items[3148800] mini batches[ 49200] memory[4999] epoch remaining[0:03:46] INFO:trainer.default_trainer:epochs[ 26] optim steps[49300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.69827/0.76227, loss_mask_bce_0: 0.27851/0.30185, loss_mask_dice_0: 0.58594/1.02487, loss_spatial_bce_0: 0.04535/0.08612, loss_spatial_dice_0: 0.11811/0.18207, loss_spatial_ce_0: 0.00007/0.05991, loss_grounding_bce_0: 0.13512/0.08083, loss_grounding_dice_0: 0.07916/0.15103, loss_grounding_ce_0: 0.01720/0.24915, loss_mask_ce_1: 0.68650/0.76295, loss_mask_bce_1: 0.26747/0.30274, loss_mask_dice_1: 0.49312/1.02873, loss_spatial_bce_1: 0.04294/0.08638, loss_spatial_dice_1: 0.11520/0.18460, loss_spatial_ce_1: 0.00006/0.06399, loss_grounding_bce_1: 0.13259/0.08102, loss_grounding_dice_1: 0.08158/0.15179, loss_grounding_ce_1: 0.01185/0.25097, loss_mask_ce_2: 0.64617/0.77100, loss_mask_bce_2: 0.27295/0.30281, loss_mask_dice_2: 0.50685/1.02967, loss_spatial_bce_2: 0.03784/0.08636, loss_spatial_dice_2: 0.08078/0.18488, loss_spatial_ce_2: 0.00008/0.06632, loss_grounding_bce_2: 0.13397/0.08095, loss_grounding_dice_2: 0.07454/0.15165, loss_grounding_ce_2: 0.01664/0.25372, loss_mask_ce_3: 0.73010/0.77379, loss_mask_bce_3: 0.28709/0.30441, loss_mask_dice_3: 0.51805/1.02719, loss_spatial_bce_3: 0.04102/0.08836, loss_spatial_dice_3: 0.09650/0.18606, loss_spatial_ce_3: 0.00207/0.07086, loss_grounding_bce_3: 0.14779/0.08140, loss_grounding_dice_3: 0.07564/0.15124, loss_grounding_ce_3: 0.01514/0.25362, loss_mask_ce_4: 0.70627/0.77987, loss_mask_bce_4: 0.29259/0.30672, loss_mask_dice_4: 0.91356/1.04627, loss_spatial_bce_4: 0.03881/0.09033, loss_spatial_dice_4: 0.09899/0.19391, loss_spatial_ce_4: 0.00475/0.08366, loss_grounding_bce_4: 0.14845/0.08202, loss_grounding_dice_4: 0.08217/0.15384, loss_grounding_ce_4: 0.01256/0.25894, loss_mask_ce_5: 0.66584/0.80302, loss_mask_bce_5: 0.29172/0.30859, loss_mask_dice_5: 0.53570/1.05376, loss_spatial_bce_5: 0.03754/0.09237, loss_spatial_dice_5: 0.09461/0.19659, loss_spatial_ce_5: 0.01353/0.09583, loss_grounding_bce_5: 0.14105/0.08234, loss_grounding_dice_5: 0.08155/0.15454, loss_grounding_ce_5: 0.00791/0.27724, loss_mask_ce_6: 0.76800/0.82946, loss_mask_bce_6: 0.30027/0.31051, loss_mask_dice_6: 0.57570/1.05657, loss_spatial_bce_6: 0.04330/0.09742, loss_spatial_dice_6: 0.08132/0.19885, loss_spatial_ce_6: 0.03108/0.11968, loss_grounding_bce_6: 0.14588/0.08331, loss_grounding_dice_6: 0.09181/0.15513, loss_grounding_ce_6: 0.00667/0.28611, loss_mask_ce_7: 0.87791/0.88488, loss_mask_bce_7: 0.29071/0.31787, loss_mask_dice_7: 0.57805/1.10334, loss_spatial_bce_7: 0.04280/0.10758, loss_spatial_dice_7: 0.09202/0.22423, loss_spatial_ce_7: 0.01417/0.15827, loss_grounding_bce_7: 0.14761/0.08495, loss_grounding_dice_7: 0.09865/0.16089, loss_grounding_ce_7: 0.01465/0.32076, loss_mask_ce_8: 0.76111/1.02303, loss_mask_bce_8: 0.30548/0.33388, loss_mask_dice_8: 0.57983/1.18031, loss_spatial_bce_8: 0.04620/0.12568, loss_spatial_dice_8: 0.11502/0.26066, loss_spatial_ce_8: 0.04439/0.20903, loss_grounding_bce_8: 0.17004/0.08907, loss_grounding_dice_8: 0.09826/0.17044, loss_grounding_ce_8: 0.03899/0.42329, loss_mask_ce_9: 3.66941/3.48169, loss_mask_bce_9: 0.30458/0.36100, loss_mask_dice_9: 2.12711/1.76363, loss_spatial_bce_9: 0.30227/0.35572, loss_spatial_dice_9: 0.92988/0.79424, loss_spatial_ce_9: 2.15733/1.39546, loss_grounding_bce_9: 0.16325/0.10109, loss_grounding_dice_9: 0.07684/0.24310, loss_grounding_ce_9: 0.06543/0.68044] items per batch[64] items per second[0.36] total items[3155200] mini batches[ 49300] memory[4999] epoch remaining[0:00:51] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00049329. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0024 s/iter. Inference: 0.3752 s/iter. Eval: 0.0874 s/iter. Total: 0.4650 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3786 s/iter. Eval: 0.0763 s/iter. Total: 0.4575 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3825 s/iter. Eval: 0.0771 s/iter. Total: 0.4624 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3868 s/iter. Eval: 0.0729 s/iter. Total: 0.4625 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0027 s/iter. Inference: 0.3869 s/iter. Eval: 0.0715 s/iter. Total: 0.4612 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0028 s/iter. Inference: 0.3857 s/iter. Eval: 0.0692 s/iter. Total: 0.4579 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval8sz_m8zv ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.528 | 82.976 | 66.079 | 133 | | Things | 61.502 | 83.937 | 72.710 | 80 | | Stuff | 46.510 | 81.526 | 56.070 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.49s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.09 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.35 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.41s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 21.17 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.47 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.562 | 69.124 | 49.076 | 25.555 | 49.772 | 67.646 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.837 | bicycle | 22.385 | car | 43.375 | | motorcycle | 41.626 | airplane | 61.804 | bus | 71.933 | | train | 74.552 | truck | 45.737 | boat | 30.899 | | traffic light | 27.445 | fire hydrant | 71.449 | stop sign | 68.550 | | parking meter | 52.447 | bench | 27.192 | bird | 34.025 | | cat | 76.437 | dog | 70.911 | horse | 51.115 | | sheep | 54.203 | cow | 56.412 | elephant | 65.987 | | bear | 79.690 | zebra | 65.639 | giraffe | 62.476 | | backpack | 22.887 | umbrella | 55.516 | handbag | 23.297 | | tie | 40.062 | suitcase | 51.941 | frisbee | 69.316 | | skis | 8.451 | snowboard | 34.001 | sports ball | 49.815 | | kite | 37.891 | baseball bat | 37.377 | baseball glove | 49.999 | | skateboard | 44.277 | surfboard | 44.168 | tennis racket | 62.650 | | bottle | 41.624 | wine glass | 37.297 | cup | 51.618 | | fork | 24.163 | knife | 24.578 | spoon | 22.377 | | bowl | 39.877 | banana | 23.198 | apple | 25.354 | | sandwich | 49.069 | orange | 30.710 | broccoli | 24.449 | | carrot | 22.634 | hot dog | 31.961 | pizza | 54.878 | | donut | 56.650 | cake | 48.125 | chair | 29.100 | | couch | 43.021 | potted plant | 23.665 | bed | 42.463 | | dining table | 15.465 | toilet | 69.850 | tv | 66.323 | | laptop | 70.777 | mouse | 64.650 | remote | 43.610 | | keyboard | 59.031 | cell phone | 45.354 | microwave | 67.099 | | oven | 33.627 | toaster | 50.980 | sink | 45.256 | | refrigerator | 70.158 | book | 14.105 | clock | 54.265 | | vase | 40.711 | scissors | 32.758 | teddy bear | 57.707 | | hair drier | 31.888 | toothbrush | 27.789 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.350 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.372 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.31987421500376, 'fwIoU': 71.25072308225482, 'IoU-person': 88.0947234911813, 'IoU-bicycle': 74.55023075574942, 'IoU-car': 74.06487562940556, 'IoU-motorcycle': 86.24873259959227, 'IoU-airplane': 84.66507754276759, 'IoU-bus': 86.85482625219223, 'IoU-train': 87.6438164066682, 'IoU-truck': 73.27549157305168, 'IoU-boat': 68.7813896367361, 'IoU-traffic light': 79.8728689436991, 'IoU-fire hydrant': 92.81565120824403, 'IoU-stop sign': 84.94278681393834, 'IoU-parking meter': 88.81108827515163, 'IoU-bench': 63.11530765469673, 'IoU-bird': 78.5316198549234, 'IoU-cat': 87.52887682003376, 'IoU-dog': 86.49369369654309, 'IoU-horse': 88.59040570855404, 'IoU-sheep': 80.64527662191269, 'IoU-cow': 85.14152471014962, 'IoU-elephant': 92.21533234364144, 'IoU-bear': 85.86943302845226, 'IoU-zebra': 82.38919090006196, 'IoU-giraffe': 88.29521096145875, 'IoU-backpack': 52.20626845942199, 'IoU-umbrella': 85.65350932551652, 'IoU-handbag': 51.72480949778044, 'IoU-tie': 65.44474132973767, 'IoU-suitcase': 84.20420705590693, 'IoU-frisbee': 84.27291980467619, 'IoU-skis': 61.35078293205353, 'IoU-snowboard': 75.2343715288577, 'IoU-sports ball': 77.64494540649873, 'IoU-kite': 79.52106504416751, 'IoU-baseball bat': 69.46969719352256, 'IoU-baseball glove': 80.54741328881173, 'IoU-skateboard': 86.18705220389383, 'IoU-surfboard': 86.5766208842159, 'IoU-tennis racket': 91.1424021164942, 'IoU-bottle': 69.4742852253095, 'IoU-wine glass': 82.62863178918607, 'IoU-cup': 69.75928563917587, 'IoU-fork': 70.50994386506117, 'IoU-knife': 62.688729448822514, 'IoU-spoon': 60.24484911213895, 'IoU-bowl': 56.03929439444967, 'IoU-banana': 82.51358856536072, 'IoU-apple': 55.46603945968451, 'IoU-sandwich': 71.08226538026788, 'IoU-orange': 76.96213969650715, 'IoU-broccoli': 67.24570024741116, 'IoU-carrot': 64.70127423031153, 'IoU-hot dog': 62.89931782594604, 'IoU-pizza': 86.91854629013727, 'IoU-donut': 64.7231581590856, 'IoU-cake': 79.8560021457736, 'IoU-chair': 64.70964495700458, 'IoU-couch': 71.85706991193896, 'IoU-potted plant': 45.98090661354747, 'IoU-bed': 70.97035706676125, 'IoU-dining table': 54.437847651553085, 'IoU-toilet': 85.48002337598649, 'IoU-tv': 74.51345870818145, 'IoU-laptop': 75.46920208418638, 'IoU-mouse': 78.763504970744, 'IoU-remote': 67.28994102286374, 'IoU-keyboard': 60.13322899276224, 'IoU-cell phone': 76.53296735316559, 'IoU-microwave': 76.50244411574889, 'IoU-oven': 71.96074794905064, 'IoU-toaster': 85.0906343443657, 'IoU-sink': 67.98841637569689, 'IoU-refrigerator': 81.53910256595135, 'IoU-book': 54.9617892147486, 'IoU-clock': 71.59176090543157, 'IoU-vase': 64.14200676664828, 'IoU-scissors': 85.0904429106173, 'IoU-teddy bear': 84.2613003122932, 'IoU-hair drier': 41.546308305945914, 'IoU-toothbrush': 75.99777389092066, 'IoU-banner': 29.489290058011193, 'IoU-blanket': 16.96104713887073, 'IoU-bridge': 35.6937571415971, 'IoU-cardboard': 55.93018601613685, 'IoU-counter': 31.95393214316125, 'IoU-curtain': 70.52900961749843, 'IoU-door-stuff': 48.71773779471103, 'IoU-floor-wood': 61.62264036411731, 'IoU-flower': 49.76251083454488, 'IoU-fruit': 45.55038413880476, 'IoU-gravel': 33.233144713243526, 'IoU-house': 27.983149950006226, 'IoU-light': 44.28104496822355, 'IoU-mirror-stuff': 61.81683003801083, 'IoU-net': 43.72417784706572, 'IoU-pillow': 25.600835388951342, 'IoU-platform': 30.227447711565453, 'IoU-playingfield': 71.09536188271306, 'IoU-railroad': 64.11962856942223, 'IoU-river': 52.698296766694256, 'IoU-road': 66.93535933852655, 'IoU-roof': 17.588020018250063, 'IoU-sand': 63.29799298570352, 'IoU-sea': 84.30702796449177, 'IoU-shelf': 39.780271802941, 'IoU-snow': 92.20686021068151, 'IoU-stairs': 33.446393495605406, 'IoU-tent': 10.557817668184668, 'IoU-towel': 43.39563373645359, 'IoU-wall-brick': 52.8444815208482, 'IoU-wall-stone': 29.881242817792675, 'IoU-wall-tile': 69.14718419422744, 'IoU-wall-wood': 45.65745284098069, 'IoU-water-other': 19.422932296027053, 'IoU-window-blind': 50.05056323072359, 'IoU-window-other': 48.74269003747715, 'IoU-tree-merged': 82.03116509088889, 'IoU-fence-merged': 54.23589375024165, 'IoU-ceiling-merged': 67.24107975500914, 'IoU-sky-other-merged': 93.65019568518127, 'IoU-cabinet-merged': 63.22796113524669, 'IoU-table-merged': 38.26326191934418, 'IoU-floor-other-merged': 54.65747654944345, 'IoU-pavement-merged': 59.032179364662326, 'IoU-mountain-merged': 59.00541622799537, 'IoU-grass-merged': 71.78424751541293, 'IoU-dirt-merged': 45.71156470325877, 'IoU-paper-merged': 38.78151828940923, 'IoU-food-other-merged': 43.37146966113871, 'IoU-building-other-merged': 58.94917341090069, 'IoU-rock-merged': 64.03944893498043, 'IoU-wall-other-merged': 67.22925599617668, 'IoU-rug-merged': 66.93745602284208, 'mACC': 77.04825772459998, 'pACC': 81.97207850164115, 'ACC-person': 92.67658913170655, 'ACC-bicycle': 82.81441711155912, 'ACC-car': 84.07963818477087, 'ACC-motorcycle': 90.7511881929941, 'ACC-airplane': 91.09595739926894, 'ACC-bus': 93.47719068652842, 'ACC-train': 96.21381306808922, 'ACC-truck': 83.07244136006545, 'ACC-boat': 76.63187745861617, 'ACC-traffic light': 91.18646456515171, 'ACC-fire hydrant': 96.09217206762118, 'ACC-stop sign': 88.46893877051332, 'ACC-parking meter': 92.06730800384129, 'ACC-bench': 74.6720799885787, 'ACC-bird': 82.14588362179653, 'ACC-cat': 93.41384258261093, 'ACC-dog': 89.04262095360068, 'ACC-horse': 93.63333049950543, 'ACC-sheep': 84.60463503804127, 'ACC-cow': 90.11489282520635, 'ACC-elephant': 94.46509028390345, 'ACC-bear': 87.69329763224808, 'ACC-zebra': 84.42073980489135, 'ACC-giraffe': 92.02714542124826, 'ACC-backpack': 69.05362354405308, 'ACC-umbrella': 89.30175457575177, 'ACC-handbag': 71.46957674567965, 'ACC-tie': 72.21651648653712, 'ACC-suitcase': 90.11167274517976, 'ACC-frisbee': 94.19781818181818, 'ACC-skis': 75.43756835674641, 'ACC-snowboard': 82.94188239327963, 'ACC-sports ball': 88.68680290673035, 'ACC-kite': 86.0571005716098, 'ACC-baseball bat': 88.08487364107022, 'ACC-baseball glove': 91.77348810733304, 'ACC-skateboard': 90.54764330695585, 'ACC-surfboard': 92.40926232952695, 'ACC-tennis racket': 94.62086342851693, 'ACC-bottle': 82.67824203561311, 'ACC-wine glass': 90.91666515566355, 'ACC-cup': 89.50467775394577, 'ACC-fork': 81.128712350876, 'ACC-knife': 80.0619183703378, 'ACC-spoon': 76.55622551860483, 'ACC-bowl': 65.35890713702767, 'ACC-banana': 89.26752507438913, 'ACC-apple': 69.46638688173172, 'ACC-sandwich': 82.73227143781855, 'ACC-orange': 87.69712753703296, 'ACC-broccoli': 76.21630303228808, 'ACC-carrot': 77.1140766701646, 'ACC-hot dog': 70.19411362259727, 'ACC-pizza': 95.49117975381614, 'ACC-donut': 74.40077819097839, 'ACC-cake': 87.40520053204062, 'ACC-chair': 81.07349300887509, 'ACC-couch': 78.3106183981995, 'ACC-potted plant': 57.57848628939266, 'ACC-bed': 79.76540280707187, 'ACC-dining table': 73.57377483418978, 'ACC-toilet': 90.76122790991573, 'ACC-tv': 86.83010842775847, 'ACC-laptop': 86.97440112654633, 'ACC-mouse': 86.96975927909989, 'ACC-remote': 71.205093540483, 'ACC-keyboard': 65.68648469365768, 'ACC-cell phone': 86.53443023543281, 'ACC-microwave': 84.66331309686338, 'ACC-oven': 92.44856355330326, 'ACC-toaster': 90.9359646361658, 'ACC-sink': 77.45475855927697, 'ACC-refrigerator': 92.0352658805038, 'ACC-book': 68.93239892409852, 'ACC-clock': 75.86340977996475, 'ACC-vase': 72.55478391324208, 'ACC-scissors': 90.2731867889706, 'ACC-teddy bear': 89.70734979074344, 'ACC-hair drier': 60.62960749153218, 'ACC-toothbrush': 83.03509381514941, 'ACC-banner': 78.1578527727301, 'ACC-blanket': 27.978566840397768, 'ACC-bridge': 56.77974817914936, 'ACC-cardboard': 72.57970045077795, 'ACC-counter': 57.852793119471755, 'ACC-curtain': 83.70768576453389, 'ACC-door-stuff': 69.03993586794479, 'ACC-floor-wood': 77.37542526996609, 'ACC-flower': 73.8823127731738, 'ACC-fruit': 68.2435827094642, 'ACC-gravel': 50.72841279469553, 'ACC-house': 36.25538882892947, 'ACC-light': 60.76685296482027, 'ACC-mirror-stuff': 79.10503919591395, 'ACC-net': 67.49689060930756, 'ACC-pillow': 50.74222596510495, 'ACC-platform': 50.5196927738491, 'ACC-playingfield': 90.58345395020177, 'ACC-railroad': 81.1552775579781, 'ACC-river': 79.0931311045891, 'ACC-road': 83.3027460529555, 'ACC-roof': 22.95562427931727, 'ACC-sand': 68.10536427721262, 'ACC-sea': 91.7365303608643, 'ACC-shelf': 59.852187546173155, 'ACC-snow': 95.65831668383555, 'ACC-stairs': 57.5853538103994, 'ACC-tent': 12.292983175178056, 'ACC-towel': 55.11744284917381, 'ACC-wall-brick': 70.79870081809905, 'ACC-wall-stone': 36.72783017287991, 'ACC-wall-tile': 86.37851479669493, 'ACC-wall-wood': 65.1516047228529, 'ACC-water-other': 26.200469940835593, 'ACC-window-blind': 66.26279987189805, 'ACC-window-other': 74.07501140494381, 'ACC-tree-merged': 89.55351361008762, 'ACC-fence-merged': 69.64843192328982, 'ACC-ceiling-merged': 83.25537581805573, 'ACC-sky-other-merged': 96.69102265982168, 'ACC-cabinet-merged': 75.79109866017882, 'ACC-table-merged': 53.7786703940513, 'ACC-floor-other-merged': 66.71961565255427, 'ACC-pavement-merged': 73.9025150150285, 'ACC-mountain-merged': 71.03677457880795, 'ACC-grass-merged': 83.5228157922774, 'ACC-dirt-merged': 65.76368643228383, 'ACC-paper-merged': 53.92746563770778, 'ACC-food-other-merged': 64.02551726644785, 'ACC-building-other-merged': 73.47012542493954, 'ACC-rock-merged': 79.07246396101795, 'ACC-wall-other-merged': 80.99320558832585, 'ACC-rug-merged': 80.26123889209869})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2905 s/iter. Inference: 0.1788 s/iter. Eval: 0.0000 s/iter. Total: 0.4693 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3406 s/iter. Inference: 0.3414 s/iter. Eval: 0.0000 s/iter. Total: 0.6820 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3480 s/iter. Inference: 0.5275 s/iter. Eval: 0.0000 s/iter. Total: 0.8757 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.405326309628329, 'noc@0.8': 2.47585601404741, 'noc@0.85': 2.896985659935616, 'noc@0.9': 3.690956979806848, 'miou@iter1': 0.8713250722597662} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0017 s/iter. Inference: 0.1442 s/iter. Eval: 0.0010 s/iter. Total: 0.1469 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.12631225585938, 'precision@0.6': 72.13369750976562, 'precision@0.7': 68.13058471679688, 'precision@0.8': 59.23046875, 'precision@0.9': 32.72444534301758, 'cIoU': 61.671852111816406, 'mIoU': 66.52096557617188} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.52772544120589, 'SQ': 82.97603455270765, 'RQ': 66.07898570984071, 'PQ_th': 61.50196431313204, 'SQ_th': 83.93685623350947, 'RQ_th': 72.71020021149171, 'PQ_st': 46.51000638924195, 'SQ_st': 81.52573767602567, 'RQ_st': 56.06960532999015}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 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'ACC-table-merged': 53.7786703940513, 'ACC-floor-other-merged': 66.71961565255427, 'ACC-pavement-merged': 73.9025150150285, 'ACC-mountain-merged': 71.03677457880795, 'ACC-grass-merged': 83.5228157922774, 'ACC-dirt-merged': 65.76368643228383, 'ACC-paper-merged': 53.92746563770778, 'ACC-food-other-merged': 64.02551726644785, 'ACC-building-other-merged': 73.47012542493954, 'ACC-rock-merged': 79.07246396101795, 'ACC-wall-other-merged': 80.99320558832585, 'ACC-rug-merged': 80.26123889209869})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.405326309628329, 'noc@0.8': 2.47585601404741, 'noc@0.85': 2.896985659935616, 'noc@0.9': 3.690956979806848, 'miou@iter1': 0.8713250722597662}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.12631225585938, 'precision@0.6': 72.13369750976562, 'precision@0.7': 68.13058471679688, 'precision@0.8': 59.23046875, 'precision@0.9': 32.72444534301758, 'cIoU': 61.671852111816406, 'mIoU': 66.52096557617188}}} INFO:trainer.default_trainer:This epoch takes 0:57:01.661733 INFO:trainer.default_trainer:PROGRESS: 54.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 27 training. INFO:trainer.default_trainer:epochs[ 27] optim steps[49400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97167/0.76252, loss_mask_bce_0: 0.12252/0.30183, loss_mask_dice_0: 0.16334/1.02489, loss_spatial_bce_0: 0.09274/0.08613, loss_spatial_dice_0: 0.13768/0.18207, loss_spatial_ce_0: 0.00167/0.05990, loss_grounding_bce_0: 0.04868/0.08081, loss_grounding_dice_0: 0.07034/0.15104, loss_grounding_ce_0: 0.40933/0.24920, loss_mask_ce_1: 0.99135/0.76328, loss_mask_bce_1: 0.12368/0.30271, loss_mask_dice_1: 0.17311/1.02876, loss_spatial_bce_1: 0.09229/0.08638, loss_spatial_dice_1: 0.13482/0.18460, loss_spatial_ce_1: 0.00133/0.06397, loss_grounding_bce_1: 0.04592/0.08100, loss_grounding_dice_1: 0.07650/0.15179, loss_grounding_ce_1: 0.41388/0.25101, loss_mask_ce_2: 0.78614/0.77123, loss_mask_bce_2: 0.11987/0.30279, loss_mask_dice_2: 0.17405/1.02976, loss_spatial_bce_2: 0.09515/0.08636, loss_spatial_dice_2: 0.12382/0.18488, loss_spatial_ce_2: 0.00096/0.06630, loss_grounding_bce_2: 0.04901/0.08093, loss_grounding_dice_2: 0.07238/0.15166, loss_grounding_ce_2: 0.42045/0.25372, loss_mask_ce_3: 0.88189/0.77407, loss_mask_bce_3: 0.12111/0.30439, loss_mask_dice_3: 0.18043/1.02724, loss_spatial_bce_3: 0.08975/0.08837, loss_spatial_dice_3: 0.12834/0.18607, loss_spatial_ce_3: 0.00103/0.07086, loss_grounding_bce_3: 0.05001/0.08137, loss_grounding_dice_3: 0.08056/0.15126, loss_grounding_ce_3: 0.39586/0.25365, loss_mask_ce_4: 0.92759/0.78016, loss_mask_bce_4: 0.12563/0.30669, loss_mask_dice_4: 0.19160/1.04633, loss_spatial_bce_4: 0.09304/0.09034, loss_spatial_dice_4: 0.12788/0.19392, loss_spatial_ce_4: 0.00484/0.08366, loss_grounding_bce_4: 0.05085/0.08200, loss_grounding_dice_4: 0.08029/0.15386, loss_grounding_ce_4: 0.41953/0.25888, loss_mask_ce_5: 0.93954/0.80333, loss_mask_bce_5: 0.12961/0.30858, loss_mask_dice_5: 0.18102/1.05387, loss_spatial_bce_5: 0.09931/0.09239, loss_spatial_dice_5: 0.13236/0.19661, loss_spatial_ce_5: 0.00274/0.09584, loss_grounding_bce_5: 0.05016/0.08232, loss_grounding_dice_5: 0.07702/0.15455, loss_grounding_ce_5: 0.42151/0.27725, loss_mask_ce_6: 0.81189/0.82971, loss_mask_bce_6: 0.13728/0.31049, loss_mask_dice_6: 0.21504/1.05667, loss_spatial_bce_6: 0.10451/0.09745, loss_spatial_dice_6: 0.12671/0.19887, loss_spatial_ce_6: 0.00290/0.11971, loss_grounding_bce_6: 0.05266/0.08328, loss_grounding_dice_6: 0.07804/0.15513, loss_grounding_ce_6: 0.37947/0.28615, loss_mask_ce_7: 0.82740/0.88514, loss_mask_bce_7: 0.12333/0.31785, loss_mask_dice_7: 0.16067/1.10338, loss_spatial_bce_7: 0.11680/0.10762, loss_spatial_dice_7: 0.15986/0.22425, loss_spatial_ce_7: 0.00709/0.15827, loss_grounding_bce_7: 0.04804/0.08492, loss_grounding_dice_7: 0.07494/0.16090, loss_grounding_ce_7: 0.40628/0.32081, loss_mask_ce_8: 0.90217/1.02331, loss_mask_bce_8: 0.13194/0.33387, loss_mask_dice_8: 0.17793/1.18033, loss_spatial_bce_8: 0.13279/0.12572, loss_spatial_dice_8: 0.16517/0.26067, loss_spatial_ce_8: 0.00798/0.20900, loss_grounding_bce_8: 0.04842/0.08906, loss_grounding_dice_8: 0.06715/0.17047, loss_grounding_ce_8: 0.32186/0.42326, loss_mask_ce_9: 2.98050/3.48206, loss_mask_bce_9: 0.13915/0.36100, loss_mask_dice_9: 0.25132/1.76356, loss_spatial_bce_9: 0.44286/0.35572, loss_spatial_dice_9: 0.46250/0.79423, loss_spatial_ce_9: 1.09799/1.39540, loss_grounding_bce_9: 0.04888/0.10107, loss_grounding_dice_9: 0.08003/0.24314, loss_grounding_ce_9: 0.47992/0.68022] items per batch[64] items per second[0.17] total items[3161600] mini batches[ 49400] memory[4999] epoch remaining[0:53:22] INFO:trainer.default_trainer:epochs[ 27] optim steps[49500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.75799/0.76234, loss_mask_bce_0: 0.25363/0.30177, loss_mask_dice_0: 0.60899/1.02498, loss_spatial_bce_0: 0.10289/0.08610, loss_spatial_dice_0: 0.19399/0.18205, loss_spatial_ce_0: 0.02542/0.05990, loss_grounding_bce_0: 0.08945/0.08080, loss_grounding_dice_0: 0.07213/0.15100, loss_grounding_ce_0: 0.11632/0.24904, loss_mask_ce_1: 1.69131/0.76308, loss_mask_bce_1: 0.26450/0.30264, loss_mask_dice_1: 0.64622/1.02884, loss_spatial_bce_1: 0.11437/0.08635, loss_spatial_dice_1: 0.17893/0.18459, loss_spatial_ce_1: 0.01395/0.06396, loss_grounding_bce_1: 0.09034/0.08098, loss_grounding_dice_1: 0.06670/0.15175, loss_grounding_ce_1: 0.17849/0.25084, loss_mask_ce_2: 1.61372/0.77102, loss_mask_bce_2: 0.26285/0.30273, loss_mask_dice_2: 0.61389/1.02986, loss_spatial_bce_2: 0.10657/0.08633, loss_spatial_dice_2: 0.18333/0.18488, loss_spatial_ce_2: 0.01570/0.06629, loss_grounding_bce_2: 0.08655/0.08092, loss_grounding_dice_2: 0.05830/0.15161, loss_grounding_ce_2: 0.13871/0.25356, loss_mask_ce_3: 1.67878/0.77390, loss_mask_bce_3: 0.26500/0.30433, loss_mask_dice_3: 0.58903/1.02735, loss_spatial_bce_3: 0.11103/0.08835, loss_spatial_dice_3: 0.20276/0.18606, loss_spatial_ce_3: 0.02792/0.07085, loss_grounding_bce_3: 0.08568/0.08136, loss_grounding_dice_3: 0.06260/0.15121, loss_grounding_ce_3: 0.11098/0.25348, loss_mask_ce_4: 1.61535/0.77995, loss_mask_bce_4: 0.24843/0.30662, loss_mask_dice_4: 0.62935/1.04634, loss_spatial_bce_4: 0.10547/0.09031, loss_spatial_dice_4: 0.18879/0.19391, loss_spatial_ce_4: 0.01614/0.08366, loss_grounding_bce_4: 0.09492/0.08198, loss_grounding_dice_4: 0.09586/0.15382, loss_grounding_ce_4: 0.08412/0.25871, loss_mask_ce_5: 1.86421/0.80313, loss_mask_bce_5: 0.26763/0.30851, loss_mask_dice_5: 0.60496/1.05393, loss_spatial_bce_5: 0.11090/0.09236, loss_spatial_dice_5: 0.24479/0.19660, loss_spatial_ce_5: 0.02010/0.09584, loss_grounding_bce_5: 0.08957/0.08230, loss_grounding_dice_5: 0.06492/0.15450, loss_grounding_ce_5: 0.05968/0.27707, loss_mask_ce_6: 2.05069/0.82951, loss_mask_bce_6: 0.26827/0.31042, loss_mask_dice_6: 0.69300/1.05673, loss_spatial_bce_6: 0.09714/0.09743, loss_spatial_dice_6: 0.21093/0.19887, loss_spatial_ce_6: 0.07842/0.11972, loss_grounding_bce_6: 0.08542/0.08326, loss_grounding_dice_6: 0.06455/0.15508, loss_grounding_ce_6: 0.05323/0.28597, loss_mask_ce_7: 2.01485/0.88491, loss_mask_bce_7: 0.24306/0.31778, loss_mask_dice_7: 0.59963/1.10346, loss_spatial_bce_7: 0.09647/0.10760, loss_spatial_dice_7: 0.22658/0.22426, loss_spatial_ce_7: 0.15631/0.15828, loss_grounding_bce_7: 0.08878/0.08490, loss_grounding_dice_7: 0.09251/0.16085, loss_grounding_ce_7: 0.04286/0.32062, loss_mask_ce_8: 2.09993/1.02317, loss_mask_bce_8: 0.27143/0.33380, loss_mask_dice_8: 0.76741/1.18045, loss_spatial_bce_8: 0.08115/0.12569, loss_spatial_dice_8: 0.22463/0.26067, loss_spatial_ce_8: 0.18645/0.20896, loss_grounding_bce_8: 0.10063/0.08904, loss_grounding_dice_8: 0.06557/0.17043, loss_grounding_ce_8: 0.22020/0.42306, loss_mask_ce_9: 4.91381/3.48173, loss_mask_bce_9: 0.41618/0.36090, loss_mask_dice_9: 1.27711/1.76351, loss_spatial_bce_9: 0.41581/0.35567, loss_spatial_dice_9: 0.74621/0.79421, loss_spatial_ce_9: 1.42849/1.39538, loss_grounding_bce_9: 0.36310/0.10107, loss_grounding_dice_9: 0.49083/0.24308, loss_grounding_ce_9: 0.48312/0.67996] items per batch[64] items per second[0.37] total items[3168000] mini batches[ 49500] memory[4999] epoch remaining[0:48:48] INFO:trainer.default_trainer:epochs[ 27] optim steps[49600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03807/0.76220, loss_mask_bce_0: 0.05699/0.30171, loss_mask_dice_0: 0.16730/1.02472, loss_spatial_bce_0: 0.03229/0.08608, loss_spatial_dice_0: 0.07625/0.18202, loss_spatial_ce_0: 0.00024/0.05986, loss_grounding_bce_0: 0.01003/0.08080, loss_grounding_dice_0: 0.01221/0.15100, loss_grounding_ce_0: 0.00008/0.24909, loss_mask_ce_1: 0.02340/0.76294, loss_mask_bce_1: 0.05610/0.30259, loss_mask_dice_1: 0.16077/1.02859, loss_spatial_bce_1: 0.03765/0.08633, loss_spatial_dice_1: 0.08874/0.18455, loss_spatial_ce_1: 0.00017/0.06394, loss_grounding_bce_1: 0.00998/0.08098, loss_grounding_dice_1: 0.01281/0.15175, loss_grounding_ce_1: 0.00006/0.25093, loss_mask_ce_2: 0.01539/0.77086, loss_mask_bce_2: 0.05462/0.30268, loss_mask_dice_2: 0.16226/1.02963, loss_spatial_bce_2: 0.03804/0.08632, loss_spatial_dice_2: 0.08595/0.18484, loss_spatial_ce_2: 0.00021/0.06627, loss_grounding_bce_2: 0.00913/0.08092, loss_grounding_dice_2: 0.01171/0.15163, loss_grounding_ce_2: 0.00006/0.25365, loss_mask_ce_3: 0.02048/0.77375, loss_mask_bce_3: 0.06215/0.30427, loss_mask_dice_3: 0.16745/1.02714, loss_spatial_bce_3: 0.03716/0.08833, loss_spatial_dice_3: 0.08570/0.18603, loss_spatial_ce_3: 0.00025/0.07084, loss_grounding_bce_3: 0.00895/0.08136, loss_grounding_dice_3: 0.01188/0.15122, loss_grounding_ce_3: 0.00008/0.25372, loss_mask_ce_4: 0.02822/0.77982, loss_mask_bce_4: 0.06027/0.30657, loss_mask_dice_4: 0.17049/1.04606, loss_spatial_bce_4: 0.03619/0.09029, loss_spatial_dice_4: 0.08160/0.19387, loss_spatial_ce_4: 0.00091/0.08365, loss_grounding_bce_4: 0.00852/0.08199, loss_grounding_dice_4: 0.01035/0.15383, loss_grounding_ce_4: 0.00022/0.25902, loss_mask_ce_5: 0.02520/0.80303, loss_mask_bce_5: 0.05815/0.30844, loss_mask_dice_5: 0.16648/1.05364, loss_spatial_bce_5: 0.04084/0.09234, loss_spatial_dice_5: 0.09357/0.19656, loss_spatial_ce_5: 0.01516/0.09582, loss_grounding_bce_5: 0.01035/0.08230, loss_grounding_dice_5: 0.01391/0.15450, loss_grounding_ce_5: 0.00005/0.27724, loss_mask_ce_6: 0.02092/0.82939, loss_mask_bce_6: 0.06427/0.31036, loss_mask_dice_6: 0.16990/1.05645, loss_spatial_bce_6: 0.04712/0.09741, loss_spatial_dice_6: 0.10578/0.19884, loss_spatial_ce_6: 0.01275/0.11969, loss_grounding_bce_6: 0.01173/0.08327, loss_grounding_dice_6: 0.01519/0.15509, loss_grounding_ce_6: 0.00011/0.28615, loss_mask_ce_7: 0.01587/0.88475, loss_mask_bce_7: 0.05992/0.31774, loss_mask_dice_7: 0.16875/1.10321, loss_spatial_bce_7: 0.03760/0.10757, loss_spatial_dice_7: 0.07762/0.22422, loss_spatial_ce_7: 0.00076/0.15827, loss_grounding_bce_7: 0.01080/0.08491, loss_grounding_dice_7: 0.01465/0.16086, loss_grounding_ce_7: 0.00014/0.32081, loss_mask_ce_8: 0.01706/1.02301, loss_mask_bce_8: 0.06488/0.33373, loss_mask_dice_8: 0.15333/1.18011, loss_spatial_bce_8: 0.03830/0.12566, loss_spatial_dice_8: 0.07875/0.26061, loss_spatial_ce_8: 0.02617/0.20891, loss_grounding_bce_8: 0.01255/0.08904, loss_grounding_dice_8: 0.01538/0.17042, loss_grounding_ce_8: 0.00033/0.42317, loss_mask_ce_9: 1.66695/3.48144, loss_mask_bce_9: 0.05069/0.36084, loss_mask_dice_9: 0.16073/1.76313, loss_spatial_bce_9: 0.30817/0.35571, loss_spatial_dice_9: 0.65940/0.79417, loss_spatial_ce_9: 0.67519/1.39523, loss_grounding_bce_9: 0.01184/0.10104, loss_grounding_dice_9: 0.01589/0.24306, loss_grounding_ce_9: 0.01682/0.68013] items per batch[64] items per second[0.37] total items[3174400] mini batches[ 49600] memory[4999] epoch remaining[0:45:33] INFO:trainer.default_trainer:epochs[ 27] optim steps[49700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50649/0.76217, loss_mask_bce_0: 0.48269/0.30171, loss_mask_dice_0: 1.62599/1.02474, loss_spatial_bce_0: 0.06680/0.08608, loss_spatial_dice_0: 0.16253/0.18202, loss_spatial_ce_0: 0.22885/0.05985, loss_grounding_bce_0: 0.11422/0.08081, loss_grounding_dice_0: 0.12831/0.15099, loss_grounding_ce_0: 0.00174/0.24927, loss_mask_ce_1: 0.62401/0.76289, loss_mask_bce_1: 0.47797/0.30260, loss_mask_dice_1: 1.82251/1.02863, loss_spatial_bce_1: 0.06772/0.08634, loss_spatial_dice_1: 0.17622/0.18456, loss_spatial_ce_1: 0.24647/0.06393, loss_grounding_bce_1: 0.11821/0.08099, loss_grounding_dice_1: 0.12793/0.15173, loss_grounding_ce_1: 0.00107/0.25113, loss_mask_ce_2: 0.59946/0.77085, loss_mask_bce_2: 0.48675/0.30268, loss_mask_dice_2: 1.83894/1.02963, loss_spatial_bce_2: 0.06765/0.08632, loss_spatial_dice_2: 0.16588/0.18485, loss_spatial_ce_2: 0.20337/0.06624, loss_grounding_bce_2: 0.10829/0.08093, loss_grounding_dice_2: 0.12116/0.15161, loss_grounding_ce_2: 0.00131/0.25389, loss_mask_ce_3: 0.59097/0.77372, loss_mask_bce_3: 0.48355/0.30428, loss_mask_dice_3: 1.67394/1.02714, loss_spatial_bce_3: 0.07350/0.08833, loss_spatial_dice_3: 0.17065/0.18605, loss_spatial_ce_3: 0.18041/0.07082, loss_grounding_bce_3: 0.11676/0.08138, loss_grounding_dice_3: 0.12332/0.15120, loss_grounding_ce_3: 0.00136/0.25394, loss_mask_ce_4: 0.51734/0.77977, loss_mask_bce_4: 0.49683/0.30658, loss_mask_dice_4: 1.99995/1.04611, loss_spatial_bce_4: 0.06828/0.09030, loss_spatial_dice_4: 0.17706/0.19389, loss_spatial_ce_4: 0.16346/0.08362, loss_grounding_bce_4: 0.11652/0.08201, loss_grounding_dice_4: 0.12409/0.15382, loss_grounding_ce_4: 0.00420/0.25922, loss_mask_ce_5: 1.02238/0.80299, loss_mask_bce_5: 0.46105/0.30843, loss_mask_dice_5: 1.85753/1.05364, loss_spatial_bce_5: 0.07138/0.09235, loss_spatial_dice_5: 0.20902/0.19658, loss_spatial_ce_5: 0.08896/0.09579, loss_grounding_bce_5: 0.11488/0.08232, loss_grounding_dice_5: 0.12689/0.15450, loss_grounding_ce_5: 0.00516/0.27738, loss_mask_ce_6: 0.64480/0.82934, loss_mask_bce_6: 0.49922/0.31035, loss_mask_dice_6: 1.95558/1.05645, loss_spatial_bce_6: 0.07794/0.09742, loss_spatial_dice_6: 0.21459/0.19887, loss_spatial_ce_6: 0.12663/0.11966, loss_grounding_bce_6: 0.12415/0.08329, loss_grounding_dice_6: 0.12904/0.15506, loss_grounding_ce_6: 0.00367/0.28626, loss_mask_ce_7: 0.71251/0.88474, loss_mask_bce_7: 0.46874/0.31774, loss_mask_dice_7: 1.79158/1.10321, loss_spatial_bce_7: 0.07569/0.10757, loss_spatial_dice_7: 0.24938/0.22424, loss_spatial_ce_7: 0.15392/0.15824, loss_grounding_bce_7: 0.11534/0.08493, loss_grounding_dice_7: 0.13332/0.16086, loss_grounding_ce_7: 0.00762/0.32093, loss_mask_ce_8: 0.75240/1.02293, loss_mask_bce_8: 0.47522/0.33373, loss_mask_dice_8: 1.76580/1.18008, loss_spatial_bce_8: 0.09746/0.12564, loss_spatial_dice_8: 0.23894/0.26061, loss_spatial_ce_8: 0.11600/0.20884, loss_grounding_bce_8: 0.12073/0.08907, loss_grounding_dice_8: 0.14629/0.17041, loss_grounding_ce_8: 0.08495/0.42328, loss_mask_ce_9: 4.68272/3.48125, loss_mask_bce_9: 0.51718/0.36081, loss_mask_dice_9: 2.60244/1.76300, loss_spatial_bce_9: 0.28090/0.35569, loss_spatial_dice_9: 0.87588/0.79417, loss_spatial_ce_9: 1.50776/1.39515, loss_grounding_bce_9: 0.13882/0.10103, loss_grounding_dice_9: 0.18312/0.24300, loss_grounding_ce_9: 0.05763/0.68009] items per batch[64] items per second[0.36] total items[3180800] mini batches[ 49700] memory[4999] epoch remaining[0:42:41] INFO:trainer.default_trainer:epochs[ 27] optim steps[49800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26971/0.76209, loss_mask_bce_0: 0.50175/0.30173, loss_mask_dice_0: 1.18723/1.02488, loss_spatial_bce_0: 0.05182/0.08606, loss_spatial_dice_0: 0.16394/0.18199, loss_spatial_ce_0: 0.00269/0.05985, loss_grounding_bce_0: 0.01673/0.08080, loss_grounding_dice_0: 0.14507/0.15099, loss_grounding_ce_0: 0.00863/0.24926, loss_mask_ce_1: 0.27431/0.76281, loss_mask_bce_1: 0.52579/0.30262, loss_mask_dice_1: 1.19097/1.02876, loss_spatial_bce_1: 0.05630/0.08632, loss_spatial_dice_1: 0.17791/0.18454, loss_spatial_ce_1: 0.00311/0.06394, loss_grounding_bce_1: 0.01538/0.08098, loss_grounding_dice_1: 0.15458/0.15173, loss_grounding_ce_1: 0.00467/0.25110, loss_mask_ce_2: 0.26847/0.77078, loss_mask_bce_2: 0.50942/0.30270, loss_mask_dice_2: 1.19120/1.02981, loss_spatial_bce_2: 0.05409/0.08631, loss_spatial_dice_2: 0.16974/0.18483, loss_spatial_ce_2: 0.00068/0.06623, loss_grounding_bce_2: 0.01604/0.08092, loss_grounding_dice_2: 0.15495/0.15161, loss_grounding_ce_2: 0.00472/0.25387, loss_mask_ce_3: 0.33347/0.77367, loss_mask_bce_3: 0.51477/0.30430, loss_mask_dice_3: 1.16695/1.02730, loss_spatial_bce_3: 0.05809/0.08832, loss_spatial_dice_3: 0.17207/0.18602, loss_spatial_ce_3: 0.00404/0.07080, loss_grounding_bce_3: 0.01435/0.08136, loss_grounding_dice_3: 0.14930/0.15120, loss_grounding_ce_3: 0.00827/0.25394, loss_mask_ce_4: 0.34993/0.77971, loss_mask_bce_4: 0.50619/0.30661, loss_mask_dice_4: 1.22949/1.04629, loss_spatial_bce_4: 0.05326/0.09029, loss_spatial_dice_4: 0.17808/0.19386, loss_spatial_ce_4: 0.01048/0.08364, loss_grounding_bce_4: 0.01372/0.08201, loss_grounding_dice_4: 0.15734/0.15382, loss_grounding_ce_4: 0.00812/0.25918, loss_mask_ce_5: 0.49549/0.80295, loss_mask_bce_5: 0.50942/0.30846, loss_mask_dice_5: 1.18162/1.05384, loss_spatial_bce_5: 0.05777/0.09234, loss_spatial_dice_5: 0.19642/0.19656, loss_spatial_ce_5: 0.02359/0.09581, loss_grounding_bce_5: 0.01032/0.08231, loss_grounding_dice_5: 0.13441/0.15450, loss_grounding_ce_5: 0.01719/0.27733, loss_mask_ce_6: 0.47284/0.82931, loss_mask_bce_6: 0.48618/0.31037, loss_mask_dice_6: 1.11786/1.05660, loss_spatial_bce_6: 0.05671/0.09741, loss_spatial_dice_6: 0.18236/0.19886, loss_spatial_ce_6: 0.03908/0.11969, loss_grounding_bce_6: 0.01121/0.08328, loss_grounding_dice_6: 0.13968/0.15507, loss_grounding_ce_6: 0.02821/0.28623, loss_mask_ce_7: 0.62778/0.88471, loss_mask_bce_7: 0.48376/0.31778, loss_mask_dice_7: 1.21508/1.10340, loss_spatial_bce_7: 0.08923/0.10758, loss_spatial_dice_7: 0.25649/0.22423, loss_spatial_ce_7: 0.07960/0.15825, loss_grounding_bce_7: 0.01571/0.08493, loss_grounding_dice_7: 0.18991/0.16087, loss_grounding_ce_7: 0.03285/0.32090, loss_mask_ce_8: 0.35877/1.02292, loss_mask_bce_8: 0.55014/0.33379, loss_mask_dice_8: 1.47951/1.18028, loss_spatial_bce_8: 0.09637/0.12562, loss_spatial_dice_8: 0.34444/0.26060, loss_spatial_ce_8: 0.39119/0.20881, loss_grounding_bce_8: 0.01489/0.08908, loss_grounding_dice_8: 0.18348/0.17042, loss_grounding_ce_8: 0.04941/0.42334, loss_mask_ce_9: 3.97117/3.48160, loss_mask_bce_9: 0.59381/0.36089, loss_mask_dice_9: 2.02340/1.76357, loss_spatial_bce_9: 0.28320/0.35566, loss_spatial_dice_9: 0.93288/0.79419, loss_spatial_ce_9: 1.58944/1.39525, loss_grounding_bce_9: 0.02606/0.10104, loss_grounding_dice_9: 0.37259/0.24304, loss_grounding_ce_9: 0.53826/0.68021] items per batch[64] items per second[0.36] total items[3187200] mini batches[ 49800] memory[4999] epoch remaining[0:39:48] INFO:trainer.default_trainer:epochs[ 27] optim steps[49900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.27814/0.76229, loss_mask_bce_0: 0.23473/0.30178, loss_mask_dice_0: 2.02482/1.02481, loss_spatial_bce_0: 0.02565/0.08607, loss_spatial_dice_0: 0.16139/0.18200, loss_spatial_ce_0: 0.00311/0.05982, loss_grounding_bce_0: 0.00623/0.08082, loss_grounding_dice_0: 0.05706/0.15101, loss_grounding_ce_0: 0.05109/0.24933, loss_mask_ce_1: 0.26072/0.76295, loss_mask_bce_1: 0.23100/0.30267, loss_mask_dice_1: 1.74018/1.02872, loss_spatial_bce_1: 0.02342/0.08632, loss_spatial_dice_1: 0.18403/0.18455, loss_spatial_ce_1: 0.00204/0.06389, loss_grounding_bce_1: 0.00549/0.08099, loss_grounding_dice_1: 0.03651/0.15175, loss_grounding_ce_1: 0.06643/0.25112, loss_mask_ce_2: 0.43272/0.77090, loss_mask_bce_2: 0.22974/0.30277, loss_mask_dice_2: 1.56730/1.02970, loss_spatial_bce_2: 0.02483/0.08632, loss_spatial_dice_2: 0.18857/0.18484, loss_spatial_ce_2: 0.00159/0.06618, loss_grounding_bce_2: 0.00601/0.08093, loss_grounding_dice_2: 0.04751/0.15163, loss_grounding_ce_2: 0.07197/0.25390, loss_mask_ce_3: 0.23928/0.77377, loss_mask_bce_3: 0.24745/0.30436, loss_mask_dice_3: 1.68941/1.02721, loss_spatial_bce_3: 0.02590/0.08832, loss_spatial_dice_3: 0.17587/0.18603, loss_spatial_ce_3: 0.00213/0.07076, loss_grounding_bce_3: 0.00535/0.08139, loss_grounding_dice_3: 0.04478/0.15123, loss_grounding_ce_3: 0.04637/0.25393, loss_mask_ce_4: 0.31708/0.77984, loss_mask_bce_4: 0.22962/0.30668, loss_mask_dice_4: 1.86027/1.04624, loss_spatial_bce_4: 0.02627/0.09029, loss_spatial_dice_4: 0.19727/0.19387, loss_spatial_ce_4: 0.17618/0.08365, loss_grounding_bce_4: 0.00571/0.08203, loss_grounding_dice_4: 0.04919/0.15384, loss_grounding_ce_4: 0.08943/0.25916, loss_mask_ce_5: 0.35671/0.80309, loss_mask_bce_5: 0.22229/0.30853, loss_mask_dice_5: 1.70226/1.05380, loss_spatial_bce_5: 0.02420/0.09235, loss_spatial_dice_5: 0.18718/0.19658, loss_spatial_ce_5: 0.01868/0.09581, loss_grounding_bce_5: 0.00543/0.08233, loss_grounding_dice_5: 0.03238/0.15453, loss_grounding_ce_5: 0.08264/0.27733, loss_mask_ce_6: 0.41774/0.82946, loss_mask_bce_6: 0.25391/0.31045, loss_mask_dice_6: 1.86168/1.05654, loss_spatial_bce_6: 0.03207/0.09742, loss_spatial_dice_6: 0.18526/0.19886, loss_spatial_ce_6: 0.07167/0.11969, loss_grounding_bce_6: 0.00620/0.08330, loss_grounding_dice_6: 0.05474/0.15508, loss_grounding_ce_6: 0.05628/0.28620, loss_mask_ce_7: 0.85173/0.88487, loss_mask_bce_7: 0.22652/0.31787, loss_mask_dice_7: 1.69381/1.10333, loss_spatial_bce_7: 0.02914/0.10757, loss_spatial_dice_7: 0.19588/0.22424, loss_spatial_ce_7: 0.41532/0.15824, loss_grounding_bce_7: 0.02146/0.08495, loss_grounding_dice_7: 0.07896/0.16090, loss_grounding_ce_7: 0.05964/0.32082, loss_mask_ce_8: 0.96037/1.02306, loss_mask_bce_8: 0.26932/0.33387, loss_mask_dice_8: 2.46649/1.18027, loss_spatial_bce_8: 0.08658/0.12560, loss_spatial_dice_8: 0.38556/0.26060, loss_spatial_ce_8: 0.12280/0.20875, loss_grounding_bce_8: 0.00566/0.08910, loss_grounding_dice_8: 0.05586/0.17046, loss_grounding_ce_8: 0.19942/0.42325, loss_mask_ce_9: 3.27964/3.48186, loss_mask_bce_9: 0.38107/0.36099, loss_mask_dice_9: 3.87861/1.76375, loss_spatial_bce_9: 0.40445/0.35572, loss_spatial_dice_9: 0.94055/0.79420, loss_spatial_ce_9: 1.35080/1.39534, loss_grounding_bce_9: 0.02675/0.10107, loss_grounding_dice_9: 0.16747/0.24312, loss_grounding_ce_9: 0.45064/0.67995] items per batch[64] items per second[0.37] total items[3193600] mini batches[ 49900] memory[4999] epoch remaining[0:36:50] INFO:trainer.default_trainer:epochs[ 27] optim steps[50000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74126/0.76215, loss_mask_bce_0: 0.11612/0.30176, loss_mask_dice_0: 0.28800/1.02455, loss_spatial_bce_0: 0.03625/0.08606, loss_spatial_dice_0: 0.09227/0.18198, loss_spatial_ce_0: 0.04035/0.05981, loss_grounding_bce_0: 0.05534/0.08082, loss_grounding_dice_0: 0.13417/0.15098, loss_grounding_ce_0: 0.07231/0.24933, loss_mask_ce_1: 0.66786/0.76282, loss_mask_bce_1: 0.11358/0.30265, loss_mask_dice_1: 0.27686/1.02845, loss_spatial_bce_1: 0.03643/0.08632, loss_spatial_dice_1: 0.08408/0.18453, loss_spatial_ce_1: 0.03994/0.06386, loss_grounding_bce_1: 0.05328/0.08100, loss_grounding_dice_1: 0.13858/0.15172, loss_grounding_ce_1: 0.04813/0.25118, loss_mask_ce_2: 0.77111/0.77080, loss_mask_bce_2: 0.11522/0.30275, loss_mask_dice_2: 0.29559/1.02948, loss_spatial_bce_2: 0.03794/0.08631, loss_spatial_dice_2: 0.08235/0.18482, loss_spatial_ce_2: 0.04132/0.06614, loss_grounding_bce_2: 0.05691/0.08093, loss_grounding_dice_2: 0.13208/0.15160, loss_grounding_ce_2: 0.05673/0.25396, loss_mask_ce_3: 0.86140/0.77365, loss_mask_bce_3: 0.11430/0.30434, loss_mask_dice_3: 0.26275/1.02700, loss_spatial_bce_3: 0.04041/0.08832, loss_spatial_dice_3: 0.09499/0.18601, loss_spatial_ce_3: 0.04072/0.07072, loss_grounding_bce_3: 0.05361/0.08139, loss_grounding_dice_3: 0.10823/0.15121, loss_grounding_ce_3: 0.06825/0.25402, loss_mask_ce_4: 0.54970/0.77965, loss_mask_bce_4: 0.11568/0.30666, loss_mask_dice_4: 0.29539/1.04604, loss_spatial_bce_4: 0.03469/0.09028, loss_spatial_dice_4: 0.08040/0.19386, loss_spatial_ce_4: 0.04020/0.08365, loss_grounding_bce_4: 0.05294/0.08204, loss_grounding_dice_4: 0.13804/0.15381, loss_grounding_ce_4: 0.09426/0.25923, loss_mask_ce_5: 0.88248/0.80294, loss_mask_bce_5: 0.11436/0.30851, loss_mask_dice_5: 0.28557/1.05358, loss_spatial_bce_5: 0.03351/0.09235, loss_spatial_dice_5: 0.09219/0.19656, loss_spatial_ce_5: 0.04000/0.09578, loss_grounding_bce_5: 0.06320/0.08233, loss_grounding_dice_5: 0.15766/0.15451, loss_grounding_ce_5: 0.10672/0.27751, loss_mask_ce_6: 0.61534/0.82932, loss_mask_bce_6: 0.12633/0.31043, loss_mask_dice_6: 0.30561/1.05630, loss_spatial_bce_6: 0.04144/0.09742, loss_spatial_dice_6: 0.10589/0.19885, loss_spatial_ce_6: 0.07242/0.11968, loss_grounding_bce_6: 0.05792/0.08329, loss_grounding_dice_6: 0.12660/0.15506, loss_grounding_ce_6: 0.09171/0.28644, loss_mask_ce_7: 0.63316/0.88473, loss_mask_bce_7: 0.14346/0.31785, loss_mask_dice_7: 0.30876/1.10313, loss_spatial_bce_7: 0.04121/0.10757, loss_spatial_dice_7: 0.11671/0.22424, loss_spatial_ce_7: 0.11556/0.15820, loss_grounding_bce_7: 0.05518/0.08496, loss_grounding_dice_7: 0.12690/0.16087, loss_grounding_ce_7: 0.05720/0.32101, loss_mask_ce_8: 0.63291/1.02287, loss_mask_bce_8: 0.14541/0.33384, loss_mask_dice_8: 0.36796/1.18005, loss_spatial_bce_8: 0.05708/0.12558, loss_spatial_dice_8: 0.17833/0.26058, loss_spatial_ce_8: 0.21024/0.20870, loss_grounding_bce_8: 0.05463/0.08910, loss_grounding_dice_8: 0.13516/0.17044, loss_grounding_ce_8: 0.13879/0.42331, loss_mask_ce_9: 3.15153/3.48156, loss_mask_bce_9: 0.13246/0.36093, loss_mask_dice_9: 0.47780/1.76341, loss_spatial_bce_9: 0.31754/0.35572, loss_spatial_dice_9: 0.80480/0.79422, loss_spatial_ce_9: 1.20623/1.39527, loss_grounding_bce_9: 0.05307/0.10108, loss_grounding_dice_9: 0.24099/0.24307, loss_grounding_ce_9: 0.11053/0.67986] items per batch[64] items per second[0.37] total items[3200000] mini batches[ 50000] memory[4999] epoch remaining[0:33:52] INFO:trainer.default_trainer:epochs[ 27] optim steps[50100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.66398/0.76213, loss_mask_bce_0: 1.13909/0.30170, loss_mask_dice_0: 2.08366/1.02450, loss_spatial_bce_0: 0.10985/0.08604, loss_spatial_dice_0: 0.33048/0.18199, loss_spatial_ce_0: 0.00653/0.05982, loss_grounding_bce_0: 0.11327/0.08082, loss_grounding_dice_0: 0.15188/0.15097, loss_grounding_ce_0: 0.61134/0.24916, loss_mask_ce_1: 1.67514/0.76281, loss_mask_bce_1: 1.10932/0.30260, loss_mask_dice_1: 2.15854/1.02842, loss_spatial_bce_1: 0.10885/0.08630, loss_spatial_dice_1: 0.32236/0.18454, loss_spatial_ce_1: 0.00442/0.06387, loss_grounding_bce_1: 0.11644/0.08099, loss_grounding_dice_1: 0.14997/0.15172, loss_grounding_ce_1: 0.60061/0.25100, loss_mask_ce_2: 1.55775/0.77083, loss_mask_bce_2: 1.19318/0.30270, loss_mask_dice_2: 2.20398/1.02941, loss_spatial_bce_2: 0.11484/0.08630, loss_spatial_dice_2: 0.34141/0.18483, loss_spatial_ce_2: 0.00354/0.06614, loss_grounding_bce_2: 0.16047/0.08092, loss_grounding_dice_2: 0.15387/0.15159, loss_grounding_ce_2: 0.61469/0.25383, loss_mask_ce_3: 1.57322/0.77368, loss_mask_bce_3: 1.22951/0.30429, loss_mask_dice_3: 2.16576/1.02694, loss_spatial_bce_3: 0.11227/0.08830, loss_spatial_dice_3: 0.35668/0.18602, loss_spatial_ce_3: 0.00267/0.07071, loss_grounding_bce_3: 0.12568/0.08138, loss_grounding_dice_3: 0.15048/0.15120, loss_grounding_ce_3: 0.63742/0.25387, loss_mask_ce_4: 1.71269/0.77968, loss_mask_bce_4: 1.30932/0.30661, loss_mask_dice_4: 2.07869/1.04595, loss_spatial_bce_4: 0.11918/0.09027, loss_spatial_dice_4: 0.33257/0.19388, loss_spatial_ce_4: 0.00636/0.08366, loss_grounding_bce_4: 0.12736/0.08204, loss_grounding_dice_4: 0.15231/0.15381, loss_grounding_ce_4: 0.65943/0.25908, loss_mask_ce_5: 1.78413/0.80296, loss_mask_bce_5: 1.18989/0.30847, loss_mask_dice_5: 2.14200/1.05348, loss_spatial_bce_5: 0.10959/0.09234, loss_spatial_dice_5: 0.31653/0.19658, loss_spatial_ce_5: 0.02165/0.09579, loss_grounding_bce_5: 0.14583/0.08232, loss_grounding_dice_5: 0.15701/0.15450, loss_grounding_ce_5: 0.64947/0.27736, loss_mask_ce_6: 2.03362/0.82940, loss_mask_bce_6: 1.13138/0.31039, loss_mask_dice_6: 2.16470/1.05618, loss_spatial_bce_6: 0.12022/0.09741, loss_spatial_dice_6: 0.32122/0.19886, loss_spatial_ce_6: 0.06344/0.11966, loss_grounding_bce_6: 0.10226/0.08329, loss_grounding_dice_6: 0.13970/0.15505, loss_grounding_ce_6: 0.64940/0.28627, loss_mask_ce_7: 1.84687/0.88483, loss_mask_bce_7: 1.13637/0.31780, loss_mask_dice_7: 2.36528/1.10303, loss_spatial_bce_7: 0.11734/0.10755, loss_spatial_dice_7: 0.34116/0.22426, loss_spatial_ce_7: 0.09037/0.15819, loss_grounding_bce_7: 0.09311/0.08497, loss_grounding_dice_7: 0.17901/0.16087, loss_grounding_ce_7: 0.61643/0.32082, loss_mask_ce_8: 1.71758/1.02294, loss_mask_bce_8: 1.08348/0.33379, loss_mask_dice_8: 2.36567/1.17991, loss_spatial_bce_8: 0.12463/0.12554, loss_spatial_dice_8: 0.38699/0.26061, loss_spatial_ce_8: 0.11183/0.20863, loss_grounding_bce_8: 0.10621/0.08912, loss_grounding_dice_8: 0.18376/0.17044, loss_grounding_ce_8: 0.62181/0.42300, loss_mask_ce_9: 3.74215/3.48189, loss_mask_bce_9: 1.21132/0.36087, loss_mask_dice_9: 4.73827/1.76323, loss_spatial_bce_9: 0.29094/0.35566, loss_spatial_dice_9: 0.95646/0.79424, loss_spatial_ce_9: 1.31128/1.39537, loss_grounding_bce_9: 0.12015/0.10108, loss_grounding_dice_9: 0.28638/0.24305, loss_grounding_ce_9: 0.70879/0.67973] items per batch[64] items per second[0.37] total items[3206400] mini batches[ 50100] memory[4999] epoch remaining[0:30:51] INFO:trainer.default_trainer:epochs[ 27] optim steps[50200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.18591/0.76211, loss_mask_bce_0: 0.44744/0.30164, loss_mask_dice_0: 0.71368/1.02461, loss_spatial_bce_0: 0.30971/0.08602, loss_spatial_dice_0: 0.28897/0.18199, loss_spatial_ce_0: 0.09090/0.05984, loss_grounding_bce_0: 0.00000/0.08079, loss_grounding_dice_0: 0.00002/0.15095, loss_grounding_ce_0: 0.06030/0.24926, loss_mask_ce_1: 1.16198/0.76281, loss_mask_bce_1: 0.43097/0.30253, loss_mask_dice_1: 0.70554/1.02851, loss_spatial_bce_1: 0.31118/0.08628, loss_spatial_dice_1: 0.28317/0.18455, loss_spatial_ce_1: 0.09903/0.06390, loss_grounding_bce_1: 0.00000/0.08097, loss_grounding_dice_1: 0.00002/0.15169, loss_grounding_ce_1: 0.04532/0.25110, loss_mask_ce_2: 1.04814/0.77078, loss_mask_bce_2: 0.43785/0.30263, loss_mask_dice_2: 0.69697/1.02953, loss_spatial_bce_2: 0.29416/0.08628, loss_spatial_dice_2: 0.27531/0.18484, loss_spatial_ce_2: 0.07332/0.06616, loss_grounding_bce_2: 0.00000/0.08090, loss_grounding_dice_2: 0.00002/0.15158, loss_grounding_ce_2: 0.05102/0.25387, loss_mask_ce_3: 0.98689/0.77372, loss_mask_bce_3: 0.39733/0.30420, loss_mask_dice_3: 0.68009/1.02703, loss_spatial_bce_3: 0.25373/0.08828, loss_spatial_dice_3: 0.27490/0.18602, loss_spatial_ce_3: 0.09697/0.07073, loss_grounding_bce_3: 0.00000/0.08136, loss_grounding_dice_3: 0.00001/0.15118, loss_grounding_ce_3: 0.03213/0.25391, loss_mask_ce_4: 0.89817/0.77968, loss_mask_bce_4: 0.42111/0.30654, loss_mask_dice_4: 0.68972/1.04606, loss_spatial_bce_4: 0.27980/0.09024, loss_spatial_dice_4: 0.27267/0.19388, loss_spatial_ce_4: 0.08617/0.08368, loss_grounding_bce_4: 0.00000/0.08202, loss_grounding_dice_4: 0.00004/0.15380, loss_grounding_ce_4: 0.01847/0.25911, loss_mask_ce_5: 0.95801/0.80300, loss_mask_bce_5: 0.46006/0.30841, loss_mask_dice_5: 0.70064/1.05352, loss_spatial_bce_5: 0.25350/0.09232, loss_spatial_dice_5: 0.29427/0.19659, loss_spatial_ce_5: 0.11979/0.09582, loss_grounding_bce_5: 0.00000/0.08229, loss_grounding_dice_5: 0.00010/0.15447, loss_grounding_ce_5: 0.02922/0.27747, loss_mask_ce_6: 1.04965/0.82939, loss_mask_bce_6: 0.46157/0.31033, loss_mask_dice_6: 0.71413/1.05627, loss_spatial_bce_6: 0.27741/0.09739, loss_spatial_dice_6: 0.29784/0.19886, loss_spatial_ce_6: 0.10985/0.11970, loss_grounding_bce_6: 0.00000/0.08327, loss_grounding_dice_6: 0.00011/0.15503, loss_grounding_ce_6: 0.05843/0.28635, loss_mask_ce_7: 1.26409/0.88497, loss_mask_bce_7: 0.43148/0.31774, loss_mask_dice_7: 0.72389/1.10308, loss_spatial_bce_7: 0.36182/0.10754, loss_spatial_dice_7: 0.27977/0.22429, loss_spatial_ce_7: 0.37304/0.15824, loss_grounding_bce_7: 0.00000/0.08494, loss_grounding_dice_7: 0.00013/0.16084, loss_grounding_ce_7: 0.08009/0.32098, loss_mask_ce_8: 1.30786/1.02296, loss_mask_bce_8: 0.34826/0.33373, loss_mask_dice_8: 0.66988/1.18003, loss_spatial_bce_8: 0.17717/0.12553, loss_spatial_dice_8: 0.26866/0.26064, loss_spatial_ce_8: 0.79799/0.20868, loss_grounding_bce_8: 0.00000/0.08910, loss_grounding_dice_8: 0.00033/0.17044, loss_grounding_ce_8: 0.15426/0.42301, loss_mask_ce_9: 4.19942/3.48208, loss_mask_bce_9: 0.30494/0.36081, loss_mask_dice_9: 0.76762/1.76328, loss_spatial_bce_9: 0.50679/0.35559, loss_spatial_dice_9: 0.77441/0.79424, loss_spatial_ce_9: 1.66298/1.39522, loss_grounding_bce_9: 0.00000/0.10106, loss_grounding_dice_9: 0.00439/0.24302, loss_grounding_ce_9: 0.61098/0.67982] items per batch[64] items per second[0.36] total items[3212800] mini batches[ 50200] memory[4999] epoch remaining[0:27:57] INFO:trainer.default_trainer:epochs[ 27] optim steps[50300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09896/0.76195, loss_mask_bce_0: 0.24134/0.30163, loss_mask_dice_0: 0.12640/1.02475, loss_spatial_bce_0: 0.15151/0.08603, loss_spatial_dice_0: 0.07881/0.18196, loss_spatial_ce_0: 0.00100/0.05983, loss_grounding_bce_0: 0.15581/0.08081, loss_grounding_dice_0: 0.08210/0.15098, loss_grounding_ce_0: 0.01812/0.24929, loss_mask_ce_1: 0.09796/0.76266, loss_mask_bce_1: 0.24540/0.30252, loss_mask_dice_1: 0.13059/1.02873, loss_spatial_bce_1: 0.14861/0.08629, loss_spatial_dice_1: 0.08132/0.18453, loss_spatial_ce_1: 0.00111/0.06390, loss_grounding_bce_1: 0.15554/0.08100, loss_grounding_dice_1: 0.08464/0.15172, loss_grounding_ce_1: 0.01185/0.25108, loss_mask_ce_2: 0.08974/0.77060, loss_mask_bce_2: 0.24663/0.30262, loss_mask_dice_2: 0.12986/1.02970, loss_spatial_bce_2: 0.15132/0.08629, loss_spatial_dice_2: 0.08492/0.18482, loss_spatial_ce_2: 0.00170/0.06616, loss_grounding_bce_2: 0.15485/0.08093, loss_grounding_dice_2: 0.08328/0.15160, loss_grounding_ce_2: 0.01249/0.25390, loss_mask_ce_3: 0.08480/0.77357, loss_mask_bce_3: 0.24423/0.30419, loss_mask_dice_3: 0.13685/1.02724, loss_spatial_bce_3: 0.15002/0.08829, loss_spatial_dice_3: 0.08994/0.18600, loss_spatial_ce_3: 0.00199/0.07075, loss_grounding_bce_3: 0.15130/0.08138, loss_grounding_dice_3: 0.08183/0.15120, loss_grounding_ce_3: 0.01389/0.25395, loss_mask_ce_4: 0.09732/0.77947, loss_mask_bce_4: 0.24340/0.30653, loss_mask_dice_4: 0.13224/1.04626, loss_spatial_bce_4: 0.14865/0.09025, loss_spatial_dice_4: 0.08722/0.19384, loss_spatial_ce_4: 0.00325/0.08370, loss_grounding_bce_4: 0.15512/0.08204, loss_grounding_dice_4: 0.08304/0.15383, loss_grounding_ce_4: 0.01623/0.25912, loss_mask_ce_5: 0.07267/0.80281, loss_mask_bce_5: 0.24289/0.30840, loss_mask_dice_5: 0.14010/1.05369, loss_spatial_bce_5: 0.15185/0.09232, loss_spatial_dice_5: 0.08539/0.19655, loss_spatial_ce_5: 0.00356/0.09583, loss_grounding_bce_5: 0.15210/0.08232, loss_grounding_dice_5: 0.08683/0.15450, loss_grounding_ce_5: 0.01317/0.27749, loss_mask_ce_6: 0.09068/0.82925, loss_mask_bce_6: 0.24817/0.31032, loss_mask_dice_6: 0.14210/1.05648, loss_spatial_bce_6: 0.16444/0.09740, loss_spatial_dice_6: 0.07986/0.19882, loss_spatial_ce_6: 0.00851/0.11975, loss_grounding_bce_6: 0.15749/0.08329, loss_grounding_dice_6: 0.08796/0.15505, loss_grounding_ce_6: 0.01480/0.28632, loss_mask_ce_7: 0.11680/0.88474, loss_mask_bce_7: 0.24682/0.31774, loss_mask_dice_7: 0.14268/1.10333, loss_spatial_bce_7: 0.15377/0.10753, loss_spatial_dice_7: 0.09215/0.22425, loss_spatial_ce_7: 0.01843/0.15822, loss_grounding_bce_7: 0.14528/0.08496, loss_grounding_dice_7: 0.08565/0.16087, loss_grounding_ce_7: 0.02072/0.32099, loss_mask_ce_8: 0.12676/1.02267, loss_mask_bce_8: 0.24379/0.33373, loss_mask_dice_8: 0.13878/1.18035, loss_spatial_bce_8: 0.16201/0.12552, loss_spatial_dice_8: 0.09178/0.26059, loss_spatial_ce_8: 0.04872/0.20864, loss_grounding_bce_8: 0.15063/0.08912, loss_grounding_dice_8: 0.08618/0.17046, loss_grounding_ce_8: 0.03905/0.42333, loss_mask_ce_9: 1.66234/3.48184, loss_mask_bce_9: 0.28011/0.36082, loss_mask_dice_9: 0.14743/1.76362, loss_spatial_bce_9: 0.57533/0.35561, loss_spatial_dice_9: 0.67650/0.79423, loss_spatial_ce_9: 1.15756/1.39510, loss_grounding_bce_9: 0.17266/0.10109, loss_grounding_dice_9: 0.09118/0.24303, loss_grounding_ce_9: 0.17180/0.67986] items per batch[64] items per second[0.37] total items[3219200] mini batches[ 50300] memory[4999] epoch remaining[0:25:01] INFO:trainer.default_trainer:epochs[ 27] optim steps[50400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59236/0.76206, loss_mask_bce_0: 0.25867/0.30160, loss_mask_dice_0: 0.34077/1.02489, loss_spatial_bce_0: 0.17451/0.08601, loss_spatial_dice_0: 0.30490/0.18196, loss_spatial_ce_0: 0.11889/0.05982, loss_grounding_bce_0: 0.02069/0.08080, loss_grounding_dice_0: 0.21210/0.15097, loss_grounding_ce_0: 0.82901/0.24924, loss_mask_ce_1: 0.58078/0.76279, loss_mask_bce_1: 0.25160/0.30249, loss_mask_dice_1: 0.34318/1.02882, loss_spatial_bce_1: 0.18781/0.08627, loss_spatial_dice_1: 0.31244/0.18451, loss_spatial_ce_1: 0.13648/0.06390, loss_grounding_bce_1: 0.01664/0.08099, loss_grounding_dice_1: 0.18913/0.15170, loss_grounding_ce_1: 0.77770/0.25100, loss_mask_ce_2: 0.57454/0.77071, loss_mask_bce_2: 0.25806/0.30258, loss_mask_dice_2: 0.33019/1.02976, loss_spatial_bce_2: 0.16667/0.08627, loss_spatial_dice_2: 0.32842/0.18482, loss_spatial_ce_2: 0.15560/0.06614, loss_grounding_bce_2: 0.02294/0.08092, loss_grounding_dice_2: 0.19063/0.15158, loss_grounding_ce_2: 0.79493/0.25383, loss_mask_ce_3: 0.67325/0.77366, loss_mask_bce_3: 0.25221/0.30416, loss_mask_dice_3: 0.31492/1.02737, loss_spatial_bce_3: 0.16475/0.08827, loss_spatial_dice_3: 0.32109/0.18599, loss_spatial_ce_3: 0.20112/0.07074, loss_grounding_bce_3: 0.02481/0.08137, loss_grounding_dice_3: 0.19916/0.15118, loss_grounding_ce_3: 0.82645/0.25389, loss_mask_ce_4: 0.66283/0.77957, loss_mask_bce_4: 0.26224/0.30650, loss_mask_dice_4: 0.36924/1.04637, loss_spatial_bce_4: 0.13870/0.09023, loss_spatial_dice_4: 0.30113/0.19383, loss_spatial_ce_4: 0.31454/0.08371, loss_grounding_bce_4: 0.02462/0.08203, loss_grounding_dice_4: 0.27205/0.15381, loss_grounding_ce_4: 0.78159/0.25904, loss_mask_ce_5: 0.74034/0.80290, loss_mask_bce_5: 0.25661/0.30837, loss_mask_dice_5: 0.33576/1.05383, loss_spatial_bce_5: 0.17483/0.09231, loss_spatial_dice_5: 0.31240/0.19654, loss_spatial_ce_5: 0.24167/0.09581, loss_grounding_bce_5: 0.02395/0.08231, loss_grounding_dice_5: 0.21415/0.15450, loss_grounding_ce_5: 0.85990/0.27750, loss_mask_ce_6: 0.74994/0.82936, loss_mask_bce_6: 0.26836/0.31029, loss_mask_dice_6: 0.34956/1.05660, loss_spatial_bce_6: 0.23191/0.09739, loss_spatial_dice_6: 0.32683/0.19881, loss_spatial_ce_6: 0.37883/0.11974, loss_grounding_bce_6: 0.02470/0.08327, loss_grounding_dice_6: 0.21919/0.15503, loss_grounding_ce_6: 0.88735/0.28625, loss_mask_ce_7: 0.84286/0.88484, loss_mask_bce_7: 0.24410/0.31769, loss_mask_dice_7: 0.32188/1.10346, loss_spatial_bce_7: 0.22016/0.10751, loss_spatial_dice_7: 0.36363/0.22425, loss_spatial_ce_7: 0.34987/0.15824, loss_grounding_bce_7: 0.01829/0.08495, loss_grounding_dice_7: 0.20038/0.16087, loss_grounding_ce_7: 0.94036/0.32090, loss_mask_ce_8: 1.01903/1.02282, loss_mask_bce_8: 0.22224/0.33368, loss_mask_dice_8: 0.40210/1.18050, loss_spatial_bce_8: 0.31705/0.12548, loss_spatial_dice_8: 0.42742/0.26057, loss_spatial_ce_8: 0.39344/0.20862, loss_grounding_bce_8: 0.00989/0.08910, loss_grounding_dice_8: 0.26294/0.17044, loss_grounding_ce_8: 1.20678/0.42328, loss_mask_ce_9: 2.76738/3.48191, loss_mask_bce_9: 0.36695/0.36078, loss_mask_dice_9: 1.00838/1.76370, loss_spatial_bce_9: 0.48038/0.35557, loss_spatial_dice_9: 0.80353/0.79420, loss_spatial_ce_9: 1.25929/1.39503, loss_grounding_bce_9: 0.08655/0.10107, loss_grounding_dice_9: 0.89098/0.24302, loss_grounding_ce_9: 0.11225/0.67990] items per batch[64] items per second[0.37] total items[3225600] mini batches[ 50400] memory[4999] epoch remaining[0:22:04] INFO:trainer.default_trainer:epochs[ 27] optim steps[50500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.96566/0.76203, loss_mask_bce_0: 0.56009/0.30153, loss_mask_dice_0: 3.35386/1.02487, loss_spatial_bce_0: 0.06140/0.08599, loss_spatial_dice_0: 0.36868/0.18194, loss_spatial_ce_0: 0.03403/0.05981, loss_grounding_bce_0: 0.06414/0.08077, loss_grounding_dice_0: 0.30521/0.15097, loss_grounding_ce_0: 0.40735/0.24914, loss_mask_ce_1: 1.71856/0.76275, loss_mask_bce_1: 0.53645/0.30241, loss_mask_dice_1: 3.38830/1.02881, loss_spatial_bce_1: 0.05677/0.08625, loss_spatial_dice_1: 0.36691/0.18450, loss_spatial_ce_1: 0.04949/0.06389, loss_grounding_bce_1: 0.04826/0.08096, loss_grounding_dice_1: 0.25767/0.15170, loss_grounding_ce_1: 0.28077/0.25088, loss_mask_ce_2: 1.87128/0.77066, loss_mask_bce_2: 0.56858/0.30251, loss_mask_dice_2: 3.38530/1.02969, loss_spatial_bce_2: 0.05884/0.08625, loss_spatial_dice_2: 0.35989/0.18480, loss_spatial_ce_2: 0.07571/0.06612, loss_grounding_bce_2: 0.05876/0.08089, loss_grounding_dice_2: 0.30143/0.15158, loss_grounding_ce_2: 0.20517/0.25372, loss_mask_ce_3: 1.74757/0.77363, loss_mask_bce_3: 0.56142/0.30409, loss_mask_dice_3: 3.22629/1.02740, loss_spatial_bce_3: 0.06482/0.08825, loss_spatial_dice_3: 0.39006/0.18598, loss_spatial_ce_3: 0.07939/0.07073, loss_grounding_bce_3: 0.05646/0.08134, loss_grounding_dice_3: 0.31083/0.15118, loss_grounding_ce_3: 0.39530/0.25382, loss_mask_ce_4: 1.68107/0.77952, loss_mask_bce_4: 0.56172/0.30643, loss_mask_dice_4: 3.27630/1.04635, loss_spatial_bce_4: 0.06233/0.09021, loss_spatial_dice_4: 0.39716/0.19381, loss_spatial_ce_4: 0.04775/0.08368, loss_grounding_bce_4: 0.04671/0.08200, loss_grounding_dice_4: 0.31895/0.15381, loss_grounding_ce_4: 0.25812/0.25897, loss_mask_ce_5: 1.91022/0.80286, loss_mask_bce_5: 0.53590/0.30831, loss_mask_dice_5: 3.13792/1.05383, loss_spatial_bce_5: 0.06290/0.09229, loss_spatial_dice_5: 0.40204/0.19653, loss_spatial_ce_5: 0.13394/0.09579, loss_grounding_bce_5: 0.05124/0.08228, loss_grounding_dice_5: 0.30996/0.15451, loss_grounding_ce_5: 0.40945/0.27736, loss_mask_ce_6: 2.21188/0.82933, loss_mask_bce_6: 0.51970/0.31022, loss_mask_dice_6: 3.20919/1.05661, loss_spatial_bce_6: 0.10552/0.09737, loss_spatial_dice_6: 0.42750/0.19881, loss_spatial_ce_6: 0.10573/0.11972, loss_grounding_bce_6: 0.05657/0.08324, loss_grounding_dice_6: 0.32899/0.15502, loss_grounding_ce_6: 0.22265/0.28614, loss_mask_ce_7: 2.14550/0.88487, loss_mask_bce_7: 0.56598/0.31761, loss_mask_dice_7: 3.41907/1.10341, loss_spatial_bce_7: 0.14578/0.10750, loss_spatial_dice_7: 0.49388/0.22426, loss_spatial_ce_7: 0.21755/0.15828, loss_grounding_bce_7: 0.07501/0.08492, loss_grounding_dice_7: 0.39772/0.16086, loss_grounding_ce_7: 0.24365/0.32077, loss_mask_ce_8: 2.71950/1.02276, loss_mask_bce_8: 0.52453/0.33363, loss_mask_dice_8: 3.67215/1.18046, loss_spatial_bce_8: 0.14672/0.12547, loss_spatial_dice_8: 0.58705/0.26057, loss_spatial_ce_8: 0.33741/0.20860, loss_grounding_bce_8: 0.06871/0.08907, loss_grounding_dice_8: 0.29723/0.17044, loss_grounding_ce_8: 0.37337/0.42313, loss_mask_ce_9: 4.28725/3.48180, loss_mask_bce_9: 0.50946/0.36071, loss_mask_dice_9: 4.85712/1.76346, loss_spatial_bce_9: 0.19976/0.35556, loss_spatial_dice_9: 0.95825/0.79417, loss_spatial_ce_9: 1.35021/1.39495, loss_grounding_bce_9: 0.11194/0.10103, loss_grounding_dice_9: 0.55625/0.24302, loss_grounding_ce_9: 0.35640/0.67959] items per batch[64] items per second[0.36] total items[3232000] mini batches[ 50500] memory[4999] epoch remaining[0:19:09] INFO:trainer.default_trainer:epochs[ 27] optim steps[50600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.82550/0.76211, loss_mask_bce_0: 0.36108/0.30156, loss_mask_dice_0: 0.76970/1.02497, loss_spatial_bce_0: 0.11929/0.08600, loss_spatial_dice_0: 0.23240/0.18193, loss_spatial_ce_0: 0.07476/0.05978, loss_grounding_bce_0: 0.13432/0.08080, loss_grounding_dice_0: 0.10496/0.15097, loss_grounding_ce_0: 0.28331/0.24916, loss_mask_ce_1: 0.97104/0.76284, loss_mask_bce_1: 0.35420/0.30245, loss_mask_dice_1: 0.72449/1.02887, loss_spatial_bce_1: 0.12868/0.08626, loss_spatial_dice_1: 0.23740/0.18449, loss_spatial_ce_1: 0.04613/0.06385, loss_grounding_bce_1: 0.14067/0.08098, loss_grounding_dice_1: 0.10119/0.15170, loss_grounding_ce_1: 0.30008/0.25087, loss_mask_ce_2: 1.07570/0.77083, loss_mask_bce_2: 0.37888/0.30255, loss_mask_dice_2: 0.72771/1.02977, loss_spatial_bce_2: 0.13659/0.08626, loss_spatial_dice_2: 0.23709/0.18479, loss_spatial_ce_2: 0.07110/0.06609, loss_grounding_bce_2: 0.13326/0.08092, loss_grounding_dice_2: 0.10282/0.15157, loss_grounding_ce_2: 0.23193/0.25372, loss_mask_ce_3: 0.99291/0.77375, loss_mask_bce_3: 0.36427/0.30413, loss_mask_dice_3: 0.67904/1.02747, loss_spatial_bce_3: 0.14079/0.08827, loss_spatial_dice_3: 0.24646/0.18597, loss_spatial_ce_3: 0.05214/0.07070, loss_grounding_bce_3: 0.15655/0.08137, loss_grounding_dice_3: 0.09814/0.15117, loss_grounding_ce_3: 0.26075/0.25382, loss_mask_ce_4: 0.88442/0.77960, loss_mask_bce_4: 0.40966/0.30648, loss_mask_dice_4: 0.76232/1.04642, loss_spatial_bce_4: 0.14472/0.09023, loss_spatial_dice_4: 0.25638/0.19380, loss_spatial_ce_4: 0.10510/0.08366, loss_grounding_bce_4: 0.15288/0.08202, loss_grounding_dice_4: 0.09690/0.15381, loss_grounding_ce_4: 0.28848/0.25909, loss_mask_ce_5: 0.98699/0.80300, loss_mask_bce_5: 0.33394/0.30835, loss_mask_dice_5: 0.76198/1.05395, loss_spatial_bce_5: 0.13310/0.09231, loss_spatial_dice_5: 0.25700/0.19653, loss_spatial_ce_5: 0.24980/0.09574, loss_grounding_bce_5: 0.11726/0.08230, loss_grounding_dice_5: 0.11074/0.15450, loss_grounding_ce_5: 0.33613/0.27750, loss_mask_ce_6: 0.71524/0.82948, loss_mask_bce_6: 0.35725/0.31027, loss_mask_dice_6: 0.78158/1.05674, loss_spatial_bce_6: 0.15354/0.09739, loss_spatial_dice_6: 0.27737/0.19880, loss_spatial_ce_6: 0.23296/0.11971, loss_grounding_bce_6: 0.12584/0.08326, loss_grounding_dice_6: 0.10169/0.15502, loss_grounding_ce_6: 0.34456/0.28625, loss_mask_ce_7: 0.96431/0.88502, loss_mask_bce_7: 0.31165/0.31767, loss_mask_dice_7: 0.74792/1.10350, loss_spatial_bce_7: 0.07920/0.10749, loss_spatial_dice_7: 0.24150/0.22425, loss_spatial_ce_7: 0.33768/0.15825, loss_grounding_bce_7: 0.08519/0.08496, loss_grounding_dice_7: 0.09979/0.16086, loss_grounding_ce_7: 0.25346/0.32075, loss_mask_ce_8: 0.96951/1.02294, loss_mask_bce_8: 0.29998/0.33368, loss_mask_dice_8: 0.83516/1.18066, loss_spatial_bce_8: 0.12121/0.12547, loss_spatial_dice_8: 0.22768/0.26057, loss_spatial_ce_8: 0.28439/0.20850, loss_grounding_bce_8: 0.11128/0.08910, loss_grounding_dice_8: 0.10027/0.17044, loss_grounding_ce_8: 0.66889/0.42311, loss_mask_ce_9: 4.01631/3.48226, loss_mask_bce_9: 0.45092/0.36077, loss_mask_dice_9: 1.30981/1.76375, loss_spatial_bce_9: 0.34677/0.35558, loss_spatial_dice_9: 0.91754/0.79419, loss_spatial_ce_9: 1.43623/1.39489, loss_grounding_bce_9: 0.12172/0.10106, loss_grounding_dice_9: 0.17895/0.24302, loss_grounding_ce_9: 2.14672/0.67964] items per batch[64] items per second[0.35] total items[3238400] mini batches[ 50600] memory[4999] epoch remaining[0:16:17] INFO:trainer.default_trainer:epochs[ 27] optim steps[50700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87213/0.76188, loss_mask_bce_0: 0.41530/0.30148, loss_mask_dice_0: 1.33825/1.02529, loss_spatial_bce_0: 0.05570/0.08599, loss_spatial_dice_0: 0.24230/0.18191, loss_spatial_ce_0: 0.15072/0.05977, loss_grounding_bce_0: 0.09632/0.08078, loss_grounding_dice_0: 0.25955/0.15098, loss_grounding_ce_0: 0.08941/0.24914, loss_mask_ce_1: 0.93737/0.76260, loss_mask_bce_1: 0.43971/0.30238, loss_mask_dice_1: 1.38579/1.02922, loss_spatial_bce_1: 0.05991/0.08625, loss_spatial_dice_1: 0.26744/0.18446, loss_spatial_ce_1: 0.04274/0.06381, loss_grounding_bce_1: 0.10089/0.08096, loss_grounding_dice_1: 0.25697/0.15173, loss_grounding_ce_1: 0.08267/0.25086, loss_mask_ce_2: 0.89243/0.77056, loss_mask_bce_2: 0.42471/0.30247, loss_mask_dice_2: 1.42310/1.03012, loss_spatial_bce_2: 0.05602/0.08626, loss_spatial_dice_2: 0.24500/0.18476, loss_spatial_ce_2: 0.22333/0.06606, loss_grounding_bce_2: 0.10303/0.08090, loss_grounding_dice_2: 0.26606/0.15159, loss_grounding_ce_2: 0.07698/0.25372, loss_mask_ce_3: 0.93552/0.77350, loss_mask_bce_3: 0.42675/0.30405, loss_mask_dice_3: 1.45298/1.02774, loss_spatial_bce_3: 0.05939/0.08826, loss_spatial_dice_3: 0.28894/0.18595, loss_spatial_ce_3: 0.07932/0.07065, loss_grounding_bce_3: 0.09939/0.08135, loss_grounding_dice_3: 0.25527/0.15119, loss_grounding_ce_3: 0.07045/0.25384, loss_mask_ce_4: 0.94750/0.77937, loss_mask_bce_4: 0.42625/0.30640, loss_mask_dice_4: 1.35819/1.04674, loss_spatial_bce_4: 0.06676/0.09023, loss_spatial_dice_4: 0.32182/0.19378, loss_spatial_ce_4: 0.10861/0.08365, loss_grounding_bce_4: 0.10171/0.08200, loss_grounding_dice_4: 0.27160/0.15382, loss_grounding_ce_4: 0.06282/0.25912, loss_mask_ce_5: 0.85063/0.80276, loss_mask_bce_5: 0.42577/0.30828, loss_mask_dice_5: 1.43896/1.05432, loss_spatial_bce_5: 0.08467/0.09231, loss_spatial_dice_5: 0.37920/0.19651, loss_spatial_ce_5: 0.23444/0.09569, loss_grounding_bce_5: 0.10525/0.08228, loss_grounding_dice_5: 0.26206/0.15453, loss_grounding_ce_5: 0.07776/0.27750, loss_mask_ce_6: 1.02383/0.82921, loss_mask_bce_6: 0.36840/0.31019, loss_mask_dice_6: 1.35922/1.05707, loss_spatial_bce_6: 0.09008/0.09740, loss_spatial_dice_6: 0.32575/0.19879, loss_spatial_ce_6: 0.17021/0.11966, loss_grounding_bce_6: 0.10164/0.08324, loss_grounding_dice_6: 0.25140/0.15504, loss_grounding_ce_6: 0.07566/0.28626, loss_mask_ce_7: 1.34399/0.88475, loss_mask_bce_7: 0.37564/0.31759, loss_mask_dice_7: 1.33715/1.10378, loss_spatial_bce_7: 0.09643/0.10750, loss_spatial_dice_7: 0.39799/0.22424, loss_spatial_ce_7: 0.26010/0.15823, loss_grounding_bce_7: 0.09491/0.08494, loss_grounding_dice_7: 0.23392/0.16088, loss_grounding_ce_7: 0.28205/0.32069, loss_mask_ce_8: 1.17665/1.02272, loss_mask_bce_8: 0.69507/0.33362, loss_mask_dice_8: 1.46855/1.18108, loss_spatial_bce_8: 0.09012/0.12547, loss_spatial_dice_8: 0.38586/0.26055, loss_spatial_ce_8: 0.24576/0.20847, loss_grounding_bce_8: 0.10388/0.08909, loss_grounding_dice_8: 0.25977/0.17047, loss_grounding_ce_8: 0.35175/0.42307, loss_mask_ce_9: 4.39580/3.48203, loss_mask_bce_9: 0.42888/0.36070, loss_mask_dice_9: 2.02046/1.76419, loss_spatial_bce_9: 0.22984/0.35555, loss_spatial_dice_9: 0.85105/0.79418, loss_spatial_ce_9: 1.01922/1.39479, loss_grounding_bce_9: 0.11495/0.10105, loss_grounding_dice_9: 0.42363/0.24304, loss_grounding_ce_9: 0.06012/0.67953] items per batch[64] items per second[0.36] total items[3244800] mini batches[ 50700] memory[4999] epoch remaining[0:13:21] INFO:trainer.default_trainer:epochs[ 27] optim steps[50800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59009/0.76192, loss_mask_bce_0: 0.13261/0.30156, loss_mask_dice_0: 1.35625/1.02541, loss_spatial_bce_0: 0.00959/0.08597, loss_spatial_dice_0: 0.08369/0.18188, loss_spatial_ce_0: 0.00032/0.05974, loss_grounding_bce_0: 0.01239/0.08075, loss_grounding_dice_0: 0.04512/0.15098, loss_grounding_ce_0: 0.03966/0.24926, loss_mask_ce_1: 0.58825/0.76264, loss_mask_bce_1: 0.13205/0.30247, loss_mask_dice_1: 1.41154/1.02932, loss_spatial_bce_1: 0.01019/0.08623, loss_spatial_dice_1: 0.08822/0.18444, loss_spatial_ce_1: 0.00013/0.06376, loss_grounding_bce_1: 0.01433/0.08094, loss_grounding_dice_1: 0.04436/0.15172, loss_grounding_ce_1: 0.02390/0.25098, loss_mask_ce_2: 0.56771/0.77062, loss_mask_bce_2: 0.13046/0.30257, loss_mask_dice_2: 1.31099/1.03022, loss_spatial_bce_2: 0.01071/0.08624, loss_spatial_dice_2: 0.09641/0.18473, loss_spatial_ce_2: 0.00051/0.06603, loss_grounding_bce_2: 0.01157/0.08087, loss_grounding_dice_2: 0.03981/0.15159, loss_grounding_ce_2: 0.03947/0.25394, loss_mask_ce_3: 0.70017/0.77359, loss_mask_bce_3: 0.13097/0.30413, loss_mask_dice_3: 1.41957/1.02788, loss_spatial_bce_3: 0.01006/0.08824, loss_spatial_dice_3: 0.08697/0.18592, loss_spatial_ce_3: 0.00034/0.07061, loss_grounding_bce_3: 0.01229/0.08132, loss_grounding_dice_3: 0.04315/0.15120, loss_grounding_ce_3: 0.06032/0.25403, loss_mask_ce_4: 0.39897/0.77941, loss_mask_bce_4: 0.13831/0.30648, loss_mask_dice_4: 1.39406/1.04692, loss_spatial_bce_4: 0.01215/0.09022, loss_spatial_dice_4: 0.12200/0.19376, loss_spatial_ce_4: 0.00068/0.08363, loss_grounding_bce_4: 0.01201/0.08197, loss_grounding_dice_4: 0.04038/0.15381, loss_grounding_ce_4: 0.02233/0.25926, loss_mask_ce_5: 0.59298/0.80287, loss_mask_bce_5: 0.13138/0.30834, loss_mask_dice_5: 1.23303/1.05439, loss_spatial_bce_5: 0.01033/0.09230, loss_spatial_dice_5: 0.09358/0.19649, loss_spatial_ce_5: 0.02667/0.09566, loss_grounding_bce_5: 0.01416/0.08225, loss_grounding_dice_5: 0.04332/0.15452, loss_grounding_ce_5: 0.31650/0.27771, loss_mask_ce_6: 0.70832/0.82931, loss_mask_bce_6: 0.13668/0.31026, loss_mask_dice_6: 1.62600/1.05719, loss_spatial_bce_6: 0.01107/0.09739, loss_spatial_dice_6: 0.07654/0.19877, loss_spatial_ce_6: 0.10321/0.11963, loss_grounding_bce_6: 0.01247/0.08320, loss_grounding_dice_6: 0.03925/0.15503, loss_grounding_ce_6: 0.21037/0.28645, loss_mask_ce_7: 0.66404/0.88492, loss_mask_bce_7: 0.14260/0.31765, loss_mask_dice_7: 1.11148/1.10385, loss_spatial_bce_7: 0.01361/0.10748, loss_spatial_dice_7: 0.11262/0.22422, loss_spatial_ce_7: 0.04622/0.15823, loss_grounding_bce_7: 0.01408/0.08491, loss_grounding_dice_7: 0.04559/0.16086, loss_grounding_ce_7: 0.04666/0.32087, loss_mask_ce_8: 1.44232/1.02286, loss_mask_bce_8: 0.16572/0.33369, loss_mask_dice_8: 1.42931/1.18115, loss_spatial_bce_8: 0.01434/0.12543, loss_spatial_dice_8: 0.14278/0.26050, loss_spatial_ce_8: 0.02074/0.20840, loss_grounding_bce_8: 0.01449/0.08905, loss_grounding_dice_8: 0.04983/0.17045, loss_grounding_ce_8: 0.78296/0.42314, loss_mask_ce_9: 3.91207/3.48247, loss_mask_bce_9: 0.20091/0.36079, loss_mask_dice_9: 2.75731/1.76436, loss_spatial_bce_9: 0.19827/0.35551, loss_spatial_dice_9: 0.93196/0.79419, loss_spatial_ce_9: 1.34773/1.39497, loss_grounding_bce_9: 0.01508/0.10103, loss_grounding_dice_9: 0.05673/0.24306, loss_grounding_ce_9: 1.60870/0.67979] items per batch[64] items per second[0.36] total items[3251200] mini batches[ 50800] memory[4999] epoch remaining[0:10:25] INFO:trainer.default_trainer:epochs[ 27] optim steps[50900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.88976/0.76195, loss_mask_bce_0: 0.23787/0.30154, loss_mask_dice_0: 0.72693/1.02531, loss_spatial_bce_0: 0.05284/0.08597, loss_spatial_dice_0: 0.14964/0.18185, loss_spatial_ce_0: 0.00263/0.05974, loss_grounding_bce_0: 0.10087/0.08075, loss_grounding_dice_0: 0.11639/0.15093, loss_grounding_ce_0: 0.05131/0.24927, loss_mask_ce_1: 0.83741/0.76268, loss_mask_bce_1: 0.24209/0.30244, loss_mask_dice_1: 0.71177/1.02925, loss_spatial_bce_1: 0.05309/0.08624, loss_spatial_dice_1: 0.17307/0.18441, loss_spatial_ce_1: 0.00353/0.06375, loss_grounding_bce_1: 0.10047/0.08093, loss_grounding_dice_1: 0.11209/0.15167, loss_grounding_ce_1: 0.05192/0.25099, loss_mask_ce_2: 1.47317/0.77069, loss_mask_bce_2: 0.22405/0.30255, loss_mask_dice_2: 0.74495/1.03016, loss_spatial_bce_2: 0.05362/0.08624, loss_spatial_dice_2: 0.17066/0.18471, loss_spatial_ce_2: 0.00144/0.06600, loss_grounding_bce_2: 0.09909/0.08087, loss_grounding_dice_2: 0.11119/0.15154, loss_grounding_ce_2: 0.05131/0.25394, loss_mask_ce_3: 1.46284/0.77364, loss_mask_bce_3: 0.23300/0.30412, loss_mask_dice_3: 0.77074/1.02780, loss_spatial_bce_3: 0.05252/0.08825, loss_spatial_dice_3: 0.14905/0.18590, loss_spatial_ce_3: 0.00099/0.07058, loss_grounding_bce_3: 0.09900/0.08132, loss_grounding_dice_3: 0.11598/0.15115, loss_grounding_ce_3: 0.06557/0.25403, loss_mask_ce_4: 1.16038/0.77949, loss_mask_bce_4: 0.22572/0.30646, loss_mask_dice_4: 0.72657/1.04685, loss_spatial_bce_4: 0.05598/0.09022, loss_spatial_dice_4: 0.16597/0.19374, loss_spatial_ce_4: 0.00084/0.08361, loss_grounding_bce_4: 0.09861/0.08197, loss_grounding_dice_4: 0.10705/0.15377, loss_grounding_ce_4: 0.05377/0.25925, loss_mask_ce_5: 1.09024/0.80294, loss_mask_bce_5: 0.22876/0.30832, loss_mask_dice_5: 0.73412/1.05431, loss_spatial_bce_5: 0.05942/0.09229, loss_spatial_dice_5: 0.20054/0.19647, loss_spatial_ce_5: 0.00192/0.09567, loss_grounding_bce_5: 0.09732/0.08224, loss_grounding_dice_5: 0.11492/0.15447, loss_grounding_ce_5: 0.06852/0.27770, loss_mask_ce_6: 1.01878/0.82945, loss_mask_bce_6: 0.23116/0.31024, loss_mask_dice_6: 0.73949/1.05711, loss_spatial_bce_6: 0.06265/0.09738, loss_spatial_dice_6: 0.18904/0.19876, loss_spatial_ce_6: 0.01142/0.11961, loss_grounding_bce_6: 0.10249/0.08320, loss_grounding_dice_6: 0.11869/0.15497, loss_grounding_ce_6: 0.07342/0.28645, loss_mask_ce_7: 1.14342/0.88505, loss_mask_bce_7: 0.22347/0.31762, loss_mask_dice_7: 0.68320/1.10376, loss_spatial_bce_7: 0.06196/0.10747, loss_spatial_dice_7: 0.20785/0.22421, loss_spatial_ce_7: 0.06855/0.15825, loss_grounding_bce_7: 0.10189/0.08490, loss_grounding_dice_7: 0.10867/0.16079, loss_grounding_ce_7: 0.06438/0.32086, loss_mask_ce_8: 1.47809/1.02292, loss_mask_bce_8: 0.23544/0.33367, loss_mask_dice_8: 0.76251/1.18105, loss_spatial_bce_8: 0.07124/0.12542, loss_spatial_dice_8: 0.22126/0.26047, loss_spatial_ce_8: 0.14369/0.20837, loss_grounding_bce_8: 0.10199/0.08906, loss_grounding_dice_8: 0.11016/0.17038, loss_grounding_ce_8: 0.03611/0.42304, loss_mask_ce_9: 3.53545/3.48282, loss_mask_bce_9: 0.24887/0.36078, loss_mask_dice_9: 1.20402/1.76449, loss_spatial_bce_9: 0.39830/0.35551, loss_spatial_dice_9: 0.80368/0.79417, loss_spatial_ce_9: 1.07834/1.39496, loss_grounding_bce_9: 0.10367/0.10105, loss_grounding_dice_9: 0.17217/0.24302, loss_grounding_ce_9: 0.22735/0.67996] items per batch[64] items per second[0.36] total items[3257600] mini batches[ 50900] memory[4999] epoch remaining[0:07:29] INFO:trainer.default_trainer:epochs[ 27] optim steps[51000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07317/0.76195, loss_mask_bce_0: 0.22348/0.30160, loss_mask_dice_0: 0.21319/1.02544, loss_spatial_bce_0: 0.08116/0.08598, loss_spatial_dice_0: 0.08128/0.18185, loss_spatial_ce_0: 0.00186/0.05974, loss_grounding_bce_0: 0.08495/0.08077, loss_grounding_dice_0: 0.08945/0.15096, loss_grounding_ce_0: 0.00330/0.24915, loss_mask_ce_1: 0.08575/0.76263, loss_mask_bce_1: 0.23793/0.30251, loss_mask_dice_1: 0.20916/1.02936, loss_spatial_bce_1: 0.08336/0.08624, loss_spatial_dice_1: 0.07462/0.18441, loss_spatial_ce_1: 0.00240/0.06376, loss_grounding_bce_1: 0.09201/0.08095, loss_grounding_dice_1: 0.09867/0.15170, loss_grounding_ce_1: 0.00287/0.25087, loss_mask_ce_2: 0.07605/0.77064, loss_mask_bce_2: 0.24444/0.30263, loss_mask_dice_2: 0.20356/1.03025, loss_spatial_bce_2: 0.07480/0.08625, loss_spatial_dice_2: 0.08269/0.18470, loss_spatial_ce_2: 0.00309/0.06600, loss_grounding_bce_2: 0.08646/0.08091, loss_grounding_dice_2: 0.09390/0.15156, loss_grounding_ce_2: 0.00380/0.25379, loss_mask_ce_3: 0.07077/0.77361, loss_mask_bce_3: 0.24667/0.30418, loss_mask_dice_3: 0.19441/1.02795, loss_spatial_bce_3: 0.08241/0.08826, loss_spatial_dice_3: 0.07852/0.18590, loss_spatial_ce_3: 0.01407/0.07061, loss_grounding_bce_3: 0.08743/0.08135, loss_grounding_dice_3: 0.09496/0.15117, loss_grounding_ce_3: 0.00342/0.25391, loss_mask_ce_4: 0.07253/0.77945, loss_mask_bce_4: 0.22901/0.30653, loss_mask_dice_4: 0.20428/1.04692, loss_spatial_bce_4: 0.07020/0.09023, loss_spatial_dice_4: 0.07673/0.19373, loss_spatial_ce_4: 0.01142/0.08362, loss_grounding_bce_4: 0.08222/0.08200, loss_grounding_dice_4: 0.09084/0.15379, loss_grounding_ce_4: 0.00195/0.25915, loss_mask_ce_5: 0.09778/0.80288, loss_mask_bce_5: 0.22269/0.30840, loss_mask_dice_5: 0.20379/1.05441, loss_spatial_bce_5: 0.07293/0.09229, loss_spatial_dice_5: 0.08159/0.19647, loss_spatial_ce_5: 0.00395/0.09569, loss_grounding_bce_5: 0.08481/0.08227, loss_grounding_dice_5: 0.09584/0.15448, loss_grounding_ce_5: 0.00395/0.27765, loss_mask_ce_6: 0.15091/0.82946, loss_mask_bce_6: 0.23668/0.31032, loss_mask_dice_6: 0.20286/1.05723, loss_spatial_bce_6: 0.10016/0.09739, loss_spatial_dice_6: 0.08225/0.19877, loss_spatial_ce_6: 0.02027/0.11962, loss_grounding_bce_6: 0.08967/0.08323, loss_grounding_dice_6: 0.09527/0.15499, loss_grounding_ce_6: 0.00632/0.28632, loss_mask_ce_7: 0.16419/0.88500, loss_mask_bce_7: 0.20196/0.31769, loss_mask_dice_7: 0.19118/1.10387, loss_spatial_bce_7: 0.09196/0.10748, loss_spatial_dice_7: 0.08760/0.22421, loss_spatial_ce_7: 0.20325/0.15825, loss_grounding_bce_7: 0.08789/0.08493, loss_grounding_dice_7: 0.09319/0.16081, loss_grounding_ce_7: 0.01195/0.32068, loss_mask_ce_8: 0.09483/1.02284, loss_mask_bce_8: 0.21314/0.33370, loss_mask_dice_8: 0.20919/1.18111, loss_spatial_bce_8: 0.20588/0.12542, loss_spatial_dice_8: 0.14450/0.26047, loss_spatial_ce_8: 0.18509/0.20835, loss_grounding_bce_8: 0.08706/0.08908, loss_grounding_dice_8: 0.09467/0.17041, loss_grounding_ce_8: 0.00761/0.42279, loss_mask_ce_9: 2.03144/3.48227, loss_mask_bce_9: 0.24027/0.36081, loss_mask_dice_9: 0.26694/1.76461, loss_spatial_bce_9: 0.46309/0.35556, loss_spatial_dice_9: 0.71214/0.79415, loss_spatial_ce_9: 0.97692/1.39486, loss_grounding_bce_9: 0.10907/0.10106, loss_grounding_dice_9: 0.11490/0.24301, loss_grounding_ce_9: 0.05648/0.67971] items per batch[64] items per second[0.37] total items[3264000] mini batches[ 51000] memory[4999] epoch remaining[0:04:33] INFO:trainer.default_trainer:epochs[ 27] optim steps[51100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.31601/0.76177, loss_mask_bce_0: 0.76336/0.30154, loss_mask_dice_0: 0.61783/1.02475, loss_spatial_bce_0: 0.23694/0.08602, loss_spatial_dice_0: 0.21270/0.18184, loss_spatial_ce_0: 0.11068/0.05971, loss_grounding_bce_0: 0.32440/0.08078, loss_grounding_dice_0: 0.21282/0.15094, loss_grounding_ce_0: 0.15735/0.24910, loss_mask_ce_1: 1.32931/0.76245, loss_mask_bce_1: 0.75887/0.30246, loss_mask_dice_1: 0.60915/1.02870, loss_spatial_bce_1: 0.23103/0.08629, loss_spatial_dice_1: 0.22344/0.18440, loss_spatial_ce_1: 0.10901/0.06375, loss_grounding_bce_1: 0.30458/0.08097, loss_grounding_dice_1: 0.21911/0.15167, loss_grounding_ce_1: 0.17902/0.25080, loss_mask_ce_2: 1.37026/0.77050, loss_mask_bce_2: 0.73988/0.30259, loss_mask_dice_2: 0.61099/1.02960, loss_spatial_bce_2: 0.22987/0.08629, loss_spatial_dice_2: 0.22927/0.18469, loss_spatial_ce_2: 0.10784/0.06598, loss_grounding_bce_2: 0.28905/0.08092, loss_grounding_dice_2: 0.23384/0.15154, loss_grounding_ce_2: 0.23188/0.25371, loss_mask_ce_3: 1.30757/0.77345, loss_mask_bce_3: 0.73140/0.30414, loss_mask_dice_3: 0.59339/1.02733, loss_spatial_bce_3: 0.25409/0.08830, loss_spatial_dice_3: 0.23626/0.18589, loss_spatial_ce_3: 0.10752/0.07058, loss_grounding_bce_3: 0.34874/0.08137, loss_grounding_dice_3: 0.18097/0.15116, loss_grounding_ce_3: 0.26005/0.25381, loss_mask_ce_4: 1.36604/0.77929, loss_mask_bce_4: 0.72666/0.30649, loss_mask_dice_4: 0.58503/1.04627, loss_spatial_bce_4: 0.23240/0.09026, loss_spatial_dice_4: 0.22430/0.19373, loss_spatial_ce_4: 0.07206/0.08360, loss_grounding_bce_4: 0.37785/0.08202, loss_grounding_dice_4: 0.21179/0.15378, loss_grounding_ce_4: 0.20071/0.25904, loss_mask_ce_5: 1.31254/0.80278, loss_mask_bce_5: 0.79681/0.30835, loss_mask_dice_5: 0.55111/1.05374, loss_spatial_bce_5: 0.28341/0.09233, loss_spatial_dice_5: 0.25446/0.19646, loss_spatial_ce_5: 0.13879/0.09567, loss_grounding_bce_5: 0.40008/0.08229, loss_grounding_dice_5: 0.24168/0.15446, loss_grounding_ce_5: 0.20034/0.27763, loss_mask_ce_6: 1.08624/0.82934, loss_mask_bce_6: 0.79502/0.31026, loss_mask_dice_6: 0.60190/1.05655, loss_spatial_bce_6: 0.24628/0.09743, loss_spatial_dice_6: 0.25390/0.19876, loss_spatial_ce_6: 0.31175/0.11960, loss_grounding_bce_6: 0.32551/0.08325, loss_grounding_dice_6: 0.28812/0.15497, loss_grounding_ce_6: 0.29860/0.28628, loss_mask_ce_7: 1.08981/0.88483, loss_mask_bce_7: 0.72997/0.31763, loss_mask_dice_7: 0.64183/1.10315, loss_spatial_bce_7: 0.27563/0.10751, loss_spatial_dice_7: 0.28531/0.22421, loss_spatial_ce_7: 0.21771/0.15826, loss_grounding_bce_7: 0.34038/0.08494, loss_grounding_dice_7: 0.27829/0.16079, loss_grounding_ce_7: 0.20819/0.32060, loss_mask_ce_8: 1.01478/1.02265, loss_mask_bce_8: 0.68244/0.33362, loss_mask_dice_8: 0.59632/1.18036, loss_spatial_bce_8: 0.44927/0.12545, loss_spatial_dice_8: 0.36187/0.26044, loss_spatial_ce_8: 0.26158/0.20835, loss_grounding_bce_8: 0.23177/0.08909, loss_grounding_dice_8: 0.26163/0.17039, loss_grounding_ce_8: 0.47144/0.42278, loss_mask_ce_9: 2.82579/3.48168, loss_mask_bce_9: 0.77376/0.36073, loss_mask_dice_9: 0.92873/1.76338, loss_spatial_bce_9: 0.46556/0.35560, loss_spatial_dice_9: 0.87672/0.79407, loss_spatial_ce_9: 1.48018/1.39470, loss_grounding_bce_9: 0.48414/0.10108, loss_grounding_dice_9: 0.63987/0.24300, loss_grounding_ce_9: 0.90874/0.67957] items per batch[64] items per second[0.36] total items[3270400] mini batches[ 51100] memory[4999] epoch remaining[0:01:38] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00051156. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0023 s/iter. Inference: 0.3628 s/iter. Eval: 0.0934 s/iter. Total: 0.4585 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3705 s/iter. Eval: 0.0778 s/iter. Total: 0.4508 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3750 s/iter. Eval: 0.0783 s/iter. Total: 0.4560 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3775 s/iter. Eval: 0.0744 s/iter. Total: 0.4546 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0027 s/iter. Inference: 0.3794 s/iter. Eval: 0.0724 s/iter. Total: 0.4546 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0028 s/iter. Inference: 0.3781 s/iter. Eval: 0.0710 s/iter. Total: 0.4519 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalyfzv4joq ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.223 | 83.096 | 65.674 | 133 | | Things | 61.404 | 84.115 | 72.533 | 80 | | Stuff | 45.892 | 81.557 | 55.322 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.53s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.15 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.36 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.84s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.27 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.695 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.671 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.674 | 69.521 | 49.244 | 25.936 | 49.768 | 67.149 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.566 | bicycle | 24.791 | car | 44.504 | | motorcycle | 42.544 | airplane | 61.912 | bus | 71.307 | | train | 74.203 | truck | 44.279 | boat | 31.369 | | traffic light | 28.751 | fire hydrant | 70.600 | stop sign | 67.981 | | parking meter | 49.180 | bench | 26.418 | bird | 33.693 | | cat | 76.786 | dog | 70.850 | horse | 49.504 | | sheep | 53.919 | cow | 57.219 | elephant | 65.872 | | bear | 79.561 | zebra | 66.536 | giraffe | 61.780 | | backpack | 23.529 | umbrella | 56.560 | handbag | 24.752 | | tie | 39.883 | suitcase | 50.948 | frisbee | 69.694 | | skis | 8.365 | snowboard | 35.121 | sports ball | 48.980 | | kite | 38.866 | baseball bat | 38.512 | baseball glove | 51.150 | | skateboard | 44.517 | surfboard | 44.565 | tennis racket | 62.389 | | bottle | 41.517 | wine glass | 37.128 | cup | 51.089 | | fork | 25.600 | knife | 25.750 | spoon | 23.046 | | bowl | 38.577 | banana | 22.357 | apple | 25.749 | | sandwich | 50.309 | orange | 31.787 | broccoli | 24.758 | | carrot | 23.299 | hot dog | 34.119 | pizza | 52.186 | | donut | 56.625 | cake | 47.037 | chair | 29.441 | | couch | 43.536 | potted plant | 22.855 | bed | 42.448 | | dining table | 15.803 | toilet | 68.822 | tv | 66.749 | | laptop | 70.079 | mouse | 65.427 | remote | 44.536 | | keyboard | 58.667 | cell phone | 46.418 | microwave | 65.826 | | oven | 31.897 | toaster | 46.761 | sink | 45.233 | | refrigerator | 70.295 | book | 14.150 | clock | 55.171 | | vase | 40.977 | scissors | 35.420 | teddy bear | 56.442 | | hair drier | 37.199 | toothbrush | 27.842 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.25138994255589, 'fwIoU': 71.93889719161086, 'IoU-person': 88.76383912146153, 'IoU-bicycle': 74.14085293803355, 'IoU-car': 72.44775163790669, 'IoU-motorcycle': 88.897298233016, 'IoU-airplane': 89.42362307599326, 'IoU-bus': 87.31902348108264, 'IoU-train': 88.33073040014776, 'IoU-truck': 69.89880236814292, 'IoU-boat': 74.18619608683886, 'IoU-traffic light': 78.74046326064867, 'IoU-fire hydrant': 93.41367438050762, 'IoU-stop sign': 94.19079358989309, 'IoU-parking meter': 84.9625459510031, 'IoU-bench': 61.68544046382133, 'IoU-bird': 77.01870196340957, 'IoU-cat': 91.0882836550747, 'IoU-dog': 85.86523551238191, 'IoU-horse': 87.5424429502593, 'IoU-sheep': 86.44335925662772, 'IoU-cow': 90.66615383834352, 'IoU-elephant': 89.37864229631138, 'IoU-bear': 82.74125808631952, 'IoU-zebra': 90.65970098465726, 'IoU-giraffe': 89.553101738503, 'IoU-backpack': 53.39473792770414, 'IoU-umbrella': 89.45972713997661, 'IoU-handbag': 50.64429001308216, 'IoU-tie': 76.06494727909961, 'IoU-suitcase': 78.36758900411009, 'IoU-frisbee': 84.44403223077023, 'IoU-skis': 59.425797834797436, 'IoU-snowboard': 71.35704796039113, 'IoU-sports ball': 79.70079410096427, 'IoU-kite': 79.66504048332442, 'IoU-baseball bat': 69.79500445121202, 'IoU-baseball glove': 82.32200074635509, 'IoU-skateboard': 85.96978093571667, 'IoU-surfboard': 86.21542113922959, 'IoU-tennis racket': 91.16930125471289, 'IoU-bottle': 70.23602240799396, 'IoU-wine glass': 82.77063123595524, 'IoU-cup': 70.62264930098793, 'IoU-fork': 69.68483457434971, 'IoU-knife': 65.1308162282321, 'IoU-spoon': 60.04128795572255, 'IoU-bowl': 58.725222718705794, 'IoU-banana': 83.06117261922917, 'IoU-apple': 57.07946224525495, 'IoU-sandwich': 69.70807528317677, 'IoU-orange': 79.46831201818179, 'IoU-broccoli': 70.75055750084323, 'IoU-carrot': 64.09982969775409, 'IoU-hot dog': 63.48819331103841, 'IoU-pizza': 86.56534237297042, 'IoU-donut': 64.83635151694835, 'IoU-cake': 76.5921758887124, 'IoU-chair': 63.10070126146614, 'IoU-couch': 71.87262645365726, 'IoU-potted plant': 43.69311072419734, 'IoU-bed': 77.34153480945668, 'IoU-dining table': 55.86591572893731, 'IoU-toilet': 86.45163860116394, 'IoU-tv': 74.49969159337404, 'IoU-laptop': 80.14248129995748, 'IoU-mouse': 81.60386849913604, 'IoU-remote': 67.6691780700232, 'IoU-keyboard': 70.45307696423207, 'IoU-cell phone': 78.54049795790492, 'IoU-microwave': 70.49000880863957, 'IoU-oven': 71.73879765387072, 'IoU-toaster': 85.91718668746749, 'IoU-sink': 73.65157190652485, 'IoU-refrigerator': 84.42446825616112, 'IoU-book': 55.835543836063565, 'IoU-clock': 79.39346584150988, 'IoU-vase': 64.92234321343763, 'IoU-scissors': 73.31097154819678, 'IoU-teddy bear': 85.93890510394613, 'IoU-hair drier': 40.709392542779014, 'IoU-toothbrush': 77.78795916726952, 'IoU-banner': 28.61051751348288, 'IoU-blanket': 17.749512354571937, 'IoU-bridge': 39.32135354700573, 'IoU-cardboard': 53.603069043315884, 'IoU-counter': 33.433871836202464, 'IoU-curtain': 72.74178396787701, 'IoU-door-stuff': 46.86205352825814, 'IoU-floor-wood': 61.54933824791497, 'IoU-flower': 48.353373029989456, 'IoU-fruit': 46.71019959347718, 'IoU-gravel': 35.62243418015187, 'IoU-house': 24.05182549471825, 'IoU-light': 44.31975420971315, 'IoU-mirror-stuff': 65.0083964364358, 'IoU-net': 50.05865284709924, 'IoU-pillow': 21.544201563784824, 'IoU-platform': 29.480689612979376, 'IoU-playingfield': 70.77443880251323, 'IoU-railroad': 63.96201852248795, 'IoU-river': 58.48834735832795, 'IoU-road': 67.3005571052473, 'IoU-roof': 18.986894076448312, 'IoU-sand': 67.41310705133307, 'IoU-sea': 85.36221016676532, 'IoU-shelf': 38.61171417843121, 'IoU-snow': 92.45005776886781, 'IoU-stairs': 36.74769748170075, 'IoU-tent': 10.349617678310006, 'IoU-towel': 45.81359981319267, 'IoU-wall-brick': 49.528560961783235, 'IoU-wall-stone': 31.270387416774653, 'IoU-wall-tile': 70.96124823765365, 'IoU-wall-wood': 44.855797417963124, 'IoU-water-other': 34.78592757634968, 'IoU-window-blind': 51.735096495736464, 'IoU-window-other': 50.38365195503922, 'IoU-tree-merged': 82.00358648514215, 'IoU-fence-merged': 55.626947912733506, 'IoU-ceiling-merged': 67.9917962530353, 'IoU-sky-other-merged': 94.03777089119087, 'IoU-cabinet-merged': 63.60509030727981, 'IoU-table-merged': 41.14659097822764, 'IoU-floor-other-merged': 55.69299138425096, 'IoU-pavement-merged': 57.702973592131215, 'IoU-mountain-merged': 58.22192995856996, 'IoU-grass-merged': 72.28047492185432, 'IoU-dirt-merged': 48.51412221289111, 'IoU-paper-merged': 37.2930911166162, 'IoU-food-other-merged': 45.1691015087416, 'IoU-building-other-merged': 59.08300862951026, 'IoU-rock-merged': 66.1726788317772, 'IoU-wall-other-merged': 66.91819637858225, 'IoU-rug-merged': 67.60325274823727, 'mACC': 77.83366489396666, 'pACC': 82.49670405139058, 'ACC-person': 93.37628180696069, 'ACC-bicycle': 83.14689671284319, 'ACC-car': 85.6307684160623, 'ACC-motorcycle': 93.59877107505342, 'ACC-airplane': 93.82597121095138, 'ACC-bus': 93.87668210940282, 'ACC-train': 95.58198843664982, 'ACC-truck': 79.87806904100779, 'ACC-boat': 83.49299587495011, 'ACC-traffic light': 90.32927290990112, 'ACC-fire hydrant': 96.0583315897125, 'ACC-stop sign': 98.36070188669858, 'ACC-parking meter': 88.15176441161871, 'ACC-bench': 78.53086157148988, 'ACC-bird': 82.66101937924776, 'ACC-cat': 94.79746677767108, 'ACC-dog': 88.63842363997365, 'ACC-horse': 92.73512583977605, 'ACC-sheep': 89.80007105107907, 'ACC-cow': 94.17929588750835, 'ACC-elephant': 91.56911559361524, 'ACC-bear': 84.51944733244657, 'ACC-zebra': 92.97776742126273, 'ACC-giraffe': 93.4597459394098, 'ACC-backpack': 72.31630309988519, 'ACC-umbrella': 93.71515662819306, 'ACC-handbag': 69.86620477594055, 'ACC-tie': 83.72789521260665, 'ACC-suitcase': 84.53644107788335, 'ACC-frisbee': 94.35745454545454, 'ACC-skis': 74.58611161272518, 'ACC-snowboard': 81.81969581935294, 'ACC-sports ball': 87.1476416410829, 'ACC-kite': 85.86140413139725, 'ACC-baseball bat': 87.22924424398175, 'ACC-baseball glove': 92.19176700316322, 'ACC-skateboard': 90.69396878688995, 'ACC-surfboard': 92.64511744111729, 'ACC-tennis racket': 94.76712727564762, 'ACC-bottle': 85.49900957626157, 'ACC-wine glass': 90.52909285416402, 'ACC-cup': 89.3072868182119, 'ACC-fork': 80.35342987133248, 'ACC-knife': 78.52458415769034, 'ACC-spoon': 77.76195349716602, 'ACC-bowl': 68.84644004547357, 'ACC-banana': 90.0298327859811, 'ACC-apple': 67.3485066569623, 'ACC-sandwich': 81.98390180343638, 'ACC-orange': 91.76977529064548, 'ACC-broccoli': 80.54069049557377, 'ACC-carrot': 77.59794083015925, 'ACC-hot dog': 71.30124107216783, 'ACC-pizza': 93.38584386755842, 'ACC-donut': 72.64663251172034, 'ACC-cake': 83.35007299808146, 'ACC-chair': 79.05689699684515, 'ACC-couch': 77.84125824854979, 'ACC-potted plant': 60.43462058529653, 'ACC-bed': 85.6751114924338, 'ACC-dining table': 76.7810923207682, 'ACC-toilet': 91.3054956489488, 'ACC-tv': 86.85559137405899, 'ACC-laptop': 92.07168064452395, 'ACC-mouse': 92.16728172606228, 'ACC-remote': 71.74818041264749, 'ACC-keyboard': 74.70898120099567, 'ACC-cell phone': 88.35483073478002, 'ACC-microwave': 74.6572814555222, 'ACC-oven': 92.60278266002489, 'ACC-toaster': 90.44533249394408, 'ACC-sink': 82.62585023463967, 'ACC-refrigerator': 94.61172948041786, 'ACC-book': 74.64963155977698, 'ACC-clock': 84.98979672290426, 'ACC-vase': 74.07402545137, 'ACC-scissors': 77.81427613879477, 'ACC-teddy bear': 91.35592968299333, 'ACC-hair drier': 59.96214385335724, 'ACC-toothbrush': 84.0653231410702, 'ACC-banner': 81.09887555620823, 'ACC-blanket': 25.401109466854223, 'ACC-bridge': 58.64758032567069, 'ACC-cardboard': 67.52025592554894, 'ACC-counter': 60.77502881583943, 'ACC-curtain': 83.15738200819332, 'ACC-door-stuff': 73.04584428023453, 'ACC-floor-wood': 79.99661704163809, 'ACC-flower': 68.93070266389174, 'ACC-fruit': 69.06079392344057, 'ACC-gravel': 47.74461554097386, 'ACC-house': 27.820539568714413, 'ACC-light': 63.10656557439805, 'ACC-mirror-stuff': 78.63980112474846, 'ACC-net': 67.47987487058062, 'ACC-pillow': 43.50661179060859, 'ACC-platform': 49.84251350542984, 'ACC-playingfield': 89.563744535622, 'ACC-railroad': 78.62815201232377, 'ACC-river': 79.46338156751548, 'ACC-road': 85.8084139291918, 'ACC-roof': 26.598377521625576, 'ACC-sand': 71.91514079002992, 'ACC-sea': 91.69983891092966, 'ACC-shelf': 55.04775510369058, 'ACC-snow': 95.50277984058737, 'ACC-stairs': 67.96442458433663, 'ACC-tent': 14.313645127158518, 'ACC-towel': 55.253651641275326, 'ACC-wall-brick': 70.84681866628264, 'ACC-wall-stone': 36.20567350434712, 'ACC-wall-tile': 85.82403275857065, 'ACC-wall-wood': 62.30116452757901, 'ACC-water-other': 49.904873261687904, 'ACC-window-blind': 65.48992114755859, 'ACC-window-other': 73.92752173950646, 'ACC-tree-merged': 90.05784069230833, 'ACC-fence-merged': 71.59136870276791, 'ACC-ceiling-merged': 83.63789741426932, 'ACC-sky-other-merged': 96.9204023070336, 'ACC-cabinet-merged': 78.74449895775703, 'ACC-table-merged': 56.54095968251467, 'ACC-floor-other-merged': 66.53457697012539, 'ACC-pavement-merged': 69.96250011987922, 'ACC-mountain-merged': 68.4074003248624, 'ACC-grass-merged': 82.7567980318593, 'ACC-dirt-merged': 71.41205781954339, 'ACC-paper-merged': 48.5769717018564, 'ACC-food-other-merged': 62.70835318631809, 'ACC-building-other-merged': 72.91783818930836, 'ACC-rock-merged': 82.82584521524169, 'ACC-wall-other-merged': 80.78654084302204, 'ACC-rug-merged': 81.19182898217824})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3376 s/iter. Inference: 0.2411 s/iter. Eval: 0.0000 s/iter. Total: 0.5787 s/iter. ETA=0:00:08 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3120 s/iter. Inference: 0.4927 s/iter. Eval: 0.0000 s/iter. Total: 0.8048 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3340 s/iter. Inference: 0.5435 s/iter. Eval: 0.0000 s/iter. Total: 0.8776 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.4108867427568041, 'noc@0.8': 2.463271875914545, 'noc@0.85': 2.902838747439274, 'noc@0.9': 3.7512437810945274, 'miou@iter1': 0.8709056498120324} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1422 s/iter. Eval: 0.0010 s/iter. Total: 0.1446 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.74815368652344, 'precision@0.6': 72.79440307617188, 'precision@0.7': 68.32491302490234, 'precision@0.8': 59.11387634277344, 'precision@0.9': 32.29692840576172, 'cIoU': 61.46056365966797, 'mIoU': 66.9311294555664} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.22268992003774, 'SQ': 83.09588458079953, 'RQ': 65.67436788496124, 'PQ_th': 61.4040451998228, 'SQ_th': 84.11536578777978, 'RQ_th': 72.53289391376812, 'PQ_st': 45.892342327909326, 'SQ_st': 81.55704502309348, 'RQ_st': 55.32187576600749}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.67350380079597, 'AP50': 69.52065634793608, 'AP75': 49.24419407917832, 'APs': 25.936171849804303, 'APm': 49.76801156456107, 'APl': 67.14872597649602, 'AP-person': 49.56559271292079, 'AP-bicycle': 24.790862299712447, 'AP-car': 44.50437592270596, 'AP-motorcycle': 42.543662982952654, 'AP-airplane': 61.91244236065152, 'AP-bus': 71.30673642623817, 'AP-train': 74.20283244399715, 'AP-truck': 44.27859066315861, 'AP-boat': 31.36898074985826, 'AP-traffic light': 28.75128864482331, 'AP-fire hydrant': 70.6000706831872, 'AP-stop sign': 67.98126262341412, 'AP-parking meter': 49.18019243235569, 'AP-bench': 26.417650747394244, 'AP-bird': 33.69331719084225, 'AP-cat': 76.78573515082535, 'AP-dog': 70.84958395465242, 'AP-horse': 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'IoU-umbrella': 89.45972713997661, 'IoU-handbag': 50.64429001308216, 'IoU-tie': 76.06494727909961, 'IoU-suitcase': 78.36758900411009, 'IoU-frisbee': 84.44403223077023, 'IoU-skis': 59.425797834797436, 'IoU-snowboard': 71.35704796039113, 'IoU-sports ball': 79.70079410096427, 'IoU-kite': 79.66504048332442, 'IoU-baseball bat': 69.79500445121202, 'IoU-baseball glove': 82.32200074635509, 'IoU-skateboard': 85.96978093571667, 'IoU-surfboard': 86.21542113922959, 'IoU-tennis racket': 91.16930125471289, 'IoU-bottle': 70.23602240799396, 'IoU-wine glass': 82.77063123595524, 'IoU-cup': 70.62264930098793, 'IoU-fork': 69.68483457434971, 'IoU-knife': 65.1308162282321, 'IoU-spoon': 60.04128795572255, 'IoU-bowl': 58.725222718705794, 'IoU-banana': 83.06117261922917, 'IoU-apple': 57.07946224525495, 'IoU-sandwich': 69.70807528317677, 'IoU-orange': 79.46831201818179, 'IoU-broccoli': 70.75055750084323, 'IoU-carrot': 64.09982969775409, 'IoU-hot dog': 63.48819331103841, 'IoU-pizza': 86.56534237297042, 'IoU-donut': 64.83635151694835, 'IoU-cake': 76.5921758887124, 'IoU-chair': 63.10070126146614, 'IoU-couch': 71.87262645365726, 'IoU-potted plant': 43.69311072419734, 'IoU-bed': 77.34153480945668, 'IoU-dining table': 55.86591572893731, 'IoU-toilet': 86.45163860116394, 'IoU-tv': 74.49969159337404, 'IoU-laptop': 80.14248129995748, 'IoU-mouse': 81.60386849913604, 'IoU-remote': 67.6691780700232, 'IoU-keyboard': 70.45307696423207, 'IoU-cell phone': 78.54049795790492, 'IoU-microwave': 70.49000880863957, 'IoU-oven': 71.73879765387072, 'IoU-toaster': 85.91718668746749, 'IoU-sink': 73.65157190652485, 'IoU-refrigerator': 84.42446825616112, 'IoU-book': 55.835543836063565, 'IoU-clock': 79.39346584150988, 'IoU-vase': 64.92234321343763, 'IoU-scissors': 73.31097154819678, 'IoU-teddy bear': 85.93890510394613, 'IoU-hair drier': 40.709392542779014, 'IoU-toothbrush': 77.78795916726952, 'IoU-banner': 28.61051751348288, 'IoU-blanket': 17.749512354571937, 'IoU-bridge': 39.32135354700573, 'IoU-cardboard': 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'ACC-laptop': 92.07168064452395, 'ACC-mouse': 92.16728172606228, 'ACC-remote': 71.74818041264749, 'ACC-keyboard': 74.70898120099567, 'ACC-cell phone': 88.35483073478002, 'ACC-microwave': 74.6572814555222, 'ACC-oven': 92.60278266002489, 'ACC-toaster': 90.44533249394408, 'ACC-sink': 82.62585023463967, 'ACC-refrigerator': 94.61172948041786, 'ACC-book': 74.64963155977698, 'ACC-clock': 84.98979672290426, 'ACC-vase': 74.07402545137, 'ACC-scissors': 77.81427613879477, 'ACC-teddy bear': 91.35592968299333, 'ACC-hair drier': 59.96214385335724, 'ACC-toothbrush': 84.0653231410702, 'ACC-banner': 81.09887555620823, 'ACC-blanket': 25.401109466854223, 'ACC-bridge': 58.64758032567069, 'ACC-cardboard': 67.52025592554894, 'ACC-counter': 60.77502881583943, 'ACC-curtain': 83.15738200819332, 'ACC-door-stuff': 73.04584428023453, 'ACC-floor-wood': 79.99661704163809, 'ACC-flower': 68.93070266389174, 'ACC-fruit': 69.06079392344057, 'ACC-gravel': 47.74461554097386, 'ACC-house': 27.820539568714413, 'ACC-light': 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66.9311294555664}}} INFO:trainer.default_trainer:This epoch takes 0:56:55.972346 INFO:trainer.default_trainer:PROGRESS: 56.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 28 training. INFO:trainer.default_trainer:epochs[ 28] optim steps[51200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97494/0.76182, loss_mask_bce_0: 0.52433/0.30157, loss_mask_dice_0: 2.55691/1.02485, loss_spatial_bce_0: 0.02806/0.08602, loss_spatial_dice_0: 0.18045/0.18182, loss_spatial_ce_0: 0.00717/0.05966, loss_grounding_bce_0: 0.03140/0.08079, loss_grounding_dice_0: 0.21648/0.15095, loss_grounding_ce_0: 0.34882/0.24914, loss_mask_ce_1: 0.90354/0.76254, loss_mask_bce_1: 0.49537/0.30250, loss_mask_dice_1: 2.87410/1.02879, loss_spatial_bce_1: 0.03028/0.08629, loss_spatial_dice_1: 0.21010/0.18439, loss_spatial_ce_1: 0.00573/0.06369, loss_grounding_bce_1: 0.03775/0.08098, loss_grounding_dice_1: 0.23051/0.15167, loss_grounding_ce_1: 0.25803/0.25081, loss_mask_ce_2: 1.06621/0.77056, loss_mask_bce_2: 0.48770/0.30263, loss_mask_dice_2: 2.76566/1.02970, loss_spatial_bce_2: 0.02640/0.08629, loss_spatial_dice_2: 0.18906/0.18468, loss_spatial_ce_2: 0.01046/0.06594, loss_grounding_bce_2: 0.03422/0.08094, loss_grounding_dice_2: 0.22895/0.15155, loss_grounding_ce_2: 0.26436/0.25374, loss_mask_ce_3: 1.03687/0.77349, loss_mask_bce_3: 0.48550/0.30418, loss_mask_dice_3: 2.75936/1.02740, loss_spatial_bce_3: 0.03259/0.08830, loss_spatial_dice_3: 0.20950/0.18587, loss_spatial_ce_3: 0.01825/0.07053, loss_grounding_bce_3: 0.03221/0.08139, loss_grounding_dice_3: 0.21432/0.15118, loss_grounding_ce_3: 0.26680/0.25380, loss_mask_ce_4: 1.16066/0.77935, loss_mask_bce_4: 0.46439/0.30653, loss_mask_dice_4: 2.82552/1.04637, loss_spatial_bce_4: 0.02964/0.09026, loss_spatial_dice_4: 0.22075/0.19372, loss_spatial_ce_4: 0.00433/0.08357, loss_grounding_bce_4: 0.03831/0.08203, loss_grounding_dice_4: 0.22494/0.15380, loss_grounding_ce_4: 0.27866/0.25905, loss_mask_ce_5: 1.00715/0.80286, loss_mask_bce_5: 0.44038/0.30839, loss_mask_dice_5: 2.95186/1.05382, loss_spatial_bce_5: 0.03019/0.09235, loss_spatial_dice_5: 0.21094/0.19646, loss_spatial_ce_5: 0.02839/0.09561, loss_grounding_bce_5: 0.03346/0.08231, loss_grounding_dice_5: 0.24644/0.15450, loss_grounding_ce_5: 0.39527/0.27760, loss_mask_ce_6: 1.20569/0.82939, loss_mask_bce_6: 0.44666/0.31030, loss_mask_dice_6: 2.63225/1.05665, loss_spatial_bce_6: 0.03511/0.09744, loss_spatial_dice_6: 0.23940/0.19876, loss_spatial_ce_6: 0.05020/0.11956, loss_grounding_bce_6: 0.03137/0.08326, loss_grounding_dice_6: 0.23079/0.15500, loss_grounding_ce_6: 0.28142/0.28631, loss_mask_ce_7: 1.13695/0.88488, loss_mask_bce_7: 0.49826/0.31765, loss_mask_dice_7: 2.78515/1.10328, loss_spatial_bce_7: 0.04063/0.10751, loss_spatial_dice_7: 0.24337/0.22421, loss_spatial_ce_7: 0.06903/0.15821, loss_grounding_bce_7: 0.03458/0.08495, loss_grounding_dice_7: 0.27518/0.16082, loss_grounding_ce_7: 0.32583/0.32062, loss_mask_ce_8: 1.46957/1.02273, loss_mask_bce_8: 0.52695/0.33365, loss_mask_dice_8: 3.44555/1.18053, loss_spatial_bce_8: 0.03970/0.12543, loss_spatial_dice_8: 0.35690/0.26042, loss_spatial_ce_8: 0.12596/0.20829, loss_grounding_bce_8: 0.03060/0.08911, loss_grounding_dice_8: 0.31965/0.17042, loss_grounding_ce_8: 0.40725/0.42270, loss_mask_ce_9: 5.55513/3.48176, loss_mask_bce_9: 0.73897/0.36077, loss_mask_dice_9: 5.74633/1.76357, loss_spatial_bce_9: 0.21366/0.35561, loss_spatial_dice_9: 0.95912/0.79411, loss_spatial_ce_9: 1.31803/1.39473, loss_grounding_bce_9: 0.05378/0.10108, loss_grounding_dice_9: 0.64060/0.24302, loss_grounding_ce_9: 0.44584/0.67932] items per batch[64] items per second[0.16] total items[3276800] mini batches[ 51200] memory[4999] epoch remaining[0:57:48] INFO:trainer.default_trainer:epochs[ 28] optim steps[51300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.02478/0.76160, loss_mask_bce_0: 0.02380/0.30144, loss_mask_dice_0: 0.61538/1.02449, loss_spatial_bce_0: 0.00704/0.08597, loss_spatial_dice_0: 0.26572/0.18179, loss_spatial_ce_0: 0.03861/0.05964, loss_grounding_bce_0: 0.00423/0.08077, loss_grounding_dice_0: 0.02559/0.15093, loss_grounding_ce_0: 0.01097/0.24907, loss_mask_ce_1: 2.05733/0.76233, loss_mask_bce_1: 0.01636/0.30237, loss_mask_dice_1: 0.63881/1.02845, loss_spatial_bce_1: 0.00929/0.08623, loss_spatial_dice_1: 0.25063/0.18434, loss_spatial_ce_1: 0.04005/0.06368, loss_grounding_bce_1: 0.00442/0.08095, loss_grounding_dice_1: 0.02566/0.15164, loss_grounding_ce_1: 0.00759/0.25076, loss_mask_ce_2: 1.88696/0.77036, loss_mask_bce_2: 0.02639/0.30251, loss_mask_dice_2: 0.70501/1.02935, loss_spatial_bce_2: 0.00892/0.08624, loss_spatial_dice_2: 0.20732/0.18463, loss_spatial_ce_2: 0.04700/0.06592, loss_grounding_bce_2: 0.00512/0.08092, loss_grounding_dice_2: 0.02333/0.15151, loss_grounding_ce_2: 0.01271/0.25378, loss_mask_ce_3: 2.08507/0.77332, loss_mask_bce_3: 0.01893/0.30406, loss_mask_dice_3: 0.72847/1.02704, loss_spatial_bce_3: 0.00852/0.08825, loss_spatial_dice_3: 0.18737/0.18583, loss_spatial_ce_3: 0.33065/0.07051, loss_grounding_bce_3: 0.00544/0.08137, loss_grounding_dice_3: 0.02655/0.15115, loss_grounding_ce_3: 0.01294/0.25379, loss_mask_ce_4: 1.93978/0.77914, loss_mask_bce_4: 0.02889/0.30640, loss_mask_dice_4: 0.66041/1.04601, loss_spatial_bce_4: 0.00806/0.09021, loss_spatial_dice_4: 0.28602/0.19368, loss_spatial_ce_4: 0.04951/0.08355, loss_grounding_bce_4: 0.00377/0.08200, loss_grounding_dice_4: 0.02241/0.15376, loss_grounding_ce_4: 0.01375/0.25913, loss_mask_ce_5: 1.65555/0.80265, loss_mask_bce_5: 0.01769/0.30826, loss_mask_dice_5: 0.54260/1.05341, loss_spatial_bce_5: 0.00768/0.09230, loss_spatial_dice_5: 0.25755/0.19641, loss_spatial_ce_5: 0.03406/0.09560, loss_grounding_bce_5: 0.00495/0.08228, loss_grounding_dice_5: 0.01852/0.15446, loss_grounding_ce_5: 0.00608/0.27760, loss_mask_ce_6: 2.34514/0.82918, loss_mask_bce_6: 0.02088/0.31018, loss_mask_dice_6: 0.75844/1.05630, loss_spatial_bce_6: 0.00983/0.09739, loss_spatial_dice_6: 0.23939/0.19872, loss_spatial_ce_6: 0.02082/0.11953, loss_grounding_bce_6: 0.00706/0.08323, loss_grounding_dice_6: 0.02931/0.15497, loss_grounding_ce_6: 0.00704/0.28633, loss_mask_ce_7: 2.31638/0.88465, loss_mask_bce_7: 0.01846/0.31753, loss_mask_dice_7: 0.51445/1.10291, loss_spatial_bce_7: 0.01819/0.10746, loss_spatial_dice_7: 0.36038/0.22416, loss_spatial_ce_7: 0.07156/0.15819, loss_grounding_bce_7: 0.00484/0.08492, loss_grounding_dice_7: 0.02350/0.16079, loss_grounding_ce_7: 0.00730/0.32061, loss_mask_ce_8: 1.74846/1.02248, loss_mask_bce_8: 0.02060/0.33353, loss_mask_dice_8: 0.79735/1.18015, loss_spatial_bce_8: 0.01035/0.12535, loss_spatial_dice_8: 0.29674/0.26037, loss_spatial_ce_8: 0.01845/0.20823, loss_grounding_bce_8: 0.00385/0.08909, loss_grounding_dice_8: 0.02821/0.17040, loss_grounding_ce_8: 0.01353/0.42259, loss_mask_ce_9: 3.71666/3.48128, loss_mask_bce_9: 0.01194/0.36065, loss_mask_dice_9: 0.56630/1.76307, loss_spatial_bce_9: 0.03551/0.35554, loss_spatial_dice_9: 0.79023/0.79407, loss_spatial_ce_9: 1.97485/1.39457, loss_grounding_bce_9: 0.00507/0.10106, loss_grounding_dice_9: 0.02701/0.24300, loss_grounding_ce_9: 0.18896/0.67917] items per batch[64] items per second[0.36] total items[3283200] mini batches[ 51300] memory[4999] epoch remaining[0:51:04] INFO:trainer.default_trainer:epochs[ 28] optim steps[51400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06814/0.76158, loss_mask_bce_0: 0.29786/0.30149, loss_mask_dice_0: 1.73819/1.02457, loss_spatial_bce_0: 0.04761/0.08596, loss_spatial_dice_0: 0.32272/0.18177, loss_spatial_ce_0: 0.06767/0.05964, loss_grounding_bce_0: 0.01698/0.08081, loss_grounding_dice_0: 0.21251/0.15094, loss_grounding_ce_0: 0.07640/0.24922, loss_mask_ce_1: 1.11236/0.76229, loss_mask_bce_1: 0.30402/0.30240, loss_mask_dice_1: 1.70045/1.02861, loss_spatial_bce_1: 0.04544/0.08622, loss_spatial_dice_1: 0.32458/0.18434, loss_spatial_ce_1: 0.07019/0.06364, loss_grounding_bce_1: 0.02046/0.08097, loss_grounding_dice_1: 0.21934/0.15164, loss_grounding_ce_1: 0.06525/0.25097, loss_mask_ce_2: 1.04178/0.77034, loss_mask_bce_2: 0.30583/0.30255, loss_mask_dice_2: 1.82015/1.02948, loss_spatial_bce_2: 0.04560/0.08622, loss_spatial_dice_2: 0.33052/0.18461, loss_spatial_ce_2: 0.07458/0.06590, loss_grounding_bce_2: 0.01981/0.08095, loss_grounding_dice_2: 0.20354/0.15151, loss_grounding_ce_2: 0.07176/0.25401, loss_mask_ce_3: 1.07344/0.77327, loss_mask_bce_3: 0.30811/0.30409, loss_mask_dice_3: 1.75129/1.02719, loss_spatial_bce_3: 0.04966/0.08824, loss_spatial_dice_3: 0.30796/0.18582, loss_spatial_ce_3: 0.08835/0.07050, loss_grounding_bce_3: 0.01555/0.08139, loss_grounding_dice_3: 0.16613/0.15116, loss_grounding_ce_3: 0.06955/0.25410, loss_mask_ce_4: 0.87484/0.77910, loss_mask_bce_4: 0.33221/0.30645, loss_mask_dice_4: 1.88927/1.04612, loss_spatial_bce_4: 0.06038/0.09021, loss_spatial_dice_4: 0.34635/0.19367, loss_spatial_ce_4: 0.12767/0.08354, loss_grounding_bce_4: 0.02151/0.08204, loss_grounding_dice_4: 0.25953/0.15377, loss_grounding_ce_4: 0.07304/0.25941, loss_mask_ce_5: 1.31037/0.80265, loss_mask_bce_5: 0.31433/0.30829, loss_mask_dice_5: 1.86106/1.05358, loss_spatial_bce_5: 0.05547/0.09230, loss_spatial_dice_5: 0.35924/0.19641, loss_spatial_ce_5: 0.21961/0.09559, loss_grounding_bce_5: 0.02482/0.08231, loss_grounding_dice_5: 0.23482/0.15447, loss_grounding_ce_5: 0.04907/0.27784, loss_mask_ce_6: 1.17255/0.82914, loss_mask_bce_6: 0.29973/0.31022, loss_mask_dice_6: 1.88424/1.05646, loss_spatial_bce_6: 0.05458/0.09739, loss_spatial_dice_6: 0.37409/0.19872, loss_spatial_ce_6: 0.25850/0.11956, loss_grounding_bce_6: 0.02842/0.08325, loss_grounding_dice_6: 0.26790/0.15499, loss_grounding_ce_6: 0.05949/0.28664, loss_mask_ce_7: 1.26557/0.88463, loss_mask_bce_7: 0.33261/0.31758, loss_mask_dice_7: 1.89023/1.10301, loss_spatial_bce_7: 0.05620/0.10744, loss_spatial_dice_7: 0.46623/0.22416, loss_spatial_ce_7: 0.20231/0.15815, loss_grounding_bce_7: 0.03111/0.08495, loss_grounding_dice_7: 0.27195/0.16080, loss_grounding_ce_7: 0.04117/0.32071, loss_mask_ce_8: 1.41675/1.02243, loss_mask_bce_8: 0.40328/0.33357, loss_mask_dice_8: 2.35868/1.18026, loss_spatial_bce_8: 0.04351/0.12532, loss_spatial_dice_8: 0.50321/0.26036, loss_spatial_ce_8: 0.25454/0.20820, loss_grounding_bce_8: 0.05521/0.08911, loss_grounding_dice_8: 0.42795/0.17040, loss_grounding_ce_8: 0.01715/0.42266, loss_mask_ce_9: 3.16382/3.48146, loss_mask_bce_9: 0.27009/0.36070, loss_mask_dice_9: 2.49402/1.76311, loss_spatial_bce_9: 0.10083/0.35557, loss_spatial_dice_9: 0.90776/0.79408, loss_spatial_ce_9: 1.63125/1.39462, loss_grounding_bce_9: 0.03846/0.10109, loss_grounding_dice_9: 0.43892/0.24300, loss_grounding_ce_9: 0.17543/0.67921] items per batch[64] items per second[0.36] total items[3289600] mini batches[ 51400] memory[4999] epoch remaining[0:47:32] INFO:trainer.default_trainer:epochs[ 28] optim steps[51500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16640/0.76147, loss_mask_bce_0: 0.35213/0.30144, loss_mask_dice_0: 0.16260/1.02445, loss_spatial_bce_0: 0.15488/0.08595, loss_spatial_dice_0: 0.11331/0.18175, loss_spatial_ce_0: 0.00071/0.05960, loss_grounding_bce_0: 0.22896/0.08081, loss_grounding_dice_0: 0.11545/0.15092, loss_grounding_ce_0: 0.00951/0.24913, loss_mask_ce_1: 0.17806/0.76213, loss_mask_bce_1: 0.34017/0.30236, loss_mask_dice_1: 0.16081/1.02847, loss_spatial_bce_1: 0.14889/0.08621, loss_spatial_dice_1: 0.10447/0.18432, loss_spatial_ce_1: 0.00155/0.06362, loss_grounding_bce_1: 0.23280/0.08098, loss_grounding_dice_1: 0.11241/0.15165, loss_grounding_ce_1: 0.01597/0.25087, loss_mask_ce_2: 0.15943/0.77020, loss_mask_bce_2: 0.36436/0.30251, loss_mask_dice_2: 0.16252/1.02932, loss_spatial_bce_2: 0.15374/0.08621, loss_spatial_dice_2: 0.10790/0.18459, loss_spatial_ce_2: 0.00139/0.06587, loss_grounding_bce_2: 0.26465/0.08096, loss_grounding_dice_2: 0.11635/0.15150, loss_grounding_ce_2: 0.01329/0.25393, loss_mask_ce_3: 0.17184/0.77312, loss_mask_bce_3: 0.33470/0.30404, loss_mask_dice_3: 0.15170/1.02705, loss_spatial_bce_3: 0.18820/0.08823, loss_spatial_dice_3: 0.10483/0.18580, loss_spatial_ce_3: 0.00174/0.07045, loss_grounding_bce_3: 0.25666/0.08140, loss_grounding_dice_3: 0.11336/0.15115, loss_grounding_ce_3: 0.01591/0.25402, loss_mask_ce_4: 0.17793/0.77894, loss_mask_bce_4: 0.36259/0.30642, loss_mask_dice_4: 0.15201/1.04598, loss_spatial_bce_4: 0.22034/0.09019, loss_spatial_dice_4: 0.11159/0.19365, loss_spatial_ce_4: 0.00179/0.08350, loss_grounding_bce_4: 0.27204/0.08204, loss_grounding_dice_4: 0.11156/0.15377, loss_grounding_ce_4: 0.04739/0.25932, loss_mask_ce_5: 0.16174/0.80251, loss_mask_bce_5: 0.36036/0.30824, loss_mask_dice_5: 0.15266/1.05341, loss_spatial_bce_5: 0.25219/0.09229, loss_spatial_dice_5: 0.11439/0.19640, loss_spatial_ce_5: 0.00173/0.09554, loss_grounding_bce_5: 0.28347/0.08232, loss_grounding_dice_5: 0.11246/0.15448, loss_grounding_ce_5: 0.02125/0.27776, loss_mask_ce_6: 0.18313/0.82899, loss_mask_bce_6: 0.39046/0.31018, loss_mask_dice_6: 0.15380/1.05632, loss_spatial_bce_6: 0.26329/0.09737, loss_spatial_dice_6: 0.12030/0.19870, loss_spatial_ce_6: 0.06370/0.11955, loss_grounding_bce_6: 0.27748/0.08326, loss_grounding_dice_6: 0.11059/0.15498, loss_grounding_ce_6: 0.02969/0.28658, loss_mask_ce_7: 0.13959/0.88443, loss_mask_bce_7: 0.37762/0.31756, loss_mask_dice_7: 0.15690/1.10287, loss_spatial_bce_7: 0.28442/0.10743, loss_spatial_dice_7: 0.10787/0.22415, loss_spatial_ce_7: 0.02485/0.15812, loss_grounding_bce_7: 0.23727/0.08496, loss_grounding_dice_7: 0.10833/0.16080, loss_grounding_ce_7: 0.01341/0.32056, loss_mask_ce_8: 0.14739/1.02222, loss_mask_bce_8: 0.45292/0.33353, loss_mask_dice_8: 0.14968/1.18011, loss_spatial_bce_8: 0.35130/0.12530, loss_spatial_dice_8: 0.10944/0.26032, loss_spatial_ce_8: 0.01138/0.20819, loss_grounding_bce_8: 0.32802/0.08913, loss_grounding_dice_8: 0.10796/0.17041, loss_grounding_ce_8: 0.01757/0.42254, loss_mask_ce_9: 1.66279/3.48105, loss_mask_bce_9: 0.49504/0.36064, loss_mask_dice_9: 0.24309/1.76272, loss_spatial_bce_9: 0.60175/0.35560, loss_spatial_dice_9: 0.49258/0.79403, loss_spatial_ce_9: 0.36655/1.39461, loss_grounding_bce_9: 0.35158/0.10108, loss_grounding_dice_9: 0.17778/0.24297, loss_grounding_ce_9: 0.09217/0.67915] items per batch[64] items per second[0.36] total items[3296000] mini batches[ 51500] memory[4999] epoch remaining[0:44:16] INFO:trainer.default_trainer:epochs[ 28] optim steps[51600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.61783/0.76149, loss_mask_bce_0: 0.07070/0.30148, loss_mask_dice_0: 1.72159/1.02459, loss_spatial_bce_0: 0.01150/0.08595, loss_spatial_dice_0: 0.22343/0.18176, loss_spatial_ce_0: 0.00349/0.05957, loss_grounding_bce_0: 0.01491/0.08081, loss_grounding_dice_0: 0.32585/0.15090, loss_grounding_ce_0: 0.71177/0.24920, loss_mask_ce_1: 1.53841/0.76211, loss_mask_bce_1: 0.08995/0.30239, loss_mask_dice_1: 2.70512/1.02866, loss_spatial_bce_1: 0.01276/0.08621, loss_spatial_dice_1: 0.22460/0.18432, loss_spatial_ce_1: 0.01768/0.06357, loss_grounding_bce_1: 0.01137/0.08098, loss_grounding_dice_1: 0.35487/0.15163, loss_grounding_ce_1: 0.73791/0.25097, loss_mask_ce_2: 1.70149/0.77019, loss_mask_bce_2: 0.08508/0.30255, loss_mask_dice_2: 2.39292/1.02948, loss_spatial_bce_2: 0.01378/0.08622, loss_spatial_dice_2: 0.22210/0.18459, loss_spatial_ce_2: 0.02755/0.06584, loss_grounding_bce_2: 0.01441/0.08096, loss_grounding_dice_2: 0.36650/0.15148, loss_grounding_ce_2: 0.74801/0.25400, loss_mask_ce_3: 1.91189/0.77314, loss_mask_bce_3: 0.07924/0.30408, loss_mask_dice_3: 2.27863/1.02722, loss_spatial_bce_3: 0.00974/0.08823, loss_spatial_dice_3: 0.23399/0.18580, loss_spatial_ce_3: 0.32086/0.07042, loss_grounding_bce_3: 0.01136/0.08140, loss_grounding_dice_3: 0.36541/0.15115, loss_grounding_ce_3: 0.71197/0.25418, loss_mask_ce_4: 2.18575/0.77900, loss_mask_bce_4: 0.06543/0.30645, loss_mask_dice_4: 1.96118/1.04614, loss_spatial_bce_4: 0.00788/0.09020, loss_spatial_dice_4: 0.25034/0.19365, loss_spatial_ce_4: 0.02655/0.08345, loss_grounding_bce_4: 0.01047/0.08205, loss_grounding_dice_4: 0.35039/0.15375, loss_grounding_ce_4: 0.70173/0.25946, loss_mask_ce_5: 1.87290/0.80252, loss_mask_bce_5: 0.06740/0.30829, loss_mask_dice_5: 1.78264/1.05357, loss_spatial_bce_5: 0.01140/0.09231, loss_spatial_dice_5: 0.25741/0.19640, loss_spatial_ce_5: 0.07814/0.09554, loss_grounding_bce_5: 0.01059/0.08232, loss_grounding_dice_5: 0.38348/0.15445, loss_grounding_ce_5: 0.64631/0.27793, loss_mask_ce_6: 1.71189/0.82900, loss_mask_bce_6: 0.08612/0.31022, loss_mask_dice_6: 2.24940/1.05651, loss_spatial_bce_6: 0.01025/0.09738, loss_spatial_dice_6: 0.25291/0.19871, loss_spatial_ce_6: 0.04495/0.11953, loss_grounding_bce_6: 0.01296/0.08326, loss_grounding_dice_6: 0.37502/0.15497, loss_grounding_ce_6: 0.72714/0.28667, loss_mask_ce_7: 2.13943/0.88446, loss_mask_bce_7: 0.06507/0.31759, loss_mask_dice_7: 2.04434/1.10304, loss_spatial_bce_7: 0.00789/0.10744, loss_spatial_dice_7: 0.30022/0.22415, loss_spatial_ce_7: 0.06681/0.15809, loss_grounding_bce_7: 0.01208/0.08498, loss_grounding_dice_7: 0.37777/0.16078, loss_grounding_ce_7: 0.83594/0.32066, loss_mask_ce_8: 2.32000/1.02216, loss_mask_bce_8: 0.08988/0.33360, loss_mask_dice_8: 2.50522/1.18035, loss_spatial_bce_8: 0.00908/0.12530, loss_spatial_dice_8: 0.35325/0.26030, loss_spatial_ce_8: 0.25255/0.20814, loss_grounding_bce_8: 0.01390/0.08916, loss_grounding_dice_8: 0.45482/0.17040, loss_grounding_ce_8: 0.62163/0.42271, loss_mask_ce_9: 6.29486/3.48134, loss_mask_bce_9: 0.06535/0.36073, loss_mask_dice_9: 2.80911/1.76311, loss_spatial_bce_9: 0.03039/0.35559, loss_spatial_dice_9: 0.88574/0.79405, loss_spatial_ce_9: 2.24021/1.39453, loss_grounding_bce_9: 0.00720/0.10109, loss_grounding_dice_9: 0.44164/0.24295, loss_grounding_ce_9: 0.68442/0.67926] items per batch[64] items per second[0.37] total items[3302400] mini batches[ 51600] memory[4999] epoch remaining[0:40:55] INFO:trainer.default_trainer:epochs[ 28] optim steps[51700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37126/0.76155, loss_mask_bce_0: 0.23095/0.30151, loss_mask_dice_0: 0.38168/1.02447, loss_spatial_bce_0: 0.05960/0.08596, loss_spatial_dice_0: 0.09465/0.18179, loss_spatial_ce_0: 0.01767/0.05956, loss_grounding_bce_0: 0.10969/0.08080, loss_grounding_dice_0: 0.10763/0.15089, loss_grounding_ce_0: 0.04377/0.24929, loss_mask_ce_1: 0.37444/0.76212, loss_mask_bce_1: 0.22063/0.30243, loss_mask_dice_1: 0.36480/1.02854, loss_spatial_bce_1: 0.06385/0.08622, loss_spatial_dice_1: 0.10381/0.18435, loss_spatial_ce_1: 0.00671/0.06357, loss_grounding_bce_1: 0.11284/0.08097, loss_grounding_dice_1: 0.10821/0.15164, loss_grounding_ce_1: 0.02550/0.25104, loss_mask_ce_2: 0.39624/0.77028, loss_mask_bce_2: 0.22046/0.30258, loss_mask_dice_2: 0.36437/1.02931, loss_spatial_bce_2: 0.06263/0.08623, loss_spatial_dice_2: 0.10390/0.18462, loss_spatial_ce_2: 0.00639/0.06584, loss_grounding_bce_2: 0.11515/0.08095, loss_grounding_dice_2: 0.10640/0.15150, loss_grounding_ce_2: 0.02289/0.25408, loss_mask_ce_3: 0.40473/0.77322, loss_mask_bce_3: 0.22619/0.30410, loss_mask_dice_3: 0.36046/1.02705, loss_spatial_bce_3: 0.06439/0.08824, loss_spatial_dice_3: 0.10525/0.18584, loss_spatial_ce_3: 0.01347/0.07042, loss_grounding_bce_3: 0.11154/0.08138, loss_grounding_dice_3: 0.09732/0.15115, loss_grounding_ce_3: 0.03323/0.25423, loss_mask_ce_4: 0.36336/0.77909, loss_mask_bce_4: 0.23842/0.30650, loss_mask_dice_4: 0.41236/1.04604, loss_spatial_bce_4: 0.06518/0.09020, loss_spatial_dice_4: 0.10928/0.19370, loss_spatial_ce_4: 0.01827/0.08346, loss_grounding_bce_4: 0.12300/0.08204, loss_grounding_dice_4: 0.11860/0.15375, loss_grounding_ce_4: 0.02314/0.25951, loss_mask_ce_5: 0.37973/0.80264, loss_mask_bce_5: 0.22837/0.30835, loss_mask_dice_5: 0.38712/1.05349, loss_spatial_bce_5: 0.07181/0.09231, loss_spatial_dice_5: 0.11498/0.19645, loss_spatial_ce_5: 0.02059/0.09552, loss_grounding_bce_5: 0.11396/0.08231, loss_grounding_dice_5: 0.10530/0.15444, loss_grounding_ce_5: 0.03474/0.27801, loss_mask_ce_6: 0.42055/0.82913, loss_mask_bce_6: 0.23403/0.31026, loss_mask_dice_6: 0.38480/1.05641, loss_spatial_bce_6: 0.07836/0.09738, loss_spatial_dice_6: 0.13551/0.19875, loss_spatial_ce_6: 0.05109/0.11955, loss_grounding_bce_6: 0.12104/0.08325, loss_grounding_dice_6: 0.10694/0.15498, loss_grounding_ce_6: 0.08918/0.28679, loss_mask_ce_7: 0.41891/0.88461, loss_mask_bce_7: 0.25204/0.31765, loss_mask_dice_7: 0.40686/1.10295, loss_spatial_bce_7: 0.12078/0.10744, loss_spatial_dice_7: 0.19859/0.22419, loss_spatial_ce_7: 0.14064/0.15818, loss_grounding_bce_7: 0.14150/0.08496, loss_grounding_dice_7: 0.13006/0.16078, loss_grounding_ce_7: 0.06072/0.32084, loss_mask_ce_8: 0.72528/1.02234, loss_mask_bce_8: 0.25521/0.33366, loss_mask_dice_8: 0.38501/1.18026, loss_spatial_bce_8: 0.11518/0.12528, loss_spatial_dice_8: 0.20571/0.26032, loss_spatial_ce_8: 0.19917/0.20823, loss_grounding_bce_8: 0.15197/0.08914, loss_grounding_dice_8: 0.13024/0.17039, loss_grounding_ce_8: 0.04281/0.42281, loss_mask_ce_9: 2.73162/3.48157, loss_mask_bce_9: 0.33618/0.36075, loss_mask_dice_9: 0.76538/1.76288, loss_spatial_bce_9: 0.37033/0.35557, loss_spatial_dice_9: 0.82016/0.79409, loss_spatial_ce_9: 1.08001/1.39487, loss_grounding_bce_9: 0.14227/0.10108, loss_grounding_dice_9: 0.20150/0.24300, loss_grounding_ce_9: 0.29458/0.67912] items per batch[64] items per second[0.37] total items[3308800] mini batches[ 51700] memory[4999] epoch remaining[0:37:52] INFO:trainer.default_trainer:epochs[ 28] optim steps[51800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06341/0.76137, loss_mask_bce_0: 0.00923/0.30146, loss_mask_dice_0: 0.07525/1.02445, loss_spatial_bce_0: 0.00613/0.08593, loss_spatial_dice_0: 0.05358/0.18176, loss_spatial_ce_0: 0.00035/0.05952, loss_grounding_bce_0: 0.00792/0.08077, loss_grounding_dice_0: 0.06153/0.15089, loss_grounding_ce_0: 0.00064/0.24928, loss_mask_ce_1: 0.07090/0.76196, loss_mask_bce_1: 0.00834/0.30239, loss_mask_dice_1: 0.09033/1.02852, loss_spatial_bce_1: 0.00639/0.08620, loss_spatial_dice_1: 0.06841/0.18433, loss_spatial_ce_1: 0.00049/0.06353, loss_grounding_bce_1: 0.00878/0.08094, loss_grounding_dice_1: 0.06088/0.15164, loss_grounding_ce_1: 0.00062/0.25100, loss_mask_ce_2: 0.08679/0.77013, loss_mask_bce_2: 0.00966/0.30253, loss_mask_dice_2: 0.10584/1.02926, loss_spatial_bce_2: 0.00572/0.08621, loss_spatial_dice_2: 0.05326/0.18460, loss_spatial_ce_2: 0.00068/0.06578, loss_grounding_bce_2: 0.00675/0.08092, loss_grounding_dice_2: 0.06669/0.15150, loss_grounding_ce_2: 0.00044/0.25408, loss_mask_ce_3: 0.06857/0.77307, loss_mask_bce_3: 0.00921/0.30405, loss_mask_dice_3: 0.07207/1.02702, loss_spatial_bce_3: 0.00485/0.08821, loss_spatial_dice_3: 0.05459/0.18582, loss_spatial_ce_3: 0.00040/0.07037, loss_grounding_bce_3: 0.00813/0.08136, loss_grounding_dice_3: 0.05828/0.15114, loss_grounding_ce_3: 0.00081/0.25421, loss_mask_ce_4: 0.06676/0.77900, loss_mask_bce_4: 0.00826/0.30645, loss_mask_dice_4: 0.06160/1.04601, loss_spatial_bce_4: 0.00661/0.09018, loss_spatial_dice_4: 0.06225/0.19367, loss_spatial_ce_4: 0.00060/0.08342, loss_grounding_bce_4: 0.00961/0.08201, loss_grounding_dice_4: 0.07016/0.15376, loss_grounding_ce_4: 0.00135/0.25946, loss_mask_ce_5: 0.08622/0.80252, loss_mask_bce_5: 0.01024/0.30829, loss_mask_dice_5: 0.09365/1.05344, loss_spatial_bce_5: 0.00627/0.09229, loss_spatial_dice_5: 0.06658/0.19643, loss_spatial_ce_5: 0.01088/0.09552, loss_grounding_bce_5: 0.01052/0.08228, loss_grounding_dice_5: 0.07738/0.15443, loss_grounding_ce_5: 0.00154/0.27798, loss_mask_ce_6: 0.11704/0.82895, loss_mask_bce_6: 0.00978/0.31021, loss_mask_dice_6: 0.08241/1.05640, loss_spatial_bce_6: 0.00638/0.09736, loss_spatial_dice_6: 0.04572/0.19873, loss_spatial_ce_6: 0.00179/0.11954, loss_grounding_bce_6: 0.01230/0.08322, loss_grounding_dice_6: 0.07237/0.15497, loss_grounding_ce_6: 0.00104/0.28680, loss_mask_ce_7: 0.10055/0.88449, loss_mask_bce_7: 0.01016/0.31759, loss_mask_dice_7: 0.11346/1.10289, loss_spatial_bce_7: 0.00676/0.10743, loss_spatial_dice_7: 0.07297/0.22417, loss_spatial_ce_7: 0.00785/0.15813, loss_grounding_bce_7: 0.00796/0.08494, loss_grounding_dice_7: 0.05966/0.16079, loss_grounding_ce_7: 0.00268/0.32083, loss_mask_ce_8: 0.09401/1.02210, loss_mask_bce_8: 0.01365/0.33360, loss_mask_dice_8: 0.08483/1.18022, loss_spatial_bce_8: 0.00667/0.12526, loss_spatial_dice_8: 0.07620/0.26032, loss_spatial_ce_8: 0.03026/0.20816, loss_grounding_bce_8: 0.00884/0.08910, loss_grounding_dice_8: 0.07672/0.17039, loss_grounding_ce_8: 0.00119/0.42272, loss_mask_ce_9: 1.72726/3.48114, loss_mask_bce_9: 0.00905/0.36068, loss_mask_dice_9: 0.11906/1.76271, loss_spatial_bce_9: 0.08321/0.35554, loss_spatial_dice_9: 0.66871/0.79405, loss_spatial_ce_9: 1.25933/1.39473, loss_grounding_bce_9: 0.00841/0.10105, loss_grounding_dice_9: 0.07536/0.24302, loss_grounding_ce_9: 0.13376/0.67915] items per batch[64] items per second[0.36] total items[3315200] mini batches[ 51800] memory[4999] epoch remaining[0:34:55] INFO:trainer.default_trainer:epochs[ 28] optim steps[51900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06685/0.76129, loss_mask_bce_0: 0.01865/0.30151, loss_mask_dice_0: 0.05167/1.02427, loss_spatial_bce_0: 0.01791/0.08592, loss_spatial_dice_0: 0.03988/0.18173, loss_spatial_ce_0: 0.00001/0.05946, loss_grounding_bce_0: 0.03983/0.08081, loss_grounding_dice_0: 0.03312/0.15092, loss_grounding_ce_0: 0.00503/0.24931, loss_mask_ce_1: 0.08257/0.76189, loss_mask_bce_1: 0.01780/0.30244, loss_mask_dice_1: 0.03730/1.02837, loss_spatial_bce_1: 0.01838/0.08619, loss_spatial_dice_1: 0.05334/0.18430, loss_spatial_ce_1: 0.00002/0.06347, loss_grounding_bce_1: 0.04029/0.08098, loss_grounding_dice_1: 0.03124/0.15167, loss_grounding_ce_1: 0.00489/0.25099, loss_mask_ce_2: 0.06632/0.77007, loss_mask_bce_2: 0.01865/0.30257, loss_mask_dice_2: 0.05855/1.02908, loss_spatial_bce_2: 0.01691/0.08619, loss_spatial_dice_2: 0.03038/0.18457, loss_spatial_ce_2: 0.00001/0.06574, loss_grounding_bce_2: 0.04151/0.08095, loss_grounding_dice_2: 0.03423/0.15153, loss_grounding_ce_2: 0.00609/0.25407, loss_mask_ce_3: 0.07703/0.77300, loss_mask_bce_3: 0.01939/0.30409, loss_mask_dice_3: 0.04113/1.02689, loss_spatial_bce_3: 0.01673/0.08821, loss_spatial_dice_3: 0.03686/0.18580, loss_spatial_ce_3: 0.00001/0.07032, loss_grounding_bce_3: 0.04266/0.08139, loss_grounding_dice_3: 0.03379/0.15117, loss_grounding_ce_3: 0.00492/0.25421, loss_mask_ce_4: 0.07252/0.77889, loss_mask_bce_4: 0.01880/0.30649, loss_mask_dice_4: 0.05206/1.04584, loss_spatial_bce_4: 0.01703/0.09017, loss_spatial_dice_4: 0.02884/0.19365, loss_spatial_ce_4: 0.00004/0.08338, loss_grounding_bce_4: 0.03923/0.08204, loss_grounding_dice_4: 0.03125/0.15378, loss_grounding_ce_4: 0.00301/0.25940, loss_mask_ce_5: 0.09968/0.80236, loss_mask_bce_5: 0.01818/0.30835, loss_mask_dice_5: 0.05219/1.05327, loss_spatial_bce_5: 0.01689/0.09229, loss_spatial_dice_5: 0.05194/0.19642, loss_spatial_ce_5: 0.00031/0.09548, loss_grounding_bce_5: 0.03406/0.08231, loss_grounding_dice_5: 0.02718/0.15446, loss_grounding_ce_5: 0.00202/0.27794, loss_mask_ce_6: 0.10549/0.82880, loss_mask_bce_6: 0.01685/0.31026, loss_mask_dice_6: 0.04340/1.05624, loss_spatial_bce_6: 0.01531/0.09735, loss_spatial_dice_6: 0.05447/0.19872, loss_spatial_ce_6: 0.00003/0.11952, loss_grounding_bce_6: 0.03738/0.08325, loss_grounding_dice_6: 0.03039/0.15500, loss_grounding_ce_6: 0.00524/0.28682, loss_mask_ce_7: 0.13296/0.88436, loss_mask_bce_7: 0.01840/0.31764, loss_mask_dice_7: 0.04392/1.10271, loss_spatial_bce_7: 0.01745/0.10741, loss_spatial_dice_7: 0.06866/0.22416, loss_spatial_ce_7: 0.00086/0.15808, loss_grounding_bce_7: 0.03280/0.08497, loss_grounding_dice_7: 0.02244/0.16082, loss_grounding_ce_7: 0.00760/0.32081, loss_mask_ce_8: 0.18280/1.02195, loss_mask_bce_8: 0.03186/0.33365, loss_mask_dice_8: 0.05806/1.18004, loss_spatial_bce_8: 0.01868/0.12525, loss_spatial_dice_8: 0.06082/0.26030, loss_spatial_ce_8: 0.09601/0.20810, loss_grounding_bce_8: 0.06927/0.08913, loss_grounding_dice_8: 0.05620/0.17041, loss_grounding_ce_8: 0.09821/0.42274, loss_mask_ce_9: 1.88970/3.48135, loss_mask_bce_9: 0.01811/0.36074, loss_mask_dice_9: 0.07866/1.76252, loss_spatial_bce_9: 0.37577/0.35554, loss_spatial_dice_9: 0.57302/0.79409, loss_spatial_ce_9: 1.02419/1.39480, loss_grounding_bce_9: 0.04243/0.10109, loss_grounding_dice_9: 0.03496/0.24304, loss_grounding_ce_9: 0.07185/0.67898] items per batch[64] items per second[0.35] total items[3321600] mini batches[ 51900] memory[4999] epoch remaining[0:32:03] INFO:trainer.default_trainer:epochs[ 28] optim steps[52000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04502/0.76108, loss_mask_bce_0: 0.21281/0.30155, loss_mask_dice_0: 1.62939/1.02434, loss_spatial_bce_0: 0.04000/0.08591, loss_spatial_dice_0: 0.21815/0.18171, loss_spatial_ce_0: 0.03315/0.05941, loss_grounding_bce_0: 0.00575/0.08081, loss_grounding_dice_0: 0.08217/0.15093, loss_grounding_ce_0: 0.00175/0.24922, loss_mask_ce_1: 1.03881/0.76169, loss_mask_bce_1: 0.20318/0.30246, loss_mask_dice_1: 1.86466/1.02848, loss_spatial_bce_1: 0.04355/0.08618, loss_spatial_dice_1: 0.23879/0.18428, loss_spatial_ce_1: 0.04430/0.06344, loss_grounding_bce_1: 0.00389/0.08098, loss_grounding_dice_1: 0.05503/0.15167, loss_grounding_ce_1: 0.00513/0.25090, loss_mask_ce_2: 1.21988/0.76985, loss_mask_bce_2: 0.19558/0.30261, loss_mask_dice_2: 1.64436/1.02916, loss_spatial_bce_2: 0.04984/0.08618, loss_spatial_dice_2: 0.24914/0.18456, loss_spatial_ce_2: 0.07573/0.06573, loss_grounding_bce_2: 0.00657/0.08095, loss_grounding_dice_2: 0.08417/0.15154, loss_grounding_ce_2: 0.00964/0.25396, loss_mask_ce_3: 1.18109/0.77278, loss_mask_bce_3: 0.19400/0.30412, loss_mask_dice_3: 1.77561/1.02693, loss_spatial_bce_3: 0.04000/0.08820, loss_spatial_dice_3: 0.23432/0.18579, loss_spatial_ce_3: 0.07406/0.07028, loss_grounding_bce_3: 0.00181/0.08139, loss_grounding_dice_3: 0.04412/0.15117, loss_grounding_ce_3: 0.00321/0.25411, loss_mask_ce_4: 1.47020/0.77870, loss_mask_bce_4: 0.20551/0.30652, loss_mask_dice_4: 1.81999/1.04593, loss_spatial_bce_4: 0.03673/0.09016, loss_spatial_dice_4: 0.25029/0.19365, loss_spatial_ce_4: 0.12504/0.08334, loss_grounding_bce_4: 0.00297/0.08204, loss_grounding_dice_4: 0.06051/0.15378, loss_grounding_ce_4: 0.00412/0.25930, loss_mask_ce_5: 1.42092/0.80217, loss_mask_bce_5: 0.21849/0.30838, loss_mask_dice_5: 1.81213/1.05338, loss_spatial_bce_5: 0.03136/0.09228, loss_spatial_dice_5: 0.24463/0.19641, loss_spatial_ce_5: 0.12249/0.09544, loss_grounding_bce_5: 0.00351/0.08231, loss_grounding_dice_5: 0.06980/0.15446, loss_grounding_ce_5: 0.00112/0.27785, loss_mask_ce_6: 1.09806/0.82863, loss_mask_bce_6: 0.20610/0.31027, loss_mask_dice_6: 1.70377/1.05635, loss_spatial_bce_6: 0.03317/0.09733, loss_spatial_dice_6: 0.25388/0.19870, loss_spatial_ce_6: 0.22960/0.11947, loss_grounding_bce_6: 0.00534/0.08325, loss_grounding_dice_6: 0.09937/0.15500, loss_grounding_ce_6: 0.00076/0.28673, loss_mask_ce_7: 1.36872/0.88426, loss_mask_bce_7: 0.16328/0.31766, loss_mask_dice_7: 1.64909/1.10276, loss_spatial_bce_7: 0.03808/0.10738, loss_spatial_dice_7: 0.31950/0.22416, loss_spatial_ce_7: 0.39495/0.15804, loss_grounding_bce_7: 0.00284/0.08497, loss_grounding_dice_7: 0.07514/0.16083, loss_grounding_ce_7: 0.00749/0.32073, loss_mask_ce_8: 2.87963/1.02179, loss_mask_bce_8: 0.18244/0.33368, loss_mask_dice_8: 2.07897/1.18017, loss_spatial_bce_8: 0.04418/0.12522, loss_spatial_dice_8: 0.39938/0.26030, loss_spatial_ce_8: 0.26870/0.20806, loss_grounding_bce_8: 0.00794/0.08913, loss_grounding_dice_8: 0.09750/0.17043, loss_grounding_ce_8: 0.12064/0.42272, loss_mask_ce_9: 5.75705/3.48140, loss_mask_bce_9: 0.21864/0.36075, loss_mask_dice_9: 2.96156/1.76273, loss_spatial_bce_9: 0.14360/0.35552, loss_spatial_dice_9: 0.87887/0.79409, loss_spatial_ce_9: 1.05433/1.39464, loss_grounding_bce_9: 0.00434/0.10109, loss_grounding_dice_9: 0.16127/0.24304, loss_grounding_ce_9: 2.63018/0.67899] items per batch[64] items per second[0.37] total items[3328000] mini batches[ 52000] memory[4999] epoch remaining[0:29:03] INFO:trainer.default_trainer:epochs[ 28] optim steps[52100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07752/0.76093, loss_mask_bce_0: 0.52833/0.30147, loss_mask_dice_0: 0.97673/1.02457, loss_spatial_bce_0: 0.11835/0.08591, loss_spatial_dice_0: 0.21146/0.18169, loss_spatial_ce_0: 0.03236/0.05939, loss_grounding_bce_0: 0.06049/0.08083, loss_grounding_dice_0: 0.06803/0.15092, loss_grounding_ce_0: 0.14719/0.24923, loss_mask_ce_1: 0.97290/0.76154, loss_mask_bce_1: 0.67037/0.30239, loss_mask_dice_1: 1.06612/1.02872, loss_spatial_bce_1: 0.11567/0.08618, loss_spatial_dice_1: 0.21343/0.18426, loss_spatial_ce_1: 0.03421/0.06340, loss_grounding_bce_1: 0.06006/0.08100, loss_grounding_dice_1: 0.07625/0.15166, loss_grounding_ce_1: 0.14542/0.25090, loss_mask_ce_2: 0.90902/0.76967, loss_mask_bce_2: 0.64643/0.30253, loss_mask_dice_2: 1.04924/1.02945, loss_spatial_bce_2: 0.11447/0.08619, loss_spatial_dice_2: 0.22717/0.18454, loss_spatial_ce_2: 0.03639/0.06570, loss_grounding_bce_2: 0.06360/0.08098, loss_grounding_dice_2: 0.07450/0.15153, loss_grounding_ce_2: 0.14758/0.25406, loss_mask_ce_3: 0.99032/0.77263, loss_mask_bce_3: 0.53710/0.30404, loss_mask_dice_3: 0.98071/1.02720, loss_spatial_bce_3: 0.12154/0.08821, loss_spatial_dice_3: 0.21684/0.18578, loss_spatial_ce_3: 0.04449/0.07027, loss_grounding_bce_3: 0.06240/0.08142, loss_grounding_dice_3: 0.06816/0.15116, loss_grounding_ce_3: 0.14665/0.25410, loss_mask_ce_4: 0.97978/0.77855, loss_mask_bce_4: 0.54197/0.30645, loss_mask_dice_4: 0.98374/1.04618, loss_spatial_bce_4: 0.11891/0.09017, loss_spatial_dice_4: 0.21001/0.19363, loss_spatial_ce_4: 0.07149/0.08336, loss_grounding_bce_4: 0.06074/0.08208, loss_grounding_dice_4: 0.07170/0.15377, loss_grounding_ce_4: 0.14656/0.25928, loss_mask_ce_5: 1.09916/0.80202, loss_mask_bce_5: 0.57561/0.30832, loss_mask_dice_5: 1.01137/1.05361, loss_spatial_bce_5: 0.12080/0.09228, loss_spatial_dice_5: 0.20422/0.19640, loss_spatial_ce_5: 0.07706/0.09550, loss_grounding_bce_5: 0.05939/0.08233, loss_grounding_dice_5: 0.06946/0.15446, loss_grounding_ce_5: 0.15074/0.27784, loss_mask_ce_6: 1.13622/0.82854, loss_mask_bce_6: 0.68988/0.31020, loss_mask_dice_6: 1.09684/1.05662, loss_spatial_bce_6: 0.11968/0.09733, loss_spatial_dice_6: 0.19743/0.19869, loss_spatial_ce_6: 0.09567/0.11948, loss_grounding_bce_6: 0.06739/0.08327, loss_grounding_dice_6: 0.07751/0.15498, loss_grounding_ce_6: 0.16512/0.28674, loss_mask_ce_7: 1.20575/0.88410, loss_mask_bce_7: 0.69750/0.31758, loss_mask_dice_7: 1.14087/1.10303, loss_spatial_bce_7: 0.16254/0.10738, loss_spatial_dice_7: 0.28882/0.22416, loss_spatial_ce_7: 0.21619/0.15806, loss_grounding_bce_7: 0.06093/0.08500, loss_grounding_dice_7: 0.07712/0.16082, loss_grounding_ce_7: 0.21194/0.32066, loss_mask_ce_8: 1.44391/1.02167, loss_mask_bce_8: 0.65130/0.33358, loss_mask_dice_8: 1.08063/1.18044, loss_spatial_bce_8: 0.18074/0.12522, loss_spatial_dice_8: 0.29987/0.26030, loss_spatial_ce_8: 0.20621/0.20805, loss_grounding_bce_8: 0.05518/0.08915, loss_grounding_dice_8: 0.07988/0.17042, loss_grounding_ce_8: 0.18907/0.42277, loss_mask_ce_9: 3.58920/3.48119, loss_mask_bce_9: 0.79586/0.36063, loss_mask_dice_9: 1.89508/1.76298, loss_spatial_bce_9: 0.37330/0.35551, loss_spatial_dice_9: 0.88589/0.79408, loss_spatial_ce_9: 1.44605/1.39457, loss_grounding_bce_9: 0.10057/0.10110, loss_grounding_dice_9: 0.17785/0.24301, loss_grounding_ce_9: 0.44828/0.67892] items per batch[64] items per second[0.36] total items[3334400] mini batches[ 52100] memory[4999] epoch remaining[0:26:05] INFO:trainer.default_trainer:epochs[ 28] optim steps[52200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52669/0.76086, loss_mask_bce_0: 0.06019/0.30144, loss_mask_dice_0: 0.18415/1.02470, loss_spatial_bce_0: 0.02102/0.08588, loss_spatial_dice_0: 0.13354/0.18166, loss_spatial_ce_0: 0.00083/0.05935, loss_grounding_bce_0: 0.04232/0.08082, loss_grounding_dice_0: 0.06730/0.15088, loss_grounding_ce_0: 0.00132/0.24906, loss_mask_ce_1: 0.66754/0.76142, loss_mask_bce_1: 0.05813/0.30237, loss_mask_dice_1: 0.16926/1.02879, loss_spatial_bce_1: 0.01818/0.08615, loss_spatial_dice_1: 0.14097/0.18424, loss_spatial_ce_1: 0.00105/0.06335, loss_grounding_bce_1: 0.04616/0.08099, loss_grounding_dice_1: 0.06846/0.15162, loss_grounding_ce_1: 0.00086/0.25072, loss_mask_ce_2: 0.78864/0.76960, loss_mask_bce_2: 0.05701/0.30251, loss_mask_dice_2: 0.19677/1.02953, loss_spatial_bce_2: 0.02151/0.08616, loss_spatial_dice_2: 0.15210/0.18451, loss_spatial_ce_2: 0.00091/0.06565, loss_grounding_bce_2: 0.04180/0.08097, loss_grounding_dice_2: 0.07120/0.15150, loss_grounding_ce_2: 0.00084/0.25388, loss_mask_ce_3: 0.67588/0.77256, loss_mask_bce_3: 0.05219/0.30402, loss_mask_dice_3: 0.17220/1.02729, loss_spatial_bce_3: 0.01818/0.08818, loss_spatial_dice_3: 0.11540/0.18575, loss_spatial_ce_3: 0.00186/0.07020, loss_grounding_bce_3: 0.04297/0.08141, loss_grounding_dice_3: 0.06673/0.15113, loss_grounding_ce_3: 0.00078/0.25393, loss_mask_ce_4: 0.96163/0.77853, loss_mask_bce_4: 0.05614/0.30643, loss_mask_dice_4: 0.20094/1.04630, loss_spatial_bce_4: 0.01911/0.09014, loss_spatial_dice_4: 0.14914/0.19360, loss_spatial_ce_4: 0.02591/0.08331, loss_grounding_bce_4: 0.04121/0.08207, loss_grounding_dice_4: 0.07033/0.15374, loss_grounding_ce_4: 0.00071/0.25911, loss_mask_ce_5: 0.86009/0.80196, loss_mask_bce_5: 0.05262/0.30831, loss_mask_dice_5: 0.22305/1.05367, loss_spatial_bce_5: 0.01761/0.09225, loss_spatial_dice_5: 0.13936/0.19636, loss_spatial_ce_5: 0.22768/0.09548, loss_grounding_bce_5: 0.03746/0.08232, loss_grounding_dice_5: 0.06470/0.15444, loss_grounding_ce_5: 0.00071/0.27767, loss_mask_ce_6: 0.80208/0.82848, loss_mask_bce_6: 0.04995/0.31021, loss_mask_dice_6: 0.18219/1.05685, loss_spatial_bce_6: 0.03617/0.09731, loss_spatial_dice_6: 0.16301/0.19866, loss_spatial_ce_6: 0.09025/0.11942, loss_grounding_bce_6: 0.04065/0.08326, loss_grounding_dice_6: 0.07093/0.15495, loss_grounding_ce_6: 0.00384/0.28677, loss_mask_ce_7: 0.67465/0.88404, loss_mask_bce_7: 0.05416/0.31756, loss_mask_dice_7: 0.21420/1.10316, loss_spatial_bce_7: 0.03825/0.10735, loss_spatial_dice_7: 0.25714/0.22414, loss_spatial_ce_7: 0.15775/0.15797, loss_grounding_bce_7: 0.03357/0.08499, loss_grounding_dice_7: 0.06290/0.16079, loss_grounding_ce_7: 0.00408/0.32054, loss_mask_ce_8: 1.02638/1.02162, loss_mask_bce_8: 0.05498/0.33355, loss_mask_dice_8: 0.31681/1.18060, loss_spatial_bce_8: 0.18819/0.12519, loss_spatial_dice_8: 0.28921/0.26026, loss_spatial_ce_8: 0.03568/0.20795, loss_grounding_bce_8: 0.04172/0.08913, loss_grounding_dice_8: 0.08701/0.17037, loss_grounding_ce_8: 0.00961/0.42260, loss_mask_ce_9: 2.02473/3.48133, loss_mask_bce_9: 0.06265/0.36060, loss_mask_dice_9: 0.41961/1.76320, loss_spatial_bce_9: 0.43505/0.35556, loss_spatial_dice_9: 0.81212/0.79409, loss_spatial_ce_9: 1.23796/1.39456, loss_grounding_bce_9: 0.04578/0.10109, loss_grounding_dice_9: 0.10612/0.24294, loss_grounding_ce_9: 0.10561/0.67896] items per batch[64] items per second[0.37] total items[3340800] mini batches[ 52200] memory[4999] epoch remaining[0:23:06] INFO:trainer.default_trainer:epochs[ 28] optim steps[52300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19271/0.76069, loss_mask_bce_0: 0.39672/0.30134, loss_mask_dice_0: 2.97756/1.02465, loss_spatial_bce_0: 0.01496/0.08586, loss_spatial_dice_0: 0.14529/0.18163, loss_spatial_ce_0: 0.07865/0.05932, loss_grounding_bce_0: 0.05483/0.08078, loss_grounding_dice_0: 0.21410/0.15084, loss_grounding_ce_0: 0.01914/0.24895, loss_mask_ce_1: 0.96502/0.76127, loss_mask_bce_1: 0.32766/0.30226, loss_mask_dice_1: 3.12558/1.02870, loss_spatial_bce_1: 0.01437/0.08613, loss_spatial_dice_1: 0.13990/0.18421, loss_spatial_ce_1: 0.05597/0.06333, loss_grounding_bce_1: 0.05672/0.08095, loss_grounding_dice_1: 0.16733/0.15159, loss_grounding_ce_1: 0.01441/0.25061, loss_mask_ce_2: 1.31666/0.76947, loss_mask_bce_2: 0.29182/0.30240, loss_mask_dice_2: 2.97982/1.02942, loss_spatial_bce_2: 0.01355/0.08614, loss_spatial_dice_2: 0.14685/0.18448, loss_spatial_ce_2: 0.08134/0.06563, loss_grounding_bce_2: 0.05383/0.08094, loss_grounding_dice_2: 0.17767/0.15147, loss_grounding_ce_2: 0.01410/0.25378, loss_mask_ce_3: 1.15739/0.77238, loss_mask_bce_3: 0.31546/0.30391, loss_mask_dice_3: 3.09736/1.02723, loss_spatial_bce_3: 0.01538/0.08816, loss_spatial_dice_3: 0.15285/0.18572, loss_spatial_ce_3: 0.03986/0.07017, loss_grounding_bce_3: 0.05303/0.08137, loss_grounding_dice_3: 0.16841/0.15109, loss_grounding_ce_3: 0.01895/0.25384, loss_mask_ce_4: 1.07701/0.77841, loss_mask_bce_4: 0.34197/0.30631, loss_mask_dice_4: 3.57833/1.04624, loss_spatial_bce_4: 0.01437/0.09013, loss_spatial_dice_4: 0.13418/0.19358, loss_spatial_ce_4: 0.08280/0.08330, loss_grounding_bce_4: 0.05483/0.08203, loss_grounding_dice_4: 0.19729/0.15371, loss_grounding_ce_4: 0.00666/0.25899, loss_mask_ce_5: 1.36222/0.80181, loss_mask_bce_5: 0.40141/0.30821, loss_mask_dice_5: 3.41624/1.05362, loss_spatial_bce_5: 0.01483/0.09223, loss_spatial_dice_5: 0.15894/0.19635, loss_spatial_ce_5: 0.13314/0.09547, loss_grounding_bce_5: 0.05408/0.08229, loss_grounding_dice_5: 0.16384/0.15441, loss_grounding_ce_5: 0.01126/0.27753, loss_mask_ce_6: 0.98597/0.82830, loss_mask_bce_6: 0.36894/0.31011, loss_mask_dice_6: 3.61266/1.05681, loss_spatial_bce_6: 0.01995/0.09729, loss_spatial_dice_6: 0.13824/0.19864, loss_spatial_ce_6: 0.16603/0.11940, loss_grounding_bce_6: 0.05420/0.08323, loss_grounding_dice_6: 0.20422/0.15493, loss_grounding_ce_6: 0.00756/0.28664, loss_mask_ce_7: 1.29371/0.88383, loss_mask_bce_7: 0.34141/0.31746, loss_mask_dice_7: 3.33071/1.10309, loss_spatial_bce_7: 0.01966/0.10732, loss_spatial_dice_7: 0.20222/0.22411, loss_spatial_ce_7: 0.09748/0.15792, loss_grounding_bce_7: 0.05289/0.08495, loss_grounding_dice_7: 0.18092/0.16077, loss_grounding_ce_7: 0.00377/0.32038, loss_mask_ce_8: 1.27182/1.02138, loss_mask_bce_8: 0.36700/0.33344, loss_mask_dice_8: 3.57484/1.18057, loss_spatial_bce_8: 0.02494/0.12514, loss_spatial_dice_8: 0.19850/0.26023, loss_spatial_ce_8: 0.19402/0.20789, loss_grounding_bce_8: 0.05319/0.08909, loss_grounding_dice_8: 0.14697/0.17034, loss_grounding_ce_8: 0.00560/0.42234, loss_mask_ce_9: 3.72902/3.48095, loss_mask_bce_9: 0.38879/0.36046, loss_mask_dice_9: 5.46870/1.76303, loss_spatial_bce_9: 0.17705/0.35559, loss_spatial_dice_9: 0.93884/0.79408, loss_spatial_ce_9: 1.33812/1.39453, loss_grounding_bce_9: 0.05740/0.10105, loss_grounding_dice_9: 0.27671/0.24290, loss_grounding_ce_9: 0.01475/0.67877] items per batch[64] items per second[0.37] total items[3347200] mini batches[ 52300] memory[4999] epoch remaining[0:20:07] INFO:trainer.default_trainer:epochs[ 28] optim steps[52400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13000/0.76085, loss_mask_bce_0: 0.26932/0.30134, loss_mask_dice_0: 0.23665/1.02451, loss_spatial_bce_0: 0.12978/0.08584, loss_spatial_dice_0: 0.11060/0.18160, loss_spatial_ce_0: 0.13008/0.05927, loss_grounding_bce_0: 0.10840/0.08076, loss_grounding_dice_0: 0.10834/0.15080, loss_grounding_ce_0: 0.02260/0.24884, loss_mask_ce_1: 0.11941/0.76147, loss_mask_bce_1: 0.27692/0.30226, loss_mask_dice_1: 0.22790/1.02857, loss_spatial_bce_1: 0.13319/0.08611, loss_spatial_dice_1: 0.10995/0.18418, loss_spatial_ce_1: 0.11041/0.06329, loss_grounding_bce_1: 0.10482/0.08093, loss_grounding_dice_1: 0.10650/0.15155, loss_grounding_ce_1: 0.02971/0.25049, loss_mask_ce_2: 0.13296/0.76966, loss_mask_bce_2: 0.27615/0.30240, loss_mask_dice_2: 0.23907/1.02934, loss_spatial_bce_2: 0.13516/0.08612, loss_spatial_dice_2: 0.11005/0.18445, loss_spatial_ce_2: 0.14764/0.06559, loss_grounding_bce_2: 0.10440/0.08092, loss_grounding_dice_2: 0.10273/0.15144, loss_grounding_ce_2: 0.02476/0.25364, loss_mask_ce_3: 0.15717/0.77259, loss_mask_bce_3: 0.26688/0.30391, loss_mask_dice_3: 0.23486/1.02709, loss_spatial_bce_3: 0.13067/0.08814, loss_spatial_dice_3: 0.11374/0.18570, loss_spatial_ce_3: 0.12729/0.07011, loss_grounding_bce_3: 0.11324/0.08135, loss_grounding_dice_3: 0.10105/0.15105, loss_grounding_ce_3: 0.02084/0.25373, loss_mask_ce_4: 0.15599/0.77860, loss_mask_bce_4: 0.27559/0.30630, loss_mask_dice_4: 0.22933/1.04612, loss_spatial_bce_4: 0.13725/0.09012, loss_spatial_dice_4: 0.11588/0.19355, loss_spatial_ce_4: 0.14183/0.08324, loss_grounding_bce_4: 0.10144/0.08201, loss_grounding_dice_4: 0.09451/0.15367, loss_grounding_ce_4: 0.03936/0.25886, loss_mask_ce_5: 0.14799/0.80204, loss_mask_bce_5: 0.28280/0.30821, loss_mask_dice_5: 0.23232/1.05348, loss_spatial_bce_5: 0.13646/0.09222, loss_spatial_dice_5: 0.11867/0.19633, loss_spatial_ce_5: 0.26120/0.09547, loss_grounding_bce_5: 0.10571/0.08226, loss_grounding_dice_5: 0.10230/0.15437, loss_grounding_ce_5: 0.00620/0.27740, loss_mask_ce_6: 0.17243/0.82851, loss_mask_bce_6: 0.28003/0.31010, loss_mask_dice_6: 0.23516/1.05666, loss_spatial_bce_6: 0.13745/0.09726, loss_spatial_dice_6: 0.11687/0.19862, loss_spatial_ce_6: 0.25094/0.11940, loss_grounding_bce_6: 0.10318/0.08320, loss_grounding_dice_6: 0.10085/0.15489, loss_grounding_ce_6: 0.00463/0.28652, loss_mask_ce_7: 0.15215/0.88405, loss_mask_bce_7: 0.27992/0.31746, loss_mask_dice_7: 0.24204/1.10296, loss_spatial_bce_7: 0.14770/0.10730, loss_spatial_dice_7: 0.12051/0.22409, loss_spatial_ce_7: 0.23746/0.15787, loss_grounding_bce_7: 0.10514/0.08493, loss_grounding_dice_7: 0.09604/0.16072, loss_grounding_ce_7: 0.00545/0.32023, loss_mask_ce_8: 0.17615/1.02160, loss_mask_bce_8: 0.29563/0.33346, loss_mask_dice_8: 0.26368/1.18042, loss_spatial_bce_8: 0.14014/0.12512, loss_spatial_dice_8: 0.14276/0.26018, loss_spatial_ce_8: 0.22254/0.20782, loss_grounding_bce_8: 0.10757/0.08908, loss_grounding_dice_8: 0.10080/0.17029, loss_grounding_ce_8: 0.00202/0.42216, loss_mask_ce_9: 1.50192/3.48134, loss_mask_bce_9: 0.25233/0.36050, loss_mask_dice_9: 0.29363/1.76318, loss_spatial_bce_9: 0.53493/0.35557, loss_spatial_dice_9: 0.62155/0.79406, loss_spatial_ce_9: 0.94294/1.39447, loss_grounding_bce_9: 0.10402/0.10103, loss_grounding_dice_9: 0.07296/0.24285, loss_grounding_ce_9: 0.69625/0.67868] items per batch[64] items per second[0.36] total items[3353600] mini batches[ 52400] memory[4999] epoch remaining[0:17:10] INFO:trainer.default_trainer:epochs[ 28] optim steps[52500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39514/0.76082, loss_mask_bce_0: 0.50136/0.30127, loss_mask_dice_0: 0.90530/1.02448, loss_spatial_bce_0: 0.08258/0.08584, loss_spatial_dice_0: 0.18905/0.18160, loss_spatial_ce_0: 0.00106/0.05922, loss_grounding_bce_0: 0.05136/0.08075, loss_grounding_dice_0: 0.04580/0.15081, loss_grounding_ce_0: 1.20485/0.24891, loss_mask_ce_1: 1.24121/0.76146, loss_mask_bce_1: 0.46781/0.30219, loss_mask_dice_1: 0.87648/1.02856, loss_spatial_bce_1: 0.08631/0.08611, loss_spatial_dice_1: 0.19110/0.18418, loss_spatial_ce_1: 0.00715/0.06324, loss_grounding_bce_1: 0.05432/0.08092, loss_grounding_dice_1: 0.04407/0.15155, loss_grounding_ce_1: 1.09620/0.25054, loss_mask_ce_2: 0.90563/0.76963, loss_mask_bce_2: 0.48476/0.30233, loss_mask_dice_2: 0.90300/1.02936, loss_spatial_bce_2: 0.08734/0.08612, loss_spatial_dice_2: 0.20042/0.18445, loss_spatial_ce_2: 0.01777/0.06554, loss_grounding_bce_2: 0.05244/0.08091, loss_grounding_dice_2: 0.04910/0.15144, loss_grounding_ce_2: 1.00947/0.25366, loss_mask_ce_3: 0.89886/0.77254, loss_mask_bce_3: 0.49335/0.30383, loss_mask_dice_3: 0.87764/1.02710, loss_spatial_bce_3: 0.10404/0.08815, loss_spatial_dice_3: 0.20794/0.18569, loss_spatial_ce_3: 0.01095/0.07008, loss_grounding_bce_3: 0.05874/0.08134, loss_grounding_dice_3: 0.04867/0.15105, loss_grounding_ce_3: 0.98789/0.25378, loss_mask_ce_4: 0.89110/0.77861, loss_mask_bce_4: 0.50182/0.30624, loss_mask_dice_4: 0.90161/1.04614, loss_spatial_bce_4: 0.12320/0.09013, loss_spatial_dice_4: 0.23533/0.19355, loss_spatial_ce_4: 0.01781/0.08324, loss_grounding_bce_4: 0.12699/0.08200, loss_grounding_dice_4: 0.15016/0.15368, loss_grounding_ce_4: 0.27725/0.25890, loss_mask_ce_5: 0.67586/0.80204, loss_mask_bce_5: 0.50415/0.30814, loss_mask_dice_5: 0.97570/1.05344, loss_spatial_bce_5: 0.13582/0.09223, loss_spatial_dice_5: 0.25892/0.19632, loss_spatial_ce_5: 0.04372/0.09547, loss_grounding_bce_5: 0.14098/0.08226, loss_grounding_dice_5: 0.15690/0.15438, loss_grounding_ce_5: 0.22293/0.27744, loss_mask_ce_6: 1.12223/0.82850, loss_mask_bce_6: 0.50127/0.31003, loss_mask_dice_6: 0.91681/1.05666, loss_spatial_bce_6: 0.14589/0.09727, loss_spatial_dice_6: 0.25472/0.19861, loss_spatial_ce_6: 0.08672/0.11945, loss_grounding_bce_6: 0.13882/0.08320, loss_grounding_dice_6: 0.15583/0.15490, loss_grounding_ce_6: 0.17109/0.28652, loss_mask_ce_7: 0.96389/0.88402, loss_mask_bce_7: 0.48057/0.31737, loss_mask_dice_7: 0.99607/1.10296, loss_spatial_bce_7: 0.12870/0.10730, loss_spatial_dice_7: 0.24632/0.22409, loss_spatial_ce_7: 0.13259/0.15785, loss_grounding_bce_7: 0.11889/0.08493, loss_grounding_dice_7: 0.13759/0.16072, loss_grounding_ce_7: 0.07072/0.32015, loss_mask_ce_8: 0.98565/1.02154, loss_mask_bce_8: 0.56309/0.33339, loss_mask_dice_8: 1.26799/1.18039, loss_spatial_bce_8: 0.12723/0.12512, loss_spatial_dice_8: 0.28400/0.26016, loss_spatial_ce_8: 0.13390/0.20778, loss_grounding_bce_8: 0.25068/0.08908, loss_grounding_dice_8: 0.25776/0.17029, loss_grounding_ce_8: 0.02158/0.42205, loss_mask_ce_9: 3.66185/3.48113, loss_mask_bce_9: 0.59277/0.36044, loss_mask_dice_9: 1.48594/1.76292, loss_spatial_bce_9: 0.33865/0.35557, loss_spatial_dice_9: 0.79613/0.79401, loss_spatial_ce_9: 1.09620/1.39429, loss_grounding_bce_9: 0.42781/0.10104, loss_grounding_dice_9: 0.34113/0.24285, loss_grounding_ce_9: 0.08304/0.67853] items per batch[64] items per second[0.35] total items[3360000] mini batches[ 52500] memory[4999] epoch remaining[0:14:15] INFO:trainer.default_trainer:epochs[ 28] optim steps[52600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.06703/0.76092, loss_mask_bce_0: 0.09194/0.30133, loss_mask_dice_0: 1.89703/1.02496, loss_spatial_bce_0: 0.01008/0.08581, loss_spatial_dice_0: 0.25567/0.18159, loss_spatial_ce_0: 0.07413/0.05919, loss_grounding_bce_0: 0.02429/0.08076, loss_grounding_dice_0: 0.21630/0.15081, loss_grounding_ce_0: 2.62557/0.24894, loss_mask_ce_1: 2.00702/0.76156, loss_mask_bce_1: 0.08487/0.30225, loss_mask_dice_1: 1.39956/1.02900, loss_spatial_bce_1: 0.01191/0.08609, loss_spatial_dice_1: 0.34071/0.18417, loss_spatial_ce_1: 0.06524/0.06321, loss_grounding_bce_1: 0.03438/0.08093, loss_grounding_dice_1: 0.23372/0.15156, loss_grounding_ce_1: 1.69368/0.25054, loss_mask_ce_2: 1.64852/0.76972, loss_mask_bce_2: 0.12966/0.30238, loss_mask_dice_2: 1.74441/1.02987, loss_spatial_bce_2: 0.00668/0.08610, loss_spatial_dice_2: 0.28436/0.18445, loss_spatial_ce_2: 0.11728/0.06552, loss_grounding_bce_2: 0.03894/0.08092, loss_grounding_dice_2: 0.24625/0.15144, loss_grounding_ce_2: 1.37385/0.25372, loss_mask_ce_3: 1.91034/0.77264, loss_mask_bce_3: 0.09011/0.30388, loss_mask_dice_3: 1.63464/1.02755, loss_spatial_bce_3: 0.00721/0.08812, loss_spatial_dice_3: 0.28549/0.18568, loss_spatial_ce_3: 0.11894/0.07004, loss_grounding_bce_3: 0.04560/0.08135, loss_grounding_dice_3: 0.23523/0.15105, loss_grounding_ce_3: 1.29917/0.25383, loss_mask_ce_4: 1.60620/0.77875, loss_mask_bce_4: 0.12423/0.30629, loss_mask_dice_4: 1.44363/1.04657, loss_spatial_bce_4: 0.01007/0.09010, loss_spatial_dice_4: 0.30086/0.19355, loss_spatial_ce_4: 0.11941/0.08319, loss_grounding_bce_4: 0.03759/0.08201, loss_grounding_dice_4: 0.24763/0.15368, loss_grounding_ce_4: 1.00061/0.25889, loss_mask_ce_5: 1.83840/0.80217, loss_mask_bce_5: 0.07786/0.30819, loss_mask_dice_5: 1.43779/1.05395, loss_spatial_bce_5: 0.01020/0.09220, loss_spatial_dice_5: 0.35411/0.19632, loss_spatial_ce_5: 0.10262/0.09546, loss_grounding_bce_5: 0.03875/0.08226, loss_grounding_dice_5: 0.23085/0.15439, loss_grounding_ce_5: 1.29563/0.27740, loss_mask_ce_6: 1.90357/0.82866, loss_mask_bce_6: 0.07653/0.31009, loss_mask_dice_6: 1.47325/1.05714, loss_spatial_bce_6: 0.01853/0.09725, loss_spatial_dice_6: 0.35933/0.19860, loss_spatial_ce_6: 0.59921/0.11944, loss_grounding_bce_6: 0.04495/0.08321, loss_grounding_dice_6: 0.23794/0.15491, loss_grounding_ce_6: 1.47488/0.28641, loss_mask_ce_7: 2.29936/0.88419, loss_mask_bce_7: 0.07214/0.31744, loss_mask_dice_7: 1.72173/1.10350, loss_spatial_bce_7: 0.01806/0.10727, loss_spatial_dice_7: 0.34413/0.22410, loss_spatial_ce_7: 0.10048/0.15779, loss_grounding_bce_7: 0.03797/0.08494, loss_grounding_dice_7: 0.24941/0.16073, loss_grounding_ce_7: 1.78458/0.32010, loss_mask_ce_8: 2.86939/1.02168, loss_mask_bce_8: 0.08036/0.33344, loss_mask_dice_8: 1.67311/1.18092, loss_spatial_bce_8: 0.02088/0.12508, loss_spatial_dice_8: 0.41766/0.26018, loss_spatial_ce_8: 0.16591/0.20770, loss_grounding_bce_8: 0.03754/0.08909, loss_grounding_dice_8: 0.30360/0.17032, loss_grounding_ce_8: 3.39657/0.42194, loss_mask_ce_9: 4.83078/3.48140, loss_mask_bce_9: 0.04832/0.36048, loss_mask_dice_9: 1.71878/1.76377, loss_spatial_bce_9: 0.04158/0.35554, loss_spatial_dice_9: 0.86324/0.79407, loss_spatial_ce_9: 1.39103/1.39446, loss_grounding_bce_9: 0.01964/0.10105, loss_grounding_dice_9: 0.25613/0.24285, loss_grounding_ce_9: 2.67978/0.67868] items per batch[64] items per second[0.36] total items[3366400] mini batches[ 52600] memory[4999] epoch remaining[0:11:18] INFO:trainer.default_trainer:epochs[ 28] optim steps[52700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 3.85504/0.76106, loss_mask_bce_0: 0.19600/0.30138, loss_mask_dice_0: 0.13975/1.02491, loss_spatial_bce_0: 0.13705/0.08581, loss_spatial_dice_0: 0.16312/0.18156, loss_spatial_ce_0: 0.00451/0.05919, loss_grounding_bce_0: 0.12401/0.08075, loss_grounding_dice_0: 0.08096/0.15078, loss_grounding_ce_0: 0.09600/0.24900, loss_mask_ce_1: 3.89270/0.76173, loss_mask_bce_1: 0.19295/0.30229, loss_mask_dice_1: 0.14083/1.02897, loss_spatial_bce_1: 0.13948/0.08609, loss_spatial_dice_1: 0.15082/0.18415, loss_spatial_ce_1: 0.00601/0.06320, loss_grounding_bce_1: 0.12532/0.08093, loss_grounding_dice_1: 0.08572/0.15153, loss_grounding_ce_1: 0.11624/0.25066, loss_mask_ce_2: 3.82946/0.76988, loss_mask_bce_2: 0.19761/0.30243, loss_mask_dice_2: 0.13927/1.02979, loss_spatial_bce_2: 0.14289/0.08610, loss_spatial_dice_2: 0.15857/0.18441, loss_spatial_ce_2: 0.00704/0.06553, loss_grounding_bce_2: 0.11447/0.08091, loss_grounding_dice_2: 0.08150/0.15143, loss_grounding_ce_2: 0.09980/0.25380, loss_mask_ce_3: 3.70491/0.77273, loss_mask_bce_3: 0.20015/0.30393, loss_mask_dice_3: 0.13948/1.02746, loss_spatial_bce_3: 0.15713/0.08813, loss_spatial_dice_3: 0.18871/0.18565, loss_spatial_ce_3: 0.01089/0.07004, loss_grounding_bce_3: 0.12500/0.08134, loss_grounding_dice_3: 0.08460/0.15103, loss_grounding_ce_3: 0.10959/0.25395, loss_mask_ce_4: 3.71330/0.77888, loss_mask_bce_4: 0.19702/0.30633, loss_mask_dice_4: 0.15889/1.04654, loss_spatial_bce_4: 0.15428/0.09011, loss_spatial_dice_4: 0.18043/0.19353, loss_spatial_ce_4: 0.11113/0.08319, loss_grounding_bce_4: 0.11471/0.08200, loss_grounding_dice_4: 0.08112/0.15366, loss_grounding_ce_4: 0.13837/0.25904, loss_mask_ce_5: 3.49967/0.80232, loss_mask_bce_5: 0.47354/0.30823, loss_mask_dice_5: 0.34350/1.05397, loss_spatial_bce_5: 0.14507/0.09221, loss_spatial_dice_5: 0.15354/0.19630, loss_spatial_ce_5: 0.18394/0.09547, loss_grounding_bce_5: 0.12658/0.08226, loss_grounding_dice_5: 0.07967/0.15437, loss_grounding_ce_5: 0.24438/0.27749, loss_mask_ce_6: 3.27325/0.82885, loss_mask_bce_6: 0.20509/0.31016, loss_mask_dice_6: 0.15964/1.05712, loss_spatial_bce_6: 0.19924/0.09726, loss_spatial_dice_6: 0.14326/0.19858, loss_spatial_ce_6: 0.72535/0.11943, loss_grounding_bce_6: 0.12680/0.08321, loss_grounding_dice_6: 0.07756/0.15490, loss_grounding_ce_6: 0.20241/0.28668, loss_mask_ce_7: 3.41525/0.88444, loss_mask_bce_7: 0.39441/0.31750, loss_mask_dice_7: 0.35219/1.10346, loss_spatial_bce_7: 0.42493/0.10728, loss_spatial_dice_7: 0.31959/0.22408, loss_spatial_ce_7: 0.64844/0.15781, loss_grounding_bce_7: 0.12488/0.08494, loss_grounding_dice_7: 0.07492/0.16069, loss_grounding_ce_7: 0.37299/0.32024, loss_mask_ce_8: 4.98548/1.02190, loss_mask_bce_8: 0.63737/0.33353, loss_mask_dice_8: 0.44260/1.18093, loss_spatial_bce_8: 0.51205/0.12510, loss_spatial_dice_8: 0.35983/0.26017, loss_spatial_ce_8: 0.37573/0.20767, loss_grounding_bce_8: 0.11185/0.08911, loss_grounding_dice_8: 0.07794/0.17030, loss_grounding_ce_8: 0.32623/0.42207, loss_mask_ce_9: 7.23643/3.48206, loss_mask_bce_9: 0.72279/0.36060, loss_mask_dice_9: 5.09400/1.76390, loss_spatial_bce_9: 0.51266/0.35554, loss_spatial_dice_9: 0.54734/0.79409, loss_spatial_ce_9: 0.82398/1.39441, loss_grounding_bce_9: 0.13458/0.10107, loss_grounding_dice_9: 0.18737/0.24282, loss_grounding_ce_9: 2.00421/0.67896] items per batch[64] items per second[0.36] total items[3372800] mini batches[ 52700] memory[4999] epoch remaining[0:08:21] INFO:trainer.default_trainer:epochs[ 28] optim steps[52800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.72090/0.76095, loss_mask_bce_0: 0.25687/0.30143, loss_mask_dice_0: 1.66882/1.02507, loss_spatial_bce_0: 0.03051/0.08582, loss_spatial_dice_0: 0.28772/0.18155, loss_spatial_ce_0: 0.04396/0.05917, loss_grounding_bce_0: 0.03042/0.08074, loss_grounding_dice_0: 0.07134/0.15077, loss_grounding_ce_0: 0.00009/0.24901, loss_mask_ce_1: 2.21493/0.76161, loss_mask_bce_1: 0.26072/0.30233, loss_mask_dice_1: 1.73912/1.02916, loss_spatial_bce_1: 0.02832/0.08609, loss_spatial_dice_1: 0.27715/0.18413, loss_spatial_ce_1: 0.05523/0.06318, loss_grounding_bce_1: 0.02659/0.08092, loss_grounding_dice_1: 0.07339/0.15152, loss_grounding_ce_1: 0.00042/0.25067, loss_mask_ce_2: 1.98850/0.76977, loss_mask_bce_2: 0.26683/0.30246, loss_mask_dice_2: 1.81312/1.02995, loss_spatial_bce_2: 0.03045/0.08610, loss_spatial_dice_2: 0.29619/0.18441, loss_spatial_ce_2: 0.05097/0.06551, loss_grounding_bce_2: 0.02907/0.08091, loss_grounding_dice_2: 0.07505/0.15142, loss_grounding_ce_2: 0.00008/0.25384, loss_mask_ce_3: 2.11632/0.77265, loss_mask_bce_3: 0.26868/0.30396, loss_mask_dice_3: 1.77137/1.02763, loss_spatial_bce_3: 0.03674/0.08814, loss_spatial_dice_3: 0.29491/0.18565, loss_spatial_ce_3: 0.12371/0.07004, loss_grounding_bce_3: 0.02566/0.08133, loss_grounding_dice_3: 0.06941/0.15102, loss_grounding_ce_3: 0.00011/0.25399, loss_mask_ce_4: 2.20171/0.77877, loss_mask_bce_4: 0.26477/0.30636, loss_mask_dice_4: 1.66986/1.04671, loss_spatial_bce_4: 0.04633/0.09012, loss_spatial_dice_4: 0.32663/0.19354, loss_spatial_ce_4: 0.11746/0.08319, loss_grounding_bce_4: 0.02406/0.08199, loss_grounding_dice_4: 0.07485/0.15365, loss_grounding_ce_4: 0.00023/0.25903, loss_mask_ce_5: 2.17938/0.80223, loss_mask_bce_5: 0.25516/0.30827, loss_mask_dice_5: 1.80159/1.05414, loss_spatial_bce_5: 0.05992/0.09224, loss_spatial_dice_5: 0.32412/0.19632, loss_spatial_ce_5: 0.12059/0.09547, loss_grounding_bce_5: 0.02332/0.08225, loss_grounding_dice_5: 0.07069/0.15437, loss_grounding_ce_5: 0.00031/0.27757, loss_mask_ce_6: 2.22571/0.82873, loss_mask_bce_6: 0.24874/0.31019, loss_mask_dice_6: 1.69738/1.05728, loss_spatial_bce_6: 0.05788/0.09729, loss_spatial_dice_6: 0.33697/0.19859, loss_spatial_ce_6: 0.16938/0.11945, loss_grounding_bce_6: 0.02210/0.08319, loss_grounding_dice_6: 0.06314/0.15489, loss_grounding_ce_6: 0.00045/0.28672, loss_mask_ce_7: 1.94967/0.88432, loss_mask_bce_7: 0.31286/0.31753, loss_mask_dice_7: 2.00918/1.10366, loss_spatial_bce_7: 0.10630/0.10730, loss_spatial_dice_7: 0.33685/0.22409, loss_spatial_ce_7: 0.21954/0.15784, loss_grounding_bce_7: 0.02304/0.08492, loss_grounding_dice_7: 0.06924/0.16068, loss_grounding_ce_7: 0.00130/0.32025, loss_mask_ce_8: 2.18905/1.02179, loss_mask_bce_8: 0.37618/0.33356, loss_mask_dice_8: 2.19485/1.18105, loss_spatial_bce_8: 0.06740/0.12511, loss_spatial_dice_8: 0.37807/0.26016, loss_spatial_ce_8: 0.15477/0.20769, loss_grounding_bce_8: 0.02186/0.08910, loss_grounding_dice_8: 0.05776/0.17029, loss_grounding_ce_8: 0.08321/0.42201, loss_mask_ce_9: 3.70820/3.48181, loss_mask_bce_9: 0.31402/0.36064, loss_mask_dice_9: 2.63150/1.76400, loss_spatial_bce_9: 0.23210/0.35560, loss_spatial_dice_9: 0.90986/0.79409, loss_spatial_ce_9: 1.94593/1.39434, loss_grounding_bce_9: 0.02046/0.10109, loss_grounding_dice_9: 0.07529/0.24282, loss_grounding_ce_9: 0.43332/0.67892] items per batch[64] items per second[0.36] total items[3379200] mini batches[ 52800] memory[4999] epoch remaining[0:05:24] INFO:trainer.default_trainer:epochs[ 28] optim steps[52900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.36333/0.76085, loss_mask_bce_0: 0.85980/0.30147, loss_mask_dice_0: 3.70293/1.02468, loss_spatial_bce_0: 0.02799/0.08583, loss_spatial_dice_0: 0.28103/0.18154, loss_spatial_ce_0: 0.00862/0.05915, loss_grounding_bce_0: 0.11229/0.08076, loss_grounding_dice_0: 0.48247/0.15077, loss_grounding_ce_0: 0.00632/0.24912, loss_mask_ce_1: 1.25931/0.76153, loss_mask_bce_1: 0.84284/0.30236, loss_mask_dice_1: 3.85363/1.02875, loss_spatial_bce_1: 0.03015/0.08611, loss_spatial_dice_1: 0.29240/0.18413, loss_spatial_ce_1: 0.02273/0.06317, loss_grounding_bce_1: 0.10534/0.08094, loss_grounding_dice_1: 0.49854/0.15153, loss_grounding_ce_1: 0.00522/0.25072, loss_mask_ce_2: 1.29383/0.76970, loss_mask_bce_2: 0.83712/0.30249, loss_mask_dice_2: 3.80334/1.02958, loss_spatial_bce_2: 0.02493/0.08612, loss_spatial_dice_2: 0.26580/0.18441, loss_spatial_ce_2: 0.02951/0.06548, loss_grounding_bce_2: 0.06456/0.08092, loss_grounding_dice_2: 0.26211/0.15142, loss_grounding_ce_2: 0.69684/0.25400, loss_mask_ce_3: 1.38251/0.77257, loss_mask_bce_3: 0.81437/0.30399, loss_mask_dice_3: 3.66978/1.02726, loss_spatial_bce_3: 0.02314/0.08815, loss_spatial_dice_3: 0.26072/0.18565, loss_spatial_ce_3: 0.02186/0.07003, loss_grounding_bce_3: 0.07026/0.08135, loss_grounding_dice_3: 0.29797/0.15102, loss_grounding_ce_3: 0.65022/0.25408, loss_mask_ce_4: 1.36984/0.77868, loss_mask_bce_4: 0.86835/0.30640, loss_mask_dice_4: 4.17557/1.04630, loss_spatial_bce_4: 0.02046/0.09014, loss_spatial_dice_4: 0.27609/0.19353, loss_spatial_ce_4: 0.03654/0.08317, loss_grounding_bce_4: 0.09947/0.08200, loss_grounding_dice_4: 0.48344/0.15365, loss_grounding_ce_4: 0.00467/0.25911, loss_mask_ce_5: 1.37665/0.80214, loss_mask_bce_5: 0.84492/0.30831, loss_mask_dice_5: 4.14914/1.05374, loss_spatial_bce_5: 0.02438/0.09226, loss_spatial_dice_5: 0.28492/0.19632, loss_spatial_ce_5: 0.01130/0.09546, loss_grounding_bce_5: 0.09687/0.08226, loss_grounding_dice_5: 0.48292/0.15438, loss_grounding_ce_5: 0.00273/0.27761, loss_mask_ce_6: 1.26215/0.82862, loss_mask_bce_6: 0.95406/0.31023, loss_mask_dice_6: 4.38097/1.05685, loss_spatial_bce_6: 0.01935/0.09731, loss_spatial_dice_6: 0.27011/0.19859, loss_spatial_ce_6: 0.11928/0.11945, loss_grounding_bce_6: 0.09178/0.08320, loss_grounding_dice_6: 0.48413/0.15489, loss_grounding_ce_6: 0.00589/0.28675, loss_mask_ce_7: 1.58437/0.88425, loss_mask_bce_7: 1.05607/0.31755, loss_mask_dice_7: 4.86794/1.10321, loss_spatial_bce_7: 0.02405/0.10732, loss_spatial_dice_7: 0.32953/0.22409, loss_spatial_ce_7: 0.15383/0.15784, loss_grounding_bce_7: 0.09559/0.08493, loss_grounding_dice_7: 0.47693/0.16068, loss_grounding_ce_7: 0.00293/0.32026, loss_mask_ce_8: 1.56146/1.02165, loss_mask_bce_8: 1.26683/0.33357, loss_mask_dice_8: 5.62615/1.18055, loss_spatial_bce_8: 0.04206/0.12514, loss_spatial_dice_8: 0.44571/0.26016, loss_spatial_ce_8: 0.09102/0.20763, loss_grounding_bce_8: 0.10017/0.08911, loss_grounding_dice_8: 0.48405/0.17028, loss_grounding_ce_8: 0.00287/0.42195, loss_mask_ce_9: 7.20537/3.48135, loss_mask_bce_9: 1.02080/0.36065, loss_mask_dice_9: 8.64590/1.76319, loss_spatial_bce_9: 0.14813/0.35566, loss_spatial_dice_9: 0.92494/0.79407, loss_spatial_ce_9: 1.34819/1.39422, loss_grounding_bce_9: 0.10734/0.10109, loss_grounding_dice_9: 0.51693/0.24280, loss_grounding_ce_9: 0.02252/0.67876] items per batch[64] items per second[0.36] total items[3385600] mini batches[ 52900] memory[4999] epoch remaining[0:02:27] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00052983. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3716 s/iter. Eval: 0.0852 s/iter. Total: 0.4596 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3714 s/iter. Eval: 0.0763 s/iter. Total: 0.4503 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3742 s/iter. Eval: 0.0753 s/iter. Total: 0.4522 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3779 s/iter. Eval: 0.0728 s/iter. Total: 0.4536 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3798 s/iter. Eval: 0.0714 s/iter. Total: 0.4542 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0028 s/iter. Inference: 0.3804 s/iter. Eval: 0.0691 s/iter. Total: 0.4524 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalw79xjbdi ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.682 | 82.929 | 66.318 | 133 | | Things | 61.981 | 84.110 | 73.200 | 80 | | Stuff | 46.174 | 81.146 | 55.930 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.51s) creating index... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.21 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.36 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.41s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.21 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.695 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.493 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.570 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.610 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.47 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.687 | 69.492 | 49.273 | 25.657 | 49.879 | 67.688 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.723 | bicycle | 23.594 | car | 43.936 | | motorcycle | 42.774 | airplane | 60.688 | bus | 70.924 | | train | 74.936 | truck | 43.039 | boat | 30.467 | | traffic light | 28.396 | fire hydrant | 70.329 | stop sign | 69.479 | | parking meter | 49.528 | bench | 26.957 | bird | 33.467 | | cat | 77.052 | dog | 70.753 | horse | 50.379 | | sheep | 53.156 | cow | 56.327 | elephant | 65.490 | | bear | 80.058 | zebra | 66.147 | giraffe | 62.170 | | backpack | 24.871 | umbrella | 56.087 | handbag | 24.624 | | tie | 40.446 | suitcase | 52.117 | frisbee | 70.601 | | skis | 9.218 | snowboard | 35.297 | sports ball | 50.222 | | kite | 36.944 | baseball bat | 39.151 | baseball glove | 49.928 | | skateboard | 44.148 | surfboard | 45.433 | tennis racket | 62.821 | | bottle | 41.944 | wine glass | 39.057 | cup | 49.830 | | fork | 26.102 | knife | 24.485 | spoon | 22.363 | | bowl | 39.823 | banana | 22.343 | apple | 25.469 | | sandwich | 48.451 | orange | 30.854 | broccoli | 25.129 | | carrot | 23.378 | hot dog | 33.773 | pizza | 52.518 | | donut | 56.241 | cake | 47.824 | chair | 28.825 | | couch | 46.564 | potted plant | 21.855 | bed | 42.590 | | dining table | 14.487 | toilet | 70.072 | tv | 65.909 | | laptop | 69.179 | mouse | 64.012 | remote | 44.205 | | keyboard | 59.386 | cell phone | 45.449 | microwave | 66.754 | | oven | 34.082 | toaster | 49.020 | sink | 43.217 | | refrigerator | 70.459 | book | 14.093 | clock | 54.706 | | vase | 40.661 | scissors | 38.269 | teddy bear | 58.033 | | hair drier | 33.579 | toothbrush | 29.314 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.56967131296237, 'fwIoU': 72.05307642002701, 'IoU-person': 89.04440748531412, 'IoU-bicycle': 74.46903237058955, 'IoU-car': 72.90476565397887, 'IoU-motorcycle': 88.45636730894579, 'IoU-airplane': 87.33032812229541, 'IoU-bus': 87.04990096706548, 'IoU-train': 89.09619346606267, 'IoU-truck': 68.30357751507773, 'IoU-boat': 67.51723947118727, 'IoU-traffic light': 79.01761199327674, 'IoU-fire hydrant': 93.3629118804919, 'IoU-stop sign': 95.42696063549405, 'IoU-parking meter': 84.79445306902316, 'IoU-bench': 64.07946500321157, 'IoU-bird': 77.05474073992572, 'IoU-cat': 92.44906454584253, 'IoU-dog': 83.33253459651276, 'IoU-horse': 88.8677949913377, 'IoU-sheep': 80.98344688807929, 'IoU-cow': 90.0333139123056, 'IoU-elephant': 90.06783617332663, 'IoU-bear': 85.92808624411747, 'IoU-zebra': 88.20431721150072, 'IoU-giraffe': 89.5810436913781, 'IoU-backpack': 53.60575156083646, 'IoU-umbrella': 89.28863580001321, 'IoU-handbag': 49.89308700917146, 'IoU-tie': 75.87002480841178, 'IoU-suitcase': 79.33506568332847, 'IoU-frisbee': 84.61483385947213, 'IoU-skis': 61.27968284361979, 'IoU-snowboard': 75.21120783027334, 'IoU-sports ball': 77.13519794325657, 'IoU-kite': 79.24123756632365, 'IoU-baseball bat': 68.29523563069668, 'IoU-baseball glove': 78.95437262357414, 'IoU-skateboard': 86.29523333237773, 'IoU-surfboard': 86.86903973760948, 'IoU-tennis racket': 90.35144502047949, 'IoU-bottle': 72.37346301988275, 'IoU-wine glass': 82.61291182619496, 'IoU-cup': 69.18683405686505, 'IoU-fork': 71.37825415122211, 'IoU-knife': 64.78742182091707, 'IoU-spoon': 60.729362697726465, 'IoU-bowl': 64.22551439603292, 'IoU-banana': 83.09969177207623, 'IoU-apple': 59.279992926897265, 'IoU-sandwich': 69.34361978759972, 'IoU-orange': 79.25175511000255, 'IoU-broccoli': 69.67480824573364, 'IoU-carrot': 63.142213442606895, 'IoU-hot dog': 65.53067476427546, 'IoU-pizza': 85.59158601671547, 'IoU-donut': 76.215251214949, 'IoU-cake': 78.64143139892008, 'IoU-chair': 61.94419441156458, 'IoU-couch': 69.40981469333167, 'IoU-potted plant': 44.59802175099094, 'IoU-bed': 73.43017069325232, 'IoU-dining table': 55.616480868642284, 'IoU-toilet': 90.0258706411077, 'IoU-tv': 80.62538139496075, 'IoU-laptop': 79.72581763536016, 'IoU-mouse': 74.10219180375664, 'IoU-remote': 71.2369917099711, 'IoU-keyboard': 70.83871350190053, 'IoU-cell phone': 83.90903812972282, 'IoU-microwave': 77.67841065479942, 'IoU-oven': 74.61953655302128, 'IoU-toaster': 85.68630347245859, 'IoU-sink': 74.61691699193037, 'IoU-refrigerator': 84.25606392576363, 'IoU-book': 57.3413776181075, 'IoU-clock': 77.39409631368103, 'IoU-vase': 65.06415848052752, 'IoU-scissors': 89.66966772808335, 'IoU-teddy bear': 84.70769709800388, 'IoU-hair drier': 48.72781793362723, 'IoU-toothbrush': 75.94638328328485, 'IoU-banner': 31.120563981061206, 'IoU-blanket': 17.010340360138102, 'IoU-bridge': 40.185823126275885, 'IoU-cardboard': 55.05086154584171, 'IoU-counter': 33.99861849718927, 'IoU-curtain': 70.18819104345103, 'IoU-door-stuff': 47.93108859029607, 'IoU-floor-wood': 63.85487138459235, 'IoU-flower': 51.44730812231381, 'IoU-fruit': 50.539718205597914, 'IoU-gravel': 31.15820754821035, 'IoU-house': 22.38436256193731, 'IoU-light': 43.91953733195735, 'IoU-mirror-stuff': 64.17019713222007, 'IoU-net': 51.09764645185816, 'IoU-pillow': 17.173018834857373, 'IoU-platform': 30.54101170721033, 'IoU-playingfield': 70.0635365919406, 'IoU-railroad': 63.512547358780004, 'IoU-river': 57.811661566103645, 'IoU-road': 68.26907337539149, 'IoU-roof': 20.57097947041757, 'IoU-sand': 63.994157695381084, 'IoU-sea': 86.6952507624702, 'IoU-shelf': 39.789818267598264, 'IoU-snow': 92.08829997742475, 'IoU-stairs': 31.44264639231639, 'IoU-tent': 10.724727941356054, 'IoU-towel': 47.37928100571443, 'IoU-wall-brick': 51.14479265025624, 'IoU-wall-stone': 31.169664618244504, 'IoU-wall-tile': 71.9126784172613, 'IoU-wall-wood': 44.751475503581965, 'IoU-water-other': 29.979972458296402, 'IoU-window-blind': 49.23915302870882, 'IoU-window-other': 49.732796641068916, 'IoU-tree-merged': 81.49157313266882, 'IoU-fence-merged': 55.220440486637735, 'IoU-ceiling-merged': 67.10116048887294, 'IoU-sky-other-merged': 93.82547002634786, 'IoU-cabinet-merged': 64.97706529893446, 'IoU-table-merged': 45.03239926529456, 'IoU-floor-other-merged': 54.52011317837776, 'IoU-pavement-merged': 57.651153813938585, 'IoU-mountain-merged': 55.95461687978681, 'IoU-grass-merged': 71.91762076008139, 'IoU-dirt-merged': 47.287915179133556, 'IoU-paper-merged': 39.98114857441484, 'IoU-food-other-merged': 44.0889902016491, 'IoU-building-other-merged': 59.39407160777446, 'IoU-rock-merged': 67.9058892086166, 'IoU-wall-other-merged': 68.11489442058134, 'IoU-rug-merged': 67.42653283130952, 'mACC': 78.47094457884145, 'pACC': 82.57574795628096, 'ACC-person': 93.2817369243422, 'ACC-bicycle': 84.23272274186431, 'ACC-car': 86.51267412420944, 'ACC-motorcycle': 93.20866844190685, 'ACC-airplane': 93.87459437625186, 'ACC-bus': 94.19597631092083, 'ACC-train': 95.44496644593521, 'ACC-truck': 77.78038469033288, 'ACC-boat': 76.29378152867766, 'ACC-traffic light': 90.39938179963669, 'ACC-fire hydrant': 96.16759256491608, 'ACC-stop sign': 98.15729536760801, 'ACC-parking meter': 88.34253262952994, 'ACC-bench': 79.44398935712826, 'ACC-bird': 82.42535908102649, 'ACC-cat': 96.14609952458972, 'ACC-dog': 86.57746911675025, 'ACC-horse': 94.16408779843644, 'ACC-sheep': 85.00618615918549, 'ACC-cow': 93.51906329941015, 'ACC-elephant': 92.24642630679455, 'ACC-bear': 87.72058637073033, 'ACC-zebra': 90.4535141256914, 'ACC-giraffe': 93.51067176228824, 'ACC-backpack': 74.29963365789958, 'ACC-umbrella': 93.74723553574937, 'ACC-handbag': 70.84787206751115, 'ACC-tie': 85.44444793930252, 'ACC-suitcase': 85.14983305035409, 'ACC-frisbee': 94.544, 'ACC-skis': 77.41884925569937, 'ACC-snowboard': 83.33227204819867, 'ACC-sports ball': 87.14815848502703, 'ACC-kite': 85.34869204302049, 'ACC-baseball bat': 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'ACC-mouse': 92.60521844528857, 'ACC-remote': 76.07824044490233, 'ACC-keyboard': 80.83419115621432, 'ACC-cell phone': 94.7890850545216, 'ACC-microwave': 82.5554851199579, 'ACC-oven': 92.36865562332068, 'ACC-toaster': 91.29931982099112, 'ACC-sink': 84.19644026799405, 'ACC-refrigerator': 93.88974826115947, 'ACC-book': 75.05151859012076, 'ACC-clock': 82.92632043921839, 'ACC-vase': 74.116895586549, 'ACC-scissors': 95.50852087522611, 'ACC-teddy bear': 90.26077747155179, 'ACC-hair drier': 61.978766401958275, 'ACC-toothbrush': 84.80107713690063, 'ACC-banner': 75.20804597597595, 'ACC-blanket': 30.105468464146618, 'ACC-bridge': 55.99064941133448, 'ACC-cardboard': 72.42274247491639, 'ACC-counter': 56.47069602682265, 'ACC-curtain': 83.11613203640562, 'ACC-door-stuff': 71.67413238324488, 'ACC-floor-wood': 80.01964402424883, 'ACC-flower': 75.64558767043158, 'ACC-fruit': 66.94465916192924, 'ACC-gravel': 44.51985799681336, 'ACC-house': 25.82585465561914, 'ACC-light': 64.80944713470888, 'ACC-mirror-stuff': 73.96797544390847, 'ACC-net': 66.75196706373751, 'ACC-pillow': 39.578872893638476, 'ACC-platform': 53.000272332829056, 'ACC-playingfield': 85.76329421849424, 'ACC-railroad': 80.55686476869536, 'ACC-river': 80.73020679065455, 'ACC-road': 87.94084898500631, 'ACC-roof': 28.528197794048737, 'ACC-sand': 67.46658811204287, 'ACC-sea': 90.65142519572687, 'ACC-shelf': 58.55659991386252, 'ACC-snow': 95.40837985654032, 'ACC-stairs': 57.99383926355739, 'ACC-tent': 12.85522205168311, 'ACC-towel': 54.385941158523934, 'ACC-wall-brick': 70.61632728857646, 'ACC-wall-stone': 41.73057553268354, 'ACC-wall-tile': 85.63892188384463, 'ACC-wall-wood': 64.25911441820253, 'ACC-water-other': 48.05498020675588, 'ACC-window-blind': 63.254927474657194, 'ACC-window-other': 75.15539314665529, 'ACC-tree-merged': 89.78393719550627, 'ACC-fence-merged': 74.60116683104361, 'ACC-ceiling-merged': 84.77896937311769, 'ACC-sky-other-merged': 97.21826122913207, 'ACC-cabinet-merged': 76.22269305438232, 'ACC-table-merged': 58.910864815874085, 'ACC-floor-other-merged': 68.22689552358723, 'ACC-pavement-merged': 68.59669226199841, 'ACC-mountain-merged': 64.4922548253345, 'ACC-grass-merged': 83.75968865095061, 'ACC-dirt-merged': 71.50882263227363, 'ACC-paper-merged': 56.40606443757681, 'ACC-food-other-merged': 62.034799130365585, 'ACC-building-other-merged': 72.91539970624407, 'ACC-rock-merged': 83.33335854211754, 'ACC-wall-other-merged': 81.05814581531534, 'ACC-rug-merged': 82.59686592859599})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2556 s/iter. Inference: 0.1740 s/iter. Eval: 0.0000 s/iter. Total: 0.4296 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.2893 s/iter. Inference: 0.4554 s/iter. Eval: 0.0000 s/iter. Total: 0.7448 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3057 s/iter. Inference: 0.5205 s/iter. Eval: 0.0000 s/iter. Total: 0.8263 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.42756804214223, 'noc@0.8': 2.5098039215686274, 'noc@0.85': 2.957565115598478, 'noc@0.9': 3.718173836698859, 'miou@iter1': 0.8757349237001602} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0021 s/iter. Inference: 0.1457 s/iter. Eval: 0.0010 s/iter. Total: 0.1489 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.4372329711914, 'precision@0.6': 72.87213134765625, 'precision@0.7': 68.71356201171875, 'precision@0.8': 59.96890640258789, 'precision@0.9': 32.72444534301758, 'cIoU': 61.97605895996094, 'mIoU': 67.00239562988281} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.68226488155755, 'SQ': 82.92874554821594, 'RQ': 66.31818736011007, 'PQ_th': 61.98122773031716, 'SQ_th': 84.10954207567178, 'RQ_th': 73.20025037723013, 'PQ_st': 46.17439643059967, 'SQ_st': 81.14641116715056, 'RQ_st': 55.9301677116269}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.68721762854852, 'AP50': 69.4915395223485, 'AP75': 49.2728877158587, 'APs': 25.65671921563069, 'APm': 49.87882823320858, 'APl': 67.68794870559722, 'AP-person': 48.72318068457669, 'AP-bicycle': 23.59431718640694, 'AP-car': 43.935760505844385, 'AP-motorcycle': 42.77376855062715, 'AP-airplane': 60.68776963073639, 'AP-bus': 70.92359068867077, 'AP-train': 74.93612922312964, 'AP-truck': 43.03912195313666, 'AP-boat': 30.466707855866538, 'AP-traffic light': 28.39587591501059, 'AP-fire hydrant': 70.32861165503624, 'AP-stop sign': 69.47916696200221, 'AP-parking meter': 49.527799214722414, 'AP-bench': 26.957123793563802, 'AP-bird': 33.46674574103285, 'AP-cat': 77.05186483520762, 'AP-dog': 70.75308440286763, 'AP-horse': 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58.03336920163681, 'AP-hair drier': 33.578547854785484, 'AP-toothbrush': 29.314223219081985}), ('sem_seg', {'mIoU': 66.56967131296237, 'fwIoU': 72.05307642002701, 'IoU-person': 89.04440748531412, 'IoU-bicycle': 74.46903237058955, 'IoU-car': 72.90476565397887, 'IoU-motorcycle': 88.45636730894579, 'IoU-airplane': 87.33032812229541, 'IoU-bus': 87.04990096706548, 'IoU-train': 89.09619346606267, 'IoU-truck': 68.30357751507773, 'IoU-boat': 67.51723947118727, 'IoU-traffic light': 79.01761199327674, 'IoU-fire hydrant': 93.3629118804919, 'IoU-stop sign': 95.42696063549405, 'IoU-parking meter': 84.79445306902316, 'IoU-bench': 64.07946500321157, 'IoU-bird': 77.05474073992572, 'IoU-cat': 92.44906454584253, 'IoU-dog': 83.33253459651276, 'IoU-horse': 88.8677949913377, 'IoU-sheep': 80.98344688807929, 'IoU-cow': 90.0333139123056, 'IoU-elephant': 90.06783617332663, 'IoU-bear': 85.92808624411747, 'IoU-zebra': 88.20431721150072, 'IoU-giraffe': 89.5810436913781, 'IoU-backpack': 53.60575156083646, 'IoU-umbrella': 89.28863580001321, 'IoU-handbag': 49.89308700917146, 'IoU-tie': 75.87002480841178, 'IoU-suitcase': 79.33506568332847, 'IoU-frisbee': 84.61483385947213, 'IoU-skis': 61.27968284361979, 'IoU-snowboard': 75.21120783027334, 'IoU-sports ball': 77.13519794325657, 'IoU-kite': 79.24123756632365, 'IoU-baseball bat': 68.29523563069668, 'IoU-baseball glove': 78.95437262357414, 'IoU-skateboard': 86.29523333237773, 'IoU-surfboard': 86.86903973760948, 'IoU-tennis racket': 90.35144502047949, 'IoU-bottle': 72.37346301988275, 'IoU-wine glass': 82.61291182619496, 'IoU-cup': 69.18683405686505, 'IoU-fork': 71.37825415122211, 'IoU-knife': 64.78742182091707, 'IoU-spoon': 60.729362697726465, 'IoU-bowl': 64.22551439603292, 'IoU-banana': 83.09969177207623, 'IoU-apple': 59.279992926897265, 'IoU-sandwich': 69.34361978759972, 'IoU-orange': 79.25175511000255, 'IoU-broccoli': 69.67480824573364, 'IoU-carrot': 63.142213442606895, 'IoU-hot dog': 65.53067476427546, 'IoU-pizza': 85.59158601671547, 'IoU-donut': 76.215251214949, 'IoU-cake': 78.64143139892008, 'IoU-chair': 61.94419441156458, 'IoU-couch': 69.40981469333167, 'IoU-potted plant': 44.59802175099094, 'IoU-bed': 73.43017069325232, 'IoU-dining table': 55.616480868642284, 'IoU-toilet': 90.0258706411077, 'IoU-tv': 80.62538139496075, 'IoU-laptop': 79.72581763536016, 'IoU-mouse': 74.10219180375664, 'IoU-remote': 71.2369917099711, 'IoU-keyboard': 70.83871350190053, 'IoU-cell phone': 83.90903812972282, 'IoU-microwave': 77.67841065479942, 'IoU-oven': 74.61953655302128, 'IoU-toaster': 85.68630347245859, 'IoU-sink': 74.61691699193037, 'IoU-refrigerator': 84.25606392576363, 'IoU-book': 57.3413776181075, 'IoU-clock': 77.39409631368103, 'IoU-vase': 65.06415848052752, 'IoU-scissors': 89.66966772808335, 'IoU-teddy bear': 84.70769709800388, 'IoU-hair drier': 48.72781793362723, 'IoU-toothbrush': 75.94638328328485, 'IoU-banner': 31.120563981061206, 'IoU-blanket': 17.010340360138102, 'IoU-bridge': 40.185823126275885, 'IoU-cardboard': 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'IoU-water-other': 29.979972458296402, 'IoU-window-blind': 49.23915302870882, 'IoU-window-other': 49.732796641068916, 'IoU-tree-merged': 81.49157313266882, 'IoU-fence-merged': 55.220440486637735, 'IoU-ceiling-merged': 67.10116048887294, 'IoU-sky-other-merged': 93.82547002634786, 'IoU-cabinet-merged': 64.97706529893446, 'IoU-table-merged': 45.03239926529456, 'IoU-floor-other-merged': 54.52011317837776, 'IoU-pavement-merged': 57.651153813938585, 'IoU-mountain-merged': 55.95461687978681, 'IoU-grass-merged': 71.91762076008139, 'IoU-dirt-merged': 47.287915179133556, 'IoU-paper-merged': 39.98114857441484, 'IoU-food-other-merged': 44.0889902016491, 'IoU-building-other-merged': 59.39407160777446, 'IoU-rock-merged': 67.9058892086166, 'IoU-wall-other-merged': 68.11489442058134, 'IoU-rug-merged': 67.42653283130952, 'mACC': 78.47094457884145, 'pACC': 82.57574795628096, 'ACC-person': 93.2817369243422, 'ACC-bicycle': 84.23272274186431, 'ACC-car': 86.51267412420944, 'ACC-motorcycle': 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'ACC-laptop': 90.63388901500007, 'ACC-mouse': 92.60521844528857, 'ACC-remote': 76.07824044490233, 'ACC-keyboard': 80.83419115621432, 'ACC-cell phone': 94.7890850545216, 'ACC-microwave': 82.5554851199579, 'ACC-oven': 92.36865562332068, 'ACC-toaster': 91.29931982099112, 'ACC-sink': 84.19644026799405, 'ACC-refrigerator': 93.88974826115947, 'ACC-book': 75.05151859012076, 'ACC-clock': 82.92632043921839, 'ACC-vase': 74.116895586549, 'ACC-scissors': 95.50852087522611, 'ACC-teddy bear': 90.26077747155179, 'ACC-hair drier': 61.978766401958275, 'ACC-toothbrush': 84.80107713690063, 'ACC-banner': 75.20804597597595, 'ACC-blanket': 30.105468464146618, 'ACC-bridge': 55.99064941133448, 'ACC-cardboard': 72.42274247491639, 'ACC-counter': 56.47069602682265, 'ACC-curtain': 83.11613203640562, 'ACC-door-stuff': 71.67413238324488, 'ACC-floor-wood': 80.01964402424883, 'ACC-flower': 75.64558767043158, 'ACC-fruit': 66.94465916192924, 'ACC-gravel': 44.51985799681336, 'ACC-house': 25.82585465561914, 'ACC-light': 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76.22269305438232, 'ACC-table-merged': 58.910864815874085, 'ACC-floor-other-merged': 68.22689552358723, 'ACC-pavement-merged': 68.59669226199841, 'ACC-mountain-merged': 64.4922548253345, 'ACC-grass-merged': 83.75968865095061, 'ACC-dirt-merged': 71.50882263227363, 'ACC-paper-merged': 56.40606443757681, 'ACC-food-other-merged': 62.034799130365585, 'ACC-building-other-merged': 72.91539970624407, 'ACC-rock-merged': 83.33335854211754, 'ACC-wall-other-merged': 81.05814581531534, 'ACC-rug-merged': 82.59686592859599})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.42756804214223, 'noc@0.8': 2.5098039215686274, 'noc@0.85': 2.957565115598478, 'noc@0.9': 3.718173836698859, 'miou@iter1': 0.8757349237001602}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.4372329711914, 'precision@0.6': 72.87213134765625, 'precision@0.7': 68.71356201171875, 'precision@0.8': 59.96890640258789, 'precision@0.9': 32.72444534301758, 'cIoU': 61.97605895996094, 'mIoU': 67.00239562988281}}} INFO:trainer.default_trainer:This epoch takes 0:57:25.550900 INFO:trainer.default_trainer:PROGRESS: 58.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 29 training. INFO:trainer.default_trainer:epochs[ 29] optim steps[53000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19059/0.76088, loss_mask_bce_0: 0.23169/0.30147, loss_mask_dice_0: 0.14796/1.02437, loss_spatial_bce_0: 0.10995/0.08583, loss_spatial_dice_0: 0.09236/0.18152, loss_spatial_ce_0: 0.00014/0.05913, loss_grounding_bce_0: 0.16842/0.08077, loss_grounding_dice_0: 0.13033/0.15077, loss_grounding_ce_0: 0.07647/0.24913, loss_mask_ce_1: 0.20031/0.76148, loss_mask_bce_1: 0.22745/0.30236, loss_mask_dice_1: 0.19553/1.02841, loss_spatial_bce_1: 0.11238/0.08611, loss_spatial_dice_1: 0.09653/0.18412, loss_spatial_ce_1: 0.00005/0.06314, loss_grounding_bce_1: 0.16225/0.08095, loss_grounding_dice_1: 0.11553/0.15153, loss_grounding_ce_1: 0.08815/0.25073, loss_mask_ce_2: 0.18143/0.76963, loss_mask_bce_2: 0.22137/0.30250, loss_mask_dice_2: 0.16041/1.02923, loss_spatial_bce_2: 0.12928/0.08612, loss_spatial_dice_2: 0.09120/0.18440, loss_spatial_ce_2: 0.00018/0.06545, loss_grounding_bce_2: 0.16294/0.08093, loss_grounding_dice_2: 0.12388/0.15142, loss_grounding_ce_2: 0.06977/0.25399, loss_mask_ce_3: 0.18217/0.77251, loss_mask_bce_3: 0.22396/0.30398, loss_mask_dice_3: 0.15870/1.02693, loss_spatial_bce_3: 0.12492/0.08815, loss_spatial_dice_3: 0.08450/0.18564, loss_spatial_ce_3: 0.00113/0.07000, loss_grounding_bce_3: 0.15870/0.08136, loss_grounding_dice_3: 0.11294/0.15103, loss_grounding_ce_3: 0.06963/0.25407, loss_mask_ce_4: 0.18901/0.77864, loss_mask_bce_4: 0.23564/0.30640, loss_mask_dice_4: 0.16733/1.04593, loss_spatial_bce_4: 0.12110/0.09015, loss_spatial_dice_4: 0.12337/0.19353, loss_spatial_ce_4: 0.00233/0.08313, loss_grounding_bce_4: 0.15873/0.08202, loss_grounding_dice_4: 0.09842/0.15365, loss_grounding_ce_4: 0.07280/0.25914, loss_mask_ce_5: 0.13720/0.80214, loss_mask_bce_5: 0.24414/0.30831, loss_mask_dice_5: 0.18011/1.05338, loss_spatial_bce_5: 0.13432/0.09228, loss_spatial_dice_5: 0.12343/0.19631, loss_spatial_ce_5: 0.03206/0.09544, loss_grounding_bce_5: 0.16170/0.08227, loss_grounding_dice_5: 0.12398/0.15438, loss_grounding_ce_5: 0.03474/0.27766, loss_mask_ce_6: 0.15057/0.82861, loss_mask_bce_6: 0.23985/0.31023, loss_mask_dice_6: 0.15340/1.05648, loss_spatial_bce_6: 0.19534/0.09732, loss_spatial_dice_6: 0.11699/0.19858, loss_spatial_ce_6: 0.01095/0.11944, loss_grounding_bce_6: 0.17681/0.08321, loss_grounding_dice_6: 0.12265/0.15490, loss_grounding_ce_6: 0.02831/0.28681, loss_mask_ce_7: 0.13536/0.88420, loss_mask_bce_7: 0.24471/0.31756, loss_mask_dice_7: 0.19496/1.10284, loss_spatial_bce_7: 0.17874/0.10733, loss_spatial_dice_7: 0.25507/0.22410, loss_spatial_ce_7: 0.00967/0.15782, loss_grounding_bce_7: 0.17128/0.08494, loss_grounding_dice_7: 0.12339/0.16069, loss_grounding_ce_7: 0.03157/0.32024, loss_mask_ce_8: 0.15257/1.02166, loss_mask_bce_8: 0.29986/0.33357, loss_mask_dice_8: 0.18299/1.18013, loss_spatial_bce_8: 0.15302/0.12517, loss_spatial_dice_8: 0.20616/0.26016, loss_spatial_ce_8: 0.09983/0.20759, loss_grounding_bce_8: 0.20837/0.08913, loss_grounding_dice_8: 0.12588/0.17030, loss_grounding_ce_8: 0.02399/0.42194, loss_mask_ce_9: 1.65181/3.48121, loss_mask_bce_9: 0.20961/0.36066, loss_mask_dice_9: 0.19207/1.76277, loss_spatial_bce_9: 0.42770/0.35565, loss_spatial_dice_9: 0.67917/0.79404, loss_spatial_ce_9: 1.03744/1.39412, loss_grounding_bce_9: 0.15074/0.10111, loss_grounding_dice_9: 0.14282/0.24284, loss_grounding_ce_9: 0.04867/0.67867] items per batch[64] items per second[0.16] total items[3392000] mini batches[ 53000] memory[4999] epoch remaining[1:10:21] INFO:trainer.default_trainer:epochs[ 29] optim steps[53100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63236/0.76075, loss_mask_bce_0: 0.07454/0.30146, loss_mask_dice_0: 0.61077/1.02431, loss_spatial_bce_0: 0.02347/0.08582, loss_spatial_dice_0: 0.19375/0.18148, loss_spatial_ce_0: 0.01935/0.05911, loss_grounding_bce_0: 0.02506/0.08078, loss_grounding_dice_0: 0.07698/0.15073, loss_grounding_ce_0: 0.00015/0.24907, loss_mask_ce_1: 0.56219/0.76134, loss_mask_bce_1: 0.07857/0.30235, loss_mask_dice_1: 0.52463/1.02830, loss_spatial_bce_1: 0.02471/0.08609, loss_spatial_dice_1: 0.20638/0.18407, loss_spatial_ce_1: 0.01229/0.06310, loss_grounding_bce_1: 0.02750/0.08096, loss_grounding_dice_1: 0.07783/0.15150, loss_grounding_ce_1: 0.00012/0.25066, loss_mask_ce_2: 0.48754/0.76948, loss_mask_bce_2: 0.07725/0.30249, loss_mask_dice_2: 0.66458/1.02913, loss_spatial_bce_2: 0.02269/0.08611, loss_spatial_dice_2: 0.18340/0.18436, loss_spatial_ce_2: 0.02066/0.06540, loss_grounding_bce_2: 0.02672/0.08094, loss_grounding_dice_2: 0.07613/0.15139, loss_grounding_ce_2: 0.00023/0.25386, loss_mask_ce_3: 0.57305/0.77240, loss_mask_bce_3: 0.07249/0.30398, loss_mask_dice_3: 0.58789/1.02685, loss_spatial_bce_3: 0.02718/0.08814, loss_spatial_dice_3: 0.21361/0.18560, loss_spatial_ce_3: 0.03168/0.06996, loss_grounding_bce_3: 0.02698/0.08137, loss_grounding_dice_3: 0.07797/0.15100, loss_grounding_ce_3: 0.00012/0.25394, loss_mask_ce_4: 0.49058/0.77846, loss_mask_bce_4: 0.07638/0.30641, loss_mask_dice_4: 0.64748/1.04588, loss_spatial_bce_4: 0.02458/0.09014, loss_spatial_dice_4: 0.18768/0.19350, loss_spatial_ce_4: 0.05502/0.08309, loss_grounding_bce_4: 0.02664/0.08203, loss_grounding_dice_4: 0.07890/0.15362, loss_grounding_ce_4: 0.00026/0.25901, loss_mask_ce_5: 0.51871/0.80198, loss_mask_bce_5: 0.08053/0.30831, loss_mask_dice_5: 0.50612/1.05331, loss_spatial_bce_5: 0.02667/0.09227, loss_spatial_dice_5: 0.19630/0.19628, loss_spatial_ce_5: 0.04026/0.09540, loss_grounding_bce_5: 0.02778/0.08228, loss_grounding_dice_5: 0.08052/0.15434, loss_grounding_ce_5: 0.00006/0.27754, loss_mask_ce_6: 1.78089/0.82851, loss_mask_bce_6: 0.08255/0.31023, loss_mask_dice_6: 0.65261/1.05638, loss_spatial_bce_6: 0.02881/0.09730, loss_spatial_dice_6: 0.19242/0.19854, loss_spatial_ce_6: 0.13482/0.11942, loss_grounding_bce_6: 0.02699/0.08322, loss_grounding_dice_6: 0.08092/0.15487, loss_grounding_ce_6: 0.00004/0.28666, loss_mask_ce_7: 0.75897/0.88407, loss_mask_bce_7: 0.07335/0.31755, loss_mask_dice_7: 0.61250/1.10275, loss_spatial_bce_7: 0.02434/0.10733, loss_spatial_dice_7: 0.21204/0.22409, loss_spatial_ce_7: 0.10353/0.15777, loss_grounding_bce_7: 0.02714/0.08495, loss_grounding_dice_7: 0.08076/0.16066, loss_grounding_ce_7: 0.00033/0.32017, loss_mask_ce_8: 0.37930/1.02149, loss_mask_bce_8: 0.07919/0.33359, loss_mask_dice_8: 0.65011/1.18014, loss_spatial_bce_8: 0.02884/0.12516, loss_spatial_dice_8: 0.27911/0.26013, loss_spatial_ce_8: 0.19760/0.20749, loss_grounding_bce_8: 0.02752/0.08913, loss_grounding_dice_8: 0.08127/0.17026, loss_grounding_ce_8: 0.09581/0.42189, loss_mask_ce_9: 2.69743/3.48100, loss_mask_bce_9: 0.07647/0.36066, loss_mask_dice_9: 0.83194/1.76271, loss_spatial_bce_9: 0.14916/0.35566, loss_spatial_dice_9: 0.84965/0.79403, loss_spatial_ce_9: 1.64393/1.39405, loss_grounding_bce_9: 0.02744/0.10112, loss_grounding_dice_9: 0.10054/0.24282, loss_grounding_ce_9: 0.96267/0.67848] items per batch[64] items per second[0.36] total items[3398400] mini batches[ 53100] memory[4999] epoch remaining[0:52:49] INFO:trainer.default_trainer:epochs[ 29] optim steps[53200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83675/0.76059, loss_mask_bce_0: 0.00990/0.30145, loss_mask_dice_0: 0.31560/1.02428, loss_spatial_bce_0: 0.00894/0.08580, loss_spatial_dice_0: 0.24399/0.18148, loss_spatial_ce_0: 0.03527/0.05909, loss_grounding_bce_0: 0.00522/0.08079, loss_grounding_dice_0: 0.11619/0.15073, loss_grounding_ce_0: 0.28464/0.24906, loss_mask_ce_1: 1.09440/0.76119, loss_mask_bce_1: 0.00897/0.30232, loss_mask_dice_1: 0.34155/1.02824, loss_spatial_bce_1: 0.00677/0.08607, loss_spatial_dice_1: 0.17983/0.18407, loss_spatial_ce_1: 0.03918/0.06307, loss_grounding_bce_1: 0.00547/0.08097, loss_grounding_dice_1: 0.13262/0.15149, loss_grounding_ce_1: 0.23869/0.25067, loss_mask_ce_2: 0.88458/0.76932, loss_mask_bce_2: 0.00891/0.30248, loss_mask_dice_2: 0.24049/1.02911, loss_spatial_bce_2: 0.00719/0.08609, loss_spatial_dice_2: 0.15998/0.18435, loss_spatial_ce_2: 0.02616/0.06539, loss_grounding_bce_2: 0.00468/0.08096, loss_grounding_dice_2: 0.08811/0.15139, loss_grounding_ce_2: 0.25057/0.25379, loss_mask_ce_3: 0.70770/0.77222, loss_mask_bce_3: 0.01017/0.30397, loss_mask_dice_3: 0.20391/1.02683, loss_spatial_bce_3: 0.00608/0.08812, loss_spatial_dice_3: 0.14003/0.18560, loss_spatial_ce_3: 0.18178/0.06995, loss_grounding_bce_3: 0.00499/0.08138, loss_grounding_dice_3: 0.11025/0.15101, loss_grounding_ce_3: 0.25451/0.25393, loss_mask_ce_4: 0.96675/0.77835, loss_mask_bce_4: 0.01417/0.30638, loss_mask_dice_4: 0.25781/1.04583, loss_spatial_bce_4: 0.01215/0.09013, loss_spatial_dice_4: 0.28578/0.19349, loss_spatial_ce_4: 0.00151/0.08303, loss_grounding_bce_4: 0.00691/0.08203, loss_grounding_dice_4: 0.13455/0.15361, loss_grounding_ce_4: 0.30752/0.25899, loss_mask_ce_5: 0.66398/0.80188, loss_mask_bce_5: 0.01432/0.30828, loss_mask_dice_5: 0.37199/1.05330, loss_spatial_bce_5: 0.02431/0.09225, loss_spatial_dice_5: 0.27993/0.19627, loss_spatial_ce_5: 0.00593/0.09536, loss_grounding_bce_5: 0.00850/0.08228, loss_grounding_dice_5: 0.17608/0.15433, loss_grounding_ce_5: 0.09193/0.27755, loss_mask_ce_6: 0.55843/0.82843, loss_mask_bce_6: 0.00835/0.31020, loss_mask_dice_6: 0.26867/1.05634, loss_spatial_bce_6: 0.04367/0.09729, loss_spatial_dice_6: 0.30046/0.19854, loss_spatial_ce_6: 0.00272/0.11936, loss_grounding_bce_6: 0.00769/0.08323, loss_grounding_dice_6: 0.19950/0.15487, loss_grounding_ce_6: 0.09269/0.28666, loss_mask_ce_7: 1.46581/0.88392, loss_mask_bce_7: 0.01304/0.31752, loss_mask_dice_7: 0.33948/1.10274, loss_spatial_bce_7: 0.03294/0.10730, loss_spatial_dice_7: 0.29379/0.22408, loss_spatial_ce_7: 0.04418/0.15771, loss_grounding_bce_7: 0.00650/0.08496, loss_grounding_dice_7: 0.16143/0.16065, loss_grounding_ce_7: 0.22022/0.32013, loss_mask_ce_8: 1.30194/1.02132, loss_mask_bce_8: 0.01065/0.33355, loss_mask_dice_8: 0.30410/1.18005, loss_spatial_bce_8: 0.03656/0.12513, loss_spatial_dice_8: 0.27822/0.26012, loss_spatial_ce_8: 0.11204/0.20740, loss_grounding_bce_8: 0.00580/0.08913, loss_grounding_dice_8: 0.24831/0.17025, loss_grounding_ce_8: 0.43502/0.42180, loss_mask_ce_9: 2.21964/3.48092, loss_mask_bce_9: 0.00685/0.36063, loss_mask_dice_9: 0.32027/1.76250, loss_spatial_bce_9: 0.02702/0.35566, loss_spatial_dice_9: 0.47266/0.79400, loss_spatial_ce_9: 0.47788/1.39393, loss_grounding_bce_9: 0.00432/0.10111, loss_grounding_dice_9: 0.22135/0.24280, loss_grounding_ce_9: 0.23425/0.67848] items per batch[64] items per second[0.36] total items[3404800] mini batches[ 53200] memory[4999] epoch remaining[0:48:36] INFO:trainer.default_trainer:epochs[ 29] optim steps[53300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.69564/0.76038, loss_mask_bce_0: 0.40114/0.30140, loss_mask_dice_0: 0.81086/1.02400, loss_spatial_bce_0: 0.08599/0.08580, loss_spatial_dice_0: 0.16896/0.18146, loss_spatial_ce_0: 0.00226/0.05908, loss_grounding_bce_0: 0.06708/0.08079, loss_grounding_dice_0: 0.28487/0.15071, loss_grounding_ce_0: 0.05302/0.24907, loss_mask_ce_1: 1.82663/0.76098, loss_mask_bce_1: 0.41087/0.30227, loss_mask_dice_1: 0.79151/1.02796, loss_spatial_bce_1: 0.11440/0.08607, loss_spatial_dice_1: 0.19247/0.18406, loss_spatial_ce_1: 0.00082/0.06304, loss_grounding_bce_1: 0.07055/0.08097, loss_grounding_dice_1: 0.29111/0.15147, loss_grounding_ce_1: 0.05783/0.25072, loss_mask_ce_2: 1.83224/0.76913, loss_mask_bce_2: 0.43241/0.30243, loss_mask_dice_2: 0.82477/1.02889, loss_spatial_bce_2: 0.11907/0.08608, loss_spatial_dice_2: 0.17414/0.18433, loss_spatial_ce_2: 0.00029/0.06535, loss_grounding_bce_2: 0.06120/0.08096, loss_grounding_dice_2: 0.29611/0.15137, loss_grounding_ce_2: 0.07654/0.25381, loss_mask_ce_3: 1.82358/0.77198, loss_mask_bce_3: 0.43037/0.30392, loss_mask_dice_3: 0.81455/1.02658, loss_spatial_bce_3: 0.09110/0.08811, loss_spatial_dice_3: 0.14164/0.18559, loss_spatial_ce_3: 0.00056/0.06991, loss_grounding_bce_3: 0.06605/0.08138, loss_grounding_dice_3: 0.27985/0.15099, loss_grounding_ce_3: 0.30911/0.25396, loss_mask_ce_4: 1.84828/0.77816, loss_mask_bce_4: 0.43454/0.30633, loss_mask_dice_4: 0.85404/1.04557, loss_spatial_bce_4: 0.13402/0.09011, loss_spatial_dice_4: 0.20090/0.19348, loss_spatial_ce_4: 0.00328/0.08299, loss_grounding_bce_4: 0.05994/0.08203, loss_grounding_dice_4: 0.29009/0.15358, loss_grounding_ce_4: 0.05475/0.25899, loss_mask_ce_5: 2.04238/0.80165, loss_mask_bce_5: 0.40873/0.30821, loss_mask_dice_5: 0.80476/1.05303, loss_spatial_bce_5: 0.11277/0.09225, loss_spatial_dice_5: 0.20952/0.19626, loss_spatial_ce_5: 0.04632/0.09534, loss_grounding_bce_5: 0.10395/0.08228, loss_grounding_dice_5: 0.26784/0.15430, loss_grounding_ce_5: 0.19288/0.27758, loss_mask_ce_6: 2.23944/0.82820, loss_mask_bce_6: 0.39089/0.31015, loss_mask_dice_6: 0.78948/1.05613, loss_spatial_bce_6: 0.11917/0.09728, loss_spatial_dice_6: 0.19266/0.19853, loss_spatial_ce_6: 0.03906/0.11938, loss_grounding_bce_6: 0.10415/0.08323, loss_grounding_dice_6: 0.26260/0.15485, loss_grounding_ce_6: 0.23004/0.28665, loss_mask_ce_7: 2.35670/0.88369, loss_mask_bce_7: 0.43771/0.31748, loss_mask_dice_7: 0.87960/1.10253, loss_spatial_bce_7: 0.13232/0.10729, loss_spatial_dice_7: 0.21499/0.22408, loss_spatial_ce_7: 0.02163/0.15772, loss_grounding_bce_7: 0.05764/0.08496, loss_grounding_dice_7: 0.28709/0.16063, loss_grounding_ce_7: 0.05364/0.32014, loss_mask_ce_8: 2.66798/1.02100, loss_mask_bce_8: 0.41894/0.33349, loss_mask_dice_8: 0.96799/1.17978, loss_spatial_bce_8: 0.10317/0.12512, loss_spatial_dice_8: 0.21111/0.26010, loss_spatial_ce_8: 0.02627/0.20738, loss_grounding_bce_8: 0.06174/0.08915, loss_grounding_dice_8: 0.33427/0.17023, loss_grounding_ce_8: 0.25267/0.42177, loss_mask_ce_9: 5.73607/3.48055, loss_mask_bce_9: 0.45873/0.36057, loss_mask_dice_9: 1.82072/1.76208, loss_spatial_bce_9: 0.47519/0.35563, loss_spatial_dice_9: 0.82318/0.79395, loss_spatial_ce_9: 1.93620/1.39377, loss_grounding_bce_9: 0.08026/0.10110, loss_grounding_dice_9: 0.47851/0.24275, loss_grounding_ce_9: 0.53807/0.67852] items per batch[64] items per second[0.36] total items[3411200] mini batches[ 53300] memory[4999] epoch remaining[0:45:08] INFO:trainer.default_trainer:epochs[ 29] optim steps[53400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.00902/0.76038, loss_mask_bce_0: 0.70372/0.30139, loss_mask_dice_0: 1.25236/1.02443, loss_spatial_bce_0: 0.26484/0.08580, loss_spatial_dice_0: 0.38451/0.18146, loss_spatial_ce_0: 0.02123/0.05907, loss_grounding_bce_0: 0.07227/0.08080, loss_grounding_dice_0: 0.98157/0.15073, loss_grounding_ce_0: 0.14792/0.24920, loss_mask_ce_1: 2.07532/0.76102, loss_mask_bce_1: 0.73667/0.30227, loss_mask_dice_1: 1.30827/1.02836, loss_spatial_bce_1: 0.25167/0.08608, loss_spatial_dice_1: 0.39629/0.18406, loss_spatial_ce_1: 0.02758/0.06304, loss_grounding_bce_1: 0.07026/0.08099, loss_grounding_dice_1: 0.98077/0.15150, loss_grounding_ce_1: 0.13254/0.25072, loss_mask_ce_2: 1.54687/0.76912, loss_mask_bce_2: 0.70146/0.30243, loss_mask_dice_2: 1.35644/1.02933, loss_spatial_bce_2: 0.27599/0.08609, loss_spatial_dice_2: 0.40831/0.18433, loss_spatial_ce_2: 0.01925/0.06534, loss_grounding_bce_2: 0.06626/0.08097, loss_grounding_dice_2: 0.98020/0.15140, loss_grounding_ce_2: 0.10759/0.25378, loss_mask_ce_3: 1.33177/0.77204, loss_mask_bce_3: 0.72070/0.30391, loss_mask_dice_3: 1.36562/1.02701, loss_spatial_bce_3: 0.30751/0.08812, loss_spatial_dice_3: 0.39481/0.18559, loss_spatial_ce_3: 0.01815/0.06990, loss_grounding_bce_3: 0.07354/0.08138, loss_grounding_dice_3: 0.98214/0.15101, loss_grounding_ce_3: 0.10860/0.25402, loss_mask_ce_4: 1.38072/0.77816, loss_mask_bce_4: 0.72111/0.30632, loss_mask_dice_4: 1.41509/1.04601, loss_spatial_bce_4: 0.30009/0.09013, loss_spatial_dice_4: 0.39927/0.19348, loss_spatial_ce_4: 0.04914/0.08299, loss_grounding_bce_4: 0.06279/0.08204, loss_grounding_dice_4: 0.97959/0.15360, loss_grounding_ce_4: 0.09627/0.25904, loss_mask_ce_5: 1.73227/0.80165, loss_mask_bce_5: 0.65919/0.30820, loss_mask_dice_5: 1.32139/1.05345, loss_spatial_bce_5: 0.30962/0.09227, loss_spatial_dice_5: 0.39973/0.19626, loss_spatial_ce_5: 0.13116/0.09536, loss_grounding_bce_5: 0.07176/0.08229, loss_grounding_dice_5: 0.98230/0.15433, loss_grounding_ce_5: 0.14061/0.27765, loss_mask_ce_6: 1.65813/0.82825, loss_mask_bce_6: 0.69518/0.31014, loss_mask_dice_6: 1.37219/1.05652, loss_spatial_bce_6: 0.22692/0.09730, loss_spatial_dice_6: 0.40663/0.19854, loss_spatial_ce_6: 0.17991/0.11940, loss_grounding_bce_6: 0.06757/0.08324, loss_grounding_dice_6: 0.98246/0.15489, loss_grounding_ce_6: 0.10715/0.28669, loss_mask_ce_7: 1.92724/0.88378, loss_mask_bce_7: 0.68384/0.31747, loss_mask_dice_7: 1.31730/1.10298, loss_spatial_bce_7: 0.26785/0.10731, loss_spatial_dice_7: 0.41381/0.22411, loss_spatial_ce_7: 0.24168/0.15774, loss_grounding_bce_7: 0.08669/0.08498, loss_grounding_dice_7: 0.98361/0.16066, loss_grounding_ce_7: 0.28765/0.32017, loss_mask_ce_8: 1.91242/1.02107, loss_mask_bce_8: 0.72923/0.33354, loss_mask_dice_8: 1.36695/1.18028, loss_spatial_bce_8: 0.29916/0.12514, loss_spatial_dice_8: 0.43664/0.26011, loss_spatial_ce_8: 0.16118/0.20736, loss_grounding_bce_8: 0.05974/0.08917, loss_grounding_dice_8: 0.91065/0.17026, loss_grounding_ce_8: 0.68137/0.42169, loss_mask_ce_9: 2.76410/3.48061, loss_mask_bce_9: 0.89026/0.36058, loss_mask_dice_9: 1.96049/1.76257, loss_spatial_bce_9: 0.42829/0.35564, loss_spatial_dice_9: 0.87203/0.79396, loss_spatial_ce_9: 1.58415/1.39377, loss_grounding_bce_9: 0.03590/0.10112, loss_grounding_dice_9: 0.96851/0.24277, loss_grounding_ce_9: 0.42381/0.67842] items per batch[64] items per second[0.36] total items[3417600] mini batches[ 53400] memory[4999] epoch remaining[0:41:59] INFO:trainer.default_trainer:epochs[ 29] optim steps[53500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09167/0.76046, loss_mask_bce_0: 0.22113/0.30142, loss_mask_dice_0: 0.86599/1.02449, loss_spatial_bce_0: 0.05054/0.08581, loss_spatial_dice_0: 0.18031/0.18147, loss_spatial_ce_0: 0.00652/0.05906, loss_grounding_bce_0: 0.06310/0.08081, loss_grounding_dice_0: 0.26405/0.15075, loss_grounding_ce_0: 0.14590/0.24933, loss_mask_ce_1: 0.09935/0.76111, loss_mask_bce_1: 0.21579/0.30229, loss_mask_dice_1: 0.87245/1.02845, loss_spatial_bce_1: 0.05104/0.08608, loss_spatial_dice_1: 0.17878/0.18406, loss_spatial_ce_1: 0.00268/0.06303, loss_grounding_bce_1: 0.06434/0.08100, loss_grounding_dice_1: 0.26466/0.15152, loss_grounding_ce_1: 0.14408/0.25086, loss_mask_ce_2: 0.10155/0.76921, loss_mask_bce_2: 0.21911/0.30246, loss_mask_dice_2: 0.88384/1.02936, loss_spatial_bce_2: 0.05057/0.08610, loss_spatial_dice_2: 0.19907/0.18435, loss_spatial_ce_2: 0.00706/0.06536, loss_grounding_bce_2: 0.06889/0.08099, loss_grounding_dice_2: 0.25552/0.15142, loss_grounding_ce_2: 0.14907/0.25396, loss_mask_ce_3: 0.07939/0.77220, loss_mask_bce_3: 0.22356/0.30393, loss_mask_dice_3: 0.92694/1.02711, loss_spatial_bce_3: 0.04849/0.08812, loss_spatial_dice_3: 0.19675/0.18559, loss_spatial_ce_3: 0.02796/0.06992, loss_grounding_bce_3: 0.07093/0.08140, loss_grounding_dice_3: 0.22618/0.15103, loss_grounding_ce_3: 0.49662/0.25419, loss_mask_ce_4: 0.15295/0.77829, loss_mask_bce_4: 0.22582/0.30635, loss_mask_dice_4: 0.87756/1.04610, loss_spatial_bce_4: 0.05066/0.09014, loss_spatial_dice_4: 0.20461/0.19350, loss_spatial_ce_4: 0.01488/0.08301, loss_grounding_bce_4: 0.06813/0.08205, loss_grounding_dice_4: 0.26175/0.15362, loss_grounding_ce_4: 0.15237/0.25920, loss_mask_ce_5: 0.09366/0.80180, loss_mask_bce_5: 0.22531/0.30825, loss_mask_dice_5: 0.89872/1.05353, loss_spatial_bce_5: 0.05219/0.09227, loss_spatial_dice_5: 0.22166/0.19628, loss_spatial_ce_5: 0.03705/0.09538, loss_grounding_bce_5: 0.06616/0.08231, loss_grounding_dice_5: 0.26076/0.15435, loss_grounding_ce_5: 0.14727/0.27782, loss_mask_ce_6: 0.12753/0.82840, loss_mask_bce_6: 0.21925/0.31018, loss_mask_dice_6: 0.88642/1.05664, loss_spatial_bce_6: 0.05566/0.09730, loss_spatial_dice_6: 0.22655/0.19855, loss_spatial_ce_6: 0.06606/0.11941, loss_grounding_bce_6: 0.06710/0.08325, loss_grounding_dice_6: 0.27800/0.15491, loss_grounding_ce_6: 0.11490/0.28684, loss_mask_ce_7: 0.14314/0.88392, loss_mask_bce_7: 0.22928/0.31753, loss_mask_dice_7: 0.98590/1.10307, loss_spatial_bce_7: 0.05720/0.10732, loss_spatial_dice_7: 0.25240/0.22413, loss_spatial_ce_7: 0.16747/0.15776, loss_grounding_bce_7: 0.06530/0.08500, loss_grounding_dice_7: 0.27454/0.16070, loss_grounding_ce_7: 0.17826/0.32034, loss_mask_ce_8: 0.38795/1.02122, loss_mask_bce_8: 0.21989/0.33359, loss_mask_dice_8: 0.89724/1.18045, loss_spatial_bce_8: 0.07043/0.12514, loss_spatial_dice_8: 0.30029/0.26015, loss_spatial_ce_8: 0.12070/0.20734, loss_grounding_bce_8: 0.06316/0.08919, loss_grounding_dice_8: 0.24772/0.17029, loss_grounding_ce_8: 0.13323/0.42194, loss_mask_ce_9: 3.01799/3.48072, loss_mask_bce_9: 0.19816/0.36063, loss_mask_dice_9: 1.27143/1.76263, loss_spatial_bce_9: 0.33285/0.35564, loss_spatial_dice_9: 0.85493/0.79399, loss_spatial_ce_9: 1.60347/1.39376, loss_grounding_bce_9: 0.04934/0.10115, loss_grounding_dice_9: 0.31763/0.24285, loss_grounding_ce_9: 0.53637/0.67851] items per batch[64] items per second[0.36] total items[3424000] mini batches[ 53500] memory[4999] epoch remaining[0:39:03] INFO:trainer.default_trainer:epochs[ 29] optim steps[53600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14578/0.76056, loss_mask_bce_0: 0.00926/0.30142, loss_mask_dice_0: 0.51494/1.02452, loss_spatial_bce_0: 0.00575/0.08580, loss_spatial_dice_0: 0.28355/0.18146, loss_spatial_ce_0: 0.00987/0.05903, loss_grounding_bce_0: 0.00341/0.08082, loss_grounding_dice_0: 0.22184/0.15073, loss_grounding_ce_0: 0.12630/0.24932, loss_mask_ce_1: 0.11847/0.76123, loss_mask_bce_1: 0.00621/0.30231, loss_mask_dice_1: 0.36605/1.02861, loss_spatial_bce_1: 0.00496/0.08607, loss_spatial_dice_1: 0.27254/0.18405, loss_spatial_ce_1: 0.03045/0.06300, loss_grounding_bce_1: 0.00366/0.08101, loss_grounding_dice_1: 0.17978/0.15151, loss_grounding_ce_1: 0.09968/0.25085, loss_mask_ce_2: 0.12654/0.76935, loss_mask_bce_2: 0.01010/0.30247, loss_mask_dice_2: 0.50602/1.02946, loss_spatial_bce_2: 0.00568/0.08608, loss_spatial_dice_2: 0.27161/0.18434, loss_spatial_ce_2: 0.03760/0.06532, loss_grounding_bce_2: 0.00514/0.08099, loss_grounding_dice_2: 0.29620/0.15140, loss_grounding_ce_2: 0.10436/0.25396, loss_mask_ce_3: 0.16932/0.77237, loss_mask_bce_3: 0.00987/0.30394, loss_mask_dice_3: 0.56793/1.02722, loss_spatial_bce_3: 0.00581/0.08811, loss_spatial_dice_3: 0.26119/0.18559, loss_spatial_ce_3: 0.04694/0.06987, loss_grounding_bce_3: 0.00322/0.08141, loss_grounding_dice_3: 0.20023/0.15103, loss_grounding_ce_3: 0.13747/0.25417, loss_mask_ce_4: 0.29237/0.77840, loss_mask_bce_4: 0.00994/0.30636, loss_mask_dice_4: 0.82178/1.04620, loss_spatial_bce_4: 0.00520/0.09013, loss_spatial_dice_4: 0.23952/0.19349, loss_spatial_ce_4: 0.05922/0.08298, loss_grounding_bce_4: 0.00206/0.08206, loss_grounding_dice_4: 0.22143/0.15359, loss_grounding_ce_4: 0.33628/0.25916, loss_mask_ce_5: 0.14802/0.80186, loss_mask_bce_5: 0.01039/0.30826, loss_mask_dice_5: 0.50639/1.05364, loss_spatial_bce_5: 0.00368/0.09227, loss_spatial_dice_5: 0.18133/0.19627, loss_spatial_ce_5: 0.09037/0.09534, loss_grounding_bce_5: 0.00382/0.08231, loss_grounding_dice_5: 0.21241/0.15433, loss_grounding_ce_5: 0.31151/0.27787, loss_mask_ce_6: 0.37512/0.82850, loss_mask_bce_6: 0.00869/0.31020, loss_mask_dice_6: 0.82614/1.05676, loss_spatial_bce_6: 0.00307/0.09729, loss_spatial_dice_6: 0.14600/0.19854, loss_spatial_ce_6: 0.02940/0.11937, loss_grounding_bce_6: 0.00227/0.08326, loss_grounding_dice_6: 0.14296/0.15491, loss_grounding_ce_6: 0.13611/0.28679, loss_mask_ce_7: 0.21675/0.88405, loss_mask_bce_7: 0.01166/0.31756, loss_mask_dice_7: 0.58362/1.10319, loss_spatial_bce_7: 0.00391/0.10730, loss_spatial_dice_7: 0.26562/0.22415, loss_spatial_ce_7: 0.07410/0.15774, loss_grounding_bce_7: 0.00413/0.08501, loss_grounding_dice_7: 0.21910/0.16067, loss_grounding_ce_7: 0.29478/0.32031, loss_mask_ce_8: 0.55970/1.02127, loss_mask_bce_8: 0.00716/0.33361, loss_mask_dice_8: 0.45230/1.18060, loss_spatial_bce_8: 0.00528/0.12513, loss_spatial_dice_8: 0.31649/0.26016, loss_spatial_ce_8: 0.11162/0.20730, loss_grounding_bce_8: 0.00488/0.08919, loss_grounding_dice_8: 0.27977/0.17028, loss_grounding_ce_8: 0.12781/0.42203, loss_mask_ce_9: 1.96923/3.48102, loss_mask_bce_9: 0.00738/0.36062, loss_mask_dice_9: 0.60852/1.76276, loss_spatial_bce_9: 0.01157/0.35558, loss_spatial_dice_9: 0.72379/0.79399, loss_spatial_ce_9: 2.05200/1.39374, loss_grounding_bce_9: 0.00271/0.10114, loss_grounding_dice_9: 0.24822/0.24283, loss_grounding_ce_9: 0.31991/0.67853] items per batch[64] items per second[0.36] total items[3430400] mini batches[ 53600] memory[4999] epoch remaining[0:35:59] INFO:trainer.default_trainer:epochs[ 29] optim steps[53700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37985/0.76057, loss_mask_bce_0: 0.27625/0.30143, loss_mask_dice_0: 0.90797/1.02426, loss_spatial_bce_0: 0.08319/0.08581, loss_spatial_dice_0: 0.22506/0.18145, loss_spatial_ce_0: 0.08566/0.05899, loss_grounding_bce_0: 0.18928/0.08086, loss_grounding_dice_0: 0.23007/0.15077, loss_grounding_ce_0: 0.00308/0.24940, loss_mask_ce_1: 0.31265/0.76118, loss_mask_bce_1: 0.27890/0.30232, loss_mask_dice_1: 1.15080/1.02836, loss_spatial_bce_1: 0.08349/0.08608, loss_spatial_dice_1: 0.25033/0.18405, loss_spatial_ce_1: 0.06996/0.06296, loss_grounding_bce_1: 0.19669/0.08106, loss_grounding_dice_1: 0.22610/0.15154, loss_grounding_ce_1: 0.00235/0.25094, loss_mask_ce_2: 0.33091/0.76932, loss_mask_bce_2: 0.27991/0.30248, loss_mask_dice_2: 1.19592/1.02921, loss_spatial_bce_2: 0.06845/0.08610, loss_spatial_dice_2: 0.22396/0.18433, loss_spatial_ce_2: 0.04910/0.06527, loss_grounding_bce_2: 0.20177/0.08104, loss_grounding_dice_2: 0.22832/0.15145, loss_grounding_ce_2: 0.00129/0.25420, loss_mask_ce_3: 0.37425/0.77235, loss_mask_bce_3: 0.27596/0.30395, loss_mask_dice_3: 1.16029/1.02694, loss_spatial_bce_3: 0.06770/0.08813, loss_spatial_dice_3: 0.19967/0.18558, loss_spatial_ce_3: 0.04182/0.06985, loss_grounding_bce_3: 0.19583/0.08145, loss_grounding_dice_3: 0.22650/0.15107, loss_grounding_ce_3: 0.00253/0.25438, loss_mask_ce_4: 0.30461/0.77836, loss_mask_bce_4: 0.28705/0.30636, loss_mask_dice_4: 1.13597/1.04596, loss_spatial_bce_4: 0.06775/0.09014, loss_spatial_dice_4: 0.22225/0.19350, loss_spatial_ce_4: 0.04016/0.08294, loss_grounding_bce_4: 0.18654/0.08210, loss_grounding_dice_4: 0.22097/0.15362, loss_grounding_ce_4: 0.00253/0.25930, loss_mask_ce_5: 0.36679/0.80181, loss_mask_bce_5: 0.27925/0.30826, loss_mask_dice_5: 1.17308/1.05339, loss_spatial_bce_5: 0.06731/0.09228, loss_spatial_dice_5: 0.22330/0.19627, loss_spatial_ce_5: 0.03083/0.09528, loss_grounding_bce_5: 0.22132/0.08235, loss_grounding_dice_5: 0.23362/0.15436, loss_grounding_ce_5: 0.00125/0.27806, loss_mask_ce_6: 0.42796/0.82851, loss_mask_bce_6: 0.29134/0.31019, loss_mask_dice_6: 1.08014/1.05649, loss_spatial_bce_6: 0.07447/0.09731, loss_spatial_dice_6: 0.22741/0.19854, loss_spatial_ce_6: 0.06003/0.11931, loss_grounding_bce_6: 0.20931/0.08330, loss_grounding_dice_6: 0.22209/0.15495, loss_grounding_ce_6: 0.00134/0.28695, loss_mask_ce_7: 0.62012/0.88402, loss_mask_bce_7: 0.28400/0.31755, loss_mask_dice_7: 1.11663/1.10292, loss_spatial_bce_7: 0.06897/0.10730, loss_spatial_dice_7: 0.23099/0.22414, loss_spatial_ce_7: 0.15131/0.15767, loss_grounding_bce_7: 0.19000/0.08504, loss_grounding_dice_7: 0.21677/0.16070, loss_grounding_ce_7: 0.00128/0.32043, loss_mask_ce_8: 0.79228/1.02126, loss_mask_bce_8: 0.29199/0.33359, loss_mask_dice_8: 1.16151/1.18031, loss_spatial_bce_8: 0.07270/0.12512, loss_spatial_dice_8: 0.24293/0.26017, loss_spatial_ce_8: 0.20059/0.20722, loss_grounding_bce_8: 0.18743/0.08922, loss_grounding_dice_8: 0.21411/0.17031, loss_grounding_ce_8: 0.00168/0.42200, loss_mask_ce_9: 2.41661/3.48082, loss_mask_bce_9: 0.31381/0.36058, loss_mask_dice_9: 2.02540/1.76245, loss_spatial_bce_9: 0.29699/0.35559, loss_spatial_dice_9: 0.89059/0.79398, loss_spatial_ce_9: 2.88933/1.39376, loss_grounding_bce_9: 0.18080/0.10117, loss_grounding_dice_9: 0.21875/0.24288, loss_grounding_ce_9: 0.00861/0.67835] items per batch[64] items per second[0.36] total items[3436800] mini batches[ 53700] memory[4999] epoch remaining[0:33:03] INFO:trainer.default_trainer:epochs[ 29] optim steps[53800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63067/0.76053, loss_mask_bce_0: 0.04139/0.30138, loss_mask_dice_0: 0.43479/1.02426, loss_spatial_bce_0: 0.08791/0.08579, loss_spatial_dice_0: 0.19373/0.18142, loss_spatial_ce_0: 0.05392/0.05895, loss_grounding_bce_0: 0.01089/0.08082, loss_grounding_dice_0: 0.90000/0.15077, loss_grounding_ce_0: 1.08979/0.24937, loss_mask_ce_1: 0.67849/0.76112, loss_mask_bce_1: 0.04203/0.30228, loss_mask_dice_1: 0.43810/1.02837, loss_spatial_bce_1: 0.08564/0.08606, loss_spatial_dice_1: 0.18774/0.18403, loss_spatial_ce_1: 0.04720/0.06291, loss_grounding_bce_1: 0.00509/0.08101, loss_grounding_dice_1: 0.80001/0.15153, loss_grounding_ce_1: 1.08445/0.25094, loss_mask_ce_2: 0.64388/0.76928, loss_mask_bce_2: 0.04210/0.30245, loss_mask_dice_2: 0.42888/1.02926, loss_spatial_bce_2: 0.08171/0.08608, loss_spatial_dice_2: 0.19218/0.18432, loss_spatial_ce_2: 0.04323/0.06522, loss_grounding_bce_2: 0.00370/0.08100, loss_grounding_dice_2: 0.75001/0.15144, loss_grounding_ce_2: 0.92001/0.25419, loss_mask_ce_3: 0.65028/0.77229, loss_mask_bce_3: 0.04166/0.30392, loss_mask_dice_3: 0.36642/1.02696, loss_spatial_bce_3: 0.09259/0.08811, loss_spatial_dice_3: 0.20257/0.18556, loss_spatial_ce_3: 0.04319/0.06980, loss_grounding_bce_3: 0.00309/0.08141, loss_grounding_dice_3: 0.71430/0.15106, loss_grounding_ce_3: 0.87852/0.25437, loss_mask_ce_4: 0.81015/0.77831, loss_mask_bce_4: 0.04413/0.30633, loss_mask_dice_4: 0.40727/1.04603, loss_spatial_bce_4: 0.09430/0.09012, loss_spatial_dice_4: 0.18635/0.19347, loss_spatial_ce_4: 0.06056/0.08290, loss_grounding_bce_4: 0.02531/0.08206, loss_grounding_dice_4: 0.87135/0.15362, loss_grounding_ce_4: 1.44112/0.25930, loss_mask_ce_5: 0.86463/0.80176, loss_mask_bce_5: 0.04366/0.30822, loss_mask_dice_5: 0.41955/1.05345, loss_spatial_bce_5: 0.12494/0.09226, loss_spatial_dice_5: 0.19429/0.19625, loss_spatial_ce_5: 0.07497/0.09522, loss_grounding_bce_5: 0.00405/0.08232, loss_grounding_dice_5: 0.75001/0.15435, loss_grounding_ce_5: 1.51812/0.27804, loss_mask_ce_6: 1.02666/0.82851, loss_mask_bce_6: 0.04167/0.31017, loss_mask_dice_6: 0.39522/1.05651, loss_spatial_bce_6: 0.02059/0.09728, loss_spatial_dice_6: 0.18343/0.19852, loss_spatial_ce_6: 0.12569/0.11929, loss_grounding_bce_6: 0.00419/0.08326, loss_grounding_dice_6: 0.75001/0.15494, loss_grounding_ce_6: 1.46802/0.28707, loss_mask_ce_7: 1.15700/0.88399, loss_mask_bce_7: 0.05284/0.31753, loss_mask_dice_7: 0.41298/1.10300, loss_spatial_bce_7: 0.09313/0.10728, loss_spatial_dice_7: 0.19954/0.22413, loss_spatial_ce_7: 0.08009/0.15762, loss_grounding_bce_7: 0.00704/0.08500, loss_grounding_dice_7: 0.75381/0.16069, loss_grounding_ce_7: 1.70448/0.32041, loss_mask_ce_8: 0.93053/1.02119, loss_mask_bce_8: 0.04339/0.33357, loss_mask_dice_8: 0.38280/1.18032, loss_spatial_bce_8: 0.08901/0.12509, loss_spatial_dice_8: 0.20732/0.26014, loss_spatial_ce_8: 0.17908/0.20719, loss_grounding_bce_8: 0.00417/0.08918, loss_grounding_dice_8: 0.75000/0.17029, loss_grounding_ce_8: 1.31193/0.42200, loss_mask_ce_9: 2.85520/3.48086, loss_mask_bce_9: 0.05855/0.36056, loss_mask_dice_9: 0.44259/1.76255, loss_spatial_bce_9: 0.40025/0.35559, loss_spatial_dice_9: 0.74164/0.79398, loss_spatial_ce_9: 0.85092/1.39374, loss_grounding_bce_9: 0.00527/0.10113, loss_grounding_dice_9: 0.84779/0.24289, loss_grounding_ce_9: 0.69545/0.67831] items per batch[64] items per second[0.36] total items[3443200] mini batches[ 53800] memory[4999] epoch remaining[0:30:02] INFO:trainer.default_trainer:epochs[ 29] optim steps[53900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14190/0.76053, loss_mask_bce_0: 0.24969/0.30135, loss_mask_dice_0: 0.18099/1.02428, loss_spatial_bce_0: 0.11544/0.08578, loss_spatial_dice_0: 0.11132/0.18141, loss_spatial_ce_0: 0.00055/0.05892, loss_grounding_bce_0: 0.24153/0.08083, loss_grounding_dice_0: 0.16662/0.15077, loss_grounding_ce_0: 0.00127/0.24939, loss_mask_ce_1: 0.13128/0.76107, loss_mask_bce_1: 0.27728/0.30226, loss_mask_dice_1: 0.18059/1.02842, loss_spatial_bce_1: 0.14839/0.08606, loss_spatial_dice_1: 0.11380/0.18402, loss_spatial_ce_1: 0.00145/0.06288, loss_grounding_bce_1: 0.28051/0.08103, loss_grounding_dice_1: 0.21900/0.15154, loss_grounding_ce_1: 0.00094/0.25095, loss_mask_ce_2: 0.14317/0.76922, loss_mask_bce_2: 0.15813/0.30241, loss_mask_dice_2: 0.19121/1.02931, loss_spatial_bce_2: 0.12459/0.08608, loss_spatial_dice_2: 0.11329/0.18431, loss_spatial_ce_2: 0.00196/0.06519, loss_grounding_bce_2: 0.16903/0.08101, loss_grounding_dice_2: 0.17653/0.15145, loss_grounding_ce_2: 0.00164/0.25423, loss_mask_ce_3: 0.15826/0.77227, loss_mask_bce_3: 0.17790/0.30389, loss_mask_dice_3: 0.16733/1.02702, loss_spatial_bce_3: 0.18213/0.08811, loss_spatial_dice_3: 0.11876/0.18555, loss_spatial_ce_3: 0.00389/0.06977, loss_grounding_bce_3: 0.23209/0.08143, loss_grounding_dice_3: 0.17350/0.15106, loss_grounding_ce_3: 0.00083/0.25441, loss_mask_ce_4: 0.18522/0.77825, loss_mask_bce_4: 0.26194/0.30630, loss_mask_dice_4: 0.22705/1.04611, loss_spatial_bce_4: 0.21161/0.09012, loss_spatial_dice_4: 0.12292/0.19347, loss_spatial_ce_4: 0.00119/0.08287, loss_grounding_bce_4: 0.25672/0.08207, loss_grounding_dice_4: 0.17401/0.15363, loss_grounding_ce_4: 0.00106/0.25933, loss_mask_ce_5: 0.13778/0.80171, loss_mask_bce_5: 0.25900/0.30820, loss_mask_dice_5: 0.17277/1.05351, loss_spatial_bce_5: 0.20537/0.09226, loss_spatial_dice_5: 0.12350/0.19625, loss_spatial_ce_5: 0.00198/0.09521, loss_grounding_bce_5: 0.27952/0.08233, loss_grounding_dice_5: 0.16932/0.15436, loss_grounding_ce_5: 0.00093/0.27806, loss_mask_ce_6: 0.11854/0.82843, loss_mask_bce_6: 0.27181/0.31015, loss_mask_dice_6: 0.20912/1.05656, loss_spatial_bce_6: 0.29410/0.09728, loss_spatial_dice_6: 0.15277/0.19852, loss_spatial_ce_6: 0.00092/0.11933, loss_grounding_bce_6: 0.25089/0.08327, loss_grounding_dice_6: 0.15790/0.15493, loss_grounding_ce_6: 0.00111/0.28710, loss_mask_ce_7: 0.09251/0.88396, loss_mask_bce_7: 0.27857/0.31750, loss_mask_dice_7: 0.16750/1.10306, loss_spatial_bce_7: 0.26987/0.10728, loss_spatial_dice_7: 0.16846/0.22413, loss_spatial_ce_7: 0.08233/0.15763, loss_grounding_bce_7: 0.26791/0.08501, loss_grounding_dice_7: 0.17953/0.16069, loss_grounding_ce_7: 0.00213/0.32045, loss_mask_ce_8: 0.14228/1.02110, loss_mask_bce_8: 0.28943/0.33352, loss_mask_dice_8: 0.17791/1.18034, loss_spatial_bce_8: 0.40286/0.12508, loss_spatial_dice_8: 0.23780/0.26014, loss_spatial_ce_8: 0.03968/0.20717, loss_grounding_bce_8: 0.30039/0.08920, loss_grounding_dice_8: 0.18521/0.17031, loss_grounding_ce_8: 0.00418/0.42215, loss_mask_ce_9: 1.15366/3.48086, loss_mask_bce_9: 0.22174/0.36053, loss_mask_dice_9: 0.15535/1.76254, loss_spatial_bce_9: 1.81009/0.35560, loss_spatial_dice_9: 0.96912/0.79399, loss_spatial_ce_9: 1.38036/1.39367, loss_grounding_bce_9: 0.24050/0.10117, loss_grounding_dice_9: 0.21859/0.24289, loss_grounding_ce_9: 0.07444/0.67840] items per batch[64] items per second[0.35] total items[3449600] mini batches[ 53900] memory[4999] epoch remaining[0:27:06] INFO:trainer.default_trainer:epochs[ 29] optim steps[54000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22335/0.76052, loss_mask_bce_0: 0.18554/0.30136, loss_mask_dice_0: 0.15131/1.02452, loss_spatial_bce_0: 0.12834/0.08578, loss_spatial_dice_0: 0.07793/0.18139, loss_spatial_ce_0: 0.00024/0.05892, loss_grounding_bce_0: 0.09566/0.08084, loss_grounding_dice_0: 0.07720/0.15077, loss_grounding_ce_0: 0.09465/0.24942, loss_mask_ce_1: 0.22392/0.76108, loss_mask_bce_1: 0.18692/0.30226, loss_mask_dice_1: 0.15637/1.02860, loss_spatial_bce_1: 0.11509/0.08607, loss_spatial_dice_1: 0.08669/0.18401, loss_spatial_ce_1: 0.00015/0.06287, loss_grounding_bce_1: 0.09402/0.08104, loss_grounding_dice_1: 0.08068/0.15156, loss_grounding_ce_1: 0.09665/0.25103, loss_mask_ce_2: 0.21313/0.76919, loss_mask_bce_2: 0.19351/0.30242, loss_mask_dice_2: 0.15220/1.02953, loss_spatial_bce_2: 0.11664/0.08608, loss_spatial_dice_2: 0.08712/0.18430, loss_spatial_ce_2: 0.00011/0.06519, loss_grounding_bce_2: 0.09755/0.08102, loss_grounding_dice_2: 0.07791/0.15147, loss_grounding_ce_2: 0.10344/0.25428, loss_mask_ce_3: 0.26140/0.77230, loss_mask_bce_3: 0.21276/0.30389, loss_mask_dice_3: 0.16706/1.02723, loss_spatial_bce_3: 0.08815/0.08812, loss_spatial_dice_3: 0.07585/0.18554, loss_spatial_ce_3: 0.00017/0.06976, loss_grounding_bce_3: 0.10751/0.08143, loss_grounding_dice_3: 0.08256/0.15108, loss_grounding_ce_3: 0.11274/0.25443, loss_mask_ce_4: 0.23314/0.77822, loss_mask_bce_4: 0.19783/0.30630, loss_mask_dice_4: 0.15708/1.04634, loss_spatial_bce_4: 0.08506/0.09014, loss_spatial_dice_4: 0.07911/0.19346, loss_spatial_ce_4: 0.00099/0.08285, loss_grounding_bce_4: 0.09915/0.08207, loss_grounding_dice_4: 0.08099/0.15365, loss_grounding_ce_4: 0.11489/0.25941, loss_mask_ce_5: 0.21471/0.80169, loss_mask_bce_5: 0.19110/0.30822, loss_mask_dice_5: 0.16643/1.05372, loss_spatial_bce_5: 0.08014/0.09227, loss_spatial_dice_5: 0.07785/0.19624, loss_spatial_ce_5: 0.00458/0.09526, loss_grounding_bce_5: 0.09675/0.08233, loss_grounding_dice_5: 0.08138/0.15436, loss_grounding_ce_5: 0.11931/0.27809, loss_mask_ce_6: 0.22833/0.82844, loss_mask_bce_6: 0.21969/0.31018, loss_mask_dice_6: 0.16851/1.05678, loss_spatial_bce_6: 0.09770/0.09729, loss_spatial_dice_6: 0.07373/0.19850, loss_spatial_ce_6: 0.02296/0.11935, loss_grounding_bce_6: 0.11009/0.08328, loss_grounding_dice_6: 0.08175/0.15495, loss_grounding_ce_6: 0.12173/0.28709, loss_mask_ce_7: 0.18549/0.88401, loss_mask_bce_7: 0.20217/0.31752, loss_mask_dice_7: 0.15878/1.10333, loss_spatial_bce_7: 0.10008/0.10733, loss_spatial_dice_7: 0.08882/0.22413, loss_spatial_ce_7: 0.01638/0.15766, loss_grounding_bce_7: 0.09850/0.08501, loss_grounding_dice_7: 0.08076/0.16071, loss_grounding_ce_7: 0.13134/0.32059, loss_mask_ce_8: 0.24097/1.02116, loss_mask_bce_8: 0.18839/0.33354, loss_mask_dice_8: 0.16856/1.18067, loss_spatial_bce_8: 0.12627/0.12509, loss_spatial_dice_8: 0.08499/0.26013, loss_spatial_ce_8: 0.00036/0.20709, loss_grounding_bce_8: 0.09505/0.08919, loss_grounding_dice_8: 0.08205/0.17033, loss_grounding_ce_8: 0.09816/0.42235, loss_mask_ce_9: 2.49352/3.48094, loss_mask_bce_9: 0.16426/0.36054, loss_mask_dice_9: 0.18826/1.76294, loss_spatial_bce_9: 0.44349/0.35561, loss_spatial_dice_9: 0.76108/0.79395, loss_spatial_ce_9: 1.69105/1.39375, loss_grounding_bce_9: 0.09043/0.10118, loss_grounding_dice_9: 0.13753/0.24289, loss_grounding_ce_9: 0.28509/0.67846] items per batch[64] items per second[0.36] total items[3456000] mini batches[ 54000] memory[4999] epoch remaining[0:24:08] INFO:trainer.default_trainer:epochs[ 29] optim steps[54100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38502/0.76052, loss_mask_bce_0: 0.24743/0.30142, loss_mask_dice_0: 0.26060/1.02446, loss_spatial_bce_0: 0.07482/0.08579, loss_spatial_dice_0: 0.08633/0.18138, loss_spatial_ce_0: 0.01269/0.05890, loss_grounding_bce_0: 0.00000/0.08085, loss_grounding_dice_0: 0.00006/0.15078, loss_grounding_ce_0: 0.02490/0.24932, loss_mask_ce_1: 0.35604/0.76107, loss_mask_bce_1: 0.22813/0.30232, loss_mask_dice_1: 0.23698/1.02855, loss_spatial_bce_1: 0.07741/0.08607, loss_spatial_dice_1: 0.09338/0.18400, loss_spatial_ce_1: 0.01471/0.06284, loss_grounding_bce_1: 0.00000/0.08104, loss_grounding_dice_1: 0.00002/0.15156, loss_grounding_ce_1: 0.01549/0.25092, loss_mask_ce_2: 0.34657/0.76918, loss_mask_bce_2: 0.23407/0.30248, loss_mask_dice_2: 0.25695/1.02947, loss_spatial_bce_2: 0.07700/0.08609, loss_spatial_dice_2: 0.09309/0.18429, loss_spatial_ce_2: 0.01148/0.06517, loss_grounding_bce_2: 0.00000/0.08102, loss_grounding_dice_2: 0.00004/0.15147, loss_grounding_ce_2: 0.01807/0.25421, loss_mask_ce_3: 0.36211/0.77230, loss_mask_bce_3: 0.22799/0.30395, loss_mask_dice_3: 0.26116/1.02714, loss_spatial_bce_3: 0.08089/0.08812, loss_spatial_dice_3: 0.08690/0.18554, loss_spatial_ce_3: 0.00654/0.06974, loss_grounding_bce_3: 0.00000/0.08143, loss_grounding_dice_3: 0.00006/0.15110, loss_grounding_ce_3: 0.04178/0.25437, loss_mask_ce_4: 0.33513/0.77827, loss_mask_bce_4: 0.24757/0.30635, loss_mask_dice_4: 0.26217/1.04627, loss_spatial_bce_4: 0.07932/0.09015, loss_spatial_dice_4: 0.09450/0.19346, loss_spatial_ce_4: 0.06527/0.08286, loss_grounding_bce_4: 0.00000/0.08207, loss_grounding_dice_4: 0.00004/0.15365, loss_grounding_ce_4: 0.02461/0.25929, loss_mask_ce_5: 0.30074/0.80173, loss_mask_bce_5: 0.24486/0.30829, loss_mask_dice_5: 0.26825/1.05366, loss_spatial_bce_5: 0.07664/0.09228, loss_spatial_dice_5: 0.08846/0.19625, loss_spatial_ce_5: 0.08079/0.09526, loss_grounding_bce_5: 0.00000/0.08233, loss_grounding_dice_5: 0.00002/0.15435, loss_grounding_ce_5: 0.03360/0.27799, loss_mask_ce_6: 0.30938/0.82845, loss_mask_bce_6: 0.25653/0.31025, loss_mask_dice_6: 0.26200/1.05668, loss_spatial_bce_6: 0.07682/0.09732, loss_spatial_dice_6: 0.08434/0.19851, loss_spatial_ce_6: 0.08447/0.11937, loss_grounding_bce_6: 0.00000/0.08328, loss_grounding_dice_6: 0.00000/0.15495, loss_grounding_ce_6: 0.06600/0.28699, loss_mask_ce_7: 0.27877/0.88407, loss_mask_bce_7: 0.24171/0.31759, loss_mask_dice_7: 0.28282/1.10326, loss_spatial_bce_7: 0.09810/0.10735, loss_spatial_dice_7: 0.08976/0.22414, loss_spatial_ce_7: 0.05251/0.15763, loss_grounding_bce_7: 0.00000/0.08501, loss_grounding_dice_7: 0.00000/0.16070, loss_grounding_ce_7: 0.06277/0.32050, loss_mask_ce_8: 0.46976/1.02117, loss_mask_bce_8: 0.24100/0.33361, loss_mask_dice_8: 0.34626/1.18059, loss_spatial_bce_8: 0.11563/0.12511, loss_spatial_dice_8: 0.19358/0.26015, loss_spatial_ce_8: 0.10372/0.20708, loss_grounding_bce_8: 0.00000/0.08920, loss_grounding_dice_8: 0.00003/0.17033, loss_grounding_ce_8: 0.06022/0.42218, loss_mask_ce_9: 4.08367/3.48078, loss_mask_bce_9: 0.29121/0.36063, loss_mask_dice_9: 0.85258/1.76298, loss_spatial_bce_9: 0.47843/0.35566, loss_spatial_dice_9: 0.80972/0.79393, loss_spatial_ce_9: 1.07210/1.39363, loss_grounding_bce_9: 0.00000/0.10118, loss_grounding_dice_9: 0.00229/0.24288, loss_grounding_ce_9: 0.52732/0.67831] items per batch[64] items per second[0.36] total items[3462400] mini batches[ 54100] memory[4999] epoch remaining[0:21:08] INFO:trainer.default_trainer:epochs[ 29] optim steps[54200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23100/0.76053, loss_mask_bce_0: 0.02829/0.30135, loss_mask_dice_0: 1.48725/1.02423, loss_spatial_bce_0: 0.00395/0.08577, loss_spatial_dice_0: 0.29787/0.18134, loss_spatial_ce_0: 0.08943/0.05886, loss_grounding_bce_0: 0.00724/0.08082, loss_grounding_dice_0: 0.44640/0.15074, loss_grounding_ce_0: 0.31761/0.24933, loss_mask_ce_1: 1.44435/0.76108, loss_mask_bce_1: 0.03293/0.30226, loss_mask_dice_1: 1.84806/1.02839, loss_spatial_bce_1: 0.00406/0.08605, loss_spatial_dice_1: 0.25490/0.18396, loss_spatial_ce_1: 0.01989/0.06280, loss_grounding_bce_1: 0.00531/0.08102, loss_grounding_dice_1: 0.32259/0.15153, loss_grounding_ce_1: 0.31614/0.25094, loss_mask_ce_2: 1.29886/0.76914, loss_mask_bce_2: 0.03032/0.30241, loss_mask_dice_2: 1.58194/1.02930, loss_spatial_bce_2: 0.00413/0.08607, loss_spatial_dice_2: 0.32998/0.18426, loss_spatial_ce_2: 0.01533/0.06512, loss_grounding_bce_2: 0.00519/0.08100, loss_grounding_dice_2: 0.39209/0.15144, loss_grounding_ce_2: 0.37436/0.25423, loss_mask_ce_3: 1.32198/0.77231, loss_mask_bce_3: 0.02318/0.30389, loss_mask_dice_3: 1.48113/1.02695, loss_spatial_bce_3: 0.00379/0.08810, loss_spatial_dice_3: 0.29094/0.18550, loss_spatial_ce_3: 0.02222/0.06970, loss_grounding_bce_3: 0.00468/0.08141, loss_grounding_dice_3: 0.26806/0.15106, loss_grounding_ce_3: 0.34614/0.25448, loss_mask_ce_4: 1.37719/0.77825, loss_mask_bce_4: 0.03314/0.30628, loss_mask_dice_4: 1.61778/1.04610, loss_spatial_bce_4: 0.00414/0.09012, loss_spatial_dice_4: 0.29133/0.19342, loss_spatial_ce_4: 0.04067/0.08282, loss_grounding_bce_4: 0.00664/0.08205, loss_grounding_dice_4: 0.41853/0.15361, loss_grounding_ce_4: 0.35429/0.25930, loss_mask_ce_5: 1.62552/0.80173, loss_mask_bce_5: 0.03328/0.30823, loss_mask_dice_5: 1.54554/1.05345, loss_spatial_bce_5: 0.00480/0.09226, loss_spatial_dice_5: 0.32214/0.19621, loss_spatial_ce_5: 0.09574/0.09522, loss_grounding_bce_5: 0.00529/0.08230, loss_grounding_dice_5: 0.32761/0.15432, loss_grounding_ce_5: 0.37530/0.27797, loss_mask_ce_6: 1.46164/0.82844, loss_mask_bce_6: 0.03259/0.31019, loss_mask_dice_6: 1.54243/1.05648, loss_spatial_bce_6: 0.00591/0.09729, loss_spatial_dice_6: 0.24034/0.19848, loss_spatial_ce_6: 0.44910/0.11933, loss_grounding_bce_6: 0.00728/0.08325, loss_grounding_dice_6: 0.47053/0.15490, loss_grounding_ce_6: 0.37079/0.28693, loss_mask_ce_7: 2.50743/0.88415, loss_mask_bce_7: 0.04098/0.31751, loss_mask_dice_7: 2.15605/1.10306, loss_spatial_bce_7: 0.00552/0.10732, loss_spatial_dice_7: 0.31237/0.22410, loss_spatial_ce_7: 0.16458/0.15759, loss_grounding_bce_7: 0.00479/0.08498, loss_grounding_dice_7: 0.22487/0.16066, loss_grounding_ce_7: 0.45949/0.32051, loss_mask_ce_8: 2.64552/1.02115, loss_mask_bce_8: 0.02986/0.33356, loss_mask_dice_8: 2.14453/1.18035, loss_spatial_bce_8: 0.00517/0.12508, loss_spatial_dice_8: 0.38138/0.26011, loss_spatial_ce_8: 0.13322/0.20701, loss_grounding_bce_8: 0.00867/0.08917, loss_grounding_dice_8: 0.48101/0.17029, loss_grounding_ce_8: 0.49270/0.42212, loss_mask_ce_9: 4.43338/3.48091, loss_mask_bce_9: 0.02329/0.36056, loss_mask_dice_9: 2.17098/1.76271, loss_spatial_bce_9: 0.02049/0.35566, loss_spatial_dice_9: 0.85263/0.79393, loss_spatial_ce_9: 2.35512/1.39349, loss_grounding_bce_9: 0.00459/0.10115, loss_grounding_dice_9: 0.39606/0.24283, loss_grounding_ce_9: 0.52786/0.67819] items per batch[64] items per second[0.36] total items[3468800] mini batches[ 54200] memory[4999] epoch remaining[0:18:09] INFO:trainer.default_trainer:epochs[ 29] optim steps[54300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46867/0.76063, loss_mask_bce_0: 0.04429/0.30135, loss_mask_dice_0: 0.45162/1.02435, loss_spatial_bce_0: 0.04445/0.08575, loss_spatial_dice_0: 0.33524/0.18135, loss_spatial_ce_0: 0.00123/0.05883, loss_grounding_bce_0: 0.03242/0.08082, loss_grounding_dice_0: 0.29035/0.15076, loss_grounding_ce_0: 0.09237/0.24932, loss_mask_ce_1: 0.47216/0.76119, loss_mask_bce_1: 0.04593/0.30224, loss_mask_dice_1: 0.41071/1.02853, loss_spatial_bce_1: 0.04479/0.08603, loss_spatial_dice_1: 0.31848/0.18396, loss_spatial_ce_1: 0.00503/0.06276, loss_grounding_bce_1: 0.02730/0.08101, loss_grounding_dice_1: 0.24656/0.15153, loss_grounding_ce_1: 0.09745/0.25092, loss_mask_ce_2: 0.45693/0.76931, loss_mask_bce_2: 0.04615/0.30240, loss_mask_dice_2: 0.43554/1.02943, loss_spatial_bce_2: 0.04449/0.08605, loss_spatial_dice_2: 0.33622/0.18426, loss_spatial_ce_2: 0.00802/0.06508, loss_grounding_bce_2: 0.03028/0.08100, loss_grounding_dice_2: 0.30084/0.15144, loss_grounding_ce_2: 0.09828/0.25425, loss_mask_ce_3: 0.43898/0.77244, loss_mask_bce_3: 0.04286/0.30388, loss_mask_dice_3: 0.43246/1.02710, loss_spatial_bce_3: 0.04563/0.08808, loss_spatial_dice_3: 0.33900/0.18550, loss_spatial_ce_3: 0.01370/0.06969, loss_grounding_bce_3: 0.02853/0.08141, loss_grounding_dice_3: 0.30376/0.15107, loss_grounding_ce_3: 0.12051/0.25450, loss_mask_ce_4: 0.38598/0.77835, loss_mask_bce_4: 0.04412/0.30629, loss_mask_dice_4: 0.34335/1.04623, loss_spatial_bce_4: 0.05536/0.09012, loss_spatial_dice_4: 0.34045/0.19343, loss_spatial_ce_4: 0.06908/0.08279, loss_grounding_bce_4: 0.03292/0.08204, loss_grounding_dice_4: 0.29848/0.15362, loss_grounding_ce_4: 0.09690/0.25927, loss_mask_ce_5: 0.47386/0.80187, loss_mask_bce_5: 0.04393/0.30822, loss_mask_dice_5: 0.43437/1.05362, loss_spatial_bce_5: 0.07028/0.09225, loss_spatial_dice_5: 0.35463/0.19622, loss_spatial_ce_5: 0.02521/0.09519, loss_grounding_bce_5: 0.02873/0.08230, loss_grounding_dice_5: 0.26175/0.15433, loss_grounding_ce_5: 0.10267/0.27791, loss_mask_ce_6: 0.31379/0.82852, loss_mask_bce_6: 0.04630/0.31020, loss_mask_dice_6: 0.45199/1.05667, loss_spatial_bce_6: 0.09069/0.09729, loss_spatial_dice_6: 0.35639/0.19849, loss_spatial_ce_6: 0.01567/0.11929, loss_grounding_bce_6: 0.02927/0.08325, loss_grounding_dice_6: 0.30285/0.15491, loss_grounding_ce_6: 0.12079/0.28699, loss_mask_ce_7: 0.45628/0.88434, loss_mask_bce_7: 0.04366/0.31749, loss_mask_dice_7: 0.43718/1.10326, loss_spatial_bce_7: 0.08499/0.10732, loss_spatial_dice_7: 0.33651/0.22412, loss_spatial_ce_7: 0.42816/0.15757, loss_grounding_bce_7: 0.03163/0.08498, loss_grounding_dice_7: 0.29054/0.16066, loss_grounding_ce_7: 0.14485/0.32047, loss_mask_ce_8: 0.47505/1.02127, loss_mask_bce_8: 0.03967/0.33355, loss_mask_dice_8: 0.42753/1.18050, loss_spatial_bce_8: 0.07523/0.12507, loss_spatial_dice_8: 0.35267/0.26012, loss_spatial_ce_8: 0.17860/0.20694, loss_grounding_bce_8: 0.02831/0.08917, loss_grounding_dice_8: 0.27382/0.17030, loss_grounding_ce_8: 0.09607/0.42224, loss_mask_ce_9: 2.04223/3.48099, loss_mask_bce_9: 0.04763/0.36059, loss_mask_dice_9: 0.45878/1.76280, loss_spatial_bce_9: 0.57000/0.35562, loss_spatial_dice_9: 0.48216/0.79392, loss_spatial_ce_9: 0.90795/1.39338, loss_grounding_bce_9: 0.03244/0.10116, loss_grounding_dice_9: 0.30345/0.24284, loss_grounding_ce_9: 0.25163/0.67815] items per batch[64] items per second[0.37] total items[3475200] mini batches[ 54300] memory[4999] epoch remaining[0:15:09] INFO:trainer.default_trainer:epochs[ 29] optim steps[54400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11253/0.76040, loss_mask_bce_0: 0.13304/0.30133, loss_mask_dice_0: 0.09756/1.02486, loss_spatial_bce_0: 0.07535/0.08572, loss_spatial_dice_0: 0.05116/0.18134, loss_spatial_ce_0: 0.00000/0.05878, loss_grounding_bce_0: 0.05942/0.08081, loss_grounding_dice_0: 0.04285/0.15077, loss_grounding_ce_0: 0.01313/0.24927, loss_mask_ce_1: 0.11515/0.76094, loss_mask_bce_1: 0.13253/0.30223, loss_mask_dice_1: 0.09691/1.02904, loss_spatial_bce_1: 0.07655/0.08600, loss_spatial_dice_1: 0.05352/0.18396, loss_spatial_ce_1: 0.00000/0.06269, loss_grounding_bce_1: 0.06124/0.08100, loss_grounding_dice_1: 0.04486/0.15154, loss_grounding_ce_1: 0.01841/0.25086, loss_mask_ce_2: 0.12616/0.76910, loss_mask_bce_2: 0.13556/0.30239, loss_mask_dice_2: 0.09810/1.02999, loss_spatial_bce_2: 0.08112/0.08602, loss_spatial_dice_2: 0.05440/0.18427, loss_spatial_ce_2: 0.00000/0.06503, loss_grounding_bce_2: 0.06049/0.08099, loss_grounding_dice_2: 0.04233/0.15145, loss_grounding_ce_2: 0.02607/0.25419, loss_mask_ce_3: 0.12113/0.77223, loss_mask_bce_3: 0.13537/0.30387, loss_mask_dice_3: 0.09630/1.02759, loss_spatial_bce_3: 0.07981/0.08805, loss_spatial_dice_3: 0.05192/0.18551, loss_spatial_ce_3: 0.00001/0.06964, loss_grounding_bce_3: 0.05999/0.08139, loss_grounding_dice_3: 0.04196/0.15107, loss_grounding_ce_3: 0.03739/0.25445, loss_mask_ce_4: 0.11053/0.77811, loss_mask_bce_4: 0.12507/0.30628, loss_mask_dice_4: 0.08661/1.04673, loss_spatial_bce_4: 0.08212/0.09008, loss_spatial_dice_4: 0.05372/0.19344, loss_spatial_ce_4: 0.00002/0.08276, loss_grounding_bce_4: 0.05769/0.08204, loss_grounding_dice_4: 0.04077/0.15364, loss_grounding_ce_4: 0.02733/0.25922, loss_mask_ce_5: 0.10881/0.80167, loss_mask_bce_5: 0.13301/0.30821, loss_mask_dice_5: 0.09258/1.05415, loss_spatial_bce_5: 0.08137/0.09223, loss_spatial_dice_5: 0.05437/0.19623, loss_spatial_ce_5: 0.00003/0.09517, loss_grounding_bce_5: 0.05939/0.08229, loss_grounding_dice_5: 0.04308/0.15434, loss_grounding_ce_5: 0.01011/0.27787, loss_mask_ce_6: 0.10039/0.82826, loss_mask_bce_6: 0.12826/0.31018, loss_mask_dice_6: 0.09132/1.05723, loss_spatial_bce_6: 0.08116/0.09726, loss_spatial_dice_6: 0.05342/0.19849, loss_spatial_ce_6: 0.00021/0.11929, loss_grounding_bce_6: 0.06386/0.08324, loss_grounding_dice_6: 0.04725/0.15492, loss_grounding_ce_6: 0.02826/0.28696, loss_mask_ce_7: 0.14769/0.88407, loss_mask_bce_7: 0.13000/0.31748, loss_mask_dice_7: 0.09311/1.10389, loss_spatial_bce_7: 0.07989/0.10728, loss_spatial_dice_7: 0.05133/0.22412, loss_spatial_ce_7: 0.06351/0.15749, loss_grounding_bce_7: 0.06289/0.08496, loss_grounding_dice_7: 0.04306/0.16066, loss_grounding_ce_7: 0.02099/0.32040, loss_mask_ce_8: 0.28184/1.02110, loss_mask_bce_8: 0.13073/0.33354, loss_mask_dice_8: 0.08837/1.18107, loss_spatial_bce_8: 0.08170/0.12502, loss_spatial_dice_8: 0.05250/0.26014, loss_spatial_ce_8: 0.06256/0.20686, loss_grounding_bce_8: 0.05933/0.08915, loss_grounding_dice_8: 0.04035/0.17029, loss_grounding_ce_8: 0.05483/0.42218, loss_mask_ce_9: 2.80692/3.48085, loss_mask_bce_9: 0.16232/0.36057, loss_mask_dice_9: 0.18458/1.76355, loss_spatial_bce_9: 0.55239/0.35555, loss_spatial_dice_9: 0.64249/0.79397, loss_spatial_ce_9: 0.74364/1.39334, loss_grounding_bce_9: 0.09414/0.10115, loss_grounding_dice_9: 0.12349/0.24284, loss_grounding_ce_9: 0.38626/0.67813] items per batch[64] items per second[0.36] total items[3481600] mini batches[ 54400] memory[4999] epoch remaining[0:12:10] INFO:trainer.default_trainer:epochs[ 29] optim steps[54500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26647/0.76062, loss_mask_bce_0: 0.46189/0.30127, loss_mask_dice_0: 0.58287/1.02482, loss_spatial_bce_0: 0.05763/0.08571, loss_spatial_dice_0: 0.17202/0.18136, loss_spatial_ce_0: 0.00004/0.05879, loss_grounding_bce_0: 0.13955/0.08082, loss_grounding_dice_0: 0.14552/0.15079, loss_grounding_ce_0: 2.42754/0.24940, loss_mask_ce_1: 0.64016/0.76117, loss_mask_bce_1: 0.15950/0.30217, loss_mask_dice_1: 0.51222/1.02898, loss_spatial_bce_1: 0.05659/0.08600, loss_spatial_dice_1: 0.17043/0.18398, loss_spatial_ce_1: 0.00004/0.06269, loss_grounding_bce_1: 0.14264/0.08101, loss_grounding_dice_1: 0.15920/0.15157, loss_grounding_ce_1: 2.45179/0.25098, loss_mask_ce_2: 1.02645/0.76935, loss_mask_bce_2: 0.12275/0.30232, loss_mask_dice_2: 0.38483/1.02990, loss_spatial_bce_2: 0.06883/0.08601, loss_spatial_dice_2: 0.19919/0.18428, loss_spatial_ce_2: 0.00029/0.06503, loss_grounding_bce_2: 0.12471/0.08100, loss_grounding_dice_2: 0.13506/0.15148, loss_grounding_ce_2: 2.29612/0.25434, loss_mask_ce_3: 1.09998/0.77254, loss_mask_bce_3: 0.13211/0.30380, loss_mask_dice_3: 0.42371/1.02752, loss_spatial_bce_3: 0.08619/0.08805, loss_spatial_dice_3: 0.20231/0.18554, loss_spatial_ce_3: 0.00176/0.06965, loss_grounding_bce_3: 0.10375/0.08141, loss_grounding_dice_3: 0.13180/0.15111, loss_grounding_ce_3: 2.28218/0.25458, loss_mask_ce_4: 0.63709/0.77833, loss_mask_bce_4: 0.15296/0.30621, loss_mask_dice_4: 0.50841/1.04661, loss_spatial_bce_4: 0.08331/0.09009, loss_spatial_dice_4: 0.20346/0.19345, loss_spatial_ce_4: 0.00159/0.08278, loss_grounding_bce_4: 0.11473/0.08205, loss_grounding_dice_4: 0.15282/0.15368, loss_grounding_ce_4: 2.41750/0.25927, loss_mask_ce_5: 0.76061/0.80190, loss_mask_bce_5: 0.15209/0.30813, loss_mask_dice_5: 0.47295/1.05407, loss_spatial_bce_5: 0.09613/0.09224, loss_spatial_dice_5: 0.20515/0.19625, loss_spatial_ce_5: 0.00425/0.09518, loss_grounding_bce_5: 0.20848/0.08230, loss_grounding_dice_5: 0.13673/0.15437, loss_grounding_ce_5: 2.09045/0.27799, loss_mask_ce_6: 0.31497/0.82851, loss_mask_bce_6: 0.51097/0.31013, loss_mask_dice_6: 0.58472/1.05720, loss_spatial_bce_6: 0.06854/0.09728, loss_spatial_dice_6: 0.18342/0.19852, loss_spatial_ce_6: 0.13506/0.11930, loss_grounding_bce_6: 0.12921/0.08324, loss_grounding_dice_6: 0.15999/0.15495, loss_grounding_ce_6: 1.68701/0.28708, loss_mask_ce_7: 0.38319/0.88438, loss_mask_bce_7: 0.35246/0.31739, loss_mask_dice_7: 0.54190/1.10381, loss_spatial_bce_7: 0.10663/0.10728, loss_spatial_dice_7: 0.21327/0.22415, loss_spatial_ce_7: 0.04947/0.15750, loss_grounding_bce_7: 0.10097/0.08495, loss_grounding_dice_7: 0.14793/0.16067, loss_grounding_ce_7: 2.47223/0.32066, loss_mask_ce_8: 0.60357/1.02139, loss_mask_bce_8: 0.27848/0.33346, loss_mask_dice_8: 0.57862/1.18099, loss_spatial_bce_8: 0.07665/0.12504, loss_spatial_dice_8: 0.19240/0.26017, loss_spatial_ce_8: 0.10733/0.20685, loss_grounding_bce_8: 0.14437/0.08914, loss_grounding_dice_8: 0.22620/0.17031, loss_grounding_ce_8: 3.14289/0.42242, loss_mask_ce_9: 1.94837/3.48140, loss_mask_bce_9: 0.46303/0.36050, loss_mask_dice_9: 1.14113/1.76344, loss_spatial_bce_9: 0.28324/0.35551, loss_spatial_dice_9: 0.83575/0.79398, loss_spatial_ce_9: 1.18172/1.39338, loss_grounding_bce_9: 0.22369/0.10115, loss_grounding_dice_9: 0.34935/0.24288, loss_grounding_ce_9: 2.22546/0.67844] items per batch[64] items per second[0.37] total items[3488000] mini batches[ 54500] memory[4999] epoch remaining[0:09:11] INFO:trainer.default_trainer:epochs[ 29] optim steps[54600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22704/0.76064, loss_mask_bce_0: 0.68855/0.30121, loss_mask_dice_0: 0.58906/1.02496, loss_spatial_bce_0: 0.26939/0.08569, loss_spatial_dice_0: 0.26823/0.18135, loss_spatial_ce_0: 0.01913/0.05877, loss_grounding_bce_0: 0.29024/0.08080, loss_grounding_dice_0: 0.25055/0.15078, loss_grounding_ce_0: 0.02147/0.24927, loss_mask_ce_1: 0.23730/0.76119, loss_mask_bce_1: 0.68359/0.30211, loss_mask_dice_1: 0.60584/1.02912, loss_spatial_bce_1: 0.26190/0.08597, loss_spatial_dice_1: 0.27994/0.18397, loss_spatial_ce_1: 0.01413/0.06268, loss_grounding_bce_1: 0.29274/0.08099, loss_grounding_dice_1: 0.25974/0.15157, loss_grounding_ce_1: 0.02459/0.25084, loss_mask_ce_2: 0.25419/0.76938, loss_mask_bce_2: 0.69261/0.30226, loss_mask_dice_2: 0.62544/1.03004, loss_spatial_bce_2: 0.27342/0.08598, loss_spatial_dice_2: 0.30891/0.18429, loss_spatial_ce_2: 0.01371/0.06500, loss_grounding_bce_2: 0.29114/0.08098, loss_grounding_dice_2: 0.26566/0.15148, loss_grounding_ce_2: 0.03587/0.25421, loss_mask_ce_3: 0.27253/0.77258, loss_mask_bce_3: 0.68303/0.30374, loss_mask_dice_3: 0.61109/1.02770, loss_spatial_bce_3: 0.27994/0.08803, loss_spatial_dice_3: 0.29779/0.18554, loss_spatial_ce_3: 0.03011/0.06965, loss_grounding_bce_3: 0.28366/0.08139, loss_grounding_dice_3: 0.25775/0.15110, loss_grounding_ce_3: 0.04992/0.25449, loss_mask_ce_4: 0.24326/0.77837, loss_mask_bce_4: 0.70088/0.30616, loss_mask_dice_4: 0.61969/1.04675, loss_spatial_bce_4: 0.26798/0.09006, loss_spatial_dice_4: 0.27563/0.19345, loss_spatial_ce_4: 0.09669/0.08277, loss_grounding_bce_4: 0.29479/0.08203, loss_grounding_dice_4: 0.26008/0.15368, loss_grounding_ce_4: 0.03673/0.25915, loss_mask_ce_5: 0.29050/0.80203, loss_mask_bce_5: 0.69327/0.30807, loss_mask_dice_5: 0.61869/1.05420, loss_spatial_bce_5: 0.28778/0.09221, loss_spatial_dice_5: 0.29405/0.19625, loss_spatial_ce_5: 0.24384/0.09516, loss_grounding_bce_5: 0.29047/0.08228, loss_grounding_dice_5: 0.25928/0.15436, loss_grounding_ce_5: 0.03359/0.27787, loss_mask_ce_6: 0.25281/0.82860, loss_mask_bce_6: 0.70648/0.31007, loss_mask_dice_6: 0.64189/1.05741, loss_spatial_bce_6: 0.29187/0.09725, loss_spatial_dice_6: 0.25602/0.19852, loss_spatial_ce_6: 0.29235/0.11932, loss_grounding_bce_6: 0.29264/0.08323, loss_grounding_dice_6: 0.26710/0.15496, loss_grounding_ce_6: 0.02242/0.28702, loss_mask_ce_7: 0.30516/0.88449, loss_mask_bce_7: 0.75034/0.31733, loss_mask_dice_7: 0.64172/1.10391, loss_spatial_bce_7: 0.37327/0.10725, loss_spatial_dice_7: 0.40186/0.22413, loss_spatial_ce_7: 0.48494/0.15751, loss_grounding_bce_7: 0.30687/0.08493, loss_grounding_dice_7: 0.26246/0.16067, loss_grounding_ce_7: 0.03866/0.32062, loss_mask_ce_8: 0.39305/1.02143, loss_mask_bce_8: 0.72421/0.33342, loss_mask_dice_8: 0.63125/1.18111, loss_spatial_bce_8: 0.38175/0.12499, loss_spatial_dice_8: 0.35384/0.26015, loss_spatial_ce_8: 0.30491/0.20686, loss_grounding_bce_8: 0.29993/0.08912, loss_grounding_dice_8: 0.25825/0.17030, loss_grounding_ce_8: 0.04443/0.42228, loss_mask_ce_9: 2.67175/3.48149, loss_mask_bce_9: 0.75950/0.36042, loss_mask_dice_9: 0.81267/1.76343, loss_spatial_bce_9: 0.50547/0.35549, loss_spatial_dice_9: 0.72403/0.79398, loss_spatial_ce_9: 0.97118/1.39349, loss_grounding_bce_9: 0.33580/0.10113, loss_grounding_dice_9: 0.35894/0.24285, loss_grounding_ce_9: 0.09018/0.67828] items per batch[64] items per second[0.36] total items[3494400] mini batches[ 54600] memory[4999] epoch remaining[0:06:13] INFO:trainer.default_trainer:epochs[ 29] optim steps[54700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37339/0.76063, loss_mask_bce_0: 0.14568/0.30114, loss_mask_dice_0: 0.18355/1.02478, loss_spatial_bce_0: 0.06671/0.08567, loss_spatial_dice_0: 0.11411/0.18135, loss_spatial_ce_0: 0.01045/0.05877, loss_grounding_bce_0: 0.00000/0.08077, loss_grounding_dice_0: 0.00016/0.15079, loss_grounding_ce_0: 0.00638/0.24927, loss_mask_ce_1: 0.37074/0.76125, loss_mask_bce_1: 0.14811/0.30203, loss_mask_dice_1: 0.16794/1.02894, loss_spatial_bce_1: 0.08718/0.08595, loss_spatial_dice_1: 0.14988/0.18396, loss_spatial_ce_1: 0.00795/0.06267, loss_grounding_bce_1: 0.00000/0.08097, loss_grounding_dice_1: 0.00011/0.15156, loss_grounding_ce_1: 0.00756/0.25084, loss_mask_ce_2: 0.43506/0.76937, loss_mask_bce_2: 0.15406/0.30219, loss_mask_dice_2: 0.18461/1.02980, loss_spatial_bce_2: 0.07165/0.08597, loss_spatial_dice_2: 0.13537/0.18428, loss_spatial_ce_2: 0.01415/0.06499, loss_grounding_bce_2: 0.00000/0.08095, loss_grounding_dice_2: 0.00011/0.15147, loss_grounding_ce_2: 0.00970/0.25421, loss_mask_ce_3: 0.34406/0.77257, loss_mask_bce_3: 0.16471/0.30367, loss_mask_dice_3: 0.18004/1.02748, loss_spatial_bce_3: 0.06845/0.08802, loss_spatial_dice_3: 0.10409/0.18554, loss_spatial_ce_3: 0.01490/0.06963, loss_grounding_bce_3: 0.00000/0.08136, loss_grounding_dice_3: 0.00038/0.15110, loss_grounding_ce_3: 0.00950/0.25443, loss_mask_ce_4: 0.46685/0.77837, loss_mask_bce_4: 0.17164/0.30608, loss_mask_dice_4: 0.18927/1.04653, loss_spatial_bce_4: 0.07697/0.09005, loss_spatial_dice_4: 0.12057/0.19344, loss_spatial_ce_4: 0.00771/0.08274, loss_grounding_bce_4: 0.00000/0.08201, loss_grounding_dice_4: 0.00009/0.15367, loss_grounding_ce_4: 0.02128/0.25920, loss_mask_ce_5: 0.52778/0.80201, loss_mask_bce_5: 0.15716/0.30799, loss_mask_dice_5: 0.17347/1.05400, loss_spatial_bce_5: 0.08101/0.09220, loss_spatial_dice_5: 0.13205/0.19625, loss_spatial_ce_5: 0.05572/0.09512, loss_grounding_bce_5: 0.00000/0.08225, loss_grounding_dice_5: 0.00009/0.15435, loss_grounding_ce_5: 0.01509/0.27781, loss_mask_ce_6: 0.84919/0.82862, loss_mask_bce_6: 0.16622/0.30998, loss_mask_dice_6: 0.17824/1.05719, loss_spatial_bce_6: 0.07384/0.09725, loss_spatial_dice_6: 0.13829/0.19852, loss_spatial_ce_6: 0.09459/0.11930, loss_grounding_bce_6: 0.00000/0.08319, loss_grounding_dice_6: 0.00030/0.15495, loss_grounding_ce_6: 0.03882/0.28706, loss_mask_ce_7: 1.05441/0.88447, loss_mask_bce_7: 0.16568/0.31723, loss_mask_dice_7: 0.17978/1.10364, loss_spatial_bce_7: 0.08949/0.10726, loss_spatial_dice_7: 0.14045/0.22414, loss_spatial_ce_7: 0.09868/0.15745, loss_grounding_bce_7: 0.00000/0.08489, loss_grounding_dice_7: 0.00151/0.16066, loss_grounding_ce_7: 0.05489/0.32062, loss_mask_ce_8: 0.22958/1.02133, loss_mask_bce_8: 0.24523/0.33332, loss_mask_dice_8: 0.42683/1.18084, loss_spatial_bce_8: 0.08587/0.12496, loss_spatial_dice_8: 0.12196/0.26014, loss_spatial_ce_8: 0.15046/0.20681, loss_grounding_bce_8: 0.00001/0.08910, loss_grounding_dice_8: 0.11020/0.17030, loss_grounding_ce_8: 0.04480/0.42229, loss_mask_ce_9: 3.69882/3.48093, loss_mask_bce_9: 0.19125/0.36030, loss_mask_dice_9: 0.56550/1.76293, loss_spatial_bce_9: 0.39287/0.35539, loss_spatial_dice_9: 0.62735/0.79392, loss_spatial_ce_9: 1.10666/1.39336, loss_grounding_bce_9: 0.00000/0.10111, loss_grounding_dice_9: 0.04104/0.24285, loss_grounding_ce_9: 1.14970/0.67799] items per batch[64] items per second[0.37] total items[3500800] mini batches[ 54700] memory[4999] epoch remaining[0:03:15] INFO:trainer.default_trainer:epochs[ 29] optim steps[54800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22979/0.76060, loss_mask_bce_0: 0.41456/0.30115, loss_mask_dice_0: 0.94228/1.02441, loss_spatial_bce_0: 0.03222/0.08570, loss_spatial_dice_0: 0.12096/0.18134, loss_spatial_ce_0: 0.00180/0.05876, loss_grounding_bce_0: 0.01880/0.08080, loss_grounding_dice_0: 0.09774/0.15079, loss_grounding_ce_0: 0.00225/0.24926, loss_mask_ce_1: 0.25752/0.76116, loss_mask_bce_1: 0.37950/0.30204, loss_mask_dice_1: 0.91471/1.02854, loss_spatial_bce_1: 0.03341/0.08599, loss_spatial_dice_1: 0.13362/0.18395, loss_spatial_ce_1: 0.00176/0.06265, loss_grounding_bce_1: 0.02211/0.08099, loss_grounding_dice_1: 0.10170/0.15156, loss_grounding_ce_1: 0.00201/0.25082, loss_mask_ce_2: 0.22298/0.76931, loss_mask_bce_2: 0.38114/0.30220, loss_mask_dice_2: 0.94264/1.02941, loss_spatial_bce_2: 0.03481/0.08601, loss_spatial_dice_2: 0.12751/0.18428, loss_spatial_ce_2: 0.00195/0.06497, loss_grounding_bce_2: 0.01647/0.08097, loss_grounding_dice_2: 0.07591/0.15148, loss_grounding_ce_2: 0.00149/0.25421, loss_mask_ce_3: 0.23924/0.77249, loss_mask_bce_3: 0.37973/0.30368, loss_mask_dice_3: 0.93669/1.02712, loss_spatial_bce_3: 0.03929/0.08806, loss_spatial_dice_3: 0.13379/0.18554, loss_spatial_ce_3: 0.00178/0.06961, loss_grounding_bce_3: 0.02273/0.08138, loss_grounding_dice_3: 0.09696/0.15110, loss_grounding_ce_3: 0.00220/0.25441, loss_mask_ce_4: 0.22472/0.77829, loss_mask_bce_4: 0.38015/0.30608, loss_mask_dice_4: 0.98704/1.04618, loss_spatial_bce_4: 0.04504/0.09009, loss_spatial_dice_4: 0.15037/0.19344, loss_spatial_ce_4: 0.00252/0.08271, loss_grounding_bce_4: 0.02039/0.08202, loss_grounding_dice_4: 0.09174/0.15366, loss_grounding_ce_4: 0.00547/0.25919, loss_mask_ce_5: 0.21162/0.80195, loss_mask_bce_5: 0.38226/0.30800, loss_mask_dice_5: 0.89859/1.05362, loss_spatial_bce_5: 0.03668/0.09223, loss_spatial_dice_5: 0.14952/0.19624, loss_spatial_ce_5: 0.00756/0.09510, loss_grounding_bce_5: 0.01886/0.08227, loss_grounding_dice_5: 0.08887/0.15435, loss_grounding_ce_5: 0.00400/0.27779, loss_mask_ce_6: 0.27042/0.82858, loss_mask_bce_6: 0.37084/0.31000, loss_mask_dice_6: 0.92023/1.05680, loss_spatial_bce_6: 0.04229/0.09729, loss_spatial_dice_6: 0.14926/0.19851, loss_spatial_ce_6: 0.03984/0.11931, loss_grounding_bce_6: 0.01969/0.08321, loss_grounding_dice_6: 0.10190/0.15495, loss_grounding_ce_6: 0.00327/0.28705, loss_mask_ce_7: 0.29013/0.88441, loss_mask_bce_7: 0.39457/0.31725, loss_mask_dice_7: 0.94037/1.10328, loss_spatial_bce_7: 0.05902/0.10730, loss_spatial_dice_7: 0.15316/0.22413, loss_spatial_ce_7: 0.15739/0.15744, loss_grounding_bce_7: 0.01631/0.08492, loss_grounding_dice_7: 0.08009/0.16066, loss_grounding_ce_7: 0.00263/0.32057, loss_mask_ce_8: 0.43545/1.02127, loss_mask_bce_8: 0.40742/0.33335, loss_mask_dice_8: 0.99014/1.18042, loss_spatial_bce_8: 0.05935/0.12502, loss_spatial_dice_8: 0.18864/0.26013, loss_spatial_ce_8: 0.14147/0.20678, loss_grounding_bce_8: 0.01503/0.08912, loss_grounding_dice_8: 0.07723/0.17030, loss_grounding_ce_8: 0.00402/0.42224, loss_mask_ce_9: 3.75475/3.48070, loss_mask_bce_9: 0.40503/0.36031, loss_mask_dice_9: 1.50802/1.76220, loss_spatial_bce_9: 0.21022/0.35541, loss_spatial_dice_9: 0.91685/0.79388, loss_spatial_ce_9: 1.31417/1.39317, loss_grounding_bce_9: 0.03338/0.10111, loss_grounding_dice_9: 0.22847/0.24282, loss_grounding_ce_9: 0.04604/0.67794] items per batch[64] items per second[0.36] total items[3507200] mini batches[ 54800] memory[4999] epoch remaining[0:00:17] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00054810. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0033 s/iter. Inference: 0.3645 s/iter. Eval: 0.0900 s/iter. Total: 0.4578 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0028 s/iter. Inference: 0.3691 s/iter. Eval: 0.0797 s/iter. Total: 0.4517 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0029 s/iter. Inference: 0.3697 s/iter. Eval: 0.0764 s/iter. Total: 0.4491 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0030 s/iter. Inference: 0.3754 s/iter. Eval: 0.0747 s/iter. Total: 0.4532 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0030 s/iter. Inference: 0.3737 s/iter. Eval: 0.0742 s/iter. Total: 0.4511 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0030 s/iter. Inference: 0.3745 s/iter. Eval: 0.0718 s/iter. Total: 0.4494 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalp4sk6pdl ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.472 | 83.139 | 66.012 | 133 | | Things | 61.723 | 84.085 | 72.939 | 80 | | Stuff | 46.037 | 81.710 | 55.556 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.52s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.27 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.38 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.94s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 22.49 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.562 | 69.432 | 49.047 | 25.747 | 49.454 | 67.725 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.907 | bicycle | 23.172 | car | 44.800 | | motorcycle | 42.384 | airplane | 62.100 | bus | 71.378 | | train | 74.724 | truck | 39.194 | boat | 31.672 | | traffic light | 28.011 | fire hydrant | 70.613 | stop sign | 69.056 | | parking meter | 52.013 | bench | 26.948 | bird | 33.992 | | cat | 75.644 | dog | 71.519 | horse | 52.085 | | sheep | 54.160 | cow | 56.954 | elephant | 65.803 | | bear | 79.421 | zebra | 65.943 | giraffe | 62.658 | | backpack | 24.802 | umbrella | 55.609 | handbag | 24.140 | | tie | 40.233 | suitcase | 50.168 | frisbee | 70.516 | | skis | 8.891 | snowboard | 34.624 | sports ball | 48.448 | | kite | 37.299 | baseball bat | 37.775 | baseball glove | 48.982 | | skateboard | 44.132 | surfboard | 45.035 | tennis racket | 63.289 | | bottle | 41.399 | wine glass | 38.510 | cup | 49.609 | | fork | 26.474 | knife | 24.488 | spoon | 22.075 | | bowl | 38.377 | banana | 21.791 | apple | 25.475 | | sandwich | 48.626 | orange | 28.851 | broccoli | 24.879 | | carrot | 22.954 | hot dog | 37.605 | pizza | 51.842 | | donut | 55.246 | cake | 47.831 | chair | 28.508 | | couch | 43.293 | potted plant | 23.958 | bed | 42.479 | | dining table | 16.217 | toilet | 69.674 | tv | 65.906 | | laptop | 69.651 | mouse | 64.588 | remote | 44.286 | | keyboard | 58.484 | cell phone | 46.373 | microwave | 63.651 | | oven | 33.785 | toaster | 50.086 | sink | 44.793 | | refrigerator | 69.755 | book | 13.869 | clock | 54.684 | | vase | 39.968 | scissors | 36.629 | teddy bear | 57.854 | | hair drier | 34.478 | toothbrush | 28.868 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.257 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.770 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.67840019553604, 'fwIoU': 71.3423728787846, 'IoU-person': 88.58609705440381, 'IoU-bicycle': 78.44488324940102, 'IoU-car': 69.47631480969842, 'IoU-motorcycle': 84.65538019353401, 'IoU-airplane': 84.01401903911943, 'IoU-bus': 87.37062069840981, 'IoU-train': 88.13879718776063, 'IoU-truck': 64.742507377318, 'IoU-boat': 67.85922731166006, 'IoU-traffic light': 78.72439234099954, 'IoU-fire hydrant': 93.24064933071732, 'IoU-stop sign': 84.82404065588274, 'IoU-parking meter': 84.95719940902478, 'IoU-bench': 62.16784893443671, 'IoU-bird': 73.30428006143556, 'IoU-cat': 89.82112486047282, 'IoU-dog': 76.33025698036766, 'IoU-horse': 88.7792333259494, 'IoU-sheep': 81.38664308595078, 'IoU-cow': 88.87309166807215, 'IoU-elephant': 88.02376053141148, 'IoU-bear': 80.0664101722325, 'IoU-zebra': 84.31724192685894, 'IoU-giraffe': 89.75905176719078, 'IoU-backpack': 52.97944263197202, 'IoU-umbrella': 81.73291525192892, 'IoU-handbag': 49.52084626502383, 'IoU-tie': 67.88080791454999, 'IoU-suitcase': 78.87934311951888, 'IoU-frisbee': 84.58745637311931, 'IoU-skis': 61.16044324063575, 'IoU-snowboard': 73.92942030460897, 'IoU-sports ball': 67.45583123385528, 'IoU-kite': 77.80239376894896, 'IoU-baseball bat': 67.94909257132277, 'IoU-baseball glove': 73.34866936005636, 'IoU-skateboard': 85.59930556817586, 'IoU-surfboard': 86.30397012516417, 'IoU-tennis racket': 90.2282990346393, 'IoU-bottle': 71.41890011049523, 'IoU-wine glass': 82.82644957650517, 'IoU-cup': 69.87312294406969, 'IoU-fork': 69.58085729720744, 'IoU-knife': 63.65245440270741, 'IoU-spoon': 61.71728627185696, 'IoU-bowl': 59.680373350633175, 'IoU-banana': 84.0583136088228, 'IoU-apple': 57.5573593671552, 'IoU-sandwich': 69.79610705782025, 'IoU-orange': 73.04138666222642, 'IoU-broccoli': 70.5357804994424, 'IoU-carrot': 64.08428480106166, 'IoU-hot dog': 64.47636983317034, 'IoU-pizza': 82.79884513919113, 'IoU-donut': 61.32784221892077, 'IoU-cake': 79.45178508583271, 'IoU-chair': 61.874185539001246, 'IoU-couch': 70.0406491389287, 'IoU-potted plant': 42.55028610081579, 'IoU-bed': 71.42528982639831, 'IoU-dining table': 54.20253777891331, 'IoU-toilet': 82.5493358140218, 'IoU-tv': 75.68379607171372, 'IoU-laptop': 77.65076760960883, 'IoU-mouse': 71.54662179351607, 'IoU-remote': 66.81139770439992, 'IoU-keyboard': 60.76072546968848, 'IoU-cell phone': 81.28905681982387, 'IoU-microwave': 58.3565498894712, 'IoU-oven': 70.40412169604193, 'IoU-toaster': 82.46651441269057, 'IoU-sink': 72.6706048532393, 'IoU-refrigerator': 82.77395739053468, 'IoU-book': 57.349084875315, 'IoU-clock': 77.97768781084157, 'IoU-vase': 56.654263345930126, 'IoU-scissors': 83.63455848040908, 'IoU-teddy bear': 80.88673924897, 'IoU-hair drier': 44.43300907462569, 'IoU-toothbrush': 75.4844151544295, 'IoU-banner': 30.92289921325657, 'IoU-blanket': 18.051924730195132, 'IoU-bridge': 38.57296956674901, 'IoU-cardboard': 44.70700073045369, 'IoU-counter': 33.17920080956661, 'IoU-curtain': 70.66684621741281, 'IoU-door-stuff': 48.66437889186855, 'IoU-floor-wood': 64.30579690729994, 'IoU-flower': 43.2120090833694, 'IoU-fruit': 49.72845065853715, 'IoU-gravel': 24.203402555152373, 'IoU-house': 26.857914877417183, 'IoU-light': 45.75300345754129, 'IoU-mirror-stuff': 65.36556569502166, 'IoU-net': 48.58752253174239, 'IoU-pillow': 22.119575220915852, 'IoU-platform': 27.999737023174383, 'IoU-playingfield': 69.23363492516732, 'IoU-railroad': 62.316347465621334, 'IoU-river': 54.35927329370753, 'IoU-road': 68.82353561905599, 'IoU-roof': 19.425755717685558, 'IoU-sand': 63.76074154858634, 'IoU-sea': 84.39235019408186, 'IoU-shelf': 39.59373272195917, 'IoU-snow': 92.28201838977577, 'IoU-stairs': 32.2917144898067, 'IoU-tent': 11.181920627711865, 'IoU-towel': 44.71876763519364, 'IoU-wall-brick': 51.35902555962897, 'IoU-wall-stone': 28.605588286969557, 'IoU-wall-tile': 69.56369905160616, 'IoU-wall-wood': 44.40488698189836, 'IoU-water-other': 18.87170231035513, 'IoU-window-blind': 50.40348076842756, 'IoU-window-other': 50.9338645845242, 'IoU-tree-merged': 82.0372990222979, 'IoU-fence-merged': 55.12446626309184, 'IoU-ceiling-merged': 67.6344244614572, 'IoU-sky-other-merged': 93.94176606366186, 'IoU-cabinet-merged': 64.85298287427453, 'IoU-table-merged': 42.608051769502424, 'IoU-floor-other-merged': 55.054635702475316, 'IoU-pavement-merged': 59.07475946906858, 'IoU-mountain-merged': 57.81289677442469, 'IoU-grass-merged': 71.25503630264285, 'IoU-dirt-merged': 46.634107157441335, 'IoU-paper-merged': 36.74883188469687, 'IoU-food-other-merged': 44.62356527006198, 'IoU-building-other-merged': 59.961810386802064, 'IoU-rock-merged': 66.51160844428017, 'IoU-wall-other-merged': 68.53611089468097, 'IoU-rug-merged': 67.81965206172006, 'mACC': 76.34668714460389, 'pACC': 82.11141696077372, 'ACC-person': 93.25663301779966, 'ACC-bicycle': 88.44463576502756, 'ACC-car': 87.57266312954538, 'ACC-motorcycle': 88.61443119569962, 'ACC-airplane': 89.82384340080675, 'ACC-bus': 93.85066062406462, 'ACC-train': 95.30726433283122, 'ACC-truck': 71.06283534677426, 'ACC-boat': 75.736798644968, 'ACC-traffic light': 91.3121111614069, 'ACC-fire hydrant': 95.9846725053056, 'ACC-stop sign': 88.3792487379565, 'ACC-parking meter': 87.86237213539879, 'ACC-bench': 80.64964388674647, 'ACC-bird': 77.74944147706846, 'ACC-cat': 94.36630187519674, 'ACC-dog': 79.02824687063529, 'ACC-horse': 93.74888958914417, 'ACC-sheep': 84.52843998943342, 'ACC-cow': 92.36194868557415, 'ACC-elephant': 90.06948254628242, 'ACC-bear': 81.66349498717112, 'ACC-zebra': 86.39934341657873, 'ACC-giraffe': 93.74561706731085, 'ACC-backpack': 73.82987440951437, 'ACC-umbrella': 84.87427995145548, 'ACC-handbag': 68.19815973262483, 'ACC-tie': 74.44699569083998, 'ACC-suitcase': 83.55277062897069, 'ACC-frisbee': 94.124, 'ACC-skis': 75.16601532553996, 'ACC-snowboard': 81.81430775636159, 'ACC-sports ball': 87.45878169545487, 'ACC-kite': 85.87335986247133, 'ACC-baseball bat': 87.9844757958777, 'ACC-baseball glove': 91.89922396835347, 'ACC-skateboard': 89.85226063889475, 'ACC-surfboard': 92.12185824911867, 'ACC-tennis racket': 94.49377349683029, 'ACC-bottle': 85.49883400034624, 'ACC-wine glass': 91.51946474225309, 'ACC-cup': 87.47063288750435, 'ACC-fork': 82.8317057296699, 'ACC-knife': 79.3844801055177, 'ACC-spoon': 76.49765512932753, 'ACC-bowl': 70.10588366391164, 'ACC-banana': 91.39838326080148, 'ACC-apple': 72.29966197682903, 'ACC-sandwich': 82.87617673681423, 'ACC-orange': 81.41281039000862, 'ACC-broccoli': 79.5713495913528, 'ACC-carrot': 74.92275202198311, 'ACC-hot dog': 72.5453636280444, 'ACC-pizza': 94.23719048137401, 'ACC-donut': 68.11149833166418, 'ACC-cake': 87.07999316841675, 'ACC-chair': 78.53768806629405, 'ACC-couch': 82.14021252777496, 'ACC-potted plant': 58.959103857489815, 'ACC-bed': 81.00347717244568, 'ACC-dining table': 73.87706044873639, 'ACC-toilet': 86.85054036140855, 'ACC-tv': 86.99150042099497, 'ACC-laptop': 90.43035239395752, 'ACC-mouse': 84.20469753868021, 'ACC-remote': 71.61646590709853, 'ACC-keyboard': 64.68268190618336, 'ACC-cell phone': 91.08826636210028, 'ACC-microwave': 61.86439229931977, 'ACC-oven': 92.60643768255125, 'ACC-toaster': 91.42317535480163, 'ACC-sink': 83.37459949648071, 'ACC-refrigerator': 92.09937730723581, 'ACC-book': 75.83627525146396, 'ACC-clock': 82.90114892768872, 'ACC-vase': 63.6317749639316, 'ACC-scissors': 88.65922467381024, 'ACC-teddy bear': 85.54448519947402, 'ACC-hair drier': 63.9000370022486, 'ACC-toothbrush': 83.17842251563586, 'ACC-banner': 80.75958741264412, 'ACC-blanket': 35.555078992088596, 'ACC-bridge': 55.96233353311053, 'ACC-cardboard': 64.31048422277156, 'ACC-counter': 51.75574925247679, 'ACC-curtain': 81.81546936369237, 'ACC-door-stuff': 69.66244189252458, 'ACC-floor-wood': 79.64754791950259, 'ACC-flower': 62.91634762393712, 'ACC-fruit': 69.48596345068374, 'ACC-gravel': 28.86660538667823, 'ACC-house': 34.48891793883699, 'ACC-light': 65.63092150595261, 'ACC-mirror-stuff': 79.7900475459429, 'ACC-net': 68.02210604962167, 'ACC-pillow': 38.38745700158134, 'ACC-platform': 44.976363463422594, 'ACC-playingfield': 89.26081854101645, 'ACC-railroad': 82.04390434283208, 'ACC-river': 81.23160399214238, 'ACC-road': 85.62846712893673, 'ACC-roof': 26.75952523560977, 'ACC-sand': 68.61953605841344, 'ACC-sea': 92.25515577897609, 'ACC-shelf': 53.467168282006384, 'ACC-snow': 95.22558859764852, 'ACC-stairs': 55.40182416746633, 'ACC-tent': 13.103067194733828, 'ACC-towel': 54.861904684535375, 'ACC-wall-brick': 67.43385564913291, 'ACC-wall-stone': 33.06086085870548, 'ACC-wall-tile': 83.9868002386978, 'ACC-wall-wood': 62.65123832523969, 'ACC-water-other': 24.05958247276819, 'ACC-window-blind': 63.6755258282351, 'ACC-window-other': 72.79968132491986, 'ACC-tree-merged': 89.9768488308746, 'ACC-fence-merged': 73.43680669900932, 'ACC-ceiling-merged': 82.73004463473382, 'ACC-sky-other-merged': 97.16406124350863, 'ACC-cabinet-merged': 77.38329916603529, 'ACC-table-merged': 58.93809263753007, 'ACC-floor-other-merged': 65.12320450157313, 'ACC-pavement-merged': 73.01184622326821, 'ACC-mountain-merged': 68.2572785786581, 'ACC-grass-merged': 83.11849599194557, 'ACC-dirt-merged': 72.3625083466301, 'ACC-paper-merged': 46.60297722634606, 'ACC-food-other-merged': 65.10919059749543, 'ACC-building-other-merged': 77.24053711736997, 'ACC-rock-merged': 85.10643350356321, 'ACC-wall-other-merged': 80.52429519817535, 'ACC-rug-merged': 80.08954740345862})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3224 s/iter. Inference: 0.1755 s/iter. Eval: 0.0000 s/iter. Total: 0.4980 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3456 s/iter. Inference: 0.3406 s/iter. Eval: 0.0000 s/iter. Total: 0.6862 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3502 s/iter. Inference: 0.5302 s/iter. Eval: 0.0000 s/iter. Total: 0.8805 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3813286508633305, 'noc@0.8': 2.379572724612233, 'noc@0.85': 2.836113549897571, 'noc@0.9': 3.62071992976295, 'miou@iter1': 0.8765090220079876} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1436 s/iter. Eval: 0.0010 s/iter. Total: 0.1460 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.28176879882812, 'precision@0.6': 72.52234649658203, 'precision@0.7': 68.83016204833984, 'precision@0.8': 59.96890640258789, 'precision@0.9': 32.841041564941406, 'cIoU': 61.8416862487793, 'mIoU': 66.97309875488281} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.472268238996115, 'SQ': 83.13854673355762, 'RQ': 66.01204445002207, 'PQ_th': 61.72326700157661, 'SQ_th': 84.0852828229083, 'RQ_th': 72.93885642283571, 'PQ_st': 46.036798408686, 'SQ_st': 81.70951112699065, 'RQ_st': 55.55647920803922}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.56200189206762, 'AP50': 69.43234125580344, 'AP75': 49.047067950201736, 'APs': 25.74682115859776, 'APm': 49.453577457185396, 'APl': 67.7249885094495, 'AP-person': 48.90655436571996, 'AP-bicycle': 23.171575574160144, 'AP-car': 44.80025444371885, 'AP-motorcycle': 42.38429791494329, 'AP-airplane': 62.09995841062599, 'AP-bus': 71.37757226740231, 'AP-train': 74.72353098024477, 'AP-truck': 39.193667192352876, 'AP-boat': 31.67206611553457, 'AP-traffic light': 28.010602519303475, 'AP-fire hydrant': 70.61309115337441, 'AP-stop sign': 69.05606150540014, 'AP-parking meter': 52.01347896867101, 'AP-bench': 26.948353541603048, 'AP-bird': 33.99160074347936, 'AP-cat': 75.64360270740396, 'AP-dog': 71.51912910069353, 'AP-horse': 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INFO:trainer.default_trainer:This epoch takes 0:57:37.300537 INFO:trainer.default_trainer:PROGRESS: 60.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 30 training. INFO:trainer.default_trainer:epochs[ 30] optim steps[54900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.19271/0.76060, loss_mask_bce_0: 0.25589/0.30115, loss_mask_dice_0: 0.44770/1.02430, loss_spatial_bce_0: 0.13017/0.08569, loss_spatial_dice_0: 0.17054/0.18132, loss_spatial_ce_0: 0.28378/0.05877, loss_grounding_bce_0: 0.01215/0.08080, loss_grounding_dice_0: 0.22335/0.15079, loss_grounding_ce_0: 0.08802/0.24912, loss_mask_ce_1: 1.25730/0.76119, loss_mask_bce_1: 0.25856/0.30204, loss_mask_dice_1: 0.46963/1.02845, loss_spatial_bce_1: 0.13497/0.08598, loss_spatial_dice_1: 0.16294/0.18394, loss_spatial_ce_1: 0.33580/0.06263, loss_grounding_bce_1: 0.00947/0.08098, loss_grounding_dice_1: 0.16118/0.15154, loss_grounding_ce_1: 0.10837/0.25071, loss_mask_ce_2: 1.32063/0.76932, loss_mask_bce_2: 0.24758/0.30219, loss_mask_dice_2: 0.41039/1.02930, loss_spatial_bce_2: 0.12827/0.08600, loss_spatial_dice_2: 0.17310/0.18427, loss_spatial_ce_2: 0.37526/0.06496, loss_grounding_bce_2: 0.01457/0.08097, loss_grounding_dice_2: 0.22105/0.15146, loss_grounding_ce_2: 0.10884/0.25408, loss_mask_ce_3: 1.36395/0.77254, loss_mask_bce_3: 0.24475/0.30368, loss_mask_dice_3: 0.45134/1.02699, loss_spatial_bce_3: 0.13353/0.08804, loss_spatial_dice_3: 0.16616/0.18552, loss_spatial_ce_3: 0.39179/0.06961, loss_grounding_bce_3: 0.01015/0.08137, loss_grounding_dice_3: 0.25048/0.15109, loss_grounding_ce_3: 0.12124/0.25428, loss_mask_ce_4: 1.36635/0.77831, loss_mask_bce_4: 0.26493/0.30609, loss_mask_dice_4: 0.47070/1.04605, loss_spatial_bce_4: 0.15377/0.09007, loss_spatial_dice_4: 0.18029/0.19342, loss_spatial_ce_4: 0.17242/0.08269, loss_grounding_bce_4: 0.01176/0.08201, loss_grounding_dice_4: 0.20523/0.15364, loss_grounding_ce_4: 0.12276/0.25908, loss_mask_ce_5: 1.45765/0.80203, loss_mask_bce_5: 0.26031/0.30799, loss_mask_dice_5: 0.39952/1.05348, loss_spatial_bce_5: 0.11593/0.09222, loss_spatial_dice_5: 0.16100/0.19622, loss_spatial_ce_5: 0.25667/0.09510, loss_grounding_bce_5: 0.01182/0.08227, loss_grounding_dice_5: 0.24156/0.15433, loss_grounding_ce_5: 0.12833/0.27763, loss_mask_ce_6: 1.37779/0.82870, loss_mask_bce_6: 0.25717/0.31000, loss_mask_dice_6: 0.44768/1.05667, loss_spatial_bce_6: 0.12819/0.09727, loss_spatial_dice_6: 0.19209/0.19851, loss_spatial_ce_6: 0.19407/0.11930, loss_grounding_bce_6: 0.01268/0.08320, loss_grounding_dice_6: 0.17525/0.15493, loss_grounding_ce_6: 0.13988/0.28692, loss_mask_ce_7: 1.36873/0.88448, loss_mask_bce_7: 0.26934/0.31725, loss_mask_dice_7: 0.44539/1.10311, loss_spatial_bce_7: 0.37612/0.10728, loss_spatial_dice_7: 0.26966/0.22412, loss_spatial_ce_7: 0.14094/0.15743, loss_grounding_bce_7: 0.01518/0.08491, loss_grounding_dice_7: 0.25389/0.16063, loss_grounding_ce_7: 0.25099/0.32042, loss_mask_ce_8: 1.31662/1.02137, loss_mask_bce_8: 0.27065/0.33335, loss_mask_dice_8: 0.46784/1.18033, loss_spatial_bce_8: 0.41716/0.12500, loss_spatial_dice_8: 0.31197/0.26012, loss_spatial_ce_8: 0.12452/0.20674, loss_grounding_bce_8: 0.01210/0.08911, loss_grounding_dice_8: 0.26298/0.17029, loss_grounding_ce_8: 0.17084/0.42201, loss_mask_ce_9: 3.74848/3.48071, loss_mask_bce_9: 0.27637/0.36034, loss_mask_dice_9: 0.75452/1.76213, loss_spatial_bce_9: 0.46771/0.35545, loss_spatial_dice_9: 0.86380/0.79391, loss_spatial_ce_9: 1.39272/1.39313, loss_grounding_bce_9: 0.01118/0.10111, loss_grounding_dice_9: 0.43969/0.24279, loss_grounding_ce_9: 0.19238/0.67761] items per batch[64] items per second[0.16] total items[3513600] mini batches[ 54900] memory[4999] epoch remaining[0:54:51] INFO:trainer.default_trainer:epochs[ 30] optim steps[55000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.52456/0.76056, loss_mask_bce_0: 0.10277/0.30113, loss_mask_dice_0: 0.18239/1.02430, loss_spatial_bce_0: 0.05932/0.08569, loss_spatial_dice_0: 0.13021/0.18131, loss_spatial_ce_0: 0.02685/0.05876, loss_grounding_bce_0: 0.21848/0.08079, loss_grounding_dice_0: 0.09071/0.15076, loss_grounding_ce_0: 0.03918/0.24920, loss_mask_ce_1: 1.52433/0.76115, loss_mask_bce_1: 0.10546/0.30201, loss_mask_dice_1: 0.16225/1.02849, loss_spatial_bce_1: 0.05965/0.08597, loss_spatial_dice_1: 0.14198/0.18393, loss_spatial_ce_1: 0.03308/0.06263, loss_grounding_bce_1: 0.21885/0.08098, loss_grounding_dice_1: 0.10655/0.15152, loss_grounding_ce_1: 0.07616/0.25073, loss_mask_ce_2: 1.51588/0.76930, loss_mask_bce_2: 0.10716/0.30216, loss_mask_dice_2: 0.16998/1.02932, loss_spatial_bce_2: 0.05745/0.08599, loss_spatial_dice_2: 0.12353/0.18426, loss_spatial_ce_2: 0.03755/0.06492, loss_grounding_bce_2: 0.23584/0.08096, loss_grounding_dice_2: 0.08838/0.15143, loss_grounding_ce_2: 0.01933/0.25414, loss_mask_ce_3: 1.45314/0.77251, loss_mask_bce_3: 0.10717/0.30365, loss_mask_dice_3: 0.18321/1.02705, loss_spatial_bce_3: 0.05735/0.08804, loss_spatial_dice_3: 0.12478/0.18551, loss_spatial_ce_3: 0.04664/0.06958, loss_grounding_bce_3: 0.26248/0.08137, loss_grounding_dice_3: 0.07967/0.15107, loss_grounding_ce_3: 0.00439/0.25434, loss_mask_ce_4: 1.48093/0.77823, loss_mask_bce_4: 0.11277/0.30606, loss_mask_dice_4: 0.22267/1.04608, loss_spatial_bce_4: 0.06238/0.09006, loss_spatial_dice_4: 0.14450/0.19341, loss_spatial_ce_4: 0.04186/0.08267, loss_grounding_bce_4: 0.24415/0.08200, loss_grounding_dice_4: 0.08143/0.15362, loss_grounding_ce_4: 0.01635/0.25916, loss_mask_ce_5: 1.40460/0.80195, loss_mask_bce_5: 0.11308/0.30797, loss_mask_dice_5: 0.24982/1.05350, loss_spatial_bce_5: 0.06150/0.09221, loss_spatial_dice_5: 0.13370/0.19621, loss_spatial_ce_5: 0.05791/0.09510, loss_grounding_bce_5: 0.23362/0.08227, loss_grounding_dice_5: 0.09993/0.15431, loss_grounding_ce_5: 0.00922/0.27762, loss_mask_ce_6: 1.42658/0.82864, loss_mask_bce_6: 0.11549/0.30998, loss_mask_dice_6: 0.21561/1.05670, loss_spatial_bce_6: 0.06479/0.09726, loss_spatial_dice_6: 0.12877/0.19850, loss_spatial_ce_6: 0.06834/0.11930, loss_grounding_bce_6: 0.26237/0.08319, loss_grounding_dice_6: 0.08260/0.15491, loss_grounding_ce_6: 0.00298/0.28697, loss_mask_ce_7: 1.64953/0.88442, loss_mask_bce_7: 0.11512/0.31722, loss_mask_dice_7: 0.29911/1.10315, loss_spatial_bce_7: 0.06077/0.10726, loss_spatial_dice_7: 0.14578/0.22410, loss_spatial_ce_7: 0.09468/0.15741, loss_grounding_bce_7: 0.24615/0.08490, loss_grounding_dice_7: 0.09955/0.16061, loss_grounding_ce_7: 0.00118/0.32048, loss_mask_ce_8: 1.61956/1.02127, loss_mask_bce_8: 0.13520/0.33333, loss_mask_dice_8: 0.28272/1.18036, loss_spatial_bce_8: 0.08044/0.12497, loss_spatial_dice_8: 0.17290/0.26010, loss_spatial_ce_8: 0.01926/0.20671, loss_grounding_bce_8: 0.23790/0.08910, loss_grounding_dice_8: 0.07660/0.17027, loss_grounding_ce_8: 0.02821/0.42206, loss_mask_ce_9: 3.70447/3.48063, loss_mask_bce_9: 0.16194/0.36034, loss_mask_dice_9: 0.51625/1.76217, loss_spatial_bce_9: 0.48948/0.35538, loss_spatial_dice_9: 0.73142/0.79387, loss_spatial_ce_9: 0.97471/1.39307, loss_grounding_bce_9: 0.22474/0.10111, loss_grounding_dice_9: 0.06145/0.24276, loss_grounding_ce_9: 1.83684/0.67770] items per batch[64] items per second[0.37] total items[3520000] mini batches[ 55000] memory[4999] epoch remaining[0:49:27] INFO:trainer.default_trainer:epochs[ 30] optim steps[55100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32907/0.76034, loss_mask_bce_0: 0.60774/0.30112, loss_mask_dice_0: 0.69327/1.02409, loss_spatial_bce_0: 0.18880/0.08572, loss_spatial_dice_0: 0.22332/0.18129, loss_spatial_ce_0: 0.04521/0.05875, loss_grounding_bce_0: 0.04923/0.08078, loss_grounding_dice_0: 0.08532/0.15076, loss_grounding_ce_0: 0.02867/0.24917, loss_mask_ce_1: 0.30920/0.76097, loss_mask_bce_1: 0.61954/0.30201, loss_mask_dice_1: 0.70067/1.02828, loss_spatial_bce_1: 0.18394/0.08600, loss_spatial_dice_1: 0.22366/0.18390, loss_spatial_ce_1: 0.03703/0.06262, loss_grounding_bce_1: 0.04753/0.08097, loss_grounding_dice_1: 0.07968/0.15152, loss_grounding_ce_1: 0.02612/0.25072, loss_mask_ce_2: 0.34712/0.76909, loss_mask_bce_2: 0.58369/0.30217, loss_mask_dice_2: 0.68818/1.02914, loss_spatial_bce_2: 0.20758/0.08603, loss_spatial_dice_2: 0.21698/0.18424, loss_spatial_ce_2: 0.06899/0.06491, loss_grounding_bce_2: 0.04635/0.08095, loss_grounding_dice_2: 0.07915/0.15143, loss_grounding_ce_2: 0.02670/0.25413, loss_mask_ce_3: 0.32582/0.77228, loss_mask_bce_3: 0.58552/0.30366, loss_mask_dice_3: 0.67198/1.02689, loss_spatial_bce_3: 0.19997/0.08807, loss_spatial_dice_3: 0.23435/0.18549, loss_spatial_ce_3: 0.05546/0.06957, loss_grounding_bce_3: 0.05102/0.08136, loss_grounding_dice_3: 0.08859/0.15108, loss_grounding_ce_3: 0.02723/0.25436, loss_mask_ce_4: 0.49393/0.77800, loss_mask_bce_4: 0.57677/0.30607, loss_mask_dice_4: 0.68348/1.04586, loss_spatial_bce_4: 0.17805/0.09009, loss_spatial_dice_4: 0.22016/0.19338, loss_spatial_ce_4: 0.10938/0.08266, loss_grounding_bce_4: 0.04871/0.08200, loss_grounding_dice_4: 0.08426/0.15363, loss_grounding_ce_4: 0.02605/0.25914, loss_mask_ce_5: 0.38795/0.80171, loss_mask_bce_5: 0.61620/0.30797, loss_mask_dice_5: 0.66630/1.05332, loss_spatial_bce_5: 0.15085/0.09223, loss_spatial_dice_5: 0.21023/0.19619, loss_spatial_ce_5: 0.07483/0.09506, loss_grounding_bce_5: 0.04820/0.08226, loss_grounding_dice_5: 0.09165/0.15432, loss_grounding_ce_5: 0.03812/0.27760, loss_mask_ce_6: 0.11528/0.82849, loss_mask_bce_6: 0.93548/0.30996, loss_mask_dice_6: 0.87789/1.05647, loss_spatial_bce_6: 0.20400/0.09728, loss_spatial_dice_6: 0.23208/0.19849, loss_spatial_ce_6: 0.06282/0.11930, loss_grounding_bce_6: 0.05111/0.08320, loss_grounding_dice_6: 0.08218/0.15491, loss_grounding_ce_6: 0.02001/0.28696, loss_mask_ce_7: 0.17840/0.88417, loss_mask_bce_7: 0.85618/0.31721, loss_mask_dice_7: 0.81499/1.10291, loss_spatial_bce_7: 0.18301/0.10726, loss_spatial_dice_7: 0.23632/0.22408, loss_spatial_ce_7: 0.16941/0.15741, loss_grounding_bce_7: 0.04836/0.08491, loss_grounding_dice_7: 0.08574/0.16060, loss_grounding_ce_7: 0.07174/0.32042, loss_mask_ce_8: 0.13316/1.02098, loss_mask_bce_8: 0.80510/0.33332, loss_mask_dice_8: 0.85395/1.18012, loss_spatial_bce_8: 0.29513/0.12497, loss_spatial_dice_8: 0.24220/0.26006, loss_spatial_ce_8: 0.06558/0.20664, loss_grounding_bce_8: 0.04393/0.08910, loss_grounding_dice_8: 0.08143/0.17027, loss_grounding_ce_8: 0.30608/0.42194, loss_mask_ce_9: 2.59490/3.48019, loss_mask_bce_9: 0.55560/0.36036, loss_mask_dice_9: 0.91405/1.76167, loss_spatial_bce_9: 0.45747/0.35543, loss_spatial_dice_9: 0.69925/0.79383, loss_spatial_ce_9: 1.07717/1.39305, loss_grounding_bce_9: 0.06592/0.10111, loss_grounding_dice_9: 0.12467/0.24272, loss_grounding_ce_9: 1.37862/0.67767] items per batch[64] items per second[0.36] total items[3526400] mini batches[ 55100] memory[4999] epoch remaining[0:46:05] INFO:trainer.default_trainer:epochs[ 30] optim steps[55200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.95840/0.76043, loss_mask_bce_0: 0.24559/0.30113, loss_mask_dice_0: 2.37023/1.02412, loss_spatial_bce_0: 0.02791/0.08573, loss_spatial_dice_0: 0.16850/0.18128, loss_spatial_ce_0: 0.03085/0.05874, loss_grounding_bce_0: 0.10622/0.08080, loss_grounding_dice_0: 0.41258/0.15077, loss_grounding_ce_0: 0.66818/0.24919, loss_mask_ce_1: 0.93157/0.76103, loss_mask_bce_1: 0.25893/0.30202, loss_mask_dice_1: 2.56195/1.02823, loss_spatial_bce_1: 0.02918/0.08600, loss_spatial_dice_1: 0.17260/0.18390, loss_spatial_ce_1: 0.02849/0.06262, loss_grounding_bce_1: 0.09523/0.08098, loss_grounding_dice_1: 0.43412/0.15153, loss_grounding_ce_1: 0.66973/0.25074, loss_mask_ce_2: 1.07313/0.76917, loss_mask_bce_2: 0.29335/0.30218, loss_mask_dice_2: 2.46100/1.02913, loss_spatial_bce_2: 0.02829/0.08603, loss_spatial_dice_2: 0.13632/0.18423, loss_spatial_ce_2: 0.02791/0.06490, loss_grounding_bce_2: 0.25371/0.08097, loss_grounding_dice_2: 0.65966/0.15145, loss_grounding_ce_2: 0.07126/0.25417, loss_mask_ce_3: 1.09340/0.77236, loss_mask_bce_3: 0.27240/0.30366, loss_mask_dice_3: 2.19867/1.02690, loss_spatial_bce_3: 0.02719/0.08808, loss_spatial_dice_3: 0.15214/0.18548, loss_spatial_ce_3: 0.03333/0.06955, loss_grounding_bce_3: 0.11430/0.08138, loss_grounding_dice_3: 0.50307/0.15110, loss_grounding_ce_3: 0.70753/0.25440, loss_mask_ce_4: 1.13699/0.77804, loss_mask_bce_4: 0.30677/0.30609, loss_mask_dice_4: 2.46170/1.04583, loss_spatial_bce_4: 0.03025/0.09010, loss_spatial_dice_4: 0.23944/0.19338, loss_spatial_ce_4: 0.05050/0.08264, loss_grounding_bce_4: 0.29030/0.08201, loss_grounding_dice_4: 0.67013/0.15366, loss_grounding_ce_4: 0.08462/0.25917, loss_mask_ce_5: 0.84719/0.80175, loss_mask_bce_5: 0.29585/0.30798, loss_mask_dice_5: 2.73050/1.05327, loss_spatial_bce_5: 0.02693/0.09224, loss_spatial_dice_5: 0.19932/0.19619, loss_spatial_ce_5: 0.06517/0.09505, loss_grounding_bce_5: 0.11484/0.08228, loss_grounding_dice_5: 0.52547/0.15435, loss_grounding_ce_5: 0.87336/0.27758, loss_mask_ce_6: 1.07156/0.82853, loss_mask_bce_6: 0.25180/0.30997, loss_mask_dice_6: 2.41887/1.05649, loss_spatial_bce_6: 0.02990/0.09729, loss_spatial_dice_6: 0.15325/0.19848, loss_spatial_ce_6: 0.05809/0.11933, loss_grounding_bce_6: 0.07983/0.08323, loss_grounding_dice_6: 0.50864/0.15495, loss_grounding_ce_6: 0.87987/0.28698, loss_mask_ce_7: 1.49511/0.88424, loss_mask_bce_7: 0.27025/0.31721, loss_mask_dice_7: 2.37004/1.10290, loss_spatial_bce_7: 0.02635/0.10727, loss_spatial_dice_7: 0.21830/0.22407, loss_spatial_ce_7: 0.15172/0.15745, loss_grounding_bce_7: 0.28291/0.08494, loss_grounding_dice_7: 0.60309/0.16062, loss_grounding_ce_7: 0.06198/0.32046, loss_mask_ce_8: 1.50958/1.02106, loss_mask_bce_8: 0.28157/0.33333, loss_mask_dice_8: 2.58117/1.18012, loss_spatial_bce_8: 0.03567/0.12498, loss_spatial_dice_8: 0.24691/0.26004, loss_spatial_ce_8: 0.37439/0.20665, loss_grounding_bce_8: 0.18011/0.08912, loss_grounding_dice_8: 0.60615/0.17031, loss_grounding_ce_8: 0.12202/0.42194, loss_mask_ce_9: 7.37343/3.48004, loss_mask_bce_9: 0.21346/0.36035, loss_mask_dice_9: 4.20592/1.76163, loss_spatial_bce_9: 0.16319/0.35540, loss_spatial_dice_9: 0.95096/0.79383, loss_spatial_ce_9: 1.57240/1.39305, loss_grounding_bce_9: 0.04546/0.10113, loss_grounding_dice_9: 0.58611/0.24275, loss_grounding_ce_9: 0.13027/0.67759] items per batch[64] items per second[0.36] total items[3532800] mini batches[ 55200] memory[4999] epoch remaining[0:42:51] INFO:trainer.default_trainer:epochs[ 30] optim steps[55300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52382/0.76061, loss_mask_bce_0: 0.04185/0.30115, loss_mask_dice_0: 0.28145/1.02442, loss_spatial_bce_0: 0.03336/0.08571, loss_spatial_dice_0: 0.20017/0.18127, loss_spatial_ce_0: 0.02225/0.05874, loss_grounding_bce_0: 0.01038/0.08080, loss_grounding_dice_0: 0.06050/0.15081, loss_grounding_ce_0: 0.08892/0.24944, loss_mask_ce_1: 0.55044/0.76123, loss_mask_bce_1: 0.04364/0.30204, loss_mask_dice_1: 0.26953/1.02853, loss_spatial_bce_1: 0.03858/0.08599, loss_spatial_dice_1: 0.19863/0.18389, loss_spatial_ce_1: 0.00939/0.06261, loss_grounding_bce_1: 0.01034/0.08099, loss_grounding_dice_1: 0.05883/0.15156, loss_grounding_ce_1: 0.08195/0.25098, loss_mask_ce_2: 0.51570/0.76937, loss_mask_bce_2: 0.03605/0.30220, loss_mask_dice_2: 0.23832/1.02944, loss_spatial_bce_2: 0.03908/0.08602, loss_spatial_dice_2: 0.19705/0.18422, loss_spatial_ce_2: 0.01630/0.06490, loss_grounding_bce_2: 0.01015/0.08098, loss_grounding_dice_2: 0.06492/0.15150, loss_grounding_ce_2: 0.05859/0.25440, loss_mask_ce_3: 0.48812/0.77252, loss_mask_bce_3: 0.04187/0.30368, loss_mask_dice_3: 0.24008/1.02724, loss_spatial_bce_3: 0.03529/0.08807, loss_spatial_dice_3: 0.20165/0.18548, loss_spatial_ce_3: 0.00804/0.06956, loss_grounding_bce_3: 0.01086/0.08138, loss_grounding_dice_3: 0.07116/0.15114, loss_grounding_ce_3: 0.05120/0.25462, loss_mask_ce_4: 0.42888/0.77821, loss_mask_bce_4: 0.04430/0.30611, loss_mask_dice_4: 0.27583/1.04617, loss_spatial_bce_4: 0.03366/0.09010, loss_spatial_dice_4: 0.19531/0.19337, loss_spatial_ce_4: 0.00731/0.08264, loss_grounding_bce_4: 0.00963/0.08202, loss_grounding_dice_4: 0.05685/0.15370, loss_grounding_ce_4: 0.07434/0.25942, loss_mask_ce_5: 0.53188/0.80190, loss_mask_bce_5: 0.03437/0.30801, loss_mask_dice_5: 0.20723/1.05363, loss_spatial_bce_5: 0.01791/0.09223, loss_spatial_dice_5: 0.13930/0.19618, loss_spatial_ce_5: 0.16649/0.09505, loss_grounding_bce_5: 0.00987/0.08228, loss_grounding_dice_5: 0.06121/0.15439, loss_grounding_ce_5: 0.06515/0.27793, loss_mask_ce_6: 0.66272/0.82872, loss_mask_bce_6: 0.04146/0.31000, loss_mask_dice_6: 0.28951/1.05683, loss_spatial_bce_6: 0.01592/0.09729, loss_spatial_dice_6: 0.12572/0.19848, loss_spatial_ce_6: 0.15811/0.11932, loss_grounding_bce_6: 0.01030/0.08323, loss_grounding_dice_6: 0.05503/0.15500, loss_grounding_ce_6: 0.06718/0.28725, loss_mask_ce_7: 1.01572/0.88450, loss_mask_bce_7: 0.03980/0.31723, loss_mask_dice_7: 0.24895/1.10330, loss_spatial_bce_7: 0.01299/0.10727, loss_spatial_dice_7: 0.12203/0.22407, loss_spatial_ce_7: 0.17157/0.15745, loss_grounding_bce_7: 0.01048/0.08494, loss_grounding_dice_7: 0.06094/0.16067, loss_grounding_ce_7: 0.09347/0.32076, loss_mask_ce_8: 0.91481/1.02129, loss_mask_bce_8: 0.04517/0.33336, loss_mask_dice_8: 0.28066/1.18051, loss_spatial_bce_8: 0.01658/0.12498, loss_spatial_dice_8: 0.10900/0.26004, loss_spatial_ce_8: 0.25678/0.20663, loss_grounding_bce_8: 0.01155/0.08912, loss_grounding_dice_8: 0.05659/0.17035, loss_grounding_ce_8: 0.23033/0.42231, loss_mask_ce_9: 3.29074/3.48054, loss_mask_bce_9: 0.03776/0.36039, loss_mask_dice_9: 0.34329/1.76228, loss_spatial_bce_9: 0.09956/0.35537, loss_spatial_dice_9: 0.70558/0.79383, loss_spatial_ce_9: 1.22630/1.39304, loss_grounding_bce_9: 0.00960/0.10114, loss_grounding_dice_9: 0.08201/0.24281, loss_grounding_ce_9: 0.23878/0.67781] items per batch[64] items per second[0.35] total items[3539200] mini batches[ 55300] memory[4999] epoch remaining[0:39:56] INFO:trainer.default_trainer:epochs[ 30] optim steps[55400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72382/0.76054, loss_mask_bce_0: 0.52197/0.30117, loss_mask_dice_0: 0.88266/1.02446, loss_spatial_bce_0: 0.10950/0.08569, loss_spatial_dice_0: 0.19794/0.18126, loss_spatial_ce_0: 0.00544/0.05873, loss_grounding_bce_0: 0.08306/0.08079, loss_grounding_dice_0: 0.06159/0.15081, loss_grounding_ce_0: 0.09338/0.24953, loss_mask_ce_1: 0.65592/0.76115, loss_mask_bce_1: 0.52677/0.30207, loss_mask_dice_1: 0.97163/1.02855, loss_spatial_bce_1: 0.10619/0.08597, loss_spatial_dice_1: 0.18746/0.18388, loss_spatial_ce_1: 0.00457/0.06259, loss_grounding_bce_1: 0.08061/0.08097, loss_grounding_dice_1: 0.06012/0.15156, loss_grounding_ce_1: 0.11978/0.25110, loss_mask_ce_2: 0.69720/0.76929, loss_mask_bce_2: 0.52886/0.30223, loss_mask_dice_2: 0.98453/1.02951, loss_spatial_bce_2: 0.10974/0.08600, loss_spatial_dice_2: 0.20057/0.18422, loss_spatial_ce_2: 0.00595/0.06489, loss_grounding_bce_2: 0.08307/0.08097, loss_grounding_dice_2: 0.06236/0.15149, loss_grounding_ce_2: 0.11336/0.25448, loss_mask_ce_3: 0.68708/0.77244, loss_mask_bce_3: 0.52223/0.30370, loss_mask_dice_3: 0.96629/1.02732, loss_spatial_bce_3: 0.10914/0.08805, loss_spatial_dice_3: 0.17555/0.18547, loss_spatial_ce_3: 0.01224/0.06954, loss_grounding_bce_3: 0.08083/0.08137, loss_grounding_dice_3: 0.05925/0.15115, loss_grounding_ce_3: 0.16092/0.25471, loss_mask_ce_4: 0.81051/0.77811, loss_mask_bce_4: 0.53930/0.30615, loss_mask_dice_4: 0.98429/1.04622, loss_spatial_bce_4: 0.10387/0.09007, loss_spatial_dice_4: 0.20240/0.19337, loss_spatial_ce_4: 0.03860/0.08263, loss_grounding_bce_4: 0.08180/0.08201, loss_grounding_dice_4: 0.05998/0.15369, loss_grounding_ce_4: 0.08283/0.25953, loss_mask_ce_5: 0.88888/0.80183, loss_mask_bce_5: 0.53553/0.30802, loss_mask_dice_5: 1.10601/1.05372, loss_spatial_bce_5: 0.10287/0.09220, loss_spatial_dice_5: 0.16050/0.19618, loss_spatial_ce_5: 0.05053/0.09503, loss_grounding_bce_5: 0.08554/0.08227, loss_grounding_dice_5: 0.05573/0.15440, loss_grounding_ce_5: 0.20129/0.27805, loss_mask_ce_6: 1.04856/0.82861, loss_mask_bce_6: 0.53766/0.31002, loss_mask_dice_6: 1.29814/1.05693, loss_spatial_bce_6: 0.11804/0.09726, loss_spatial_dice_6: 0.21331/0.19849, loss_spatial_ce_6: 0.09933/0.11929, loss_grounding_bce_6: 0.07680/0.08322, loss_grounding_dice_6: 0.05773/0.15500, loss_grounding_ce_6: 0.26283/0.28734, loss_mask_ce_7: 0.63075/0.88440, loss_mask_bce_7: 0.58924/0.31726, loss_mask_dice_7: 1.51989/1.10344, loss_spatial_bce_7: 0.12935/0.10723, loss_spatial_dice_7: 0.26688/0.22408, loss_spatial_ce_7: 0.17742/0.15743, loss_grounding_bce_7: 0.07792/0.08493, loss_grounding_dice_7: 0.04936/0.16067, loss_grounding_ce_7: 0.50810/0.32081, loss_mask_ce_8: 1.59918/1.02113, loss_mask_bce_8: 0.57060/0.33340, loss_mask_dice_8: 1.45490/1.18067, loss_spatial_bce_8: 0.27484/0.12494, loss_spatial_dice_8: 0.41022/0.26004, loss_spatial_ce_8: 0.15875/0.20663, loss_grounding_bce_8: 0.07676/0.08913, loss_grounding_dice_8: 0.05102/0.17037, loss_grounding_ce_8: 1.27457/0.42234, loss_mask_ce_9: 3.66213/3.48060, loss_mask_bce_9: 0.58543/0.36043, loss_mask_dice_9: 1.91582/1.76256, loss_spatial_bce_9: 0.47732/0.35527, loss_spatial_dice_9: 0.92217/0.79387, loss_spatial_ce_9: 1.76642/1.39305, loss_grounding_bce_9: 0.07052/0.10112, loss_grounding_dice_9: 0.04103/0.24281, loss_grounding_ce_9: 2.10328/0.67778] items per batch[64] items per second[0.37] total items[3545600] mini batches[ 55400] memory[4999] epoch remaining[0:36:41] INFO:trainer.default_trainer:epochs[ 30] optim steps[55500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57645/0.76055, loss_mask_bce_0: 0.06128/0.30119, loss_mask_dice_0: 0.49385/1.02421, loss_spatial_bce_0: 0.02606/0.08571, loss_spatial_dice_0: 0.21156/0.18125, loss_spatial_ce_0: 0.08665/0.05872, loss_grounding_bce_0: 0.02033/0.08079, loss_grounding_dice_0: 0.05986/0.15080, loss_grounding_ce_0: 0.03611/0.24954, loss_mask_ce_1: 0.62243/0.76118, loss_mask_bce_1: 0.06200/0.30208, loss_mask_dice_1: 0.55218/1.02832, loss_spatial_bce_1: 0.02397/0.08598, loss_spatial_dice_1: 0.19966/0.18388, loss_spatial_ce_1: 0.11643/0.06261, loss_grounding_bce_1: 0.02009/0.08097, loss_grounding_dice_1: 0.06711/0.15154, loss_grounding_ce_1: 0.03781/0.25108, loss_mask_ce_2: 0.91682/0.76928, loss_mask_bce_2: 0.05367/0.30225, loss_mask_dice_2: 0.51414/1.02925, loss_spatial_bce_2: 0.02440/0.08602, loss_spatial_dice_2: 0.20269/0.18423, loss_spatial_ce_2: 0.13244/0.06487, loss_grounding_bce_2: 0.02018/0.08097, loss_grounding_dice_2: 0.06209/0.15146, loss_grounding_ce_2: 0.06186/0.25451, loss_mask_ce_3: 0.82178/0.77246, loss_mask_bce_3: 0.05532/0.30371, loss_mask_dice_3: 0.53331/1.02703, loss_spatial_bce_3: 0.02182/0.08807, loss_spatial_dice_3: 0.21215/0.18548, loss_spatial_ce_3: 0.12214/0.06954, loss_grounding_bce_3: 0.01837/0.08137, loss_grounding_dice_3: 0.05964/0.15114, loss_grounding_ce_3: 0.04523/0.25475, loss_mask_ce_4: 0.99798/0.77810, loss_mask_bce_4: 0.05328/0.30615, loss_mask_dice_4: 0.51330/1.04594, loss_spatial_bce_4: 0.02263/0.09009, loss_spatial_dice_4: 0.22802/0.19337, loss_spatial_ce_4: 0.17206/0.08263, loss_grounding_bce_4: 0.01943/0.08200, loss_grounding_dice_4: 0.06935/0.15367, loss_grounding_ce_4: 0.04277/0.25951, loss_mask_ce_5: 0.98314/0.80182, loss_mask_bce_5: 0.05626/0.30802, loss_mask_dice_5: 0.53805/1.05341, loss_spatial_bce_5: 0.02723/0.09221, loss_spatial_dice_5: 0.25696/0.19617, loss_spatial_ce_5: 0.18047/0.09502, loss_grounding_bce_5: 0.01975/0.08226, loss_grounding_dice_5: 0.06912/0.15437, loss_grounding_ce_5: 0.07809/0.27808, loss_mask_ce_6: 0.79001/0.82863, loss_mask_bce_6: 0.05610/0.31002, loss_mask_dice_6: 0.54065/1.05663, loss_spatial_bce_6: 0.02530/0.09728, loss_spatial_dice_6: 0.23775/0.19848, loss_spatial_ce_6: 0.30799/0.11931, loss_grounding_bce_6: 0.02356/0.08321, loss_grounding_dice_6: 0.07094/0.15499, loss_grounding_ce_6: 0.12827/0.28739, loss_mask_ce_7: 1.01652/0.88446, loss_mask_bce_7: 0.05400/0.31725, loss_mask_dice_7: 0.49541/1.10315, loss_spatial_bce_7: 0.02184/0.10724, loss_spatial_dice_7: 0.28006/0.22408, loss_spatial_ce_7: 0.18544/0.15745, loss_grounding_bce_7: 0.02056/0.08492, loss_grounding_dice_7: 0.08022/0.16064, loss_grounding_ce_7: 0.15462/0.32083, loss_mask_ce_8: 1.43041/1.02115, loss_mask_bce_8: 0.05332/0.33339, loss_mask_dice_8: 0.49297/1.18028, loss_spatial_bce_8: 0.02572/0.12495, loss_spatial_dice_8: 0.33882/0.26002, loss_spatial_ce_8: 0.40944/0.20663, loss_grounding_bce_8: 0.02174/0.08912, loss_grounding_dice_8: 0.06989/0.17033, loss_grounding_ce_8: 0.25201/0.42228, loss_mask_ce_9: 2.62236/3.48031, loss_mask_bce_9: 0.04804/0.36040, loss_mask_dice_9: 0.74311/1.76197, loss_spatial_bce_9: 0.12373/0.35531, loss_spatial_dice_9: 0.77557/0.79384, loss_spatial_ce_9: 0.88668/1.39298, loss_grounding_bce_9: 0.02423/0.10110, loss_grounding_dice_9: 0.14186/0.24276, loss_grounding_ce_9: 0.25149/0.67760] items per batch[64] items per second[0.37] total items[3552000] mini batches[ 55500] memory[4999] epoch remaining[0:33:33] INFO:trainer.default_trainer:epochs[ 30] optim steps[55600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.74661/0.76052, loss_mask_bce_0: 0.05928/0.30118, loss_mask_dice_0: 0.20856/1.02405, loss_spatial_bce_0: 0.05975/0.08570, loss_spatial_dice_0: 0.20906/0.18123, loss_spatial_ce_0: 0.09412/0.05874, loss_grounding_bce_0: 0.04994/0.08077, loss_grounding_dice_0: 0.16723/0.15078, loss_grounding_ce_0: 0.29591/0.24958, loss_mask_ce_1: 0.86273/0.76119, loss_mask_bce_1: 0.06446/0.30207, loss_mask_dice_1: 0.25211/1.02816, loss_spatial_bce_1: 0.06500/0.08597, loss_spatial_dice_1: 0.21617/0.18386, loss_spatial_ce_1: 0.02671/0.06263, loss_grounding_bce_1: 0.05872/0.08095, loss_grounding_dice_1: 0.20981/0.15153, loss_grounding_ce_1: 0.32828/0.25112, loss_mask_ce_2: 0.89168/0.76929, loss_mask_bce_2: 0.05718/0.30225, loss_mask_dice_2: 0.24375/1.02908, loss_spatial_bce_2: 0.05731/0.08601, loss_spatial_dice_2: 0.21027/0.18421, loss_spatial_ce_2: 0.06037/0.06489, loss_grounding_bce_2: 0.04471/0.08095, loss_grounding_dice_2: 0.16774/0.15144, loss_grounding_ce_2: 0.23247/0.25456, loss_mask_ce_3: 0.75021/0.77247, loss_mask_bce_3: 0.05905/0.30369, loss_mask_dice_3: 0.20629/1.02684, loss_spatial_bce_3: 0.05375/0.08806, loss_spatial_dice_3: 0.19367/0.18547, loss_spatial_ce_3: 0.19924/0.06957, loss_grounding_bce_3: 0.05187/0.08135, loss_grounding_dice_3: 0.19273/0.15112, loss_grounding_ce_3: 0.28756/0.25480, loss_mask_ce_4: 0.90642/0.77810, loss_mask_bce_4: 0.05667/0.30616, loss_mask_dice_4: 0.23412/1.04577, loss_spatial_bce_4: 0.05407/0.09008, loss_spatial_dice_4: 0.23114/0.19335, loss_spatial_ce_4: 0.05778/0.08262, loss_grounding_bce_4: 0.04830/0.08198, loss_grounding_dice_4: 0.21187/0.15366, loss_grounding_ce_4: 0.18257/0.25954, loss_mask_ce_5: 0.91425/0.80186, loss_mask_bce_5: 0.04937/0.30801, loss_mask_dice_5: 0.19628/1.05320, loss_spatial_bce_5: 0.05544/0.09220, loss_spatial_dice_5: 0.22801/0.19615, loss_spatial_ce_5: 0.19829/0.09500, loss_grounding_bce_5: 0.03702/0.08224, loss_grounding_dice_5: 0.13494/0.15436, loss_grounding_ce_5: 0.30987/0.27808, loss_mask_ce_6: 0.68253/0.82864, loss_mask_bce_6: 0.05678/0.31002, loss_mask_dice_6: 0.20037/1.05651, loss_spatial_bce_6: 0.04865/0.09726, loss_spatial_dice_6: 0.19503/0.19847, loss_spatial_ce_6: 0.31416/0.11933, loss_grounding_bce_6: 0.04412/0.08319, loss_grounding_dice_6: 0.18452/0.15497, loss_grounding_ce_6: 0.32044/0.28742, loss_mask_ce_7: 0.93115/0.88448, loss_mask_bce_7: 0.05515/0.31726, loss_mask_dice_7: 0.28614/1.10295, loss_spatial_bce_7: 0.15268/0.10723, loss_spatial_dice_7: 0.32069/0.22407, loss_spatial_ce_7: 0.12782/0.15745, loss_grounding_bce_7: 0.04189/0.08489, loss_grounding_dice_7: 0.24405/0.16062, loss_grounding_ce_7: 0.29717/0.32084, loss_mask_ce_8: 1.12718/1.02120, loss_mask_bce_8: 0.08337/0.33339, loss_mask_dice_8: 0.35784/1.18011, loss_spatial_bce_8: 0.10044/0.12494, loss_spatial_dice_8: 0.41106/0.26000, loss_spatial_ce_8: 0.55336/0.20660, loss_grounding_bce_8: 0.07140/0.08911, loss_grounding_dice_8: 0.27981/0.17032, loss_grounding_ce_8: 0.33954/0.42223, loss_mask_ce_9: 1.92506/3.48042, loss_mask_bce_9: 0.05713/0.36042, loss_mask_dice_9: 0.44905/1.76192, loss_spatial_bce_9: 0.14335/0.35528, loss_spatial_dice_9: 0.60231/0.79384, loss_spatial_ce_9: 1.51173/1.39297, loss_grounding_bce_9: 0.04579/0.10108, loss_grounding_dice_9: 0.34678/0.24275, loss_grounding_ce_9: 0.19590/0.67749] items per batch[64] items per second[0.37] total items[3558400] mini batches[ 55600] memory[4999] epoch remaining[0:30:31] INFO:trainer.default_trainer:epochs[ 30] optim steps[55700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24509/0.76049, loss_mask_bce_0: 0.04457/0.30112, loss_mask_dice_0: 0.72128/1.02419, loss_spatial_bce_0: 0.02252/0.08567, loss_spatial_dice_0: 0.16652/0.18124, loss_spatial_ce_0: 0.00041/0.05873, loss_grounding_bce_0: 0.03239/0.08078, loss_grounding_dice_0: 0.20626/0.15082, loss_grounding_ce_0: 0.00206/0.24954, loss_mask_ce_1: 0.32679/0.76120, loss_mask_bce_1: 0.04116/0.30199, loss_mask_dice_1: 0.61229/1.02828, loss_spatial_bce_1: 0.02285/0.08594, loss_spatial_dice_1: 0.20910/0.18387, loss_spatial_ce_1: 0.00081/0.06263, loss_grounding_bce_1: 0.02224/0.08096, loss_grounding_dice_1: 0.14310/0.15156, loss_grounding_ce_1: 0.00130/0.25103, loss_mask_ce_2: 0.32898/0.76928, loss_mask_bce_2: 0.04276/0.30217, loss_mask_dice_2: 0.92853/1.02921, loss_spatial_bce_2: 0.01999/0.08598, loss_spatial_dice_2: 0.19169/0.18422, loss_spatial_ce_2: 0.00026/0.06488, loss_grounding_bce_2: 0.03182/0.08095, loss_grounding_dice_2: 0.18387/0.15147, loss_grounding_ce_2: 0.00144/0.25449, loss_mask_ce_3: 0.32575/0.77244, loss_mask_bce_3: 0.04628/0.30361, loss_mask_dice_3: 0.90338/1.02695, loss_spatial_bce_3: 0.02141/0.08803, loss_spatial_dice_3: 0.20445/0.18548, loss_spatial_ce_3: 0.00053/0.06956, loss_grounding_bce_3: 0.03344/0.08136, loss_grounding_dice_3: 0.17278/0.15115, loss_grounding_ce_3: 0.00105/0.25475, loss_mask_ce_4: 0.24264/0.77810, loss_mask_bce_4: 0.05623/0.30607, loss_mask_dice_4: 0.78789/1.04588, loss_spatial_bce_4: 0.02196/0.09005, loss_spatial_dice_4: 0.19092/0.19336, loss_spatial_ce_4: 0.00087/0.08260, loss_grounding_bce_4: 0.03342/0.08199, loss_grounding_dice_4: 0.16266/0.15369, loss_grounding_ce_4: 0.00219/0.25947, loss_mask_ce_5: 0.24338/0.80190, loss_mask_bce_5: 0.05743/0.30793, loss_mask_dice_5: 0.60901/1.05328, loss_spatial_bce_5: 0.02141/0.09217, loss_spatial_dice_5: 0.17512/0.19618, loss_spatial_ce_5: 0.00604/0.09499, loss_grounding_bce_5: 0.02173/0.08226, loss_grounding_dice_5: 0.13870/0.15440, loss_grounding_ce_5: 0.00391/0.27802, loss_mask_ce_6: 0.28564/0.82867, loss_mask_bce_6: 0.05993/0.30994, loss_mask_dice_6: 0.54673/1.05661, loss_spatial_bce_6: 0.02185/0.09724, loss_spatial_dice_6: 0.23532/0.19848, loss_spatial_ce_6: 0.00413/0.11936, loss_grounding_bce_6: 0.03102/0.08319, loss_grounding_dice_6: 0.20453/0.15499, loss_grounding_ce_6: 0.00108/0.28741, loss_mask_ce_7: 0.29814/0.88455, loss_mask_bce_7: 0.05366/0.31717, loss_mask_dice_7: 0.52669/1.10312, loss_spatial_bce_7: 0.02523/0.10720, loss_spatial_dice_7: 0.15363/0.22409, loss_spatial_ce_7: 0.03533/0.15745, loss_grounding_bce_7: 0.02963/0.08489, loss_grounding_dice_7: 0.15067/0.16065, loss_grounding_ce_7: 0.00024/0.32082, loss_mask_ce_8: 0.26901/1.02128, loss_mask_bce_8: 0.05155/0.33332, loss_mask_dice_8: 0.97682/1.18033, loss_spatial_bce_8: 0.02462/0.12490, loss_spatial_dice_8: 0.21331/0.26001, loss_spatial_ce_8: 0.01478/0.20657, loss_grounding_bce_8: 0.02031/0.08911, loss_grounding_dice_8: 0.13715/0.17034, loss_grounding_ce_8: 0.00838/0.42223, loss_mask_ce_9: 1.97506/3.48069, loss_mask_bce_9: 0.06129/0.36033, loss_mask_dice_9: 0.65475/1.76204, loss_spatial_bce_9: 0.13209/0.35522, loss_spatial_dice_9: 0.69305/0.79386, loss_spatial_ce_9: 1.49992/1.39308, loss_grounding_bce_9: 0.01818/0.10109, loss_grounding_dice_9: 0.17702/0.24281, loss_grounding_ce_9: 0.52260/0.67765] items per batch[64] items per second[0.36] total items[3564800] mini batches[ 55700] memory[4999] epoch remaining[0:27:37] INFO:trainer.default_trainer:epochs[ 30] optim steps[55800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.61277/0.76045, loss_mask_bce_0: 0.24988/0.30110, loss_mask_dice_0: 2.57211/1.02427, loss_spatial_bce_0: 0.02145/0.08566, loss_spatial_dice_0: 0.39013/0.18120, loss_spatial_ce_0: 0.19774/0.05871, loss_grounding_bce_0: 0.01826/0.08078, loss_grounding_dice_0: 0.12529/0.15081, loss_grounding_ce_0: 0.00708/0.24949, loss_mask_ce_1: 1.62435/0.76116, loss_mask_bce_1: 0.23926/0.30198, loss_mask_dice_1: 2.92730/1.02839, loss_spatial_bce_1: 0.01868/0.08594, loss_spatial_dice_1: 0.34751/0.18383, loss_spatial_ce_1: 0.01183/0.06258, loss_grounding_bce_1: 0.01938/0.08095, loss_grounding_dice_1: 0.14162/0.15155, loss_grounding_ce_1: 0.00650/0.25095, loss_mask_ce_2: 1.64656/0.76924, loss_mask_bce_2: 0.24768/0.30216, loss_mask_dice_2: 2.71185/1.02933, loss_spatial_bce_2: 0.01994/0.08598, loss_spatial_dice_2: 0.33599/0.18418, loss_spatial_ce_2: 0.22851/0.06485, loss_grounding_bce_2: 0.02499/0.08095, loss_grounding_dice_2: 0.16696/0.15146, loss_grounding_ce_2: 0.00423/0.25440, loss_mask_ce_3: 1.37189/0.77239, loss_mask_bce_3: 0.22881/0.30360, loss_mask_dice_3: 3.07427/1.02707, loss_spatial_bce_3: 0.02521/0.08804, loss_spatial_dice_3: 0.33173/0.18544, loss_spatial_ce_3: 0.03657/0.06952, loss_grounding_bce_3: 0.01813/0.08136, loss_grounding_dice_3: 0.16791/0.15114, loss_grounding_ce_3: 0.00471/0.25466, loss_mask_ce_4: 1.59445/0.77814, loss_mask_bce_4: 0.24712/0.30606, loss_mask_dice_4: 3.06604/1.04598, loss_spatial_bce_4: 0.02212/0.09005, loss_spatial_dice_4: 0.40338/0.19332, loss_spatial_ce_4: 0.36301/0.08259, loss_grounding_bce_4: 0.01893/0.08199, loss_grounding_dice_4: 0.16057/0.15367, loss_grounding_ce_4: 0.00248/0.25941, loss_mask_ce_5: 1.82446/0.80193, loss_mask_bce_5: 0.22466/0.30792, loss_mask_dice_5: 3.06517/1.05339, loss_spatial_bce_5: 0.02071/0.09217, loss_spatial_dice_5: 0.41222/0.19614, loss_spatial_ce_5: 0.15752/0.09498, loss_grounding_bce_5: 0.02074/0.08225, loss_grounding_dice_5: 0.17545/0.15440, loss_grounding_ce_5: 0.00079/0.27797, loss_mask_ce_6: 1.87440/0.82864, loss_mask_bce_6: 0.23010/0.30993, loss_mask_dice_6: 2.99612/1.05668, loss_spatial_bce_6: 0.01891/0.09724, loss_spatial_dice_6: 0.36353/0.19845, loss_spatial_ce_6: 0.38506/0.11934, loss_grounding_bce_6: 0.01941/0.08319, loss_grounding_dice_6: 0.16322/0.15498, loss_grounding_ce_6: 0.00189/0.28732, loss_mask_ce_7: 1.92726/0.88457, loss_mask_bce_7: 0.30443/0.31717, loss_mask_dice_7: 3.38238/1.10326, loss_spatial_bce_7: 0.03450/0.10719, loss_spatial_dice_7: 0.43809/0.22405, loss_spatial_ce_7: 0.25002/0.15741, loss_grounding_bce_7: 0.01951/0.08489, loss_grounding_dice_7: 0.12980/0.16064, loss_grounding_ce_7: 0.00065/0.32074, loss_mask_ce_8: 1.82244/1.02134, loss_mask_bce_8: 0.28294/0.33332, loss_mask_dice_8: 3.36626/1.18050, loss_spatial_bce_8: 0.05192/0.12489, loss_spatial_dice_8: 0.53536/0.25996, loss_spatial_ce_8: 0.07575/0.20649, loss_grounding_bce_8: 0.02473/0.08910, loss_grounding_dice_8: 0.18705/0.17035, loss_grounding_ce_8: 0.00212/0.42216, loss_mask_ce_9: 4.93472/3.48082, loss_mask_bce_9: 0.19407/0.36033, loss_mask_dice_9: 4.11350/1.76235, loss_spatial_bce_9: 0.06176/0.35523, loss_spatial_dice_9: 0.89800/0.79384, loss_spatial_ce_9: 2.27765/1.39298, loss_grounding_bce_9: 0.01827/0.10109, loss_grounding_dice_9: 0.15808/0.24278, loss_grounding_ce_9: 2.11123/0.67745] items per batch[64] items per second[0.37] total items[3571200] mini batches[ 55800] memory[4999] epoch remaining[0:24:38] INFO:trainer.default_trainer:epochs[ 30] optim steps[55900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06361/0.76045, loss_mask_bce_0: 0.05657/0.30111, loss_mask_dice_0: 0.43614/1.02417, loss_spatial_bce_0: 0.01834/0.08566, loss_spatial_dice_0: 0.12742/0.18121, loss_spatial_ce_0: 0.00028/0.05868, loss_grounding_bce_0: 0.01546/0.08077, loss_grounding_dice_0: 0.07214/0.15082, loss_grounding_ce_0: 0.00787/0.24941, loss_mask_ce_1: 0.30601/0.76113, loss_mask_bce_1: 0.05685/0.30198, loss_mask_dice_1: 0.57192/1.02827, loss_spatial_bce_1: 0.01797/0.08594, loss_spatial_dice_1: 0.11159/0.18383, loss_spatial_ce_1: 0.00060/0.06257, loss_grounding_bce_1: 0.01901/0.08095, loss_grounding_dice_1: 0.08511/0.15157, loss_grounding_ce_1: 0.00642/0.25085, loss_mask_ce_2: 0.50922/0.76918, loss_mask_bce_2: 0.06256/0.30217, loss_mask_dice_2: 0.58017/1.02920, loss_spatial_bce_2: 0.01973/0.08598, loss_spatial_dice_2: 0.12422/0.18418, loss_spatial_ce_2: 0.00052/0.06483, loss_grounding_bce_2: 0.01888/0.08095, loss_grounding_dice_2: 0.08608/0.15147, loss_grounding_ce_2: 0.00375/0.25428, loss_mask_ce_3: 0.34066/0.77234, loss_mask_bce_3: 0.06386/0.30361, loss_mask_dice_3: 0.63215/1.02696, loss_spatial_bce_3: 0.01748/0.08804, loss_spatial_dice_3: 0.12037/0.18545, loss_spatial_ce_3: 0.00011/0.06951, loss_grounding_bce_3: 0.01762/0.08135, loss_grounding_dice_3: 0.07749/0.15116, loss_grounding_ce_3: 0.00369/0.25455, loss_mask_ce_4: 0.49521/0.77813, loss_mask_bce_4: 0.06331/0.30605, loss_mask_dice_4: 0.62365/1.04588, loss_spatial_bce_4: 0.01759/0.09005, loss_spatial_dice_4: 0.11545/0.19333, loss_spatial_ce_4: 0.00022/0.08256, loss_grounding_bce_4: 0.01731/0.08198, loss_grounding_dice_4: 0.08399/0.15368, loss_grounding_ce_4: 0.00631/0.25930, loss_mask_ce_5: 1.12702/0.80193, loss_mask_bce_5: 0.06416/0.30791, loss_mask_dice_5: 0.45782/1.05329, loss_spatial_bce_5: 0.01757/0.09218, loss_spatial_dice_5: 0.12484/0.19615, loss_spatial_ce_5: 0.00045/0.09496, loss_grounding_bce_5: 0.01822/0.08225, loss_grounding_dice_5: 0.08263/0.15442, loss_grounding_ce_5: 0.00509/0.27783, loss_mask_ce_6: 1.22646/0.82863, loss_mask_bce_6: 0.07110/0.30993, loss_mask_dice_6: 0.47837/1.05655, loss_spatial_bce_6: 0.02183/0.09723, loss_spatial_dice_6: 0.14773/0.19846, loss_spatial_ce_6: 0.01390/0.11933, loss_grounding_bce_6: 0.01818/0.08319, loss_grounding_dice_6: 0.08654/0.15501, loss_grounding_ce_6: 0.01929/0.28722, loss_mask_ce_7: 1.39240/0.88459, loss_mask_bce_7: 0.07141/0.31718, loss_mask_dice_7: 0.47848/1.10312, loss_spatial_bce_7: 0.02065/0.10719, loss_spatial_dice_7: 0.13830/0.22404, loss_spatial_ce_7: 0.05324/0.15739, loss_grounding_bce_7: 0.01855/0.08489, loss_grounding_dice_7: 0.07736/0.16065, loss_grounding_ce_7: 0.01832/0.32065, loss_mask_ce_8: 0.69930/1.02127, loss_mask_bce_8: 0.06956/0.33332, loss_mask_dice_8: 0.63337/1.18038, loss_spatial_bce_8: 0.02681/0.12486, loss_spatial_dice_8: 0.20341/0.25994, loss_spatial_ce_8: 0.02255/0.20648, loss_grounding_bce_8: 0.01988/0.08910, loss_grounding_dice_8: 0.09607/0.17035, loss_grounding_ce_8: 0.00641/0.42203, loss_mask_ce_9: 2.91950/3.48043, loss_mask_bce_9: 0.06372/0.36031, loss_mask_dice_9: 0.63743/1.76245, loss_spatial_bce_9: 0.20962/0.35522, loss_spatial_dice_9: 0.87514/0.79383, loss_spatial_ce_9: 1.75624/1.39295, loss_grounding_bce_9: 0.01918/0.10108, loss_grounding_dice_9: 0.12022/0.24275, loss_grounding_ce_9: 0.02866/0.67731] items per batch[64] items per second[0.37] total items[3577600] mini batches[ 55900] memory[4999] epoch remaining[0:21:39] INFO:trainer.default_trainer:epochs[ 30] optim steps[56000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22241/0.76042, loss_mask_bce_0: 0.34533/0.30113, loss_mask_dice_0: 0.53485/1.02418, loss_spatial_bce_0: 0.08882/0.08564, loss_spatial_dice_0: 0.10743/0.18116, loss_spatial_ce_0: 0.00700/0.05865, loss_grounding_bce_0: 0.07039/0.08075, loss_grounding_dice_0: 0.06636/0.15081, loss_grounding_ce_0: 0.20500/0.24939, loss_mask_ce_1: 0.22394/0.76110, loss_mask_bce_1: 0.34738/0.30200, loss_mask_dice_1: 0.54159/1.02828, loss_spatial_bce_1: 0.08889/0.08591, loss_spatial_dice_1: 0.12982/0.18379, loss_spatial_ce_1: 0.00639/0.06253, loss_grounding_bce_1: 0.06836/0.08093, loss_grounding_dice_1: 0.06886/0.15156, loss_grounding_ce_1: 0.20292/0.25082, loss_mask_ce_2: 0.22007/0.76917, loss_mask_bce_2: 0.34824/0.30219, loss_mask_dice_2: 0.53563/1.02920, loss_spatial_bce_2: 0.08457/0.08596, loss_spatial_dice_2: 0.12667/0.18414, loss_spatial_ce_2: 0.00762/0.06481, loss_grounding_bce_2: 0.06985/0.08093, loss_grounding_dice_2: 0.07092/0.15147, loss_grounding_ce_2: 0.19474/0.25422, loss_mask_ce_3: 0.21928/0.77236, loss_mask_bce_3: 0.33882/0.30363, loss_mask_dice_3: 0.56067/1.02697, loss_spatial_bce_3: 0.08069/0.08802, loss_spatial_dice_3: 0.11156/0.18541, loss_spatial_ce_3: 0.02909/0.06949, loss_grounding_bce_3: 0.06862/0.08133, loss_grounding_dice_3: 0.06884/0.15115, loss_grounding_ce_3: 0.20620/0.25450, loss_mask_ce_4: 0.21507/0.77813, loss_mask_bce_4: 0.34368/0.30607, loss_mask_dice_4: 0.55470/1.04590, loss_spatial_bce_4: 0.07949/0.09002, loss_spatial_dice_4: 0.10955/0.19330, loss_spatial_ce_4: 0.03854/0.08254, loss_grounding_bce_4: 0.07315/0.08196, loss_grounding_dice_4: 0.07349/0.15367, loss_grounding_ce_4: 0.22288/0.25927, loss_mask_ce_5: 0.22400/0.80190, loss_mask_bce_5: 0.35357/0.30793, loss_mask_dice_5: 0.54025/1.05331, loss_spatial_bce_5: 0.08443/0.09215, loss_spatial_dice_5: 0.11095/0.19613, loss_spatial_ce_5: 0.03087/0.09495, loss_grounding_bce_5: 0.07080/0.08223, loss_grounding_dice_5: 0.07068/0.15442, loss_grounding_ce_5: 0.21949/0.27778, loss_mask_ce_6: 0.19207/0.82857, loss_mask_bce_6: 0.35886/0.30996, loss_mask_dice_6: 0.52053/1.05660, loss_spatial_bce_6: 0.10284/0.09720, loss_spatial_dice_6: 0.11734/0.19843, loss_spatial_ce_6: 0.04499/0.11933, loss_grounding_bce_6: 0.06984/0.08316, loss_grounding_dice_6: 0.07315/0.15500, loss_grounding_ce_6: 0.21991/0.28720, loss_mask_ce_7: 0.27281/0.88459, loss_mask_bce_7: 0.35173/0.31719, loss_mask_dice_7: 0.51885/1.10315, loss_spatial_bce_7: 0.09883/0.10718, loss_spatial_dice_7: 0.12330/0.22402, loss_spatial_ce_7: 0.09908/0.15732, loss_grounding_bce_7: 0.07050/0.08487, loss_grounding_dice_7: 0.07337/0.16066, loss_grounding_ce_7: 0.24659/0.32060, loss_mask_ce_8: 0.39357/1.02125, loss_mask_bce_8: 0.38271/0.33334, loss_mask_dice_8: 0.55033/1.18039, loss_spatial_bce_8: 0.10529/0.12485, loss_spatial_dice_8: 0.15702/0.25992, loss_spatial_ce_8: 0.15963/0.20643, loss_grounding_bce_8: 0.07486/0.08908, loss_grounding_dice_8: 0.07989/0.17037, loss_grounding_ce_8: 0.24460/0.42189, loss_mask_ce_9: 3.49928/3.48050, loss_mask_bce_9: 0.43106/0.36032, loss_mask_dice_9: 0.85542/1.76251, loss_spatial_bce_9: 0.32194/0.35522, loss_spatial_dice_9: 0.83095/0.79383, loss_spatial_ce_9: 1.48218/1.39296, loss_grounding_bce_9: 0.09929/0.10105, loss_grounding_dice_9: 0.10017/0.24276, loss_grounding_ce_9: 0.31934/0.67716] items per batch[64] items per second[0.36] total items[3584000] mini batches[ 56000] memory[4999] epoch remaining[0:18:42] INFO:trainer.default_trainer:epochs[ 30] optim steps[56100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01914/0.76053, loss_mask_bce_0: 0.16663/0.30110, loss_mask_dice_0: 0.14634/1.02423, loss_spatial_bce_0: 0.11118/0.08562, loss_spatial_dice_0: 0.09846/0.18115, loss_spatial_ce_0: 0.00015/0.05863, loss_grounding_bce_0: 0.16332/0.08075, loss_grounding_dice_0: 0.13950/0.15084, loss_grounding_ce_0: 0.00065/0.24940, loss_mask_ce_1: 0.02684/0.76120, loss_mask_bce_1: 0.16344/0.30197, loss_mask_dice_1: 0.14285/1.02833, loss_spatial_bce_1: 0.11122/0.08590, loss_spatial_dice_1: 0.10411/0.18378, loss_spatial_ce_1: 0.00007/0.06251, loss_grounding_bce_1: 0.16503/0.08093, loss_grounding_dice_1: 0.14249/0.15159, loss_grounding_ce_1: 0.00147/0.25086, loss_mask_ce_2: 0.02424/0.76924, loss_mask_bce_2: 0.16271/0.30215, loss_mask_dice_2: 0.14153/1.02925, loss_spatial_bce_2: 0.11147/0.08594, loss_spatial_dice_2: 0.10911/0.18414, loss_spatial_ce_2: 0.00007/0.06478, loss_grounding_bce_2: 0.16271/0.08093, loss_grounding_dice_2: 0.13848/0.15151, loss_grounding_ce_2: 0.00107/0.25425, loss_mask_ce_3: 0.02509/0.77244, loss_mask_bce_3: 0.15874/0.30359, loss_mask_dice_3: 0.14585/1.02702, loss_spatial_bce_3: 0.11264/0.08800, loss_spatial_dice_3: 0.10202/0.18541, loss_spatial_ce_3: 0.00056/0.06947, loss_grounding_bce_3: 0.16445/0.08134, loss_grounding_dice_3: 0.14782/0.15117, loss_grounding_ce_3: 0.00072/0.25453, loss_mask_ce_4: 0.02164/0.77827, loss_mask_bce_4: 0.16807/0.30603, loss_mask_dice_4: 0.14960/1.04593, loss_spatial_bce_4: 0.12075/0.09001, loss_spatial_dice_4: 0.10732/0.19330, loss_spatial_ce_4: 0.00036/0.08253, loss_grounding_bce_4: 0.16400/0.08197, loss_grounding_dice_4: 0.14195/0.15372, loss_grounding_ce_4: 0.00067/0.25929, loss_mask_ce_5: 0.03426/0.80198, loss_mask_bce_5: 0.16264/0.30790, loss_mask_dice_5: 0.15195/1.05341, loss_spatial_bce_5: 0.10857/0.09214, loss_spatial_dice_5: 0.10568/0.19614, loss_spatial_ce_5: 0.00061/0.09493, loss_grounding_bce_5: 0.15835/0.08223, loss_grounding_dice_5: 0.14421/0.15445, loss_grounding_ce_5: 0.00069/0.27781, loss_mask_ce_6: 0.02912/0.82869, loss_mask_bce_6: 0.16673/0.30991, loss_mask_dice_6: 0.16239/1.05664, loss_spatial_bce_6: 0.10792/0.09718, loss_spatial_dice_6: 0.11181/0.19844, loss_spatial_ce_6: 0.00267/0.11931, loss_grounding_bce_6: 0.16166/0.08316, loss_grounding_dice_6: 0.15144/0.15503, loss_grounding_ce_6: 0.00063/0.28726, loss_mask_ce_7: 0.04797/0.88466, loss_mask_bce_7: 0.16097/0.31716, loss_mask_dice_7: 0.15869/1.10318, loss_spatial_bce_7: 0.12069/0.10716, loss_spatial_dice_7: 0.10530/0.22403, loss_spatial_ce_7: 0.05596/0.15731, loss_grounding_bce_7: 0.15819/0.08486, loss_grounding_dice_7: 0.15218/0.16068, loss_grounding_ce_7: 0.00081/0.32063, loss_mask_ce_8: 0.06747/1.02126, loss_mask_bce_8: 0.16700/0.33331, loss_mask_dice_8: 0.15625/1.18038, loss_spatial_bce_8: 0.13418/0.12482, loss_spatial_dice_8: 0.12278/0.25992, loss_spatial_ce_8: 0.06295/0.20641, loss_grounding_bce_8: 0.15626/0.08908, loss_grounding_dice_8: 0.14321/0.17039, loss_grounding_ce_8: 0.00523/0.42184, loss_mask_ce_9: 1.96840/3.48059, loss_mask_bce_9: 0.17303/0.36026, loss_mask_dice_9: 0.14431/1.76236, loss_spatial_bce_9: 0.47854/0.35521, loss_spatial_dice_9: 0.47700/0.79383, loss_spatial_ce_9: 0.40708/1.39300, loss_grounding_bce_9: 0.16553/0.10105, loss_grounding_dice_9: 0.12703/0.24277, loss_grounding_ce_9: 0.01926/0.67711] items per batch[64] items per second[0.36] total items[3590400] mini batches[ 56100] memory[4999] epoch remaining[0:15:46] INFO:trainer.default_trainer:epochs[ 30] optim steps[56200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06169/0.76046, loss_mask_bce_0: 1.12723/0.30111, loss_mask_dice_0: 2.78016/1.02446, loss_spatial_bce_0: 0.10013/0.08561, loss_spatial_dice_0: 0.17580/0.18112, loss_spatial_ce_0: 0.00670/0.05859, loss_grounding_bce_0: 0.27668/0.08075, loss_grounding_dice_0: 0.24577/0.15083, loss_grounding_ce_0: 0.00082/0.24944, loss_mask_ce_1: 1.17105/0.76116, loss_mask_bce_1: 0.99944/0.30198, loss_mask_dice_1: 2.64173/1.02857, loss_spatial_bce_1: 0.11306/0.08589, loss_spatial_dice_1: 0.18906/0.18375, loss_spatial_ce_1: 0.01148/0.06249, loss_grounding_bce_1: 0.17754/0.08093, loss_grounding_dice_1: 0.17786/0.15158, loss_grounding_ce_1: 0.00060/0.25096, loss_mask_ce_2: 1.21452/0.76919, loss_mask_bce_2: 1.01374/0.30217, loss_mask_dice_2: 2.66165/1.02949, loss_spatial_bce_2: 0.11246/0.08593, loss_spatial_dice_2: 0.18436/0.18411, loss_spatial_ce_2: 0.01626/0.06474, loss_grounding_bce_2: 0.17602/0.08093, loss_grounding_dice_2: 0.16494/0.15150, loss_grounding_ce_2: 0.00041/0.25428, loss_mask_ce_3: 1.16148/0.77236, loss_mask_bce_3: 1.05263/0.30361, loss_mask_dice_3: 2.60214/1.02725, loss_spatial_bce_3: 0.10776/0.08799, loss_spatial_dice_3: 0.17955/0.18538, loss_spatial_ce_3: 0.02089/0.06943, loss_grounding_bce_3: 0.25120/0.08134, loss_grounding_dice_3: 0.23289/0.15116, loss_grounding_ce_3: 0.00084/0.25458, loss_mask_ce_4: 1.18195/0.77819, loss_mask_bce_4: 1.00943/0.30605, loss_mask_dice_4: 2.74063/1.04619, loss_spatial_bce_4: 0.09925/0.09001, loss_spatial_dice_4: 0.23368/0.19327, loss_spatial_ce_4: 0.02620/0.08250, loss_grounding_bce_4: 0.17403/0.08196, loss_grounding_dice_4: 0.17873/0.15371, loss_grounding_ce_4: 0.00071/0.25935, loss_mask_ce_5: 1.52970/0.80193, loss_mask_bce_5: 0.91417/0.30791, loss_mask_dice_5: 2.58312/1.05364, loss_spatial_bce_5: 0.12129/0.09213, loss_spatial_dice_5: 0.20577/0.19612, loss_spatial_ce_5: 0.01843/0.09488, loss_grounding_bce_5: 0.15177/0.08224, loss_grounding_dice_5: 0.16738/0.15443, loss_grounding_ce_5: 0.00086/0.27781, loss_mask_ce_6: 1.70596/0.82864, loss_mask_bce_6: 0.97167/0.30993, loss_mask_dice_6: 2.83583/1.05694, loss_spatial_bce_6: 0.13422/0.09718, loss_spatial_dice_6: 0.29219/0.19842, loss_spatial_ce_6: 0.06059/0.11926, loss_grounding_bce_6: 0.13399/0.08316, loss_grounding_dice_6: 0.15972/0.15501, loss_grounding_ce_6: 0.00203/0.28732, loss_mask_ce_7: 1.33437/0.88461, loss_mask_bce_7: 1.01117/0.31717, loss_mask_dice_7: 2.96351/1.10347, loss_spatial_bce_7: 0.15378/0.10716, loss_spatial_dice_7: 0.34690/0.22401, loss_spatial_ce_7: 0.12116/0.15726, loss_grounding_bce_7: 0.13376/0.08486, loss_grounding_dice_7: 0.11682/0.16066, loss_grounding_ce_7: 0.01011/0.32060, loss_mask_ce_8: 1.84510/1.02115, loss_mask_bce_8: 1.24032/0.33332, loss_mask_dice_8: 3.02356/1.18068, loss_spatial_bce_8: 0.11118/0.12482, loss_spatial_dice_8: 0.32361/0.25990, loss_spatial_ce_8: 0.11976/0.20631, loss_grounding_bce_8: 0.09262/0.08908, loss_grounding_dice_8: 0.09503/0.17038, loss_grounding_ce_8: 0.00164/0.42180, loss_mask_ce_9: 3.99703/3.48058, loss_mask_bce_9: 1.12199/0.36029, loss_mask_dice_9: 5.14896/1.76286, loss_spatial_bce_9: 0.22708/0.35515, loss_spatial_dice_9: 0.83947/0.79382, loss_spatial_ce_9: 0.98782/1.39286, loss_grounding_bce_9: 0.12033/0.10105, loss_grounding_dice_9: 0.11117/0.24276, loss_grounding_ce_9: 0.13465/0.67701] items per batch[64] items per second[0.36] total items[3596800] mini batches[ 56200] memory[4999] epoch remaining[0:12:50] INFO:trainer.default_trainer:epochs[ 30] optim steps[56300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53079/0.76060, loss_mask_bce_0: 0.44061/0.30111, loss_mask_dice_0: 0.54439/1.02479, loss_spatial_bce_0: 0.11743/0.08558, loss_spatial_dice_0: 0.13207/0.18110, loss_spatial_ce_0: 0.10221/0.05856, loss_grounding_bce_0: 0.14638/0.08074, loss_grounding_dice_0: 0.09099/0.15085, loss_grounding_ce_0: 0.28325/0.24941, loss_mask_ce_1: 0.50456/0.76131, loss_mask_bce_1: 0.47988/0.30198, loss_mask_dice_1: 0.54592/1.02892, loss_spatial_bce_1: 0.16685/0.08586, loss_spatial_dice_1: 0.14614/0.18374, loss_spatial_ce_1: 0.09737/0.06246, loss_grounding_bce_1: 0.14984/0.08092, loss_grounding_dice_1: 0.09033/0.15159, loss_grounding_ce_1: 0.26229/0.25093, loss_mask_ce_2: 0.56639/0.76931, loss_mask_bce_2: 0.51230/0.30218, loss_mask_dice_2: 0.60204/1.02984, loss_spatial_bce_2: 0.12976/0.08590, loss_spatial_dice_2: 0.14449/0.18410, loss_spatial_ce_2: 0.11275/0.06469, loss_grounding_bce_2: 0.16315/0.08092, loss_grounding_dice_2: 0.09336/0.15152, loss_grounding_ce_2: 0.34770/0.25427, loss_mask_ce_3: 0.49095/0.77251, loss_mask_bce_3: 0.50807/0.30361, loss_mask_dice_3: 0.57781/1.02759, loss_spatial_bce_3: 0.11934/0.08796, loss_spatial_dice_3: 0.13965/0.18536, loss_spatial_ce_3: 0.10487/0.06939, loss_grounding_bce_3: 0.15994/0.08133, loss_grounding_dice_3: 0.09622/0.15118, loss_grounding_ce_3: 0.40116/0.25456, loss_mask_ce_4: 0.44423/0.77832, loss_mask_bce_4: 0.50672/0.30606, loss_mask_dice_4: 0.57805/1.04655, loss_spatial_bce_4: 0.15665/0.08997, loss_spatial_dice_4: 0.14159/0.19324, loss_spatial_ce_4: 0.13719/0.08249, loss_grounding_bce_4: 0.19729/0.08195, loss_grounding_dice_4: 0.09805/0.15372, loss_grounding_ce_4: 0.32944/0.25933, loss_mask_ce_5: 0.48469/0.80213, loss_mask_bce_5: 0.51930/0.30792, loss_mask_dice_5: 0.55436/1.05397, loss_spatial_bce_5: 0.14142/0.09210, loss_spatial_dice_5: 0.15213/0.19611, loss_spatial_ce_5: 0.16826/0.09486, loss_grounding_bce_5: 0.16126/0.08223, loss_grounding_dice_5: 0.08666/0.15445, loss_grounding_ce_5: 0.28937/0.27780, loss_mask_ce_6: 0.49418/0.82882, loss_mask_bce_6: 0.52093/0.30995, loss_mask_dice_6: 0.61360/1.05729, loss_spatial_bce_6: 0.24532/0.09714, loss_spatial_dice_6: 0.19358/0.19841, loss_spatial_ce_6: 0.17517/0.11921, loss_grounding_bce_6: 0.20459/0.08316, loss_grounding_dice_6: 0.14710/0.15503, loss_grounding_ce_6: 0.20785/0.28731, loss_mask_ce_7: 0.74936/0.88475, loss_mask_bce_7: 0.56900/0.31719, loss_mask_dice_7: 0.60413/1.10388, loss_spatial_bce_7: 0.16486/0.10713, loss_spatial_dice_7: 0.15852/0.22400, loss_spatial_ce_7: 0.27303/0.15723, loss_grounding_bce_7: 0.17256/0.08485, loss_grounding_dice_7: 0.11236/0.16067, loss_grounding_ce_7: 0.25621/0.32054, loss_mask_ce_8: 0.66068/1.02131, loss_mask_bce_8: 0.41434/0.33331, loss_mask_dice_8: 0.50813/1.18113, loss_spatial_bce_8: 0.16673/0.12478, loss_spatial_dice_8: 0.20147/0.25988, loss_spatial_ce_8: 0.22199/0.20626, loss_grounding_bce_8: 0.13885/0.08907, loss_grounding_dice_8: 0.08264/0.17040, loss_grounding_ce_8: 0.16574/0.42185, loss_mask_ce_9: 2.65136/3.48091, loss_mask_bce_9: 0.68469/0.36027, loss_mask_dice_9: 1.20832/1.76330, loss_spatial_bce_9: 0.43889/0.35510, loss_spatial_dice_9: 0.88690/0.79387, loss_spatial_ce_9: 1.16565/1.39302, loss_grounding_bce_9: 0.35140/0.10106, loss_grounding_dice_9: 0.26891/0.24283, loss_grounding_ce_9: 0.54689/0.67726] items per batch[64] items per second[0.37] total items[3603200] mini batches[ 56300] memory[4999] epoch remaining[0:09:53] INFO:trainer.default_trainer:epochs[ 30] optim steps[56400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.23375/0.76055, loss_mask_bce_0: 0.14268/0.30107, loss_mask_dice_0: 1.66409/1.02487, loss_spatial_bce_0: 0.01265/0.08556, loss_spatial_dice_0: 0.25051/0.18109, loss_spatial_ce_0: 0.04912/0.05853, loss_grounding_bce_0: 0.04097/0.08074, loss_grounding_dice_0: 0.34501/0.15086, loss_grounding_ce_0: 0.32157/0.24958, loss_mask_ce_1: 1.88901/0.76127, loss_mask_bce_1: 0.14534/0.30194, loss_mask_dice_1: 1.65367/1.02897, loss_spatial_bce_1: 0.01320/0.08584, loss_spatial_dice_1: 0.25536/0.18372, loss_spatial_ce_1: 0.02478/0.06243, loss_grounding_bce_1: 0.04464/0.08093, loss_grounding_dice_1: 0.35265/0.15161, loss_grounding_ce_1: 0.55431/0.25110, loss_mask_ce_2: 2.44581/0.76925, loss_mask_bce_2: 0.13047/0.30214, loss_mask_dice_2: 1.51956/1.02991, loss_spatial_bce_2: 0.01304/0.08588, loss_spatial_dice_2: 0.24838/0.18408, loss_spatial_ce_2: 0.03135/0.06466, loss_grounding_bce_2: 0.04790/0.08093, loss_grounding_dice_2: 0.38145/0.15153, loss_grounding_ce_2: 0.30146/0.25442, loss_mask_ce_3: 2.30180/0.77245, loss_mask_bce_3: 0.12869/0.30357, loss_mask_dice_3: 1.50733/1.02766, loss_spatial_bce_3: 0.01433/0.08795, loss_spatial_dice_3: 0.26066/0.18535, loss_spatial_ce_3: 0.03019/0.06936, loss_grounding_bce_3: 0.04549/0.08134, loss_grounding_dice_3: 0.35516/0.15120, loss_grounding_ce_3: 0.53798/0.25474, loss_mask_ce_4: 2.21763/0.77827, loss_mask_bce_4: 0.13999/0.30602, loss_mask_dice_4: 1.50404/1.04662, loss_spatial_bce_4: 0.01635/0.08996, loss_spatial_dice_4: 0.30640/0.19324, loss_spatial_ce_4: 0.04377/0.08246, loss_grounding_bce_4: 0.03776/0.08195, loss_grounding_dice_4: 0.38423/0.15376, loss_grounding_ce_4: 0.40462/0.25948, loss_mask_ce_5: 2.43404/0.80205, loss_mask_bce_5: 0.12229/0.30789, loss_mask_dice_5: 1.43538/1.05406, loss_spatial_bce_5: 0.02159/0.09209, loss_spatial_dice_5: 0.29394/0.19611, loss_spatial_ce_5: 0.07173/0.09485, loss_grounding_bce_5: 0.03714/0.08224, loss_grounding_dice_5: 0.37381/0.15447, loss_grounding_ce_5: 0.46384/0.27797, loss_mask_ce_6: 2.75282/0.82880, loss_mask_bce_6: 0.10016/0.30992, loss_mask_dice_6: 1.44058/1.05737, loss_spatial_bce_6: 0.01949/0.09713, loss_spatial_dice_6: 0.26783/0.19841, loss_spatial_ce_6: 0.12738/0.11920, loss_grounding_bce_6: 0.04767/0.08317, loss_grounding_dice_6: 0.41572/0.15506, loss_grounding_ce_6: 0.41932/0.28746, loss_mask_ce_7: 3.05884/0.88471, loss_mask_bce_7: 0.10002/0.31715, loss_mask_dice_7: 1.32583/1.10394, loss_spatial_bce_7: 0.02544/0.10712, loss_spatial_dice_7: 0.30423/0.22400, loss_spatial_ce_7: 0.18144/0.15722, loss_grounding_bce_7: 0.07963/0.08486, loss_grounding_dice_7: 0.42694/0.16070, loss_grounding_ce_7: 0.45491/0.32065, loss_mask_ce_8: 3.00688/1.02124, loss_mask_bce_8: 0.08636/0.33326, loss_mask_dice_8: 1.45842/1.18122, loss_spatial_bce_8: 0.01852/0.12475, loss_spatial_dice_8: 0.39131/0.25986, loss_spatial_ce_8: 0.25333/0.20619, loss_grounding_bce_8: 0.02392/0.08908, loss_grounding_dice_8: 0.33417/0.17045, loss_grounding_ce_8: 0.33627/0.42193, loss_mask_ce_9: 4.43379/3.48137, loss_mask_bce_9: 0.07112/0.36024, loss_mask_dice_9: 1.59215/1.76340, loss_spatial_bce_9: 0.03935/0.35505, loss_spatial_dice_9: 0.76213/0.79386, loss_spatial_ce_9: 1.18207/1.39314, loss_grounding_bce_9: 0.02605/0.10108, loss_grounding_dice_9: 0.45969/0.24289, loss_grounding_ce_9: 0.55697/0.67728] items per batch[64] items per second[0.36] total items[3609600] mini batches[ 56400] memory[4999] epoch remaining[0:06:57] INFO:trainer.default_trainer:epochs[ 30] optim steps[56500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.42816/0.76040, loss_mask_bce_0: 0.29062/0.30102, loss_mask_dice_0: 0.78987/1.02467, loss_spatial_bce_0: 0.06206/0.08555, loss_spatial_dice_0: 0.15092/0.18105, loss_spatial_ce_0: 0.02631/0.05849, loss_grounding_bce_0: 0.01115/0.08073, loss_grounding_dice_0: 0.04425/0.15086, loss_grounding_ce_0: 0.00158/0.24953, loss_mask_ce_1: 0.40050/0.76112, loss_mask_bce_1: 0.27112/0.30189, loss_mask_dice_1: 0.81171/1.02878, loss_spatial_bce_1: 0.05441/0.08583, loss_spatial_dice_1: 0.15074/0.18368, loss_spatial_ce_1: 0.01997/0.06240, loss_grounding_bce_1: 0.00995/0.08092, loss_grounding_dice_1: 0.03873/0.15160, loss_grounding_ce_1: 0.00412/0.25103, loss_mask_ce_2: 0.41671/0.76910, loss_mask_bce_2: 0.26446/0.30209, loss_mask_dice_2: 0.76425/1.02968, loss_spatial_bce_2: 0.05733/0.08587, loss_spatial_dice_2: 0.16256/0.18405, loss_spatial_ce_2: 0.03351/0.06462, loss_grounding_bce_2: 0.01026/0.08092, loss_grounding_dice_2: 0.04552/0.15153, loss_grounding_ce_2: 0.00351/0.25436, loss_mask_ce_3: 0.51946/0.77233, loss_mask_bce_3: 0.27537/0.30353, loss_mask_dice_3: 0.79048/1.02751, loss_spatial_bce_3: 0.05718/0.08793, loss_spatial_dice_3: 0.15105/0.18531, loss_spatial_ce_3: 0.03350/0.06934, loss_grounding_bce_3: 0.01024/0.08134, loss_grounding_dice_3: 0.04422/0.15120, loss_grounding_ce_3: 0.00400/0.25467, loss_mask_ce_4: 0.50056/0.77811, loss_mask_bce_4: 0.25903/0.30597, loss_mask_dice_4: 0.75737/1.04644, loss_spatial_bce_4: 0.06816/0.08994, loss_spatial_dice_4: 0.17096/0.19320, loss_spatial_ce_4: 0.01077/0.08246, loss_grounding_bce_4: 0.01023/0.08194, loss_grounding_dice_4: 0.04137/0.15375, loss_grounding_ce_4: 0.00177/0.25943, loss_mask_ce_5: 0.71999/0.80192, loss_mask_bce_5: 0.30118/0.30783, loss_mask_dice_5: 0.81310/1.05382, loss_spatial_bce_5: 0.06448/0.09207, loss_spatial_dice_5: 0.19495/0.19607, loss_spatial_ce_5: 0.00765/0.09485, loss_grounding_bce_5: 0.01237/0.08224, loss_grounding_dice_5: 0.04721/0.15447, loss_grounding_ce_5: 0.01478/0.27786, loss_mask_ce_6: 0.67619/0.82862, loss_mask_bce_6: 0.30193/0.30989, loss_mask_dice_6: 0.80858/1.05719, loss_spatial_bce_6: 0.07211/0.09712, loss_spatial_dice_6: 0.21904/0.19837, loss_spatial_ce_6: 0.01063/0.11921, loss_grounding_bce_6: 0.00881/0.08317, loss_grounding_dice_6: 0.04094/0.15505, loss_grounding_ce_6: 0.01747/0.28737, loss_mask_ce_7: 0.61574/0.88458, loss_mask_bce_7: 0.29097/0.31710, loss_mask_dice_7: 0.81342/1.10379, loss_spatial_bce_7: 0.07441/0.10710, loss_spatial_dice_7: 0.21661/0.22397, loss_spatial_ce_7: 0.01896/0.15723, loss_grounding_bce_7: 0.01295/0.08484, loss_grounding_dice_7: 0.05875/0.16068, loss_grounding_ce_7: 0.02874/0.32057, loss_mask_ce_8: 0.73376/1.02099, loss_mask_bce_8: 0.23069/0.33318, loss_mask_dice_8: 0.76406/1.18105, loss_spatial_bce_8: 0.07703/0.12471, loss_spatial_dice_8: 0.29470/0.25982, loss_spatial_ce_8: 0.13113/0.20616, loss_grounding_bce_8: 0.00984/0.08907, loss_grounding_dice_8: 0.04742/0.17045, loss_grounding_ce_8: 0.16818/0.42176, loss_mask_ce_9: 3.76882/3.48085, loss_mask_bce_9: 0.29154/0.36015, loss_mask_dice_9: 1.46756/1.76303, loss_spatial_bce_9: 0.16947/0.35501, loss_spatial_dice_9: 0.89534/0.79383, loss_spatial_ce_9: 1.54866/1.39313, loss_grounding_bce_9: 0.04699/0.10106, loss_grounding_dice_9: 0.19975/0.24286, loss_grounding_ce_9: 1.27739/0.67724] items per batch[64] items per second[0.37] total items[3616000] mini batches[ 56500] memory[4999] epoch remaining[0:04:01] INFO:trainer.default_trainer:epochs[ 30] optim steps[56600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.49455/0.76038, loss_mask_bce_0: 0.36513/0.30102, loss_mask_dice_0: 1.24111/1.02433, loss_spatial_bce_0: 0.08188/0.08557, loss_spatial_dice_0: 0.14092/0.18106, loss_spatial_ce_0: 0.00190/0.05847, loss_grounding_bce_0: 0.11637/0.08074, loss_grounding_dice_0: 0.09373/0.15086, loss_grounding_ce_0: 0.00413/0.24949, loss_mask_ce_1: 1.50284/0.76102, loss_mask_bce_1: 0.37787/0.30190, loss_mask_dice_1: 1.53176/1.02845, loss_spatial_bce_1: 0.07629/0.08586, loss_spatial_dice_1: 0.14717/0.18369, loss_spatial_ce_1: 0.00259/0.06238, loss_grounding_bce_1: 0.12401/0.08093, loss_grounding_dice_1: 0.10291/0.15163, loss_grounding_ce_1: 0.00324/0.25096, loss_mask_ce_2: 1.59925/0.76906, loss_mask_bce_2: 0.38357/0.30209, loss_mask_dice_2: 1.15646/1.02934, loss_spatial_bce_2: 0.07684/0.08590, loss_spatial_dice_2: 0.15506/0.18405, loss_spatial_ce_2: 0.00084/0.06461, loss_grounding_bce_2: 0.12465/0.08093, loss_grounding_dice_2: 0.10048/0.15153, loss_grounding_ce_2: 0.00456/0.25428, loss_mask_ce_3: 1.93702/0.77231, loss_mask_bce_3: 0.38191/0.30353, loss_mask_dice_3: 1.26775/1.02714, loss_spatial_bce_3: 0.07683/0.08796, loss_spatial_dice_3: 0.13764/0.18532, loss_spatial_ce_3: 0.00066/0.06935, loss_grounding_bce_3: 0.14370/0.08135, loss_grounding_dice_3: 0.12421/0.15119, loss_grounding_ce_3: 0.00591/0.25466, loss_mask_ce_4: 1.72982/0.77806, loss_mask_bce_4: 0.38458/0.30599, loss_mask_dice_4: 1.34432/1.04607, loss_spatial_bce_4: 0.07680/0.08998, loss_spatial_dice_4: 0.14466/0.19322, loss_spatial_ce_4: 0.00209/0.08246, loss_grounding_bce_4: 0.13520/0.08195, loss_grounding_dice_4: 0.13106/0.15375, loss_grounding_ce_4: 0.00730/0.25946, loss_mask_ce_5: 1.80616/0.80189, loss_mask_bce_5: 0.38532/0.30785, loss_mask_dice_5: 1.33891/1.05349, loss_spatial_bce_5: 0.07951/0.09211, loss_spatial_dice_5: 0.15023/0.19609, loss_spatial_ce_5: 0.01130/0.09485, loss_grounding_bce_5: 0.13694/0.08224, loss_grounding_dice_5: 0.13934/0.15447, loss_grounding_ce_5: 0.00269/0.27790, loss_mask_ce_6: 1.29655/0.82857, loss_mask_bce_6: 0.56435/0.30990, loss_mask_dice_6: 1.40117/1.05682, loss_spatial_bce_6: 0.08460/0.09715, loss_spatial_dice_6: 0.16790/0.19838, loss_spatial_ce_6: 0.00184/0.11921, loss_grounding_bce_6: 0.13418/0.08317, loss_grounding_dice_6: 0.11183/0.15505, loss_grounding_ce_6: 0.00967/0.28734, loss_mask_ce_7: 1.02505/0.88451, loss_mask_bce_7: 0.58449/0.31712, loss_mask_dice_7: 1.44218/1.10342, loss_spatial_bce_7: 0.09274/0.10715, loss_spatial_dice_7: 0.18319/0.22398, loss_spatial_ce_7: 0.01557/0.15726, loss_grounding_bce_7: 0.13080/0.08486, loss_grounding_dice_7: 0.12673/0.16069, loss_grounding_ce_7: 0.00372/0.32052, loss_mask_ce_8: 1.03488/1.02085, loss_mask_bce_8: 0.60248/0.33320, loss_mask_dice_8: 1.20031/1.18063, loss_spatial_bce_8: 0.08856/0.12473, loss_spatial_dice_8: 0.27828/0.25982, loss_spatial_ce_8: 0.08065/0.20615, loss_grounding_bce_8: 0.13992/0.08907, loss_grounding_dice_8: 0.19728/0.17043, loss_grounding_ce_8: 0.00500/0.42170, loss_mask_ce_9: 2.92990/3.48048, loss_mask_bce_9: 0.49243/0.36013, loss_mask_dice_9: 1.92917/1.76228, loss_spatial_bce_9: 0.37116/0.35500, loss_spatial_dice_9: 0.83508/0.79381, loss_spatial_ce_9: 1.15659/1.39296, loss_grounding_bce_9: 0.14177/0.10106, loss_grounding_dice_9: 0.12609/0.24283, loss_grounding_ce_9: 0.02167/0.67712] items per batch[64] items per second[0.36] total items[3622400] mini batches[ 56600] memory[4999] epoch remaining[0:01:05] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00056637. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0029 s/iter. Inference: 0.3830 s/iter. Eval: 0.0815 s/iter. Total: 0.4674 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3885 s/iter. Eval: 0.0786 s/iter. Total: 0.4698 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0027 s/iter. Inference: 0.3898 s/iter. Eval: 0.0738 s/iter. Total: 0.4665 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0028 s/iter. Inference: 0.3891 s/iter. Eval: 0.0730 s/iter. Total: 0.4650 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3885 s/iter. Eval: 0.0710 s/iter. Total: 0.4625 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 67/79. Dataloading: 0.0029 s/iter. Inference: 0.3893 s/iter. Eval: 0.0692 s/iter. Total: 0.4615 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/79. Dataloading: 0.0029 s/iter. Inference: 0.3870 s/iter. Eval: 0.0685 s/iter. Total: 0.4585 s/iter. ETA=0:00:00 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalhk74kc1t ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.589 | 83.037 | 66.136 | 133 | | Things | 61.750 | 84.044 | 72.959 | 80 | | Stuff | 46.290 | 81.517 | 55.837 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.50s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.80 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.07 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.18s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.80 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.44 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.695 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.376 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.608 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.760 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.757 | 69.452 | 49.476 | 26.215 | 49.926 | 67.673 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.873 | bicycle | 23.156 | car | 43.087 | | motorcycle | 41.393 | airplane | 60.997 | bus | 71.515 | | train | 74.897 | truck | 44.142 | boat | 31.799 | | traffic light | 28.084 | fire hydrant | 72.576 | stop sign | 69.539 | | parking meter | 53.335 | bench | 26.658 | bird | 33.348 | | cat | 76.781 | dog | 70.602 | horse | 50.801 | | sheep | 51.715 | cow | 56.464 | elephant | 66.821 | | bear | 79.784 | zebra | 65.860 | giraffe | 62.477 | | backpack | 24.176 | umbrella | 55.623 | handbag | 24.065 | | tie | 39.827 | suitcase | 51.201 | frisbee | 70.253 | | skis | 8.966 | snowboard | 35.218 | sports ball | 49.430 | | kite | 38.172 | baseball bat | 37.408 | baseball glove | 51.499 | | skateboard | 43.745 | surfboard | 45.337 | tennis racket | 63.465 | | bottle | 42.656 | wine glass | 37.571 | cup | 50.599 | | fork | 23.526 | knife | 24.036 | spoon | 21.635 | | bowl | 39.056 | banana | 22.344 | apple | 26.401 | | sandwich | 49.204 | orange | 32.173 | broccoli | 23.991 | | carrot | 23.305 | hot dog | 38.217 | pizza | 54.392 | | donut | 56.391 | cake | 48.616 | chair | 28.097 | | couch | 44.224 | potted plant | 23.182 | bed | 43.072 | | dining table | 16.148 | toilet | 70.018 | tv | 65.795 | | laptop | 70.607 | mouse | 63.215 | remote | 44.046 | | keyboard | 58.492 | cell phone | 45.817 | microwave | 66.308 | | oven | 35.404 | toaster | 55.899 | sink | 43.820 | | refrigerator | 70.449 | book | 14.694 | clock | 53.852 | | vase | 39.864 | scissors | 31.845 | teddy bear | 56.811 | | hair drier | 35.866 | toothbrush | 25.794 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.36174414398047, 'fwIoU': 71.46495792364222, 'IoU-person': 88.91709056360997, 'IoU-bicycle': 77.56911429579412, 'IoU-car': 73.03769692411076, 'IoU-motorcycle': 86.475543385945, 'IoU-airplane': 86.83932565850155, 'IoU-bus': 87.44273044274502, 'IoU-train': 88.5012129714048, 'IoU-truck': 69.99597465834077, 'IoU-boat': 74.75823553265391, 'IoU-traffic light': 78.75711669099194, 'IoU-fire hydrant': 93.33964011842626, 'IoU-stop sign': 84.85309354718157, 'IoU-parking meter': 84.88767418007154, 'IoU-bench': 64.421547417132, 'IoU-bird': 73.09980868461933, 'IoU-cat': 91.00841963382388, 'IoU-dog': 86.07379296918056, 'IoU-horse': 88.81171010367235, 'IoU-sheep': 80.4721982383168, 'IoU-cow': 86.06840405621624, 'IoU-elephant': 84.0016249105332, 'IoU-bear': 78.14250565905878, 'IoU-zebra': 85.9700079250056, 'IoU-giraffe': 87.64837428473876, 'IoU-backpack': 54.63015270722965, 'IoU-umbrella': 85.23874013664063, 'IoU-handbag': 48.12299372808534, 'IoU-tie': 76.18931121416104, 'IoU-suitcase': 80.24888941577836, 'IoU-frisbee': 84.69232605361854, 'IoU-skis': 58.71738806201158, 'IoU-snowboard': 71.31179813749657, 'IoU-sports ball': 80.15889570267777, 'IoU-kite': 79.44871889071996, 'IoU-baseball bat': 66.7747465817844, 'IoU-baseball glove': 78.81429898152837, 'IoU-skateboard': 86.0675838119407, 'IoU-surfboard': 86.2806568000524, 'IoU-tennis racket': 91.10086945971008, 'IoU-bottle': 71.02137500925647, 'IoU-wine glass': 82.65037317087021, 'IoU-cup': 71.06143772540739, 'IoU-fork': 68.65369811058464, 'IoU-knife': 63.81999635988594, 'IoU-spoon': 59.8791585072634, 'IoU-bowl': 57.19153546067277, 'IoU-banana': 83.97254925401987, 'IoU-apple': 58.1954447088226, 'IoU-sandwich': 68.9992344299607, 'IoU-orange': 79.03248292707816, 'IoU-broccoli': 69.39397775010605, 'IoU-carrot': 64.81659271772786, 'IoU-hot dog': 65.41988213903367, 'IoU-pizza': 80.04751847257748, 'IoU-donut': 64.42379405092402, 'IoU-cake': 78.7527904545158, 'IoU-chair': 62.984216815198224, 'IoU-couch': 72.69458573577208, 'IoU-potted plant': 40.44233820503604, 'IoU-bed': 76.88570480983905, 'IoU-dining table': 53.796419980248125, 'IoU-toilet': 82.79757230997687, 'IoU-tv': 74.32380356759805, 'IoU-laptop': 75.95668140302764, 'IoU-mouse': 72.13922884669046, 'IoU-remote': 67.3370459963681, 'IoU-keyboard': 65.77296977551865, 'IoU-cell phone': 65.14477096136528, 'IoU-microwave': 79.03958302522554, 'IoU-oven': 72.42748858730805, 'IoU-toaster': 85.03637682927612, 'IoU-sink': 69.9162653857832, 'IoU-refrigerator': 82.667134598894, 'IoU-book': 55.97598054570978, 'IoU-clock': 73.92566227551485, 'IoU-vase': 62.587487779484775, 'IoU-scissors': 67.3248907995373, 'IoU-teddy bear': 86.04156051159914, 'IoU-hair drier': 48.64357618153899, 'IoU-toothbrush': 75.2144958959269, 'IoU-banner': 36.29293660122681, 'IoU-blanket': 19.26091266941516, 'IoU-bridge': 42.626409467022754, 'IoU-cardboard': 52.138132865894825, 'IoU-counter': 30.939285440101273, 'IoU-curtain': 72.00799650817665, 'IoU-door-stuff': 47.17616125860567, 'IoU-floor-wood': 66.2390999753131, 'IoU-flower': 43.81682278701029, 'IoU-fruit': 48.07873369736678, 'IoU-gravel': 32.371734093135636, 'IoU-house': 23.51311526247436, 'IoU-light': 42.97633396278871, 'IoU-mirror-stuff': 65.42884542713652, 'IoU-net': 44.520006205446045, 'IoU-pillow': 26.674976245344634, 'IoU-platform': 29.96360091704662, 'IoU-playingfield': 70.75969057503342, 'IoU-railroad': 64.01320499562485, 'IoU-river': 51.531671693951665, 'IoU-road': 67.7898691139958, 'IoU-roof': 19.037625074097935, 'IoU-sand': 67.4449469741107, 'IoU-sea': 86.1961101146635, 'IoU-shelf': 40.14794017085463, 'IoU-snow': 92.20550748193834, 'IoU-stairs': 37.01705961485896, 'IoU-tent': 11.506944685035148, 'IoU-towel': 45.75900954296046, 'IoU-wall-brick': 49.85988089391812, 'IoU-wall-stone': 35.556998135183896, 'IoU-wall-tile': 70.77635745607151, 'IoU-wall-wood': 43.71494327208775, 'IoU-water-other': 21.495037961071393, 'IoU-window-blind': 49.05185689246728, 'IoU-window-other': 48.645606994084936, 'IoU-tree-merged': 81.13204331555576, 'IoU-fence-merged': 55.2141306691855, 'IoU-ceiling-merged': 68.37176316518088, 'IoU-sky-other-merged': 93.81002431364604, 'IoU-cabinet-merged': 64.70286542392931, 'IoU-table-merged': 39.17519676451029, 'IoU-floor-other-merged': 55.49270693570896, 'IoU-pavement-merged': 57.72544701556929, 'IoU-mountain-merged': 57.769641278751635, 'IoU-grass-merged': 71.4843077609877, 'IoU-dirt-merged': 46.187047692328825, 'IoU-paper-merged': 37.758196404427224, 'IoU-food-other-merged': 43.029931142460725, 'IoU-building-other-merged': 59.25814764581726, 'IoU-rock-merged': 63.80946844762298, 'IoU-wall-other-merged': 68.3715484507086, 'IoU-rug-merged': 67.98624509084325, 'mACC': 76.77450001656753, 'pACC': 82.18847218449028, 'ACC-person': 92.93655129128014, 'ACC-bicycle': 87.98937449706241, 'ACC-car': 86.1431928907897, 'ACC-motorcycle': 90.47315885177125, 'ACC-airplane': 90.77002713832594, 'ACC-bus': 93.8378290686476, 'ACC-train': 95.30652980065068, 'ACC-truck': 79.0225470851211, 'ACC-boat': 83.33693179414843, 'ACC-traffic light': 91.00193605639286, 'ACC-fire hydrant': 95.90119220964442, 'ACC-stop sign': 87.55220189029927, 'ACC-parking meter': 87.95075795375166, 'ACC-bench': 79.96379004396952, 'ACC-bird': 78.28988196120726, 'ACC-cat': 94.53426511050583, 'ACC-dog': 89.31105474836536, 'ACC-horse': 93.64363918481462, 'ACC-sheep': 84.77355549566165, 'ACC-cow': 89.30168074042881, 'ACC-elephant': 85.8422071736378, 'ACC-bear': 79.65012206451554, 'ACC-zebra': 88.03389967150582, 'ACC-giraffe': 91.42172078865033, 'ACC-backpack': 74.05259375465596, 'ACC-umbrella': 89.58957496384097, 'ACC-handbag': 69.92531650338435, 'ACC-tie': 85.17886911558969, 'ACC-suitcase': 85.62305975429726, 'ACC-frisbee': 94.18618181818181, 'ACC-skis': 71.09237913845797, 'ACC-snowboard': 81.61935784085621, 'ACC-sports ball': 89.37989063582143, 'ACC-kite': 85.73014278918396, 'ACC-baseball bat': 88.56813192576554, 'ACC-baseball glove': 91.41789700467636, 'ACC-skateboard': 90.38572024902263, 'ACC-surfboard': 92.54296535612028, 'ACC-tennis racket': 94.97081248211857, 'ACC-bottle': 84.53218324085749, 'ACC-wine glass': 91.05066091276677, 'ACC-cup': 88.31430450537488, 'ACC-fork': 80.92805100322944, 'ACC-knife': 77.0825089763318, 'ACC-spoon': 77.23091530105181, 'ACC-bowl': 65.05690894255697, 'ACC-banana': 90.94746162358788, 'ACC-apple': 69.47779312692253, 'ACC-sandwich': 79.62265628600169, 'ACC-orange': 88.74286624961924, 'ACC-broccoli': 80.0482460588463, 'ACC-carrot': 76.84016108914192, 'ACC-hot dog': 72.82953995790464, 'ACC-pizza': 85.249312672939, 'ACC-donut': 73.56749940315234, 'ACC-cake': 86.17292262974811, 'ACC-chair': 77.31059785803114, 'ACC-couch': 82.15973341238858, 'ACC-potted plant': 60.56260270460514, 'ACC-bed': 87.3954599127636, 'ACC-dining table': 77.07571152827549, 'ACC-toilet': 86.54799291881014, 'ACC-tv': 85.58993837446755, 'ACC-laptop': 86.15509639498097, 'ACC-mouse': 86.73419694165774, 'ACC-remote': 72.48239097328181, 'ACC-keyboard': 73.03059413712838, 'ACC-cell phone': 72.34905677724906, 'ACC-microwave': 84.4701467927217, 'ACC-oven': 92.26407629128519, 'ACC-toaster': 90.61845652739193, 'ACC-sink': 78.09123828281068, 'ACC-refrigerator': 92.06226621255044, 'ACC-book': 72.66026449244907, 'ACC-clock': 79.13320871639493, 'ACC-vase': 71.40910558049649, 'ACC-scissors': 71.61070251310727, 'ACC-teddy bear': 91.86356830593812, 'ACC-hair drier': 60.86158312697464, 'ACC-toothbrush': 84.37456567060458, 'ACC-banner': 69.52749215970508, 'ACC-blanket': 28.463933179929583, 'ACC-bridge': 57.71036284956777, 'ACC-cardboard': 65.86331249091172, 'ACC-counter': 55.70159611294183, 'ACC-curtain': 83.21010898028052, 'ACC-door-stuff': 65.07152095935655, 'ACC-floor-wood': 81.96084357613877, 'ACC-flower': 61.26248032395494, 'ACC-fruit': 67.48962081052782, 'ACC-gravel': 47.547299553007726, 'ACC-house': 27.220826139480874, 'ACC-light': 62.31834733759523, 'ACC-mirror-stuff': 76.74064623814122, 'ACC-net': 67.9176283053211, 'ACC-pillow': 51.58252660424031, 'ACC-platform': 47.82580391529979, 'ACC-playingfield': 90.47006268362196, 'ACC-railroad': 79.93557887131276, 'ACC-river': 77.24999142658979, 'ACC-road': 87.59992871046718, 'ACC-roof': 26.511467768419937, 'ACC-sand': 72.9668413568434, 'ACC-sea': 93.05183505882736, 'ACC-shelf': 60.3521262439523, 'ACC-snow': 95.90613640744988, 'ACC-stairs': 58.49633650460785, 'ACC-tent': 14.58506718636951, 'ACC-towel': 57.842340805410075, 'ACC-wall-brick': 68.93122724036299, 'ACC-wall-stone': 46.307909746445986, 'ACC-wall-tile': 86.66590447484984, 'ACC-wall-wood': 65.3775030385095, 'ACC-water-other': 29.401369156440666, 'ACC-window-blind': 67.27123569749953, 'ACC-window-other': 69.83499210575526, 'ACC-tree-merged': 90.04213296489316, 'ACC-fence-merged': 71.69920092004364, 'ACC-ceiling-merged': 82.22490247687982, 'ACC-sky-other-merged': 97.14697507777683, 'ACC-cabinet-merged': 76.67312031437827, 'ACC-table-merged': 55.469893059382755, 'ACC-floor-other-merged': 65.12227626560977, 'ACC-pavement-merged': 69.32493057576382, 'ACC-mountain-merged': 67.09919212557614, 'ACC-grass-merged': 84.14166209144342, 'ACC-dirt-merged': 64.8278420140481, 'ACC-paper-merged': 50.650031655632944, 'ACC-food-other-merged': 61.052265499818745, 'ACC-building-other-merged': 74.64927996284501, 'ACC-rock-merged': 84.17748476431501, 'ACC-wall-other-merged': 82.75710003601957, 'ACC-rug-merged': 82.02052110939836})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2715 s/iter. Inference: 0.1776 s/iter. Eval: 0.0000 s/iter. Total: 0.4492 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3052 s/iter. Inference: 0.3434 s/iter. Eval: 0.0000 s/iter. Total: 0.6487 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3176 s/iter. Inference: 0.5545 s/iter. Eval: 0.0000 s/iter. Total: 0.8722 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3804506877377818, 'noc@0.8': 2.419373719637109, 'noc@0.85': 2.861574480538484, 'noc@0.9': 3.635645302897278, 'miou@iter1': 0.8756778718157403} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1421 s/iter. Eval: 0.0010 s/iter. Total: 0.1444 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.59269714355469, 'precision@0.6': 72.75553894042969, 'precision@0.7': 68.75242614746094, 'precision@0.8': 59.30820083618164, 'precision@0.9': 32.41352462768555, 'cIoU': 61.87751007080078, 'mIoU': 67.1303482055664} INFO:trainer.default_trainer:This epoch takes 0:57:07.949203 INFO:trainer.default_trainer:PROGRESS: 62.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 31 training. INFO:trainer.default_trainer:epochs[ 31] optim steps[56700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.55841/0.76039, loss_mask_bce_0: 0.66022/0.30106, loss_mask_dice_0: 1.00397/1.02425, loss_spatial_bce_0: 0.15633/0.08557, loss_spatial_dice_0: 0.18434/0.18104, loss_spatial_ce_0: 0.19092/0.05843, loss_grounding_bce_0: 0.08670/0.08076, loss_grounding_dice_0: 0.12308/0.15084, loss_grounding_ce_0: 0.07127/0.24940, loss_mask_ce_1: 0.44773/0.76099, loss_mask_bce_1: 0.72395/0.30194, loss_mask_dice_1: 1.05497/1.02835, loss_spatial_bce_1: 0.16325/0.08586, loss_spatial_dice_1: 0.20156/0.18367, loss_spatial_ce_1: 0.18472/0.06234, loss_grounding_bce_1: 0.08499/0.08096, loss_grounding_dice_1: 0.11863/0.15161, loss_grounding_ce_1: 0.11049/0.25088, loss_mask_ce_2: 0.73762/0.76907, loss_mask_bce_2: 0.66893/0.30213, loss_mask_dice_2: 0.96897/1.02924, loss_spatial_bce_2: 0.13385/0.08590, loss_spatial_dice_2: 0.17335/0.18404, loss_spatial_ce_2: 0.10311/0.06458, loss_grounding_bce_2: 0.06776/0.08096, loss_grounding_dice_2: 0.10369/0.15152, loss_grounding_ce_2: 0.11491/0.25423, loss_mask_ce_3: 0.59518/0.77230, loss_mask_bce_3: 0.70621/0.30357, loss_mask_dice_3: 1.05168/1.02705, loss_spatial_bce_3: 0.14804/0.08796, loss_spatial_dice_3: 0.19251/0.18530, loss_spatial_ce_3: 0.14951/0.06930, loss_grounding_bce_3: 0.05740/0.08137, loss_grounding_dice_3: 0.09682/0.15119, loss_grounding_ce_3: 0.02270/0.25460, loss_mask_ce_4: 0.58284/0.77808, loss_mask_bce_4: 0.71337/0.30602, loss_mask_dice_4: 1.04767/1.04598, loss_spatial_bce_4: 0.14084/0.08999, loss_spatial_dice_4: 0.20703/0.19321, loss_spatial_ce_4: 0.21756/0.08242, loss_grounding_bce_4: 0.05559/0.08198, loss_grounding_dice_4: 0.09368/0.15376, loss_grounding_ce_4: 0.03029/0.25939, loss_mask_ce_5: 0.70057/0.80191, loss_mask_bce_5: 0.76948/0.30789, loss_mask_dice_5: 1.07105/1.05341, loss_spatial_bce_5: 0.13357/0.09211, loss_spatial_dice_5: 0.20668/0.19609, loss_spatial_ce_5: 0.23407/0.09482, loss_grounding_bce_5: 0.04783/0.08227, loss_grounding_dice_5: 0.09448/0.15446, loss_grounding_ce_5: 0.07536/0.27782, loss_mask_ce_6: 0.71473/0.82859, loss_mask_bce_6: 0.72831/0.30994, loss_mask_dice_6: 1.11113/1.05673, loss_spatial_bce_6: 0.19024/0.09715, loss_spatial_dice_6: 0.24017/0.19838, loss_spatial_ce_6: 0.14394/0.11918, loss_grounding_bce_6: 0.05445/0.08319, loss_grounding_dice_6: 0.10395/0.15504, loss_grounding_ce_6: 0.04790/0.28726, loss_mask_ce_7: 0.65849/0.88453, loss_mask_bce_7: 0.77512/0.31717, loss_mask_dice_7: 1.21100/1.10334, loss_spatial_bce_7: 0.33919/0.10715, loss_spatial_dice_7: 0.29129/0.22396, loss_spatial_ce_7: 0.15755/0.15724, loss_grounding_bce_7: 0.04990/0.08488, loss_grounding_dice_7: 0.09361/0.16067, loss_grounding_ce_7: 0.03538/0.32041, loss_mask_ce_8: 1.11538/1.02076, loss_mask_bce_8: 0.77457/0.33322, loss_mask_dice_8: 1.12731/1.18054, loss_spatial_bce_8: 0.27211/0.12472, loss_spatial_dice_8: 0.30183/0.25979, loss_spatial_ce_8: 0.25105/0.20608, loss_grounding_bce_8: 0.05252/0.08908, loss_grounding_dice_8: 0.10776/0.17043, loss_grounding_ce_8: 1.25813/0.42154, loss_mask_ce_9: 3.55562/3.48054, loss_mask_bce_9: 0.86264/0.36020, loss_mask_dice_9: 1.89009/1.76212, loss_spatial_bce_9: 0.38478/0.35497, loss_spatial_dice_9: 0.89188/0.79383, loss_spatial_ce_9: 1.42045/1.39300, loss_grounding_bce_9: 0.11858/0.10108, loss_grounding_dice_9: 0.30209/0.24281, loss_grounding_ce_9: 2.56842/0.67694] items per batch[64] items per second[0.16] total items[3628800] mini batches[ 56700] memory[4999] epoch remaining[0:57:58] INFO:trainer.default_trainer:epochs[ 31] optim steps[56800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98943/0.76014, loss_mask_bce_0: 0.05421/0.30104, loss_mask_dice_0: 0.17465/1.02404, loss_spatial_bce_0: 0.03210/0.08556, loss_spatial_dice_0: 0.16096/0.18099, loss_spatial_ce_0: 0.00299/0.05842, loss_grounding_bce_0: 0.00949/0.08076, loss_grounding_dice_0: 0.02795/0.15083, loss_grounding_ce_0: 0.82077/0.24932, loss_mask_ce_1: 1.05116/0.76071, loss_mask_bce_1: 0.06314/0.30192, loss_mask_dice_1: 0.18949/1.02816, loss_spatial_bce_1: 0.04390/0.08585, loss_spatial_dice_1: 0.17397/0.18363, loss_spatial_ce_1: 0.00165/0.06235, loss_grounding_bce_1: 0.00999/0.08096, loss_grounding_dice_1: 0.02951/0.15159, loss_grounding_ce_1: 0.82143/0.25083, loss_mask_ce_2: 1.19344/0.76879, loss_mask_bce_2: 0.07147/0.30211, loss_mask_dice_2: 0.22977/1.02898, loss_spatial_bce_2: 0.03202/0.08589, loss_spatial_dice_2: 0.15465/0.18400, loss_spatial_ce_2: 0.00325/0.06458, loss_grounding_bce_2: 0.01037/0.08096, loss_grounding_dice_2: 0.03037/0.15149, loss_grounding_ce_2: 0.81408/0.25418, loss_mask_ce_3: 1.00515/0.77201, loss_mask_bce_3: 0.06410/0.30355, loss_mask_dice_3: 0.20655/1.02685, loss_spatial_bce_3: 0.03043/0.08795, loss_spatial_dice_3: 0.15530/0.18526, loss_spatial_ce_3: 0.01147/0.06929, loss_grounding_bce_3: 0.00856/0.08137, loss_grounding_dice_3: 0.02828/0.15116, loss_grounding_ce_3: 0.80372/0.25454, loss_mask_ce_4: 1.06451/0.77780, loss_mask_bce_4: 0.07170/0.30600, loss_mask_dice_4: 0.21809/1.04573, loss_spatial_bce_4: 0.03040/0.08999, loss_spatial_dice_4: 0.15356/0.19317, loss_spatial_ce_4: 0.00608/0.08239, loss_grounding_bce_4: 0.01009/0.08197, loss_grounding_dice_4: 0.02836/0.15373, loss_grounding_ce_4: 0.76393/0.25936, loss_mask_ce_5: 0.98558/0.80165, loss_mask_bce_5: 0.07735/0.30786, loss_mask_dice_5: 0.25619/1.05321, loss_spatial_bce_5: 0.03504/0.09210, loss_spatial_dice_5: 0.17316/0.19605, loss_spatial_ce_5: 0.02135/0.09482, loss_grounding_bce_5: 0.01004/0.08227, loss_grounding_dice_5: 0.03082/0.15443, loss_grounding_ce_5: 0.74720/0.27776, loss_mask_ce_6: 1.25129/0.82837, loss_mask_bce_6: 0.06103/0.30993, loss_mask_dice_6: 0.19261/1.05654, loss_spatial_bce_6: 0.01780/0.09714, loss_spatial_dice_6: 0.09978/0.19833, loss_spatial_ce_6: 0.01583/0.11916, loss_grounding_bce_6: 0.01044/0.08319, loss_grounding_dice_6: 0.03459/0.15500, loss_grounding_ce_6: 0.76631/0.28719, loss_mask_ce_7: 1.13583/0.88429, loss_mask_bce_7: 0.09723/0.31715, loss_mask_dice_7: 0.33134/1.10314, loss_spatial_bce_7: 0.01742/0.10715, loss_spatial_dice_7: 0.10623/0.22393, loss_spatial_ce_7: 0.18863/0.15729, loss_grounding_bce_7: 0.01527/0.08488, loss_grounding_dice_7: 0.05388/0.16064, loss_grounding_ce_7: 0.76990/0.32034, loss_mask_ce_8: 1.00816/1.02049, loss_mask_bce_8: 0.21190/0.33321, loss_mask_dice_8: 0.66574/1.18031, loss_spatial_bce_8: 0.12546/0.12471, loss_spatial_dice_8: 0.26957/0.25975, loss_spatial_ce_8: 0.02355/0.20608, loss_grounding_bce_8: 0.05851/0.08908, loss_grounding_dice_8: 0.09892/0.17040, loss_grounding_ce_8: 0.75290/0.42145, loss_mask_ce_9: 7.21281/3.48011, loss_mask_bce_9: 0.46724/0.36018, loss_mask_dice_9: 1.74059/1.76169, loss_spatial_bce_9: 0.37750/0.35498, loss_spatial_dice_9: 0.81636/0.79380, loss_spatial_ce_9: 1.06163/1.39284, loss_grounding_bce_9: 0.07823/0.10109, loss_grounding_dice_9: 0.28721/0.24277, loss_grounding_ce_9: 0.74517/0.67679] items per batch[64] items per second[0.36] total items[3635200] mini batches[ 56800] memory[4999] epoch remaining[0:51:14] INFO:trainer.default_trainer:epochs[ 31] optim steps[56900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04266/0.76000, loss_mask_bce_0: 0.08427/0.30097, loss_mask_dice_0: 0.22518/1.02395, loss_spatial_bce_0: 0.04271/0.08551, loss_spatial_dice_0: 0.11464/0.18096, loss_spatial_ce_0: 0.00040/0.05839, loss_grounding_bce_0: 0.03785/0.08075, loss_grounding_dice_0: 0.15734/0.15082, loss_grounding_ce_0: 0.06792/0.24934, loss_mask_ce_1: 0.04168/0.76060, loss_mask_bce_1: 0.08537/0.30184, loss_mask_dice_1: 0.23674/1.02809, loss_spatial_bce_1: 0.04860/0.08580, loss_spatial_dice_1: 0.12427/0.18360, loss_spatial_ce_1: 0.00039/0.06231, loss_grounding_bce_1: 0.04028/0.08095, loss_grounding_dice_1: 0.13088/0.15159, loss_grounding_ce_1: 0.05988/0.25083, loss_mask_ce_2: 0.04043/0.76865, loss_mask_bce_2: 0.08961/0.30204, loss_mask_dice_2: 0.24053/1.02894, loss_spatial_bce_2: 0.04983/0.08584, loss_spatial_dice_2: 0.12352/0.18397, loss_spatial_ce_2: 0.00289/0.06454, loss_grounding_bce_2: 0.04034/0.08095, loss_grounding_dice_2: 0.14440/0.15149, loss_grounding_ce_2: 0.05658/0.25423, loss_mask_ce_3: 0.07157/0.77192, loss_mask_bce_3: 0.09013/0.30348, loss_mask_dice_3: 0.24171/1.02680, loss_spatial_bce_3: 0.04881/0.08791, loss_spatial_dice_3: 0.12324/0.18524, loss_spatial_ce_3: 0.00424/0.06923, loss_grounding_bce_3: 0.04217/0.08136, loss_grounding_dice_3: 0.15292/0.15117, loss_grounding_ce_3: 0.07270/0.25455, loss_mask_ce_4: 0.04544/0.77771, loss_mask_bce_4: 0.08874/0.30593, loss_mask_dice_4: 0.24733/1.04565, loss_spatial_bce_4: 0.05012/0.08994, loss_spatial_dice_4: 0.13190/0.19313, loss_spatial_ce_4: 0.09287/0.08236, loss_grounding_bce_4: 0.03089/0.08196, loss_grounding_dice_4: 0.12288/0.15373, loss_grounding_ce_4: 0.09530/0.25944, loss_mask_ce_5: 0.03000/0.80159, loss_mask_bce_5: 0.09493/0.30778, loss_mask_dice_5: 0.24585/1.05314, loss_spatial_bce_5: 0.04920/0.09206, loss_spatial_dice_5: 0.14227/0.19602, loss_spatial_ce_5: 0.05451/0.09478, loss_grounding_bce_5: 0.03032/0.08225, loss_grounding_dice_5: 0.12794/0.15441, loss_grounding_ce_5: 0.12824/0.27778, loss_mask_ce_6: 0.04582/0.82826, loss_mask_bce_6: 0.08603/0.30986, loss_mask_dice_6: 0.23571/1.05653, loss_spatial_bce_6: 0.05751/0.09709, loss_spatial_dice_6: 0.13506/0.19831, loss_spatial_ce_6: 0.13468/0.11912, loss_grounding_bce_6: 0.03986/0.08318, loss_grounding_dice_6: 0.13970/0.15501, loss_grounding_ce_6: 0.12984/0.28724, loss_mask_ce_7: 0.06929/0.88419, loss_mask_bce_7: 0.09598/0.31707, loss_mask_dice_7: 0.24521/1.10304, loss_spatial_bce_7: 0.04755/0.10709, loss_spatial_dice_7: 0.12814/0.22391, loss_spatial_ce_7: 0.07775/0.15728, loss_grounding_bce_7: 0.03526/0.08486, loss_grounding_dice_7: 0.12616/0.16063, loss_grounding_ce_7: 0.14076/0.32043, loss_mask_ce_8: 0.09623/1.02037, loss_mask_bce_8: 0.09122/0.33312, loss_mask_dice_8: 0.20962/1.18020, loss_spatial_bce_8: 0.06238/0.12465, loss_spatial_dice_8: 0.13418/0.25973, loss_spatial_ce_8: 0.11853/0.20599, loss_grounding_bce_8: 0.04221/0.08907, loss_grounding_dice_8: 0.12645/0.17039, loss_grounding_ce_8: 0.47263/0.42152, loss_mask_ce_9: 3.90849/3.47995, loss_mask_bce_9: 0.11896/0.36008, loss_mask_dice_9: 0.35285/1.76147, loss_spatial_bce_9: 0.24940/0.35489, loss_spatial_dice_9: 0.73240/0.79377, loss_spatial_ce_9: 0.96665/1.39277, loss_grounding_bce_9: 0.02757/0.10107, loss_grounding_dice_9: 0.21582/0.24275, loss_grounding_ce_9: 2.49100/0.67689] items per batch[64] items per second[0.36] total items[3641600] mini batches[ 56900] memory[4999] epoch remaining[0:47:22] INFO:trainer.default_trainer:epochs[ 31] optim steps[57000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.78371/0.75985, loss_mask_bce_0: 0.39500/0.30094, loss_mask_dice_0: 0.95056/1.02385, loss_spatial_bce_0: 0.17123/0.08550, loss_spatial_dice_0: 0.34177/0.18094, loss_spatial_ce_0: 0.03147/0.05837, loss_grounding_bce_0: 0.14780/0.08073, loss_grounding_dice_0: 0.53503/0.15084, loss_grounding_ce_0: 0.74853/0.24931, loss_mask_ce_1: 1.78060/0.76046, loss_mask_bce_1: 0.35088/0.30180, loss_mask_dice_1: 0.94684/1.02799, loss_spatial_bce_1: 0.18621/0.08580, loss_spatial_dice_1: 0.34016/0.18358, loss_spatial_ce_1: 0.04031/0.06228, loss_grounding_bce_1: 0.14376/0.08092, loss_grounding_dice_1: 0.54854/0.15160, loss_grounding_ce_1: 0.30851/0.25085, loss_mask_ce_2: 2.01102/0.76853, loss_mask_bce_2: 0.35151/0.30201, loss_mask_dice_2: 0.96148/1.02887, loss_spatial_bce_2: 0.22348/0.08584, loss_spatial_dice_2: 0.33948/0.18395, loss_spatial_ce_2: 0.03062/0.06451, loss_grounding_bce_2: 0.14318/0.08092, loss_grounding_dice_2: 0.55251/0.15150, loss_grounding_ce_2: 0.30746/0.25418, loss_mask_ce_3: 1.81537/0.77175, loss_mask_bce_3: 0.35651/0.30345, loss_mask_dice_3: 0.96811/1.02669, loss_spatial_bce_3: 0.20654/0.08790, loss_spatial_dice_3: 0.33205/0.18522, loss_spatial_ce_3: 0.04081/0.06922, loss_grounding_bce_3: 0.15131/0.08134, loss_grounding_dice_3: 0.54165/0.15118, loss_grounding_ce_3: 0.29293/0.25455, loss_mask_ce_4: 1.46937/0.77755, loss_mask_bce_4: 0.36822/0.30590, loss_mask_dice_4: 0.96430/1.04558, loss_spatial_bce_4: 0.17742/0.08994, loss_spatial_dice_4: 0.33621/0.19311, loss_spatial_ce_4: 0.10796/0.08236, loss_grounding_bce_4: 0.16623/0.08193, loss_grounding_dice_4: 0.54840/0.15375, loss_grounding_ce_4: 0.31143/0.25944, loss_mask_ce_5: 1.72020/0.80144, loss_mask_bce_5: 0.35283/0.30775, loss_mask_dice_5: 0.97583/1.05307, loss_spatial_bce_5: 0.21298/0.09206, loss_spatial_dice_5: 0.33481/0.19602, loss_spatial_ce_5: 0.18030/0.09478, loss_grounding_bce_5: 0.15887/0.08223, loss_grounding_dice_5: 0.55115/0.15442, loss_grounding_ce_5: 0.48941/0.27772, loss_mask_ce_6: 1.82840/0.82811, loss_mask_bce_6: 0.35681/0.30982, loss_mask_dice_6: 0.98047/1.05640, loss_spatial_bce_6: 0.24515/0.09710, loss_spatial_dice_6: 0.34477/0.19830, loss_spatial_ce_6: 0.15285/0.11911, loss_grounding_bce_6: 0.16191/0.08317, loss_grounding_dice_6: 0.53959/0.15501, loss_grounding_ce_6: 0.36942/0.28717, loss_mask_ce_7: 1.65576/0.88404, loss_mask_bce_7: 0.38171/0.31704, loss_mask_dice_7: 0.97023/1.10294, loss_spatial_bce_7: 0.27800/0.10709, loss_spatial_dice_7: 0.34128/0.22390, loss_spatial_ce_7: 0.21196/0.15727, loss_grounding_bce_7: 0.16368/0.08483, loss_grounding_dice_7: 0.53152/0.16064, loss_grounding_ce_7: 0.44366/0.32033, loss_mask_ce_8: 2.03205/1.02022, loss_mask_bce_8: 0.37443/0.33310, loss_mask_dice_8: 1.00199/1.18008, loss_spatial_bce_8: 0.13639/0.12463, loss_spatial_dice_8: 0.35324/0.25970, loss_spatial_ce_8: 0.77465/0.20595, loss_grounding_bce_8: 0.14044/0.08905, loss_grounding_dice_8: 0.57257/0.17041, loss_grounding_ce_8: 0.39646/0.42143, loss_mask_ce_9: 3.64772/3.47958, loss_mask_bce_9: 0.35897/0.36001, loss_mask_dice_9: 1.06179/1.76127, loss_spatial_bce_9: 0.50761/0.35496, loss_spatial_dice_9: 0.71535/0.79376, loss_spatial_ce_9: 1.38788/1.39260, loss_grounding_bce_9: 0.15887/0.10104, loss_grounding_dice_9: 0.58699/0.24275, loss_grounding_ce_9: 0.44799/0.67673] items per batch[64] items per second[0.36] total items[3648000] mini batches[ 57000] memory[4999] epoch remaining[0:44:01] INFO:trainer.default_trainer:epochs[ 31] optim steps[57100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50676/0.75991, loss_mask_bce_0: 0.11240/0.30094, loss_mask_dice_0: 0.14733/1.02437, loss_spatial_bce_0: 0.03626/0.08551, loss_spatial_dice_0: 0.06384/0.18094, loss_spatial_ce_0: 0.00036/0.05839, loss_grounding_bce_0: 0.05546/0.08070, loss_grounding_dice_0: 0.03434/0.15085, loss_grounding_ce_0: 0.56644/0.24943, loss_mask_ce_1: 0.43577/0.76048, loss_mask_bce_1: 0.11541/0.30181, loss_mask_dice_1: 0.14490/1.02846, loss_spatial_bce_1: 0.03551/0.08580, loss_spatial_dice_1: 0.08986/0.18358, loss_spatial_ce_1: 0.00104/0.06229, loss_grounding_bce_1: 0.04564/0.08089, loss_grounding_dice_1: 0.03111/0.15161, loss_grounding_ce_1: 0.51798/0.25095, loss_mask_ce_2: 0.44393/0.76859, loss_mask_bce_2: 0.11192/0.30201, loss_mask_dice_2: 0.15632/1.02939, loss_spatial_bce_2: 0.03663/0.08585, loss_spatial_dice_2: 0.07696/0.18395, loss_spatial_ce_2: 0.00020/0.06451, loss_grounding_bce_2: 0.05036/0.08090, loss_grounding_dice_2: 0.03548/0.15152, loss_grounding_ce_2: 0.57683/0.25429, loss_mask_ce_3: 0.37751/0.77185, loss_mask_bce_3: 0.11822/0.30345, loss_mask_dice_3: 0.14245/1.02712, loss_spatial_bce_3: 0.03426/0.08791, loss_spatial_dice_3: 0.06678/0.18523, loss_spatial_ce_3: 0.00833/0.06922, loss_grounding_bce_3: 0.04993/0.08131, loss_grounding_dice_3: 0.03150/0.15120, loss_grounding_ce_3: 0.63790/0.25465, loss_mask_ce_4: 0.31658/0.77759, loss_mask_bce_4: 0.11292/0.30592, loss_mask_dice_4: 0.13218/1.04608, loss_spatial_bce_4: 0.03578/0.08994, loss_spatial_dice_4: 0.04807/0.19312, loss_spatial_ce_4: 0.00968/0.08238, loss_grounding_bce_4: 0.05342/0.08191, loss_grounding_dice_4: 0.03612/0.15377, loss_grounding_ce_4: 0.60824/0.25953, loss_mask_ce_5: 0.42668/0.80152, loss_mask_bce_5: 0.11756/0.30777, loss_mask_dice_5: 0.17367/1.05359, loss_spatial_bce_5: 0.03563/0.09208, loss_spatial_dice_5: 0.04606/0.19604, loss_spatial_ce_5: 0.00932/0.09478, loss_grounding_bce_5: 0.05011/0.08221, loss_grounding_dice_5: 0.03606/0.15444, loss_grounding_ce_5: 0.56511/0.27782, loss_mask_ce_6: 0.43341/0.82817, loss_mask_bce_6: 0.11960/0.30983, loss_mask_dice_6: 0.17301/1.05689, loss_spatial_bce_6: 0.03882/0.09712, loss_spatial_dice_6: 0.05474/0.19833, loss_spatial_ce_6: 0.02776/0.11912, loss_grounding_bce_6: 0.04842/0.08314, loss_grounding_dice_6: 0.03376/0.15503, loss_grounding_ce_6: 0.50548/0.28722, loss_mask_ce_7: 0.47560/0.88413, loss_mask_bce_7: 0.12239/0.31706, loss_mask_dice_7: 0.15602/1.10344, loss_spatial_bce_7: 0.03778/0.10710, loss_spatial_dice_7: 0.04926/0.22393, loss_spatial_ce_7: 0.05160/0.15729, loss_grounding_bce_7: 0.05001/0.08482, loss_grounding_dice_7: 0.04425/0.16066, loss_grounding_ce_7: 0.66219/0.32042, loss_mask_ce_8: 0.68936/1.02030, loss_mask_bce_8: 0.12007/0.33313, loss_mask_dice_8: 0.16265/1.18062, loss_spatial_bce_8: 0.04270/0.12464, loss_spatial_dice_8: 0.05913/0.25971, loss_spatial_ce_8: 0.19160/0.20591, loss_grounding_bce_8: 0.05310/0.08902, loss_grounding_dice_8: 0.03149/0.17043, loss_grounding_ce_8: 0.92637/0.42163, loss_mask_ce_9: 4.59210/3.47995, loss_mask_bce_9: 0.19122/0.36005, loss_mask_dice_9: 0.34234/1.76205, loss_spatial_bce_9: 0.49429/0.35492, loss_spatial_dice_9: 0.85875/0.79376, loss_spatial_ce_9: 1.79238/1.39266, loss_grounding_bce_9: 0.08020/0.10103, loss_grounding_dice_9: 0.09075/0.24280, loss_grounding_ce_9: 2.01611/0.67694] items per batch[64] items per second[0.36] total items[3654400] mini batches[ 57100] memory[4999] epoch remaining[0:40:55] INFO:trainer.default_trainer:epochs[ 31] optim steps[57200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38448/0.75996, loss_mask_bce_0: 0.14112/0.30096, loss_mask_dice_0: 0.36070/1.02437, loss_spatial_bce_0: 0.08856/0.08549, loss_spatial_dice_0: 0.16422/0.18093, loss_spatial_ce_0: 0.00259/0.05839, loss_grounding_bce_0: 0.08223/0.08069, loss_grounding_dice_0: 0.20953/0.15088, loss_grounding_ce_0: 0.07440/0.24952, loss_mask_ce_1: 0.34220/0.76055, loss_mask_bce_1: 0.15167/0.30183, loss_mask_dice_1: 0.35605/1.02842, loss_spatial_bce_1: 0.08586/0.08578, loss_spatial_dice_1: 0.18013/0.18358, loss_spatial_ce_1: 0.00656/0.06227, loss_grounding_bce_1: 0.07943/0.08088, loss_grounding_dice_1: 0.23106/0.15166, loss_grounding_ce_1: 0.06623/0.25107, loss_mask_ce_2: 0.42698/0.76866, loss_mask_bce_2: 0.15322/0.30202, loss_mask_dice_2: 0.37596/1.02936, loss_spatial_bce_2: 0.08456/0.08582, loss_spatial_dice_2: 0.14510/0.18394, loss_spatial_ce_2: 0.02951/0.06451, loss_grounding_bce_2: 0.07762/0.08089, loss_grounding_dice_2: 0.19432/0.15155, loss_grounding_ce_2: 0.10100/0.25438, loss_mask_ce_3: 0.42245/0.77193, loss_mask_bce_3: 0.14593/0.30347, loss_mask_dice_3: 0.39944/1.02714, loss_spatial_bce_3: 0.08008/0.08789, loss_spatial_dice_3: 0.16937/0.18523, loss_spatial_ce_3: 0.00687/0.06919, loss_grounding_bce_3: 0.08337/0.08130, loss_grounding_dice_3: 0.21603/0.15123, loss_grounding_ce_3: 0.04607/0.25470, loss_mask_ce_4: 0.39104/0.77764, loss_mask_bce_4: 0.13853/0.30596, loss_mask_dice_4: 0.35342/1.04606, loss_spatial_bce_4: 0.08755/0.08991, loss_spatial_dice_4: 0.15425/0.19312, loss_spatial_ce_4: 0.04210/0.08236, loss_grounding_bce_4: 0.07672/0.08190, loss_grounding_dice_4: 0.20356/0.15380, loss_grounding_ce_4: 0.06924/0.25960, loss_mask_ce_5: 0.37976/0.80154, loss_mask_bce_5: 0.13081/0.30780, loss_mask_dice_5: 0.30495/1.05358, loss_spatial_bce_5: 0.10053/0.09206, loss_spatial_dice_5: 0.17564/0.19604, loss_spatial_ce_5: 0.00339/0.09477, loss_grounding_bce_5: 0.07593/0.08220, loss_grounding_dice_5: 0.18043/0.15448, loss_grounding_ce_5: 0.09529/0.27788, loss_mask_ce_6: 0.45148/0.82817, loss_mask_bce_6: 0.13578/0.30986, loss_mask_dice_6: 0.30547/1.05688, loss_spatial_bce_6: 0.07978/0.09711, loss_spatial_dice_6: 0.16590/0.19834, loss_spatial_ce_6: 0.01538/0.11911, loss_grounding_bce_6: 0.07969/0.08313, loss_grounding_dice_6: 0.15583/0.15507, loss_grounding_ce_6: 0.07977/0.28726, loss_mask_ce_7: 0.35902/0.88417, loss_mask_bce_7: 0.14416/0.31708, loss_mask_dice_7: 0.38558/1.10346, loss_spatial_bce_7: 0.09910/0.10708, loss_spatial_dice_7: 0.22789/0.22395, loss_spatial_ce_7: 0.12519/0.15729, loss_grounding_bce_7: 0.07715/0.08480, loss_grounding_dice_7: 0.17518/0.16069, loss_grounding_ce_7: 0.14840/0.32047, loss_mask_ce_8: 0.71628/1.02022, loss_mask_bce_8: 0.14413/0.33316, loss_mask_dice_8: 0.28712/1.18067, loss_spatial_bce_8: 0.12758/0.12462, loss_spatial_dice_8: 0.21185/0.25972, loss_spatial_ce_8: 0.66768/0.20589, loss_grounding_bce_8: 0.07773/0.08901, loss_grounding_dice_8: 0.19441/0.17045, loss_grounding_ce_8: 0.30545/0.42168, loss_mask_ce_9: 1.78593/3.47999, loss_mask_bce_9: 0.13075/0.36009, loss_mask_dice_9: 0.65800/1.76231, loss_spatial_bce_9: 0.23762/0.35488, loss_spatial_dice_9: 0.77004/0.79379, loss_spatial_ce_9: 0.75666/1.39267, loss_grounding_bce_9: 0.10346/0.10101, loss_grounding_dice_9: 0.36665/0.24286, loss_grounding_ce_9: 0.07520/0.67670] items per batch[64] items per second[0.36] total items[3660800] mini batches[ 57200] memory[4999] epoch remaining[0:37:47] INFO:trainer.default_trainer:epochs[ 31] optim steps[57300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32143/0.75984, loss_mask_bce_0: 0.27383/0.30098, loss_mask_dice_0: 1.12307/1.02399, loss_spatial_bce_0: 0.03338/0.08550, loss_spatial_dice_0: 0.14714/0.18093, loss_spatial_ce_0: 0.00389/0.05839, loss_grounding_bce_0: 0.16262/0.08070, loss_grounding_dice_0: 0.35087/0.15087, loss_grounding_ce_0: 0.01065/0.24947, loss_mask_ce_1: 0.33840/0.76040, loss_mask_bce_1: 0.26966/0.30186, loss_mask_dice_1: 1.41180/1.02806, loss_spatial_bce_1: 0.03380/0.08579, loss_spatial_dice_1: 0.13256/0.18357, loss_spatial_ce_1: 0.00074/0.06227, loss_grounding_bce_1: 0.16123/0.08088, loss_grounding_dice_1: 0.34331/0.15165, loss_grounding_ce_1: 0.01387/0.25102, loss_mask_ce_2: 0.32165/0.76851, loss_mask_bce_2: 0.26342/0.30205, loss_mask_dice_2: 1.15708/1.02898, loss_spatial_bce_2: 0.03579/0.08583, loss_spatial_dice_2: 0.14207/0.18393, loss_spatial_ce_2: 0.00083/0.06452, loss_grounding_bce_2: 0.15590/0.08089, loss_grounding_dice_2: 0.33490/0.15154, loss_grounding_ce_2: 0.02193/0.25432, loss_mask_ce_3: 0.30241/0.77182, loss_mask_bce_3: 0.27833/0.30351, loss_mask_dice_3: 1.33398/1.02678, loss_spatial_bce_3: 0.03631/0.08790, loss_spatial_dice_3: 0.13748/0.18522, loss_spatial_ce_3: 0.00090/0.06920, loss_grounding_bce_3: 0.16322/0.08130, loss_grounding_dice_3: 0.33698/0.15123, loss_grounding_ce_3: 0.01417/0.25463, loss_mask_ce_4: 0.57064/0.77754, loss_mask_bce_4: 0.29931/0.30599, loss_mask_dice_4: 1.55243/1.04569, loss_spatial_bce_4: 0.03313/0.08992, loss_spatial_dice_4: 0.14947/0.19311, loss_spatial_ce_4: 0.00798/0.08236, loss_grounding_bce_4: 0.19600/0.08190, loss_grounding_dice_4: 0.36323/0.15380, loss_grounding_ce_4: 0.02833/0.25958, loss_mask_ce_5: 0.47376/0.80142, loss_mask_bce_5: 0.27131/0.30783, loss_mask_dice_5: 1.40575/1.05319, loss_spatial_bce_5: 0.03505/0.09207, loss_spatial_dice_5: 0.16408/0.19605, loss_spatial_ce_5: 0.03605/0.09476, loss_grounding_bce_5: 0.16170/0.08220, loss_grounding_dice_5: 0.30293/0.15447, loss_grounding_ce_5: 0.03625/0.27782, loss_mask_ce_6: 0.31532/0.82798, loss_mask_bce_6: 0.28595/0.30989, loss_mask_dice_6: 1.50444/1.05651, loss_spatial_bce_6: 0.03775/0.09713, loss_spatial_dice_6: 0.13278/0.19834, loss_spatial_ce_6: 0.05183/0.11911, loss_grounding_bce_6: 0.16856/0.08314, loss_grounding_dice_6: 0.31351/0.15507, loss_grounding_ce_6: 0.03532/0.28726, loss_mask_ce_7: 0.41868/0.88402, loss_mask_bce_7: 0.31425/0.31710, loss_mask_dice_7: 1.66765/1.10306, loss_spatial_bce_7: 0.03941/0.10709, loss_spatial_dice_7: 0.24858/0.22395, loss_spatial_ce_7: 0.05216/0.15733, loss_grounding_bce_7: 0.17997/0.08481, loss_grounding_dice_7: 0.36362/0.16069, loss_grounding_ce_7: 0.04466/0.32048, loss_mask_ce_8: 0.51109/1.01995, loss_mask_bce_8: 0.28345/0.33317, loss_mask_dice_8: 1.34922/1.18020, loss_spatial_bce_8: 0.09817/0.12464, loss_spatial_dice_8: 0.32888/0.25971, loss_spatial_ce_8: 0.27555/0.20586, loss_grounding_bce_8: 0.15370/0.08901, loss_grounding_dice_8: 0.33219/0.17044, loss_grounding_ce_8: 0.07969/0.42165, loss_mask_ce_9: 3.16426/3.47955, loss_mask_bce_9: 0.27040/0.36009, loss_mask_dice_9: 2.16337/1.76178, loss_spatial_bce_9: 0.21865/0.35486, loss_spatial_dice_9: 0.95219/0.79377, loss_spatial_ce_9: 1.65636/1.39256, loss_grounding_bce_9: 0.14651/0.10100, loss_grounding_dice_9: 0.34102/0.24283, loss_grounding_ce_9: 0.02485/0.67670] items per batch[64] items per second[0.36] total items[3667200] mini batches[ 57300] memory[4999] epoch remaining[0:34:47] INFO:trainer.default_trainer:epochs[ 31] optim steps[57400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15235/0.75998, loss_mask_bce_0: 0.27325/0.30098, loss_mask_dice_0: 0.26819/1.02378, loss_spatial_bce_0: 0.09436/0.08548, loss_spatial_dice_0: 0.09000/0.18091, loss_spatial_ce_0: 0.00200/0.05834, loss_grounding_bce_0: 0.02505/0.08071, loss_grounding_dice_0: 0.04991/0.15089, loss_grounding_ce_0: 0.00005/0.24947, loss_mask_ce_1: 0.15674/0.76053, loss_mask_bce_1: 0.27792/0.30184, loss_mask_dice_1: 0.27417/1.02786, loss_spatial_bce_1: 0.09401/0.08577, loss_spatial_dice_1: 0.09026/0.18356, loss_spatial_ce_1: 0.00263/0.06221, loss_grounding_bce_1: 0.02244/0.08088, loss_grounding_dice_1: 0.05424/0.15166, loss_grounding_ce_1: 0.00039/0.25110, loss_mask_ce_2: 0.13452/0.76867, loss_mask_bce_2: 0.27572/0.30204, loss_mask_dice_2: 0.27680/1.02879, loss_spatial_bce_2: 0.09875/0.08581, loss_spatial_dice_2: 0.08874/0.18392, loss_spatial_ce_2: 0.00184/0.06446, loss_grounding_bce_2: 0.02450/0.08089, loss_grounding_dice_2: 0.05226/0.15156, loss_grounding_ce_2: 0.00094/0.25438, loss_mask_ce_3: 0.14696/0.77197, loss_mask_bce_3: 0.27462/0.30351, loss_mask_dice_3: 0.27728/1.02662, loss_spatial_bce_3: 0.14071/0.08788, loss_spatial_dice_3: 0.13915/0.18521, loss_spatial_ce_3: 0.00266/0.06916, loss_grounding_bce_3: 0.02156/0.08130, loss_grounding_dice_3: 0.04200/0.15124, loss_grounding_ce_3: 0.00050/0.25466, loss_mask_ce_4: 0.13677/0.77769, loss_mask_bce_4: 0.29019/0.30597, loss_mask_dice_4: 0.27635/1.04554, loss_spatial_bce_4: 0.10995/0.08991, loss_spatial_dice_4: 0.09196/0.19311, loss_spatial_ce_4: 0.00473/0.08237, loss_grounding_bce_4: 0.02456/0.08190, loss_grounding_dice_4: 0.04972/0.15378, loss_grounding_ce_4: 0.00173/0.25965, loss_mask_ce_5: 0.14362/0.80153, loss_mask_bce_5: 0.28699/0.30781, loss_mask_dice_5: 0.26717/1.05302, loss_spatial_bce_5: 0.10878/0.09206, loss_spatial_dice_5: 0.09115/0.19606, loss_spatial_ce_5: 0.01582/0.09471, loss_grounding_bce_5: 0.02038/0.08221, loss_grounding_dice_5: 0.04765/0.15449, loss_grounding_ce_5: 0.00901/0.27785, loss_mask_ce_6: 0.11619/0.82813, loss_mask_bce_6: 0.30528/0.30988, loss_mask_dice_6: 0.28120/1.05628, loss_spatial_bce_6: 0.11782/0.09713, loss_spatial_dice_6: 0.08610/0.19834, loss_spatial_ce_6: 0.05570/0.11907, loss_grounding_bce_6: 0.02768/0.08314, loss_grounding_dice_6: 0.05493/0.15506, loss_grounding_ce_6: 0.00054/0.28730, loss_mask_ce_7: 0.17724/0.88428, loss_mask_bce_7: 0.29018/0.31708, loss_mask_dice_7: 0.27509/1.10282, loss_spatial_bce_7: 0.12486/0.10708, loss_spatial_dice_7: 0.10508/0.22395, loss_spatial_ce_7: 0.02198/0.15730, loss_grounding_bce_7: 0.02645/0.08482, loss_grounding_dice_7: 0.05280/0.16069, loss_grounding_ce_7: 0.00189/0.32051, loss_mask_ce_8: 0.15926/1.02023, loss_mask_bce_8: 0.29667/0.33315, loss_mask_dice_8: 0.27217/1.17999, loss_spatial_bce_8: 0.10627/0.12462, loss_spatial_dice_8: 0.09472/0.25972, loss_spatial_ce_8: 0.19142/0.20579, loss_grounding_bce_8: 0.02377/0.08901, loss_grounding_dice_8: 0.04796/0.17045, loss_grounding_ce_8: 0.15376/0.42156, loss_mask_ce_9: 2.48563/3.47978, loss_mask_bce_9: 0.25802/0.36008, loss_mask_dice_9: 0.33971/1.76154, loss_spatial_bce_9: 0.42773/0.35482, loss_spatial_dice_9: 0.69969/0.79377, loss_spatial_ce_9: 0.91175/1.39257, loss_grounding_bce_9: 0.02181/0.10101, loss_grounding_dice_9: 0.07395/0.24281, loss_grounding_ce_9: 1.56690/0.67650] items per batch[64] items per second[0.36] total items[3673600] mini batches[ 57400] memory[4999] epoch remaining[0:31:45] INFO:trainer.default_trainer:epochs[ 31] optim steps[57500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39851/0.75987, loss_mask_bce_0: 0.08888/0.30091, loss_mask_dice_0: 0.13935/1.02390, loss_spatial_bce_0: 0.05916/0.08546, loss_spatial_dice_0: 0.09080/0.18091, loss_spatial_ce_0: 0.03376/0.05833, loss_grounding_bce_0: 0.05328/0.08068, loss_grounding_dice_0: 0.09827/0.15090, loss_grounding_ce_0: 0.21112/0.24947, loss_mask_ce_1: 0.39929/0.76043, loss_mask_bce_1: 0.09411/0.30178, loss_mask_dice_1: 0.15003/1.02798, loss_spatial_bce_1: 0.05611/0.08574, loss_spatial_dice_1: 0.08403/0.18355, loss_spatial_ce_1: 0.05268/0.06220, loss_grounding_bce_1: 0.05179/0.08086, loss_grounding_dice_1: 0.10293/0.15167, loss_grounding_ce_1: 0.17833/0.25110, loss_mask_ce_2: 0.36335/0.76862, loss_mask_bce_2: 0.09054/0.30197, loss_mask_dice_2: 0.14176/1.02887, loss_spatial_bce_2: 0.06125/0.08579, loss_spatial_dice_2: 0.08748/0.18393, loss_spatial_ce_2: 0.05939/0.06445, loss_grounding_bce_2: 0.05231/0.08087, loss_grounding_dice_2: 0.10373/0.15157, loss_grounding_ce_2: 0.16440/0.25440, loss_mask_ce_3: 0.47201/0.77189, loss_mask_bce_3: 0.09566/0.30345, loss_mask_dice_3: 0.14447/1.02674, loss_spatial_bce_3: 0.07863/0.08787, loss_spatial_dice_3: 0.09153/0.18522, loss_spatial_ce_3: 0.06060/0.06913, loss_grounding_bce_3: 0.05089/0.08128, loss_grounding_dice_3: 0.10612/0.15125, loss_grounding_ce_3: 0.23675/0.25468, loss_mask_ce_4: 0.39120/0.77762, loss_mask_bce_4: 0.09815/0.30591, loss_mask_dice_4: 0.14470/1.04565, loss_spatial_bce_4: 0.07127/0.08989, loss_spatial_dice_4: 0.08479/0.19312, loss_spatial_ce_4: 0.06929/0.08236, loss_grounding_bce_4: 0.06429/0.08188, loss_grounding_dice_4: 0.11179/0.15380, loss_grounding_ce_4: 0.19106/0.25969, loss_mask_ce_5: 0.44753/0.80141, loss_mask_bce_5: 0.08361/0.30777, loss_mask_dice_5: 0.13278/1.05312, loss_spatial_bce_5: 0.07344/0.09204, loss_spatial_dice_5: 0.09128/0.19606, loss_spatial_ce_5: 0.08020/0.09471, loss_grounding_bce_5: 0.04823/0.08219, loss_grounding_dice_5: 0.10056/0.15450, loss_grounding_ce_5: 0.16708/0.27791, loss_mask_ce_6: 0.48855/0.82802, loss_mask_bce_6: 0.09023/0.30983, loss_mask_dice_6: 0.14179/1.05638, loss_spatial_bce_6: 0.05783/0.09711, loss_spatial_dice_6: 0.07437/0.19835, loss_spatial_ce_6: 0.16114/0.11906, loss_grounding_bce_6: 0.05206/0.08311, loss_grounding_dice_6: 0.10940/0.15507, loss_grounding_ce_6: 0.25191/0.28733, loss_mask_ce_7: 0.29716/0.88417, loss_mask_bce_7: 0.08472/0.31706, loss_mask_dice_7: 0.14677/1.10294, loss_spatial_bce_7: 0.07701/0.10706, loss_spatial_dice_7: 0.09365/0.22396, loss_spatial_ce_7: 0.15540/0.15729, loss_grounding_bce_7: 0.04960/0.08481, loss_grounding_dice_7: 0.11559/0.16070, loss_grounding_ce_7: 0.09823/0.32067, loss_mask_ce_8: 0.30990/1.02027, loss_mask_bce_8: 0.08925/0.33312, loss_mask_dice_8: 0.13811/1.18010, loss_spatial_bce_8: 0.17295/0.12459, loss_spatial_dice_8: 0.16186/0.25973, loss_spatial_ce_8: 0.22508/0.20572, loss_grounding_bce_8: 0.05197/0.08898, loss_grounding_dice_8: 0.10421/0.17044, loss_grounding_ce_8: 0.09466/0.42168, loss_mask_ce_9: 2.33082/3.47989, loss_mask_bce_9: 0.17530/0.36003, loss_mask_dice_9: 0.28845/1.76172, loss_spatial_bce_9: 0.65930/0.35476, loss_spatial_dice_9: 0.73427/0.79379, loss_spatial_ce_9: 1.74759/1.39242, loss_grounding_bce_9: 0.06197/0.10098, loss_grounding_dice_9: 0.18486/0.24282, loss_grounding_ce_9: 0.45094/0.67655] items per batch[64] items per second[0.37] total items[3680000] mini batches[ 57500] memory[4999] epoch remaining[0:28:41] INFO:trainer.default_trainer:epochs[ 31] optim steps[57600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.14097/0.75973, loss_mask_bce_0: 0.39051/0.30089, loss_mask_dice_0: 1.34327/1.02379, loss_spatial_bce_0: 0.06259/0.08545, loss_spatial_dice_0: 0.13998/0.18086, loss_spatial_ce_0: 0.00336/0.05829, loss_grounding_bce_0: 0.10009/0.08067, loss_grounding_dice_0: 0.20904/0.15088, loss_grounding_ce_0: 0.04420/0.24936, loss_mask_ce_1: 1.09855/0.76030, loss_mask_bce_1: 0.39817/0.30175, loss_mask_dice_1: 1.32341/1.02790, loss_spatial_bce_1: 0.06165/0.08573, loss_spatial_dice_1: 0.14735/0.18350, loss_spatial_ce_1: 0.00486/0.06216, loss_grounding_bce_1: 0.11132/0.08085, loss_grounding_dice_1: 0.21109/0.15165, loss_grounding_ce_1: 0.04485/0.25096, loss_mask_ce_2: 1.08300/0.76847, loss_mask_bce_2: 0.42181/0.30195, loss_mask_dice_2: 1.39823/1.02875, loss_spatial_bce_2: 0.06421/0.08578, loss_spatial_dice_2: 0.15313/0.18388, loss_spatial_ce_2: 0.01118/0.06442, loss_grounding_bce_2: 0.11323/0.08086, loss_grounding_dice_2: 0.21288/0.15156, loss_grounding_ce_2: 0.04420/0.25426, loss_mask_ce_3: 1.11584/0.77175, loss_mask_bce_3: 0.41710/0.30343, loss_mask_dice_3: 1.47088/1.02663, loss_spatial_bce_3: 0.06859/0.08785, loss_spatial_dice_3: 0.15378/0.18518, loss_spatial_ce_3: 0.01439/0.06909, loss_grounding_bce_3: 0.10997/0.08127, loss_grounding_dice_3: 0.20914/0.15122, loss_grounding_ce_3: 0.05375/0.25456, loss_mask_ce_4: 1.09051/0.77743, loss_mask_bce_4: 0.40366/0.30590, loss_mask_dice_4: 1.36130/1.04555, loss_spatial_bce_4: 0.08695/0.08989, loss_spatial_dice_4: 0.17581/0.19308, loss_spatial_ce_4: 0.00830/0.08231, loss_grounding_bce_4: 0.11436/0.08187, loss_grounding_dice_4: 0.20745/0.15379, loss_grounding_ce_4: 0.06478/0.25955, loss_mask_ce_5: 0.95438/0.80125, loss_mask_bce_5: 0.43951/0.30775, loss_mask_dice_5: 1.24205/1.05305, loss_spatial_bce_5: 0.08981/0.09203, loss_spatial_dice_5: 0.18593/0.19602, loss_spatial_ce_5: 0.01987/0.09468, loss_grounding_bce_5: 0.13430/0.08218, loss_grounding_dice_5: 0.22697/0.15449, loss_grounding_ce_5: 0.04087/0.27777, loss_mask_ce_6: 0.98058/0.82786, loss_mask_bce_6: 0.48320/0.30981, loss_mask_dice_6: 1.46210/1.05633, loss_spatial_bce_6: 0.08713/0.09711, loss_spatial_dice_6: 0.17643/0.19832, loss_spatial_ce_6: 0.03638/0.11904, loss_grounding_bce_6: 0.13353/0.08310, loss_grounding_dice_6: 0.22124/0.15506, loss_grounding_ce_6: 0.03244/0.28717, loss_mask_ce_7: 0.75812/0.88396, loss_mask_bce_7: 0.51371/0.31703, loss_mask_dice_7: 1.64327/1.10293, loss_spatial_bce_7: 0.09442/0.10704, loss_spatial_dice_7: 0.17915/0.22392, loss_spatial_ce_7: 0.10178/0.15724, loss_grounding_bce_7: 0.12998/0.08479, loss_grounding_dice_7: 0.22435/0.16068, loss_grounding_ce_7: 0.04136/0.32051, loss_mask_ce_8: 0.67559/1.02008, loss_mask_bce_8: 0.58479/0.33310, loss_mask_dice_8: 1.76885/1.18011, loss_spatial_bce_8: 0.11862/0.12458, loss_spatial_dice_8: 0.22068/0.25967, loss_spatial_ce_8: 0.10227/0.20565, loss_grounding_bce_8: 0.12833/0.08897, loss_grounding_dice_8: 0.20783/0.17042, loss_grounding_ce_8: 0.03181/0.42153, loss_mask_ce_9: 2.62133/3.47963, loss_mask_bce_9: 0.66630/0.36003, loss_mask_dice_9: 2.06140/1.76196, loss_spatial_bce_9: 0.27316/0.35477, loss_spatial_dice_9: 0.90235/0.79378, loss_spatial_ce_9: 1.76881/1.39244, loss_grounding_bce_9: 0.13160/0.10098, loss_grounding_dice_9: 0.29999/0.24282, loss_grounding_ce_9: 0.07532/0.67640] items per batch[64] items per second[0.36] total items[3686400] mini batches[ 57600] memory[4999] epoch remaining[0:25:40] INFO:trainer.default_trainer:epochs[ 31] optim steps[57700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43964/0.75978, loss_mask_bce_0: 0.06049/0.30087, loss_mask_dice_0: 0.42173/1.02392, loss_spatial_bce_0: 0.01909/0.08544, loss_spatial_dice_0: 0.08640/0.18085, loss_spatial_ce_0: 0.00052/0.05829, loss_grounding_bce_0: 0.00351/0.08067, loss_grounding_dice_0: 0.38148/0.15087, loss_grounding_ce_0: 0.48059/0.24934, loss_mask_ce_1: 0.45089/0.76039, loss_mask_bce_1: 0.06295/0.30174, loss_mask_dice_1: 0.40421/1.02799, loss_spatial_bce_1: 0.01942/0.08572, loss_spatial_dice_1: 0.12331/0.18350, loss_spatial_ce_1: 0.00095/0.06216, loss_grounding_bce_1: 0.00270/0.08085, loss_grounding_dice_1: 0.31446/0.15164, loss_grounding_ce_1: 0.48529/0.25094, loss_mask_ce_2: 0.24335/0.76851, loss_mask_bce_2: 0.06193/0.30194, loss_mask_dice_2: 0.64202/1.02885, loss_spatial_bce_2: 0.01994/0.08576, loss_spatial_dice_2: 0.10733/0.18388, loss_spatial_ce_2: 0.00123/0.06443, loss_grounding_bce_2: 0.00350/0.08086, loss_grounding_dice_2: 0.24243/0.15155, loss_grounding_ce_2: 0.55756/0.25426, loss_mask_ce_3: 0.88502/0.77181, loss_mask_bce_3: 0.06055/0.30341, loss_mask_dice_3: 0.33395/1.02674, loss_spatial_bce_3: 0.01919/0.08784, loss_spatial_dice_3: 0.14610/0.18517, loss_spatial_ce_3: 0.00671/0.06909, loss_grounding_bce_3: 0.00327/0.08126, loss_grounding_dice_3: 0.25425/0.15121, loss_grounding_ce_3: 0.69922/0.25456, loss_mask_ce_4: 0.73295/0.77758, loss_mask_bce_4: 0.06720/0.30587, loss_mask_dice_4: 0.43411/1.04565, loss_spatial_bce_4: 0.01759/0.08987, loss_spatial_dice_4: 0.11294/0.19307, loss_spatial_ce_4: 0.02394/0.08232, loss_grounding_bce_4: 0.00424/0.08186, loss_grounding_dice_4: 0.38926/0.15378, loss_grounding_ce_4: 0.87169/0.25955, loss_mask_ce_5: 0.51801/0.80139, loss_mask_bce_5: 0.06529/0.30773, loss_mask_dice_5: 0.49629/1.05317, loss_spatial_bce_5: 0.01800/0.09202, loss_spatial_dice_5: 0.09933/0.19602, loss_spatial_ce_5: 0.02129/0.09468, loss_grounding_bce_5: 0.00358/0.08217, loss_grounding_dice_5: 0.30329/0.15449, loss_grounding_ce_5: 0.71699/0.27777, loss_mask_ce_6: 0.43034/0.82803, loss_mask_bce_6: 0.05831/0.30980, loss_mask_dice_6: 0.58327/1.05649, loss_spatial_bce_6: 0.01916/0.09710, loss_spatial_dice_6: 0.12256/0.19831, loss_spatial_ce_6: 0.02520/0.11905, loss_grounding_bce_6: 0.00338/0.08310, loss_grounding_dice_6: 0.30277/0.15505, loss_grounding_ce_6: 0.49334/0.28718, loss_mask_ce_7: 0.17969/0.88409, loss_mask_bce_7: 0.06725/0.31702, loss_mask_dice_7: 0.45065/1.10307, loss_spatial_bce_7: 0.01835/0.10702, loss_spatial_dice_7: 0.13362/0.22391, loss_spatial_ce_7: 0.05137/0.15723, loss_grounding_bce_7: 0.00333/0.08478, loss_grounding_dice_7: 0.20489/0.16067, loss_grounding_ce_7: 0.83694/0.32048, loss_mask_ce_8: 1.10489/1.02022, loss_mask_bce_8: 0.06820/0.33308, loss_mask_dice_8: 0.44313/1.18033, loss_spatial_bce_8: 0.02191/0.12457, loss_spatial_dice_8: 0.19882/0.25967, loss_spatial_ce_8: 0.02916/0.20565, loss_grounding_bce_8: 0.00363/0.08896, loss_grounding_dice_8: 0.22104/0.17040, loss_grounding_ce_8: 0.58957/0.42155, loss_mask_ce_9: 1.70097/3.47971, loss_mask_bce_9: 0.06979/0.36001, loss_mask_dice_9: 0.76568/1.76218, loss_spatial_bce_9: 0.37829/0.35476, loss_spatial_dice_9: 0.86751/0.79378, loss_spatial_ce_9: 1.59086/1.39248, loss_grounding_bce_9: 0.00457/0.10095, loss_grounding_dice_9: 0.43288/0.24278, loss_grounding_ce_9: 0.58585/0.67638] items per batch[64] items per second[0.37] total items[3692800] mini batches[ 57700] memory[4999] epoch remaining[0:22:38] INFO:trainer.default_trainer:epochs[ 31] optim steps[57800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.07724/0.75973, loss_mask_bce_0: 0.65804/0.30085, loss_mask_dice_0: 2.18281/1.02381, loss_spatial_bce_0: 0.12002/0.08541, loss_spatial_dice_0: 0.29958/0.18082, loss_spatial_ce_0: 0.11355/0.05828, loss_grounding_bce_0: 0.03027/0.08067, loss_grounding_dice_0: 0.03214/0.15087, loss_grounding_ce_0: 0.27845/0.24921, loss_mask_ce_1: 2.12160/0.76033, loss_mask_bce_1: 0.70351/0.30173, loss_mask_dice_1: 2.27069/1.02791, loss_spatial_bce_1: 0.15264/0.08569, loss_spatial_dice_1: 0.29606/0.18346, loss_spatial_ce_1: 0.08957/0.06214, loss_grounding_bce_1: 0.03815/0.08085, loss_grounding_dice_1: 0.03966/0.15163, loss_grounding_ce_1: 0.26276/0.25081, loss_mask_ce_2: 2.11979/0.76847, loss_mask_bce_2: 0.66105/0.30192, loss_mask_dice_2: 2.27582/1.02878, loss_spatial_bce_2: 0.14451/0.08574, loss_spatial_dice_2: 0.29597/0.18384, loss_spatial_ce_2: 0.09135/0.06441, loss_grounding_bce_2: 0.03343/0.08086, loss_grounding_dice_2: 0.03391/0.15153, loss_grounding_ce_2: 0.21387/0.25412, loss_mask_ce_3: 2.06470/0.77178, loss_mask_bce_3: 0.65025/0.30339, loss_mask_dice_3: 2.27914/1.02662, loss_spatial_bce_3: 0.11631/0.08782, loss_spatial_dice_3: 0.31237/0.18513, loss_spatial_ce_3: 0.12907/0.06906, loss_grounding_bce_3: 0.02894/0.08126, loss_grounding_dice_3: 0.03051/0.15120, loss_grounding_ce_3: 0.22635/0.25443, loss_mask_ce_4: 2.16559/0.77756, loss_mask_bce_4: 0.69532/0.30586, loss_mask_dice_4: 2.25514/1.04554, loss_spatial_bce_4: 0.09105/0.08985, loss_spatial_dice_4: 0.29275/0.19303, loss_spatial_ce_4: 0.20687/0.08231, loss_grounding_bce_4: 0.02777/0.08187, loss_grounding_dice_4: 0.02927/0.15378, loss_grounding_ce_4: 0.31974/0.25940, loss_mask_ce_5: 2.07480/0.80138, loss_mask_bce_5: 0.68628/0.30773, loss_mask_dice_5: 2.27298/1.05308, loss_spatial_bce_5: 0.09826/0.09200, loss_spatial_dice_5: 0.31690/0.19599, loss_spatial_ce_5: 0.20512/0.09466, loss_grounding_bce_5: 0.03380/0.08218, loss_grounding_dice_5: 0.03368/0.15447, loss_grounding_ce_5: 0.24415/0.27764, loss_mask_ce_6: 2.08661/0.82802, loss_mask_bce_6: 0.71014/0.30979, loss_mask_dice_6: 2.36604/1.05640, loss_spatial_bce_6: 0.10583/0.09708, loss_spatial_dice_6: 0.30434/0.19828, loss_spatial_ce_6: 0.21155/0.11905, loss_grounding_bce_6: 0.03152/0.08310, loss_grounding_dice_6: 0.02688/0.15504, loss_grounding_ce_6: 0.24772/0.28704, loss_mask_ce_7: 2.84103/0.88410, loss_mask_bce_7: 0.83663/0.31701, loss_mask_dice_7: 2.68740/1.10295, loss_spatial_bce_7: 0.17933/0.10701, loss_spatial_dice_7: 0.41017/0.22389, loss_spatial_ce_7: 0.10357/0.15718, loss_grounding_bce_7: 0.04301/0.08479, loss_grounding_dice_7: 0.03953/0.16065, loss_grounding_ce_7: 1.06564/0.32036, loss_mask_ce_8: 2.85184/1.02019, loss_mask_bce_8: 0.94529/0.33308, loss_mask_dice_8: 3.30523/1.18024, loss_spatial_bce_8: 0.20777/0.12453, loss_spatial_dice_8: 0.43104/0.25963, loss_spatial_ce_8: 0.25675/0.20562, loss_grounding_bce_8: 0.04175/0.08896, loss_grounding_dice_8: 0.05188/0.17040, loss_grounding_ce_8: 1.32395/0.42134, loss_mask_ce_9: 7.05052/3.47964, loss_mask_bce_9: 1.54435/0.35998, loss_mask_dice_9: 5.19854/1.76206, loss_spatial_bce_9: 0.25564/0.35476, loss_spatial_dice_9: 0.95957/0.79374, loss_spatial_ce_9: 1.41813/1.39251, loss_grounding_bce_9: 0.27565/0.10096, loss_grounding_dice_9: 0.29671/0.24277, loss_grounding_ce_9: 3.35227/0.67618] items per batch[64] items per second[0.36] total items[3699200] mini batches[ 57800] memory[4999] epoch remaining[0:19:41] INFO:trainer.default_trainer:epochs[ 31] optim steps[57900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.35374/0.75985, loss_mask_bce_0: 0.06106/0.30086, loss_mask_dice_0: 2.42147/1.02388, loss_spatial_bce_0: 0.00624/0.08540, loss_spatial_dice_0: 0.41721/0.18081, loss_spatial_ce_0: 0.05862/0.05824, loss_grounding_bce_0: 0.00539/0.08067, loss_grounding_dice_0: 0.18536/0.15086, loss_grounding_ce_0: 0.51291/0.24927, loss_mask_ce_1: 1.49638/0.76043, loss_mask_bce_1: 0.05952/0.30173, loss_mask_dice_1: 2.63175/1.02800, loss_spatial_bce_1: 0.00928/0.08569, loss_spatial_dice_1: 0.41759/0.18346, loss_spatial_ce_1: 0.03873/0.06210, loss_grounding_bce_1: 0.00810/0.08085, loss_grounding_dice_1: 0.26357/0.15164, loss_grounding_ce_1: 0.47497/0.25084, loss_mask_ce_2: 1.42107/0.76856, loss_mask_bce_2: 0.05861/0.30193, loss_mask_dice_2: 2.59335/1.02884, loss_spatial_bce_2: 0.00510/0.08574, loss_spatial_dice_2: 0.34531/0.18384, loss_spatial_ce_2: 0.08473/0.06439, loss_grounding_bce_2: 0.00769/0.08086, loss_grounding_dice_2: 0.21250/0.15153, loss_grounding_ce_2: 0.86229/0.25415, loss_mask_ce_3: 1.38323/0.77185, loss_mask_bce_3: 0.05258/0.30340, loss_mask_dice_3: 2.21687/1.02674, loss_spatial_bce_3: 0.00581/0.08782, loss_spatial_dice_3: 0.37724/0.18514, loss_spatial_ce_3: 0.07006/0.06904, loss_grounding_bce_3: 0.00907/0.08126, loss_grounding_dice_3: 0.27226/0.15119, loss_grounding_ce_3: 0.65776/0.25448, loss_mask_ce_4: 1.95583/0.77775, loss_mask_bce_4: 0.05759/0.30585, loss_mask_dice_4: 2.62171/1.04564, loss_spatial_bce_4: 0.00866/0.08985, loss_spatial_dice_4: 0.44065/0.19304, loss_spatial_ce_4: 0.04482/0.08229, loss_grounding_bce_4: 0.01192/0.08186, loss_grounding_dice_4: 0.33958/0.15377, loss_grounding_ce_4: 0.76015/0.25945, loss_mask_ce_5: 1.78072/0.80155, loss_mask_bce_5: 0.06949/0.30772, loss_mask_dice_5: 2.70375/1.05319, loss_spatial_bce_5: 0.00772/0.09200, loss_spatial_dice_5: 0.42174/0.19600, loss_spatial_ce_5: 0.05325/0.09464, loss_grounding_bce_5: 0.00687/0.08217, loss_grounding_dice_5: 0.25948/0.15446, loss_grounding_ce_5: 0.80878/0.27767, loss_mask_ce_6: 2.04332/0.82821, loss_mask_bce_6: 0.06728/0.30978, loss_mask_dice_6: 2.83674/1.05655, loss_spatial_bce_6: 0.01000/0.09708, loss_spatial_dice_6: 0.32725/0.19829, loss_spatial_ce_6: 0.06064/0.11903, loss_grounding_bce_6: 0.00771/0.08309, loss_grounding_dice_6: 0.29527/0.15504, loss_grounding_ce_6: 0.77381/0.28707, loss_mask_ce_7: 2.14922/0.88426, loss_mask_bce_7: 0.07031/0.31700, loss_mask_dice_7: 2.89343/1.10307, loss_spatial_bce_7: 0.01609/0.10700, loss_spatial_dice_7: 0.55318/0.22391, loss_spatial_ce_7: 0.46107/0.15717, loss_grounding_bce_7: 0.00743/0.08478, loss_grounding_dice_7: 0.28487/0.16065, loss_grounding_ce_7: 0.61875/0.32037, loss_mask_ce_8: 2.03307/1.02034, loss_mask_bce_8: 0.08042/0.33308, loss_mask_dice_8: 2.85286/1.18034, loss_spatial_bce_8: 0.01441/0.12453, loss_spatial_dice_8: 0.59198/0.25965, loss_spatial_ce_8: 0.58411/0.20555, loss_grounding_bce_8: 0.00824/0.08895, loss_grounding_dice_8: 0.30282/0.17039, loss_grounding_ce_8: 0.59683/0.42139, loss_mask_ce_9: 3.82109/3.48001, loss_mask_bce_9: 0.05406/0.35999, loss_mask_dice_9: 3.68777/1.76217, loss_spatial_bce_9: 0.03322/0.35478, loss_spatial_dice_9: 0.87703/0.79374, loss_spatial_ce_9: 1.10874/1.39251, loss_grounding_bce_9: 0.00788/0.10096, loss_grounding_dice_9: 0.46204/0.24274, loss_grounding_ce_9: 0.64282/0.67615] items per batch[64] items per second[0.36] total items[3705600] mini batches[ 57900] memory[4999] epoch remaining[0:16:42] INFO:trainer.default_trainer:epochs[ 31] optim steps[58000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60474/0.75974, loss_mask_bce_0: 0.01487/0.30077, loss_mask_dice_0: 2.13545/1.02407, loss_spatial_bce_0: 0.00154/0.08539, loss_spatial_dice_0: 0.38127/0.18081, loss_spatial_ce_0: 0.14519/0.05821, loss_grounding_bce_0: 0.00268/0.08065, loss_grounding_dice_0: 0.43380/0.15085, loss_grounding_ce_0: 0.57482/0.24915, loss_mask_ce_1: 0.40545/0.76029, loss_mask_bce_1: 0.01495/0.30164, loss_mask_dice_1: 1.63436/1.02821, loss_spatial_bce_1: 0.00157/0.08567, loss_spatial_dice_1: 0.31379/0.18346, loss_spatial_ce_1: 0.57858/0.06207, loss_grounding_bce_1: 0.00248/0.08083, loss_grounding_dice_1: 0.37683/0.15162, loss_grounding_ce_1: 0.54518/0.25076, loss_mask_ce_2: 0.34838/0.76843, loss_mask_bce_2: 0.02688/0.30184, loss_mask_dice_2: 2.91262/1.02908, loss_spatial_bce_2: 0.00177/0.08572, loss_spatial_dice_2: 0.42101/0.18384, loss_spatial_ce_2: 0.16793/0.06435, loss_grounding_bce_2: 0.00202/0.08083, loss_grounding_dice_2: 0.33051/0.15150, loss_grounding_ce_2: 0.52702/0.25405, loss_mask_ce_3: 0.37116/0.77170, loss_mask_bce_3: 0.03208/0.30331, loss_mask_dice_3: 2.55085/1.02697, loss_spatial_bce_3: 0.00192/0.08780, loss_spatial_dice_3: 0.51954/0.18515, loss_spatial_ce_3: 0.30493/0.06902, loss_grounding_bce_3: 0.00171/0.08125, loss_grounding_dice_3: 0.36522/0.15116, loss_grounding_ce_3: 0.49843/0.25438, loss_mask_ce_4: 0.40639/0.77764, loss_mask_bce_4: 0.02091/0.30577, loss_mask_dice_4: 1.64457/1.04588, loss_spatial_bce_4: 0.00125/0.08983, loss_spatial_dice_4: 0.38445/0.19305, loss_spatial_ce_4: 0.53996/0.08226, loss_grounding_bce_4: 0.00149/0.08184, loss_grounding_dice_4: 0.28488/0.15376, loss_grounding_ce_4: 0.55839/0.25933, loss_mask_ce_5: 0.91993/0.80151, loss_mask_bce_5: 0.01467/0.30762, loss_mask_dice_5: 2.14914/1.05338, loss_spatial_bce_5: 0.00187/0.09199, loss_spatial_dice_5: 0.32663/0.19601, loss_spatial_ce_5: 0.58054/0.09461, loss_grounding_bce_5: 0.00127/0.08214, loss_grounding_dice_5: 0.30360/0.15444, loss_grounding_ce_5: 0.55359/0.27761, loss_mask_ce_6: 0.48455/0.82817, loss_mask_bce_6: 0.01655/0.30968, loss_mask_dice_6: 1.94287/1.05677, loss_spatial_bce_6: 0.00249/0.09706, loss_spatial_dice_6: 0.41819/0.19830, loss_spatial_ce_6: 0.52807/0.11902, loss_grounding_bce_6: 0.00226/0.08306, loss_grounding_dice_6: 0.22015/0.15502, loss_grounding_ce_6: 0.52050/0.28699, loss_mask_ce_7: 0.47147/0.88423, loss_mask_bce_7: 0.02201/0.31692, loss_mask_dice_7: 2.65360/1.10333, loss_spatial_bce_7: 0.00217/0.10699, loss_spatial_dice_7: 0.45340/0.22394, loss_spatial_ce_7: 0.43137/0.15718, loss_grounding_bce_7: 0.00191/0.08476, loss_grounding_dice_7: 0.26086/0.16064, loss_grounding_ce_7: 0.66832/0.32032, loss_mask_ce_8: 0.87162/1.02024, loss_mask_bce_8: 0.05409/0.33300, loss_mask_dice_8: 3.43143/1.18072, loss_spatial_bce_8: 0.00135/0.12453, loss_spatial_dice_8: 0.37534/0.25968, loss_spatial_ce_8: 0.44110/0.20549, loss_grounding_bce_8: 0.00222/0.08893, loss_grounding_dice_8: 0.26811/0.17038, loss_grounding_ce_8: 0.63727/0.42125, loss_mask_ce_9: 2.59215/3.47976, loss_mask_bce_9: 0.01078/0.35991, loss_mask_dice_9: 1.78010/1.76253, loss_spatial_bce_9: 0.00638/0.35470, loss_spatial_dice_9: 0.66517/0.79371, loss_spatial_ce_9: 2.26277/1.39249, loss_grounding_bce_9: 0.00130/0.10093, loss_grounding_dice_9: 0.31936/0.24271, loss_grounding_ce_9: 0.68685/0.67601] items per batch[64] items per second[0.36] total items[3712000] mini batches[ 58000] memory[4999] epoch remaining[0:13:44] INFO:trainer.default_trainer:epochs[ 31] optim steps[58100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.23817/0.75974, loss_mask_bce_0: 0.11721/0.30083, loss_mask_dice_0: 3.45792/1.02416, loss_spatial_bce_0: 0.00374/0.08537, loss_spatial_dice_0: 0.16696/0.18079, loss_spatial_ce_0: 0.10339/0.05818, loss_grounding_bce_0: 0.00344/0.08063, loss_grounding_dice_0: 0.30959/0.15082, loss_grounding_ce_0: 0.67023/0.24917, loss_mask_ce_1: 1.01851/0.76028, loss_mask_bce_1: 0.13736/0.30171, loss_mask_dice_1: 4.09001/1.02836, loss_spatial_bce_1: 0.00458/0.08565, loss_spatial_dice_1: 0.16253/0.18344, loss_spatial_ce_1: 0.05383/0.06203, loss_grounding_bce_1: 0.00274/0.08081, loss_grounding_dice_1: 0.30151/0.15160, loss_grounding_ce_1: 0.61134/0.25078, loss_mask_ce_2: 1.00211/0.76841, loss_mask_bce_2: 0.12279/0.30190, loss_mask_dice_2: 3.52293/1.02917, loss_spatial_bce_2: 0.00467/0.08570, loss_spatial_dice_2: 0.18616/0.18383, loss_spatial_ce_2: 0.04941/0.06431, loss_grounding_bce_2: 0.00577/0.08081, loss_grounding_dice_2: 0.37575/0.15149, loss_grounding_ce_2: 0.69593/0.25405, loss_mask_ce_3: 1.15766/0.77172, loss_mask_bce_3: 0.12630/0.30337, loss_mask_dice_3: 3.13155/1.02706, loss_spatial_bce_3: 0.00428/0.08779, loss_spatial_dice_3: 0.22295/0.18514, loss_spatial_ce_3: 0.06983/0.06897, loss_grounding_bce_3: 0.00387/0.08123, loss_grounding_dice_3: 0.33733/0.15115, loss_grounding_ce_3: 0.64957/0.25440, loss_mask_ce_4: 0.80498/0.77764, loss_mask_bce_4: 0.11765/0.30584, loss_mask_dice_4: 3.94677/1.04598, loss_spatial_bce_4: 0.00636/0.08982, loss_spatial_dice_4: 0.21226/0.19304, loss_spatial_ce_4: 0.06829/0.08223, loss_grounding_bce_4: 0.00521/0.08182, loss_grounding_dice_4: 0.37475/0.15374, loss_grounding_ce_4: 0.70701/0.25933, loss_mask_ce_5: 1.38030/0.80155, loss_mask_bce_5: 0.11867/0.30770, loss_mask_dice_5: 3.54761/1.05348, loss_spatial_bce_5: 0.00597/0.09198, loss_spatial_dice_5: 0.25243/0.19600, loss_spatial_ce_5: 0.11766/0.09458, loss_grounding_bce_5: 0.00335/0.08213, loss_grounding_dice_5: 0.32221/0.15442, loss_grounding_ce_5: 0.63142/0.27760, loss_mask_ce_6: 1.56191/0.82823, loss_mask_bce_6: 0.11328/0.30975, loss_mask_dice_6: 3.13647/1.05682, loss_spatial_bce_6: 0.00871/0.09705, loss_spatial_dice_6: 0.28141/0.19829, loss_spatial_ce_6: 0.51702/0.11898, loss_grounding_bce_6: 0.00354/0.08304, loss_grounding_dice_6: 0.33283/0.15499, loss_grounding_ce_6: 0.63943/0.28702, loss_mask_ce_7: 0.52911/0.88425, loss_mask_bce_7: 0.12814/0.31699, loss_mask_dice_7: 3.56933/1.10348, loss_spatial_bce_7: 0.00763/0.10698, loss_spatial_dice_7: 0.29771/0.22393, loss_spatial_ce_7: 0.14449/0.15714, loss_grounding_bce_7: 0.00297/0.08474, loss_grounding_dice_7: 0.37922/0.16061, loss_grounding_ce_7: 0.66441/0.32029, loss_mask_ce_8: 2.00263/1.02024, loss_mask_bce_8: 0.11307/0.33307, loss_mask_dice_8: 4.55940/1.18077, loss_spatial_bce_8: 0.00877/0.12451, loss_spatial_dice_8: 0.31334/0.25966, loss_spatial_ce_8: 0.21846/0.20540, loss_grounding_bce_8: 0.00822/0.08893, loss_grounding_dice_8: 0.42786/0.17036, loss_grounding_ce_8: 0.64544/0.42118, loss_mask_ce_9: 4.61331/3.48005, loss_mask_bce_9: 0.09417/0.35996, loss_mask_dice_9: 4.77633/1.76257, loss_spatial_bce_9: 0.04768/0.35471, loss_spatial_dice_9: 0.87422/0.79375, loss_spatial_ce_9: 2.09507/1.39251, loss_grounding_bce_9: 0.00575/0.10093, loss_grounding_dice_9: 0.49300/0.24269, loss_grounding_ce_9: 0.81543/0.67620] items per batch[64] items per second[0.37] total items[3718400] mini batches[ 58100] memory[4999] epoch remaining[0:10:46] INFO:trainer.default_trainer:epochs[ 31] optim steps[58200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.13284/0.75961, loss_mask_bce_0: 0.25851/0.30076, loss_mask_dice_0: 0.32380/1.02412, loss_spatial_bce_0: 0.16738/0.08535, loss_spatial_dice_0: 0.28444/0.18076, loss_spatial_ce_0: 0.23134/0.05817, loss_grounding_bce_0: 0.11875/0.08064, loss_grounding_dice_0: 0.11768/0.15082, loss_grounding_ce_0: 0.00391/0.24922, loss_mask_ce_1: 1.01850/0.76011, loss_mask_bce_1: 0.26935/0.30164, loss_mask_dice_1: 0.34822/1.02836, loss_spatial_bce_1: 0.16785/0.08563, loss_spatial_dice_1: 0.29593/0.18342, loss_spatial_ce_1: 0.17519/0.06201, loss_grounding_bce_1: 0.11733/0.08081, loss_grounding_dice_1: 0.11254/0.15160, loss_grounding_ce_1: 0.00330/0.25078, loss_mask_ce_2: 1.26832/0.76826, loss_mask_bce_2: 0.26146/0.30184, loss_mask_dice_2: 0.35835/1.02914, loss_spatial_bce_2: 0.15707/0.08568, loss_spatial_dice_2: 0.26661/0.18380, loss_spatial_ce_2: 0.20306/0.06428, loss_grounding_bce_2: 0.11925/0.08082, loss_grounding_dice_2: 0.11641/0.15149, loss_grounding_ce_2: 0.00361/0.25406, loss_mask_ce_3: 1.21450/0.77160, loss_mask_bce_3: 0.26804/0.30330, loss_mask_dice_3: 0.35863/1.02705, loss_spatial_bce_3: 0.16480/0.08776, loss_spatial_dice_3: 0.28571/0.18511, loss_spatial_ce_3: 0.23269/0.06895, loss_grounding_bce_3: 0.11961/0.08124, loss_grounding_dice_3: 0.11217/0.15115, loss_grounding_ce_3: 0.00449/0.25445, loss_mask_ce_4: 0.95827/0.77753, loss_mask_bce_4: 0.26260/0.30577, loss_mask_dice_4: 0.38929/1.04598, loss_spatial_bce_4: 0.16392/0.08979, loss_spatial_dice_4: 0.32060/0.19301, loss_spatial_ce_4: 0.36808/0.08222, loss_grounding_bce_4: 0.11062/0.08183, loss_grounding_dice_4: 0.10953/0.15374, loss_grounding_ce_4: 0.00390/0.25932, loss_mask_ce_5: 1.03596/0.80140, loss_mask_bce_5: 0.25883/0.30762, loss_mask_dice_5: 0.34814/1.05349, loss_spatial_bce_5: 0.31263/0.09196, loss_spatial_dice_5: 0.46461/0.19598, loss_spatial_ce_5: 0.46278/0.09458, loss_grounding_bce_5: 0.11250/0.08214, loss_grounding_dice_5: 0.12262/0.15442, loss_grounding_ce_5: 0.00438/0.27767, loss_mask_ce_6: 1.00107/0.82814, loss_mask_bce_6: 0.29281/0.30967, loss_mask_dice_6: 0.40052/1.05682, loss_spatial_bce_6: 0.29411/0.09703, loss_spatial_dice_6: 0.49997/0.19826, loss_spatial_ce_6: 0.50482/0.11896, loss_grounding_bce_6: 0.11452/0.08305, loss_grounding_dice_6: 0.11240/0.15500, loss_grounding_ce_6: 0.00467/0.28720, loss_mask_ce_7: 0.10629/0.88409, loss_mask_bce_7: 0.29225/0.31690, loss_mask_dice_7: 0.53968/1.10348, loss_spatial_bce_7: 0.40069/0.10696, loss_spatial_dice_7: 0.54290/0.22392, loss_spatial_ce_7: 0.52698/0.15710, loss_grounding_bce_7: 0.11539/0.08475, loss_grounding_dice_7: 0.11898/0.16063, loss_grounding_ce_7: 0.01162/0.32040, loss_mask_ce_8: 1.04400/1.02001, loss_mask_bce_8: 0.29542/0.33299, loss_mask_dice_8: 0.46686/1.18075, loss_spatial_bce_8: 0.79233/0.12450, loss_spatial_dice_8: 0.53755/0.25963, loss_spatial_ce_8: 0.50178/0.20533, loss_grounding_bce_8: 0.10542/0.08893, loss_grounding_dice_8: 0.11262/0.17037, loss_grounding_ce_8: 0.00206/0.42124, loss_mask_ce_9: 1.69889/3.48030, loss_mask_bce_9: 0.26489/0.35986, loss_mask_dice_9: 0.50746/1.76250, loss_spatial_bce_9: 0.88587/0.35469, loss_spatial_dice_9: 0.76195/0.79376, loss_spatial_ce_9: 1.55710/1.39246, loss_grounding_bce_9: 0.11529/0.10093, loss_grounding_dice_9: 0.11514/0.24270, loss_grounding_ce_9: 0.21803/0.67647] items per batch[64] items per second[0.36] total items[3724800] mini batches[ 58200] memory[4999] epoch remaining[0:07:48] INFO:trainer.default_trainer:epochs[ 31] optim steps[58300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.12517/0.75962, loss_mask_bce_0: 0.00690/0.30074, loss_mask_dice_0: 0.11497/1.02435, loss_spatial_bce_0: 0.00390/0.08534, loss_spatial_dice_0: 0.11960/0.18075, loss_spatial_ce_0: 0.20595/0.05817, loss_grounding_bce_0: 0.04639/0.08063, loss_grounding_dice_0: 0.21768/0.15083, loss_grounding_ce_0: 0.05866/0.24912, loss_mask_ce_1: 2.02068/0.76010, loss_mask_bce_1: 0.00652/0.30162, loss_mask_dice_1: 0.14088/1.02856, loss_spatial_bce_1: 0.00675/0.08562, loss_spatial_dice_1: 0.15301/0.18340, loss_spatial_ce_1: 0.19323/0.06201, loss_grounding_bce_1: 0.00336/0.08081, loss_grounding_dice_1: 0.06236/0.15160, loss_grounding_ce_1: 0.65041/0.25069, loss_mask_ce_2: 1.60602/0.76827, loss_mask_bce_2: 0.00884/0.30182, loss_mask_dice_2: 0.15215/1.02937, loss_spatial_bce_2: 0.00821/0.08567, loss_spatial_dice_2: 0.15619/0.18380, loss_spatial_ce_2: 0.20795/0.06425, loss_grounding_bce_2: 0.00458/0.08083, loss_grounding_dice_2: 0.09610/0.15150, loss_grounding_ce_2: 0.62508/0.25397, loss_mask_ce_3: 1.79962/0.77158, loss_mask_bce_3: 0.00679/0.30328, loss_mask_dice_3: 0.13029/1.02730, loss_spatial_bce_3: 0.00482/0.08775, loss_spatial_dice_3: 0.09562/0.18510, loss_spatial_ce_3: 0.12219/0.06894, loss_grounding_bce_3: 0.00575/0.08124, loss_grounding_dice_3: 0.10793/0.15116, loss_grounding_ce_3: 0.52354/0.25438, loss_mask_ce_4: 1.49806/0.77751, loss_mask_bce_4: 0.06969/0.30575, loss_mask_dice_4: 0.33821/1.04621, loss_spatial_bce_4: 0.01028/0.08978, loss_spatial_dice_4: 0.21842/0.19300, loss_spatial_ce_4: 0.31044/0.08220, loss_grounding_bce_4: 0.00530/0.08183, loss_grounding_dice_4: 0.11854/0.15374, loss_grounding_ce_4: 0.71020/0.25925, loss_mask_ce_5: 1.51415/0.80134, loss_mask_bce_5: 0.05037/0.30759, loss_mask_dice_5: 0.28454/1.05374, loss_spatial_bce_5: 0.00928/0.09196, loss_spatial_dice_5: 0.20080/0.19598, loss_spatial_ce_5: 0.26146/0.09456, loss_grounding_bce_5: 0.00657/0.08214, loss_grounding_dice_5: 0.07472/0.15442, loss_grounding_ce_5: 1.14885/0.27756, loss_mask_ce_6: 1.37963/0.82812, loss_mask_bce_6: 0.07662/0.30964, loss_mask_dice_6: 0.31557/1.05703, loss_spatial_bce_6: 0.00503/0.09702, loss_spatial_dice_6: 0.14876/0.19825, loss_spatial_ce_6: 0.40673/0.11896, loss_grounding_bce_6: 0.00475/0.08304, loss_grounding_dice_6: 0.08422/0.15500, loss_grounding_ce_6: 0.68770/0.28707, loss_mask_ce_7: 2.23041/0.88401, loss_mask_bce_7: 0.01935/0.31688, loss_mask_dice_7: 0.17525/1.10370, loss_spatial_bce_7: 0.01523/0.10695, loss_spatial_dice_7: 0.33774/0.22391, loss_spatial_ce_7: 0.38485/0.15706, loss_grounding_bce_7: 0.01721/0.08475, loss_grounding_dice_7: 0.14398/0.16062, loss_grounding_ce_7: 0.82583/0.32024, loss_mask_ce_8: 2.16773/1.01990, loss_mask_bce_8: 0.02323/0.33296, loss_mask_dice_8: 0.23778/1.18100, loss_spatial_bce_8: 0.02713/0.12448, loss_spatial_dice_8: 0.45786/0.25963, loss_spatial_ce_8: 0.55300/0.20528, loss_grounding_bce_8: 0.02136/0.08892, loss_grounding_dice_8: 0.17789/0.17037, loss_grounding_ce_8: 0.68645/0.42097, loss_mask_ce_9: 4.39690/3.48010, loss_mask_bce_9: 0.00761/0.35982, loss_mask_dice_9: 0.19194/1.76269, loss_spatial_bce_9: 0.01850/0.35468, loss_spatial_dice_9: 0.47981/0.79377, loss_spatial_ce_9: 0.59607/1.39248, loss_grounding_bce_9: 0.01937/0.10093, loss_grounding_dice_9: 0.21589/0.24271, loss_grounding_ce_9: 0.34964/0.67627] items per batch[64] items per second[0.37] total items[3731200] mini batches[ 58300] memory[4999] epoch remaining[0:04:50] INFO:trainer.default_trainer:epochs[ 31] optim steps[58400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24536/0.75953, loss_mask_bce_0: 0.18762/0.30074, loss_mask_dice_0: 0.15172/1.02381, loss_spatial_bce_0: 0.45446/0.08538, loss_spatial_dice_0: 0.31229/0.18074, loss_spatial_ce_0: 0.42263/0.05816, loss_grounding_bce_0: 0.10323/0.08063, loss_grounding_dice_0: 0.05370/0.15082, loss_grounding_ce_0: 0.62434/0.24913, loss_mask_ce_1: 0.25062/0.75996, loss_mask_bce_1: 0.20569/0.30161, loss_mask_dice_1: 0.17322/1.02804, loss_spatial_bce_1: 0.30956/0.08565, loss_spatial_dice_1: 0.23258/0.18340, loss_spatial_ce_1: 0.53106/0.06202, loss_grounding_bce_1: 0.11822/0.08081, loss_grounding_dice_1: 0.05707/0.15159, loss_grounding_ce_1: 0.63491/0.25069, loss_mask_ce_2: 0.34367/0.76816, loss_mask_bce_2: 0.22136/0.30181, loss_mask_dice_2: 0.18049/1.02882, loss_spatial_bce_2: 0.28224/0.08571, loss_spatial_dice_2: 0.20780/0.18380, loss_spatial_ce_2: 0.66982/0.06423, loss_grounding_bce_2: 0.10857/0.08083, loss_grounding_dice_2: 0.06474/0.15148, loss_grounding_ce_2: 0.71843/0.25403, loss_mask_ce_3: 0.23305/0.77146, loss_mask_bce_3: 0.13263/0.30328, loss_mask_dice_3: 0.08818/1.02678, loss_spatial_bce_3: 0.35404/0.08779, loss_spatial_dice_3: 0.25037/0.18510, loss_spatial_ce_3: 0.68115/0.06892, loss_grounding_bce_3: 0.12811/0.08124, loss_grounding_dice_3: 0.05378/0.15114, loss_grounding_ce_3: 0.55535/0.25439, loss_mask_ce_4: 0.52134/0.77737, loss_mask_bce_4: 0.15668/0.30575, loss_mask_dice_4: 0.12120/1.04567, loss_spatial_bce_4: 0.15811/0.08983, loss_spatial_dice_4: 0.10674/0.19300, loss_spatial_ce_4: 1.12340/0.08221, loss_grounding_bce_4: 0.09413/0.08183, loss_grounding_dice_4: 0.05437/0.15373, loss_grounding_ce_4: 0.77230/0.25923, loss_mask_ce_5: 0.34712/0.80127, loss_mask_bce_5: 0.31831/0.30760, loss_mask_dice_5: 0.26633/1.05318, loss_spatial_bce_5: 0.23160/0.09200, loss_spatial_dice_5: 0.14254/0.19598, loss_spatial_ce_5: 1.38759/0.09458, loss_grounding_bce_5: 0.23193/0.08214, loss_grounding_dice_5: 0.20898/0.15441, loss_grounding_ce_5: 0.27745/0.27749, loss_mask_ce_6: 0.51608/0.82799, loss_mask_bce_6: 0.12519/0.30963, loss_mask_dice_6: 0.11666/1.05653, loss_spatial_bce_6: 0.21673/0.09707, loss_spatial_dice_6: 0.14389/0.19825, loss_spatial_ce_6: 1.18714/0.11898, loss_grounding_bce_6: 0.10582/0.08304, loss_grounding_dice_6: 0.08640/0.15498, loss_grounding_ce_6: 0.32281/0.28704, loss_mask_ce_7: 2.10646/0.88394, loss_mask_bce_7: 0.09789/0.31689, loss_mask_dice_7: 0.05906/1.10317, loss_spatial_bce_7: 0.92992/0.10703, loss_spatial_dice_7: 0.36146/0.22392, loss_spatial_ce_7: 1.65264/0.15711, loss_grounding_bce_7: 0.09903/0.08476, loss_grounding_dice_7: 0.06242/0.16062, loss_grounding_ce_7: 1.10579/0.32017, loss_mask_ce_8: 1.48424/1.01980, loss_mask_bce_8: 0.21608/0.33295, loss_mask_dice_8: 0.15529/1.18043, loss_spatial_bce_8: 1.88356/0.12457, loss_spatial_dice_8: 0.45661/0.25963, loss_spatial_ce_8: 0.40962/0.20525, loss_grounding_bce_8: 0.19265/0.08893, loss_grounding_dice_8: 0.11853/0.17035, loss_grounding_ce_8: 0.45263/0.42083, loss_mask_ce_9: 2.70487/3.47956, loss_mask_bce_9: 0.57808/0.35979, loss_mask_dice_9: 0.35709/1.76164, loss_spatial_bce_9: 0.75458/0.35475, loss_spatial_dice_9: 0.48587/0.79370, loss_spatial_ce_9: 0.74232/1.39229, loss_grounding_bce_9: 0.64242/0.10094, loss_grounding_dice_9: 0.26670/0.24269, loss_grounding_ce_9: 0.28933/0.67621] items per batch[64] items per second[0.36] total items[3737600] mini batches[ 58400] memory[4999] epoch remaining[0:01:53] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00058464. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3703 s/iter. Eval: 0.1035 s/iter. Total: 0.4765 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3772 s/iter. Eval: 0.0901 s/iter. Total: 0.4700 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0028 s/iter. Inference: 0.3822 s/iter. Eval: 0.0838 s/iter. Total: 0.4690 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0029 s/iter. Inference: 0.3833 s/iter. Eval: 0.0837 s/iter. Total: 0.4700 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 55/79. Dataloading: 0.0030 s/iter. Inference: 0.3843 s/iter. Eval: 0.0813 s/iter. Total: 0.4687 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 67/79. Dataloading: 0.0030 s/iter. Inference: 0.3854 s/iter. Eval: 0.0779 s/iter. Total: 0.4664 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/79. Dataloading: 0.0030 s/iter. Inference: 0.3818 s/iter. Eval: 0.0772 s/iter. Total: 0.4621 s/iter. ETA=0:00:00 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalwa4nweb_ ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.504 | 83.242 | 65.930 | 133 | | Things | 61.615 | 84.150 | 72.714 | 80 | | Stuff | 46.280 | 81.872 | 55.690 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.77 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.41 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.98s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.51 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.697 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.259 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.380 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.608 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.769 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.54 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.936 | 69.676 | 49.543 | 25.860 | 49.939 | 67.990 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.708 | bicycle | 23.772 | car | 44.772 | | motorcycle | 42.435 | airplane | 62.993 | bus | 72.135 | | train | 74.154 | truck | 42.522 | boat | 31.260 | | traffic light | 29.736 | fire hydrant | 71.948 | stop sign | 69.787 | | parking meter | 51.793 | bench | 27.070 | bird | 33.943 | | cat | 76.842 | dog | 71.206 | horse | 52.733 | | sheep | 53.848 | cow | 57.254 | elephant | 67.006 | | bear | 79.785 | zebra | 65.638 | giraffe | 62.621 | | backpack | 24.344 | umbrella | 56.710 | handbag | 24.631 | | tie | 40.281 | suitcase | 50.521 | frisbee | 69.849 | | skis | 8.096 | snowboard | 35.357 | sports ball | 48.700 | | kite | 37.953 | baseball bat | 38.436 | baseball glove | 50.467 | | skateboard | 44.575 | surfboard | 45.078 | tennis racket | 63.556 | | bottle | 41.954 | wine glass | 38.732 | cup | 50.867 | | fork | 27.898 | knife | 25.143 | spoon | 22.389 | | bowl | 40.210 | banana | 22.233 | apple | 27.657 | | sandwich | 49.597 | orange | 30.872 | broccoli | 24.000 | | carrot | 21.273 | hot dog | 33.451 | pizza | 53.307 | | donut | 55.294 | cake | 46.962 | chair | 29.351 | | couch | 44.484 | potted plant | 23.051 | bed | 45.076 | | dining table | 15.719 | toilet | 69.494 | tv | 66.494 | | laptop | 69.574 | mouse | 63.894 | remote | 42.502 | | keyboard | 58.851 | cell phone | 46.498 | microwave | 66.814 | | oven | 34.389 | toaster | 57.006 | sink | 45.037 | | refrigerator | 71.074 | book | 13.886 | clock | 54.601 | | vase | 40.466 | scissors | 31.409 | teddy bear | 57.355 | | hair drier | 36.853 | toothbrush | 26.618 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.13577517121175, 'fwIoU': 71.82781800438785, 'IoU-person': 89.0339440688215, 'IoU-bicycle': 77.2809697230771, 'IoU-car': 71.68356263577118, 'IoU-motorcycle': 89.06350138661679, 'IoU-airplane': 87.40614613474892, 'IoU-bus': 86.00642684202414, 'IoU-train': 86.86346781407164, 'IoU-truck': 64.64301471993663, 'IoU-boat': 72.2693636298323, 'IoU-traffic light': 79.47319586662654, 'IoU-fire hydrant': 93.43153409252987, 'IoU-stop sign': 94.90192740010657, 'IoU-parking meter': 85.23433501956285, 'IoU-bench': 64.20054317427652, 'IoU-bird': 76.90715554398766, 'IoU-cat': 82.75241733265362, 'IoU-dog': 80.8840420503824, 'IoU-horse': 88.97110177350844, 'IoU-sheep': 86.97075507270648, 'IoU-cow': 90.57006792431311, 'IoU-elephant': 90.48595300209028, 'IoU-bear': 86.09447204434662, 'IoU-zebra': 92.8550862243921, 'IoU-giraffe': 89.66819132192688, 'IoU-backpack': 51.91677483836453, 'IoU-umbrella': 89.2401601710204, 'IoU-handbag': 51.4517196888894, 'IoU-tie': 76.0145109171438, 'IoU-suitcase': 83.35325657923599, 'IoU-frisbee': 84.66086740156037, 'IoU-skis': 59.230992857123454, 'IoU-snowboard': 71.86626541145806, 'IoU-sports ball': 77.83156988764466, 'IoU-kite': 79.82304035808086, 'IoU-baseball bat': 68.61322175146188, 'IoU-baseball glove': 77.32108814718431, 'IoU-skateboard': 86.35310803749479, 'IoU-surfboard': 86.50508797938129, 'IoU-tennis racket': 90.34932568994167, 'IoU-bottle': 69.14369682826515, 'IoU-wine glass': 82.58560463355954, 'IoU-cup': 71.11618459424834, 'IoU-fork': 70.31689284883844, 'IoU-knife': 63.4351666656891, 'IoU-spoon': 60.77619355606826, 'IoU-bowl': 60.11196930780418, 'IoU-banana': 81.8817757170322, 'IoU-apple': 60.56042156413229, 'IoU-sandwich': 69.53388221561987, 'IoU-orange': 80.2929448793295, 'IoU-broccoli': 69.94608036049924, 'IoU-carrot': 66.06025809560985, 'IoU-hot dog': 66.99668681721988, 'IoU-pizza': 84.27914844836532, 'IoU-donut': 65.63672716751523, 'IoU-cake': 76.064747699745, 'IoU-chair': 62.159827357569164, 'IoU-couch': 68.98084591828692, 'IoU-potted plant': 44.30100971848267, 'IoU-bed': 73.80460196839003, 'IoU-dining table': 55.392035242874535, 'IoU-toilet': 88.14890363097247, 'IoU-tv': 77.75262878206398, 'IoU-laptop': 79.83299828599571, 'IoU-mouse': 82.6323754839045, 'IoU-remote': 68.20326475238102, 'IoU-keyboard': 69.30551260876935, 'IoU-cell phone': 85.44911609688448, 'IoU-microwave': 79.41958996403785, 'IoU-oven': 71.53094692430344, 'IoU-toaster': 71.1390910879402, 'IoU-sink': 74.87249360299539, 'IoU-refrigerator': 82.35698747552081, 'IoU-book': 53.61458646887357, 'IoU-clock': 82.6095225528759, 'IoU-vase': 71.40183649589876, 'IoU-scissors': 88.80248766657522, 'IoU-teddy bear': 82.13037363924307, 'IoU-hair drier': 50.87790704247646, 'IoU-toothbrush': 73.3862058080808, 'IoU-banner': 30.343635119339616, 'IoU-blanket': 16.965710934596014, 'IoU-bridge': 39.72451574822825, 'IoU-cardboard': 49.09082660595453, 'IoU-counter': 31.950972257175025, 'IoU-curtain': 70.7249497138406, 'IoU-door-stuff': 47.88966938561428, 'IoU-floor-wood': 64.45468188392822, 'IoU-flower': 45.63012593661507, 'IoU-fruit': 49.40212375043388, 'IoU-gravel': 30.43775175060753, 'IoU-house': 23.60633019460705, 'IoU-light': 44.38210305281861, 'IoU-mirror-stuff': 63.636877716456866, 'IoU-net': 39.71575341336595, 'IoU-pillow': 29.355680924006883, 'IoU-platform': 28.11029040998731, 'IoU-playingfield': 69.6112574057403, 'IoU-railroad': 63.95176905916278, 'IoU-river': 54.67770828086391, 'IoU-road': 67.19374192703216, 'IoU-roof': 19.184964189077846, 'IoU-sand': 65.83296985168009, 'IoU-sea': 85.51433252072962, 'IoU-shelf': 39.83426319485435, 'IoU-snow': 92.32287601189948, 'IoU-stairs': 34.529562704657195, 'IoU-tent': 11.517222582226129, 'IoU-towel': 45.67584217372108, 'IoU-wall-brick': 52.4419981580395, 'IoU-wall-stone': 31.16108350204786, 'IoU-wall-tile': 71.96827431569821, 'IoU-wall-wood': 45.66437684202042, 'IoU-water-other': 27.14271437016076, 'IoU-window-blind': 50.30951098174491, 'IoU-window-other': 51.14987420517053, 'IoU-tree-merged': 82.23085577706713, 'IoU-fence-merged': 55.00957644347031, 'IoU-ceiling-merged': 68.42294076174089, 'IoU-sky-other-merged': 94.19055008222973, 'IoU-cabinet-merged': 63.610841730136954, 'IoU-table-merged': 41.07419375186779, 'IoU-floor-other-merged': 54.87745438602233, 'IoU-pavement-merged': 56.66204596016065, 'IoU-mountain-merged': 58.6023923503029, 'IoU-grass-merged': 72.38429047864405, 'IoU-dirt-merged': 47.567469524054296, 'IoU-paper-merged': 30.666836407575897, 'IoU-food-other-merged': 43.74149834714874, 'IoU-building-other-merged': 60.4599087579284, 'IoU-rock-merged': 67.36944043950295, 'IoU-wall-other-merged': 68.46181008080418, 'IoU-rug-merged': 66.59395692916848, 'mACC': 78.1540519205809, 'pACC': 82.44381142495108, 'ACC-person': 93.07363705250403, 'ACC-bicycle': 88.17069160948681, 'ACC-car': 87.372905718234, 'ACC-motorcycle': 94.14375464721012, 'ACC-airplane': 94.3699702058871, 'ACC-bus': 94.81383143776529, 'ACC-train': 95.63528282708197, 'ACC-truck': 73.36255300218386, 'ACC-boat': 82.13755928724729, 'ACC-traffic light': 91.14943942747193, 'ACC-fire hydrant': 96.15419515173771, 'ACC-stop sign': 98.52607197592378, 'ACC-parking meter': 88.28330635535505, 'ACC-bench': 77.16452304769255, 'ACC-bird': 82.67460796398062, 'ACC-cat': 87.40218969135015, 'ACC-dog': 83.28032989164831, 'ACC-horse': 94.94161720618847, 'ACC-sheep': 90.71430446834128, 'ACC-cow': 94.12588364367429, 'ACC-elephant': 92.83226496125259, 'ACC-bear': 87.9652917215131, 'ACC-zebra': 95.32984978942469, 'ACC-giraffe': 93.7788167348851, 'ACC-backpack': 68.57214222487094, 'ACC-umbrella': 93.68029130457892, 'ACC-handbag': 73.47237894653752, 'ACC-tie': 84.1894444041776, 'ACC-suitcase': 95.00267303221315, 'ACC-frisbee': 94.03236363636364, 'ACC-skis': 74.27844236908724, 'ACC-snowboard': 82.11456981215252, 'ACC-sports ball': 85.99921439720491, 'ACC-kite': 86.9010493356389, 'ACC-baseball bat': 88.08899444814901, 'ACC-baseball glove': 92.05846537400114, 'ACC-skateboard': 90.86531771929745, 'ACC-surfboard': 92.88070230909653, 'ACC-tennis racket': 94.86731349351751, 'ACC-bottle': 82.99578863609469, 'ACC-wine glass': 90.90234084604087, 'ACC-cup': 88.76353224995529, 'ACC-fork': 82.27996432000074, 'ACC-knife': 79.24910236682055, 'ACC-spoon': 77.63186557992906, 'ACC-bowl': 68.67748635284504, 'ACC-banana': 92.32860198038568, 'ACC-apple': 72.77727586275572, 'ACC-sandwich': 82.7255381883882, 'ACC-orange': 90.8633124002687, 'ACC-broccoli': 81.42956868591642, 'ACC-carrot': 82.39160904299834, 'ACC-hot dog': 74.03019448402685, 'ACC-pizza': 92.85189698725658, 'ACC-donut': 74.75858410709131, 'ACC-cake': 88.3536934372014, 'ACC-chair': 84.05152829438376, 'ACC-couch': 74.59259263115237, 'ACC-potted plant': 60.17953465534123, 'ACC-bed': 88.87458158476227, 'ACC-dining table': 77.0785348711334, 'ACC-toilet': 93.03087031326194, 'ACC-tv': 89.52171740537868, 'ACC-laptop': 91.52246696068056, 'ACC-mouse': 91.78453802004985, 'ACC-remote': 72.26257699647456, 'ACC-keyboard': 77.39542607203312, 'ACC-cell phone': 94.66986692928026, 'ACC-microwave': 84.41290600378986, 'ACC-oven': 93.07485166319516, 'ACC-toaster': 91.55182088163242, 'ACC-sink': 84.54545120641801, 'ACC-refrigerator': 92.87648899171569, 'ACC-book': 72.98548653722227, 'ACC-clock': 88.91425084635534, 'ACC-vase': 82.12731841645778, 'ACC-scissors': 94.72552773416338, 'ACC-teddy bear': 86.4620623847428, 'ACC-hair drier': 64.04377650641847, 'ACC-toothbrush': 80.78092425295344, 'ACC-banner': 81.24942002989268, 'ACC-blanket': 25.374988819956577, 'ACC-bridge': 56.01744156516969, 'ACC-cardboard': 64.82777373854879, 'ACC-counter': 53.438043912650016, 'ACC-curtain': 84.11983332898477, 'ACC-door-stuff': 73.50421117382318, 'ACC-floor-wood': 84.2056006136229, 'ACC-flower': 62.856547509960414, 'ACC-fruit': 69.03761021655663, 'ACC-gravel': 43.86548292082981, 'ACC-house': 27.1258490062901, 'ACC-light': 61.63067507560387, 'ACC-mirror-stuff': 80.0936506965157, 'ACC-net': 66.76327393898278, 'ACC-pillow': 55.744430335301864, 'ACC-platform': 45.868334852924, 'ACC-playingfield': 87.54157241939738, 'ACC-railroad': 79.91215680861849, 'ACC-river': 78.6173861507775, 'ACC-road': 88.12769799592608, 'ACC-roof': 26.185423771630667, 'ACC-sand': 71.06189172963491, 'ACC-sea': 91.8915380256473, 'ACC-shelf': 54.3260730246426, 'ACC-snow': 95.27037719979481, 'ACC-stairs': 56.381833534767, 'ACC-tent': 14.336542442631247, 'ACC-towel': 55.28046013117205, 'ACC-wall-brick': 68.74803134698037, 'ACC-wall-stone': 38.21821961921441, 'ACC-wall-tile': 87.09002169161218, 'ACC-wall-wood': 62.506949218031004, 'ACC-water-other': 39.46718407746532, 'ACC-window-blind': 65.97970923793103, 'ACC-window-other': 72.7832571982136, 'ACC-tree-merged': 89.96420818793214, 'ACC-fence-merged': 71.6070002852939, 'ACC-ceiling-merged': 83.49258053314055, 'ACC-sky-other-merged': 96.90922604054978, 'ACC-cabinet-merged': 79.04161344095006, 'ACC-table-merged': 54.72900171423687, 'ACC-floor-other-merged': 65.54027123364261, 'ACC-pavement-merged': 67.10478665933674, 'ACC-mountain-merged': 71.08673282343126, 'ACC-grass-merged': 82.3337536538519, 'ACC-dirt-merged': 71.95458548331867, 'ACC-paper-merged': 39.345951888608674, 'ACC-food-other-merged': 60.187484219947166, 'ACC-building-other-merged': 76.2513570835609, 'ACC-rock-merged': 83.13530824998804, 'ACC-wall-other-merged': 80.81845183377968, 'ACC-rug-merged': 82.65540243408942})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3442 s/iter. Inference: 0.1905 s/iter. Eval: 0.0000 s/iter. Total: 0.5347 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3290 s/iter. Inference: 0.3506 s/iter. Eval: 0.0000 s/iter. Total: 0.6797 s/iter. ETA=0:00:04 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3511 s/iter. Inference: 0.5335 s/iter. Eval: 0.0000 s/iter. Total: 0.8848 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.376353526485221, 'noc@0.8': 2.4000585308750364, 'noc@0.85': 2.8065554580040972, 'noc@0.9': 3.616915422885572, 'miou@iter1': 0.8743550979082857} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0019 s/iter. Inference: 0.1464 s/iter. Eval: 0.0010 s/iter. Total: 0.1493 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.24290466308594, 'precision@0.6': 72.6000747680664, 'precision@0.7': 68.86902618408203, 'precision@0.8': 59.580257415771484, 'precision@0.9': 32.91877365112305, 'cIoU': 61.822696685791016, 'mIoU': 67.16964721679688} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.504320657327874, 'SQ': 83.24246198131927, 'RQ': 65.92966790992912, 'PQ_th': 61.61549771051968, 'SQ_th': 84.15009960901455, 'RQ_th': 72.71375517331356, 'PQ_st': 46.27990246383079, 'SQ_st': 81.87244292064723, 'RQ_st': 55.689536191613065}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.93559351867576, 'AP50': 69.67620095559887, 'AP75': 49.54322025352765, 'APs': 25.86033458552856, 'APm': 49.938508733031085, 'APl': 67.98965056345413, 'AP-person': 48.70837889362993, 'AP-bicycle': 23.77198251939471, 'AP-car': 44.772221952510456, 'AP-motorcycle': 42.43548840896436, 'AP-airplane': 62.99332983637457, 'AP-bus': 72.13501426792823, 'AP-train': 74.15417207348392, 'AP-truck': 42.52205706281871, 'AP-boat': 31.260467997190126, 'AP-traffic light': 29.73576442513449, 'AP-fire hydrant': 71.94751299624158, 'AP-stop sign': 69.7867529400979, 'AP-parking meter': 51.79301639810385, 'AP-bench': 27.070144755040804, 'AP-bird': 33.94258916856822, 'AP-cat': 76.84170775857558, 'AP-dog': 71.20642396297363, 'AP-horse': 52.73303311572308, 'AP-sheep': 53.84799241970426, 'AP-cow': 57.253542524451504, 'AP-elephant': 67.0063305772018, 'AP-bear': 79.78481734996997, 'AP-zebra': 65.63752519433676, 'AP-giraffe': 62.62119544128804, 'AP-backpack': 24.343531013623583, 'AP-umbrella': 56.70975973320459, 'AP-handbag': 24.63095077270626, 'AP-tie': 40.28078911669316, 'AP-suitcase': 50.5210824393008, 'AP-frisbee': 69.8493237573657, 'AP-skis': 8.09599167604696, 'AP-snowboard': 35.35718313398573, 'AP-sports ball': 48.699877300732794, 'AP-kite': 37.95318829141212, 'AP-baseball bat': 38.43583626661833, 'AP-baseball glove': 50.46708616639931, 'AP-skateboard': 44.574544582138685, 'AP-surfboard': 45.07766968243597, 'AP-tennis racket': 63.55555207810583, 'AP-bottle': 41.95367746315814, 'AP-wine glass': 38.731526713542394, 'AP-cup': 50.86711334825231, 'AP-fork': 27.89807352334886, 'AP-knife': 25.14326992630105, 'AP-spoon': 22.388801857058837, 'AP-bowl': 40.21026733589953, 'AP-banana': 22.232513127238988, 'AP-apple': 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'AP-hair drier': 36.852745439379106, 'AP-toothbrush': 26.61838367872889}), ('sem_seg', {'mIoU': 66.13577517121175, 'fwIoU': 71.82781800438785, 'IoU-person': 89.0339440688215, 'IoU-bicycle': 77.2809697230771, 'IoU-car': 71.68356263577118, 'IoU-motorcycle': 89.06350138661679, 'IoU-airplane': 87.40614613474892, 'IoU-bus': 86.00642684202414, 'IoU-train': 86.86346781407164, 'IoU-truck': 64.64301471993663, 'IoU-boat': 72.2693636298323, 'IoU-traffic light': 79.47319586662654, 'IoU-fire hydrant': 93.43153409252987, 'IoU-stop sign': 94.90192740010657, 'IoU-parking meter': 85.23433501956285, 'IoU-bench': 64.20054317427652, 'IoU-bird': 76.90715554398766, 'IoU-cat': 82.75241733265362, 'IoU-dog': 80.8840420503824, 'IoU-horse': 88.97110177350844, 'IoU-sheep': 86.97075507270648, 'IoU-cow': 90.57006792431311, 'IoU-elephant': 90.48595300209028, 'IoU-bear': 86.09447204434662, 'IoU-zebra': 92.8550862243921, 'IoU-giraffe': 89.66819132192688, 'IoU-backpack': 51.91677483836453, 'IoU-umbrella': 89.2401601710204, 'IoU-handbag': 51.4517196888894, 'IoU-tie': 76.0145109171438, 'IoU-suitcase': 83.35325657923599, 'IoU-frisbee': 84.66086740156037, 'IoU-skis': 59.230992857123454, 'IoU-snowboard': 71.86626541145806, 'IoU-sports ball': 77.83156988764466, 'IoU-kite': 79.82304035808086, 'IoU-baseball bat': 68.61322175146188, 'IoU-baseball glove': 77.32108814718431, 'IoU-skateboard': 86.35310803749479, 'IoU-surfboard': 86.50508797938129, 'IoU-tennis racket': 90.34932568994167, 'IoU-bottle': 69.14369682826515, 'IoU-wine glass': 82.58560463355954, 'IoU-cup': 71.11618459424834, 'IoU-fork': 70.31689284883844, 'IoU-knife': 63.4351666656891, 'IoU-spoon': 60.77619355606826, 'IoU-bowl': 60.11196930780418, 'IoU-banana': 81.8817757170322, 'IoU-apple': 60.56042156413229, 'IoU-sandwich': 69.53388221561987, 'IoU-orange': 80.2929448793295, 'IoU-broccoli': 69.94608036049924, 'IoU-carrot': 66.06025809560985, 'IoU-hot dog': 66.99668681721988, 'IoU-pizza': 84.27914844836532, 'IoU-donut': 65.63672716751523, 'IoU-cake': 76.064747699745, 'IoU-chair': 62.159827357569164, 'IoU-couch': 68.98084591828692, 'IoU-potted plant': 44.30100971848267, 'IoU-bed': 73.80460196839003, 'IoU-dining table': 55.392035242874535, 'IoU-toilet': 88.14890363097247, 'IoU-tv': 77.75262878206398, 'IoU-laptop': 79.83299828599571, 'IoU-mouse': 82.6323754839045, 'IoU-remote': 68.20326475238102, 'IoU-keyboard': 69.30551260876935, 'IoU-cell phone': 85.44911609688448, 'IoU-microwave': 79.41958996403785, 'IoU-oven': 71.53094692430344, 'IoU-toaster': 71.1390910879402, 'IoU-sink': 74.87249360299539, 'IoU-refrigerator': 82.35698747552081, 'IoU-book': 53.61458646887357, 'IoU-clock': 82.6095225528759, 'IoU-vase': 71.40183649589876, 'IoU-scissors': 88.80248766657522, 'IoU-teddy bear': 82.13037363924307, 'IoU-hair drier': 50.87790704247646, 'IoU-toothbrush': 73.3862058080808, 'IoU-banner': 30.343635119339616, 'IoU-blanket': 16.965710934596014, 'IoU-bridge': 39.72451574822825, 'IoU-cardboard': 49.09082660595453, 'IoU-counter': 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'IoU-window-blind': 50.30951098174491, 'IoU-window-other': 51.14987420517053, 'IoU-tree-merged': 82.23085577706713, 'IoU-fence-merged': 55.00957644347031, 'IoU-ceiling-merged': 68.42294076174089, 'IoU-sky-other-merged': 94.19055008222973, 'IoU-cabinet-merged': 63.610841730136954, 'IoU-table-merged': 41.07419375186779, 'IoU-floor-other-merged': 54.87745438602233, 'IoU-pavement-merged': 56.66204596016065, 'IoU-mountain-merged': 58.6023923503029, 'IoU-grass-merged': 72.38429047864405, 'IoU-dirt-merged': 47.567469524054296, 'IoU-paper-merged': 30.666836407575897, 'IoU-food-other-merged': 43.74149834714874, 'IoU-building-other-merged': 60.4599087579284, 'IoU-rock-merged': 67.36944043950295, 'IoU-wall-other-merged': 68.46181008080418, 'IoU-rug-merged': 66.59395692916848, 'mACC': 78.1540519205809, 'pACC': 82.44381142495108, 'ACC-person': 93.07363705250403, 'ACC-bicycle': 88.17069160948681, 'ACC-car': 87.372905718234, 'ACC-motorcycle': 94.14375464721012, 'ACC-airplane': 94.3699702058871, 'ACC-bus': 94.81383143776529, 'ACC-train': 95.63528282708197, 'ACC-truck': 73.36255300218386, 'ACC-boat': 82.13755928724729, 'ACC-traffic light': 91.14943942747193, 'ACC-fire hydrant': 96.15419515173771, 'ACC-stop sign': 98.52607197592378, 'ACC-parking meter': 88.28330635535505, 'ACC-bench': 77.16452304769255, 'ACC-bird': 82.67460796398062, 'ACC-cat': 87.40218969135015, 'ACC-dog': 83.28032989164831, 'ACC-horse': 94.94161720618847, 'ACC-sheep': 90.71430446834128, 'ACC-cow': 94.12588364367429, 'ACC-elephant': 92.83226496125259, 'ACC-bear': 87.9652917215131, 'ACC-zebra': 95.32984978942469, 'ACC-giraffe': 93.7788167348851, 'ACC-backpack': 68.57214222487094, 'ACC-umbrella': 93.68029130457892, 'ACC-handbag': 73.47237894653752, 'ACC-tie': 84.1894444041776, 'ACC-suitcase': 95.00267303221315, 'ACC-frisbee': 94.03236363636364, 'ACC-skis': 74.27844236908724, 'ACC-snowboard': 82.11456981215252, 'ACC-sports ball': 85.99921439720491, 'ACC-kite': 86.9010493356389, 'ACC-baseball bat': 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'ACC-mouse': 91.78453802004985, 'ACC-remote': 72.26257699647456, 'ACC-keyboard': 77.39542607203312, 'ACC-cell phone': 94.66986692928026, 'ACC-microwave': 84.41290600378986, 'ACC-oven': 93.07485166319516, 'ACC-toaster': 91.55182088163242, 'ACC-sink': 84.54545120641801, 'ACC-refrigerator': 92.87648899171569, 'ACC-book': 72.98548653722227, 'ACC-clock': 88.91425084635534, 'ACC-vase': 82.12731841645778, 'ACC-scissors': 94.72552773416338, 'ACC-teddy bear': 86.4620623847428, 'ACC-hair drier': 64.04377650641847, 'ACC-toothbrush': 80.78092425295344, 'ACC-banner': 81.24942002989268, 'ACC-blanket': 25.374988819956577, 'ACC-bridge': 56.01744156516969, 'ACC-cardboard': 64.82777373854879, 'ACC-counter': 53.438043912650016, 'ACC-curtain': 84.11983332898477, 'ACC-door-stuff': 73.50421117382318, 'ACC-floor-wood': 84.2056006136229, 'ACC-flower': 62.856547509960414, 'ACC-fruit': 69.03761021655663, 'ACC-gravel': 43.86548292082981, 'ACC-house': 27.1258490062901, 'ACC-light': 61.63067507560387, 'ACC-mirror-stuff': 80.0936506965157, 'ACC-net': 66.76327393898278, 'ACC-pillow': 55.744430335301864, 'ACC-platform': 45.868334852924, 'ACC-playingfield': 87.54157241939738, 'ACC-railroad': 79.91215680861849, 'ACC-river': 78.6173861507775, 'ACC-road': 88.12769799592608, 'ACC-roof': 26.185423771630667, 'ACC-sand': 71.06189172963491, 'ACC-sea': 91.8915380256473, 'ACC-shelf': 54.3260730246426, 'ACC-snow': 95.27037719979481, 'ACC-stairs': 56.381833534767, 'ACC-tent': 14.336542442631247, 'ACC-towel': 55.28046013117205, 'ACC-wall-brick': 68.74803134698037, 'ACC-wall-stone': 38.21821961921441, 'ACC-wall-tile': 87.09002169161218, 'ACC-wall-wood': 62.506949218031004, 'ACC-water-other': 39.46718407746532, 'ACC-window-blind': 65.97970923793103, 'ACC-window-other': 72.7832571982136, 'ACC-tree-merged': 89.96420818793214, 'ACC-fence-merged': 71.6070002852939, 'ACC-ceiling-merged': 83.49258053314055, 'ACC-sky-other-merged': 96.90922604054978, 'ACC-cabinet-merged': 79.04161344095006, 'ACC-table-merged': 54.72900171423687, 'ACC-floor-other-merged': 65.54027123364261, 'ACC-pavement-merged': 67.10478665933674, 'ACC-mountain-merged': 71.08673282343126, 'ACC-grass-merged': 82.3337536538519, 'ACC-dirt-merged': 71.95458548331867, 'ACC-paper-merged': 39.345951888608674, 'ACC-food-other-merged': 60.187484219947166, 'ACC-building-other-merged': 76.2513570835609, 'ACC-rock-merged': 83.13530824998804, 'ACC-wall-other-merged': 80.81845183377968, 'ACC-rug-merged': 82.65540243408942})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.376353526485221, 'noc@0.8': 2.4000585308750364, 'noc@0.85': 2.8065554580040972, 'noc@0.9': 3.616915422885572, 'miou@iter1': 0.8743550979082857}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.24290466308594, 'precision@0.6': 72.6000747680664, 'precision@0.7': 68.86902618408203, 'precision@0.8': 59.580257415771484, 'precision@0.9': 32.91877365112305, 'cIoU': 61.822696685791016, 'mIoU': 67.16964721679688}}} INFO:trainer.default_trainer:This epoch takes 0:57:25.969294 INFO:trainer.default_trainer:PROGRESS: 64.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 32 training. INFO:trainer.default_trainer:epochs[ 32] optim steps[58500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.41979/0.75961, loss_mask_bce_0: 0.19907/0.30078, loss_mask_dice_0: 0.10399/1.02380, loss_spatial_bce_0: 0.14573/0.08538, loss_spatial_dice_0: 0.05888/0.18075, loss_spatial_ce_0: 0.14663/0.05815, loss_grounding_bce_0: 0.06620/0.08065, loss_grounding_dice_0: 0.05507/0.15082, loss_grounding_ce_0: 0.00305/0.24916, loss_mask_ce_1: 0.41986/0.76008, loss_mask_bce_1: 0.20246/0.30165, loss_mask_dice_1: 0.11400/1.02803, loss_spatial_bce_1: 0.14803/0.08566, loss_spatial_dice_1: 0.06665/0.18341, loss_spatial_ce_1: 0.13208/0.06201, loss_grounding_bce_1: 0.05774/0.08082, loss_grounding_dice_1: 0.07386/0.15158, loss_grounding_ce_1: 0.00398/0.25076, loss_mask_ce_2: 0.48993/0.76830, loss_mask_bce_2: 0.20231/0.30185, loss_mask_dice_2: 0.11552/1.02882, loss_spatial_bce_2: 0.14191/0.08571, loss_spatial_dice_2: 0.06988/0.18381, loss_spatial_ce_2: 0.14109/0.06422, loss_grounding_bce_2: 0.06203/0.08084, loss_grounding_dice_2: 0.06821/0.15147, loss_grounding_ce_2: 0.00452/0.25404, loss_mask_ce_3: 0.56695/0.77161, loss_mask_bce_3: 0.20788/0.30332, loss_mask_dice_3: 0.10812/1.02677, loss_spatial_bce_3: 0.15199/0.08780, loss_spatial_dice_3: 0.07421/0.18511, loss_spatial_ce_3: 0.14524/0.06888, loss_grounding_bce_3: 0.06250/0.08125, loss_grounding_dice_3: 0.07169/0.15113, loss_grounding_ce_3: 0.00822/0.25447, loss_mask_ce_4: 0.41246/0.77755, loss_mask_bce_4: 0.20870/0.30581, loss_mask_dice_4: 0.10948/1.04563, loss_spatial_bce_4: 0.14460/0.08984, loss_spatial_dice_4: 0.06609/0.19302, loss_spatial_ce_4: 0.12902/0.08217, loss_grounding_bce_4: 0.05491/0.08184, loss_grounding_dice_4: 0.06369/0.15373, loss_grounding_ce_4: 0.01728/0.25927, loss_mask_ce_5: 0.53980/0.80144, loss_mask_bce_5: 0.20328/0.30764, loss_mask_dice_5: 0.10829/1.05315, loss_spatial_bce_5: 0.15043/0.09202, loss_spatial_dice_5: 0.08183/0.19600, loss_spatial_ce_5: 0.14741/0.09456, loss_grounding_bce_5: 0.05794/0.08215, loss_grounding_dice_5: 0.06091/0.15441, loss_grounding_ce_5: 0.00473/0.27751, loss_mask_ce_6: 0.74840/0.82815, loss_mask_bce_6: 0.20102/0.30968, loss_mask_dice_6: 0.10747/1.05655, loss_spatial_bce_6: 0.14396/0.09709, loss_spatial_dice_6: 0.06828/0.19827, loss_spatial_ce_6: 0.16010/0.11895, loss_grounding_bce_6: 0.06508/0.08304, loss_grounding_dice_6: 0.05603/0.15497, loss_grounding_ce_6: 0.01733/0.28705, loss_mask_ce_7: 0.94393/0.88408, loss_mask_bce_7: 0.20187/0.31694, loss_mask_dice_7: 0.11101/1.10314, loss_spatial_bce_7: 0.14673/0.10705, loss_spatial_dice_7: 0.06948/0.22395, loss_spatial_ce_7: 0.11307/0.15708, loss_grounding_bce_7: 0.06237/0.08477, loss_grounding_dice_7: 0.06553/0.16061, loss_grounding_ce_7: 0.00932/0.32021, loss_mask_ce_8: 1.62270/1.01995, loss_mask_bce_8: 0.21908/0.33302, loss_mask_dice_8: 0.14868/1.18043, loss_spatial_bce_8: 0.14033/0.12457, loss_spatial_dice_8: 0.08461/0.25965, loss_spatial_ce_8: 0.11165/0.20519, loss_grounding_bce_8: 0.08616/0.08894, loss_grounding_dice_8: 0.05406/0.17035, loss_grounding_ce_8: 0.00473/0.42081, loss_mask_ce_9: 3.36167/3.47981, loss_mask_bce_9: 0.36411/0.35987, loss_mask_dice_9: 0.27277/1.76183, loss_spatial_bce_9: 0.66441/0.35473, loss_spatial_dice_9: 0.56190/0.79371, loss_spatial_ce_9: 0.93545/1.39226, loss_grounding_bce_9: 0.09820/0.10094, loss_grounding_dice_9: 0.09602/0.24267, loss_grounding_ce_9: 2.83401/0.67622] items per batch[64] items per second[0.16] total items[3744000] mini batches[ 58500] memory[4999] epoch remaining[0:58:17] INFO:trainer.default_trainer:epochs[ 32] optim steps[58600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.87145/0.75943, loss_mask_bce_0: 0.11640/0.30069, loss_mask_dice_0: 0.20802/1.02373, loss_spatial_bce_0: 0.02660/0.08535, loss_spatial_dice_0: 0.07315/0.18072, loss_spatial_ce_0: 0.01063/0.05812, loss_grounding_bce_0: 0.02160/0.08062, loss_grounding_dice_0: 0.02527/0.15081, loss_grounding_ce_0: 0.58002/0.24906, loss_mask_ce_1: 0.91173/0.75990, loss_mask_bce_1: 0.10944/0.30156, loss_mask_dice_1: 0.20916/1.02802, loss_spatial_bce_1: 0.02542/0.08563, loss_spatial_dice_1: 0.06373/0.18338, loss_spatial_ce_1: 0.02272/0.06199, loss_grounding_bce_1: 0.02018/0.08080, loss_grounding_dice_1: 0.02261/0.15157, loss_grounding_ce_1: 0.67496/0.25066, loss_mask_ce_2: 0.73668/0.76815, loss_mask_bce_2: 0.10967/0.30176, loss_mask_dice_2: 0.21769/1.02875, loss_spatial_bce_2: 0.02795/0.08568, loss_spatial_dice_2: 0.07274/0.18379, loss_spatial_ce_2: 0.02149/0.06418, loss_grounding_bce_2: 0.02258/0.08081, loss_grounding_dice_2: 0.02497/0.15146, loss_grounding_ce_2: 0.87599/0.25398, loss_mask_ce_3: 0.71460/0.77140, loss_mask_bce_3: 0.12560/0.30323, loss_mask_dice_3: 0.21051/1.02667, loss_spatial_bce_3: 0.03064/0.08777, loss_spatial_dice_3: 0.07013/0.18508, loss_spatial_ce_3: 0.00854/0.06887, loss_grounding_bce_3: 0.02109/0.08123, loss_grounding_dice_3: 0.02320/0.15112, loss_grounding_ce_3: 0.97882/0.25437, loss_mask_ce_4: 0.65798/0.77738, loss_mask_bce_4: 0.10975/0.30571, loss_mask_dice_4: 0.23363/1.04555, loss_spatial_bce_4: 0.02709/0.08981, loss_spatial_dice_4: 0.07749/0.19299, loss_spatial_ce_4: 0.00683/0.08214, loss_grounding_bce_4: 0.01834/0.08181, loss_grounding_dice_4: 0.02024/0.15371, loss_grounding_ce_4: 0.45578/0.25918, loss_mask_ce_5: 0.58157/0.80128, loss_mask_bce_5: 0.11205/0.30756, loss_mask_dice_5: 0.22170/1.05308, loss_spatial_bce_5: 0.02807/0.09199, loss_spatial_dice_5: 0.07471/0.19597, loss_spatial_ce_5: 0.06017/0.09455, loss_grounding_bce_5: 0.01976/0.08213, loss_grounding_dice_5: 0.02282/0.15440, loss_grounding_ce_5: 0.54215/0.27738, loss_mask_ce_6: 0.63068/0.82799, loss_mask_bce_6: 0.09899/0.30959, loss_mask_dice_6: 0.17374/1.05653, loss_spatial_bce_6: 0.03323/0.09707, loss_spatial_dice_6: 0.08013/0.19825, loss_spatial_ce_6: 0.12603/0.11894, loss_grounding_bce_6: 0.02070/0.08302, loss_grounding_dice_6: 0.02267/0.15495, loss_grounding_ce_6: 0.59955/0.28692, loss_mask_ce_7: 0.94599/0.88391, loss_mask_bce_7: 0.08618/0.31684, loss_mask_dice_7: 0.19128/1.10307, loss_spatial_bce_7: 0.04772/0.10702, loss_spatial_dice_7: 0.09645/0.22392, loss_spatial_ce_7: 0.17067/0.15703, loss_grounding_bce_7: 0.01866/0.08474, loss_grounding_dice_7: 0.02097/0.16060, loss_grounding_ce_7: 0.72602/0.32008, loss_mask_ce_8: 0.95338/1.01980, loss_mask_bce_8: 0.09399/0.33294, loss_mask_dice_8: 0.19584/1.18035, loss_spatial_bce_8: 0.05270/0.12452, loss_spatial_dice_8: 0.09853/0.25961, loss_spatial_ce_8: 0.23062/0.20514, loss_grounding_bce_8: 0.01645/0.08891, loss_grounding_dice_8: 0.01772/0.17033, loss_grounding_ce_8: 1.88265/0.42065, loss_mask_ce_9: 2.58220/3.47937, loss_mask_bce_9: 0.14112/0.35980, loss_mask_dice_9: 0.46706/1.76159, loss_spatial_bce_9: 0.34392/0.35474, loss_spatial_dice_9: 0.83882/0.79368, loss_spatial_ce_9: 1.16948/1.39224, loss_grounding_bce_9: 0.01821/0.10092, loss_grounding_dice_9: 0.02126/0.24265, loss_grounding_ce_9: 1.55707/0.67594] items per batch[64] items per second[0.36] total items[3750400] mini batches[ 58600] memory[4999] epoch remaining[0:51:22] INFO:trainer.default_trainer:epochs[ 32] optim steps[58700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.76892/0.75935, loss_mask_bce_0: 0.67026/0.30067, loss_mask_dice_0: 0.48194/1.02386, loss_spatial_bce_0: 0.25490/0.08533, loss_spatial_dice_0: 0.18935/0.18070, loss_spatial_ce_0: 0.00341/0.05810, loss_grounding_bce_0: 0.02773/0.08059, loss_grounding_dice_0: 0.03029/0.15078, loss_grounding_ce_0: 0.04490/0.24898, loss_mask_ce_1: 0.74744/0.75986, loss_mask_bce_1: 0.66860/0.30154, loss_mask_dice_1: 0.47363/1.02814, loss_spatial_bce_1: 0.24566/0.08561, loss_spatial_dice_1: 0.18095/0.18336, loss_spatial_ce_1: 0.00262/0.06198, loss_grounding_bce_1: 0.02985/0.08077, loss_grounding_dice_1: 0.02791/0.15155, loss_grounding_ce_1: 0.03954/0.25058, loss_mask_ce_2: 0.73360/0.76807, loss_mask_bce_2: 0.67867/0.30175, loss_mask_dice_2: 0.49017/1.02888, loss_spatial_bce_2: 0.24429/0.08567, loss_spatial_dice_2: 0.18029/0.18377, loss_spatial_ce_2: 0.00347/0.06417, loss_grounding_bce_2: 0.02962/0.08078, loss_grounding_dice_2: 0.02761/0.15144, loss_grounding_ce_2: 0.03000/0.25391, loss_mask_ce_3: 0.73532/0.77132, loss_mask_bce_3: 0.68403/0.30322, loss_mask_dice_3: 0.48950/1.02683, loss_spatial_bce_3: 0.23526/0.08775, loss_spatial_dice_3: 0.18859/0.18507, loss_spatial_ce_3: 0.00459/0.06884, loss_grounding_bce_3: 0.02915/0.08120, loss_grounding_dice_3: 0.02610/0.15111, loss_grounding_ce_3: 0.02944/0.25427, loss_mask_ce_4: 0.82910/0.77735, loss_mask_bce_4: 0.68873/0.30569, loss_mask_dice_4: 0.45982/1.04563, loss_spatial_bce_4: 0.23391/0.08979, loss_spatial_dice_4: 0.18216/0.19298, loss_spatial_ce_4: 0.01026/0.08212, loss_grounding_bce_4: 0.02976/0.08178, loss_grounding_dice_4: 0.02786/0.15370, loss_grounding_ce_4: 0.04284/0.25912, loss_mask_ce_5: 0.89127/0.80124, loss_mask_bce_5: 0.66644/0.30753, loss_mask_dice_5: 0.47940/1.05321, loss_spatial_bce_5: 0.24889/0.09197, loss_spatial_dice_5: 0.20158/0.19597, loss_spatial_ce_5: 0.02341/0.09454, loss_grounding_bce_5: 0.03030/0.08210, loss_grounding_dice_5: 0.03045/0.15438, loss_grounding_ce_5: 0.06828/0.27731, loss_mask_ce_6: 1.05291/0.82795, loss_mask_bce_6: 0.77000/0.30959, loss_mask_dice_6: 0.57042/1.05668, loss_spatial_bce_6: 0.25583/0.09705, loss_spatial_dice_6: 0.20480/0.19823, loss_spatial_ce_6: 0.11375/0.11895, loss_grounding_bce_6: 0.03108/0.08298, loss_grounding_dice_6: 0.03076/0.15493, loss_grounding_ce_6: 0.07104/0.28688, loss_mask_ce_7: 1.17308/0.88385, loss_mask_bce_7: 0.67391/0.31683, loss_mask_dice_7: 0.45626/1.10321, loss_spatial_bce_7: 0.26521/0.10700, loss_spatial_dice_7: 0.18712/0.22391, loss_spatial_ce_7: 0.21518/0.15704, loss_grounding_bce_7: 0.03059/0.08472, loss_grounding_dice_7: 0.02815/0.16057, loss_grounding_ce_7: 0.09122/0.32000, loss_mask_ce_8: 1.21621/1.01977, loss_mask_bce_8: 0.67218/0.33292, loss_mask_dice_8: 0.47413/1.18046, loss_spatial_bce_8: 0.30423/0.12451, loss_spatial_dice_8: 0.23327/0.25958, loss_spatial_ce_8: 0.25004/0.20508, loss_grounding_bce_8: 0.02856/0.08889, loss_grounding_dice_8: 0.02794/0.17030, loss_grounding_ce_8: 0.05699/0.42062, loss_mask_ce_9: 2.57391/3.47924, loss_mask_bce_9: 0.68423/0.35977, loss_mask_dice_9: 0.52554/1.76148, loss_spatial_bce_9: 0.49624/0.35477, loss_spatial_dice_9: 0.82074/0.79368, loss_spatial_ce_9: 1.16433/1.39222, loss_grounding_bce_9: 0.03520/0.10089, loss_grounding_dice_9: 0.03143/0.24262, loss_grounding_ce_9: 0.33448/0.67595] items per batch[64] items per second[0.36] total items[3756800] mini batches[ 58700] memory[4999] epoch remaining[0:47:42] INFO:trainer.default_trainer:epochs[ 32] optim steps[58800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.13048/0.75915, loss_mask_bce_0: 0.78157/0.30060, loss_mask_dice_0: 5.06073/1.02388, loss_spatial_bce_0: 0.06388/0.08532, loss_spatial_dice_0: 0.25786/0.18071, loss_spatial_ce_0: 0.03366/0.05809, loss_grounding_bce_0: 0.00000/0.08060, loss_grounding_dice_0: 0.00001/0.15083, loss_grounding_ce_0: 0.26458/0.24894, loss_mask_ce_1: 1.06456/0.75968, loss_mask_bce_1: 0.80478/0.30147, loss_mask_dice_1: 5.04797/1.02813, loss_spatial_bce_1: 0.06215/0.08560, loss_spatial_dice_1: 0.27664/0.18336, loss_spatial_ce_1: 0.02517/0.06197, loss_grounding_bce_1: 0.00000/0.08078, loss_grounding_dice_1: 0.00001/0.15160, loss_grounding_ce_1: 0.52068/0.25058, loss_mask_ce_2: 1.05048/0.76788, loss_mask_bce_2: 0.81021/0.30167, loss_mask_dice_2: 5.28889/1.02889, loss_spatial_bce_2: 0.06843/0.08565, loss_spatial_dice_2: 0.27100/0.18378, loss_spatial_ce_2: 0.06621/0.06415, loss_grounding_bce_2: 0.00000/0.08079, loss_grounding_dice_2: 0.00001/0.15149, loss_grounding_ce_2: 0.38361/0.25388, loss_mask_ce_3: 1.14419/0.77111, loss_mask_bce_3: 0.81605/0.30315, loss_mask_dice_3: 5.08548/1.02683, loss_spatial_bce_3: 0.06726/0.08774, loss_spatial_dice_3: 0.26735/0.18508, loss_spatial_ce_3: 0.10155/0.06881, loss_grounding_bce_3: 0.00000/0.08121, loss_grounding_dice_3: 0.00000/0.15115, loss_grounding_ce_3: 0.54565/0.25426, loss_mask_ce_4: 0.92243/0.77714, loss_mask_bce_4: 0.82750/0.30562, loss_mask_dice_4: 5.26756/1.04563, loss_spatial_bce_4: 0.05832/0.08978, loss_spatial_dice_4: 0.24592/0.19300, loss_spatial_ce_4: 0.20114/0.08210, loss_grounding_bce_4: 0.00000/0.08179, loss_grounding_dice_4: 0.00006/0.15375, loss_grounding_ce_4: 0.47546/0.25910, loss_mask_ce_5: 1.19041/0.80104, loss_mask_bce_5: 0.84217/0.30746, loss_mask_dice_5: 5.01790/1.05323, loss_spatial_bce_5: 0.06203/0.09196, loss_spatial_dice_5: 0.25819/0.19599, loss_spatial_ce_5: 0.08533/0.09454, loss_grounding_bce_5: 0.00000/0.08211, loss_grounding_dice_5: 0.00004/0.15444, loss_grounding_ce_5: 0.95866/0.27729, loss_mask_ce_6: 1.38296/0.82774, loss_mask_bce_6: 0.86404/0.30951, loss_mask_dice_6: 5.21082/1.05664, loss_spatial_bce_6: 0.08443/0.09705, loss_spatial_dice_6: 0.31593/0.19825, loss_spatial_ce_6: 0.05079/0.11895, loss_grounding_bce_6: 0.00000/0.08299, loss_grounding_dice_6: 0.00014/0.15496, loss_grounding_ce_6: 0.36592/0.28684, loss_mask_ce_7: 1.38861/0.88364, loss_mask_bce_7: 0.87812/0.31674, loss_mask_dice_7: 5.48079/1.10321, loss_spatial_bce_7: 0.10702/0.10698, loss_spatial_dice_7: 0.33647/0.22394, loss_spatial_ce_7: 0.04942/0.15704, loss_grounding_bce_7: 0.00000/0.08472, loss_grounding_dice_7: 0.00003/0.16061, loss_grounding_ce_7: 0.95749/0.31995, loss_mask_ce_8: 1.28300/1.01959, loss_mask_bce_8: 0.89672/0.33282, loss_mask_dice_8: 5.80471/1.18048, loss_spatial_bce_8: 0.12566/0.12448, loss_spatial_dice_8: 0.35442/0.25960, loss_spatial_ce_8: 0.03952/0.20505, loss_grounding_bce_8: 0.00000/0.08889, loss_grounding_dice_8: 0.00053/0.17035, loss_grounding_ce_8: 3.20585/0.42053, loss_mask_ce_9: 7.71724/3.47912, loss_mask_bce_9: 1.17858/0.35968, loss_mask_dice_9: 10.38051/1.76140, loss_spatial_bce_9: 0.18797/0.35468, loss_spatial_dice_9: 0.96949/0.79364, loss_spatial_ce_9: 1.34425/1.39215, loss_grounding_bce_9: 0.00000/0.10088, loss_grounding_dice_9: 0.00210/0.24266, loss_grounding_ce_9: 3.45717/0.67583] items per batch[64] items per second[0.36] total items[3763200] mini batches[ 58800] memory[4999] epoch remaining[0:44:32] INFO:trainer.default_trainer:epochs[ 32] optim steps[58900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83237/0.75917, loss_mask_bce_0: 0.26537/0.30062, loss_mask_dice_0: 0.57791/1.02400, loss_spatial_bce_0: 0.01644/0.08532, loss_spatial_dice_0: 0.14558/0.18071, loss_spatial_ce_0: 0.00202/0.05806, loss_grounding_bce_0: 0.13533/0.08059, loss_grounding_dice_0: 0.18181/0.15084, loss_grounding_ce_0: 1.30886/0.24891, loss_mask_ce_1: 0.82788/0.75966, loss_mask_bce_1: 0.26597/0.30149, loss_mask_dice_1: 0.52877/1.02823, loss_spatial_bce_1: 0.02430/0.08559, loss_spatial_dice_1: 0.18995/0.18336, loss_spatial_ce_1: 0.00160/0.06196, loss_grounding_bce_1: 0.13801/0.08077, loss_grounding_dice_1: 0.18552/0.15159, loss_grounding_ce_1: 1.44134/0.25056, loss_mask_ce_2: 0.87514/0.76790, loss_mask_bce_2: 0.26121/0.30169, loss_mask_dice_2: 0.52834/1.02900, loss_spatial_bce_2: 0.02216/0.08564, loss_spatial_dice_2: 0.16671/0.18378, loss_spatial_ce_2: 0.00326/0.06413, loss_grounding_bce_2: 0.14401/0.08078, loss_grounding_dice_2: 0.18910/0.15148, loss_grounding_ce_2: 1.24716/0.25387, loss_mask_ce_3: 0.89192/0.77111, loss_mask_bce_3: 0.27305/0.30317, loss_mask_dice_3: 0.52289/1.02695, loss_spatial_bce_3: 0.01624/0.08773, loss_spatial_dice_3: 0.15300/0.18509, loss_spatial_ce_3: 0.00293/0.06878, loss_grounding_bce_3: 0.13730/0.08120, loss_grounding_dice_3: 0.18133/0.15114, loss_grounding_ce_3: 1.20307/0.25424, loss_mask_ce_4: 0.83868/0.77713, loss_mask_bce_4: 0.34817/0.30564, loss_mask_dice_4: 0.57868/1.04574, loss_spatial_bce_4: 0.04235/0.08977, loss_spatial_dice_4: 0.17420/0.19302, loss_spatial_ce_4: 0.05699/0.08208, loss_grounding_bce_4: 0.18342/0.08177, loss_grounding_dice_4: 0.19079/0.15374, loss_grounding_ce_4: 0.98836/0.25915, loss_mask_ce_5: 0.90572/0.80108, loss_mask_bce_5: 0.33129/0.30749, loss_mask_dice_5: 0.53158/1.05335, loss_spatial_bce_5: 0.02419/0.09195, loss_spatial_dice_5: 0.20156/0.19600, loss_spatial_ce_5: 0.08541/0.09456, loss_grounding_bce_5: 0.03917/0.08209, loss_grounding_dice_5: 0.17309/0.15444, loss_grounding_ce_5: 3.17004/0.27729, loss_mask_ce_6: 0.95355/0.82780, loss_mask_bce_6: 0.34446/0.30952, loss_mask_dice_6: 0.57906/1.05676, loss_spatial_bce_6: 0.04167/0.09705, loss_spatial_dice_6: 0.22566/0.19827, loss_spatial_ce_6: 0.13841/0.11897, loss_grounding_bce_6: 0.29014/0.08297, loss_grounding_dice_6: 0.25367/0.15496, loss_grounding_ce_6: 0.24841/0.28679, loss_mask_ce_7: 1.17512/0.88361, loss_mask_bce_7: 0.23203/0.31676, loss_mask_dice_7: 0.55203/1.10334, loss_spatial_bce_7: 0.02548/0.10697, loss_spatial_dice_7: 0.19738/0.22395, loss_spatial_ce_7: 0.18929/0.15702, loss_grounding_bce_7: 0.24313/0.08470, loss_grounding_dice_7: 0.22039/0.16062, loss_grounding_ce_7: 0.65424/0.31989, loss_mask_ce_8: 1.45556/1.01962, loss_mask_bce_8: 0.31418/0.33287, loss_mask_dice_8: 0.67441/1.18062, loss_spatial_bce_8: 0.04886/0.12446, loss_spatial_dice_8: 0.30984/0.25960, loss_spatial_ce_8: 0.11955/0.20502, loss_grounding_bce_8: 0.41723/0.08887, loss_grounding_dice_8: 0.29229/0.17034, loss_grounding_ce_8: 0.21549/0.42062, loss_mask_ce_9: 4.21147/3.47916, loss_mask_bce_9: 0.19987/0.35972, loss_mask_dice_9: 0.82245/1.76175, loss_spatial_bce_9: 0.11887/0.35464, loss_spatial_dice_9: 0.88906/0.79364, loss_spatial_ce_9: 1.16942/1.39215, loss_grounding_bce_9: 0.15430/0.10086, loss_grounding_dice_9: 0.19046/0.24265, loss_grounding_ce_9: 3.18081/0.67601] items per batch[64] items per second[0.36] total items[3769600] mini batches[ 58900] memory[4999] epoch remaining[0:41:31] INFO:trainer.default_trainer:epochs[ 32] optim steps[59000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34250/0.75926, loss_mask_bce_0: 0.36932/0.30055, loss_mask_dice_0: 0.17186/1.02408, loss_spatial_bce_0: 0.20459/0.08528, loss_spatial_dice_0: 0.08396/0.18070, loss_spatial_ce_0: 0.06996/0.05803, loss_grounding_bce_0: 0.19482/0.08055, loss_grounding_dice_0: 0.09127/0.15086, loss_grounding_ce_0: 0.07442/0.24891, loss_mask_ce_1: 0.33401/0.75976, loss_mask_bce_1: 0.36157/0.30141, loss_mask_dice_1: 0.16757/1.02827, loss_spatial_bce_1: 0.19068/0.08556, loss_spatial_dice_1: 0.08208/0.18335, loss_spatial_ce_1: 0.07043/0.06192, loss_grounding_bce_1: 0.19633/0.08073, loss_grounding_dice_1: 0.08910/0.15162, loss_grounding_ce_1: 0.07388/0.25057, loss_mask_ce_2: 0.30621/0.76800, loss_mask_bce_2: 0.35449/0.30161, loss_mask_dice_2: 0.16760/1.02907, loss_spatial_bce_2: 0.21059/0.08561, loss_spatial_dice_2: 0.08631/0.18377, loss_spatial_ce_2: 0.06973/0.06407, loss_grounding_bce_2: 0.19685/0.08074, loss_grounding_dice_2: 0.09024/0.15150, loss_grounding_ce_2: 0.06294/0.25389, loss_mask_ce_3: 0.32208/0.77123, loss_mask_bce_3: 0.35946/0.30309, loss_mask_dice_3: 0.17278/1.02702, loss_spatial_bce_3: 0.20402/0.08770, loss_spatial_dice_3: 0.08680/0.18508, loss_spatial_ce_3: 0.06980/0.06874, loss_grounding_bce_3: 0.19442/0.08116, loss_grounding_dice_3: 0.09081/0.15116, loss_grounding_ce_3: 0.08356/0.25428, loss_mask_ce_4: 0.28119/0.77720, loss_mask_bce_4: 0.36114/0.30557, loss_mask_dice_4: 0.17449/1.04582, loss_spatial_bce_4: 0.20986/0.08974, loss_spatial_dice_4: 0.09987/0.19301, loss_spatial_ce_4: 0.06972/0.08203, loss_grounding_bce_4: 0.19087/0.08173, loss_grounding_dice_4: 0.09571/0.15378, loss_grounding_ce_4: 0.07145/0.25915, loss_mask_ce_5: 0.27998/0.80119, loss_mask_bce_5: 0.36694/0.30741, loss_mask_dice_5: 0.17946/1.05345, loss_spatial_bce_5: 0.21744/0.09191, loss_spatial_dice_5: 0.10080/0.19599, loss_spatial_ce_5: 0.06969/0.09452, loss_grounding_bce_5: 0.18526/0.08205, loss_grounding_dice_5: 0.09659/0.15447, loss_grounding_ce_5: 0.06425/0.27729, loss_mask_ce_6: 0.27649/0.82794, loss_mask_bce_6: 0.37146/0.30946, loss_mask_dice_6: 0.17982/1.05686, loss_spatial_bce_6: 0.24898/0.09701, loss_spatial_dice_6: 0.12215/0.19826, loss_spatial_ce_6: 0.07191/0.11895, loss_grounding_bce_6: 0.19308/0.08293, loss_grounding_dice_6: 0.09194/0.15500, loss_grounding_ce_6: 0.06586/0.28686, loss_mask_ce_7: 0.25706/0.88368, loss_mask_bce_7: 0.36912/0.31667, loss_mask_dice_7: 0.17807/1.10344, loss_spatial_bce_7: 0.18670/0.10693, loss_spatial_dice_7: 0.09074/0.22394, loss_spatial_ce_7: 0.08572/0.15697, loss_grounding_bce_7: 0.20066/0.08466, loss_grounding_dice_7: 0.09866/0.16065, loss_grounding_ce_7: 0.09587/0.31988, loss_mask_ce_8: 0.31402/1.01985, loss_mask_bce_8: 0.37273/0.33279, loss_mask_dice_8: 0.17280/1.18071, loss_spatial_bce_8: 0.22545/0.12441, loss_spatial_dice_8: 0.11625/0.25959, loss_spatial_ce_8: 0.08262/0.20493, loss_grounding_bce_8: 0.19392/0.08883, loss_grounding_dice_8: 0.09002/0.17037, loss_grounding_ce_8: 0.06105/0.42058, loss_mask_ce_9: 1.97837/3.47943, loss_mask_bce_9: 0.30629/0.35962, loss_mask_dice_9: 0.15602/1.76177, loss_spatial_bce_9: 0.58025/0.35462, loss_spatial_dice_9: 0.61043/0.79367, loss_spatial_ce_9: 1.00235/1.39219, loss_grounding_bce_9: 0.15535/0.10081, loss_grounding_dice_9: 0.08128/0.24270, loss_grounding_ce_9: 0.17865/0.67584] items per batch[64] items per second[0.37] total items[3776000] mini batches[ 59000] memory[4999] epoch remaining[0:38:19] INFO:trainer.default_trainer:epochs[ 32] optim steps[59100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09096/0.75917, loss_mask_bce_0: 0.13030/0.30056, loss_mask_dice_0: 0.14524/1.02383, loss_spatial_bce_0: 0.09514/0.08528, loss_spatial_dice_0: 0.11094/0.18070, loss_spatial_ce_0: 0.00046/0.05802, loss_grounding_bce_0: 0.07480/0.08056, loss_grounding_dice_0: 0.13544/0.15087, loss_grounding_ce_0: 0.00323/0.24896, loss_mask_ce_1: 0.07992/0.75971, loss_mask_bce_1: 0.12951/0.30141, loss_mask_dice_1: 0.14060/1.02799, loss_spatial_bce_1: 0.09622/0.08556, loss_spatial_dice_1: 0.11230/0.18335, loss_spatial_ce_1: 0.00053/0.06193, loss_grounding_bce_1: 0.07312/0.08073, loss_grounding_dice_1: 0.13964/0.15163, loss_grounding_ce_1: 0.00322/0.25064, loss_mask_ce_2: 0.06988/0.76788, loss_mask_bce_2: 0.13041/0.30162, loss_mask_dice_2: 0.15115/1.02880, loss_spatial_bce_2: 0.09276/0.08561, loss_spatial_dice_2: 0.10941/0.18378, loss_spatial_ce_2: 0.00068/0.06411, loss_grounding_bce_2: 0.07291/0.08075, loss_grounding_dice_2: 0.13541/0.15151, loss_grounding_ce_2: 0.00296/0.25398, loss_mask_ce_3: 0.06030/0.77112, loss_mask_bce_3: 0.13333/0.30309, loss_mask_dice_3: 0.15378/1.02676, loss_spatial_bce_3: 0.09147/0.08769, loss_spatial_dice_3: 0.10945/0.18509, loss_spatial_ce_3: 0.00068/0.06876, loss_grounding_bce_3: 0.07522/0.08116, loss_grounding_dice_3: 0.12861/0.15117, loss_grounding_ce_3: 0.00262/0.25438, loss_mask_ce_4: 0.08469/0.77715, loss_mask_bce_4: 0.13373/0.30558, loss_mask_dice_4: 0.14612/1.04556, loss_spatial_bce_4: 0.09149/0.08974, loss_spatial_dice_4: 0.10737/0.19303, loss_spatial_ce_4: 0.00135/0.08205, loss_grounding_bce_4: 0.07644/0.08174, loss_grounding_dice_4: 0.13129/0.15379, loss_grounding_ce_4: 0.00217/0.25921, loss_mask_ce_5: 0.09160/0.80113, loss_mask_bce_5: 0.13334/0.30741, loss_mask_dice_5: 0.13900/1.05318, loss_spatial_bce_5: 0.09734/0.09191, loss_spatial_dice_5: 0.09986/0.19601, loss_spatial_ce_5: 0.00155/0.09453, loss_grounding_bce_5: 0.07920/0.08205, loss_grounding_dice_5: 0.13015/0.15447, loss_grounding_ce_5: 0.00229/0.27745, loss_mask_ce_6: 0.11463/0.82786, loss_mask_bce_6: 0.13529/0.30947, loss_mask_dice_6: 0.15076/1.05662, loss_spatial_bce_6: 0.10358/0.09701, loss_spatial_dice_6: 0.11198/0.19828, loss_spatial_ce_6: 0.02885/0.11898, loss_grounding_bce_6: 0.07892/0.08294, loss_grounding_dice_6: 0.13347/0.15500, loss_grounding_ce_6: 0.01031/0.28699, loss_mask_ce_7: 0.12219/0.88360, loss_mask_bce_7: 0.13543/0.31669, loss_mask_dice_7: 0.13710/1.10317, loss_spatial_bce_7: 0.10225/0.10693, loss_spatial_dice_7: 0.12099/0.22395, loss_spatial_ce_7: 0.01589/0.15702, loss_grounding_bce_7: 0.08016/0.08467, loss_grounding_dice_7: 0.12134/0.16066, loss_grounding_ce_7: 0.00445/0.32003, loss_mask_ce_8: 0.15943/1.01979, loss_mask_bce_8: 0.14125/0.33280, loss_mask_dice_8: 0.17683/1.18039, loss_spatial_bce_8: 0.10672/0.12441, loss_spatial_dice_8: 0.12840/0.25960, loss_spatial_ce_8: 0.15400/0.20492, loss_grounding_bce_8: 0.08120/0.08884, loss_grounding_dice_8: 0.17184/0.17037, loss_grounding_ce_8: 0.00149/0.42072, loss_mask_ce_9: 2.07394/3.47929, loss_mask_bce_9: 0.21253/0.35965, loss_mask_dice_9: 0.19400/1.76163, loss_spatial_bce_9: 0.48803/0.35468, loss_spatial_dice_9: 0.57483/0.79366, loss_spatial_ce_9: 0.97790/1.39221, loss_grounding_bce_9: 0.08278/0.10081, loss_grounding_dice_9: 0.18208/0.24268, loss_grounding_ce_9: 0.13407/0.67606] items per batch[64] items per second[0.36] total items[3782400] mini batches[ 59100] memory[4999] epoch remaining[0:35:21] INFO:trainer.default_trainer:epochs[ 32] optim steps[59200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.04011/0.75912, loss_mask_bce_0: 0.28497/0.30056, loss_mask_dice_0: 1.05206/1.02365, loss_spatial_bce_0: 0.01493/0.08527, loss_spatial_dice_0: 0.12911/0.18068, loss_spatial_ce_0: 0.00104/0.05797, loss_grounding_bce_0: 0.19376/0.08056, loss_grounding_dice_0: 0.41372/0.15086, loss_grounding_ce_0: 0.02293/0.24902, loss_mask_ce_1: 1.04335/0.75969, loss_mask_bce_1: 0.27833/0.30142, loss_mask_dice_1: 0.94376/1.02780, loss_spatial_bce_1: 0.01579/0.08555, loss_spatial_dice_1: 0.17518/0.18334, loss_spatial_ce_1: 0.00093/0.06189, loss_grounding_bce_1: 0.18495/0.08073, loss_grounding_dice_1: 0.42228/0.15162, loss_grounding_ce_1: 0.02469/0.25071, loss_mask_ce_2: 1.11358/0.76785, loss_mask_bce_2: 0.28459/0.30162, loss_mask_dice_2: 1.11706/1.02861, loss_spatial_bce_2: 0.01533/0.08560, loss_spatial_dice_2: 0.15105/0.18376, loss_spatial_ce_2: 0.00226/0.06406, loss_grounding_bce_2: 0.19056/0.08074, loss_grounding_dice_2: 0.44756/0.15150, loss_grounding_ce_2: 0.02407/0.25402, loss_mask_ce_3: 1.03923/0.77109, loss_mask_bce_3: 0.26658/0.30310, loss_mask_dice_3: 0.97541/1.02653, loss_spatial_bce_3: 0.01630/0.08769, loss_spatial_dice_3: 0.15398/0.18508, loss_spatial_ce_3: 0.00119/0.06872, loss_grounding_bce_3: 0.18134/0.08115, loss_grounding_dice_3: 0.40607/0.15115, loss_grounding_ce_3: 0.04212/0.25445, loss_mask_ce_4: 1.07918/0.77710, loss_mask_bce_4: 0.25877/0.30558, loss_mask_dice_4: 0.96838/1.04535, loss_spatial_bce_4: 0.01931/0.08973, loss_spatial_dice_4: 0.16675/0.19302, loss_spatial_ce_4: 0.00079/0.08201, loss_grounding_bce_4: 0.17095/0.08173, loss_grounding_dice_4: 0.45754/0.15377, loss_grounding_ce_4: 0.01889/0.25925, loss_mask_ce_5: 0.98829/0.80110, loss_mask_bce_5: 0.23432/0.30740, loss_mask_dice_5: 0.93844/1.05292, loss_spatial_bce_5: 0.01484/0.09191, loss_spatial_dice_5: 0.14475/0.19600, loss_spatial_ce_5: 0.03005/0.09449, loss_grounding_bce_5: 0.16637/0.08204, loss_grounding_dice_5: 0.43143/0.15446, loss_grounding_ce_5: 0.02533/0.27750, loss_mask_ce_6: 1.09159/0.82781, loss_mask_bce_6: 0.23424/0.30946, loss_mask_dice_6: 0.95883/1.05642, loss_spatial_bce_6: 0.01809/0.09700, loss_spatial_dice_6: 0.16072/0.19827, loss_spatial_ce_6: 0.02143/0.11895, loss_grounding_bce_6: 0.16283/0.08293, loss_grounding_dice_6: 0.42670/0.15498, loss_grounding_ce_6: 0.02603/0.28708, loss_mask_ce_7: 1.08626/0.88351, loss_mask_bce_7: 0.23550/0.31669, loss_mask_dice_7: 0.91909/1.10293, loss_spatial_bce_7: 0.02223/0.10691, loss_spatial_dice_7: 0.21863/0.22394, loss_spatial_ce_7: 0.04257/0.15696, loss_grounding_bce_7: 0.16810/0.08467, loss_grounding_dice_7: 0.43728/0.16065, loss_grounding_ce_7: 0.02776/0.32007, loss_mask_ce_8: 1.36687/1.01968, loss_mask_bce_8: 0.30702/0.33281, loss_mask_dice_8: 1.12865/1.18020, loss_spatial_bce_8: 0.01982/0.12440, loss_spatial_dice_8: 0.21810/0.25958, loss_spatial_ce_8: 0.06634/0.20484, loss_grounding_bce_8: 0.15791/0.08883, loss_grounding_dice_8: 0.45496/0.17036, loss_grounding_ce_8: 0.07091/0.42068, loss_mask_ce_9: 3.63288/3.47938, loss_mask_bce_9: 0.30444/0.35965, loss_mask_dice_9: 2.00546/1.76149, loss_spatial_bce_9: 0.13786/0.35467, loss_spatial_dice_9: 0.92385/0.79368, loss_spatial_ce_9: 1.35445/1.39222, loss_grounding_bce_9: 0.17242/0.10080, loss_grounding_dice_9: 0.47047/0.24265, loss_grounding_ce_9: 0.04854/0.67594] items per batch[64] items per second[0.36] total items[3788800] mini batches[ 59200] memory[4999] epoch remaining[0:32:22] INFO:trainer.default_trainer:epochs[ 32] optim steps[59300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63437/0.75914, loss_mask_bce_0: 0.48865/0.30057, loss_mask_dice_0: 2.32867/1.02350, loss_spatial_bce_0: 0.02848/0.08525, loss_spatial_dice_0: 0.17297/0.18067, loss_spatial_ce_0: 0.00231/0.05795, loss_grounding_bce_0: 0.04997/0.08054, loss_grounding_dice_0: 0.10462/0.15085, loss_grounding_ce_0: 0.00076/0.24918, loss_mask_ce_1: 0.52825/0.75965, loss_mask_bce_1: 0.48539/0.30143, loss_mask_dice_1: 2.70334/1.02761, loss_spatial_bce_1: 0.03104/0.08553, loss_spatial_dice_1: 0.19764/0.18332, loss_spatial_ce_1: 0.00122/0.06185, loss_grounding_bce_1: 0.04609/0.08071, loss_grounding_dice_1: 0.09820/0.15161, loss_grounding_ce_1: 0.00070/0.25084, loss_mask_ce_2: 0.72252/0.76779, loss_mask_bce_2: 0.46603/0.30164, loss_mask_dice_2: 2.44226/1.02844, loss_spatial_bce_2: 0.02813/0.08557, loss_spatial_dice_2: 0.18656/0.18374, loss_spatial_ce_2: 0.00241/0.06403, loss_grounding_bce_2: 0.04537/0.08073, loss_grounding_dice_2: 0.10269/0.15150, loss_grounding_ce_2: 0.00068/0.25419, loss_mask_ce_3: 0.52569/0.77109, loss_mask_bce_3: 0.47974/0.30312, loss_mask_dice_3: 2.68172/1.02636, loss_spatial_bce_3: 0.02552/0.08766, loss_spatial_dice_3: 0.17226/0.18506, loss_spatial_ce_3: 0.00154/0.06869, loss_grounding_bce_3: 0.04304/0.08114, loss_grounding_dice_3: 0.10517/0.15114, loss_grounding_ce_3: 0.00144/0.25461, loss_mask_ce_4: 0.69093/0.77712, loss_mask_bce_4: 0.48354/0.30560, loss_mask_dice_4: 2.41470/1.04514, loss_spatial_bce_4: 0.03213/0.08971, loss_spatial_dice_4: 0.26530/0.19300, loss_spatial_ce_4: 0.00899/0.08200, loss_grounding_bce_4: 0.05913/0.08172, loss_grounding_dice_4: 0.10245/0.15375, loss_grounding_ce_4: 0.00043/0.25926, loss_mask_ce_5: 0.73356/0.80113, loss_mask_bce_5: 0.47569/0.30740, loss_mask_dice_5: 2.55260/1.05275, loss_spatial_bce_5: 0.03589/0.09190, loss_spatial_dice_5: 0.27578/0.19600, loss_spatial_ce_5: 0.01422/0.09450, loss_grounding_bce_5: 0.05762/0.08203, loss_grounding_dice_5: 0.09806/0.15445, loss_grounding_ce_5: 0.00050/0.27762, loss_mask_ce_6: 0.63363/0.82781, loss_mask_bce_6: 0.48689/0.30947, loss_mask_dice_6: 2.42646/1.05624, loss_spatial_bce_6: 0.04096/0.09699, loss_spatial_dice_6: 0.23777/0.19827, loss_spatial_ce_6: 0.08356/0.11896, loss_grounding_bce_6: 0.06734/0.08292, loss_grounding_dice_6: 0.10239/0.15498, loss_grounding_ce_6: 0.00027/0.28717, loss_mask_ce_7: 0.72446/0.88353, loss_mask_bce_7: 0.49162/0.31670, loss_mask_dice_7: 2.60728/1.10273, loss_spatial_bce_7: 0.04319/0.10690, loss_spatial_dice_7: 0.30029/0.22393, loss_spatial_ce_7: 0.09771/0.15700, loss_grounding_bce_7: 0.06986/0.08466, loss_grounding_dice_7: 0.09906/0.16065, loss_grounding_ce_7: 0.00095/0.32012, loss_mask_ce_8: 0.98963/1.01971, loss_mask_bce_8: 0.54615/0.33282, loss_mask_dice_8: 3.25987/1.17993, loss_spatial_bce_8: 0.05139/0.12439, loss_spatial_dice_8: 0.37372/0.25958, loss_spatial_ce_8: 0.17539/0.20484, loss_grounding_bce_8: 0.04153/0.08883, loss_grounding_dice_8: 0.10768/0.17035, loss_grounding_ce_8: 0.00059/0.42077, loss_mask_ce_9: 5.28088/3.47944, loss_mask_bce_9: 0.72933/0.35966, loss_mask_dice_9: 5.32696/1.76156, loss_spatial_bce_9: 0.19730/0.35462, loss_spatial_dice_9: 0.97421/0.79365, loss_spatial_ce_9: 1.27383/1.39222, loss_grounding_bce_9: 0.45275/0.10081, loss_grounding_dice_9: 0.73329/0.24265, loss_grounding_ce_9: 0.06532/0.67595] items per batch[64] items per second[0.37] total items[3795200] mini batches[ 59300] memory[4999] epoch remaining[0:29:20] INFO:trainer.default_trainer:epochs[ 32] optim steps[59400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24780/0.75911, loss_mask_bce_0: 0.34434/0.30059, loss_mask_dice_0: 0.30776/1.02329, loss_spatial_bce_0: 0.13922/0.08524, loss_spatial_dice_0: 0.11066/0.18064, loss_spatial_ce_0: 0.01740/0.05792, loss_grounding_bce_0: 0.12919/0.08056, loss_grounding_dice_0: 0.11802/0.15083, loss_grounding_ce_0: 0.00576/0.24919, loss_mask_ce_1: 0.22681/0.75960, loss_mask_bce_1: 0.34212/0.30146, loss_mask_dice_1: 0.31138/1.02741, loss_spatial_bce_1: 0.14781/0.08552, loss_spatial_dice_1: 0.10520/0.18329, loss_spatial_ce_1: 0.00632/0.06182, loss_grounding_bce_1: 0.13232/0.08073, loss_grounding_dice_1: 0.11550/0.15159, loss_grounding_ce_1: 0.00484/0.25088, loss_mask_ce_2: 0.21748/0.76774, loss_mask_bce_2: 0.33837/0.30167, loss_mask_dice_2: 0.29665/1.02820, loss_spatial_bce_2: 0.14280/0.08557, loss_spatial_dice_2: 0.10450/0.18371, loss_spatial_ce_2: 0.01649/0.06400, loss_grounding_bce_2: 0.12702/0.08075, loss_grounding_dice_2: 0.11355/0.15147, loss_grounding_ce_2: 0.00369/0.25420, loss_mask_ce_3: 0.22018/0.77105, loss_mask_bce_3: 0.34379/0.30315, loss_mask_dice_3: 0.31125/1.02617, loss_spatial_bce_3: 0.14640/0.08766, loss_spatial_dice_3: 0.10794/0.18503, loss_spatial_ce_3: 0.07726/0.06867, loss_grounding_bce_3: 0.12791/0.08116, loss_grounding_dice_3: 0.11663/0.15111, loss_grounding_ce_3: 0.00418/0.25464, loss_mask_ce_4: 0.24654/0.77705, loss_mask_bce_4: 0.35061/0.30563, loss_mask_dice_4: 0.32258/1.04495, loss_spatial_bce_4: 0.17766/0.08972, loss_spatial_dice_4: 0.13441/0.19299, loss_spatial_ce_4: 0.21522/0.08198, loss_grounding_bce_4: 0.12981/0.08175, loss_grounding_dice_4: 0.11933/0.15373, loss_grounding_ce_4: 0.00370/0.25925, loss_mask_ce_5: 0.24533/0.80108, loss_mask_bce_5: 0.34596/0.30744, loss_mask_dice_5: 0.32404/1.05255, loss_spatial_bce_5: 0.22173/0.09191, loss_spatial_dice_5: 0.16523/0.19599, loss_spatial_ce_5: 0.23657/0.09452, loss_grounding_bce_5: 0.12849/0.08205, loss_grounding_dice_5: 0.12480/0.15442, loss_grounding_ce_5: 0.00485/0.27760, loss_mask_ce_6: 0.32108/0.82777, loss_mask_bce_6: 0.33050/0.30950, loss_mask_dice_6: 0.30340/1.05599, loss_spatial_bce_6: 0.52413/0.09701, loss_spatial_dice_6: 0.25985/0.19827, loss_spatial_ce_6: 0.35602/0.11898, loss_grounding_bce_6: 0.13405/0.08295, loss_grounding_dice_6: 0.11604/0.15496, loss_grounding_ce_6: 0.00781/0.28715, loss_mask_ce_7: 0.30182/0.88350, loss_mask_bce_7: 0.34106/0.31673, loss_mask_dice_7: 0.31369/1.10255, loss_spatial_bce_7: 0.21037/0.10691, loss_spatial_dice_7: 0.16440/0.22392, loss_spatial_ce_7: 0.12557/0.15695, loss_grounding_bce_7: 0.13115/0.08468, loss_grounding_dice_7: 0.12067/0.16062, loss_grounding_ce_7: 0.01244/0.32008, loss_mask_ce_8: 0.43499/1.01971, loss_mask_bce_8: 0.33707/0.33284, loss_mask_dice_8: 0.36652/1.17976, loss_spatial_bce_8: 0.22254/0.12438, loss_spatial_dice_8: 0.15240/0.25953, loss_spatial_ce_8: 0.21426/0.20479, loss_grounding_bce_8: 0.11642/0.08885, loss_grounding_dice_8: 0.12561/0.17033, loss_grounding_ce_8: 0.01051/0.42079, loss_mask_ce_9: 2.10033/3.47932, loss_mask_bce_9: 0.44852/0.35971, loss_mask_dice_9: 0.44923/1.76173, loss_spatial_bce_9: 0.48475/0.35463, loss_spatial_dice_9: 0.65603/0.79365, loss_spatial_ce_9: 0.82810/1.39208, loss_grounding_bce_9: 0.12368/0.10082, loss_grounding_dice_9: 0.13340/0.24263, loss_grounding_ce_9: 0.07612/0.67585] items per batch[64] items per second[0.36] total items[3801600] mini batches[ 59400] memory[4999] epoch remaining[0:26:23] INFO:trainer.default_trainer:epochs[ 32] optim steps[59500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.92156/0.75906, loss_mask_bce_0: 0.03860/0.30055, loss_mask_dice_0: 1.81189/1.02341, loss_spatial_bce_0: 0.01118/0.08521, loss_spatial_dice_0: 0.27008/0.18062, loss_spatial_ce_0: 0.00946/0.05788, loss_grounding_bce_0: 0.00517/0.08054, loss_grounding_dice_0: 0.12757/0.15081, loss_grounding_ce_0: 0.05378/0.24908, loss_mask_ce_1: 1.53353/0.75958, loss_mask_bce_1: 0.04041/0.30141, loss_mask_dice_1: 1.87334/1.02756, loss_spatial_bce_1: 0.00789/0.08550, loss_spatial_dice_1: 0.29147/0.18326, loss_spatial_ce_1: 0.00817/0.06178, loss_grounding_bce_1: 0.00271/0.08072, loss_grounding_dice_1: 0.11679/0.15158, loss_grounding_ce_1: 0.05706/0.25075, loss_mask_ce_2: 1.88002/0.76772, loss_mask_bce_2: 0.04052/0.30163, loss_mask_dice_2: 2.08603/1.02832, loss_spatial_bce_2: 0.00806/0.08554, loss_spatial_dice_2: 0.19720/0.18369, loss_spatial_ce_2: 0.00856/0.06395, loss_grounding_bce_2: 0.00624/0.08073, loss_grounding_dice_2: 0.10261/0.15147, loss_grounding_ce_2: 0.09746/0.25407, loss_mask_ce_3: 1.67620/0.77107, loss_mask_bce_3: 0.03830/0.30311, loss_mask_dice_3: 1.87390/1.02628, loss_spatial_bce_3: 0.00893/0.08763, loss_spatial_dice_3: 0.27549/0.18501, loss_spatial_ce_3: 0.00627/0.06862, loss_grounding_bce_3: 0.00392/0.08114, loss_grounding_dice_3: 0.08055/0.15111, loss_grounding_ce_3: 0.07890/0.25459, loss_mask_ce_4: 1.96123/0.77707, loss_mask_bce_4: 0.04118/0.30558, loss_mask_dice_4: 1.85600/1.04509, loss_spatial_bce_4: 0.00807/0.08970, loss_spatial_dice_4: 0.29687/0.19298, loss_spatial_ce_4: 0.01594/0.08192, loss_grounding_bce_4: 0.00555/0.08173, loss_grounding_dice_4: 0.11226/0.15373, loss_grounding_ce_4: 0.10366/0.25911, loss_mask_ce_5: 1.66944/0.80111, loss_mask_bce_5: 0.04166/0.30741, loss_mask_dice_5: 1.74544/1.05274, loss_spatial_bce_5: 0.00868/0.09189, loss_spatial_dice_5: 0.29765/0.19599, loss_spatial_ce_5: 0.00896/0.09450, loss_grounding_bce_5: 0.00456/0.08204, loss_grounding_dice_5: 0.15627/0.15442, loss_grounding_ce_5: 0.07957/0.27748, loss_mask_ce_6: 1.71276/0.82777, loss_mask_bce_6: 0.05843/0.30947, loss_mask_dice_6: 2.92138/1.05617, loss_spatial_bce_6: 0.00933/0.09698, loss_spatial_dice_6: 0.28814/0.19827, loss_spatial_ce_6: 0.08497/0.11895, loss_grounding_bce_6: 0.00430/0.08293, loss_grounding_dice_6: 0.26276/0.15497, loss_grounding_ce_6: 0.08834/0.28703, loss_mask_ce_7: 1.90329/0.88353, loss_mask_bce_7: 0.04181/0.31671, loss_mask_dice_7: 2.02102/1.10275, loss_spatial_bce_7: 0.01017/0.10688, loss_spatial_dice_7: 0.45063/0.22391, loss_spatial_ce_7: 0.26545/0.15688, loss_grounding_bce_7: 0.00431/0.08466, loss_grounding_dice_7: 0.43029/0.16063, loss_grounding_ce_7: 0.15862/0.31993, loss_mask_ce_8: 2.09253/1.01974, loss_mask_bce_8: 0.05226/0.33280, loss_mask_dice_8: 2.35011/1.17994, loss_spatial_bce_8: 0.01207/0.12435, loss_spatial_dice_8: 0.46978/0.25950, loss_spatial_ce_8: 0.14009/0.20474, loss_grounding_bce_8: 0.00427/0.08883, loss_grounding_dice_8: 0.08089/0.17032, loss_grounding_ce_8: 0.07404/0.42072, loss_mask_ce_9: 3.94522/3.47939, loss_mask_bce_9: 0.02571/0.35967, loss_mask_dice_9: 2.86268/1.76195, loss_spatial_bce_9: 0.01178/0.35461, loss_spatial_dice_9: 0.78094/0.79365, loss_spatial_ce_9: 1.86102/1.39200, loss_grounding_bce_9: 0.00383/0.10080, loss_grounding_dice_9: 0.41247/0.24262, loss_grounding_ce_9: 0.24639/0.67579] items per batch[64] items per second[0.36] total items[3808000] mini batches[ 59500] memory[4999] epoch remaining[0:23:26] INFO:trainer.default_trainer:epochs[ 32] optim steps[59600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.41343/0.75893, loss_mask_bce_0: 0.72542/0.30052, loss_mask_dice_0: 0.47558/1.02325, loss_spatial_bce_0: 0.19420/0.08520, loss_spatial_dice_0: 0.16352/0.18060, loss_spatial_ce_0: 0.00061/0.05787, loss_grounding_bce_0: 0.46435/0.08054, loss_grounding_dice_0: 0.23177/0.15080, loss_grounding_ce_0: 0.17701/0.24891, loss_mask_ce_1: 0.42968/0.75946, loss_mask_bce_1: 0.69572/0.30139, loss_mask_dice_1: 0.46524/1.02741, loss_spatial_bce_1: 0.20036/0.08549, loss_spatial_dice_1: 0.16646/0.18324, loss_spatial_ce_1: 0.00043/0.06175, loss_grounding_bce_1: 0.43144/0.08072, loss_grounding_dice_1: 0.20925/0.15156, loss_grounding_ce_1: 0.17718/0.25061, loss_mask_ce_2: 0.48583/0.76759, loss_mask_bce_2: 0.63244/0.30160, loss_mask_dice_2: 0.45671/1.02818, loss_spatial_bce_2: 0.18576/0.08553, loss_spatial_dice_2: 0.17197/0.18367, loss_spatial_ce_2: 0.00038/0.06392, loss_grounding_bce_2: 0.40530/0.08073, loss_grounding_dice_2: 0.19517/0.15145, loss_grounding_ce_2: 0.20566/0.25393, loss_mask_ce_3: 0.44880/0.77097, loss_mask_bce_3: 0.63788/0.30307, loss_mask_dice_3: 0.43546/1.02609, loss_spatial_bce_3: 0.18215/0.08763, loss_spatial_dice_3: 0.16659/0.18499, loss_spatial_ce_3: 0.00050/0.06860, loss_grounding_bce_3: 0.40945/0.08114, loss_grounding_dice_3: 0.20155/0.15109, loss_grounding_ce_3: 0.21524/0.25448, loss_mask_ce_4: 0.45243/0.77691, loss_mask_bce_4: 0.62810/0.30555, loss_mask_dice_4: 0.46942/1.04496, loss_spatial_bce_4: 0.19758/0.08969, loss_spatial_dice_4: 0.18142/0.19297, loss_spatial_ce_4: 0.00349/0.08192, loss_grounding_bce_4: 0.40070/0.08173, loss_grounding_dice_4: 0.21168/0.15372, loss_grounding_ce_4: 0.18465/0.25893, loss_mask_ce_5: 0.42324/0.80095, loss_mask_bce_5: 0.63371/0.30740, loss_mask_dice_5: 0.55248/1.05258, loss_spatial_bce_5: 0.25226/0.09189, loss_spatial_dice_5: 0.22486/0.19598, loss_spatial_ce_5: 0.01802/0.09452, loss_grounding_bce_5: 0.41569/0.08203, loss_grounding_dice_5: 0.29790/0.15441, loss_grounding_ce_5: 0.16525/0.27729, loss_mask_ce_6: 0.45616/0.82757, loss_mask_bce_6: 0.58207/0.30945, loss_mask_dice_6: 0.46398/1.05605, loss_spatial_bce_6: 0.35941/0.09699, loss_spatial_dice_6: 0.26087/0.19828, loss_spatial_ce_6: 0.11233/0.11898, loss_grounding_bce_6: 0.39744/0.08292, loss_grounding_dice_6: 0.20984/0.15496, loss_grounding_ce_6: 0.16579/0.28686, loss_mask_ce_7: 0.52636/0.88336, loss_mask_bce_7: 0.64593/0.31669, loss_mask_dice_7: 0.53118/1.10261, loss_spatial_bce_7: 0.28696/0.10688, loss_spatial_dice_7: 0.28319/0.22389, loss_spatial_ce_7: 0.12749/0.15690, loss_grounding_bce_7: 0.39696/0.08466, loss_grounding_dice_7: 0.21196/0.16061, loss_grounding_ce_7: 0.42176/0.31975, loss_mask_ce_8: 0.40245/1.01950, loss_mask_bce_8: 0.75760/0.33279, loss_mask_dice_8: 0.69017/1.17977, loss_spatial_bce_8: 0.28190/0.12434, loss_spatial_dice_8: 0.24136/0.25948, loss_spatial_ce_8: 0.15508/0.20471, loss_grounding_bce_8: 0.36390/0.08883, loss_grounding_dice_8: 0.37716/0.17030, loss_grounding_ce_8: 0.12330/0.42053, loss_mask_ce_9: 2.97073/3.47905, loss_mask_bce_9: 0.64989/0.35965, loss_mask_dice_9: 0.77826/1.76168, loss_spatial_bce_9: 0.46882/0.35462, loss_spatial_dice_9: 0.73316/0.79361, loss_spatial_ce_9: 1.06691/1.39186, loss_grounding_bce_9: 0.39390/0.10080, loss_grounding_dice_9: 0.40709/0.24261, loss_grounding_ce_9: 0.03613/0.67563] items per batch[64] items per second[0.36] total items[3814400] mini batches[ 59600] memory[4999] epoch remaining[0:20:29] INFO:trainer.default_trainer:epochs[ 32] optim steps[59700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72633/0.75892, loss_mask_bce_0: 0.65984/0.30053, loss_mask_dice_0: 0.72994/1.02318, loss_spatial_bce_0: 0.21155/0.08518, loss_spatial_dice_0: 0.19915/0.18055, loss_spatial_ce_0: 0.00712/0.05783, loss_grounding_bce_0: 0.13800/0.08051, loss_grounding_dice_0: 0.11419/0.15077, loss_grounding_ce_0: 0.14971/0.24894, loss_mask_ce_1: 0.81723/0.75947, loss_mask_bce_1: 0.59034/0.30140, loss_mask_dice_1: 0.65611/1.02736, loss_spatial_bce_1: 0.19066/0.08546, loss_spatial_dice_1: 0.19352/0.18320, loss_spatial_ce_1: 0.01133/0.06171, loss_grounding_bce_1: 0.14295/0.08069, loss_grounding_dice_1: 0.11997/0.15153, loss_grounding_ce_1: 0.19427/0.25066, loss_mask_ce_2: 0.87453/0.76758, loss_mask_bce_2: 0.58339/0.30160, loss_mask_dice_2: 0.62930/1.02811, loss_spatial_bce_2: 0.18480/0.08551, loss_spatial_dice_2: 0.20141/0.18364, loss_spatial_ce_2: 0.02002/0.06388, loss_grounding_bce_2: 0.14882/0.08070, loss_grounding_dice_2: 0.11467/0.15141, loss_grounding_ce_2: 0.32131/0.25393, loss_mask_ce_3: 0.93314/0.77097, loss_mask_bce_3: 0.59345/0.30308, loss_mask_dice_3: 0.63800/1.02606, loss_spatial_bce_3: 0.19243/0.08761, loss_spatial_dice_3: 0.20534/0.18495, loss_spatial_ce_3: 0.02634/0.06856, loss_grounding_bce_3: 0.15225/0.08111, loss_grounding_dice_3: 0.13113/0.15106, loss_grounding_ce_3: 0.36359/0.25454, loss_mask_ce_4: 0.91343/0.77692, loss_mask_bce_4: 0.58108/0.30557, loss_mask_dice_4: 0.63061/1.04490, loss_spatial_bce_4: 0.19507/0.08967, loss_spatial_dice_4: 0.20507/0.19293, loss_spatial_ce_4: 0.03966/0.08189, loss_grounding_bce_4: 0.14269/0.08169, loss_grounding_dice_4: 0.13048/0.15369, loss_grounding_ce_4: 0.38243/0.25905, loss_mask_ce_5: 0.93530/0.80093, loss_mask_bce_5: 0.55819/0.30740, loss_mask_dice_5: 0.60861/1.05249, loss_spatial_bce_5: 0.25158/0.09187, loss_spatial_dice_5: 0.24747/0.19594, loss_spatial_ce_5: 0.09407/0.09450, loss_grounding_bce_5: 0.14488/0.08200, loss_grounding_dice_5: 0.12034/0.15438, loss_grounding_ce_5: 1.25221/0.27732, loss_mask_ce_6: 0.90063/0.82761, loss_mask_bce_6: 0.59057/0.30948, loss_mask_dice_6: 0.63692/1.05598, loss_spatial_bce_6: 0.22351/0.09697, loss_spatial_dice_6: 0.22055/0.19825, loss_spatial_ce_6: 0.06573/0.11895, loss_grounding_bce_6: 0.13473/0.08288, loss_grounding_dice_6: 0.13339/0.15493, loss_grounding_ce_6: 1.36319/0.28693, loss_mask_ce_7: 0.97873/0.88338, loss_mask_bce_7: 0.58462/0.31671, loss_mask_dice_7: 0.68250/1.10254, loss_spatial_bce_7: 0.24202/0.10685, loss_spatial_dice_7: 0.22664/0.22385, loss_spatial_ce_7: 0.07607/0.15685, loss_grounding_bce_7: 0.14993/0.08462, loss_grounding_dice_7: 0.11217/0.16057, loss_grounding_ce_7: 1.96788/0.31980, loss_mask_ce_8: 0.88669/1.01945, loss_mask_bce_8: 0.63882/0.33283, loss_mask_dice_8: 0.71094/1.17970, loss_spatial_bce_8: 0.24410/0.12430, loss_spatial_dice_8: 0.22156/0.25941, loss_spatial_ce_8: 0.10268/0.20460, loss_grounding_bce_8: 0.14706/0.08879, loss_grounding_dice_8: 0.13730/0.17026, loss_grounding_ce_8: 1.88163/0.42054, loss_mask_ce_9: 3.14665/3.47927, loss_mask_bce_9: 0.74960/0.35970, loss_mask_dice_9: 1.71647/1.76187, loss_spatial_bce_9: 0.41584/0.35460, loss_spatial_dice_9: 0.72054/0.79359, loss_spatial_ce_9: 1.10156/1.39174, loss_grounding_bce_9: 0.14707/0.10077, loss_grounding_dice_9: 0.19557/0.24257, loss_grounding_ce_9: 1.61945/0.67561] items per batch[64] items per second[0.36] total items[3820800] mini batches[ 59700] memory[4999] epoch remaining[0:17:32] INFO:trainer.default_trainer:epochs[ 32] optim steps[59800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.25785/0.75880, loss_mask_bce_0: 0.13141/0.30048, loss_mask_dice_0: 0.09235/1.02301, loss_spatial_bce_0: 0.43197/0.08519, loss_spatial_dice_0: 0.34514/0.18054, loss_spatial_ce_0: 0.00016/0.05780, loss_grounding_bce_0: 0.22218/0.08050, loss_grounding_dice_0: 0.18425/0.15075, loss_grounding_ce_0: 0.46830/0.24892, loss_mask_ce_1: 0.24638/0.75941, loss_mask_bce_1: 0.14287/0.30135, loss_mask_dice_1: 0.10590/1.02722, loss_spatial_bce_1: 0.34913/0.08548, loss_spatial_dice_1: 0.31002/0.18318, loss_spatial_ce_1: 0.00009/0.06169, loss_grounding_bce_1: 0.26340/0.08068, loss_grounding_dice_1: 0.22046/0.15151, loss_grounding_ce_1: 0.45857/0.25064, loss_mask_ce_2: 0.25603/0.76746, loss_mask_bce_2: 0.16366/0.30156, loss_mask_dice_2: 0.12622/1.02794, loss_spatial_bce_2: 0.37679/0.08553, loss_spatial_dice_2: 0.33776/0.18362, loss_spatial_ce_2: 0.00056/0.06385, loss_grounding_bce_2: 0.31841/0.08069, loss_grounding_dice_2: 0.23220/0.15139, loss_grounding_ce_2: 0.29890/0.25396, loss_mask_ce_3: 0.36108/0.77087, loss_mask_bce_3: 0.11952/0.30303, loss_mask_dice_3: 0.10149/1.02590, loss_spatial_bce_3: 0.39290/0.08762, loss_spatial_dice_3: 0.33278/0.18494, loss_spatial_ce_3: 0.00135/0.06852, loss_grounding_bce_3: 0.27034/0.08110, loss_grounding_dice_3: 0.22321/0.15105, loss_grounding_ce_3: 0.46396/0.25459, loss_mask_ce_4: 0.31471/0.77676, loss_mask_bce_4: 0.13127/0.30551, loss_mask_dice_4: 0.11971/1.04475, loss_spatial_bce_4: 0.39756/0.08970, loss_spatial_dice_4: 0.32651/0.19293, loss_spatial_ce_4: 0.00365/0.08186, loss_grounding_bce_4: 0.27059/0.08169, loss_grounding_dice_4: 0.25453/0.15367, loss_grounding_ce_4: 0.40198/0.25906, loss_mask_ce_5: 0.33711/0.80086, loss_mask_bce_5: 0.13555/0.30735, loss_mask_dice_5: 0.13224/1.05237, loss_spatial_bce_5: 0.37674/0.09189, loss_spatial_dice_5: 0.32274/0.19593, loss_spatial_ce_5: 0.13624/0.09451, loss_grounding_bce_5: 0.28412/0.08200, loss_grounding_dice_5: 0.27715/0.15436, loss_grounding_ce_5: 0.30578/0.27732, loss_mask_ce_6: 0.28916/0.82748, loss_mask_bce_6: 0.13862/0.30943, loss_mask_dice_6: 0.12533/1.05583, loss_spatial_bce_6: 0.40096/0.09700, loss_spatial_dice_6: 0.31872/0.19824, loss_spatial_ce_6: 0.20496/0.11895, loss_grounding_bce_6: 0.28714/0.08288, loss_grounding_dice_6: 0.25985/0.15491, loss_grounding_ce_6: 0.15775/0.28690, loss_mask_ce_7: 0.30594/0.88328, loss_mask_bce_7: 0.20815/0.31667, loss_mask_dice_7: 0.19106/1.10237, loss_spatial_bce_7: 0.28832/0.10687, loss_spatial_dice_7: 0.21108/0.22383, loss_spatial_ce_7: 0.91478/0.15682, loss_grounding_bce_7: 0.43248/0.08461, loss_grounding_dice_7: 0.39365/0.16055, loss_grounding_ce_7: 0.11667/0.31974, loss_mask_ce_8: 0.28224/1.01931, loss_mask_bce_8: 0.25849/0.33278, loss_mask_dice_8: 0.20486/1.17955, loss_spatial_bce_8: 0.30511/0.12432, loss_spatial_dice_8: 0.29629/0.25939, loss_spatial_ce_8: 0.21443/0.20455, loss_grounding_bce_8: 0.52460/0.08879, loss_grounding_dice_8: 0.41763/0.17024, loss_grounding_ce_8: 0.04128/0.42045, loss_mask_ce_9: 1.03555/3.47884, loss_mask_bce_9: 0.16766/0.35968, loss_mask_dice_9: 0.13799/1.76161, loss_spatial_bce_9: 0.63626/0.35466, loss_spatial_dice_9: 0.62757/0.79356, loss_spatial_ce_9: 0.00758/1.39165, loss_grounding_bce_9: 0.34319/0.10077, loss_grounding_dice_9: 0.28627/0.24253, loss_grounding_ce_9: 0.03695/0.67542] items per batch[64] items per second[0.36] total items[3827200] mini batches[ 59800] memory[4999] epoch remaining[0:14:34] INFO:trainer.default_trainer:epochs[ 32] optim steps[59900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39143/0.75893, loss_mask_bce_0: 1.14684/0.30058, loss_mask_dice_0: 0.79128/1.02349, loss_spatial_bce_0: 0.33511/0.08520, loss_spatial_dice_0: 0.19177/0.18056, loss_spatial_ce_0: 0.25496/0.05778, loss_grounding_bce_0: 0.49451/0.08051, loss_grounding_dice_0: 0.33485/0.15076, loss_grounding_ce_0: 0.02440/0.24882, loss_mask_ce_1: 0.41468/0.75954, loss_mask_bce_1: 1.11579/0.30145, loss_mask_dice_1: 0.83115/1.02770, loss_spatial_bce_1: 0.18894/0.08548, loss_spatial_dice_1: 0.16595/0.18319, loss_spatial_ce_1: 0.22411/0.06165, loss_grounding_bce_1: 0.48362/0.08069, loss_grounding_dice_1: 0.34496/0.15152, loss_grounding_ce_1: 0.03650/0.25058, loss_mask_ce_2: 0.43558/0.76759, loss_mask_bce_2: 1.04323/0.30165, loss_mask_dice_2: 0.78368/1.02844, loss_spatial_bce_2: 0.20922/0.08553, loss_spatial_dice_2: 0.15697/0.18364, loss_spatial_ce_2: 0.31473/0.06382, loss_grounding_bce_2: 0.45614/0.08070, loss_grounding_dice_2: 0.33581/0.15141, loss_grounding_ce_2: 0.03425/0.25387, loss_mask_ce_3: 0.45225/0.77100, loss_mask_bce_3: 1.10000/0.30314, loss_mask_dice_3: 0.83164/1.02641, loss_spatial_bce_3: 0.18305/0.08763, loss_spatial_dice_3: 0.17628/0.18496, loss_spatial_ce_3: 0.17848/0.06850, loss_grounding_bce_3: 0.47245/0.08111, loss_grounding_dice_3: 0.35449/0.15105, loss_grounding_ce_3: 0.03273/0.25451, loss_mask_ce_4: 0.40695/0.77693, loss_mask_bce_4: 1.03213/0.30560, loss_mask_dice_4: 0.80137/1.04526, loss_spatial_bce_4: 0.20890/0.08971, loss_spatial_dice_4: 0.25365/0.19296, loss_spatial_ce_4: 0.14215/0.08185, loss_grounding_bce_4: 0.42790/0.08170, loss_grounding_dice_4: 0.33622/0.15368, loss_grounding_ce_4: 0.04297/0.25901, loss_mask_ce_5: 0.35541/0.80098, loss_mask_bce_5: 1.07912/0.30747, loss_mask_dice_5: 0.86295/1.05298, loss_spatial_bce_5: 0.21807/0.09190, loss_spatial_dice_5: 0.25348/0.19596, loss_spatial_ce_5: 0.14615/0.09454, loss_grounding_bce_5: 0.44163/0.08201, loss_grounding_dice_5: 0.34104/0.15437, loss_grounding_ce_5: 0.04318/0.27725, loss_mask_ce_6: 0.38247/0.82765, loss_mask_bce_6: 1.08608/0.30954, loss_mask_dice_6: 0.84380/1.05645, loss_spatial_bce_6: 0.22342/0.09700, loss_spatial_dice_6: 0.25554/0.19828, loss_spatial_ce_6: 0.20069/0.11896, loss_grounding_bce_6: 0.44082/0.08289, loss_grounding_dice_6: 0.34368/0.15493, loss_grounding_ce_6: 0.04550/0.28684, loss_mask_ce_7: 0.47315/0.88349, loss_mask_bce_7: 0.98837/0.31677, loss_mask_dice_7: 0.87687/1.10297, loss_spatial_bce_7: 0.25797/0.10686, loss_spatial_dice_7: 0.25951/0.22386, loss_spatial_ce_7: 0.15418/0.15682, loss_grounding_bce_7: 0.41175/0.08462, loss_grounding_dice_7: 0.34011/0.16055, loss_grounding_ce_7: 0.02691/0.31971, loss_mask_ce_8: 0.65097/1.01948, loss_mask_bce_8: 1.03460/0.33288, loss_mask_dice_8: 0.84394/1.18015, loss_spatial_bce_8: 0.18118/0.12431, loss_spatial_dice_8: 0.17942/0.25941, loss_spatial_ce_8: 0.08853/0.20450, loss_grounding_bce_8: 0.44061/0.08879, loss_grounding_dice_8: 0.29373/0.17024, loss_grounding_ce_8: 0.14663/0.42047, loss_mask_ce_9: 3.72151/3.47909, loss_mask_bce_9: 0.93676/0.35976, loss_mask_dice_9: 1.36247/1.76245, loss_spatial_bce_9: 0.48310/0.35465, loss_spatial_dice_9: 0.63207/0.79362, loss_spatial_ce_9: 1.06915/1.39177, loss_grounding_bce_9: 0.37411/0.10078, loss_grounding_dice_9: 0.53125/0.24256, loss_grounding_ce_9: 0.30502/0.67545] items per batch[64] items per second[0.36] total items[3833600] mini batches[ 59900] memory[4999] epoch remaining[0:11:35] INFO:trainer.default_trainer:epochs[ 32] optim steps[60000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73556/0.75899, loss_mask_bce_0: 0.26131/0.30059, loss_mask_dice_0: 0.28520/1.02339, loss_spatial_bce_0: 0.11923/0.08517, loss_spatial_dice_0: 0.14399/0.18053, loss_spatial_ce_0: 0.00178/0.05776, loss_grounding_bce_0: 0.10309/0.08049, loss_grounding_dice_0: 0.10956/0.15072, loss_grounding_ce_0: 0.06899/0.24888, loss_mask_ce_1: 0.82092/0.75956, loss_mask_bce_1: 0.26222/0.30146, loss_mask_dice_1: 0.30539/1.02762, loss_spatial_bce_1: 0.10423/0.08546, loss_spatial_dice_1: 0.12496/0.18316, loss_spatial_ce_1: 0.00150/0.06164, loss_grounding_bce_1: 0.11469/0.08067, loss_grounding_dice_1: 0.12381/0.15148, loss_grounding_ce_1: 0.06079/0.25063, loss_mask_ce_2: 0.81273/0.76763, loss_mask_bce_2: 0.27213/0.30166, loss_mask_dice_2: 0.31480/1.02834, loss_spatial_bce_2: 0.11267/0.08551, loss_spatial_dice_2: 0.13593/0.18360, loss_spatial_ce_2: 0.00427/0.06384, loss_grounding_bce_2: 0.13512/0.08068, loss_grounding_dice_2: 0.15654/0.15136, loss_grounding_ce_2: 0.06848/0.25393, loss_mask_ce_3: 0.83320/0.77106, loss_mask_bce_3: 0.27135/0.30314, loss_mask_dice_3: 0.30172/1.02636, loss_spatial_bce_3: 0.12778/0.08761, loss_spatial_dice_3: 0.13962/0.18493, loss_spatial_ce_3: 0.44699/0.06849, loss_grounding_bce_3: 0.13267/0.08109, loss_grounding_dice_3: 0.14483/0.15101, loss_grounding_ce_3: 0.07297/0.25453, loss_mask_ce_4: 0.79661/0.77700, loss_mask_bce_4: 0.28961/0.30561, loss_mask_dice_4: 0.34278/1.04516, loss_spatial_bce_4: 0.13104/0.08969, loss_spatial_dice_4: 0.15339/0.19293, loss_spatial_ce_4: 0.28171/0.08182, loss_grounding_bce_4: 0.10913/0.08168, loss_grounding_dice_4: 0.12005/0.15363, loss_grounding_ce_4: 0.03223/0.25908, loss_mask_ce_5: 0.81615/0.80107, loss_mask_bce_5: 0.32013/0.30747, loss_mask_dice_5: 0.38502/1.05286, loss_spatial_bce_5: 0.14882/0.09189, loss_spatial_dice_5: 0.19556/0.19593, loss_spatial_ce_5: 0.35214/0.09451, loss_grounding_bce_5: 0.10471/0.08199, loss_grounding_dice_5: 0.10424/0.15432, loss_grounding_ce_5: 0.03919/0.27737, loss_mask_ce_6: 0.92654/0.82778, loss_mask_bce_6: 0.30460/0.30954, loss_mask_dice_6: 0.35509/1.05638, loss_spatial_bce_6: 0.25735/0.09700, loss_spatial_dice_6: 0.27093/0.19827, loss_spatial_ce_6: 0.11627/0.11891, loss_grounding_bce_6: 0.08566/0.08287, loss_grounding_dice_6: 0.08553/0.15489, loss_grounding_ce_6: 0.02959/0.28699, loss_mask_ce_7: 1.35761/0.88361, loss_mask_bce_7: 0.33130/0.31679, loss_mask_dice_7: 0.29069/1.10285, loss_spatial_bce_7: 0.14209/0.10684, loss_spatial_dice_7: 0.13951/0.22383, loss_spatial_ce_7: 0.09526/0.15679, loss_grounding_bce_7: 0.13717/0.08460, loss_grounding_dice_7: 0.13431/0.16050, loss_grounding_ce_7: 0.22287/0.31979, loss_mask_ce_8: 0.87450/1.01953, loss_mask_bce_8: 0.33687/0.33290, loss_mask_dice_8: 0.37611/1.18005, loss_spatial_bce_8: 0.18522/0.12429, loss_spatial_dice_8: 0.17012/0.25938, loss_spatial_ce_8: 0.12808/0.20445, loss_grounding_bce_8: 0.18546/0.08879, loss_grounding_dice_8: 0.19872/0.17021, loss_grounding_ce_8: 0.04078/0.42061, loss_mask_ce_9: 3.08920/3.47962, loss_mask_bce_9: 0.61664/0.35979, loss_mask_dice_9: 0.77024/1.76236, loss_spatial_bce_9: 0.56675/0.35462, loss_spatial_dice_9: 0.70096/0.79360, loss_spatial_ce_9: 0.84934/1.39184, loss_grounding_bce_9: 0.27119/0.10078, loss_grounding_dice_9: 0.42320/0.24254, loss_grounding_ce_9: 0.12481/0.67574] items per batch[64] items per second[0.37] total items[3840000] mini batches[ 60000] memory[4999] epoch remaining[0:08:37] INFO:trainer.default_trainer:epochs[ 32] optim steps[60100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58049/0.75891, loss_mask_bce_0: 0.35260/0.30062, loss_mask_dice_0: 1.19884/1.02351, loss_spatial_bce_0: 0.03074/0.08518, loss_spatial_dice_0: 0.25014/0.18050, loss_spatial_ce_0: 0.00322/0.05775, loss_grounding_bce_0: 0.13935/0.08049, loss_grounding_dice_0: 0.10233/0.15069, loss_grounding_ce_0: 0.38137/0.24880, loss_mask_ce_1: 0.53617/0.75953, loss_mask_bce_1: 0.34632/0.30150, loss_mask_dice_1: 1.27543/1.02770, loss_spatial_bce_1: 0.03453/0.08548, loss_spatial_dice_1: 0.22760/0.18315, loss_spatial_ce_1: 0.00540/0.06162, loss_grounding_bce_1: 0.15903/0.08067, loss_grounding_dice_1: 0.11222/0.15144, loss_grounding_ce_1: 0.33795/0.25053, loss_mask_ce_2: 0.52031/0.76760, loss_mask_bce_2: 0.34866/0.30169, loss_mask_dice_2: 1.24965/1.02844, loss_spatial_bce_2: 0.04143/0.08553, loss_spatial_dice_2: 0.29044/0.18359, loss_spatial_ce_2: 0.00698/0.06382, loss_grounding_bce_2: 0.15558/0.08068, loss_grounding_dice_2: 0.10031/0.15133, loss_grounding_ce_2: 0.23537/0.25382, loss_mask_ce_3: 0.50066/0.77103, loss_mask_bce_3: 0.34578/0.30317, loss_mask_dice_3: 1.25432/1.02645, loss_spatial_bce_3: 0.04162/0.08763, loss_spatial_dice_3: 0.30783/0.18492, loss_spatial_ce_3: 0.01409/0.06849, loss_grounding_bce_3: 0.16787/0.08110, loss_grounding_dice_3: 0.10399/0.15098, loss_grounding_ce_3: 0.17574/0.25441, loss_mask_ce_4: 0.56717/0.77696, loss_mask_bce_4: 0.34301/0.30564, loss_mask_dice_4: 1.19319/1.04525, loss_spatial_bce_4: 0.02900/0.08971, loss_spatial_dice_4: 0.22617/0.19292, loss_spatial_ce_4: 0.04162/0.08182, loss_grounding_bce_4: 0.17032/0.08169, loss_grounding_dice_4: 0.11036/0.15359, loss_grounding_ce_4: 0.21681/0.25896, loss_mask_ce_5: 0.45260/0.80108, loss_mask_bce_5: 0.43082/0.30751, loss_mask_dice_5: 1.04330/1.05293, loss_spatial_bce_5: 0.03278/0.09190, loss_spatial_dice_5: 0.19912/0.19592, loss_spatial_ce_5: 0.13096/0.09451, loss_grounding_bce_5: 0.09187/0.08199, loss_grounding_dice_5: 0.07372/0.15429, loss_grounding_ce_5: 0.14188/0.27731, loss_mask_ce_6: 0.46731/0.82774, loss_mask_bce_6: 0.41833/0.30958, loss_mask_dice_6: 1.10325/1.05647, loss_spatial_bce_6: 0.04617/0.09701, loss_spatial_dice_6: 0.25043/0.19825, loss_spatial_ce_6: 0.13156/0.11892, loss_grounding_bce_6: 0.08613/0.08287, loss_grounding_dice_6: 0.07510/0.15486, loss_grounding_ce_6: 0.12866/0.28695, loss_mask_ce_7: 0.56096/0.88357, loss_mask_bce_7: 0.41887/0.31682, loss_mask_dice_7: 1.14270/1.10287, loss_spatial_bce_7: 0.05881/0.10685, loss_spatial_dice_7: 0.22574/0.22381, loss_spatial_ce_7: 0.21498/0.15675, loss_grounding_bce_7: 0.09361/0.08461, loss_grounding_dice_7: 0.08478/0.16047, loss_grounding_ce_7: 0.24080/0.31964, loss_mask_ce_8: 0.57831/1.01954, loss_mask_bce_8: 0.43661/0.33294, loss_mask_dice_8: 1.24016/1.18009, loss_spatial_bce_8: 0.06826/0.12428, loss_spatial_dice_8: 0.30397/0.25934, loss_spatial_ce_8: 0.26836/0.20439, loss_grounding_bce_8: 0.07365/0.08879, loss_grounding_dice_8: 0.06528/0.17018, loss_grounding_ce_8: 0.38897/0.42056, loss_mask_ce_9: 3.34985/3.47975, loss_mask_bce_9: 0.57138/0.35982, loss_mask_dice_9: 3.28231/1.76254, loss_spatial_bce_9: 0.19922/0.35464, loss_spatial_dice_9: 0.91065/0.79359, loss_spatial_ce_9: 1.84607/1.39182, loss_grounding_bce_9: 0.11902/0.10080, loss_grounding_dice_9: 0.10082/0.24252, loss_grounding_ce_9: 1.60138/0.67571] items per batch[64] items per second[0.36] total items[3846400] mini batches[ 60100] memory[4999] epoch remaining[0:05:39] INFO:trainer.default_trainer:epochs[ 32] optim steps[60200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61764/0.75871, loss_mask_bce_0: 0.42664/0.30061, loss_mask_dice_0: 0.78550/1.02300, loss_spatial_bce_0: 0.09640/0.08519, loss_spatial_dice_0: 0.26383/0.18049, loss_spatial_ce_0: 0.00190/0.05775, loss_grounding_bce_0: 0.08727/0.08049, loss_grounding_dice_0: 0.35832/0.15065, loss_grounding_ce_0: 0.17135/0.24881, loss_mask_ce_1: 0.58969/0.75938, loss_mask_bce_1: 0.42378/0.30147, loss_mask_dice_1: 0.77144/1.02717, loss_spatial_bce_1: 0.10547/0.08549, loss_spatial_dice_1: 0.25597/0.18313, loss_spatial_ce_1: 0.00986/0.06160, loss_grounding_bce_1: 0.08644/0.08067, loss_grounding_dice_1: 0.18630/0.15140, loss_grounding_ce_1: 0.15498/0.25054, loss_mask_ce_2: 0.64948/0.76746, loss_mask_bce_2: 0.42262/0.30167, loss_mask_dice_2: 0.81761/1.02793, loss_spatial_bce_2: 0.09812/0.08554, loss_spatial_dice_2: 0.24648/0.18357, loss_spatial_ce_2: 0.43892/0.06380, loss_grounding_bce_2: 0.09121/0.08069, loss_grounding_dice_2: 0.36451/0.15129, loss_grounding_ce_2: 0.17131/0.25384, loss_mask_ce_3: 0.64263/0.77085, loss_mask_bce_3: 0.44593/0.30315, loss_mask_dice_3: 0.95086/1.02595, loss_spatial_bce_3: 0.10417/0.08763, loss_spatial_dice_3: 0.25420/0.18490, loss_spatial_ce_3: 0.02936/0.06848, loss_grounding_bce_3: 0.10035/0.08110, loss_grounding_dice_3: 0.40322/0.15094, loss_grounding_ce_3: 0.18257/0.25440, loss_mask_ce_4: 0.57516/0.77682, loss_mask_bce_4: 0.44264/0.30562, loss_mask_dice_4: 0.79533/1.04472, loss_spatial_bce_4: 0.11500/0.08973, loss_spatial_dice_4: 0.26994/0.19291, loss_spatial_ce_4: 0.01993/0.08182, loss_grounding_bce_4: 0.09297/0.08169, loss_grounding_dice_4: 0.19597/0.15356, loss_grounding_ce_4: 0.17265/0.25896, loss_mask_ce_5: 0.61080/0.80091, loss_mask_bce_5: 0.43735/0.30748, loss_mask_dice_5: 1.06739/1.05242, loss_spatial_bce_5: 0.12690/0.09192, loss_spatial_dice_5: 0.28176/0.19591, loss_spatial_ce_5: 0.05052/0.09456, loss_grounding_bce_5: 0.09605/0.08199, loss_grounding_dice_5: 0.20398/0.15425, loss_grounding_ce_5: 0.20110/0.27728, loss_mask_ce_6: 0.59738/0.82758, loss_mask_bce_6: 0.43008/0.30956, loss_mask_dice_6: 0.81850/1.05595, loss_spatial_bce_6: 0.13938/0.09703, loss_spatial_dice_6: 0.27859/0.19824, loss_spatial_ce_6: 0.08618/0.11892, loss_grounding_bce_6: 0.09380/0.08287, loss_grounding_dice_6: 0.36772/0.15481, loss_grounding_ce_6: 0.21440/0.28694, loss_mask_ce_7: 0.66356/0.88339, loss_mask_bce_7: 0.44679/0.31679, loss_mask_dice_7: 1.09950/1.10233, loss_spatial_bce_7: 0.14669/0.10686, loss_spatial_dice_7: 0.29070/0.22379, loss_spatial_ce_7: 0.12663/0.15673, loss_grounding_bce_7: 0.09240/0.08461, loss_grounding_dice_7: 0.37330/0.16042, loss_grounding_ce_7: 0.14977/0.31958, loss_mask_ce_8: 0.56689/1.01940, loss_mask_bce_8: 0.45248/0.33289, loss_mask_dice_8: 1.01013/1.17944, loss_spatial_bce_8: 0.16860/0.12428, loss_spatial_dice_8: 0.30937/0.25931, loss_spatial_ce_8: 0.17397/0.20438, loss_grounding_bce_8: 0.09831/0.08879, loss_grounding_dice_8: 0.21723/0.17013, loss_grounding_ce_8: 0.08515/0.42039, loss_mask_ce_9: 2.39362/3.47895, loss_mask_bce_9: 0.43217/0.35978, loss_mask_dice_9: 1.16866/1.76149, loss_spatial_bce_9: 0.35517/0.35471, loss_spatial_dice_9: 0.81269/0.79353, loss_spatial_ce_9: 1.94722/1.39184, loss_grounding_bce_9: 0.08114/0.10079, loss_grounding_dice_9: 0.44993/0.24242, loss_grounding_ce_9: 0.24496/0.67569] items per batch[64] items per second[0.36] total items[3852800] mini batches[ 60200] memory[4999] epoch remaining[0:02:41] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00060291. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3822 s/iter. Eval: 0.0828 s/iter. Total: 0.4679 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0026 s/iter. Inference: 0.3834 s/iter. Eval: 0.0811 s/iter. Total: 0.4672 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0029 s/iter. Inference: 0.3909 s/iter. Eval: 0.0749 s/iter. Total: 0.4688 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0029 s/iter. Inference: 0.3889 s/iter. Eval: 0.0742 s/iter. Total: 0.4661 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0029 s/iter. Inference: 0.3892 s/iter. Eval: 0.0714 s/iter. Total: 0.4636 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0030 s/iter. Inference: 0.3882 s/iter. Eval: 0.0692 s/iter. Total: 0.4605 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evals42h21v4 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.370 | 83.170 | 65.826 | 133 | | Things | 61.516 | 84.087 | 72.665 | 80 | | Stuff | 46.093 | 81.785 | 55.503 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.52s) creating index... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.59 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.35 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.75s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 22.15 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.488 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.609 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.373 | 69.265 | 48.833 | 26.606 | 49.824 | 67.352 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.802 | bicycle | 22.439 | car | 43.632 | | motorcycle | 40.197 | airplane | 62.052 | bus | 72.373 | | train | 74.200 | truck | 42.561 | boat | 31.091 | | traffic light | 28.654 | fire hydrant | 71.713 | stop sign | 68.955 | | parking meter | 50.440 | bench | 27.376 | bird | 34.290 | | cat | 76.692 | dog | 70.644 | horse | 49.074 | | sheep | 53.388 | cow | 55.872 | elephant | 65.474 | | bear | 80.059 | zebra | 65.612 | giraffe | 61.719 | | backpack | 24.358 | umbrella | 55.357 | handbag | 24.081 | | tie | 39.341 | suitcase | 51.810 | frisbee | 71.007 | | skis | 9.050 | snowboard | 35.318 | sports ball | 49.101 | | kite | 38.295 | baseball bat | 37.567 | baseball glove | 50.029 | | skateboard | 43.336 | surfboard | 44.771 | tennis racket | 63.356 | | bottle | 41.622 | wine glass | 38.031 | cup | 49.167 | | fork | 26.212 | knife | 24.228 | spoon | 21.952 | | bowl | 39.564 | banana | 22.701 | apple | 25.318 | | sandwich | 47.622 | orange | 32.099 | broccoli | 24.232 | | carrot | 20.830 | hot dog | 31.960 | pizza | 50.607 | | donut | 56.334 | cake | 47.944 | chair | 28.072 | | couch | 45.284 | potted plant | 23.145 | bed | 44.023 | | dining table | 15.283 | toilet | 69.861 | tv | 65.783 | | laptop | 70.679 | mouse | 64.675 | remote | 43.482 | | keyboard | 59.528 | cell phone | 45.507 | microwave | 63.941 | | oven | 33.743 | toaster | 50.025 | sink | 44.282 | | refrigerator | 69.628 | book | 13.946 | clock | 53.668 | | vase | 40.293 | scissors | 36.918 | teddy bear | 56.275 | | hair drier | 30.239 | toothbrush | 27.077 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.9723977130351, 'fwIoU': 71.79862741204586, 'IoU-person': 89.04135289450595, 'IoU-bicycle': 78.4881534996937, 'IoU-car': 72.68213157406271, 'IoU-motorcycle': 88.22138043680357, 'IoU-airplane': 84.09254221296241, 'IoU-bus': 87.71895942764732, 'IoU-train': 87.48389515309172, 'IoU-truck': 68.86480133190615, 'IoU-boat': 72.08783801807535, 'IoU-traffic light': 78.72682116305972, 'IoU-fire hydrant': 93.2945609164062, 'IoU-stop sign': 94.48539673878457, 'IoU-parking meter': 85.29983638226915, 'IoU-bench': 64.5887177304204, 'IoU-bird': 77.50914523019324, 'IoU-cat': 88.46016685576326, 'IoU-dog': 83.18430237740947, 'IoU-horse': 89.60632020341619, 'IoU-sheep': 80.36742839314715, 'IoU-cow': 89.93717356784792, 'IoU-elephant': 90.66279288252777, 'IoU-bear': 70.23224881183707, 'IoU-zebra': 89.26117216360532, 'IoU-giraffe': 89.48803850400775, 'IoU-backpack': 54.2338052442518, 'IoU-umbrella': 86.5033799238979, 'IoU-handbag': 51.59263169239679, 'IoU-tie': 75.72641213440401, 'IoU-suitcase': 86.39406525396211, 'IoU-frisbee': 84.09703154530159, 'IoU-skis': 59.15484126010442, 'IoU-snowboard': 71.19438337465289, 'IoU-sports ball': 80.17141102353412, 'IoU-kite': 79.61302622164527, 'IoU-baseball bat': 69.14983677555227, 'IoU-baseball glove': 77.370605961172, 'IoU-skateboard': 85.80644543978703, 'IoU-surfboard': 86.6691813453108, 'IoU-tennis racket': 91.0913728328016, 'IoU-bottle': 72.41314268617603, 'IoU-wine glass': 82.69957506429948, 'IoU-cup': 71.05429064189336, 'IoU-fork': 69.620884072695, 'IoU-knife': 63.73106709615517, 'IoU-spoon': 61.44761162581675, 'IoU-bowl': 63.5799913492178, 'IoU-banana': 82.9234651482487, 'IoU-apple': 56.34588563458857, 'IoU-sandwich': 70.17404038812985, 'IoU-orange': 80.71239076015786, 'IoU-broccoli': 70.50317177320625, 'IoU-carrot': 64.28434813812298, 'IoU-hot dog': 62.977510644600045, 'IoU-pizza': 83.9126282975394, 'IoU-donut': 69.00989885125468, 'IoU-cake': 79.35949619874326, 'IoU-chair': 63.32153887863985, 'IoU-couch': 72.46079502682143, 'IoU-potted plant': 44.10268548533736, 'IoU-bed': 68.8464809878325, 'IoU-dining table': 54.90117518800979, 'IoU-toilet': 87.46898245358481, 'IoU-tv': 78.09780218868488, 'IoU-laptop': 82.36634529989273, 'IoU-mouse': 75.93116237667829, 'IoU-remote': 68.1480911499269, 'IoU-keyboard': 69.53327889732496, 'IoU-cell phone': 81.43874132706095, 'IoU-microwave': 70.49169510075323, 'IoU-oven': 69.11435235087833, 'IoU-toaster': 84.10857596411878, 'IoU-sink': 71.59018164649177, 'IoU-refrigerator': 83.58922912177717, 'IoU-book': 54.25807933829569, 'IoU-clock': 76.71806952716005, 'IoU-vase': 66.27722730016401, 'IoU-scissors': 85.26509135306257, 'IoU-teddy bear': 82.9296046160716, 'IoU-hair drier': 47.66481672112071, 'IoU-toothbrush': 75.56126834422882, 'IoU-banner': 37.96090736263128, 'IoU-blanket': 15.766089516658727, 'IoU-bridge': 39.83868456265954, 'IoU-cardboard': 48.198318988889675, 'IoU-counter': 31.948390476612182, 'IoU-curtain': 72.2094287547123, 'IoU-door-stuff': 48.31991402078757, 'IoU-floor-wood': 65.79712655259613, 'IoU-flower': 44.047919267393226, 'IoU-fruit': 48.701090872462395, 'IoU-gravel': 28.450621693096362, 'IoU-house': 25.30429692251107, 'IoU-light': 44.855431178052534, 'IoU-mirror-stuff': 64.44312552531119, 'IoU-net': 48.71344980180785, 'IoU-pillow': 26.025718377423328, 'IoU-platform': 29.98456750044083, 'IoU-playingfield': 70.13544498664007, 'IoU-railroad': 64.48678041041879, 'IoU-river': 56.72514493560868, 'IoU-road': 67.60353985383385, 'IoU-roof': 20.72137033917437, 'IoU-sand': 63.053644220818924, 'IoU-sea': 86.36907202331739, 'IoU-shelf': 37.80870282261877, 'IoU-snow': 92.10306725847654, 'IoU-stairs': 31.28951873621655, 'IoU-tent': 10.713252432521031, 'IoU-towel': 46.7658986818224, 'IoU-wall-brick': 50.195137984275185, 'IoU-wall-stone': 30.894694843264737, 'IoU-wall-tile': 70.45165727843006, 'IoU-wall-wood': 44.70752981644569, 'IoU-water-other': 27.541061504452202, 'IoU-window-blind': 47.88075807543271, 'IoU-window-other': 49.21500832055207, 'IoU-tree-merged': 82.0409013990846, 'IoU-fence-merged': 54.44071981732155, 'IoU-ceiling-merged': 68.31889714402031, 'IoU-sky-other-merged': 94.25060608365285, 'IoU-cabinet-merged': 63.719987505553824, 'IoU-table-merged': 41.30434237374238, 'IoU-floor-other-merged': 54.42228861736127, 'IoU-pavement-merged': 57.86087990931009, 'IoU-mountain-merged': 58.22763892877073, 'IoU-grass-merged': 71.86377904095927, 'IoU-dirt-merged': 47.522890786834594, 'IoU-paper-merged': 35.7808477409792, 'IoU-food-other-merged': 44.905030848205655, 'IoU-building-other-merged': 59.66731367336974, 'IoU-rock-merged': 63.788714862744534, 'IoU-wall-other-merged': 68.10712054610761, 'IoU-rug-merged': 67.39229514430336, 'mACC': 77.54065370138807, 'pACC': 82.40771544980353, 'ACC-person': 92.94438426021537, 'ACC-bicycle': 87.24429138797795, 'ACC-car': 87.58466647837884, 'ACC-motorcycle': 92.82300153971046, 'ACC-airplane': 90.2154129470409, 'ACC-bus': 93.89372675045405, 'ACC-train': 95.86676021523425, 'ACC-truck': 79.1981291750805, 'ACC-boat': 81.50774895126631, 'ACC-traffic light': 91.27938571713511, 'ACC-fire hydrant': 95.84616140093173, 'ACC-stop sign': 98.17169814655875, 'ACC-parking meter': 88.23872465225624, 'ACC-bench': 80.77992320247253, 'ACC-bird': 82.13668190300105, 'ACC-cat': 91.92507267392823, 'ACC-dog': 85.58029170095124, 'ACC-horse': 94.28343946612718, 'ACC-sheep': 84.6103684207424, 'ACC-cow': 93.34519100213373, 'ACC-elephant': 93.0486073700463, 'ACC-bear': 71.57314534552673, 'ACC-zebra': 91.51510600181521, 'ACC-giraffe': 93.28020492615488, 'ACC-backpack': 75.63737390557489, 'ACC-umbrella': 90.32251946335032, 'ACC-handbag': 71.2784278971474, 'ACC-tie': 84.49741737585694, 'ACC-suitcase': 92.26247716841293, 'ACC-frisbee': 94.33454545454546, 'ACC-skis': 73.92818325229901, 'ACC-snowboard': 82.00631872841714, 'ACC-sports ball': 89.05531263890181, 'ACC-kite': 85.98008049353257, 'ACC-baseball bat': 87.20938944623845, 'ACC-baseball glove': 92.59491436271013, 'ACC-skateboard': 90.56671948148714, 'ACC-surfboard': 92.36413164647271, 'ACC-tennis racket': 94.90074236180476, 'ACC-bottle': 86.00881180403903, 'ACC-wine glass': 91.18411270874508, 'ACC-cup': 88.3784114930647, 'ACC-fork': 84.43297710018072, 'ACC-knife': 78.36044551916173, 'ACC-spoon': 77.13412002614089, 'ACC-bowl': 75.64282438895239, 'ACC-banana': 89.48753119577157, 'ACC-apple': 70.38968923715153, 'ACC-sandwich': 83.7156362526745, 'ACC-orange': 91.38672643315508, 'ACC-broccoli': 81.87947324743014, 'ACC-carrot': 77.04727154174302, 'ACC-hot dog': 67.05684398136648, 'ACC-pizza': 93.83877995042309, 'ACC-donut': 78.56304811140753, 'ACC-cake': 88.66587193438595, 'ACC-chair': 79.3200505991067, 'ACC-couch': 80.83448658381657, 'ACC-potted plant': 58.836684359706716, 'ACC-bed': 81.17776400789249, 'ACC-dining table': 75.41281731339514, 'ACC-toilet': 92.27993193541076, 'ACC-tv': 88.84157321255918, 'ACC-laptop': 95.69578705265006, 'ACC-mouse': 92.31554531057795, 'ACC-remote': 71.86482527179702, 'ACC-keyboard': 75.67339778051625, 'ACC-cell phone': 89.84066451590495, 'ACC-microwave': 74.83660744950423, 'ACC-oven': 89.01233981292681, 'ACC-toaster': 91.3650111538409, 'ACC-sink': 80.69090453497924, 'ACC-refrigerator': 92.30191809592893, 'ACC-book': 71.05022974979687, 'ACC-clock': 81.8775890785023, 'ACC-vase': 74.51019625649107, 'ACC-scissors': 90.57492952832182, 'ACC-teddy bear': 87.79784372354843, 'ACC-hair drier': 60.625338001309316, 'ACC-toothbrush': 83.23488533703961, 'ACC-banner': 69.48438292531966, 'ACC-blanket': 30.544386805109486, 'ACC-bridge': 59.938492325508165, 'ACC-cardboard': 63.82518540061073, 'ACC-counter': 53.47309823277444, 'ACC-curtain': 83.28007546327784, 'ACC-door-stuff': 73.07111105207098, 'ACC-floor-wood': 79.73160886493392, 'ACC-flower': 63.319031850715625, 'ACC-fruit': 69.32292591100116, 'ACC-gravel': 33.448517412372084, 'ACC-house': 30.695741408390703, 'ACC-light': 64.19767781265551, 'ACC-mirror-stuff': 76.07477244729904, 'ACC-net': 64.36898767994005, 'ACC-pillow': 50.90299979144282, 'ACC-platform': 47.60052354449314, 'ACC-playingfield': 87.69183734132666, 'ACC-railroad': 84.24317329215651, 'ACC-river': 83.31188344150434, 'ACC-road': 88.28137642279269, 'ACC-roof': 29.8810236852506, 'ACC-sand': 66.66694825033655, 'ACC-sea': 91.09491934774486, 'ACC-shelf': 52.797020487516576, 'ACC-snow': 95.6824481519332, 'ACC-stairs': 57.15078220348385, 'ACC-tent': 14.11180372295712, 'ACC-towel': 55.05191098498183, 'ACC-wall-brick': 66.82563151328911, 'ACC-wall-stone': 38.03513582054558, 'ACC-wall-tile': 84.51020252954285, 'ACC-wall-wood': 61.55562804005158, 'ACC-water-other': 41.942524375550725, 'ACC-window-blind': 62.12481314641705, 'ACC-window-other': 73.29206085760308, 'ACC-tree-merged': 89.80736582053916, 'ACC-fence-merged': 74.63095796366042, 'ACC-ceiling-merged': 82.83704973654629, 'ACC-sky-other-merged': 97.02360538818498, 'ACC-cabinet-merged': 77.8785562176186, 'ACC-table-merged': 53.661215820435636, 'ACC-floor-other-merged': 64.44209589286012, 'ACC-pavement-merged': 69.45777748641329, 'ACC-mountain-merged': 68.57983551860343, 'ACC-grass-merged': 84.00501654744605, 'ACC-dirt-merged': 74.21311234583852, 'ACC-paper-merged': 46.68275885038421, 'ACC-food-other-merged': 64.52092987614685, 'ACC-building-other-merged': 72.68692843233529, 'ACC-rock-merged': 83.89852940019831, 'ACC-wall-other-merged': 82.92479016869552, 'ACC-rug-merged': 81.15120137259626})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2991 s/iter. Inference: 0.2233 s/iter. Eval: 0.0000 s/iter. Total: 0.5225 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3517 s/iter. Inference: 0.3690 s/iter. Eval: 0.0000 s/iter. Total: 0.7208 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3614 s/iter. Inference: 0.5500 s/iter. Eval: 0.0000 s/iter. Total: 0.9115 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.408838162130524, 'noc@0.8': 2.410886742756804, 'noc@0.85': 2.845771144278607, 'noc@0.9': 3.696810067310506, 'miou@iter1': 0.8651512130366664} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1481 s/iter. Eval: 0.0011 s/iter. Total: 0.1505 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.20404052734375, 'precision@0.6': 72.40575408935547, 'precision@0.7': 67.85852813720703, 'precision@0.8': 58.68635940551758, 'precision@0.9': 31.90827751159668, 'cIoU': 61.77457046508789, 'mIoU': 66.67576599121094} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.37036576675788, 'SQ': 83.16952906659239, 'RQ': 65.82566882160106, 'PQ_th': 61.51648973794964, 'SQ_th': 84.08665387016312, 'RQ_th': 72.66475180747813, 'PQ_st': 46.093197508355104, 'SQ_st': 81.78518974044792, 'RQ_st': 55.50252469197533}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.37329931137894, 'AP50': 69.26543686048232, 'AP75': 48.833348673328494, 'APs': 26.606128795857554, 'APm': 49.82377545987694, 'APl': 67.35179056358818, 'AP-person': 48.80164322231025, 'AP-bicycle': 22.43929613906864, 'AP-car': 43.63215807932998, 'AP-motorcycle': 40.19670849518704, 'AP-airplane': 62.05201189884134, 'AP-bus': 72.3729071293317, 'AP-train': 74.19969463544507, 'AP-truck': 42.56149141415208, 'AP-boat': 31.091344625630967, 'AP-traffic light': 28.654001624276948, 'AP-fire hydrant': 71.71303719621424, 'AP-stop sign': 68.9546170064268, 'AP-parking meter': 50.44044339336439, 'AP-bench': 27.376150513039466, 'AP-bird': 34.28964010154877, 'AP-cat': 76.69240956784509, 'AP-dog': 70.6436262198852, 'AP-horse': 49.07431766218669, 'AP-sheep': 53.38823488899431, 'AP-cow': 55.872297611682754, 'AP-elephant': 65.47369052396006, 'AP-bear': 80.05896854181373, 'AP-zebra': 65.61224307243853, 'AP-giraffe': 61.719136835005315, 'AP-backpack': 24.358127144350277, 'AP-umbrella': 55.357056052826415, 'AP-handbag': 24.080656202190106, 'AP-tie': 39.341178680514986, 'AP-suitcase': 51.810277405866934, 'AP-frisbee': 71.00696230166432, 'AP-skis': 9.049661874293712, 'AP-snowboard': 35.31818560856683, 'AP-sports ball': 49.100557714180944, 'AP-kite': 38.294613441376114, 'AP-baseball bat': 37.56730008521535, 'AP-baseball glove': 50.02866264775155, 'AP-skateboard': 43.33626043116687, 'AP-surfboard': 44.77106736749576, 'AP-tennis racket': 63.35556195956039, 'AP-bottle': 41.62191558574448, 'AP-wine glass': 38.03054049821464, 'AP-cup': 49.16727250564051, 'AP-fork': 26.212138814379653, 'AP-knife': 24.22816982082582, 'AP-spoon': 21.951922356659743, 'AP-bowl': 39.56369808124318, 'AP-banana': 22.700594106594544, 'AP-apple': 25.317680927554864, 'AP-sandwich': 47.622434675411945, 'AP-orange': 32.09858772322659, 'AP-broccoli': 24.23156055525847, 'AP-carrot': 20.830422652129542, 'AP-hot dog': 31.96045752336148, 'AP-pizza': 50.60681078679279, 'AP-donut': 56.333525219076066, 'AP-cake': 47.943638889990034, 'AP-chair': 28.07165186582325, 'AP-couch': 45.283551939870115, 'AP-potted plant': 23.144596993031094, 'AP-bed': 44.0225353199023, 'AP-dining table': 15.28285349347123, 'AP-toilet': 69.86077543586357, 'AP-tv': 65.78285296307924, 'AP-laptop': 70.67893599367923, 'AP-mouse': 64.67512400585863, 'AP-remote': 43.48230729523068, 'AP-keyboard': 59.52814042239745, 'AP-cell phone': 45.50668047656911, 'AP-microwave': 63.9414491611198, 'AP-oven': 33.74286898991162, 'AP-toaster': 50.024516737388026, 'AP-sink': 44.28239412626755, 'AP-refrigerator': 69.6283062008261, 'AP-book': 13.945914089042514, 'AP-clock': 53.668479525371474, 'AP-vase': 40.29278665421692, 'AP-scissors': 36.91820653233223, 'AP-teddy bear': 56.27529077008955, 'AP-hair drier': 30.238823882388232, 'AP-toothbrush': 27.077331999481192}), ('sem_seg', {'mIoU': 65.9723977130351, 'fwIoU': 71.79862741204586, 'IoU-person': 89.04135289450595, 'IoU-bicycle': 78.4881534996937, 'IoU-car': 72.68213157406271, 'IoU-motorcycle': 88.22138043680357, 'IoU-airplane': 84.09254221296241, 'IoU-bus': 87.71895942764732, 'IoU-train': 87.48389515309172, 'IoU-truck': 68.86480133190615, 'IoU-boat': 72.08783801807535, 'IoU-traffic light': 78.72682116305972, 'IoU-fire hydrant': 93.2945609164062, 'IoU-stop sign': 94.48539673878457, 'IoU-parking meter': 85.29983638226915, 'IoU-bench': 64.5887177304204, 'IoU-bird': 77.50914523019324, 'IoU-cat': 88.46016685576326, 'IoU-dog': 83.18430237740947, 'IoU-horse': 89.60632020341619, 'IoU-sheep': 80.36742839314715, 'IoU-cow': 89.93717356784792, 'IoU-elephant': 90.66279288252777, 'IoU-bear': 70.23224881183707, 'IoU-zebra': 89.26117216360532, 'IoU-giraffe': 89.48803850400775, 'IoU-backpack': 54.2338052442518, 'IoU-umbrella': 86.5033799238979, 'IoU-handbag': 51.59263169239679, 'IoU-tie': 75.72641213440401, 'IoU-suitcase': 86.39406525396211, 'IoU-frisbee': 84.09703154530159, 'IoU-skis': 59.15484126010442, 'IoU-snowboard': 71.19438337465289, 'IoU-sports ball': 80.17141102353412, 'IoU-kite': 79.61302622164527, 'IoU-baseball bat': 69.14983677555227, 'IoU-baseball glove': 77.370605961172, 'IoU-skateboard': 85.80644543978703, 'IoU-surfboard': 86.6691813453108, 'IoU-tennis racket': 91.0913728328016, 'IoU-bottle': 72.41314268617603, 'IoU-wine glass': 82.69957506429948, 'IoU-cup': 71.05429064189336, 'IoU-fork': 69.620884072695, 'IoU-knife': 63.73106709615517, 'IoU-spoon': 61.44761162581675, 'IoU-bowl': 63.5799913492178, 'IoU-banana': 82.9234651482487, 'IoU-apple': 56.34588563458857, 'IoU-sandwich': 70.17404038812985, 'IoU-orange': 80.71239076015786, 'IoU-broccoli': 70.50317177320625, 'IoU-carrot': 64.28434813812298, 'IoU-hot dog': 62.977510644600045, 'IoU-pizza': 83.9126282975394, 'IoU-donut': 69.00989885125468, 'IoU-cake': 79.35949619874326, 'IoU-chair': 63.32153887863985, 'IoU-couch': 72.46079502682143, 'IoU-potted plant': 44.10268548533736, 'IoU-bed': 68.8464809878325, 'IoU-dining table': 54.90117518800979, 'IoU-toilet': 87.46898245358481, 'IoU-tv': 78.09780218868488, 'IoU-laptop': 82.36634529989273, 'IoU-mouse': 75.93116237667829, 'IoU-remote': 68.1480911499269, 'IoU-keyboard': 69.53327889732496, 'IoU-cell phone': 81.43874132706095, 'IoU-microwave': 70.49169510075323, 'IoU-oven': 69.11435235087833, 'IoU-toaster': 84.10857596411878, 'IoU-sink': 71.59018164649177, 'IoU-refrigerator': 83.58922912177717, 'IoU-book': 54.25807933829569, 'IoU-clock': 76.71806952716005, 'IoU-vase': 66.27722730016401, 'IoU-scissors': 85.26509135306257, 'IoU-teddy bear': 82.9296046160716, 'IoU-hair drier': 47.66481672112071, 'IoU-toothbrush': 75.56126834422882, 'IoU-banner': 37.96090736263128, 'IoU-blanket': 15.766089516658727, 'IoU-bridge': 39.83868456265954, 'IoU-cardboard': 48.198318988889675, 'IoU-counter': 31.948390476612182, 'IoU-curtain': 72.2094287547123, 'IoU-door-stuff': 48.31991402078757, 'IoU-floor-wood': 65.79712655259613, 'IoU-flower': 44.047919267393226, 'IoU-fruit': 48.701090872462395, 'IoU-gravel': 28.450621693096362, 'IoU-house': 25.30429692251107, 'IoU-light': 44.855431178052534, 'IoU-mirror-stuff': 64.44312552531119, 'IoU-net': 48.71344980180785, 'IoU-pillow': 26.025718377423328, 'IoU-platform': 29.98456750044083, 'IoU-playingfield': 70.13544498664007, 'IoU-railroad': 64.48678041041879, 'IoU-river': 56.72514493560868, 'IoU-road': 67.60353985383385, 'IoU-roof': 20.72137033917437, 'IoU-sand': 63.053644220818924, 'IoU-sea': 86.36907202331739, 'IoU-shelf': 37.80870282261877, 'IoU-snow': 92.10306725847654, 'IoU-stairs': 31.28951873621655, 'IoU-tent': 10.713252432521031, 'IoU-towel': 46.7658986818224, 'IoU-wall-brick': 50.195137984275185, 'IoU-wall-stone': 30.894694843264737, 'IoU-wall-tile': 70.45165727843006, 'IoU-wall-wood': 44.70752981644569, 'IoU-water-other': 27.541061504452202, 'IoU-window-blind': 47.88075807543271, 'IoU-window-other': 49.21500832055207, 'IoU-tree-merged': 82.0409013990846, 'IoU-fence-merged': 54.44071981732155, 'IoU-ceiling-merged': 68.31889714402031, 'IoU-sky-other-merged': 94.25060608365285, 'IoU-cabinet-merged': 63.719987505553824, 'IoU-table-merged': 41.30434237374238, 'IoU-floor-other-merged': 54.42228861736127, 'IoU-pavement-merged': 57.86087990931009, 'IoU-mountain-merged': 58.22763892877073, 'IoU-grass-merged': 71.86377904095927, 'IoU-dirt-merged': 47.522890786834594, 'IoU-paper-merged': 35.7808477409792, 'IoU-food-other-merged': 44.905030848205655, 'IoU-building-other-merged': 59.66731367336974, 'IoU-rock-merged': 63.788714862744534, 'IoU-wall-other-merged': 68.10712054610761, 'IoU-rug-merged': 67.39229514430336, 'mACC': 77.54065370138807, 'pACC': 82.40771544980353, 'ACC-person': 92.94438426021537, 'ACC-bicycle': 87.24429138797795, 'ACC-car': 87.58466647837884, 'ACC-motorcycle': 92.82300153971046, 'ACC-airplane': 90.2154129470409, 'ACC-bus': 93.89372675045405, 'ACC-train': 95.86676021523425, 'ACC-truck': 79.1981291750805, 'ACC-boat': 81.50774895126631, 'ACC-traffic light': 91.27938571713511, 'ACC-fire hydrant': 95.84616140093173, 'ACC-stop sign': 98.17169814655875, 'ACC-parking meter': 88.23872465225624, 'ACC-bench': 80.77992320247253, 'ACC-bird': 82.13668190300105, 'ACC-cat': 91.92507267392823, 'ACC-dog': 85.58029170095124, 'ACC-horse': 94.28343946612718, 'ACC-sheep': 84.6103684207424, 'ACC-cow': 93.34519100213373, 'ACC-elephant': 93.0486073700463, 'ACC-bear': 71.57314534552673, 'ACC-zebra': 91.51510600181521, 'ACC-giraffe': 93.28020492615488, 'ACC-backpack': 75.63737390557489, 'ACC-umbrella': 90.32251946335032, 'ACC-handbag': 71.2784278971474, 'ACC-tie': 84.49741737585694, 'ACC-suitcase': 92.26247716841293, 'ACC-frisbee': 94.33454545454546, 'ACC-skis': 73.92818325229901, 'ACC-snowboard': 82.00631872841714, 'ACC-sports ball': 89.05531263890181, 'ACC-kite': 85.98008049353257, 'ACC-baseball bat': 87.20938944623845, 'ACC-baseball glove': 92.59491436271013, 'ACC-skateboard': 90.56671948148714, 'ACC-surfboard': 92.36413164647271, 'ACC-tennis racket': 94.90074236180476, 'ACC-bottle': 86.00881180403903, 'ACC-wine glass': 91.18411270874508, 'ACC-cup': 88.3784114930647, 'ACC-fork': 84.43297710018072, 'ACC-knife': 78.36044551916173, 'ACC-spoon': 77.13412002614089, 'ACC-bowl': 75.64282438895239, 'ACC-banana': 89.48753119577157, 'ACC-apple': 70.38968923715153, 'ACC-sandwich': 83.7156362526745, 'ACC-orange': 91.38672643315508, 'ACC-broccoli': 81.87947324743014, 'ACC-carrot': 77.04727154174302, 'ACC-hot dog': 67.05684398136648, 'ACC-pizza': 93.83877995042309, 'ACC-donut': 78.56304811140753, 'ACC-cake': 88.66587193438595, 'ACC-chair': 79.3200505991067, 'ACC-couch': 80.83448658381657, 'ACC-potted plant': 58.836684359706716, 'ACC-bed': 81.17776400789249, 'ACC-dining table': 75.41281731339514, 'ACC-toilet': 92.27993193541076, 'ACC-tv': 88.84157321255918, 'ACC-laptop': 95.69578705265006, 'ACC-mouse': 92.31554531057795, 'ACC-remote': 71.86482527179702, 'ACC-keyboard': 75.67339778051625, 'ACC-cell phone': 89.84066451590495, 'ACC-microwave': 74.83660744950423, 'ACC-oven': 89.01233981292681, 'ACC-toaster': 91.3650111538409, 'ACC-sink': 80.69090453497924, 'ACC-refrigerator': 92.30191809592893, 'ACC-book': 71.05022974979687, 'ACC-clock': 81.8775890785023, 'ACC-vase': 74.51019625649107, 'ACC-scissors': 90.57492952832182, 'ACC-teddy bear': 87.79784372354843, 'ACC-hair drier': 60.625338001309316, 'ACC-toothbrush': 83.23488533703961, 'ACC-banner': 69.48438292531966, 'ACC-blanket': 30.544386805109486, 'ACC-bridge': 59.938492325508165, 'ACC-cardboard': 63.82518540061073, 'ACC-counter': 53.47309823277444, 'ACC-curtain': 83.28007546327784, 'ACC-door-stuff': 73.07111105207098, 'ACC-floor-wood': 79.73160886493392, 'ACC-flower': 63.319031850715625, 'ACC-fruit': 69.32292591100116, 'ACC-gravel': 33.448517412372084, 'ACC-house': 30.695741408390703, 'ACC-light': 64.19767781265551, 'ACC-mirror-stuff': 76.07477244729904, 'ACC-net': 64.36898767994005, 'ACC-pillow': 50.90299979144282, 'ACC-platform': 47.60052354449314, 'ACC-playingfield': 87.69183734132666, 'ACC-railroad': 84.24317329215651, 'ACC-river': 83.31188344150434, 'ACC-road': 88.28137642279269, 'ACC-roof': 29.8810236852506, 'ACC-sand': 66.66694825033655, 'ACC-sea': 91.09491934774486, 'ACC-shelf': 52.797020487516576, 'ACC-snow': 95.6824481519332, 'ACC-stairs': 57.15078220348385, 'ACC-tent': 14.11180372295712, 'ACC-towel': 55.05191098498183, 'ACC-wall-brick': 66.82563151328911, 'ACC-wall-stone': 38.03513582054558, 'ACC-wall-tile': 84.51020252954285, 'ACC-wall-wood': 61.55562804005158, 'ACC-water-other': 41.942524375550725, 'ACC-window-blind': 62.12481314641705, 'ACC-window-other': 73.29206085760308, 'ACC-tree-merged': 89.80736582053916, 'ACC-fence-merged': 74.63095796366042, 'ACC-ceiling-merged': 82.83704973654629, 'ACC-sky-other-merged': 97.02360538818498, 'ACC-cabinet-merged': 77.8785562176186, 'ACC-table-merged': 53.661215820435636, 'ACC-floor-other-merged': 64.44209589286012, 'ACC-pavement-merged': 69.45777748641329, 'ACC-mountain-merged': 68.57983551860343, 'ACC-grass-merged': 84.00501654744605, 'ACC-dirt-merged': 74.21311234583852, 'ACC-paper-merged': 46.68275885038421, 'ACC-food-other-merged': 64.52092987614685, 'ACC-building-other-merged': 72.68692843233529, 'ACC-rock-merged': 83.89852940019831, 'ACC-wall-other-merged': 82.92479016869552, 'ACC-rug-merged': 81.15120137259626})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.408838162130524, 'noc@0.8': 2.410886742756804, 'noc@0.85': 2.845771144278607, 'noc@0.9': 3.696810067310506, 'miou@iter1': 0.8651512130366664}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.20404052734375, 'precision@0.6': 72.40575408935547, 'precision@0.7': 67.85852813720703, 'precision@0.8': 58.68635940551758, 'precision@0.9': 31.90827751159668, 'cIoU': 61.77457046508789, 'mIoU': 66.67576599121094}}} INFO:trainer.default_trainer:This epoch takes 0:57:30.630021 INFO:trainer.default_trainer:PROGRESS: 66.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 33 training. INFO:trainer.default_trainer:epochs[ 33] optim steps[60300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.40289/0.75877, loss_mask_bce_0: 0.08225/0.30060, loss_mask_dice_0: 1.79824/1.02275, loss_spatial_bce_0: 0.01437/0.08520, loss_spatial_dice_0: 0.19854/0.18048, loss_spatial_ce_0: 0.00014/0.05776, loss_grounding_bce_0: 0.00700/0.08049, loss_grounding_dice_0: 0.11738/0.15066, loss_grounding_ce_0: 0.11887/0.24891, loss_mask_ce_1: 1.66816/0.75936, loss_mask_bce_1: 0.08161/0.30147, loss_mask_dice_1: 1.64428/1.02693, loss_spatial_bce_1: 0.01364/0.08550, loss_spatial_dice_1: 0.19937/0.18312, loss_spatial_ce_1: 0.00014/0.06163, loss_grounding_bce_1: 0.00902/0.08067, loss_grounding_dice_1: 0.13793/0.15140, loss_grounding_ce_1: 0.11996/0.25064, loss_mask_ce_2: 1.70374/0.76747, loss_mask_bce_2: 0.07473/0.30166, loss_mask_dice_2: 1.79576/1.02764, loss_spatial_bce_2: 0.01448/0.08555, loss_spatial_dice_2: 0.22815/0.18357, loss_spatial_ce_2: 0.00031/0.06382, loss_grounding_bce_2: 0.00733/0.08068, loss_grounding_dice_2: 0.13041/0.15130, loss_grounding_ce_2: 0.12817/0.25396, loss_mask_ce_3: 2.05286/0.77089, loss_mask_bce_3: 0.07404/0.30314, loss_mask_dice_3: 1.52472/1.02572, loss_spatial_bce_3: 0.01406/0.08765, loss_spatial_dice_3: 0.23474/0.18491, loss_spatial_ce_3: 0.00429/0.06849, loss_grounding_bce_3: 0.00742/0.08109, loss_grounding_dice_3: 0.10619/0.15095, loss_grounding_ce_3: 0.12093/0.25456, loss_mask_ce_4: 1.86861/0.77684, loss_mask_bce_4: 0.08992/0.30563, loss_mask_dice_4: 1.82566/1.04445, loss_spatial_bce_4: 0.01867/0.08974, loss_spatial_dice_4: 0.24601/0.19291, loss_spatial_ce_4: 0.01982/0.08182, loss_grounding_bce_4: 0.00587/0.08168, loss_grounding_dice_4: 0.09046/0.15356, loss_grounding_ce_4: 0.10769/0.25911, loss_mask_ce_5: 1.55059/0.80093, loss_mask_bce_5: 0.08856/0.30747, loss_mask_dice_5: 1.53894/1.05216, loss_spatial_bce_5: 0.01942/0.09194, loss_spatial_dice_5: 0.21821/0.19593, loss_spatial_ce_5: 0.05530/0.09457, loss_grounding_bce_5: 0.00850/0.08198, loss_grounding_dice_5: 0.12694/0.15425, loss_grounding_ce_5: 0.13885/0.27743, loss_mask_ce_6: 1.61176/0.82759, loss_mask_bce_6: 0.09138/0.30957, loss_mask_dice_6: 1.49948/1.05568, loss_spatial_bce_6: 0.01913/0.09705, loss_spatial_dice_6: 0.21242/0.19826, loss_spatial_ce_6: 0.05118/0.11895, loss_grounding_bce_6: 0.00747/0.08286, loss_grounding_dice_6: 0.12006/0.15481, loss_grounding_ce_6: 0.13218/0.28707, loss_mask_ce_7: 2.50716/0.88339, loss_mask_bce_7: 0.10397/0.31680, loss_mask_dice_7: 2.00831/1.10207, loss_spatial_bce_7: 0.01847/0.10690, loss_spatial_dice_7: 0.17884/0.22380, loss_spatial_ce_7: 0.13730/0.15677, loss_grounding_bce_7: 0.00784/0.08460, loss_grounding_dice_7: 0.12523/0.16042, loss_grounding_ce_7: 0.12071/0.31964, loss_mask_ce_8: 2.47956/1.01937, loss_mask_bce_8: 0.10251/0.33291, loss_mask_dice_8: 2.07753/1.17917, loss_spatial_bce_8: 0.01247/0.12431, loss_spatial_dice_8: 0.26727/0.25931, loss_spatial_ce_8: 0.10084/0.20442, loss_grounding_bce_8: 0.01686/0.08878, loss_grounding_dice_8: 0.18204/0.17016, loss_grounding_ce_8: 0.17645/0.42038, loss_mask_ce_9: 4.87200/3.47895, loss_mask_bce_9: 0.08611/0.35978, loss_mask_dice_9: 3.45636/1.76112, loss_spatial_bce_9: 0.18709/0.35474, loss_spatial_dice_9: 0.92430/0.79356, loss_spatial_ce_9: 2.23725/1.39175, loss_grounding_bce_9: 0.00615/0.10079, loss_grounding_dice_9: 0.25108/0.24245, loss_grounding_ce_9: 0.64204/0.67557] items per batch[64] items per second[0.16] total items[3859200] mini batches[ 60300] memory[4999] epoch remaining[1:24:56] INFO:trainer.default_trainer:epochs[ 33] optim steps[60400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10753/0.75870, loss_mask_bce_0: 0.06929/0.30060, loss_mask_dice_0: 0.12452/1.02285, loss_spatial_bce_0: 0.03195/0.08520, loss_spatial_dice_0: 0.05627/0.18048, loss_spatial_ce_0: 0.00052/0.05775, loss_grounding_bce_0: 0.05172/0.08050, loss_grounding_dice_0: 0.02672/0.15065, loss_grounding_ce_0: 0.00126/0.24878, loss_mask_ce_1: 0.11938/0.75934, loss_mask_bce_1: 0.06841/0.30146, loss_mask_dice_1: 0.11759/1.02702, loss_spatial_bce_1: 0.03299/0.08550, loss_spatial_dice_1: 0.03821/0.18312, loss_spatial_ce_1: 0.00018/0.06164, loss_grounding_bce_1: 0.05364/0.08068, loss_grounding_dice_1: 0.02745/0.15140, loss_grounding_ce_1: 0.00128/0.25048, loss_mask_ce_2: 0.09533/0.76747, loss_mask_bce_2: 0.06922/0.30167, loss_mask_dice_2: 0.11750/1.02770, loss_spatial_bce_2: 0.03357/0.08555, loss_spatial_dice_2: 0.04567/0.18357, loss_spatial_ce_2: 0.00003/0.06380, loss_grounding_bce_2: 0.05642/0.08069, loss_grounding_dice_2: 0.02845/0.15129, loss_grounding_ce_2: 0.00181/0.25381, loss_mask_ce_3: 0.09105/0.77084, loss_mask_bce_3: 0.06545/0.30315, loss_mask_dice_3: 0.11103/1.02581, loss_spatial_bce_3: 0.03401/0.08765, loss_spatial_dice_3: 0.04603/0.18491, loss_spatial_ce_3: 0.00011/0.06847, loss_grounding_bce_3: 0.05382/0.08110, loss_grounding_dice_3: 0.02699/0.15094, loss_grounding_ce_3: 0.00145/0.25446, loss_mask_ce_4: 0.08800/0.77683, loss_mask_bce_4: 0.07074/0.30563, loss_mask_dice_4: 0.13052/1.04451, loss_spatial_bce_4: 0.03285/0.08974, loss_spatial_dice_4: 0.05028/0.19291, loss_spatial_ce_4: 0.00003/0.08180, loss_grounding_bce_4: 0.05584/0.08169, loss_grounding_dice_4: 0.03009/0.15355, loss_grounding_ce_4: 0.00236/0.25898, loss_mask_ce_5: 0.08333/0.80094, loss_mask_bce_5: 0.07147/0.30749, loss_mask_dice_5: 0.11360/1.05222, loss_spatial_bce_5: 0.03659/0.09194, loss_spatial_dice_5: 0.05666/0.19593, loss_spatial_ce_5: 0.00066/0.09458, loss_grounding_bce_5: 0.05688/0.08198, loss_grounding_dice_5: 0.03202/0.15425, loss_grounding_ce_5: 0.00149/0.27727, loss_mask_ce_6: 0.07882/0.82762, loss_mask_bce_6: 0.08167/0.30958, loss_mask_dice_6: 0.12785/1.05574, loss_spatial_bce_6: 0.03381/0.09706, loss_spatial_dice_6: 0.04747/0.19827, loss_spatial_ce_6: 0.02894/0.11893, loss_grounding_bce_6: 0.06292/0.08286, loss_grounding_dice_6: 0.03161/0.15481, loss_grounding_ce_6: 0.00126/0.28693, loss_mask_ce_7: 0.09362/0.88338, loss_mask_bce_7: 0.07256/0.31681, loss_mask_dice_7: 0.11257/1.10215, loss_spatial_bce_7: 0.03511/0.10689, loss_spatial_dice_7: 0.05070/0.22379, loss_spatial_ce_7: 0.00297/0.15675, loss_grounding_bce_7: 0.05565/0.08460, loss_grounding_dice_7: 0.02994/0.16040, loss_grounding_ce_7: 0.00076/0.31947, loss_mask_ce_8: 0.12196/1.01933, loss_mask_bce_8: 0.07052/0.33291, loss_mask_dice_8: 0.11351/1.17924, loss_spatial_bce_8: 0.03515/0.12431, loss_spatial_dice_8: 0.05212/0.25932, loss_spatial_ce_8: 0.02690/0.20438, loss_grounding_bce_8: 0.05936/0.08878, loss_grounding_dice_8: 0.03085/0.17015, loss_grounding_ce_8: 0.00159/0.42018, loss_mask_ce_9: 2.40511/3.47875, loss_mask_bce_9: 0.07765/0.35977, loss_mask_dice_9: 0.15402/1.76119, loss_spatial_bce_9: 0.46623/0.35473, loss_spatial_dice_9: 0.75273/0.79353, loss_spatial_ce_9: 0.66964/1.39163, loss_grounding_bce_9: 0.05901/0.10079, loss_grounding_dice_9: 0.02778/0.24244, loss_grounding_ce_9: 0.02515/0.67534] items per batch[64] items per second[0.36] total items[3865600] mini batches[ 60400] memory[4999] epoch remaining[0:53:27] INFO:trainer.default_trainer:epochs[ 33] optim steps[60500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.20480/0.75857, loss_mask_bce_0: 0.67624/0.30062, loss_mask_dice_0: 1.60687/1.02295, loss_spatial_bce_0: 0.06227/0.08518, loss_spatial_dice_0: 0.20836/0.18047, loss_spatial_ce_0: 0.01427/0.05776, loss_grounding_bce_0: 0.22741/0.08049, loss_grounding_dice_0: 0.19443/0.15065, loss_grounding_ce_0: 0.01711/0.24887, loss_mask_ce_1: 1.18985/0.75920, loss_mask_bce_1: 0.66690/0.30149, loss_mask_dice_1: 1.64099/1.02711, loss_spatial_bce_1: 0.05749/0.08549, loss_spatial_dice_1: 0.16834/0.18312, loss_spatial_ce_1: 0.01182/0.06165, loss_grounding_bce_1: 0.23294/0.08068, loss_grounding_dice_1: 0.18647/0.15139, loss_grounding_ce_1: 0.02034/0.25060, loss_mask_ce_2: 1.22630/0.76731, loss_mask_bce_2: 0.67742/0.30169, loss_mask_dice_2: 1.68920/1.02785, loss_spatial_bce_2: 0.07034/0.08554, loss_spatial_dice_2: 0.20371/0.18357, loss_spatial_ce_2: 0.01276/0.06380, loss_grounding_bce_2: 0.23113/0.08069, loss_grounding_dice_2: 0.18827/0.15129, loss_grounding_ce_2: 0.04166/0.25390, loss_mask_ce_3: 1.28330/0.77071, loss_mask_bce_3: 0.68054/0.30318, loss_mask_dice_3: 1.64468/1.02593, loss_spatial_bce_3: 0.06834/0.08764, loss_spatial_dice_3: 0.19938/0.18491, loss_spatial_ce_3: 0.00653/0.06846, loss_grounding_bce_3: 0.23047/0.08110, loss_grounding_dice_3: 0.18189/0.15094, loss_grounding_ce_3: 0.03341/0.25458, loss_mask_ce_4: 1.28855/0.77666, loss_mask_bce_4: 0.64781/0.30565, loss_mask_dice_4: 1.77526/1.04460, loss_spatial_bce_4: 0.06553/0.08973, loss_spatial_dice_4: 0.18462/0.19291, loss_spatial_ce_4: 0.00495/0.08182, loss_grounding_bce_4: 0.22457/0.08169, loss_grounding_dice_4: 0.16742/0.15355, loss_grounding_ce_4: 0.01499/0.25911, loss_mask_ce_5: 1.47076/0.80082, loss_mask_bce_5: 0.66337/0.30751, loss_mask_dice_5: 2.03952/1.05230, loss_spatial_bce_5: 0.08025/0.09193, loss_spatial_dice_5: 0.20826/0.19593, loss_spatial_ce_5: 0.02628/0.09463, loss_grounding_bce_5: 0.20867/0.08198, loss_grounding_dice_5: 0.17437/0.15425, loss_grounding_ce_5: 0.01614/0.27738, loss_mask_ce_6: 1.43189/0.82748, loss_mask_bce_6: 0.64239/0.30962, loss_mask_dice_6: 2.44698/1.05589, loss_spatial_bce_6: 0.05882/0.09706, loss_spatial_dice_6: 0.18097/0.19827, loss_spatial_ce_6: 0.06317/0.11899, loss_grounding_bce_6: 0.19240/0.08286, loss_grounding_dice_6: 0.17060/0.15480, loss_grounding_ce_6: 0.02925/0.28699, loss_mask_ce_7: 1.37747/0.88326, loss_mask_bce_7: 0.60451/0.31682, loss_mask_dice_7: 2.78814/1.10231, loss_spatial_bce_7: 0.07530/0.10688, loss_spatial_dice_7: 0.21490/0.22379, loss_spatial_ce_7: 0.07724/0.15676, loss_grounding_bce_7: 0.19429/0.08459, loss_grounding_dice_7: 0.19701/0.16040, loss_grounding_ce_7: 0.02301/0.31957, loss_mask_ce_8: 1.26348/1.01917, loss_mask_bce_8: 0.56751/0.33292, loss_mask_dice_8: 2.62585/1.17938, loss_spatial_bce_8: 0.12267/0.12429, loss_spatial_dice_8: 0.23146/0.25930, loss_spatial_ce_8: 0.02378/0.20440, loss_grounding_bce_8: 0.18685/0.08877, loss_grounding_dice_8: 0.18864/0.17014, loss_grounding_ce_8: 0.03380/0.42018, loss_mask_ce_9: 4.03995/3.47873, loss_mask_bce_9: 0.74523/0.35979, loss_mask_dice_9: 5.92373/1.76132, loss_spatial_bce_9: 0.15601/0.35470, loss_spatial_dice_9: 0.96423/0.79353, loss_spatial_ce_9: 1.15731/1.39173, loss_grounding_bce_9: 0.17710/0.10079, loss_grounding_dice_9: 0.16257/0.24243, loss_grounding_ce_9: 0.01819/0.67538] items per batch[64] items per second[0.37] total items[3872000] mini batches[ 60500] memory[4999] epoch remaining[0:48:38] INFO:trainer.default_trainer:epochs[ 33] optim steps[60600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48435/0.75832, loss_mask_bce_0: 0.06691/0.30056, loss_mask_dice_0: 0.05816/1.02250, loss_spatial_bce_0: 0.17862/0.08518, loss_spatial_dice_0: 0.08914/0.18043, loss_spatial_ce_0: 0.09926/0.05776, loss_grounding_bce_0: 0.04752/0.08049, loss_grounding_dice_0: 0.04019/0.15065, loss_grounding_ce_0: 0.23199/0.24877, loss_mask_ce_1: 0.48438/0.75894, loss_mask_bce_1: 0.06999/0.30143, loss_mask_dice_1: 0.06115/1.02667, loss_spatial_bce_1: 0.15731/0.08548, loss_spatial_dice_1: 0.10724/0.18308, loss_spatial_ce_1: 0.08212/0.06164, loss_grounding_bce_1: 0.04813/0.08068, loss_grounding_dice_1: 0.04136/0.15137, loss_grounding_ce_1: 0.22804/0.25050, loss_mask_ce_2: 0.45483/0.76703, loss_mask_bce_2: 0.06982/0.30164, loss_mask_dice_2: 0.06226/1.02740, loss_spatial_bce_2: 0.16513/0.08554, loss_spatial_dice_2: 0.10261/0.18353, loss_spatial_ce_2: 0.08975/0.06380, loss_grounding_bce_2: 0.04803/0.08070, loss_grounding_dice_2: 0.04120/0.15129, loss_grounding_ce_2: 0.21081/0.25380, loss_mask_ce_3: 0.44929/0.77044, loss_mask_bce_3: 0.07614/0.30313, loss_mask_dice_3: 0.06932/1.02547, loss_spatial_bce_3: 0.08233/0.08763, loss_spatial_dice_3: 0.07146/0.18487, loss_spatial_ce_3: 0.11461/0.06845, loss_grounding_bce_3: 0.05207/0.08109, loss_grounding_dice_3: 0.04689/0.15093, loss_grounding_ce_3: 0.20404/0.25456, loss_mask_ce_4: 0.47359/0.77634, loss_mask_bce_4: 0.07321/0.30561, loss_mask_dice_4: 0.06729/1.04416, loss_spatial_bce_4: 0.07526/0.08973, loss_spatial_dice_4: 0.06911/0.19288, loss_spatial_ce_4: 0.19885/0.08183, loss_grounding_bce_4: 0.05222/0.08169, loss_grounding_dice_4: 0.04748/0.15353, loss_grounding_ce_4: 0.22415/0.25908, loss_mask_ce_5: 0.63385/0.80054, loss_mask_bce_5: 0.08253/0.30745, loss_mask_dice_5: 0.07729/1.05186, loss_spatial_bce_5: 0.08953/0.09194, loss_spatial_dice_5: 0.07522/0.19590, loss_spatial_ce_5: 0.18562/0.09464, loss_grounding_bce_5: 0.05852/0.08198, loss_grounding_dice_5: 0.05794/0.15423, loss_grounding_ce_5: 0.33264/0.27736, loss_mask_ce_6: 0.85044/0.82720, loss_mask_bce_6: 0.07789/0.30956, loss_mask_dice_6: 0.07193/1.05545, loss_spatial_bce_6: 0.08148/0.09706, loss_spatial_dice_6: 0.07047/0.19824, loss_spatial_ce_6: 0.26454/0.11902, loss_grounding_bce_6: 0.04879/0.08286, loss_grounding_dice_6: 0.04662/0.15478, loss_grounding_ce_6: 0.58482/0.28689, loss_mask_ce_7: 0.88885/0.88296, loss_mask_bce_7: 0.08680/0.31676, loss_mask_dice_7: 0.07926/1.10183, loss_spatial_bce_7: 0.10104/0.10688, loss_spatial_dice_7: 0.08856/0.22373, loss_spatial_ce_7: 0.26038/0.15674, loss_grounding_bce_7: 0.05126/0.08458, loss_grounding_dice_7: 0.04284/0.16038, loss_grounding_ce_7: 0.64019/0.31964, loss_mask_ce_8: 1.56355/1.01885, loss_mask_bce_8: 0.09010/0.33286, loss_mask_dice_8: 0.07376/1.17889, loss_spatial_bce_8: 0.18049/0.12428, loss_spatial_dice_8: 0.13598/0.25924, loss_spatial_ce_8: 0.24648/0.20429, loss_grounding_bce_8: 0.06007/0.08877, loss_grounding_dice_8: 0.05011/0.17012, loss_grounding_ce_8: 0.66753/0.42008, loss_mask_ce_9: 3.02035/3.47827, loss_mask_bce_9: 0.26401/0.35971, loss_mask_dice_9: 0.24451/1.76064, loss_spatial_bce_9: 0.70880/0.35473, loss_spatial_dice_9: 0.57469/0.79347, loss_spatial_ce_9: 0.39918/1.39158, loss_grounding_bce_9: 0.18265/0.10079, loss_grounding_dice_9: 0.16670/0.24238, loss_grounding_ce_9: 0.57871/0.67524] items per batch[64] items per second[0.36] total items[3878400] mini batches[ 60600] memory[4999] epoch remaining[0:45:30] INFO:trainer.default_trainer:epochs[ 33] optim steps[60700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43390/0.75832, loss_mask_bce_0: 0.19860/0.30057, loss_mask_dice_0: 0.36391/1.02279, loss_spatial_bce_0: 0.04589/0.08518, loss_spatial_dice_0: 0.09284/0.18042, loss_spatial_ce_0: 0.00159/0.05774, loss_grounding_bce_0: 0.07815/0.08050, loss_grounding_dice_0: 0.10502/0.15070, loss_grounding_ce_0: 0.04237/0.24886, loss_mask_ce_1: 0.44484/0.75896, loss_mask_bce_1: 0.20541/0.30144, loss_mask_dice_1: 0.35652/1.02696, loss_spatial_bce_1: 0.04699/0.08548, loss_spatial_dice_1: 0.12902/0.18308, loss_spatial_ce_1: 0.00238/0.06162, loss_grounding_bce_1: 0.07637/0.08068, loss_grounding_dice_1: 0.09931/0.15141, loss_grounding_ce_1: 0.03588/0.25059, loss_mask_ce_2: 0.49511/0.76701, loss_mask_bce_2: 0.20362/0.30165, loss_mask_dice_2: 0.35971/1.02766, loss_spatial_bce_2: 0.04823/0.08554, loss_spatial_dice_2: 0.12566/0.18353, loss_spatial_ce_2: 0.00634/0.06378, loss_grounding_bce_2: 0.06606/0.08070, loss_grounding_dice_2: 0.09768/0.15133, loss_grounding_ce_2: 0.04299/0.25381, loss_mask_ce_3: 0.47189/0.77042, loss_mask_bce_3: 0.19244/0.30314, loss_mask_dice_3: 0.36681/1.02572, loss_spatial_bce_3: 0.05212/0.08763, loss_spatial_dice_3: 0.13949/0.18487, loss_spatial_ce_3: 0.01092/0.06844, loss_grounding_bce_3: 0.07635/0.08110, loss_grounding_dice_3: 0.09985/0.15096, loss_grounding_ce_3: 0.03766/0.25467, loss_mask_ce_4: 0.49487/0.77633, loss_mask_bce_4: 0.20423/0.30562, loss_mask_dice_4: 0.37176/1.04443, loss_spatial_bce_4: 0.05627/0.08975, loss_spatial_dice_4: 0.13010/0.19288, loss_spatial_ce_4: 0.00445/0.08184, loss_grounding_bce_4: 0.08072/0.08170, loss_grounding_dice_4: 0.09967/0.15358, loss_grounding_ce_4: 0.02564/0.25910, loss_mask_ce_5: 0.51377/0.80056, loss_mask_bce_5: 0.19961/0.30747, loss_mask_dice_5: 0.37624/1.05213, loss_spatial_bce_5: 0.06109/0.09195, loss_spatial_dice_5: 0.14925/0.19591, loss_spatial_ce_5: 0.03630/0.09464, loss_grounding_bce_5: 0.06414/0.08198, loss_grounding_dice_5: 0.09092/0.15426, loss_grounding_ce_5: 0.06431/0.27740, loss_mask_ce_6: 0.55655/0.82722, loss_mask_bce_6: 0.19368/0.30957, loss_mask_dice_6: 0.39558/1.05578, loss_spatial_bce_6: 0.05996/0.09707, loss_spatial_dice_6: 0.12448/0.19826, loss_spatial_ce_6: 0.05325/0.11902, loss_grounding_bce_6: 0.05539/0.08287, loss_grounding_dice_6: 0.09046/0.15483, loss_grounding_ce_6: 0.10692/0.28692, loss_mask_ce_7: 0.72687/0.88295, loss_mask_bce_7: 0.20688/0.31677, loss_mask_dice_7: 0.42043/1.10209, loss_spatial_bce_7: 0.06304/0.10689, loss_spatial_dice_7: 0.10344/0.22374, loss_spatial_ce_7: 0.07448/0.15672, loss_grounding_bce_7: 0.05953/0.08458, loss_grounding_dice_7: 0.09845/0.16043, loss_grounding_ce_7: 0.05391/0.31961, loss_mask_ce_8: 0.61209/1.01887, loss_mask_bce_8: 0.20574/0.33288, loss_mask_dice_8: 0.43113/1.17920, loss_spatial_bce_8: 0.06810/0.12428, loss_spatial_dice_8: 0.13267/0.25925, loss_spatial_ce_8: 0.10464/0.20428, loss_grounding_bce_8: 0.07450/0.08879, loss_grounding_dice_8: 0.11036/0.17018, loss_grounding_ce_8: 0.29782/0.41992, loss_mask_ce_9: 3.32381/3.47834, loss_mask_bce_9: 0.21396/0.35973, loss_mask_dice_9: 0.64246/1.76113, loss_spatial_bce_9: 0.37914/0.35470, loss_spatial_dice_9: 0.89886/0.79350, loss_spatial_ce_9: 1.29772/1.39153, loss_grounding_bce_9: 0.05483/0.10079, loss_grounding_dice_9: 0.15557/0.24244, loss_grounding_ce_9: 2.21463/0.67516] items per batch[64] items per second[0.36] total items[3884800] mini batches[ 60700] memory[4999] epoch remaining[0:42:18] INFO:trainer.default_trainer:epochs[ 33] optim steps[60800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33398/0.75838, loss_mask_bce_0: 0.53463/0.30066, loss_mask_dice_0: 0.77722/1.02279, loss_spatial_bce_0: 0.15062/0.08520, loss_spatial_dice_0: 0.19323/0.18042, loss_spatial_ce_0: 0.01693/0.05773, loss_grounding_bce_0: 0.21157/0.08051, loss_grounding_dice_0: 0.18764/0.15072, loss_grounding_ce_0: 0.35090/0.24913, loss_mask_ce_1: 0.32296/0.75900, loss_mask_bce_1: 0.53929/0.30154, loss_mask_dice_1: 0.77162/1.02696, loss_spatial_bce_1: 0.15237/0.08550, loss_spatial_dice_1: 0.19880/0.18308, loss_spatial_ce_1: 0.01575/0.06160, loss_grounding_bce_1: 0.21450/0.08070, loss_grounding_dice_1: 0.18970/0.15144, loss_grounding_ce_1: 0.31796/0.25081, loss_mask_ce_2: 0.31769/0.76704, loss_mask_bce_2: 0.54365/0.30175, loss_mask_dice_2: 0.81012/1.02771, loss_spatial_bce_2: 0.15835/0.08556, loss_spatial_dice_2: 0.19484/0.18353, loss_spatial_ce_2: 0.03558/0.06376, loss_grounding_bce_2: 0.20983/0.08071, loss_grounding_dice_2: 0.19124/0.15135, loss_grounding_ce_2: 0.31672/0.25410, loss_mask_ce_3: 0.46228/0.77052, loss_mask_bce_3: 0.55416/0.30322, loss_mask_dice_3: 0.80934/1.02571, loss_spatial_bce_3: 0.13554/0.08765, loss_spatial_dice_3: 0.17791/0.18488, loss_spatial_ce_3: 0.03560/0.06843, loss_grounding_bce_3: 0.22144/0.08111, loss_grounding_dice_3: 0.19584/0.15099, loss_grounding_ce_3: 0.31415/0.25488, loss_mask_ce_4: 0.31287/0.77642, loss_mask_bce_4: 0.52851/0.30574, loss_mask_dice_4: 0.80717/1.04450, loss_spatial_bce_4: 0.13489/0.08977, loss_spatial_dice_4: 0.19934/0.19290, loss_spatial_ce_4: 0.03942/0.08183, loss_grounding_bce_4: 0.21749/0.08172, loss_grounding_dice_4: 0.21748/0.15360, loss_grounding_ce_4: 0.14593/0.25934, loss_mask_ce_5: 0.44327/0.80058, loss_mask_bce_5: 0.53050/0.30759, loss_mask_dice_5: 0.82948/1.05220, loss_spatial_bce_5: 0.12989/0.09197, loss_spatial_dice_5: 0.20127/0.19592, loss_spatial_ce_5: 0.05722/0.09466, loss_grounding_bce_5: 0.19631/0.08201, loss_grounding_dice_5: 0.20572/0.15428, loss_grounding_ce_5: 0.20056/0.27757, loss_mask_ce_6: 0.48662/0.82733, loss_mask_bce_6: 0.54276/0.30969, loss_mask_dice_6: 0.85839/1.05586, loss_spatial_bce_6: 0.11749/0.09709, loss_spatial_dice_6: 0.18449/0.19826, loss_spatial_ce_6: 0.10823/0.11906, loss_grounding_bce_6: 0.20029/0.08289, loss_grounding_dice_6: 0.19988/0.15487, loss_grounding_ce_6: 0.29798/0.28707, loss_mask_ce_7: 0.60317/0.88303, loss_mask_bce_7: 0.54147/0.31690, loss_mask_dice_7: 0.83987/1.10213, loss_spatial_bce_7: 0.15344/0.10691, loss_spatial_dice_7: 0.25474/0.22374, loss_spatial_ce_7: 0.09888/0.15672, loss_grounding_bce_7: 0.21373/0.08462, loss_grounding_dice_7: 0.21782/0.16047, loss_grounding_ce_7: 0.35101/0.31989, loss_mask_ce_8: 0.62619/1.01889, loss_mask_bce_8: 0.57409/0.33303, loss_mask_dice_8: 0.84793/1.17924, loss_spatial_bce_8: 0.16540/0.12429, loss_spatial_dice_8: 0.28828/0.25926, loss_spatial_ce_8: 0.10120/0.20428, loss_grounding_bce_8: 0.20723/0.08882, loss_grounding_dice_8: 0.22621/0.17021, loss_grounding_ce_8: 0.34273/0.42009, loss_mask_ce_9: 2.75829/3.47847, loss_mask_bce_9: 0.63954/0.35988, loss_mask_dice_9: 1.24588/1.76129, loss_spatial_bce_9: 0.35675/0.35473, loss_spatial_dice_9: 0.87148/0.79352, loss_spatial_ce_9: 1.27883/1.39154, loss_grounding_bce_9: 0.26558/0.10085, loss_grounding_dice_9: 0.32730/0.24253, loss_grounding_ce_9: 0.19651/0.67516] items per batch[64] items per second[0.35] total items[3891200] mini batches[ 60800] memory[4999] epoch remaining[0:39:22] INFO:trainer.default_trainer:epochs[ 33] optim steps[60900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58072/0.75840, loss_mask_bce_0: 0.29633/0.30071, loss_mask_dice_0: 0.51820/1.02276, loss_spatial_bce_0: 0.06984/0.08518, loss_spatial_dice_0: 0.13360/0.18040, loss_spatial_ce_0: 0.00051/0.05771, loss_grounding_bce_0: 0.02454/0.08051, loss_grounding_dice_0: 0.05792/0.15071, loss_grounding_ce_0: 0.00066/0.24942, loss_mask_ce_1: 0.60084/0.75900, loss_mask_bce_1: 0.28551/0.30159, loss_mask_dice_1: 0.51088/1.02696, loss_spatial_bce_1: 0.07412/0.08549, loss_spatial_dice_1: 0.15310/0.18305, loss_spatial_ce_1: 0.00049/0.06159, loss_grounding_bce_1: 0.02420/0.08069, loss_grounding_dice_1: 0.05976/0.15144, loss_grounding_ce_1: 0.00071/0.25099, loss_mask_ce_2: 0.71241/0.76703, loss_mask_bce_2: 0.29178/0.30180, loss_mask_dice_2: 0.45473/1.02769, loss_spatial_bce_2: 0.07914/0.08555, loss_spatial_dice_2: 0.15355/0.18351, loss_spatial_ce_2: 0.00049/0.06374, loss_grounding_bce_2: 0.02260/0.08071, loss_grounding_dice_2: 0.05898/0.15135, loss_grounding_ce_2: 0.00043/0.25426, loss_mask_ce_3: 0.65636/0.77053, loss_mask_bce_3: 0.27235/0.30326, loss_mask_dice_3: 0.48017/1.02571, loss_spatial_bce_3: 0.07779/0.08764, loss_spatial_dice_3: 0.13835/0.18486, loss_spatial_ce_3: 0.00053/0.06843, loss_grounding_bce_3: 0.02658/0.08111, loss_grounding_dice_3: 0.06281/0.15098, loss_grounding_ce_3: 0.00082/0.25501, loss_mask_ce_4: 0.83252/0.77641, loss_mask_bce_4: 0.32184/0.30579, loss_mask_dice_4: 0.49075/1.04449, loss_spatial_bce_4: 0.07334/0.08975, loss_spatial_dice_4: 0.15321/0.19288, loss_spatial_ce_4: 0.00064/0.08182, loss_grounding_bce_4: 0.02659/0.08172, loss_grounding_dice_4: 0.06269/0.15360, loss_grounding_ce_4: 0.00021/0.25960, loss_mask_ce_5: 0.95518/0.80058, loss_mask_bce_5: 0.32697/0.30764, loss_mask_dice_5: 0.46617/1.05222, loss_spatial_bce_5: 0.08560/0.09196, loss_spatial_dice_5: 0.15448/0.19590, loss_spatial_ce_5: 0.01677/0.09466, loss_grounding_bce_5: 0.02784/0.08200, loss_grounding_dice_5: 0.06536/0.15429, loss_grounding_ce_5: 0.00036/0.27782, loss_mask_ce_6: 0.86477/0.82733, loss_mask_bce_6: 0.28551/0.30975, loss_mask_dice_6: 0.47491/1.05586, loss_spatial_bce_6: 0.09339/0.09707, loss_spatial_dice_6: 0.15953/0.19824, loss_spatial_ce_6: 0.03822/0.11907, loss_grounding_bce_6: 0.02665/0.08289, loss_grounding_dice_6: 0.06118/0.15487, loss_grounding_ce_6: 0.00031/0.28714, loss_mask_ce_7: 1.05242/0.88308, loss_mask_bce_7: 0.28877/0.31698, loss_mask_dice_7: 0.49685/1.10214, loss_spatial_bce_7: 0.08413/0.10689, loss_spatial_dice_7: 0.15063/0.22372, loss_spatial_ce_7: 0.09898/0.15670, loss_grounding_bce_7: 0.03014/0.08462, loss_grounding_dice_7: 0.05670/0.16047, loss_grounding_ce_7: 0.00181/0.32004, loss_mask_ce_8: 1.05617/1.01885, loss_mask_bce_8: 0.35334/0.33309, loss_mask_dice_8: 0.55808/1.17930, loss_spatial_bce_8: 0.10950/0.12427, loss_spatial_dice_8: 0.17192/0.25925, loss_spatial_ce_8: 0.10473/0.20422, loss_grounding_bce_8: 0.03052/0.08882, loss_grounding_dice_8: 0.07071/0.17023, loss_grounding_ce_8: 0.02147/0.42022, loss_mask_ce_9: 2.85962/3.47859, loss_mask_bce_9: 0.35538/0.35995, loss_mask_dice_9: 0.70628/1.76162, loss_spatial_bce_9: 0.44191/0.35473, loss_spatial_dice_9: 0.82424/0.79354, loss_spatial_ce_9: 1.15832/1.39141, loss_grounding_bce_9: 0.02862/0.10084, loss_grounding_dice_9: 0.06886/0.24256, loss_grounding_ce_9: 0.08816/0.67523] items per batch[64] items per second[0.36] total items[3897600] mini batches[ 60900] memory[4999] epoch remaining[0:36:18] INFO:trainer.default_trainer:epochs[ 33] optim steps[61000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.46539/0.75826, loss_mask_bce_0: 0.47589/0.30067, loss_mask_dice_0: 0.99916/1.02248, loss_spatial_bce_0: 0.10076/0.08517, loss_spatial_dice_0: 0.22241/0.18037, loss_spatial_ce_0: 0.20613/0.05768, loss_grounding_bce_0: 0.11526/0.08050, loss_grounding_dice_0: 0.10124/0.15072, loss_grounding_ce_0: 0.01970/0.24934, loss_mask_ce_1: 0.55198/0.75888, loss_mask_bce_1: 0.45950/0.30154, loss_mask_dice_1: 0.80123/1.02664, loss_spatial_bce_1: 0.11935/0.08548, loss_spatial_dice_1: 0.23400/0.18302, loss_spatial_ce_1: 0.23217/0.06156, loss_grounding_bce_1: 0.11528/0.08069, loss_grounding_dice_1: 0.09676/0.15144, loss_grounding_ce_1: 0.02350/0.25091, loss_mask_ce_2: 0.23418/0.76693, loss_mask_bce_2: 0.49100/0.30175, loss_mask_dice_2: 1.22149/1.02741, loss_spatial_bce_2: 0.10366/0.08553, loss_spatial_dice_2: 0.22466/0.18349, loss_spatial_ce_2: 0.21045/0.06374, loss_grounding_bce_2: 0.12058/0.08071, loss_grounding_dice_2: 0.09661/0.15135, loss_grounding_ce_2: 0.02449/0.25419, loss_mask_ce_3: 0.56904/0.77041, loss_mask_bce_3: 0.48373/0.30321, loss_mask_dice_3: 0.86437/1.02546, loss_spatial_bce_3: 0.10024/0.08763, loss_spatial_dice_3: 0.21348/0.18485, loss_spatial_ce_3: 0.25156/0.06840, loss_grounding_bce_3: 0.12082/0.08111, loss_grounding_dice_3: 0.08945/0.15099, loss_grounding_ce_3: 0.02042/0.25491, loss_mask_ce_4: 0.28141/0.77624, loss_mask_bce_4: 0.48734/0.30575, loss_mask_dice_4: 1.04229/1.04423, loss_spatial_bce_4: 0.21633/0.08975, loss_spatial_dice_4: 0.20658/0.19287, loss_spatial_ce_4: 0.03906/0.08179, loss_grounding_bce_4: 0.11808/0.08172, loss_grounding_dice_4: 0.08455/0.15361, loss_grounding_ce_4: 0.05740/0.25950, loss_mask_ce_5: 0.64245/0.80043, loss_mask_bce_5: 0.46336/0.30761, loss_mask_dice_5: 0.94193/1.05190, loss_spatial_bce_5: 0.19617/0.09196, loss_spatial_dice_5: 0.25728/0.19590, loss_spatial_ce_5: 0.15419/0.09464, loss_grounding_bce_5: 0.12546/0.08200, loss_grounding_dice_5: 0.09463/0.15430, loss_grounding_ce_5: 0.06012/0.27779, loss_mask_ce_6: 0.71823/0.82721, loss_mask_bce_6: 0.46579/0.30970, loss_mask_dice_6: 0.84574/1.05555, loss_spatial_bce_6: 0.26386/0.09708, loss_spatial_dice_6: 0.24469/0.19824, loss_spatial_ce_6: 0.05826/0.11905, loss_grounding_bce_6: 0.12424/0.08289, loss_grounding_dice_6: 0.08823/0.15489, loss_grounding_ce_6: 0.04800/0.28708, loss_mask_ce_7: 0.38430/0.88292, loss_mask_bce_7: 0.49943/0.31693, loss_mask_dice_7: 1.35827/1.10185, loss_spatial_bce_7: 0.13543/0.10689, loss_spatial_dice_7: 0.22758/0.22371, loss_spatial_ce_7: 0.12366/0.15666, loss_grounding_bce_7: 0.12926/0.08461, loss_grounding_dice_7: 0.08824/0.16048, loss_grounding_ce_7: 0.01857/0.31996, loss_mask_ce_8: 1.08048/1.01871, loss_mask_bce_8: 0.51210/0.33303, loss_mask_dice_8: 0.92071/1.17898, loss_spatial_bce_8: 0.20510/0.12426, loss_spatial_dice_8: 0.25576/0.25921, loss_spatial_ce_8: 0.14678/0.20416, loss_grounding_bce_8: 0.15870/0.08882, loss_grounding_dice_8: 0.11056/0.17024, loss_grounding_ce_8: 0.00526/0.42011, loss_mask_ce_9: 4.80623/3.47833, loss_mask_bce_9: 0.51221/0.35988, loss_mask_dice_9: 1.44341/1.76109, loss_spatial_bce_9: 0.36367/0.35473, loss_spatial_dice_9: 0.92627/0.79353, loss_spatial_ce_9: 1.50362/1.39129, loss_grounding_bce_9: 0.15387/0.10085, loss_grounding_dice_9: 0.10289/0.24256, loss_grounding_ce_9: 0.04661/0.67501] items per batch[64] items per second[0.36] total items[3904000] mini batches[ 61000] memory[4999] epoch remaining[0:33:14] INFO:trainer.default_trainer:epochs[ 33] optim steps[61100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.52463/0.75813, loss_mask_bce_0: 0.32231/0.30068, loss_mask_dice_0: 1.50626/1.02254, loss_spatial_bce_0: 0.04834/0.08515, loss_spatial_dice_0: 0.12180/0.18036, loss_spatial_ce_0: 0.00008/0.05769, loss_grounding_bce_0: 0.04095/0.08049, loss_grounding_dice_0: 0.23638/0.15069, loss_grounding_ce_0: 0.61137/0.24924, loss_mask_ce_1: 0.53971/0.75880, loss_mask_bce_1: 0.34440/0.30154, loss_mask_dice_1: 1.53541/1.02664, loss_spatial_bce_1: 0.04816/0.08546, loss_spatial_dice_1: 0.11521/0.18302, loss_spatial_ce_1: 0.00002/0.06156, loss_grounding_bce_1: 0.04317/0.08067, loss_grounding_dice_1: 0.23927/0.15142, loss_grounding_ce_1: 0.60780/0.25083, loss_mask_ce_2: 0.57055/0.76681, loss_mask_bce_2: 0.33681/0.30176, loss_mask_dice_2: 1.60203/1.02745, loss_spatial_bce_2: 0.04524/0.08552, loss_spatial_dice_2: 0.12117/0.18349, loss_spatial_ce_2: 0.00002/0.06377, loss_grounding_bce_2: 0.04227/0.08069, loss_grounding_dice_2: 0.24554/0.15132, loss_grounding_ce_2: 0.60828/0.25409, loss_mask_ce_3: 0.56031/0.77031, loss_mask_bce_3: 0.31899/0.30323, loss_mask_dice_3: 1.56275/1.02548, loss_spatial_bce_3: 0.04909/0.08761, loss_spatial_dice_3: 0.11784/0.18484, loss_spatial_ce_3: 0.00005/0.06841, loss_grounding_bce_3: 0.04119/0.08110, loss_grounding_dice_3: 0.24149/0.15097, loss_grounding_ce_3: 0.60929/0.25483, loss_mask_ce_4: 0.54277/0.77606, loss_mask_bce_4: 0.34213/0.30578, loss_mask_dice_4: 1.54169/1.04429, loss_spatial_bce_4: 0.05281/0.08974, loss_spatial_dice_4: 0.12705/0.19286, loss_spatial_ce_4: 0.00014/0.08179, loss_grounding_bce_4: 0.04509/0.08170, loss_grounding_dice_4: 0.23844/0.15358, loss_grounding_ce_4: 0.61343/0.25944, loss_mask_ce_5: 0.58649/0.80031, loss_mask_bce_5: 0.34609/0.30762, loss_mask_dice_5: 1.62312/1.05196, loss_spatial_bce_5: 0.04657/0.09195, loss_spatial_dice_5: 0.12626/0.19591, loss_spatial_ce_5: 0.00494/0.09465, loss_grounding_bce_5: 0.04725/0.08199, loss_grounding_dice_5: 0.25471/0.15428, loss_grounding_ce_5: 0.60554/0.27769, loss_mask_ce_6: 0.58293/0.82709, loss_mask_bce_6: 0.32738/0.30973, loss_mask_dice_6: 1.74985/1.05561, loss_spatial_bce_6: 0.06279/0.09707, loss_spatial_dice_6: 0.17333/0.19825, loss_spatial_ce_6: 0.02905/0.11908, loss_grounding_bce_6: 0.04649/0.08287, loss_grounding_dice_6: 0.24724/0.15486, loss_grounding_ce_6: 0.60253/0.28702, loss_mask_ce_7: 0.62189/0.88275, loss_mask_bce_7: 0.36319/0.31694, loss_mask_dice_7: 1.74042/1.10187, loss_spatial_bce_7: 0.05771/0.10686, loss_spatial_dice_7: 0.19862/0.22370, loss_spatial_ce_7: 0.02351/0.15664, loss_grounding_bce_7: 0.04475/0.08459, loss_grounding_dice_7: 0.26998/0.16045, loss_grounding_ce_7: 0.63939/0.31987, loss_mask_ce_8: 0.70247/1.01850, loss_mask_bce_8: 0.42571/0.33304, loss_mask_dice_8: 2.04126/1.17900, loss_spatial_bce_8: 0.06190/0.12422, loss_spatial_dice_8: 0.16205/0.25921, loss_spatial_ce_8: 0.05595/0.20415, loss_grounding_bce_8: 0.05588/0.08880, loss_grounding_dice_8: 0.29919/0.17021, loss_grounding_ce_8: 0.61364/0.41998, loss_mask_ce_9: 3.01582/3.47839, loss_mask_bce_9: 0.44671/0.35990, loss_mask_dice_9: 3.01037/1.76122, loss_spatial_bce_9: 0.24561/0.35468, loss_spatial_dice_9: 0.94577/0.79355, loss_spatial_ce_9: 1.26446/1.39115, loss_grounding_bce_9: 0.05403/0.10084, loss_grounding_dice_9: 0.43775/0.24255, loss_grounding_ce_9: 0.72467/0.67494] items per batch[64] items per second[0.37] total items[3910400] mini batches[ 61100] memory[4999] epoch remaining[0:30:10] INFO:trainer.default_trainer:epochs[ 33] optim steps[61200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.73562/0.75811, loss_mask_bce_0: 0.31892/0.30079, loss_mask_dice_0: 0.60481/1.02232, loss_spatial_bce_0: 0.07063/0.08517, loss_spatial_dice_0: 0.11462/0.18036, loss_spatial_ce_0: 0.00238/0.05770, loss_grounding_bce_0: 0.14850/0.08056, loss_grounding_dice_0: 0.40980/0.15073, loss_grounding_ce_0: 0.00611/0.24925, loss_mask_ce_1: 0.73491/0.75878, loss_mask_bce_1: 0.32664/0.30166, loss_mask_dice_1: 0.62171/1.02642, loss_spatial_bce_1: 0.06805/0.08547, loss_spatial_dice_1: 0.12172/0.18302, loss_spatial_ce_1: 0.00279/0.06156, loss_grounding_bce_1: 0.15661/0.08075, loss_grounding_dice_1: 0.40937/0.15146, loss_grounding_ce_1: 0.00825/0.25084, loss_mask_ce_2: 0.66468/0.76681, loss_mask_bce_2: 0.33185/0.30187, loss_mask_dice_2: 0.59983/1.02725, loss_spatial_bce_2: 0.07143/0.08554, loss_spatial_dice_2: 0.11361/0.18349, loss_spatial_ce_2: 0.00319/0.06378, loss_grounding_bce_2: 0.15729/0.08077, loss_grounding_dice_2: 0.37998/0.15136, loss_grounding_ce_2: 0.00557/0.25410, loss_mask_ce_3: 0.72759/0.77031, loss_mask_bce_3: 0.33208/0.30334, loss_mask_dice_3: 0.56974/1.02526, loss_spatial_bce_3: 0.07441/0.08764, loss_spatial_dice_3: 0.10847/0.18485, loss_spatial_ce_3: 0.00393/0.06841, loss_grounding_bce_3: 0.15261/0.08117, loss_grounding_dice_3: 0.38678/0.15101, loss_grounding_ce_3: 0.00859/0.25486, loss_mask_ce_4: 0.65319/0.77605, loss_mask_bce_4: 0.33608/0.30589, loss_mask_dice_4: 0.58362/1.04408, loss_spatial_bce_4: 0.07916/0.08976, loss_spatial_dice_4: 0.12221/0.19288, loss_spatial_ce_4: 0.02316/0.08182, loss_grounding_bce_4: 0.13905/0.08177, loss_grounding_dice_4: 0.32858/0.15361, loss_grounding_ce_4: 0.00304/0.25949, loss_mask_ce_5: 0.64482/0.80030, loss_mask_bce_5: 0.31520/0.30773, loss_mask_dice_5: 0.57262/1.05175, loss_spatial_bce_5: 0.07756/0.09198, loss_spatial_dice_5: 0.12859/0.19591, loss_spatial_ce_5: 0.01454/0.09471, loss_grounding_bce_5: 0.14894/0.08208, loss_grounding_dice_5: 0.36463/0.15432, loss_grounding_ce_5: 0.00196/0.27765, loss_mask_ce_6: 0.69450/0.82711, loss_mask_bce_6: 0.35997/0.30985, loss_mask_dice_6: 0.58000/1.05541, loss_spatial_bce_6: 0.08856/0.09710, loss_spatial_dice_6: 0.11664/0.19826, loss_spatial_ce_6: 0.01803/0.11911, loss_grounding_bce_6: 0.16538/0.08296, loss_grounding_dice_6: 0.44260/0.15489, loss_grounding_ce_6: 0.00121/0.28703, loss_mask_ce_7: 0.84261/0.88276, loss_mask_bce_7: 0.36517/0.31705, loss_mask_dice_7: 0.64862/1.10168, loss_spatial_bce_7: 0.09345/0.10689, loss_spatial_dice_7: 0.14537/0.22371, loss_spatial_ce_7: 0.07748/0.15670, loss_grounding_bce_7: 0.17448/0.08466, loss_grounding_dice_7: 0.47550/0.16049, loss_grounding_ce_7: 0.00265/0.31982, loss_mask_ce_8: 0.85309/1.01842, loss_mask_bce_8: 0.35239/0.33314, loss_mask_dice_8: 0.66547/1.17876, loss_spatial_bce_8: 0.10622/0.12425, loss_spatial_dice_8: 0.19469/0.25921, loss_spatial_ce_8: 0.11435/0.20413, loss_grounding_bce_8: 0.14374/0.08887, loss_grounding_dice_8: 0.37296/0.17024, loss_grounding_ce_8: 0.00333/0.41998, loss_mask_ce_9: 3.48847/3.47849, loss_mask_bce_9: 0.53668/0.36004, loss_mask_dice_9: 1.01620/1.76101, loss_spatial_bce_9: 0.36856/0.35468, loss_spatial_dice_9: 0.86480/0.79355, loss_spatial_ce_9: 1.34152/1.39111, loss_grounding_bce_9: 0.17732/0.10092, loss_grounding_dice_9: 0.30955/0.24256, loss_grounding_ce_9: 0.01665/0.67498] items per batch[64] items per second[0.36] total items[3916800] mini batches[ 61200] memory[4999] epoch remaining[0:27:11] INFO:trainer.default_trainer:epochs[ 33] optim steps[61300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.81752/0.75806, loss_mask_bce_0: 0.36810/0.30081, loss_mask_dice_0: 0.43198/1.02237, loss_spatial_bce_0: 0.24037/0.08516, loss_spatial_dice_0: 0.36897/0.18034, loss_spatial_ce_0: 0.56809/0.05767, loss_grounding_bce_0: 0.10875/0.08059, loss_grounding_dice_0: 0.05541/0.15071, loss_grounding_ce_0: 0.14889/0.24918, loss_mask_ce_1: 2.00581/0.75877, loss_mask_bce_1: 0.29974/0.30167, loss_mask_dice_1: 0.34533/1.02646, loss_spatial_bce_1: 0.24097/0.08546, loss_spatial_dice_1: 0.33568/0.18300, loss_spatial_ce_1: 0.56836/0.06154, loss_grounding_bce_1: 0.09863/0.08078, loss_grounding_dice_1: 0.04822/0.15145, loss_grounding_ce_1: 0.12657/0.25076, loss_mask_ce_2: 2.02256/0.76678, loss_mask_bce_2: 0.31092/0.30190, loss_mask_dice_2: 0.38469/1.02732, loss_spatial_bce_2: 0.25209/0.08553, loss_spatial_dice_2: 0.31548/0.18347, loss_spatial_ce_2: 0.58775/0.06375, loss_grounding_bce_2: 0.10207/0.08080, loss_grounding_dice_2: 0.05420/0.15134, loss_grounding_ce_2: 0.08332/0.25401, loss_mask_ce_3: 2.23856/0.77029, loss_mask_bce_3: 0.27573/0.30336, loss_mask_dice_3: 0.30988/1.02534, loss_spatial_bce_3: 0.24117/0.08763, loss_spatial_dice_3: 0.31840/0.18483, loss_spatial_ce_3: 0.57331/0.06838, loss_grounding_bce_3: 0.10017/0.08121, loss_grounding_dice_3: 0.05371/0.15100, loss_grounding_ce_3: 0.05961/0.25476, loss_mask_ce_4: 2.03832/0.77603, loss_mask_bce_4: 0.28660/0.30590, loss_mask_dice_4: 0.48144/1.04417, loss_spatial_bce_4: 0.25124/0.08975, loss_spatial_dice_4: 0.35672/0.19286, loss_spatial_ce_4: 0.49526/0.08180, loss_grounding_bce_4: 0.10237/0.08180, loss_grounding_dice_4: 0.04900/0.15359, loss_grounding_ce_4: 0.13750/0.25939, loss_mask_ce_5: 1.90539/0.80031, loss_mask_bce_5: 0.32049/0.30774, loss_mask_dice_5: 0.34358/1.05184, loss_spatial_bce_5: 0.21843/0.09198, loss_spatial_dice_5: 0.35021/0.19591, loss_spatial_ce_5: 0.47895/0.09469, loss_grounding_bce_5: 0.09522/0.08211, loss_grounding_dice_5: 0.04664/0.15430, loss_grounding_ce_5: 0.53448/0.27757, loss_mask_ce_6: 2.46867/0.82710, loss_mask_bce_6: 0.28215/0.30986, loss_mask_dice_6: 0.36428/1.05549, loss_spatial_bce_6: 0.24391/0.09710, loss_spatial_dice_6: 0.39564/0.19825, loss_spatial_ce_6: 0.61279/0.11910, loss_grounding_bce_6: 0.09835/0.08299, loss_grounding_dice_6: 0.05178/0.15489, loss_grounding_ce_6: 0.63781/0.28695, loss_mask_ce_7: 2.39362/0.88279, loss_mask_bce_7: 0.37976/0.31708, loss_mask_dice_7: 0.65543/1.10174, loss_spatial_bce_7: 0.33214/0.10690, loss_spatial_dice_7: 0.53082/0.22369, loss_spatial_ce_7: 0.85144/0.15664, loss_grounding_bce_7: 0.09584/0.08470, loss_grounding_dice_7: 0.04630/0.16048, loss_grounding_ce_7: 1.06059/0.31977, loss_mask_ce_8: 2.25644/1.01852, loss_mask_bce_8: 0.54739/0.33317, loss_mask_dice_8: 0.84089/1.17892, loss_spatial_bce_8: 0.27005/0.12425, loss_spatial_dice_8: 0.52213/0.25918, loss_spatial_ce_8: 1.09366/0.20409, loss_grounding_bce_8: 0.11059/0.08889, loss_grounding_dice_8: 0.05255/0.17022, loss_grounding_ce_8: 1.50925/0.41991, loss_mask_ce_9: 5.49363/3.47861, loss_mask_bce_9: 0.64615/0.36008, loss_mask_dice_9: 1.12910/1.76142, loss_spatial_bce_9: 0.62636/0.35463, loss_spatial_dice_9: 0.84328/0.79350, loss_spatial_ce_9: 1.33138/1.39101, loss_grounding_bce_9: 0.34896/0.10095, loss_grounding_dice_9: 0.18181/0.24251, loss_grounding_ce_9: 1.32094/0.67477] items per batch[64] items per second[0.36] total items[3923200] mini batches[ 61300] memory[4999] epoch remaining[0:24:12] INFO:trainer.default_trainer:epochs[ 33] optim steps[61400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03967/0.75809, loss_mask_bce_0: 0.19313/0.30077, loss_mask_dice_0: 0.11489/1.02248, loss_spatial_bce_0: 0.11177/0.08514, loss_spatial_dice_0: 0.07785/0.18032, loss_spatial_ce_0: 0.00431/0.05764, loss_grounding_bce_0: 0.10907/0.08058, loss_grounding_dice_0: 0.06386/0.15071, loss_grounding_ce_0: 0.00169/0.24916, loss_mask_ce_1: 0.04088/0.75878, loss_mask_bce_1: 0.19631/0.30163, loss_mask_dice_1: 0.11528/1.02658, loss_spatial_bce_1: 0.10560/0.08544, loss_spatial_dice_1: 0.06820/0.18299, loss_spatial_ce_1: 0.00467/0.06153, loss_grounding_bce_1: 0.11070/0.08077, loss_grounding_dice_1: 0.06251/0.15145, loss_grounding_ce_1: 0.00173/0.25073, loss_mask_ce_2: 0.03639/0.76679, loss_mask_bce_2: 0.19902/0.30186, loss_mask_dice_2: 0.11443/1.02744, loss_spatial_bce_2: 0.10547/0.08551, loss_spatial_dice_2: 0.07314/0.18346, loss_spatial_ce_2: 0.00405/0.06373, loss_grounding_bce_2: 0.11500/0.08079, loss_grounding_dice_2: 0.06438/0.15133, loss_grounding_ce_2: 0.00183/0.25397, loss_mask_ce_3: 0.04483/0.77029, loss_mask_bce_3: 0.19643/0.30332, loss_mask_dice_3: 0.11121/1.02547, loss_spatial_bce_3: 0.10713/0.08760, loss_spatial_dice_3: 0.07392/0.18482, loss_spatial_ce_3: 0.00678/0.06836, loss_grounding_bce_3: 0.10938/0.08119, loss_grounding_dice_3: 0.06425/0.15099, loss_grounding_ce_3: 0.00192/0.25473, loss_mask_ce_4: 0.04607/0.77602, loss_mask_bce_4: 0.19120/0.30587, loss_mask_dice_4: 0.11245/1.04430, loss_spatial_bce_4: 0.10510/0.08973, loss_spatial_dice_4: 0.06893/0.19285, loss_spatial_ce_4: 0.02486/0.08180, loss_grounding_bce_4: 0.10622/0.08179, loss_grounding_dice_4: 0.06107/0.15358, loss_grounding_ce_4: 0.00292/0.25936, loss_mask_ce_5: 0.04084/0.80031, loss_mask_bce_5: 0.19691/0.30770, loss_mask_dice_5: 0.10659/1.05197, loss_spatial_bce_5: 0.10415/0.09196, loss_spatial_dice_5: 0.06958/0.19591, loss_spatial_ce_5: 0.07344/0.09469, loss_grounding_bce_5: 0.11425/0.08209, loss_grounding_dice_5: 0.06234/0.15429, loss_grounding_ce_5: 0.00138/0.27759, loss_mask_ce_6: 0.03007/0.82706, loss_mask_bce_6: 0.20194/0.30982, loss_mask_dice_6: 0.11059/1.05561, loss_spatial_bce_6: 0.11167/0.09710, loss_spatial_dice_6: 0.06611/0.19826, loss_spatial_ce_6: 0.13865/0.11909, loss_grounding_bce_6: 0.11408/0.08297, loss_grounding_dice_6: 0.06238/0.15487, loss_grounding_ce_6: 0.00091/0.28699, loss_mask_ce_7: 0.04371/0.88277, loss_mask_bce_7: 0.18788/0.31703, loss_mask_dice_7: 0.10791/1.10185, loss_spatial_bce_7: 0.11585/0.10688, loss_spatial_dice_7: 0.07122/0.22368, loss_spatial_ce_7: 0.16112/0.15661, loss_grounding_bce_7: 0.10913/0.08468, loss_grounding_dice_7: 0.06339/0.16046, loss_grounding_ce_7: 0.00165/0.31977, loss_mask_ce_8: 0.09402/1.01846, loss_mask_bce_8: 0.19849/0.33313, loss_mask_dice_8: 0.10789/1.17902, loss_spatial_bce_8: 0.10237/0.12422, loss_spatial_dice_8: 0.07808/0.25916, loss_spatial_ce_8: 0.07144/0.20406, loss_grounding_bce_8: 0.11183/0.08887, loss_grounding_dice_8: 0.06103/0.17022, loss_grounding_ce_8: 0.00456/0.41982, loss_mask_ce_9: 2.01548/3.47834, loss_mask_bce_9: 0.19045/0.36004, loss_mask_dice_9: 0.16246/1.76172, loss_spatial_bce_9: 0.52941/0.35467, loss_spatial_dice_9: 0.49052/0.79352, loss_spatial_ce_9: 0.87109/1.39099, loss_grounding_bce_9: 0.11051/0.10094, loss_grounding_dice_9: 0.10701/0.24250, loss_grounding_ce_9: 0.11783/0.67478] items per batch[64] items per second[0.36] total items[3929600] mini batches[ 61400] memory[4999] epoch remaining[0:21:14] INFO:trainer.default_trainer:epochs[ 33] optim steps[61500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.05995/0.75806, loss_mask_bce_0: 0.32231/0.30075, loss_mask_dice_0: 0.29111/1.02221, loss_spatial_bce_0: 0.14628/0.08513, loss_spatial_dice_0: 0.11163/0.18028, loss_spatial_ce_0: 0.12005/0.05762, loss_grounding_bce_0: 0.09862/0.08057, loss_grounding_dice_0: 0.08734/0.15066, loss_grounding_ce_0: 0.26745/0.24906, loss_mask_ce_1: 1.12076/0.75873, loss_mask_bce_1: 0.29656/0.30161, loss_mask_dice_1: 0.24402/1.02632, loss_spatial_bce_1: 0.15563/0.08543, loss_spatial_dice_1: 0.12649/0.18294, loss_spatial_ce_1: 0.12717/0.06150, loss_grounding_bce_1: 0.09842/0.08076, loss_grounding_dice_1: 0.09141/0.15141, loss_grounding_ce_1: 0.25960/0.25062, loss_mask_ce_2: 1.12863/0.76674, loss_mask_bce_2: 0.29745/0.30184, loss_mask_dice_2: 0.25932/1.02717, loss_spatial_bce_2: 0.15414/0.08550, loss_spatial_dice_2: 0.11948/0.18342, loss_spatial_ce_2: 0.16048/0.06370, loss_grounding_bce_2: 0.10324/0.08078, loss_grounding_dice_2: 0.09547/0.15130, loss_grounding_ce_2: 0.28177/0.25386, loss_mask_ce_3: 1.11721/0.77022, loss_mask_bce_3: 0.31294/0.30330, loss_mask_dice_3: 0.27543/1.02518, loss_spatial_bce_3: 0.16689/0.08759, loss_spatial_dice_3: 0.11157/0.18478, loss_spatial_ce_3: 0.09610/0.06835, loss_grounding_bce_3: 0.10261/0.08118, loss_grounding_dice_3: 0.09568/0.15095, loss_grounding_ce_3: 0.27188/0.25461, loss_mask_ce_4: 1.05264/0.77597, loss_mask_bce_4: 0.33050/0.30585, loss_mask_dice_4: 0.27764/1.04402, loss_spatial_bce_4: 0.15921/0.08973, loss_spatial_dice_4: 0.10919/0.19282, loss_spatial_ce_4: 0.10284/0.08180, loss_grounding_bce_4: 0.11068/0.08178, loss_grounding_dice_4: 0.09525/0.15354, loss_grounding_ce_4: 0.24318/0.25925, loss_mask_ce_5: 1.19669/0.80025, loss_mask_bce_5: 0.30606/0.30768, loss_mask_dice_5: 0.22039/1.05169, loss_spatial_bce_5: 0.15581/0.09196, loss_spatial_dice_5: 0.11167/0.19588, loss_spatial_ce_5: 0.20051/0.09474, loss_grounding_bce_5: 0.10832/0.08209, loss_grounding_dice_5: 0.09223/0.15424, loss_grounding_ce_5: 0.25007/0.27749, loss_mask_ce_6: 1.14419/0.82698, loss_mask_bce_6: 0.35948/0.30980, loss_mask_dice_6: 0.27101/1.05530, loss_spatial_bce_6: 0.24329/0.09710, loss_spatial_dice_6: 0.16967/0.19824, loss_spatial_ce_6: 0.12158/0.11911, loss_grounding_bce_6: 0.13392/0.08296, loss_grounding_dice_6: 0.11080/0.15483, loss_grounding_ce_6: 0.25530/0.28686, loss_mask_ce_7: 1.47155/0.88270, loss_mask_bce_7: 0.35002/0.31702, loss_mask_dice_7: 0.27575/1.10156, loss_spatial_bce_7: 0.18338/0.10688, loss_spatial_dice_7: 0.13824/0.22366, loss_spatial_ce_7: 0.08413/0.15660, loss_grounding_bce_7: 0.11340/0.08467, loss_grounding_dice_7: 0.10778/0.16041, loss_grounding_ce_7: 0.33346/0.31964, loss_mask_ce_8: 1.65233/1.01841, loss_mask_bce_8: 0.44262/0.33311, loss_mask_dice_8: 0.32501/1.17873, loss_spatial_bce_8: 0.21811/0.12422, loss_spatial_dice_8: 0.16419/0.25913, loss_spatial_ce_8: 0.26760/0.20401, loss_grounding_bce_8: 0.13425/0.08887, loss_grounding_dice_8: 0.11935/0.17018, loss_grounding_ce_8: 0.41578/0.41977, loss_mask_ce_9: 3.54373/3.47820, loss_mask_bce_9: 0.83680/0.36003, loss_mask_dice_9: 0.53819/1.76140, loss_spatial_bce_9: 0.66731/0.35471, loss_spatial_dice_9: 0.68085/0.79347, loss_spatial_ce_9: 1.12804/1.39080, loss_grounding_bce_9: 0.31030/0.10093, loss_grounding_dice_9: 0.21581/0.24244, loss_grounding_ce_9: 0.29653/0.67466] items per batch[64] items per second[0.36] total items[3936000] mini batches[ 61500] memory[4999] epoch remaining[0:18:15] INFO:trainer.default_trainer:epochs[ 33] optim steps[61600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.53148/0.75799, loss_mask_bce_0: 0.45043/0.30080, loss_mask_dice_0: 0.59194/1.02206, loss_spatial_bce_0: 0.09179/0.08514, loss_spatial_dice_0: 0.12921/0.18026, loss_spatial_ce_0: 0.06194/0.05759, loss_grounding_bce_0: 0.02068/0.08058, loss_grounding_dice_0: 0.02611/0.15064, loss_grounding_ce_0: 0.00095/0.24903, loss_mask_ce_1: 0.54381/0.75863, loss_mask_bce_1: 0.44484/0.30166, loss_mask_dice_1: 0.60318/1.02620, loss_spatial_bce_1: 0.09681/0.08544, loss_spatial_dice_1: 0.13279/0.18292, loss_spatial_ce_1: 0.04832/0.06148, loss_grounding_bce_1: 0.02271/0.08078, loss_grounding_dice_1: 0.02724/0.15140, loss_grounding_ce_1: 0.00350/0.25058, loss_mask_ce_2: 0.52628/0.76665, loss_mask_bce_2: 0.47280/0.30190, loss_mask_dice_2: 0.59564/1.02705, loss_spatial_bce_2: 0.09449/0.08551, loss_spatial_dice_2: 0.12779/0.18341, loss_spatial_ce_2: 0.10272/0.06367, loss_grounding_bce_2: 0.02334/0.08079, loss_grounding_dice_2: 0.02784/0.15128, loss_grounding_ce_2: 0.00675/0.25384, loss_mask_ce_3: 0.49598/0.77014, loss_mask_bce_3: 0.44507/0.30336, loss_mask_dice_3: 0.58490/1.02502, loss_spatial_bce_3: 0.09633/0.08761, loss_spatial_dice_3: 0.12922/0.18476, loss_spatial_ce_3: 0.11698/0.06833, loss_grounding_bce_3: 0.02203/0.08120, loss_grounding_dice_3: 0.03021/0.15093, loss_grounding_ce_3: 0.00257/0.25458, loss_mask_ce_4: 0.48416/0.77593, loss_mask_bce_4: 0.47668/0.30591, loss_mask_dice_4: 0.63144/1.04389, loss_spatial_bce_4: 0.14577/0.08975, loss_spatial_dice_4: 0.18083/0.19280, loss_spatial_ce_4: 0.04260/0.08177, loss_grounding_bce_4: 0.02271/0.08179, loss_grounding_dice_4: 0.02786/0.15351, loss_grounding_ce_4: 0.00286/0.25921, loss_mask_ce_5: 0.48261/0.80022, loss_mask_bce_5: 0.49596/0.30774, loss_mask_dice_5: 0.64495/1.05158, loss_spatial_bce_5: 0.15923/0.09200, loss_spatial_dice_5: 0.18181/0.19587, loss_spatial_ce_5: 0.09957/0.09472, loss_grounding_bce_5: 0.02159/0.08210, loss_grounding_dice_5: 0.02657/0.15422, loss_grounding_ce_5: 0.02450/0.27747, loss_mask_ce_6: 0.51743/0.82692, loss_mask_bce_6: 0.52823/0.30986, loss_mask_dice_6: 0.68206/1.05519, loss_spatial_bce_6: 0.10265/0.09712, loss_spatial_dice_6: 0.15327/0.19822, loss_spatial_ce_6: 0.08339/0.11912, loss_grounding_bce_6: 0.02161/0.08297, loss_grounding_dice_6: 0.02919/0.15480, loss_grounding_ce_6: 0.00326/0.28687, loss_mask_ce_7: 0.50860/0.88263, loss_mask_bce_7: 0.50608/0.31708, loss_mask_dice_7: 0.68369/1.10143, loss_spatial_bce_7: 0.10205/0.10690, loss_spatial_dice_7: 0.14406/0.22363, loss_spatial_ce_7: 0.10295/0.15656, loss_grounding_bce_7: 0.02083/0.08468, loss_grounding_dice_7: 0.02898/0.16038, loss_grounding_ce_7: 0.02077/0.31965, loss_mask_ce_8: 1.02438/1.01831, loss_mask_bce_8: 0.42307/0.33319, loss_mask_dice_8: 0.63469/1.17856, loss_spatial_bce_8: 0.11416/0.12423, loss_spatial_dice_8: 0.14330/0.25909, loss_spatial_ce_8: 0.01709/0.20395, loss_grounding_bce_8: 0.02252/0.08888, loss_grounding_dice_8: 0.02758/0.17015, loss_grounding_ce_8: 0.14287/0.41965, loss_mask_ce_9: 3.02320/3.47825, loss_mask_bce_9: 0.67628/0.36014, loss_mask_dice_9: 1.09040/1.76141, loss_spatial_bce_9: 0.27173/0.35474, loss_spatial_dice_9: 0.66098/0.79345, loss_spatial_ce_9: 1.10151/1.39056, loss_grounding_bce_9: 0.03404/0.10095, loss_grounding_dice_9: 0.04349/0.24242, loss_grounding_ce_9: 0.86643/0.67467] items per batch[64] items per second[0.36] total items[3942400] mini batches[ 61600] memory[4999] epoch remaining[0:15:18] INFO:trainer.default_trainer:epochs[ 33] optim steps[61700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.73693/0.75807, loss_mask_bce_0: 0.38572/0.30087, loss_mask_dice_0: 0.78986/1.02228, loss_spatial_bce_0: 0.15526/0.08515, loss_spatial_dice_0: 0.43775/0.18027, loss_spatial_ce_0: 0.01661/0.05756, loss_grounding_bce_0: 0.00000/0.08058, loss_grounding_dice_0: 0.00000/0.15061, loss_grounding_ce_0: 0.03170/0.24906, loss_mask_ce_1: 1.61878/0.75871, loss_mask_bce_1: 0.37418/0.30173, loss_mask_dice_1: 1.00940/1.02640, loss_spatial_bce_1: 0.17164/0.08546, loss_spatial_dice_1: 0.40736/0.18294, loss_spatial_ce_1: 0.00601/0.06144, loss_grounding_bce_1: 0.00000/0.08078, loss_grounding_dice_1: 0.00000/0.15137, loss_grounding_ce_1: 0.03857/0.25059, loss_mask_ce_2: 1.67551/0.76675, loss_mask_bce_2: 0.38150/0.30196, loss_mask_dice_2: 0.84334/1.02726, loss_spatial_bce_2: 0.18018/0.08553, loss_spatial_dice_2: 0.38221/0.18342, loss_spatial_ce_2: 0.00857/0.06363, loss_grounding_bce_2: 0.00000/0.08078, loss_grounding_dice_2: 0.00000/0.15126, loss_grounding_ce_2: 0.05984/0.25388, loss_mask_ce_3: 1.79340/0.77024, loss_mask_bce_3: 0.37849/0.30343, loss_mask_dice_3: 0.73391/1.02518, loss_spatial_bce_3: 0.16432/0.08763, loss_spatial_dice_3: 0.41712/0.18477, loss_spatial_ce_3: 0.07033/0.06832, loss_grounding_bce_3: 0.00000/0.08119, loss_grounding_dice_3: 0.00000/0.15091, loss_grounding_ce_3: 0.03996/0.25463, loss_mask_ce_4: 1.62338/0.77601, loss_mask_bce_4: 0.42591/0.30598, loss_mask_dice_4: 0.99709/1.04413, loss_spatial_bce_4: 0.34935/0.08977, loss_spatial_dice_4: 0.42593/0.19283, loss_spatial_ce_4: 0.01703/0.08176, loss_grounding_bce_4: 0.00000/0.08179, loss_grounding_dice_4: 0.00001/0.15348, loss_grounding_ce_4: 0.06005/0.25922, loss_mask_ce_5: 1.73945/0.80031, loss_mask_bce_5: 0.48544/0.30782, loss_mask_dice_5: 0.84467/1.05178, loss_spatial_bce_5: 0.19591/0.09202, loss_spatial_dice_5: 0.37420/0.19589, loss_spatial_ce_5: 0.10742/0.09474, loss_grounding_bce_5: 0.00000/0.08210, loss_grounding_dice_5: 0.00001/0.15418, loss_grounding_ce_5: 0.03769/0.27748, loss_mask_ce_6: 1.71916/0.82700, loss_mask_bce_6: 0.47123/0.30993, loss_mask_dice_6: 0.71292/1.05540, loss_spatial_bce_6: 0.22601/0.09715, loss_spatial_dice_6: 0.39765/0.19825, loss_spatial_ce_6: 0.17382/0.11912, loss_grounding_bce_6: 0.00000/0.08296, loss_grounding_dice_6: 0.00001/0.15477, loss_grounding_ce_6: 0.04775/0.28683, loss_mask_ce_7: 1.82948/0.88273, loss_mask_bce_7: 0.49066/0.31715, loss_mask_dice_7: 1.08470/1.10165, loss_spatial_bce_7: 0.35205/0.10691, loss_spatial_dice_7: 0.41963/0.22363, loss_spatial_ce_7: 0.13148/0.15656, loss_grounding_bce_7: 0.00000/0.08467, loss_grounding_dice_7: 0.00001/0.16035, loss_grounding_ce_7: 0.02850/0.31970, loss_mask_ce_8: 2.24047/1.01836, loss_mask_bce_8: 0.38685/0.33327, loss_mask_dice_8: 0.72366/1.17875, loss_spatial_bce_8: 0.21215/0.12424, loss_spatial_dice_8: 0.48745/0.25908, loss_spatial_ce_8: 0.43471/0.20391, loss_grounding_bce_8: 0.00000/0.08887, loss_grounding_dice_8: 0.00000/0.17010, loss_grounding_ce_8: 0.34928/0.41957, loss_mask_ce_9: 4.46489/3.47828, loss_mask_bce_9: 0.59001/0.36021, loss_mask_dice_9: 5.52241/1.76203, loss_spatial_bce_9: 0.46711/0.35474, loss_spatial_dice_9: 0.94071/0.79347, loss_spatial_ce_9: 1.48239/1.39056, loss_grounding_bce_9: 0.00000/0.10094, loss_grounding_dice_9: 0.00048/0.24237, loss_grounding_ce_9: 3.20550/0.67470] items per batch[64] items per second[0.37] total items[3948800] mini batches[ 61700] memory[4999] epoch remaining[0:12:20] INFO:trainer.default_trainer:epochs[ 33] optim steps[61800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18804/0.75812, loss_mask_bce_0: 0.37572/0.30090, loss_mask_dice_0: 0.54939/1.02212, loss_spatial_bce_0: 0.06798/0.08515, loss_spatial_dice_0: 0.09992/0.18023, loss_spatial_ce_0: 0.00064/0.05753, loss_grounding_bce_0: 0.05579/0.08060, loss_grounding_dice_0: 0.09931/0.15060, loss_grounding_ce_0: 0.00748/0.24908, loss_mask_ce_1: 0.41650/0.75875, loss_mask_bce_1: 0.33676/0.30176, loss_mask_dice_1: 0.44568/1.02620, loss_spatial_bce_1: 0.07291/0.08547, loss_spatial_dice_1: 0.10628/0.18291, loss_spatial_ce_1: 0.00083/0.06142, loss_grounding_bce_1: 0.04717/0.08080, loss_grounding_dice_1: 0.09243/0.15137, loss_grounding_ce_1: 0.00509/0.25063, loss_mask_ce_2: 0.14815/0.76675, loss_mask_bce_2: 0.38682/0.30200, loss_mask_dice_2: 0.56551/1.02707, loss_spatial_bce_2: 0.07268/0.08553, loss_spatial_dice_2: 0.10781/0.18339, loss_spatial_ce_2: 0.00179/0.06359, loss_grounding_bce_2: 0.05298/0.08081, loss_grounding_dice_2: 0.09800/0.15126, loss_grounding_ce_2: 0.00267/0.25387, loss_mask_ce_3: 0.17868/0.77026, loss_mask_bce_3: 0.38301/0.30348, loss_mask_dice_3: 0.57717/1.02501, loss_spatial_bce_3: 0.07908/0.08763, loss_spatial_dice_3: 0.10654/0.18474, loss_spatial_ce_3: 0.00440/0.06827, loss_grounding_bce_3: 0.05402/0.08121, loss_grounding_dice_3: 0.09413/0.15091, loss_grounding_ce_3: 0.00182/0.25463, loss_mask_ce_4: 0.16424/0.77598, loss_mask_bce_4: 0.39421/0.30604, loss_mask_dice_4: 0.58667/1.04397, loss_spatial_bce_4: 0.07366/0.08978, loss_spatial_dice_4: 0.11410/0.19281, loss_spatial_ce_4: 0.02613/0.08175, loss_grounding_bce_4: 0.04690/0.08182, loss_grounding_dice_4: 0.08067/0.15348, loss_grounding_ce_4: 0.00058/0.25912, loss_mask_ce_5: 0.15413/0.80029, loss_mask_bce_5: 0.38935/0.30789, loss_mask_dice_5: 0.59807/1.05161, loss_spatial_bce_5: 0.08437/0.09202, loss_spatial_dice_5: 0.12106/0.19587, loss_spatial_ce_5: 0.05335/0.09472, loss_grounding_bce_5: 0.04417/0.08213, loss_grounding_dice_5: 0.10478/0.15419, loss_grounding_ce_5: 0.00415/0.27746, loss_mask_ce_6: 0.73698/0.82705, loss_mask_bce_6: 0.24900/0.30998, loss_mask_dice_6: 0.39535/1.05523, loss_spatial_bce_6: 0.08851/0.09715, loss_spatial_dice_6: 0.12355/0.19822, loss_spatial_ce_6: 0.04485/0.11911, loss_grounding_bce_6: 0.04365/0.08299, loss_grounding_dice_6: 0.09966/0.15477, loss_grounding_ce_6: 0.00993/0.28686, loss_mask_ce_7: 0.15265/0.88277, loss_mask_bce_7: 0.39054/0.31718, loss_mask_dice_7: 0.61419/1.10150, loss_spatial_bce_7: 0.10254/0.10691, loss_spatial_dice_7: 0.14178/0.22359, loss_spatial_ce_7: 0.03871/0.15650, loss_grounding_bce_7: 0.05180/0.08469, loss_grounding_dice_7: 0.13552/0.16035, loss_grounding_ce_7: 0.02127/0.31976, loss_mask_ce_8: 0.21616/1.01836, loss_mask_bce_8: 0.40310/0.33333, loss_mask_dice_8: 0.63324/1.17862, loss_spatial_bce_8: 0.11604/0.12422, loss_spatial_dice_8: 0.17175/0.25904, loss_spatial_ce_8: 0.10885/0.20381, loss_grounding_bce_8: 0.06564/0.08890, loss_grounding_dice_8: 0.14054/0.17011, loss_grounding_ce_8: 0.29291/0.41955, loss_mask_ce_9: 3.33997/3.47865, loss_mask_bce_9: 0.33427/0.36025, loss_mask_dice_9: 0.69496/1.76180, loss_spatial_bce_9: 0.39931/0.35472, loss_spatial_dice_9: 0.77918/0.79347, loss_spatial_ce_9: 1.56444/1.39040, loss_grounding_bce_9: 0.05041/0.10096, loss_grounding_dice_9: 0.15258/0.24237, loss_grounding_ce_9: 2.45318/0.67483] items per batch[64] items per second[0.37] total items[3955200] mini batches[ 61800] memory[4999] epoch remaining[0:09:22] INFO:trainer.default_trainer:epochs[ 33] optim steps[61900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16863/0.75811, loss_mask_bce_0: 0.66116/0.30087, loss_mask_dice_0: 0.57528/1.02205, loss_spatial_bce_0: 0.35609/0.08515, loss_spatial_dice_0: 0.26521/0.18023, loss_spatial_ce_0: 0.18981/0.05753, loss_grounding_bce_0: 0.09423/0.08060, loss_grounding_dice_0: 0.08786/0.15058, loss_grounding_ce_0: 0.20020/0.24908, loss_mask_ce_1: 0.16860/0.75877, loss_mask_bce_1: 0.66882/0.30173, loss_mask_dice_1: 0.60546/1.02618, loss_spatial_bce_1: 0.33235/0.08546, loss_spatial_dice_1: 0.25613/0.18290, loss_spatial_ce_1: 0.20197/0.06141, loss_grounding_bce_1: 0.08891/0.08081, loss_grounding_dice_1: 0.08882/0.15135, loss_grounding_ce_1: 0.22897/0.25060, loss_mask_ce_2: 0.16782/0.76675, loss_mask_bce_2: 0.67862/0.30198, loss_mask_dice_2: 0.60485/1.02702, loss_spatial_bce_2: 0.31963/0.08552, loss_spatial_dice_2: 0.24305/0.18338, loss_spatial_ce_2: 0.18111/0.06358, loss_grounding_bce_2: 0.08362/0.08081, loss_grounding_dice_2: 0.07596/0.15124, loss_grounding_ce_2: 0.22784/0.25386, loss_mask_ce_3: 0.15808/0.77023, loss_mask_bce_3: 0.66117/0.30345, loss_mask_dice_3: 0.58538/1.02498, loss_spatial_bce_3: 0.33161/0.08762, loss_spatial_dice_3: 0.24891/0.18473, loss_spatial_ce_3: 0.18766/0.06828, loss_grounding_bce_3: 0.09039/0.08122, loss_grounding_dice_3: 0.08576/0.15088, loss_grounding_ce_3: 0.19351/0.25463, loss_mask_ce_4: 0.14812/0.77600, loss_mask_bce_4: 0.67908/0.30601, loss_mask_dice_4: 0.60937/1.04397, loss_spatial_bce_4: 0.37506/0.08978, loss_spatial_dice_4: 0.26741/0.19282, loss_spatial_ce_4: 0.24103/0.08174, loss_grounding_bce_4: 0.09682/0.08183, loss_grounding_dice_4: 0.09426/0.15345, loss_grounding_ce_4: 0.21975/0.25910, loss_mask_ce_5: 0.16767/0.80036, loss_mask_bce_5: 0.67890/0.30786, loss_mask_dice_5: 0.58848/1.05156, loss_spatial_bce_5: 0.31194/0.09202, loss_spatial_dice_5: 0.24585/0.19588, loss_spatial_ce_5: 0.28715/0.09473, loss_grounding_bce_5: 0.09356/0.08213, loss_grounding_dice_5: 0.08392/0.15416, loss_grounding_ce_5: 0.36021/0.27745, loss_mask_ce_6: 0.16418/0.82720, loss_mask_bce_6: 0.67145/0.30995, loss_mask_dice_6: 0.59449/1.05520, loss_spatial_bce_6: 0.34246/0.09716, loss_spatial_dice_6: 0.27448/0.19822, loss_spatial_ce_6: 0.18415/0.11912, loss_grounding_bce_6: 0.09097/0.08299, loss_grounding_dice_6: 0.07822/0.15474, loss_grounding_ce_6: 0.27116/0.28684, loss_mask_ce_7: 0.13900/0.88283, loss_mask_bce_7: 0.65882/0.31716, loss_mask_dice_7: 0.56563/1.10152, loss_spatial_bce_7: 0.46301/0.10692, loss_spatial_dice_7: 0.32200/0.22358, loss_spatial_ce_7: 0.21633/0.15647, loss_grounding_bce_7: 0.08336/0.08469, loss_grounding_dice_7: 0.07741/0.16034, loss_grounding_ce_7: 0.43315/0.31978, loss_mask_ce_8: 0.17660/1.01846, loss_mask_bce_8: 0.68001/0.33330, loss_mask_dice_8: 0.59714/1.17861, loss_spatial_bce_8: 0.28292/0.12421, loss_spatial_dice_8: 0.29105/0.25903, loss_spatial_ce_8: 0.27245/0.20376, loss_grounding_bce_8: 0.08336/0.08891, loss_grounding_dice_8: 0.05790/0.17009, loss_grounding_ce_8: 0.13212/0.41957, loss_mask_ce_9: 1.97431/3.47900, loss_mask_bce_9: 0.60642/0.36023, loss_mask_dice_9: 0.64635/1.76201, loss_spatial_bce_9: 0.45275/0.35474, loss_spatial_dice_9: 0.52349/0.79349, loss_spatial_ce_9: 0.75016/1.39035, loss_grounding_bce_9: 0.09115/0.10098, loss_grounding_dice_9: 0.09677/0.24237, loss_grounding_ce_9: 2.29727/0.67485] items per batch[64] items per second[0.37] total items[3961600] mini batches[ 61900] memory[4999] epoch remaining[0:06:25] INFO:trainer.default_trainer:epochs[ 33] optim steps[62000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75451/0.75797, loss_mask_bce_0: 0.15580/0.30084, loss_mask_dice_0: 0.22063/1.02191, loss_spatial_bce_0: 0.04753/0.08516, loss_spatial_dice_0: 0.06226/0.18021, loss_spatial_ce_0: 0.00063/0.05751, loss_grounding_bce_0: 0.04190/0.08060, loss_grounding_dice_0: 0.03588/0.15057, loss_grounding_ce_0: 0.00089/0.24905, loss_mask_ce_1: 0.70394/0.75862, loss_mask_bce_1: 0.15047/0.30169, loss_mask_dice_1: 0.21312/1.02601, loss_spatial_bce_1: 0.04724/0.08547, loss_spatial_dice_1: 0.06495/0.18289, loss_spatial_ce_1: 0.00077/0.06140, loss_grounding_bce_1: 0.03784/0.08080, loss_grounding_dice_1: 0.03415/0.15133, loss_grounding_ce_1: 0.00121/0.25057, loss_mask_ce_2: 0.55709/0.76663, loss_mask_bce_2: 0.16499/0.30193, loss_mask_dice_2: 0.25037/1.02687, loss_spatial_bce_2: 0.04937/0.08553, loss_spatial_dice_2: 0.06500/0.18337, loss_spatial_ce_2: 0.00049/0.06354, loss_grounding_bce_2: 0.03798/0.08081, loss_grounding_dice_2: 0.03408/0.15122, loss_grounding_ce_2: 0.00055/0.25382, loss_mask_ce_3: 0.77342/0.77012, loss_mask_bce_3: 0.14272/0.30341, loss_mask_dice_3: 0.21417/1.02483, loss_spatial_bce_3: 0.05379/0.08763, loss_spatial_dice_3: 0.07791/0.18472, loss_spatial_ce_3: 0.00189/0.06828, loss_grounding_bce_3: 0.03963/0.08121, loss_grounding_dice_3: 0.03397/0.15086, loss_grounding_ce_3: 0.00075/0.25459, loss_mask_ce_4: 0.78020/0.77587, loss_mask_bce_4: 0.15095/0.30597, loss_mask_dice_4: 0.19633/1.04385, loss_spatial_bce_4: 0.04837/0.08979, loss_spatial_dice_4: 0.07041/0.19280, loss_spatial_ce_4: 0.00557/0.08174, loss_grounding_bce_4: 0.03918/0.08181, loss_grounding_dice_4: 0.03612/0.15343, loss_grounding_ce_4: 0.00074/0.25906, loss_mask_ce_5: 0.86517/0.80020, loss_mask_bce_5: 0.14872/0.30782, loss_mask_dice_5: 0.20514/1.05145, loss_spatial_bce_5: 0.05078/0.09203, loss_spatial_dice_5: 0.07475/0.19586, loss_spatial_ce_5: 0.00310/0.09470, loss_grounding_bce_5: 0.03975/0.08212, loss_grounding_dice_5: 0.03549/0.15415, loss_grounding_ce_5: 0.00572/0.27737, loss_mask_ce_6: 0.86865/0.82706, loss_mask_bce_6: 0.15347/0.30992, loss_mask_dice_6: 0.21537/1.05507, loss_spatial_bce_6: 0.05114/0.09718, loss_spatial_dice_6: 0.07844/0.19820, loss_spatial_ce_6: 0.03498/0.11908, loss_grounding_bce_6: 0.03936/0.08298, loss_grounding_dice_6: 0.03479/0.15472, loss_grounding_ce_6: 0.03379/0.28678, loss_mask_ce_7: 0.84784/0.88270, loss_mask_bce_7: 0.15239/0.31711, loss_mask_dice_7: 0.22525/1.10139, loss_spatial_bce_7: 0.05462/0.10693, loss_spatial_dice_7: 0.06785/0.22355, loss_spatial_ce_7: 0.02475/0.15644, loss_grounding_bce_7: 0.03458/0.08468, loss_grounding_dice_7: 0.03143/0.16032, loss_grounding_ce_7: 0.00259/0.31968, loss_mask_ce_8: 0.86259/1.01831, loss_mask_bce_8: 0.19333/0.33326, loss_mask_dice_8: 0.28897/1.17848, loss_spatial_bce_8: 0.06753/0.12423, loss_spatial_dice_8: 0.09706/0.25900, loss_spatial_ce_8: 0.06161/0.20373, loss_grounding_bce_8: 0.04141/0.08889, loss_grounding_dice_8: 0.03492/0.17006, loss_grounding_ce_8: 0.00330/0.41943, loss_mask_ce_9: 2.95393/3.47858, loss_mask_bce_9: 0.22325/0.36017, loss_mask_dice_9: 0.54019/1.76157, loss_spatial_bce_9: 0.54919/0.35475, loss_spatial_dice_9: 0.79028/0.79344, loss_spatial_ce_9: 1.44206/1.39025, loss_grounding_bce_9: 0.06351/0.10097, loss_grounding_dice_9: 0.06984/0.24231, loss_grounding_ce_9: 0.13756/0.67464] items per batch[64] items per second[0.36] total items[3968000] mini batches[ 62000] memory[4999] epoch remaining[0:03:28] INFO:trainer.default_trainer:epochs[ 33] optim steps[62100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02574/0.75790, loss_mask_bce_0: 0.07558/0.30083, loss_mask_dice_0: 0.37752/1.02154, loss_spatial_bce_0: 0.02450/0.08517, loss_spatial_dice_0: 0.07885/0.18020, loss_spatial_ce_0: 0.00105/0.05750, loss_grounding_bce_0: 0.01772/0.08060, loss_grounding_dice_0: 0.03315/0.15056, loss_grounding_ce_0: 0.00165/0.24901, loss_mask_ce_1: 0.02577/0.75855, loss_mask_bce_1: 0.07060/0.30168, loss_mask_dice_1: 0.31356/1.02563, loss_spatial_bce_1: 0.02529/0.08549, loss_spatial_dice_1: 0.09380/0.18287, loss_spatial_ce_1: 0.00065/0.06137, loss_grounding_bce_1: 0.02131/0.08080, loss_grounding_dice_1: 0.03867/0.15130, loss_grounding_ce_1: 0.00205/0.25051, loss_mask_ce_2: 0.02616/0.76651, loss_mask_bce_2: 0.07130/0.30193, loss_mask_dice_2: 0.31608/1.02646, loss_spatial_bce_2: 0.02790/0.08555, loss_spatial_dice_2: 0.10616/0.18337, loss_spatial_ce_2: 0.00171/0.06352, loss_grounding_bce_2: 0.01935/0.08081, loss_grounding_dice_2: 0.03431/0.15120, loss_grounding_ce_2: 0.00213/0.25375, loss_mask_ce_3: 0.02426/0.77004, loss_mask_bce_3: 0.07978/0.30341, loss_mask_dice_3: 0.32045/1.02448, loss_spatial_bce_3: 0.02607/0.08765, loss_spatial_dice_3: 0.10435/0.18471, loss_spatial_ce_3: 0.00334/0.06827, loss_grounding_bce_3: 0.02047/0.08121, loss_grounding_dice_3: 0.04110/0.15086, loss_grounding_ce_3: 0.00406/0.25451, loss_mask_ce_4: 0.02091/0.77574, loss_mask_bce_4: 0.07238/0.30597, loss_mask_dice_4: 0.36403/1.04350, loss_spatial_bce_4: 0.02681/0.08981, loss_spatial_dice_4: 0.09812/0.19280, loss_spatial_ce_4: 0.01585/0.08173, loss_grounding_bce_4: 0.02272/0.08182, loss_grounding_dice_4: 0.04451/0.15342, loss_grounding_ce_4: 0.00196/0.25899, loss_mask_ce_5: 0.01957/0.80007, loss_mask_bce_5: 0.07570/0.30783, loss_mask_dice_5: 0.35900/1.05107, loss_spatial_bce_5: 0.02632/0.09205, loss_spatial_dice_5: 0.09583/0.19587, loss_spatial_ce_5: 0.00483/0.09472, loss_grounding_bce_5: 0.02206/0.08213, loss_grounding_dice_5: 0.04433/0.15413, loss_grounding_ce_5: 0.00204/0.27731, loss_mask_ce_6: 0.02492/0.82694, loss_mask_bce_6: 0.07553/0.30993, loss_mask_dice_6: 0.34464/1.05468, loss_spatial_bce_6: 0.02856/0.09721, loss_spatial_dice_6: 0.09871/0.19821, loss_spatial_ce_6: 0.00854/0.11912, loss_grounding_bce_6: 0.02060/0.08299, loss_grounding_dice_6: 0.03965/0.15472, loss_grounding_ce_6: 0.00722/0.28670, loss_mask_ce_7: 0.08241/0.88255, loss_mask_bce_7: 0.08200/0.31712, loss_mask_dice_7: 0.31613/1.10104, loss_spatial_bce_7: 0.02534/0.10695, loss_spatial_dice_7: 0.09860/0.22355, loss_spatial_ce_7: 0.06200/0.15644, loss_grounding_bce_7: 0.02162/0.08468, loss_grounding_dice_7: 0.03988/0.16031, loss_grounding_ce_7: 0.00134/0.31966, loss_mask_ce_8: 0.06796/1.01811, loss_mask_bce_8: 0.07849/0.33325, loss_mask_dice_8: 0.31012/1.17801, loss_spatial_bce_8: 0.04930/0.12425, loss_spatial_dice_8: 0.12056/0.25897, loss_spatial_ce_8: 0.08861/0.20372, loss_grounding_bce_8: 0.01893/0.08888, loss_grounding_dice_8: 0.03436/0.17005, loss_grounding_ce_8: 0.00132/0.41938, loss_mask_ce_9: 1.80005/3.47833, loss_mask_bce_9: 0.07698/0.36014, loss_mask_dice_9: 0.36113/1.76098, loss_spatial_bce_9: 0.27763/0.35474, loss_spatial_dice_9: 0.74079/0.79339, loss_spatial_ce_9: 0.93615/1.39017, loss_grounding_bce_9: 0.01840/0.10095, loss_grounding_dice_9: 0.04190/0.24229, loss_grounding_ce_9: 0.07701/0.67449] items per batch[64] items per second[0.37] total items[3974400] mini batches[ 62100] memory[4999] epoch remaining[0:00:31] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00062118. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0021 s/iter. Inference: 0.3690 s/iter. Eval: 0.1037 s/iter. Total: 0.4748 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3728 s/iter. Eval: 0.0879 s/iter. Total: 0.4634 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0027 s/iter. Inference: 0.3774 s/iter. Eval: 0.0805 s/iter. Total: 0.4608 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0028 s/iter. Inference: 0.3784 s/iter. Eval: 0.0811 s/iter. Total: 0.4624 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 55/79. Dataloading: 0.0029 s/iter. Inference: 0.3794 s/iter. Eval: 0.0787 s/iter. Total: 0.4611 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 67/79. Dataloading: 0.0029 s/iter. Inference: 0.3794 s/iter. Eval: 0.0758 s/iter. Total: 0.4583 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 79/79. Dataloading: 0.0030 s/iter. Inference: 0.3746 s/iter. Eval: 0.0755 s/iter. Total: 0.4531 s/iter. ETA=0:00:00 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval776pajlk ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.568 | 83.130 | 66.065 | 133 | | Things | 61.811 | 84.141 | 72.928 | 80 | | Stuff | 46.145 | 81.603 | 55.707 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 15.05 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.91s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.91 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.457 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.383 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.609 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.53 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.746 | 69.133 | 49.475 | 26.411 | 50.075 | 67.406 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.332 | bicycle | 22.399 | car | 43.564 | | motorcycle | 41.821 | airplane | 61.956 | bus | 71.190 | | train | 75.274 | truck | 41.781 | boat | 31.787 | | traffic light | 28.206 | fire hydrant | 72.165 | stop sign | 68.597 | | parking meter | 50.534 | bench | 26.932 | bird | 34.124 | | cat | 77.258 | dog | 71.466 | horse | 51.673 | | sheep | 53.992 | cow | 57.154 | elephant | 66.304 | | bear | 81.042 | zebra | 66.496 | giraffe | 61.450 | | backpack | 23.344 | umbrella | 55.284 | handbag | 23.852 | | tie | 40.437 | suitcase | 51.367 | frisbee | 69.122 | | skis | 8.968 | snowboard | 34.948 | sports ball | 49.394 | | kite | 38.379 | baseball bat | 39.452 | baseball glove | 49.226 | | skateboard | 44.652 | surfboard | 44.941 | tennis racket | 63.552 | | bottle | 41.966 | wine glass | 36.985 | cup | 49.875 | | fork | 26.346 | knife | 24.825 | spoon | 21.718 | | bowl | 39.099 | banana | 22.476 | apple | 25.867 | | sandwich | 48.691 | orange | 30.399 | broccoli | 23.817 | | carrot | 21.643 | hot dog | 37.522 | pizza | 55.189 | | donut | 56.719 | cake | 48.245 | chair | 28.432 | | couch | 44.716 | potted plant | 22.693 | bed | 42.880 | | dining table | 14.998 | toilet | 69.902 | tv | 66.449 | | laptop | 69.402 | mouse | 64.310 | remote | 45.095 | | keyboard | 58.315 | cell phone | 45.569 | microwave | 65.663 | | oven | 34.390 | toaster | 54.078 | sink | 44.736 | | refrigerator | 69.593 | book | 14.201 | clock | 54.237 | | vase | 40.960 | scissors | 34.630 | teddy bear | 56.065 | | hair drier | 34.192 | toothbrush | 29.397 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 64.8060935139619, 'fwIoU': 71.33415197043274, 'IoU-person': 88.54410303680528, 'IoU-bicycle': 73.42472958552617, 'IoU-car': 71.04753101754262, 'IoU-motorcycle': 84.10062763299374, 'IoU-airplane': 84.77493623640342, 'IoU-bus': 85.82293387429003, 'IoU-train': 87.58981443236215, 'IoU-truck': 66.73881635294414, 'IoU-boat': 72.26390489307929, 'IoU-traffic light': 79.3739675871465, 'IoU-fire hydrant': 93.40870008675833, 'IoU-stop sign': 85.68507920447719, 'IoU-parking meter': 84.80622537371981, 'IoU-bench': 63.220384377724436, 'IoU-bird': 78.36553609595522, 'IoU-cat': 90.10656270642023, 'IoU-dog': 82.9400206014808, 'IoU-horse': 89.05932980902648, 'IoU-sheep': 84.20505480586495, 'IoU-cow': 88.19628637609645, 'IoU-elephant': 89.55238674475714, 'IoU-bear': 73.05513409145121, 'IoU-zebra': 88.18600895291615, 'IoU-giraffe': 89.60820465367735, 'IoU-backpack': 53.5431158060823, 'IoU-umbrella': 86.7548561555937, 'IoU-handbag': 50.408081062370194, 'IoU-tie': 75.20831961606275, 'IoU-suitcase': 74.4758836300392, 'IoU-frisbee': 84.10115115814372, 'IoU-skis': 58.85431511627591, 'IoU-snowboard': 72.49672767893307, 'IoU-sports ball': 79.35440484196369, 'IoU-kite': 78.98616923103711, 'IoU-baseball bat': 67.3239444689543, 'IoU-baseball glove': 77.67699894328989, 'IoU-skateboard': 86.29280273443736, 'IoU-surfboard': 86.47090778731994, 'IoU-tennis racket': 90.37784434784595, 'IoU-bottle': 71.43878293267618, 'IoU-wine glass': 82.42872084145067, 'IoU-cup': 68.03722371076542, 'IoU-fork': 69.09480511368635, 'IoU-knife': 63.17825182178495, 'IoU-spoon': 60.69681683215734, 'IoU-bowl': 57.247649428476755, 'IoU-banana': 83.98312574414479, 'IoU-apple': 58.54662794701638, 'IoU-sandwich': 70.40523976810161, 'IoU-orange': 76.78923646225245, 'IoU-broccoli': 69.82201471121114, 'IoU-carrot': 63.746670021137874, 'IoU-hot dog': 67.00999526894155, 'IoU-pizza': 84.97984731770151, 'IoU-donut': 70.52579655377103, 'IoU-cake': 75.72643287600005, 'IoU-chair': 60.91966946494083, 'IoU-couch': 70.09564568661565, 'IoU-potted plant': 45.518683120397114, 'IoU-bed': 72.72403908177208, 'IoU-dining table': 55.13205239736023, 'IoU-toilet': 77.26144859506087, 'IoU-tv': 75.03446258041521, 'IoU-laptop': 76.70885126604934, 'IoU-mouse': 76.43135307805966, 'IoU-remote': 51.29041460631419, 'IoU-keyboard': 66.86772022355395, 'IoU-cell phone': 76.7130747089721, 'IoU-microwave': 79.72341180703052, 'IoU-oven': 75.39307010322034, 'IoU-toaster': 85.91724279994335, 'IoU-sink': 68.02481957301559, 'IoU-refrigerator': 83.24812377315665, 'IoU-book': 54.137557299536624, 'IoU-clock': 73.87170262250675, 'IoU-vase': 68.51989784431312, 'IoU-scissors': 64.30901583673821, 'IoU-teddy bear': 78.52084553634889, 'IoU-hair drier': 27.44138167676999, 'IoU-toothbrush': 75.12460986630643, 'IoU-banner': 30.212851564709563, 'IoU-blanket': 14.221337878281293, 'IoU-bridge': 39.33192762322433, 'IoU-cardboard': 51.305751040238434, 'IoU-counter': 34.53581220942633, 'IoU-curtain': 70.65268968656466, 'IoU-door-stuff': 46.13129451723897, 'IoU-floor-wood': 62.016825370738935, 'IoU-flower': 49.65431515626557, 'IoU-fruit': 50.651709216037254, 'IoU-gravel': 34.274675637876484, 'IoU-house': 24.190942155318258, 'IoU-light': 43.568124562421836, 'IoU-mirror-stuff': 58.19502478879117, 'IoU-net': 40.750875107191845, 'IoU-pillow': 16.284715910752034, 'IoU-platform': 27.655689185423554, 'IoU-playingfield': 70.32839188769164, 'IoU-railroad': 64.27753614824341, 'IoU-river': 53.9515613716847, 'IoU-road': 66.96847563350428, 'IoU-roof': 17.96938796745907, 'IoU-sand': 64.38134636159441, 'IoU-sea': 85.66918713853897, 'IoU-shelf': 39.07605663034482, 'IoU-snow': 92.11864161057576, 'IoU-stairs': 34.391247358523756, 'IoU-tent': 9.714642341368783, 'IoU-towel': 45.16036782838636, 'IoU-wall-brick': 46.84741274621278, 'IoU-wall-stone': 32.08502217679075, 'IoU-wall-tile': 66.50702689375382, 'IoU-wall-wood': 43.363847466790666, 'IoU-water-other': 27.680430439246635, 'IoU-window-blind': 50.93703063832823, 'IoU-window-other': 51.08003216057138, 'IoU-tree-merged': 82.29207353251982, 'IoU-fence-merged': 54.031473480566646, 'IoU-ceiling-merged': 67.43561032306759, 'IoU-sky-other-merged': 93.93372538497063, 'IoU-cabinet-merged': 62.957519580598664, 'IoU-table-merged': 42.313888971019544, 'IoU-floor-other-merged': 55.08365339920947, 'IoU-pavement-merged': 57.70216567562932, 'IoU-mountain-merged': 58.613196797048815, 'IoU-grass-merged': 72.17397071162797, 'IoU-dirt-merged': 47.30665193479212, 'IoU-paper-merged': 36.1638193039515, 'IoU-food-other-merged': 43.95009088575199, 'IoU-building-other-merged': 60.01830021671265, 'IoU-rock-merged': 64.51810283819015, 'IoU-wall-other-merged': 67.6625794228496, 'IoU-rug-merged': 67.92327648087472, 'mACC': 76.77465608359374, 'pACC': 82.06317236226394, 'ACC-person': 93.22710404144972, 'ACC-bicycle': 83.8278633913352, 'ACC-car': 85.49902157576736, 'ACC-motorcycle': 88.30431389051991, 'ACC-airplane': 91.11412648066086, 'ACC-bus': 94.16499002757386, 'ACC-train': 95.51511880332556, 'ACC-truck': 76.04656052228019, 'ACC-boat': 81.26392411232347, 'ACC-traffic light': 91.7099522375724, 'ACC-fire hydrant': 96.20084590519542, 'ACC-stop sign': 88.70678102473154, 'ACC-parking meter': 87.97693674452482, 'ACC-bench': 80.31593299779843, 'ACC-bird': 82.36372896444286, 'ACC-cat': 93.14234021991167, 'ACC-dog': 85.71928413132444, 'ACC-horse': 95.00237481565271, 'ACC-sheep': 88.23520586326794, 'ACC-cow': 91.74169366784916, 'ACC-elephant': 91.79836942795612, 'ACC-bear': 74.57608803398902, 'ACC-zebra': 90.53393050177131, 'ACC-giraffe': 93.6981774776995, 'ACC-backpack': 73.16314055091541, 'ACC-umbrella': 90.87511962271458, 'ACC-handbag': 71.84308392788907, 'ACC-tie': 84.35190971961057, 'ACC-suitcase': 81.75616324469893, 'ACC-frisbee': 94.12509090909091, 'ACC-skis': 73.87264605771098, 'ACC-snowboard': 83.28769807254292, 'ACC-sports ball': 89.04187469635418, 'ACC-kite': 85.63500033979446, 'ACC-baseball bat': 88.03729705025137, 'ACC-baseball glove': 92.1615039305967, 'ACC-skateboard': 90.99436242936206, 'ACC-surfboard': 92.80807433861254, 'ACC-tennis racket': 95.34164203002575, 'ACC-bottle': 85.63100754941321, 'ACC-wine glass': 92.10050588384617, 'ACC-cup': 85.96349901346781, 'ACC-fork': 83.79497295400404, 'ACC-knife': 78.69678317578955, 'ACC-spoon': 78.13844806964327, 'ACC-bowl': 68.10812618337646, 'ACC-banana': 91.23377812991491, 'ACC-apple': 72.44341687653554, 'ACC-sandwich': 82.54961413944177, 'ACC-orange': 87.09790259165192, 'ACC-broccoli': 83.33786486920567, 'ACC-carrot': 78.9192705869682, 'ACC-hot dog': 75.37770340641191, 'ACC-pizza': 94.71474179985894, 'ACC-donut': 79.78954640712843, 'ACC-cake': 85.88727330468839, 'ACC-chair': 79.66305134661906, 'ACC-couch': 76.9598772943221, 'ACC-potted plant': 56.780438309481674, 'ACC-bed': 86.3486704062862, 'ACC-dining table': 74.91711943438881, 'ACC-toilet': 80.7722471960274, 'ACC-tv': 90.02211970559574, 'ACC-laptop': 87.99519407766161, 'ACC-mouse': 92.18171127200054, 'ACC-remote': 54.708467547371576, 'ACC-keyboard': 75.1366652319613, 'ACC-cell phone': 84.33553517394404, 'ACC-microwave': 84.62760167929096, 'ACC-oven': 90.47649614920533, 'ACC-toaster': 91.33079691798164, 'ACC-sink': 77.94754470070231, 'ACC-refrigerator': 92.36949552273217, 'ACC-book': 73.05479672746631, 'ACC-clock': 78.95451057325914, 'ACC-vase': 78.66706020877349, 'ACC-scissors': 68.4658751801963, 'ACC-teddy bear': 83.46449165744995, 'ACC-hair drier': 34.14453647567814, 'ACC-toothbrush': 84.05316191799861, 'ACC-banner': 78.67764112756099, 'ACC-blanket': 22.155796954149626, 'ACC-bridge': 58.71646536574464, 'ACC-cardboard': 68.88608404827686, 'ACC-counter': 54.58336017391445, 'ACC-curtain': 84.27588531771522, 'ACC-door-stuff': 72.47080833879379, 'ACC-floor-wood': 78.32093868006874, 'ACC-flower': 77.80145458379673, 'ACC-fruit': 67.40156897615316, 'ACC-gravel': 47.355097377932644, 'ACC-house': 28.18891356326063, 'ACC-light': 60.96830379359185, 'ACC-mirror-stuff': 71.86051327623562, 'ACC-net': 67.48026285159395, 'ACC-pillow': 33.821374735013585, 'ACC-platform': 43.21614582862417, 'ACC-playingfield': 88.33282756773822, 'ACC-railroad': 79.48269133093518, 'ACC-river': 80.39781529868024, 'ACC-road': 86.30059702916172, 'ACC-roof': 23.57181565190464, 'ACC-sand': 70.14171961703177, 'ACC-sea': 90.19027163581235, 'ACC-shelf': 58.51247578786827, 'ACC-snow': 95.58879486420516, 'ACC-stairs': 63.08868530019828, 'ACC-tent': 11.79107192893677, 'ACC-towel': 54.87932568975447, 'ACC-wall-brick': 66.70929571420294, 'ACC-wall-stone': 44.19326495652881, 'ACC-wall-tile': 87.18761393266628, 'ACC-wall-wood': 60.70675015433111, 'ACC-water-other': 42.95286374252481, 'ACC-window-blind': 64.20156053138452, 'ACC-window-other': 77.22882158822145, 'ACC-tree-merged': 89.9508894179314, 'ACC-fence-merged': 72.17287408725227, 'ACC-ceiling-merged': 83.3599471295812, 'ACC-sky-other-merged': 96.91781167816413, 'ACC-cabinet-merged': 78.40743797883034, 'ACC-table-merged': 59.21584319988502, 'ACC-floor-other-merged': 66.19580178469866, 'ACC-pavement-merged': 70.422048668119, 'ACC-mountain-merged': 68.97017675996803, 'ACC-grass-merged': 83.48815609527252, 'ACC-dirt-merged': 69.5868578098727, 'ACC-paper-merged': 49.50145609828318, 'ACC-food-other-merged': 56.12261137892855, 'ACC-building-other-merged': 73.58264161870758, 'ACC-rock-merged': 82.90006491766106, 'ACC-wall-other-merged': 80.15968226523829, 'ACC-rug-merged': 79.2948894882237})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2797 s/iter. Inference: 0.4894 s/iter. Eval: 0.0000 s/iter. Total: 0.7692 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.2969 s/iter. Inference: 0.6041 s/iter. Eval: 0.0000 s/iter. Total: 0.9011 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3337 s/iter. Inference: 0.6107 s/iter. Eval: 0.0000 s/iter. Total: 0.9445 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3690371671056483, 'noc@0.8': 2.3465027802165643, 'noc@0.85': 2.7851916886157446, 'noc@0.9': 3.5750658472344163, 'miou@iter1': 0.8636196171082633} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0010 s/iter. Inference: 0.1417 s/iter. Eval: 0.0010 s/iter. Total: 0.1437 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.05907440185547, 'precision@0.6': 72.94986724853516, 'precision@0.7': 68.9856185913086, 'precision@0.8': 60.08550262451172, 'precision@0.9': 33.30742263793945, 'cIoU': 62.92966842651367, 'mIoU': 67.35633087158203} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.56805694429149, 'SQ': 83.12975075176986, 'RQ': 66.06541728226946, 'PQ_th': 61.81064864503802, 'SQ_th': 84.1411688856625, 'RQ_th': 72.92798198706664, 'PQ_st': 46.145277018636364, 'SQ_st': 81.60308187042246, 'RQ_st': 55.70682904861324}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.74624592766309, 'AP50': 69.13291996648219, 'AP75': 49.4746332661048, 'APs': 26.41072683204338, 'APm': 50.07536992646619, 'APl': 67.40585885749788, 'AP-person': 49.33162016909632, 'AP-bicycle': 22.398743575578766, 'AP-car': 43.56427754503437, 'AP-motorcycle': 41.82098956963674, 'AP-airplane': 61.95552695735594, 'AP-bus': 71.19022976358853, 'AP-train': 75.27386287194958, 'AP-truck': 41.7805655538038, 'AP-boat': 31.787117493160434, 'AP-traffic light': 28.20632919447503, 'AP-fire hydrant': 72.16514317835045, 'AP-stop sign': 68.5968929380031, 'AP-parking meter': 50.53351972998065, 'AP-bench': 26.931873400694045, 'AP-bird': 34.123739393654276, 'AP-cat': 77.25843009123578, 'AP-dog': 71.46610216870415, 'AP-horse': 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'ACC-tv': 90.02211970559574, 'ACC-laptop': 87.99519407766161, 'ACC-mouse': 92.18171127200054, 'ACC-remote': 54.708467547371576, 'ACC-keyboard': 75.1366652319613, 'ACC-cell phone': 84.33553517394404, 'ACC-microwave': 84.62760167929096, 'ACC-oven': 90.47649614920533, 'ACC-toaster': 91.33079691798164, 'ACC-sink': 77.94754470070231, 'ACC-refrigerator': 92.36949552273217, 'ACC-book': 73.05479672746631, 'ACC-clock': 78.95451057325914, 'ACC-vase': 78.66706020877349, 'ACC-scissors': 68.4658751801963, 'ACC-teddy bear': 83.46449165744995, 'ACC-hair drier': 34.14453647567814, 'ACC-toothbrush': 84.05316191799861, 'ACC-banner': 78.67764112756099, 'ACC-blanket': 22.155796954149626, 'ACC-bridge': 58.71646536574464, 'ACC-cardboard': 68.88608404827686, 'ACC-counter': 54.58336017391445, 'ACC-curtain': 84.27588531771522, 'ACC-door-stuff': 72.47080833879379, 'ACC-floor-wood': 78.32093868006874, 'ACC-flower': 77.80145458379673, 'ACC-fruit': 67.40156897615316, 'ACC-gravel': 47.355097377932644, 'ACC-house': 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'ACC-cabinet-merged': 78.40743797883034, 'ACC-table-merged': 59.21584319988502, 'ACC-floor-other-merged': 66.19580178469866, 'ACC-pavement-merged': 70.422048668119, 'ACC-mountain-merged': 68.97017675996803, 'ACC-grass-merged': 83.48815609527252, 'ACC-dirt-merged': 69.5868578098727, 'ACC-paper-merged': 49.50145609828318, 'ACC-food-other-merged': 56.12261137892855, 'ACC-building-other-merged': 73.58264161870758, 'ACC-rock-merged': 82.90006491766106, 'ACC-wall-other-merged': 80.15968226523829, 'ACC-rug-merged': 79.2948894882237})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3690371671056483, 'noc@0.8': 2.3465027802165643, 'noc@0.85': 2.7851916886157446, 'noc@0.9': 3.5750658472344163, 'miou@iter1': 0.8636196171082633}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 76.05907440185547, 'precision@0.6': 72.94986724853516, 'precision@0.7': 68.9856185913086, 'precision@0.8': 60.08550262451172, 'precision@0.9': 33.30742263793945, 'cIoU': 62.92966842651367, 'mIoU': 67.35633087158203}}} INFO:trainer.default_trainer:This epoch takes 0:57:18.808602 INFO:trainer.default_trainer:PROGRESS: 68.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 34 training. INFO:trainer.default_trainer:epochs[ 34] optim steps[62200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60496/0.75801, loss_mask_bce_0: 0.15748/0.30083, loss_mask_dice_0: 0.07089/1.02140, loss_spatial_bce_0: 0.07004/0.08517, loss_spatial_dice_0: 0.02562/0.18020, loss_spatial_ce_0: 0.01451/0.05749, loss_grounding_bce_0: 0.00000/0.08060, loss_grounding_dice_0: 0.00000/0.15054, loss_grounding_ce_0: 0.01069/0.24906, loss_mask_ce_1: 0.60994/0.75864, loss_mask_bce_1: 0.34695/0.30168, loss_mask_dice_1: 0.14320/1.02552, loss_spatial_bce_1: 0.11500/0.08549, loss_spatial_dice_1: 0.04868/0.18287, loss_spatial_ce_1: 0.01098/0.06135, loss_grounding_bce_1: 0.00000/0.08080, loss_grounding_dice_1: 0.00000/0.15128, loss_grounding_ce_1: 0.00977/0.25050, loss_mask_ce_2: 0.60535/0.76660, loss_mask_bce_2: 0.31887/0.30192, loss_mask_dice_2: 0.13224/1.02632, loss_spatial_bce_2: 0.14611/0.08555, loss_spatial_dice_2: 0.06548/0.18337, loss_spatial_ce_2: 0.01912/0.06351, loss_grounding_bce_2: 0.00000/0.08081, loss_grounding_dice_2: 0.00000/0.15117, loss_grounding_ce_2: 0.01045/0.25378, loss_mask_ce_3: 0.52731/0.77014, loss_mask_bce_3: 0.23470/0.30342, loss_mask_dice_3: 0.10063/1.02438, loss_spatial_bce_3: 0.10959/0.08765, loss_spatial_dice_3: 0.04583/0.18472, loss_spatial_ce_3: 0.01960/0.06826, loss_grounding_bce_3: 0.00000/0.08122, loss_grounding_dice_3: 0.00000/0.15082, loss_grounding_ce_3: 0.01338/0.25454, loss_mask_ce_4: 0.93866/0.77581, loss_mask_bce_4: 0.00796/0.30597, loss_mask_dice_4: 0.00237/1.04340, loss_spatial_bce_4: 0.09473/0.08982, loss_spatial_dice_4: 0.03508/0.19281, loss_spatial_ce_4: 0.02932/0.08173, loss_grounding_bce_4: 0.00000/0.08181, loss_grounding_dice_4: 0.00000/0.15340, loss_grounding_ce_4: 0.01016/0.25904, loss_mask_ce_5: 0.54971/0.80017, loss_mask_bce_5: 0.06442/0.30783, loss_mask_dice_5: 0.02380/1.05096, loss_spatial_bce_5: 0.11502/0.09206, loss_spatial_dice_5: 0.03857/0.19588, loss_spatial_ce_5: 0.14520/0.09473, loss_grounding_bce_5: 0.00000/0.08212, loss_grounding_dice_5: 0.00000/0.15410, loss_grounding_ce_5: 0.01945/0.27736, loss_mask_ce_6: 0.65472/0.82705, loss_mask_bce_6: 0.14488/0.30994, loss_mask_dice_6: 0.06333/1.05464, loss_spatial_bce_6: 0.06511/0.09721, loss_spatial_dice_6: 0.02020/0.19822, loss_spatial_ce_6: 0.19220/0.11916, loss_grounding_bce_6: 0.00000/0.08298, loss_grounding_dice_6: 0.00000/0.15469, loss_grounding_ce_6: 0.02606/0.28663, loss_mask_ce_7: 0.51319/0.88266, loss_mask_bce_7: 0.36077/0.31712, loss_mask_dice_7: 0.14947/1.10096, loss_spatial_bce_7: 0.07584/0.10696, loss_spatial_dice_7: 0.02139/0.22356, loss_spatial_ce_7: 0.07561/0.15643, loss_grounding_bce_7: 0.00000/0.08467, loss_grounding_dice_7: 0.00004/0.16028, loss_grounding_ce_7: 0.01472/0.31955, loss_mask_ce_8: 1.28887/1.01820, loss_mask_bce_8: 0.00908/0.33325, loss_mask_dice_8: 0.00245/1.17792, loss_spatial_bce_8: 0.10083/0.12424, loss_spatial_dice_8: 0.02317/0.25897, loss_spatial_ce_8: 0.11668/0.20370, loss_grounding_bce_8: 0.00000/0.08888, loss_grounding_dice_8: 0.00008/0.17003, loss_grounding_ce_8: 0.06679/0.41926, loss_mask_ce_9: 3.40332/3.47834, loss_mask_bce_9: 0.10639/0.36015, loss_mask_dice_9: 0.05029/1.76081, loss_spatial_bce_9: 0.73868/0.35470, loss_spatial_dice_9: 0.35044/0.79337, loss_spatial_ce_9: 0.73810/1.39025, loss_grounding_bce_9: 0.00000/0.10094, loss_grounding_dice_9: 0.00074/0.24227, loss_grounding_ce_9: 0.74003/0.67431] items per batch[64] items per second[0.16] total items[3980800] mini batches[ 62200] memory[4999] epoch remaining[0:54:16] INFO:trainer.default_trainer:epochs[ 34] optim steps[62300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80066/0.75792, loss_mask_bce_0: 0.04484/0.30085, loss_mask_dice_0: 2.98259/1.02139, loss_spatial_bce_0: 0.00422/0.08516, loss_spatial_dice_0: 0.30830/0.18019, loss_spatial_ce_0: 0.06387/0.05748, loss_grounding_bce_0: 0.00258/0.08062, loss_grounding_dice_0: 0.13415/0.15056, loss_grounding_ce_0: 0.51818/0.24911, loss_mask_ce_1: 1.20958/0.75858, loss_mask_bce_1: 0.03184/0.30170, loss_mask_dice_1: 2.91066/1.02554, loss_spatial_bce_1: 0.00348/0.08547, loss_spatial_dice_1: 0.29285/0.18286, loss_spatial_ce_1: 0.07307/0.06133, loss_grounding_bce_1: 0.00183/0.08082, loss_grounding_dice_1: 0.41036/0.15131, loss_grounding_ce_1: 0.37538/0.25055, loss_mask_ce_2: 0.99041/0.76650, loss_mask_bce_2: 0.02453/0.30194, loss_mask_dice_2: 2.82977/1.02635, loss_spatial_bce_2: 0.00380/0.08554, loss_spatial_dice_2: 0.22485/0.18336, loss_spatial_ce_2: 0.47860/0.06350, loss_grounding_bce_2: 0.00110/0.08082, loss_grounding_dice_2: 0.30534/0.15120, loss_grounding_ce_2: 0.35900/0.25379, loss_mask_ce_3: 0.98923/0.77009, loss_mask_bce_3: 0.03013/0.30343, loss_mask_dice_3: 2.74700/1.02438, loss_spatial_bce_3: 0.00396/0.08764, loss_spatial_dice_3: 0.21169/0.18471, loss_spatial_ce_3: 0.07967/0.06825, loss_grounding_bce_3: 0.00167/0.08123, loss_grounding_dice_3: 0.24649/0.15084, loss_grounding_ce_3: 0.36022/0.25460, loss_mask_ce_4: 0.76290/0.77576, loss_mask_bce_4: 0.03715/0.30599, loss_mask_dice_4: 4.83465/1.04344, loss_spatial_bce_4: 0.00403/0.08980, loss_spatial_dice_4: 0.20095/0.19280, loss_spatial_ce_4: 0.09525/0.08170, loss_grounding_bce_4: 0.00457/0.08182, loss_grounding_dice_4: 0.20290/0.15343, loss_grounding_ce_4: 0.63773/0.25912, loss_mask_ce_5: 1.11107/0.80010, loss_mask_bce_5: 0.02760/0.30784, loss_mask_dice_5: 3.42319/1.05096, loss_spatial_bce_5: 0.00344/0.09205, loss_spatial_dice_5: 0.21364/0.19587, loss_spatial_ce_5: 0.43514/0.09470, loss_grounding_bce_5: 0.00110/0.08215, loss_grounding_dice_5: 0.30145/0.15413, loss_grounding_ce_5: 0.45999/0.27737, loss_mask_ce_6: 1.16094/0.82697, loss_mask_bce_6: 0.03481/0.30996, loss_mask_dice_6: 3.73683/1.05464, loss_spatial_bce_6: 0.00460/0.09720, loss_spatial_dice_6: 0.31729/0.19822, loss_spatial_ce_6: 0.12592/0.11913, loss_grounding_bce_6: 0.00414/0.08300, loss_grounding_dice_6: 0.34270/0.15472, loss_grounding_ce_6: 0.41235/0.28665, loss_mask_ce_7: 1.27017/0.88256, loss_mask_bce_7: 0.02740/0.31714, loss_mask_dice_7: 2.80555/1.10099, loss_spatial_bce_7: 0.00438/0.10694, loss_spatial_dice_7: 0.30822/0.22355, loss_spatial_ce_7: 0.20524/0.15637, loss_grounding_bce_7: 0.00168/0.08469, loss_grounding_dice_7: 0.32894/0.16032, loss_grounding_ce_7: 0.39613/0.31953, loss_mask_ce_8: 1.17324/1.01813, loss_mask_bce_8: 0.04164/0.33328, loss_mask_dice_8: 2.38646/1.17788, loss_spatial_bce_8: 0.01577/0.12422, loss_spatial_dice_8: 0.48267/0.25897, loss_spatial_ce_8: 0.34609/0.20365, loss_grounding_bce_8: 0.00286/0.08889, loss_grounding_dice_8: 0.23501/0.17004, loss_grounding_ce_8: 0.46929/0.41930, loss_mask_ce_9: 4.93899/3.47829, loss_mask_bce_9: 0.02675/0.36020, loss_mask_dice_9: 4.74369/1.76109, loss_spatial_bce_9: 0.01806/0.35467, loss_spatial_dice_9: 0.77073/0.79337, loss_spatial_ce_9: 3.32000/1.39016, loss_grounding_bce_9: 0.00076/0.10096, loss_grounding_dice_9: 0.20233/0.24228, loss_grounding_ce_9: 0.46555/0.67430] items per batch[64] items per second[0.37] total items[3987200] mini batches[ 62300] memory[4999] epoch remaining[0:49:09] INFO:trainer.default_trainer:epochs[ 34] optim steps[62400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.12132/0.75776, loss_mask_bce_0: 0.38567/0.30084, loss_mask_dice_0: 0.35698/1.02116, loss_spatial_bce_0: 0.06696/0.08516, loss_spatial_dice_0: 0.08691/0.18017, loss_spatial_ce_0: 0.03396/0.05745, loss_grounding_bce_0: 0.02558/0.08063, loss_grounding_dice_0: 0.06593/0.15056, loss_grounding_ce_0: 0.49274/0.24907, loss_mask_ce_1: 0.11551/0.75843, loss_mask_bce_1: 0.40378/0.30169, loss_mask_dice_1: 0.35677/1.02532, loss_spatial_bce_1: 0.08162/0.08548, loss_spatial_dice_1: 0.13665/0.18284, loss_spatial_ce_1: 0.02930/0.06130, loss_grounding_bce_1: 0.02162/0.08084, loss_grounding_dice_1: 0.06184/0.15130, loss_grounding_ce_1: 0.48708/0.25057, loss_mask_ce_2: 0.13909/0.76635, loss_mask_bce_2: 0.39565/0.30193, loss_mask_dice_2: 0.37251/1.02615, loss_spatial_bce_2: 0.07171/0.08554, loss_spatial_dice_2: 0.12437/0.18334, loss_spatial_ce_2: 0.03456/0.06348, loss_grounding_bce_2: 0.02427/0.08084, loss_grounding_dice_2: 0.06394/0.15118, loss_grounding_ce_2: 0.53051/0.25372, loss_mask_ce_3: 0.14850/0.76996, loss_mask_bce_3: 0.40457/0.30343, loss_mask_dice_3: 0.37714/1.02414, loss_spatial_bce_3: 0.08274/0.08764, loss_spatial_dice_3: 0.14597/0.18469, loss_spatial_ce_3: 0.05143/0.06821, loss_grounding_bce_3: 0.02514/0.08125, loss_grounding_dice_3: 0.06896/0.15083, loss_grounding_ce_3: 0.52058/0.25454, loss_mask_ce_4: 0.14288/0.77562, loss_mask_bce_4: 0.43097/0.30597, loss_mask_dice_4: 0.34588/1.04317, loss_spatial_bce_4: 0.06445/0.08981, loss_spatial_dice_4: 0.08766/0.19279, loss_spatial_ce_4: 0.07303/0.08169, loss_grounding_bce_4: 0.02378/0.08184, loss_grounding_dice_4: 0.05914/0.15342, loss_grounding_ce_4: 0.51081/0.25909, loss_mask_ce_5: 0.10591/0.79990, loss_mask_bce_5: 0.38718/0.30782, loss_mask_dice_5: 0.35494/1.05075, loss_spatial_bce_5: 0.06542/0.09206, loss_spatial_dice_5: 0.09602/0.19586, loss_spatial_ce_5: 0.11782/0.09468, loss_grounding_bce_5: 0.02472/0.08217, loss_grounding_dice_5: 0.06223/0.15412, loss_grounding_ce_5: 0.51118/0.27738, loss_mask_ce_6: 0.10330/0.82677, loss_mask_bce_6: 0.46830/0.30994, loss_mask_dice_6: 0.36859/1.05441, loss_spatial_bce_6: 0.11294/0.09721, loss_spatial_dice_6: 0.16494/0.19821, loss_spatial_ce_6: 0.09114/0.11912, loss_grounding_bce_6: 0.02735/0.08302, loss_grounding_dice_6: 0.08566/0.15472, loss_grounding_ce_6: 0.32458/0.28658, loss_mask_ce_7: 0.36153/0.88239, loss_mask_bce_7: 0.46702/0.31711, loss_mask_dice_7: 0.35328/1.10074, loss_spatial_bce_7: 0.08055/0.10694, loss_spatial_dice_7: 0.11484/0.22353, loss_spatial_ce_7: 0.15564/0.15634, loss_grounding_bce_7: 0.02466/0.08470, loss_grounding_dice_7: 0.07434/0.16031, loss_grounding_ce_7: 0.40198/0.31950, loss_mask_ce_8: 0.38332/1.01791, loss_mask_bce_8: 0.35263/0.33325, loss_mask_dice_8: 0.32633/1.17764, loss_spatial_bce_8: 0.07181/0.12422, loss_spatial_dice_8: 0.08779/0.25895, loss_spatial_ce_8: 0.15882/0.20364, loss_grounding_bce_8: 0.02396/0.08890, loss_grounding_dice_8: 0.06932/0.17002, loss_grounding_ce_8: 0.47053/0.41934, loss_mask_ce_9: 4.24116/3.47796, loss_mask_bce_9: 0.33291/0.36015, loss_mask_dice_9: 0.64689/1.76059, loss_spatial_bce_9: 0.40283/0.35469, loss_spatial_dice_9: 0.66267/0.79332, loss_spatial_ce_9: 1.19555/1.39008, loss_grounding_bce_9: 0.04329/0.10096, loss_grounding_dice_9: 0.20381/0.24224, loss_grounding_ce_9: 0.63833/0.67415] items per batch[64] items per second[0.36] total items[3993600] mini batches[ 62400] memory[4999] epoch remaining[0:46:00] INFO:trainer.default_trainer:epochs[ 34] optim steps[62500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.61312/0.75771, loss_mask_bce_0: 0.11806/0.30086, loss_mask_dice_0: 1.09308/1.02105, loss_spatial_bce_0: 0.01664/0.08516, loss_spatial_dice_0: 0.23088/0.18015, loss_spatial_ce_0: 0.00183/0.05742, loss_grounding_bce_0: 0.01147/0.08063, loss_grounding_dice_0: 0.27188/0.15053, loss_grounding_ce_0: 0.38307/0.24905, loss_mask_ce_1: 0.52891/0.75841, loss_mask_bce_1: 0.11016/0.30170, loss_mask_dice_1: 1.17767/1.02517, loss_spatial_bce_1: 0.01762/0.08548, loss_spatial_dice_1: 0.26719/0.18282, loss_spatial_ce_1: 0.07219/0.06127, loss_grounding_bce_1: 0.01024/0.08083, loss_grounding_dice_1: 0.26500/0.15128, loss_grounding_ce_1: 0.39076/0.25057, loss_mask_ce_2: 0.57093/0.76632, loss_mask_bce_2: 0.10962/0.30194, loss_mask_dice_2: 1.00293/1.02605, loss_spatial_bce_2: 0.02200/0.08555, loss_spatial_dice_2: 0.24845/0.18332, loss_spatial_ce_2: 0.10625/0.06345, loss_grounding_bce_2: 0.00968/0.08084, loss_grounding_dice_2: 0.27704/0.15116, loss_grounding_ce_2: 0.40311/0.25373, loss_mask_ce_3: 0.56359/0.76992, loss_mask_bce_3: 0.13219/0.30344, loss_mask_dice_3: 1.29733/1.02402, loss_spatial_bce_3: 0.01997/0.08765, loss_spatial_dice_3: 0.25372/0.18467, loss_spatial_ce_3: 0.02042/0.06821, loss_grounding_bce_3: 0.00924/0.08124, loss_grounding_dice_3: 0.20775/0.15081, loss_grounding_ce_3: 0.43110/0.25455, loss_mask_ce_4: 0.54209/0.77556, loss_mask_bce_4: 0.11614/0.30600, loss_mask_dice_4: 1.12881/1.04308, loss_spatial_bce_4: 0.02225/0.08981, loss_spatial_dice_4: 0.28780/0.19277, loss_spatial_ce_4: 0.24113/0.08167, loss_grounding_bce_4: 0.01343/0.08184, loss_grounding_dice_4: 0.43176/0.15340, loss_grounding_ce_4: 0.50951/0.25908, loss_mask_ce_5: 0.52931/0.79988, loss_mask_bce_5: 0.11962/0.30784, loss_mask_dice_5: 1.02146/1.05066, loss_spatial_bce_5: 0.02305/0.09206, loss_spatial_dice_5: 0.26645/0.19585, loss_spatial_ce_5: 0.04162/0.09466, loss_grounding_bce_5: 0.01433/0.08217, loss_grounding_dice_5: 0.29728/0.15411, loss_grounding_ce_5: 0.48458/0.27734, loss_mask_ce_6: 0.56550/0.82679, loss_mask_bce_6: 0.11738/0.30996, loss_mask_dice_6: 1.04941/1.05430, loss_spatial_bce_6: 0.02406/0.09721, loss_spatial_dice_6: 0.24950/0.19820, loss_spatial_ce_6: 0.05432/0.11913, loss_grounding_bce_6: 0.00963/0.08302, loss_grounding_dice_6: 0.29008/0.15471, loss_grounding_ce_6: 0.42081/0.28653, loss_mask_ce_7: 0.58754/0.88237, loss_mask_bce_7: 0.12573/0.31713, loss_mask_dice_7: 1.17468/1.10063, loss_spatial_bce_7: 0.02238/0.10694, loss_spatial_dice_7: 0.26625/0.22351, loss_spatial_ce_7: 0.17817/0.15636, loss_grounding_bce_7: 0.01015/0.08470, loss_grounding_dice_7: 0.25170/0.16029, loss_grounding_ce_7: 0.52051/0.31944, loss_mask_ce_8: 1.00113/1.01786, loss_mask_bce_8: 0.13305/0.33328, loss_mask_dice_8: 1.02025/1.17751, loss_spatial_bce_8: 0.02645/0.12422, loss_spatial_dice_8: 0.32542/0.25894, loss_spatial_ce_8: 0.28167/0.20361, loss_grounding_bce_8: 0.01812/0.08891, loss_grounding_dice_8: 0.33086/0.17000, loss_grounding_ce_8: 0.44227/0.41938, loss_mask_ce_9: 4.09825/3.47786, loss_mask_bce_9: 0.19070/0.36018, loss_mask_dice_9: 1.85701/1.76053, loss_spatial_bce_9: 0.16877/0.35472, loss_spatial_dice_9: 0.88813/0.79332, loss_spatial_ce_9: 1.26101/1.38996, loss_grounding_bce_9: 0.02550/0.10096, loss_grounding_dice_9: 0.53916/0.24221, loss_grounding_ce_9: 0.65470/0.67401] items per batch[64] items per second[0.38] total items[4000000] mini batches[ 62500] memory[4999] epoch remaining[0:42:29] INFO:trainer.default_trainer:epochs[ 34] optim steps[62600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.02611/0.75767, loss_mask_bce_0: 0.05841/0.30090, loss_mask_dice_0: 0.66313/1.02134, loss_spatial_bce_0: 0.01536/0.08516, loss_spatial_dice_0: 0.26513/0.18014, loss_spatial_ce_0: 0.09376/0.05744, loss_grounding_bce_0: 0.01541/0.08063, loss_grounding_dice_0: 0.25644/0.15055, loss_grounding_ce_0: 0.14314/0.24901, loss_mask_ce_1: 0.99392/0.75841, loss_mask_bce_1: 0.07560/0.30174, loss_mask_dice_1: 0.76640/1.02542, loss_spatial_bce_1: 0.01534/0.08548, loss_spatial_dice_1: 0.25939/0.18282, loss_spatial_ce_1: 0.01443/0.06128, loss_grounding_bce_1: 0.01887/0.08083, loss_grounding_dice_1: 0.37006/0.15131, loss_grounding_ce_1: 0.12306/0.25053, loss_mask_ce_2: 0.95908/0.76630, loss_mask_bce_2: 0.06864/0.30199, loss_mask_dice_2: 0.81980/1.02629, loss_spatial_bce_2: 0.01806/0.08556, loss_spatial_dice_2: 0.25268/0.18332, loss_spatial_ce_2: 0.07012/0.06345, loss_grounding_bce_2: 0.02103/0.08084, loss_grounding_dice_2: 0.39315/0.15119, loss_grounding_ce_2: 0.16353/0.25371, loss_mask_ce_3: 0.89351/0.76991, loss_mask_bce_3: 0.06094/0.30347, loss_mask_dice_3: 0.72124/1.02434, loss_spatial_bce_3: 0.02879/0.08767, loss_spatial_dice_3: 0.28575/0.18467, loss_spatial_ce_3: 0.09916/0.06822, loss_grounding_bce_3: 0.02073/0.08124, loss_grounding_dice_3: 0.32706/0.15083, loss_grounding_ce_3: 0.13673/0.25454, loss_mask_ce_4: 0.77529/0.77558, loss_mask_bce_4: 0.06512/0.30605, loss_mask_dice_4: 0.78528/1.04339, loss_spatial_bce_4: 0.04272/0.08983, loss_spatial_dice_4: 0.32317/0.19277, loss_spatial_ce_4: 0.01755/0.08168, loss_grounding_bce_4: 0.01649/0.08184, loss_grounding_dice_4: 0.35325/0.15343, loss_grounding_ce_4: 0.15979/0.25909, loss_mask_ce_5: 0.89626/0.79987, loss_mask_bce_5: 0.06484/0.30789, loss_mask_dice_5: 0.81039/1.05094, loss_spatial_bce_5: 0.01993/0.09210, loss_spatial_dice_5: 0.27742/0.19586, loss_spatial_ce_5: 0.21250/0.09468, loss_grounding_bce_5: 0.01691/0.08217, loss_grounding_dice_5: 0.28743/0.15413, loss_grounding_ce_5: 0.21461/0.27734, loss_mask_ce_6: 0.83320/0.82681, loss_mask_bce_6: 0.06700/0.31001, loss_mask_dice_6: 0.75621/1.05464, loss_spatial_bce_6: 0.02135/0.09725, loss_spatial_dice_6: 0.30269/0.19821, loss_spatial_ce_6: 0.13847/0.11914, loss_grounding_bce_6: 0.01825/0.08302, loss_grounding_dice_6: 0.38600/0.15472, loss_grounding_ce_6: 0.18859/0.28652, loss_mask_ce_7: 0.97481/0.88238, loss_mask_bce_7: 0.07482/0.31718, loss_mask_dice_7: 0.79702/1.10090, loss_spatial_bce_7: 0.02968/0.10697, loss_spatial_dice_7: 0.30448/0.22350, loss_spatial_ce_7: 0.12451/0.15636, loss_grounding_bce_7: 0.02339/0.08470, loss_grounding_dice_7: 0.40223/0.16031, loss_grounding_ce_7: 0.18984/0.31945, loss_mask_ce_8: 1.41914/1.01789, loss_mask_bce_8: 0.07482/0.33335, loss_mask_dice_8: 0.86402/1.17784, loss_spatial_bce_8: 0.02458/0.12423, loss_spatial_dice_8: 0.31403/0.25895, loss_spatial_ce_8: 0.09462/0.20361, loss_grounding_bce_8: 0.02054/0.08891, loss_grounding_dice_8: 0.33775/0.17004, loss_grounding_ce_8: 0.33459/0.41942, loss_mask_ce_9: 4.47241/3.47820, loss_mask_bce_9: 0.06393/0.36026, loss_mask_dice_9: 1.02077/1.76095, loss_spatial_bce_9: 0.11397/0.35475, loss_spatial_dice_9: 0.84959/0.79333, loss_spatial_ce_9: 1.36073/1.39001, loss_grounding_bce_9: 0.02149/0.10098, loss_grounding_dice_9: 0.39418/0.24227, loss_grounding_ce_9: 0.43863/0.67405] items per batch[64] items per second[0.36] total items[4006400] mini batches[ 62600] memory[4999] epoch remaining[0:39:35] INFO:trainer.default_trainer:epochs[ 34] optim steps[62700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.04520/0.75761, loss_mask_bce_0: 0.10110/0.30090, loss_mask_dice_0: 0.06709/1.02153, loss_spatial_bce_0: 0.10063/0.08514, loss_spatial_dice_0: 0.05903/0.18015, loss_spatial_ce_0: 0.00020/0.05746, loss_grounding_bce_0: 0.09944/0.08063, loss_grounding_dice_0: 0.06421/0.15053, loss_grounding_ce_0: 0.09312/0.24899, loss_mask_ce_1: 0.03782/0.75830, loss_mask_bce_1: 0.09844/0.30174, loss_mask_dice_1: 0.06342/1.02563, loss_spatial_bce_1: 0.09970/0.08546, loss_spatial_dice_1: 0.05841/0.18283, loss_spatial_ce_1: 0.00003/0.06129, loss_grounding_bce_1: 0.10030/0.08083, loss_grounding_dice_1: 0.06396/0.15129, loss_grounding_ce_1: 0.07486/0.25049, loss_mask_ce_2: 0.04066/0.76620, loss_mask_bce_2: 0.09994/0.30199, loss_mask_dice_2: 0.06380/1.02647, loss_spatial_bce_2: 0.10064/0.08554, loss_spatial_dice_2: 0.06196/0.18333, loss_spatial_ce_2: 0.00004/0.06345, loss_grounding_bce_2: 0.09594/0.08084, loss_grounding_dice_2: 0.06188/0.15118, loss_grounding_ce_2: 0.07482/0.25366, loss_mask_ce_3: 0.03628/0.76982, loss_mask_bce_3: 0.09606/0.30348, loss_mask_dice_3: 0.06425/1.02453, loss_spatial_bce_3: 0.10300/0.08764, loss_spatial_dice_3: 0.06390/0.18468, loss_spatial_ce_3: 0.00011/0.06824, loss_grounding_bce_3: 0.09437/0.08124, loss_grounding_dice_3: 0.06317/0.15082, loss_grounding_ce_3: 0.06145/0.25450, loss_mask_ce_4: 0.03521/0.77548, loss_mask_bce_4: 0.10442/0.30606, loss_mask_dice_4: 0.07047/1.04362, loss_spatial_bce_4: 0.10950/0.08981, loss_spatial_dice_4: 0.06620/0.19279, loss_spatial_ce_4: 0.00055/0.08167, loss_grounding_bce_4: 0.10418/0.08184, loss_grounding_dice_4: 0.06826/0.15342, loss_grounding_ce_4: 0.06515/0.25903, loss_mask_ce_5: 0.05039/0.79981, loss_mask_bce_5: 0.10651/0.30789, loss_mask_dice_5: 0.06955/1.05111, loss_spatial_bce_5: 0.10867/0.09208, loss_spatial_dice_5: 0.06934/0.19587, loss_spatial_ce_5: 0.00089/0.09469, loss_grounding_bce_5: 0.09912/0.08217, loss_grounding_dice_5: 0.06451/0.15412, loss_grounding_ce_5: 0.10261/0.27732, loss_mask_ce_6: 0.05323/0.82680, loss_mask_bce_6: 0.10807/0.31001, loss_mask_dice_6: 0.07050/1.05485, loss_spatial_bce_6: 0.09689/0.09724, loss_spatial_dice_6: 0.06369/0.19823, loss_spatial_ce_6: 0.00328/0.11916, loss_grounding_bce_6: 0.10415/0.08302, loss_grounding_dice_6: 0.06871/0.15472, loss_grounding_ce_6: 0.08589/0.28648, loss_mask_ce_7: 0.05632/0.88229, loss_mask_bce_7: 0.11137/0.31719, loss_mask_dice_7: 0.06940/1.10112, loss_spatial_bce_7: 0.10109/0.10694, loss_spatial_dice_7: 0.06067/0.22351, loss_spatial_ce_7: 0.01967/0.15633, loss_grounding_bce_7: 0.11187/0.08470, loss_grounding_dice_7: 0.06996/0.16030, loss_grounding_ce_7: 0.10305/0.31938, loss_mask_ce_8: 0.03933/1.01785, loss_mask_bce_8: 0.08989/0.33334, loss_mask_dice_8: 0.05781/1.17805, loss_spatial_bce_8: 0.11246/0.12421, loss_spatial_dice_8: 0.07618/0.25897, loss_spatial_ce_8: 0.08631/0.20356, loss_grounding_bce_8: 0.08784/0.08892, loss_grounding_dice_8: 0.05757/0.17003, loss_grounding_ce_8: 0.02308/0.41944, loss_mask_ce_9: 1.27783/3.47828, loss_mask_bce_9: 0.10957/0.36029, loss_mask_dice_9: 0.11112/1.76119, loss_spatial_bce_9: 0.78703/0.35473, loss_spatial_dice_9: 0.71004/0.79335, loss_spatial_ce_9: 0.65294/1.39016, loss_grounding_bce_9: 0.10672/0.10099, loss_grounding_dice_9: 0.10791/0.24228, loss_grounding_ce_9: 0.04692/0.67393] items per batch[64] items per second[0.36] total items[4012800] mini batches[ 62700] memory[4999] epoch remaining[0:36:38] INFO:trainer.default_trainer:epochs[ 34] optim steps[62800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75635/0.75754, loss_mask_bce_0: 0.25358/0.30088, loss_mask_dice_0: 0.18415/1.02127, loss_spatial_bce_0: 0.16539/0.08514, loss_spatial_dice_0: 0.16677/0.18013, loss_spatial_ce_0: 0.00177/0.05747, loss_grounding_bce_0: 0.06167/0.08062, loss_grounding_dice_0: 0.03739/0.15051, loss_grounding_ce_0: 0.09650/0.24903, loss_mask_ce_1: 0.76591/0.75823, loss_mask_bce_1: 0.25584/0.30173, loss_mask_dice_1: 0.17272/1.02536, loss_spatial_bce_1: 0.14850/0.08546, loss_spatial_dice_1: 0.15066/0.18280, loss_spatial_ce_1: 0.00082/0.06130, loss_grounding_bce_1: 0.06082/0.08083, loss_grounding_dice_1: 0.03678/0.15127, loss_grounding_ce_1: 0.10960/0.25054, loss_mask_ce_2: 0.78318/0.76612, loss_mask_bce_2: 0.25530/0.30198, loss_mask_dice_2: 0.18302/1.02620, loss_spatial_bce_2: 0.15395/0.08553, loss_spatial_dice_2: 0.16385/0.18330, loss_spatial_ce_2: 0.00104/0.06347, loss_grounding_bce_2: 0.05739/0.08084, loss_grounding_dice_2: 0.03422/0.15116, loss_grounding_ce_2: 0.13518/0.25370, loss_mask_ce_3: 0.71829/0.76977, loss_mask_bce_3: 0.26087/0.30346, loss_mask_dice_3: 0.19788/1.02425, loss_spatial_bce_3: 0.13653/0.08764, loss_spatial_dice_3: 0.13450/0.18465, loss_spatial_ce_3: 0.00198/0.06824, loss_grounding_bce_3: 0.05755/0.08124, loss_grounding_dice_3: 0.03854/0.15081, loss_grounding_ce_3: 0.13435/0.25455, loss_mask_ce_4: 0.73775/0.77543, loss_mask_bce_4: 0.27222/0.30604, loss_mask_dice_4: 0.21433/1.04334, loss_spatial_bce_4: 0.14398/0.08982, loss_spatial_dice_4: 0.14630/0.19277, loss_spatial_ce_4: 0.01548/0.08168, loss_grounding_bce_4: 0.06132/0.08184, loss_grounding_dice_4: 0.04051/0.15339, loss_grounding_ce_4: 0.09699/0.25908, loss_mask_ce_5: 0.82440/0.79973, loss_mask_bce_5: 0.27835/0.30787, loss_mask_dice_5: 0.23321/1.05083, loss_spatial_bce_5: 0.18171/0.09209, loss_spatial_dice_5: 0.19696/0.19586, loss_spatial_ce_5: 0.01618/0.09469, loss_grounding_bce_5: 0.05998/0.08216, loss_grounding_dice_5: 0.03960/0.15409, loss_grounding_ce_5: 0.18833/0.27738, loss_mask_ce_6: 0.83064/0.82678, loss_mask_bce_6: 0.26809/0.30999, loss_mask_dice_6: 0.20275/1.05455, loss_spatial_bce_6: 0.13922/0.09724, loss_spatial_dice_6: 0.11373/0.19822, loss_spatial_ce_6: 0.01722/0.11920, loss_grounding_bce_6: 0.06539/0.08302, loss_grounding_dice_6: 0.04152/0.15471, loss_grounding_ce_6: 0.16137/0.28654, loss_mask_ce_7: 1.15056/0.88228, loss_mask_bce_7: 0.28669/0.31716, loss_mask_dice_7: 0.18799/1.10085, loss_spatial_bce_7: 0.18950/0.10695, loss_spatial_dice_7: 0.17270/0.22350, loss_spatial_ce_7: 0.05713/0.15634, loss_grounding_bce_7: 0.06621/0.08470, loss_grounding_dice_7: 0.04163/0.16029, loss_grounding_ce_7: 0.30858/0.31941, loss_mask_ce_8: 0.99747/1.01779, loss_mask_bce_8: 0.28434/0.33331, loss_mask_dice_8: 0.23708/1.17771, loss_spatial_bce_8: 0.17608/0.12421, loss_spatial_dice_8: 0.16029/0.25895, loss_spatial_ce_8: 0.07676/0.20354, loss_grounding_bce_8: 0.06530/0.08891, loss_grounding_dice_8: 0.04513/0.17000, loss_grounding_ce_8: 0.21282/0.41950, loss_mask_ce_9: 3.47285/3.47807, loss_mask_bce_9: 0.43854/0.36023, loss_mask_dice_9: 0.44811/1.76081, loss_spatial_bce_9: 0.48069/0.35472, loss_spatial_dice_9: 0.61592/0.79335, loss_spatial_ce_9: 1.23071/1.39001, loss_grounding_bce_9: 0.07401/0.10098, loss_grounding_dice_9: 0.09341/0.24225, loss_grounding_ce_9: 0.48461/0.67396] items per batch[64] items per second[0.36] total items[4019200] mini batches[ 62800] memory[4999] epoch remaining[0:33:44] INFO:trainer.default_trainer:epochs[ 34] optim steps[62900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.96132/0.75773, loss_mask_bce_0: 0.30770/0.30084, loss_mask_dice_0: 0.21334/1.02136, loss_spatial_bce_0: 0.34549/0.08513, loss_spatial_dice_0: 0.17084/0.18014, loss_spatial_ce_0: 0.03553/0.05745, loss_grounding_bce_0: 0.37859/0.08060, loss_grounding_dice_0: 0.19309/0.15052, loss_grounding_ce_0: 0.81870/0.24902, loss_mask_ce_1: 1.27665/0.75842, loss_mask_bce_1: 0.31535/0.30170, loss_mask_dice_1: 0.16928/1.02538, loss_spatial_bce_1: 0.34617/0.08545, loss_spatial_dice_1: 0.18479/0.18282, loss_spatial_ce_1: 0.05293/0.06127, loss_grounding_bce_1: 0.36543/0.08081, loss_grounding_dice_1: 0.22793/0.15128, loss_grounding_ce_1: 1.01646/0.25054, loss_mask_ce_2: 1.38658/0.76634, loss_mask_bce_2: 0.30513/0.30195, loss_mask_dice_2: 0.16974/1.02629, loss_spatial_bce_2: 0.35851/0.08552, loss_spatial_dice_2: 0.20352/0.18332, loss_spatial_ce_2: 0.04164/0.06345, loss_grounding_bce_2: 0.34020/0.08082, loss_grounding_dice_2: 0.22405/0.15117, loss_grounding_ce_2: 0.92819/0.25370, loss_mask_ce_3: 1.38894/0.76993, loss_mask_bce_3: 0.29470/0.30343, loss_mask_dice_3: 0.16526/1.02432, loss_spatial_bce_3: 0.37958/0.08763, loss_spatial_dice_3: 0.19395/0.18467, loss_spatial_ce_3: 0.04173/0.06822, loss_grounding_bce_3: 0.37387/0.08121, loss_grounding_dice_3: 0.24094/0.15083, loss_grounding_ce_3: 0.93698/0.25457, loss_mask_ce_4: 2.03528/0.77554, loss_mask_bce_4: 0.30076/0.30602, loss_mask_dice_4: 0.18369/1.04341, loss_spatial_bce_4: 0.30636/0.08981, loss_spatial_dice_4: 0.16672/0.19280, loss_spatial_ce_4: 0.06359/0.08172, loss_grounding_bce_4: 0.31998/0.08182, loss_grounding_dice_4: 0.22192/0.15340, loss_grounding_ce_4: 0.95857/0.25906, loss_mask_ce_5: 1.38452/0.79992, loss_mask_bce_5: 0.27965/0.30784, loss_mask_dice_5: 0.15895/1.05089, loss_spatial_bce_5: 0.30448/0.09208, loss_spatial_dice_5: 0.16939/0.19589, loss_spatial_ce_5: 0.09001/0.09472, loss_grounding_bce_5: 0.33398/0.08214, loss_grounding_dice_5: 0.18148/0.15411, loss_grounding_ce_5: 0.68471/0.27746, loss_mask_ce_6: 1.42479/0.82692, loss_mask_bce_6: 0.31699/0.30997, loss_mask_dice_6: 0.17835/1.05466, loss_spatial_bce_6: 0.32619/0.09724, loss_spatial_dice_6: 0.17892/0.19825, loss_spatial_ce_6: 0.07285/0.11922, loss_grounding_bce_6: 0.36989/0.08300, loss_grounding_dice_6: 0.21467/0.15473, loss_grounding_ce_6: 0.67624/0.28659, loss_mask_ce_7: 1.51871/0.88251, loss_mask_bce_7: 0.31665/0.31712, loss_mask_dice_7: 0.21207/1.10088, loss_spatial_bce_7: 0.33160/0.10695, loss_spatial_dice_7: 0.17920/0.22354, loss_spatial_ce_7: 0.26158/0.15636, loss_grounding_bce_7: 0.32280/0.08468, loss_grounding_dice_7: 0.21705/0.16031, loss_grounding_ce_7: 0.73950/0.31952, loss_mask_ce_8: 1.91481/1.01800, loss_mask_bce_8: 0.33418/0.33328, loss_mask_dice_8: 0.19833/1.17778, loss_spatial_bce_8: 0.34202/0.12420, loss_spatial_dice_8: 0.19548/0.25898, loss_spatial_ce_8: 0.29597/0.20355, loss_grounding_bce_8: 0.37346/0.08888, loss_grounding_dice_8: 0.22658/0.17002, loss_grounding_ce_8: 0.86129/0.41950, loss_mask_ce_9: 1.91582/3.47829, loss_mask_bce_9: 0.31134/0.36020, loss_mask_dice_9: 0.19345/1.76084, loss_spatial_bce_9: 0.54738/0.35468, loss_spatial_dice_9: 0.33491/0.79336, loss_spatial_ce_9: 0.52160/1.38997, loss_grounding_bce_9: 0.34285/0.10095, loss_grounding_dice_9: 0.21169/0.24224, loss_grounding_ce_9: 0.45386/0.67395] items per batch[64] items per second[0.37] total items[4025600] mini batches[ 62900] memory[4999] epoch remaining[0:30:43] INFO:trainer.default_trainer:epochs[ 34] optim steps[63000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17119/0.75754, loss_mask_bce_0: 0.19010/0.30084, loss_mask_dice_0: 0.65389/1.02125, loss_spatial_bce_0: 0.03505/0.08512, loss_spatial_dice_0: 0.11885/0.18011, loss_spatial_ce_0: 0.11773/0.05745, loss_grounding_bce_0: 0.04571/0.08060, loss_grounding_dice_0: 0.16332/0.15051, loss_grounding_ce_0: 0.00422/0.24897, loss_mask_ce_1: 0.16555/0.75824, loss_mask_bce_1: 0.18803/0.30169, loss_mask_dice_1: 0.64197/1.02528, loss_spatial_bce_1: 0.03899/0.08544, loss_spatial_dice_1: 0.12695/0.18280, loss_spatial_ce_1: 0.09730/0.06125, loss_grounding_bce_1: 0.03947/0.08081, loss_grounding_dice_1: 0.16765/0.15127, loss_grounding_ce_1: 0.00538/0.25047, loss_mask_ce_2: 0.15992/0.76618, loss_mask_bce_2: 0.19014/0.30195, loss_mask_dice_2: 0.69640/1.02619, loss_spatial_bce_2: 0.02974/0.08551, loss_spatial_dice_2: 0.10638/0.18330, loss_spatial_ce_2: 0.13541/0.06343, loss_grounding_bce_2: 0.04238/0.08082, loss_grounding_dice_2: 0.16731/0.15116, loss_grounding_ce_2: 0.00338/0.25362, loss_mask_ce_3: 0.16736/0.76979, loss_mask_bce_3: 0.18434/0.30343, loss_mask_dice_3: 0.64435/1.02423, loss_spatial_bce_3: 0.04345/0.08762, loss_spatial_dice_3: 0.12421/0.18465, loss_spatial_ce_3: 0.04877/0.06820, loss_grounding_bce_3: 0.04593/0.08121, loss_grounding_dice_3: 0.16676/0.15082, loss_grounding_ce_3: 0.00339/0.25452, loss_mask_ce_4: 0.22745/0.77539, loss_mask_bce_4: 0.19196/0.30601, loss_mask_dice_4: 0.73255/1.04331, loss_spatial_bce_4: 0.04382/0.08979, loss_spatial_dice_4: 0.13220/0.19278, loss_spatial_ce_4: 0.08368/0.08173, loss_grounding_bce_4: 0.03238/0.08181, loss_grounding_dice_4: 0.15752/0.15338, loss_grounding_ce_4: 0.00317/0.25902, loss_mask_ce_5: 0.16722/0.79977, loss_mask_bce_5: 0.18777/0.30783, loss_mask_dice_5: 0.65783/1.05082, loss_spatial_bce_5: 0.05278/0.09207, loss_spatial_dice_5: 0.13230/0.19587, loss_spatial_ce_5: 0.05687/0.09470, loss_grounding_bce_5: 0.04220/0.08214, loss_grounding_dice_5: 0.16760/0.15409, loss_grounding_ce_5: 0.00351/0.27740, loss_mask_ce_6: 0.19483/0.82676, loss_mask_bce_6: 0.22257/0.30997, loss_mask_dice_6: 0.62891/1.05455, loss_spatial_bce_6: 0.05180/0.09722, loss_spatial_dice_6: 0.12928/0.19823, loss_spatial_ce_6: 0.08792/0.11921, loss_grounding_bce_6: 0.04504/0.08299, loss_grounding_dice_6: 0.15210/0.15471, loss_grounding_ce_6: 0.00410/0.28649, loss_mask_ce_7: 0.24100/0.88234, loss_mask_bce_7: 0.15848/0.31712, loss_mask_dice_7: 0.67192/1.10077, loss_spatial_bce_7: 0.03958/0.10694, loss_spatial_dice_7: 0.12118/0.22353, loss_spatial_ce_7: 0.05610/0.15634, loss_grounding_bce_7: 0.04279/0.08467, loss_grounding_dice_7: 0.16139/0.16030, loss_grounding_ce_7: 0.00284/0.31942, loss_mask_ce_8: 0.18037/1.01786, loss_mask_bce_8: 0.14725/0.33325, loss_mask_dice_8: 0.78279/1.17766, loss_spatial_bce_8: 0.04188/0.12419, loss_spatial_dice_8: 0.11763/0.25897, loss_spatial_ce_8: 0.12239/0.20353, loss_grounding_bce_8: 0.03740/0.08888, loss_grounding_dice_8: 0.16060/0.17001, loss_grounding_ce_8: 0.00420/0.41946, loss_mask_ce_9: 3.43093/3.47830, loss_mask_bce_9: 0.13724/0.36019, loss_mask_dice_9: 0.85740/1.76080, loss_spatial_bce_9: 0.29428/0.35468, loss_spatial_dice_9: 0.79962/0.79337, loss_spatial_ce_9: 1.16978/1.38995, loss_grounding_bce_9: 0.04117/0.10096, loss_grounding_dice_9: 0.21623/0.24222, loss_grounding_ce_9: 0.09287/0.67396] items per batch[64] items per second[0.36] total items[4032000] mini batches[ 63000] memory[4999] epoch remaining[0:27:49] INFO:trainer.default_trainer:epochs[ 34] optim steps[63100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.49871/0.75746, loss_mask_bce_0: 0.15507/0.30089, loss_mask_dice_0: 0.23846/1.02124, loss_spatial_bce_0: 0.25076/0.08513, loss_spatial_dice_0: 0.11940/0.18008, loss_spatial_ce_0: 0.00033/0.05742, loss_grounding_bce_0: 0.03317/0.08062, loss_grounding_dice_0: 0.04807/0.15051, loss_grounding_ce_0: 0.35119/0.24896, loss_mask_ce_1: 0.52480/0.75813, loss_mask_bce_1: 0.15503/0.30174, loss_mask_dice_1: 0.24966/1.02528, loss_spatial_bce_1: 0.26107/0.08545, loss_spatial_dice_1: 0.11383/0.18276, loss_spatial_ce_1: 0.00009/0.06122, loss_grounding_bce_1: 0.03404/0.08084, loss_grounding_dice_1: 0.04918/0.15126, loss_grounding_ce_1: 0.33994/0.25042, loss_mask_ce_2: 0.57281/0.76614, loss_mask_bce_2: 0.15481/0.30199, loss_mask_dice_2: 0.26714/1.02618, loss_spatial_bce_2: 0.24191/0.08553, loss_spatial_dice_2: 0.11514/0.18327, loss_spatial_ce_2: 0.00013/0.06339, loss_grounding_bce_2: 0.03345/0.08084, loss_grounding_dice_2: 0.05118/0.15115, loss_grounding_ce_2: 0.35389/0.25359, loss_mask_ce_3: 0.58608/0.76976, loss_mask_bce_3: 0.15626/0.30347, loss_mask_dice_3: 0.27002/1.02424, loss_spatial_bce_3: 0.26212/0.08765, loss_spatial_dice_3: 0.12327/0.18463, loss_spatial_ce_3: 0.00017/0.06816, loss_grounding_bce_3: 0.03390/0.08124, loss_grounding_dice_3: 0.05075/0.15082, loss_grounding_ce_3: 0.35725/0.25450, loss_mask_ce_4: 0.65619/0.77528, loss_mask_bce_4: 0.17623/0.30608, loss_mask_dice_4: 0.27398/1.04335, loss_spatial_bce_4: 0.32698/0.08983, loss_spatial_dice_4: 0.12966/0.19275, loss_spatial_ce_4: 0.00025/0.08170, loss_grounding_bce_4: 0.03761/0.08186, loss_grounding_dice_4: 0.05538/0.15338, loss_grounding_ce_4: 0.40815/0.25901, loss_mask_ce_5: 0.61239/0.79971, loss_mask_bce_5: 0.18962/0.30788, loss_mask_dice_5: 0.27682/1.05085, loss_spatial_bce_5: 0.20753/0.09210, loss_spatial_dice_5: 0.12382/0.19585, loss_spatial_ce_5: 0.15133/0.09468, loss_grounding_bce_5: 0.04268/0.08216, loss_grounding_dice_5: 0.06547/0.15407, loss_grounding_ce_5: 0.28820/0.27741, loss_mask_ce_6: 0.71691/0.82671, loss_mask_bce_6: 0.16481/0.31003, loss_mask_dice_6: 0.25164/1.05457, loss_spatial_bce_6: 0.28363/0.09726, loss_spatial_dice_6: 0.12734/0.19821, loss_spatial_ce_6: 0.02585/0.11919, loss_grounding_bce_6: 0.03527/0.08301, loss_grounding_dice_6: 0.05661/0.15470, loss_grounding_ce_6: 0.30093/0.28654, loss_mask_ce_7: 0.81268/0.88232, loss_mask_bce_7: 0.20574/0.31717, loss_mask_dice_7: 0.27222/1.10083, loss_spatial_bce_7: 0.09789/0.10696, loss_spatial_dice_7: 0.09441/0.22350, loss_spatial_ce_7: 0.12698/0.15629, loss_grounding_bce_7: 0.04164/0.08469, loss_grounding_dice_7: 0.05765/0.16029, loss_grounding_ce_7: 0.32044/0.31944, loss_mask_ce_8: 1.38596/1.01777, loss_mask_bce_8: 0.18159/0.33329, loss_mask_dice_8: 0.32523/1.17774, loss_spatial_bce_8: 0.12522/0.12421, loss_spatial_dice_8: 0.11414/0.25893, loss_spatial_ce_8: 0.03351/0.20346, loss_grounding_bce_8: 0.03730/0.08889, loss_grounding_dice_8: 0.06345/0.16999, loss_grounding_ce_8: 0.53560/0.41950, loss_mask_ce_9: 4.90975/3.47832, loss_mask_bce_9: 0.45157/0.36026, loss_mask_dice_9: 0.69711/1.76110, loss_spatial_bce_9: 0.34809/0.35476, loss_spatial_dice_9: 0.87984/0.79336, loss_spatial_ce_9: 1.72168/1.39000, loss_grounding_bce_9: 0.09494/0.10099, loss_grounding_dice_9: 0.14627/0.24222, loss_grounding_ce_9: 0.65202/0.67388] items per batch[64] items per second[0.37] total items[4038400] mini batches[ 63100] memory[4999] epoch remaining[0:24:49] INFO:trainer.default_trainer:epochs[ 34] optim steps[63200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16915/0.75750, loss_mask_bce_0: 0.13137/0.30088, loss_mask_dice_0: 0.12287/1.02137, loss_spatial_bce_0: 0.08000/0.08514, loss_spatial_dice_0: 0.10732/0.18008, loss_spatial_ce_0: 0.00007/0.05741, loss_grounding_bce_0: 0.01089/0.08063, loss_grounding_dice_0: 0.07109/0.15051, loss_grounding_ce_0: 0.14708/0.24898, loss_mask_ce_1: 0.15627/0.75822, loss_mask_bce_1: 0.12517/0.30173, loss_mask_dice_1: 0.12321/1.02538, loss_spatial_bce_1: 0.07691/0.08546, loss_spatial_dice_1: 0.09897/0.18277, loss_spatial_ce_1: 0.00007/0.06121, loss_grounding_bce_1: 0.01260/0.08085, loss_grounding_dice_1: 0.08634/0.15127, loss_grounding_ce_1: 0.14733/0.25043, loss_mask_ce_2: 0.21305/0.76615, loss_mask_bce_2: 0.13000/0.30199, loss_mask_dice_2: 0.30996/1.02632, loss_spatial_bce_2: 0.08194/0.08554, loss_spatial_dice_2: 0.10240/0.18329, loss_spatial_ce_2: 0.00009/0.06339, loss_grounding_bce_2: 0.01264/0.08085, loss_grounding_dice_2: 0.07758/0.15115, loss_grounding_ce_2: 0.15665/0.25361, loss_mask_ce_3: 0.17858/0.76982, loss_mask_bce_3: 0.12646/0.30345, loss_mask_dice_3: 0.12845/1.02439, loss_spatial_bce_3: 0.08109/0.08767, loss_spatial_dice_3: 0.10060/0.18464, loss_spatial_ce_3: 0.00039/0.06814, loss_grounding_bce_3: 0.01254/0.08125, loss_grounding_dice_3: 0.07656/0.15082, loss_grounding_ce_3: 0.14831/0.25451, loss_mask_ce_4: 0.19018/0.77535, loss_mask_bce_4: 0.11926/0.30607, loss_mask_dice_4: 0.11301/1.04350, loss_spatial_bce_4: 0.08217/0.08985, loss_spatial_dice_4: 0.11461/0.19276, loss_spatial_ce_4: 0.00742/0.08166, loss_grounding_bce_4: 0.01458/0.08186, loss_grounding_dice_4: 0.08055/0.15338, loss_grounding_ce_4: 0.14772/0.25911, loss_mask_ce_5: 0.16881/0.79975, loss_mask_bce_5: 0.13317/0.30787, loss_mask_dice_5: 0.12056/1.05097, loss_spatial_bce_5: 0.08747/0.09212, loss_spatial_dice_5: 0.08651/0.19586, loss_spatial_ce_5: 0.02164/0.09465, loss_grounding_bce_5: 0.01367/0.08217, loss_grounding_dice_5: 0.06000/0.15408, loss_grounding_ce_5: 0.15837/0.27756, loss_mask_ce_6: 0.17227/0.82675, loss_mask_bce_6: 0.13420/0.31001, loss_mask_dice_6: 0.12464/1.05471, loss_spatial_bce_6: 0.09241/0.09729, loss_spatial_dice_6: 0.08519/0.19822, loss_spatial_ce_6: 0.00432/0.11915, loss_grounding_bce_6: 0.01402/0.08302, loss_grounding_dice_6: 0.06856/0.15470, loss_grounding_ce_6: 0.16125/0.28650, loss_mask_ce_7: 0.20144/0.88236, loss_mask_bce_7: 0.13015/0.31715, loss_mask_dice_7: 0.10933/1.10095, loss_spatial_bce_7: 0.08372/0.10698, loss_spatial_dice_7: 0.10126/0.22352, loss_spatial_ce_7: 0.00717/0.15623, loss_grounding_bce_7: 0.01156/0.08470, loss_grounding_dice_7: 0.07221/0.16030, loss_grounding_ce_7: 0.17120/0.31942, loss_mask_ce_8: 0.18706/1.01776, loss_mask_bce_8: 0.13166/0.33325, loss_mask_dice_8: 0.12490/1.17786, loss_spatial_bce_8: 0.07997/0.12423, loss_spatial_dice_8: 0.11295/0.25895, loss_spatial_ce_8: 0.02257/0.20342, loss_grounding_bce_8: 0.01208/0.08891, loss_grounding_dice_8: 0.07543/0.16999, loss_grounding_ce_8: 0.14680/0.41937, loss_mask_ce_9: 2.41773/3.47809, loss_mask_bce_9: 0.12748/0.36021, loss_mask_dice_9: 0.17268/1.76123, loss_spatial_bce_9: 0.48905/0.35483, loss_spatial_dice_9: 0.71982/0.79334, loss_spatial_ce_9: 1.94048/1.38991, loss_grounding_bce_9: 0.01031/0.10099, loss_grounding_dice_9: 0.13392/0.24221, loss_grounding_ce_9: 0.38312/0.67379] items per batch[64] items per second[0.37] total items[4044800] mini batches[ 63200] memory[4999] epoch remaining[0:21:49] INFO:trainer.default_trainer:epochs[ 34] optim steps[63300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03940/0.75751, loss_mask_bce_0: 0.03735/0.30082, loss_mask_dice_0: 0.11450/1.02128, loss_spatial_bce_0: 0.01824/0.08510, loss_spatial_dice_0: 0.05229/0.18004, loss_spatial_ce_0: 0.00013/0.05737, loss_grounding_bce_0: 0.01214/0.08062, loss_grounding_dice_0: 0.06419/0.15051, loss_grounding_ce_0: 0.00262/0.24893, loss_mask_ce_1: 0.04024/0.75823, loss_mask_bce_1: 0.03576/0.30167, loss_mask_dice_1: 0.10023/1.02530, loss_spatial_bce_1: 0.01987/0.08543, loss_spatial_dice_1: 0.06136/0.18273, loss_spatial_ce_1: 0.00018/0.06119, loss_grounding_bce_1: 0.01081/0.08084, loss_grounding_dice_1: 0.05558/0.15127, loss_grounding_ce_1: 0.00220/0.25037, loss_mask_ce_2: 0.03184/0.76613, loss_mask_bce_2: 0.03649/0.30193, loss_mask_dice_2: 0.11228/1.02624, loss_spatial_bce_2: 0.01947/0.08551, loss_spatial_dice_2: 0.05337/0.18325, loss_spatial_ce_2: 0.00033/0.06335, loss_grounding_bce_2: 0.01282/0.08084, loss_grounding_dice_2: 0.06900/0.15115, loss_grounding_ce_2: 0.00142/0.25354, loss_mask_ce_3: 0.04084/0.76980, loss_mask_bce_3: 0.03527/0.30339, loss_mask_dice_3: 0.07739/1.02433, loss_spatial_bce_3: 0.02047/0.08764, loss_spatial_dice_3: 0.06769/0.18460, loss_spatial_ce_3: 0.00603/0.06811, loss_grounding_bce_3: 0.01266/0.08123, loss_grounding_dice_3: 0.05816/0.15082, loss_grounding_ce_3: 0.00211/0.25446, loss_mask_ce_4: 0.03251/0.77535, loss_mask_bce_4: 0.03799/0.30600, loss_mask_dice_4: 0.11003/1.04339, loss_spatial_bce_4: 0.01950/0.08981, loss_spatial_dice_4: 0.05624/0.19272, loss_spatial_ce_4: 0.05377/0.08162, loss_grounding_bce_4: 0.01030/0.08184, loss_grounding_dice_4: 0.05984/0.15338, loss_grounding_ce_4: 0.00183/0.25902, loss_mask_ce_5: 0.03086/0.79972, loss_mask_bce_5: 0.03842/0.30780, loss_mask_dice_5: 0.10717/1.05090, loss_spatial_bce_5: 0.02075/0.09209, loss_spatial_dice_5: 0.06677/0.19582, loss_spatial_ce_5: 0.01995/0.09462, loss_grounding_bce_5: 0.01450/0.08215, loss_grounding_dice_5: 0.06611/0.15407, loss_grounding_ce_5: 0.00158/0.27742, loss_mask_ce_6: 0.02746/0.82676, loss_mask_bce_6: 0.03868/0.30994, loss_mask_dice_6: 0.12708/1.05462, loss_spatial_bce_6: 0.02148/0.09725, loss_spatial_dice_6: 0.05529/0.19818, loss_spatial_ce_6: 0.00566/0.11914, loss_grounding_bce_6: 0.01252/0.08299, loss_grounding_dice_6: 0.06165/0.15469, loss_grounding_ce_6: 0.00399/0.28642, loss_mask_ce_7: 0.03353/0.88235, loss_mask_bce_7: 0.03662/0.31709, loss_mask_dice_7: 0.12514/1.10086, loss_spatial_bce_7: 0.02351/0.10693, loss_spatial_dice_7: 0.09319/0.22348, loss_spatial_ce_7: 0.00595/0.15619, loss_grounding_bce_7: 0.01285/0.08468, loss_grounding_dice_7: 0.06322/0.16029, loss_grounding_ce_7: 0.00391/0.31933, loss_mask_ce_8: 0.06025/1.01778, loss_mask_bce_8: 0.03765/0.33319, loss_mask_dice_8: 0.14190/1.17778, loss_spatial_bce_8: 0.02618/0.12419, loss_spatial_dice_8: 0.10280/0.25889, loss_spatial_ce_8: 0.05554/0.20337, loss_grounding_bce_8: 0.01570/0.08888, loss_grounding_dice_8: 0.09853/0.16998, loss_grounding_ce_8: 0.00429/0.41919, loss_mask_ce_9: 1.80602/3.47794, loss_mask_bce_9: 0.04318/0.36015, loss_mask_dice_9: 0.13285/1.76110, loss_spatial_bce_9: 0.42219/0.35479, loss_spatial_dice_9: 0.61664/0.79331, loss_spatial_ce_9: 0.48327/1.38983, loss_grounding_bce_9: 0.01618/0.10096, loss_grounding_dice_9: 0.10932/0.24220, loss_grounding_ce_9: 0.15108/0.67368] items per batch[64] items per second[0.36] total items[4051200] mini batches[ 63300] memory[4999] epoch remaining[0:18:53] INFO:trainer.default_trainer:epochs[ 34] optim steps[63400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.24960/0.75739, loss_mask_bce_0: 0.35565/0.30081, loss_mask_dice_0: 0.32792/1.02123, loss_spatial_bce_0: 0.10001/0.08508, loss_spatial_dice_0: 0.08504/0.18003, loss_spatial_ce_0: 0.01331/0.05733, loss_grounding_bce_0: 0.07195/0.08061, loss_grounding_dice_0: 0.07030/0.15049, loss_grounding_ce_0: 0.00718/0.24886, loss_mask_ce_1: 0.26538/0.75806, loss_mask_bce_1: 0.35911/0.30167, loss_mask_dice_1: 0.33020/1.02524, loss_spatial_bce_1: 0.10443/0.08541, loss_spatial_dice_1: 0.08905/0.18271, loss_spatial_ce_1: 0.01187/0.06116, loss_grounding_bce_1: 0.07748/0.08083, loss_grounding_dice_1: 0.06750/0.15126, loss_grounding_ce_1: 0.00731/0.25032, loss_mask_ce_2: 0.23137/0.76599, loss_mask_bce_2: 0.35675/0.30193, loss_mask_dice_2: 0.33494/1.02619, loss_spatial_bce_2: 0.10783/0.08549, loss_spatial_dice_2: 0.09069/0.18323, loss_spatial_ce_2: 0.01654/0.06332, loss_grounding_bce_2: 0.07459/0.08083, loss_grounding_dice_2: 0.07092/0.15113, loss_grounding_ce_2: 0.00940/0.25346, loss_mask_ce_3: 0.21792/0.76967, loss_mask_bce_3: 0.35876/0.30338, loss_mask_dice_3: 0.33763/1.02426, loss_spatial_bce_3: 0.10532/0.08762, loss_spatial_dice_3: 0.09596/0.18458, loss_spatial_ce_3: 0.04449/0.06809, loss_grounding_bce_3: 0.08349/0.08122, loss_grounding_dice_3: 0.06718/0.15080, loss_grounding_ce_3: 0.00933/0.25437, loss_mask_ce_4: 0.26276/0.77523, loss_mask_bce_4: 0.36947/0.30600, loss_mask_dice_4: 0.33313/1.04334, loss_spatial_bce_4: 0.10673/0.08980, loss_spatial_dice_4: 0.09580/0.19270, loss_spatial_ce_4: 0.08082/0.08159, loss_grounding_bce_4: 0.07277/0.08183, loss_grounding_dice_4: 0.06287/0.15336, loss_grounding_ce_4: 0.00500/0.25895, loss_mask_ce_5: 0.21704/0.79963, loss_mask_bce_5: 0.35766/0.30780, loss_mask_dice_5: 0.34241/1.05086, loss_spatial_bce_5: 0.12047/0.09208, loss_spatial_dice_5: 0.11109/0.19580, loss_spatial_ce_5: 0.14429/0.09460, loss_grounding_bce_5: 0.07109/0.08214, loss_grounding_dice_5: 0.05858/0.15406, loss_grounding_ce_5: 0.00247/0.27733, loss_mask_ce_6: 0.25874/0.82662, loss_mask_bce_6: 0.35989/0.30994, loss_mask_dice_6: 0.34139/1.05459, loss_spatial_bce_6: 0.11836/0.09724, loss_spatial_dice_6: 0.10289/0.19816, loss_spatial_ce_6: 0.17395/0.11912, loss_grounding_bce_6: 0.07114/0.08298, loss_grounding_dice_6: 0.05791/0.15468, loss_grounding_ce_6: 0.00255/0.28632, loss_mask_ce_7: 0.25265/0.88220, loss_mask_bce_7: 0.35494/0.31708, loss_mask_dice_7: 0.32320/1.10080, loss_spatial_bce_7: 0.13455/0.10692, loss_spatial_dice_7: 0.11980/0.22347, loss_spatial_ce_7: 0.26241/0.15614, loss_grounding_bce_7: 0.07332/0.08467, loss_grounding_dice_7: 0.05997/0.16028, loss_grounding_ce_7: 0.00274/0.31927, loss_mask_ce_8: 0.37089/1.01756, loss_mask_bce_8: 0.36437/0.33319, loss_mask_dice_8: 0.37638/1.17777, loss_spatial_bce_8: 0.16316/0.12418, loss_spatial_dice_8: 0.15840/0.25887, loss_spatial_ce_8: 0.18047/0.20328, loss_grounding_bce_8: 0.06655/0.08887, loss_grounding_dice_8: 0.05885/0.16997, loss_grounding_ce_8: 0.20579/0.41907, loss_mask_ce_9: 2.97799/3.47809, loss_mask_bce_9: 0.36749/0.36014, loss_mask_dice_9: 0.39139/1.76112, loss_spatial_bce_9: 0.48521/0.35477, loss_spatial_dice_9: 0.76040/0.79330, loss_spatial_ce_9: 1.42619/1.38978, loss_grounding_bce_9: 0.09727/0.10094, loss_grounding_dice_9: 0.09205/0.24216, loss_grounding_ce_9: 0.95769/0.67356] items per batch[64] items per second[0.36] total items[4057600] mini batches[ 63400] memory[4999] epoch remaining[0:15:58] INFO:trainer.default_trainer:epochs[ 34] optim steps[63500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84374/0.75725, loss_mask_bce_0: 0.01104/0.30081, loss_mask_dice_0: 0.15938/1.02144, loss_spatial_bce_0: 0.00662/0.08507, loss_spatial_dice_0: 0.12466/0.18002, loss_spatial_ce_0: 0.00183/0.05732, loss_grounding_bce_0: 0.01188/0.08063, loss_grounding_dice_0: 0.01109/0.15049, loss_grounding_ce_0: 0.00208/0.24884, loss_mask_ce_1: 0.89017/0.75789, loss_mask_bce_1: 0.01382/0.30167, loss_mask_dice_1: 0.27729/1.02544, loss_spatial_bce_1: 0.00837/0.08540, loss_spatial_dice_1: 0.13464/0.18271, loss_spatial_ce_1: 0.00076/0.06114, loss_grounding_bce_1: 0.01196/0.08084, loss_grounding_dice_1: 0.01148/0.15125, loss_grounding_ce_1: 0.00296/0.25036, loss_mask_ce_2: 0.02470/0.76582, loss_mask_bce_2: 0.01578/0.30193, loss_mask_dice_2: 0.42380/1.02633, loss_spatial_bce_2: 0.00770/0.08548, loss_spatial_dice_2: 0.11770/0.18323, loss_spatial_ce_2: 0.00101/0.06329, loss_grounding_bce_2: 0.01259/0.08084, loss_grounding_dice_2: 0.01131/0.15113, loss_grounding_ce_2: 0.00155/0.25345, loss_mask_ce_3: 1.10003/0.76949, loss_mask_bce_3: 0.01312/0.30338, loss_mask_dice_3: 0.26044/1.02444, loss_spatial_bce_3: 0.00745/0.08761, loss_spatial_dice_3: 0.09751/0.18458, loss_spatial_ce_3: 0.00481/0.06807, loss_grounding_bce_3: 0.01299/0.08124, loss_grounding_dice_3: 0.01206/0.15080, loss_grounding_ce_3: 0.00235/0.25438, loss_mask_ce_4: 0.05743/0.77507, loss_mask_bce_4: 0.03080/0.30600, loss_mask_dice_4: 0.53551/1.04357, loss_spatial_bce_4: 0.00655/0.08979, loss_spatial_dice_4: 0.14767/0.19271, loss_spatial_ce_4: 0.00314/0.08157, loss_grounding_bce_4: 0.01442/0.08185, loss_grounding_dice_4: 0.01395/0.15336, loss_grounding_ce_4: 0.00198/0.25895, loss_mask_ce_5: 1.15564/0.79948, loss_mask_bce_5: 0.01183/0.30780, loss_mask_dice_5: 0.21394/1.05109, loss_spatial_bce_5: 0.00743/0.09208, loss_spatial_dice_5: 0.09410/0.19581, loss_spatial_ce_5: 0.01493/0.09456, loss_grounding_bce_5: 0.01510/0.08216, loss_grounding_dice_5: 0.01444/0.15405, loss_grounding_ce_5: 0.00132/0.27727, loss_mask_ce_6: 0.10458/0.82647, loss_mask_bce_6: 0.02353/0.30993, loss_mask_dice_6: 0.49115/1.05478, loss_spatial_bce_6: 0.00767/0.09725, loss_spatial_dice_6: 0.08504/0.19817, loss_spatial_ce_6: 0.13192/0.11912, loss_grounding_bce_6: 0.01410/0.08300, loss_grounding_dice_6: 0.01336/0.15468, loss_grounding_ce_6: 0.00107/0.28626, loss_mask_ce_7: 1.41149/0.88208, loss_mask_bce_7: 0.01688/0.31707, loss_mask_dice_7: 0.26283/1.10099, loss_spatial_bce_7: 0.00975/0.10694, loss_spatial_dice_7: 0.17282/0.22348, loss_spatial_ce_7: 0.06330/0.15613, loss_grounding_bce_7: 0.01370/0.08468, loss_grounding_dice_7: 0.01327/0.16026, loss_grounding_ce_7: 0.00093/0.31919, loss_mask_ce_8: 0.08129/1.01743, loss_mask_bce_8: 0.02228/0.33318, loss_mask_dice_8: 0.38393/1.17790, loss_spatial_bce_8: 0.00800/0.12419, loss_spatial_dice_8: 0.12349/0.25888, loss_spatial_ce_8: 0.04068/0.20321, loss_grounding_bce_8: 0.01320/0.08889, loss_grounding_dice_8: 0.01191/0.16994, loss_grounding_ce_8: 0.00105/0.41895, loss_mask_ce_9: 2.45726/3.47793, loss_mask_bce_9: 0.01253/0.36015, loss_mask_dice_9: 0.48553/1.76143, loss_spatial_bce_9: 0.81788/0.35477, loss_spatial_dice_9: 0.73309/0.79329, loss_spatial_ce_9: 1.09665/1.38971, loss_grounding_bce_9: 0.01071/0.10096, loss_grounding_dice_9: 0.00946/0.24215, loss_grounding_ce_9: 0.05897/0.67356] items per batch[64] items per second[0.36] total items[4064000] mini batches[ 63500] memory[4999] epoch remaining[0:13:02] INFO:trainer.default_trainer:epochs[ 34] optim steps[63600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20828/0.75736, loss_mask_bce_0: 0.19889/0.30086, loss_mask_dice_0: 0.18819/1.02163, loss_spatial_bce_0: 0.05846/0.08506, loss_spatial_dice_0: 0.05857/0.18001, loss_spatial_ce_0: 0.00025/0.05730, loss_grounding_bce_0: 0.00179/0.08059, loss_grounding_dice_0: 0.02499/0.15047, loss_grounding_ce_0: 0.00033/0.24883, loss_mask_ce_1: 0.21749/0.75800, loss_mask_bce_1: 0.20303/0.30172, loss_mask_dice_1: 0.19776/1.02558, loss_spatial_bce_1: 0.05976/0.08539, loss_spatial_dice_1: 0.06395/0.18271, loss_spatial_ce_1: 0.00022/0.06114, loss_grounding_bce_1: 0.00220/0.08080, loss_grounding_dice_1: 0.03297/0.15123, loss_grounding_ce_1: 0.00022/0.25035, loss_mask_ce_2: 0.20817/0.76593, loss_mask_bce_2: 0.19636/0.30198, loss_mask_dice_2: 0.19864/1.02654, loss_spatial_bce_2: 0.05690/0.08547, loss_spatial_dice_2: 0.06335/0.18322, loss_spatial_ce_2: 0.00053/0.06330, loss_grounding_bce_2: 0.00158/0.08080, loss_grounding_dice_2: 0.02575/0.15111, loss_grounding_ce_2: 0.00030/0.25341, loss_mask_ce_3: 0.21363/0.76957, loss_mask_bce_3: 0.19252/0.30343, loss_mask_dice_3: 0.17254/1.02461, loss_spatial_bce_3: 0.05683/0.08760, loss_spatial_dice_3: 0.06296/0.18457, loss_spatial_ce_3: 0.00095/0.06807, loss_grounding_bce_3: 0.00163/0.08120, loss_grounding_dice_3: 0.02574/0.15077, loss_grounding_ce_3: 0.00033/0.25435, loss_mask_ce_4: 0.19988/0.77515, loss_mask_bce_4: 0.19628/0.30604, loss_mask_dice_4: 0.19623/1.04371, loss_spatial_bce_4: 0.06008/0.08977, loss_spatial_dice_4: 0.06621/0.19272, loss_spatial_ce_4: 0.00442/0.08158, loss_grounding_bce_4: 0.00232/0.08181, loss_grounding_dice_4: 0.03093/0.15334, loss_grounding_ce_4: 0.00088/0.25893, loss_mask_ce_5: 0.24154/0.79960, loss_mask_bce_5: 0.20176/0.30784, loss_mask_dice_5: 0.19177/1.05127, loss_spatial_bce_5: 0.06424/0.09206, loss_spatial_dice_5: 0.05688/0.19582, loss_spatial_ce_5: 0.02713/0.09457, loss_grounding_bce_5: 0.00196/0.08212, loss_grounding_dice_5: 0.03172/0.15402, loss_grounding_ce_5: 0.00067/0.27723, loss_mask_ce_6: 0.26942/0.82657, loss_mask_bce_6: 0.19855/0.30998, loss_mask_dice_6: 0.18901/1.05494, loss_spatial_bce_6: 0.08039/0.09724, loss_spatial_dice_6: 0.06754/0.19817, loss_spatial_ce_6: 0.01691/0.11912, loss_grounding_bce_6: 0.00175/0.08296, loss_grounding_dice_6: 0.03326/0.15465, loss_grounding_ce_6: 0.00334/0.28621, loss_mask_ce_7: 0.32321/0.88224, loss_mask_bce_7: 0.21959/0.31711, loss_mask_dice_7: 0.20497/1.10120, loss_spatial_bce_7: 0.17824/0.10693, loss_spatial_dice_7: 0.16821/0.22349, loss_spatial_ce_7: 0.04816/0.15611, loss_grounding_bce_7: 0.00150/0.08464, loss_grounding_dice_7: 0.02682/0.16024, loss_grounding_ce_7: 0.00109/0.31918, loss_mask_ce_8: 0.39927/1.01763, loss_mask_bce_8: 0.21429/0.33323, loss_mask_dice_8: 0.21848/1.17807, loss_spatial_bce_8: 0.08334/0.12417, loss_spatial_dice_8: 0.07241/0.25891, loss_spatial_ce_8: 0.06173/0.20320, loss_grounding_bce_8: 0.00180/0.08884, loss_grounding_dice_8: 0.03963/0.16992, loss_grounding_ce_8: 0.01381/0.41902, loss_mask_ce_9: 2.49771/3.47833, loss_mask_bce_9: 0.20156/0.36020, loss_mask_dice_9: 0.29490/1.76171, loss_spatial_bce_9: 0.42374/0.35472, loss_spatial_dice_9: 0.79831/0.79333, loss_spatial_ce_9: 1.25525/1.38990, loss_grounding_bce_9: 0.00519/0.10092, loss_grounding_dice_9: 0.11400/0.24216, loss_grounding_ce_9: 0.55794/0.67364] items per batch[64] items per second[0.38] total items[4070400] mini batches[ 63600] memory[4999] epoch remaining[0:10:05] INFO:trainer.default_trainer:epochs[ 34] optim steps[63700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00823/0.75727, loss_mask_bce_0: 0.06122/0.30089, loss_mask_dice_0: 0.15916/1.02175, loss_spatial_bce_0: 0.03028/0.08506, loss_spatial_dice_0: 0.07419/0.18000, loss_spatial_ce_0: 0.36355/0.05729, loss_grounding_bce_0: 0.03372/0.08059, loss_grounding_dice_0: 0.01156/0.15045, loss_grounding_ce_0: 0.00002/0.24893, loss_mask_ce_1: 0.01350/0.75791, loss_mask_bce_1: 0.06706/0.30174, loss_mask_dice_1: 0.15937/1.02571, loss_spatial_bce_1: 0.03472/0.08539, loss_spatial_dice_1: 0.11253/0.18270, loss_spatial_ce_1: 0.24672/0.06112, loss_grounding_bce_1: 0.03667/0.08080, loss_grounding_dice_1: 0.01257/0.15120, loss_grounding_ce_1: 0.00002/0.25045, loss_mask_ce_2: 0.01014/0.76584, loss_mask_bce_2: 0.06096/0.30201, loss_mask_dice_2: 0.15829/1.02665, loss_spatial_bce_2: 0.03299/0.08547, loss_spatial_dice_2: 0.08742/0.18322, loss_spatial_ce_2: 0.25841/0.06328, loss_grounding_bce_2: 0.03569/0.08080, loss_grounding_dice_2: 0.01262/0.15109, loss_grounding_ce_2: 0.00001/0.25351, loss_mask_ce_3: 0.01289/0.76947, loss_mask_bce_3: 0.06359/0.30345, loss_mask_dice_3: 0.15491/1.02473, loss_spatial_bce_3: 0.03198/0.08760, loss_spatial_dice_3: 0.08906/0.18457, loss_spatial_ce_3: 0.28601/0.06804, loss_grounding_bce_3: 0.03539/0.08120, loss_grounding_dice_3: 0.01277/0.15076, loss_grounding_ce_3: 0.00002/0.25447, loss_mask_ce_4: 0.00968/0.77508, loss_mask_bce_4: 0.06057/0.30606, loss_mask_dice_4: 0.17914/1.04375, loss_spatial_bce_4: 0.03650/0.08978, loss_spatial_dice_4: 0.09039/0.19271, loss_spatial_ce_4: 0.45269/0.08160, loss_grounding_bce_4: 0.03540/0.08181, loss_grounding_dice_4: 0.01341/0.15333, loss_grounding_ce_4: 0.00003/0.25903, loss_mask_ce_5: 0.00986/0.79957, loss_mask_bce_5: 0.06239/0.30787, loss_mask_dice_5: 0.18418/1.05136, loss_spatial_bce_5: 0.03852/0.09207, loss_spatial_dice_5: 0.09106/0.19582, loss_spatial_ce_5: 0.52414/0.09457, loss_grounding_bce_5: 0.03654/0.08212, loss_grounding_dice_5: 0.01281/0.15401, loss_grounding_ce_5: 0.00002/0.27731, loss_mask_ce_6: 0.01003/0.82647, loss_mask_bce_6: 0.06565/0.31001, loss_mask_dice_6: 0.16744/1.05504, loss_spatial_bce_6: 0.03931/0.09724, loss_spatial_dice_6: 0.07990/0.19817, loss_spatial_ce_6: 0.73986/0.11911, loss_grounding_bce_6: 0.03486/0.08296, loss_grounding_dice_6: 0.01244/0.15464, loss_grounding_ce_6: 0.00007/0.28630, loss_mask_ce_7: 0.01395/0.88216, loss_mask_bce_7: 0.06499/0.31713, loss_mask_dice_7: 0.14232/1.10128, loss_spatial_bce_7: 0.03605/0.10693, loss_spatial_dice_7: 0.08763/0.22348, loss_spatial_ce_7: 0.62862/0.15607, loss_grounding_bce_7: 0.03329/0.08466, loss_grounding_dice_7: 0.01200/0.16023, loss_grounding_ce_7: 0.00010/0.31926, loss_mask_ce_8: 0.01411/1.01754, loss_mask_bce_8: 0.06915/0.33323, loss_mask_dice_8: 0.15709/1.17811, loss_spatial_bce_8: 0.04371/0.12417, loss_spatial_dice_8: 0.20136/0.25891, loss_spatial_ce_8: 0.18446/0.20316, loss_grounding_bce_8: 0.03534/0.08884, loss_grounding_dice_8: 0.01333/0.16990, loss_grounding_ce_8: 0.00033/0.41904, loss_mask_ce_9: 1.78850/3.47866, loss_mask_bce_9: 0.06719/0.36023, loss_mask_dice_9: 0.18447/1.76190, loss_spatial_bce_9: 0.38399/0.35478, loss_spatial_dice_9: 0.56029/0.79334, loss_spatial_ce_9: 1.22529/1.38991, loss_grounding_bce_9: 0.04026/0.10093, loss_grounding_dice_9: 0.02006/0.24215, loss_grounding_ce_9: 0.01123/0.67367] items per batch[64] items per second[0.37] total items[4076800] mini batches[ 63700] memory[4999] epoch remaining[0:07:09] INFO:trainer.default_trainer:epochs[ 34] optim steps[63800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26067/0.75715, loss_mask_bce_0: 0.29806/0.30086, loss_mask_dice_0: 0.39487/1.02159, loss_spatial_bce_0: 0.05407/0.08505, loss_spatial_dice_0: 0.06922/0.17999, loss_spatial_ce_0: 0.00088/0.05728, loss_grounding_bce_0: 0.12447/0.08061, loss_grounding_dice_0: 0.09681/0.15046, loss_grounding_ce_0: 0.01338/0.24885, loss_mask_ce_1: 0.27114/0.75777, loss_mask_bce_1: 0.30620/0.30172, loss_mask_dice_1: 0.41779/1.02554, loss_spatial_bce_1: 0.05462/0.08539, loss_spatial_dice_1: 0.07107/0.18270, loss_spatial_ce_1: 0.00058/0.06112, loss_grounding_bce_1: 0.10981/0.08081, loss_grounding_dice_1: 0.11179/0.15121, loss_grounding_ce_1: 0.01432/0.25031, loss_mask_ce_2: 0.27090/0.76569, loss_mask_bce_2: 0.30506/0.30199, loss_mask_dice_2: 0.41100/1.02651, loss_spatial_bce_2: 0.05872/0.08547, loss_spatial_dice_2: 0.07725/0.18322, loss_spatial_ce_2: 0.00073/0.06329, loss_grounding_bce_2: 0.11043/0.08082, loss_grounding_dice_2: 0.10604/0.15110, loss_grounding_ce_2: 0.01624/0.25338, loss_mask_ce_3: 0.31370/0.76930, loss_mask_bce_3: 0.32213/0.30343, loss_mask_dice_3: 0.43494/1.02457, loss_spatial_bce_3: 0.06473/0.08760, loss_spatial_dice_3: 0.08507/0.18457, loss_spatial_ce_3: 0.00409/0.06804, loss_grounding_bce_3: 0.12374/0.08122, loss_grounding_dice_3: 0.10688/0.15077, loss_grounding_ce_3: 0.01565/0.25437, loss_mask_ce_4: 0.25787/0.77494, loss_mask_bce_4: 0.30160/0.30603, loss_mask_dice_4: 0.42326/1.04357, loss_spatial_bce_4: 0.06756/0.08978, loss_spatial_dice_4: 0.08562/0.19272, loss_spatial_ce_4: 0.02835/0.08161, loss_grounding_bce_4: 0.12457/0.08182, loss_grounding_dice_4: 0.09680/0.15335, loss_grounding_ce_4: 0.00548/0.25888, loss_mask_ce_5: 0.23759/0.79950, loss_mask_bce_5: 0.31150/0.30784, loss_mask_dice_5: 0.40702/1.05120, loss_spatial_bce_5: 0.07593/0.09207, loss_spatial_dice_5: 0.09731/0.19583, loss_spatial_ce_5: 0.02791/0.09459, loss_grounding_bce_5: 0.11517/0.08213, loss_grounding_dice_5: 0.08350/0.15402, loss_grounding_ce_5: 0.01644/0.27721, loss_mask_ce_6: 0.28916/0.82636, loss_mask_bce_6: 0.32641/0.30998, loss_mask_dice_6: 0.40814/1.05487, loss_spatial_bce_6: 0.10512/0.09725, loss_spatial_dice_6: 0.10825/0.19818, loss_spatial_ce_6: 0.05254/0.11913, loss_grounding_bce_6: 0.13175/0.08297, loss_grounding_dice_6: 0.09053/0.15464, loss_grounding_ce_6: 0.12392/0.28618, loss_mask_ce_7: 0.29442/0.88209, loss_mask_bce_7: 0.32425/0.31710, loss_mask_dice_7: 0.42036/1.10109, loss_spatial_bce_7: 0.06732/0.10692, loss_spatial_dice_7: 0.08212/0.22348, loss_spatial_ce_7: 0.10283/0.15607, loss_grounding_bce_7: 0.13392/0.08467, loss_grounding_dice_7: 0.08386/0.16023, loss_grounding_ce_7: 0.14992/0.31917, loss_mask_ce_8: 0.42486/1.01742, loss_mask_bce_8: 0.31069/0.33320, loss_mask_dice_8: 0.43643/1.17789, loss_spatial_bce_8: 0.07820/0.12415, loss_spatial_dice_8: 0.10713/0.25889, loss_spatial_ce_8: 0.11769/0.20312, loss_grounding_bce_8: 0.11404/0.08885, loss_grounding_dice_8: 0.12557/0.16989, loss_grounding_ce_8: 0.01247/0.41895, loss_mask_ce_9: 4.06016/3.47811, loss_mask_bce_9: 0.38222/0.36018, loss_mask_dice_9: 1.04037/1.76155, loss_spatial_bce_9: 0.39852/0.35485, loss_spatial_dice_9: 0.82721/0.79333, loss_spatial_ce_9: 1.19106/1.38984, loss_grounding_bce_9: 0.17827/0.10093, loss_grounding_dice_9: 0.13635/0.24213, loss_grounding_ce_9: 0.23944/0.67365] items per batch[64] items per second[0.37] total items[4083200] mini batches[ 63800] memory[4999] epoch remaining[0:04:14] INFO:trainer.default_trainer:epochs[ 34] optim steps[63900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03520/0.75706, loss_mask_bce_0: 0.14159/0.30085, loss_mask_dice_0: 0.06755/1.02120, loss_spatial_bce_0: 0.29622/0.08508, loss_spatial_dice_0: 0.14950/0.17999, loss_spatial_ce_0: 0.00000/0.05727, loss_grounding_bce_0: 0.32328/0.08062, loss_grounding_dice_0: 0.15041/0.15046, loss_grounding_ce_0: 0.00322/0.24894, loss_mask_ce_1: 0.02879/0.75769, loss_mask_bce_1: 0.13927/0.30169, loss_mask_dice_1: 0.06863/1.02514, loss_spatial_bce_1: 0.30840/0.08541, loss_spatial_dice_1: 0.15365/0.18269, loss_spatial_ce_1: 0.00000/0.06111, loss_grounding_bce_1: 0.30625/0.08083, loss_grounding_dice_1: 0.15363/0.15121, loss_grounding_ce_1: 0.00257/0.25040, loss_mask_ce_2: 0.02974/0.76558, loss_mask_bce_2: 0.13302/0.30196, loss_mask_dice_2: 0.06713/1.02611, loss_spatial_bce_2: 0.35187/0.08549, loss_spatial_dice_2: 0.15745/0.18321, loss_spatial_ce_2: 0.00001/0.06331, loss_grounding_bce_2: 0.29840/0.08084, loss_grounding_dice_2: 0.15299/0.15110, loss_grounding_ce_2: 0.00489/0.25349, loss_mask_ce_3: 0.03007/0.76920, loss_mask_bce_3: 0.14321/0.30339, loss_mask_dice_3: 0.06802/1.02416, loss_spatial_bce_3: 0.43271/0.08763, loss_spatial_dice_3: 0.17011/0.18457, loss_spatial_ce_3: 0.00001/0.06804, loss_grounding_bce_3: 0.31634/0.08123, loss_grounding_dice_3: 0.14833/0.15077, loss_grounding_ce_3: 0.00493/0.25447, loss_mask_ce_4: 0.02521/0.77480, loss_mask_bce_4: 0.13688/0.30601, loss_mask_dice_4: 0.06684/1.04318, loss_spatial_bce_4: 0.35310/0.08982, loss_spatial_dice_4: 0.15810/0.19272, loss_spatial_ce_4: 0.00011/0.08164, loss_grounding_bce_4: 0.31529/0.08183, loss_grounding_dice_4: 0.15004/0.15335, loss_grounding_ce_4: 0.00312/0.25899, loss_mask_ce_5: 0.03870/0.79935, loss_mask_bce_5: 0.13395/0.30782, loss_mask_dice_5: 0.06600/1.05080, loss_spatial_bce_5: 0.28391/0.09212, loss_spatial_dice_5: 0.15655/0.19583, loss_spatial_ce_5: 0.00009/0.09463, loss_grounding_bce_5: 0.30752/0.08213, loss_grounding_dice_5: 0.14705/0.15402, loss_grounding_ce_5: 0.00847/0.27729, loss_mask_ce_6: 0.04050/0.82623, loss_mask_bce_6: 0.13688/0.30995, loss_mask_dice_6: 0.06723/1.05447, loss_spatial_bce_6: 0.27163/0.09730, loss_spatial_dice_6: 0.16443/0.19819, loss_spatial_ce_6: 0.00095/0.11916, loss_grounding_bce_6: 0.31011/0.08297, loss_grounding_dice_6: 0.15029/0.15465, loss_grounding_ce_6: 0.00877/0.28623, loss_mask_ce_7: 0.03156/0.88198, loss_mask_bce_7: 0.14368/0.31708, loss_mask_dice_7: 0.07243/1.10068, loss_spatial_bce_7: 0.28822/0.10697, loss_spatial_dice_7: 0.18727/0.22349, loss_spatial_ce_7: 0.00071/0.15610, loss_grounding_bce_7: 0.31759/0.08468, loss_grounding_dice_7: 0.15966/0.16024, loss_grounding_ce_7: 0.00247/0.31932, loss_mask_ce_8: 0.09341/1.01723, loss_mask_bce_8: 0.13863/0.33318, loss_mask_dice_8: 0.06785/1.17745, loss_spatial_bce_8: 0.34124/0.12419, loss_spatial_dice_8: 0.14591/0.25889, loss_spatial_ce_8: 0.07294/0.20311, loss_grounding_bce_8: 0.31182/0.08886, loss_grounding_dice_8: 0.15112/0.16990, loss_grounding_ce_8: 0.00373/0.41908, loss_mask_ce_9: 1.23447/3.47782, loss_mask_bce_9: 0.16159/0.36014, loss_mask_dice_9: 0.07817/1.76081, loss_spatial_bce_9: 0.59872/0.35486, loss_spatial_dice_9: 0.31993/0.79329, loss_spatial_ce_9: 0.15362/1.38968, loss_grounding_bce_9: 0.35827/0.10095, loss_grounding_dice_9: 0.17546/0.24214, loss_grounding_ce_9: 0.08715/0.67358] items per batch[64] items per second[0.37] total items[4089600] mini batches[ 63900] memory[4999] epoch remaining[0:01:18] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00063945. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0023 s/iter. Inference: 0.3777 s/iter. Eval: 0.1001 s/iter. Total: 0.4801 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3783 s/iter. Eval: 0.0850 s/iter. Total: 0.4660 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0028 s/iter. Inference: 0.3801 s/iter. Eval: 0.0803 s/iter. Total: 0.4633 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0028 s/iter. Inference: 0.3826 s/iter. Eval: 0.0786 s/iter. Total: 0.4641 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3829 s/iter. Eval: 0.0757 s/iter. Total: 0.4616 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0029 s/iter. Inference: 0.3823 s/iter. Eval: 0.0727 s/iter. Total: 0.4579 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval0plsn5ay ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.270 | 82.916 | 65.840 | 133 | | Things | 61.379 | 83.946 | 72.611 | 80 | | Stuff | 46.048 | 81.360 | 55.620 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.73 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.35 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.47s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.32 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.45 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.453 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.691 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.254 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.548 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.568 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.607 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.759 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.350 | 69.128 | 49.000 | 25.431 | 49.810 | 67.250 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.740 | bicycle | 22.372 | car | 43.078 | | motorcycle | 42.298 | airplane | 61.610 | bus | 70.532 | | train | 74.203 | truck | 44.107 | boat | 31.336 | | traffic light | 29.668 | fire hydrant | 71.196 | stop sign | 67.609 | | parking meter | 50.711 | bench | 26.842 | bird | 33.431 | | cat | 76.680 | dog | 70.896 | horse | 49.498 | | sheep | 54.775 | cow | 56.308 | elephant | 65.415 | | bear | 80.593 | zebra | 65.942 | giraffe | 61.297 | | backpack | 24.449 | umbrella | 54.543 | handbag | 23.829 | | tie | 40.097 | suitcase | 51.495 | frisbee | 71.018 | | skis | 8.891 | snowboard | 33.516 | sports ball | 50.290 | | kite | 37.947 | baseball bat | 39.501 | baseball glove | 50.486 | | skateboard | 43.045 | surfboard | 45.330 | tennis racket | 63.214 | | bottle | 42.201 | wine glass | 38.157 | cup | 51.047 | | fork | 26.338 | knife | 23.912 | spoon | 22.064 | | bowl | 37.043 | banana | 21.895 | apple | 27.127 | | sandwich | 48.656 | orange | 30.047 | broccoli | 24.417 | | carrot | 22.308 | hot dog | 34.909 | pizza | 53.382 | | donut | 54.528 | cake | 46.991 | chair | 27.950 | | couch | 42.380 | potted plant | 23.721 | bed | 38.947 | | dining table | 14.779 | toilet | 69.950 | tv | 65.558 | | laptop | 70.263 | mouse | 63.602 | remote | 43.868 | | keyboard | 57.881 | cell phone | 45.404 | microwave | 66.541 | | oven | 32.194 | toaster | 49.716 | sink | 43.348 | | refrigerator | 69.402 | book | 14.466 | clock | 54.879 | | vase | 40.070 | scissors | 34.630 | teddy bear | 56.579 | | hair drier | 31.941 | toothbrush | 28.091 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.17558351467426, 'fwIoU': 71.26677612695103, 'IoU-person': 88.85425706179603, 'IoU-bicycle': 73.57707494610278, 'IoU-car': 74.02571212448811, 'IoU-motorcycle': 86.59827708788171, 'IoU-airplane': 83.13325590363425, 'IoU-bus': 87.21030658265447, 'IoU-train': 88.08721167172021, 'IoU-truck': 69.3998149914196, 'IoU-boat': 74.57672257759059, 'IoU-traffic light': 79.45964415079152, 'IoU-fire hydrant': 93.2699182587887, 'IoU-stop sign': 95.0519032456627, 'IoU-parking meter': 84.92621021236695, 'IoU-bench': 62.96971642249277, 'IoU-bird': 78.11914519459452, 'IoU-cat': 85.59924665970874, 'IoU-dog': 86.31252939384589, 'IoU-horse': 88.11817335891202, 'IoU-sheep': 84.35169746163677, 'IoU-cow': 84.65664057717693, 'IoU-elephant': 89.1911615496233, 'IoU-bear': 75.77062986559434, 'IoU-zebra': 86.41434568714708, 'IoU-giraffe': 87.96852229970608, 'IoU-backpack': 53.403796042976836, 'IoU-umbrella': 81.57311011446983, 'IoU-handbag': 51.749129726491105, 'IoU-tie': 76.01558020690514, 'IoU-suitcase': 77.94531019019495, 'IoU-frisbee': 84.12620555015965, 'IoU-skis': 58.67995589905415, 'IoU-snowboard': 71.12790162200884, 'IoU-sports ball': 79.69949401744542, 'IoU-kite': 78.94816926862042, 'IoU-baseball bat': 66.92433426411496, 'IoU-baseball glove': 55.81970592277037, 'IoU-skateboard': 86.05027287914956, 'IoU-surfboard': 86.66575323091067, 'IoU-tennis racket': 90.9466496421388, 'IoU-bottle': 70.99839308947375, 'IoU-wine glass': 82.5136463287293, 'IoU-cup': 69.89864810808024, 'IoU-fork': 69.25323485765045, 'IoU-knife': 62.89961335002716, 'IoU-spoon': 61.66774915088426, 'IoU-bowl': 57.74574741091625, 'IoU-banana': 82.88446643040668, 'IoU-apple': 57.9588479060927, 'IoU-sandwich': 69.64413025140956, 'IoU-orange': 74.5903424897579, 'IoU-broccoli': 68.64816292550994, 'IoU-carrot': 65.48729102861714, 'IoU-hot dog': 72.12312299200802, 'IoU-pizza': 85.7430478156525, 'IoU-donut': 66.4031664333147, 'IoU-cake': 79.75495822444212, 'IoU-chair': 63.442024926512644, 'IoU-couch': 71.1009381230731, 'IoU-potted plant': 45.15465115314074, 'IoU-bed': 72.27675365268432, 'IoU-dining table': 55.39037939016167, 'IoU-toilet': 80.09169379432738, 'IoU-tv': 80.0010758488274, 'IoU-laptop': 78.05653610120275, 'IoU-mouse': 82.97790735847155, 'IoU-remote': 74.44104460139961, 'IoU-keyboard': 62.44593311785446, 'IoU-cell phone': 79.8450795075821, 'IoU-microwave': 77.05080893523915, 'IoU-oven': 74.45827400807048, 'IoU-toaster': 85.28990419667183, 'IoU-sink': 68.50384359353477, 'IoU-refrigerator': 82.59097626998796, 'IoU-book': 49.72554026163544, 'IoU-clock': 70.26744607203472, 'IoU-vase': 66.47516960804111, 'IoU-scissors': 66.25018104154552, 'IoU-teddy bear': 78.9766861140377, 'IoU-hair drier': 48.47013443757445, 'IoU-toothbrush': 72.81852428073888, 'IoU-banner': 33.29955453113237, 'IoU-blanket': 18.326027728478277, 'IoU-bridge': 35.326721132400465, 'IoU-cardboard': 51.577280594909936, 'IoU-counter': 35.52543618911176, 'IoU-curtain': 70.41067362487927, 'IoU-door-stuff': 47.56698394642085, 'IoU-floor-wood': 64.51950643092592, 'IoU-flower': 44.03453166195521, 'IoU-fruit': 48.79939786184888, 'IoU-gravel': 31.918773484736597, 'IoU-house': 22.57290078415678, 'IoU-light': 43.565592893926414, 'IoU-mirror-stuff': 64.40417722955843, 'IoU-net': 47.21502567961057, 'IoU-pillow': 18.34695956859919, 'IoU-platform': 27.190851535913346, 'IoU-playingfield': 69.95038087755118, 'IoU-railroad': 64.52968052032959, 'IoU-river': 56.540582690361916, 'IoU-road': 66.9733035085702, 'IoU-roof': 19.927414054578136, 'IoU-sand': 64.42966368474508, 'IoU-sea': 86.20476703980916, 'IoU-shelf': 39.23208277692063, 'IoU-snow': 92.04101295745127, 'IoU-stairs': 33.11259509216559, 'IoU-tent': 11.020853792159883, 'IoU-towel': 46.06984585523019, 'IoU-wall-brick': 48.17568769319253, 'IoU-wall-stone': 30.342910526721255, 'IoU-wall-tile': 66.77205862668549, 'IoU-wall-wood': 44.348178024293105, 'IoU-water-other': 27.612126803549742, 'IoU-window-blind': 50.02583940761526, 'IoU-window-other': 49.01802473202623, 'IoU-tree-merged': 82.04713787189209, 'IoU-fence-merged': 56.3814193412009, 'IoU-ceiling-merged': 68.07960128597006, 'IoU-sky-other-merged': 93.6410223414243, 'IoU-cabinet-merged': 64.08296209076013, 'IoU-table-merged': 41.230237676351614, 'IoU-floor-other-merged': 53.489259894384375, 'IoU-pavement-merged': 56.24666749244448, 'IoU-mountain-merged': 57.98254163607347, 'IoU-grass-merged': 71.81915715718404, 'IoU-dirt-merged': 46.25863350509867, 'IoU-paper-merged': 34.88546520104456, 'IoU-food-other-merged': 41.806815515573156, 'IoU-building-other-merged': 59.118930862733556, 'IoU-rock-merged': 63.94943016576985, 'IoU-wall-other-merged': 66.68393832772847, 'IoU-rug-merged': 68.08842249345261, 'mACC': 76.46991394519917, 'pACC': 82.06299156573206, 'ACC-person': 93.18817734720479, 'ACC-bicycle': 82.38944025750405, 'ACC-car': 86.59507106184246, 'ACC-motorcycle': 91.03443520491797, 'ACC-airplane': 86.90840158997352, 'ACC-bus': 93.99988427125768, 'ACC-train': 95.29887615669548, 'ACC-truck': 77.39715403501457, 'ACC-boat': 83.42826036182002, 'ACC-traffic light': 90.54843783777974, 'ACC-fire hydrant': 95.94859022917584, 'ACC-stop sign': 98.2634176616041, 'ACC-parking meter': 88.27112414578735, 'ACC-bench': 79.82851185350458, 'ACC-bird': 82.13331150600037, 'ACC-cat': 93.01794760322504, 'ACC-dog': 89.7598056141003, 'ACC-horse': 93.45100676402478, 'ACC-sheep': 88.11049139376391, 'ACC-cow': 87.68964548282334, 'ACC-elephant': 91.19982670489223, 'ACC-bear': 77.24283461698944, 'ACC-zebra': 88.49373998361486, 'ACC-giraffe': 91.68959758945742, 'ACC-backpack': 71.95905382073778, 'ACC-umbrella': 84.9389529023099, 'ACC-handbag': 70.57931286877556, 'ACC-tie': 83.40337484768877, 'ACC-suitcase': 85.93298554184173, 'ACC-frisbee': 94.17454545454545, 'ACC-skis': 72.35542441660392, 'ACC-snowboard': 81.70850579216771, 'ACC-sports ball': 88.16737474287014, 'ACC-kite': 84.88569062595174, 'ACC-baseball bat': 88.34860529411324, 'ACC-baseball glove': 62.92773610600722, 'ACC-skateboard': 90.56896373731435, 'ACC-surfboard': 92.73281149292029, 'ACC-tennis racket': 94.95675830325906, 'ACC-bottle': 86.07721618065499, 'ACC-wine glass': 90.69708618157422, 'ACC-cup': 88.5914911775434, 'ACC-fork': 81.2338353989497, 'ACC-knife': 79.3306221147505, 'ACC-spoon': 78.24613538185838, 'ACC-bowl': 67.60261407509182, 'ACC-banana': 89.97192483868008, 'ACC-apple': 71.34902084325346, 'ACC-sandwich': 82.77756567246179, 'ACC-orange': 84.41042793424342, 'ACC-broccoli': 82.39208268994074, 'ACC-carrot': 78.17684583734189, 'ACC-hot dog': 80.08194649619355, 'ACC-pizza': 92.82100245522457, 'ACC-donut': 72.58363718018519, 'ACC-cake': 90.60730160818642, 'ACC-chair': 78.6755588203366, 'ACC-couch': 78.56441592602262, 'ACC-potted plant': 56.89139797043781, 'ACC-bed': 81.17428936394359, 'ACC-dining table': 78.19988488358733, 'ACC-toilet': 83.41921536844853, 'ACC-tv': 87.99838535405777, 'ACC-laptop': 91.82199912440147, 'ACC-mouse': 92.09260882583177, 'ACC-remote': 79.70422920932172, 'ACC-keyboard': 70.55062682440789, 'ACC-cell phone': 87.9490600462979, 'ACC-microwave': 81.50174199953182, 'ACC-oven': 91.60395637913365, 'ACC-toaster': 91.25689416852563, 'ACC-sink': 76.35087679203771, 'ACC-refrigerator': 92.06102151639226, 'ACC-book': 63.51328065898966, 'ACC-clock': 74.39087635547855, 'ACC-vase': 74.81178109341027, 'ACC-scissors': 70.35093518442001, 'ACC-teddy bear': 83.70301576275281, 'ACC-hair drier': 60.803233427262114, 'ACC-toothbrush': 80.09468380820014, 'ACC-banner': 77.3127856493548, 'ACC-blanket': 29.889185442363825, 'ACC-bridge': 53.56830856434688, 'ACC-cardboard': 70.19520139595754, 'ACC-counter': 58.99357514664339, 'ACC-curtain': 79.63160589805767, 'ACC-door-stuff': 67.65475016666038, 'ACC-floor-wood': 81.3557073717565, 'ACC-flower': 62.71872105079495, 'ACC-fruit': 66.42178274033728, 'ACC-gravel': 44.13622554294863, 'ACC-house': 26.08958043176206, 'ACC-light': 61.42958312598093, 'ACC-mirror-stuff': 75.48009340680295, 'ACC-net': 67.16184129137818, 'ACC-pillow': 38.98171296724068, 'ACC-platform': 45.88569471453697, 'ACC-playingfield': 89.11430705788193, 'ACC-railroad': 81.48033709994607, 'ACC-river': 84.52258493076991, 'ACC-road': 86.90354975560041, 'ACC-roof': 27.401304664930915, 'ACC-sand': 69.27878119035526, 'ACC-sea': 92.13137629843841, 'ACC-shelf': 54.00965925396321, 'ACC-snow': 95.60853839334584, 'ACC-stairs': 57.32923520987073, 'ACC-tent': 14.696417145175253, 'ACC-towel': 54.93185949823894, 'ACC-wall-brick': 68.27511475322677, 'ACC-wall-stone': 37.22127719640746, 'ACC-wall-tile': 85.27776471322073, 'ACC-wall-wood': 62.214719364101526, 'ACC-water-other': 36.3481282695822, 'ACC-window-blind': 61.51371360771255, 'ACC-window-other': 75.71847367466911, 'ACC-tree-merged': 89.57084019964681, 'ACC-fence-merged': 73.53911879465312, 'ACC-ceiling-merged': 83.60603762933798, 'ACC-sky-other-merged': 97.22126958930768, 'ACC-cabinet-merged': 77.91770775821327, 'ACC-table-merged': 58.15630599697236, 'ACC-floor-other-merged': 67.20442298248301, 'ACC-pavement-merged': 67.64497419715397, 'ACC-mountain-merged': 69.35380749958266, 'ACC-grass-merged': 83.85041194702802, 'ACC-dirt-merged': 67.10172293617867, 'ACC-paper-merged': 47.12391503606826, 'ACC-food-other-merged': 49.90647402148936, 'ACC-building-other-merged': 74.25337452188276, 'ACC-rock-merged': 82.89133763657362, 'ACC-wall-other-merged': 81.90787344909344, 'ACC-rug-merged': 80.40265338201824})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2811 s/iter. Inference: 0.2210 s/iter. Eval: 0.0000 s/iter. Total: 0.5021 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3088 s/iter. Inference: 0.3640 s/iter. Eval: 0.0000 s/iter. Total: 0.6729 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3335 s/iter. Inference: 0.5591 s/iter. Eval: 0.0000 s/iter. Total: 0.8928 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3617208077260754, 'noc@0.8': 2.369037167105648, 'noc@0.85': 2.8282118817676323, 'noc@0.9': 3.5940883816213054, 'miou@iter1': 0.8673903582770817} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1434 s/iter. Eval: 0.0010 s/iter. Total: 0.1463 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.048583984375, 'precision@0.6': 72.40575408935547, 'precision@0.7': 68.09172058105469, 'precision@0.8': 58.21997833251953, 'precision@0.9': 31.986007690429688, 'cIoU': 62.06315231323242, 'mIoU': 66.70787811279297} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.26997553024573, 'SQ': 82.91566231845684, 'RQ': 65.84043561236847, 'PQ_th': 61.37947915020331, 'SQ_th': 83.9463153846307, 'RQ_th': 72.61137425000207, 'PQ_st': 46.04808327370594, 'SQ_st': 81.3599595770624, 'RQ_st': 55.620150876317666}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.34965534479438, 'AP50': 69.12774404396488, 'AP75': 49.00040598450358, 'APs': 25.43110234891786, 'APm': 49.80964998948239, 'APl': 67.25021705465922, 'AP-person': 48.74006276134366, 'AP-bicycle': 22.371586506564732, 'AP-car': 43.07822350902604, 'AP-motorcycle': 42.298301169512534, 'AP-airplane': 61.61028483603922, 'AP-bus': 70.53174932609068, 'AP-train': 74.20340051304117, 'AP-truck': 44.10657429072371, 'AP-boat': 31.335952225687308, 'AP-traffic light': 29.668005571432865, 'AP-fire hydrant': 71.19645803382754, 'AP-stop sign': 67.60874729728077, 'AP-parking meter': 50.71147506047863, 'AP-bench': 26.842156240379445, 'AP-bird': 33.43059496814509, 'AP-cat': 76.68006082817818, 'AP-dog': 70.89635575005231, 'AP-horse': 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INFO:trainer.default_trainer:This epoch takes 0:56:44.984573 INFO:trainer.default_trainer:PROGRESS: 70.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 35 training. INFO:trainer.default_trainer:epochs[ 35] optim steps[64000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.09289/0.75709, loss_mask_bce_0: 0.16306/0.30087, loss_mask_dice_0: 0.37283/1.02127, loss_spatial_bce_0: 0.06019/0.08509, loss_spatial_dice_0: 0.10183/0.17998, loss_spatial_ce_0: 0.00020/0.05726, loss_grounding_bce_0: 0.02264/0.08062, loss_grounding_dice_0: 0.10695/0.15047, loss_grounding_ce_0: 0.90101/0.24889, loss_mask_ce_1: 1.06909/0.75769, loss_mask_bce_1: 0.16682/0.30171, loss_mask_dice_1: 0.39785/1.02520, loss_spatial_bce_1: 0.05193/0.08543, loss_spatial_dice_1: 0.09319/0.18269, loss_spatial_ce_1: 0.00021/0.06109, loss_grounding_bce_1: 0.02485/0.08084, loss_grounding_dice_1: 0.11517/0.15122, loss_grounding_ce_1: 0.34339/0.25033, loss_mask_ce_2: 1.04917/0.76561, loss_mask_bce_2: 0.17615/0.30198, loss_mask_dice_2: 0.39014/1.02615, loss_spatial_bce_2: 0.05166/0.08550, loss_spatial_dice_2: 0.09455/0.18321, loss_spatial_ce_2: 0.00041/0.06331, loss_grounding_bce_2: 0.02314/0.08085, loss_grounding_dice_2: 0.10052/0.15112, loss_grounding_ce_2: 0.42994/0.25346, loss_mask_ce_3: 1.21256/0.76922, loss_mask_bce_3: 0.18072/0.30341, loss_mask_dice_3: 0.41798/1.02423, loss_spatial_bce_3: 0.05483/0.08764, loss_spatial_dice_3: 0.10242/0.18457, loss_spatial_ce_3: 0.00016/0.06803, loss_grounding_bce_3: 0.02662/0.08123, loss_grounding_dice_3: 0.11989/0.15078, loss_grounding_ce_3: 0.44721/0.25442, loss_mask_ce_4: 1.16425/0.77484, loss_mask_bce_4: 0.17918/0.30603, loss_mask_dice_4: 0.40491/1.04325, loss_spatial_bce_4: 0.04982/0.08983, loss_spatial_dice_4: 0.08998/0.19273, loss_spatial_ce_4: 0.00051/0.08162, loss_grounding_bce_4: 0.02403/0.08185, loss_grounding_dice_4: 0.11622/0.15337, loss_grounding_ce_4: 0.89265/0.25894, loss_mask_ce_5: 1.03428/0.79939, loss_mask_bce_5: 0.17538/0.30784, loss_mask_dice_5: 0.41228/1.05085, loss_spatial_bce_5: 0.05116/0.09213, loss_spatial_dice_5: 0.10559/0.19585, loss_spatial_ce_5: 0.00088/0.09461, loss_grounding_bce_5: 0.02580/0.08214, loss_grounding_dice_5: 0.12001/0.15403, loss_grounding_ce_5: 0.37131/0.27723, loss_mask_ce_6: 1.14339/0.82627, loss_mask_bce_6: 0.17027/0.30998, loss_mask_dice_6: 0.39984/1.05457, loss_spatial_bce_6: 0.05875/0.09731, loss_spatial_dice_6: 0.11247/0.19820, loss_spatial_ce_6: 0.01125/0.11917, loss_grounding_bce_6: 0.02500/0.08298, loss_grounding_dice_6: 0.11063/0.15466, loss_grounding_ce_6: 0.31936/0.28618, loss_mask_ce_7: 1.11877/0.88202, loss_mask_bce_7: 0.17741/0.31711, loss_mask_dice_7: 0.46059/1.10077, loss_spatial_bce_7: 0.05531/0.10697, loss_spatial_dice_7: 0.11549/0.22349, loss_spatial_ce_7: 0.04728/0.15607, loss_grounding_bce_7: 0.02991/0.08469, loss_grounding_dice_7: 0.14728/0.16025, loss_grounding_ce_7: 0.37372/0.31925, loss_mask_ce_8: 0.94733/1.01732, loss_mask_bce_8: 0.18225/0.33320, loss_mask_dice_8: 0.47706/1.17748, loss_spatial_bce_8: 0.06811/0.12418, loss_spatial_dice_8: 0.13062/0.25888, loss_spatial_ce_8: 0.06921/0.20304, loss_grounding_bce_8: 0.03168/0.08886, loss_grounding_dice_8: 0.15412/0.16990, loss_grounding_ce_8: 0.29041/0.41891, loss_mask_ce_9: 2.80980/3.47784, loss_mask_bce_9: 0.29555/0.36015, loss_mask_dice_9: 1.22154/1.76091, loss_spatial_bce_9: 0.33708/0.35488, loss_spatial_dice_9: 0.91123/0.79330, loss_spatial_ce_9: 1.25723/1.38959, loss_grounding_bce_9: 0.06757/0.10095, loss_grounding_dice_9: 0.35481/0.24216, loss_grounding_ce_9: 0.25000/0.67336] items per batch[64] items per second[0.16] total items[4096000] mini batches[ 64000] memory[4999] epoch remaining[0:56:20] INFO:trainer.default_trainer:epochs[ 35] optim steps[64100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22525/0.75700, loss_mask_bce_0: 0.30474/0.30086, loss_mask_dice_0: 0.28708/1.02099, loss_spatial_bce_0: 0.09855/0.08508, loss_spatial_dice_0: 0.08939/0.17995, loss_spatial_ce_0: 0.00309/0.05725, loss_grounding_bce_0: 0.06641/0.08064, loss_grounding_dice_0: 0.08016/0.15047, loss_grounding_ce_0: 0.00239/0.24888, loss_mask_ce_1: 0.23171/0.75757, loss_mask_bce_1: 0.29858/0.30170, loss_mask_dice_1: 0.25360/1.02492, loss_spatial_bce_1: 0.09501/0.08542, loss_spatial_dice_1: 0.08713/0.18266, loss_spatial_ce_1: 0.00551/0.06108, loss_grounding_bce_1: 0.06466/0.08085, loss_grounding_dice_1: 0.07851/0.15122, loss_grounding_ce_1: 0.00304/0.25031, loss_mask_ce_2: 0.28658/0.76554, loss_mask_bce_2: 0.30263/0.30197, loss_mask_dice_2: 0.27292/1.02588, loss_spatial_bce_2: 0.09836/0.08549, loss_spatial_dice_2: 0.08825/0.18318, loss_spatial_ce_2: 0.00847/0.06331, loss_grounding_bce_2: 0.06951/0.08086, loss_grounding_dice_2: 0.08392/0.15111, loss_grounding_ce_2: 0.00186/0.25343, loss_mask_ce_3: 0.27702/0.76910, loss_mask_bce_3: 0.29046/0.30341, loss_mask_dice_3: 0.27601/1.02395, loss_spatial_bce_3: 0.09504/0.08763, loss_spatial_dice_3: 0.08684/0.18453, loss_spatial_ce_3: 0.00939/0.06803, loss_grounding_bce_3: 0.06702/0.08124, loss_grounding_dice_3: 0.09295/0.15078, loss_grounding_ce_3: 0.00110/0.25441, loss_mask_ce_4: 0.23531/0.77472, loss_mask_bce_4: 0.35140/0.30602, loss_mask_dice_4: 0.33350/1.04294, loss_spatial_bce_4: 0.11384/0.08982, loss_spatial_dice_4: 0.09717/0.19271, loss_spatial_ce_4: 0.04330/0.08162, loss_grounding_bce_4: 0.07043/0.08186, loss_grounding_dice_4: 0.08809/0.15337, loss_grounding_ce_4: 0.00258/0.25892, loss_mask_ce_5: 0.27583/0.79929, loss_mask_bce_5: 0.32353/0.30783, loss_mask_dice_5: 0.27992/1.05054, loss_spatial_bce_5: 0.12796/0.09213, loss_spatial_dice_5: 0.11611/0.19583, loss_spatial_ce_5: 0.07680/0.09462, loss_grounding_bce_5: 0.07022/0.08215, loss_grounding_dice_5: 0.09091/0.15404, loss_grounding_ce_5: 0.00106/0.27723, loss_mask_ce_6: 0.27697/0.82617, loss_mask_bce_6: 0.32207/0.30998, loss_mask_dice_6: 0.28426/1.05427, loss_spatial_bce_6: 0.13331/0.09732, loss_spatial_dice_6: 0.11313/0.19818, loss_spatial_ce_6: 0.07615/0.11918, loss_grounding_bce_6: 0.05885/0.08300, loss_grounding_dice_6: 0.07428/0.15467, loss_grounding_ce_6: 0.00059/0.28617, loss_mask_ce_7: 0.14727/0.88196, loss_mask_bce_7: 0.42877/0.31709, loss_mask_dice_7: 0.37501/1.10044, loss_spatial_bce_7: 0.09882/0.10697, loss_spatial_dice_7: 0.09578/0.22345, loss_spatial_ce_7: 0.12005/0.15606, loss_grounding_bce_7: 0.06494/0.08470, loss_grounding_dice_7: 0.08042/0.16025, loss_grounding_ce_7: 0.00485/0.31927, loss_mask_ce_8: 0.17889/1.01721, loss_mask_bce_8: 0.42965/0.33320, loss_mask_dice_8: 0.37962/1.17713, loss_spatial_bce_8: 0.11224/0.12418, loss_spatial_dice_8: 0.10600/0.25884, loss_spatial_ce_8: 0.12456/0.20303, loss_grounding_bce_8: 0.06721/0.08889, loss_grounding_dice_8: 0.08427/0.16992, loss_grounding_ce_8: 0.00188/0.41898, loss_mask_ce_9: 1.85229/3.47746, loss_mask_bce_9: 0.40949/0.36015, loss_mask_dice_9: 0.38731/1.76047, loss_spatial_bce_9: 0.42305/0.35492, loss_spatial_dice_9: 0.63706/0.79329, loss_spatial_ce_9: 0.67490/1.38951, loss_grounding_bce_9: 0.06478/0.10097, loss_grounding_dice_9: 0.11644/0.24218, loss_grounding_ce_9: 0.07980/0.67337] items per batch[64] items per second[0.37] total items[4102400] mini batches[ 64100] memory[4999] epoch remaining[0:49:45] INFO:trainer.default_trainer:epochs[ 35] optim steps[64200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.44325/0.75696, loss_mask_bce_0: 0.03963/0.30083, loss_mask_dice_0: 1.49120/1.02088, loss_spatial_bce_0: 0.00511/0.08506, loss_spatial_dice_0: 0.24605/0.17994, loss_spatial_ce_0: 0.09134/0.05723, loss_grounding_bce_0: 0.00155/0.08060, loss_grounding_dice_0: 0.05598/0.15044, loss_grounding_ce_0: 0.40810/0.24902, loss_mask_ce_1: 1.38761/0.75752, loss_mask_bce_1: 0.04288/0.30168, loss_mask_dice_1: 1.53694/1.02488, loss_spatial_bce_1: 0.00480/0.08539, loss_spatial_dice_1: 0.25206/0.18265, loss_spatial_ce_1: 0.07239/0.06107, loss_grounding_bce_1: 0.00209/0.08082, loss_grounding_dice_1: 0.07532/0.15118, loss_grounding_ce_1: 0.36423/0.25047, loss_mask_ce_2: 1.32964/0.76554, loss_mask_bce_2: 0.06003/0.30195, loss_mask_dice_2: 1.46158/1.02581, loss_spatial_bce_2: 0.00601/0.08547, loss_spatial_dice_2: 0.27973/0.18317, loss_spatial_ce_2: 0.07630/0.06332, loss_grounding_bce_2: 0.00071/0.08082, loss_grounding_dice_2: 0.02915/0.15108, loss_grounding_ce_2: 0.28716/0.25360, loss_mask_ce_3: 1.51652/0.76909, loss_mask_bce_3: 0.03677/0.30338, loss_mask_dice_3: 1.52391/1.02389, loss_spatial_bce_3: 0.00642/0.08760, loss_spatial_dice_3: 0.29594/0.18452, loss_spatial_ce_3: 0.12071/0.06804, loss_grounding_bce_3: 0.00141/0.08121, loss_grounding_dice_3: 0.06897/0.15075, loss_grounding_ce_3: 0.30956/0.25465, loss_mask_ce_4: 2.16930/0.77471, loss_mask_bce_4: 0.05163/0.30600, loss_mask_dice_4: 1.56060/1.04290, loss_spatial_bce_4: 0.00597/0.08979, loss_spatial_dice_4: 0.29246/0.19270, loss_spatial_ce_4: 0.11233/0.08164, loss_grounding_bce_4: 0.00094/0.08182, loss_grounding_dice_4: 0.05444/0.15334, loss_grounding_ce_4: 0.36191/0.25908, loss_mask_ce_5: 1.89841/0.79926, loss_mask_bce_5: 0.04756/0.30781, loss_mask_dice_5: 1.51705/1.05047, loss_spatial_bce_5: 0.00420/0.09211, loss_spatial_dice_5: 0.29193/0.19582, loss_spatial_ce_5: 0.14956/0.09464, loss_grounding_bce_5: 0.00127/0.08212, loss_grounding_dice_5: 0.05401/0.15401, loss_grounding_ce_5: 0.45207/0.27744, loss_mask_ce_6: 2.53723/0.82617, loss_mask_bce_6: 0.04508/0.30996, loss_mask_dice_6: 1.41553/1.05421, loss_spatial_bce_6: 0.01082/0.09728, loss_spatial_dice_6: 0.31439/0.19818, loss_spatial_ce_6: 0.23198/0.11918, loss_grounding_bce_6: 0.00108/0.08296, loss_grounding_dice_6: 0.05799/0.15464, loss_grounding_ce_6: 0.60219/0.28637, loss_mask_ce_7: 1.73629/0.88196, loss_mask_bce_7: 0.05964/0.31707, loss_mask_dice_7: 1.74228/1.10039, loss_spatial_bce_7: 0.00949/0.10694, loss_spatial_dice_7: 0.33014/0.22345, loss_spatial_ce_7: 0.24047/0.15606, loss_grounding_bce_7: 0.00146/0.08467, loss_grounding_dice_7: 0.05338/0.16023, loss_grounding_ce_7: 1.04341/0.31945, loss_mask_ce_8: 1.98463/1.01714, loss_mask_bce_8: 0.07720/0.33318, loss_mask_dice_8: 1.71242/1.17704, loss_spatial_bce_8: 0.01487/0.12415, loss_spatial_dice_8: 0.42033/0.25883, loss_spatial_ce_8: 0.54191/0.20300, loss_grounding_bce_8: 0.00127/0.08886, loss_grounding_dice_8: 0.04491/0.16989, loss_grounding_ce_8: 1.55883/0.41917, loss_mask_ce_9: 4.93490/3.47747, loss_mask_bce_9: 0.03588/0.36014, loss_mask_dice_9: 1.86038/1.76046, loss_spatial_bce_9: 0.02294/0.35485, loss_spatial_dice_9: 0.85623/0.79329, loss_spatial_ce_9: 1.72360/1.38952, loss_grounding_bce_9: 0.00179/0.10093, loss_grounding_dice_9: 0.11194/0.24216, loss_grounding_ce_9: 2.94418/0.67346] items per batch[64] items per second[0.36] total items[4108800] mini batches[ 64200] memory[4999] epoch remaining[0:46:45] INFO:trainer.default_trainer:epochs[ 35] optim steps[64300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18333/0.75679, loss_mask_bce_0: 0.30346/0.30086, loss_mask_dice_0: 0.57953/1.02063, loss_spatial_bce_0: 0.11896/0.08507, loss_spatial_dice_0: 0.19677/0.17993, loss_spatial_ce_0: 0.00032/0.05721, loss_grounding_bce_0: 0.10735/0.08065, loss_grounding_dice_0: 0.23969/0.15045, loss_grounding_ce_0: 0.06512/0.24899, loss_mask_ce_1: 0.15669/0.75733, loss_mask_bce_1: 0.31522/0.30170, loss_mask_dice_1: 0.57614/1.02462, loss_spatial_bce_1: 0.10829/0.08540, loss_spatial_dice_1: 0.18734/0.18264, loss_spatial_ce_1: 0.00053/0.06104, loss_grounding_bce_1: 0.10485/0.08085, loss_grounding_dice_1: 0.23091/0.15119, loss_grounding_ce_1: 0.06654/0.25046, loss_mask_ce_2: 0.15708/0.76534, loss_mask_bce_2: 0.31360/0.30198, loss_mask_dice_2: 0.56432/1.02553, loss_spatial_bce_2: 0.10725/0.08547, loss_spatial_dice_2: 0.19370/0.18316, loss_spatial_ce_2: 0.00046/0.06332, loss_grounding_bce_2: 0.10584/0.08085, loss_grounding_dice_2: 0.23893/0.15109, loss_grounding_ce_2: 0.06339/0.25361, loss_mask_ce_3: 0.16686/0.76890, loss_mask_bce_3: 0.31979/0.30341, loss_mask_dice_3: 0.59308/1.02364, loss_spatial_bce_3: 0.10674/0.08761, loss_spatial_dice_3: 0.17602/0.18451, loss_spatial_ce_3: 0.00487/0.06801, loss_grounding_bce_3: 0.11494/0.08124, loss_grounding_dice_3: 0.23511/0.15076, loss_grounding_ce_3: 0.06627/0.25464, loss_mask_ce_4: 0.16156/0.77453, loss_mask_bce_4: 0.32523/0.30603, loss_mask_dice_4: 0.55129/1.04264, loss_spatial_bce_4: 0.10803/0.08981, loss_spatial_dice_4: 0.18971/0.19270, loss_spatial_ce_4: 0.00142/0.08162, loss_grounding_bce_4: 0.11954/0.08186, loss_grounding_dice_4: 0.24879/0.15335, loss_grounding_ce_4: 0.06644/0.25905, loss_mask_ce_5: 0.17577/0.79909, loss_mask_bce_5: 0.32160/0.30783, loss_mask_dice_5: 0.58114/1.05021, loss_spatial_bce_5: 0.10977/0.09212, loss_spatial_dice_5: 0.21745/0.19582, loss_spatial_ce_5: 0.00240/0.09463, loss_grounding_bce_5: 0.11710/0.08216, loss_grounding_dice_5: 0.24910/0.15402, loss_grounding_ce_5: 0.06426/0.27741, loss_mask_ce_6: 0.14553/0.82598, loss_mask_bce_6: 0.29855/0.31000, loss_mask_dice_6: 0.59087/1.05394, loss_spatial_bce_6: 0.10007/0.09730, loss_spatial_dice_6: 0.17691/0.19817, loss_spatial_ce_6: 0.01256/0.11918, loss_grounding_bce_6: 0.11587/0.08299, loss_grounding_dice_6: 0.26953/0.15465, loss_grounding_ce_6: 0.06285/0.28645, loss_mask_ce_7: 0.15889/0.88185, loss_mask_bce_7: 0.31084/0.31708, loss_mask_dice_7: 0.60860/1.10008, loss_spatial_bce_7: 0.11979/0.10696, loss_spatial_dice_7: 0.19137/0.22344, loss_spatial_ce_7: 0.03238/0.15606, loss_grounding_bce_7: 0.10777/0.08470, loss_grounding_dice_7: 0.23741/0.16024, loss_grounding_ce_7: 0.33590/0.31946, loss_mask_ce_8: 0.14666/1.01700, loss_mask_bce_8: 0.32038/0.33320, loss_mask_dice_8: 0.55258/1.17674, loss_spatial_bce_8: 0.12863/0.12416, loss_spatial_dice_8: 0.22531/0.25881, loss_spatial_ce_8: 0.07604/0.20298, loss_grounding_bce_8: 0.11137/0.08889, loss_grounding_dice_8: 0.26129/0.16989, loss_grounding_ce_8: 0.08374/0.41919, loss_mask_ce_9: 2.18778/3.47717, loss_mask_bce_9: 0.31205/0.36016, loss_mask_dice_9: 0.70901/1.76011, loss_spatial_bce_9: 0.47832/0.35488, loss_spatial_dice_9: 0.86824/0.79329, loss_spatial_ce_9: 1.24986/1.38942, loss_grounding_bce_9: 0.10924/0.10097, loss_grounding_dice_9: 0.29006/0.24215, loss_grounding_ce_9: 0.24358/0.67341] items per batch[64] items per second[0.36] total items[4115200] mini batches[ 64300] memory[4999] epoch remaining[0:43:36] INFO:trainer.default_trainer:epochs[ 35] optim steps[64400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.45572/0.75695, loss_mask_bce_0: 0.33606/0.30092, loss_mask_dice_0: 4.55721/1.02092, loss_spatial_bce_0: 0.00785/0.08507, loss_spatial_dice_0: 0.48753/0.17993, loss_spatial_ce_0: 0.00829/0.05721, loss_grounding_bce_0: 0.00210/0.08065, loss_grounding_dice_0: 0.64847/0.15045, loss_grounding_ce_0: 0.46202/0.24922, loss_mask_ce_1: 2.29769/0.75749, loss_mask_bce_1: 0.29331/0.30177, loss_mask_dice_1: 4.46685/1.02487, loss_spatial_bce_1: 0.00791/0.08541, loss_spatial_dice_1: 0.51729/0.18265, loss_spatial_ce_1: 0.00745/0.06106, loss_grounding_bce_1: 0.00517/0.08086, loss_grounding_dice_1: 0.72962/0.15120, loss_grounding_ce_1: 0.48549/0.25071, loss_mask_ce_2: 2.67242/0.76546, loss_mask_bce_2: 0.29399/0.30204, loss_mask_dice_2: 4.43384/1.02582, loss_spatial_bce_2: 0.00482/0.08547, loss_spatial_dice_2: 0.49725/0.18316, loss_spatial_ce_2: 0.01622/0.06334, loss_grounding_bce_2: 0.00346/0.08086, loss_grounding_dice_2: 0.74917/0.15110, loss_grounding_ce_2: 0.43912/0.25382, loss_mask_ce_3: 2.37293/0.76905, loss_mask_bce_3: 0.30862/0.30347, loss_mask_dice_3: 4.26130/1.02391, loss_spatial_bce_3: 0.00707/0.08761, loss_spatial_dice_3: 0.51842/0.18451, loss_spatial_ce_3: 0.02409/0.06800, loss_grounding_bce_3: 0.00273/0.08125, loss_grounding_dice_3: 0.66331/0.15077, loss_grounding_ce_3: 0.45482/0.25478, loss_mask_ce_4: 2.57081/0.77468, loss_mask_bce_4: 0.29148/0.30611, loss_mask_dice_4: 4.99129/1.04294, loss_spatial_bce_4: 0.00653/0.08981, loss_spatial_dice_4: 0.54556/0.19271, loss_spatial_ce_4: 0.22100/0.08165, loss_grounding_bce_4: 0.00149/0.08188, loss_grounding_dice_4: 0.51866/0.15337, loss_grounding_ce_4: 0.45703/0.25917, loss_mask_ce_5: 3.22713/0.79925, loss_mask_bce_5: 0.30513/0.30791, loss_mask_dice_5: 4.94105/1.05049, loss_spatial_bce_5: 0.00925/0.09213, loss_spatial_dice_5: 0.55648/0.19583, loss_spatial_ce_5: 0.12965/0.09466, loss_grounding_bce_5: 0.00209/0.08217, loss_grounding_dice_5: 0.62369/0.15403, loss_grounding_ce_5: 0.44271/0.27761, loss_mask_ce_6: 3.37715/0.82618, loss_mask_bce_6: 0.11219/0.31007, loss_mask_dice_6: 4.13506/1.05421, loss_spatial_bce_6: 0.00825/0.09730, loss_spatial_dice_6: 0.52175/0.19818, loss_spatial_ce_6: 0.28435/0.11921, loss_grounding_bce_6: 0.00435/0.08300, loss_grounding_dice_6: 0.63308/0.15466, loss_grounding_ce_6: 0.48307/0.28662, loss_mask_ce_7: 3.38587/0.88201, loss_mask_bce_7: 0.30980/0.31714, loss_mask_dice_7: 4.83357/1.10033, loss_spatial_bce_7: 0.01194/0.10695, loss_spatial_dice_7: 0.58322/0.22345, loss_spatial_ce_7: 0.16079/0.15606, loss_grounding_bce_7: 0.00778/0.08471, loss_grounding_dice_7: 0.72322/0.16024, loss_grounding_ce_7: 0.60480/0.31969, loss_mask_ce_8: 3.07562/1.01721, loss_mask_bce_8: 0.30179/0.33328, loss_mask_dice_8: 5.30836/1.17707, loss_spatial_bce_8: 0.00672/0.12415, loss_spatial_dice_8: 0.64430/0.25882, loss_spatial_ce_8: 0.93494/0.20302, loss_grounding_bce_8: 0.01070/0.08890, loss_grounding_dice_8: 0.70969/0.16991, loss_grounding_ce_8: 0.61898/0.41960, loss_mask_ce_9: 5.21259/3.47758, loss_mask_bce_9: 0.09234/0.36026, loss_mask_dice_9: 5.87217/1.76072, loss_spatial_bce_9: 0.03173/0.35485, loss_spatial_dice_9: 0.82009/0.79331, loss_spatial_ce_9: 2.81701/1.38940, loss_grounding_bce_9: 0.00106/0.10097, loss_grounding_dice_9: 0.46047/0.24216, loss_grounding_ce_9: 0.63346/0.67368] items per batch[64] items per second[0.36] total items[4121600] mini batches[ 64400] memory[4999] epoch remaining[0:40:33] INFO:trainer.default_trainer:epochs[ 35] optim steps[64500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07238/0.75686, loss_mask_bce_0: 0.19209/0.30092, loss_mask_dice_0: 0.15193/1.02096, loss_spatial_bce_0: 0.11803/0.08505, loss_spatial_dice_0: 0.06392/0.17994, loss_spatial_ce_0: 0.00451/0.05720, loss_grounding_bce_0: 0.13880/0.08064, loss_grounding_dice_0: 0.10428/0.15046, loss_grounding_ce_0: 0.72295/0.24917, loss_mask_ce_1: 0.06664/0.75739, loss_mask_bce_1: 0.19125/0.30176, loss_mask_dice_1: 0.15702/1.02491, loss_spatial_bce_1: 0.12540/0.08539, loss_spatial_dice_1: 0.06845/0.18265, loss_spatial_ce_1: 0.00236/0.06103, loss_grounding_bce_1: 0.14202/0.08083, loss_grounding_dice_1: 0.10450/0.15120, loss_grounding_ce_1: 0.69972/0.25068, loss_mask_ce_2: 0.08276/0.76535, loss_mask_bce_2: 0.19586/0.30204, loss_mask_dice_2: 0.15903/1.02585, loss_spatial_bce_2: 0.12628/0.08545, loss_spatial_dice_2: 0.07871/0.18316, loss_spatial_ce_2: 0.00268/0.06331, loss_grounding_bce_2: 0.14995/0.08084, loss_grounding_dice_2: 0.10108/0.15109, loss_grounding_ce_2: 0.55314/0.25378, loss_mask_ce_3: 0.06555/0.76894, loss_mask_bce_3: 0.18716/0.30348, loss_mask_dice_3: 0.15373/1.02394, loss_spatial_bce_3: 0.12537/0.08759, loss_spatial_dice_3: 0.07627/0.18452, loss_spatial_ce_3: 0.00190/0.06797, loss_grounding_bce_3: 0.13866/0.08122, loss_grounding_dice_3: 0.09661/0.15075, loss_grounding_ce_3: 0.58733/0.25475, loss_mask_ce_4: 0.07388/0.77453, loss_mask_bce_4: 0.19557/0.30610, loss_mask_dice_4: 0.16098/1.04298, loss_spatial_bce_4: 0.12060/0.08979, loss_spatial_dice_4: 0.06818/0.19272, loss_spatial_ce_4: 0.01439/0.08161, loss_grounding_bce_4: 0.14359/0.08185, loss_grounding_dice_4: 0.10884/0.15337, loss_grounding_ce_4: 0.41082/0.25912, loss_mask_ce_5: 0.07231/0.79912, loss_mask_bce_5: 0.19199/0.30791, loss_mask_dice_5: 0.15909/1.05056, loss_spatial_bce_5: 0.10957/0.09212, loss_spatial_dice_5: 0.07496/0.19585, loss_spatial_ce_5: 0.06453/0.09463, loss_grounding_bce_5: 0.15171/0.08215, loss_grounding_dice_5: 0.10670/0.15404, loss_grounding_ce_5: 0.59544/0.27757, loss_mask_ce_6: 0.05952/0.82605, loss_mask_bce_6: 0.19514/0.31008, loss_mask_dice_6: 0.16810/1.05427, loss_spatial_bce_6: 0.11416/0.09729, loss_spatial_dice_6: 0.09123/0.19819, loss_spatial_ce_6: 0.05888/0.11918, loss_grounding_bce_6: 0.15232/0.08298, loss_grounding_dice_6: 0.09968/0.15467, loss_grounding_ce_6: 0.81530/0.28658, loss_mask_ce_7: 0.09568/0.88187, loss_mask_bce_7: 0.19594/0.31715, loss_mask_dice_7: 0.16357/1.10037, loss_spatial_bce_7: 0.13215/0.10693, loss_spatial_dice_7: 0.10037/0.22346, loss_spatial_ce_7: 0.14967/0.15602, loss_grounding_bce_7: 0.14467/0.08469, loss_grounding_dice_7: 0.09709/0.16025, loss_grounding_ce_7: 0.37302/0.31961, loss_mask_ce_8: 0.09558/1.01703, loss_mask_bce_8: 0.20396/0.33327, loss_mask_dice_8: 0.16568/1.17711, loss_spatial_bce_8: 0.26110/0.12413, loss_spatial_dice_8: 0.25029/0.25883, loss_spatial_ce_8: 0.17650/0.20297, loss_grounding_bce_8: 0.16104/0.08889, loss_grounding_dice_8: 0.08040/0.16992, loss_grounding_ce_8: 0.73176/0.41944, loss_mask_ce_9: 2.69113/3.47757, loss_mask_bce_9: 0.22133/0.36026, loss_mask_dice_9: 0.25865/1.76077, loss_spatial_bce_9: 0.57078/0.35480, loss_spatial_dice_9: 0.49251/0.79333, loss_spatial_ce_9: 0.73831/1.38942, loss_grounding_bce_9: 0.16218/0.10095, loss_grounding_dice_9: 0.11396/0.24218, loss_grounding_ce_9: 1.56153/0.67344] items per batch[64] items per second[0.36] total items[4128000] mini batches[ 64500] memory[4999] epoch remaining[0:37:36] INFO:trainer.default_trainer:epochs[ 35] optim steps[64600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02270/0.75675, loss_mask_bce_0: 0.12227/0.30086, loss_mask_dice_0: 0.18026/1.02082, loss_spatial_bce_0: 0.04425/0.08503, loss_spatial_dice_0: 0.06617/0.17993, loss_spatial_ce_0: 0.01198/0.05720, loss_grounding_bce_0: 0.02310/0.08063, loss_grounding_dice_0: 0.03546/0.15047, loss_grounding_ce_0: 0.00044/0.24910, loss_mask_ce_1: 0.02694/0.75727, loss_mask_bce_1: 0.13002/0.30170, loss_mask_dice_1: 0.18544/1.02476, loss_spatial_bce_1: 0.04045/0.08537, loss_spatial_dice_1: 0.06639/0.18266, loss_spatial_ce_1: 0.03085/0.06101, loss_grounding_bce_1: 0.01856/0.08082, loss_grounding_dice_1: 0.03102/0.15121, loss_grounding_ce_1: 0.00027/0.25073, loss_mask_ce_2: 0.03104/0.76525, loss_mask_bce_2: 0.12048/0.30199, loss_mask_dice_2: 0.17567/1.02571, loss_spatial_bce_2: 0.04082/0.08544, loss_spatial_dice_2: 0.06377/0.18316, loss_spatial_ce_2: 0.03297/0.06330, loss_grounding_bce_2: 0.02532/0.08082, loss_grounding_dice_2: 0.03865/0.15109, loss_grounding_ce_2: 0.00022/0.25376, loss_mask_ce_3: 0.03115/0.76890, loss_mask_bce_3: 0.11730/0.30342, loss_mask_dice_3: 0.17377/1.02381, loss_spatial_bce_3: 0.03837/0.08758, loss_spatial_dice_3: 0.06460/0.18452, loss_spatial_ce_3: 0.03735/0.06795, loss_grounding_bce_3: 0.02470/0.08121, loss_grounding_dice_3: 0.03478/0.15075, loss_grounding_ce_3: 0.00023/0.25474, loss_mask_ce_4: 0.03756/0.77445, loss_mask_bce_4: 0.12162/0.30604, loss_mask_dice_4: 0.19167/1.04287, loss_spatial_bce_4: 0.03899/0.08977, loss_spatial_dice_4: 0.06818/0.19272, loss_spatial_ce_4: 0.11884/0.08161, loss_grounding_bce_4: 0.02079/0.08184, loss_grounding_dice_4: 0.03509/0.15337, loss_grounding_ce_4: 0.00036/0.25908, loss_mask_ce_5: 0.02849/0.79905, loss_mask_bce_5: 0.13453/0.30784, loss_mask_dice_5: 0.20848/1.05042, loss_spatial_bce_5: 0.03846/0.09209, loss_spatial_dice_5: 0.06702/0.19586, loss_spatial_ce_5: 0.17488/0.09464, loss_grounding_bce_5: 0.02484/0.08214, loss_grounding_dice_5: 0.03301/0.15405, loss_grounding_ce_5: 0.00017/0.27751, loss_mask_ce_6: 0.02924/0.82600, loss_mask_bce_6: 0.14035/0.31001, loss_mask_dice_6: 0.20176/1.05412, loss_spatial_bce_6: 0.04535/0.09728, loss_spatial_dice_6: 0.07781/0.19820, loss_spatial_ce_6: 0.03877/0.11919, loss_grounding_bce_6: 0.01804/0.08297, loss_grounding_dice_6: 0.02940/0.15467, loss_grounding_ce_6: 0.00012/0.28654, loss_mask_ce_7: 0.05369/0.88178, loss_mask_bce_7: 0.12684/0.31708, loss_mask_dice_7: 0.19686/1.10021, loss_spatial_bce_7: 0.05357/0.10690, loss_spatial_dice_7: 0.07126/0.22346, loss_spatial_ce_7: 0.33617/0.15601, loss_grounding_bce_7: 0.02156/0.08467, loss_grounding_dice_7: 0.03325/0.16024, loss_grounding_ce_7: 0.00083/0.31962, loss_mask_ce_8: 0.06623/1.01694, loss_mask_bce_8: 0.16108/0.33322, loss_mask_dice_8: 0.24828/1.17697, loss_spatial_bce_8: 0.04929/0.12411, loss_spatial_dice_8: 0.07299/0.25883, loss_spatial_ce_8: 0.62110/0.20295, loss_grounding_bce_8: 0.02389/0.08887, loss_grounding_dice_8: 0.03503/0.16992, loss_grounding_ce_8: 0.00428/0.41949, loss_mask_ce_9: 1.88061/3.47734, loss_mask_bce_9: 0.13531/0.36016, loss_mask_dice_9: 0.34090/1.76046, loss_spatial_bce_9: 0.30229/0.35478, loss_spatial_dice_9: 0.65857/0.79331, loss_spatial_ce_9: 1.01616/1.38935, loss_grounding_bce_9: 0.02833/0.10092, loss_grounding_dice_9: 0.04581/0.24218, loss_grounding_ce_9: 0.01203/0.67363] items per batch[64] items per second[0.36] total items[4134400] mini batches[ 64600] memory[4999] epoch remaining[0:34:40] INFO:trainer.default_trainer:epochs[ 35] optim steps[64700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.97996/0.75671, loss_mask_bce_0: 0.25321/0.30084, loss_mask_dice_0: 1.00480/1.02075, loss_spatial_bce_0: 0.02762/0.08503, loss_spatial_dice_0: 0.12551/0.17993, loss_spatial_ce_0: 0.00849/0.05718, loss_grounding_bce_0: 0.07039/0.08065, loss_grounding_dice_0: 0.24652/0.15050, loss_grounding_ce_0: 0.02981/0.24906, loss_mask_ce_1: 0.99730/0.75724, loss_mask_bce_1: 0.24446/0.30168, loss_mask_dice_1: 1.09362/1.02469, loss_spatial_bce_1: 0.03068/0.08537, loss_spatial_dice_1: 0.13966/0.18265, loss_spatial_ce_1: 0.00852/0.06097, loss_grounding_bce_1: 0.07008/0.08084, loss_grounding_dice_1: 0.25219/0.15123, loss_grounding_ce_1: 0.05089/0.25070, loss_mask_ce_2: 0.99216/0.76519, loss_mask_bce_2: 0.25319/0.30197, loss_mask_dice_2: 1.07113/1.02564, loss_spatial_bce_2: 0.03065/0.08543, loss_spatial_dice_2: 0.14225/0.18315, loss_spatial_ce_2: 0.01723/0.06326, loss_grounding_bce_2: 0.07127/0.08084, loss_grounding_dice_2: 0.23082/0.15111, loss_grounding_ce_2: 0.03205/0.25372, loss_mask_ce_3: 1.08530/0.76884, loss_mask_bce_3: 0.24500/0.30341, loss_mask_dice_3: 1.03079/1.02374, loss_spatial_bce_3: 0.03700/0.08757, loss_spatial_dice_3: 0.16892/0.18452, loss_spatial_ce_3: 0.02719/0.06794, loss_grounding_bce_3: 0.07401/0.08124, loss_grounding_dice_3: 0.22902/0.15079, loss_grounding_ce_3: 0.02449/0.25471, loss_mask_ce_4: 0.86908/0.77441, loss_mask_bce_4: 0.24155/0.30602, loss_mask_dice_4: 1.09607/1.04279, loss_spatial_bce_4: 0.03844/0.08977, loss_spatial_dice_4: 0.20055/0.19272, loss_spatial_ce_4: 0.01259/0.08158, loss_grounding_bce_4: 0.06689/0.08186, loss_grounding_dice_4: 0.22347/0.15339, loss_grounding_ce_4: 0.04179/0.25905, loss_mask_ce_5: 1.12194/0.79898, loss_mask_bce_5: 0.24618/0.30782, loss_mask_dice_5: 1.06853/1.05031, loss_spatial_bce_5: 0.03847/0.09210, loss_spatial_dice_5: 0.19773/0.19586, loss_spatial_ce_5: 0.05983/0.09461, loss_grounding_bce_5: 0.06683/0.08216, loss_grounding_dice_5: 0.22536/0.15409, loss_grounding_ce_5: 0.01554/0.27747, loss_mask_ce_6: 1.17029/0.82594, loss_mask_bce_6: 0.23870/0.30999, loss_mask_dice_6: 1.04668/1.05403, loss_spatial_bce_6: 0.04678/0.09728, loss_spatial_dice_6: 0.15661/0.19821, loss_spatial_ce_6: 0.01014/0.11917, loss_grounding_bce_6: 0.06678/0.08299, loss_grounding_dice_6: 0.23950/0.15470, loss_grounding_ce_6: 0.00835/0.28649, loss_mask_ce_7: 1.27543/0.88173, loss_mask_bce_7: 0.26527/0.31706, loss_mask_dice_7: 1.09353/1.10009, loss_spatial_bce_7: 0.05452/0.10690, loss_spatial_dice_7: 0.14751/0.22345, loss_spatial_ce_7: 0.25218/0.15600, loss_grounding_bce_7: 0.06699/0.08468, loss_grounding_dice_7: 0.18890/0.16027, loss_grounding_ce_7: 0.01777/0.31963, loss_mask_ce_8: 1.87628/1.01697, loss_mask_bce_8: 0.25464/0.33320, loss_mask_dice_8: 1.14341/1.17680, loss_spatial_bce_8: 0.05224/0.12412, loss_spatial_dice_8: 0.22059/0.25884, loss_spatial_ce_8: 0.16621/0.20290, loss_grounding_bce_8: 0.06451/0.08888, loss_grounding_dice_8: 0.21773/0.16994, loss_grounding_ce_8: 0.02190/0.41941, loss_mask_ce_9: 3.45563/3.47739, loss_mask_bce_9: 0.33034/0.36013, loss_mask_dice_9: 1.91226/1.76016, loss_spatial_bce_9: 0.35864/0.35476, loss_spatial_dice_9: 0.95746/0.79331, loss_spatial_ce_9: 1.55200/1.38932, loss_grounding_bce_9: 0.07906/0.10093, loss_grounding_dice_9: 0.33937/0.24221, loss_grounding_ce_9: 0.24953/0.67345] items per batch[64] items per second[0.36] total items[4140800] mini batches[ 64700] memory[4999] epoch remaining[0:31:42] INFO:trainer.default_trainer:epochs[ 35] optim steps[64800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77369/0.75661, loss_mask_bce_0: 0.42696/0.30085, loss_mask_dice_0: 3.05619/1.02077, loss_spatial_bce_0: 0.02052/0.08501, loss_spatial_dice_0: 0.12098/0.17991, loss_spatial_ce_0: 0.00032/0.05717, loss_grounding_bce_0: 0.02258/0.08066, loss_grounding_dice_0: 0.08131/0.15049, loss_grounding_ce_0: 0.00695/0.24908, loss_mask_ce_1: 0.79128/0.75720, loss_mask_bce_1: 0.41618/0.30169, loss_mask_dice_1: 3.00596/1.02469, loss_spatial_bce_1: 0.01475/0.08535, loss_spatial_dice_1: 0.09750/0.18263, loss_spatial_ce_1: 0.00031/0.06098, loss_grounding_bce_1: 0.02984/0.08086, loss_grounding_dice_1: 0.09009/0.15123, loss_grounding_ce_1: 0.00739/0.25072, loss_mask_ce_2: 0.82655/0.76511, loss_mask_bce_2: 0.37442/0.30198, loss_mask_dice_2: 2.96604/1.02565, loss_spatial_bce_2: 0.01579/0.08541, loss_spatial_dice_2: 0.09022/0.18314, loss_spatial_ce_2: 0.00025/0.06325, loss_grounding_bce_2: 0.03103/0.08085, loss_grounding_dice_2: 0.09769/0.15112, loss_grounding_ce_2: 0.00889/0.25376, loss_mask_ce_3: 0.79510/0.76879, loss_mask_bce_3: 0.37241/0.30342, loss_mask_dice_3: 2.74483/1.02370, loss_spatial_bce_3: 0.01557/0.08755, loss_spatial_dice_3: 0.10050/0.18450, loss_spatial_ce_3: 0.00164/0.06793, loss_grounding_bce_3: 0.03180/0.08124, loss_grounding_dice_3: 0.10090/0.15079, loss_grounding_ce_3: 0.00797/0.25476, loss_mask_ce_4: 0.75413/0.77432, loss_mask_bce_4: 0.41530/0.30603, loss_mask_dice_4: 3.26912/1.04279, loss_spatial_bce_4: 0.01842/0.08975, loss_spatial_dice_4: 0.11239/0.19271, loss_spatial_ce_4: 0.02508/0.08157, loss_grounding_bce_4: 0.03499/0.08187, loss_grounding_dice_4: 0.10931/0.15339, loss_grounding_ce_4: 0.00600/0.25911, loss_mask_ce_5: 0.92315/0.79896, loss_mask_bce_5: 0.39537/0.30785, loss_mask_dice_5: 3.00786/1.05029, loss_spatial_bce_5: 0.01827/0.09208, loss_spatial_dice_5: 0.11504/0.19585, loss_spatial_ce_5: 0.09480/0.09460, loss_grounding_bce_5: 0.03785/0.08217, loss_grounding_dice_5: 0.11023/0.15408, loss_grounding_ce_5: 0.05584/0.27748, loss_mask_ce_6: 1.12539/0.82588, loss_mask_bce_6: 0.37058/0.31001, loss_mask_dice_6: 2.84403/1.05406, loss_spatial_bce_6: 0.02193/0.09726, loss_spatial_dice_6: 0.13528/0.19821, loss_spatial_ce_6: 0.04782/0.11917, loss_grounding_bce_6: 0.03937/0.08300, loss_grounding_dice_6: 0.12180/0.15470, loss_grounding_ce_6: 0.06741/0.28649, loss_mask_ce_7: 1.02197/0.88168, loss_mask_bce_7: 0.42149/0.31708, loss_mask_dice_7: 3.38779/1.10010, loss_spatial_bce_7: 0.01880/0.10690, loss_spatial_dice_7: 0.20397/0.22345, loss_spatial_ce_7: 0.03881/0.15600, loss_grounding_bce_7: 0.02595/0.08471, loss_grounding_dice_7: 0.08886/0.16027, loss_grounding_ce_7: 0.09708/0.31956, loss_mask_ce_8: 1.50217/1.01694, loss_mask_bce_8: 0.44784/0.33322, loss_mask_dice_8: 3.80106/1.17676, loss_spatial_bce_8: 0.02827/0.12412, loss_spatial_dice_8: 0.25828/0.25883, loss_spatial_ce_8: 0.05398/0.20287, loss_grounding_bce_8: 0.04309/0.08891, loss_grounding_dice_8: 0.11858/0.16994, loss_grounding_ce_8: 0.16383/0.41933, loss_mask_ce_9: 5.07050/3.47766, loss_mask_bce_9: 0.42312/0.36015, loss_mask_dice_9: 6.63291/1.76037, loss_spatial_bce_9: 0.23362/0.35478, loss_spatial_dice_9: 0.97851/0.79332, loss_spatial_ce_9: 1.70611/1.38928, loss_grounding_bce_9: 0.05933/0.10095, loss_grounding_dice_9: 0.19954/0.24222, loss_grounding_ce_9: 0.67074/0.67340] items per batch[64] items per second[0.37] total items[4147200] mini batches[ 64800] memory[4999] epoch remaining[0:28:42] INFO:trainer.default_trainer:epochs[ 35] optim steps[64900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22579/0.75658, loss_mask_bce_0: 0.22881/0.30076, loss_mask_dice_0: 0.23591/1.02065, loss_spatial_bce_0: 0.07837/0.08499, loss_spatial_dice_0: 0.08080/0.17988, loss_spatial_ce_0: 0.00064/0.05714, loss_grounding_bce_0: 0.06910/0.08065, loss_grounding_dice_0: 0.04727/0.15045, loss_grounding_ce_0: 0.00294/0.24899, loss_mask_ce_1: 0.23551/0.75713, loss_mask_bce_1: 0.22820/0.30159, loss_mask_dice_1: 0.24772/1.02458, loss_spatial_bce_1: 0.07480/0.08533, loss_spatial_dice_1: 0.07859/0.18260, loss_spatial_ce_1: 0.00068/0.06095, loss_grounding_bce_1: 0.06617/0.08084, loss_grounding_dice_1: 0.04460/0.15118, loss_grounding_ce_1: 0.00348/0.25064, loss_mask_ce_2: 0.22491/0.76504, loss_mask_bce_2: 0.22599/0.30189, loss_mask_dice_2: 0.23866/1.02550, loss_spatial_bce_2: 0.07536/0.08539, loss_spatial_dice_2: 0.07603/0.18311, loss_spatial_ce_2: 0.00111/0.06322, loss_grounding_bce_2: 0.07006/0.08084, loss_grounding_dice_2: 0.04331/0.15108, loss_grounding_ce_2: 0.00402/0.25367, loss_mask_ce_3: 0.25607/0.76874, loss_mask_bce_3: 0.22659/0.30333, loss_mask_dice_3: 0.24619/1.02359, loss_spatial_bce_3: 0.07689/0.08754, loss_spatial_dice_3: 0.07861/0.18447, loss_spatial_ce_3: 0.00157/0.06791, loss_grounding_bce_3: 0.05805/0.08123, loss_grounding_dice_3: 0.03912/0.15074, loss_grounding_ce_3: 0.00201/0.25466, loss_mask_ce_4: 0.24106/0.77424, loss_mask_bce_4: 0.22852/0.30595, loss_mask_dice_4: 0.24656/1.04264, loss_spatial_bce_4: 0.07605/0.08974, loss_spatial_dice_4: 0.08427/0.19269, loss_spatial_ce_4: 0.00500/0.08156, loss_grounding_bce_4: 0.05860/0.08186, loss_grounding_dice_4: 0.03805/0.15335, loss_grounding_ce_4: 0.00180/0.25902, loss_mask_ce_5: 0.23388/0.79885, loss_mask_bce_5: 0.22579/0.30776, loss_mask_dice_5: 0.24875/1.05018, loss_spatial_bce_5: 0.07780/0.09207, loss_spatial_dice_5: 0.08113/0.19582, loss_spatial_ce_5: 0.00858/0.09458, loss_grounding_bce_5: 0.06191/0.08216, loss_grounding_dice_5: 0.04029/0.15403, loss_grounding_ce_5: 0.00426/0.27740, loss_mask_ce_6: 0.26984/0.82581, loss_mask_bce_6: 0.22564/0.30993, loss_mask_dice_6: 0.23223/1.05393, loss_spatial_bce_6: 0.09385/0.09725, loss_spatial_dice_6: 0.07737/0.19817, loss_spatial_ce_6: 0.04114/0.11919, loss_grounding_bce_6: 0.06565/0.08299, loss_grounding_dice_6: 0.04187/0.15465, loss_grounding_ce_6: 0.00154/0.28643, loss_mask_ce_7: 0.21571/0.88156, loss_mask_bce_7: 0.22562/0.31699, loss_mask_dice_7: 0.25797/1.10000, loss_spatial_bce_7: 0.09025/0.10687, loss_spatial_dice_7: 0.09464/0.22341, loss_spatial_ce_7: 0.08851/0.15601, loss_grounding_bce_7: 0.05336/0.08470, loss_grounding_dice_7: 0.03601/0.16023, loss_grounding_ce_7: 0.00155/0.31953, loss_mask_ce_8: 0.24774/1.01678, loss_mask_bce_8: 0.23853/0.33312, loss_mask_dice_8: 0.26546/1.17670, loss_spatial_bce_8: 0.10594/0.12409, loss_spatial_dice_8: 0.17000/0.25879, loss_spatial_ce_8: 0.13293/0.20282, loss_grounding_bce_8: 0.07171/0.08890, loss_grounding_dice_8: 0.06330/0.16990, loss_grounding_ce_8: 0.08998/0.41932, loss_mask_ce_9: 1.90469/3.47741, loss_mask_bce_9: 0.24726/0.36009, loss_mask_dice_9: 0.46740/1.76039, loss_spatial_bce_9: 0.53433/0.35477, loss_spatial_dice_9: 0.87999/0.79330, loss_spatial_ce_9: 1.29122/1.38929, loss_grounding_bce_9: 0.07662/0.10095, loss_grounding_dice_9: 0.04465/0.24215, loss_grounding_ce_9: 0.15450/0.67331] items per batch[64] items per second[0.37] total items[4153600] mini batches[ 64900] memory[4999] epoch remaining[0:25:42] INFO:trainer.default_trainer:epochs[ 35] optim steps[65000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20288/0.75661, loss_mask_bce_0: 0.29049/0.30070, loss_mask_dice_0: 0.20744/1.02093, loss_spatial_bce_0: 0.11679/0.08496, loss_spatial_dice_0: 0.08809/0.17987, loss_spatial_ce_0: 0.00025/0.05711, loss_grounding_bce_0: 0.20584/0.08062, loss_grounding_dice_0: 0.13196/0.15044, loss_grounding_ce_0: 0.00905/0.24896, loss_mask_ce_1: 0.19343/0.75719, loss_mask_bce_1: 0.31046/0.30153, loss_mask_dice_1: 0.20946/1.02492, loss_spatial_bce_1: 0.11589/0.08531, loss_spatial_dice_1: 0.08271/0.18260, loss_spatial_ce_1: 0.00016/0.06091, loss_grounding_bce_1: 0.23234/0.08082, loss_grounding_dice_1: 0.13633/0.15118, loss_grounding_ce_1: 0.01169/0.25062, loss_mask_ce_2: 0.20008/0.76511, loss_mask_bce_2: 0.30589/0.30183, loss_mask_dice_2: 0.20877/1.02583, loss_spatial_bce_2: 0.12377/0.08537, loss_spatial_dice_2: 0.09111/0.18311, loss_spatial_ce_2: 0.00026/0.06317, loss_grounding_bce_2: 0.22274/0.08081, loss_grounding_dice_2: 0.13719/0.15107, loss_grounding_ce_2: 0.01493/0.25366, loss_mask_ce_3: 0.23610/0.76883, loss_mask_bce_3: 0.31301/0.30327, loss_mask_dice_3: 0.20643/1.02388, loss_spatial_bce_3: 0.13769/0.08751, loss_spatial_dice_3: 0.10990/0.18448, loss_spatial_ce_3: 0.00023/0.06788, loss_grounding_bce_3: 0.21876/0.08120, loss_grounding_dice_3: 0.13218/0.15073, loss_grounding_ce_3: 0.01209/0.25464, loss_mask_ce_4: 0.21255/0.77435, loss_mask_bce_4: 0.31912/0.30589, loss_mask_dice_4: 0.20347/1.04295, loss_spatial_bce_4: 0.12883/0.08971, loss_spatial_dice_4: 0.11637/0.19269, loss_spatial_ce_4: 0.00171/0.08155, loss_grounding_bce_4: 0.23593/0.08182, loss_grounding_dice_4: 0.13837/0.15335, loss_grounding_ce_4: 0.01080/0.25904, loss_mask_ce_5: 0.24156/0.79894, loss_mask_bce_5: 0.29719/0.30771, loss_mask_dice_5: 0.21797/1.05055, loss_spatial_bce_5: 0.12460/0.09204, loss_spatial_dice_5: 0.09974/0.19583, loss_spatial_ce_5: 0.00629/0.09456, loss_grounding_bce_5: 0.21735/0.08213, loss_grounding_dice_5: 0.13980/0.15403, loss_grounding_ce_5: 0.03375/0.27737, loss_mask_ce_6: 0.25073/0.82592, loss_mask_bce_6: 0.29007/0.30987, loss_mask_dice_6: 0.22345/1.05427, loss_spatial_bce_6: 0.16525/0.09722, loss_spatial_dice_6: 0.12219/0.19817, loss_spatial_ce_6: 0.09141/0.11916, loss_grounding_bce_6: 0.21198/0.08295, loss_grounding_dice_6: 0.14695/0.15465, loss_grounding_ce_6: 0.01747/0.28648, loss_mask_ce_7: 0.18385/0.88172, loss_mask_bce_7: 0.26822/0.31693, loss_mask_dice_7: 0.21295/1.10039, loss_spatial_bce_7: 0.13909/0.10684, loss_spatial_dice_7: 0.14357/0.22341, loss_spatial_ce_7: 0.14457/0.15599, loss_grounding_bce_7: 0.19548/0.08467, loss_grounding_dice_7: 0.12928/0.16023, loss_grounding_ce_7: 0.02209/0.31949, loss_mask_ce_8: 0.20770/1.01690, loss_mask_bce_8: 0.26189/0.33305, loss_mask_dice_8: 0.27696/1.17705, loss_spatial_bce_8: 0.15636/0.12406, loss_spatial_dice_8: 0.14878/0.25879, loss_spatial_ce_8: 0.08648/0.20278, loss_grounding_bce_8: 0.19363/0.08887, loss_grounding_dice_8: 0.20493/0.16991, loss_grounding_ce_8: 0.01671/0.41939, loss_mask_ce_9: 2.25089/3.47761, loss_mask_bce_9: 0.24435/0.36001, loss_mask_dice_9: 0.28373/1.76088, loss_spatial_bce_9: 0.53259/0.35471, loss_spatial_dice_9: 0.71868/0.79329, loss_spatial_ce_9: 0.94676/1.38932, loss_grounding_bce_9: 0.17648/0.10091, loss_grounding_dice_9: 0.18242/0.24217, loss_grounding_ce_9: 0.34267/0.67340] items per batch[64] items per second[0.37] total items[4160000] mini batches[ 65000] memory[4999] epoch remaining[0:22:43] INFO:trainer.default_trainer:epochs[ 35] optim steps[65100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71578/0.75654, loss_mask_bce_0: 0.22604/0.30066, loss_mask_dice_0: 2.60600/1.02073, loss_spatial_bce_0: 0.04987/0.08495, loss_spatial_dice_0: 0.39162/0.17984, loss_spatial_ce_0: 0.22359/0.05708, loss_grounding_bce_0: 0.00352/0.08061, loss_grounding_dice_0: 0.33075/0.15043, loss_grounding_ce_0: 0.83945/0.24886, loss_mask_ce_1: 0.68217/0.75710, loss_mask_bce_1: 0.26211/0.30149, loss_mask_dice_1: 2.66887/1.02470, loss_spatial_bce_1: 0.05190/0.08530, loss_spatial_dice_1: 0.28912/0.18257, loss_spatial_ce_1: 0.35158/0.06090, loss_grounding_bce_1: 0.00713/0.08081, loss_grounding_dice_1: 0.48611/0.15116, loss_grounding_ce_1: 0.74482/0.25051, loss_mask_ce_2: 0.69555/0.76502, loss_mask_bce_2: 0.25106/0.30177, loss_mask_dice_2: 2.51195/1.02560, loss_spatial_bce_2: 0.05334/0.08536, loss_spatial_dice_2: 0.36741/0.18308, loss_spatial_ce_2: 0.17348/0.06314, loss_grounding_bce_2: 0.00650/0.08080, loss_grounding_dice_2: 0.44864/0.15105, loss_grounding_ce_2: 0.81342/0.25356, loss_mask_ce_3: 0.76672/0.76875, loss_mask_bce_3: 0.26565/0.30322, loss_mask_dice_3: 2.37217/1.02366, loss_spatial_bce_3: 0.05601/0.08750, loss_spatial_dice_3: 0.34879/0.18444, loss_spatial_ce_3: 0.23075/0.06785, loss_grounding_bce_3: 0.00764/0.08119, loss_grounding_dice_3: 0.40498/0.15072, loss_grounding_ce_3: 0.75024/0.25453, loss_mask_ce_4: 0.74717/0.77425, loss_mask_bce_4: 0.25417/0.30584, loss_mask_dice_4: 2.50185/1.04273, loss_spatial_bce_4: 0.05588/0.08971, loss_spatial_dice_4: 0.36904/0.19267, loss_spatial_ce_4: 0.27410/0.08152, loss_grounding_bce_4: 0.00689/0.08181, loss_grounding_dice_4: 0.41681/0.15333, loss_grounding_ce_4: 0.75041/0.25895, loss_mask_ce_5: 0.78229/0.79882, loss_mask_bce_5: 0.20718/0.30766, loss_mask_dice_5: 2.33229/1.05035, loss_spatial_bce_5: 0.05065/0.09203, loss_spatial_dice_5: 0.34950/0.19580, loss_spatial_ce_5: 0.14352/0.09456, loss_grounding_bce_5: 0.00877/0.08212, loss_grounding_dice_5: 0.53896/0.15402, loss_grounding_ce_5: 0.79965/0.27724, loss_mask_ce_6: 1.08395/0.82582, loss_mask_bce_6: 0.23531/0.30982, loss_mask_dice_6: 2.48462/1.05405, loss_spatial_bce_6: 0.05096/0.09721, loss_spatial_dice_6: 0.38514/0.19814, loss_spatial_ce_6: 0.09349/0.11917, loss_grounding_bce_6: 0.00798/0.08294, loss_grounding_dice_6: 0.37367/0.15463, loss_grounding_ce_6: 0.78622/0.28634, loss_mask_ce_7: 0.82575/0.88154, loss_mask_bce_7: 0.23321/0.31688, loss_mask_dice_7: 2.43763/1.10015, loss_spatial_bce_7: 0.05392/0.10685, loss_spatial_dice_7: 0.43842/0.22339, loss_spatial_ce_7: 0.18176/0.15596, loss_grounding_bce_7: 0.00755/0.08466, loss_grounding_dice_7: 0.40387/0.16021, loss_grounding_ce_7: 0.82541/0.31937, loss_mask_ce_8: 1.07206/1.01679, loss_mask_bce_8: 0.21837/0.33300, loss_mask_dice_8: 2.64689/1.17678, loss_spatial_bce_8: 0.06028/0.12405, loss_spatial_dice_8: 0.43504/0.25875, loss_spatial_ce_8: 0.29387/0.20274, loss_grounding_bce_8: 0.00652/0.08885, loss_grounding_dice_8: 0.39711/0.16989, loss_grounding_ce_8: 0.79240/0.41915, loss_mask_ce_9: 6.14742/3.47746, loss_mask_bce_9: 0.26974/0.35995, loss_mask_dice_9: 3.64158/1.76063, loss_spatial_bce_9: 0.27998/0.35476, loss_spatial_dice_9: 0.92057/0.79325, loss_spatial_ce_9: 1.17926/1.38907, loss_grounding_bce_9: 0.00491/0.10090, loss_grounding_dice_9: 0.62565/0.24216, loss_grounding_ce_9: 0.66597/0.67319] items per batch[64] items per second[0.37] total items[4166400] mini batches[ 65100] memory[4999] epoch remaining[0:19:45] INFO:trainer.default_trainer:epochs[ 35] optim steps[65200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31962/0.75661, loss_mask_bce_0: 0.03954/0.30062, loss_mask_dice_0: 0.24622/1.02083, loss_spatial_bce_0: 0.02998/0.08494, loss_spatial_dice_0: 0.15745/0.17983, loss_spatial_ce_0: 0.02589/0.05707, loss_grounding_bce_0: 0.01767/0.08060, loss_grounding_dice_0: 0.09509/0.15039, loss_grounding_ce_0: 0.06319/0.24891, loss_mask_ce_1: 0.28848/0.75719, loss_mask_bce_1: 0.04262/0.30145, loss_mask_dice_1: 0.25681/1.02480, loss_spatial_bce_1: 0.02783/0.08529, loss_spatial_dice_1: 0.14265/0.18256, loss_spatial_ce_1: 0.01680/0.06089, loss_grounding_bce_1: 0.01738/0.08080, loss_grounding_dice_1: 0.09964/0.15112, loss_grounding_ce_1: 0.05382/0.25052, loss_mask_ce_2: 0.29119/0.76510, loss_mask_bce_2: 0.03286/0.30174, loss_mask_dice_2: 0.19251/1.02567, loss_spatial_bce_2: 0.02590/0.08534, loss_spatial_dice_2: 0.15476/0.18307, loss_spatial_ce_2: 0.00897/0.06313, loss_grounding_bce_2: 0.02043/0.08080, loss_grounding_dice_2: 0.09791/0.15102, loss_grounding_ce_2: 0.04673/0.25356, loss_mask_ce_3: 0.31254/0.76886, loss_mask_bce_3: 0.03349/0.30318, loss_mask_dice_3: 0.20116/1.02373, loss_spatial_bce_3: 0.02361/0.08749, loss_spatial_dice_3: 0.12220/0.18444, loss_spatial_ce_3: 0.00665/0.06783, loss_grounding_bce_3: 0.01735/0.08119, loss_grounding_dice_3: 0.08928/0.15068, loss_grounding_ce_3: 0.05376/0.25462, loss_mask_ce_4: 0.27581/0.77433, loss_mask_bce_4: 0.03461/0.30581, loss_mask_dice_4: 0.21177/1.04286, loss_spatial_bce_4: 0.02442/0.08969, loss_spatial_dice_4: 0.12037/0.19266, loss_spatial_ce_4: 0.04969/0.08150, loss_grounding_bce_4: 0.01945/0.08181, loss_grounding_dice_4: 0.08558/0.15331, loss_grounding_ce_4: 0.05754/0.25896, loss_mask_ce_5: 0.26770/0.79889, loss_mask_bce_5: 0.03848/0.30763, loss_mask_dice_5: 0.23689/1.05042, loss_spatial_bce_5: 0.04001/0.09202, loss_spatial_dice_5: 0.14590/0.19580, loss_spatial_ce_5: 0.05052/0.09454, loss_grounding_bce_5: 0.02099/0.08211, loss_grounding_dice_5: 0.09647/0.15399, loss_grounding_ce_5: 0.05622/0.27730, loss_mask_ce_6: 0.32085/0.82587, loss_mask_bce_6: 0.04055/0.30979, loss_mask_dice_6: 0.24563/1.05414, loss_spatial_bce_6: 0.03213/0.09720, loss_spatial_dice_6: 0.12910/0.19814, loss_spatial_ce_6: 0.03007/0.11916, loss_grounding_bce_6: 0.01704/0.08294, loss_grounding_dice_6: 0.08203/0.15461, loss_grounding_ce_6: 0.04956/0.28643, loss_mask_ce_7: 0.35278/0.88165, loss_mask_bce_7: 0.03045/0.31685, loss_mask_dice_7: 0.24360/1.10025, loss_spatial_bce_7: 0.02458/0.10683, loss_spatial_dice_7: 0.13850/0.22338, loss_spatial_ce_7: 0.05512/0.15591, loss_grounding_bce_7: 0.01908/0.08466, loss_grounding_dice_7: 0.08861/0.16019, loss_grounding_ce_7: 0.04111/0.31940, loss_mask_ce_8: 0.39860/1.01688, loss_mask_bce_8: 0.02981/0.33297, loss_mask_dice_8: 0.20472/1.17685, loss_spatial_bce_8: 0.02772/0.12401, loss_spatial_dice_8: 0.17994/0.25873, loss_spatial_ce_8: 0.10396/0.20268, loss_grounding_bce_8: 0.01485/0.08885, loss_grounding_dice_8: 0.08773/0.16987, loss_grounding_ce_8: 0.05589/0.41916, loss_mask_ce_9: 1.92279/3.47770, loss_mask_bce_9: 0.03160/0.35997, loss_mask_dice_9: 0.36718/1.76079, loss_spatial_bce_9: 0.07823/0.35471, loss_spatial_dice_9: 0.78900/0.79324, loss_spatial_ce_9: 1.80960/1.38900, loss_grounding_bce_9: 0.01689/0.10090, loss_grounding_dice_9: 0.13469/0.24212, loss_grounding_ce_9: 0.09106/0.67311] items per batch[64] items per second[0.37] total items[4172800] mini batches[ 65200] memory[4999] epoch remaining[0:16:48] INFO:trainer.default_trainer:epochs[ 35] optim steps[65300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44540/0.75665, loss_mask_bce_0: 0.02414/0.30061, loss_mask_dice_0: 1.95902/1.02092, loss_spatial_bce_0: 0.00409/0.08493, loss_spatial_dice_0: 0.27438/0.17982, loss_spatial_ce_0: 0.09105/0.05705, loss_grounding_bce_0: 0.00202/0.08060, loss_grounding_dice_0: 0.43331/0.15039, loss_grounding_ce_0: 0.35290/0.24885, loss_mask_ce_1: 0.46057/0.75723, loss_mask_bce_1: 0.01059/0.30145, loss_mask_dice_1: 1.38896/1.02493, loss_spatial_bce_1: 0.00439/0.08528, loss_spatial_dice_1: 0.19482/0.18255, loss_spatial_ce_1: 0.05845/0.06087, loss_grounding_bce_1: 0.00179/0.08080, loss_grounding_dice_1: 0.44586/0.15112, loss_grounding_ce_1: 0.62100/0.25047, loss_mask_ce_2: 0.43539/0.76512, loss_mask_bce_2: 0.00726/0.30174, loss_mask_dice_2: 0.78137/1.02576, loss_spatial_bce_2: 0.00372/0.08534, loss_spatial_dice_2: 0.14865/0.18306, loss_spatial_ce_2: 0.03095/0.06309, loss_grounding_bce_2: 0.00169/0.08080, loss_grounding_dice_2: 0.27420/0.15102, loss_grounding_ce_2: 0.60274/0.25353, loss_mask_ce_3: 0.48610/0.76887, loss_mask_bce_3: 0.01056/0.30318, loss_mask_dice_3: 0.83882/1.02388, loss_spatial_bce_3: 0.00298/0.08749, loss_spatial_dice_3: 0.19174/0.18443, loss_spatial_ce_3: 0.02787/0.06780, loss_grounding_bce_3: 0.00377/0.08119, loss_grounding_dice_3: 0.57246/0.15070, loss_grounding_ce_3: 0.30032/0.25459, loss_mask_ce_4: 0.40978/0.77439, loss_mask_bce_4: 0.00603/0.30581, loss_mask_dice_4: 0.84220/1.04297, loss_spatial_bce_4: 0.00370/0.08968, loss_spatial_dice_4: 0.14057/0.19265, loss_spatial_ce_4: 0.16649/0.08148, loss_grounding_bce_4: 0.00160/0.08182, loss_grounding_dice_4: 0.43975/0.15332, loss_grounding_ce_4: 0.74090/0.25892, loss_mask_ce_5: 0.29827/0.79891, loss_mask_bce_5: 0.00928/0.30763, loss_mask_dice_5: 1.11999/1.05058, loss_spatial_bce_5: 0.00343/0.09202, loss_spatial_dice_5: 0.19993/0.19580, loss_spatial_ce_5: 0.01176/0.09454, loss_grounding_bce_5: 0.00124/0.08212, loss_grounding_dice_5: 0.45755/0.15401, loss_grounding_ce_5: 0.30334/0.27726, loss_mask_ce_6: 0.33462/0.82590, loss_mask_bce_6: 0.00636/0.30979, loss_mask_dice_6: 0.43526/1.05425, loss_spatial_bce_6: 0.00494/0.09720, loss_spatial_dice_6: 0.26216/0.19813, loss_spatial_ce_6: 0.04312/0.11914, loss_grounding_bce_6: 0.00125/0.08294, loss_grounding_dice_6: 0.32757/0.15462, loss_grounding_ce_6: 0.40824/0.28635, loss_mask_ce_7: 0.88783/0.88166, loss_mask_bce_7: 0.01158/0.31686, loss_mask_dice_7: 0.84390/1.10037, loss_spatial_bce_7: 0.00362/0.10683, loss_spatial_dice_7: 0.30855/0.22337, loss_spatial_ce_7: 0.04873/0.15589, loss_grounding_bce_7: 0.00190/0.08467, loss_grounding_dice_7: 0.36689/0.16021, loss_grounding_ce_7: 0.40155/0.31930, loss_mask_ce_8: 0.73484/1.01691, loss_mask_bce_8: 0.01725/0.33298, loss_mask_dice_8: 1.60460/1.17700, loss_spatial_bce_8: 0.01941/0.12401, loss_spatial_dice_8: 0.32886/0.25872, loss_spatial_ce_8: 0.56050/0.20261, loss_grounding_bce_8: 0.00338/0.08885, loss_grounding_dice_8: 0.44700/0.16987, loss_grounding_ce_8: 0.41086/0.41906, loss_mask_ce_9: 2.08047/3.47769, loss_mask_bce_9: 0.00865/0.35998, loss_mask_dice_9: 0.90405/1.76094, loss_spatial_bce_9: 0.01541/0.35472, loss_spatial_dice_9: 0.78050/0.79323, loss_spatial_ce_9: 2.47775/1.38894, loss_grounding_bce_9: 0.00097/0.10090, loss_grounding_dice_9: 0.33488/0.24211, loss_grounding_ce_9: 0.46749/0.67301] items per batch[64] items per second[0.37] total items[4179200] mini batches[ 65300] memory[4999] epoch remaining[0:13:51] INFO:trainer.default_trainer:epochs[ 35] optim steps[65400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44648/0.75671, loss_mask_bce_0: 0.17618/0.30067, loss_mask_dice_0: 0.12102/1.02101, loss_spatial_bce_0: 0.10779/0.08492, loss_spatial_dice_0: 0.05794/0.17980, loss_spatial_ce_0: 0.00073/0.05702, loss_grounding_bce_0: 0.10539/0.08057, loss_grounding_dice_0: 0.07200/0.15038, loss_grounding_ce_0: 0.00748/0.24889, loss_mask_ce_1: 0.46106/0.75730, loss_mask_bce_1: 0.19540/0.30151, loss_mask_dice_1: 0.13339/1.02499, loss_spatial_bce_1: 0.11998/0.08527, loss_spatial_dice_1: 0.06532/0.18254, loss_spatial_ce_1: 0.00035/0.06084, loss_grounding_bce_1: 0.09287/0.08078, loss_grounding_dice_1: 0.07753/0.15112, loss_grounding_ce_1: 0.01236/0.25052, loss_mask_ce_2: 0.45163/0.76513, loss_mask_bce_2: 0.19134/0.30180, loss_mask_dice_2: 0.12789/1.02585, loss_spatial_bce_2: 0.11430/0.08533, loss_spatial_dice_2: 0.06559/0.18305, loss_spatial_ce_2: 0.00081/0.06307, loss_grounding_bce_2: 0.08932/0.08078, loss_grounding_dice_2: 0.07368/0.15101, loss_grounding_ce_2: 0.00965/0.25356, loss_mask_ce_3: 0.46704/0.76890, loss_mask_bce_3: 0.23948/0.30324, loss_mask_dice_3: 0.14017/1.02398, loss_spatial_bce_3: 0.14884/0.08748, loss_spatial_dice_3: 0.07566/0.18442, loss_spatial_ce_3: 0.00019/0.06778, loss_grounding_bce_3: 0.13159/0.08117, loss_grounding_dice_3: 0.08456/0.15068, loss_grounding_ce_3: 0.00889/0.25465, loss_mask_ce_4: 0.33836/0.77450, loss_mask_bce_4: 0.24522/0.30587, loss_mask_dice_4: 0.13781/1.04306, loss_spatial_bce_4: 0.16500/0.08968, loss_spatial_dice_4: 0.09076/0.19265, loss_spatial_ce_4: 0.00048/0.08146, loss_grounding_bce_4: 0.15117/0.08180, loss_grounding_dice_4: 0.09437/0.15330, loss_grounding_ce_4: 0.00228/0.25899, loss_mask_ce_5: 0.34861/0.79895, loss_mask_bce_5: 0.25070/0.30771, loss_mask_dice_5: 0.14225/1.05069, loss_spatial_bce_5: 0.15050/0.09201, loss_spatial_dice_5: 0.08031/0.19579, loss_spatial_ce_5: 0.03787/0.09453, loss_grounding_bce_5: 0.14843/0.08210, loss_grounding_dice_5: 0.09530/0.15400, loss_grounding_ce_5: 0.00056/0.27734, loss_mask_ce_6: 0.36186/0.82599, loss_mask_bce_6: 0.24407/0.30987, loss_mask_dice_6: 0.13433/1.05435, loss_spatial_bce_6: 0.15000/0.09719, loss_spatial_dice_6: 0.07662/0.19813, loss_spatial_ce_6: 0.00287/0.11912, loss_grounding_bce_6: 0.15945/0.08292, loss_grounding_dice_6: 0.12049/0.15461, loss_grounding_ce_6: 0.00005/0.28640, loss_mask_ce_7: 0.40637/0.88176, loss_mask_bce_7: 0.26228/0.31693, loss_mask_dice_7: 0.14702/1.10045, loss_spatial_bce_7: 0.19919/0.10681, loss_spatial_dice_7: 0.10110/0.22336, loss_spatial_ce_7: 0.10608/0.15590, loss_grounding_bce_7: 0.20718/0.08464, loss_grounding_dice_7: 0.13973/0.16020, loss_grounding_ce_7: 0.00048/0.31940, loss_mask_ce_8: 0.57112/1.01703, loss_mask_bce_8: 0.16100/0.33304, loss_mask_dice_8: 0.12706/1.17710, loss_spatial_bce_8: 0.30623/0.12399, loss_spatial_dice_8: 0.11522/0.25873, loss_spatial_ce_8: 0.00005/0.20256, loss_grounding_bce_8: 0.14810/0.08882, loss_grounding_dice_8: 0.13534/0.16985, loss_grounding_ce_8: 0.47304/0.41921, loss_mask_ce_9: 2.01968/3.47785, loss_mask_bce_9: 0.18902/0.36004, loss_mask_dice_9: 0.16229/1.76117, loss_spatial_bce_9: 0.62219/0.35466, loss_spatial_dice_9: 0.72148/0.79326, loss_spatial_ce_9: 0.91554/1.38890, loss_grounding_bce_9: 0.18018/0.10089, loss_grounding_dice_9: 0.13570/0.24212, loss_grounding_ce_9: 2.01820/0.67338] items per batch[64] items per second[0.37] total items[4185600] mini batches[ 65400] memory[4999] epoch remaining[0:10:54] INFO:trainer.default_trainer:epochs[ 35] optim steps[65500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14366/0.75672, loss_mask_bce_0: 0.03464/0.30063, loss_mask_dice_0: 0.26522/1.02101, loss_spatial_bce_0: 0.02121/0.08491, loss_spatial_dice_0: 0.15730/0.17978, loss_spatial_ce_0: 0.00021/0.05702, loss_grounding_bce_0: 0.01783/0.08057, loss_grounding_dice_0: 0.05862/0.15034, loss_grounding_ce_0: 0.02568/0.24887, loss_mask_ce_1: 0.15977/0.75730, loss_mask_bce_1: 0.02851/0.30147, loss_mask_dice_1: 0.24047/1.02501, loss_spatial_bce_1: 0.01863/0.08526, loss_spatial_dice_1: 0.15630/0.18251, loss_spatial_ce_1: 0.00009/0.06082, loss_grounding_bce_1: 0.01701/0.08078, loss_grounding_dice_1: 0.06153/0.15108, loss_grounding_ce_1: 0.04512/0.25048, loss_mask_ce_2: 0.16663/0.76510, loss_mask_bce_2: 0.02850/0.30176, loss_mask_dice_2: 0.24777/1.02585, loss_spatial_bce_2: 0.02204/0.08533, loss_spatial_dice_2: 0.15741/0.18304, loss_spatial_ce_2: 0.00015/0.06304, loss_grounding_bce_2: 0.01587/0.08077, loss_grounding_dice_2: 0.05390/0.15097, loss_grounding_ce_2: 0.05467/0.25352, loss_mask_ce_3: 0.20364/0.76887, loss_mask_bce_3: 0.03387/0.30320, loss_mask_dice_3: 0.28661/1.02399, loss_spatial_bce_3: 0.02365/0.08747, loss_spatial_dice_3: 0.16421/0.18441, loss_spatial_ce_3: 0.00028/0.06775, loss_grounding_bce_3: 0.01714/0.08116, loss_grounding_dice_3: 0.06266/0.15065, loss_grounding_ce_3: 0.07850/0.25462, loss_mask_ce_4: 0.18842/0.77451, loss_mask_bce_4: 0.02859/0.30584, loss_mask_dice_4: 0.26307/1.04309, loss_spatial_bce_4: 0.02713/0.08968, loss_spatial_dice_4: 0.19086/0.19263, loss_spatial_ce_4: 0.00368/0.08146, loss_grounding_bce_4: 0.01857/0.08180, loss_grounding_dice_4: 0.07172/0.15326, loss_grounding_ce_4: 0.08973/0.25897, loss_mask_ce_5: 0.16495/0.79895, loss_mask_bce_5: 0.03223/0.30767, loss_mask_dice_5: 0.30399/1.05065, loss_spatial_bce_5: 0.03101/0.09201, loss_spatial_dice_5: 0.19404/0.19578, loss_spatial_ce_5: 0.03124/0.09454, loss_grounding_bce_5: 0.01871/0.08209, loss_grounding_dice_5: 0.09651/0.15396, loss_grounding_ce_5: 0.06125/0.27732, loss_mask_ce_6: 0.11753/0.82599, loss_mask_bce_6: 0.03810/0.30983, loss_mask_dice_6: 0.30596/1.05438, loss_spatial_bce_6: 0.03832/0.09719, loss_spatial_dice_6: 0.20352/0.19811, loss_spatial_ce_6: 0.04568/0.11912, loss_grounding_bce_6: 0.01908/0.08293, loss_grounding_dice_6: 0.08947/0.15458, loss_grounding_ce_6: 0.03557/0.28639, loss_mask_ce_7: 0.20598/0.88181, loss_mask_bce_7: 0.04241/0.31689, loss_mask_dice_7: 0.30087/1.10045, loss_spatial_bce_7: 0.02538/0.10681, loss_spatial_dice_7: 0.25495/0.22335, loss_spatial_ce_7: 0.12296/0.15590, loss_grounding_bce_7: 0.01975/0.08465, loss_grounding_dice_7: 0.09372/0.16018, loss_grounding_ce_7: 0.01842/0.31934, loss_mask_ce_8: 0.18541/1.01709, loss_mask_bce_8: 0.03943/0.33298, loss_mask_dice_8: 0.31246/1.17708, loss_spatial_bce_8: 0.03696/0.12398, loss_spatial_dice_8: 0.27980/0.25871, loss_spatial_ce_8: 0.06957/0.20256, loss_grounding_bce_8: 0.02304/0.08883, loss_grounding_dice_8: 0.12091/0.16983, loss_grounding_ce_8: 0.01468/0.41927, loss_mask_ce_9: 1.67194/3.47819, loss_mask_bce_9: 0.03047/0.36002, loss_mask_dice_9: 0.29278/1.76108, loss_spatial_bce_9: 0.13237/0.35466, loss_spatial_dice_9: 0.63565/0.79325, loss_spatial_ce_9: 0.81025/1.38891, loss_grounding_bce_9: 0.02045/0.10090, loss_grounding_dice_9: 0.08983/0.24210, loss_grounding_ce_9: 0.05440/0.67349] items per batch[64] items per second[0.37] total items[4192000] mini batches[ 65500] memory[4999] epoch remaining[0:07:57] INFO:trainer.default_trainer:epochs[ 35] optim steps[65600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39064/0.75660, loss_mask_bce_0: 0.17871/0.30065, loss_mask_dice_0: 2.77837/1.02108, loss_spatial_bce_0: 0.01906/0.08490, loss_spatial_dice_0: 0.29847/0.17976, loss_spatial_ce_0: 0.00789/0.05702, loss_grounding_bce_0: 0.02598/0.08056, loss_grounding_dice_0: 0.36991/0.15037, loss_grounding_ce_0: 0.63682/0.24886, loss_mask_ce_1: 0.38070/0.75721, loss_mask_bce_1: 0.18428/0.30149, loss_mask_dice_1: 2.68157/1.02512, loss_spatial_bce_1: 0.01852/0.08526, loss_spatial_dice_1: 0.29420/0.18250, loss_spatial_ce_1: 0.00822/0.06080, loss_grounding_bce_1: 0.03215/0.08077, loss_grounding_dice_1: 0.38546/0.15110, loss_grounding_ce_1: 0.64543/0.25049, loss_mask_ce_2: 0.43330/0.76501, loss_mask_bce_2: 0.18817/0.30178, loss_mask_dice_2: 2.77850/1.02597, loss_spatial_bce_2: 0.01924/0.08532, loss_spatial_dice_2: 0.33172/0.18302, loss_spatial_ce_2: 0.00666/0.06304, loss_grounding_bce_2: 0.03010/0.08076, loss_grounding_dice_2: 0.38926/0.15099, loss_grounding_ce_2: 0.67411/0.25347, loss_mask_ce_3: 0.49180/0.76881, loss_mask_bce_3: 0.18578/0.30321, loss_mask_dice_3: 2.63366/1.02410, loss_spatial_bce_3: 0.01854/0.08747, loss_spatial_dice_3: 0.30359/0.18440, loss_spatial_ce_3: 0.02787/0.06775, loss_grounding_bce_3: 0.02969/0.08115, loss_grounding_dice_3: 0.39506/0.15068, loss_grounding_ce_3: 0.61070/0.25459, loss_mask_ce_4: 0.40326/0.77446, loss_mask_bce_4: 0.16792/0.30586, loss_mask_dice_4: 2.74900/1.04317, loss_spatial_bce_4: 0.02098/0.08968, loss_spatial_dice_4: 0.34062/0.19264, loss_spatial_ce_4: 0.02232/0.08144, loss_grounding_bce_4: 0.02676/0.08179, loss_grounding_dice_4: 0.37514/0.15328, loss_grounding_ce_4: 0.58020/0.25898, loss_mask_ce_5: 0.41336/0.79891, loss_mask_bce_5: 0.18034/0.30770, loss_mask_dice_5: 2.66307/1.05075, loss_spatial_bce_5: 0.02351/0.09201, loss_spatial_dice_5: 0.34623/0.19577, loss_spatial_ce_5: 0.02664/0.09454, loss_grounding_bce_5: 0.02776/0.08209, loss_grounding_dice_5: 0.40026/0.15398, loss_grounding_ce_5: 0.58760/0.27732, loss_mask_ce_6: 0.49025/0.82594, loss_mask_bce_6: 0.17540/0.30986, loss_mask_dice_6: 2.71141/1.05449, loss_spatial_bce_6: 0.01910/0.09719, loss_spatial_dice_6: 0.32069/0.19811, loss_spatial_ce_6: 0.06383/0.11913, loss_grounding_bce_6: 0.02464/0.08292, loss_grounding_dice_6: 0.37740/0.15460, loss_grounding_ce_6: 0.64896/0.28635, loss_mask_ce_7: 0.59849/0.88172, loss_mask_bce_7: 0.16716/0.31692, loss_mask_dice_7: 2.62926/1.10056, loss_spatial_bce_7: 0.02097/0.10681, loss_spatial_dice_7: 0.34648/0.22334, loss_spatial_ce_7: 0.12274/0.15590, loss_grounding_bce_7: 0.02717/0.08464, loss_grounding_dice_7: 0.37581/0.16019, loss_grounding_ce_7: 0.71171/0.31935, loss_mask_ce_8: 0.57517/1.01700, loss_mask_bce_8: 0.20674/0.33302, loss_mask_dice_8: 2.75253/1.17720, loss_spatial_bce_8: 0.02628/0.12398, loss_spatial_dice_8: 0.39891/0.25870, loss_spatial_ce_8: 0.08761/0.20252, loss_grounding_bce_8: 0.03020/0.08882, loss_grounding_dice_8: 0.39638/0.16984, loss_grounding_ce_8: 0.61716/0.41926, loss_mask_ce_9: 3.82767/3.47799, loss_mask_bce_9: 0.15524/0.36005, loss_mask_dice_9: 3.07162/1.76116, loss_spatial_bce_9: 0.14768/0.35469, loss_spatial_dice_9: 0.97771/0.79325, loss_spatial_ce_9: 2.22404/1.38892, loss_grounding_bce_9: 0.02110/0.10090, loss_grounding_dice_9: 0.42085/0.24211, loss_grounding_ce_9: 0.59967/0.67337] items per batch[64] items per second[0.37] total items[4198400] mini batches[ 65600] memory[4999] epoch remaining[0:05:01] INFO:trainer.default_trainer:epochs[ 35] optim steps[65700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56077/0.75654, loss_mask_bce_0: 0.27292/0.30064, loss_mask_dice_0: 0.44828/1.02066, loss_spatial_bce_0: 0.10623/0.08491, loss_spatial_dice_0: 0.12708/0.17975, loss_spatial_ce_0: 0.00153/0.05701, loss_grounding_bce_0: 0.05209/0.08057, loss_grounding_dice_0: 0.08286/0.15036, loss_grounding_ce_0: 0.01978/0.24877, loss_mask_ce_1: 0.60976/0.75715, loss_mask_bce_1: 0.30367/0.30148, loss_mask_dice_1: 0.48021/1.02470, loss_spatial_bce_1: 0.10136/0.08527, loss_spatial_dice_1: 0.12894/0.18249, loss_spatial_ce_1: 0.00196/0.06080, loss_grounding_bce_1: 0.04837/0.08078, loss_grounding_dice_1: 0.08039/0.15109, loss_grounding_ce_1: 0.01675/0.25040, loss_mask_ce_2: 0.60908/0.76499, loss_mask_bce_2: 0.29417/0.30177, loss_mask_dice_2: 0.51635/1.02557, loss_spatial_bce_2: 0.10683/0.08533, loss_spatial_dice_2: 0.13146/0.18302, loss_spatial_ce_2: 0.00352/0.06302, loss_grounding_bce_2: 0.04895/0.08078, loss_grounding_dice_2: 0.08829/0.15100, loss_grounding_ce_2: 0.01606/0.25340, loss_mask_ce_3: 0.64985/0.76876, loss_mask_bce_3: 0.30262/0.30321, loss_mask_dice_3: 0.51847/1.02369, loss_spatial_bce_3: 0.11195/0.08749, loss_spatial_dice_3: 0.13053/0.18439, loss_spatial_ce_3: 0.01707/0.06775, loss_grounding_bce_3: 0.05033/0.08116, loss_grounding_dice_3: 0.08567/0.15068, loss_grounding_ce_3: 0.01583/0.25449, loss_mask_ce_4: 0.73353/0.77440, loss_mask_bce_4: 0.32687/0.30585, loss_mask_dice_4: 0.47765/1.04276, loss_spatial_bce_4: 0.07987/0.08970, loss_spatial_dice_4: 0.12770/0.19265, loss_spatial_ce_4: 0.01891/0.08143, loss_grounding_bce_4: 0.06085/0.08180, loss_grounding_dice_4: 0.09370/0.15329, loss_grounding_ce_4: 0.02086/0.25889, loss_mask_ce_5: 0.61201/0.79882, loss_mask_bce_5: 0.31972/0.30769, loss_mask_dice_5: 0.53502/1.05033, loss_spatial_bce_5: 0.10123/0.09203, loss_spatial_dice_5: 0.12886/0.19578, loss_spatial_ce_5: 0.01749/0.09456, loss_grounding_bce_5: 0.05912/0.08210, loss_grounding_dice_5: 0.09392/0.15398, loss_grounding_ce_5: 0.03080/0.27721, loss_mask_ce_6: 0.78916/0.82591, loss_mask_bce_6: 0.29853/0.30984, loss_mask_dice_6: 0.50792/1.05407, loss_spatial_bce_6: 0.10982/0.09721, loss_spatial_dice_6: 0.13333/0.19811, loss_spatial_ce_6: 0.04594/0.11916, loss_grounding_bce_6: 0.05707/0.08293, loss_grounding_dice_6: 0.08737/0.15461, loss_grounding_ce_6: 0.05512/0.28623, loss_mask_ce_7: 1.01286/0.88168, loss_mask_bce_7: 0.34366/0.31691, loss_mask_dice_7: 0.56192/1.10012, loss_spatial_bce_7: 0.10189/0.10683, loss_spatial_dice_7: 0.13474/0.22334, loss_spatial_ce_7: 0.16628/0.15591, loss_grounding_bce_7: 0.06043/0.08466, loss_grounding_dice_7: 0.10934/0.16021, loss_grounding_ce_7: 0.11085/0.31921, loss_mask_ce_8: 1.02863/1.01693, loss_mask_bce_8: 0.41256/0.33299, loss_mask_dice_8: 0.62490/1.17672, loss_spatial_bce_8: 0.11505/0.12400, loss_spatial_dice_8: 0.11701/0.25868, loss_spatial_ce_8: 0.13365/0.20250, loss_grounding_bce_8: 0.07454/0.08883, loss_grounding_dice_8: 0.09789/0.16987, loss_grounding_ce_8: 0.11123/0.41916, loss_mask_ce_9: 3.98714/3.47755, loss_mask_bce_9: 0.32206/0.36003, loss_mask_dice_9: 0.65573/1.76041, loss_spatial_bce_9: 0.43593/0.35474, loss_spatial_dice_9: 0.89172/0.79324, loss_spatial_ce_9: 1.05110/1.38882, loss_grounding_bce_9: 0.06567/0.10091, loss_grounding_dice_9: 0.15488/0.24212, loss_grounding_ce_9: 0.77305/0.67331] items per batch[64] items per second[0.37] total items[4204800] mini batches[ 65700] memory[4999] epoch remaining[0:02:06] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00065772. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3765 s/iter. Eval: 0.0926 s/iter. Total: 0.4719 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3731 s/iter. Eval: 0.0779 s/iter. Total: 0.4536 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0027 s/iter. Inference: 0.3790 s/iter. Eval: 0.0736 s/iter. Total: 0.4553 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3814 s/iter. Eval: 0.0701 s/iter. Total: 0.4544 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3829 s/iter. Eval: 0.0690 s/iter. Total: 0.4548 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3814 s/iter. Eval: 0.0675 s/iter. Total: 0.4518 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9d8ir71g ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.486 | 83.210 | 65.927 | 133 | | Things | 61.412 | 84.195 | 72.465 | 80 | | Stuff | 46.540 | 81.723 | 56.060 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... DONE (t=0.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.24 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.33 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.34s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.08 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.695 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.549 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.567 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.49 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.549 | 69.459 | 49.037 | 26.603 | 49.729 | 67.591 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.172 | bicycle | 22.527 | car | 43.944 | | motorcycle | 41.323 | airplane | 61.394 | bus | 71.293 | | train | 73.922 | truck | 42.219 | boat | 30.822 | | traffic light | 28.885 | fire hydrant | 71.382 | stop sign | 69.140 | | parking meter | 51.679 | bench | 26.302 | bird | 34.069 | | cat | 76.106 | dog | 70.228 | horse | 50.168 | | sheep | 52.791 | cow | 57.106 | elephant | 65.885 | | bear | 79.613 | zebra | 65.798 | giraffe | 62.729 | | backpack | 23.636 | umbrella | 55.479 | handbag | 25.444 | | tie | 40.704 | suitcase | 51.739 | frisbee | 70.573 | | skis | 9.700 | snowboard | 34.411 | sports ball | 49.693 | | kite | 37.515 | baseball bat | 38.664 | baseball glove | 50.030 | | skateboard | 44.482 | surfboard | 44.636 | tennis racket | 62.643 | | bottle | 43.052 | wine glass | 37.322 | cup | 50.001 | | fork | 26.562 | knife | 23.714 | spoon | 22.502 | | bowl | 39.008 | banana | 23.068 | apple | 25.918 | | sandwich | 48.207 | orange | 29.406 | broccoli | 24.050 | | carrot | 22.967 | hot dog | 33.322 | pizza | 51.791 | | donut | 54.531 | cake | 48.367 | chair | 29.354 | | couch | 42.964 | potted plant | 22.869 | bed | 44.264 | | dining table | 14.647 | toilet | 69.103 | tv | 65.689 | | laptop | 70.131 | mouse | 64.366 | remote | 43.612 | | keyboard | 58.484 | cell phone | 45.376 | microwave | 65.253 | | oven | 35.035 | toaster | 50.021 | sink | 43.557 | | refrigerator | 70.657 | book | 14.408 | clock | 54.622 | | vase | 40.995 | scissors | 37.734 | teddy bear | 57.819 | | hair drier | 32.875 | toothbrush | 28.424 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.03145307698439, 'fwIoU': 71.93861260456748, 'IoU-person': 89.01181929509583, 'IoU-bicycle': 77.5181067008796, 'IoU-car': 70.89777369770984, 'IoU-motorcycle': 86.1304338114503, 'IoU-airplane': 86.77308042183147, 'IoU-bus': 87.07152098868313, 'IoU-train': 89.73793457102826, 'IoU-truck': 68.24525961126014, 'IoU-boat': 75.46665371832069, 'IoU-traffic light': 79.32521034473426, 'IoU-fire hydrant': 93.27804198446734, 'IoU-stop sign': 95.6449481194658, 'IoU-parking meter': 84.80930874341229, 'IoU-bench': 62.89607897431274, 'IoU-bird': 78.43555473065031, 'IoU-cat': 91.58532626405928, 'IoU-dog': 86.0872665956384, 'IoU-horse': 89.34543750467358, 'IoU-sheep': 86.1608811493696, 'IoU-cow': 87.8970441898884, 'IoU-elephant': 91.70732570271373, 'IoU-bear': 78.68788719984369, 'IoU-zebra': 86.43661844021588, 'IoU-giraffe': 89.47396701152105, 'IoU-backpack': 53.0389551078523, 'IoU-umbrella': 89.2487454230646, 'IoU-handbag': 48.78012428877347, 'IoU-tie': 76.7133235841267, 'IoU-suitcase': 79.78711869125226, 'IoU-frisbee': 84.59098333877819, 'IoU-skis': 60.888170999747956, 'IoU-snowboard': 74.94370586539847, 'IoU-sports ball': 78.85677496493527, 'IoU-kite': 78.08851073188822, 'IoU-baseball bat': 68.89302461899179, 'IoU-baseball glove': 82.8784917954678, 'IoU-skateboard': 86.15233150773575, 'IoU-surfboard': 86.27059116588356, 'IoU-tennis racket': 90.54636969057483, 'IoU-bottle': 70.78523327661091, 'IoU-wine glass': 83.103323290777, 'IoU-cup': 69.99019028084929, 'IoU-fork': 69.02395994724178, 'IoU-knife': 66.14363027071852, 'IoU-spoon': 60.77142718545666, 'IoU-bowl': 60.18871169885074, 'IoU-banana': 82.36438442044302, 'IoU-apple': 59.76902436725968, 'IoU-sandwich': 69.5929270936607, 'IoU-orange': 77.5694675218017, 'IoU-broccoli': 70.06480718362947, 'IoU-carrot': 64.71402471068106, 'IoU-hot dog': 61.75641493275818, 'IoU-pizza': 82.79759599740463, 'IoU-donut': 73.91665578071243, 'IoU-cake': 79.79620184422595, 'IoU-chair': 62.22456252132733, 'IoU-couch': 67.23532289318295, 'IoU-potted plant': 45.326175672577385, 'IoU-bed': 74.44908917290917, 'IoU-dining table': 55.159028655515186, 'IoU-toilet': 82.10445248790926, 'IoU-tv': 78.6056137690129, 'IoU-laptop': 79.42136774754586, 'IoU-mouse': 72.50014339665931, 'IoU-remote': 70.87813289752317, 'IoU-keyboard': 56.2716061244793, 'IoU-cell phone': 83.76648833553968, 'IoU-microwave': 70.9491917981277, 'IoU-oven': 73.43598468895242, 'IoU-toaster': 86.18270008084075, 'IoU-sink': 68.54594010941028, 'IoU-refrigerator': 84.66705623463852, 'IoU-book': 56.29424050437528, 'IoU-clock': 75.23529278386104, 'IoU-vase': 71.00488115029297, 'IoU-scissors': 84.00543078503358, 'IoU-teddy bear': 86.01618718072122, 'IoU-hair drier': 48.701388092233564, 'IoU-toothbrush': 73.98854753397512, 'IoU-banner': 30.25501858280747, 'IoU-blanket': 20.24359444012592, 'IoU-bridge': 39.204533037003664, 'IoU-cardboard': 50.138415149767646, 'IoU-counter': 33.50434134026928, 'IoU-curtain': 70.49889740294988, 'IoU-door-stuff': 49.747018719170974, 'IoU-floor-wood': 64.17128174715408, 'IoU-flower': 50.673345991984945, 'IoU-fruit': 48.51625473039187, 'IoU-gravel': 27.941808861680386, 'IoU-house': 24.83778707481775, 'IoU-light': 43.62210864898067, 'IoU-mirror-stuff': 61.47768497247689, 'IoU-net': 46.48632886035577, 'IoU-pillow': 25.496191298837573, 'IoU-platform': 30.44582580118307, 'IoU-playingfield': 70.59724702235566, 'IoU-railroad': 63.41947712152485, 'IoU-river': 51.07991796075678, 'IoU-road': 67.59473407217908, 'IoU-roof': 18.501740385328112, 'IoU-sand': 64.91584347342032, 'IoU-sea': 85.80641242514055, 'IoU-shelf': 39.81866993985807, 'IoU-snow': 92.2667590754703, 'IoU-stairs': 33.07124625836116, 'IoU-tent': 10.69881200886437, 'IoU-towel': 43.54542093006449, 'IoU-wall-brick': 50.60981913482136, 'IoU-wall-stone': 29.28206064046269, 'IoU-wall-tile': 70.55351837034846, 'IoU-wall-wood': 45.20372612621753, 'IoU-water-other': 23.47033780543176, 'IoU-window-blind': 50.01328159785394, 'IoU-window-other': 50.11400187207173, 'IoU-tree-merged': 82.3599642709103, 'IoU-fence-merged': 55.56541251392751, 'IoU-ceiling-merged': 67.89677096286898, 'IoU-sky-other-merged': 94.16917754869738, 'IoU-cabinet-merged': 65.40686781231351, 'IoU-table-merged': 43.58203803086674, 'IoU-floor-other-merged': 54.76044590494599, 'IoU-pavement-merged': 57.180052207332324, 'IoU-mountain-merged': 59.176131718716285, 'IoU-grass-merged': 72.26158264290717, 'IoU-dirt-merged': 48.4932326304892, 'IoU-paper-merged': 37.411363375604274, 'IoU-food-other-merged': 43.40687147919778, 'IoU-building-other-merged': 59.39874900877198, 'IoU-rock-merged': 65.19898027577587, 'IoU-wall-other-merged': 68.7435740638363, 'IoU-rug-merged': 67.73107594585099, 'mACC': 77.80664980321905, 'pACC': 82.44465272179816, 'ACC-person': 92.91388674954145, 'ACC-bicycle': 89.21524430869336, 'ACC-car': 85.3395863383734, 'ACC-motorcycle': 90.45514580332936, 'ACC-airplane': 90.8281522958642, 'ACC-bus': 95.12879892270875, 'ACC-train': 94.70729663411058, 'ACC-truck': 75.39568362025574, 'ACC-boat': 85.17766404107763, 'ACC-traffic light': 91.00671349351283, 'ACC-fire hydrant': 95.91811244859876, 'ACC-stop sign': 98.44908257571443, 'ACC-parking meter': 87.78020701980387, 'ACC-bench': 80.37034461789877, 'ACC-bird': 82.29402059473063, 'ACC-cat': 95.36317792182074, 'ACC-dog': 88.99071416603525, 'ACC-horse': 94.58239134009345, 'ACC-sheep': 90.53812351206662, 'ACC-cow': 91.05408950204571, 'ACC-elephant': 93.87738541697338, 'ACC-bear': 80.18788599012879, 'ACC-zebra': 88.53815397205177, 'ACC-giraffe': 93.3300580618742, 'ACC-backpack': 69.21302179647856, 'ACC-umbrella': 93.30986187571695, 'ACC-handbag': 73.3945201006783, 'ACC-tie': 84.36490147463257, 'ACC-suitcase': 85.51824662060078, 'ACC-frisbee': 94.15745454545454, 'ACC-skis': 76.5483122485068, 'ACC-snowboard': 81.51208640493742, 'ACC-sports ball': 88.62736585315429, 'ACC-kite': 84.00373522208714, 'ACC-baseball bat': 88.05902494212138, 'ACC-baseball glove': 92.74839137358322, 'ACC-skateboard': 90.53485104874075, 'ACC-surfboard': 91.9553206237763, 'ACC-tennis racket': 94.84633261222011, 'ACC-bottle': 86.66342904678783, 'ACC-wine glass': 91.25219850954652, 'ACC-cup': 88.24236722657004, 'ACC-fork': 82.4390402265711, 'ACC-knife': 78.31794533597127, 'ACC-spoon': 74.66347714932778, 'ACC-bowl': 70.34062337405227, 'ACC-banana': 88.89120606386018, 'ACC-apple': 72.7272452952256, 'ACC-sandwich': 82.56086009859673, 'ACC-orange': 85.51317613025437, 'ACC-broccoli': 78.8033430501564, 'ACC-carrot': 78.5920127846677, 'ACC-hot dog': 67.87745283882185, 'ACC-pizza': 91.70879930207295, 'ACC-donut': 82.63727590782327, 'ACC-cake': 87.72378631716161, 'ACC-chair': 82.7903978689429, 'ACC-couch': 71.83351079986434, 'ACC-potted plant': 57.300187916229426, 'ACC-bed': 83.34362540271408, 'ACC-dining table': 72.95501204363374, 'ACC-toilet': 85.62496136596222, 'ACC-tv': 88.72811602073266, 'ACC-laptop': 92.38378254467703, 'ACC-mouse': 86.63355085873835, 'ACC-remote': 75.22245840994944, 'ACC-keyboard': 61.341127656591546, 'ACC-cell phone': 93.86062410228854, 'ACC-microwave': 74.84950798551722, 'ACC-oven': 90.01907008003128, 'ACC-toaster': 91.18778141208995, 'ACC-sink': 77.23038272444828, 'ACC-refrigerator': 94.77805918549382, 'ACC-book': 77.916561598162, 'ACC-clock': 79.62121282610943, 'ACC-vase': 81.09051302468949, 'ACC-scissors': 89.26603861939785, 'ACC-teddy bear': 91.67400044765515, 'ACC-hair drier': 60.41755614379644, 'ACC-toothbrush': 82.38359972202919, 'ACC-banner': 80.11727333416819, 'ACC-blanket': 34.54150641937766, 'ACC-bridge': 59.22246883983625, 'ACC-cardboard': 67.66371964519412, 'ACC-counter': 56.973433140685714, 'ACC-curtain': 80.78753351260077, 'ACC-door-stuff': 72.85942050713014, 'ACC-floor-wood': 82.40996501100561, 'ACC-flower': 75.74781523722972, 'ACC-fruit': 72.41578272761804, 'ACC-gravel': 38.84963653333659, 'ACC-house': 29.9311005942646, 'ACC-light': 61.763041668261685, 'ACC-mirror-stuff': 77.04163307587041, 'ACC-net': 67.68095988719729, 'ACC-pillow': 55.871412618501395, 'ACC-platform': 52.57311123801492, 'ACC-playingfield': 90.31086195035208, 'ACC-railroad': 78.26518846264743, 'ACC-river': 68.7145006052752, 'ACC-road': 87.45955159821153, 'ACC-roof': 24.71374380161922, 'ACC-sand': 68.14982561667726, 'ACC-sea': 92.458729845572, 'ACC-shelf': 60.31536736647006, 'ACC-snow': 95.4302141353742, 'ACC-stairs': 61.61888298750829, 'ACC-tent': 14.607859947890312, 'ACC-towel': 56.01936349243315, 'ACC-wall-brick': 71.06093926575147, 'ACC-wall-stone': 35.60618022958146, 'ACC-wall-tile': 87.14400888879105, 'ACC-wall-wood': 62.61493905463, 'ACC-water-other': 38.99260243355336, 'ACC-window-blind': 66.43114015326353, 'ACC-window-other': 73.47029957825428, 'ACC-tree-merged': 90.85573917212254, 'ACC-fence-merged': 73.88534432824734, 'ACC-ceiling-merged': 83.05230950364071, 'ACC-sky-other-merged': 97.23902186712225, 'ACC-cabinet-merged': 76.49764343120052, 'ACC-table-merged': 60.693764895794445, 'ACC-floor-other-merged': 66.39483104584191, 'ACC-pavement-merged': 68.98552934064436, 'ACC-mountain-merged': 69.91934223908859, 'ACC-grass-merged': 83.15803408147569, 'ACC-dirt-merged': 70.80345239322384, 'ACC-paper-merged': 50.38244536608831, 'ACC-food-other-merged': 63.96217109949023, 'ACC-building-other-merged': 72.75789662620028, 'ACC-rock-merged': 84.96741714225558, 'ACC-wall-other-merged': 80.90802990097299, 'ACC-rug-merged': 82.34621448337002})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3158 s/iter. Inference: 0.4973 s/iter. Eval: 0.0000 s/iter. Total: 0.8131 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3279 s/iter. Inference: 0.5000 s/iter. Eval: 0.0000 s/iter. Total: 0.8280 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3239 s/iter. Inference: 0.6399 s/iter. Eval: 0.0000 s/iter. Total: 0.9639 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3977172958735733, 'noc@0.8': 2.4056189640035117, 'noc@0.85': 2.82294410301434, 'noc@0.9': 3.6072578285045362, 'miou@iter1': 0.8717565115062612} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1456 s/iter. Eval: 0.0011 s/iter. Total: 0.1479 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.12631225585938, 'precision@0.6': 72.44461822509766, 'precision@0.7': 68.24718475341797, 'precision@0.8': 58.91954803466797, 'precision@0.9': 32.56898498535156, 'cIoU': 61.222923278808594, 'mIoU': 66.76914978027344} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.48591265980164, 'SQ': 83.20998512479282, 'RQ': 65.92746425429593, 'PQ_th': 61.412289800167386, 'SQ_th': 84.19516122842516, 'RQ_th': 72.46478651851795, 'PQ_st': 46.54043773094767, 'SQ_st': 81.72292685515906, 'RQ_st': 56.05980800641358}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.54868806582315, 'AP50': 69.45904290194068, 'AP75': 49.0368790613228, 'APs': 26.603285116527793, 'APm': 49.72949747650627, 'APl': 67.59066673150845, 'AP-person': 49.171956401147106, 'AP-bicycle': 22.527380192412352, 'AP-car': 43.94399370658279, 'AP-motorcycle': 41.32328011192248, 'AP-airplane': 61.393583685047325, 'AP-bus': 71.29260826286064, 'AP-train': 73.92165935111792, 'AP-truck': 42.21945136418355, 'AP-boat': 30.821911824339885, 'AP-traffic light': 28.884745106817096, 'AP-fire hydrant': 71.38228652224315, 'AP-stop sign': 69.14023706816972, 'AP-parking meter': 51.67926426978228, 'AP-bench': 26.30235671210305, 'AP-bird': 34.068625467965575, 'AP-cat': 76.10608922256951, 'AP-dog': 70.2278669775098, 'AP-horse': 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57.818518581102886, 'AP-hair drier': 32.87516986992817, 'AP-toothbrush': 28.42408669199249}), ('sem_seg', {'mIoU': 66.03145307698439, 'fwIoU': 71.93861260456748, 'IoU-person': 89.01181929509583, 'IoU-bicycle': 77.5181067008796, 'IoU-car': 70.89777369770984, 'IoU-motorcycle': 86.1304338114503, 'IoU-airplane': 86.77308042183147, 'IoU-bus': 87.07152098868313, 'IoU-train': 89.73793457102826, 'IoU-truck': 68.24525961126014, 'IoU-boat': 75.46665371832069, 'IoU-traffic light': 79.32521034473426, 'IoU-fire hydrant': 93.27804198446734, 'IoU-stop sign': 95.6449481194658, 'IoU-parking meter': 84.80930874341229, 'IoU-bench': 62.89607897431274, 'IoU-bird': 78.43555473065031, 'IoU-cat': 91.58532626405928, 'IoU-dog': 86.0872665956384, 'IoU-horse': 89.34543750467358, 'IoU-sheep': 86.1608811493696, 'IoU-cow': 87.8970441898884, 'IoU-elephant': 91.70732570271373, 'IoU-bear': 78.68788719984369, 'IoU-zebra': 86.43661844021588, 'IoU-giraffe': 89.47396701152105, 'IoU-backpack': 53.0389551078523, 'IoU-umbrella': 89.2487454230646, 'IoU-handbag': 48.78012428877347, 'IoU-tie': 76.7133235841267, 'IoU-suitcase': 79.78711869125226, 'IoU-frisbee': 84.59098333877819, 'IoU-skis': 60.888170999747956, 'IoU-snowboard': 74.94370586539847, 'IoU-sports ball': 78.85677496493527, 'IoU-kite': 78.08851073188822, 'IoU-baseball bat': 68.89302461899179, 'IoU-baseball glove': 82.8784917954678, 'IoU-skateboard': 86.15233150773575, 'IoU-surfboard': 86.27059116588356, 'IoU-tennis racket': 90.54636969057483, 'IoU-bottle': 70.78523327661091, 'IoU-wine glass': 83.103323290777, 'IoU-cup': 69.99019028084929, 'IoU-fork': 69.02395994724178, 'IoU-knife': 66.14363027071852, 'IoU-spoon': 60.77142718545666, 'IoU-bowl': 60.18871169885074, 'IoU-banana': 82.36438442044302, 'IoU-apple': 59.76902436725968, 'IoU-sandwich': 69.5929270936607, 'IoU-orange': 77.5694675218017, 'IoU-broccoli': 70.06480718362947, 'IoU-carrot': 64.71402471068106, 'IoU-hot dog': 61.75641493275818, 'IoU-pizza': 82.79759599740463, 'IoU-donut': 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'IoU-water-other': 23.47033780543176, 'IoU-window-blind': 50.01328159785394, 'IoU-window-other': 50.11400187207173, 'IoU-tree-merged': 82.3599642709103, 'IoU-fence-merged': 55.56541251392751, 'IoU-ceiling-merged': 67.89677096286898, 'IoU-sky-other-merged': 94.16917754869738, 'IoU-cabinet-merged': 65.40686781231351, 'IoU-table-merged': 43.58203803086674, 'IoU-floor-other-merged': 54.76044590494599, 'IoU-pavement-merged': 57.180052207332324, 'IoU-mountain-merged': 59.176131718716285, 'IoU-grass-merged': 72.26158264290717, 'IoU-dirt-merged': 48.4932326304892, 'IoU-paper-merged': 37.411363375604274, 'IoU-food-other-merged': 43.40687147919778, 'IoU-building-other-merged': 59.39874900877198, 'IoU-rock-merged': 65.19898027577587, 'IoU-wall-other-merged': 68.7435740638363, 'IoU-rug-merged': 67.73107594585099, 'mACC': 77.80664980321905, 'pACC': 82.44465272179816, 'ACC-person': 92.91388674954145, 'ACC-bicycle': 89.21524430869336, 'ACC-car': 85.3395863383734, 'ACC-motorcycle': 90.45514580332936, 'ACC-airplane': 90.8281522958642, 'ACC-bus': 95.12879892270875, 'ACC-train': 94.70729663411058, 'ACC-truck': 75.39568362025574, 'ACC-boat': 85.17766404107763, 'ACC-traffic light': 91.00671349351283, 'ACC-fire hydrant': 95.91811244859876, 'ACC-stop sign': 98.44908257571443, 'ACC-parking meter': 87.78020701980387, 'ACC-bench': 80.37034461789877, 'ACC-bird': 82.29402059473063, 'ACC-cat': 95.36317792182074, 'ACC-dog': 88.99071416603525, 'ACC-horse': 94.58239134009345, 'ACC-sheep': 90.53812351206662, 'ACC-cow': 91.05408950204571, 'ACC-elephant': 93.87738541697338, 'ACC-bear': 80.18788599012879, 'ACC-zebra': 88.53815397205177, 'ACC-giraffe': 93.3300580618742, 'ACC-backpack': 69.21302179647856, 'ACC-umbrella': 93.30986187571695, 'ACC-handbag': 73.3945201006783, 'ACC-tie': 84.36490147463257, 'ACC-suitcase': 85.51824662060078, 'ACC-frisbee': 94.15745454545454, 'ACC-skis': 76.5483122485068, 'ACC-snowboard': 81.51208640493742, 'ACC-sports ball': 88.62736585315429, 'ACC-kite': 84.00373522208714, 'ACC-baseball bat': 88.05902494212138, 'ACC-baseball glove': 92.74839137358322, 'ACC-skateboard': 90.53485104874075, 'ACC-surfboard': 91.9553206237763, 'ACC-tennis racket': 94.84633261222011, 'ACC-bottle': 86.66342904678783, 'ACC-wine glass': 91.25219850954652, 'ACC-cup': 88.24236722657004, 'ACC-fork': 82.4390402265711, 'ACC-knife': 78.31794533597127, 'ACC-spoon': 74.66347714932778, 'ACC-bowl': 70.34062337405227, 'ACC-banana': 88.89120606386018, 'ACC-apple': 72.7272452952256, 'ACC-sandwich': 82.56086009859673, 'ACC-orange': 85.51317613025437, 'ACC-broccoli': 78.8033430501564, 'ACC-carrot': 78.5920127846677, 'ACC-hot dog': 67.87745283882185, 'ACC-pizza': 91.70879930207295, 'ACC-donut': 82.63727590782327, 'ACC-cake': 87.72378631716161, 'ACC-chair': 82.7903978689429, 'ACC-couch': 71.83351079986434, 'ACC-potted plant': 57.300187916229426, 'ACC-bed': 83.34362540271408, 'ACC-dining table': 72.95501204363374, 'ACC-toilet': 85.62496136596222, 'ACC-tv': 88.72811602073266, 'ACC-laptop': 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76.49764343120052, 'ACC-table-merged': 60.693764895794445, 'ACC-floor-other-merged': 66.39483104584191, 'ACC-pavement-merged': 68.98552934064436, 'ACC-mountain-merged': 69.91934223908859, 'ACC-grass-merged': 83.15803408147569, 'ACC-dirt-merged': 70.80345239322384, 'ACC-paper-merged': 50.38244536608831, 'ACC-food-other-merged': 63.96217109949023, 'ACC-building-other-merged': 72.75789662620028, 'ACC-rock-merged': 84.96741714225558, 'ACC-wall-other-merged': 80.90802990097299, 'ACC-rug-merged': 82.34621448337002})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3977172958735733, 'noc@0.8': 2.4056189640035117, 'noc@0.85': 2.82294410301434, 'noc@0.9': 3.6072578285045362, 'miou@iter1': 0.8717565115062612}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.12631225585938, 'precision@0.6': 72.44461822509766, 'precision@0.7': 68.24718475341797, 'precision@0.8': 58.91954803466797, 'precision@0.9': 32.56898498535156, 'cIoU': 61.222923278808594, 'mIoU': 66.76914978027344}}} INFO:trainer.default_trainer:This epoch takes 0:56:51.043473 INFO:trainer.default_trainer:PROGRESS: 72.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 36 training. INFO:trainer.default_trainer:epochs[ 36] optim steps[65800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24685/0.75655, loss_mask_bce_0: 0.69024/0.30067, loss_mask_dice_0: 3.91859/1.02062, loss_spatial_bce_0: 0.09317/0.08491, loss_spatial_dice_0: 0.21689/0.17974, loss_spatial_ce_0: 0.02876/0.05700, loss_grounding_bce_0: 0.06081/0.08058, loss_grounding_dice_0: 0.10601/0.15033, loss_grounding_ce_0: 0.41813/0.24881, loss_mask_ce_1: 1.30478/0.75722, loss_mask_bce_1: 0.72207/0.30151, loss_mask_dice_1: 4.01806/1.02469, loss_spatial_bce_1: 0.09101/0.08527, loss_spatial_dice_1: 0.21461/0.18248, loss_spatial_ce_1: 0.02558/0.06079, loss_grounding_bce_1: 0.06121/0.08078, loss_grounding_dice_1: 0.10946/0.15107, loss_grounding_ce_1: 0.41265/0.25045, loss_mask_ce_2: 1.13906/0.76502, loss_mask_bce_2: 0.71540/0.30179, loss_mask_dice_2: 4.60014/1.02554, loss_spatial_bce_2: 0.08266/0.08533, loss_spatial_dice_2: 0.18900/0.18300, loss_spatial_ce_2: 0.04153/0.06297, loss_grounding_bce_2: 0.05514/0.08078, loss_grounding_dice_2: 0.10619/0.15097, loss_grounding_ce_2: 0.42781/0.25344, loss_mask_ce_3: 1.16826/0.76880, loss_mask_bce_3: 0.60857/0.30322, loss_mask_dice_3: 4.16135/1.02367, loss_spatial_bce_3: 0.08145/0.08748, loss_spatial_dice_3: 0.16444/0.18438, loss_spatial_ce_3: 0.03160/0.06773, loss_grounding_bce_3: 0.06132/0.08117, loss_grounding_dice_3: 0.10900/0.15065, loss_grounding_ce_3: 0.42723/0.25454, loss_mask_ce_4: 1.09647/0.77445, loss_mask_bce_4: 0.55372/0.30588, loss_mask_dice_4: 4.18334/1.04273, loss_spatial_bce_4: 0.08319/0.08970, loss_spatial_dice_4: 0.17889/0.19264, loss_spatial_ce_4: 0.04363/0.08141, loss_grounding_bce_4: 0.05862/0.08180, loss_grounding_dice_4: 0.10815/0.15326, loss_grounding_ce_4: 0.41023/0.25894, loss_mask_ce_5: 1.07808/0.79889, loss_mask_bce_5: 0.57211/0.30771, loss_mask_dice_5: 4.69118/1.05031, loss_spatial_bce_5: 0.09609/0.09202, loss_spatial_dice_5: 0.19581/0.19576, loss_spatial_ce_5: 0.19660/0.09455, loss_grounding_bce_5: 0.05667/0.08210, loss_grounding_dice_5: 0.10900/0.15396, loss_grounding_ce_5: 0.42564/0.27724, loss_mask_ce_6: 1.09259/0.82597, loss_mask_bce_6: 0.59144/0.30986, loss_mask_dice_6: 4.69281/1.05403, loss_spatial_bce_6: 0.11389/0.09721, loss_spatial_dice_6: 0.19275/0.19810, loss_spatial_ce_6: 0.04577/0.11914, loss_grounding_bce_6: 0.10309/0.08294, loss_grounding_dice_6: 0.13577/0.15459, loss_grounding_ce_6: 0.36307/0.28627, loss_mask_ce_7: 1.19558/0.88174, loss_mask_bce_7: 0.65075/0.31693, loss_mask_dice_7: 4.54562/1.10006, loss_spatial_bce_7: 0.10471/0.10682, loss_spatial_dice_7: 0.22556/0.22334, loss_spatial_ce_7: 0.15969/0.15587, loss_grounding_bce_7: 0.05830/0.08466, loss_grounding_dice_7: 0.12846/0.16017, loss_grounding_ce_7: 0.42432/0.31917, loss_mask_ce_8: 1.12682/1.01694, loss_mask_bce_8: 0.69591/0.33299, loss_mask_dice_8: 4.77716/1.17671, loss_spatial_bce_8: 0.09495/0.12400, loss_spatial_dice_8: 0.20065/0.25866, loss_spatial_ce_8: 0.08156/0.20242, loss_grounding_bce_8: 0.09985/0.08884, loss_grounding_dice_8: 0.12977/0.16984, loss_grounding_ce_8: 0.35443/0.41914, loss_mask_ce_9: 5.31572/3.47766, loss_mask_bce_9: 0.67573/0.36004, loss_mask_dice_9: 5.23518/1.76033, loss_spatial_bce_9: 0.36208/0.35477, loss_spatial_dice_9: 0.84394/0.79324, loss_spatial_ce_9: 1.63220/1.38898, loss_grounding_bce_9: 0.08789/0.10092, loss_grounding_dice_9: 0.18753/0.24210, loss_grounding_ce_9: 0.50027/0.67329] items per batch[64] items per second[0.16] total items[4211200] mini batches[ 65800] memory[4999] epoch remaining[1:00:00] INFO:trainer.default_trainer:epochs[ 36] optim steps[65900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.02760/0.75635, loss_mask_bce_0: 0.10343/0.30066, loss_mask_dice_0: 0.29362/1.02053, loss_spatial_bce_0: 0.02291/0.08489, loss_spatial_dice_0: 0.07861/0.17972, loss_spatial_ce_0: 0.05475/0.05697, loss_grounding_bce_0: 0.00041/0.08059, loss_grounding_dice_0: 0.01416/0.15033, loss_grounding_ce_0: 0.01822/0.24882, loss_mask_ce_1: 0.02373/0.75703, loss_mask_bce_1: 0.10530/0.30149, loss_mask_dice_1: 0.29591/1.02458, loss_spatial_bce_1: 0.02225/0.08525, loss_spatial_dice_1: 0.07449/0.18246, loss_spatial_ce_1: 0.02044/0.06076, loss_grounding_bce_1: 0.00124/0.08080, loss_grounding_dice_1: 0.06709/0.15107, loss_grounding_ce_1: 0.01755/0.25045, loss_mask_ce_2: 0.02481/0.76484, loss_mask_bce_2: 0.09413/0.30178, loss_mask_dice_2: 0.23713/1.02543, loss_spatial_bce_2: 0.02434/0.08532, loss_spatial_dice_2: 0.08081/0.18299, loss_spatial_ce_2: 0.00984/0.06293, loss_grounding_bce_2: 0.00125/0.08079, loss_grounding_dice_2: 0.10562/0.15097, loss_grounding_ce_2: 0.03894/0.25342, loss_mask_ce_3: 0.01863/0.76861, loss_mask_bce_3: 0.10452/0.30321, loss_mask_dice_3: 0.26866/1.02353, loss_spatial_bce_3: 0.02435/0.08747, loss_spatial_dice_3: 0.07895/0.18438, loss_spatial_ce_3: 0.00767/0.06770, loss_grounding_bce_3: 0.00092/0.08118, loss_grounding_dice_3: 0.07145/0.15066, loss_grounding_ce_3: 0.02454/0.25450, loss_mask_ce_4: 0.02566/0.77424, loss_mask_bce_4: 0.10256/0.30587, loss_mask_dice_4: 0.34278/1.04262, loss_spatial_bce_4: 0.02560/0.08969, loss_spatial_dice_4: 0.09369/0.19263, loss_spatial_ce_4: 0.02383/0.08138, loss_grounding_bce_4: 0.00081/0.08183, loss_grounding_dice_4: 0.07591/0.15326, loss_grounding_ce_4: 0.01361/0.25886, loss_mask_ce_5: 0.04774/0.79870, loss_mask_bce_5: 0.11685/0.30769, loss_mask_dice_5: 0.34138/1.05022, loss_spatial_bce_5: 0.02671/0.09202, loss_spatial_dice_5: 0.07066/0.19575, loss_spatial_ce_5: 0.03971/0.09453, loss_grounding_bce_5: 0.00058/0.08211, loss_grounding_dice_5: 0.04196/0.15397, loss_grounding_ce_5: 0.04278/0.27726, loss_mask_ce_6: 0.06042/0.82581, loss_mask_bce_6: 0.10209/0.30983, loss_mask_dice_6: 0.29881/1.05392, loss_spatial_bce_6: 0.02515/0.09720, loss_spatial_dice_6: 0.08875/0.19809, loss_spatial_ce_6: 0.09929/0.11911, loss_grounding_bce_6: 0.00033/0.08296, loss_grounding_dice_6: 0.02958/0.15458, loss_grounding_ce_6: 0.01799/0.28617, loss_mask_ce_7: 0.12871/0.88157, loss_mask_bce_7: 0.11202/0.31691, loss_mask_dice_7: 0.27973/1.09994, loss_spatial_bce_7: 0.02884/0.10681, loss_spatial_dice_7: 0.08737/0.22333, loss_spatial_ce_7: 0.15523/0.15580, loss_grounding_bce_7: 0.00144/0.08468, loss_grounding_dice_7: 0.07328/0.16017, loss_grounding_ce_7: 0.04306/0.31913, loss_mask_ce_8: 0.11312/1.01681, loss_mask_bce_8: 0.11600/0.33297, loss_mask_dice_8: 0.26756/1.17659, loss_spatial_bce_8: 0.02762/0.12398, loss_spatial_dice_8: 0.12007/0.25865, loss_spatial_ce_8: 0.08165/0.20235, loss_grounding_bce_8: 0.00170/0.08885, loss_grounding_dice_8: 0.12913/0.16985, loss_grounding_ce_8: 0.20578/0.41904, loss_mask_ce_9: 3.24997/3.47730, loss_mask_bce_9: 0.10471/0.36001, loss_mask_dice_9: 0.48669/1.76018, loss_spatial_bce_9: 0.31092/0.35477, loss_spatial_dice_9: 0.73950/0.79322, loss_spatial_ce_9: 1.48311/1.38884, loss_grounding_bce_9: 0.00175/0.10093, loss_grounding_dice_9: 0.14503/0.24210, loss_grounding_ce_9: 0.87398/0.67318] items per batch[64] items per second[0.36] total items[4217600] mini batches[ 65900] memory[4999] epoch remaining[0:51:29] INFO:trainer.default_trainer:epochs[ 36] optim steps[66000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.13901/0.75627, loss_mask_bce_0: 0.01413/0.30061, loss_mask_dice_0: 0.13966/1.02057, loss_spatial_bce_0: 0.00278/0.08486, loss_spatial_dice_0: 0.04456/0.17969, loss_spatial_ce_0: 0.00096/0.05694, loss_grounding_bce_0: 0.02209/0.08055, loss_grounding_dice_0: 0.07512/0.15031, loss_grounding_ce_0: 0.06623/0.24877, loss_mask_ce_1: 0.13745/0.75700, loss_mask_bce_1: 0.00969/0.30144, loss_mask_dice_1: 0.12216/1.02457, loss_spatial_bce_1: 0.00533/0.08522, loss_spatial_dice_1: 0.07995/0.18244, loss_spatial_ce_1: 0.01995/0.06072, loss_grounding_bce_1: 0.01416/0.08076, loss_grounding_dice_1: 0.05938/0.15107, loss_grounding_ce_1: 0.05155/0.25037, loss_mask_ce_2: 0.16300/0.76479, loss_mask_bce_2: 0.01021/0.30173, loss_mask_dice_2: 0.09546/1.02543, loss_spatial_bce_2: 0.00505/0.08529, loss_spatial_dice_2: 0.08053/0.18298, loss_spatial_ce_2: 0.12565/0.06291, loss_grounding_bce_2: 0.01384/0.08076, loss_grounding_dice_2: 0.06832/0.15097, loss_grounding_ce_2: 0.08420/0.25335, loss_mask_ce_3: 0.14767/0.76859, loss_mask_bce_3: 0.01189/0.30315, loss_mask_dice_3: 0.11598/1.02349, loss_spatial_bce_3: 0.01224/0.08744, loss_spatial_dice_3: 0.14792/0.18436, loss_spatial_ce_3: 0.15321/0.06767, loss_grounding_bce_3: 0.01976/0.08114, loss_grounding_dice_3: 0.07808/0.15066, loss_grounding_ce_3: 0.02927/0.25442, loss_mask_ce_4: 0.14189/0.77424, loss_mask_bce_4: 0.01276/0.30581, loss_mask_dice_4: 0.11605/1.04268, loss_spatial_bce_4: 0.02275/0.08966, loss_spatial_dice_4: 0.18336/0.19262, loss_spatial_ce_4: 0.03378/0.08135, loss_grounding_bce_4: 0.01539/0.08179, loss_grounding_dice_4: 0.06409/0.15325, loss_grounding_ce_4: 0.01036/0.25878, loss_mask_ce_5: 0.09704/0.79866, loss_mask_bce_5: 0.01384/0.30764, loss_mask_dice_5: 0.14812/1.05026, loss_spatial_bce_5: 0.03362/0.09198, loss_spatial_dice_5: 0.23057/0.19574, loss_spatial_ce_5: 0.00143/0.09451, loss_grounding_bce_5: 0.01734/0.08207, loss_grounding_dice_5: 0.08203/0.15396, loss_grounding_ce_5: 0.02482/0.27718, loss_mask_ce_6: 0.13530/0.82577, loss_mask_bce_6: 0.01166/0.30978, loss_mask_dice_6: 0.11796/1.05393, loss_spatial_bce_6: 0.03733/0.09717, loss_spatial_dice_6: 0.22806/0.19808, loss_spatial_ce_6: 0.00042/0.11910, loss_grounding_bce_6: 0.01536/0.08292, loss_grounding_dice_6: 0.07624/0.15457, loss_grounding_ce_6: 0.12186/0.28609, loss_mask_ce_7: 0.23483/0.88153, loss_mask_bce_7: 0.01352/0.31686, loss_mask_dice_7: 0.14757/1.09996, loss_spatial_bce_7: 0.07955/0.10678, loss_spatial_dice_7: 0.29475/0.22332, loss_spatial_ce_7: 0.00269/0.15576, loss_grounding_bce_7: 0.01140/0.08464, loss_grounding_dice_7: 0.06090/0.16016, loss_grounding_ce_7: 0.01179/0.31913, loss_mask_ce_8: 0.20306/1.01669, loss_mask_bce_8: 0.01586/0.33292, loss_mask_dice_8: 0.14260/1.17660, loss_spatial_bce_8: 0.08059/0.12395, loss_spatial_dice_8: 0.28095/0.25865, loss_spatial_ce_8: 0.08990/0.20232, loss_grounding_bce_8: 0.01552/0.08881, loss_grounding_dice_8: 0.06206/0.16984, loss_grounding_ce_8: 0.00287/0.41899, loss_mask_ce_9: 2.04145/3.47721, loss_mask_bce_9: 0.02121/0.35997, loss_mask_dice_9: 0.19684/1.76010, loss_spatial_bce_9: 0.02726/0.35471, loss_spatial_dice_9: 0.41429/0.79322, loss_spatial_ce_9: 2.45737/1.38897, loss_grounding_bce_9: 0.01564/0.10090, loss_grounding_dice_9: 0.08914/0.24206, loss_grounding_ce_9: 0.04562/0.67318] items per batch[64] items per second[0.37] total items[4224000] mini batches[ 66000] memory[4999] epoch remaining[0:47:36] INFO:trainer.default_trainer:epochs[ 36] optim steps[66100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32168/0.75622, loss_mask_bce_0: 0.07533/0.30061, loss_mask_dice_0: 0.43285/1.02044, loss_spatial_bce_0: 0.02776/0.08485, loss_spatial_dice_0: 0.16341/0.17968, loss_spatial_ce_0: 0.05304/0.05692, loss_grounding_bce_0: 0.01789/0.08057, loss_grounding_dice_0: 0.16339/0.15033, loss_grounding_ce_0: 0.24132/0.24873, loss_mask_ce_1: 0.36558/0.75695, loss_mask_bce_1: 0.08118/0.30143, loss_mask_dice_1: 0.45456/1.02449, loss_spatial_bce_1: 0.02798/0.08522, loss_spatial_dice_1: 0.13522/0.18243, loss_spatial_ce_1: 0.09487/0.06070, loss_grounding_bce_1: 0.01857/0.08076, loss_grounding_dice_1: 0.20036/0.15108, loss_grounding_ce_1: 0.37055/0.25035, loss_mask_ce_2: 0.37778/0.76471, loss_mask_bce_2: 0.07422/0.30173, loss_mask_dice_2: 0.71392/1.02533, loss_spatial_bce_2: 0.03236/0.08528, loss_spatial_dice_2: 0.16818/0.18296, loss_spatial_ce_2: 0.04773/0.06290, loss_grounding_bce_2: 0.01968/0.08076, loss_grounding_dice_2: 0.26247/0.15098, loss_grounding_ce_2: 0.41549/0.25334, loss_mask_ce_3: 0.41662/0.76854, loss_mask_bce_3: 0.07172/0.30315, loss_mask_dice_3: 0.39487/1.02338, loss_spatial_bce_3: 0.02945/0.08743, loss_spatial_dice_3: 0.14928/0.18434, loss_spatial_ce_3: 0.08731/0.06765, loss_grounding_bce_3: 0.01766/0.08115, loss_grounding_dice_3: 0.17626/0.15067, loss_grounding_ce_3: 0.35956/0.25442, loss_mask_ce_4: 0.40771/0.77421, loss_mask_bce_4: 0.08021/0.30582, loss_mask_dice_4: 0.42992/1.04256, loss_spatial_bce_4: 0.02759/0.08965, loss_spatial_dice_4: 0.17065/0.19261, loss_spatial_ce_4: 0.07753/0.08135, loss_grounding_bce_4: 0.01831/0.08179, loss_grounding_dice_4: 0.20446/0.15327, loss_grounding_ce_4: 0.35189/0.25878, loss_mask_ce_5: 0.39533/0.79861, loss_mask_bce_5: 0.07878/0.30764, loss_mask_dice_5: 0.44456/1.05017, loss_spatial_bce_5: 0.02304/0.09198, loss_spatial_dice_5: 0.16828/0.19573, loss_spatial_ce_5: 0.03408/0.09450, loss_grounding_bce_5: 0.01762/0.08208, loss_grounding_dice_5: 0.17883/0.15397, loss_grounding_ce_5: 0.30297/0.27718, loss_mask_ce_6: 0.47783/0.82572, loss_mask_bce_6: 0.07420/0.30979, loss_mask_dice_6: 0.43487/1.05384, loss_spatial_bce_6: 0.02463/0.09717, loss_spatial_dice_6: 0.15556/0.19807, loss_spatial_ce_6: 0.04934/0.11911, loss_grounding_bce_6: 0.01776/0.08293, loss_grounding_dice_6: 0.20515/0.15459, loss_grounding_ce_6: 0.46963/0.28610, loss_mask_ce_7: 0.40042/0.88148, loss_mask_bce_7: 0.07455/0.31687, loss_mask_dice_7: 0.55739/1.09988, loss_spatial_bce_7: 0.05006/0.10677, loss_spatial_dice_7: 0.20297/0.22331, loss_spatial_ce_7: 0.12970/0.15574, loss_grounding_bce_7: 0.01772/0.08464, loss_grounding_dice_7: 0.18676/0.16018, loss_grounding_ce_7: 0.40989/0.31913, loss_mask_ce_8: 0.45538/1.01658, loss_mask_bce_8: 0.07715/0.33292, loss_mask_dice_8: 0.49253/1.17648, loss_spatial_bce_8: 0.07105/0.12394, loss_spatial_dice_8: 0.26671/0.25864, loss_spatial_ce_8: 0.12719/0.20229, loss_grounding_bce_8: 0.01807/0.08881, loss_grounding_dice_8: 0.18229/0.16985, loss_grounding_ce_8: 0.26471/0.41893, loss_mask_ce_9: 2.83759/3.47702, loss_mask_bce_9: 0.07134/0.35995, loss_mask_dice_9: 0.53398/1.75993, loss_spatial_bce_9: 0.10425/0.35469, loss_spatial_dice_9: 0.88142/0.79321, loss_spatial_ce_9: 1.24548/1.38872, loss_grounding_bce_9: 0.01284/0.10089, loss_grounding_dice_9: 0.22856/0.24205, loss_grounding_ce_9: 0.51298/0.67308] items per batch[64] items per second[0.38] total items[4230400] mini batches[ 66100] memory[4999] epoch remaining[0:43:55] INFO:trainer.default_trainer:epochs[ 36] optim steps[66200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.40111/0.75633, loss_mask_bce_0: 0.09324/0.30059, loss_mask_dice_0: 3.15922/1.02079, loss_spatial_bce_0: 0.00189/0.08487, loss_spatial_dice_0: 0.19972/0.17969, loss_spatial_ce_0: 0.00718/0.05691, loss_grounding_bce_0: 0.01432/0.08057, loss_grounding_dice_0: 0.27681/0.15032, loss_grounding_ce_0: 0.11017/0.24878, loss_mask_ce_1: 0.34939/0.75706, loss_mask_bce_1: 0.09864/0.30141, loss_mask_dice_1: 3.89933/1.02488, loss_spatial_bce_1: 0.00204/0.08524, loss_spatial_dice_1: 0.28043/0.18244, loss_spatial_ce_1: 0.00172/0.06068, loss_grounding_bce_1: 0.01451/0.08076, loss_grounding_dice_1: 0.26258/0.15107, loss_grounding_ce_1: 0.11651/0.25039, loss_mask_ce_2: 0.57645/0.76481, loss_mask_bce_2: 0.11070/0.30171, loss_mask_dice_2: 4.00034/1.02569, loss_spatial_bce_2: 0.00227/0.08530, loss_spatial_dice_2: 0.31212/0.18298, loss_spatial_ce_2: 0.00238/0.06288, loss_grounding_bce_2: 0.01384/0.08076, loss_grounding_dice_2: 0.24962/0.15095, loss_grounding_ce_2: 0.13849/0.25339, loss_mask_ce_3: 0.47712/0.76870, loss_mask_bce_3: 0.10860/0.30313, loss_mask_dice_3: 3.87454/1.02374, loss_spatial_bce_3: 0.00186/0.08745, loss_spatial_dice_3: 0.18982/0.18435, loss_spatial_ce_3: 0.01012/0.06764, loss_grounding_bce_3: 0.01537/0.08114, loss_grounding_dice_3: 0.25843/0.15065, loss_grounding_ce_3: 0.15310/0.25444, loss_mask_ce_4: 0.48841/0.77432, loss_mask_bce_4: 0.08996/0.30580, loss_mask_dice_4: 2.82406/1.04288, loss_spatial_bce_4: 0.00185/0.08967, loss_spatial_dice_4: 0.19702/0.19263, loss_spatial_ce_4: 0.18005/0.08137, loss_grounding_bce_4: 0.01255/0.08179, loss_grounding_dice_4: 0.25401/0.15324, loss_grounding_ce_4: 0.15143/0.25881, loss_mask_ce_5: 0.54707/0.79869, loss_mask_bce_5: 0.09425/0.30764, loss_mask_dice_5: 3.48421/1.05049, loss_spatial_bce_5: 0.00192/0.09200, loss_spatial_dice_5: 0.25972/0.19575, loss_spatial_ce_5: 0.63906/0.09458, loss_grounding_bce_5: 0.02474/0.08209, loss_grounding_dice_5: 0.26599/0.15396, loss_grounding_ce_5: 0.39232/0.27722, loss_mask_ce_6: 0.51780/0.82585, loss_mask_bce_6: 0.09280/0.30979, loss_mask_dice_6: 3.67730/1.05419, loss_spatial_bce_6: 0.00310/0.09719, loss_spatial_dice_6: 0.23196/0.19809, loss_spatial_ce_6: 0.15297/0.11915, loss_grounding_bce_6: 0.01401/0.08295, loss_grounding_dice_6: 0.26157/0.15457, loss_grounding_ce_6: 0.29921/0.28614, loss_mask_ce_7: 0.74997/0.88158, loss_mask_bce_7: 0.14339/0.31687, loss_mask_dice_7: 3.72231/1.10028, loss_spatial_bce_7: 0.00358/0.10679, loss_spatial_dice_7: 0.35387/0.22332, loss_spatial_ce_7: 0.39201/0.15575, loss_grounding_bce_7: 0.05427/0.08466, loss_grounding_dice_7: 0.48539/0.16017, loss_grounding_ce_7: 0.04594/0.31922, loss_mask_ce_8: 0.82253/1.01671, loss_mask_bce_8: 0.14939/0.33293, loss_mask_dice_8: 3.96617/1.17688, loss_spatial_bce_8: 0.00587/0.12396, loss_spatial_dice_8: 0.40768/0.25865, loss_spatial_ce_8: 0.12475/0.20229, loss_grounding_bce_8: 0.03229/0.08884, loss_grounding_dice_8: 0.30262/0.16985, loss_grounding_ce_8: 0.40789/0.41910, loss_mask_ce_9: 3.19349/3.47711, loss_mask_bce_9: 0.11864/0.35994, loss_mask_dice_9: 4.81283/1.76034, loss_spatial_bce_9: 0.06907/0.35469, loss_spatial_dice_9: 0.89326/0.79321, loss_spatial_ce_9: 2.71832/1.38872, loss_grounding_bce_9: 0.01760/0.10091, loss_grounding_dice_9: 0.42933/0.24205, loss_grounding_ce_9: 0.03435/0.67315] items per batch[64] items per second[0.36] total items[4236800] mini batches[ 66200] memory[4999] epoch remaining[0:40:59] INFO:trainer.default_trainer:epochs[ 36] optim steps[66300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.48724/0.75637, loss_mask_bce_0: 0.47859/0.30067, loss_mask_dice_0: 0.92818/1.02068, loss_spatial_bce_0: 0.07311/0.08488, loss_spatial_dice_0: 0.12967/0.17970, loss_spatial_ce_0: 0.09642/0.05691, loss_grounding_bce_0: 0.12576/0.08055, loss_grounding_dice_0: 0.12335/0.15032, loss_grounding_ce_0: 0.18306/0.24886, loss_mask_ce_1: 1.51215/0.75712, loss_mask_bce_1: 0.47831/0.30147, loss_mask_dice_1: 0.92828/1.02479, loss_spatial_bce_1: 0.07462/0.08525, loss_spatial_dice_1: 0.12077/0.18244, loss_spatial_ce_1: 0.08803/0.06068, loss_grounding_bce_1: 0.11449/0.08074, loss_grounding_dice_1: 0.10852/0.15107, loss_grounding_ce_1: 0.19054/0.25047, loss_mask_ce_2: 1.42773/0.76488, loss_mask_bce_2: 0.47833/0.30177, loss_mask_dice_2: 0.91915/1.02558, loss_spatial_bce_2: 0.07355/0.08531, loss_spatial_dice_2: 0.11850/0.18299, loss_spatial_ce_2: 0.09514/0.06288, loss_grounding_bce_2: 0.12775/0.08074, loss_grounding_dice_2: 0.12100/0.15095, loss_grounding_ce_2: 0.20339/0.25346, loss_mask_ce_3: 1.47023/0.76878, loss_mask_bce_3: 0.47445/0.30319, loss_mask_dice_3: 0.93253/1.02360, loss_spatial_bce_3: 0.08023/0.08746, loss_spatial_dice_3: 0.13033/0.18436, loss_spatial_ce_3: 0.10609/0.06764, loss_grounding_bce_3: 0.12996/0.08113, loss_grounding_dice_3: 0.11372/0.15064, loss_grounding_ce_3: 0.20776/0.25449, loss_mask_ce_4: 1.42666/0.77435, loss_mask_bce_4: 0.45645/0.30586, loss_mask_dice_4: 0.91828/1.04278, loss_spatial_bce_4: 0.08193/0.08969, loss_spatial_dice_4: 0.12772/0.19265, loss_spatial_ce_4: 0.07396/0.08138, loss_grounding_bce_4: 0.11607/0.08177, loss_grounding_dice_4: 0.10636/0.15324, loss_grounding_ce_4: 0.21403/0.25888, loss_mask_ce_5: 1.49863/0.79877, loss_mask_bce_5: 0.47048/0.30771, loss_mask_dice_5: 0.96013/1.05038, loss_spatial_bce_5: 0.07802/0.09202, loss_spatial_dice_5: 0.14114/0.19576, loss_spatial_ce_5: 0.10354/0.09460, loss_grounding_bce_5: 0.11646/0.08207, loss_grounding_dice_5: 0.10356/0.15396, loss_grounding_ce_5: 0.16356/0.27730, loss_mask_ce_6: 1.57181/0.82592, loss_mask_bce_6: 0.47368/0.30985, loss_mask_dice_6: 0.92786/1.05411, loss_spatial_bce_6: 0.08121/0.09722, loss_spatial_dice_6: 0.14668/0.19811, loss_spatial_ce_6: 0.09950/0.11915, loss_grounding_bce_6: 0.11970/0.08293, loss_grounding_dice_6: 0.11314/0.15457, loss_grounding_ce_6: 0.17073/0.28617, loss_mask_ce_7: 1.58841/0.88162, loss_mask_bce_7: 0.47318/0.31694, loss_mask_dice_7: 1.00749/1.10017, loss_spatial_bce_7: 0.08154/0.10680, loss_spatial_dice_7: 0.15836/0.22333, loss_spatial_ce_7: 0.15719/0.15575, loss_grounding_bce_7: 0.11985/0.08464, loss_grounding_dice_7: 0.12372/0.16016, loss_grounding_ce_7: 0.11662/0.31934, loss_mask_ce_8: 1.77989/1.01683, loss_mask_bce_8: 0.50390/0.33298, loss_mask_dice_8: 1.01656/1.17678, loss_spatial_bce_8: 0.17610/0.12397, loss_spatial_dice_8: 0.21650/0.25865, loss_spatial_ce_8: 0.12121/0.20224, loss_grounding_bce_8: 0.14039/0.08882, loss_grounding_dice_8: 0.12182/0.16984, loss_grounding_ce_8: 0.10931/0.41914, loss_mask_ce_9: 4.68234/3.47725, loss_mask_bce_9: 0.76189/0.36001, loss_mask_dice_9: 2.76988/1.76030, loss_spatial_bce_9: 0.43775/0.35470, loss_spatial_dice_9: 0.90218/0.79323, loss_spatial_ce_9: 1.32841/1.38876, loss_grounding_bce_9: 0.21029/0.10088, loss_grounding_dice_9: 0.19342/0.24206, loss_grounding_ce_9: 0.14804/0.67316] items per batch[64] items per second[0.35] total items[4243200] mini batches[ 66300] memory[4999] epoch remaining[0:38:15] INFO:trainer.default_trainer:epochs[ 36] optim steps[66400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98544/0.75637, loss_mask_bce_0: 0.08934/0.30068, loss_mask_dice_0: 0.40944/1.02064, loss_spatial_bce_0: 0.02752/0.08488, loss_spatial_dice_0: 0.11414/0.17968, loss_spatial_ce_0: 0.00276/0.05690, loss_grounding_bce_0: 0.02242/0.08055, loss_grounding_dice_0: 0.10160/0.15031, loss_grounding_ce_0: 0.07271/0.24877, loss_mask_ce_1: 0.96941/0.75713, loss_mask_bce_1: 0.09534/0.30149, loss_mask_dice_1: 0.49092/1.02477, loss_spatial_bce_1: 0.02596/0.08524, loss_spatial_dice_1: 0.13474/0.18243, loss_spatial_ce_1: 0.00495/0.06068, loss_grounding_bce_1: 0.02164/0.08074, loss_grounding_dice_1: 0.11439/0.15106, loss_grounding_ce_1: 0.07571/0.25037, loss_mask_ce_2: 0.96183/0.76494, loss_mask_bce_2: 0.10294/0.30179, loss_mask_dice_2: 0.47931/1.02558, loss_spatial_bce_2: 0.02678/0.08531, loss_spatial_dice_2: 0.14554/0.18297, loss_spatial_ce_2: 0.00524/0.06288, loss_grounding_bce_2: 0.02208/0.08074, loss_grounding_dice_2: 0.11624/0.15094, loss_grounding_ce_2: 0.21864/0.25338, loss_mask_ce_3: 1.13460/0.76881, loss_mask_bce_3: 0.09678/0.30321, loss_mask_dice_3: 0.71734/1.02357, loss_spatial_bce_3: 0.02636/0.08745, loss_spatial_dice_3: 0.13136/0.18435, loss_spatial_ce_3: 0.01380/0.06762, loss_grounding_bce_3: 0.02050/0.08113, loss_grounding_dice_3: 0.11092/0.15064, loss_grounding_ce_3: 0.11441/0.25442, loss_mask_ce_4: 1.01446/0.77439, loss_mask_bce_4: 0.09237/0.30589, loss_mask_dice_4: 0.35954/1.04272, loss_spatial_bce_4: 0.02649/0.08968, loss_spatial_dice_4: 0.16256/0.19264, loss_spatial_ce_4: 0.04767/0.08135, loss_grounding_bce_4: 0.02182/0.08178, loss_grounding_dice_4: 0.18451/0.15324, loss_grounding_ce_4: 0.30082/0.25880, loss_mask_ce_5: 0.95531/0.79878, loss_mask_bce_5: 0.08916/0.30773, loss_mask_dice_5: 0.39250/1.05033, loss_spatial_bce_5: 0.02734/0.09201, loss_spatial_dice_5: 0.13330/0.19576, loss_spatial_ce_5: 0.08815/0.09457, loss_grounding_bce_5: 0.02259/0.08208, loss_grounding_dice_5: 0.09852/0.15395, loss_grounding_ce_5: 0.16091/0.27721, loss_mask_ce_6: 0.83821/0.82592, loss_mask_bce_6: 0.08821/0.30988, loss_mask_dice_6: 0.32986/1.05408, loss_spatial_bce_6: 0.02799/0.09721, loss_spatial_dice_6: 0.12474/0.19809, loss_spatial_ce_6: 0.13647/0.11913, loss_grounding_bce_6: 0.02533/0.08293, loss_grounding_dice_6: 0.14817/0.15456, loss_grounding_ce_6: 0.15545/0.28611, loss_mask_ce_7: 0.98088/0.88155, loss_mask_bce_7: 0.08103/0.31698, loss_mask_dice_7: 0.37376/1.10014, loss_spatial_bce_7: 0.03870/0.10680, loss_spatial_dice_7: 0.19719/0.22332, loss_spatial_ce_7: 0.12274/0.15575, loss_grounding_bce_7: 0.02324/0.08464, loss_grounding_dice_7: 0.14866/0.16015, loss_grounding_ce_7: 0.21732/0.31929, loss_mask_ce_8: 1.02331/1.01669, loss_mask_bce_8: 0.09716/0.33301, loss_mask_dice_8: 0.37154/1.17673, loss_spatial_bce_8: 0.05558/0.12396, loss_spatial_dice_8: 0.25879/0.25864, loss_spatial_ce_8: 0.08258/0.20222, loss_grounding_bce_8: 0.02175/0.08882, loss_grounding_dice_8: 0.10158/0.16983, loss_grounding_ce_8: 0.31078/0.41914, loss_mask_ce_9: 2.32748/3.47717, loss_mask_bce_9: 0.08381/0.36002, loss_mask_dice_9: 0.69607/1.76024, loss_spatial_bce_9: 0.30249/0.35470, loss_spatial_dice_9: 0.79326/0.79321, loss_spatial_ce_9: 0.95945/1.38867, loss_grounding_bce_9: 0.02300/0.10089, loss_grounding_dice_9: 0.16241/0.24206, loss_grounding_ce_9: 0.33084/0.67318] items per batch[64] items per second[0.37] total items[4249600] mini batches[ 66400] memory[4999] epoch remaining[0:35:10] INFO:trainer.default_trainer:epochs[ 36] optim steps[66500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.71547/0.75635, loss_mask_bce_0: 0.29004/0.30071, loss_mask_dice_0: 0.26462/1.02051, loss_spatial_bce_0: 0.07364/0.08489, loss_spatial_dice_0: 0.06917/0.17967, loss_spatial_ce_0: 0.00021/0.05687, loss_grounding_bce_0: 0.11290/0.08056, loss_grounding_dice_0: 0.06347/0.15035, loss_grounding_ce_0: 0.08255/0.24881, loss_mask_ce_1: 1.77889/0.75714, loss_mask_bce_1: 0.28202/0.30153, loss_mask_dice_1: 0.24722/1.02460, loss_spatial_bce_1: 0.07197/0.08525, loss_spatial_dice_1: 0.06305/0.18242, loss_spatial_ce_1: 0.00015/0.06067, loss_grounding_bce_1: 0.11625/0.08076, loss_grounding_dice_1: 0.06519/0.15110, loss_grounding_ce_1: 0.13489/0.25042, loss_mask_ce_2: 1.69091/0.76492, loss_mask_bce_2: 0.29172/0.30183, loss_mask_dice_2: 0.25757/1.02542, loss_spatial_bce_2: 0.07754/0.08531, loss_spatial_dice_2: 0.06551/0.18296, loss_spatial_ce_2: 0.00019/0.06285, loss_grounding_bce_2: 0.11611/0.08076, loss_grounding_dice_2: 0.06449/0.15098, loss_grounding_ce_2: 0.13825/0.25344, loss_mask_ce_3: 1.78067/0.76877, loss_mask_bce_3: 0.28235/0.30325, loss_mask_dice_3: 0.24861/1.02342, loss_spatial_bce_3: 0.07205/0.08746, loss_spatial_dice_3: 0.05909/0.18433, loss_spatial_ce_3: 0.00015/0.06761, loss_grounding_bce_3: 0.11523/0.08113, loss_grounding_dice_3: 0.06024/0.15068, loss_grounding_ce_3: 0.11119/0.25446, loss_mask_ce_4: 1.80234/0.77439, loss_mask_bce_4: 0.31328/0.30592, loss_mask_dice_4: 0.25971/1.04255, loss_spatial_bce_4: 0.07878/0.08969, loss_spatial_dice_4: 0.06854/0.19263, loss_spatial_ce_4: 0.00045/0.08133, loss_grounding_bce_4: 0.10826/0.08178, loss_grounding_dice_4: 0.06110/0.15329, loss_grounding_ce_4: 0.08153/0.25886, loss_mask_ce_5: 2.02178/0.79877, loss_mask_bce_5: 0.30056/0.30776, loss_mask_dice_5: 0.29935/1.05018, loss_spatial_bce_5: 0.10221/0.09203, loss_spatial_dice_5: 0.09386/0.19575, loss_spatial_ce_5: 0.00256/0.09455, loss_grounding_bce_5: 0.11352/0.08209, loss_grounding_dice_5: 0.06613/0.15400, loss_grounding_ce_5: 0.11691/0.27723, loss_mask_ce_6: 2.24979/0.82595, loss_mask_bce_6: 0.29299/0.30990, loss_mask_dice_6: 0.28728/1.05389, loss_spatial_bce_6: 0.10109/0.09723, loss_spatial_dice_6: 0.09473/0.19809, loss_spatial_ce_6: 0.00267/0.11912, loss_grounding_bce_6: 0.10698/0.08294, loss_grounding_dice_6: 0.06594/0.15461, loss_grounding_ce_6: 0.14463/0.28608, loss_mask_ce_7: 2.56236/0.88154, loss_mask_bce_7: 0.51329/0.31702, loss_mask_dice_7: 0.32948/1.09997, loss_spatial_bce_7: 0.10713/0.10680, loss_spatial_dice_7: 0.09392/0.22331, loss_spatial_ce_7: 0.05425/0.15574, loss_grounding_bce_7: 0.10788/0.08465, loss_grounding_dice_7: 0.06603/0.16020, loss_grounding_ce_7: 0.18514/0.31929, loss_mask_ce_8: 2.66171/1.01675, loss_mask_bce_8: 0.29288/0.33303, loss_mask_dice_8: 0.26926/1.17658, loss_spatial_bce_8: 0.11462/0.12396, loss_spatial_dice_8: 0.09051/0.25861, loss_spatial_ce_8: 0.01834/0.20219, loss_grounding_bce_8: 0.11704/0.08882, loss_grounding_dice_8: 0.07358/0.16988, loss_grounding_ce_8: 0.53185/0.41913, loss_mask_ce_9: 2.86070/3.47715, loss_mask_bce_9: 0.72483/0.36004, loss_mask_dice_9: 4.06619/1.76006, loss_spatial_bce_9: 0.39179/0.35474, loss_spatial_dice_9: 0.57004/0.79322, loss_spatial_ce_9: 0.89640/1.38873, loss_grounding_bce_9: 0.17254/0.10089, loss_grounding_dice_9: 0.12076/0.24211, loss_grounding_ce_9: 1.47583/0.67308] items per batch[64] items per second[0.36] total items[4256000] mini batches[ 66500] memory[4999] epoch remaining[0:32:13] INFO:trainer.default_trainer:epochs[ 36] optim steps[66600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43063/0.75630, loss_mask_bce_0: 0.10382/0.30071, loss_mask_dice_0: 0.99105/1.02051, loss_spatial_bce_0: 0.06814/0.08489, loss_spatial_dice_0: 0.28702/0.17966, loss_spatial_ce_0: 0.03293/0.05686, loss_grounding_bce_0: 0.01508/0.08055, loss_grounding_dice_0: 0.34042/0.15037, loss_grounding_ce_0: 0.09175/0.24879, loss_mask_ce_1: 0.91675/0.75704, loss_mask_bce_1: 0.11162/0.30152, loss_mask_dice_1: 0.69398/1.02460, loss_spatial_bce_1: 0.07220/0.08525, loss_spatial_dice_1: 0.29160/0.18241, loss_spatial_ce_1: 0.04695/0.06065, loss_grounding_bce_1: 0.01214/0.08074, loss_grounding_dice_1: 0.24871/0.15111, loss_grounding_ce_1: 0.12467/0.25037, loss_mask_ce_2: 1.09121/0.76482, loss_mask_bce_2: 0.11881/0.30183, loss_mask_dice_2: 1.11950/1.02546, loss_spatial_bce_2: 0.08395/0.08532, loss_spatial_dice_2: 0.30752/0.18296, loss_spatial_ce_2: 0.04946/0.06283, loss_grounding_bce_2: 0.01400/0.08074, loss_grounding_dice_2: 0.25933/0.15100, loss_grounding_ce_2: 0.12642/0.25337, loss_mask_ce_3: 0.95444/0.76874, loss_mask_bce_3: 0.11857/0.30325, loss_mask_dice_3: 1.14541/1.02342, loss_spatial_bce_3: 0.04277/0.08746, loss_spatial_dice_3: 0.29825/0.18432, loss_spatial_ce_3: 0.07610/0.06763, loss_grounding_bce_3: 0.01321/0.08112, loss_grounding_dice_3: 0.27671/0.15070, loss_grounding_ce_3: 0.13271/0.25439, loss_mask_ce_4: 1.39992/0.77433, loss_mask_bce_4: 0.11642/0.30592, loss_mask_dice_4: 1.06752/1.04257, loss_spatial_bce_4: 0.04366/0.08969, loss_spatial_dice_4: 0.30066/0.19262, loss_spatial_ce_4: 0.03478/0.08132, loss_grounding_bce_4: 0.01299/0.08177, loss_grounding_dice_4: 0.27025/0.15331, loss_grounding_ce_4: 0.19296/0.25880, loss_mask_ce_5: 1.58533/0.79874, loss_mask_bce_5: 0.10524/0.30776, loss_mask_dice_5: 1.02476/1.05021, loss_spatial_bce_5: 0.04107/0.09203, loss_spatial_dice_5: 0.29140/0.19575, loss_spatial_ce_5: 0.08205/0.09456, loss_grounding_bce_5: 0.01244/0.08207, loss_grounding_dice_5: 0.30736/0.15403, loss_grounding_ce_5: 0.17390/0.27718, loss_mask_ce_6: 1.09072/0.82592, loss_mask_bce_6: 0.11779/0.30989, loss_mask_dice_6: 1.11567/1.05391, loss_spatial_bce_6: 0.03617/0.09723, loss_spatial_dice_6: 0.25474/0.19808, loss_spatial_ce_6: 0.13180/0.11914, loss_grounding_bce_6: 0.01495/0.08292, loss_grounding_dice_6: 0.34553/0.15463, loss_grounding_ce_6: 0.24465/0.28599, loss_mask_ce_7: 1.44947/0.88151, loss_mask_bce_7: 0.12711/0.31702, loss_mask_dice_7: 1.06217/1.09996, loss_spatial_bce_7: 0.03630/0.10680, loss_spatial_dice_7: 0.26940/0.22330, loss_spatial_ce_7: 0.07271/0.15574, loss_grounding_bce_7: 0.01202/0.08463, loss_grounding_dice_7: 0.31138/0.16023, loss_grounding_ce_7: 0.16528/0.31920, loss_mask_ce_8: 1.40497/1.01672, loss_mask_bce_8: 0.12319/0.33302, loss_mask_dice_8: 1.05828/1.17660, loss_spatial_bce_8: 0.02831/0.12395, loss_spatial_dice_8: 0.33853/0.25861, loss_spatial_ce_8: 0.33204/0.20219, loss_grounding_bce_8: 0.01484/0.08880, loss_grounding_dice_8: 0.33746/0.16991, loss_grounding_ce_8: 0.23684/0.41903, loss_mask_ce_9: 3.97694/3.47727, loss_mask_bce_9: 0.10794/0.36003, loss_mask_dice_9: 1.49828/1.76022, loss_spatial_bce_9: 0.20057/0.35471, loss_spatial_dice_9: 0.87880/0.79321, loss_spatial_ce_9: 1.26761/1.38851, loss_grounding_bce_9: 0.01670/0.10087, loss_grounding_dice_9: 0.38167/0.24213, loss_grounding_ce_9: 0.41863/0.67294] items per batch[64] items per second[0.37] total items[4262400] mini batches[ 66600] memory[4999] epoch remaining[0:29:15] INFO:trainer.default_trainer:epochs[ 36] optim steps[66700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.10962/0.75624, loss_mask_bce_0: 0.16287/0.30073, loss_mask_dice_0: 0.16037/1.02074, loss_spatial_bce_0: 0.09861/0.08491, loss_spatial_dice_0: 0.08014/0.17966, loss_spatial_ce_0: 0.00190/0.05686, loss_grounding_bce_0: 0.11512/0.08057, loss_grounding_dice_0: 0.10677/0.15037, loss_grounding_ce_0: 0.02343/0.24878, loss_mask_ce_1: 0.10453/0.75692, loss_mask_bce_1: 0.17122/0.30154, loss_mask_dice_1: 0.16311/1.02481, loss_spatial_bce_1: 0.09820/0.08527, loss_spatial_dice_1: 0.08857/0.18241, loss_spatial_ce_1: 0.00080/0.06067, loss_grounding_bce_1: 0.11466/0.08077, loss_grounding_dice_1: 0.10904/0.15110, loss_grounding_ce_1: 0.02449/0.25037, loss_mask_ce_2: 0.08248/0.76472, loss_mask_bce_2: 0.17741/0.30185, loss_mask_dice_2: 0.17713/1.02564, loss_spatial_bce_2: 0.10079/0.08534, loss_spatial_dice_2: 0.09153/0.18296, loss_spatial_ce_2: 0.00067/0.06284, loss_grounding_bce_2: 0.11602/0.08076, loss_grounding_dice_2: 0.11347/0.15100, loss_grounding_ce_2: 0.01243/0.25334, loss_mask_ce_3: 0.09325/0.76862, loss_mask_bce_3: 0.17141/0.30328, loss_mask_dice_3: 0.15606/1.02363, loss_spatial_bce_3: 0.09368/0.08748, loss_spatial_dice_3: 0.07357/0.18433, loss_spatial_ce_3: 0.00076/0.06766, loss_grounding_bce_3: 0.11811/0.08115, loss_grounding_dice_3: 0.10712/0.15069, loss_grounding_ce_3: 0.00929/0.25435, loss_mask_ce_4: 0.09016/0.77428, loss_mask_bce_4: 0.16382/0.30595, loss_mask_dice_4: 0.16022/1.04280, loss_spatial_bce_4: 0.10100/0.08971, loss_spatial_dice_4: 0.09725/0.19264, loss_spatial_ce_4: 0.00181/0.08136, loss_grounding_bce_4: 0.12296/0.08179, loss_grounding_dice_4: 0.11723/0.15331, loss_grounding_ce_4: 0.00906/0.25882, loss_mask_ce_5: 0.09841/0.79871, loss_mask_bce_5: 0.16360/0.30779, loss_mask_dice_5: 0.16125/1.05046, loss_spatial_bce_5: 0.09773/0.09204, loss_spatial_dice_5: 0.09293/0.19577, loss_spatial_ce_5: 0.02775/0.09462, loss_grounding_bce_5: 0.11325/0.08210, loss_grounding_dice_5: 0.11161/0.15403, loss_grounding_ce_5: 0.01817/0.27717, loss_mask_ce_6: 0.08879/0.82590, loss_mask_bce_6: 0.16373/0.30991, loss_mask_dice_6: 0.15981/1.05411, loss_spatial_bce_6: 0.10982/0.09726, loss_spatial_dice_6: 0.10616/0.19810, loss_spatial_ce_6: 0.10448/0.11920, loss_grounding_bce_6: 0.10965/0.08294, loss_grounding_dice_6: 0.10773/0.15464, loss_grounding_ce_6: 0.00787/0.28604, loss_mask_ce_7: 0.09586/0.88148, loss_mask_bce_7: 0.16137/0.31705, loss_mask_dice_7: 0.15645/1.10020, loss_spatial_bce_7: 0.10561/0.10681, loss_spatial_dice_7: 0.11374/0.22331, loss_spatial_ce_7: 0.09201/0.15578, loss_grounding_bce_7: 0.11345/0.08465, loss_grounding_dice_7: 0.10900/0.16023, loss_grounding_ce_7: 0.00563/0.31921, loss_mask_ce_8: 0.10946/1.01677, loss_mask_bce_8: 0.16321/0.33305, loss_mask_dice_8: 0.16735/1.17684, loss_spatial_bce_8: 0.10246/0.12396, loss_spatial_dice_8: 0.11720/0.25862, loss_spatial_ce_8: 0.19983/0.20220, loss_grounding_bce_8: 0.11500/0.08883, loss_grounding_dice_8: 0.10761/0.16993, loss_grounding_ce_8: 0.00673/0.41903, loss_mask_ce_9: 1.70917/3.47714, loss_mask_bce_9: 0.16793/0.36008, loss_mask_dice_9: 0.23888/1.76058, loss_spatial_bce_9: 0.55297/0.35470, loss_spatial_dice_9: 0.75397/0.79320, loss_spatial_ce_9: 1.39774/1.38847, loss_grounding_bce_9: 0.10599/0.10089, loss_grounding_dice_9: 0.13274/0.24213, loss_grounding_ce_9: 0.13206/0.67291] items per batch[64] items per second[0.36] total items[4268800] mini batches[ 66700] memory[4999] epoch remaining[0:26:22] INFO:trainer.default_trainer:epochs[ 36] optim steps[66800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71788/0.75619, loss_mask_bce_0: 0.42710/0.30075, loss_mask_dice_0: 1.59702/1.02093, loss_spatial_bce_0: 0.04435/0.08491, loss_spatial_dice_0: 0.11016/0.17966, loss_spatial_ce_0: 0.25726/0.05685, loss_grounding_bce_0: 0.01355/0.08058, loss_grounding_dice_0: 0.07042/0.15036, loss_grounding_ce_0: 0.00261/0.24891, loss_mask_ce_1: 0.58096/0.75688, loss_mask_bce_1: 0.44430/0.30158, loss_mask_dice_1: 1.83443/1.02502, loss_spatial_bce_1: 0.04659/0.08528, loss_spatial_dice_1: 0.11201/0.18242, loss_spatial_ce_1: 0.07219/0.06067, loss_grounding_bce_1: 0.01333/0.08078, loss_grounding_dice_1: 0.06846/0.15110, loss_grounding_ce_1: 0.00581/0.25034, loss_mask_ce_2: 0.67495/0.76469, loss_mask_bce_2: 0.44235/0.30188, loss_mask_dice_2: 2.51595/1.02584, loss_spatial_bce_2: 0.04648/0.08535, loss_spatial_dice_2: 0.11210/0.18296, loss_spatial_ce_2: 0.09183/0.06284, loss_grounding_bce_2: 0.01376/0.08078, loss_grounding_dice_2: 0.07736/0.15101, loss_grounding_ce_2: 0.00484/0.25332, loss_mask_ce_3: 0.65572/0.76859, loss_mask_bce_3: 0.42656/0.30330, loss_mask_dice_3: 2.41733/1.02385, loss_spatial_bce_3: 0.04794/0.08749, loss_spatial_dice_3: 0.12007/0.18433, loss_spatial_ce_3: 0.10210/0.06765, loss_grounding_bce_3: 0.01367/0.08116, loss_grounding_dice_3: 0.07475/0.15069, loss_grounding_ce_3: 0.00384/0.25435, loss_mask_ce_4: 0.68638/0.77429, loss_mask_bce_4: 0.40778/0.30597, loss_mask_dice_4: 1.89552/1.04303, loss_spatial_bce_4: 0.04954/0.08971, loss_spatial_dice_4: 0.15766/0.19264, loss_spatial_ce_4: 0.19484/0.08136, loss_grounding_bce_4: 0.01129/0.08180, loss_grounding_dice_4: 0.07039/0.15331, loss_grounding_ce_4: 0.01110/0.25883, loss_mask_ce_5: 0.74406/0.79873, loss_mask_bce_5: 0.40843/0.30783, loss_mask_dice_5: 1.90078/1.05071, loss_spatial_bce_5: 0.04714/0.09205, loss_spatial_dice_5: 0.19005/0.19578, loss_spatial_ce_5: 0.16039/0.09460, loss_grounding_bce_5: 0.01198/0.08211, loss_grounding_dice_5: 0.06963/0.15404, loss_grounding_ce_5: 0.01018/0.27719, loss_mask_ce_6: 0.86120/0.82592, loss_mask_bce_6: 0.41307/0.30994, loss_mask_dice_6: 1.68481/1.05435, loss_spatial_bce_6: 0.05691/0.09727, loss_spatial_dice_6: 0.15292/0.19810, loss_spatial_ce_6: 0.23362/0.11918, loss_grounding_bce_6: 0.01272/0.08296, loss_grounding_dice_6: 0.06550/0.15463, loss_grounding_ce_6: 0.00678/0.28597, loss_mask_ce_7: 0.63119/0.88147, loss_mask_bce_7: 0.44246/0.31710, loss_mask_dice_7: 2.01934/1.10044, loss_spatial_bce_7: 0.06807/0.10682, loss_spatial_dice_7: 0.23488/0.22331, loss_spatial_ce_7: 0.17574/0.15577, loss_grounding_bce_7: 0.01343/0.08467, loss_grounding_dice_7: 0.06902/0.16023, loss_grounding_ce_7: 0.35078/0.31920, loss_mask_ce_8: 0.55329/1.01676, loss_mask_bce_8: 0.44448/0.33309, loss_mask_dice_8: 2.27452/1.17709, loss_spatial_bce_8: 0.07791/0.12397, loss_spatial_dice_8: 0.22732/0.25862, loss_spatial_ce_8: 0.15395/0.20215, loss_grounding_bce_8: 0.02147/0.08885, loss_grounding_dice_8: 0.08771/0.16993, loss_grounding_ce_8: 0.77353/0.41903, loss_mask_ce_9: 4.29560/3.47726, loss_mask_bce_9: 0.42782/0.36012, loss_mask_dice_9: 3.18555/1.76082, loss_spatial_bce_9: 0.23818/0.35470, loss_spatial_dice_9: 0.94711/0.79319, loss_spatial_ce_9: 1.62044/1.38848, loss_grounding_bce_9: 0.04096/0.10093, loss_grounding_dice_9: 0.19463/0.24214, loss_grounding_ce_9: 1.43826/0.67291] items per batch[64] items per second[0.37] total items[4275200] mini batches[ 66800] memory[4999] epoch remaining[0:23:25] INFO:trainer.default_trainer:epochs[ 36] optim steps[66900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.23549/0.75620, loss_mask_bce_0: 0.47648/0.30075, loss_mask_dice_0: 0.29400/1.02079, loss_spatial_bce_0: 0.18535/0.08489, loss_spatial_dice_0: 0.12099/0.17963, loss_spatial_ce_0: 0.00014/0.05681, loss_grounding_bce_0: 0.31704/0.08057, loss_grounding_dice_0: 0.18042/0.15033, loss_grounding_ce_0: 0.00544/0.24884, loss_mask_ce_1: 0.23073/0.75691, loss_mask_bce_1: 0.45518/0.30156, loss_mask_dice_1: 0.28555/1.02488, loss_spatial_bce_1: 0.18274/0.08526, loss_spatial_dice_1: 0.12532/0.18239, loss_spatial_ce_1: 0.00006/0.06064, loss_grounding_bce_1: 0.32666/0.08077, loss_grounding_dice_1: 0.17548/0.15107, loss_grounding_ce_1: 0.00897/0.25026, loss_mask_ce_2: 0.21821/0.76470, loss_mask_bce_2: 0.44872/0.30186, loss_mask_dice_2: 0.28864/1.02570, loss_spatial_bce_2: 0.18089/0.08533, loss_spatial_dice_2: 0.12900/0.18293, loss_spatial_ce_2: 0.00012/0.06280, loss_grounding_bce_2: 0.30982/0.08076, loss_grounding_dice_2: 0.17263/0.15098, loss_grounding_ce_2: 0.00892/0.25325, loss_mask_ce_3: 0.20144/0.76861, loss_mask_bce_3: 0.48419/0.30328, loss_mask_dice_3: 0.28337/1.02371, loss_spatial_bce_3: 0.17483/0.08747, loss_spatial_dice_3: 0.12380/0.18430, loss_spatial_ce_3: 0.00038/0.06761, loss_grounding_bce_3: 0.31711/0.08115, loss_grounding_dice_3: 0.17068/0.15067, loss_grounding_ce_3: 0.00880/0.25428, loss_mask_ce_4: 0.13382/0.77432, loss_mask_bce_4: 0.44554/0.30594, loss_mask_dice_4: 0.29532/1.04287, loss_spatial_bce_4: 0.17683/0.08970, loss_spatial_dice_4: 0.13030/0.19261, loss_spatial_ce_4: 0.00300/0.08133, loss_grounding_bce_4: 0.30562/0.08179, loss_grounding_dice_4: 0.18351/0.15329, loss_grounding_ce_4: 0.00659/0.25873, loss_mask_ce_5: 0.22143/0.79876, loss_mask_bce_5: 0.46321/0.30781, loss_mask_dice_5: 0.28451/1.05054, loss_spatial_bce_5: 0.18227/0.09204, loss_spatial_dice_5: 0.12587/0.19576, loss_spatial_ce_5: 0.02232/0.09457, loss_grounding_bce_5: 0.30155/0.08210, loss_grounding_dice_5: 0.17192/0.15401, loss_grounding_ce_5: 0.00539/0.27710, loss_mask_ce_6: 0.19800/0.82593, loss_mask_bce_6: 0.49668/0.30993, loss_mask_dice_6: 0.30376/1.05421, loss_spatial_bce_6: 0.16591/0.09726, loss_spatial_dice_6: 0.12465/0.19808, loss_spatial_ce_6: 0.03353/0.11915, loss_grounding_bce_6: 0.32660/0.08296, loss_grounding_dice_6: 0.16980/0.15461, loss_grounding_ce_6: 0.00453/0.28595, loss_mask_ce_7: 0.21563/0.88148, loss_mask_bce_7: 0.50546/0.31708, loss_mask_dice_7: 0.30507/1.10027, loss_spatial_bce_7: 0.18283/0.10681, loss_spatial_dice_7: 0.12351/0.22329, loss_spatial_ce_7: 0.08995/0.15571, loss_grounding_bce_7: 0.33679/0.08467, loss_grounding_dice_7: 0.17669/0.16020, loss_grounding_ce_7: 0.01162/0.31908, loss_mask_ce_8: 0.22304/1.01673, loss_mask_bce_8: 0.41698/0.33306, loss_mask_dice_8: 0.30433/1.17690, loss_spatial_bce_8: 0.19219/0.12395, loss_spatial_dice_8: 0.12071/0.25858, loss_spatial_ce_8: 0.08065/0.20209, loss_grounding_bce_8: 0.26600/0.08884, loss_grounding_dice_8: 0.17306/0.16990, loss_grounding_ce_8: 0.01026/0.41882, loss_mask_ce_9: 3.54088/3.47708, loss_mask_bce_9: 0.33607/0.36008, loss_mask_dice_9: 0.47855/1.76054, loss_spatial_bce_9: 1.96629/0.35475, loss_spatial_dice_9: 0.88780/0.79315, loss_spatial_ce_9: 2.31040/1.38847, loss_grounding_bce_9: 0.21229/0.10091, loss_grounding_dice_9: 0.35237/0.24211, loss_grounding_ce_9: 0.20685/0.67270] items per batch[64] items per second[0.37] total items[4281600] mini batches[ 66900] memory[4999] epoch remaining[0:20:27] INFO:trainer.default_trainer:epochs[ 36] optim steps[67000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43808/0.75623, loss_mask_bce_0: 0.08487/0.30073, loss_mask_dice_0: 5.19063/1.02096, loss_spatial_bce_0: 0.00379/0.08487, loss_spatial_dice_0: 0.51566/0.17962, loss_spatial_ce_0: 0.20757/0.05679, loss_grounding_bce_0: 0.00370/0.08055, loss_grounding_dice_0: 0.25328/0.15035, loss_grounding_ce_0: 2.02414/0.24884, loss_mask_ce_1: 1.82679/0.75699, loss_mask_bce_1: 0.09181/0.30155, loss_mask_dice_1: 4.99790/1.02510, loss_spatial_bce_1: 0.00445/0.08524, loss_spatial_dice_1: 0.51654/0.18239, loss_spatial_ce_1: 0.06881/0.06062, loss_grounding_bce_1: 0.00363/0.08074, loss_grounding_dice_1: 0.29653/0.15108, loss_grounding_ce_1: 2.10633/0.25027, loss_mask_ce_2: 1.93226/0.76475, loss_mask_bce_2: 0.06414/0.30185, loss_mask_dice_2: 4.99309/1.02591, loss_spatial_bce_2: 0.00335/0.08531, loss_spatial_dice_2: 0.60660/0.18293, loss_spatial_ce_2: 0.06652/0.06279, loss_grounding_bce_2: 0.00337/0.08074, loss_grounding_dice_2: 0.21021/0.15098, loss_grounding_ce_2: 1.84200/0.25324, loss_mask_ce_3: 1.49022/0.76865, loss_mask_bce_3: 0.07381/0.30327, loss_mask_dice_3: 5.39337/1.02391, loss_spatial_bce_3: 0.00526/0.08745, loss_spatial_dice_3: 0.58817/0.18430, loss_spatial_ce_3: 0.14633/0.06760, loss_grounding_bce_3: 0.00149/0.08112, loss_grounding_dice_3: 0.22120/0.15067, loss_grounding_ce_3: 1.60739/0.25426, loss_mask_ce_4: 1.72754/0.77440, loss_mask_bce_4: 0.07842/0.30593, loss_mask_dice_4: 5.55015/1.04307, loss_spatial_bce_4: 0.00457/0.08968, loss_spatial_dice_4: 0.65532/0.19261, loss_spatial_ce_4: 0.12169/0.08132, loss_grounding_bce_4: 0.00270/0.08176, loss_grounding_dice_4: 0.33984/0.15330, loss_grounding_ce_4: 1.53032/0.25876, loss_mask_ce_5: 1.91929/0.79883, loss_mask_bce_5: 0.07975/0.30780, loss_mask_dice_5: 4.97011/1.05075, loss_spatial_bce_5: 0.00660/0.09202, loss_spatial_dice_5: 0.68407/0.19576, loss_spatial_ce_5: 0.11872/0.09456, loss_grounding_bce_5: 0.00769/0.08207, loss_grounding_dice_5: 0.34557/0.15403, loss_grounding_ce_5: 0.93981/0.27710, loss_mask_ce_6: 1.70517/0.82599, loss_mask_bce_6: 0.10337/0.30992, loss_mask_dice_6: 5.35696/1.05443, loss_spatial_bce_6: 0.00598/0.09724, loss_spatial_dice_6: 0.51342/0.19808, loss_spatial_ce_6: 0.14262/0.11913, loss_grounding_bce_6: 0.00535/0.08293, loss_grounding_dice_6: 0.42348/0.15463, loss_grounding_ce_6: 1.61848/0.28595, loss_mask_ce_7: 1.90115/0.88156, loss_mask_bce_7: 0.09287/0.31707, loss_mask_dice_7: 5.08898/1.10052, loss_spatial_bce_7: 0.00526/0.10679, loss_spatial_dice_7: 0.72196/0.22329, loss_spatial_ce_7: 0.17237/0.15567, loss_grounding_bce_7: 0.00975/0.08464, loss_grounding_dice_7: 0.39658/0.16023, loss_grounding_ce_7: 1.63340/0.31903, loss_mask_ce_8: 3.09430/1.01677, loss_mask_bce_8: 0.18103/0.33305, loss_mask_dice_8: 5.77049/1.17716, loss_spatial_bce_8: 0.00728/0.12392, loss_spatial_dice_8: 0.67648/0.25858, loss_spatial_ce_8: 0.37706/0.20208, loss_grounding_bce_8: 0.00960/0.08882, loss_grounding_dice_8: 0.44541/0.16991, loss_grounding_ce_8: 1.94660/0.41873, loss_mask_ce_9: 6.52887/3.47738, loss_mask_bce_9: 0.04788/0.36008, loss_mask_dice_9: 6.20092/1.76084, loss_spatial_bce_9: 0.00387/0.35474, loss_spatial_dice_9: 0.79296/0.79315, loss_spatial_ce_9: 2.28460/1.38852, loss_grounding_bce_9: 0.00704/0.10088, loss_grounding_dice_9: 0.62608/0.24212, loss_grounding_ce_9: 1.79629/0.67260] items per batch[64] items per second[0.37] total items[4288000] mini batches[ 67000] memory[4999] epoch remaining[0:17:31] INFO:trainer.default_trainer:epochs[ 36] optim steps[67100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05757/0.75608, loss_mask_bce_0: 0.05484/0.30067, loss_mask_dice_0: 0.04758/1.02065, loss_spatial_bce_0: 0.04074/0.08485, loss_spatial_dice_0: 0.03079/0.17958, loss_spatial_ce_0: 0.00003/0.05676, loss_grounding_bce_0: 0.03511/0.08055, loss_grounding_dice_0: 0.02919/0.15035, loss_grounding_ce_0: 0.00195/0.24869, loss_mask_ce_1: 0.05445/0.75685, loss_mask_bce_1: 0.05882/0.30148, loss_mask_dice_1: 0.04973/1.02479, loss_spatial_bce_1: 0.04425/0.08523, loss_spatial_dice_1: 0.03319/0.18235, loss_spatial_ce_1: 0.00001/0.06058, loss_grounding_bce_1: 0.03510/0.08074, loss_grounding_dice_1: 0.02889/0.15108, loss_grounding_ce_1: 0.00298/0.25012, loss_mask_ce_2: 0.06044/0.76458, loss_mask_bce_2: 0.05348/0.30178, loss_mask_dice_2: 0.04512/1.02562, loss_spatial_bce_2: 0.04137/0.08530, loss_spatial_dice_2: 0.03295/0.18290, loss_spatial_ce_2: 0.00002/0.06275, loss_grounding_bce_2: 0.03229/0.08074, loss_grounding_dice_2: 0.02615/0.15098, loss_grounding_ce_2: 0.00242/0.25311, loss_mask_ce_3: 0.06517/0.76851, loss_mask_bce_3: 0.05821/0.30321, loss_mask_dice_3: 0.04899/1.02360, loss_spatial_bce_3: 0.04074/0.08743, loss_spatial_dice_3: 0.03026/0.18426, loss_spatial_ce_3: 0.00003/0.06757, loss_grounding_bce_3: 0.03480/0.08112, loss_grounding_dice_3: 0.02759/0.15067, loss_grounding_ce_3: 0.00354/0.25412, loss_mask_ce_4: 0.03419/0.77425, loss_mask_bce_4: 0.05947/0.30586, loss_mask_dice_4: 0.04986/1.04276, loss_spatial_bce_4: 0.04079/0.08966, loss_spatial_dice_4: 0.03316/0.19258, loss_spatial_ce_4: 0.00084/0.08126, loss_grounding_bce_4: 0.04151/0.08176, loss_grounding_dice_4: 0.03016/0.15330, loss_grounding_ce_4: 0.00231/0.25863, loss_mask_ce_5: 0.05121/0.79870, loss_mask_bce_5: 0.06135/0.30772, loss_mask_dice_5: 0.05177/1.05043, loss_spatial_bce_5: 0.04456/0.09200, loss_spatial_dice_5: 0.03708/0.19572, loss_spatial_ce_5: 0.00094/0.09451, loss_grounding_bce_5: 0.04040/0.08207, loss_grounding_dice_5: 0.03074/0.15403, loss_grounding_ce_5: 0.00305/0.27695, loss_mask_ce_6: 0.05353/0.82585, loss_mask_bce_6: 0.06039/0.30985, loss_mask_dice_6: 0.05182/1.05412, loss_spatial_bce_6: 0.04094/0.09722, loss_spatial_dice_6: 0.03143/0.19805, loss_spatial_ce_6: 0.00128/0.11908, loss_grounding_bce_6: 0.04068/0.08293, loss_grounding_dice_6: 0.03116/0.15462, loss_grounding_ce_6: 0.00564/0.28577, loss_mask_ce_7: 0.07865/0.88140, loss_mask_bce_7: 0.05532/0.31700, loss_mask_dice_7: 0.04977/1.10019, loss_spatial_bce_7: 0.04233/0.10677, loss_spatial_dice_7: 0.03687/0.22325, loss_spatial_ce_7: 0.02324/0.15562, loss_grounding_bce_7: 0.03547/0.08463, loss_grounding_dice_7: 0.02882/0.16021, loss_grounding_ce_7: 0.02045/0.31890, loss_mask_ce_8: 0.07671/1.01664, loss_mask_bce_8: 0.05659/0.33297, loss_mask_dice_8: 0.04610/1.17683, loss_spatial_bce_8: 0.03613/0.12390, loss_spatial_dice_8: 0.03984/0.25854, loss_spatial_ce_8: 0.07513/0.20202, loss_grounding_bce_8: 0.04002/0.08881, loss_grounding_dice_8: 0.03141/0.16990, loss_grounding_ce_8: 0.00331/0.41862, loss_mask_ce_9: 1.38279/3.47701, loss_mask_bce_9: 0.05519/0.36002, loss_mask_dice_9: 0.04753/1.76039, loss_spatial_bce_9: 0.56109/0.35472, loss_spatial_dice_9: 0.78455/0.79314, loss_spatial_ce_9: 1.14041/1.38836, loss_grounding_bce_9: 0.05350/0.10087, loss_grounding_dice_9: 0.04149/0.24208, loss_grounding_ce_9: 0.07756/0.67254] items per batch[64] items per second[0.37] total items[4294400] mini batches[ 67100] memory[4999] epoch remaining[0:14:35] INFO:trainer.default_trainer:epochs[ 36] optim steps[67200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.56726/0.75619, loss_mask_bce_0: 0.15265/0.30074, loss_mask_dice_0: 0.11227/1.02115, loss_spatial_bce_0: 0.11325/0.08484, loss_spatial_dice_0: 0.06944/0.17959, loss_spatial_ce_0: 0.00049/0.05674, loss_grounding_bce_0: 0.10812/0.08057, loss_grounding_dice_0: 0.07217/0.15036, loss_grounding_ce_0: 0.09578/0.24880, loss_mask_ce_1: 1.68494/0.75701, loss_mask_bce_1: 0.15875/0.30154, loss_mask_dice_1: 0.11496/1.02528, loss_spatial_bce_1: 0.11410/0.08521, loss_spatial_dice_1: 0.07825/0.18236, loss_spatial_ce_1: 0.00021/0.06058, loss_grounding_bce_1: 0.10658/0.08075, loss_grounding_dice_1: 0.07091/0.15110, loss_grounding_ce_1: 0.10533/0.25024, loss_mask_ce_2: 1.66094/0.76468, loss_mask_bce_2: 0.15601/0.30185, loss_mask_dice_2: 0.10282/1.02609, loss_spatial_bce_2: 0.11338/0.08529, loss_spatial_dice_2: 0.07205/0.18291, loss_spatial_ce_2: 0.00054/0.06273, loss_grounding_bce_2: 0.10834/0.08075, loss_grounding_dice_2: 0.07114/0.15099, loss_grounding_ce_2: 0.08987/0.25318, loss_mask_ce_3: 1.73704/0.76861, loss_mask_bce_3: 0.15555/0.30327, loss_mask_dice_3: 0.10235/1.02404, loss_spatial_bce_3: 0.10978/0.08743, loss_spatial_dice_3: 0.06310/0.18427, loss_spatial_ce_3: 0.00359/0.06756, loss_grounding_bce_3: 0.10951/0.08113, loss_grounding_dice_3: 0.07125/0.15068, loss_grounding_ce_3: 0.08365/0.25425, loss_mask_ce_4: 2.06533/0.77437, loss_mask_bce_4: 0.15766/0.30593, loss_mask_dice_4: 0.11625/1.04329, loss_spatial_bce_4: 0.10835/0.08966, loss_spatial_dice_4: 0.07154/0.19259, loss_spatial_ce_4: 0.00482/0.08126, loss_grounding_bce_4: 0.11313/0.08177, loss_grounding_dice_4: 0.08064/0.15330, loss_grounding_ce_4: 0.08486/0.25872, loss_mask_ce_5: 1.74441/0.79881, loss_mask_bce_5: 0.16279/0.30779, loss_mask_dice_5: 0.10735/1.05089, loss_spatial_bce_5: 0.10807/0.09200, loss_spatial_dice_5: 0.07648/0.19573, loss_spatial_ce_5: 0.00436/0.09448, loss_grounding_bce_5: 0.11395/0.08208, loss_grounding_dice_5: 0.08464/0.15404, loss_grounding_ce_5: 0.10190/0.27699, loss_mask_ce_6: 1.59958/0.82595, loss_mask_bce_6: 0.16150/0.30992, loss_mask_dice_6: 0.11247/1.05460, loss_spatial_bce_6: 0.09811/0.09722, loss_spatial_dice_6: 0.06543/0.19805, loss_spatial_ce_6: 0.00592/0.11905, loss_grounding_bce_6: 0.11233/0.08294, loss_grounding_dice_6: 0.07696/0.15464, loss_grounding_ce_6: 0.11475/0.28581, loss_mask_ce_7: 1.91289/0.88151, loss_mask_bce_7: 0.16008/0.31708, loss_mask_dice_7: 0.13345/1.10069, loss_spatial_bce_7: 0.09960/0.10675, loss_spatial_dice_7: 0.07537/0.22324, loss_spatial_ce_7: 0.01170/0.15560, loss_grounding_bce_7: 0.11543/0.08464, loss_grounding_dice_7: 0.08251/0.16022, loss_grounding_ce_7: 0.15613/0.31895, loss_mask_ce_8: 1.97501/1.01673, loss_mask_bce_8: 0.14211/0.33305, loss_mask_dice_8: 0.10610/1.17735, loss_spatial_bce_8: 0.09789/0.12388, loss_spatial_dice_8: 0.07258/0.25853, loss_spatial_ce_8: 0.01243/0.20200, loss_grounding_bce_8: 0.10692/0.08881, loss_grounding_dice_8: 0.07124/0.16991, loss_grounding_ce_8: 0.22503/0.41868, loss_mask_ce_9: 2.62399/3.47717, loss_mask_bce_9: 0.15429/0.36009, loss_mask_dice_9: 0.11015/1.76110, loss_spatial_bce_9: 0.43543/0.35471, loss_spatial_dice_9: 0.30303/0.79319, loss_spatial_ce_9: 0.40950/1.38844, loss_grounding_bce_9: 0.12326/0.10088, loss_grounding_dice_9: 0.08405/0.24210, loss_grounding_ce_9: 0.38195/0.67259] items per batch[64] items per second[0.36] total items[4300800] mini batches[ 67200] memory[4999] epoch remaining[0:11:40] INFO:trainer.default_trainer:epochs[ 36] optim steps[67300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03501/0.75624, loss_mask_bce_0: 0.27925/0.30078, loss_mask_dice_0: 0.38479/1.02114, loss_spatial_bce_0: 0.06943/0.08484, loss_spatial_dice_0: 0.09867/0.17958, loss_spatial_ce_0: 0.00161/0.05675, loss_grounding_bce_0: 0.08112/0.08055, loss_grounding_dice_0: 0.11181/0.15034, loss_grounding_ce_0: 0.00457/0.24873, loss_mask_ce_1: 0.03299/0.75710, loss_mask_bce_1: 0.28101/0.30160, loss_mask_dice_1: 0.39675/1.02528, loss_spatial_bce_1: 0.06784/0.08522, loss_spatial_dice_1: 0.09454/0.18235, loss_spatial_ce_1: 0.01003/0.06057, loss_grounding_bce_1: 0.08637/0.08073, loss_grounding_dice_1: 0.12245/0.15107, loss_grounding_ce_1: 0.00414/0.25020, loss_mask_ce_2: 0.03600/0.76472, loss_mask_bce_2: 0.28358/0.30190, loss_mask_dice_2: 0.38153/1.02614, loss_spatial_bce_2: 0.06986/0.08530, loss_spatial_dice_2: 0.09530/0.18290, loss_spatial_ce_2: 0.02327/0.06272, loss_grounding_bce_2: 0.09005/0.08074, loss_grounding_dice_2: 0.11817/0.15096, loss_grounding_ce_2: 0.00534/0.25313, loss_mask_ce_3: 0.04289/0.76866, loss_mask_bce_3: 0.29030/0.30333, loss_mask_dice_3: 0.37346/1.02405, loss_spatial_bce_3: 0.07310/0.08743, loss_spatial_dice_3: 0.09673/0.18427, loss_spatial_ce_3: 0.00462/0.06757, loss_grounding_bce_3: 0.08809/0.08111, loss_grounding_dice_3: 0.11454/0.15065, loss_grounding_ce_3: 0.00458/0.25420, loss_mask_ce_4: 0.04181/0.77444, loss_mask_bce_4: 0.26195/0.30598, loss_mask_dice_4: 0.37587/1.04331, loss_spatial_bce_4: 0.07226/0.08966, loss_spatial_dice_4: 0.10598/0.19259, loss_spatial_ce_4: 0.00068/0.08127, loss_grounding_bce_4: 0.08051/0.08176, loss_grounding_dice_4: 0.11557/0.15328, loss_grounding_ce_4: 0.00504/0.25867, loss_mask_ce_5: 0.04394/0.79888, loss_mask_bce_5: 0.28566/0.30784, loss_mask_dice_5: 0.38584/1.05088, loss_spatial_bce_5: 0.07700/0.09200, loss_spatial_dice_5: 0.10342/0.19573, loss_spatial_ce_5: 0.00554/0.09449, loss_grounding_bce_5: 0.08211/0.08207, loss_grounding_dice_5: 0.12141/0.15402, loss_grounding_ce_5: 0.00669/0.27696, loss_mask_ce_6: 0.06359/0.82604, loss_mask_bce_6: 0.29623/0.30998, loss_mask_dice_6: 0.39419/1.05462, loss_spatial_bce_6: 0.07500/0.09724, loss_spatial_dice_6: 0.10168/0.19805, loss_spatial_ce_6: 0.02218/0.11907, loss_grounding_bce_6: 0.08423/0.08293, loss_grounding_dice_6: 0.11744/0.15462, loss_grounding_ce_6: 0.01026/0.28575, loss_mask_ce_7: 0.08245/0.88166, loss_mask_bce_7: 0.27997/0.31713, loss_mask_dice_7: 0.39806/1.10072, loss_spatial_bce_7: 0.08219/0.10676, loss_spatial_dice_7: 0.10340/0.22324, loss_spatial_ce_7: 0.02655/0.15557, loss_grounding_bce_7: 0.07753/0.08463, loss_grounding_dice_7: 0.10977/0.16020, loss_grounding_ce_7: 0.01292/0.31889, loss_mask_ce_8: 0.09232/1.01675, loss_mask_bce_8: 0.30884/0.33313, loss_mask_dice_8: 0.38244/1.17738, loss_spatial_bce_8: 0.08015/0.12388, loss_spatial_dice_8: 0.10981/0.25851, loss_spatial_ce_8: 0.09059/0.20196, loss_grounding_bce_8: 0.09017/0.08881, loss_grounding_dice_8: 0.10392/0.16989, loss_grounding_ce_8: 0.00795/0.41871, loss_mask_ce_9: 2.63633/3.47751, loss_mask_bce_9: 0.28703/0.36016, loss_mask_dice_9: 0.55293/1.76102, loss_spatial_bce_9: 0.53437/0.35471, loss_spatial_dice_9: 0.78528/0.79319, loss_spatial_ce_9: 1.78935/1.38833, loss_grounding_bce_9: 0.08796/0.10088, loss_grounding_dice_9: 0.14050/0.24210, loss_grounding_ce_9: 0.20494/0.67273] items per batch[64] items per second[0.37] total items[4307200] mini batches[ 67300] memory[4999] epoch remaining[0:08:44] INFO:trainer.default_trainer:epochs[ 36] optim steps[67400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.38592/0.75631, loss_mask_bce_0: 0.43237/0.30078, loss_mask_dice_0: 0.93277/1.02144, loss_spatial_bce_0: 0.17501/0.08484, loss_spatial_dice_0: 0.23790/0.17958, loss_spatial_ce_0: 0.07193/0.05673, loss_grounding_bce_0: 0.12060/0.08057, loss_grounding_dice_0: 0.39165/0.15038, loss_grounding_ce_0: 0.04093/0.24868, loss_mask_ce_1: 0.39050/0.75720, loss_mask_bce_1: 0.44836/0.30159, loss_mask_dice_1: 0.94327/1.02556, loss_spatial_bce_1: 0.10121/0.08521, loss_spatial_dice_1: 0.19928/0.18234, loss_spatial_ce_1: 0.07004/0.06056, loss_grounding_bce_1: 0.12767/0.08075, loss_grounding_dice_1: 0.38489/0.15111, loss_grounding_ce_1: 0.03441/0.25016, loss_mask_ce_2: 0.38805/0.76482, loss_mask_bce_2: 0.43398/0.30190, loss_mask_dice_2: 0.90911/1.02642, loss_spatial_bce_2: 0.20855/0.08529, loss_spatial_dice_2: 0.26977/0.18290, loss_spatial_ce_2: 0.07037/0.06270, loss_grounding_bce_2: 0.11623/0.08076, loss_grounding_dice_2: 0.36991/0.15099, loss_grounding_ce_2: 0.03646/0.25309, loss_mask_ce_3: 0.38403/0.76875, loss_mask_bce_3: 0.45193/0.30332, loss_mask_dice_3: 0.96299/1.02435, loss_spatial_bce_3: 0.20720/0.08742, loss_spatial_dice_3: 0.25996/0.18426, loss_spatial_ce_3: 0.08112/0.06756, loss_grounding_bce_3: 0.11551/0.08113, loss_grounding_dice_3: 0.36243/0.15069, loss_grounding_ce_3: 0.04374/0.25416, loss_mask_ce_4: 0.35963/0.77451, loss_mask_bce_4: 0.44771/0.30596, loss_mask_dice_4: 0.92255/1.04357, loss_spatial_bce_4: 0.14107/0.08966, loss_spatial_dice_4: 0.23522/0.19259, loss_spatial_ce_4: 0.39467/0.08125, loss_grounding_bce_4: 0.11567/0.08178, loss_grounding_dice_4: 0.35703/0.15331, loss_grounding_ce_4: 0.04415/0.25862, loss_mask_ce_5: 0.35149/0.79899, loss_mask_bce_5: 0.45313/0.30784, loss_mask_dice_5: 0.95577/1.05117, loss_spatial_bce_5: 0.59554/0.09200, loss_spatial_dice_5: 0.35274/0.19574, loss_spatial_ce_5: 0.17189/0.09449, loss_grounding_bce_5: 0.11898/0.08210, loss_grounding_dice_5: 0.35898/0.15405, loss_grounding_ce_5: 0.04258/0.27690, loss_mask_ce_6: 0.35991/0.82617, loss_mask_bce_6: 0.46606/0.30997, loss_mask_dice_6: 0.92929/1.05491, loss_spatial_bce_6: 0.59764/0.09724, loss_spatial_dice_6: 0.35859/0.19806, loss_spatial_ce_6: 0.27421/0.11908, loss_grounding_bce_6: 0.12129/0.08295, loss_grounding_dice_6: 0.38359/0.15464, loss_grounding_ce_6: 0.05452/0.28569, loss_mask_ce_7: 0.42966/0.88181, loss_mask_bce_7: 0.43405/0.31712, loss_mask_dice_7: 0.87951/1.10102, loss_spatial_bce_7: 0.13417/0.10675, loss_spatial_dice_7: 0.26829/0.22323, loss_spatial_ce_7: 0.20720/0.15556, loss_grounding_bce_7: 0.11056/0.08464, loss_grounding_dice_7: 0.35632/0.16022, loss_grounding_ce_7: 0.01825/0.31883, loss_mask_ce_8: 0.51434/1.01693, loss_mask_bce_8: 0.43768/0.33313, loss_mask_dice_8: 0.94310/1.17771, loss_spatial_bce_8: 0.14721/0.12388, loss_spatial_dice_8: 0.23635/0.25851, loss_spatial_ce_8: 0.19029/0.20194, loss_grounding_bce_8: 0.11726/0.08883, loss_grounding_dice_8: 0.33933/0.16992, loss_grounding_ce_8: 0.01368/0.41867, loss_mask_ce_9: 3.99083/3.47758, loss_mask_bce_9: 0.44960/0.36014, loss_mask_dice_9: 1.14043/1.76142, loss_spatial_bce_9: 0.46189/0.35470, loss_spatial_dice_9: 0.90750/0.79321, loss_spatial_ce_9: 1.57540/1.38858, loss_grounding_bce_9: 0.10493/0.10090, loss_grounding_dice_9: 0.39131/0.24213, loss_grounding_ce_9: 0.03359/0.67267] items per batch[64] items per second[0.37] total items[4313600] mini batches[ 67400] memory[4999] epoch remaining[0:05:48] INFO:trainer.default_trainer:epochs[ 36] optim steps[67500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.06363/0.75617, loss_mask_bce_0: 0.05486/0.30078, loss_mask_dice_0: 0.12123/1.02091, loss_spatial_bce_0: 0.11961/0.08485, loss_spatial_dice_0: 0.09653/0.17956, loss_spatial_ce_0: 0.07587/0.05671, loss_grounding_bce_0: 0.02949/0.08058, loss_grounding_dice_0: 0.03939/0.15035, loss_grounding_ce_0: 0.44736/0.24877, loss_mask_ce_1: 1.22537/0.75708, loss_mask_bce_1: 0.06735/0.30159, loss_mask_dice_1: 0.14739/1.02503, loss_spatial_bce_1: 0.14074/0.08522, loss_spatial_dice_1: 0.10492/0.18233, loss_spatial_ce_1: 0.07449/0.06055, loss_grounding_bce_1: 0.03214/0.08076, loss_grounding_dice_1: 0.04066/0.15109, loss_grounding_ce_1: 0.42843/0.25027, loss_mask_ce_2: 0.98277/0.76472, loss_mask_bce_2: 0.06044/0.30190, loss_mask_dice_2: 0.10682/1.02589, loss_spatial_bce_2: 0.11176/0.08531, loss_spatial_dice_2: 0.09390/0.18288, loss_spatial_ce_2: 0.06894/0.06269, loss_grounding_bce_2: 0.03268/0.08076, loss_grounding_dice_2: 0.03933/0.15097, loss_grounding_ce_2: 0.40387/0.25318, loss_mask_ce_3: 0.91725/0.76865, loss_mask_bce_3: 0.05454/0.30332, loss_mask_dice_3: 0.12043/1.02384, loss_spatial_bce_3: 0.16526/0.08744, loss_spatial_dice_3: 0.12730/0.18425, loss_spatial_ce_3: 0.08507/0.06753, loss_grounding_bce_3: 0.03313/0.08114, loss_grounding_dice_3: 0.04137/0.15067, loss_grounding_ce_3: 0.50469/0.25426, loss_mask_ce_4: 1.15359/0.77439, loss_mask_bce_4: 0.05931/0.30597, loss_mask_dice_4: 0.13399/1.04306, loss_spatial_bce_4: 0.14167/0.08968, loss_spatial_dice_4: 0.11303/0.19257, loss_spatial_ce_4: 0.10125/0.08123, loss_grounding_bce_4: 0.03246/0.08179, loss_grounding_dice_4: 0.03952/0.15329, loss_grounding_ce_4: 0.55369/0.25861, loss_mask_ce_5: 1.27018/0.79888, loss_mask_bce_5: 0.05373/0.30784, loss_mask_dice_5: 0.13088/1.05066, loss_spatial_bce_5: 0.19532/0.09202, loss_spatial_dice_5: 0.16054/0.19572, loss_spatial_ce_5: 0.08999/0.09447, loss_grounding_bce_5: 0.03329/0.08210, loss_grounding_dice_5: 0.04204/0.15403, loss_grounding_ce_5: 0.61462/0.27698, loss_mask_ce_6: 1.19767/0.82600, loss_mask_bce_6: 0.06587/0.30997, loss_mask_dice_6: 0.13471/1.05439, loss_spatial_bce_6: 0.19376/0.09727, loss_spatial_dice_6: 0.13920/0.19804, loss_spatial_ce_6: 0.10515/0.11906, loss_grounding_bce_6: 0.02870/0.08295, loss_grounding_dice_6: 0.04132/0.15462, loss_grounding_ce_6: 0.44350/0.28570, loss_mask_ce_7: 0.94864/0.88163, loss_mask_bce_7: 0.05376/0.31711, loss_mask_dice_7: 0.10882/1.10046, loss_spatial_bce_7: 0.28691/0.10677, loss_spatial_dice_7: 0.19096/0.22320, loss_spatial_ce_7: 0.13421/0.15551, loss_grounding_bce_7: 0.03116/0.08465, loss_grounding_dice_7: 0.04155/0.16020, loss_grounding_ce_7: 0.60890/0.31888, loss_mask_ce_8: 1.64189/1.01673, loss_mask_bce_8: 0.13257/0.33310, loss_mask_dice_8: 0.19695/1.17709, loss_spatial_bce_8: 0.21894/0.12389, loss_spatial_dice_8: 0.20693/0.25847, loss_spatial_ce_8: 0.13462/0.20186, loss_grounding_bce_8: 0.03347/0.08883, loss_grounding_dice_8: 0.04448/0.16989, loss_grounding_ce_8: 0.85329/0.41872, loss_mask_ce_9: 5.03373/3.47704, loss_mask_bce_9: 0.77652/0.36013, loss_mask_dice_9: 0.62513/1.76057, loss_spatial_bce_9: 0.71149/0.35479, loss_spatial_dice_9: 0.65624/0.79319, loss_spatial_ce_9: 1.77065/1.38859, loss_grounding_bce_9: 0.03963/0.10091, loss_grounding_dice_9: 0.11724/0.24211, loss_grounding_ce_9: 2.66656/0.67259] items per batch[64] items per second[0.37] total items[4320000] mini batches[ 67500] memory[4999] epoch remaining[0:02:53] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00067599. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3756 s/iter. Eval: 0.0965 s/iter. Total: 0.4747 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3768 s/iter. Eval: 0.0775 s/iter. Total: 0.4568 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0026 s/iter. Inference: 0.3811 s/iter. Eval: 0.0764 s/iter. Total: 0.4603 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3839 s/iter. Eval: 0.0742 s/iter. Total: 0.4608 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3850 s/iter. Eval: 0.0720 s/iter. Total: 0.4598 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3827 s/iter. Eval: 0.0705 s/iter. Total: 0.4560 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalxhqgmb__ ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.621 | 82.973 | 66.243 | 133 | | Things | 61.609 | 84.022 | 72.848 | 80 | | Stuff | 46.581 | 81.390 | 56.274 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.47s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.94 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.34 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.08s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.70 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.679 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.351 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.546 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.563 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.363 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.762 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.478 | 69.306 | 49.203 | 25.592 | 49.790 | 67.920 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.689 | bicycle | 22.850 | car | 42.682 | | motorcycle | 41.570 | airplane | 61.167 | bus | 71.106 | | train | 75.075 | truck | 43.664 | boat | 31.345 | | traffic light | 27.644 | fire hydrant | 71.742 | stop sign | 68.787 | | parking meter | 51.163 | bench | 26.149 | bird | 33.183 | | cat | 77.950 | dog | 71.178 | horse | 50.816 | | sheep | 52.761 | cow | 57.000 | elephant | 66.875 | | bear | 80.651 | zebra | 65.473 | giraffe | 62.325 | | backpack | 24.748 | umbrella | 55.753 | handbag | 23.820 | | tie | 40.561 | suitcase | 50.519 | frisbee | 69.664 | | skis | 9.019 | snowboard | 34.604 | sports ball | 50.189 | | kite | 37.302 | baseball bat | 38.890 | baseball glove | 49.254 | | skateboard | 44.768 | surfboard | 44.415 | tennis racket | 63.383 | | bottle | 41.982 | wine glass | 38.331 | cup | 50.429 | | fork | 26.991 | knife | 24.873 | spoon | 22.168 | | bowl | 38.909 | banana | 21.351 | apple | 25.845 | | sandwich | 49.217 | orange | 31.340 | broccoli | 24.470 | | carrot | 23.029 | hot dog | 33.973 | pizza | 52.097 | | donut | 54.119 | cake | 48.024 | chair | 28.917 | | couch | 44.053 | potted plant | 22.991 | bed | 41.867 | | dining table | 16.062 | toilet | 69.180 | tv | 65.422 | | laptop | 70.255 | mouse | 64.585 | remote | 43.323 | | keyboard | 58.380 | cell phone | 46.258 | microwave | 65.646 | | oven | 32.088 | toaster | 49.219 | sink | 43.520 | | refrigerator | 67.862 | book | 14.087 | clock | 54.406 | | vase | 41.336 | scissors | 37.690 | teddy bear | 57.202 | | hair drier | 30.884 | toothbrush | 27.085 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.20253811307951, 'fwIoU': 71.92825034271793, 'IoU-person': 88.75488852334725, 'IoU-bicycle': 78.70801453644474, 'IoU-car': 71.1431905416192, 'IoU-motorcycle': 89.05469109984759, 'IoU-airplane': 84.68223962860965, 'IoU-bus': 87.46317688308831, 'IoU-train': 89.01093168972616, 'IoU-truck': 65.98971489339455, 'IoU-boat': 75.23513366031096, 'IoU-traffic light': 80.30664477801919, 'IoU-fire hydrant': 92.80343428245632, 'IoU-stop sign': 93.95677856613099, 'IoU-parking meter': 84.58463027943807, 'IoU-bench': 58.79811963097124, 'IoU-bird': 76.37725656129432, 'IoU-cat': 90.1352526980071, 'IoU-dog': 83.81917549578085, 'IoU-horse': 87.91350764246583, 'IoU-sheep': 84.55834724914746, 'IoU-cow': 90.26125019426135, 'IoU-elephant': 89.87400507413204, 'IoU-bear': 85.7715422370663, 'IoU-zebra': 83.59142898581706, 'IoU-giraffe': 89.44298291659206, 'IoU-backpack': 53.348009071430944, 'IoU-umbrella': 88.67019048241002, 'IoU-handbag': 50.256442638938594, 'IoU-tie': 76.28216307543504, 'IoU-suitcase': 81.29696968767674, 'IoU-frisbee': 84.68888080943069, 'IoU-skis': 61.29276665619714, 'IoU-snowboard': 74.76656551922852, 'IoU-sports ball': 77.5949197055949, 'IoU-kite': 79.58919743125617, 'IoU-baseball bat': 67.93150401089305, 'IoU-baseball glove': 79.23543963614183, 'IoU-skateboard': 86.27015120721028, 'IoU-surfboard': 85.93512334466449, 'IoU-tennis racket': 90.74185980037255, 'IoU-bottle': 70.65703750689138, 'IoU-wine glass': 82.68517638109726, 'IoU-cup': 71.38918212579959, 'IoU-fork': 70.051764978061, 'IoU-knife': 66.32521329587985, 'IoU-spoon': 63.670865263518316, 'IoU-bowl': 61.712171325711594, 'IoU-banana': 82.03519636242288, 'IoU-apple': 58.6447376432985, 'IoU-sandwich': 69.54036670507094, 'IoU-orange': 78.75228310171852, 'IoU-broccoli': 70.53269295257837, 'IoU-carrot': 65.04573804326208, 'IoU-hot dog': 63.25713347125298, 'IoU-pizza': 83.37673071434185, 'IoU-donut': 69.09165722045086, 'IoU-cake': 78.65950518675481, 'IoU-chair': 62.15360874247644, 'IoU-couch': 70.53088963802114, 'IoU-potted plant': 44.44873165802384, 'IoU-bed': 71.27033152160222, 'IoU-dining table': 55.08325732813121, 'IoU-toilet': 86.99543239757313, 'IoU-tv': 80.56980489916103, 'IoU-laptop': 76.81604611013778, 'IoU-mouse': 77.32661916881806, 'IoU-remote': 71.51488004195255, 'IoU-keyboard': 67.72443629929722, 'IoU-cell phone': 81.93279879259993, 'IoU-microwave': 70.74884738522029, 'IoU-oven': 73.17136809715214, 'IoU-toaster': 85.3817630312261, 'IoU-sink': 70.27457062650585, 'IoU-refrigerator': 85.20905455232561, 'IoU-book': 55.48107490682382, 'IoU-clock': 80.45344072566603, 'IoU-vase': 63.444877150516646, 'IoU-scissors': 89.07036777015716, 'IoU-teddy bear': 83.4185741589825, 'IoU-hair drier': 48.603301184836816, 'IoU-toothbrush': 75.60270978008141, 'IoU-banner': 40.41572337054256, 'IoU-blanket': 17.544363332129787, 'IoU-bridge': 38.11417130271108, 'IoU-cardboard': 51.36182524571269, 'IoU-counter': 34.08454599725582, 'IoU-curtain': 71.97228361474556, 'IoU-door-stuff': 47.58221422814762, 'IoU-floor-wood': 63.48474819960709, 'IoU-flower': 47.560568337897266, 'IoU-fruit': 49.11852634683845, 'IoU-gravel': 32.66462071664665, 'IoU-house': 23.25593794853913, 'IoU-light': 44.19482609233816, 'IoU-mirror-stuff': 65.05193293048468, 'IoU-net': 42.47130790682315, 'IoU-pillow': 25.55251042251015, 'IoU-platform': 28.831615027287317, 'IoU-playingfield': 68.28438236461642, 'IoU-railroad': 65.27525698130894, 'IoU-river': 52.82821262170404, 'IoU-road': 67.60356820899696, 'IoU-roof': 21.20324861873967, 'IoU-sand': 65.69951549981053, 'IoU-sea': 85.71607172115392, 'IoU-shelf': 40.248157095152834, 'IoU-snow': 92.15435588955275, 'IoU-stairs': 36.56343202585417, 'IoU-tent': 11.030577429734093, 'IoU-towel': 46.091394125635546, 'IoU-wall-brick': 48.69720144073001, 'IoU-wall-stone': 26.287635464040456, 'IoU-wall-tile': 67.62648383767808, 'IoU-wall-wood': 44.42390345520293, 'IoU-water-other': 28.190567999358347, 'IoU-window-blind': 51.462695339961215, 'IoU-window-other': 51.09068214770605, 'IoU-tree-merged': 82.25983983574446, 'IoU-fence-merged': 53.98038446093527, 'IoU-ceiling-merged': 68.01350462978564, 'IoU-sky-other-merged': 94.10039888486865, 'IoU-cabinet-merged': 64.8139046612065, 'IoU-table-merged': 42.78923035953777, 'IoU-floor-other-merged': 56.48271903448795, 'IoU-pavement-merged': 58.17774218166192, 'IoU-mountain-merged': 58.16354572951972, 'IoU-grass-merged': 72.86704865890579, 'IoU-dirt-merged': 46.771204399230584, 'IoU-paper-merged': 35.91521475363891, 'IoU-food-other-merged': 44.11700996899517, 'IoU-building-other-merged': 60.22199284512898, 'IoU-rock-merged': 61.59315905826867, 'IoU-wall-other-merged': 69.55736901266609, 'IoU-rug-merged': 68.58347790759026, 'mACC': 77.61509611255798, 'pACC': 82.4903485813366, 'ACC-person': 93.07101488208754, 'ACC-bicycle': 88.39704597174031, 'ACC-car': 85.49676464358123, 'ACC-motorcycle': 93.58752535972783, 'ACC-airplane': 88.5345940104848, 'ACC-bus': 93.96638062554008, 'ACC-train': 95.476651080982, 'ACC-truck': 76.36956134077334, 'ACC-boat': 85.14503881372171, 'ACC-traffic light': 91.0277342168407, 'ACC-fire hydrant': 96.003834542481, 'ACC-stop sign': 98.36286230354119, 'ACC-parking meter': 87.40942721583376, 'ACC-bench': 75.4749445289476, 'ACC-bird': 82.35270830123432, 'ACC-cat': 94.39107543490022, 'ACC-dog': 87.13862137688656, 'ACC-horse': 93.28739138336448, 'ACC-sheep': 88.52058900594396, 'ACC-cow': 93.19640513249728, 'ACC-elephant': 92.05370876551704, 'ACC-bear': 87.49948131225693, 'ACC-zebra': 85.50342510445019, 'ACC-giraffe': 93.21817679117972, 'ACC-backpack': 75.58489846714753, 'ACC-umbrella': 93.47084179502328, 'ACC-handbag': 70.67564973565666, 'ACC-tie': 84.34233684748911, 'ACC-suitcase': 85.97958116909618, 'ACC-frisbee': 94.02036363636364, 'ACC-skis': 75.79873048106114, 'ACC-snowboard': 81.34505645220543, 'ACC-sports ball': 86.5827312101384, 'ACC-kite': 85.6596669007473, 'ACC-baseball bat': 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'ACC-mouse': 91.73367387061747, 'ACC-remote': 75.88030643546352, 'ACC-keyboard': 74.66926768383459, 'ACC-cell phone': 95.16676235445071, 'ACC-microwave': 74.64890892221575, 'ACC-oven': 89.17505684702223, 'ACC-toaster': 90.9140675252159, 'ACC-sink': 77.84784477991539, 'ACC-refrigerator': 94.12509158091349, 'ACC-book': 72.33241573505926, 'ACC-clock': 86.21061503209367, 'ACC-vase': 70.62363597596556, 'ACC-scissors': 94.64989119356527, 'ACC-teddy bear': 90.19681570029738, 'ACC-hair drier': 60.89004639512709, 'ACC-toothbrush': 85.01824183460737, 'ACC-banner': 76.63767792131506, 'ACC-blanket': 27.483341735305356, 'ACC-bridge': 54.470480379508956, 'ACC-cardboard': 68.58208521157482, 'ACC-counter': 56.186165019853696, 'ACC-curtain': 83.11531568073578, 'ACC-door-stuff': 67.5367798258382, 'ACC-floor-wood': 81.13352850325639, 'ACC-flower': 69.14575210819251, 'ACC-fruit': 69.70299994854489, 'ACC-gravel': 45.686740043985225, 'ACC-house': 27.05877649213935, 'ACC-light': 62.36039601117789, 'ACC-mirror-stuff': 74.15151644153073, 'ACC-net': 66.15397747049681, 'ACC-pillow': 54.5939338900138, 'ACC-platform': 50.09556965481724, 'ACC-playingfield': 82.27924326852794, 'ACC-railroad': 81.29445061800524, 'ACC-river': 79.93045491345538, 'ACC-road': 85.68576960579314, 'ACC-roof': 29.79256561014069, 'ACC-sand': 71.56859793353686, 'ACC-sea': 92.11564100241804, 'ACC-shelf': 58.63305927722661, 'ACC-snow': 95.79695183628657, 'ACC-stairs': 58.6335428977482, 'ACC-tent': 14.244221303076815, 'ACC-towel': 55.02104060267652, 'ACC-wall-brick': 64.3268693621459, 'ACC-wall-stone': 34.59999688978846, 'ACC-wall-tile': 81.59296755464514, 'ACC-wall-wood': 65.78070325642355, 'ACC-water-other': 36.63117824813812, 'ACC-window-blind': 67.00636013349765, 'ACC-window-other': 72.94617177201465, 'ACC-tree-merged': 90.33504174813886, 'ACC-fence-merged': 70.87429029928288, 'ACC-ceiling-merged': 80.60053530318983, 'ACC-sky-other-merged': 96.79739129317862, 'ACC-cabinet-merged': 77.20278756697734, 'ACC-table-merged': 58.21584119127522, 'ACC-floor-other-merged': 66.45509934419613, 'ACC-pavement-merged': 70.68542367280706, 'ACC-mountain-merged': 68.31152866259083, 'ACC-grass-merged': 84.1922839898975, 'ACC-dirt-merged': 72.4323838028208, 'ACC-paper-merged': 47.29924907029163, 'ACC-food-other-merged': 69.7307886145875, 'ACC-building-other-merged': 79.21623569535294, 'ACC-rock-merged': 86.24134305142047, 'ACC-wall-other-merged': 82.84217893023057, 'ACC-rug-merged': 82.45417954157904})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3106 s/iter. Inference: 0.4805 s/iter. Eval: 0.0000 s/iter. Total: 0.7911 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3260 s/iter. Inference: 0.4915 s/iter. Eval: 0.0000 s/iter. Total: 0.8177 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3495 s/iter. Inference: 0.6286 s/iter. Eval: 0.0000 s/iter. Total: 0.9782 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.385425812115891, 'noc@0.8': 2.3292361720807726, 'noc@0.85': 2.756218905472637, 'noc@0.9': 3.5387767047117356, 'miou@iter1': 0.8717408368627265} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0012 s/iter. Inference: 0.1452 s/iter. Eval: 0.0011 s/iter. Total: 0.1475 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.33113098144531, 'precision@0.6': 73.53284454345703, 'precision@0.7': 69.41313934326172, 'precision@0.8': 60.51301956176758, 'precision@0.9': 32.60784912109375, 'cIoU': 62.695430755615234, 'mIoU': 67.65573120117188} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.62069262953935, 'SQ': 82.97338538588969, 'RQ': 66.24337092686102, 'PQ_th': 61.60920561612653, 'SQ_th': 84.02205011156254, 'RQ_th': 72.84817114502414, 'PQ_st': 46.58142774412466, 'SQ_st': 81.39049523393065, 'RQ_st': 56.27386116359588}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.47755278915846, 'AP50': 69.3058356854913, 'AP75': 49.20257739866559, 'APs': 25.592255467483866, 'APm': 49.7900878780523, 'APl': 67.92031705396062, 'AP-person': 48.68899672855098, 'AP-bicycle': 22.849799226881522, 'AP-car': 42.68222034395925, 'AP-motorcycle': 41.570494339056125, 'AP-airplane': 61.167415016801364, 'AP-bus': 71.10621844203003, 'AP-train': 75.07492312799052, 'AP-truck': 43.66420614267945, 'AP-boat': 31.34495610945507, 'AP-traffic light': 27.64396775071529, 'AP-fire hydrant': 71.74248833860644, 'AP-stop sign': 68.78743524944898, 'AP-parking meter': 51.16276545352313, 'AP-bench': 26.148853238377338, 'AP-bird': 33.183218144975044, 'AP-cat': 77.94954313318108, 'AP-dog': 71.17826148622616, 'AP-horse': 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57.20164952983229, 'AP-hair drier': 30.883764346931585, 'AP-toothbrush': 27.085491891866546}), ('sem_seg', {'mIoU': 66.20253811307951, 'fwIoU': 71.92825034271793, 'IoU-person': 88.75488852334725, 'IoU-bicycle': 78.70801453644474, 'IoU-car': 71.1431905416192, 'IoU-motorcycle': 89.05469109984759, 'IoU-airplane': 84.68223962860965, 'IoU-bus': 87.46317688308831, 'IoU-train': 89.01093168972616, 'IoU-truck': 65.98971489339455, 'IoU-boat': 75.23513366031096, 'IoU-traffic light': 80.30664477801919, 'IoU-fire hydrant': 92.80343428245632, 'IoU-stop sign': 93.95677856613099, 'IoU-parking meter': 84.58463027943807, 'IoU-bench': 58.79811963097124, 'IoU-bird': 76.37725656129432, 'IoU-cat': 90.1352526980071, 'IoU-dog': 83.81917549578085, 'IoU-horse': 87.91350764246583, 'IoU-sheep': 84.55834724914746, 'IoU-cow': 90.26125019426135, 'IoU-elephant': 89.87400507413204, 'IoU-bear': 85.7715422370663, 'IoU-zebra': 83.59142898581706, 'IoU-giraffe': 89.44298291659206, 'IoU-backpack': 53.348009071430944, 'IoU-umbrella': 88.67019048241002, 'IoU-handbag': 50.256442638938594, 'IoU-tie': 76.28216307543504, 'IoU-suitcase': 81.29696968767674, 'IoU-frisbee': 84.68888080943069, 'IoU-skis': 61.29276665619714, 'IoU-snowboard': 74.76656551922852, 'IoU-sports ball': 77.5949197055949, 'IoU-kite': 79.58919743125617, 'IoU-baseball bat': 67.93150401089305, 'IoU-baseball glove': 79.23543963614183, 'IoU-skateboard': 86.27015120721028, 'IoU-surfboard': 85.93512334466449, 'IoU-tennis racket': 90.74185980037255, 'IoU-bottle': 70.65703750689138, 'IoU-wine glass': 82.68517638109726, 'IoU-cup': 71.38918212579959, 'IoU-fork': 70.051764978061, 'IoU-knife': 66.32521329587985, 'IoU-spoon': 63.670865263518316, 'IoU-bowl': 61.712171325711594, 'IoU-banana': 82.03519636242288, 'IoU-apple': 58.6447376432985, 'IoU-sandwich': 69.54036670507094, 'IoU-orange': 78.75228310171852, 'IoU-broccoli': 70.53269295257837, 'IoU-carrot': 65.04573804326208, 'IoU-hot dog': 63.25713347125298, 'IoU-pizza': 83.37673071434185, 'IoU-donut': 69.09165722045086, 'IoU-cake': 78.65950518675481, 'IoU-chair': 62.15360874247644, 'IoU-couch': 70.53088963802114, 'IoU-potted plant': 44.44873165802384, 'IoU-bed': 71.27033152160222, 'IoU-dining table': 55.08325732813121, 'IoU-toilet': 86.99543239757313, 'IoU-tv': 80.56980489916103, 'IoU-laptop': 76.81604611013778, 'IoU-mouse': 77.32661916881806, 'IoU-remote': 71.51488004195255, 'IoU-keyboard': 67.72443629929722, 'IoU-cell phone': 81.93279879259993, 'IoU-microwave': 70.74884738522029, 'IoU-oven': 73.17136809715214, 'IoU-toaster': 85.3817630312261, 'IoU-sink': 70.27457062650585, 'IoU-refrigerator': 85.20905455232561, 'IoU-book': 55.48107490682382, 'IoU-clock': 80.45344072566603, 'IoU-vase': 63.444877150516646, 'IoU-scissors': 89.07036777015716, 'IoU-teddy bear': 83.4185741589825, 'IoU-hair drier': 48.603301184836816, 'IoU-toothbrush': 75.60270978008141, 'IoU-banner': 40.41572337054256, 'IoU-blanket': 17.544363332129787, 'IoU-bridge': 38.11417130271108, 'IoU-cardboard': 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'IoU-water-other': 28.190567999358347, 'IoU-window-blind': 51.462695339961215, 'IoU-window-other': 51.09068214770605, 'IoU-tree-merged': 82.25983983574446, 'IoU-fence-merged': 53.98038446093527, 'IoU-ceiling-merged': 68.01350462978564, 'IoU-sky-other-merged': 94.10039888486865, 'IoU-cabinet-merged': 64.8139046612065, 'IoU-table-merged': 42.78923035953777, 'IoU-floor-other-merged': 56.48271903448795, 'IoU-pavement-merged': 58.17774218166192, 'IoU-mountain-merged': 58.16354572951972, 'IoU-grass-merged': 72.86704865890579, 'IoU-dirt-merged': 46.771204399230584, 'IoU-paper-merged': 35.91521475363891, 'IoU-food-other-merged': 44.11700996899517, 'IoU-building-other-merged': 60.22199284512898, 'IoU-rock-merged': 61.59315905826867, 'IoU-wall-other-merged': 69.55736901266609, 'IoU-rug-merged': 68.58347790759026, 'mACC': 77.61509611255798, 'pACC': 82.4903485813366, 'ACC-person': 93.07101488208754, 'ACC-bicycle': 88.39704597174031, 'ACC-car': 85.49676464358123, 'ACC-motorcycle': 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'ACC-laptop': 88.26792942549228, 'ACC-mouse': 91.73367387061747, 'ACC-remote': 75.88030643546352, 'ACC-keyboard': 74.66926768383459, 'ACC-cell phone': 95.16676235445071, 'ACC-microwave': 74.64890892221575, 'ACC-oven': 89.17505684702223, 'ACC-toaster': 90.9140675252159, 'ACC-sink': 77.84784477991539, 'ACC-refrigerator': 94.12509158091349, 'ACC-book': 72.33241573505926, 'ACC-clock': 86.21061503209367, 'ACC-vase': 70.62363597596556, 'ACC-scissors': 94.64989119356527, 'ACC-teddy bear': 90.19681570029738, 'ACC-hair drier': 60.89004639512709, 'ACC-toothbrush': 85.01824183460737, 'ACC-banner': 76.63767792131506, 'ACC-blanket': 27.483341735305356, 'ACC-bridge': 54.470480379508956, 'ACC-cardboard': 68.58208521157482, 'ACC-counter': 56.186165019853696, 'ACC-curtain': 83.11531568073578, 'ACC-door-stuff': 67.5367798258382, 'ACC-floor-wood': 81.13352850325639, 'ACC-flower': 69.14575210819251, 'ACC-fruit': 69.70299994854489, 'ACC-gravel': 45.686740043985225, 'ACC-house': 27.05877649213935, 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67.65573120117188}}} INFO:trainer.default_trainer:This epoch takes 0:56:44.836277 INFO:trainer.default_trainer:PROGRESS: 74.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 37 training. INFO:trainer.default_trainer:epochs[ 37] optim steps[67600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81884/0.75612, loss_mask_bce_0: 0.36484/0.30082, loss_mask_dice_0: 0.96994/1.02077, loss_spatial_bce_0: 0.06615/0.08487, loss_spatial_dice_0: 0.15561/0.17956, loss_spatial_ce_0: 0.04418/0.05671, loss_grounding_bce_0: 0.04021/0.08058, loss_grounding_dice_0: 0.13910/0.15036, loss_grounding_ce_0: 0.44676/0.24878, loss_mask_ce_1: 1.32644/0.75703, loss_mask_bce_1: 0.34904/0.30163, loss_mask_dice_1: 0.77248/1.02491, loss_spatial_bce_1: 0.06587/0.08524, loss_spatial_dice_1: 0.17406/0.18233, loss_spatial_ce_1: 0.04987/0.06054, loss_grounding_bce_1: 0.04272/0.08076, loss_grounding_dice_1: 0.15319/0.15110, loss_grounding_ce_1: 0.46932/0.25028, loss_mask_ce_2: 1.37748/0.76467, loss_mask_bce_2: 0.35151/0.30194, loss_mask_dice_2: 0.77397/1.02576, loss_spatial_bce_2: 0.06607/0.08532, loss_spatial_dice_2: 0.17268/0.18288, loss_spatial_ce_2: 0.03927/0.06268, loss_grounding_bce_2: 0.04082/0.08076, loss_grounding_dice_2: 0.15231/0.15099, loss_grounding_ce_2: 0.47207/0.25321, loss_mask_ce_3: 0.81692/0.76863, loss_mask_bce_3: 0.36590/0.30336, loss_mask_dice_3: 0.96577/1.02374, loss_spatial_bce_3: 0.06782/0.08746, loss_spatial_dice_3: 0.18480/0.18425, loss_spatial_ce_3: 0.03320/0.06751, loss_grounding_bce_3: 0.03949/0.08114, loss_grounding_dice_3: 0.15321/0.15068, loss_grounding_ce_3: 0.47462/0.25427, loss_mask_ce_4: 1.33180/0.77436, loss_mask_bce_4: 0.34680/0.30602, loss_mask_dice_4: 0.79391/1.04293, loss_spatial_bce_4: 0.07176/0.08969, loss_spatial_dice_4: 0.21747/0.19258, loss_spatial_ce_4: 0.03466/0.08120, loss_grounding_bce_4: 0.04198/0.08179, loss_grounding_dice_4: 0.17079/0.15330, loss_grounding_ce_4: 0.45916/0.25863, loss_mask_ce_5: 1.23246/0.79886, loss_mask_bce_5: 0.40455/0.30789, loss_mask_dice_5: 0.95275/1.05055, loss_spatial_bce_5: 0.08000/0.09204, loss_spatial_dice_5: 0.23962/0.19573, loss_spatial_ce_5: 0.04102/0.09448, loss_grounding_bce_5: 0.05618/0.08211, loss_grounding_dice_5: 0.21304/0.15405, loss_grounding_ce_5: 0.45997/0.27697, loss_mask_ce_6: 1.40504/0.82598, loss_mask_bce_6: 0.35629/0.31001, loss_mask_dice_6: 0.77451/1.05427, loss_spatial_bce_6: 0.08577/0.09729, loss_spatial_dice_6: 0.24182/0.19805, loss_spatial_ce_6: 0.08174/0.11905, loss_grounding_bce_6: 0.05452/0.08295, loss_grounding_dice_6: 0.17152/0.15463, loss_grounding_ce_6: 0.44970/0.28570, loss_mask_ce_7: 0.81312/0.88163, loss_mask_bce_7: 0.40428/0.31716, loss_mask_dice_7: 1.13487/1.10032, loss_spatial_bce_7: 0.13779/0.10678, loss_spatial_dice_7: 0.29490/0.22321, loss_spatial_ce_7: 0.07367/0.15549, loss_grounding_bce_7: 0.05540/0.08464, loss_grounding_dice_7: 0.18512/0.16022, loss_grounding_ce_7: 0.41727/0.31891, loss_mask_ce_8: 1.77433/1.01668, loss_mask_bce_8: 0.37381/0.33312, loss_mask_dice_8: 1.02030/1.17697, loss_spatial_bce_8: 0.13614/0.12390, loss_spatial_dice_8: 0.29770/0.25847, loss_spatial_ce_8: 0.13527/0.20181, loss_grounding_bce_8: 0.05009/0.08883, loss_grounding_dice_8: 0.18987/0.16990, loss_grounding_ce_8: 0.57813/0.41873, loss_mask_ce_9: 4.53887/3.47712, loss_mask_bce_9: 0.40707/0.36016, loss_mask_dice_9: 1.30007/1.76030, loss_spatial_bce_9: 0.55653/0.35476, loss_spatial_dice_9: 0.92519/0.79319, loss_spatial_ce_9: 1.40221/1.38856, loss_grounding_bce_9: 0.06885/0.10091, loss_grounding_dice_9: 0.25579/0.24215, loss_grounding_ce_9: 0.44639/0.67250] items per batch[64] items per second[0.16] total items[4326400] mini batches[ 67600] memory[4999] epoch remaining[4:34:13] INFO:trainer.default_trainer:epochs[ 37] optim steps[67700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.71862/0.75610, loss_mask_bce_0: 0.66414/0.30080, loss_mask_dice_0: 1.13941/1.02071, loss_spatial_bce_0: 0.10470/0.08485, loss_spatial_dice_0: 0.23793/0.17953, loss_spatial_ce_0: 0.00758/0.05669, loss_grounding_bce_0: 0.36716/0.08060, loss_grounding_dice_0: 0.18177/0.15038, loss_grounding_ce_0: 0.00251/0.24875, loss_mask_ce_1: 0.62624/0.75701, loss_mask_bce_1: 0.65768/0.30162, loss_mask_dice_1: 1.12202/1.02488, loss_spatial_bce_1: 0.11763/0.08522, loss_spatial_dice_1: 0.24148/0.18231, loss_spatial_ce_1: 0.01612/0.06052, loss_grounding_bce_1: 0.39911/0.08078, loss_grounding_dice_1: 0.18208/0.15112, loss_grounding_ce_1: 0.00351/0.25025, loss_mask_ce_2: 0.63980/0.76467, loss_mask_bce_2: 0.66818/0.30193, loss_mask_dice_2: 1.15155/1.02573, loss_spatial_bce_2: 0.11533/0.08531, loss_spatial_dice_2: 0.26021/0.18286, loss_spatial_ce_2: 0.05073/0.06266, loss_grounding_bce_2: 0.35758/0.08077, loss_grounding_dice_2: 0.16463/0.15101, loss_grounding_ce_2: 0.00567/0.25317, loss_mask_ce_3: 0.73297/0.76862, loss_mask_bce_3: 0.67099/0.30334, loss_mask_dice_3: 1.16012/1.02368, loss_spatial_bce_3: 0.10169/0.08744, loss_spatial_dice_3: 0.23706/0.18423, loss_spatial_ce_3: 0.04165/0.06749, loss_grounding_bce_3: 0.37876/0.08116, loss_grounding_dice_3: 0.17773/0.15070, loss_grounding_ce_3: 0.00900/0.25426, loss_mask_ce_4: 0.88242/0.77432, loss_mask_bce_4: 0.72929/0.30601, loss_mask_dice_4: 1.12068/1.04288, loss_spatial_bce_4: 0.13291/0.08968, loss_spatial_dice_4: 0.26331/0.19256, loss_spatial_ce_4: 0.10867/0.08117, loss_grounding_bce_4: 0.39024/0.08181, loss_grounding_dice_4: 0.17847/0.15333, loss_grounding_ce_4: 0.00398/0.25861, loss_mask_ce_5: 0.74033/0.79882, loss_mask_bce_5: 0.81573/0.30787, loss_mask_dice_5: 1.17672/1.05052, loss_spatial_bce_5: 0.16066/0.09203, loss_spatial_dice_5: 0.30106/0.19571, loss_spatial_ce_5: 0.32114/0.09446, loss_grounding_bce_5: 0.36881/0.08213, loss_grounding_dice_5: 0.17549/0.15408, loss_grounding_ce_5: 0.00712/0.27696, loss_mask_ce_6: 0.97566/0.82592, loss_mask_bce_6: 0.67725/0.30998, loss_mask_dice_6: 1.15931/1.05421, loss_spatial_bce_6: 0.16449/0.09728, loss_spatial_dice_6: 0.32036/0.19803, loss_spatial_ce_6: 0.25438/0.11903, loss_grounding_bce_6: 0.35720/0.08298, loss_grounding_dice_6: 0.16960/0.15466, loss_grounding_ce_6: 0.00524/0.28565, loss_mask_ce_7: 0.92851/0.88155, loss_mask_bce_7: 0.67999/0.31714, loss_mask_dice_7: 1.07043/1.10024, loss_spatial_bce_7: 0.18621/0.10676, loss_spatial_dice_7: 0.35872/0.22319, loss_spatial_ce_7: 0.24695/0.15546, loss_grounding_bce_7: 0.41984/0.08466, loss_grounding_dice_7: 0.17034/0.16025, loss_grounding_ce_7: 0.00324/0.31880, loss_mask_ce_8: 0.91238/1.01661, loss_mask_bce_8: 0.68088/0.33311, loss_mask_dice_8: 1.25679/1.17694, loss_spatial_bce_8: 0.13486/0.12388, loss_spatial_dice_8: 0.36067/0.25845, loss_spatial_ce_8: 0.44105/0.20177, loss_grounding_bce_8: 0.37206/0.08884, loss_grounding_dice_8: 0.16893/0.16993, loss_grounding_ce_8: 0.00492/0.41852, loss_mask_ce_9: 4.18663/3.47688, loss_mask_bce_9: 0.73760/0.36015, loss_mask_dice_9: 1.84670/1.76010, loss_spatial_bce_9: 0.34679/0.35475, loss_spatial_dice_9: 0.89011/0.79316, loss_spatial_ce_9: 1.91649/1.38845, loss_grounding_bce_9: 0.42209/0.10092, loss_grounding_dice_9: 0.13936/0.24216, loss_grounding_ce_9: 0.15813/0.67230] items per batch[64] items per second[0.36] total items[4332800] mini batches[ 67700] memory[4999] epoch remaining[0:52:50] INFO:trainer.default_trainer:epochs[ 37] optim steps[67800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15079/0.75614, loss_mask_bce_0: 0.03279/0.30078, loss_mask_dice_0: 0.06150/1.02081, loss_spatial_bce_0: 0.02954/0.08483, loss_spatial_dice_0: 0.04794/0.17951, loss_spatial_ce_0: 0.00129/0.05667, loss_grounding_bce_0: 0.02817/0.08058, loss_grounding_dice_0: 0.04085/0.15036, loss_grounding_ce_0: 0.00612/0.24875, loss_mask_ce_1: 0.16648/0.75705, loss_mask_bce_1: 0.02894/0.30159, loss_mask_dice_1: 0.05943/1.02495, loss_spatial_bce_1: 0.02805/0.08521, loss_spatial_dice_1: 0.04822/0.18230, loss_spatial_ce_1: 0.00089/0.06050, loss_grounding_bce_1: 0.03023/0.08076, loss_grounding_dice_1: 0.04652/0.15110, loss_grounding_ce_1: 0.00630/0.25029, loss_mask_ce_2: 0.14642/0.76469, loss_mask_bce_2: 0.02936/0.30191, loss_mask_dice_2: 0.05617/1.02580, loss_spatial_bce_2: 0.02762/0.08529, loss_spatial_dice_2: 0.04420/0.18285, loss_spatial_ce_2: 0.00090/0.06267, loss_grounding_bce_2: 0.02951/0.08075, loss_grounding_dice_2: 0.04485/0.15099, loss_grounding_ce_2: 0.00308/0.25318, loss_mask_ce_3: 0.18468/0.76870, loss_mask_bce_3: 0.03116/0.30331, loss_mask_dice_3: 0.05959/1.02375, loss_spatial_bce_3: 0.02934/0.08743, loss_spatial_dice_3: 0.04861/0.18421, loss_spatial_ce_3: 0.00052/0.06748, loss_grounding_bce_3: 0.03025/0.08113, loss_grounding_dice_3: 0.04764/0.15069, loss_grounding_ce_3: 0.00550/0.25432, loss_mask_ce_4: 0.13560/0.77437, loss_mask_bce_4: 0.03118/0.30598, loss_mask_dice_4: 0.05703/1.04302, loss_spatial_bce_4: 0.02850/0.08966, loss_spatial_dice_4: 0.04511/0.19255, loss_spatial_ce_4: 0.00141/0.08116, loss_grounding_bce_4: 0.02990/0.08179, loss_grounding_dice_4: 0.04348/0.15330, loss_grounding_ce_4: 0.01214/0.25864, loss_mask_ce_5: 0.16528/0.79887, loss_mask_bce_5: 0.03190/0.30784, loss_mask_dice_5: 0.05769/1.05062, loss_spatial_bce_5: 0.02965/0.09201, loss_spatial_dice_5: 0.04645/0.19570, loss_spatial_ce_5: 0.00108/0.09446, loss_grounding_bce_5: 0.02668/0.08210, loss_grounding_dice_5: 0.03827/0.15406, loss_grounding_ce_5: 0.02767/0.27693, loss_mask_ce_6: 0.23748/0.82597, loss_mask_bce_6: 0.03099/0.30996, loss_mask_dice_6: 0.05750/1.05431, loss_spatial_bce_6: 0.02891/0.09725, loss_spatial_dice_6: 0.05068/0.19801, loss_spatial_ce_6: 0.00594/0.11904, loss_grounding_bce_6: 0.02913/0.08295, loss_grounding_dice_6: 0.04371/0.15464, loss_grounding_ce_6: 0.02204/0.28558, loss_mask_ce_7: 0.21114/0.88158, loss_mask_bce_7: 0.03311/0.31713, loss_mask_dice_7: 0.05962/1.10034, loss_spatial_bce_7: 0.02846/0.10674, loss_spatial_dice_7: 0.04200/0.22318, loss_spatial_ce_7: 0.02273/0.15544, loss_grounding_bce_7: 0.02916/0.08465, loss_grounding_dice_7: 0.04066/0.16024, loss_grounding_ce_7: 0.06519/0.31870, loss_mask_ce_8: 0.15934/1.01665, loss_mask_bce_8: 0.03396/0.33309, loss_mask_dice_8: 0.06366/1.17705, loss_spatial_bce_8: 0.03083/0.12386, loss_spatial_dice_8: 0.05211/0.25842, loss_spatial_ce_8: 0.03208/0.20170, loss_grounding_bce_8: 0.02769/0.08882, loss_grounding_dice_8: 0.03861/0.16991, loss_grounding_ce_8: 0.02230/0.41860, loss_mask_ce_9: 2.75466/3.47690, loss_mask_bce_9: 0.04766/0.36012, loss_mask_dice_9: 0.10832/1.76024, loss_spatial_bce_9: 0.24965/0.35474, loss_spatial_dice_9: 0.58874/0.79316, loss_spatial_ce_9: 0.45113/1.38844, loss_grounding_bce_9: 0.03969/0.10091, loss_grounding_dice_9: 0.06183/0.24214, loss_grounding_ce_9: 0.25921/0.67229] items per batch[64] items per second[0.37] total items[4339200] mini batches[ 67800] memory[4999] epoch remaining[0:48:13] INFO:trainer.default_trainer:epochs[ 37] optim steps[67900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.43030/0.75600, loss_mask_bce_0: 0.10523/0.30072, loss_mask_dice_0: 0.13568/1.02071, loss_spatial_bce_0: 0.09661/0.08481, loss_spatial_dice_0: 0.13033/0.17949, loss_spatial_ce_0: 0.00535/0.05664, loss_grounding_bce_0: 0.12856/0.08059, loss_grounding_dice_0: 0.18211/0.15037, loss_grounding_ce_0: 0.42095/0.24872, loss_mask_ce_1: 1.45341/0.75690, loss_mask_bce_1: 0.09665/0.30153, loss_mask_dice_1: 0.12089/1.02483, loss_spatial_bce_1: 0.10378/0.08519, loss_spatial_dice_1: 0.13043/0.18228, loss_spatial_ce_1: 0.00301/0.06046, loss_grounding_bce_1: 0.11794/0.08076, loss_grounding_dice_1: 0.16310/0.15112, loss_grounding_ce_1: 0.44705/0.25028, loss_mask_ce_2: 1.26224/0.76455, loss_mask_bce_2: 0.17350/0.30185, loss_mask_dice_2: 0.18659/1.02567, loss_spatial_bce_2: 0.16238/0.08527, loss_spatial_dice_2: 0.18596/0.18283, loss_spatial_ce_2: 0.01553/0.06264, loss_grounding_bce_2: 0.13944/0.08076, loss_grounding_dice_2: 0.18780/0.15101, loss_grounding_ce_2: 0.41112/0.25317, loss_mask_ce_3: 1.32125/0.76860, loss_mask_bce_3: 0.16826/0.30326, loss_mask_dice_3: 0.18175/1.02365, loss_spatial_bce_3: 0.24155/0.08741, loss_spatial_dice_3: 0.23579/0.18419, loss_spatial_ce_3: 0.01977/0.06747, loss_grounding_bce_3: 0.10940/0.08114, loss_grounding_dice_3: 0.15221/0.15070, loss_grounding_ce_3: 0.44698/0.25434, loss_mask_ce_4: 1.37550/0.77424, loss_mask_bce_4: 0.28318/0.30592, loss_mask_dice_4: 0.22090/1.04292, loss_spatial_bce_4: 0.19319/0.08965, loss_spatial_dice_4: 0.12450/0.19253, loss_spatial_ce_4: 0.45133/0.08113, loss_grounding_bce_4: 0.35218/0.08180, loss_grounding_dice_4: 0.25223/0.15334, loss_grounding_ce_4: 0.47315/0.25862, loss_mask_ce_5: 1.36977/0.79871, loss_mask_bce_5: 0.21567/0.30778, loss_mask_dice_5: 0.19437/1.05048, loss_spatial_bce_5: 0.13460/0.09200, loss_spatial_dice_5: 0.12872/0.19568, loss_spatial_ce_5: 0.47967/0.09446, loss_grounding_bce_5: 0.25929/0.08211, loss_grounding_dice_5: 0.24276/0.15409, loss_grounding_ce_5: 0.36045/0.27688, loss_mask_ce_6: 1.35385/0.82582, loss_mask_bce_6: 0.11649/0.30991, loss_mask_dice_6: 0.15101/1.05419, loss_spatial_bce_6: 0.13278/0.09724, loss_spatial_dice_6: 0.13702/0.19799, loss_spatial_ce_6: 0.62184/0.11902, loss_grounding_bce_6: 0.12531/0.08296, loss_grounding_dice_6: 0.18057/0.15466, loss_grounding_ce_6: 0.36895/0.28555, loss_mask_ce_7: 1.35785/0.88143, loss_mask_bce_7: 0.10706/0.31707, loss_mask_dice_7: 0.11362/1.10021, loss_spatial_bce_7: 0.29234/0.10672, loss_spatial_dice_7: 0.24528/0.22317, loss_spatial_ce_7: 0.45305/0.15543, loss_grounding_bce_7: 0.12303/0.08466, loss_grounding_dice_7: 0.18721/0.16025, loss_grounding_ce_7: 0.47597/0.31873, loss_mask_ce_8: 1.49430/1.01641, loss_mask_bce_8: 0.18824/0.33303, loss_mask_dice_8: 0.19642/1.17691, loss_spatial_bce_8: 0.44063/0.12384, loss_spatial_dice_8: 0.23593/0.25840, loss_spatial_ce_8: 0.95502/0.20165, loss_grounding_bce_8: 0.26068/0.08884, loss_grounding_dice_8: 0.25230/0.16993, loss_grounding_ce_8: 0.53057/0.41854, loss_mask_ce_9: 2.71326/3.47654, loss_mask_bce_9: 0.72525/0.36007, loss_mask_dice_9: 0.22874/1.75993, loss_spatial_bce_9: 0.29470/0.35470, loss_spatial_dice_9: 0.44081/0.79314, loss_spatial_ce_9: 0.40310/1.38829, loss_grounding_bce_9: 0.83411/0.10092, loss_grounding_dice_9: 0.25790/0.24214, loss_grounding_ce_9: 0.37698/0.67228] items per batch[64] items per second[0.37] total items[4345600] mini batches[ 67900] memory[4999] epoch remaining[0:44:55] INFO:trainer.default_trainer:epochs[ 37] optim steps[68000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.43008/0.75605, loss_mask_bce_0: 0.25259/0.30079, loss_mask_dice_0: 0.95883/1.02090, loss_spatial_bce_0: 0.06200/0.08481, loss_spatial_dice_0: 0.21269/0.17949, loss_spatial_ce_0: 0.22715/0.05665, loss_grounding_bce_0: 0.03125/0.08061, loss_grounding_dice_0: 0.07978/0.15039, loss_grounding_ce_0: 0.00239/0.24867, loss_mask_ce_1: 0.46539/0.75696, loss_mask_bce_1: 0.25144/0.30160, loss_mask_dice_1: 0.90983/1.02503, loss_spatial_bce_1: 0.06102/0.08518, loss_spatial_dice_1: 0.21333/0.18228, loss_spatial_ce_1: 0.21780/0.06047, loss_grounding_bce_1: 0.03340/0.08079, loss_grounding_dice_1: 0.07937/0.15113, loss_grounding_ce_1: 0.00106/0.25021, loss_mask_ce_2: 0.50843/0.76460, loss_mask_bce_2: 0.21353/0.30190, loss_mask_dice_2: 0.90277/1.02589, loss_spatial_bce_2: 0.06362/0.08527, loss_spatial_dice_2: 0.23809/0.18283, loss_spatial_ce_2: 0.19585/0.06265, loss_grounding_bce_2: 0.03447/0.08078, loss_grounding_dice_2: 0.08076/0.15103, loss_grounding_ce_2: 0.00123/0.25311, loss_mask_ce_3: 0.45982/0.76868, loss_mask_bce_3: 0.25274/0.30332, loss_mask_dice_3: 0.92588/1.02387, loss_spatial_bce_3: 0.06484/0.08741, loss_spatial_dice_3: 0.22221/0.18419, loss_spatial_ce_3: 0.19966/0.06746, loss_grounding_bce_3: 0.03314/0.08116, loss_grounding_dice_3: 0.06817/0.15071, loss_grounding_ce_3: 0.00125/0.25430, loss_mask_ce_4: 0.50245/0.77431, loss_mask_bce_4: 0.22671/0.30598, loss_mask_dice_4: 0.95623/1.04309, loss_spatial_bce_4: 0.06596/0.08965, loss_spatial_dice_4: 0.20900/0.19253, loss_spatial_ce_4: 0.26606/0.08113, loss_grounding_bce_4: 0.02786/0.08183, loss_grounding_dice_4: 0.06066/0.15336, loss_grounding_ce_4: 0.00082/0.25857, loss_mask_ce_5: 0.56999/0.79880, loss_mask_bce_5: 0.24494/0.30784, loss_mask_dice_5: 0.88601/1.05069, loss_spatial_bce_5: 0.05382/0.09201, loss_spatial_dice_5: 0.22268/0.19570, loss_spatial_ce_5: 0.29632/0.09445, loss_grounding_bce_5: 0.03137/0.08214, loss_grounding_dice_5: 0.07609/0.15411, loss_grounding_ce_5: 0.00185/0.27681, loss_mask_ce_6: 0.45048/0.82585, loss_mask_bce_6: 0.26526/0.30996, loss_mask_dice_6: 0.86964/1.05442, loss_spatial_bce_6: 0.05097/0.09725, loss_spatial_dice_6: 0.22826/0.19799, loss_spatial_ce_6: 0.33250/0.11901, loss_grounding_bce_6: 0.03210/0.08298, loss_grounding_dice_6: 0.07418/0.15467, loss_grounding_ce_6: 0.00406/0.28549, loss_mask_ce_7: 0.60954/0.88149, loss_mask_bce_7: 0.24530/0.31715, loss_mask_dice_7: 0.89608/1.10042, loss_spatial_bce_7: 0.06224/0.10673, loss_spatial_dice_7: 0.21348/0.22316, loss_spatial_ce_7: 0.57761/0.15543, loss_grounding_bce_7: 0.03283/0.08468, loss_grounding_dice_7: 0.07824/0.16028, loss_grounding_ce_7: 0.07264/0.31864, loss_mask_ce_8: 0.60663/1.01652, loss_mask_bce_8: 0.24962/0.33311, loss_mask_dice_8: 0.95011/1.17710, loss_spatial_bce_8: 0.06711/0.12385, loss_spatial_dice_8: 0.25449/0.25839, loss_spatial_ce_8: 0.55687/0.20166, loss_grounding_bce_8: 0.03895/0.08886, loss_grounding_dice_8: 0.07321/0.16995, loss_grounding_ce_8: 0.00710/0.41849, loss_mask_ce_9: 2.53726/3.47659, loss_mask_bce_9: 0.34028/0.36011, loss_mask_dice_9: 1.56999/1.76020, loss_spatial_bce_9: 0.31342/0.35471, loss_spatial_dice_9: 0.89803/0.79317, loss_spatial_ce_9: 1.37199/1.38826, loss_grounding_bce_9: 0.04680/0.10093, loss_grounding_dice_9: 0.12360/0.24216, loss_grounding_ce_9: 0.73666/0.67204] items per batch[64] items per second[0.37] total items[4352000] mini batches[ 68000] memory[4999] epoch remaining[0:41:44] INFO:trainer.default_trainer:epochs[ 37] optim steps[68100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.33334/0.75605, loss_mask_bce_0: 0.22008/0.30080, loss_mask_dice_0: 1.34929/1.02093, loss_spatial_bce_0: 0.05097/0.08481, loss_spatial_dice_0: 0.22446/0.17948, loss_spatial_ce_0: 0.03016/0.05665, loss_grounding_bce_0: 0.11877/0.08063, loss_grounding_dice_0: 0.09993/0.15043, loss_grounding_ce_0: 0.00070/0.24869, loss_mask_ce_1: 0.18182/0.75699, loss_mask_bce_1: 0.21295/0.30160, loss_mask_dice_1: 0.95148/1.02504, loss_spatial_bce_1: 0.05189/0.08519, loss_spatial_dice_1: 0.26230/0.18228, loss_spatial_ce_1: 0.10175/0.06047, loss_grounding_bce_1: 0.12396/0.08080, loss_grounding_dice_1: 0.11639/0.15117, loss_grounding_ce_1: 0.00068/0.25027, loss_mask_ce_2: 0.17465/0.76462, loss_mask_bce_2: 0.21096/0.30192, loss_mask_dice_2: 0.94271/1.02590, loss_spatial_bce_2: 0.04590/0.08529, loss_spatial_dice_2: 0.24744/0.18283, loss_spatial_ce_2: 0.12711/0.06263, loss_grounding_bce_2: 0.12637/0.08079, loss_grounding_dice_2: 0.11328/0.15106, loss_grounding_ce_2: 0.00119/0.25327, loss_mask_ce_3: 0.17444/0.76872, loss_mask_bce_3: 0.22402/0.30333, loss_mask_dice_3: 1.02765/1.02386, loss_spatial_bce_3: 0.04710/0.08743, loss_spatial_dice_3: 0.25553/0.18420, loss_spatial_ce_3: 0.05037/0.06744, loss_grounding_bce_3: 0.11341/0.08118, loss_grounding_dice_3: 0.10474/0.15073, loss_grounding_ce_3: 0.00226/0.25437, loss_mask_ce_4: 0.18893/0.77435, loss_mask_bce_4: 0.23581/0.30599, loss_mask_dice_4: 0.87469/1.04310, loss_spatial_bce_4: 0.04944/0.08968, loss_spatial_dice_4: 0.26729/0.19254, loss_spatial_ce_4: 0.09590/0.08112, loss_grounding_bce_4: 0.11982/0.08184, loss_grounding_dice_4: 0.09619/0.15340, loss_grounding_ce_4: 0.00164/0.25868, loss_mask_ce_5: 0.21203/0.79888, loss_mask_bce_5: 0.22632/0.30785, loss_mask_dice_5: 0.93594/1.05073, loss_spatial_bce_5: 0.05329/0.09203, loss_spatial_dice_5: 0.25734/0.19571, loss_spatial_ce_5: 0.13342/0.09444, loss_grounding_bce_5: 0.11749/0.08214, loss_grounding_dice_5: 0.10320/0.15414, loss_grounding_ce_5: 0.00085/0.27693, loss_mask_ce_6: 0.19989/0.82592, loss_mask_bce_6: 0.22714/0.30999, loss_mask_dice_6: 1.07179/1.05445, loss_spatial_bce_6: 0.05530/0.09728, loss_spatial_dice_6: 0.28851/0.19800, loss_spatial_ce_6: 0.05876/0.11901, loss_grounding_bce_6: 0.12173/0.08299, loss_grounding_dice_6: 0.10673/0.15471, loss_grounding_ce_6: 0.00093/0.28565, loss_mask_ce_7: 0.22496/0.88159, loss_mask_bce_7: 0.24376/0.31717, loss_mask_dice_7: 0.92283/1.10043, loss_spatial_bce_7: 0.07380/0.10675, loss_spatial_dice_7: 0.36415/0.22317, loss_spatial_ce_7: 0.22736/0.15542, loss_grounding_bce_7: 0.12877/0.08469, loss_grounding_dice_7: 0.11170/0.16033, loss_grounding_ce_7: 0.00434/0.31872, loss_mask_ce_8: 0.41750/1.01660, loss_mask_bce_8: 0.22074/0.33312, loss_mask_dice_8: 1.09905/1.17715, loss_spatial_bce_8: 0.07866/0.12386, loss_spatial_dice_8: 0.38719/0.25839, loss_spatial_ce_8: 0.26166/0.20161, loss_grounding_bce_8: 0.53657/0.08887, loss_grounding_dice_8: 0.25441/0.16999, loss_grounding_ce_8: 0.02715/0.41851, loss_mask_ce_9: 3.90233/3.47679, loss_mask_bce_9: 0.33594/0.36013, loss_mask_dice_9: 1.80039/1.76026, loss_spatial_bce_9: 0.29737/0.35469, loss_spatial_dice_9: 0.80264/0.79317, loss_spatial_ce_9: 1.83078/1.38819, loss_grounding_bce_9: 0.37501/0.10094, loss_grounding_dice_9: 0.25338/0.24221, loss_grounding_ce_9: 0.98750/0.67208] items per batch[64] items per second[0.37] total items[4358400] mini batches[ 68100] memory[4999] epoch remaining[0:38:47] INFO:trainer.default_trainer:epochs[ 37] optim steps[68200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.36171/0.75612, loss_mask_bce_0: 0.36705/0.30086, loss_mask_dice_0: 2.68029/1.02106, loss_spatial_bce_0: 0.01276/0.08482, loss_spatial_dice_0: 0.23713/0.17949, loss_spatial_ce_0: 0.00316/0.05665, loss_grounding_bce_0: 0.01584/0.08063, loss_grounding_dice_0: 0.13669/0.15047, loss_grounding_ce_0: 0.27571/0.24868, loss_mask_ce_1: 1.48207/0.75707, loss_mask_bce_1: 0.30609/0.30166, loss_mask_dice_1: 2.90838/1.02512, loss_spatial_bce_1: 0.01676/0.08519, loss_spatial_dice_1: 0.27108/0.18229, loss_spatial_ce_1: 0.02394/0.06048, loss_grounding_bce_1: 0.01581/0.08081, loss_grounding_dice_1: 0.15446/0.15119, loss_grounding_ce_1: 0.28112/0.25026, loss_mask_ce_2: 1.23087/0.76467, loss_mask_bce_2: 0.30479/0.30197, loss_mask_dice_2: 2.79434/1.02600, loss_spatial_bce_2: 0.01399/0.08529, loss_spatial_dice_2: 0.26997/0.18284, loss_spatial_ce_2: 0.01114/0.06263, loss_grounding_bce_2: 0.01506/0.08079, loss_grounding_dice_2: 0.15882/0.15109, loss_grounding_ce_2: 0.28796/0.25330, loss_mask_ce_3: 1.19644/0.76875, loss_mask_bce_3: 0.39015/0.30339, loss_mask_dice_3: 2.89447/1.02397, loss_spatial_bce_3: 0.01565/0.08743, loss_spatial_dice_3: 0.25214/0.18421, loss_spatial_ce_3: 0.01992/0.06744, loss_grounding_bce_3: 0.01509/0.08118, loss_grounding_dice_3: 0.14135/0.15076, loss_grounding_ce_3: 0.28236/0.25439, loss_mask_ce_4: 0.86730/0.77443, loss_mask_bce_4: 0.38431/0.30604, loss_mask_dice_4: 3.00922/1.04324, loss_spatial_bce_4: 0.02034/0.08968, loss_spatial_dice_4: 0.31956/0.19255, loss_spatial_ce_4: 0.10438/0.08112, loss_grounding_bce_4: 0.02540/0.08184, loss_grounding_dice_4: 0.22173/0.15344, loss_grounding_ce_4: 0.12799/0.25870, loss_mask_ce_5: 1.07802/0.79897, loss_mask_bce_5: 0.32080/0.30789, loss_mask_dice_5: 2.70483/1.05086, loss_spatial_bce_5: 0.02231/0.09205, loss_spatial_dice_5: 0.33681/0.19572, loss_spatial_ce_5: 0.10129/0.09446, loss_grounding_bce_5: 0.02389/0.08214, loss_grounding_dice_5: 0.17550/0.15417, loss_grounding_ce_5: 0.16976/0.27694, loss_mask_ce_6: 1.22744/0.82604, loss_mask_bce_6: 0.33407/0.31004, loss_mask_dice_6: 2.58981/1.05457, loss_spatial_bce_6: 0.02430/0.09729, loss_spatial_dice_6: 0.31945/0.19802, loss_spatial_ce_6: 0.19633/0.11903, loss_grounding_bce_6: 0.02308/0.08299, loss_grounding_dice_6: 0.19115/0.15474, loss_grounding_ce_6: 0.14035/0.28562, loss_mask_ce_7: 1.33121/0.88173, loss_mask_bce_7: 0.31423/0.31722, loss_mask_dice_7: 2.80547/1.10056, loss_spatial_bce_7: 0.03293/0.10674, loss_spatial_dice_7: 0.37301/0.22318, loss_spatial_ce_7: 0.30127/0.15546, loss_grounding_bce_7: 0.01500/0.08468, loss_grounding_dice_7: 0.14334/0.16035, loss_grounding_ce_7: 0.43376/0.31875, loss_mask_ce_8: 1.33242/1.01671, loss_mask_bce_8: 0.43621/0.33318, loss_mask_dice_8: 3.17141/1.17730, loss_spatial_bce_8: 0.03388/0.12386, loss_spatial_dice_8: 0.40106/0.25841, loss_spatial_ce_8: 0.35057/0.20158, loss_grounding_bce_8: 0.02322/0.08887, loss_grounding_dice_8: 0.16769/0.17002, loss_grounding_ce_8: 0.50674/0.41852, loss_mask_ce_9: 5.57546/3.47705, loss_mask_bce_9: 0.42289/0.36018, loss_mask_dice_9: 4.86939/1.76056, loss_spatial_bce_9: 0.16650/0.35465, loss_spatial_dice_9: 0.98729/0.79319, loss_spatial_ce_9: 1.57680/1.38825, loss_grounding_bce_9: 0.05735/0.10093, loss_grounding_dice_9: 0.45449/0.24229, loss_grounding_ce_9: 0.58787/0.67210] items per batch[64] items per second[0.36] total items[4364800] mini batches[ 68200] memory[4999] epoch remaining[0:35:53] INFO:trainer.default_trainer:epochs[ 37] optim steps[68300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.85183/0.75607, loss_mask_bce_0: 0.02764/0.30082, loss_mask_dice_0: 1.07993/1.02087, loss_spatial_bce_0: 0.00481/0.08481, loss_spatial_dice_0: 0.25656/0.17948, loss_spatial_ce_0: 0.13045/0.05664, loss_grounding_bce_0: 0.00311/0.08062, loss_grounding_dice_0: 0.10497/0.15050, loss_grounding_ce_0: 0.29242/0.24861, loss_mask_ce_1: 0.83339/0.75698, loss_mask_bce_1: 0.02717/0.30162, loss_mask_dice_1: 1.26151/1.02497, loss_spatial_bce_1: 0.00376/0.08519, loss_spatial_dice_1: 0.22928/0.18228, loss_spatial_ce_1: 0.14566/0.06048, loss_grounding_bce_1: 0.00355/0.08079, loss_grounding_dice_1: 0.08788/0.15122, loss_grounding_ce_1: 0.29658/0.25016, loss_mask_ce_2: 0.87763/0.76460, loss_mask_bce_2: 0.02426/0.30193, loss_mask_dice_2: 0.80983/1.02585, loss_spatial_bce_2: 0.00262/0.08529, loss_spatial_dice_2: 0.21882/0.18283, loss_spatial_ce_2: 0.13197/0.06263, loss_grounding_bce_2: 0.00339/0.08078, loss_grounding_dice_2: 0.05146/0.15110, loss_grounding_ce_2: 0.29904/0.25320, loss_mask_ce_3: 0.84376/0.76863, loss_mask_bce_3: 0.02878/0.30335, loss_mask_dice_3: 0.77040/1.02380, loss_spatial_bce_3: 0.00459/0.08742, loss_spatial_dice_3: 0.24833/0.18420, loss_spatial_ce_3: 0.30778/0.06744, loss_grounding_bce_3: 0.00389/0.08116, loss_grounding_dice_3: 0.07482/0.15078, loss_grounding_ce_3: 0.29762/0.25429, loss_mask_ce_4: 0.84167/0.77438, loss_mask_bce_4: 0.02682/0.30600, loss_mask_dice_4: 1.12163/1.04305, loss_spatial_bce_4: 0.00371/0.08967, loss_spatial_dice_4: 0.20965/0.19254, loss_spatial_ce_4: 0.05574/0.08113, loss_grounding_bce_4: 0.00407/0.08183, loss_grounding_dice_4: 0.25227/0.15346, loss_grounding_ce_4: 0.28841/0.25861, loss_mask_ce_5: 0.87394/0.79888, loss_mask_bce_5: 0.02466/0.30785, loss_mask_dice_5: 1.01586/1.05068, loss_spatial_bce_5: 0.00378/0.09204, loss_spatial_dice_5: 0.19280/0.19572, loss_spatial_ce_5: 0.23799/0.09448, loss_grounding_bce_5: 0.00305/0.08214, loss_grounding_dice_5: 0.02994/0.15419, loss_grounding_ce_5: 0.29119/0.27683, loss_mask_ce_6: 0.87348/0.82597, loss_mask_bce_6: 0.01704/0.30998, loss_mask_dice_6: 0.65787/1.05437, loss_spatial_bce_6: 0.00916/0.09729, loss_spatial_dice_6: 0.28568/0.19801, loss_spatial_ce_6: 0.15559/0.11903, loss_grounding_bce_6: 0.00311/0.08297, loss_grounding_dice_6: 0.28952/0.15477, loss_grounding_ce_6: 0.30140/0.28554, loss_mask_ce_7: 0.93212/0.88167, loss_mask_bce_7: 0.01906/0.31716, loss_mask_dice_7: 1.06080/1.10039, loss_spatial_bce_7: 0.01038/0.10672, loss_spatial_dice_7: 0.38586/0.22318, loss_spatial_ce_7: 0.28125/0.15546, loss_grounding_bce_7: 0.00345/0.08467, loss_grounding_dice_7: 0.16634/0.16037, loss_grounding_ce_7: 0.31138/0.31870, loss_mask_ce_8: 1.20901/1.01659, loss_mask_bce_8: 0.01519/0.33313, loss_mask_dice_8: 0.99090/1.17706, loss_spatial_bce_8: 0.00329/0.12384, loss_spatial_dice_8: 0.28519/0.25840, loss_spatial_ce_8: 0.32071/0.20154, loss_grounding_bce_8: 0.00264/0.08885, loss_grounding_dice_8: 0.14133/0.17004, loss_grounding_ce_8: 0.31567/0.41844, loss_mask_ce_9: 2.53565/3.47666, loss_mask_bce_9: 0.01700/0.36011, loss_mask_dice_9: 1.37313/1.76012, loss_spatial_bce_9: 0.01481/0.35461, loss_spatial_dice_9: 0.87700/0.79319, loss_spatial_ce_9: 2.61341/1.38828, loss_grounding_bce_9: 0.00265/0.10091, loss_grounding_dice_9: 0.08798/0.24230, loss_grounding_ce_9: 0.42584/0.67193] items per batch[64] items per second[0.37] total items[4371200] mini batches[ 68300] memory[4999] epoch remaining[0:32:56] INFO:trainer.default_trainer:epochs[ 37] optim steps[68400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86043/0.75608, loss_mask_bce_0: 0.50582/0.30078, loss_mask_dice_0: 3.94586/1.02099, loss_spatial_bce_0: 0.01771/0.08480, loss_spatial_dice_0: 0.21841/0.17949, loss_spatial_ce_0: 0.00509/0.05666, loss_grounding_bce_0: 0.04883/0.08060, loss_grounding_dice_0: 0.52574/0.15051, loss_grounding_ce_0: 0.12653/0.24864, loss_mask_ce_1: 0.88388/0.75698, loss_mask_bce_1: 0.52943/0.30158, loss_mask_dice_1: 4.17490/1.02509, loss_spatial_bce_1: 0.01977/0.08518, loss_spatial_dice_1: 0.25278/0.18229, loss_spatial_ce_1: 0.00764/0.06049, loss_grounding_bce_1: 0.05954/0.08078, loss_grounding_dice_1: 0.54248/0.15123, loss_grounding_ce_1: 0.09653/0.25016, loss_mask_ce_2: 0.94952/0.76460, loss_mask_bce_2: 0.57377/0.30189, loss_mask_dice_2: 4.07522/1.02600, loss_spatial_bce_2: 0.01768/0.08528, loss_spatial_dice_2: 0.22163/0.18284, loss_spatial_ce_2: 0.00357/0.06263, loss_grounding_bce_2: 0.05624/0.08076, loss_grounding_dice_2: 0.55197/0.15110, loss_grounding_ce_2: 0.11509/0.25320, loss_mask_ce_3: 1.03868/0.76862, loss_mask_bce_3: 0.55036/0.30331, loss_mask_dice_3: 3.64122/1.02393, loss_spatial_bce_3: 0.02463/0.08741, loss_spatial_dice_3: 0.25530/0.18422, loss_spatial_ce_3: 0.01060/0.06745, loss_grounding_bce_3: 0.06773/0.08114, loss_grounding_dice_3: 0.59635/0.15080, loss_grounding_ce_3: 0.09559/0.25439, loss_mask_ce_4: 1.09478/0.77438, loss_mask_bce_4: 0.57745/0.30596, loss_mask_dice_4: 3.92549/1.04321, loss_spatial_bce_4: 0.02826/0.08966, loss_spatial_dice_4: 0.26385/0.19256, loss_spatial_ce_4: 0.01233/0.08116, loss_grounding_bce_4: 0.08345/0.08181, loss_grounding_dice_4: 0.63972/0.15348, loss_grounding_ce_4: 0.09773/0.25866, loss_mask_ce_5: 0.83453/0.79894, loss_mask_bce_5: 0.57546/0.30782, loss_mask_dice_5: 4.11264/1.05081, loss_spatial_bce_5: 0.02298/0.09203, loss_spatial_dice_5: 0.26676/0.19574, loss_spatial_ce_5: 0.08809/0.09453, loss_grounding_bce_5: 0.07006/0.08212, loss_grounding_dice_5: 0.58682/0.15420, loss_grounding_ce_5: 0.15463/0.27681, loss_mask_ce_6: 0.87326/0.82606, loss_mask_bce_6: 0.53058/0.30994, loss_mask_dice_6: 3.99060/1.05452, loss_spatial_bce_6: 0.02121/0.09729, loss_spatial_dice_6: 0.25530/0.19802, loss_spatial_ce_6: 0.11888/0.11907, loss_grounding_bce_6: 0.05336/0.08296, loss_grounding_dice_6: 0.65740/0.15478, loss_grounding_ce_6: 0.07002/0.28563, loss_mask_ce_7: 1.02619/0.88174, loss_mask_bce_7: 0.54895/0.31713, loss_mask_dice_7: 4.22392/1.10056, loss_spatial_bce_7: 0.02568/0.10671, loss_spatial_dice_7: 0.25076/0.22319, loss_spatial_ce_7: 0.23263/0.15549, loss_grounding_bce_7: 0.06121/0.08465, loss_grounding_dice_7: 0.65666/0.16037, loss_grounding_ce_7: 0.04360/0.31889, loss_mask_ce_8: 1.56666/1.01665, loss_mask_bce_8: 0.56241/0.33310, loss_mask_dice_8: 4.48060/1.17719, loss_spatial_bce_8: 0.03021/0.12382, loss_spatial_dice_8: 0.36355/0.25842, loss_spatial_ce_8: 0.12965/0.20155, loss_grounding_bce_8: 0.04855/0.08883, loss_grounding_dice_8: 0.66078/0.17005, loss_grounding_ce_8: 0.02552/0.41839, loss_mask_ce_9: 6.70511/3.47692, loss_mask_bce_9: 0.60530/0.36009, loss_mask_dice_9: 6.25385/1.76025, loss_spatial_bce_9: 0.08801/0.35461, loss_spatial_dice_9: 0.98369/0.79319, loss_spatial_ce_9: 1.35757/1.38826, loss_grounding_bce_9: 0.04796/0.10091, loss_grounding_dice_9: 0.68765/0.24234, loss_grounding_ce_9: 0.06154/0.67191] items per batch[64] items per second[0.36] total items[4377600] mini batches[ 68400] memory[4999] epoch remaining[0:30:01] INFO:trainer.default_trainer:epochs[ 37] optim steps[68500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16316/0.75599, loss_mask_bce_0: 0.03382/0.30083, loss_mask_dice_0: 2.23555/1.02102, loss_spatial_bce_0: 0.00099/0.08479, loss_spatial_dice_0: 0.22163/0.17948, loss_spatial_ce_0: 0.11473/0.05665, loss_grounding_bce_0: 0.00368/0.08062, loss_grounding_dice_0: 0.27186/0.15052, loss_grounding_ce_0: 0.75499/0.24876, loss_mask_ce_1: 0.68282/0.75687, loss_mask_bce_1: 0.03638/0.30163, loss_mask_dice_1: 2.30405/1.02515, loss_spatial_bce_1: 0.00105/0.08518, loss_spatial_dice_1: 0.25343/0.18229, loss_spatial_ce_1: 0.25507/0.06047, loss_grounding_bce_1: 0.00330/0.08079, loss_grounding_dice_1: 0.24654/0.15125, loss_grounding_ce_1: 0.49118/0.25026, loss_mask_ce_2: 0.76758/0.76452, loss_mask_bce_2: 0.02335/0.30195, loss_mask_dice_2: 1.28729/1.02601, loss_spatial_bce_2: 0.00063/0.08527, loss_spatial_dice_2: 0.19766/0.18284, loss_spatial_ce_2: 0.08635/0.06261, loss_grounding_bce_2: 0.00275/0.08077, loss_grounding_dice_2: 0.22013/0.15112, loss_grounding_ce_2: 0.48213/0.25329, loss_mask_ce_3: 0.49950/0.76854, loss_mask_bce_3: 0.02755/0.30337, loss_mask_dice_3: 1.74947/1.02397, loss_spatial_bce_3: 0.00080/0.08741, loss_spatial_dice_3: 0.20838/0.18422, loss_spatial_ce_3: 0.09360/0.06743, loss_grounding_bce_3: 0.00391/0.08116, loss_grounding_dice_3: 0.25852/0.15082, loss_grounding_ce_3: 0.49206/0.25452, loss_mask_ce_4: 0.60600/0.77430, loss_mask_bce_4: 0.02798/0.30601, loss_mask_dice_4: 1.27969/1.04323, loss_spatial_bce_4: 0.00107/0.08966, loss_spatial_dice_4: 0.28741/0.19256, loss_spatial_ce_4: 0.55160/0.08114, loss_grounding_bce_4: 0.00340/0.08183, loss_grounding_dice_4: 0.22429/0.15349, loss_grounding_ce_4: 0.55965/0.25875, loss_mask_ce_5: 0.36203/0.79887, loss_mask_bce_5: 0.02911/0.30787, loss_mask_dice_5: 1.61727/1.05085, loss_spatial_bce_5: 0.00133/0.09204, loss_spatial_dice_5: 0.35438/0.19574, loss_spatial_ce_5: 0.12317/0.09453, loss_grounding_bce_5: 0.00369/0.08214, loss_grounding_dice_5: 0.24455/0.15421, loss_grounding_ce_5: 0.54666/0.27692, loss_mask_ce_6: 0.55966/0.82596, loss_mask_bce_6: 0.03923/0.30999, loss_mask_dice_6: 2.33797/1.05456, loss_spatial_bce_6: 0.00209/0.09729, loss_spatial_dice_6: 0.22637/0.19803, loss_spatial_ce_6: 0.22432/0.11907, loss_grounding_bce_6: 0.00455/0.08297, loss_grounding_dice_6: 0.35093/0.15478, loss_grounding_ce_6: 0.54500/0.28574, loss_mask_ce_7: 0.54826/0.88169, loss_mask_bce_7: 0.06116/0.31719, loss_mask_dice_7: 1.75050/1.10056, loss_spatial_bce_7: 0.00102/0.10671, loss_spatial_dice_7: 0.32047/0.22320, loss_spatial_ce_7: 0.20115/0.15546, loss_grounding_bce_7: 0.00360/0.08467, loss_grounding_dice_7: 0.13250/0.16040, loss_grounding_ce_7: 0.59762/0.31896, loss_mask_ce_8: 0.56136/1.01658, loss_mask_bce_8: 0.04189/0.33316, loss_mask_dice_8: 1.70086/1.17722, loss_spatial_bce_8: 0.00199/0.12382, loss_spatial_dice_8: 0.35331/0.25842, loss_spatial_ce_8: 0.11959/0.20151, loss_grounding_bce_8: 0.00495/0.08884, loss_grounding_dice_8: 0.33767/0.17006, loss_grounding_ce_8: 0.62808/0.41855, loss_mask_ce_9: 5.35226/3.47687, loss_mask_bce_9: 0.06383/0.36016, loss_mask_dice_9: 2.52047/1.76018, loss_spatial_bce_9: 0.04881/0.35459, loss_spatial_dice_9: 0.71689/0.79318, loss_spatial_ce_9: 4.87892/1.38823, loss_grounding_bce_9: 0.00448/0.10093, loss_grounding_dice_9: 0.28815/0.24234, loss_grounding_ce_9: 0.52675/0.67197] items per batch[64] items per second[0.36] total items[4384000] mini batches[ 68500] memory[4999] epoch remaining[0:27:08] INFO:trainer.default_trainer:epochs[ 37] optim steps[68600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.99058/0.75591, loss_mask_bce_0: 0.28804/0.30083, loss_mask_dice_0: 0.38826/1.02109, loss_spatial_bce_0: 0.08458/0.08478, loss_spatial_dice_0: 0.08015/0.17945, loss_spatial_ce_0: 0.00757/0.05663, loss_grounding_bce_0: 0.20559/0.08063, loss_grounding_dice_0: 0.06195/0.15053, loss_grounding_ce_0: 0.00867/0.24887, loss_mask_ce_1: 0.94814/0.75678, loss_mask_bce_1: 0.27531/0.30163, loss_mask_dice_1: 0.37936/1.02517, loss_spatial_bce_1: 0.08227/0.08517, loss_spatial_dice_1: 0.09077/0.18225, loss_spatial_ce_1: 0.00531/0.06046, loss_grounding_bce_1: 0.20552/0.08080, loss_grounding_dice_1: 0.06023/0.15126, loss_grounding_ce_1: 0.01126/0.25041, loss_mask_ce_2: 0.91899/0.76440, loss_mask_bce_2: 0.27180/0.30195, loss_mask_dice_2: 0.38293/1.02605, loss_spatial_bce_2: 0.15235/0.08526, loss_spatial_dice_2: 0.21047/0.18280, loss_spatial_ce_2: 0.00832/0.06261, loss_grounding_bce_2: 0.20172/0.08078, loss_grounding_dice_2: 0.06143/0.15112, loss_grounding_ce_2: 0.00954/0.25337, loss_mask_ce_3: 0.97243/0.76846, loss_mask_bce_3: 0.27374/0.30337, loss_mask_dice_3: 0.38249/1.02400, loss_spatial_bce_3: 0.21772/0.08740, loss_spatial_dice_3: 0.23408/0.18418, loss_spatial_ce_3: 0.01245/0.06742, loss_grounding_bce_3: 0.21200/0.08117, loss_grounding_dice_3: 0.06167/0.15082, loss_grounding_ce_3: 0.00412/0.25459, loss_mask_ce_4: 0.99704/0.77426, loss_mask_bce_4: 0.27581/0.30601, loss_mask_dice_4: 0.38899/1.04331, loss_spatial_bce_4: 0.08685/0.08965, loss_spatial_dice_4: 0.11580/0.19252, loss_spatial_ce_4: 0.01610/0.08112, loss_grounding_bce_4: 0.21330/0.08184, loss_grounding_dice_4: 0.06169/0.15350, loss_grounding_ce_4: 0.00701/0.25884, loss_mask_ce_5: 0.94044/0.79880, loss_mask_bce_5: 0.28121/0.30788, loss_mask_dice_5: 0.42266/1.05097, loss_spatial_bce_5: 0.10909/0.09203, loss_spatial_dice_5: 0.14332/0.19570, loss_spatial_ce_5: 0.04611/0.09451, loss_grounding_bce_5: 0.20148/0.08215, loss_grounding_dice_5: 0.05939/0.15422, loss_grounding_ce_5: 0.01003/0.27684, loss_mask_ce_6: 1.03806/0.82589, loss_mask_bce_6: 0.28577/0.30999, loss_mask_dice_6: 0.42624/1.05466, loss_spatial_bce_6: 0.10250/0.09730, loss_spatial_dice_6: 0.14384/0.19800, loss_spatial_ce_6: 0.06719/0.11907, loss_grounding_bce_6: 0.21248/0.08298, loss_grounding_dice_6: 0.06486/0.15479, loss_grounding_ce_6: 0.00994/0.28573, loss_mask_ce_7: 1.14894/0.88166, loss_mask_bce_7: 0.28345/0.31720, loss_mask_dice_7: 0.42081/1.10067, loss_spatial_bce_7: 0.11126/0.10670, loss_spatial_dice_7: 0.18108/0.22316, loss_spatial_ce_7: 0.18157/0.15541, loss_grounding_bce_7: 0.21857/0.08468, loss_grounding_dice_7: 0.06735/0.16040, loss_grounding_ce_7: 0.01203/0.31897, loss_mask_ce_8: 0.72880/1.01648, loss_mask_bce_8: 0.29949/0.33319, loss_mask_dice_8: 0.49201/1.17740, loss_spatial_bce_8: 0.12615/0.12380, loss_spatial_dice_8: 0.21264/0.25837, loss_spatial_ce_8: 0.23422/0.20145, loss_grounding_bce_8: 0.25050/0.08886, loss_grounding_dice_8: 0.07759/0.17007, loss_grounding_ce_8: 0.13024/0.41851, loss_mask_ce_9: 3.18098/3.47692, loss_mask_bce_9: 0.28274/0.36016, loss_mask_dice_9: 0.73400/1.76032, loss_spatial_bce_9: 0.43968/0.35458, loss_spatial_dice_9: 0.80511/0.79315, loss_spatial_ce_9: 0.85199/1.38819, loss_grounding_bce_9: 0.23201/0.10094, loss_grounding_dice_9: 0.06259/0.24233, loss_grounding_ce_9: 0.22190/0.67189] items per batch[64] items per second[0.36] total items[4390400] mini batches[ 68600] memory[4999] epoch remaining[0:24:12] INFO:trainer.default_trainer:epochs[ 37] optim steps[68700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98221/0.75589, loss_mask_bce_0: 0.23016/0.30085, loss_mask_dice_0: 0.94155/1.02121, loss_spatial_bce_0: 0.03854/0.08478, loss_spatial_dice_0: 0.18436/0.17944, loss_spatial_ce_0: 0.01736/0.05661, loss_grounding_bce_0: 0.12805/0.08061, loss_grounding_dice_0: 0.22487/0.15054, loss_grounding_ce_0: 0.00307/0.24884, loss_mask_ce_1: 0.86657/0.75674, loss_mask_bce_1: 0.22618/0.30165, loss_mask_dice_1: 1.04896/1.02531, loss_spatial_bce_1: 0.03988/0.08516, loss_spatial_dice_1: 0.17907/0.18224, loss_spatial_ce_1: 0.03692/0.06043, loss_grounding_bce_1: 0.13550/0.08079, loss_grounding_dice_1: 0.22964/0.15126, loss_grounding_ce_1: 0.00195/0.25036, loss_mask_ce_2: 0.82004/0.76437, loss_mask_bce_2: 0.26409/0.30197, loss_mask_dice_2: 0.97426/1.02618, loss_spatial_bce_2: 0.04011/0.08526, loss_spatial_dice_2: 0.16862/0.18280, loss_spatial_ce_2: 0.01412/0.06258, loss_grounding_bce_2: 0.14469/0.08077, loss_grounding_dice_2: 0.22501/0.15113, loss_grounding_ce_2: 0.00322/0.25330, loss_mask_ce_3: 0.93842/0.76841, loss_mask_bce_3: 0.23065/0.30338, loss_mask_dice_3: 1.01227/1.02410, loss_spatial_bce_3: 0.04031/0.08740, loss_spatial_dice_3: 0.16905/0.18418, loss_spatial_ce_3: 0.02947/0.06740, loss_grounding_bce_3: 0.13833/0.08115, loss_grounding_dice_3: 0.23004/0.15082, loss_grounding_ce_3: 0.00288/0.25451, loss_mask_ce_4: 0.83239/0.77426, loss_mask_bce_4: 0.24120/0.30602, loss_mask_dice_4: 1.04360/1.04342, loss_spatial_bce_4: 0.05368/0.08965, loss_spatial_dice_4: 0.19504/0.19251, loss_spatial_ce_4: 0.05002/0.08111, loss_grounding_bce_4: 0.14641/0.08182, loss_grounding_dice_4: 0.22967/0.15349, loss_grounding_ce_4: 0.00144/0.25879, loss_mask_ce_5: 0.79535/0.79877, loss_mask_bce_5: 0.24988/0.30789, loss_mask_dice_5: 1.03935/1.05109, loss_spatial_bce_5: 0.05270/0.09204, loss_spatial_dice_5: 0.19477/0.19570, loss_spatial_ce_5: 0.07583/0.09451, loss_grounding_bce_5: 0.13554/0.08213, loss_grounding_dice_5: 0.23999/0.15422, loss_grounding_ce_5: 0.00244/0.27679, loss_mask_ce_6: 1.04946/0.82586, loss_mask_bce_6: 0.24767/0.31001, loss_mask_dice_6: 1.14084/1.05478, loss_spatial_bce_6: 0.06096/0.09730, loss_spatial_dice_6: 0.20270/0.19799, loss_spatial_ce_6: 0.18797/0.11907, loss_grounding_bce_6: 0.15795/0.08297, loss_grounding_dice_6: 0.24511/0.15478, loss_grounding_ce_6: 0.00602/0.28571, loss_mask_ce_7: 1.07651/0.88161, loss_mask_bce_7: 0.23894/0.31722, loss_mask_dice_7: 1.07102/1.10079, loss_spatial_bce_7: 0.07028/0.10669, loss_spatial_dice_7: 0.22743/0.22315, loss_spatial_ce_7: 0.15303/0.15536, loss_grounding_bce_7: 0.13962/0.08467, loss_grounding_dice_7: 0.21846/0.16041, loss_grounding_ce_7: 0.01167/0.31896, loss_mask_ce_8: 1.00056/1.01640, loss_mask_bce_8: 0.22652/0.33320, loss_mask_dice_8: 1.13445/1.17759, loss_spatial_bce_8: 0.06566/0.12380, loss_spatial_dice_8: 0.23329/0.25835, loss_spatial_ce_8: 0.14241/0.20142, loss_grounding_bce_8: 0.10761/0.08884, loss_grounding_dice_8: 0.21095/0.17009, loss_grounding_ce_8: 0.01795/0.41839, loss_mask_ce_9: 2.20348/3.47667, loss_mask_bce_9: 0.27983/0.36018, loss_mask_dice_9: 1.60565/1.76058, loss_spatial_bce_9: 0.30847/0.35458, loss_spatial_dice_9: 0.89112/0.79316, loss_spatial_ce_9: 1.21097/1.38822, loss_grounding_bce_9: 0.08647/0.10092, loss_grounding_dice_9: 0.19793/0.24234, loss_grounding_ce_9: 0.03462/0.67186] items per batch[64] items per second[0.37] total items[4396800] mini batches[ 68700] memory[4999] epoch remaining[0:21:15] INFO:trainer.default_trainer:epochs[ 37] optim steps[68800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37341/0.75580, loss_mask_bce_0: 0.27146/0.30080, loss_mask_dice_0: 0.46507/1.02107, loss_spatial_bce_0: 0.10799/0.08476, loss_spatial_dice_0: 0.16271/0.17941, loss_spatial_ce_0: 0.00333/0.05661, loss_grounding_bce_0: 0.25287/0.08061, loss_grounding_dice_0: 0.33241/0.15052, loss_grounding_ce_0: 0.02965/0.24876, loss_mask_ce_1: 0.31107/0.75666, loss_mask_bce_1: 0.26885/0.30160, loss_mask_dice_1: 0.46760/1.02518, loss_spatial_bce_1: 0.10238/0.08515, loss_spatial_dice_1: 0.15227/0.18221, loss_spatial_ce_1: 0.00355/0.06044, loss_grounding_bce_1: 0.24629/0.08078, loss_grounding_dice_1: 0.32968/0.15126, loss_grounding_ce_1: 0.04549/0.25036, loss_mask_ce_2: 0.37357/0.76430, loss_mask_bce_2: 0.27196/0.30192, loss_mask_dice_2: 0.47082/1.02606, loss_spatial_bce_2: 0.10187/0.08525, loss_spatial_dice_2: 0.15992/0.18277, loss_spatial_ce_2: 0.00290/0.06259, loss_grounding_bce_2: 0.25291/0.08076, loss_grounding_dice_2: 0.34183/0.15113, loss_grounding_ce_2: 0.04111/0.25329, loss_mask_ce_3: 0.31699/0.76837, loss_mask_bce_3: 0.27648/0.30333, loss_mask_dice_3: 0.47290/1.02393, loss_spatial_bce_3: 0.10126/0.08739, loss_spatial_dice_3: 0.16895/0.18415, loss_spatial_ce_3: 0.00559/0.06740, loss_grounding_bce_3: 0.24976/0.08115, loss_grounding_dice_3: 0.35835/0.15081, loss_grounding_ce_3: 0.05249/0.25447, loss_mask_ce_4: 0.28552/0.77419, loss_mask_bce_4: 0.27012/0.30598, loss_mask_dice_4: 0.46071/1.04329, loss_spatial_bce_4: 0.10512/0.08964, loss_spatial_dice_4: 0.16724/0.19249, loss_spatial_ce_4: 0.00748/0.08109, loss_grounding_bce_4: 0.24847/0.08181, loss_grounding_dice_4: 0.32560/0.15349, loss_grounding_ce_4: 0.03611/0.25879, loss_mask_ce_5: 0.27531/0.79873, loss_mask_bce_5: 0.27414/0.30785, loss_mask_dice_5: 0.45650/1.05097, loss_spatial_bce_5: 0.10062/0.09202, loss_spatial_dice_5: 0.18205/0.19567, loss_spatial_ce_5: 0.00399/0.09449, loss_grounding_bce_5: 0.24969/0.08212, loss_grounding_dice_5: 0.33382/0.15420, loss_grounding_ce_5: 0.05156/0.27672, loss_mask_ce_6: 0.29863/0.82582, loss_mask_bce_6: 0.29023/0.30997, loss_mask_dice_6: 0.47104/1.05465, loss_spatial_bce_6: 0.09975/0.09729, loss_spatial_dice_6: 0.18147/0.19796, loss_spatial_ce_6: 0.01590/0.11905, loss_grounding_bce_6: 0.26806/0.08296, loss_grounding_dice_6: 0.35433/0.15477, loss_grounding_ce_6: 0.02596/0.28565, loss_mask_ce_7: 0.37839/0.88156, loss_mask_bce_7: 0.30002/0.31718, loss_mask_dice_7: 0.46989/1.10065, loss_spatial_bce_7: 0.11970/0.10668, loss_spatial_dice_7: 0.20851/0.22312, loss_spatial_ce_7: 0.03925/0.15532, loss_grounding_bce_7: 0.26326/0.08466, loss_grounding_dice_7: 0.34941/0.16040, loss_grounding_ce_7: 0.01340/0.31886, loss_mask_ce_8: 0.42488/1.01635, loss_mask_bce_8: 0.27988/0.33315, loss_mask_dice_8: 0.44108/1.17743, loss_spatial_bce_8: 0.11287/0.12379, loss_spatial_dice_8: 0.19946/0.25831, loss_spatial_ce_8: 0.17771/0.20137, loss_grounding_bce_8: 0.25484/0.08884, loss_grounding_dice_8: 0.32274/0.17009, loss_grounding_ce_8: 0.02054/0.41839, loss_mask_ce_9: 2.22351/3.47662, loss_mask_bce_9: 0.33851/0.36013, loss_mask_dice_9: 0.70506/1.76047, loss_spatial_bce_9: 0.46044/0.35456, loss_spatial_dice_9: 0.81924/0.79314, loss_spatial_ce_9: 1.32920/1.38813, loss_grounding_bce_9: 0.29530/0.10091, loss_grounding_dice_9: 0.35092/0.24234, loss_grounding_ce_9: 0.05071/0.67176] items per batch[64] items per second[0.37] total items[4403200] mini batches[ 68800] memory[4999] epoch remaining[0:18:18] INFO:trainer.default_trainer:epochs[ 37] optim steps[68900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.72650/0.75579, loss_mask_bce_0: 0.29363/0.30080, loss_mask_dice_0: 5.16584/1.02106, loss_spatial_bce_0: 0.01882/0.08475, loss_spatial_dice_0: 0.33869/0.17938, loss_spatial_ce_0: 0.32774/0.05660, loss_grounding_bce_0: 0.06055/0.08060, loss_grounding_dice_0: 0.15563/0.15051, loss_grounding_ce_0: 0.00236/0.24871, loss_mask_ce_1: 0.76836/0.75666, loss_mask_bce_1: 0.30680/0.30160, loss_mask_dice_1: 4.96170/1.02518, loss_spatial_bce_1: 0.02513/0.08514, loss_spatial_dice_1: 0.36004/0.18219, loss_spatial_ce_1: 0.72111/0.06042, loss_grounding_bce_1: 0.06063/0.08077, loss_grounding_dice_1: 0.16370/0.15125, loss_grounding_ce_1: 0.00264/0.25030, loss_mask_ce_2: 0.64715/0.76432, loss_mask_bce_2: 0.34747/0.30192, loss_mask_dice_2: 4.82879/1.02605, loss_spatial_bce_2: 0.02298/0.08524, loss_spatial_dice_2: 0.26756/0.18275, loss_spatial_ce_2: 0.04945/0.06256, loss_grounding_bce_2: 0.05617/0.08075, loss_grounding_dice_2: 0.15718/0.15112, loss_grounding_ce_2: 0.00359/0.25323, loss_mask_ce_3: 0.71205/0.76838, loss_mask_bce_3: 0.34930/0.30333, loss_mask_dice_3: 4.36523/1.02392, loss_spatial_bce_3: 0.04617/0.08738, loss_spatial_dice_3: 0.34827/0.18413, loss_spatial_ce_3: 0.04877/0.06736, loss_grounding_bce_3: 0.06185/0.08114, loss_grounding_dice_3: 0.16073/0.15080, loss_grounding_ce_3: 0.00266/0.25447, loss_mask_ce_4: 0.91473/0.77421, loss_mask_bce_4: 0.34025/0.30599, loss_mask_dice_4: 4.49501/1.04331, loss_spatial_bce_4: 0.05136/0.08963, loss_spatial_dice_4: 0.38188/0.19247, loss_spatial_ce_4: 0.22216/0.08106, loss_grounding_bce_4: 0.06090/0.08180, loss_grounding_dice_4: 0.15235/0.15349, loss_grounding_ce_4: 0.00197/0.25874, loss_mask_ce_5: 0.89083/0.79878, loss_mask_bce_5: 0.34215/0.30784, loss_mask_dice_5: 4.61347/1.05097, loss_spatial_bce_5: 0.02567/0.09202, loss_spatial_dice_5: 0.30770/0.19566, loss_spatial_ce_5: 0.31639/0.09446, loss_grounding_bce_5: 0.06396/0.08211, loss_grounding_dice_5: 0.16307/0.15420, loss_grounding_ce_5: 0.00121/0.27672, loss_mask_ce_6: 0.84076/0.82584, loss_mask_bce_6: 0.33582/0.30997, loss_mask_dice_6: 4.44445/1.05465, loss_spatial_bce_6: 0.03042/0.09729, loss_spatial_dice_6: 0.30426/0.19795, loss_spatial_ce_6: 0.06157/0.11903, loss_grounding_bce_6: 0.06622/0.08294, loss_grounding_dice_6: 0.16452/0.15476, loss_grounding_ce_6: 0.00104/0.28561, loss_mask_ce_7: 1.17171/0.88157, loss_mask_bce_7: 0.34155/0.31720, loss_mask_dice_7: 5.50787/1.10068, loss_spatial_bce_7: 0.08827/0.10668, loss_spatial_dice_7: 0.48102/0.22311, loss_spatial_ce_7: 0.17574/0.15526, loss_grounding_bce_7: 0.06655/0.08465, loss_grounding_dice_7: 0.16644/0.16040, loss_grounding_ce_7: 0.00098/0.31878, loss_mask_ce_8: 0.96871/1.01628, loss_mask_bce_8: 0.33432/0.33316, loss_mask_dice_8: 4.68145/1.17741, loss_spatial_bce_8: 0.03393/0.12378, loss_spatial_dice_8: 0.49778/0.25829, loss_spatial_ce_8: 0.65721/0.20132, loss_grounding_bce_8: 0.05889/0.08883, loss_grounding_dice_8: 0.15728/0.17009, loss_grounding_ce_8: 0.00052/0.41829, loss_mask_ce_9: 4.48850/3.47677, loss_mask_bce_9: 0.34344/0.36018, loss_mask_dice_9: 7.13694/1.76065, loss_spatial_bce_9: 0.09760/0.35453, loss_spatial_dice_9: 0.85203/0.79315, loss_spatial_ce_9: 2.68133/1.38797, loss_grounding_bce_9: 0.06352/0.10090, loss_grounding_dice_9: 0.17160/0.24235, loss_grounding_ce_9: 0.02098/0.67177] items per batch[64] items per second[0.37] total items[4409600] mini batches[ 68900] memory[4999] epoch remaining[0:15:22] INFO:trainer.default_trainer:epochs[ 37] optim steps[69000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.20428/0.75576, loss_mask_bce_0: 0.13506/0.30082, loss_mask_dice_0: 5.85012/1.02161, loss_spatial_bce_0: 0.01434/0.08475, loss_spatial_dice_0: 0.46166/0.17938, loss_spatial_ce_0: 0.16762/0.05658, loss_grounding_bce_0: 0.06681/0.08060, loss_grounding_dice_0: 0.52512/0.15051, loss_grounding_ce_0: 0.19552/0.24873, loss_mask_ce_1: 2.21823/0.75662, loss_mask_bce_1: 0.14926/0.30162, loss_mask_dice_1: 6.59432/1.02567, loss_spatial_bce_1: 0.01587/0.08514, loss_spatial_dice_1: 0.46815/0.18220, loss_spatial_ce_1: 0.25912/0.06039, loss_grounding_bce_1: 0.07558/0.08077, loss_grounding_dice_1: 0.50476/0.15124, loss_grounding_ce_1: 0.28460/0.25031, loss_mask_ce_2: 2.34944/0.76429, loss_mask_bce_2: 0.17123/0.30194, loss_mask_dice_2: 6.50992/1.02658, loss_spatial_bce_2: 0.01695/0.08524, loss_spatial_dice_2: 0.49989/0.18275, loss_spatial_ce_2: 0.19677/0.06253, loss_grounding_bce_2: 0.07562/0.08076, loss_grounding_dice_2: 0.49655/0.15111, loss_grounding_ce_2: 0.27545/0.25320, loss_mask_ce_3: 2.33642/0.76835, loss_mask_bce_3: 0.17422/0.30335, loss_mask_dice_3: 6.68112/1.02448, loss_spatial_bce_3: 0.01202/0.08738, loss_spatial_dice_3: 0.38292/0.18413, loss_spatial_ce_3: 0.34258/0.06736, loss_grounding_bce_3: 0.07687/0.08115, loss_grounding_dice_3: 0.50355/0.15080, loss_grounding_ce_3: 0.27057/0.25438, loss_mask_ce_4: 2.39566/0.77417, loss_mask_bce_4: 0.15760/0.30600, loss_mask_dice_4: 6.38561/1.04390, loss_spatial_bce_4: 0.02637/0.08964, loss_spatial_dice_4: 0.46806/0.19248, loss_spatial_ce_4: 0.19610/0.08105, loss_grounding_bce_4: 0.07472/0.08182, loss_grounding_dice_4: 0.50785/0.15348, loss_grounding_ce_4: 0.29658/0.25867, loss_mask_ce_5: 2.55524/0.79878, loss_mask_bce_5: 0.16169/0.30785, loss_mask_dice_5: 6.30466/1.05158, loss_spatial_bce_5: 0.02537/0.09203, loss_spatial_dice_5: 0.50630/0.19567, loss_spatial_ce_5: 0.14666/0.09445, loss_grounding_bce_5: 0.07028/0.08212, loss_grounding_dice_5: 0.49365/0.15419, loss_grounding_ce_5: 0.36693/0.27664, loss_mask_ce_6: 2.14867/0.82578, loss_mask_bce_6: 0.17426/0.30998, loss_mask_dice_6: 7.45054/1.05525, loss_spatial_bce_6: 0.02777/0.09730, loss_spatial_dice_6: 0.45909/0.19796, loss_spatial_ce_6: 0.19440/0.11903, loss_grounding_bce_6: 0.06728/0.08295, loss_grounding_dice_6: 0.45802/0.15476, loss_grounding_ce_6: 0.25445/0.28560, loss_mask_ce_7: 2.26655/0.88155, loss_mask_bce_7: 0.17932/0.31722, loss_mask_dice_7: 6.84807/1.10130, loss_spatial_bce_7: 0.03737/0.10668, loss_spatial_dice_7: 0.54784/0.22313, loss_spatial_ce_7: 0.11281/0.15523, loss_grounding_bce_7: 0.07533/0.08466, loss_grounding_dice_7: 0.50615/0.16039, loss_grounding_ce_7: 0.36265/0.31873, loss_mask_ce_8: 3.13055/1.01629, loss_mask_bce_8: 0.20719/0.33318, loss_mask_dice_8: 6.42582/1.17796, loss_spatial_bce_8: 0.03505/0.12378, loss_spatial_dice_8: 0.59602/0.25831, loss_spatial_ce_8: 0.21109/0.20127, loss_grounding_bce_8: 0.07824/0.08883, loss_grounding_dice_8: 0.51895/0.17008, loss_grounding_ce_8: 0.33367/0.41813, loss_mask_ce_9: 5.33908/3.47667, loss_mask_bce_9: 0.11680/0.36016, loss_mask_dice_9: 8.34575/1.76129, loss_spatial_bce_9: 0.02280/0.35452, loss_spatial_dice_9: 0.92007/0.79318, loss_spatial_ce_9: 2.05274/1.38800, loss_grounding_bce_9: 0.04842/0.10091, loss_grounding_dice_9: 0.56959/0.24233, loss_grounding_ce_9: 0.35251/0.67160] items per batch[64] items per second[0.37] total items[4416000] mini batches[ 69000] memory[4999] epoch remaining[0:12:26] INFO:trainer.default_trainer:epochs[ 37] optim steps[69100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.21794/0.75601, loss_mask_bce_0: 0.21765/0.30083, loss_mask_dice_0: 0.35421/1.02171, loss_spatial_bce_0: 0.14647/0.08473, loss_spatial_dice_0: 0.29433/0.17937, loss_spatial_ce_0: 0.27473/0.05658, loss_grounding_bce_0: 0.10548/0.08063, loss_grounding_dice_0: 0.36185/0.15055, loss_grounding_ce_0: 0.54845/0.24867, loss_mask_ce_1: 1.12398/0.75682, loss_mask_bce_1: 0.21976/0.30163, loss_mask_dice_1: 0.39960/1.02577, loss_spatial_bce_1: 0.14317/0.08512, loss_spatial_dice_1: 0.13187/0.18219, loss_spatial_ce_1: 0.59723/0.06039, loss_grounding_bce_1: 0.11094/0.08081, loss_grounding_dice_1: 0.04731/0.15127, loss_grounding_ce_1: 0.06931/0.25023, loss_mask_ce_2: 1.24155/0.76448, loss_mask_bce_2: 0.21322/0.30197, loss_mask_dice_2: 0.31203/1.02669, loss_spatial_bce_2: 0.14095/0.08522, loss_spatial_dice_2: 0.33660/0.18275, loss_spatial_ce_2: 0.33581/0.06252, loss_grounding_bce_2: 0.11236/0.08080, loss_grounding_dice_2: 0.04897/0.15116, loss_grounding_ce_2: 0.07984/0.25312, loss_mask_ce_3: 1.79866/0.76858, loss_mask_bce_3: 0.21216/0.30337, loss_mask_dice_3: 0.29994/1.02458, loss_spatial_bce_3: 0.14433/0.08736, loss_spatial_dice_3: 0.35904/0.18413, loss_spatial_ce_3: 0.19214/0.06734, loss_grounding_bce_3: 0.11013/0.08118, loss_grounding_dice_3: 0.05196/0.15084, loss_grounding_ce_3: 0.07660/0.25428, loss_mask_ce_4: 2.12963/0.77443, loss_mask_bce_4: 0.24714/0.30603, loss_mask_dice_4: 0.40273/1.04397, loss_spatial_bce_4: 0.15030/0.08962, loss_spatial_dice_4: 0.35252/0.19248, loss_spatial_ce_4: 0.17196/0.08103, loss_grounding_bce_4: 0.11709/0.08184, loss_grounding_dice_4: 0.30692/0.15352, loss_grounding_ce_4: 0.07079/0.25862, loss_mask_ce_5: 1.81671/0.79906, loss_mask_bce_5: 0.30247/0.30788, loss_mask_dice_5: 0.13936/1.05169, loss_spatial_bce_5: 0.15004/0.09200, loss_spatial_dice_5: 0.31180/0.19567, loss_spatial_ce_5: 0.20788/0.09445, loss_grounding_bce_5: 0.14550/0.08213, loss_grounding_dice_5: 0.37119/0.15422, loss_grounding_ce_5: 0.07073/0.27657, loss_mask_ce_6: 1.81000/0.82604, loss_mask_bce_6: 0.27031/0.31001, loss_mask_dice_6: 0.12291/1.05535, loss_spatial_bce_6: 0.18102/0.09728, loss_spatial_dice_6: 0.37056/0.19797, loss_spatial_ce_6: 0.21717/0.11904, loss_grounding_bce_6: 0.15540/0.08297, loss_grounding_dice_6: 0.32722/0.15479, loss_grounding_ce_6: 0.14410/0.28551, loss_mask_ce_7: 2.52341/0.88184, loss_mask_bce_7: 0.27069/0.31724, loss_mask_dice_7: 0.26986/1.10135, loss_spatial_bce_7: 0.27255/0.10666, loss_spatial_dice_7: 0.43969/0.22314, loss_spatial_ce_7: 0.22788/0.15522, loss_grounding_bce_7: 0.18214/0.08469, loss_grounding_dice_7: 0.30149/0.16040, loss_grounding_ce_7: 0.24656/0.31868, loss_mask_ce_8: 2.12198/1.01661, loss_mask_bce_8: 0.28552/0.33320, loss_mask_dice_8: 0.11174/1.17798, loss_spatial_bce_8: 0.24868/0.12374, loss_spatial_dice_8: 0.40939/0.25831, loss_spatial_ce_8: 0.54415/0.20128, loss_grounding_bce_8: 0.20929/0.08884, loss_grounding_dice_8: 0.08036/0.17010, loss_grounding_ce_8: 0.07803/0.41810, loss_mask_ce_9: 2.54927/3.47713, loss_mask_bce_9: 0.25686/0.36020, loss_mask_dice_9: 0.13671/1.76137, loss_spatial_bce_9: 0.59425/0.35447, loss_spatial_dice_9: 0.68420/0.79322, loss_spatial_ce_9: 0.53244/1.38809, loss_grounding_bce_9: 0.15773/0.10094, loss_grounding_dice_9: 0.32581/0.24237, loss_grounding_ce_9: 0.27861/0.67180] items per batch[64] items per second[0.37] total items[4422400] mini batches[ 69100] memory[4999] epoch remaining[0:09:31] INFO:trainer.default_trainer:epochs[ 37] optim steps[69200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.19827/0.75596, loss_mask_bce_0: 0.11897/0.30081, loss_mask_dice_0: 0.05760/1.02175, loss_spatial_bce_0: 0.12088/0.08473, loss_spatial_dice_0: 0.06138/0.17937, loss_spatial_ce_0: 0.00000/0.05659, loss_grounding_bce_0: 0.07801/0.08063, loss_grounding_dice_0: 0.03897/0.15054, loss_grounding_ce_0: 0.01993/0.24870, loss_mask_ce_1: 0.20591/0.75679, loss_mask_bce_1: 0.11852/0.30161, loss_mask_dice_1: 0.05847/1.02584, loss_spatial_bce_1: 0.12712/0.08512, loss_spatial_dice_1: 0.06521/0.18219, loss_spatial_ce_1: 0.00000/0.06040, loss_grounding_bce_1: 0.07581/0.08081, loss_grounding_dice_1: 0.03855/0.15127, loss_grounding_ce_1: 0.02329/0.25022, loss_mask_ce_2: 0.25766/0.76444, loss_mask_bce_2: 0.11817/0.30195, loss_mask_dice_2: 0.05617/1.02679, loss_spatial_bce_2: 0.12974/0.08522, loss_spatial_dice_2: 0.06438/0.18276, loss_spatial_ce_2: 0.00001/0.06252, loss_grounding_bce_2: 0.07635/0.08080, loss_grounding_dice_2: 0.03636/0.15116, loss_grounding_ce_2: 0.02653/0.25311, loss_mask_ce_3: 0.30259/0.76849, loss_mask_bce_3: 0.12554/0.30335, loss_mask_dice_3: 0.06083/1.02466, loss_spatial_bce_3: 0.12870/0.08737, loss_spatial_dice_3: 0.07031/0.18414, loss_spatial_ce_3: 0.00002/0.06736, loss_grounding_bce_3: 0.07388/0.08119, loss_grounding_dice_3: 0.03709/0.15085, loss_grounding_ce_3: 0.06566/0.25427, loss_mask_ce_4: 0.22383/0.77440, loss_mask_bce_4: 0.11898/0.30601, loss_mask_dice_4: 0.05827/1.04411, loss_spatial_bce_4: 0.12128/0.08963, loss_spatial_dice_4: 0.05842/0.19249, loss_spatial_ce_4: 0.00008/0.08106, loss_grounding_bce_4: 0.07932/0.08185, loss_grounding_dice_4: 0.03887/0.15352, loss_grounding_ce_4: 0.03837/0.25862, loss_mask_ce_5: 0.24127/0.79904, loss_mask_bce_5: 0.11686/0.30786, loss_mask_dice_5: 0.05860/1.05184, loss_spatial_bce_5: 0.11646/0.09202, loss_spatial_dice_5: 0.05919/0.19568, loss_spatial_ce_5: 0.00044/0.09445, loss_grounding_bce_5: 0.07343/0.08214, loss_grounding_dice_5: 0.03839/0.15423, loss_grounding_ce_5: 0.07612/0.27658, loss_mask_ce_6: 0.42995/0.82603, loss_mask_bce_6: 0.11933/0.31001, loss_mask_dice_6: 0.05745/1.05547, loss_spatial_bce_6: 0.09577/0.09730, loss_spatial_dice_6: 0.05089/0.19798, loss_spatial_ce_6: 0.16483/0.11902, loss_grounding_bce_6: 0.07758/0.08298, loss_grounding_dice_6: 0.03791/0.15479, loss_grounding_ce_6: 0.20343/0.28552, loss_mask_ce_7: 0.34740/0.88183, loss_mask_bce_7: 0.12042/0.31723, loss_mask_dice_7: 0.05838/1.10148, loss_spatial_bce_7: 0.14343/0.10667, loss_spatial_dice_7: 0.07853/0.22315, loss_spatial_ce_7: 0.00948/0.15520, loss_grounding_bce_7: 0.08283/0.08470, loss_grounding_dice_7: 0.04001/0.16041, loss_grounding_ce_7: 0.19408/0.31871, loss_mask_ce_8: 0.29379/1.01662, loss_mask_bce_8: 0.12625/0.33318, loss_mask_dice_8: 0.06143/1.17806, loss_spatial_bce_8: 0.14866/0.12374, loss_spatial_dice_8: 0.09197/0.25831, loss_spatial_ce_8: 0.14985/0.20127, loss_grounding_bce_8: 0.08385/0.08885, loss_grounding_dice_8: 0.04155/0.17011, loss_grounding_ce_8: 0.11246/0.41805, loss_mask_ce_9: 2.01136/3.47740, loss_mask_bce_9: 0.11624/0.36020, loss_mask_dice_9: 0.08711/1.76146, loss_spatial_bce_9: 0.91565/0.35450, loss_spatial_dice_9: 0.69078/0.79321, loss_spatial_ce_9: 0.95286/1.38810, loss_grounding_bce_9: 0.10333/0.10095, loss_grounding_dice_9: 0.06009/0.24238, loss_grounding_ce_9: 0.28515/0.67185] items per batch[64] items per second[0.37] total items[4428800] mini batches[ 69200] memory[4999] epoch remaining[0:06:35] INFO:trainer.default_trainer:epochs[ 37] optim steps[69300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05777/0.75600, loss_mask_bce_0: 0.01477/0.30085, loss_mask_dice_0: 0.02017/1.02145, loss_spatial_bce_0: 0.01743/0.08475, loss_spatial_dice_0: 0.02153/0.17934, loss_spatial_ce_0: 0.00163/0.05658, loss_grounding_bce_0: 0.01832/0.08064, loss_grounding_dice_0: 0.02555/0.15052, loss_grounding_ce_0: 0.01575/0.24880, loss_mask_ce_1: 0.06221/0.75680, loss_mask_bce_1: 0.01327/0.30165, loss_mask_dice_1: 0.01934/1.02558, loss_spatial_bce_1: 0.02001/0.08515, loss_spatial_dice_1: 0.02320/0.18217, loss_spatial_ce_1: 0.00131/0.06041, loss_grounding_bce_1: 0.01848/0.08082, loss_grounding_dice_1: 0.02585/0.15125, loss_grounding_ce_1: 0.02174/0.25025, loss_mask_ce_2: 0.07281/0.76450, loss_mask_bce_2: 0.01411/0.30198, loss_mask_dice_2: 0.02071/1.02653, loss_spatial_bce_2: 0.01734/0.08525, loss_spatial_dice_2: 0.02032/0.18273, loss_spatial_ce_2: 0.00069/0.06251, loss_grounding_bce_2: 0.01785/0.08081, loss_grounding_dice_2: 0.02438/0.15115, loss_grounding_ce_2: 0.01514/0.25311, loss_mask_ce_3: 0.06697/0.76857, loss_mask_bce_3: 0.01414/0.30339, loss_mask_dice_3: 0.01765/1.02440, loss_spatial_bce_3: 0.02262/0.08740, loss_spatial_dice_3: 0.02711/0.18411, loss_spatial_ce_3: 0.00080/0.06734, loss_grounding_bce_3: 0.01754/0.08120, loss_grounding_dice_3: 0.02393/0.15083, loss_grounding_ce_3: 0.01547/0.25431, loss_mask_ce_4: 0.07620/0.77448, loss_mask_bce_4: 0.01462/0.30605, loss_mask_dice_4: 0.01886/1.04383, loss_spatial_bce_4: 0.02238/0.08966, loss_spatial_dice_4: 0.02483/0.19247, loss_spatial_ce_4: 0.00101/0.08105, loss_grounding_bce_4: 0.01983/0.08187, loss_grounding_dice_4: 0.02589/0.15350, loss_grounding_ce_4: 0.02403/0.25861, loss_mask_ce_5: 0.06741/0.79914, loss_mask_bce_5: 0.01684/0.30790, loss_mask_dice_5: 0.02242/1.05155, loss_spatial_bce_5: 0.01790/0.09204, loss_spatial_dice_5: 0.02511/0.19566, loss_spatial_ce_5: 0.00026/0.09443, loss_grounding_bce_5: 0.01716/0.08215, loss_grounding_dice_5: 0.02587/0.15421, loss_grounding_ce_5: 0.01168/0.27654, loss_mask_ce_6: 0.06967/0.82607, loss_mask_bce_6: 0.01438/0.31004, loss_mask_dice_6: 0.02096/1.05520, loss_spatial_bce_6: 0.02149/0.09732, loss_spatial_dice_6: 0.02448/0.19796, loss_spatial_ce_6: 0.01197/0.11901, loss_grounding_bce_6: 0.01743/0.08299, loss_grounding_dice_6: 0.02451/0.15477, loss_grounding_ce_6: 0.02393/0.28545, loss_mask_ce_7: 0.07649/0.88190, loss_mask_bce_7: 0.01594/0.31727, loss_mask_dice_7: 0.02289/1.10118, loss_spatial_bce_7: 0.01895/0.10669, loss_spatial_dice_7: 0.02458/0.22313, loss_spatial_ce_7: 0.00051/0.15515, loss_grounding_bce_7: 0.01906/0.08471, loss_grounding_dice_7: 0.02725/0.16040, loss_grounding_ce_7: 0.03355/0.31865, loss_mask_ce_8: 0.10550/1.01663, loss_mask_bce_8: 0.01605/0.33320, loss_mask_dice_8: 0.02422/1.17773, loss_spatial_bce_8: 0.02178/0.12375, loss_spatial_dice_8: 0.03058/0.25827, loss_spatial_ce_8: 0.04060/0.20122, loss_grounding_bce_8: 0.01783/0.08886, loss_grounding_dice_8: 0.02698/0.17008, loss_grounding_ce_8: 0.03033/0.41803, loss_mask_ce_9: 1.49898/3.47710, loss_mask_bce_9: 0.02258/0.36023, loss_mask_dice_9: 0.03962/1.76100, loss_spatial_bce_9: 0.35637/0.35452, loss_spatial_dice_9: 0.43725/0.79316, loss_spatial_ce_9: 0.27728/1.38802, loss_grounding_bce_9: 0.02876/0.10096, loss_grounding_dice_9: 0.05029/0.24233, loss_grounding_ce_9: 0.06165/0.67185] items per batch[64] items per second[0.37] total items[4435200] mini batches[ 69300] memory[4999] epoch remaining[0:03:40] INFO:trainer.default_trainer:epochs[ 37] optim steps[69400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.30557/0.75592, loss_mask_bce_0: 0.28450/0.30082, loss_mask_dice_0: 0.36410/1.02123, loss_spatial_bce_0: 0.07874/0.08479, loss_spatial_dice_0: 0.10500/0.17935, loss_spatial_ce_0: 0.03874/0.05658, loss_grounding_bce_0: 0.11150/0.08068, loss_grounding_dice_0: 0.09405/0.15057, loss_grounding_ce_0: 0.54068/0.24883, loss_mask_ce_1: 0.32743/0.75673, loss_mask_bce_1: 0.26877/0.30161, loss_mask_dice_1: 0.34387/1.02528, loss_spatial_bce_1: 0.08152/0.08519, loss_spatial_dice_1: 0.10374/0.18219, loss_spatial_ce_1: 0.01220/0.06040, loss_grounding_bce_1: 0.12507/0.08086, loss_grounding_dice_1: 0.09883/0.15130, loss_grounding_ce_1: 0.51504/0.25026, loss_mask_ce_2: 0.32865/0.76439, loss_mask_bce_2: 0.26975/0.30195, loss_mask_dice_2: 0.34811/1.02625, loss_spatial_bce_2: 0.07395/0.08529, loss_spatial_dice_2: 0.10422/0.18275, loss_spatial_ce_2: 0.01111/0.06251, loss_grounding_bce_2: 0.12669/0.08084, loss_grounding_dice_2: 0.10331/0.15119, loss_grounding_ce_2: 0.46429/0.25312, loss_mask_ce_3: 0.33583/0.76850, loss_mask_bce_3: 0.27425/0.30335, loss_mask_dice_3: 0.35154/1.02417, loss_spatial_bce_3: 0.08018/0.08744, loss_spatial_dice_3: 0.11022/0.18412, loss_spatial_ce_3: 0.04542/0.06734, loss_grounding_bce_3: 0.14010/0.08123, loss_grounding_dice_3: 0.10906/0.15087, loss_grounding_ce_3: 0.47354/0.25435, loss_mask_ce_4: 0.31046/0.77442, loss_mask_bce_4: 0.26145/0.30601, loss_mask_dice_4: 0.34051/1.04359, loss_spatial_bce_4: 0.08552/0.08970, loss_spatial_dice_4: 0.12234/0.19249, loss_spatial_ce_4: 0.00513/0.08106, loss_grounding_bce_4: 0.15632/0.08190, loss_grounding_dice_4: 0.10504/0.15355, loss_grounding_ce_4: 0.61338/0.25866, loss_mask_ce_5: 0.36364/0.79911, loss_mask_bce_5: 0.26482/0.30786, loss_mask_dice_5: 0.33694/1.05131, loss_spatial_bce_5: 0.08110/0.09208, loss_spatial_dice_5: 0.10322/0.19568, loss_spatial_ce_5: 0.00409/0.09446, loss_grounding_bce_5: 0.11684/0.08217, loss_grounding_dice_5: 0.09654/0.15427, loss_grounding_ce_5: 1.09447/0.27654, loss_mask_ce_6: 0.35390/0.82604, loss_mask_bce_6: 0.27380/0.31002, loss_mask_dice_6: 0.35190/1.05495, loss_spatial_bce_6: 0.09182/0.09736, loss_spatial_dice_6: 0.10843/0.19798, loss_spatial_ce_6: 0.05038/0.11907, loss_grounding_bce_6: 0.10608/0.08301, loss_grounding_dice_6: 0.10248/0.15481, loss_grounding_ce_6: 0.84324/0.28545, loss_mask_ce_7: 0.38521/0.88185, loss_mask_bce_7: 0.27438/0.31726, loss_mask_dice_7: 0.35060/1.10096, loss_spatial_bce_7: 0.09186/0.10672, loss_spatial_dice_7: 0.11250/0.22313, loss_spatial_ce_7: 0.02027/0.15519, loss_grounding_bce_7: 0.12309/0.08474, loss_grounding_dice_7: 0.09201/0.16045, loss_grounding_ce_7: 0.83981/0.31865, loss_mask_ce_8: 0.54687/1.01655, loss_mask_bce_8: 0.32423/0.33316, loss_mask_dice_8: 0.37835/1.17739, loss_spatial_bce_8: 0.13278/0.12378, loss_spatial_dice_8: 0.14308/0.25827, loss_spatial_ce_8: 0.01279/0.20127, loss_grounding_bce_8: 0.12030/0.08887, loss_grounding_dice_8: 0.09522/0.17011, loss_grounding_ce_8: 2.11712/0.41794, loss_mask_ce_9: 2.33296/3.47677, loss_mask_bce_9: 0.34922/0.36019, loss_mask_dice_9: 0.59237/1.76038, loss_spatial_bce_9: 0.45325/0.35453, loss_spatial_dice_9: 0.83502/0.79312, loss_spatial_ce_9: 1.24660/1.38803, loss_grounding_bce_9: 0.16341/0.10098, loss_grounding_dice_9: 0.15866/0.24236, loss_grounding_ce_9: 1.39297/0.67173] items per batch[64] items per second[0.37] total items[4441600] mini batches[ 69400] memory[4999] epoch remaining[0:00:45] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00069426. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0025 s/iter. Inference: 0.3697 s/iter. Eval: 0.0951 s/iter. Total: 0.4673 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0024 s/iter. Inference: 0.3695 s/iter. Eval: 0.0819 s/iter. Total: 0.4539 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0027 s/iter. Inference: 0.3734 s/iter. Eval: 0.0803 s/iter. Total: 0.4565 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3771 s/iter. Eval: 0.0772 s/iter. Total: 0.4572 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3781 s/iter. Eval: 0.0744 s/iter. Total: 0.4555 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0029 s/iter. Inference: 0.3758 s/iter. Eval: 0.0727 s/iter. Total: 0.4515 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalfg_ijww5 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.811 | 83.148 | 66.319 | 133 | | Things | 61.984 | 84.084 | 73.196 | 80 | | Stuff | 46.493 | 81.737 | 55.938 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.55s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.67 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.55s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.18 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.458 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.676 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.605 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.60 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.799 | 69.347 | 49.501 | 26.574 | 49.716 | 67.604 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.161 | bicycle | 24.541 | car | 42.922 | | motorcycle | 41.741 | airplane | 62.704 | bus | 71.475 | | train | 75.349 | truck | 44.385 | boat | 31.414 | | traffic light | 28.685 | fire hydrant | 71.510 | stop sign | 67.861 | | parking meter | 51.543 | bench | 26.297 | bird | 34.128 | | cat | 76.764 | dog | 70.710 | horse | 51.906 | | sheep | 53.004 | cow | 56.964 | elephant | 66.804 | | bear | 80.465 | zebra | 66.074 | giraffe | 63.130 | | backpack | 22.708 | umbrella | 55.384 | handbag | 24.651 | | tie | 41.024 | suitcase | 50.668 | frisbee | 71.184 | | skis | 8.609 | snowboard | 35.374 | sports ball | 50.123 | | kite | 39.166 | baseball bat | 37.802 | baseball glove | 50.380 | | skateboard | 43.743 | surfboard | 44.768 | tennis racket | 63.050 | | bottle | 42.299 | wine glass | 38.117 | cup | 50.991 | | fork | 27.775 | knife | 24.500 | spoon | 22.194 | | bowl | 40.269 | banana | 22.493 | apple | 26.114 | | sandwich | 49.597 | orange | 32.163 | broccoli | 24.971 | | carrot | 22.268 | hot dog | 32.774 | pizza | 54.675 | | donut | 55.177 | cake | 47.599 | chair | 29.079 | | couch | 44.144 | potted plant | 22.251 | bed | 41.955 | | dining table | 14.490 | toilet | 69.467 | tv | 67.178 | | laptop | 70.054 | mouse | 63.883 | remote | 44.288 | | keyboard | 59.327 | cell phone | 45.830 | microwave | 66.981 | | oven | 31.351 | toaster | 53.780 | sink | 45.310 | | refrigerator | 69.146 | book | 14.068 | clock | 53.585 | | vase | 39.858 | scissors | 38.246 | teddy bear | 56.302 | | hair drier | 32.962 | toothbrush | 28.265 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.65557693771461, 'fwIoU': 71.6114486204486, 'IoU-person': 88.52233948910124, 'IoU-bicycle': 73.7639896822292, 'IoU-car': 72.85358656257714, 'IoU-motorcycle': 88.82498452088525, 'IoU-airplane': 87.35889787731561, 'IoU-bus': 87.44278322542095, 'IoU-train': 87.90744247981425, 'IoU-truck': 70.17618994252413, 'IoU-boat': 72.2603816594071, 'IoU-traffic light': 79.07581925314598, 'IoU-fire hydrant': 93.37879281136148, 'IoU-stop sign': 77.6952291539493, 'IoU-parking meter': 84.45489849713637, 'IoU-bench': 58.08034579121512, 'IoU-bird': 77.05787043961195, 'IoU-cat': 89.89776093488767, 'IoU-dog': 79.24565063796624, 'IoU-horse': 88.55069835806898, 'IoU-sheep': 82.23846363415153, 'IoU-cow': 89.72442522643915, 'IoU-elephant': 89.65949494644025, 'IoU-bear': 92.96742505753079, 'IoU-zebra': 82.57232075952012, 'IoU-giraffe': 89.02872935661183, 'IoU-backpack': 52.12221162033052, 'IoU-umbrella': 82.29473291163436, 'IoU-handbag': 51.988185953695464, 'IoU-tie': 72.63780756709637, 'IoU-suitcase': 79.44624796107543, 'IoU-frisbee': 85.03311519356619, 'IoU-skis': 60.999632488055866, 'IoU-snowboard': 75.40843566470903, 'IoU-sports ball': 78.51384408791478, 'IoU-kite': 79.39794125265745, 'IoU-baseball bat': 68.63538986366123, 'IoU-baseball glove': 76.7427367865992, 'IoU-skateboard': 85.77845950407601, 'IoU-surfboard': 86.30604958852905, 'IoU-tennis racket': 90.94006861364387, 'IoU-bottle': 70.84837618207582, 'IoU-wine glass': 82.26385916488049, 'IoU-cup': 68.39002723964599, 'IoU-fork': 71.99572025353302, 'IoU-knife': 65.59551573051814, 'IoU-spoon': 61.5591458519395, 'IoU-bowl': 59.69463843999211, 'IoU-banana': 82.63069839678779, 'IoU-apple': 61.7088923529185, 'IoU-sandwich': 69.59802585410063, 'IoU-orange': 77.92045167337233, 'IoU-broccoli': 67.34820883524326, 'IoU-carrot': 64.80364849560772, 'IoU-hot dog': 64.75460886588736, 'IoU-pizza': 84.31558143585161, 'IoU-donut': 71.32714271887778, 'IoU-cake': 79.93249426195511, 'IoU-chair': 61.235055376585954, 'IoU-couch': 69.47738638501156, 'IoU-potted plant': 44.335906239996795, 'IoU-bed': 71.65592485032532, 'IoU-dining table': 56.227030474295766, 'IoU-toilet': 85.18169270335545, 'IoU-tv': 75.3730374705097, 'IoU-laptop': 76.02649961349208, 'IoU-mouse': 83.75421207813405, 'IoU-remote': 70.06308321744564, 'IoU-keyboard': 65.57346308382907, 'IoU-cell phone': 80.99962173191561, 'IoU-microwave': 77.25806958594838, 'IoU-oven': 70.41310445319253, 'IoU-toaster': 86.33638962195366, 'IoU-sink': 71.8136063201351, 'IoU-refrigerator': 82.21688549573224, 'IoU-book': 53.553767994580106, 'IoU-clock': 81.57925624744801, 'IoU-vase': 71.52749524820577, 'IoU-scissors': 59.91422012153949, 'IoU-teddy bear': 83.89638626263095, 'IoU-hair drier': 50.37151667558003, 'IoU-toothbrush': 75.42227510856296, 'IoU-banner': 37.637327613672824, 'IoU-blanket': 16.696185860423288, 'IoU-bridge': 35.85164037424303, 'IoU-cardboard': 48.810460954968505, 'IoU-counter': 32.81120858123981, 'IoU-curtain': 71.16025100886827, 'IoU-door-stuff': 47.38840671769236, 'IoU-floor-wood': 63.20154614783179, 'IoU-flower': 50.262318402206915, 'IoU-fruit': 51.16360207243956, 'IoU-gravel': 32.93594020930183, 'IoU-house': 24.534174492180703, 'IoU-light': 44.528509054101576, 'IoU-mirror-stuff': 63.79331447936353, 'IoU-net': 44.075951822069435, 'IoU-pillow': 17.867143453002026, 'IoU-platform': 29.465021553772125, 'IoU-playingfield': 68.37305521955986, 'IoU-railroad': 64.48233394191011, 'IoU-river': 51.797290133769366, 'IoU-road': 69.01909386122288, 'IoU-roof': 19.909493529618086, 'IoU-sand': 67.14192575494693, 'IoU-sea': 85.59589940381603, 'IoU-shelf': 37.59900277259044, 'IoU-snow': 92.42601195927318, 'IoU-stairs': 32.004857191451656, 'IoU-tent': 11.314638700117431, 'IoU-towel': 44.703784510444834, 'IoU-wall-brick': 48.66742551072202, 'IoU-wall-stone': 29.448829206619674, 'IoU-wall-tile': 69.27074194586889, 'IoU-wall-wood': 45.1778025539358, 'IoU-water-other': 28.79696654122238, 'IoU-window-blind': 50.54779288898248, 'IoU-window-other': 51.4157172053846, 'IoU-tree-merged': 81.97494850471483, 'IoU-fence-merged': 54.91696523328973, 'IoU-ceiling-merged': 67.53783936605234, 'IoU-sky-other-merged': 93.82778134940143, 'IoU-cabinet-merged': 62.67710182872334, 'IoU-table-merged': 42.58045511405321, 'IoU-floor-other-merged': 54.357152605462154, 'IoU-pavement-merged': 58.75816246994714, 'IoU-mountain-merged': 56.75671645557424, 'IoU-grass-merged': 72.49569372832768, 'IoU-dirt-merged': 46.635223435168186, 'IoU-paper-merged': 38.17126596278624, 'IoU-food-other-merged': 44.790017312920675, 'IoU-building-other-merged': 59.28466877188519, 'IoU-rock-merged': 65.04680615139318, 'IoU-wall-other-merged': 67.8560555666342, 'IoU-rug-merged': 66.77291176272541, 'mACC': 77.44985395275799, 'pACC': 82.29278645871443, 'ACC-person': 92.71753255759576, 'ACC-bicycle': 82.63253757430498, 'ACC-car': 85.86147984143476, 'ACC-motorcycle': 93.4533160643124, 'ACC-airplane': 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'ACC-mouse': 91.8148400665202, 'ACC-remote': 75.55804784322932, 'ACC-keyboard': 83.5534214995235, 'ACC-cell phone': 89.90762893843768, 'ACC-microwave': 81.89456759282832, 'ACC-oven': 88.79685339110705, 'ACC-toaster': 91.56071658295583, 'ACC-sink': 81.79572494248788, 'ACC-refrigerator': 91.67585507753692, 'ACC-book': 70.37460424196577, 'ACC-clock': 88.15636307897537, 'ACC-vase': 81.49884314012196, 'ACC-scissors': 63.651879091718776, 'ACC-teddy bear': 89.30407391292509, 'ACC-hair drier': 63.000597728631206, 'ACC-toothbrush': 84.78978457261988, 'ACC-banner': 76.46834917308651, 'ACC-blanket': 26.568204363062765, 'ACC-bridge': 61.53338734089778, 'ACC-cardboard': 65.07954049730988, 'ACC-counter': 53.98840611977758, 'ACC-curtain': 82.98782013347575, 'ACC-door-stuff': 67.59220677328412, 'ACC-floor-wood': 83.43561114799108, 'ACC-flower': 73.58828299362082, 'ACC-fruit': 67.91132145394798, 'ACC-gravel': 44.35849951081073, 'ACC-house': 29.167285360583463, 'ACC-light': 62.09805535351989, 'ACC-mirror-stuff': 78.69651746588372, 'ACC-net': 66.00399509591999, 'ACC-pillow': 36.30082921278904, 'ACC-platform': 49.342260159949426, 'ACC-playingfield': 84.54511426454809, 'ACC-railroad': 83.80377435038412, 'ACC-river': 72.08133545178397, 'ACC-road': 84.97738845434864, 'ACC-roof': 29.002799203021674, 'ACC-sand': 73.96055012784981, 'ACC-sea': 92.05447848553548, 'ACC-shelf': 51.86140937623082, 'ACC-snow': 95.45932650715271, 'ACC-stairs': 54.82496444914597, 'ACC-tent': 13.388290375599619, 'ACC-towel': 55.16988639338255, 'ACC-wall-brick': 70.68953679686216, 'ACC-wall-stone': 36.055366550392606, 'ACC-wall-tile': 84.0815988883007, 'ACC-wall-wood': 65.57362568819835, 'ACC-water-other': 42.52795447917644, 'ACC-window-blind': 63.220607896989875, 'ACC-window-other': 78.12101440982008, 'ACC-tree-merged': 90.02183015102267, 'ACC-fence-merged': 72.77091647103823, 'ACC-ceiling-merged': 83.46392224415719, 'ACC-sky-other-merged': 96.96313161679116, 'ACC-cabinet-merged': 78.24254157000233, 'ACC-table-merged': 60.61088965568187, 'ACC-floor-other-merged': 67.45093151315199, 'ACC-pavement-merged': 71.39363006428925, 'ACC-mountain-merged': 66.20516185791973, 'ACC-grass-merged': 84.55965655683309, 'ACC-dirt-merged': 69.06685294503241, 'ACC-paper-merged': 51.12231879526402, 'ACC-food-other-merged': 63.900760291541694, 'ACC-building-other-merged': 71.44697646753382, 'ACC-rock-merged': 84.61053638444324, 'ACC-wall-other-merged': 82.16627355935108, 'ACC-rug-merged': 81.76460664170044})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3411 s/iter. Inference: 0.4282 s/iter. Eval: 0.0000 s/iter. Total: 0.7694 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3417 s/iter. Inference: 0.4666 s/iter. Eval: 0.0000 s/iter. Total: 0.8085 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3539 s/iter. Inference: 0.5443 s/iter. Eval: 0.0000 s/iter. Total: 0.8984 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3959613696224757, 'noc@0.8': 2.3804506877377816, 'noc@0.85': 2.8527948492829966, 'noc@0.9': 3.6341820310213637, 'miou@iter1': 0.8696367304542102} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0018 s/iter. Inference: 0.1494 s/iter. Eval: 0.0026 s/iter. Total: 0.1538 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.21453857421875, 'precision@0.6': 73.18305206298828, 'precision@0.7': 69.02448272705078, 'precision@0.8': 60.124366760253906, 'precision@0.9': 32.80217742919922, 'cIoU': 62.51237487792969, 'mIoU': 67.4573745727539} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.81083816781883, 'SQ': 83.14829648034262, 'RQ': 66.31869314823415, 'PQ_th': 61.984214502500315, 'SQ_th': 84.08359440299773, 'RQ_th': 73.19573997092733, 'PQ_st': 46.492534266412896, 'SQ_st': 81.73652603105184, 'RQ_st': 55.938245113980244}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.79930815554021, 'AP50': 69.34689378168835, 'AP75': 49.50106114006818, 'APs': 26.574016590580023, 'APm': 49.71634568444548, 'APl': 67.60412052306997, 'AP-person': 49.16098464078434, 'AP-bicycle': 24.540999640234997, 'AP-car': 42.9219101219976, 'AP-motorcycle': 41.74065990910501, 'AP-airplane': 62.70373346133052, 'AP-bus': 71.4754914137324, 'AP-train': 75.34853620260917, 'AP-truck': 44.38545492372219, 'AP-boat': 31.41427374010378, 'AP-traffic light': 28.6846688032706, 'AP-fire hydrant': 71.5098229191863, 'AP-stop sign': 67.8613214123957, 'AP-parking meter': 51.542701597065644, 'AP-bench': 26.296746852235504, 'AP-bird': 34.12782137942176, 'AP-cat': 76.76387575994629, 'AP-dog': 70.71023421730462, 'AP-horse': 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'ACC-laptop': 85.43791378874016, 'ACC-mouse': 91.8148400665202, 'ACC-remote': 75.55804784322932, 'ACC-keyboard': 83.5534214995235, 'ACC-cell phone': 89.90762893843768, 'ACC-microwave': 81.89456759282832, 'ACC-oven': 88.79685339110705, 'ACC-toaster': 91.56071658295583, 'ACC-sink': 81.79572494248788, 'ACC-refrigerator': 91.67585507753692, 'ACC-book': 70.37460424196577, 'ACC-clock': 88.15636307897537, 'ACC-vase': 81.49884314012196, 'ACC-scissors': 63.651879091718776, 'ACC-teddy bear': 89.30407391292509, 'ACC-hair drier': 63.000597728631206, 'ACC-toothbrush': 84.78978457261988, 'ACC-banner': 76.46834917308651, 'ACC-blanket': 26.568204363062765, 'ACC-bridge': 61.53338734089778, 'ACC-cardboard': 65.07954049730988, 'ACC-counter': 53.98840611977758, 'ACC-curtain': 82.98782013347575, 'ACC-door-stuff': 67.59220677328412, 'ACC-floor-wood': 83.43561114799108, 'ACC-flower': 73.58828299362082, 'ACC-fruit': 67.91132145394798, 'ACC-gravel': 44.35849951081073, 'ACC-house': 29.167285360583463, 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'ACC-cabinet-merged': 78.24254157000233, 'ACC-table-merged': 60.61088965568187, 'ACC-floor-other-merged': 67.45093151315199, 'ACC-pavement-merged': 71.39363006428925, 'ACC-mountain-merged': 66.20516185791973, 'ACC-grass-merged': 84.55965655683309, 'ACC-dirt-merged': 69.06685294503241, 'ACC-paper-merged': 51.12231879526402, 'ACC-food-other-merged': 63.900760291541694, 'ACC-building-other-merged': 71.44697646753382, 'ACC-rock-merged': 84.61053638444324, 'ACC-wall-other-merged': 82.16627355935108, 'ACC-rug-merged': 81.76460664170044})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3959613696224757, 'noc@0.8': 2.3804506877377816, 'noc@0.85': 2.8527948492829966, 'noc@0.9': 3.6341820310213637, 'miou@iter1': 0.8696367304542102}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 76.21453857421875, 'precision@0.6': 73.18305206298828, 'precision@0.7': 69.02448272705078, 'precision@0.8': 60.124366760253906, 'precision@0.9': 32.80217742919922, 'cIoU': 62.51237487792969, 'mIoU': 67.4573745727539}}} INFO:trainer.default_trainer:This epoch takes 0:56:43.155166 INFO:trainer.default_trainer:PROGRESS: 76.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 38 training. INFO:trainer.default_trainer:epochs[ 38] optim steps[69500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.58827/0.75599, loss_mask_bce_0: 0.14740/0.30088, loss_mask_dice_0: 0.45122/1.02103, loss_spatial_bce_0: 0.02081/0.08480, loss_spatial_dice_0: 0.06184/0.17934, loss_spatial_ce_0: 0.02817/0.05657, loss_grounding_bce_0: 0.02205/0.08071, loss_grounding_dice_0: 0.02992/0.15055, loss_grounding_ce_0: 0.01750/0.24877, loss_mask_ce_1: 0.55623/0.75679, loss_mask_bce_1: 0.14286/0.30166, loss_mask_dice_1: 0.42119/1.02511, loss_spatial_bce_1: 0.02261/0.08520, loss_spatial_dice_1: 0.06158/0.18217, loss_spatial_ce_1: 0.02788/0.06038, loss_grounding_bce_1: 0.02068/0.08089, loss_grounding_dice_1: 0.02574/0.15128, loss_grounding_ce_1: 0.01482/0.25023, loss_mask_ce_2: 0.54008/0.76444, loss_mask_bce_2: 0.13448/0.30201, loss_mask_dice_2: 0.43262/1.02605, loss_spatial_bce_2: 0.02219/0.08530, loss_spatial_dice_2: 0.06830/0.18273, loss_spatial_ce_2: 0.02789/0.06249, loss_grounding_bce_2: 0.02093/0.08088, loss_grounding_dice_2: 0.02297/0.15117, loss_grounding_ce_2: 0.02286/0.25305, loss_mask_ce_3: 0.55221/0.76856, loss_mask_bce_3: 0.14073/0.30340, loss_mask_dice_3: 0.42150/1.02399, loss_spatial_bce_3: 0.02286/0.08746, loss_spatial_dice_3: 0.06016/0.18411, loss_spatial_ce_3: 0.02870/0.06732, loss_grounding_bce_3: 0.02414/0.08127, loss_grounding_dice_3: 0.03204/0.15085, loss_grounding_ce_3: 0.02523/0.25428, loss_mask_ce_4: 0.50735/0.77450, loss_mask_bce_4: 0.14071/0.30606, loss_mask_dice_4: 0.42656/1.04340, loss_spatial_bce_4: 0.02520/0.08972, loss_spatial_dice_4: 0.07063/0.19248, loss_spatial_ce_4: 0.03815/0.08104, loss_grounding_bce_4: 0.02179/0.08194, loss_grounding_dice_4: 0.02904/0.15353, loss_grounding_ce_4: 0.00741/0.25859, loss_mask_ce_5: 0.46793/0.79917, loss_mask_bce_5: 0.12962/0.30791, loss_mask_dice_5: 0.42093/1.05111, loss_spatial_bce_5: 0.02245/0.09210, loss_spatial_dice_5: 0.07693/0.19567, loss_spatial_ce_5: 0.02802/0.09444, loss_grounding_bce_5: 0.02299/0.08221, loss_grounding_dice_5: 0.02811/0.15425, loss_grounding_ce_5: 0.01065/0.27647, loss_mask_ce_6: 0.37943/0.82607, loss_mask_bce_6: 0.13426/0.31007, loss_mask_dice_6: 0.43172/1.05472, loss_spatial_bce_6: 0.02602/0.09738, loss_spatial_dice_6: 0.08343/0.19797, loss_spatial_ce_6: 0.15613/0.11909, loss_grounding_bce_6: 0.02268/0.08305, loss_grounding_dice_6: 0.02911/0.15480, loss_grounding_ce_6: 0.01333/0.28535, loss_mask_ce_7: 0.74761/0.88193, loss_mask_bce_7: 0.14253/0.31730, loss_mask_dice_7: 0.46214/1.10071, loss_spatial_bce_7: 0.03188/0.10674, loss_spatial_dice_7: 0.10780/0.22311, loss_spatial_ce_7: 0.05002/0.15516, loss_grounding_bce_7: 0.03002/0.08478, loss_grounding_dice_7: 0.02940/0.16042, loss_grounding_ce_7: 0.03054/0.31863, loss_mask_ce_8: 1.55904/1.01659, loss_mask_bce_8: 0.15579/0.33323, loss_mask_dice_8: 0.63373/1.17720, loss_spatial_bce_8: 0.03731/0.12378, loss_spatial_dice_8: 0.12954/0.25823, loss_spatial_ce_8: 0.07869/0.20123, loss_grounding_bce_8: 0.02349/0.08891, loss_grounding_dice_8: 0.03717/0.17010, loss_grounding_ce_8: 0.01099/0.41783, loss_mask_ce_9: 4.24557/3.47696, loss_mask_bce_9: 0.24628/0.36025, loss_mask_dice_9: 1.25271/1.76015, loss_spatial_bce_9: 0.34751/0.35454, loss_spatial_dice_9: 0.92927/0.79311, loss_spatial_ce_9: 1.87057/1.38787, loss_grounding_bce_9: 0.03590/0.10101, loss_grounding_dice_9: 0.18020/0.24233, loss_grounding_ce_9: 0.47493/0.67157] items per batch[64] items per second[0.16] total items[4448000] mini batches[ 69500] memory[4999] epoch remaining[0:54:24] INFO:trainer.default_trainer:epochs[ 38] optim steps[69600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.89935/0.75593, loss_mask_bce_0: 0.44061/0.30084, loss_mask_dice_0: 0.58648/1.02103, loss_spatial_bce_0: 0.07405/0.08478, loss_spatial_dice_0: 0.10432/0.17933, loss_spatial_ce_0: 0.25644/0.05655, loss_grounding_bce_0: 0.09793/0.08070, loss_grounding_dice_0: 0.08847/0.15053, loss_grounding_ce_0: 0.06255/0.24880, loss_mask_ce_1: 0.91050/0.75671, loss_mask_bce_1: 0.43208/0.30163, loss_mask_dice_1: 0.60639/1.02511, loss_spatial_bce_1: 0.09295/0.08518, loss_spatial_dice_1: 0.11963/0.18216, loss_spatial_ce_1: 0.22484/0.06034, loss_grounding_bce_1: 0.09520/0.08088, loss_grounding_dice_1: 0.09281/0.15126, loss_grounding_ce_1: 0.08817/0.25023, loss_mask_ce_2: 0.96467/0.76435, loss_mask_bce_2: 0.43034/0.30198, loss_mask_dice_2: 0.57426/1.02607, loss_spatial_bce_2: 0.10554/0.08528, loss_spatial_dice_2: 0.12611/0.18273, loss_spatial_ce_2: 0.21599/0.06246, loss_grounding_bce_2: 0.11103/0.08087, loss_grounding_dice_2: 0.09468/0.15117, loss_grounding_ce_2: 0.10586/0.25299, loss_mask_ce_3: 0.93383/0.76851, loss_mask_bce_3: 0.43424/0.30337, loss_mask_dice_3: 0.59808/1.02400, loss_spatial_bce_3: 0.11873/0.08743, loss_spatial_dice_3: 0.15004/0.18410, loss_spatial_ce_3: 0.24607/0.06731, loss_grounding_bce_3: 0.09712/0.08126, loss_grounding_dice_3: 0.08624/0.15083, loss_grounding_ce_3: 0.07381/0.25424, loss_mask_ce_4: 0.94280/0.77444, loss_mask_bce_4: 0.52992/0.30603, loss_mask_dice_4: 0.73885/1.04344, loss_spatial_bce_4: 0.10167/0.08970, loss_spatial_dice_4: 0.13923/0.19249, loss_spatial_ce_4: 0.24290/0.08102, loss_grounding_bce_4: 0.09546/0.08193, loss_grounding_dice_4: 0.09369/0.15351, loss_grounding_ce_4: 0.06265/0.25855, loss_mask_ce_5: 0.99942/0.79913, loss_mask_bce_5: 0.52316/0.30787, loss_mask_dice_5: 0.62769/1.05111, loss_spatial_bce_5: 0.11942/0.09208, loss_spatial_dice_5: 0.15950/0.19567, loss_spatial_ce_5: 0.25654/0.09443, loss_grounding_bce_5: 0.08953/0.08221, loss_grounding_dice_5: 0.08098/0.15425, loss_grounding_ce_5: 0.03983/0.27638, loss_mask_ce_6: 0.85872/0.82608, loss_mask_bce_6: 0.38375/0.31003, loss_mask_dice_6: 0.65221/1.05476, loss_spatial_bce_6: 0.12884/0.09737, loss_spatial_dice_6: 0.17646/0.19798, loss_spatial_ce_6: 0.24968/0.11909, loss_grounding_bce_6: 0.08935/0.08304, loss_grounding_dice_6: 0.08075/0.15479, loss_grounding_ce_6: 0.04911/0.28532, loss_mask_ce_7: 0.96020/0.88192, loss_mask_bce_7: 0.40299/0.31727, loss_mask_dice_7: 0.66625/1.10076, loss_spatial_bce_7: 0.09890/0.10671, loss_spatial_dice_7: 0.16230/0.22312, loss_spatial_ce_7: 0.24248/0.15513, loss_grounding_bce_7: 0.09147/0.08477, loss_grounding_dice_7: 0.09298/0.16040, loss_grounding_ce_7: 0.07492/0.31856, loss_mask_ce_8: 1.18246/1.01661, loss_mask_bce_8: 0.43718/0.33319, loss_mask_dice_8: 0.65028/1.17725, loss_spatial_bce_8: 0.12938/0.12375, loss_spatial_dice_8: 0.18533/0.25822, loss_spatial_ce_8: 0.44652/0.20118, loss_grounding_bce_8: 0.12614/0.08889, loss_grounding_dice_8: 0.10520/0.17009, loss_grounding_ce_8: 0.90312/0.41774, loss_mask_ce_9: 3.82950/3.47673, loss_mask_bce_9: 0.64507/0.36021, loss_mask_dice_9: 1.59990/1.76015, loss_spatial_bce_9: 0.33610/0.35447, loss_spatial_dice_9: 0.84311/0.79310, loss_spatial_ce_9: 1.59791/1.38792, loss_grounding_bce_9: 0.10546/0.10099, loss_grounding_dice_9: 0.10479/0.24231, loss_grounding_ce_9: 1.78079/0.67147] items per batch[64] items per second[0.37] total items[4454400] mini batches[ 69600] memory[4999] epoch remaining[0:49:08] INFO:trainer.default_trainer:epochs[ 38] optim steps[69700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.85835/0.75571, loss_mask_bce_0: 0.40665/0.30080, loss_mask_dice_0: 0.80089/1.02078, loss_spatial_bce_0: 0.06602/0.08477, loss_spatial_dice_0: 0.14997/0.17932, loss_spatial_ce_0: 0.00061/0.05653, loss_grounding_bce_0: 0.18924/0.08071, loss_grounding_dice_0: 0.21692/0.15051, loss_grounding_ce_0: 0.48177/0.24887, loss_mask_ce_1: 1.78617/0.75650, loss_mask_bce_1: 0.38884/0.30159, loss_mask_dice_1: 0.77216/1.02486, loss_spatial_bce_1: 0.05932/0.08517, loss_spatial_dice_1: 0.14352/0.18214, loss_spatial_ce_1: 0.00036/0.06031, loss_grounding_bce_1: 0.18952/0.08089, loss_grounding_dice_1: 0.22037/0.15123, loss_grounding_ce_1: 0.43245/0.25030, loss_mask_ce_2: 1.62132/0.76416, loss_mask_bce_2: 0.41897/0.30195, loss_mask_dice_2: 0.81824/1.02582, loss_spatial_bce_2: 0.05927/0.08527, loss_spatial_dice_2: 0.14590/0.18271, loss_spatial_ce_2: 0.00071/0.06245, loss_grounding_bce_2: 0.21480/0.08088, loss_grounding_dice_2: 0.22197/0.15116, loss_grounding_ce_2: 0.60449/0.25304, loss_mask_ce_3: 1.67915/0.76830, loss_mask_bce_3: 0.42746/0.30333, loss_mask_dice_3: 0.81239/1.02373, loss_spatial_bce_3: 0.05396/0.08742, loss_spatial_dice_3: 0.14272/0.18409, loss_spatial_ce_3: 0.00399/0.06729, loss_grounding_bce_3: 0.21753/0.08127, loss_grounding_dice_3: 0.23307/0.15081, loss_grounding_ce_3: 0.48204/0.25427, loss_mask_ce_4: 1.41113/0.77422, loss_mask_bce_4: 0.45938/0.30599, loss_mask_dice_4: 0.76086/1.04319, loss_spatial_bce_4: 0.06780/0.08970, loss_spatial_dice_4: 0.14721/0.19247, loss_spatial_ce_4: 0.03059/0.08099, loss_grounding_bce_4: 0.24771/0.08194, loss_grounding_dice_4: 0.25261/0.15350, loss_grounding_ce_4: 0.34664/0.25865, loss_mask_ce_5: 1.33968/0.79892, loss_mask_bce_5: 0.47137/0.30783, loss_mask_dice_5: 0.96670/1.05087, loss_spatial_bce_5: 0.07493/0.09208, loss_spatial_dice_5: 0.14526/0.19566, loss_spatial_ce_5: 0.06158/0.09440, loss_grounding_bce_5: 0.23102/0.08221, loss_grounding_dice_5: 0.24791/0.15423, loss_grounding_ce_5: 0.38047/0.27646, loss_mask_ce_6: 1.27891/0.82584, loss_mask_bce_6: 0.48029/0.30999, loss_mask_dice_6: 0.94894/1.05453, loss_spatial_bce_6: 0.08634/0.09737, loss_spatial_dice_6: 0.16262/0.19797, loss_spatial_ce_6: 0.06487/0.11907, loss_grounding_bce_6: 0.18747/0.08304, loss_grounding_dice_6: 0.20841/0.15478, loss_grounding_ce_6: 0.15993/0.28531, loss_mask_ce_7: 1.43824/0.88169, loss_mask_bce_7: 0.48513/0.31723, loss_mask_dice_7: 1.14838/1.10050, loss_spatial_bce_7: 0.09512/0.10671, loss_spatial_dice_7: 0.18763/0.22310, loss_spatial_ce_7: 0.04341/0.15509, loss_grounding_bce_7: 0.16052/0.08478, loss_grounding_dice_7: 0.21538/0.16038, loss_grounding_ce_7: 0.40813/0.31859, loss_mask_ce_8: 1.10041/1.01632, loss_mask_bce_8: 0.50099/0.33315, loss_mask_dice_8: 1.30982/1.17695, loss_spatial_bce_8: 0.12990/0.12375, loss_spatial_dice_8: 0.25297/0.25821, loss_spatial_ce_8: 0.17723/0.20113, loss_grounding_bce_8: 0.15231/0.08890, loss_grounding_dice_8: 0.20740/0.17007, loss_grounding_ce_8: 1.15273/0.41770, loss_mask_ce_9: 5.10801/3.47645, loss_mask_bce_9: 0.50672/0.36017, loss_mask_dice_9: 1.92777/1.75981, loss_spatial_bce_9: 0.24789/0.35451, loss_spatial_dice_9: 0.93767/0.79309, loss_spatial_ce_9: 1.47649/1.38790, loss_grounding_bce_9: 0.08381/0.10099, loss_grounding_dice_9: 0.25882/0.24229, loss_grounding_ce_9: 0.80214/0.67150] items per batch[64] items per second[0.36] total items[4460800] mini batches[ 69700] memory[4999] epoch remaining[0:46:14] INFO:trainer.default_trainer:epochs[ 38] optim steps[69800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16641/0.75563, loss_mask_bce_0: 0.07984/0.30086, loss_mask_dice_0: 0.08941/1.02070, loss_spatial_bce_0: 0.10430/0.08479, loss_spatial_dice_0: 0.21157/0.17931, loss_spatial_ce_0: 0.00080/0.05651, loss_grounding_bce_0: 0.08733/0.08071, loss_grounding_dice_0: 0.10367/0.15048, loss_grounding_ce_0: 0.04588/0.24887, loss_mask_ce_1: 0.16281/0.75638, loss_mask_bce_1: 0.08252/0.30165, loss_mask_dice_1: 0.09077/1.02478, loss_spatial_bce_1: 0.08717/0.08519, loss_spatial_dice_1: 0.12661/0.18214, loss_spatial_ce_1: 0.00214/0.06030, loss_grounding_bce_1: 0.09291/0.08089, loss_grounding_dice_1: 0.10523/0.15120, loss_grounding_ce_1: 0.04763/0.25028, loss_mask_ce_2: 0.17826/0.76407, loss_mask_bce_2: 0.08008/0.30200, loss_mask_dice_2: 0.08199/1.02573, loss_spatial_bce_2: 0.08607/0.08529, loss_spatial_dice_2: 0.12198/0.18271, loss_spatial_ce_2: 0.00483/0.06244, loss_grounding_bce_2: 0.08992/0.08088, loss_grounding_dice_2: 0.09000/0.15113, loss_grounding_ce_2: 0.04539/0.25303, loss_mask_ce_3: 0.18230/0.76821, loss_mask_bce_3: 0.07713/0.30339, loss_mask_dice_3: 0.07265/1.02363, loss_spatial_bce_3: 0.09246/0.08744, loss_spatial_dice_3: 0.17935/0.18408, loss_spatial_ce_3: 0.02124/0.06728, loss_grounding_bce_3: 0.08186/0.08126, loss_grounding_dice_3: 0.07860/0.15078, loss_grounding_ce_3: 0.08274/0.25424, loss_mask_ce_4: 0.21316/0.77413, loss_mask_bce_4: 0.08401/0.30604, loss_mask_dice_4: 0.07792/1.04310, loss_spatial_bce_4: 0.11193/0.08972, loss_spatial_dice_4: 0.23136/0.19247, loss_spatial_ce_4: 0.01547/0.08098, loss_grounding_bce_4: 0.08689/0.08194, loss_grounding_dice_4: 0.07339/0.15347, loss_grounding_ce_4: 0.10040/0.25861, loss_mask_ce_5: 0.19560/0.79883, loss_mask_bce_5: 0.08562/0.30788, loss_mask_dice_5: 0.06993/1.05078, loss_spatial_bce_5: 0.13472/0.09211, loss_spatial_dice_5: 0.29305/0.19566, loss_spatial_ce_5: 0.01011/0.09441, loss_grounding_bce_5: 0.08576/0.08222, loss_grounding_dice_5: 0.06995/0.15420, loss_grounding_ce_5: 0.08118/0.27645, loss_mask_ce_6: 0.29483/0.82577, loss_mask_bce_6: 0.09443/0.31004, loss_mask_dice_6: 0.07739/1.05443, loss_spatial_bce_6: 0.16018/0.09740, loss_spatial_dice_6: 0.33331/0.19797, loss_spatial_ce_6: 0.02524/0.11907, loss_grounding_bce_6: 0.09074/0.08304, loss_grounding_dice_6: 0.07922/0.15474, loss_grounding_ce_6: 0.25285/0.28531, loss_mask_ce_7: 0.49180/0.88165, loss_mask_bce_7: 0.09169/0.31728, loss_mask_dice_7: 0.08942/1.10039, loss_spatial_bce_7: 0.16584/0.10674, loss_spatial_dice_7: 0.33322/0.22310, loss_spatial_ce_7: 0.28623/0.15506, loss_grounding_bce_7: 0.08507/0.08479, loss_grounding_dice_7: 0.07504/0.16035, loss_grounding_ce_7: 0.37736/0.31864, loss_mask_ce_8: 0.56988/1.01621, loss_mask_bce_8: 0.08421/0.33321, loss_mask_dice_8: 0.07947/1.17689, loss_spatial_bce_8: 0.15716/0.12376, loss_spatial_dice_8: 0.17808/0.25819, loss_spatial_ce_8: 0.12009/0.20110, loss_grounding_bce_8: 0.08638/0.08891, loss_grounding_dice_8: 0.08431/0.17005, loss_grounding_ce_8: 0.36028/0.41764, loss_mask_ce_9: 2.99132/3.47629, loss_mask_bce_9: 0.25464/0.36021, loss_mask_dice_9: 0.28133/1.75971, loss_spatial_bce_9: 0.58662/0.35455, loss_spatial_dice_9: 0.55557/0.79308, loss_spatial_ce_9: 1.03600/1.38783, loss_grounding_bce_9: 0.26525/0.10099, loss_grounding_dice_9: 0.29267/0.24224, loss_grounding_ce_9: 0.56774/0.67146] items per batch[64] items per second[0.37] total items[4467200] mini batches[ 69800] memory[4999] epoch remaining[0:42:59] INFO:trainer.default_trainer:epochs[ 38] optim steps[69900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32998/0.75572, loss_mask_bce_0: 0.39808/0.30090, loss_mask_dice_0: 0.05974/1.02081, loss_spatial_bce_0: 0.44882/0.08479, loss_spatial_dice_0: 0.06951/0.17929, loss_spatial_ce_0: 0.24459/0.05650, loss_grounding_bce_0: 0.49144/0.08070, loss_grounding_dice_0: 0.07341/0.15048, loss_grounding_ce_0: 0.05078/0.24889, loss_mask_ce_1: 0.34673/0.75648, loss_mask_bce_1: 0.36297/0.30169, loss_mask_dice_1: 0.06458/1.02492, loss_spatial_bce_1: 0.42217/0.08519, loss_spatial_dice_1: 0.07333/0.18212, loss_spatial_ce_1: 0.24163/0.06028, loss_grounding_bce_1: 0.42739/0.08088, loss_grounding_dice_1: 0.07418/0.15120, loss_grounding_ce_1: 0.06892/0.25029, loss_mask_ce_2: 0.32568/0.76414, loss_mask_bce_2: 0.38770/0.30205, loss_mask_dice_2: 0.06292/1.02587, loss_spatial_bce_2: 0.40286/0.08529, loss_spatial_dice_2: 0.07681/0.18269, loss_spatial_ce_2: 0.26746/0.06243, loss_grounding_bce_2: 0.46616/0.08087, loss_grounding_dice_2: 0.07335/0.15112, loss_grounding_ce_2: 0.07886/0.25305, loss_mask_ce_3: 0.32352/0.76830, loss_mask_bce_3: 0.42471/0.30343, loss_mask_dice_3: 0.06592/1.02377, loss_spatial_bce_3: 0.38592/0.08745, loss_spatial_dice_3: 0.07106/0.18406, loss_spatial_ce_3: 0.25199/0.06727, loss_grounding_bce_3: 0.51509/0.08126, loss_grounding_dice_3: 0.07748/0.15078, loss_grounding_ce_3: 0.05797/0.25428, loss_mask_ce_4: 0.30764/0.77425, loss_mask_bce_4: 0.33261/0.30609, loss_mask_dice_4: 0.06708/1.04325, loss_spatial_bce_4: 0.35769/0.08973, loss_spatial_dice_4: 0.07076/0.19246, loss_spatial_ce_4: 0.24816/0.08097, loss_grounding_bce_4: 0.40918/0.08194, loss_grounding_dice_4: 0.07754/0.15346, loss_grounding_ce_4: 0.06919/0.25863, loss_mask_ce_5: 0.35505/0.79900, loss_mask_bce_5: 0.33162/0.30792, loss_mask_dice_5: 0.06801/1.05093, loss_spatial_bce_5: 0.27917/0.09212, loss_spatial_dice_5: 0.07285/0.19565, loss_spatial_ce_5: 0.43810/0.09439, loss_grounding_bce_5: 0.42149/0.08221, loss_grounding_dice_5: 0.08268/0.15420, loss_grounding_ce_5: 0.10699/0.27650, loss_mask_ce_6: 0.70635/0.82589, loss_mask_bce_6: 0.36782/0.31008, loss_mask_dice_6: 0.07139/1.05460, loss_spatial_bce_6: 0.30238/0.09741, loss_spatial_dice_6: 0.08439/0.19796, loss_spatial_ce_6: 0.50221/0.11905, loss_grounding_bce_6: 0.44755/0.08304, loss_grounding_dice_6: 0.08638/0.15474, loss_grounding_ce_6: 0.34139/0.28537, loss_mask_ce_7: 0.81899/0.88180, loss_mask_bce_7: 0.20637/0.31731, loss_mask_dice_7: 0.05645/1.10055, loss_spatial_bce_7: 0.35158/0.10675, loss_spatial_dice_7: 0.07717/0.22310, loss_spatial_ce_7: 0.36113/0.15506, loss_grounding_bce_7: 0.27932/0.08478, loss_grounding_dice_7: 0.07090/0.16034, loss_grounding_ce_7: 0.51704/0.31873, loss_mask_ce_8: 2.00010/1.01634, loss_mask_bce_8: 0.16045/0.33325, loss_mask_dice_8: 0.05516/1.17704, loss_spatial_bce_8: 0.37655/0.12377, loss_spatial_dice_8: 0.15268/0.25817, loss_spatial_ce_8: 0.73707/0.20105, loss_grounding_bce_8: 0.18130/0.08890, loss_grounding_dice_8: 0.06654/0.17004, loss_grounding_ce_8: 1.30866/0.41776, loss_mask_ce_9: 2.78129/3.47667, loss_mask_bce_9: 0.26468/0.36025, loss_mask_dice_9: 0.09277/1.76009, loss_spatial_bce_9: 0.78036/0.35456, loss_spatial_dice_9: 0.36986/0.79309, loss_spatial_ce_9: 0.60297/1.38775, loss_grounding_bce_9: 0.31758/0.10099, loss_grounding_dice_9: 0.11104/0.24223, loss_grounding_ce_9: 0.79297/0.67164] items per batch[64] items per second[0.36] total items[4473600] mini batches[ 69900] memory[4999] epoch remaining[0:39:56] INFO:trainer.default_trainer:epochs[ 38] optim steps[70000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36553/0.75570, loss_mask_bce_0: 0.18069/0.30092, loss_mask_dice_0: 0.38474/1.02091, loss_spatial_bce_0: 0.04930/0.08478, loss_spatial_dice_0: 0.12836/0.17930, loss_spatial_ce_0: 0.00082/0.05648, loss_grounding_bce_0: 0.04596/0.08069, loss_grounding_dice_0: 0.07753/0.15049, loss_grounding_ce_0: 0.05959/0.24887, loss_mask_ce_1: 0.36303/0.75646, loss_mask_bce_1: 0.18555/0.30171, loss_mask_dice_1: 0.45153/1.02503, loss_spatial_bce_1: 0.05313/0.08518, loss_spatial_dice_1: 0.13533/0.18213, loss_spatial_ce_1: 0.00054/0.06030, loss_grounding_bce_1: 0.04590/0.08087, loss_grounding_dice_1: 0.07319/0.15122, loss_grounding_ce_1: 0.07028/0.25026, loss_mask_ce_2: 0.35451/0.76412, loss_mask_bce_2: 0.16984/0.30207, loss_mask_dice_2: 0.41954/1.02595, loss_spatial_bce_2: 0.05687/0.08528, loss_spatial_dice_2: 0.13892/0.18270, loss_spatial_ce_2: 0.00064/0.06242, loss_grounding_bce_2: 0.04637/0.08086, loss_grounding_dice_2: 0.07540/0.15112, loss_grounding_ce_2: 0.07764/0.25303, loss_mask_ce_3: 0.46517/0.76833, loss_mask_bce_3: 0.16952/0.30345, loss_mask_dice_3: 0.45096/1.02388, loss_spatial_bce_3: 0.05798/0.08743, loss_spatial_dice_3: 0.14584/0.18407, loss_spatial_ce_3: 0.00087/0.06726, loss_grounding_bce_3: 0.04450/0.08125, loss_grounding_dice_3: 0.06869/0.15081, loss_grounding_ce_3: 0.03505/0.25424, loss_mask_ce_4: 0.33654/0.77423, loss_mask_bce_4: 0.18294/0.30611, loss_mask_dice_4: 0.39419/1.04337, loss_spatial_bce_4: 0.05284/0.08971, loss_spatial_dice_4: 0.11113/0.19247, loss_spatial_ce_4: 0.00908/0.08097, loss_grounding_bce_4: 0.04801/0.08193, loss_grounding_dice_4: 0.07299/0.15348, loss_grounding_ce_4: 0.04524/0.25858, loss_mask_ce_5: 0.38504/0.79899, loss_mask_bce_5: 0.19687/0.30794, loss_mask_dice_5: 0.48356/1.05104, loss_spatial_bce_5: 0.05516/0.09211, loss_spatial_dice_5: 0.11246/0.19568, loss_spatial_ce_5: 0.00203/0.09437, loss_grounding_bce_5: 0.04641/0.08220, loss_grounding_dice_5: 0.07248/0.15420, loss_grounding_ce_5: 0.04621/0.27646, loss_mask_ce_6: 0.78194/0.82591, loss_mask_bce_6: 0.19155/0.31011, loss_mask_dice_6: 0.39057/1.05473, loss_spatial_bce_6: 0.06351/0.09741, loss_spatial_dice_6: 0.10940/0.19799, loss_spatial_ce_6: 0.00642/0.11905, loss_grounding_bce_6: 0.04967/0.08303, loss_grounding_dice_6: 0.07738/0.15475, loss_grounding_ce_6: 0.03692/0.28535, loss_mask_ce_7: 0.46986/0.88184, loss_mask_bce_7: 0.20904/0.31734, loss_mask_dice_7: 0.63209/1.10071, loss_spatial_bce_7: 0.08327/0.10674, loss_spatial_dice_7: 0.13709/0.22313, loss_spatial_ce_7: 0.09441/0.15506, loss_grounding_bce_7: 0.04370/0.08477, loss_grounding_dice_7: 0.06717/0.16036, loss_grounding_ce_7: 0.02373/0.31871, loss_mask_ce_8: 0.71741/1.01632, loss_mask_bce_8: 0.21717/0.33327, loss_mask_dice_8: 0.42421/1.17717, loss_spatial_bce_8: 0.09686/0.12374, loss_spatial_dice_8: 0.15055/0.25820, loss_spatial_ce_8: 0.11326/0.20104, loss_grounding_bce_8: 0.04579/0.08889, loss_grounding_dice_8: 0.07044/0.17007, loss_grounding_ce_8: 0.03781/0.41770, loss_mask_ce_9: 3.52522/3.47675, loss_mask_bce_9: 0.26429/0.36025, loss_mask_dice_9: 0.89570/1.76024, loss_spatial_bce_9: 0.35990/0.35452, loss_spatial_dice_9: 0.76472/0.79313, loss_spatial_ce_9: 0.94124/1.38802, loss_grounding_bce_9: 0.13432/0.10097, loss_grounding_dice_9: 0.18865/0.24226, loss_grounding_ce_9: 1.36256/0.67151] items per batch[64] items per second[0.37] total items[4480000] mini batches[ 70000] memory[4999] epoch remaining[0:36:54] INFO:trainer.default_trainer:epochs[ 38] optim steps[70100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28564/0.75557, loss_mask_bce_0: 0.26344/0.30089, loss_mask_dice_0: 2.26527/1.02083, loss_spatial_bce_0: 0.01155/0.08478, loss_spatial_dice_0: 0.15386/0.17929, loss_spatial_ce_0: 0.11249/0.05646, loss_grounding_bce_0: 0.00823/0.08067, loss_grounding_dice_0: 0.19547/0.15046, loss_grounding_ce_0: 0.00810/0.24884, loss_mask_ce_1: 1.25384/0.75630, loss_mask_bce_1: 0.25843/0.30167, loss_mask_dice_1: 2.07944/1.02498, loss_spatial_bce_1: 0.01348/0.08517, loss_spatial_dice_1: 0.19572/0.18212, loss_spatial_ce_1: 0.07126/0.06028, loss_grounding_bce_1: 0.01045/0.08085, loss_grounding_dice_1: 0.22383/0.15120, loss_grounding_ce_1: 0.00756/0.25021, loss_mask_ce_2: 1.36115/0.76399, loss_mask_bce_2: 0.27491/0.30203, loss_mask_dice_2: 2.56120/1.02587, loss_spatial_bce_2: 0.01449/0.08527, loss_spatial_dice_2: 0.22317/0.18269, loss_spatial_ce_2: 0.05573/0.06241, loss_grounding_bce_2: 0.01415/0.08084, loss_grounding_dice_2: 0.24418/0.15110, loss_grounding_ce_2: 0.00643/0.25298, loss_mask_ce_3: 1.40726/0.76817, loss_mask_bce_3: 0.29722/0.30341, loss_mask_dice_3: 2.11853/1.02379, loss_spatial_bce_3: 0.01799/0.08742, loss_spatial_dice_3: 0.23791/0.18406, loss_spatial_ce_3: 0.01136/0.06722, loss_grounding_bce_3: 0.01068/0.08123, loss_grounding_dice_3: 0.20934/0.15079, loss_grounding_ce_3: 0.00752/0.25420, loss_mask_ce_4: 1.30085/0.77411, loss_mask_bce_4: 0.26021/0.30606, loss_mask_dice_4: 2.02609/1.04329, loss_spatial_bce_4: 0.01759/0.08970, loss_spatial_dice_4: 0.22608/0.19246, loss_spatial_ce_4: 0.04727/0.08095, loss_grounding_bce_4: 0.01209/0.08190, loss_grounding_dice_4: 0.20433/0.15346, loss_grounding_ce_4: 0.01391/0.25854, loss_mask_ce_5: 1.37787/0.79887, loss_mask_bce_5: 0.25471/0.30790, loss_mask_dice_5: 2.21620/1.05100, loss_spatial_bce_5: 0.02016/0.09209, loss_spatial_dice_5: 0.26172/0.19567, loss_spatial_ce_5: 0.01834/0.09437, loss_grounding_bce_5: 0.01131/0.08218, loss_grounding_dice_5: 0.20561/0.15417, loss_grounding_ce_5: 0.00852/0.27646, loss_mask_ce_6: 1.25230/0.82579, loss_mask_bce_6: 0.26940/0.31006, loss_mask_dice_6: 2.28319/1.05466, loss_spatial_bce_6: 0.02216/0.09741, loss_spatial_dice_6: 0.24642/0.19798, loss_spatial_ce_6: 0.08971/0.11904, loss_grounding_bce_6: 0.01129/0.08301, loss_grounding_dice_6: 0.20636/0.15473, loss_grounding_ce_6: 0.00824/0.28532, loss_mask_ce_7: 1.65980/0.88176, loss_mask_bce_7: 0.22347/0.31730, loss_mask_dice_7: 2.04373/1.10063, loss_spatial_bce_7: 0.04068/0.10672, loss_spatial_dice_7: 0.36128/0.22311, loss_spatial_ce_7: 0.13172/0.15505, loss_grounding_bce_7: 0.01858/0.08475, loss_grounding_dice_7: 0.25126/0.16033, loss_grounding_ce_7: 0.00744/0.31880, loss_mask_ce_8: 1.40836/1.01622, loss_mask_bce_8: 0.26614/0.33322, loss_mask_dice_8: 2.44143/1.17704, loss_spatial_bce_8: 0.03458/0.12372, loss_spatial_dice_8: 0.35712/0.25818, loss_spatial_ce_8: 0.07199/0.20102, loss_grounding_bce_8: 0.01031/0.08886, loss_grounding_dice_8: 0.23055/0.17004, loss_grounding_ce_8: 0.00572/0.41773, loss_mask_ce_9: 4.03243/3.47650, loss_mask_bce_9: 0.19423/0.36017, loss_mask_dice_9: 3.18183/1.75999, loss_spatial_bce_9: 0.06728/0.35451, loss_spatial_dice_9: 0.93805/0.79312, loss_spatial_ce_9: 1.41983/1.38796, loss_grounding_bce_9: 0.01349/0.10094, loss_grounding_dice_9: 0.47524/0.24223, loss_grounding_ce_9: 0.27647/0.67151] items per batch[64] items per second[0.37] total items[4486400] mini batches[ 70100] memory[4999] epoch remaining[0:33:53] INFO:trainer.default_trainer:epochs[ 38] optim steps[70200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.98172/0.75552, loss_mask_bce_0: 0.53484/0.30094, loss_mask_dice_0: 2.61097/1.02064, loss_spatial_bce_0: 0.04629/0.08478, loss_spatial_dice_0: 0.27717/0.17926, loss_spatial_ce_0: 0.15368/0.05645, loss_grounding_bce_0: 0.10650/0.08068, loss_grounding_dice_0: 0.32577/0.15044, loss_grounding_ce_0: 0.02254/0.24884, loss_mask_ce_1: 0.98991/0.75628, loss_mask_bce_1: 0.53792/0.30172, loss_mask_dice_1: 2.61445/1.02480, loss_spatial_bce_1: 0.04975/0.08517, loss_spatial_dice_1: 0.29433/0.18210, loss_spatial_ce_1: 0.03168/0.06026, loss_grounding_bce_1: 0.09899/0.08086, loss_grounding_dice_1: 0.31661/0.15119, loss_grounding_ce_1: 0.02491/0.25021, loss_mask_ce_2: 1.18789/0.76396, loss_mask_bce_2: 0.53523/0.30209, loss_mask_dice_2: 2.45899/1.02569, loss_spatial_bce_2: 0.06346/0.08527, loss_spatial_dice_2: 0.34877/0.18268, loss_spatial_ce_2: 0.08792/0.06238, loss_grounding_bce_2: 0.10181/0.08085, loss_grounding_dice_2: 0.31755/0.15109, loss_grounding_ce_2: 0.02723/0.25304, loss_mask_ce_3: 0.97434/0.76815, loss_mask_bce_3: 0.56569/0.30346, loss_mask_dice_3: 2.25588/1.02363, loss_spatial_bce_3: 0.08500/0.08742, loss_spatial_dice_3: 0.34977/0.18405, loss_spatial_ce_3: 0.11164/0.06720, loss_grounding_bce_3: 0.10482/0.08124, loss_grounding_dice_3: 0.31456/0.15078, loss_grounding_ce_3: 0.02399/0.25426, loss_mask_ce_4: 1.11447/0.77408, loss_mask_bce_4: 0.58644/0.30611, loss_mask_dice_4: 2.70844/1.04311, loss_spatial_bce_4: 0.08188/0.08971, loss_spatial_dice_4: 0.37901/0.19245, loss_spatial_ce_4: 0.06164/0.08092, loss_grounding_bce_4: 0.12082/0.08191, loss_grounding_dice_4: 0.32203/0.15344, loss_grounding_ce_4: 0.02685/0.25860, loss_mask_ce_5: 1.26925/0.79884, loss_mask_bce_5: 0.55527/0.30795, loss_mask_dice_5: 2.16663/1.05083, loss_spatial_bce_5: 0.08676/0.09211, loss_spatial_dice_5: 0.39922/0.19567, loss_spatial_ce_5: 0.08597/0.09434, loss_grounding_bce_5: 0.11864/0.08219, loss_grounding_dice_5: 0.30884/0.15416, loss_grounding_ce_5: 0.02245/0.27643, loss_mask_ce_6: 1.38799/0.82575, loss_mask_bce_6: 0.57099/0.31012, loss_mask_dice_6: 2.37819/1.05452, loss_spatial_bce_6: 0.11532/0.09742, loss_spatial_dice_6: 0.37257/0.19797, loss_spatial_ce_6: 0.12616/0.11903, loss_grounding_bce_6: 0.11664/0.08302, loss_grounding_dice_6: 0.30824/0.15472, loss_grounding_ce_6: 0.02441/0.28530, loss_mask_ce_7: 1.47043/0.88175, loss_mask_bce_7: 0.57680/0.31736, loss_mask_dice_7: 2.16691/1.10048, loss_spatial_bce_7: 0.13408/0.10675, loss_spatial_dice_7: 0.41701/0.22311, loss_spatial_ce_7: 0.08591/0.15503, loss_grounding_bce_7: 0.12024/0.08476, loss_grounding_dice_7: 0.31841/0.16033, loss_grounding_ce_7: 0.07453/0.31879, loss_mask_ce_8: 1.51614/1.01613, loss_mask_bce_8: 0.58677/0.33329, loss_mask_dice_8: 2.34328/1.17683, loss_spatial_bce_8: 0.10441/0.12373, loss_spatial_dice_8: 0.43115/0.25818, loss_spatial_ce_8: 0.07938/0.20095, loss_grounding_bce_8: 0.13418/0.08888, loss_grounding_dice_8: 0.32381/0.17003, loss_grounding_ce_8: 0.01891/0.41769, loss_mask_ce_9: 4.34853/3.47671, loss_mask_bce_9: 0.72116/0.36026, loss_mask_dice_9: 4.11231/1.75979, loss_spatial_bce_9: 0.33564/0.35453, loss_spatial_dice_9: 0.94974/0.79314, loss_spatial_ce_9: 2.03934/1.38796, loss_grounding_bce_9: 0.13903/0.10097, loss_grounding_dice_9: 0.35741/0.24222, loss_grounding_ce_9: 0.14683/0.67157] items per batch[64] items per second[0.36] total items[4492800] mini batches[ 70200] memory[4999] epoch remaining[0:30:57] INFO:trainer.default_trainer:epochs[ 38] optim steps[70300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84048/0.75546, loss_mask_bce_0: 0.80812/0.30096, loss_mask_dice_0: 0.95748/1.02051, loss_spatial_bce_0: 0.12948/0.08480, loss_spatial_dice_0: 0.18241/0.17926, loss_spatial_ce_0: 0.00057/0.05644, loss_grounding_bce_0: 0.06059/0.08067, loss_grounding_dice_0: 0.11495/0.15045, loss_grounding_ce_0: 0.03599/0.24879, loss_mask_ce_1: 0.80298/0.75624, loss_mask_bce_1: 0.84597/0.30175, loss_mask_dice_1: 1.00046/1.02467, loss_spatial_bce_1: 0.12561/0.08519, loss_spatial_dice_1: 0.17527/0.18210, loss_spatial_ce_1: 0.00151/0.06026, loss_grounding_bce_1: 0.06094/0.08086, loss_grounding_dice_1: 0.11272/0.15119, loss_grounding_ce_1: 0.05224/0.25016, loss_mask_ce_2: 0.85744/0.76393, loss_mask_bce_2: 0.84699/0.30212, loss_mask_dice_2: 0.97385/1.02558, loss_spatial_bce_2: 0.12897/0.08529, loss_spatial_dice_2: 0.17397/0.18268, loss_spatial_ce_2: 0.00138/0.06237, loss_grounding_bce_2: 0.05471/0.08084, loss_grounding_dice_2: 0.09865/0.15109, loss_grounding_ce_2: 0.03743/0.25298, loss_mask_ce_3: 0.91771/0.76809, loss_mask_bce_3: 0.83126/0.30351, loss_mask_dice_3: 0.90812/1.02353, loss_spatial_bce_3: 0.13042/0.08745, loss_spatial_dice_3: 0.19417/0.18405, loss_spatial_ce_3: 0.01281/0.06720, loss_grounding_bce_3: 0.05784/0.08123, loss_grounding_dice_3: 0.10796/0.15078, loss_grounding_ce_3: 0.02071/0.25422, loss_mask_ce_4: 0.95039/0.77404, loss_mask_bce_4: 0.84960/0.30615, loss_mask_dice_4: 1.43560/1.04302, loss_spatial_bce_4: 0.14766/0.08973, loss_spatial_dice_4: 0.21628/0.19245, loss_spatial_ce_4: 0.00189/0.08091, loss_grounding_bce_4: 0.05838/0.08190, loss_grounding_dice_4: 0.10410/0.15344, loss_grounding_ce_4: 0.08117/0.25854, loss_mask_ce_5: 1.01971/0.79880, loss_mask_bce_5: 0.84598/0.30801, loss_mask_dice_5: 1.06942/1.05074, loss_spatial_bce_5: 0.15048/0.09213, loss_spatial_dice_5: 0.19883/0.19567, loss_spatial_ce_5: 0.00451/0.09434, loss_grounding_bce_5: 0.05224/0.08219, loss_grounding_dice_5: 0.10030/0.15415, loss_grounding_ce_5: 0.08049/0.27638, loss_mask_ce_6: 1.31923/0.82573, loss_mask_bce_6: 0.83257/0.31016, loss_mask_dice_6: 1.06670/1.05443, loss_spatial_bce_6: 0.17261/0.09745, loss_spatial_dice_6: 0.17847/0.19797, loss_spatial_ce_6: 0.02903/0.11905, loss_grounding_bce_6: 0.05281/0.08301, loss_grounding_dice_6: 0.08867/0.15472, loss_grounding_ce_6: 0.05006/0.28528, loss_mask_ce_7: 1.39012/0.88173, loss_mask_bce_7: 0.88267/0.31739, loss_mask_dice_7: 1.06607/1.10036, loss_spatial_bce_7: 0.13618/0.10678, loss_spatial_dice_7: 0.18579/0.22312, loss_spatial_ce_7: 0.12866/0.15503, loss_grounding_bce_7: 0.05500/0.08475, loss_grounding_dice_7: 0.10136/0.16033, loss_grounding_ce_7: 0.45598/0.31877, loss_mask_ce_8: 1.25884/1.01611, loss_mask_bce_8: 0.87541/0.33332, loss_mask_dice_8: 1.26812/1.17670, loss_spatial_bce_8: 0.17829/0.12375, loss_spatial_dice_8: 0.21381/0.25816, loss_spatial_ce_8: 0.66371/0.20094, loss_grounding_bce_8: 0.06803/0.08887, loss_grounding_dice_8: 0.09493/0.17002, loss_grounding_ce_8: 2.81574/0.41767, loss_mask_ce_9: 3.62936/3.47673, loss_mask_bce_9: 0.81084/0.36031, loss_mask_dice_9: 1.33588/1.75974, loss_spatial_bce_9: 0.35040/0.35454, loss_spatial_dice_9: 0.78842/0.79316, loss_spatial_ce_9: 1.84517/1.38810, loss_grounding_bce_9: 0.07422/0.10096, loss_grounding_dice_9: 0.14310/0.24221, loss_grounding_ce_9: 2.53090/0.67160] items per batch[64] items per second[0.36] total items[4499200] mini batches[ 70300] memory[4999] epoch remaining[0:28:00] INFO:trainer.default_trainer:epochs[ 38] optim steps[70400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.80445/0.75535, loss_mask_bce_0: 0.32226/0.30094, loss_mask_dice_0: 0.35847/1.02060, loss_spatial_bce_0: 0.09097/0.08479, loss_spatial_dice_0: 0.13006/0.17924, loss_spatial_ce_0: 0.16004/0.05642, loss_grounding_bce_0: 0.20005/0.08066, loss_grounding_dice_0: 0.12522/0.15042, loss_grounding_ce_0: 0.29583/0.24868, loss_mask_ce_1: 0.71819/0.75612, loss_mask_bce_1: 0.33423/0.30174, loss_mask_dice_1: 0.32306/1.02480, loss_spatial_bce_1: 0.09878/0.08519, loss_spatial_dice_1: 0.13139/0.18208, loss_spatial_ce_1: 0.15314/0.06024, loss_grounding_bce_1: 0.19936/0.08085, loss_grounding_dice_1: 0.12936/0.15117, loss_grounding_ce_1: 0.25997/0.25006, loss_mask_ce_2: 0.79513/0.76382, loss_mask_bce_2: 0.34448/0.30210, loss_mask_dice_2: 0.34990/1.02572, loss_spatial_bce_2: 0.09296/0.08528, loss_spatial_dice_2: 0.12215/0.18267, loss_spatial_ce_2: 0.14937/0.06234, loss_grounding_bce_2: 0.20017/0.08084, loss_grounding_dice_2: 0.12824/0.15107, loss_grounding_ce_2: 0.28792/0.25288, loss_mask_ce_3: 0.74453/0.76805, loss_mask_bce_3: 0.33237/0.30348, loss_mask_dice_3: 0.37198/1.02363, loss_spatial_bce_3: 0.08850/0.08744, loss_spatial_dice_3: 0.12774/0.18403, loss_spatial_ce_3: 0.14746/0.06717, loss_grounding_bce_3: 0.19920/0.08123, loss_grounding_dice_3: 0.12832/0.15076, loss_grounding_ce_3: 0.27511/0.25411, loss_mask_ce_4: 1.00396/0.77399, loss_mask_bce_4: 0.33956/0.30614, loss_mask_dice_4: 0.36972/1.04315, loss_spatial_bce_4: 0.15019/0.08972, loss_spatial_dice_4: 0.16155/0.19244, loss_spatial_ce_4: 0.19851/0.08090, loss_grounding_bce_4: 0.19317/0.08191, loss_grounding_dice_4: 0.12960/0.15342, loss_grounding_ce_4: 0.38860/0.25845, loss_mask_ce_5: 1.22330/0.79870, loss_mask_bce_5: 0.34351/0.30801, loss_mask_dice_5: 0.30202/1.05090, loss_spatial_bce_5: 0.15245/0.09213, loss_spatial_dice_5: 0.18856/0.19566, loss_spatial_ce_5: 0.22200/0.09435, loss_grounding_bce_5: 0.19753/0.08218, loss_grounding_dice_5: 0.12106/0.15413, loss_grounding_ce_5: 0.36633/0.27630, loss_mask_ce_6: 1.45592/0.82567, loss_mask_bce_6: 0.31129/0.31015, loss_mask_dice_6: 0.33462/1.05454, loss_spatial_bce_6: 0.14439/0.09746, loss_spatial_dice_6: 0.16169/0.19795, loss_spatial_ce_6: 0.24178/0.11903, loss_grounding_bce_6: 0.21949/0.08303, loss_grounding_dice_6: 0.12259/0.15469, loss_grounding_ce_6: 0.45844/0.28522, loss_mask_ce_7: 1.69178/0.88168, loss_mask_bce_7: 0.32693/0.31737, loss_mask_dice_7: 0.38479/1.10054, loss_spatial_bce_7: 0.23969/0.10677, loss_spatial_dice_7: 0.25533/0.22310, loss_spatial_ce_7: 0.36388/0.15507, loss_grounding_bce_7: 0.21645/0.08476, loss_grounding_dice_7: 0.11866/0.16030, loss_grounding_ce_7: 0.30034/0.31871, loss_mask_ce_8: 2.14630/1.01604, loss_mask_bce_8: 0.40715/0.33330, loss_mask_dice_8: 0.42208/1.17691, loss_spatial_bce_8: 0.25686/0.12374, loss_spatial_dice_8: 0.31223/0.25813, loss_spatial_ce_8: 0.26131/0.20091, loss_grounding_bce_8: 0.30064/0.08888, loss_grounding_dice_8: 0.16306/0.17000, loss_grounding_ce_8: 0.90338/0.41758, loss_mask_ce_9: 4.30450/3.47663, loss_mask_bce_9: 0.30809/0.36028, loss_mask_dice_9: 0.81454/1.75997, loss_spatial_bce_9: 0.62007/0.35452, loss_spatial_dice_9: 0.66432/0.79313, loss_spatial_ce_9: 1.41919/1.38804, loss_grounding_bce_9: 0.27157/0.10097, loss_grounding_dice_9: 0.25932/0.24218, loss_grounding_ce_9: 2.14117/0.67159] items per batch[64] items per second[0.37] total items[4505600] mini batches[ 70400] memory[4999] epoch remaining[0:25:02] INFO:trainer.default_trainer:epochs[ 38] optim steps[70500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.60731/0.75529, loss_mask_bce_0: 0.15591/0.30090, loss_mask_dice_0: 0.54745/1.02077, loss_spatial_bce_0: 0.04349/0.08479, loss_spatial_dice_0: 0.21457/0.17925, loss_spatial_ce_0: 0.04896/0.05641, loss_grounding_bce_0: 0.03436/0.08066, loss_grounding_dice_0: 0.04163/0.15042, loss_grounding_ce_0: 0.00577/0.24863, loss_mask_ce_1: 0.57154/0.75603, loss_mask_bce_1: 0.14645/0.30171, loss_mask_dice_1: 0.31796/1.02492, loss_spatial_bce_1: 0.04069/0.08519, loss_spatial_dice_1: 0.20652/0.18208, loss_spatial_ce_1: 0.03647/0.06023, loss_grounding_bce_1: 0.03470/0.08085, loss_grounding_dice_1: 0.04167/0.15116, loss_grounding_ce_1: 0.00745/0.24998, loss_mask_ce_2: 0.54904/0.76373, loss_mask_bce_2: 0.15056/0.30207, loss_mask_dice_2: 0.55821/1.02587, loss_spatial_bce_2: 0.04234/0.08527, loss_spatial_dice_2: 0.20509/0.18268, loss_spatial_ce_2: 0.09254/0.06233, loss_grounding_bce_2: 0.03373/0.08084, loss_grounding_dice_2: 0.04306/0.15106, loss_grounding_ce_2: 0.00854/0.25281, loss_mask_ce_3: 0.60076/0.76796, loss_mask_bce_3: 0.15586/0.30344, loss_mask_dice_3: 0.32800/1.02376, loss_spatial_bce_3: 0.04196/0.08743, loss_spatial_dice_3: 0.20609/0.18404, loss_spatial_ce_3: 0.17891/0.06716, loss_grounding_bce_3: 0.03075/0.08123, loss_grounding_dice_3: 0.04034/0.15076, loss_grounding_ce_3: 0.00875/0.25408, loss_mask_ce_4: 0.56978/0.77390, loss_mask_bce_4: 0.16741/0.30610, loss_mask_dice_4: 0.67008/1.04330, loss_spatial_bce_4: 0.04494/0.08972, loss_spatial_dice_4: 0.20442/0.19245, loss_spatial_ce_4: 0.19921/0.08087, loss_grounding_bce_4: 0.03202/0.08190, loss_grounding_dice_4: 0.04185/0.15342, loss_grounding_ce_4: 0.00950/0.25839, loss_mask_ce_5: 0.54284/0.79868, loss_mask_bce_5: 0.15761/0.30798, loss_mask_dice_5: 0.60961/1.05107, loss_spatial_bce_5: 0.04519/0.09212, loss_spatial_dice_5: 0.21287/0.19567, loss_spatial_ce_5: 0.35550/0.09435, loss_grounding_bce_5: 0.03286/0.08218, loss_grounding_dice_5: 0.04281/0.15414, loss_grounding_ce_5: 0.01958/0.27622, loss_mask_ce_6: 0.58274/0.82561, loss_mask_bce_6: 0.15873/0.31012, loss_mask_dice_6: 0.63888/1.05471, loss_spatial_bce_6: 0.06344/0.09745, loss_spatial_dice_6: 0.25997/0.19796, loss_spatial_ce_6: 0.42301/0.11902, loss_grounding_bce_6: 0.03336/0.08303, loss_grounding_dice_6: 0.04520/0.15468, loss_grounding_ce_6: 0.03419/0.28514, loss_mask_ce_7: 0.61780/0.88165, loss_mask_bce_7: 0.17149/0.31733, loss_mask_dice_7: 0.48258/1.10067, loss_spatial_bce_7: 0.06594/0.10676, loss_spatial_dice_7: 0.24844/0.22311, loss_spatial_ce_7: 0.34918/0.15505, loss_grounding_bce_7: 0.03973/0.08475, loss_grounding_dice_7: 0.04487/0.16030, loss_grounding_ce_7: 0.03964/0.31865, loss_mask_ce_8: 0.74465/1.01603, loss_mask_bce_8: 0.17298/0.33326, loss_mask_dice_8: 0.33877/1.17708, loss_spatial_bce_8: 0.09256/0.12372, loss_spatial_dice_8: 0.29682/0.25813, loss_spatial_ce_8: 0.28655/0.20088, loss_grounding_bce_8: 0.03923/0.08887, loss_grounding_dice_8: 0.04469/0.17000, loss_grounding_ce_8: 0.03687/0.41752, loss_mask_ce_9: 3.65427/3.47666, loss_mask_bce_9: 0.19294/0.36025, loss_mask_dice_9: 0.81144/1.76014, loss_spatial_bce_9: 0.38569/0.35452, loss_spatial_dice_9: 0.72189/0.79312, loss_spatial_ce_9: 1.03382/1.38819, loss_grounding_bce_9: 0.04956/0.10097, loss_grounding_dice_9: 0.08941/0.24219, loss_grounding_ce_9: 0.47689/0.67151] items per batch[64] items per second[0.37] total items[4512000] mini batches[ 70500] memory[4999] epoch remaining[0:22:02] INFO:trainer.default_trainer:epochs[ 38] optim steps[70600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.74193/0.75521, loss_mask_bce_0: 0.28775/0.30090, loss_mask_dice_0: 1.03396/1.02070, loss_spatial_bce_0: 0.07900/0.08479, loss_spatial_dice_0: 0.19328/0.17922, loss_spatial_ce_0: 0.00060/0.05638, loss_grounding_bce_0: 0.08360/0.08066, loss_grounding_dice_0: 0.14402/0.15043, loss_grounding_ce_0: 1.96618/0.24854, loss_mask_ce_1: 1.70569/0.75591, loss_mask_bce_1: 0.30089/0.30170, loss_mask_dice_1: 1.04072/1.02488, loss_spatial_bce_1: 0.07274/0.08519, loss_spatial_dice_1: 0.18909/0.18206, loss_spatial_ce_1: 0.00058/0.06019, loss_grounding_bce_1: 0.08380/0.08085, loss_grounding_dice_1: 0.13202/0.15117, loss_grounding_ce_1: 1.78070/0.24989, loss_mask_ce_2: 1.68858/0.76362, loss_mask_bce_2: 0.30554/0.30207, loss_mask_dice_2: 1.08371/1.02579, loss_spatial_bce_2: 0.07441/0.08528, loss_spatial_dice_2: 0.18345/0.18265, loss_spatial_ce_2: 0.00081/0.06228, loss_grounding_bce_2: 0.08423/0.08083, loss_grounding_dice_2: 0.13915/0.15106, loss_grounding_ce_2: 1.08959/0.25274, loss_mask_ce_3: 1.89556/0.76787, loss_mask_bce_3: 0.31909/0.30344, loss_mask_dice_3: 1.10071/1.02371, loss_spatial_bce_3: 0.07236/0.08743, loss_spatial_dice_3: 0.16857/0.18402, loss_spatial_ce_3: 0.00255/0.06713, loss_grounding_bce_3: 0.08706/0.08122, loss_grounding_dice_3: 0.15185/0.15075, loss_grounding_ce_3: 2.11155/0.25401, loss_mask_ce_4: 1.91167/0.77382, loss_mask_bce_4: 0.29256/0.30611, loss_mask_dice_4: 1.00092/1.04324, loss_spatial_bce_4: 0.07441/0.08972, loss_spatial_dice_4: 0.19284/0.19243, loss_spatial_ce_4: 0.00993/0.08086, loss_grounding_bce_4: 0.08557/0.08190, loss_grounding_dice_4: 0.11190/0.15343, loss_grounding_ce_4: 1.34508/0.25829, loss_mask_ce_5: 1.96007/0.79858, loss_mask_bce_5: 0.29398/0.30798, loss_mask_dice_5: 0.94754/1.05099, loss_spatial_bce_5: 0.08005/0.09212, loss_spatial_dice_5: 0.18199/0.19565, loss_spatial_ce_5: 0.01711/0.09434, loss_grounding_bce_5: 0.08047/0.08218, loss_grounding_dice_5: 0.11443/0.15414, loss_grounding_ce_5: 1.30393/0.27611, loss_mask_ce_6: 2.00548/0.82555, loss_mask_bce_6: 0.28103/0.31012, loss_mask_dice_6: 0.95983/1.05467, loss_spatial_bce_6: 0.08645/0.09746, loss_spatial_dice_6: 0.19288/0.19794, loss_spatial_ce_6: 0.05548/0.11900, loss_grounding_bce_6: 0.08030/0.08302, loss_grounding_dice_6: 0.11195/0.15468, loss_grounding_ce_6: 1.74974/0.28506, loss_mask_ce_7: 1.99701/0.88153, loss_mask_bce_7: 0.29670/0.31733, loss_mask_dice_7: 0.98853/1.10064, loss_spatial_bce_7: 0.09059/0.10676, loss_spatial_dice_7: 0.23526/0.22309, loss_spatial_ce_7: 0.05753/0.15501, loss_grounding_bce_7: 0.07644/0.08474, loss_grounding_dice_7: 0.13514/0.16030, loss_grounding_ce_7: 1.41360/0.31856, loss_mask_ce_8: 2.58437/1.01586, loss_mask_bce_8: 0.36597/0.33327, loss_mask_dice_8: 1.26429/1.17703, loss_spatial_bce_8: 0.08013/0.12371, loss_spatial_dice_8: 0.19763/0.25810, loss_spatial_ce_8: 0.04382/0.20082, loss_grounding_bce_8: 0.07488/0.08886, loss_grounding_dice_8: 0.20215/0.17000, loss_grounding_ce_8: 2.18184/0.41729, loss_mask_ce_9: 3.88509/3.47647, loss_mask_bce_9: 0.45436/0.36024, loss_mask_dice_9: 4.63268/1.76011, loss_spatial_bce_9: 0.42585/0.35455, loss_spatial_dice_9: 0.88946/0.79311, loss_spatial_ce_9: 1.08823/1.38808, loss_grounding_bce_9: 0.13143/0.10095, loss_grounding_dice_9: 0.24961/0.24218, loss_grounding_ce_9: 2.44969/0.67131] items per batch[64] items per second[0.36] total items[4518400] mini batches[ 70600] memory[4999] epoch remaining[0:19:07] INFO:trainer.default_trainer:epochs[ 38] optim steps[70700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.70026/0.75519, loss_mask_bce_0: 0.64665/0.30085, loss_mask_dice_0: 0.45244/1.02069, loss_spatial_bce_0: 0.34425/0.08476, loss_spatial_dice_0: 0.27876/0.17919, loss_spatial_ce_0: 0.01193/0.05634, loss_grounding_bce_0: 0.44390/0.08065, loss_grounding_dice_0: 0.31824/0.15042, loss_grounding_ce_0: 0.23492/0.24848, loss_mask_ce_1: 0.73742/0.75589, loss_mask_bce_1: 0.61026/0.30165, loss_mask_dice_1: 0.45173/1.02486, loss_spatial_bce_1: 0.37396/0.08516, loss_spatial_dice_1: 0.27672/0.18203, loss_spatial_ce_1: 0.00597/0.06016, loss_grounding_bce_1: 0.43208/0.08084, loss_grounding_dice_1: 0.31401/0.15115, loss_grounding_ce_1: 0.25141/0.24983, loss_mask_ce_2: 0.75076/0.76363, loss_mask_bce_2: 0.58214/0.30202, loss_mask_dice_2: 0.45467/1.02577, loss_spatial_bce_2: 0.38963/0.08525, loss_spatial_dice_2: 0.27986/0.18263, loss_spatial_ce_2: 0.02012/0.06225, loss_grounding_bce_2: 0.42091/0.08082, loss_grounding_dice_2: 0.31395/0.15105, loss_grounding_ce_2: 0.26820/0.25268, loss_mask_ce_3: 0.71679/0.76786, loss_mask_bce_3: 0.58494/0.30339, loss_mask_dice_3: 0.41915/1.02371, loss_spatial_bce_3: 0.29025/0.08740, loss_spatial_dice_3: 0.28940/0.18399, loss_spatial_ce_3: 0.12449/0.06711, loss_grounding_bce_3: 0.41160/0.08121, loss_grounding_dice_3: 0.29348/0.15074, loss_grounding_ce_3: 0.22272/0.25396, loss_mask_ce_4: 0.69948/0.77382, loss_mask_bce_4: 0.43336/0.30605, loss_mask_dice_4: 0.42331/1.04323, loss_spatial_bce_4: 0.36962/0.08970, loss_spatial_dice_4: 0.28742/0.19241, loss_spatial_ce_4: 0.05956/0.08083, loss_grounding_bce_4: 0.31606/0.08189, loss_grounding_dice_4: 0.30084/0.15342, loss_grounding_ce_4: 0.23881/0.25822, loss_mask_ce_5: 0.65813/0.79861, loss_mask_bce_5: 0.44311/0.30794, loss_mask_dice_5: 0.44807/1.05098, loss_spatial_bce_5: 0.28668/0.09210, loss_spatial_dice_5: 0.29946/0.19563, loss_spatial_ce_5: 0.04997/0.09432, loss_grounding_bce_5: 0.32364/0.08217, loss_grounding_dice_5: 0.33111/0.15414, loss_grounding_ce_5: 0.22055/0.27611, loss_mask_ce_6: 0.64775/0.82552, loss_mask_bce_6: 0.42187/0.31009, loss_mask_dice_6: 0.43278/1.05467, loss_spatial_bce_6: 0.31391/0.09744, loss_spatial_dice_6: 0.30174/0.19793, loss_spatial_ce_6: 0.09613/0.11895, loss_grounding_bce_6: 0.30543/0.08301, loss_grounding_dice_6: 0.30031/0.15468, loss_grounding_ce_6: 0.20606/0.28504, loss_mask_ce_7: 0.53864/0.88156, loss_mask_bce_7: 0.40982/0.31729, loss_mask_dice_7: 0.45186/1.10066, loss_spatial_bce_7: 0.34857/0.10674, loss_spatial_dice_7: 0.28573/0.22307, loss_spatial_ce_7: 0.09447/0.15495, loss_grounding_bce_7: 0.28530/0.08473, loss_grounding_dice_7: 0.31767/0.16030, loss_grounding_ce_7: 0.21193/0.31852, loss_mask_ce_8: 0.68205/1.01589, loss_mask_bce_8: 0.48147/0.33322, loss_mask_dice_8: 0.40541/1.17702, loss_spatial_bce_8: 0.42280/0.12368, loss_spatial_dice_8: 0.36930/0.25808, loss_spatial_ce_8: 0.32429/0.20074, loss_grounding_bce_8: 0.34068/0.08885, loss_grounding_dice_8: 0.28406/0.16999, loss_grounding_ce_8: 0.25594/0.41736, loss_mask_ce_9: 1.93937/3.47669, loss_mask_bce_9: 0.46519/0.36019, loss_mask_dice_9: 0.52117/1.76018, loss_spatial_bce_9: 0.66388/0.35451, loss_spatial_dice_9: 0.78773/0.79311, loss_spatial_ce_9: 0.97035/1.38803, loss_grounding_bce_9: 0.32397/0.10094, loss_grounding_dice_9: 0.36600/0.24218, loss_grounding_ce_9: 0.17556/0.67133] items per batch[64] items per second[0.36] total items[4524800] mini batches[ 70700] memory[4999] epoch remaining[0:16:12] INFO:trainer.default_trainer:epochs[ 38] optim steps[70800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.65153/0.75509, loss_mask_bce_0: 0.49656/0.30085, loss_mask_dice_0: 0.72578/1.02093, loss_spatial_bce_0: 0.22072/0.08476, loss_spatial_dice_0: 0.25907/0.17918, loss_spatial_ce_0: 0.15057/0.05630, loss_grounding_bce_0: 0.06373/0.08065, loss_grounding_dice_0: 0.08593/0.15043, loss_grounding_ce_0: 0.01369/0.24840, loss_mask_ce_1: 1.04212/0.75580, loss_mask_bce_1: 0.40350/0.30165, loss_mask_dice_1: 0.64858/1.02509, loss_spatial_bce_1: 0.23695/0.08517, loss_spatial_dice_1: 0.25251/0.18203, loss_spatial_ce_1: 0.11395/0.06011, loss_grounding_bce_1: 0.07796/0.08084, loss_grounding_dice_1: 0.14906/0.15116, loss_grounding_ce_1: 0.00790/0.24976, loss_mask_ce_2: 0.65047/0.76351, loss_mask_bce_2: 0.53150/0.30202, loss_mask_dice_2: 0.72911/1.02601, loss_spatial_bce_2: 0.26936/0.08526, loss_spatial_dice_2: 0.26960/0.18263, loss_spatial_ce_2: 0.10509/0.06221, loss_grounding_bce_2: 0.06605/0.08083, loss_grounding_dice_2: 0.09233/0.15106, loss_grounding_ce_2: 0.00613/0.25261, loss_mask_ce_3: 0.94806/0.76774, loss_mask_bce_3: 0.38863/0.30339, loss_mask_dice_3: 0.67775/1.02394, loss_spatial_bce_3: 0.25855/0.08740, loss_spatial_dice_3: 0.28570/0.18398, loss_spatial_ce_3: 0.16906/0.06706, loss_grounding_bce_3: 0.06405/0.08122, loss_grounding_dice_3: 0.10276/0.15075, loss_grounding_ce_3: 0.00849/0.25389, loss_mask_ce_4: 0.58799/0.77370, loss_mask_bce_4: 0.51162/0.30605, loss_mask_dice_4: 0.77427/1.04348, loss_spatial_bce_4: 0.28248/0.08970, loss_spatial_dice_4: 0.29793/0.19242, loss_spatial_ce_4: 0.18054/0.08080, loss_grounding_bce_4: 0.06644/0.08190, loss_grounding_dice_4: 0.08276/0.15342, loss_grounding_ce_4: 0.01223/0.25813, loss_mask_ce_5: 1.04272/0.79850, loss_mask_bce_5: 0.43669/0.30793, loss_mask_dice_5: 0.54308/1.05127, loss_spatial_bce_5: 0.28824/0.09209, loss_spatial_dice_5: 0.30517/0.19563, loss_spatial_ce_5: 0.16095/0.09429, loss_grounding_bce_5: 0.06871/0.08217, loss_grounding_dice_5: 0.10801/0.15414, loss_grounding_ce_5: 0.00705/0.27603, loss_mask_ce_6: 0.64868/0.82540, loss_mask_bce_6: 0.52839/0.31007, loss_mask_dice_6: 0.80528/1.05493, loss_spatial_bce_6: 0.26829/0.09744, loss_spatial_dice_6: 0.28278/0.19792, loss_spatial_ce_6: 0.07539/0.11895, loss_grounding_bce_6: 0.06383/0.08301, loss_grounding_dice_6: 0.09556/0.15469, loss_grounding_ce_6: 0.00322/0.28493, loss_mask_ce_7: 1.26891/0.88145, loss_mask_bce_7: 0.40368/0.31727, loss_mask_dice_7: 0.76335/1.10091, loss_spatial_bce_7: 0.24371/0.10674, loss_spatial_dice_7: 0.27721/0.22307, loss_spatial_ce_7: 0.16184/0.15491, loss_grounding_bce_7: 0.06690/0.08474, loss_grounding_dice_7: 0.10361/0.16030, loss_grounding_ce_7: 0.36092/0.31840, loss_mask_ce_8: 1.19053/1.01576, loss_mask_bce_8: 0.45516/0.33322, loss_mask_dice_8: 0.67172/1.17732, loss_spatial_bce_8: 0.29656/0.12367, loss_spatial_dice_8: 0.30318/0.25807, loss_spatial_ce_8: 0.18201/0.20071, loss_grounding_bce_8: 0.09744/0.08886, loss_grounding_dice_8: 0.20750/0.17000, loss_grounding_ce_8: 0.01569/0.41727, loss_mask_ce_9: 3.44224/3.47654, loss_mask_bce_9: 0.56800/0.36018, loss_mask_dice_9: 0.96611/1.76059, loss_spatial_bce_9: 0.78260/0.35450, loss_spatial_dice_9: 0.79032/0.79310, loss_spatial_ce_9: 2.76727/1.38798, loss_grounding_bce_9: 0.09056/0.10094, loss_grounding_dice_9: 0.15303/0.24217, loss_grounding_ce_9: 1.27854/0.67126] items per batch[64] items per second[0.37] total items[4531200] mini batches[ 70800] memory[4999] epoch remaining[0:13:15] INFO:trainer.default_trainer:epochs[ 38] optim steps[70900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26051/0.75514, loss_mask_bce_0: 0.33475/0.30090, loss_mask_dice_0: 0.41057/1.02100, loss_spatial_bce_0: 0.10826/0.08475, loss_spatial_dice_0: 0.11462/0.17918, loss_spatial_ce_0: 0.07302/0.05630, loss_grounding_bce_0: 0.12208/0.08066, loss_grounding_dice_0: 0.14440/0.15044, loss_grounding_ce_0: 0.00504/0.24834, loss_mask_ce_1: 0.25974/0.75586, loss_mask_bce_1: 0.34849/0.30169, loss_mask_dice_1: 0.45567/1.02520, loss_spatial_bce_1: 0.18173/0.08516, loss_spatial_dice_1: 0.16926/0.18203, loss_spatial_ce_1: 0.14304/0.06010, loss_grounding_bce_1: 0.12047/0.08085, loss_grounding_dice_1: 0.14449/0.15117, loss_grounding_ce_1: 0.00260/0.24971, loss_mask_ce_2: 0.27166/0.76355, loss_mask_bce_2: 0.34004/0.30205, loss_mask_dice_2: 0.41756/1.02612, loss_spatial_bce_2: 0.17817/0.08524, loss_spatial_dice_2: 0.16958/0.18263, loss_spatial_ce_2: 0.12860/0.06219, loss_grounding_bce_2: 0.11555/0.08083, loss_grounding_dice_2: 0.13704/0.15107, loss_grounding_ce_2: 0.00383/0.25256, loss_mask_ce_3: 0.30022/0.76780, loss_mask_bce_3: 0.34732/0.30343, loss_mask_dice_3: 0.41337/1.02404, loss_spatial_bce_3: 0.23116/0.08739, loss_spatial_dice_3: 0.20490/0.18399, loss_spatial_ce_3: 0.11965/0.06704, loss_grounding_bce_3: 0.11948/0.08122, loss_grounding_dice_3: 0.14274/0.15076, loss_grounding_ce_3: 0.00413/0.25385, loss_mask_ce_4: 0.26449/0.77375, loss_mask_bce_4: 0.35686/0.30609, loss_mask_dice_4: 0.42744/1.04357, loss_spatial_bce_4: 0.26176/0.08969, loss_spatial_dice_4: 0.27732/0.19243, loss_spatial_ce_4: 0.20879/0.08080, loss_grounding_bce_4: 0.12867/0.08190, loss_grounding_dice_4: 0.13847/0.15344, loss_grounding_ce_4: 0.00289/0.25805, loss_mask_ce_5: 0.30996/0.79856, loss_mask_bce_5: 0.35396/0.30797, loss_mask_dice_5: 0.42436/1.05141, loss_spatial_bce_5: 0.20850/0.09209, loss_spatial_dice_5: 0.25612/0.19565, loss_spatial_ce_5: 0.12607/0.09430, loss_grounding_bce_5: 0.12614/0.08218, loss_grounding_dice_5: 0.15549/0.15417, loss_grounding_ce_5: 0.00324/0.27604, loss_mask_ce_6: 0.32635/0.82548, loss_mask_bce_6: 0.34872/0.31010, loss_mask_dice_6: 0.46997/1.05507, loss_spatial_bce_6: 0.17153/0.09743, loss_spatial_dice_6: 0.24665/0.19794, loss_spatial_ce_6: 0.08603/0.11895, loss_grounding_bce_6: 0.12399/0.08302, loss_grounding_dice_6: 0.14331/0.15471, loss_grounding_ce_6: 0.00484/0.28491, loss_mask_ce_7: 0.35900/0.88159, loss_mask_bce_7: 0.35079/0.31729, loss_mask_dice_7: 0.42618/1.10100, loss_spatial_bce_7: 0.24546/0.10674, loss_spatial_dice_7: 0.21595/0.22310, loss_spatial_ce_7: 0.10928/0.15489, loss_grounding_bce_7: 0.12745/0.08474, loss_grounding_dice_7: 0.13709/0.16031, loss_grounding_ce_7: 0.01005/0.31839, loss_mask_ce_8: 0.50304/1.01596, loss_mask_bce_8: 0.42056/0.33326, loss_mask_dice_8: 0.59963/1.17742, loss_spatial_bce_8: 0.11764/0.12365, loss_spatial_dice_8: 0.18158/0.25809, loss_spatial_ce_8: 0.22732/0.20069, loss_grounding_bce_8: 0.12011/0.08887, loss_grounding_dice_8: 0.14191/0.17002, loss_grounding_ce_8: 0.00326/0.41727, loss_mask_ce_9: 3.84006/3.47686, loss_mask_bce_9: 0.35877/0.36022, loss_mask_dice_9: 0.90875/1.76070, loss_spatial_bce_9: 0.37244/0.35444, loss_spatial_dice_9: 0.85742/0.79311, loss_spatial_ce_9: 1.32658/1.38804, loss_grounding_bce_9: 0.11401/0.10094, loss_grounding_dice_9: 0.17631/0.24219, loss_grounding_ce_9: 0.06300/0.67138] items per batch[64] items per second[0.37] total items[4537600] mini batches[ 70900] memory[4999] epoch remaining[0:10:18] INFO:trainer.default_trainer:epochs[ 38] optim steps[71000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.34206/0.75510, loss_mask_bce_0: 0.32063/0.30088, loss_mask_dice_0: 2.64505/1.02098, loss_spatial_bce_0: 0.01191/0.08474, loss_spatial_dice_0: 0.24430/0.17918, loss_spatial_ce_0: 0.00354/0.05630, loss_grounding_bce_0: 0.10936/0.08064, loss_grounding_dice_0: 0.30851/0.15044, loss_grounding_ce_0: 0.00125/0.24837, loss_mask_ce_1: 0.40066/0.75587, loss_mask_bce_1: 0.27263/0.30168, loss_mask_dice_1: 2.23724/1.02520, loss_spatial_bce_1: 0.01342/0.08515, loss_spatial_dice_1: 0.16139/0.18204, loss_spatial_ce_1: 0.00535/0.06009, loss_grounding_bce_1: 0.10585/0.08083, loss_grounding_dice_1: 0.32017/0.15117, loss_grounding_ce_1: 0.00107/0.24979, loss_mask_ce_2: 0.28474/0.76352, loss_mask_bce_2: 0.28585/0.30204, loss_mask_dice_2: 2.07949/1.02613, loss_spatial_bce_2: 0.01462/0.08524, loss_spatial_dice_2: 0.26793/0.18264, loss_spatial_ce_2: 0.00331/0.06219, loss_grounding_bce_2: 0.10985/0.08081, loss_grounding_dice_2: 0.31414/0.15107, loss_grounding_ce_2: 0.00068/0.25258, loss_mask_ce_3: 0.47002/0.76778, loss_mask_bce_3: 0.27782/0.30341, loss_mask_dice_3: 1.98353/1.02406, loss_spatial_bce_3: 0.01304/0.08740, loss_spatial_dice_3: 0.22569/0.18400, loss_spatial_ce_3: 0.00548/0.06705, loss_grounding_bce_3: 0.10299/0.08120, loss_grounding_dice_3: 0.29055/0.15077, loss_grounding_ce_3: 0.00116/0.25388, loss_mask_ce_4: 0.38891/0.77375, loss_mask_bce_4: 0.26618/0.30606, loss_mask_dice_4: 2.20071/1.04356, loss_spatial_bce_4: 0.01986/0.08970, loss_spatial_dice_4: 0.27106/0.19244, loss_spatial_ce_4: 0.02378/0.08079, loss_grounding_bce_4: 0.09723/0.08188, loss_grounding_dice_4: 0.31040/0.15344, loss_grounding_ce_4: 0.00138/0.25810, loss_mask_ce_5: 0.40660/0.79856, loss_mask_bce_5: 0.27209/0.30795, loss_mask_dice_5: 2.05063/1.05143, loss_spatial_bce_5: 0.01845/0.09208, loss_spatial_dice_5: 0.22940/0.19566, loss_spatial_ce_5: 0.07092/0.09429, loss_grounding_bce_5: 0.09439/0.08216, loss_grounding_dice_5: 0.30953/0.15417, loss_grounding_ce_5: 0.00077/0.27612, loss_mask_ce_6: 0.46318/0.82549, loss_mask_bce_6: 0.28332/0.31005, loss_mask_dice_6: 2.10539/1.05510, loss_spatial_bce_6: 0.01643/0.09743, loss_spatial_dice_6: 0.28807/0.19796, loss_spatial_ce_6: 0.07277/0.11893, loss_grounding_bce_6: 0.10006/0.08300, loss_grounding_dice_6: 0.28550/0.15471, loss_grounding_ce_6: 0.00084/0.28490, loss_mask_ce_7: 0.52204/0.88157, loss_mask_bce_7: 0.28814/0.31726, loss_mask_dice_7: 2.58954/1.10099, loss_spatial_bce_7: 0.02427/0.10674, loss_spatial_dice_7: 0.31874/0.22311, loss_spatial_ce_7: 0.20187/0.15487, loss_grounding_bce_7: 0.10536/0.08472, loss_grounding_dice_7: 0.29219/0.16031, loss_grounding_ce_7: 0.00091/0.31838, loss_mask_ce_8: 0.61182/1.01592, loss_mask_bce_8: 0.31455/0.33323, loss_mask_dice_8: 3.04390/1.17742, loss_spatial_bce_8: 0.03946/0.12364, loss_spatial_dice_8: 0.36332/0.25810, loss_spatial_ce_8: 0.11197/0.20068, loss_grounding_bce_8: 0.10438/0.08885, loss_grounding_dice_8: 0.30660/0.17002, loss_grounding_ce_8: 0.00080/0.41727, loss_mask_ce_9: 4.94721/3.47707, loss_mask_bce_9: 0.24494/0.36020, loss_mask_dice_9: 3.02635/1.76070, loss_spatial_bce_9: 0.08606/0.35445, loss_spatial_dice_9: 0.88852/0.79315, loss_spatial_ce_9: 1.66843/1.38819, loss_grounding_bce_9: 0.07942/0.10092, loss_grounding_dice_9: 0.30704/0.24219, loss_grounding_ce_9: 0.02896/0.67150] items per batch[64] items per second[0.37] total items[4544000] mini batches[ 71000] memory[4999] epoch remaining[0:07:23] INFO:trainer.default_trainer:epochs[ 38] optim steps[71100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.35300/0.75508, loss_mask_bce_0: 0.35726/0.30093, loss_mask_dice_0: 0.66055/1.02093, loss_spatial_bce_0: 0.07481/0.08475, loss_spatial_dice_0: 0.26567/0.17917, loss_spatial_ce_0: 0.01921/0.05631, loss_grounding_bce_0: 0.05734/0.08064, loss_grounding_dice_0: 0.04004/0.15045, loss_grounding_ce_0: 0.12954/0.24847, loss_mask_ce_1: 1.42154/0.75587, loss_mask_bce_1: 0.35798/0.30172, loss_mask_dice_1: 0.65178/1.02514, loss_spatial_bce_1: 0.07823/0.08517, loss_spatial_dice_1: 0.22520/0.18204, loss_spatial_ce_1: 0.04656/0.06008, loss_grounding_bce_1: 0.05742/0.08083, loss_grounding_dice_1: 0.03880/0.15119, loss_grounding_ce_1: 0.12715/0.24985, loss_mask_ce_2: 1.47203/0.76351, loss_mask_bce_2: 0.36267/0.30208, loss_mask_dice_2: 0.64821/1.02607, loss_spatial_bce_2: 0.07160/0.08526, loss_spatial_dice_2: 0.20615/0.18263, loss_spatial_ce_2: 0.03281/0.06218, loss_grounding_bce_2: 0.05992/0.08081, loss_grounding_dice_2: 0.04354/0.15108, loss_grounding_ce_2: 0.12988/0.25266, loss_mask_ce_3: 1.48860/0.76780, loss_mask_bce_3: 0.35427/0.30344, loss_mask_dice_3: 0.64253/1.02400, loss_spatial_bce_3: 0.06568/0.08743, loss_spatial_dice_3: 0.21563/0.18400, loss_spatial_ce_3: 0.19884/0.06706, loss_grounding_bce_3: 0.05836/0.08120, loss_grounding_dice_3: 0.04201/0.15077, loss_grounding_ce_3: 0.20115/0.25395, loss_mask_ce_4: 1.47556/0.77378, loss_mask_bce_4: 0.28621/0.30610, loss_mask_dice_4: 0.64921/1.04348, loss_spatial_bce_4: 0.08070/0.08972, loss_spatial_dice_4: 0.22986/0.19244, loss_spatial_ce_4: 0.01778/0.08080, loss_grounding_bce_4: 0.06593/0.08188, loss_grounding_dice_4: 0.04133/0.15347, loss_grounding_ce_4: 0.32728/0.25816, loss_mask_ce_5: 1.40498/0.79859, loss_mask_bce_5: 0.32721/0.30798, loss_mask_dice_5: 0.64870/1.05135, loss_spatial_bce_5: 0.10920/0.09210, loss_spatial_dice_5: 0.21958/0.19566, loss_spatial_ce_5: 0.09715/0.09431, loss_grounding_bce_5: 0.06637/0.08216, loss_grounding_dice_5: 0.03976/0.15418, loss_grounding_ce_5: 0.44847/0.27620, loss_mask_ce_6: 1.27170/0.82550, loss_mask_bce_6: 0.31405/0.31009, loss_mask_dice_6: 0.65236/1.05503, loss_spatial_bce_6: 0.09670/0.09744, loss_spatial_dice_6: 0.22685/0.19796, loss_spatial_ce_6: 0.04898/0.11895, loss_grounding_bce_6: 0.06590/0.08301, loss_grounding_dice_6: 0.04250/0.15472, loss_grounding_ce_6: 0.56587/0.28492, loss_mask_ce_7: 1.39423/0.88158, loss_mask_bce_7: 0.35839/0.31729, loss_mask_dice_7: 0.72195/1.10088, loss_spatial_bce_7: 0.13803/0.10676, loss_spatial_dice_7: 0.23982/0.22310, loss_spatial_ce_7: 0.11161/0.15485, loss_grounding_bce_7: 0.06919/0.08472, loss_grounding_dice_7: 0.05425/0.16033, loss_grounding_ce_7: 0.51374/0.31844, loss_mask_ce_8: 1.42262/1.01587, loss_mask_bce_8: 0.41323/0.33327, loss_mask_dice_8: 0.79110/1.17731, loss_spatial_bce_8: 0.10445/0.12364, loss_spatial_dice_8: 0.27056/0.25808, loss_spatial_ce_8: 0.10736/0.20066, loss_grounding_bce_8: 0.09593/0.08885, loss_grounding_dice_8: 0.08467/0.17005, loss_grounding_ce_8: 0.79533/0.41725, loss_mask_ce_9: 4.81431/3.47669, loss_mask_bce_9: 0.38351/0.36023, loss_mask_dice_9: 1.01679/1.76066, loss_spatial_bce_9: 0.26083/0.35446, loss_spatial_dice_9: 0.93669/0.79311, loss_spatial_ce_9: 1.39324/1.38812, loss_grounding_bce_9: 0.19129/0.10093, loss_grounding_dice_9: 0.15604/0.24221, loss_grounding_ce_9: 1.42589/0.67147] items per batch[64] items per second[0.36] total items[4550400] mini batches[ 71100] memory[4999] epoch remaining[0:04:28] INFO:trainer.default_trainer:epochs[ 38] optim steps[71200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.33247/0.75487, loss_mask_bce_0: 0.07227/0.30094, loss_mask_dice_0: 2.56703/1.02052, loss_spatial_bce_0: 0.00496/0.08478, loss_spatial_dice_0: 0.18439/0.17917, loss_spatial_ce_0: 0.02621/0.05630, loss_grounding_bce_0: 0.00604/0.08065, loss_grounding_dice_0: 0.12614/0.15046, loss_grounding_ce_0: 0.01462/0.24853, loss_mask_ce_1: 1.43716/0.75566, loss_mask_bce_1: 0.06707/0.30172, loss_mask_dice_1: 2.76758/1.02473, loss_spatial_bce_1: 0.00366/0.08520, loss_spatial_dice_1: 0.18579/0.18203, loss_spatial_ce_1: 0.02869/0.06007, loss_grounding_bce_1: 0.00908/0.08084, loss_grounding_dice_1: 0.13493/0.15120, loss_grounding_ce_1: 0.03181/0.24989, loss_mask_ce_2: 1.25514/0.76331, loss_mask_bce_2: 0.07559/0.30207, loss_mask_dice_2: 2.95871/1.02566, loss_spatial_bce_2: 0.00489/0.08529, loss_spatial_dice_2: 0.20783/0.18262, loss_spatial_ce_2: 0.01250/0.06217, loss_grounding_bce_2: 0.01079/0.08083, loss_grounding_dice_2: 0.17575/0.15109, loss_grounding_ce_2: 0.02880/0.25271, loss_mask_ce_3: 1.26142/0.76760, loss_mask_bce_3: 0.09434/0.30344, loss_mask_dice_3: 2.68551/1.02360, loss_spatial_bce_3: 0.00544/0.08745, loss_spatial_dice_3: 0.15178/0.18399, loss_spatial_ce_3: 0.01933/0.06705, loss_grounding_bce_3: 0.01134/0.08122, loss_grounding_dice_3: 0.15632/0.15077, loss_grounding_ce_3: 0.01568/0.25399, loss_mask_ce_4: 1.39659/0.77356, loss_mask_bce_4: 0.07493/0.30609, loss_mask_dice_4: 2.81615/1.04304, loss_spatial_bce_4: 0.00448/0.08975, loss_spatial_dice_4: 0.20782/0.19243, loss_spatial_ce_4: 0.02757/0.08079, loss_grounding_bce_4: 0.01538/0.08189, loss_grounding_dice_4: 0.18600/0.15347, loss_grounding_ce_4: 0.01736/0.25821, loss_mask_ce_5: 1.69626/0.79842, loss_mask_bce_5: 0.08515/0.30797, loss_mask_dice_5: 2.98712/1.05091, loss_spatial_bce_5: 0.00694/0.09214, loss_spatial_dice_5: 0.20357/0.19565, loss_spatial_ce_5: 0.02664/0.09432, loss_grounding_bce_5: 0.01145/0.08217, loss_grounding_dice_5: 0.15289/0.15418, loss_grounding_ce_5: 0.02273/0.27624, loss_mask_ce_6: 1.96250/0.82533, loss_mask_bce_6: 0.07829/0.31007, loss_mask_dice_6: 2.66567/1.05459, loss_spatial_bce_6: 0.00693/0.09749, loss_spatial_dice_6: 0.18504/0.19795, loss_spatial_ce_6: 0.02848/0.11893, loss_grounding_bce_6: 0.00965/0.08301, loss_grounding_dice_6: 0.14297/0.15472, loss_grounding_ce_6: 0.01805/0.28496, loss_mask_ce_7: 1.75983/0.88143, loss_mask_bce_7: 0.09305/0.31726, loss_mask_dice_7: 3.50047/1.10044, loss_spatial_bce_7: 0.00556/0.10681, loss_spatial_dice_7: 0.21771/0.22310, loss_spatial_ce_7: 0.18384/0.15481, loss_grounding_bce_7: 0.00928/0.08472, loss_grounding_dice_7: 0.11271/0.16032, loss_grounding_ce_7: 0.02928/0.31845, loss_mask_ce_8: 2.35166/1.01573, loss_mask_bce_8: 0.10855/0.33325, loss_mask_dice_8: 3.57382/1.17683, loss_spatial_bce_8: 0.00468/0.12369, loss_spatial_dice_8: 0.26165/0.25806, loss_spatial_ce_8: 0.14477/0.20063, loss_grounding_bce_8: 0.01477/0.08886, loss_grounding_dice_8: 0.11339/0.17006, loss_grounding_ce_8: 0.03415/0.41721, loss_mask_ce_9: 4.26644/3.47621, loss_mask_bce_9: 0.08371/0.36020, loss_mask_dice_9: 4.53165/1.75986, loss_spatial_bce_9: 0.02219/0.35451, loss_spatial_dice_9: 0.91457/0.79307, loss_spatial_ce_9: 2.79639/1.38799, loss_grounding_bce_9: 0.00857/0.10092, loss_grounding_dice_9: 0.20445/0.24222, loss_grounding_ce_9: 0.02824/0.67136] items per batch[64] items per second[0.37] total items[4556800] mini batches[ 71200] memory[4999] epoch remaining[0:01:32] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00071253. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0033 s/iter. Inference: 0.3736 s/iter. Eval: 0.0991 s/iter. Total: 0.4760 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0027 s/iter. Inference: 0.3723 s/iter. Eval: 0.0806 s/iter. Total: 0.4557 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0027 s/iter. Inference: 0.3722 s/iter. Eval: 0.0759 s/iter. Total: 0.4510 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0027 s/iter. Inference: 0.3780 s/iter. Eval: 0.0741 s/iter. Total: 0.4549 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3794 s/iter. Eval: 0.0747 s/iter. Total: 0.4570 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3773 s/iter. Eval: 0.0734 s/iter. Total: 0.4536 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evala87m3c35 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.566 | 83.108 | 66.067 | 133 | | Things | 61.829 | 83.998 | 73.107 | 80 | | Stuff | 46.112 | 81.764 | 55.440 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.54s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.70 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.44 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.53s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 18.56 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.543 | 69.373 | 49.061 | 25.797 | 49.623 | 67.409 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 47.703 | bicycle | 22.962 | car | 43.421 | | motorcycle | 41.652 | airplane | 61.773 | bus | 71.418 | | train | 74.281 | truck | 41.368 | boat | 30.185 | | traffic light | 26.446 | fire hydrant | 69.948 | stop sign | 69.524 | | parking meter | 52.413 | bench | 26.160 | bird | 33.921 | | cat | 77.095 | dog | 70.845 | horse | 51.671 | | sheep | 52.698 | cow | 55.586 | elephant | 66.401 | | bear | 79.574 | zebra | 65.322 | giraffe | 61.744 | | backpack | 24.090 | umbrella | 54.603 | handbag | 24.310 | | tie | 40.008 | suitcase | 50.761 | frisbee | 70.084 | | skis | 8.690 | snowboard | 34.146 | sports ball | 51.004 | | kite | 36.299 | baseball bat | 39.871 | baseball glove | 47.863 | | skateboard | 43.970 | surfboard | 44.262 | tennis racket | 63.519 | | bottle | 42.098 | wine glass | 38.458 | cup | 50.610 | | fork | 27.318 | knife | 23.943 | spoon | 23.502 | | bowl | 40.958 | banana | 22.361 | apple | 26.235 | | sandwich | 48.062 | orange | 30.049 | broccoli | 24.186 | | carrot | 23.565 | hot dog | 36.200 | pizza | 53.477 | | donut | 55.813 | cake | 47.655 | chair | 28.352 | | couch | 42.602 | potted plant | 22.280 | bed | 42.389 | | dining table | 15.637 | toilet | 70.281 | tv | 65.152 | | laptop | 70.939 | mouse | 63.484 | remote | 42.799 | | keyboard | 58.905 | cell phone | 46.932 | microwave | 66.299 | | oven | 31.595 | toaster | 56.597 | sink | 44.128 | | refrigerator | 68.882 | book | 14.043 | clock | 54.488 | | vase | 40.875 | scissors | 36.317 | teddy bear | 56.897 | | hair drier | 32.097 | toothbrush | 29.372 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.455 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.491 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.603 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.760 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.58613171152992, 'fwIoU': 71.28711797868309, 'IoU-person': 88.95703265119998, 'IoU-bicycle': 71.68570428759425, 'IoU-car': 70.64599123887064, 'IoU-motorcycle': 84.11111392782239, 'IoU-airplane': 81.48364882379445, 'IoU-bus': 86.93324674427487, 'IoU-train': 88.29777068063645, 'IoU-truck': 66.9355130852729, 'IoU-boat': 71.88800186643616, 'IoU-traffic light': 79.35928523445409, 'IoU-fire hydrant': 93.25293933104378, 'IoU-stop sign': 83.15714153108289, 'IoU-parking meter': 86.32816727432856, 'IoU-bench': 62.769641387058094, 'IoU-bird': 77.12404431863386, 'IoU-cat': 91.08214113887597, 'IoU-dog': 83.30360476047048, 'IoU-horse': 88.17071962969979, 'IoU-sheep': 84.8857553693198, 'IoU-cow': 88.03319151108145, 'IoU-elephant': 91.49590583453207, 'IoU-bear': 78.37493975140241, 'IoU-zebra': 85.28662680459918, 'IoU-giraffe': 89.48210085018225, 'IoU-backpack': 47.58126584972246, 'IoU-umbrella': 88.789585694814, 'IoU-handbag': 49.63330881720841, 'IoU-tie': 76.08497092465029, 'IoU-suitcase': 85.70690015229539, 'IoU-frisbee': 84.5022122450045, 'IoU-skis': 58.59197186012829, 'IoU-snowboard': 72.14706296181019, 'IoU-sports ball': 79.0249878643582, 'IoU-kite': 79.01340286868418, 'IoU-baseball bat': 68.84685981116283, 'IoU-baseball glove': 81.00974561957027, 'IoU-skateboard': 86.17228161765878, 'IoU-surfboard': 86.75935928996658, 'IoU-tennis racket': 90.84482271466547, 'IoU-bottle': 70.99770549496145, 'IoU-wine glass': 81.77901014068014, 'IoU-cup': 72.109684956014, 'IoU-fork': 71.06470897273473, 'IoU-knife': 65.67390183662006, 'IoU-spoon': 61.282525146204506, 'IoU-bowl': 59.539384461492325, 'IoU-banana': 82.74451935102778, 'IoU-apple': 58.389957516140676, 'IoU-sandwich': 69.62992037110664, 'IoU-orange': 76.0559473865333, 'IoU-broccoli': 70.59763874215751, 'IoU-carrot': 65.46753196929363, 'IoU-hot dog': 71.4764772656384, 'IoU-pizza': 80.96520975275546, 'IoU-donut': 66.6102404865297, 'IoU-cake': 78.90391533048677, 'IoU-chair': 63.29566646801121, 'IoU-couch': 68.52926974211162, 'IoU-potted plant': 45.808645231402565, 'IoU-bed': 73.30390964455769, 'IoU-dining table': 53.584082707603386, 'IoU-toilet': 85.93103467847325, 'IoU-tv': 77.60500710444643, 'IoU-laptop': 76.66416914410877, 'IoU-mouse': 76.48118738306849, 'IoU-remote': 68.14310633785956, 'IoU-keyboard': 61.53465995721452, 'IoU-cell phone': 80.15894454522063, 'IoU-microwave': 79.6387108399619, 'IoU-oven': 75.9005046910299, 'IoU-toaster': 86.7213466629375, 'IoU-sink': 74.94444539893851, 'IoU-refrigerator': 82.43194269572041, 'IoU-book': 56.524481115962, 'IoU-clock': 78.51610508669019, 'IoU-vase': 66.90705347442965, 'IoU-scissors': 81.79259376247381, 'IoU-teddy bear': 82.40646288968833, 'IoU-hair drier': 48.2016496146392, 'IoU-toothbrush': 75.52986811481769, 'IoU-banner': 36.201649417406344, 'IoU-blanket': 15.084179743863343, 'IoU-bridge': 36.997495985979526, 'IoU-cardboard': 47.387023298819805, 'IoU-counter': 34.488914259570755, 'IoU-curtain': 71.91829673045689, 'IoU-door-stuff': 48.066696787173754, 'IoU-floor-wood': 62.934609570552425, 'IoU-flower': 48.66458829174035, 'IoU-fruit': 46.46168749997372, 'IoU-gravel': 24.48313407466334, 'IoU-house': 24.47697873273405, 'IoU-light': 44.797031240817084, 'IoU-mirror-stuff': 61.328885831803234, 'IoU-net': 44.618900916659356, 'IoU-pillow': 19.22853516339307, 'IoU-platform': 27.40648923391155, 'IoU-playingfield': 67.78381912058245, 'IoU-railroad': 64.42489513259768, 'IoU-river': 54.21650660041624, 'IoU-road': 65.91820307478899, 'IoU-roof': 20.420573423646214, 'IoU-sand': 60.94278926823734, 'IoU-sea': 84.92808549183535, 'IoU-shelf': 40.03539144786122, 'IoU-snow': 92.10413175660109, 'IoU-stairs': 33.635958971878864, 'IoU-tent': 11.109940356670341, 'IoU-towel': 64.05372061102005, 'IoU-wall-brick': 51.3347273500341, 'IoU-wall-stone': 27.16748786384334, 'IoU-wall-tile': 70.07379797025048, 'IoU-wall-wood': 45.334337256553525, 'IoU-water-other': 25.46496930299747, 'IoU-window-blind': 48.679243936596286, 'IoU-window-other': 50.014544706420025, 'IoU-tree-merged': 82.09332712201959, 'IoU-fence-merged': 54.51700459177147, 'IoU-ceiling-merged': 67.63594687512125, 'IoU-sky-other-merged': 94.05556475952189, 'IoU-cabinet-merged': 63.4768729960517, 'IoU-table-merged': 42.953588358806314, 'IoU-floor-other-merged': 53.82917217212388, 'IoU-pavement-merged': 56.367160587931984, 'IoU-mountain-merged': 56.59493799497002, 'IoU-grass-merged': 71.80491960601755, 'IoU-dirt-merged': 45.510686174316675, 'IoU-paper-merged': 32.87038353948165, 'IoU-food-other-merged': 40.91196781175729, 'IoU-building-other-merged': 59.21566584147268, 'IoU-rock-merged': 63.67092571822472, 'IoU-wall-other-merged': 67.99984623066, 'IoU-rug-merged': 65.66323403476896, 'mACC': 76.93883914248505, 'pACC': 82.07476923753093, 'ACC-person': 92.93383771805732, 'ACC-bicycle': 79.23276600531275, 'ACC-car': 86.22024379911034, 'ACC-motorcycle': 88.21059485711118, 'ACC-airplane': 87.35805653645625, 'ACC-bus': 93.93387343576084, 'ACC-train': 94.34890840809919, 'ACC-truck': 74.26395468441147, 'ACC-boat': 79.56101230158657, 'ACC-traffic light': 90.72496413936261, 'ACC-fire hydrant': 95.82988167575485, 'ACC-stop sign': 88.56026548249638, 'ACC-parking meter': 89.94008685656226, 'ACC-bench': 76.52723851873672, 'ACC-bird': 82.25090091246811, 'ACC-cat': 94.63137789296644, 'ACC-dog': 86.18910432301054, 'ACC-horse': 93.36298840896514, 'ACC-sheep': 88.86984310357114, 'ACC-cow': 90.95283977607538, 'ACC-elephant': 93.59882210198285, 'ACC-bear': 79.8441977283782, 'ACC-zebra': 87.27028461212673, 'ACC-giraffe': 93.185540284364, 'ACC-backpack': 73.48368988878275, 'ACC-umbrella': 93.30332901790074, 'ACC-handbag': 69.91500110329932, 'ACC-tie': 84.52750354538156, 'ACC-suitcase': 90.9574212185243, 'ACC-frisbee': 94.17418181818182, 'ACC-skis': 73.32715496240166, 'ACC-snowboard': 82.88457299600793, 'ACC-sports ball': 88.34671959148655, 'ACC-kite': 85.00147244266913, 'ACC-baseball bat': 87.82076736920183, 'ACC-baseball glove': 91.5796603330379, 'ACC-skateboard': 90.94240790696213, 'ACC-surfboard': 92.67072302326933, 'ACC-tennis racket': 94.8922094674972, 'ACC-bottle': 86.04399721747289, 'ACC-wine glass': 90.16228173559863, 'ACC-cup': 88.98492105630064, 'ACC-fork': 83.85449115033987, 'ACC-knife': 78.18055250238147, 'ACC-spoon': 77.89286345495424, 'ACC-bowl': 68.68987407368775, 'ACC-banana': 89.47226906313371, 'ACC-apple': 72.84311826224345, 'ACC-sandwich': 81.75005270797205, 'ACC-orange': 86.8502159541826, 'ACC-broccoli': 81.24634731243093, 'ACC-carrot': 76.99392932046521, 'ACC-hot dog': 79.26357095856265, 'ACC-pizza': 88.30217273498003, 'ACC-donut': 77.19372256250632, 'ACC-cake': 86.8735884948252, 'ACC-chair': 80.0599359805433, 'ACC-couch': 77.66296750241213, 'ACC-potted plant': 62.727173489800535, 'ACC-bed': 86.38713618296006, 'ACC-dining table': 80.9102843692174, 'ACC-toilet': 90.98805263844723, 'ACC-tv': 86.5220442913209, 'ACC-laptop': 88.27677479557418, 'ACC-mouse': 83.9088918469458, 'ACC-remote': 72.17128971540103, 'ACC-keyboard': 68.60259152520089, 'ACC-cell phone': 89.42881543971771, 'ACC-microwave': 84.57898972570555, 'ACC-oven': 91.27763139920202, 'ACC-toaster': 91.23431277285853, 'ACC-sink': 83.66867957392262, 'ACC-refrigerator': 91.2940397437228, 'ACC-book': 73.82988708638032, 'ACC-clock': 83.1885644941628, 'ACC-vase': 75.27583785763403, 'ACC-scissors': 86.78357619664123, 'ACC-teddy bear': 87.50704168503734, 'ACC-hair drier': 60.880084251273736, 'ACC-toothbrush': 84.57088255733149, 'ACC-banner': 70.92401513192686, 'ACC-blanket': 19.497182629058354, 'ACC-bridge': 56.264856312790144, 'ACC-cardboard': 59.88724734622656, 'ACC-counter': 51.38894534965824, 'ACC-curtain': 81.51031241291206, 'ACC-door-stuff': 70.21572184212117, 'ACC-floor-wood': 82.71739208502072, 'ACC-flower': 67.12198771361219, 'ACC-fruit': 63.88579740677809, 'ACC-gravel': 33.02143363010601, 'ACC-house': 28.966893398576204, 'ACC-light': 63.82833135551047, 'ACC-mirror-stuff': 77.96856167536258, 'ACC-net': 65.375133299191, 'ACC-pillow': 37.75360157130675, 'ACC-platform': 42.95719455956404, 'ACC-playingfield': 82.02193966572025, 'ACC-railroad': 83.83428054141562, 'ACC-river': 81.9827858008615, 'ACC-road': 87.50933159584623, 'ACC-roof': 28.46414616158776, 'ACC-sand': 66.00713273096088, 'ACC-sea': 91.26672890284807, 'ACC-shelf': 58.088143162467055, 'ACC-snow': 95.61155592494086, 'ACC-stairs': 62.91133333276869, 'ACC-tent': 13.669906445123814, 'ACC-towel': 82.43330823986467, 'ACC-wall-brick': 70.86359106407954, 'ACC-wall-stone': 32.40021005890262, 'ACC-wall-tile': 84.68491457280733, 'ACC-wall-wood': 64.5923709355155, 'ACC-water-other': 33.49998947952352, 'ACC-window-blind': 62.59748732974415, 'ACC-window-other': 72.09802012862376, 'ACC-tree-merged': 90.12227051919956, 'ACC-fence-merged': 71.66508131109957, 'ACC-ceiling-merged': 83.28526733634557, 'ACC-sky-other-merged': 97.2635151644758, 'ACC-cabinet-merged': 76.10484095502882, 'ACC-table-merged': 58.947729500965366, 'ACC-floor-other-merged': 62.313872574139104, 'ACC-pavement-merged': 69.0306426225744, 'ACC-mountain-merged': 66.90381566128187, 'ACC-grass-merged': 83.80311212194421, 'ACC-dirt-merged': 73.2447110005174, 'ACC-paper-merged': 43.75911059457689, 'ACC-food-other-merged': 48.7961084547681, 'ACC-building-other-merged': 71.46159486087818, 'ACC-rock-merged': 84.10387007271625, 'ACC-wall-other-merged': 84.09696308784426, 'ACC-rug-merged': 82.17946438805083})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3021 s/iter. Inference: 0.2004 s/iter. Eval: 0.0000 s/iter. Total: 0.5025 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3216 s/iter. Inference: 0.3545 s/iter. Eval: 0.0000 s/iter. Total: 0.6762 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3380 s/iter. Inference: 0.5360 s/iter. Eval: 0.0000 s/iter. Total: 0.8741 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3693298214808312, 'noc@0.8': 2.3678665496049165, 'noc@0.85': 2.790166812993854, 'noc@0.9': 3.6087211003804507, 'miou@iter1': 0.8684041098889671} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1544 s/iter. Eval: 0.0010 s/iter. Total: 0.1571 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.51496124267578, 'precision@0.6': 72.83326721191406, 'precision@0.7': 68.59696960449219, 'precision@0.8': 59.26933670043945, 'precision@0.9': 32.60784912109375, 'cIoU': 62.40196990966797, 'mIoU': 66.96471405029297} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.56626340294638, 'SQ': 83.10769737611332, 'RQ': 66.06684934989944, 'PQ_th': 61.829472692886675, 'SQ_th': 83.99802182709377, 'RQ_th': 73.10710859867443, 'PQ_st': 46.112362587942144, 'SQ_st': 81.76381141236926, 'RQ_st': 55.44004293665419}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.54277718080637, 'AP50': 69.37319867239087, 'AP75': 49.06143611122416, 'APs': 25.79700960054913, 'APm': 49.62336372613743, 'APl': 67.40857266435872, 'AP-person': 47.7028895999268, 'AP-bicycle': 22.961930401694175, 'AP-car': 43.42087790271309, 'AP-motorcycle': 41.65174051988183, 'AP-airplane': 61.77310693513846, 'AP-bus': 71.41766020376336, 'AP-train': 74.28058882716101, 'AP-truck': 41.3680609407344, 'AP-boat': 30.18541406053587, 'AP-traffic light': 26.446418934359052, 'AP-fire hydrant': 69.94830805968724, 'AP-stop sign': 69.52429654099063, 'AP-parking meter': 52.4126799917487, 'AP-bench': 26.16001255662877, 'AP-bird': 33.92062428349857, 'AP-cat': 77.0949976134129, 'AP-dog': 70.84527920976292, 'AP-horse': 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'IoU-umbrella': 88.789585694814, 'IoU-handbag': 49.63330881720841, 'IoU-tie': 76.08497092465029, 'IoU-suitcase': 85.70690015229539, 'IoU-frisbee': 84.5022122450045, 'IoU-skis': 58.59197186012829, 'IoU-snowboard': 72.14706296181019, 'IoU-sports ball': 79.0249878643582, 'IoU-kite': 79.01340286868418, 'IoU-baseball bat': 68.84685981116283, 'IoU-baseball glove': 81.00974561957027, 'IoU-skateboard': 86.17228161765878, 'IoU-surfboard': 86.75935928996658, 'IoU-tennis racket': 90.84482271466547, 'IoU-bottle': 70.99770549496145, 'IoU-wine glass': 81.77901014068014, 'IoU-cup': 72.109684956014, 'IoU-fork': 71.06470897273473, 'IoU-knife': 65.67390183662006, 'IoU-spoon': 61.282525146204506, 'IoU-bowl': 59.539384461492325, 'IoU-banana': 82.74451935102778, 'IoU-apple': 58.389957516140676, 'IoU-sandwich': 69.62992037110664, 'IoU-orange': 76.0559473865333, 'IoU-broccoli': 70.59763874215751, 'IoU-carrot': 65.46753196929363, 'IoU-hot dog': 71.4764772656384, 'IoU-pizza': 80.96520975275546, 'IoU-donut': 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'ACC-laptop': 88.27677479557418, 'ACC-mouse': 83.9088918469458, 'ACC-remote': 72.17128971540103, 'ACC-keyboard': 68.60259152520089, 'ACC-cell phone': 89.42881543971771, 'ACC-microwave': 84.57898972570555, 'ACC-oven': 91.27763139920202, 'ACC-toaster': 91.23431277285853, 'ACC-sink': 83.66867957392262, 'ACC-refrigerator': 91.2940397437228, 'ACC-book': 73.82988708638032, 'ACC-clock': 83.1885644941628, 'ACC-vase': 75.27583785763403, 'ACC-scissors': 86.78357619664123, 'ACC-teddy bear': 87.50704168503734, 'ACC-hair drier': 60.880084251273736, 'ACC-toothbrush': 84.57088255733149, 'ACC-banner': 70.92401513192686, 'ACC-blanket': 19.497182629058354, 'ACC-bridge': 56.264856312790144, 'ACC-cardboard': 59.88724734622656, 'ACC-counter': 51.38894534965824, 'ACC-curtain': 81.51031241291206, 'ACC-door-stuff': 70.21572184212117, 'ACC-floor-wood': 82.71739208502072, 'ACC-flower': 67.12198771361219, 'ACC-fruit': 63.88579740677809, 'ACC-gravel': 33.02143363010601, 'ACC-house': 28.966893398576204, 'ACC-light': 63.82833135551047, 'ACC-mirror-stuff': 77.96856167536258, 'ACC-net': 65.375133299191, 'ACC-pillow': 37.75360157130675, 'ACC-platform': 42.95719455956404, 'ACC-playingfield': 82.02193966572025, 'ACC-railroad': 83.83428054141562, 'ACC-river': 81.9827858008615, 'ACC-road': 87.50933159584623, 'ACC-roof': 28.46414616158776, 'ACC-sand': 66.00713273096088, 'ACC-sea': 91.26672890284807, 'ACC-shelf': 58.088143162467055, 'ACC-snow': 95.61155592494086, 'ACC-stairs': 62.91133333276869, 'ACC-tent': 13.669906445123814, 'ACC-towel': 82.43330823986467, 'ACC-wall-brick': 70.86359106407954, 'ACC-wall-stone': 32.40021005890262, 'ACC-wall-tile': 84.68491457280733, 'ACC-wall-wood': 64.5923709355155, 'ACC-water-other': 33.49998947952352, 'ACC-window-blind': 62.59748732974415, 'ACC-window-other': 72.09802012862376, 'ACC-tree-merged': 90.12227051919956, 'ACC-fence-merged': 71.66508131109957, 'ACC-ceiling-merged': 83.28526733634557, 'ACC-sky-other-merged': 97.2635151644758, 'ACC-cabinet-merged': 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66.96471405029297}}} INFO:trainer.default_trainer:This epoch takes 0:56:47.160348 INFO:trainer.default_trainer:PROGRESS: 78.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 39 training. INFO:trainer.default_trainer:epochs[ 39] optim steps[71300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.27685/0.75491, loss_mask_bce_0: 0.05279/0.30098, loss_mask_dice_0: 2.36606/1.02069, loss_spatial_bce_0: 0.01075/0.08477, loss_spatial_dice_0: 0.29697/0.17918, loss_spatial_ce_0: 0.01144/0.05628, loss_grounding_bce_0: 0.00605/0.08067, loss_grounding_dice_0: 0.25358/0.15047, loss_grounding_ce_0: 0.18808/0.24852, loss_mask_ce_1: 1.51291/0.75568, loss_mask_bce_1: 0.06128/0.30176, loss_mask_dice_1: 2.83438/1.02495, loss_spatial_bce_1: 0.01045/0.08519, loss_spatial_dice_1: 0.32960/0.18204, loss_spatial_ce_1: 0.00267/0.06005, loss_grounding_bce_1: 0.00573/0.08085, loss_grounding_dice_1: 0.12768/0.15122, loss_grounding_ce_1: 0.18562/0.24988, loss_mask_ce_2: 1.15842/0.76333, loss_mask_bce_2: 0.04966/0.30211, loss_mask_dice_2: 1.86567/1.02581, loss_spatial_bce_2: 0.01232/0.08528, loss_spatial_dice_2: 0.29118/0.18264, loss_spatial_ce_2: 0.00361/0.06215, loss_grounding_bce_2: 0.00630/0.08084, loss_grounding_dice_2: 0.10441/0.15111, loss_grounding_ce_2: 0.19322/0.25274, loss_mask_ce_3: 1.36213/0.76762, loss_mask_bce_3: 0.05562/0.30348, loss_mask_dice_3: 2.01278/1.02376, loss_spatial_bce_3: 0.00953/0.08744, loss_spatial_dice_3: 0.31244/0.18400, loss_spatial_ce_3: 0.00933/0.06704, loss_grounding_bce_3: 0.00592/0.08123, loss_grounding_dice_3: 0.13579/0.15078, loss_grounding_ce_3: 0.18985/0.25397, loss_mask_ce_4: 1.20308/0.77359, loss_mask_bce_4: 0.05955/0.30613, loss_mask_dice_4: 2.73095/1.04321, loss_spatial_bce_4: 0.01040/0.08974, loss_spatial_dice_4: 0.28046/0.19245, loss_spatial_ce_4: 0.00596/0.08077, loss_grounding_bce_4: 0.00778/0.08190, loss_grounding_dice_4: 0.25324/0.15348, loss_grounding_ce_4: 0.37383/0.25823, loss_mask_ce_5: 1.86180/0.79844, loss_mask_bce_5: 0.07118/0.30801, loss_mask_dice_5: 1.99968/1.05109, loss_spatial_bce_5: 0.01127/0.09213, loss_spatial_dice_5: 0.30268/0.19567, loss_spatial_ce_5: 0.02784/0.09429, loss_grounding_bce_5: 0.00789/0.08218, loss_grounding_dice_5: 0.22036/0.15420, loss_grounding_ce_5: 0.24485/0.27627, loss_mask_ce_6: 1.29447/0.82534, loss_mask_bce_6: 0.05803/0.31011, loss_mask_dice_6: 1.83591/1.05477, loss_spatial_bce_6: 0.01290/0.09748, loss_spatial_dice_6: 0.28991/0.19797, loss_spatial_ce_6: 0.02645/0.11892, loss_grounding_bce_6: 0.01079/0.08302, loss_grounding_dice_6: 0.18595/0.15473, loss_grounding_ce_6: 0.24313/0.28496, loss_mask_ce_7: 1.52174/0.88144, loss_mask_bce_7: 0.05597/0.31729, loss_mask_dice_7: 2.35307/1.10062, loss_spatial_bce_7: 0.01428/0.10681, loss_spatial_dice_7: 0.36324/0.22311, loss_spatial_ce_7: 0.09262/0.15477, loss_grounding_bce_7: 0.00693/0.08474, loss_grounding_dice_7: 0.17890/0.16034, loss_grounding_ce_7: 0.19993/0.31840, loss_mask_ce_8: 1.46668/1.01571, loss_mask_bce_8: 0.08079/0.33329, loss_mask_dice_8: 2.88794/1.17700, loss_spatial_bce_8: 0.01743/0.12367, loss_spatial_dice_8: 0.42002/0.25807, loss_spatial_ce_8: 0.32801/0.20060, loss_grounding_bce_8: 0.00858/0.08888, loss_grounding_dice_8: 0.17106/0.17009, loss_grounding_ce_8: 0.45127/0.41714, loss_mask_ce_9: 3.27516/3.47624, loss_mask_bce_9: 0.06226/0.36024, loss_mask_dice_9: 3.51517/1.76003, loss_spatial_bce_9: 0.17564/0.35447, loss_spatial_dice_9: 0.96316/0.79308, loss_spatial_ce_9: 1.74106/1.38795, loss_grounding_bce_9: 0.01237/0.10095, loss_grounding_dice_9: 0.33030/0.24226, loss_grounding_ce_9: 0.49963/0.67123] items per batch[64] items per second[0.16] total items[4563200] mini batches[ 71300] memory[4999] epoch remaining[0:58:03] INFO:trainer.default_trainer:epochs[ 39] optim steps[71400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.83630/0.75477, loss_mask_bce_0: 0.34723/0.30098, loss_mask_dice_0: 0.21764/1.02048, loss_spatial_bce_0: 0.39791/0.08477, loss_spatial_dice_0: 0.29487/0.17916, loss_spatial_ce_0: 0.00025/0.05625, loss_grounding_bce_0: 0.21227/0.08067, loss_grounding_dice_0: 0.13463/0.15044, loss_grounding_ce_0: 0.06586/0.24845, loss_mask_ce_1: 0.83259/0.75554, loss_mask_bce_1: 0.35452/0.30176, loss_mask_dice_1: 0.22450/1.02472, loss_spatial_bce_1: 0.38457/0.08519, loss_spatial_dice_1: 0.29843/0.18202, loss_spatial_ce_1: 0.00034/0.06002, loss_grounding_bce_1: 0.21701/0.08087, loss_grounding_dice_1: 0.13763/0.15119, loss_grounding_ce_1: 0.06547/0.24982, loss_mask_ce_2: 0.80529/0.76317, loss_mask_bce_2: 0.35350/0.30211, loss_mask_dice_2: 0.22665/1.02560, loss_spatial_bce_2: 0.36961/0.08528, loss_spatial_dice_2: 0.28873/0.18261, loss_spatial_ce_2: 0.00107/0.06214, loss_grounding_bce_2: 0.21419/0.08085, loss_grounding_dice_2: 0.13545/0.15108, loss_grounding_ce_2: 0.06943/0.25267, loss_mask_ce_3: 0.87691/0.76746, loss_mask_bce_3: 0.35043/0.30349, loss_mask_dice_3: 0.23362/1.02354, loss_spatial_bce_3: 0.39822/0.08745, loss_spatial_dice_3: 0.27368/0.18398, loss_spatial_ce_3: 0.00249/0.06703, loss_grounding_bce_3: 0.21130/0.08124, loss_grounding_dice_3: 0.13618/0.15075, loss_grounding_ce_3: 0.06070/0.25395, loss_mask_ce_4: 0.94583/0.77344, loss_mask_bce_4: 0.35399/0.30613, loss_mask_dice_4: 0.24051/1.04299, loss_spatial_bce_4: 0.47975/0.08974, loss_spatial_dice_4: 0.32935/0.19242, loss_spatial_ce_4: 0.06288/0.08075, loss_grounding_bce_4: 0.21198/0.08191, loss_grounding_dice_4: 0.14086/0.15345, loss_grounding_ce_4: 0.06518/0.25816, loss_mask_ce_5: 1.14893/0.79829, loss_mask_bce_5: 0.36349/0.30801, loss_mask_dice_5: 0.23537/1.05088, loss_spatial_bce_5: 0.41208/0.09213, loss_spatial_dice_5: 0.26075/0.19565, loss_spatial_ce_5: 0.07439/0.09427, loss_grounding_bce_5: 0.21588/0.08220, loss_grounding_dice_5: 0.13909/0.15418, loss_grounding_ce_5: 0.06980/0.27618, loss_mask_ce_6: 1.21871/0.82520, loss_mask_bce_6: 0.35233/0.31011, loss_mask_dice_6: 0.23350/1.05455, loss_spatial_bce_6: 0.42289/0.09749, loss_spatial_dice_6: 0.24937/0.19796, loss_spatial_ce_6: 0.34823/0.11890, loss_grounding_bce_6: 0.20710/0.08304, loss_grounding_dice_6: 0.14061/0.15470, loss_grounding_ce_6: 0.08711/0.28489, loss_mask_ce_7: 1.38943/0.88129, loss_mask_bce_7: 0.37592/0.31728, loss_mask_dice_7: 0.23865/1.10043, loss_spatial_bce_7: 0.44467/0.10681, loss_spatial_dice_7: 0.33137/0.22309, loss_spatial_ce_7: 0.25062/0.15475, loss_grounding_bce_7: 0.21596/0.08475, loss_grounding_dice_7: 0.14050/0.16030, loss_grounding_ce_7: 0.07763/0.31828, loss_mask_ce_8: 1.67727/1.01557, loss_mask_bce_8: 0.36018/0.33329, loss_mask_dice_8: 0.24474/1.17676, loss_spatial_bce_8: 0.47810/0.12367, loss_spatial_dice_8: 0.30140/0.25804, loss_spatial_ce_8: 0.26972/0.20056, loss_grounding_bce_8: 0.22267/0.08889, loss_grounding_dice_8: 0.15074/0.17004, loss_grounding_ce_8: 0.11641/0.41695, loss_mask_ce_9: 2.53240/3.47599, loss_mask_bce_9: 0.38315/0.36022, loss_mask_dice_9: 0.29444/1.75971, loss_spatial_bce_9: 0.55002/0.35451, loss_spatial_dice_9: 0.72204/0.79308, loss_spatial_ce_9: 0.96084/1.38783, loss_grounding_bce_9: 0.22740/0.10095, loss_grounding_dice_9: 0.17254/0.24220, loss_grounding_ce_9: 0.23084/0.67104] items per batch[64] items per second[0.36] total items[4569600] mini batches[ 71400] memory[4999] epoch remaining[0:51:08] INFO:trainer.default_trainer:epochs[ 39] optim steps[71500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.39438/0.75468, loss_mask_bce_0: 0.04563/0.30098, loss_mask_dice_0: 0.03207/1.02044, loss_spatial_bce_0: 0.04514/0.08476, loss_spatial_dice_0: 0.03337/0.17915, loss_spatial_ce_0: 0.00065/0.05626, loss_grounding_bce_0: 0.05659/0.08069, loss_grounding_dice_0: 0.03972/0.15047, loss_grounding_ce_0: 0.16879/0.24851, loss_mask_ce_1: 0.41092/0.75545, loss_mask_bce_1: 0.04361/0.30177, loss_mask_dice_1: 0.03118/1.02471, loss_spatial_bce_1: 0.04750/0.08518, loss_spatial_dice_1: 0.03490/0.18200, loss_spatial_ce_1: 0.00026/0.06001, loss_grounding_bce_1: 0.05510/0.08088, loss_grounding_dice_1: 0.04012/0.15122, loss_grounding_ce_1: 0.17840/0.24987, loss_mask_ce_2: 0.39891/0.76306, loss_mask_bce_2: 0.04668/0.30212, loss_mask_dice_2: 0.03434/1.02560, loss_spatial_bce_2: 0.04841/0.08527, loss_spatial_dice_2: 0.03437/0.18261, loss_spatial_ce_2: 0.00057/0.06215, loss_grounding_bce_2: 0.05133/0.08086, loss_grounding_dice_2: 0.03991/0.15112, loss_grounding_ce_2: 0.19171/0.25271, loss_mask_ce_3: 0.35650/0.76734, loss_mask_bce_3: 0.04766/0.30349, loss_mask_dice_3: 0.03574/1.02352, loss_spatial_bce_3: 0.05299/0.08744, loss_spatial_dice_3: 0.03757/0.18397, loss_spatial_ce_3: 0.00183/0.06702, loss_grounding_bce_3: 0.05576/0.08125, loss_grounding_dice_3: 0.04272/0.15077, loss_grounding_ce_3: 0.14635/0.25399, loss_mask_ce_4: 0.46768/0.77334, loss_mask_bce_4: 0.04648/0.30615, loss_mask_dice_4: 0.03294/1.04297, loss_spatial_bce_4: 0.05151/0.08973, loss_spatial_dice_4: 0.03748/0.19241, loss_spatial_ce_4: 0.05997/0.08077, loss_grounding_bce_4: 0.05593/0.08192, loss_grounding_dice_4: 0.04036/0.15348, loss_grounding_ce_4: 0.16498/0.25821, loss_mask_ce_5: 0.38251/0.79819, loss_mask_bce_5: 0.04296/0.30802, loss_mask_dice_5: 0.03154/1.05087, loss_spatial_bce_5: 0.05363/0.09212, loss_spatial_dice_5: 0.04295/0.19564, loss_spatial_ce_5: 0.03138/0.09427, loss_grounding_bce_5: 0.05261/0.08221, loss_grounding_dice_5: 0.03714/0.15421, loss_grounding_ce_5: 0.14478/0.27619, loss_mask_ce_6: 0.45046/0.82513, loss_mask_bce_6: 0.04571/0.31011, loss_mask_dice_6: 0.03453/1.05453, loss_spatial_bce_6: 0.05272/0.09748, loss_spatial_dice_6: 0.04066/0.19795, loss_spatial_ce_6: 0.00766/0.11889, loss_grounding_bce_6: 0.05098/0.08304, loss_grounding_dice_6: 0.03883/0.15473, loss_grounding_ce_6: 0.18882/0.28492, loss_mask_ce_7: 0.40402/0.88121, loss_mask_bce_7: 0.03873/0.31728, loss_mask_dice_7: 0.03230/1.10042, loss_spatial_bce_7: 0.05910/0.10680, loss_spatial_dice_7: 0.05182/0.22308, loss_spatial_ce_7: 0.12411/0.15472, loss_grounding_bce_7: 0.05111/0.08475, loss_grounding_dice_7: 0.03945/0.16033, loss_grounding_ce_7: 0.23914/0.31829, loss_mask_ce_8: 0.38374/1.01538, loss_mask_bce_8: 0.04017/0.33331, loss_mask_dice_8: 0.03283/1.17676, loss_spatial_bce_8: 0.07324/0.12365, loss_spatial_dice_8: 0.07083/0.25802, loss_spatial_ce_8: 0.29246/0.20053, loss_grounding_bce_8: 0.04950/0.08889, loss_grounding_dice_8: 0.04032/0.17006, loss_grounding_ce_8: 0.16760/0.41696, loss_mask_ce_9: 1.62306/3.47600, loss_mask_bce_9: 0.04348/0.36021, loss_mask_dice_9: 0.04041/1.75970, loss_spatial_bce_9: 0.52152/0.35450, loss_spatial_dice_9: 0.45602/0.79308, loss_spatial_ce_9: 0.29087/1.38777, loss_grounding_bce_9: 0.05242/0.10095, loss_grounding_dice_9: 0.04861/0.24221, loss_grounding_ce_9: 0.10403/0.67106] items per batch[64] items per second[0.36] total items[4576000] mini batches[ 71500] memory[4999] epoch remaining[0:47:25] INFO:trainer.default_trainer:epochs[ 39] optim steps[71600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.79877/0.75460, loss_mask_bce_0: 0.02336/0.30096, loss_mask_dice_0: 0.37530/1.02025, loss_spatial_bce_0: 0.01422/0.08475, loss_spatial_dice_0: 0.17814/0.17913, loss_spatial_ce_0: 0.13461/0.05623, loss_grounding_bce_0: 0.00388/0.08069, loss_grounding_dice_0: 0.04224/0.15046, loss_grounding_ce_0: 0.02028/0.24846, loss_mask_ce_1: 0.42405/0.75534, loss_mask_bce_1: 0.05131/0.30175, loss_mask_dice_1: 0.48962/1.02454, loss_spatial_bce_1: 0.01537/0.08517, loss_spatial_dice_1: 0.18610/0.18199, loss_spatial_ce_1: 0.23571/0.05998, loss_grounding_bce_1: 0.00369/0.08088, loss_grounding_dice_1: 0.04370/0.15120, loss_grounding_ce_1: 0.02081/0.24981, loss_mask_ce_2: 0.41003/0.76292, loss_mask_bce_2: 0.04820/0.30210, loss_mask_dice_2: 0.44907/1.02544, loss_spatial_bce_2: 0.01445/0.08526, loss_spatial_dice_2: 0.20085/0.18258, loss_spatial_ce_2: 0.21717/0.06212, loss_grounding_bce_2: 0.00514/0.08087, loss_grounding_dice_2: 0.06546/0.15111, loss_grounding_ce_2: 0.01909/0.25265, loss_mask_ce_3: 0.40556/0.76723, loss_mask_bce_3: 0.04898/0.30347, loss_mask_dice_3: 0.50461/1.02336, loss_spatial_bce_3: 0.01305/0.08743, loss_spatial_dice_3: 0.21298/0.18395, loss_spatial_ce_3: 0.22500/0.06699, loss_grounding_bce_3: 0.00453/0.08125, loss_grounding_dice_3: 0.05121/0.15076, loss_grounding_ce_3: 0.01165/0.25391, loss_mask_ce_4: 0.40928/0.77323, loss_mask_bce_4: 0.05177/0.30614, loss_mask_dice_4: 0.45088/1.04280, loss_spatial_bce_4: 0.05152/0.08973, loss_spatial_dice_4: 0.24791/0.19240, loss_spatial_ce_4: 0.01993/0.08075, loss_grounding_bce_4: 0.00412/0.08193, loss_grounding_dice_4: 0.04954/0.15346, loss_grounding_ce_4: 0.02052/0.25817, loss_mask_ce_5: 0.98777/0.79808, loss_mask_bce_5: 0.01993/0.30801, loss_mask_dice_5: 0.33151/1.05072, loss_spatial_bce_5: 0.04488/0.09211, loss_spatial_dice_5: 0.26543/0.19562, loss_spatial_ce_5: 0.06184/0.09424, loss_grounding_bce_5: 0.00527/0.08221, loss_grounding_dice_5: 0.06947/0.15419, loss_grounding_ce_5: 0.04278/0.27616, loss_mask_ce_6: 0.55407/0.82499, loss_mask_bce_6: 0.04488/0.31010, loss_mask_dice_6: 0.44260/1.05435, loss_spatial_bce_6: 0.05433/0.09749, loss_spatial_dice_6: 0.22137/0.19794, loss_spatial_ce_6: 0.01256/0.11886, loss_grounding_bce_6: 0.00449/0.08305, loss_grounding_dice_6: 0.06485/0.15472, loss_grounding_ce_6: 0.07391/0.28485, loss_mask_ce_7: 0.58539/0.88104, loss_mask_bce_7: 0.04214/0.31727, loss_mask_dice_7: 0.49593/1.10023, loss_spatial_bce_7: 0.05125/0.10681, loss_spatial_dice_7: 0.24370/0.22306, loss_spatial_ce_7: 0.00624/0.15471, loss_grounding_bce_7: 0.00409/0.08475, loss_grounding_dice_7: 0.06547/0.16031, loss_grounding_ce_7: 0.10564/0.31835, loss_mask_ce_8: 0.46350/1.01518, loss_mask_bce_8: 0.04951/0.33330, loss_mask_dice_8: 0.46886/1.17658, loss_spatial_bce_8: 0.02562/0.12364, loss_spatial_dice_8: 0.23235/0.25799, loss_spatial_ce_8: 0.22042/0.20050, loss_grounding_bce_8: 0.00359/0.08890, loss_grounding_dice_8: 0.05616/0.17005, loss_grounding_ce_8: 0.39642/0.41690, loss_mask_ce_9: 2.72715/3.47574, loss_mask_bce_9: 0.03508/0.36019, loss_mask_dice_9: 0.53804/1.75946, loss_spatial_bce_9: 0.06156/0.35453, loss_spatial_dice_9: 0.78819/0.79307, loss_spatial_ce_9: 1.47759/1.38772, loss_grounding_bce_9: 0.00307/0.10095, loss_grounding_dice_9: 0.07111/0.24218, loss_grounding_ce_9: 1.36158/0.67093] items per batch[64] items per second[0.37] total items[4582400] mini batches[ 71600] memory[4999] epoch remaining[0:43:56] INFO:trainer.default_trainer:epochs[ 39] optim steps[71700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.77440/0.75470, loss_mask_bce_0: 0.16228/0.30098, loss_mask_dice_0: 4.87470/1.02042, loss_spatial_bce_0: 0.01430/0.08474, loss_spatial_dice_0: 0.24681/0.17913, loss_spatial_ce_0: 0.01035/0.05622, loss_grounding_bce_0: 0.02661/0.08069, loss_grounding_dice_0: 0.38906/0.15046, loss_grounding_ce_0: 0.50937/0.24853, loss_mask_ce_1: 2.14603/0.75544, loss_mask_bce_1: 0.15030/0.30178, loss_mask_dice_1: 4.79246/1.02467, loss_spatial_bce_1: 0.01348/0.08516, loss_spatial_dice_1: 0.23822/0.18199, loss_spatial_ce_1: 0.03090/0.05999, loss_grounding_bce_1: 0.02728/0.08088, loss_grounding_dice_1: 0.34948/0.15121, loss_grounding_ce_1: 0.58435/0.24990, loss_mask_ce_2: 1.94175/0.76300, loss_mask_bce_2: 0.16861/0.30213, loss_mask_dice_2: 4.80126/1.02558, loss_spatial_bce_2: 0.01420/0.08526, loss_spatial_dice_2: 0.19364/0.18259, loss_spatial_ce_2: 0.04750/0.06211, loss_grounding_bce_2: 0.02335/0.08087, loss_grounding_dice_2: 0.36614/0.15112, loss_grounding_ce_2: 0.64164/0.25275, loss_mask_ce_3: 2.20210/0.76730, loss_mask_bce_3: 0.17166/0.30350, loss_mask_dice_3: 4.33267/1.02349, loss_spatial_bce_3: 0.01648/0.08742, loss_spatial_dice_3: 0.21200/0.18395, loss_spatial_ce_3: 0.10637/0.06698, loss_grounding_bce_3: 0.02736/0.08125, loss_grounding_dice_3: 0.34393/0.15077, loss_grounding_ce_3: 0.75227/0.25397, loss_mask_ce_4: 1.69213/0.77329, loss_mask_bce_4: 0.14831/0.30617, loss_mask_dice_4: 5.07065/1.04294, loss_spatial_bce_4: 0.01268/0.08972, loss_spatial_dice_4: 0.28456/0.19240, loss_spatial_ce_4: 0.03257/0.08073, loss_grounding_bce_4: 0.02733/0.08193, loss_grounding_dice_4: 0.33008/0.15347, loss_grounding_ce_4: 1.24879/0.25823, loss_mask_ce_5: 1.50636/0.79816, loss_mask_bce_5: 0.16898/0.30803, loss_mask_dice_5: 4.76046/1.05088, loss_spatial_bce_5: 0.02306/0.09211, loss_spatial_dice_5: 0.33148/0.19562, loss_spatial_ce_5: 0.13648/0.09425, loss_grounding_bce_5: 0.03176/0.08222, loss_grounding_dice_5: 0.36568/0.15419, loss_grounding_ce_5: 1.20013/0.27629, loss_mask_ce_6: 1.70469/0.82509, loss_mask_bce_6: 0.19091/0.31013, loss_mask_dice_6: 5.04070/1.05453, loss_spatial_bce_6: 0.02539/0.09749, loss_spatial_dice_6: 0.31589/0.19794, loss_spatial_ce_6: 0.05026/0.11887, loss_grounding_bce_6: 0.02753/0.08306, loss_grounding_dice_6: 0.33542/0.15471, loss_grounding_ce_6: 0.96519/0.28495, loss_mask_ce_7: 1.76966/0.88111, loss_mask_bce_7: 0.17114/0.31732, loss_mask_dice_7: 4.74801/1.10035, loss_spatial_bce_7: 0.04091/0.10682, loss_spatial_dice_7: 0.37305/0.22307, loss_spatial_ce_7: 0.20879/0.15470, loss_grounding_bce_7: 0.02549/0.08476, loss_grounding_dice_7: 0.32058/0.16031, loss_grounding_ce_7: 1.44876/0.31847, loss_mask_ce_8: 2.55267/1.01527, loss_mask_bce_8: 0.17224/0.33335, loss_mask_dice_8: 5.50574/1.17672, loss_spatial_bce_8: 0.03692/0.12365, loss_spatial_dice_8: 0.47329/0.25800, loss_spatial_ce_8: 0.13730/0.20050, loss_grounding_bce_8: 0.02467/0.08891, loss_grounding_dice_8: 0.37802/0.17005, loss_grounding_ce_8: 1.23033/0.41697, loss_mask_ce_9: 5.02821/3.47595, loss_mask_bce_9: 0.10133/0.36025, loss_mask_dice_9: 6.33002/1.75982, loss_spatial_bce_9: 0.02410/0.35451, loss_spatial_dice_9: 0.94825/0.79308, loss_spatial_ce_9: 1.86600/1.38772, loss_grounding_bce_9: 0.03618/0.10096, loss_grounding_dice_9: 0.48626/0.24221, loss_grounding_ce_9: 1.58235/0.67097] items per batch[64] items per second[0.37] total items[4588800] mini batches[ 71700] memory[4999] epoch remaining[0:40:44] INFO:trainer.default_trainer:epochs[ 39] optim steps[71800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.56357/0.75468, loss_mask_bce_0: 0.57145/0.30094, loss_mask_dice_0: 1.99841/1.02061, loss_spatial_bce_0: 0.07355/0.08471, loss_spatial_dice_0: 0.22679/0.17914, loss_spatial_ce_0: 0.01147/0.05620, loss_grounding_bce_0: 0.08280/0.08068, loss_grounding_dice_0: 0.41770/0.15048, loss_grounding_ce_0: 1.22042/0.24856, loss_mask_ce_1: 1.94076/0.75541, loss_mask_bce_1: 0.52869/0.30174, loss_mask_dice_1: 1.92646/1.02485, loss_spatial_bce_1: 0.07692/0.08513, loss_spatial_dice_1: 0.21692/0.18200, loss_spatial_ce_1: 0.02566/0.05996, loss_grounding_bce_1: 0.08405/0.08087, loss_grounding_dice_1: 0.39271/0.15124, loss_grounding_ce_1: 1.17455/0.24993, loss_mask_ce_2: 1.76028/0.76302, loss_mask_bce_2: 0.55500/0.30209, loss_mask_dice_2: 1.95646/1.02574, loss_spatial_bce_2: 0.09304/0.08523, loss_spatial_dice_2: 0.22823/0.18260, loss_spatial_ce_2: 0.03557/0.06209, loss_grounding_bce_2: 0.08714/0.08086, loss_grounding_dice_2: 0.44097/0.15115, loss_grounding_ce_2: 1.14144/0.25276, loss_mask_ce_3: 2.01767/0.76735, loss_mask_bce_3: 0.55418/0.30345, loss_mask_dice_3: 2.00978/1.02367, loss_spatial_bce_3: 0.16464/0.08739, loss_spatial_dice_3: 0.25062/0.18396, loss_spatial_ce_3: 0.06690/0.06697, loss_grounding_bce_3: 0.07305/0.08124, loss_grounding_dice_3: 0.41768/0.15079, loss_grounding_ce_3: 1.14968/0.25400, loss_mask_ce_4: 2.04622/0.77331, loss_mask_bce_4: 0.56255/0.30612, loss_mask_dice_4: 2.22117/1.04312, loss_spatial_bce_4: 0.28614/0.08969, loss_spatial_dice_4: 0.28995/0.19242, loss_spatial_ce_4: 0.16223/0.08073, loss_grounding_bce_4: 0.06777/0.08192, loss_grounding_dice_4: 0.43522/0.15349, loss_grounding_ce_4: 1.13424/0.25825, loss_mask_ce_5: 2.10331/0.79817, loss_mask_bce_5: 0.57594/0.30799, loss_mask_dice_5: 2.13489/1.05103, loss_spatial_bce_5: 0.28019/0.09208, loss_spatial_dice_5: 0.27480/0.19564, loss_spatial_ce_5: 0.07606/0.09425, loss_grounding_bce_5: 0.08568/0.08220, loss_grounding_dice_5: 0.44220/0.15421, loss_grounding_ce_5: 1.18819/0.27632, loss_mask_ce_6: 1.93063/0.82512, loss_mask_bce_6: 0.59925/0.31008, loss_mask_dice_6: 2.16267/1.05473, loss_spatial_bce_6: 0.41527/0.09746, loss_spatial_dice_6: 0.31821/0.19796, loss_spatial_ce_6: 0.06454/0.11887, loss_grounding_bce_6: 0.11504/0.08304, loss_grounding_dice_6: 0.61059/0.15474, loss_grounding_ce_6: 0.27819/0.28494, loss_mask_ce_7: 1.56836/0.88113, loss_mask_bce_7: 0.61848/0.31728, loss_mask_dice_7: 2.47297/1.10055, loss_spatial_bce_7: 0.51095/0.10678, loss_spatial_dice_7: 0.36208/0.22307, loss_spatial_ce_7: 0.12566/0.15468, loss_grounding_bce_7: 0.13683/0.08475, loss_grounding_dice_7: 0.62897/0.16034, loss_grounding_ce_7: 0.45265/0.31847, loss_mask_ce_8: 2.10779/1.01533, loss_mask_bce_8: 0.88165/0.33332, loss_mask_dice_8: 2.92384/1.17692, loss_spatial_bce_8: 0.31528/0.12360, loss_spatial_dice_8: 0.44277/0.25801, loss_spatial_ce_8: 0.19398/0.20047, loss_grounding_bce_8: 0.21290/0.08890, loss_grounding_dice_8: 0.71644/0.17009, loss_grounding_ce_8: 0.62280/0.41702, loss_mask_ce_9: 5.67245/3.47611, loss_mask_bce_9: 0.81072/0.36020, loss_mask_dice_9: 4.40759/1.76002, loss_spatial_bce_9: 0.26551/0.35448, loss_spatial_dice_9: 0.85543/0.79310, loss_spatial_ce_9: 1.30198/1.38788, loss_grounding_bce_9: 0.37505/0.10094, loss_grounding_dice_9: 0.81451/0.24225, loss_grounding_ce_9: 0.04937/0.67085] items per batch[64] items per second[0.36] total items[4595200] mini batches[ 71800] memory[4999] epoch remaining[0:37:43] INFO:trainer.default_trainer:epochs[ 39] optim steps[71900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20899/0.75464, loss_mask_bce_0: 0.14070/0.30090, loss_mask_dice_0: 0.24493/1.02043, loss_spatial_bce_0: 0.05733/0.08471, loss_spatial_dice_0: 0.11079/0.17912, loss_spatial_ce_0: 0.00034/0.05622, loss_grounding_bce_0: 0.10638/0.08067, loss_grounding_dice_0: 0.11749/0.15045, loss_grounding_ce_0: 0.01419/0.24855, loss_mask_ce_1: 0.19945/0.75535, loss_mask_bce_1: 0.14156/0.30169, loss_mask_dice_1: 0.24751/1.02469, loss_spatial_bce_1: 0.06092/0.08514, loss_spatial_dice_1: 0.10762/0.18198, loss_spatial_ce_1: 0.00038/0.05999, loss_grounding_bce_1: 0.10224/0.08085, loss_grounding_dice_1: 0.11407/0.15121, loss_grounding_ce_1: 0.02647/0.24992, loss_mask_ce_2: 0.19928/0.76294, loss_mask_bce_2: 0.14068/0.30205, loss_mask_dice_2: 0.24832/1.02555, loss_spatial_bce_2: 0.05714/0.08523, loss_spatial_dice_2: 0.10050/0.18259, loss_spatial_ce_2: 0.00036/0.06211, loss_grounding_bce_2: 0.09064/0.08085, loss_grounding_dice_2: 0.10830/0.15112, loss_grounding_ce_2: 0.02220/0.25271, loss_mask_ce_3: 0.21739/0.76727, loss_mask_bce_3: 0.14038/0.30341, loss_mask_dice_3: 0.24138/1.02347, loss_spatial_bce_3: 0.06380/0.08739, loss_spatial_dice_3: 0.11838/0.18395, loss_spatial_ce_3: 0.00079/0.06699, loss_grounding_bce_3: 0.08596/0.08122, loss_grounding_dice_3: 0.11300/0.15076, loss_grounding_ce_3: 0.00971/0.25398, loss_mask_ce_4: 0.21315/0.77325, loss_mask_bce_4: 0.14005/0.30607, loss_mask_dice_4: 0.24426/1.04294, loss_spatial_bce_4: 0.05943/0.08970, loss_spatial_dice_4: 0.12140/0.19241, loss_spatial_ce_4: 0.00131/0.08076, loss_grounding_bce_4: 0.08649/0.08190, loss_grounding_dice_4: 0.10962/0.15347, loss_grounding_ce_4: 0.01502/0.25820, loss_mask_ce_5: 0.19042/0.79814, loss_mask_bce_5: 0.13312/0.30794, loss_mask_dice_5: 0.23044/1.05085, loss_spatial_bce_5: 0.06079/0.09209, loss_spatial_dice_5: 0.11589/0.19562, loss_spatial_ce_5: 0.00085/0.09427, loss_grounding_bce_5: 0.08744/0.08219, loss_grounding_dice_5: 0.11496/0.15417, loss_grounding_ce_5: 0.01533/0.27632, loss_mask_ce_6: 0.20126/0.82512, loss_mask_bce_6: 0.13296/0.31003, loss_mask_dice_6: 0.22577/1.05455, loss_spatial_bce_6: 0.08579/0.09747, loss_spatial_dice_6: 0.11446/0.19795, loss_spatial_ce_6: 0.00232/0.11887, loss_grounding_bce_6: 0.08116/0.08303, loss_grounding_dice_6: 0.10647/0.15470, loss_grounding_ce_6: 0.03156/0.28494, loss_mask_ce_7: 0.28545/0.88111, loss_mask_bce_7: 0.13985/0.31723, loss_mask_dice_7: 0.26250/1.10039, loss_spatial_bce_7: 0.07049/0.10679, loss_spatial_dice_7: 0.11526/0.22306, loss_spatial_ce_7: 0.01449/0.15468, loss_grounding_bce_7: 0.07953/0.08474, loss_grounding_dice_7: 0.10694/0.16031, loss_grounding_ce_7: 0.37071/0.31856, loss_mask_ce_8: 0.44953/1.01523, loss_mask_bce_8: 0.12046/0.33326, loss_mask_dice_8: 0.26091/1.17672, loss_spatial_bce_8: 0.07330/0.12359, loss_spatial_dice_8: 0.14687/0.25798, loss_spatial_ce_8: 0.06171/0.20044, loss_grounding_bce_8: 0.07736/0.08889, loss_grounding_dice_8: 0.11384/0.17006, loss_grounding_ce_8: 0.94244/0.41706, loss_mask_ce_9: 3.03038/3.47580, loss_mask_bce_9: 0.14326/0.36012, loss_mask_dice_9: 0.34995/1.75957, loss_spatial_bce_9: 0.38300/0.35446, loss_spatial_dice_9: 0.70646/0.79307, loss_spatial_ce_9: 0.73809/1.38775, loss_grounding_bce_9: 0.07908/0.10092, loss_grounding_dice_9: 0.17844/0.24222, loss_grounding_ce_9: 3.02061/0.67087] items per batch[64] items per second[0.36] total items[4601600] mini batches[ 71900] memory[4999] epoch remaining[0:34:50] INFO:trainer.default_trainer:epochs[ 39] optim steps[72000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56129/0.75464, loss_mask_bce_0: 0.04201/0.30091, loss_mask_dice_0: 1.68309/1.02040, loss_spatial_bce_0: 0.01465/0.08470, loss_spatial_dice_0: 0.45146/0.17912, loss_spatial_ce_0: 0.01657/0.05621, loss_grounding_bce_0: 0.00281/0.08066, loss_grounding_dice_0: 0.19126/0.15044, loss_grounding_ce_0: 1.59856/0.24866, loss_mask_ce_1: 0.56698/0.75532, loss_mask_bce_1: 0.02548/0.30170, loss_mask_dice_1: 1.29726/1.02462, loss_spatial_bce_1: 0.01546/0.08513, loss_spatial_dice_1: 0.44329/0.18199, loss_spatial_ce_1: 0.01209/0.05999, loss_grounding_bce_1: 0.00507/0.08085, loss_grounding_dice_1: 0.21863/0.15120, loss_grounding_ce_1: 2.06096/0.25001, loss_mask_ce_2: 0.49790/0.76292, loss_mask_bce_2: 0.01976/0.30207, loss_mask_dice_2: 1.19820/1.02549, loss_spatial_bce_2: 0.01588/0.08522, loss_spatial_dice_2: 0.45114/0.18260, loss_spatial_ce_2: 0.02323/0.06213, loss_grounding_bce_2: 0.00779/0.08085, loss_grounding_dice_2: 0.23933/0.15110, loss_grounding_ce_2: 2.23656/0.25287, loss_mask_ce_3: 0.50036/0.76726, loss_mask_bce_3: 0.03308/0.30341, loss_mask_dice_3: 1.68534/1.02342, loss_spatial_bce_3: 0.01559/0.08738, loss_spatial_dice_3: 0.46046/0.18395, loss_spatial_ce_3: 0.00993/0.06699, loss_grounding_bce_3: 0.00494/0.08122, loss_grounding_dice_3: 0.20970/0.15074, loss_grounding_ce_3: 2.56170/0.25410, loss_mask_ce_4: 0.56264/0.77324, loss_mask_bce_4: 0.02752/0.30609, loss_mask_dice_4: 1.53514/1.04289, loss_spatial_bce_4: 0.02204/0.08969, loss_spatial_dice_4: 0.42810/0.19242, loss_spatial_ce_4: 0.01937/0.08074, loss_grounding_bce_4: 0.00374/0.08190, loss_grounding_dice_4: 0.14493/0.15345, loss_grounding_ce_4: 2.11043/0.25831, loss_mask_ce_5: 0.63648/0.79813, loss_mask_bce_5: 0.02713/0.30796, loss_mask_dice_5: 1.20247/1.05081, loss_spatial_bce_5: 0.02570/0.09209, loss_spatial_dice_5: 0.47804/0.19564, loss_spatial_ce_5: 0.06955/0.09426, loss_grounding_bce_5: 0.00831/0.08219, loss_grounding_dice_5: 0.26992/0.15416, loss_grounding_ce_5: 1.58977/0.27638, loss_mask_ce_6: 1.02135/0.82512, loss_mask_bce_6: 0.02017/0.31006, loss_mask_dice_6: 1.27168/1.05452, loss_spatial_bce_6: 0.02264/0.09748, loss_spatial_dice_6: 0.35636/0.19797, loss_spatial_ce_6: 0.04317/0.11886, loss_grounding_bce_6: 0.00547/0.08303, loss_grounding_dice_6: 0.21942/0.15468, loss_grounding_ce_6: 1.77309/0.28503, loss_mask_ce_7: 1.31161/0.88113, loss_mask_bce_7: 0.02938/0.31726, loss_mask_dice_7: 1.35131/1.10033, loss_spatial_bce_7: 0.01615/0.10678, loss_spatial_dice_7: 0.45147/0.22307, loss_spatial_ce_7: 0.21685/0.15464, loss_grounding_bce_7: 0.00223/0.08474, loss_grounding_dice_7: 0.15836/0.16030, loss_grounding_ce_7: 1.91890/0.31861, loss_mask_ce_8: 0.70505/1.01521, loss_mask_bce_8: 0.03611/0.33330, loss_mask_dice_8: 1.87752/1.17663, loss_spatial_bce_8: 0.01841/0.12358, loss_spatial_dice_8: 0.53274/0.25798, loss_spatial_ce_8: 0.29688/0.20041, loss_grounding_bce_8: 0.00456/0.08889, loss_grounding_dice_8: 0.31667/0.17004, loss_grounding_ce_8: 2.28302/0.41710, loss_mask_ce_9: 3.05161/3.47597, loss_mask_bce_9: 0.01677/0.36014, loss_mask_dice_9: 1.64518/1.75949, loss_spatial_bce_9: 0.01531/0.35443, loss_spatial_dice_9: 0.87380/0.79309, loss_spatial_ce_9: 1.03975/1.38774, loss_grounding_bce_9: 0.00328/0.10091, loss_grounding_dice_9: 0.29768/0.24219, loss_grounding_ce_9: 2.07195/0.67094] items per batch[64] items per second[0.37] total items[4608000] mini batches[ 72000] memory[4999] epoch remaining[0:31:46] INFO:trainer.default_trainer:epochs[ 39] optim steps[72100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.11823/0.75470, loss_mask_bce_0: 0.26660/0.30091, loss_mask_dice_0: 0.38261/1.02044, loss_spatial_bce_0: 0.08541/0.08470, loss_spatial_dice_0: 0.13470/0.17912, loss_spatial_ce_0: 0.00055/0.05618, loss_grounding_bce_0: 0.06068/0.08068, loss_grounding_dice_0: 0.09772/0.15044, loss_grounding_ce_0: 0.25570/0.24870, loss_mask_ce_1: 0.13006/0.75538, loss_mask_bce_1: 0.25445/0.30170, loss_mask_dice_1: 0.34403/1.02463, loss_spatial_bce_1: 0.08812/0.08513, loss_spatial_dice_1: 0.13453/0.18198, loss_spatial_ce_1: 0.00042/0.05997, loss_grounding_bce_1: 0.05998/0.08086, loss_grounding_dice_1: 0.09173/0.15119, loss_grounding_ce_1: 0.26444/0.25010, loss_mask_ce_2: 0.11200/0.76298, loss_mask_bce_2: 0.26082/0.30207, loss_mask_dice_2: 0.37145/1.02547, loss_spatial_bce_2: 0.09093/0.08522, loss_spatial_dice_2: 0.13377/0.18259, loss_spatial_ce_2: 0.00109/0.06211, loss_grounding_bce_2: 0.06122/0.08086, loss_grounding_dice_2: 0.08318/0.15110, loss_grounding_ce_2: 0.36387/0.25294, loss_mask_ce_3: 0.08172/0.76734, loss_mask_bce_3: 0.26085/0.30341, loss_mask_dice_3: 0.38703/1.02344, loss_spatial_bce_3: 0.08758/0.08738, loss_spatial_dice_3: 0.13258/0.18395, loss_spatial_ce_3: 0.00116/0.06697, loss_grounding_bce_3: 0.06042/0.08123, loss_grounding_dice_3: 0.08962/0.15074, loss_grounding_ce_3: 0.35684/0.25413, loss_mask_ce_4: 0.10972/0.77329, loss_mask_bce_4: 0.25560/0.30609, loss_mask_dice_4: 0.36895/1.04289, loss_spatial_bce_4: 0.08361/0.08970, loss_spatial_dice_4: 0.12668/0.19242, loss_spatial_ce_4: 0.00542/0.08072, loss_grounding_bce_4: 0.05990/0.08193, loss_grounding_dice_4: 0.09520/0.15344, loss_grounding_ce_4: 0.35352/0.25826, loss_mask_ce_5: 0.13273/0.79822, loss_mask_bce_5: 0.25013/0.30796, loss_mask_dice_5: 0.36711/1.05082, loss_spatial_bce_5: 0.08721/0.09209, loss_spatial_dice_5: 0.12174/0.19564, loss_spatial_ce_5: 0.01322/0.09424, loss_grounding_bce_5: 0.06002/0.08221, loss_grounding_dice_5: 0.07367/0.15416, loss_grounding_ce_5: 0.35830/0.27633, loss_mask_ce_6: 0.12745/0.82519, loss_mask_bce_6: 0.25924/0.31006, loss_mask_dice_6: 0.35670/1.05454, loss_spatial_bce_6: 0.09193/0.09748, loss_spatial_dice_6: 0.12456/0.19796, loss_spatial_ce_6: 0.00586/0.11886, loss_grounding_bce_6: 0.05951/0.08304, loss_grounding_dice_6: 0.08109/0.15467, loss_grounding_ce_6: 0.26604/0.28503, loss_mask_ce_7: 0.13486/0.88122, loss_mask_bce_7: 0.26498/0.31726, loss_mask_dice_7: 0.37444/1.10033, loss_spatial_bce_7: 0.10159/0.10679, loss_spatial_dice_7: 0.13468/0.22307, loss_spatial_ce_7: 0.05403/0.15461, loss_grounding_bce_7: 0.05876/0.08475, loss_grounding_dice_7: 0.08308/0.16030, loss_grounding_ce_7: 0.26649/0.31856, loss_mask_ce_8: 0.25420/1.01538, loss_mask_bce_8: 0.25180/0.33330, loss_mask_dice_8: 0.31358/1.17666, loss_spatial_bce_8: 0.10562/0.12359, loss_spatial_dice_8: 0.15310/0.25797, loss_spatial_ce_8: 0.03289/0.20043, loss_grounding_bce_8: 0.06239/0.08890, loss_grounding_dice_8: 0.08539/0.17004, loss_grounding_ce_8: 0.39921/0.41707, loss_mask_ce_9: 3.40034/3.47613, loss_mask_bce_9: 0.24580/0.36016, loss_mask_dice_9: 0.50028/1.75963, loss_spatial_bce_9: 0.28330/0.35446, loss_spatial_dice_9: 0.77813/0.79308, loss_spatial_ce_9: 1.30283/1.38765, loss_grounding_bce_9: 0.05761/0.10094, loss_grounding_dice_9: 0.13316/0.24220, loss_grounding_ce_9: 0.48362/0.67087] items per batch[64] items per second[0.37] total items[4614400] mini batches[ 72100] memory[4999] epoch remaining[0:28:45] INFO:trainer.default_trainer:epochs[ 39] optim steps[72200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00667/0.75462, loss_mask_bce_0: 0.04523/0.30090, loss_mask_dice_0: 0.12923/1.02042, loss_spatial_bce_0: 0.02094/0.08469, loss_spatial_dice_0: 0.05341/0.17910, loss_spatial_ce_0: 0.00398/0.05619, loss_grounding_bce_0: 0.02121/0.08067, loss_grounding_dice_0: 0.05871/0.15041, loss_grounding_ce_0: 0.22952/0.24888, loss_mask_ce_1: 0.00785/0.75531, loss_mask_bce_1: 0.04392/0.30169, loss_mask_dice_1: 0.12513/1.02464, loss_spatial_bce_1: 0.01945/0.08513, loss_spatial_dice_1: 0.05234/0.18197, loss_spatial_ce_1: 0.00130/0.05996, loss_grounding_bce_1: 0.02001/0.08085, loss_grounding_dice_1: 0.05438/0.15116, loss_grounding_ce_1: 0.22718/0.25025, loss_mask_ce_2: 0.00832/0.76291, loss_mask_bce_2: 0.04802/0.30206, loss_mask_dice_2: 0.13927/1.02551, loss_spatial_bce_2: 0.02086/0.08522, loss_spatial_dice_2: 0.05562/0.18257, loss_spatial_ce_2: 0.00070/0.06211, loss_grounding_bce_2: 0.02007/0.08085, loss_grounding_dice_2: 0.05351/0.15107, loss_grounding_ce_2: 0.23110/0.25307, loss_mask_ce_3: 0.00830/0.76727, loss_mask_bce_3: 0.04736/0.30341, loss_mask_dice_3: 0.12079/1.02348, loss_spatial_bce_3: 0.01946/0.08737, loss_spatial_dice_3: 0.04906/0.18392, loss_spatial_ce_3: 0.00045/0.06697, loss_grounding_bce_3: 0.01998/0.08122, loss_grounding_dice_3: 0.05361/0.15071, loss_grounding_ce_3: 0.22630/0.25429, loss_mask_ce_4: 0.01524/0.77322, loss_mask_bce_4: 0.04526/0.30608, loss_mask_dice_4: 0.13037/1.04288, loss_spatial_bce_4: 0.02039/0.08970, loss_spatial_dice_4: 0.05598/0.19239, loss_spatial_ce_4: 0.00064/0.08072, loss_grounding_bce_4: 0.01877/0.08192, loss_grounding_dice_4: 0.05041/0.15341, loss_grounding_ce_4: 0.22606/0.25846, loss_mask_ce_5: 0.01297/0.79821, loss_mask_bce_5: 0.04940/0.30795, loss_mask_dice_5: 0.13872/1.05084, loss_spatial_bce_5: 0.02096/0.09210, loss_spatial_dice_5: 0.05485/0.19562, loss_spatial_ce_5: 0.00112/0.09424, loss_grounding_bce_5: 0.02134/0.08221, loss_grounding_dice_5: 0.05626/0.15414, loss_grounding_ce_5: 0.24070/0.27645, loss_mask_ce_6: 0.03489/0.82513, loss_mask_bce_6: 0.04941/0.31006, loss_mask_dice_6: 0.14061/1.05457, loss_spatial_bce_6: 0.02364/0.09748, loss_spatial_dice_6: 0.05575/0.19794, loss_spatial_ce_6: 0.00042/0.11885, loss_grounding_bce_6: 0.01927/0.08305, loss_grounding_dice_6: 0.05112/0.15464, loss_grounding_ce_6: 0.30239/0.28520, loss_mask_ce_7: 0.11668/0.88120, loss_mask_bce_7: 0.04425/0.31725, loss_mask_dice_7: 0.11839/1.10033, loss_spatial_bce_7: 0.01879/0.10681, loss_spatial_dice_7: 0.04821/0.22305, loss_spatial_ce_7: 0.02998/0.15460, loss_grounding_bce_7: 0.01965/0.08475, loss_grounding_dice_7: 0.05479/0.16028, loss_grounding_ce_7: 0.28880/0.31858, loss_mask_ce_8: 0.01848/1.01527, loss_mask_bce_8: 0.05476/0.33328, loss_mask_dice_8: 0.13551/1.17672, loss_spatial_bce_8: 0.02346/0.12359, loss_spatial_dice_8: 0.05779/0.25794, loss_spatial_ce_8: 0.06575/0.20038, loss_grounding_bce_8: 0.02182/0.08889, loss_grounding_dice_8: 0.05500/0.17002, loss_grounding_ce_8: 0.27251/0.41712, loss_mask_ce_9: 3.48322/3.47593, loss_mask_bce_9: 0.04775/0.36016, loss_mask_dice_9: 0.19354/1.75980, loss_spatial_bce_9: 0.47884/0.35448, loss_spatial_dice_9: 0.79431/0.79311, loss_spatial_ce_9: 1.25953/1.38773, loss_grounding_bce_9: 0.02121/0.10094, loss_grounding_dice_9: 0.07660/0.24217, loss_grounding_ce_9: 0.63250/0.67087] items per batch[64] items per second[0.37] total items[4620800] mini batches[ 72200] memory[4999] epoch remaining[0:25:48] INFO:trainer.default_trainer:epochs[ 39] optim steps[72300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21403/0.75471, loss_mask_bce_0: 0.08551/0.30090, loss_mask_dice_0: 0.21482/1.02062, loss_spatial_bce_0: 0.03058/0.08468, loss_spatial_dice_0: 0.07995/0.17909, loss_spatial_ce_0: 0.00005/0.05615, loss_grounding_bce_0: 0.10528/0.08066, loss_grounding_dice_0: 0.07639/0.15042, loss_grounding_ce_0: 0.00007/0.24882, loss_mask_ce_1: 0.22237/0.75540, loss_mask_bce_1: 0.08439/0.30168, loss_mask_dice_1: 0.23530/1.02487, loss_spatial_bce_1: 0.03312/0.08511, loss_spatial_dice_1: 0.08504/0.18197, loss_spatial_ce_1: 0.00006/0.05991, loss_grounding_bce_1: 0.11151/0.08085, loss_grounding_dice_1: 0.08057/0.15116, loss_grounding_ce_1: 0.00005/0.25018, loss_mask_ce_2: 0.24104/0.76301, loss_mask_bce_2: 0.09258/0.30206, loss_mask_dice_2: 0.24849/1.02579, loss_spatial_bce_2: 0.03477/0.08520, loss_spatial_dice_2: 0.10176/0.18257, loss_spatial_ce_2: 0.00005/0.06208, loss_grounding_bce_2: 0.10938/0.08084, loss_grounding_dice_2: 0.08185/0.15108, loss_grounding_ce_2: 0.00002/0.25301, loss_mask_ce_3: 0.22495/0.76737, loss_mask_bce_3: 0.08667/0.30341, loss_mask_dice_3: 0.22577/1.02372, loss_spatial_bce_3: 0.03441/0.08735, loss_spatial_dice_3: 0.08921/0.18392, loss_spatial_ce_3: 0.00010/0.06694, loss_grounding_bce_3: 0.11037/0.08121, loss_grounding_dice_3: 0.07755/0.15072, loss_grounding_ce_3: 0.00010/0.25421, loss_mask_ce_4: 0.22609/0.77332, loss_mask_bce_4: 0.09322/0.30607, loss_mask_dice_4: 0.27170/1.04313, loss_spatial_bce_4: 0.03875/0.08968, loss_spatial_dice_4: 0.09628/0.19240, loss_spatial_ce_4: 0.00069/0.08070, loss_grounding_bce_4: 0.10386/0.08191, loss_grounding_dice_4: 0.08234/0.15341, loss_grounding_ce_4: 0.00001/0.25838, loss_mask_ce_5: 0.29653/0.79836, loss_mask_bce_5: 0.08114/0.30795, loss_mask_dice_5: 0.25448/1.05109, loss_spatial_bce_5: 0.03559/0.09208, loss_spatial_dice_5: 0.09667/0.19562, loss_spatial_ce_5: 0.00019/0.09422, loss_grounding_bce_5: 0.10654/0.08219, loss_grounding_dice_5: 0.08620/0.15415, loss_grounding_ce_5: 0.00000/0.27638, loss_mask_ce_6: 0.39326/0.82526, loss_mask_bce_6: 0.07988/0.31006, loss_mask_dice_6: 0.18526/1.05488, loss_spatial_bce_6: 0.04307/0.09747, loss_spatial_dice_6: 0.09279/0.19794, loss_spatial_ce_6: 0.00042/0.11880, loss_grounding_bce_6: 0.09060/0.08304, loss_grounding_dice_6: 0.07735/0.15466, loss_grounding_ce_6: 0.00003/0.28512, loss_mask_ce_7: 0.50848/0.88135, loss_mask_bce_7: 0.07982/0.31724, loss_mask_dice_7: 0.23506/1.10061, loss_spatial_bce_7: 0.03657/0.10679, loss_spatial_dice_7: 0.11476/0.22306, loss_spatial_ce_7: 0.05521/0.15457, loss_grounding_bce_7: 0.09829/0.08474, loss_grounding_dice_7: 0.08869/0.16028, loss_grounding_ce_7: 0.00041/0.31849, loss_mask_ce_8: 0.60296/1.01542, loss_mask_bce_8: 0.08038/0.33328, loss_mask_dice_8: 0.21293/1.17702, loss_spatial_bce_8: 0.03611/0.12357, loss_spatial_dice_8: 0.11484/0.25795, loss_spatial_ce_8: 0.05208/0.20034, loss_grounding_bce_8: 0.09993/0.08888, loss_grounding_dice_8: 0.07538/0.17003, loss_grounding_ce_8: 0.00245/0.41703, loss_mask_ce_9: 1.96925/3.47615, loss_mask_bce_9: 0.08283/0.36015, loss_mask_dice_9: 0.49377/1.76022, loss_spatial_bce_9: 0.25965/0.35449, loss_spatial_dice_9: 0.73526/0.79313, loss_spatial_ce_9: 0.82346/1.38777, loss_grounding_bce_9: 0.10674/0.10093, loss_grounding_dice_9: 0.08070/0.24218, loss_grounding_ce_9: 0.27452/0.67079] items per batch[64] items per second[0.37] total items[4627200] mini batches[ 72300] memory[4999] epoch remaining[0:22:49] INFO:trainer.default_trainer:epochs[ 39] optim steps[72400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07692/0.75459, loss_mask_bce_0: 0.30840/0.30087, loss_mask_dice_0: 0.69801/1.02050, loss_spatial_bce_0: 0.09090/0.08467, loss_spatial_dice_0: 0.21426/0.17907, loss_spatial_ce_0: 0.00885/0.05614, loss_grounding_bce_0: 0.08945/0.08068, loss_grounding_dice_0: 0.10885/0.15042, loss_grounding_ce_0: 0.09600/0.24874, loss_mask_ce_1: 1.07673/0.75527, loss_mask_bce_1: 0.29554/0.30166, loss_mask_dice_1: 0.67084/1.02474, loss_spatial_bce_1: 0.09036/0.08510, loss_spatial_dice_1: 0.20923/0.18195, loss_spatial_ce_1: 0.02249/0.05990, loss_grounding_bce_1: 0.07877/0.08087, loss_grounding_dice_1: 0.10044/0.15116, loss_grounding_ce_1: 0.09102/0.25011, loss_mask_ce_2: 1.17694/0.76290, loss_mask_bce_2: 0.29472/0.30204, loss_mask_dice_2: 0.68442/1.02570, loss_spatial_bce_2: 0.08362/0.08519, loss_spatial_dice_2: 0.19392/0.18254, loss_spatial_ce_2: 0.00658/0.06205, loss_grounding_bce_2: 0.08126/0.08086, loss_grounding_dice_2: 0.10617/0.15108, loss_grounding_ce_2: 0.10979/0.25293, loss_mask_ce_3: 1.11576/0.76724, loss_mask_bce_3: 0.31771/0.30338, loss_mask_dice_3: 0.79367/1.02365, loss_spatial_bce_3: 0.09173/0.08734, loss_spatial_dice_3: 0.23642/0.18389, loss_spatial_ce_3: 0.01304/0.06691, loss_grounding_bce_3: 0.08562/0.08123, loss_grounding_dice_3: 0.11356/0.15072, loss_grounding_ce_3: 0.10055/0.25412, loss_mask_ce_4: 1.40846/0.77321, loss_mask_bce_4: 0.31870/0.30605, loss_mask_dice_4: 0.61838/1.04298, loss_spatial_bce_4: 0.10195/0.08967, loss_spatial_dice_4: 0.22226/0.19237, loss_spatial_ce_4: 0.02911/0.08067, loss_grounding_bce_4: 0.07736/0.08194, loss_grounding_dice_4: 0.10058/0.15341, loss_grounding_ce_4: 0.11923/0.25834, loss_mask_ce_5: 1.17308/0.79824, loss_mask_bce_5: 0.34844/0.30793, loss_mask_dice_5: 0.82233/1.05100, loss_spatial_bce_5: 0.10173/0.09208, loss_spatial_dice_5: 0.28167/0.19559, loss_spatial_ce_5: 0.02017/0.09419, loss_grounding_bce_5: 0.08039/0.08222, loss_grounding_dice_5: 0.10670/0.15415, loss_grounding_ce_5: 0.18053/0.27628, loss_mask_ce_6: 1.27699/0.82512, loss_mask_bce_6: 0.38483/0.31004, loss_mask_dice_6: 0.85010/1.05479, loss_spatial_bce_6: 0.13121/0.09747, loss_spatial_dice_6: 0.25822/0.19791, loss_spatial_ce_6: 0.14512/0.11876, loss_grounding_bce_6: 0.08051/0.08306, loss_grounding_dice_6: 0.10581/0.15465, loss_grounding_ce_6: 0.18894/0.28502, loss_mask_ce_7: 1.06125/0.88120, loss_mask_bce_7: 0.41749/0.31723, loss_mask_dice_7: 0.85904/1.10052, loss_spatial_bce_7: 0.13828/0.10679, loss_spatial_dice_7: 0.34005/0.22303, loss_spatial_ce_7: 0.37387/0.15452, loss_grounding_bce_7: 0.09639/0.08476, loss_grounding_dice_7: 0.11509/0.16027, loss_grounding_ce_7: 0.18693/0.31833, loss_mask_ce_8: 1.62074/1.01520, loss_mask_bce_8: 0.32374/0.33327, loss_mask_dice_8: 0.72141/1.17690, loss_spatial_bce_8: 0.12443/0.12356, loss_spatial_dice_8: 0.33799/0.25792, loss_spatial_ce_8: 0.19175/0.20028, loss_grounding_bce_8: 0.12425/0.08890, loss_grounding_dice_8: 0.13984/0.17002, loss_grounding_ce_8: 0.16783/0.41687, loss_mask_ce_9: 2.58356/3.47576, loss_mask_bce_9: 0.34114/0.36013, loss_mask_dice_9: 1.57365/1.76009, loss_spatial_bce_9: 0.36911/0.35450, loss_spatial_dice_9: 0.94671/0.79311, loss_spatial_ce_9: 1.31155/1.38757, loss_grounding_bce_9: 0.09842/0.10095, loss_grounding_dice_9: 0.14240/0.24216, loss_grounding_ce_9: 0.22534/0.67053] items per batch[64] items per second[0.38] total items[4633600] mini batches[ 72400] memory[4999] epoch remaining[0:19:51] INFO:trainer.default_trainer:epochs[ 39] optim steps[72500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06056/0.75459, loss_mask_bce_0: 0.14509/0.30086, loss_mask_dice_0: 0.13625/1.02048, loss_spatial_bce_0: 0.10945/0.08464, loss_spatial_dice_0: 0.09238/0.17904, loss_spatial_ce_0: 0.02039/0.05614, loss_grounding_bce_0: 0.22460/0.08066, loss_grounding_dice_0: 0.09202/0.15041, loss_grounding_ce_0: 0.00027/0.24879, loss_mask_ce_1: 0.05885/0.75528, loss_mask_bce_1: 0.14274/0.30166, loss_mask_dice_1: 0.13624/1.02474, loss_spatial_bce_1: 0.11712/0.08508, loss_spatial_dice_1: 0.10221/0.18192, loss_spatial_ce_1: 0.02960/0.05987, loss_grounding_bce_1: 0.22811/0.08084, loss_grounding_dice_1: 0.09207/0.15114, loss_grounding_ce_1: 0.00018/0.25014, loss_mask_ce_2: 0.06266/0.76289, loss_mask_bce_2: 0.15134/0.30203, loss_mask_dice_2: 0.13870/1.02569, loss_spatial_bce_2: 0.11182/0.08516, loss_spatial_dice_2: 0.10150/0.18251, loss_spatial_ce_2: 0.03347/0.06203, loss_grounding_bce_2: 0.22819/0.08083, loss_grounding_dice_2: 0.09231/0.15106, loss_grounding_ce_2: 0.00023/0.25297, loss_mask_ce_3: 0.06213/0.76726, loss_mask_bce_3: 0.14760/0.30338, loss_mask_dice_3: 0.13226/1.02360, loss_spatial_bce_3: 0.10896/0.08732, loss_spatial_dice_3: 0.09626/0.18387, loss_spatial_ce_3: 0.04172/0.06687, loss_grounding_bce_3: 0.22114/0.08120, loss_grounding_dice_3: 0.08942/0.15070, loss_grounding_ce_3: 0.00030/0.25415, loss_mask_ce_4: 0.07020/0.77323, loss_mask_bce_4: 0.15571/0.30604, loss_mask_dice_4: 0.13563/1.04297, loss_spatial_bce_4: 0.11500/0.08965, loss_spatial_dice_4: 0.10120/0.19235, loss_spatial_ce_4: 0.03675/0.08063, loss_grounding_bce_4: 0.24464/0.08191, loss_grounding_dice_4: 0.09940/0.15339, loss_grounding_ce_4: 0.00021/0.25841, loss_mask_ce_5: 0.08239/0.79827, loss_mask_bce_5: 0.15795/0.30791, loss_mask_dice_5: 0.14112/1.05100, loss_spatial_bce_5: 0.11493/0.09205, loss_spatial_dice_5: 0.09113/0.19557, loss_spatial_ce_5: 0.12008/0.09413, loss_grounding_bce_5: 0.23458/0.08219, loss_grounding_dice_5: 0.09472/0.15413, loss_grounding_ce_5: 0.00037/0.27634, loss_mask_ce_6: 0.06675/0.82519, loss_mask_bce_6: 0.15222/0.31003, loss_mask_dice_6: 0.13332/1.05481, loss_spatial_bce_6: 0.13246/0.09744, loss_spatial_dice_6: 0.09169/0.19789, loss_spatial_ce_6: 0.07140/0.11871, loss_grounding_bce_6: 0.25457/0.08303, loss_grounding_dice_6: 0.10239/0.15463, loss_grounding_ce_6: 0.00089/0.28507, loss_mask_ce_7: 0.05562/0.88124, loss_mask_bce_7: 0.14852/0.31721, loss_mask_dice_7: 0.13084/1.10052, loss_spatial_bce_7: 0.12993/0.10676, loss_spatial_dice_7: 0.10883/0.22300, loss_spatial_ce_7: 0.12859/0.15446, loss_grounding_bce_7: 0.22805/0.08473, loss_grounding_dice_7: 0.08707/0.16025, loss_grounding_ce_7: 0.00032/0.31842, loss_mask_ce_8: 0.07506/1.01525, loss_mask_bce_8: 0.15558/0.33326, loss_mask_dice_8: 0.14171/1.17692, loss_spatial_bce_8: 0.12000/0.12352, loss_spatial_dice_8: 0.15135/0.25788, loss_spatial_ce_8: 0.30028/0.20020, loss_grounding_bce_8: 0.22553/0.08887, loss_grounding_dice_8: 0.08668/0.17001, loss_grounding_ce_8: 0.00069/0.41680, loss_mask_ce_9: 1.79979/3.47597, loss_mask_bce_9: 0.19402/0.36014, loss_mask_dice_9: 0.16131/1.76025, loss_spatial_bce_9: 0.49182/0.35449, loss_spatial_dice_9: 0.74112/0.79313, loss_spatial_ce_9: 1.00157/1.38757, loss_grounding_bce_9: 0.32096/0.10092, loss_grounding_dice_9: 0.12031/0.24215, loss_grounding_ce_9: 0.00991/0.67053] items per batch[64] items per second[0.37] total items[4640000] mini batches[ 72500] memory[4999] epoch remaining[0:16:54] INFO:trainer.default_trainer:epochs[ 39] optim steps[72600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59104/0.75443, loss_mask_bce_0: 0.46120/0.30081, loss_mask_dice_0: 2.94637/1.02051, loss_spatial_bce_0: 0.21935/0.08464, loss_spatial_dice_0: 0.30812/0.17903, loss_spatial_ce_0: 0.01507/0.05612, loss_grounding_bce_0: 0.20651/0.08064, loss_grounding_dice_0: 0.33574/0.15041, loss_grounding_ce_0: 0.57571/0.24881, loss_mask_ce_1: 0.67081/0.75514, loss_mask_bce_1: 0.47024/0.30161, loss_mask_dice_1: 2.96821/1.02479, loss_spatial_bce_1: 0.19490/0.08507, loss_spatial_dice_1: 0.27311/0.18192, loss_spatial_ce_1: 0.00712/0.05984, loss_grounding_bce_1: 0.21071/0.08083, loss_grounding_dice_1: 0.42465/0.15114, loss_grounding_ce_1: 0.60226/0.25017, loss_mask_ce_2: 0.50623/0.76279, loss_mask_bce_2: 0.40858/0.30198, loss_mask_dice_2: 3.26565/1.02574, loss_spatial_bce_2: 0.23630/0.08516, loss_spatial_dice_2: 0.32148/0.18251, loss_spatial_ce_2: 0.01272/0.06200, loss_grounding_bce_2: 0.18174/0.08082, loss_grounding_dice_2: 0.34430/0.15107, loss_grounding_ce_2: 0.52730/0.25298, loss_mask_ce_3: 0.43642/0.76714, loss_mask_bce_3: 0.42340/0.30333, loss_mask_dice_3: 2.88638/1.02363, loss_spatial_bce_3: 0.24023/0.08731, loss_spatial_dice_3: 0.29519/0.18386, loss_spatial_ce_3: 0.00958/0.06685, loss_grounding_bce_3: 0.18406/0.08119, loss_grounding_dice_3: 0.47711/0.15071, loss_grounding_ce_3: 0.27422/0.25416, loss_mask_ce_4: 0.65858/0.77313, loss_mask_bce_4: 0.44146/0.30600, loss_mask_dice_4: 3.50647/1.04300, loss_spatial_bce_4: 0.21927/0.08964, loss_spatial_dice_4: 0.30059/0.19234, loss_spatial_ce_4: 0.03144/0.08061, loss_grounding_bce_4: 0.16696/0.08190, loss_grounding_dice_4: 0.46628/0.15339, loss_grounding_ce_4: 0.24041/0.25843, loss_mask_ce_5: 0.68270/0.79815, loss_mask_bce_5: 0.42942/0.30786, loss_mask_dice_5: 3.06928/1.05099, loss_spatial_bce_5: 0.21546/0.09205, loss_spatial_dice_5: 0.31598/0.19556, loss_spatial_ce_5: 0.05732/0.09410, loss_grounding_bce_5: 0.17700/0.08218, loss_grounding_dice_5: 0.45632/0.15413, loss_grounding_ce_5: 0.29719/0.27634, loss_mask_ce_6: 0.45082/0.82509, loss_mask_bce_6: 0.44717/0.30997, loss_mask_dice_6: 3.54024/1.05484, loss_spatial_bce_6: 0.27202/0.09744, loss_spatial_dice_6: 0.30174/0.19788, loss_spatial_ce_6: 0.08104/0.11870, loss_grounding_bce_6: 0.18553/0.08301, loss_grounding_dice_6: 0.44965/0.15464, loss_grounding_ce_6: 0.28776/0.28511, loss_mask_ce_7: 0.81536/0.88116, loss_mask_bce_7: 0.43964/0.31716, loss_mask_dice_7: 3.14566/1.10055, loss_spatial_bce_7: 0.15173/0.10674, loss_spatial_dice_7: 0.34151/0.22299, loss_spatial_ce_7: 0.29591/0.15444, loss_grounding_bce_7: 0.17939/0.08473, loss_grounding_dice_7: 0.50759/0.16025, loss_grounding_ce_7: 0.30136/0.31841, loss_mask_ce_8: 1.34223/1.01517, loss_mask_bce_8: 0.47449/0.33320, loss_mask_dice_8: 3.54015/1.17695, loss_spatial_bce_8: 0.09736/0.12350, loss_spatial_dice_8: 0.40657/0.25787, loss_spatial_ce_8: 0.07083/0.20018, loss_grounding_bce_8: 0.18916/0.08886, loss_grounding_dice_8: 0.51976/0.17002, loss_grounding_ce_8: 0.70643/0.41675, loss_mask_ce_9: 3.44150/3.47568, loss_mask_bce_9: 0.49270/0.36009, loss_mask_dice_9: 4.34738/1.76020, loss_spatial_bce_9: 0.22706/0.35446, loss_spatial_dice_9: 0.87945/0.79310, loss_spatial_ce_9: 1.51753/1.38737, loss_grounding_bce_9: 0.18229/0.10091, loss_grounding_dice_9: 0.59814/0.24216, loss_grounding_ce_9: 0.44735/0.67043] items per batch[64] items per second[0.37] total items[4646400] mini batches[ 72600] memory[4999] epoch remaining[0:13:59] INFO:trainer.default_trainer:epochs[ 39] optim steps[72700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.90484/0.75444, loss_mask_bce_0: 0.05625/0.30083, loss_mask_dice_0: 1.02919/1.02073, loss_spatial_bce_0: 0.00981/0.08461, loss_spatial_dice_0: 0.16704/0.17902, loss_spatial_ce_0: 0.00288/0.05609, loss_grounding_bce_0: 0.02651/0.08062, loss_grounding_dice_0: 0.14720/0.15039, loss_grounding_ce_0: 0.00003/0.24880, loss_mask_ce_1: 0.91427/0.75519, loss_mask_bce_1: 0.05628/0.30163, loss_mask_dice_1: 0.94797/1.02501, loss_spatial_bce_1: 0.00994/0.08505, loss_spatial_dice_1: 0.16595/0.18191, loss_spatial_ce_1: 0.00120/0.05982, loss_grounding_bce_1: 0.03259/0.08080, loss_grounding_dice_1: 0.17310/0.15112, loss_grounding_ce_1: 0.00007/0.25015, loss_mask_ce_2: 1.16358/0.76282, loss_mask_bce_2: 0.05432/0.30200, loss_mask_dice_2: 0.90775/1.02597, loss_spatial_bce_2: 0.01071/0.08514, loss_spatial_dice_2: 0.18091/0.18251, loss_spatial_ce_2: 0.00203/0.06198, loss_grounding_bce_2: 0.03294/0.08079, loss_grounding_dice_2: 0.16443/0.15105, loss_grounding_ce_2: 0.00009/0.25299, loss_mask_ce_3: 0.84876/0.76719, loss_mask_bce_3: 0.05195/0.30335, loss_mask_dice_3: 0.96191/1.02388, loss_spatial_bce_3: 0.01068/0.08729, loss_spatial_dice_3: 0.17255/0.18386, loss_spatial_ce_3: 0.02306/0.06684, loss_grounding_bce_3: 0.03217/0.08117, loss_grounding_dice_3: 0.17765/0.15069, loss_grounding_ce_3: 0.00025/0.25415, loss_mask_ce_4: 0.90212/0.77317, loss_mask_bce_4: 0.05502/0.30602, loss_mask_dice_4: 0.99047/1.04328, loss_spatial_bce_4: 0.01053/0.08961, loss_spatial_dice_4: 0.17490/0.19234, loss_spatial_ce_4: 0.02300/0.08059, loss_grounding_bce_4: 0.03659/0.08188, loss_grounding_dice_4: 0.18931/0.15337, loss_grounding_ce_4: 0.00118/0.25842, loss_mask_ce_5: 0.97939/0.79821, loss_mask_bce_5: 0.06612/0.30787, loss_mask_dice_5: 0.99580/1.05121, loss_spatial_bce_5: 0.01486/0.09202, loss_spatial_dice_5: 0.20814/0.19556, loss_spatial_ce_5: 0.08969/0.09407, loss_grounding_bce_5: 0.03272/0.08215, loss_grounding_dice_5: 0.16178/0.15412, loss_grounding_ce_5: 0.00087/0.27630, loss_mask_ce_6: 0.99801/0.82517, loss_mask_bce_6: 0.07637/0.30999, loss_mask_dice_6: 1.13854/1.05512, loss_spatial_bce_6: 0.02071/0.09741, loss_spatial_dice_6: 0.21366/0.19788, loss_spatial_ce_6: 0.17663/0.11867, loss_grounding_bce_6: 0.02958/0.08298, loss_grounding_dice_6: 0.16834/0.15463, loss_grounding_ce_6: 0.00021/0.28508, loss_mask_ce_7: 1.11989/0.88123, loss_mask_bce_7: 0.06124/0.31720, loss_mask_dice_7: 1.01950/1.10082, loss_spatial_bce_7: 0.01858/0.10671, loss_spatial_dice_7: 0.26416/0.22298, loss_spatial_ce_7: 0.06667/0.15439, loss_grounding_bce_7: 0.03000/0.08470, loss_grounding_dice_7: 0.15807/0.16023, loss_grounding_ce_7: 0.00213/0.31839, loss_mask_ce_8: 1.93052/1.01532, loss_mask_bce_8: 0.07835/0.33322, loss_mask_dice_8: 1.11606/1.17722, loss_spatial_bce_8: 0.01608/0.12346, loss_spatial_dice_8: 0.26485/0.25787, loss_spatial_ce_8: 0.06369/0.20012, loss_grounding_bce_8: 0.03454/0.08883, loss_grounding_dice_8: 0.16871/0.17000, loss_grounding_ce_8: 0.01981/0.41680, loss_mask_ce_9: 4.26972/3.47586, loss_mask_bce_9: 0.06755/0.36010, loss_mask_dice_9: 1.81039/1.76063, loss_spatial_bce_9: 0.16968/0.35437, loss_spatial_dice_9: 0.91847/0.79313, loss_spatial_ce_9: 1.25270/1.38733, loss_grounding_bce_9: 0.04160/0.10089, loss_grounding_dice_9: 0.18946/0.24214, loss_grounding_ce_9: 0.15894/0.67048] items per batch[64] items per second[0.37] total items[4652800] mini batches[ 72700] memory[4999] epoch remaining[0:11:04] INFO:trainer.default_trainer:epochs[ 39] optim steps[72800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.21844/0.75445, loss_mask_bce_0: 0.06899/0.30083, loss_mask_dice_0: 0.11181/1.02073, loss_spatial_bce_0: 0.04500/0.08460, loss_spatial_dice_0: 0.06879/0.17901, loss_spatial_ce_0: 0.00032/0.05607, loss_grounding_bce_0: 0.03373/0.08062, loss_grounding_dice_0: 0.08776/0.15039, loss_grounding_ce_0: 0.08964/0.24883, loss_mask_ce_1: 0.20636/0.75519, loss_mask_bce_1: 0.06885/0.30164, loss_mask_dice_1: 0.10980/1.02500, loss_spatial_bce_1: 0.04727/0.08504, loss_spatial_dice_1: 0.07101/0.18189, loss_spatial_ce_1: 0.00028/0.05979, loss_grounding_bce_1: 0.02845/0.08080, loss_grounding_dice_1: 0.07428/0.15111, loss_grounding_ce_1: 0.07075/0.25019, loss_mask_ce_2: 0.22987/0.76282, loss_mask_bce_2: 0.06931/0.30200, loss_mask_dice_2: 0.11973/1.02596, loss_spatial_bce_2: 0.04509/0.08513, loss_spatial_dice_2: 0.06452/0.18250, loss_spatial_ce_2: 0.00026/0.06196, loss_grounding_bce_2: 0.02761/0.08080, loss_grounding_dice_2: 0.07391/0.15104, loss_grounding_ce_2: 0.05722/0.25302, loss_mask_ce_3: 0.22997/0.76718, loss_mask_bce_3: 0.07169/0.30335, loss_mask_dice_3: 0.11613/1.02387, loss_spatial_bce_3: 0.04422/0.08729, loss_spatial_dice_3: 0.07084/0.18385, loss_spatial_ce_3: 0.00036/0.06682, loss_grounding_bce_3: 0.02957/0.08117, loss_grounding_dice_3: 0.07549/0.15068, loss_grounding_ce_3: 0.05689/0.25417, loss_mask_ce_4: 0.27143/0.77322, loss_mask_bce_4: 0.06674/0.30601, loss_mask_dice_4: 0.11014/1.04327, loss_spatial_bce_4: 0.04584/0.08961, loss_spatial_dice_4: 0.07184/0.19233, loss_spatial_ce_4: 0.00082/0.08057, loss_grounding_bce_4: 0.02959/0.08188, loss_grounding_dice_4: 0.08175/0.15336, loss_grounding_ce_4: 0.06115/0.25848, loss_mask_ce_5: 0.27643/0.79830, loss_mask_bce_5: 0.07716/0.30787, loss_mask_dice_5: 0.11718/1.05123, loss_spatial_bce_5: 0.04923/0.09202, loss_spatial_dice_5: 0.08175/0.19555, loss_spatial_ce_5: 0.01744/0.09405, loss_grounding_bce_5: 0.02930/0.08215, loss_grounding_dice_5: 0.07056/0.15411, loss_grounding_ce_5: 0.04863/0.27634, loss_mask_ce_6: 0.33233/0.82522, loss_mask_bce_6: 0.07179/0.30999, loss_mask_dice_6: 0.12268/1.05515, loss_spatial_bce_6: 0.04336/0.09741, loss_spatial_dice_6: 0.06742/0.19788, loss_spatial_ce_6: 0.03836/0.11866, loss_grounding_bce_6: 0.03044/0.08298, loss_grounding_dice_6: 0.08353/0.15463, loss_grounding_ce_6: 0.05757/0.28506, loss_mask_ce_7: 0.39689/0.88132, loss_mask_bce_7: 0.06802/0.31719, loss_mask_dice_7: 0.11577/1.10083, loss_spatial_bce_7: 0.04300/0.10670, loss_spatial_dice_7: 0.07663/0.22298, loss_spatial_ce_7: 0.02054/0.15438, loss_grounding_bce_7: 0.03100/0.08470, loss_grounding_dice_7: 0.07239/0.16023, loss_grounding_ce_7: 0.07883/0.31840, loss_mask_ce_8: 0.37645/1.01541, loss_mask_bce_8: 0.06831/0.33322, loss_mask_dice_8: 0.11909/1.17723, loss_spatial_bce_8: 0.04412/0.12345, loss_spatial_dice_8: 0.09948/0.25787, loss_spatial_ce_8: 0.00605/0.20010, loss_grounding_bce_8: 0.02896/0.08883, loss_grounding_dice_8: 0.08581/0.17000, loss_grounding_ce_8: 0.05462/0.41683, loss_mask_ce_9: 2.22954/3.47618, loss_mask_bce_9: 0.08101/0.36010, loss_mask_dice_9: 0.15557/1.76061, loss_spatial_bce_9: 0.52286/0.35437, loss_spatial_dice_9: 0.69607/0.79315, loss_spatial_ce_9: 1.54958/1.38746, loss_grounding_bce_9: 0.03491/0.10091, loss_grounding_dice_9: 0.13833/0.24216, loss_grounding_ce_9: 0.01108/0.67063] items per batch[64] items per second[0.37] total items[4659200] mini batches[ 72800] memory[4999] epoch remaining[0:08:09] INFO:trainer.default_trainer:epochs[ 39] optim steps[72900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.63431/0.75440, loss_mask_bce_0: 0.07964/0.30084, loss_mask_dice_0: 1.48103/1.02085, loss_spatial_bce_0: 0.00853/0.08460, loss_spatial_dice_0: 0.32634/0.17899, loss_spatial_ce_0: 0.11867/0.05603, loss_grounding_bce_0: 0.00386/0.08062, loss_grounding_dice_0: 0.11637/0.15039, loss_grounding_ce_0: 0.12635/0.24877, loss_mask_ce_1: 1.43362/0.75514, loss_mask_bce_1: 0.04036/0.30165, loss_mask_dice_1: 1.24082/1.02508, loss_spatial_bce_1: 0.00775/0.08504, loss_spatial_dice_1: 0.27911/0.18188, loss_spatial_ce_1: 0.14794/0.05977, loss_grounding_bce_1: 0.00389/0.08081, loss_grounding_dice_1: 0.12728/0.15111, loss_grounding_ce_1: 0.12493/0.25012, loss_mask_ce_2: 1.46704/0.76278, loss_mask_bce_2: 0.04854/0.30201, loss_mask_dice_2: 1.26374/1.02603, loss_spatial_bce_2: 0.00826/0.08513, loss_spatial_dice_2: 0.27186/0.18249, loss_spatial_ce_2: 0.09204/0.06192, loss_grounding_bce_2: 0.00468/0.08080, loss_grounding_dice_2: 0.18662/0.15104, loss_grounding_ce_2: 0.20348/0.25295, loss_mask_ce_3: 1.48910/0.76714, loss_mask_bce_3: 0.08420/0.30337, loss_mask_dice_3: 1.76462/1.02397, loss_spatial_bce_3: 0.01075/0.08729, loss_spatial_dice_3: 0.26787/0.18383, loss_spatial_ce_3: 0.10854/0.06680, loss_grounding_bce_3: 0.00420/0.08117, loss_grounding_dice_3: 0.08631/0.15068, loss_grounding_ce_3: 0.13140/0.25411, loss_mask_ce_4: 1.51707/0.77314, loss_mask_bce_4: 0.03426/0.30603, loss_mask_dice_4: 1.34060/1.04337, loss_spatial_bce_4: 0.00872/0.08962, loss_spatial_dice_4: 0.33021/0.19231, loss_spatial_ce_4: 0.14385/0.08053, loss_grounding_bce_4: 0.00491/0.08188, loss_grounding_dice_4: 0.23284/0.15336, loss_grounding_ce_4: 0.17180/0.25841, loss_mask_ce_5: 1.74120/0.79826, loss_mask_bce_5: 0.04237/0.30788, loss_mask_dice_5: 0.99803/1.05134, loss_spatial_bce_5: 0.01146/0.09202, loss_spatial_dice_5: 0.33822/0.19553, loss_spatial_ce_5: 0.51604/0.09405, loss_grounding_bce_5: 0.00597/0.08215, loss_grounding_dice_5: 0.28055/0.15411, loss_grounding_ce_5: 0.18062/0.27629, loss_mask_ce_6: 1.59968/0.82518, loss_mask_bce_6: 0.04046/0.31001, loss_mask_dice_6: 1.05687/1.05525, loss_spatial_bce_6: 0.01194/0.09742, loss_spatial_dice_6: 0.34579/0.19787, loss_spatial_ce_6: 0.19941/0.11862, loss_grounding_bce_6: 0.00540/0.08299, loss_grounding_dice_6: 0.21135/0.15462, loss_grounding_ce_6: 0.16377/0.28502, loss_mask_ce_7: 2.70207/0.88127, loss_mask_bce_7: 0.07154/0.31721, loss_mask_dice_7: 1.41121/1.10092, loss_spatial_bce_7: 0.00834/0.10671, loss_spatial_dice_7: 0.42050/0.22296, loss_spatial_ce_7: 0.17969/0.15434, loss_grounding_bce_7: 0.00607/0.08471, loss_grounding_dice_7: 0.33195/0.16023, loss_grounding_ce_7: 0.17533/0.31835, loss_mask_ce_8: 1.38268/1.01539, loss_mask_bce_8: 0.04850/0.33324, loss_mask_dice_8: 1.60449/1.17732, loss_spatial_bce_8: 0.01870/0.12345, loss_spatial_dice_8: 0.46264/0.25785, loss_spatial_ce_8: 0.28415/0.20003, loss_grounding_bce_8: 0.00407/0.08883, loss_grounding_dice_8: 0.17584/0.17001, loss_grounding_ce_8: 0.22370/0.41674, loss_mask_ce_9: 3.57183/3.47602, loss_mask_bce_9: 0.02317/0.36012, loss_mask_dice_9: 1.36941/1.76077, loss_spatial_bce_9: 0.02645/0.35436, loss_spatial_dice_9: 0.88444/0.79312, loss_spatial_ce_9: 1.33282/1.38734, loss_grounding_bce_9: 0.00379/0.10092, loss_grounding_dice_9: 0.28998/0.24217, loss_grounding_ce_9: 0.23720/0.67048] items per batch[64] items per second[0.37] total items[4665600] mini batches[ 72900] memory[4999] epoch remaining[0:05:14] INFO:trainer.default_trainer:epochs[ 39] optim steps[73000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08838/0.75432, loss_mask_bce_0: 0.06089/0.30084, loss_mask_dice_0: 0.43722/1.02054, loss_spatial_bce_0: 0.02202/0.08462, loss_spatial_dice_0: 0.14841/0.17898, loss_spatial_ce_0: 0.00043/0.05602, loss_grounding_bce_0: 0.02323/0.08064, loss_grounding_dice_0: 0.16196/0.15039, loss_grounding_ce_0: 0.16797/0.24869, loss_mask_ce_1: 0.09777/0.75505, loss_mask_bce_1: 0.05875/0.30165, loss_mask_dice_1: 0.49558/1.02476, loss_spatial_bce_1: 0.02075/0.08505, loss_spatial_dice_1: 0.17752/0.18187, loss_spatial_ce_1: 0.00055/0.05977, loss_grounding_bce_1: 0.02267/0.08082, loss_grounding_dice_1: 0.14463/0.15113, loss_grounding_ce_1: 0.16529/0.25005, loss_mask_ce_2: 0.09705/0.76267, loss_mask_bce_2: 0.05981/0.30201, loss_mask_dice_2: 0.51424/1.02576, loss_spatial_bce_2: 0.02391/0.08515, loss_spatial_dice_2: 0.15924/0.18248, loss_spatial_ce_2: 0.00090/0.06191, loss_grounding_bce_2: 0.02033/0.08081, loss_grounding_dice_2: 0.12966/0.15104, loss_grounding_ce_2: 0.16006/0.25289, loss_mask_ce_3: 0.09785/0.76703, loss_mask_bce_3: 0.05823/0.30336, loss_mask_dice_3: 0.45972/1.02368, loss_spatial_bce_3: 0.02334/0.08731, loss_spatial_dice_3: 0.17205/0.18382, loss_spatial_ce_3: 0.00061/0.06680, loss_grounding_bce_3: 0.02365/0.08118, loss_grounding_dice_3: 0.15869/0.15068, loss_grounding_ce_3: 0.16257/0.25403, loss_mask_ce_4: 0.10296/0.77304, loss_mask_bce_4: 0.06231/0.30603, loss_mask_dice_4: 0.52504/1.04305, loss_spatial_bce_4: 0.02163/0.08964, loss_spatial_dice_4: 0.20331/0.19230, loss_spatial_ce_4: 0.00150/0.08053, loss_grounding_bce_4: 0.02542/0.08190, loss_grounding_dice_4: 0.14503/0.15337, loss_grounding_ce_4: 0.15383/0.25835, loss_mask_ce_5: 0.09177/0.79818, loss_mask_bce_5: 0.05890/0.30788, loss_mask_dice_5: 0.49576/1.05102, loss_spatial_bce_5: 0.02222/0.09205, loss_spatial_dice_5: 0.18362/0.19553, loss_spatial_ce_5: 0.00424/0.09405, loss_grounding_bce_5: 0.01877/0.08217, loss_grounding_dice_5: 0.13167/0.15412, loss_grounding_ce_5: 0.16280/0.27625, loss_mask_ce_6: 0.12824/0.82509, loss_mask_bce_6: 0.05754/0.31001, loss_mask_dice_6: 0.43483/1.05497, loss_spatial_bce_6: 0.03289/0.09745, loss_spatial_dice_6: 0.20361/0.19786, loss_spatial_ce_6: 0.00358/0.11861, loss_grounding_bce_6: 0.02728/0.08301, loss_grounding_dice_6: 0.16899/0.15464, loss_grounding_ce_6: 0.16307/0.28496, loss_mask_ce_7: 0.14680/0.88119, loss_mask_bce_7: 0.06567/0.31721, loss_mask_dice_7: 0.44967/1.10060, loss_spatial_bce_7: 0.02946/0.10673, loss_spatial_dice_7: 0.19554/0.22295, loss_spatial_ce_7: 0.01408/0.15430, loss_grounding_bce_7: 0.02180/0.08473, loss_grounding_dice_7: 0.14738/0.16024, loss_grounding_ce_7: 0.16864/0.31832, loss_mask_ce_8: 0.11936/1.01531, loss_mask_bce_8: 0.06102/0.33322, loss_mask_dice_8: 0.50727/1.17698, loss_spatial_bce_8: 0.03037/0.12347, loss_spatial_dice_8: 0.21928/0.25784, loss_spatial_ce_8: 0.10763/0.20000, loss_grounding_bce_8: 0.02266/0.08885, loss_grounding_dice_8: 0.14169/0.17001, loss_grounding_ce_8: 0.17578/0.41669, loss_mask_ce_9: 2.29971/3.47561, loss_mask_bce_9: 0.06614/0.36011, loss_mask_dice_9: 0.64408/1.76014, loss_spatial_bce_9: 0.20698/0.35439, loss_spatial_dice_9: 0.76863/0.79310, loss_spatial_ce_9: 1.28795/1.38726, loss_grounding_bce_9: 0.01493/0.10094, loss_grounding_dice_9: 0.16275/0.24217, loss_grounding_ce_9: 0.40606/0.67036] items per batch[64] items per second[0.37] total items[4672000] mini batches[ 73000] memory[4999] epoch remaining[0:02:19] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00073080. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0026 s/iter. Inference: 0.3789 s/iter. Eval: 0.0881 s/iter. Total: 0.4695 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0026 s/iter. Inference: 0.3798 s/iter. Eval: 0.0777 s/iter. Total: 0.4602 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3778 s/iter. Eval: 0.0764 s/iter. Total: 0.4571 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3806 s/iter. Eval: 0.0733 s/iter. Total: 0.4568 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3823 s/iter. Eval: 0.0717 s/iter. Total: 0.4570 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0029 s/iter. Inference: 0.3822 s/iter. Eval: 0.0698 s/iter. Total: 0.4550 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalq65sfnje ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.741 | 83.085 | 66.282 | 133 | | Things | 61.704 | 84.089 | 72.868 | 80 | | Stuff | 46.741 | 81.569 | 56.341 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.56s) creating index... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.51 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=2.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.58 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.459 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.698 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.493 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.551 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.570 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.379 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.876 | 69.771 | 49.317 | 26.455 | 49.824 | 67.653 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.829 | bicycle | 22.946 | car | 43.455 | | motorcycle | 42.094 | airplane | 61.168 | bus | 71.442 | | train | 74.566 | truck | 43.652 | boat | 31.602 | | traffic light | 27.716 | fire hydrant | 71.008 | stop sign | 68.282 | | parking meter | 52.084 | bench | 26.852 | bird | 34.661 | | cat | 76.245 | dog | 71.081 | horse | 50.710 | | sheep | 54.030 | cow | 56.721 | elephant | 66.963 | | bear | 80.076 | zebra | 65.467 | giraffe | 62.375 | | backpack | 23.470 | umbrella | 55.413 | handbag | 24.252 | | tie | 41.087 | suitcase | 53.574 | frisbee | 69.937 | | skis | 9.122 | snowboard | 35.143 | sports ball | 51.481 | | kite | 38.333 | baseball bat | 38.099 | baseball glove | 49.793 | | skateboard | 43.861 | surfboard | 46.253 | tennis racket | 63.416 | | bottle | 42.522 | wine glass | 38.875 | cup | 49.861 | | fork | 26.425 | knife | 24.685 | spoon | 22.171 | | bowl | 41.011 | banana | 21.866 | apple | 26.672 | | sandwich | 50.585 | orange | 29.943 | broccoli | 23.978 | | carrot | 23.074 | hot dog | 35.710 | pizza | 52.126 | | donut | 56.493 | cake | 46.573 | chair | 28.652 | | couch | 44.801 | potted plant | 23.830 | bed | 41.920 | | dining table | 14.608 | toilet | 69.316 | tv | 65.933 | | laptop | 71.602 | mouse | 63.146 | remote | 44.091 | | keyboard | 59.024 | cell phone | 45.632 | microwave | 66.611 | | oven | 32.424 | toaster | 48.003 | sink | 44.913 | | refrigerator | 70.241 | book | 14.461 | clock | 54.900 | | vase | 42.249 | scissors | 38.079 | teddy bear | 58.045 | | hair drier | 37.930 | toothbrush | 29.810 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.12552520442503, 'fwIoU': 71.49221963889771, 'IoU-person': 88.38828379624078, 'IoU-bicycle': 72.00778236205836, 'IoU-car': 72.59751261944601, 'IoU-motorcycle': 88.58484828288894, 'IoU-airplane': 89.26577317316784, 'IoU-bus': 87.6541886130688, 'IoU-train': 88.46168773869734, 'IoU-truck': 70.07187481200286, 'IoU-boat': 70.93563281716283, 'IoU-traffic light': 78.50735377246986, 'IoU-fire hydrant': 93.10616089902899, 'IoU-stop sign': 85.15504758836536, 'IoU-parking meter': 84.76675356080695, 'IoU-bench': 65.18108139124033, 'IoU-bird': 76.96750463870859, 'IoU-cat': 89.50884806747226, 'IoU-dog': 84.12639817191032, 'IoU-horse': 87.79702418469874, 'IoU-sheep': 86.64229704944434, 'IoU-cow': 89.91448121070282, 'IoU-elephant': 86.69160255646514, 'IoU-bear': 85.51618391611137, 'IoU-zebra': 86.8388777218557, 'IoU-giraffe': 89.6001507429437, 'IoU-backpack': 51.63055106107003, 'IoU-umbrella': 77.99014141168384, 'IoU-handbag': 50.207183454632506, 'IoU-tie': 73.1834617352497, 'IoU-suitcase': 77.37436400746101, 'IoU-frisbee': 74.28469856997053, 'IoU-skis': 58.65918903570623, 'IoU-snowboard': 71.27137381446292, 'IoU-sports ball': 77.60451655056274, 'IoU-kite': 79.33938118099623, 'IoU-baseball bat': 70.06632859076981, 'IoU-baseball glove': 79.2185955730154, 'IoU-skateboard': 85.78142098585693, 'IoU-surfboard': 80.29516922157569, 'IoU-tennis racket': 90.99214582382561, 'IoU-bottle': 70.02431462268818, 'IoU-wine glass': 82.53165159877837, 'IoU-cup': 69.1267624970105, 'IoU-fork': 68.80438535118383, 'IoU-knife': 64.9755059029015, 'IoU-spoon': 58.59700780269742, 'IoU-bowl': 60.449154634912915, 'IoU-banana': 83.27755403898547, 'IoU-apple': 59.910577087349914, 'IoU-sandwich': 68.34726882086713, 'IoU-orange': 78.33334892197358, 'IoU-broccoli': 64.31548690700724, 'IoU-carrot': 65.41412311738543, 'IoU-hot dog': 68.26267061978378, 'IoU-pizza': 81.42398729085328, 'IoU-donut': 64.94404354506969, 'IoU-cake': 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32.22909417070609, 'IoU-curtain': 70.71437100263793, 'IoU-door-stuff': 47.604852134124194, 'IoU-floor-wood': 66.51620691508266, 'IoU-flower': 44.30467739798003, 'IoU-fruit': 49.03357508276277, 'IoU-gravel': 34.26842589467626, 'IoU-house': 26.77408951820964, 'IoU-light': 43.69239231196079, 'IoU-mirror-stuff': 62.631984411916385, 'IoU-net': 42.97993237599736, 'IoU-pillow': 19.584735086761707, 'IoU-platform': 29.340010738799137, 'IoU-playingfield': 71.0480706244373, 'IoU-railroad': 65.75232680197055, 'IoU-river': 53.160419245666745, 'IoU-road': 67.5156175929016, 'IoU-roof': 17.016346759193688, 'IoU-sand': 65.9282018215983, 'IoU-sea': 85.99518293848561, 'IoU-shelf': 39.42504551943064, 'IoU-snow': 92.22727003622909, 'IoU-stairs': 36.36718317228203, 'IoU-tent': 9.807183704472571, 'IoU-towel': 45.336101041080404, 'IoU-wall-brick': 52.788976733732305, 'IoU-wall-stone': 29.60045560629332, 'IoU-wall-tile': 67.8465127420982, 'IoU-wall-wood': 45.11821635280646, 'IoU-water-other': 26.686043013705994, 'IoU-window-blind': 50.011814923710105, 'IoU-window-other': 49.73935363442633, 'IoU-tree-merged': 81.57783942276387, 'IoU-fence-merged': 54.623807181954916, 'IoU-ceiling-merged': 67.22200043951986, 'IoU-sky-other-merged': 93.64484772997994, 'IoU-cabinet-merged': 63.21583310218735, 'IoU-table-merged': 40.981949003192334, 'IoU-floor-other-merged': 54.390051907159446, 'IoU-pavement-merged': 58.57628512619949, 'IoU-mountain-merged': 58.49728599476145, 'IoU-grass-merged': 72.69541694038062, 'IoU-dirt-merged': 47.25127060086551, 'IoU-paper-merged': 38.38336188310505, 'IoU-food-other-merged': 43.899352767176794, 'IoU-building-other-merged': 60.73109475689734, 'IoU-rock-merged': 65.86416318280433, 'IoU-wall-other-merged': 67.41078946330052, 'IoU-rug-merged': 67.2427875465256, 'mACC': 76.94134981938603, 'pACC': 82.17296732885573, 'ACC-person': 92.66507153955096, 'ACC-bicycle': 79.91654480795354, 'ACC-car': 84.41791323293558, 'ACC-motorcycle': 93.16196531315265, 'ACC-airplane': 93.4481576869527, 'ACC-bus': 94.10702950715813, 'ACC-train': 95.2829612927839, 'ACC-truck': 79.77071164214742, 'ACC-boat': 79.56204218475, 'ACC-traffic light': 91.2314919100074, 'ACC-fire hydrant': 96.0244377356557, 'ACC-stop sign': 88.598694715424, 'ACC-parking meter': 87.94972116995868, 'ACC-bench': 76.99925160879424, 'ACC-bird': 82.68632410593533, 'ACC-cat': 93.03611273822594, 'ACC-dog': 86.8167884404968, 'ACC-horse': 92.82278784522006, 'ACC-sheep': 90.80780683108436, 'ACC-cow': 93.27397467702167, 'ACC-elephant': 88.61535743567477, 'ACC-bear': 87.23451832350901, 'ACC-zebra': 88.89335544277507, 'ACC-giraffe': 93.47141141226504, 'ACC-backpack': 67.67841649065302, 'ACC-umbrella': 81.66287690667544, 'ACC-handbag': 71.29726471469398, 'ACC-tie': 81.76381536391273, 'ACC-suitcase': 85.65657401055915, 'ACC-frisbee': 94.2210909090909, 'ACC-skis': 73.55577740147258, 'ACC-snowboard': 81.78883691312973, 'ACC-sports ball': 87.1714164625133, 'ACC-kite': 85.70761567484439, 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89.48806908550605, 'ACC-mouse': 86.76125234029199, 'ACC-remote': 75.70946880945446, 'ACC-keyboard': 61.942721979740654, 'ACC-cell phone': 85.3953199479404, 'ACC-microwave': 84.41042841740325, 'ACC-oven': 89.6203984979685, 'ACC-toaster': 90.98660170523752, 'ACC-sink': 77.96635831653643, 'ACC-refrigerator': 92.11013914171114, 'ACC-book': 67.07443753327169, 'ACC-clock': 79.30289604606484, 'ACC-vase': 63.756899624580754, 'ACC-scissors': 83.59226510205768, 'ACC-teddy bear': 90.08911195685361, 'ACC-hair drier': 63.2283038738508, 'ACC-toothbrush': 83.56236970118137, 'ACC-banner': 79.39369587133358, 'ACC-blanket': 33.969901290380285, 'ACC-bridge': 53.79058185955324, 'ACC-cardboard': 65.12141922349862, 'ACC-counter': 54.97862030448789, 'ACC-curtain': 83.99102520986344, 'ACC-door-stuff': 71.939623259419, 'ACC-floor-wood': 84.54319288828277, 'ACC-flower': 64.29019373529789, 'ACC-fruit': 68.46293179654072, 'ACC-gravel': 50.51996863528093, 'ACC-house': 32.84113013471232, 'ACC-light': 64.33345375722543, 'ACC-mirror-stuff': 78.19642478632706, 'ACC-net': 67.48109423947962, 'ACC-pillow': 49.82483836818526, 'ACC-platform': 47.01473671419049, 'ACC-playingfield': 90.95218652337951, 'ACC-railroad': 81.00413205965411, 'ACC-river': 75.28201987429794, 'ACC-road': 82.76448389107422, 'ACC-roof': 22.224245906114646, 'ACC-sand': 73.49906337329294, 'ACC-sea': 90.75446668936613, 'ACC-shelf': 55.64252137606196, 'ACC-snow': 95.58889149549348, 'ACC-stairs': 58.10538297946224, 'ACC-tent': 11.528432401687919, 'ACC-towel': 53.87883645737992, 'ACC-wall-brick': 69.35468725406778, 'ACC-wall-stone': 34.25838202010632, 'ACC-wall-tile': 87.9070679698312, 'ACC-wall-wood': 60.74889204816019, 'ACC-water-other': 43.523899474585775, 'ACC-window-blind': 65.15902169015662, 'ACC-window-other': 75.01719873084156, 'ACC-tree-merged': 89.17518297851443, 'ACC-fence-merged': 73.28343493861975, 'ACC-ceiling-merged': 84.43966797220187, 'ACC-sky-other-merged': 96.92142883178175, 'ACC-cabinet-merged': 78.16454132886594, 'ACC-table-merged': 56.16189930571287, 'ACC-floor-other-merged': 64.77759646808279, 'ACC-pavement-merged': 74.01280113903367, 'ACC-mountain-merged': 70.89552469227924, 'ACC-grass-merged': 84.13476698372547, 'ACC-dirt-merged': 65.92765620095689, 'ACC-paper-merged': 54.02596841675755, 'ACC-food-other-merged': 63.01600649966254, 'ACC-building-other-merged': 75.02179889229926, 'ACC-rock-merged': 82.05120448579223, 'ACC-wall-other-merged': 81.30023343431212, 'ACC-rug-merged': 83.62473421736517})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3437 s/iter. Inference: 0.1935 s/iter. Eval: 0.0000 s/iter. Total: 0.5373 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3309 s/iter. Inference: 0.4591 s/iter. Eval: 0.0000 s/iter. Total: 0.7903 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3619 s/iter. Inference: 0.5076 s/iter. Eval: 0.0000 s/iter. Total: 0.8697 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3690371671056483, 'noc@0.8': 2.42551946151595, 'noc@0.85': 2.845771144278607, 'noc@0.9': 3.584723441615452, 'miou@iter1': 0.8751246363299612} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1433 s/iter. Eval: 0.0010 s/iter. Total: 0.1459 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.32064056396484, 'precision@0.6': 72.79440307617188, 'precision@0.7': 68.20832061767578, 'precision@0.8': 59.7357177734375, 'precision@0.9': 33.89039993286133, 'cIoU': 61.6942253112793, 'mIoU': 66.84247589111328} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.741235354812424, 'SQ': 83.08488607984141, 'RQ': 66.28204177907958, 'PQ_th': 61.70366687914877, 'SQ_th': 84.0889857693788, 'RQ_th': 72.8679081406568, 'PQ_st': 46.74133871430473, 'SQ_st': 81.56926390695467, 'RQ_st': 56.34111142198197}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.875589960827746, 'AP50': 69.77055172902857, 'AP75': 49.31727318992799, 'APs': 26.454528668283807, 'APm': 49.82361215455684, 'APl': 67.65251542118752, 'AP-person': 48.828818790944524, 'AP-bicycle': 22.94599458560316, 'AP-car': 43.45479112560992, 'AP-motorcycle': 42.09415262377615, 'AP-airplane': 61.167676912045884, 'AP-bus': 71.44209519238554, 'AP-train': 74.56582343188862, 'AP-truck': 43.65242758333846, 'AP-boat': 31.601697008285452, 'AP-traffic light': 27.716119002040713, 'AP-fire hydrant': 71.00763785370961, 'AP-stop sign': 68.28193054817505, 'AP-parking meter': 52.08436039322281, 'AP-bench': 26.851589842209883, 'AP-bird': 34.660542332396446, 'AP-cat': 76.2450546795755, 'AP-dog': 71.08071232275988, 'AP-horse': 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58.044666006549214, 'AP-hair drier': 37.92968204383463, 'AP-toothbrush': 29.809855896260096}), ('sem_seg', {'mIoU': 65.12552520442503, 'fwIoU': 71.49221963889771, 'IoU-person': 88.38828379624078, 'IoU-bicycle': 72.00778236205836, 'IoU-car': 72.59751261944601, 'IoU-motorcycle': 88.58484828288894, 'IoU-airplane': 89.26577317316784, 'IoU-bus': 87.6541886130688, 'IoU-train': 88.46168773869734, 'IoU-truck': 70.07187481200286, 'IoU-boat': 70.93563281716283, 'IoU-traffic light': 78.50735377246986, 'IoU-fire hydrant': 93.10616089902899, 'IoU-stop sign': 85.15504758836536, 'IoU-parking meter': 84.76675356080695, 'IoU-bench': 65.18108139124033, 'IoU-bird': 76.96750463870859, 'IoU-cat': 89.50884806747226, 'IoU-dog': 84.12639817191032, 'IoU-horse': 87.79702418469874, 'IoU-sheep': 86.64229704944434, 'IoU-cow': 89.91448121070282, 'IoU-elephant': 86.69160255646514, 'IoU-bear': 85.51618391611137, 'IoU-zebra': 86.8388777218557, 'IoU-giraffe': 89.6001507429437, 'IoU-backpack': 51.63055106107003, 'IoU-umbrella': 77.99014141168384, 'IoU-handbag': 50.207183454632506, 'IoU-tie': 73.1834617352497, 'IoU-suitcase': 77.37436400746101, 'IoU-frisbee': 74.28469856997053, 'IoU-skis': 58.65918903570623, 'IoU-snowboard': 71.27137381446292, 'IoU-sports ball': 77.60451655056274, 'IoU-kite': 79.33938118099623, 'IoU-baseball bat': 70.06632859076981, 'IoU-baseball glove': 79.2185955730154, 'IoU-skateboard': 85.78142098585693, 'IoU-surfboard': 80.29516922157569, 'IoU-tennis racket': 90.99214582382561, 'IoU-bottle': 70.02431462268818, 'IoU-wine glass': 82.53165159877837, 'IoU-cup': 69.1267624970105, 'IoU-fork': 68.80438535118383, 'IoU-knife': 64.9755059029015, 'IoU-spoon': 58.59700780269742, 'IoU-bowl': 60.449154634912915, 'IoU-banana': 83.27755403898547, 'IoU-apple': 59.910577087349914, 'IoU-sandwich': 68.34726882086713, 'IoU-orange': 78.33334892197358, 'IoU-broccoli': 64.31548690700724, 'IoU-carrot': 65.41412311738543, 'IoU-hot dog': 68.26267061978378, 'IoU-pizza': 81.42398729085328, 'IoU-donut': 64.94404354506969, 'IoU-cake': 76.24378048196697, 'IoU-chair': 62.26002432112425, 'IoU-couch': 71.06321599341516, 'IoU-potted plant': 43.22884962948513, 'IoU-bed': 72.96069520160896, 'IoU-dining table': 55.429575673154474, 'IoU-toilet': 75.95168725546742, 'IoU-tv': 76.42806992990681, 'IoU-laptop': 76.94530618911023, 'IoU-mouse': 73.62611123356109, 'IoU-remote': 70.17134123739585, 'IoU-keyboard': 58.43047818216062, 'IoU-cell phone': 75.51053775951452, 'IoU-microwave': 79.27643192307445, 'IoU-oven': 72.76513164679234, 'IoU-toaster': 85.87704171591326, 'IoU-sink': 68.27989578158451, 'IoU-refrigerator': 81.5845922472421, 'IoU-book': 50.93244990561677, 'IoU-clock': 74.32642512017426, 'IoU-vase': 56.23676823497937, 'IoU-scissors': 78.6774952522313, 'IoU-teddy bear': 84.93570053912136, 'IoU-hair drier': 46.81263566054833, 'IoU-toothbrush': 75.24031504931445, 'IoU-banner': 31.318839487780192, 'IoU-blanket': 18.294022601207892, 'IoU-bridge': 38.48069104998703, 'IoU-cardboard': 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'IoU-water-other': 26.686043013705994, 'IoU-window-blind': 50.011814923710105, 'IoU-window-other': 49.73935363442633, 'IoU-tree-merged': 81.57783942276387, 'IoU-fence-merged': 54.623807181954916, 'IoU-ceiling-merged': 67.22200043951986, 'IoU-sky-other-merged': 93.64484772997994, 'IoU-cabinet-merged': 63.21583310218735, 'IoU-table-merged': 40.981949003192334, 'IoU-floor-other-merged': 54.390051907159446, 'IoU-pavement-merged': 58.57628512619949, 'IoU-mountain-merged': 58.49728599476145, 'IoU-grass-merged': 72.69541694038062, 'IoU-dirt-merged': 47.25127060086551, 'IoU-paper-merged': 38.38336188310505, 'IoU-food-other-merged': 43.899352767176794, 'IoU-building-other-merged': 60.73109475689734, 'IoU-rock-merged': 65.86416318280433, 'IoU-wall-other-merged': 67.41078946330052, 'IoU-rug-merged': 67.2427875465256, 'mACC': 76.94134981938603, 'pACC': 82.17296732885573, 'ACC-person': 92.66507153955096, 'ACC-bicycle': 79.91654480795354, 'ACC-car': 84.41791323293558, 'ACC-motorcycle': 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'ACC-laptop': 89.48806908550605, 'ACC-mouse': 86.76125234029199, 'ACC-remote': 75.70946880945446, 'ACC-keyboard': 61.942721979740654, 'ACC-cell phone': 85.3953199479404, 'ACC-microwave': 84.41042841740325, 'ACC-oven': 89.6203984979685, 'ACC-toaster': 90.98660170523752, 'ACC-sink': 77.96635831653643, 'ACC-refrigerator': 92.11013914171114, 'ACC-book': 67.07443753327169, 'ACC-clock': 79.30289604606484, 'ACC-vase': 63.756899624580754, 'ACC-scissors': 83.59226510205768, 'ACC-teddy bear': 90.08911195685361, 'ACC-hair drier': 63.2283038738508, 'ACC-toothbrush': 83.56236970118137, 'ACC-banner': 79.39369587133358, 'ACC-blanket': 33.969901290380285, 'ACC-bridge': 53.79058185955324, 'ACC-cardboard': 65.12141922349862, 'ACC-counter': 54.97862030448789, 'ACC-curtain': 83.99102520986344, 'ACC-door-stuff': 71.939623259419, 'ACC-floor-wood': 84.54319288828277, 'ACC-flower': 64.29019373529789, 'ACC-fruit': 68.46293179654072, 'ACC-gravel': 50.51996863528093, 'ACC-house': 32.84113013471232, 'ACC-light': 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78.16454132886594, 'ACC-table-merged': 56.16189930571287, 'ACC-floor-other-merged': 64.77759646808279, 'ACC-pavement-merged': 74.01280113903367, 'ACC-mountain-merged': 70.89552469227924, 'ACC-grass-merged': 84.13476698372547, 'ACC-dirt-merged': 65.92765620095689, 'ACC-paper-merged': 54.02596841675755, 'ACC-food-other-merged': 63.01600649966254, 'ACC-building-other-merged': 75.02179889229926, 'ACC-rock-merged': 82.05120448579223, 'ACC-wall-other-merged': 81.30023343431212, 'ACC-rug-merged': 83.62473421736517})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3690371671056483, 'noc@0.8': 2.42551946151595, 'noc@0.85': 2.845771144278607, 'noc@0.9': 3.584723441615452, 'miou@iter1': 0.8751246363299612}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.32064056396484, 'precision@0.6': 72.79440307617188, 'precision@0.7': 68.20832061767578, 'precision@0.8': 59.7357177734375, 'precision@0.9': 33.89039993286133, 'cIoU': 61.6942253112793, 'mIoU': 66.84247589111328}}} INFO:trainer.default_trainer:This epoch takes 0:56:36.837304 INFO:trainer.default_trainer:PROGRESS: 80.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 40 training. INFO:trainer.default_trainer:epochs[ 40] optim steps[73100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20948/0.75425, loss_mask_bce_0: 0.21447/0.30087, loss_mask_dice_0: 0.37564/1.02039, loss_spatial_bce_0: 0.04697/0.08466, loss_spatial_dice_0: 0.06286/0.17897, loss_spatial_ce_0: 0.00031/0.05601, loss_grounding_bce_0: 0.02741/0.08067, loss_grounding_dice_0: 0.06643/0.15041, loss_grounding_ce_0: 0.23914/0.24878, loss_mask_ce_1: 0.21444/0.75499, loss_mask_bce_1: 0.21357/0.30168, loss_mask_dice_1: 0.36656/1.02461, loss_spatial_bce_1: 0.04532/0.08509, loss_spatial_dice_1: 0.06448/0.18186, loss_spatial_ce_1: 0.00019/0.05976, loss_grounding_bce_1: 0.02791/0.08085, loss_grounding_dice_1: 0.06707/0.15113, loss_grounding_ce_1: 0.23759/0.25015, loss_mask_ce_2: 0.20049/0.76260, loss_mask_bce_2: 0.20944/0.30204, loss_mask_dice_2: 0.38014/1.02561, loss_spatial_bce_2: 0.04592/0.08519, loss_spatial_dice_2: 0.06386/0.18247, loss_spatial_ce_2: 0.00033/0.06189, loss_grounding_bce_2: 0.02843/0.08084, loss_grounding_dice_2: 0.06824/0.15106, loss_grounding_ce_2: 0.22471/0.25297, loss_mask_ce_3: 0.21865/0.76698, loss_mask_bce_3: 0.20100/0.30339, loss_mask_dice_3: 0.34889/1.02351, loss_spatial_bce_3: 0.04686/0.08735, loss_spatial_dice_3: 0.06890/0.18381, loss_spatial_ce_3: 0.00070/0.06679, loss_grounding_bce_3: 0.02837/0.08120, loss_grounding_dice_3: 0.06803/0.15069, loss_grounding_ce_3: 0.23250/0.25412, loss_mask_ce_4: 0.22287/0.77297, loss_mask_bce_4: 0.19424/0.30606, loss_mask_dice_4: 0.36586/1.04289, loss_spatial_bce_4: 0.05164/0.08967, loss_spatial_dice_4: 0.07642/0.19230, loss_spatial_ce_4: 0.00051/0.08052, loss_grounding_bce_4: 0.02846/0.08192, loss_grounding_dice_4: 0.06726/0.15338, loss_grounding_ce_4: 0.23905/0.25843, loss_mask_ce_5: 0.21218/0.79815, loss_mask_bce_5: 0.21352/0.30791, loss_mask_dice_5: 0.38596/1.05086, loss_spatial_bce_5: 0.05539/0.09209, loss_spatial_dice_5: 0.08554/0.19554, loss_spatial_ce_5: 0.00096/0.09402, loss_grounding_bce_5: 0.02928/0.08220, loss_grounding_dice_5: 0.07687/0.15413, loss_grounding_ce_5: 0.23368/0.27632, loss_mask_ce_6: 0.31900/0.82504, loss_mask_bce_6: 0.19500/0.31005, loss_mask_dice_6: 0.37617/1.05481, loss_spatial_bce_6: 0.06156/0.09749, loss_spatial_dice_6: 0.09301/0.19786, loss_spatial_ce_6: 0.00259/0.11861, loss_grounding_bce_6: 0.02807/0.08304, loss_grounding_dice_6: 0.07402/0.15465, loss_grounding_ce_6: 0.28897/0.28502, loss_mask_ce_7: 0.31903/0.88115, loss_mask_bce_7: 0.21643/0.31724, loss_mask_dice_7: 0.36852/1.10043, loss_spatial_bce_7: 0.05307/0.10677, loss_spatial_dice_7: 0.08874/0.22294, loss_spatial_ce_7: 0.01920/0.15428, loss_grounding_bce_7: 0.02686/0.08475, loss_grounding_dice_7: 0.06961/0.16025, loss_grounding_ce_7: 0.27944/0.31830, loss_mask_ce_8: 0.64355/1.01520, loss_mask_bce_8: 0.22839/0.33328, loss_mask_dice_8: 0.40991/1.17682, loss_spatial_bce_8: 0.05802/0.12349, loss_spatial_dice_8: 0.13283/0.25783, loss_spatial_ce_8: 0.06692/0.19999, loss_grounding_bce_8: 0.03579/0.08888, loss_grounding_dice_8: 0.08619/0.17003, loss_grounding_ce_8: 0.34269/0.41665, loss_mask_ce_9: 4.04481/3.47554, loss_mask_bce_9: 0.40817/0.36017, loss_mask_dice_9: 1.24531/1.75992, loss_spatial_bce_9: 0.45968/0.35444, loss_spatial_dice_9: 0.79708/0.79312, loss_spatial_ce_9: 1.22767/1.38721, loss_grounding_bce_9: 0.08447/0.10098, loss_grounding_dice_9: 0.32648/0.24221, loss_grounding_ce_9: 0.53855/0.67030] items per batch[64] items per second[0.17] total items[4678400] mini batches[ 73100] memory[4999] epoch remaining[1:02:35] INFO:trainer.default_trainer:epochs[ 40] optim steps[73200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.44861/0.75426, loss_mask_bce_0: 0.53994/0.30088, loss_mask_dice_0: 1.61993/1.02043, loss_spatial_bce_0: 0.06406/0.08466, loss_spatial_dice_0: 0.25222/0.17896, loss_spatial_ce_0: 0.03384/0.05600, loss_grounding_bce_0: 0.10907/0.08066, loss_grounding_dice_0: 0.30280/0.15042, loss_grounding_ce_0: 0.21759/0.24872, loss_mask_ce_1: 0.44573/0.75501, loss_mask_bce_1: 0.54042/0.30169, loss_mask_dice_1: 1.70500/1.02468, loss_spatial_bce_1: 0.06771/0.08510, loss_spatial_dice_1: 0.26783/0.18186, loss_spatial_ce_1: 0.03144/0.05976, loss_grounding_bce_1: 0.11017/0.08084, loss_grounding_dice_1: 0.32039/0.15114, loss_grounding_ce_1: 0.19460/0.25012, loss_mask_ce_2: 0.50260/0.76262, loss_mask_bce_2: 0.53181/0.30205, loss_mask_dice_2: 1.65764/1.02565, loss_spatial_bce_2: 0.06144/0.08519, loss_spatial_dice_2: 0.25613/0.18247, loss_spatial_ce_2: 0.03225/0.06189, loss_grounding_bce_2: 0.11164/0.08083, loss_grounding_dice_2: 0.33350/0.15107, loss_grounding_ce_2: 0.24961/0.25291, loss_mask_ce_3: 0.57429/0.76701, loss_mask_bce_3: 0.53806/0.30341, loss_mask_dice_3: 1.59422/1.02355, loss_spatial_bce_3: 0.06535/0.08734, loss_spatial_dice_3: 0.25260/0.18380, loss_spatial_ce_3: 0.03464/0.06677, loss_grounding_bce_3: 0.11406/0.08120, loss_grounding_dice_3: 0.31726/0.15070, loss_grounding_ce_3: 0.17962/0.25407, loss_mask_ce_4: 0.65649/0.77296, loss_mask_bce_4: 0.54300/0.30608, loss_mask_dice_4: 1.63769/1.04293, loss_spatial_bce_4: 0.08466/0.08968, loss_spatial_dice_4: 0.29757/0.19230, loss_spatial_ce_4: 0.02823/0.08052, loss_grounding_bce_4: 0.11851/0.08193, loss_grounding_dice_4: 0.31733/0.15340, loss_grounding_ce_4: 0.21365/0.25837, loss_mask_ce_5: 0.72117/0.79813, loss_mask_bce_5: 0.51713/0.30793, loss_mask_dice_5: 1.61400/1.05089, loss_spatial_bce_5: 0.07314/0.09209, loss_spatial_dice_5: 0.28312/0.19554, loss_spatial_ce_5: 0.04056/0.09404, loss_grounding_bce_5: 0.11184/0.08220, loss_grounding_dice_5: 0.33843/0.15414, loss_grounding_ce_5: 0.19885/0.27627, loss_mask_ce_6: 0.65742/0.82505, loss_mask_bce_6: 0.54024/0.31006, loss_mask_dice_6: 1.69151/1.05484, loss_spatial_bce_6: 0.06937/0.09750, loss_spatial_dice_6: 0.25701/0.19786, loss_spatial_ce_6: 0.06461/0.11861, loss_grounding_bce_6: 0.10833/0.08304, loss_grounding_dice_6: 0.30494/0.15465, loss_grounding_ce_6: 0.26804/0.28494, loss_mask_ce_7: 0.73699/0.88113, loss_mask_bce_7: 0.54108/0.31727, loss_mask_dice_7: 1.82407/1.10044, loss_spatial_bce_7: 0.07296/0.10678, loss_spatial_dice_7: 0.28285/0.22295, loss_spatial_ce_7: 0.07463/0.15427, loss_grounding_bce_7: 0.11024/0.08476, loss_grounding_dice_7: 0.34503/0.16026, loss_grounding_ce_7: 0.21255/0.31822, loss_mask_ce_8: 0.53598/1.01519, loss_mask_bce_8: 0.52089/0.33330, loss_mask_dice_8: 1.81929/1.17685, loss_spatial_bce_8: 0.08433/0.12350, loss_spatial_dice_8: 0.30193/0.25782, loss_spatial_ce_8: 0.10672/0.19994, loss_grounding_bce_8: 0.10724/0.08889, loss_grounding_dice_8: 0.31947/0.17003, loss_grounding_ce_8: 0.19864/0.41651, loss_mask_ce_9: 3.69865/3.47538, loss_mask_bce_9: 0.55423/0.36019, loss_mask_dice_9: 2.08886/1.75990, loss_spatial_bce_9: 0.29914/0.35447, loss_spatial_dice_9: 0.85429/0.79311, loss_spatial_ce_9: 1.10580/1.38716, loss_grounding_bce_9: 0.09669/0.10098, loss_grounding_dice_9: 0.37536/0.24221, loss_grounding_ce_9: 0.65590/0.67016] items per batch[64] items per second[0.37] total items[4684800] mini batches[ 73200] memory[4999] epoch remaining[0:51:17] INFO:trainer.default_trainer:epochs[ 40] optim steps[73300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.34568/0.75415, loss_mask_bce_0: 0.05512/0.30086, loss_mask_dice_0: 1.16720/1.02042, loss_spatial_bce_0: 0.00889/0.08464, loss_spatial_dice_0: 0.27963/0.17894, loss_spatial_ce_0: 0.02389/0.05596, loss_grounding_bce_0: 0.00942/0.08066, loss_grounding_dice_0: 0.11055/0.15043, loss_grounding_ce_0: 0.01028/0.24876, loss_mask_ce_1: 1.21622/0.75495, loss_mask_bce_1: 0.04814/0.30167, loss_mask_dice_1: 0.97310/1.02466, loss_spatial_bce_1: 0.00821/0.08508, loss_spatial_dice_1: 0.28371/0.18184, loss_spatial_ce_1: 0.07177/0.05973, loss_grounding_bce_1: 0.00683/0.08084, loss_grounding_dice_1: 0.10622/0.15115, loss_grounding_ce_1: 0.01759/0.25015, loss_mask_ce_2: 0.94403/0.76254, loss_mask_bce_2: 0.05564/0.30204, loss_mask_dice_2: 1.50710/1.02564, loss_spatial_bce_2: 0.00799/0.08518, loss_spatial_dice_2: 0.28581/0.18245, loss_spatial_ce_2: 0.12837/0.06185, loss_grounding_bce_2: 0.00525/0.08083, loss_grounding_dice_2: 0.11199/0.15108, loss_grounding_ce_2: 0.03286/0.25292, loss_mask_ce_3: 0.93136/0.76692, loss_mask_bce_3: 0.04960/0.30339, loss_mask_dice_3: 1.15187/1.02358, loss_spatial_bce_3: 0.00882/0.08733, loss_spatial_dice_3: 0.31120/0.18378, loss_spatial_ce_3: 0.03034/0.06674, loss_grounding_bce_3: 0.00919/0.08120, loss_grounding_dice_3: 0.10578/0.15070, loss_grounding_ce_3: 0.06525/0.25409, loss_mask_ce_4: 0.96034/0.77284, loss_mask_bce_4: 0.04954/0.30606, loss_mask_dice_4: 1.23930/1.04293, loss_spatial_bce_4: 0.01024/0.08966, loss_spatial_dice_4: 0.33623/0.19229, loss_spatial_ce_4: 0.06451/0.08048, loss_grounding_bce_4: 0.00733/0.08193, loss_grounding_dice_4: 0.10660/0.15340, loss_grounding_ce_4: 0.16075/0.25845, loss_mask_ce_5: 1.41051/0.79808, loss_mask_bce_5: 0.06337/0.30792, loss_mask_dice_5: 1.32539/1.05087, loss_spatial_bce_5: 0.01026/0.09208, loss_spatial_dice_5: 0.33246/0.19552, loss_spatial_ce_5: 0.04430/0.09400, loss_grounding_bce_5: 0.00751/0.08220, loss_grounding_dice_5: 0.13711/0.15414, loss_grounding_ce_5: 0.16085/0.27633, loss_mask_ce_6: 1.76902/0.82495, loss_mask_bce_6: 0.05439/0.31005, loss_mask_dice_6: 1.40348/1.05484, loss_spatial_bce_6: 0.01145/0.09749, loss_spatial_dice_6: 0.30037/0.19785, loss_spatial_ce_6: 0.06498/0.11859, loss_grounding_bce_6: 0.00814/0.08304, loss_grounding_dice_6: 0.10377/0.15464, loss_grounding_ce_6: 0.06812/0.28501, loss_mask_ce_7: 1.11415/0.88102, loss_mask_bce_7: 0.05263/0.31725, loss_mask_dice_7: 1.19352/1.10044, loss_spatial_bce_7: 0.01009/0.10676, loss_spatial_dice_7: 0.34965/0.22293, loss_spatial_ce_7: 0.24229/0.15421, loss_grounding_bce_7: 0.01301/0.08475, loss_grounding_dice_7: 0.13647/0.16025, loss_grounding_ce_7: 0.04339/0.31829, loss_mask_ce_8: 1.52511/1.01507, loss_mask_bce_8: 0.04688/0.33328, loss_mask_dice_8: 1.62501/1.17684, loss_spatial_bce_8: 0.01474/0.12348, loss_spatial_dice_8: 0.42316/0.25780, loss_spatial_ce_8: 0.09214/0.19990, loss_grounding_bce_8: 0.00884/0.08888, loss_grounding_dice_8: 0.12655/0.17003, loss_grounding_ce_8: 0.25585/0.41654, loss_mask_ce_9: 2.61165/3.47528, loss_mask_bce_9: 0.04958/0.36017, loss_mask_dice_9: 1.36244/1.75973, loss_spatial_bce_9: 0.01556/0.35448, loss_spatial_dice_9: 0.79260/0.79312, loss_spatial_ce_9: 1.54962/1.38725, loss_grounding_bce_9: 0.00849/0.10099, loss_grounding_dice_9: 0.12656/0.24218, loss_grounding_ce_9: 0.41614/0.67020] items per batch[64] items per second[0.37] total items[4691200] mini batches[ 73300] memory[4999] epoch remaining[0:47:39] INFO:trainer.default_trainer:epochs[ 40] optim steps[73400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.80588/0.75403, loss_mask_bce_0: 1.18949/0.30091, loss_mask_dice_0: 3.28754/1.02024, loss_spatial_bce_0: 0.04674/0.08464, loss_spatial_dice_0: 0.15749/0.17891, loss_spatial_ce_0: 0.00890/0.05596, loss_grounding_bce_0: 0.07630/0.08067, loss_grounding_dice_0: 0.18216/0.15041, loss_grounding_ce_0: 0.41205/0.24875, loss_mask_ce_1: 1.67180/0.75485, loss_mask_bce_1: 1.22598/0.30171, loss_mask_dice_1: 3.44860/1.02449, loss_spatial_bce_1: 0.04554/0.08508, loss_spatial_dice_1: 0.15943/0.18181, loss_spatial_ce_1: 0.00355/0.05973, loss_grounding_bce_1: 0.07894/0.08085, loss_grounding_dice_1: 0.29570/0.15115, loss_grounding_ce_1: 0.40555/0.25013, loss_mask_ce_2: 1.68017/0.76244, loss_mask_bce_2: 1.26002/0.30208, loss_mask_dice_2: 3.45352/1.02547, loss_spatial_bce_2: 0.05199/0.08517, loss_spatial_dice_2: 0.15707/0.18242, loss_spatial_ce_2: 0.00497/0.06185, loss_grounding_bce_2: 0.08517/0.08084, loss_grounding_dice_2: 0.31040/0.15108, loss_grounding_ce_2: 0.42243/0.25290, loss_mask_ce_3: 1.69800/0.76678, loss_mask_bce_3: 1.28624/0.30343, loss_mask_dice_3: 3.32279/1.02337, loss_spatial_bce_3: 0.05176/0.08732, loss_spatial_dice_3: 0.16636/0.18375, loss_spatial_ce_3: 0.01151/0.06673, loss_grounding_bce_3: 0.08760/0.08121, loss_grounding_dice_3: 0.30016/0.15070, loss_grounding_ce_3: 0.42480/0.25409, loss_mask_ce_4: 1.66591/0.77274, loss_mask_bce_4: 1.29303/0.30610, loss_mask_dice_4: 3.40078/1.04273, loss_spatial_bce_4: 0.05146/0.08966, loss_spatial_dice_4: 0.15340/0.19226, loss_spatial_ce_4: 0.01950/0.08046, loss_grounding_bce_4: 0.08206/0.08194, loss_grounding_dice_4: 0.26974/0.15339, loss_grounding_ce_4: 0.43651/0.25845, loss_mask_ce_5: 2.03680/0.79797, loss_mask_bce_5: 0.93408/0.30796, loss_mask_dice_5: 3.52526/1.05067, loss_spatial_bce_5: 0.04749/0.09208, loss_spatial_dice_5: 0.16308/0.19549, loss_spatial_ce_5: 0.00937/0.09397, loss_grounding_bce_5: 0.08064/0.08221, loss_grounding_dice_5: 0.27678/0.15414, loss_grounding_ce_5: 0.43336/0.27633, loss_mask_ce_6: 2.16563/0.82483, loss_mask_bce_6: 0.76629/0.31009, loss_mask_dice_6: 3.33983/1.05465, loss_spatial_bce_6: 0.05993/0.09749, loss_spatial_dice_6: 0.17584/0.19784, loss_spatial_ce_6: 0.02148/0.11857, loss_grounding_bce_6: 0.08165/0.08305, loss_grounding_dice_6: 0.26399/0.15464, loss_grounding_ce_6: 0.42438/0.28498, loss_mask_ce_7: 1.64661/0.88089, loss_mask_bce_7: 1.15376/0.31729, loss_mask_dice_7: 3.53972/1.10025, loss_spatial_bce_7: 0.05984/0.10677, loss_spatial_dice_7: 0.18415/0.22291, loss_spatial_ce_7: 0.04732/0.15418, loss_grounding_bce_7: 0.08404/0.08476, loss_grounding_dice_7: 0.26108/0.16025, loss_grounding_ce_7: 0.48176/0.31830, loss_mask_ce_8: 1.54307/1.01490, loss_mask_bce_8: 1.31392/0.33332, loss_mask_dice_8: 4.04771/1.17666, loss_spatial_bce_8: 0.07164/0.12349, loss_spatial_dice_8: 0.28426/0.25778, loss_spatial_ce_8: 0.12029/0.19984, loss_grounding_bce_8: 0.08911/0.08889, loss_grounding_dice_8: 0.26276/0.17003, loss_grounding_ce_8: 0.45510/0.41659, loss_mask_ce_9: 6.61591/3.47507, loss_mask_bce_9: 1.29552/0.36019, loss_mask_dice_9: 7.46726/1.75950, loss_spatial_bce_9: 0.22886/0.35449, loss_spatial_dice_9: 0.96097/0.79311, loss_spatial_ce_9: 1.17620/1.38713, loss_grounding_bce_9: 0.12775/0.10099, loss_grounding_dice_9: 0.54211/0.24217, loss_grounding_ce_9: 0.57130/0.67015] items per batch[64] items per second[0.37] total items[4697600] mini batches[ 73400] memory[4999] epoch remaining[0:44:17] INFO:trainer.default_trainer:epochs[ 40] optim steps[73500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28371/0.75395, loss_mask_bce_0: 0.31462/0.30089, loss_mask_dice_0: 0.23503/1.02039, loss_spatial_bce_0: 0.33663/0.08463, loss_spatial_dice_0: 0.21755/0.17891, loss_spatial_ce_0: 0.40477/0.05595, loss_grounding_bce_0: 0.06680/0.08069, loss_grounding_dice_0: 0.06533/0.15043, loss_grounding_ce_0: 0.56654/0.24881, loss_mask_ce_1: 1.29639/0.75476, loss_mask_bce_1: 0.29232/0.30171, loss_mask_dice_1: 0.21619/1.02464, loss_spatial_bce_1: 0.46827/0.08507, loss_spatial_dice_1: 0.24534/0.18181, loss_spatial_ce_1: 0.16546/0.05972, loss_grounding_bce_1: 0.06276/0.08086, loss_grounding_dice_1: 0.06179/0.15115, loss_grounding_ce_1: 0.51609/0.25018, loss_mask_ce_2: 1.30667/0.76235, loss_mask_bce_2: 0.30365/0.30207, loss_mask_dice_2: 0.21653/1.02564, loss_spatial_bce_2: 0.36090/0.08516, loss_spatial_dice_2: 0.22367/0.18242, loss_spatial_ce_2: 0.30706/0.06183, loss_grounding_bce_2: 0.05542/0.08086, loss_grounding_dice_2: 0.05242/0.15109, loss_grounding_ce_2: 0.52007/0.25294, loss_mask_ce_3: 1.64580/0.76668, loss_mask_bce_3: 0.29714/0.30343, loss_mask_dice_3: 0.26751/1.02353, loss_spatial_bce_3: 0.38735/0.08731, loss_spatial_dice_3: 0.26530/0.18375, loss_spatial_ce_3: 0.28879/0.06670, loss_grounding_bce_3: 0.05431/0.08122, loss_grounding_dice_3: 0.05385/0.15070, loss_grounding_ce_3: 0.42141/0.25414, loss_mask_ce_4: 1.70782/0.77263, loss_mask_bce_4: 0.35785/0.30610, loss_mask_dice_4: 0.31305/1.04289, loss_spatial_bce_4: 0.49342/0.08965, loss_spatial_dice_4: 0.29865/0.19226, loss_spatial_ce_4: 0.29612/0.08043, loss_grounding_bce_4: 0.07339/0.08195, loss_grounding_dice_4: 0.06625/0.15339, loss_grounding_ce_4: 0.51328/0.25849, loss_mask_ce_5: 1.79962/0.79789, loss_mask_bce_5: 0.38330/0.30795, loss_mask_dice_5: 0.33283/1.05083, loss_spatial_bce_5: 0.59144/0.09207, loss_spatial_dice_5: 0.30465/0.19549, loss_spatial_ce_5: 0.32932/0.09395, loss_grounding_bce_5: 0.09059/0.08222, loss_grounding_dice_5: 0.09821/0.15415, loss_grounding_ce_5: 0.57475/0.27640, loss_mask_ce_6: 1.68077/0.82479, loss_mask_bce_6: 0.54600/0.31008, loss_mask_dice_6: 0.53269/1.05482, loss_spatial_bce_6: 0.54605/0.09749, loss_spatial_dice_6: 0.32154/0.19784, loss_spatial_ce_6: 0.45586/0.11855, loss_grounding_bce_6: 0.11384/0.08306, loss_grounding_dice_6: 0.09109/0.15466, loss_grounding_ce_6: 0.63136/0.28505, loss_mask_ce_7: 1.80413/0.88081, loss_mask_bce_7: 0.56855/0.31730, loss_mask_dice_7: 0.46658/1.10042, loss_spatial_bce_7: 0.56768/0.10678, loss_spatial_dice_7: 0.29393/0.22291, loss_spatial_ce_7: 0.18696/0.15413, loss_grounding_bce_7: 0.14079/0.08478, loss_grounding_dice_7: 0.13467/0.16027, loss_grounding_ce_7: 0.54949/0.31837, loss_mask_ce_8: 1.96702/1.01482, loss_mask_bce_8: 0.60735/0.33333, loss_mask_dice_8: 0.56261/1.17689, loss_spatial_bce_8: 0.46704/0.12348, loss_spatial_dice_8: 0.26690/0.25777, loss_spatial_ce_8: 0.47105/0.19981, loss_grounding_bce_8: 0.09729/0.08891, loss_grounding_dice_8: 0.12058/0.17006, loss_grounding_ce_8: 0.57977/0.41661, loss_mask_ce_9: 4.19407/3.47510, loss_mask_bce_9: 0.91309/0.36021, loss_mask_dice_9: 0.63752/1.75990, loss_spatial_bce_9: 0.93985/0.35451, loss_spatial_dice_9: 0.61387/0.79312, loss_spatial_ce_9: 0.64157/1.38713, loss_grounding_bce_9: 0.37373/0.10102, loss_grounding_dice_9: 0.21860/0.24219, loss_grounding_ce_9: 0.91671/0.67011] items per batch[64] items per second[0.37] total items[4704000] mini batches[ 73500] memory[4999] epoch remaining[0:41:11] INFO:trainer.default_trainer:epochs[ 40] optim steps[73600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57726/0.75391, loss_mask_bce_0: 0.08535/0.30094, loss_mask_dice_0: 2.76323/1.02034, loss_spatial_bce_0: 0.01541/0.08463, loss_spatial_dice_0: 0.28833/0.17890, loss_spatial_ce_0: 0.00660/0.05593, loss_grounding_bce_0: 0.01578/0.08068, loss_grounding_dice_0: 0.15550/0.15042, loss_grounding_ce_0: 0.09190/0.24882, loss_mask_ce_1: 0.50562/0.75470, loss_mask_bce_1: 0.08640/0.30175, loss_mask_dice_1: 2.90970/1.02460, loss_spatial_bce_1: 0.01729/0.08507, loss_spatial_dice_1: 0.32092/0.18180, loss_spatial_ce_1: 0.02815/0.05968, loss_grounding_bce_1: 0.01300/0.08085, loss_grounding_dice_1: 0.12239/0.15114, loss_grounding_ce_1: 0.08047/0.25021, loss_mask_ce_2: 1.15567/0.76231, loss_mask_bce_2: 0.08632/0.30211, loss_mask_dice_2: 2.50694/1.02560, loss_spatial_bce_2: 0.01802/0.08516, loss_spatial_dice_2: 0.32534/0.18241, loss_spatial_ce_2: 0.01367/0.06181, loss_grounding_bce_2: 0.01581/0.08085, loss_grounding_dice_2: 0.13559/0.15108, loss_grounding_ce_2: 0.18292/0.25296, loss_mask_ce_3: 0.64144/0.76664, loss_mask_bce_3: 0.09560/0.30347, loss_mask_dice_3: 3.05421/1.02349, loss_spatial_bce_3: 0.01869/0.08731, loss_spatial_dice_3: 0.38439/0.18375, loss_spatial_ce_3: 0.02754/0.06667, loss_grounding_bce_3: 0.01282/0.08121, loss_grounding_dice_3: 0.12161/0.15070, loss_grounding_ce_3: 0.16387/0.25415, loss_mask_ce_4: 1.08452/0.77258, loss_mask_bce_4: 0.08064/0.30613, loss_mask_dice_4: 2.58132/1.04283, loss_spatial_bce_4: 0.01683/0.08965, loss_spatial_dice_4: 0.34194/0.19226, loss_spatial_ce_4: 0.06177/0.08040, loss_grounding_bce_4: 0.01545/0.08194, loss_grounding_dice_4: 0.13676/0.15339, loss_grounding_ce_4: 0.09843/0.25851, loss_mask_ce_5: 0.95685/0.79787, loss_mask_bce_5: 0.08938/0.30799, loss_mask_dice_5: 2.71900/1.05083, loss_spatial_bce_5: 0.02010/0.09206, loss_spatial_dice_5: 0.32113/0.19548, loss_spatial_ce_5: 0.05688/0.09392, loss_grounding_bce_5: 0.01417/0.08221, loss_grounding_dice_5: 0.14939/0.15416, loss_grounding_ce_5: 0.36168/0.27642, loss_mask_ce_6: 1.23754/0.82479, loss_mask_bce_6: 0.08497/0.31012, loss_mask_dice_6: 2.53847/1.05479, loss_spatial_bce_6: 0.02242/0.09749, loss_spatial_dice_6: 0.33427/0.19784, loss_spatial_ce_6: 0.03616/0.11851, loss_grounding_bce_6: 0.01418/0.08306, loss_grounding_dice_6: 0.12579/0.15466, loss_grounding_ce_6: 0.90269/0.28505, loss_mask_ce_7: 1.41172/0.88082, loss_mask_bce_7: 0.10576/0.31732, loss_mask_dice_7: 2.68405/1.10033, loss_spatial_bce_7: 0.02011/0.10678, loss_spatial_dice_7: 0.40888/0.22291, loss_spatial_ce_7: 0.20414/0.15408, loss_grounding_bce_7: 0.01483/0.08477, loss_grounding_dice_7: 0.10071/0.16028, loss_grounding_ce_7: 0.73413/0.31839, loss_mask_ce_8: 1.36005/1.01483, loss_mask_bce_8: 0.10245/0.33337, loss_mask_dice_8: 3.03007/1.17685, loss_spatial_bce_8: 0.02105/0.12347, loss_spatial_dice_8: 0.45384/0.25777, loss_spatial_ce_8: 0.17160/0.19974, loss_grounding_bce_8: 0.01948/0.08890, loss_grounding_dice_8: 0.17122/0.17005, loss_grounding_ce_8: 1.49249/0.41664, loss_mask_ce_9: 3.53725/3.47510, loss_mask_bce_9: 0.12191/0.36024, loss_mask_dice_9: 3.65311/1.75982, loss_spatial_bce_9: 0.06399/0.35454, loss_spatial_dice_9: 0.93490/0.79314, loss_spatial_ce_9: 1.16447/1.38709, loss_grounding_bce_9: 0.03888/0.10102, loss_grounding_dice_9: 0.39258/0.24223, loss_grounding_ce_9: 1.92912/0.67009] items per batch[64] items per second[0.36] total items[4710400] mini batches[ 73600] memory[4999] epoch remaining[0:38:21] INFO:trainer.default_trainer:epochs[ 40] optim steps[73700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.16510/0.75403, loss_mask_bce_0: 0.55180/0.30094, loss_mask_dice_0: 0.52046/1.02032, loss_spatial_bce_0: 0.24044/0.08462, loss_spatial_dice_0: 0.18762/0.17889, loss_spatial_ce_0: 0.23588/0.05590, loss_grounding_bce_0: 0.09804/0.08066, loss_grounding_dice_0: 0.08688/0.15042, loss_grounding_ce_0: 0.00220/0.24886, loss_mask_ce_1: 0.51145/0.75478, loss_mask_bce_1: 0.47679/0.30176, loss_mask_dice_1: 0.47534/1.02459, loss_spatial_bce_1: 0.22151/0.08506, loss_spatial_dice_1: 0.18025/0.18179, loss_spatial_ce_1: 0.30245/0.05966, loss_grounding_bce_1: 0.10060/0.08084, loss_grounding_dice_1: 0.08557/0.15114, loss_grounding_ce_1: 0.00259/0.25025, loss_mask_ce_2: 0.65363/0.76241, loss_mask_bce_2: 0.47612/0.30212, loss_mask_dice_2: 0.51833/1.02559, loss_spatial_bce_2: 0.20953/0.08515, loss_spatial_dice_2: 0.19172/0.18240, loss_spatial_ce_2: 0.42149/0.06179, loss_grounding_bce_2: 0.10350/0.08084, loss_grounding_dice_2: 0.08593/0.15108, loss_grounding_ce_2: 0.00253/0.25300, loss_mask_ce_3: 0.74251/0.76669, loss_mask_bce_3: 0.51496/0.30348, loss_mask_dice_3: 0.49626/1.02349, loss_spatial_bce_3: 0.24219/0.08730, loss_spatial_dice_3: 0.18459/0.18373, loss_spatial_ce_3: 0.28579/0.06665, loss_grounding_bce_3: 0.11201/0.08120, loss_grounding_dice_3: 0.09083/0.15070, loss_grounding_ce_3: 0.00269/0.25420, loss_mask_ce_4: 0.78886/0.77268, loss_mask_bce_4: 0.47823/0.30614, loss_mask_dice_4: 0.47569/1.04282, loss_spatial_bce_4: 0.25153/0.08964, loss_spatial_dice_4: 0.19032/0.19224, loss_spatial_ce_4: 0.30142/0.08038, loss_grounding_bce_4: 0.10525/0.08193, loss_grounding_dice_4: 0.08651/0.15340, loss_grounding_ce_4: 0.00380/0.25855, loss_mask_ce_5: 0.81513/0.79796, loss_mask_bce_5: 0.48251/0.30800, loss_mask_dice_5: 0.45512/1.05082, loss_spatial_bce_5: 0.19805/0.09205, loss_spatial_dice_5: 0.18525/0.19547, loss_spatial_ce_5: 0.19351/0.09391, loss_grounding_bce_5: 0.10375/0.08220, loss_grounding_dice_5: 0.08459/0.15415, loss_grounding_ce_5: 0.00524/0.27645, loss_mask_ce_6: 0.88494/0.82490, loss_mask_bce_6: 0.69301/0.31013, loss_mask_dice_6: 0.55590/1.05481, loss_spatial_bce_6: 0.26868/0.09748, loss_spatial_dice_6: 0.22650/0.19783, loss_spatial_ce_6: 0.34058/0.11846, loss_grounding_bce_6: 0.10380/0.08304, loss_grounding_dice_6: 0.09140/0.15466, loss_grounding_ce_6: 0.00639/0.28503, loss_mask_ce_7: 0.80919/0.88092, loss_mask_bce_7: 0.60759/0.31734, loss_mask_dice_7: 0.51781/1.10031, loss_spatial_bce_7: 0.30416/0.10676, loss_spatial_dice_7: 0.25614/0.22290, loss_spatial_ce_7: 0.34636/0.15404, loss_grounding_bce_7: 0.09811/0.08476, loss_grounding_dice_7: 0.08958/0.16029, loss_grounding_ce_7: 0.00389/0.31839, loss_mask_ce_8: 0.91817/1.01487, loss_mask_bce_8: 0.45544/0.33337, loss_mask_dice_8: 0.57291/1.17684, loss_spatial_bce_8: 0.27256/0.12346, loss_spatial_dice_8: 0.23645/0.25776, loss_spatial_ce_8: 0.96564/0.19971, loss_grounding_bce_8: 0.08906/0.08888, loss_grounding_dice_8: 0.07598/0.17004, loss_grounding_ce_8: 0.00763/0.41671, loss_mask_ce_9: 2.28194/3.47519, loss_mask_bce_9: 0.47548/0.36024, loss_mask_dice_9: 0.53507/1.76002, loss_spatial_bce_9: 0.62196/0.35452, loss_spatial_dice_9: 0.76191/0.79316, loss_spatial_ce_9: 1.82307/1.38706, loss_grounding_bce_9: 0.08930/0.10100, loss_grounding_dice_9: 0.09382/0.24223, loss_grounding_ce_9: 0.05984/0.67003] items per batch[64] items per second[0.37] total items[4716800] mini batches[ 73700] memory[4999] epoch remaining[0:35:20] INFO:trainer.default_trainer:epochs[ 40] optim steps[73800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68571/0.75402, loss_mask_bce_0: 0.11576/0.30089, loss_mask_dice_0: 0.28344/1.02021, loss_spatial_bce_0: 0.03712/0.08461, loss_spatial_dice_0: 0.08307/0.17888, loss_spatial_ce_0: 0.00005/0.05590, loss_grounding_bce_0: 0.04569/0.08066, loss_grounding_dice_0: 0.05003/0.15041, loss_grounding_ce_0: 0.00223/0.24877, loss_mask_ce_1: 0.66835/0.75479, loss_mask_bce_1: 0.10786/0.30171, loss_mask_dice_1: 0.27352/1.02451, loss_spatial_bce_1: 0.03982/0.08505, loss_spatial_dice_1: 0.09046/0.18178, loss_spatial_ce_1: 0.00006/0.05966, loss_grounding_bce_1: 0.04442/0.08084, loss_grounding_dice_1: 0.04752/0.15114, loss_grounding_ce_1: 0.00322/0.25015, loss_mask_ce_2: 0.65042/0.76244, loss_mask_bce_2: 0.11867/0.30207, loss_mask_dice_2: 0.31557/1.02541, loss_spatial_bce_2: 0.03650/0.08514, loss_spatial_dice_2: 0.08681/0.18240, loss_spatial_ce_2: 0.00009/0.06179, loss_grounding_bce_2: 0.04226/0.08083, loss_grounding_dice_2: 0.04961/0.15108, loss_grounding_ce_2: 0.00269/0.25288, loss_mask_ce_3: 0.70636/0.76672, loss_mask_bce_3: 0.11993/0.30343, loss_mask_dice_3: 0.28525/1.02335, loss_spatial_bce_3: 0.03560/0.08729, loss_spatial_dice_3: 0.08150/0.18372, loss_spatial_ce_3: 0.00171/0.06664, loss_grounding_bce_3: 0.04359/0.08119, loss_grounding_dice_3: 0.05272/0.15070, loss_grounding_ce_3: 0.00351/0.25409, loss_mask_ce_4: 0.31461/0.77269, loss_mask_bce_4: 0.13338/0.30608, loss_mask_dice_4: 0.32021/1.04267, loss_spatial_bce_4: 0.03640/0.08963, loss_spatial_dice_4: 0.11685/0.19223, loss_spatial_ce_4: 0.00951/0.08037, loss_grounding_bce_4: 0.04145/0.08192, loss_grounding_dice_4: 0.04722/0.15339, loss_grounding_ce_4: 0.00350/0.25844, loss_mask_ce_5: 0.36284/0.79797, loss_mask_bce_5: 0.12167/0.30794, loss_mask_dice_5: 0.30044/1.05071, loss_spatial_bce_5: 0.04105/0.09204, loss_spatial_dice_5: 0.10612/0.19546, loss_spatial_ce_5: 0.57486/0.09390, loss_grounding_bce_5: 0.04489/0.08219, loss_grounding_dice_5: 0.05107/0.15415, loss_grounding_ce_5: 0.00323/0.27635, loss_mask_ce_6: 0.70950/0.82492, loss_mask_bce_6: 0.12393/0.31008, loss_mask_dice_6: 0.31016/1.05473, loss_spatial_bce_6: 0.04686/0.09747, loss_spatial_dice_6: 0.14852/0.19782, loss_spatial_ce_6: 0.00080/0.11846, loss_grounding_bce_6: 0.04540/0.08304, loss_grounding_dice_6: 0.04870/0.15465, loss_grounding_ce_6: 0.00529/0.28495, loss_mask_ce_7: 0.37461/0.88094, loss_mask_bce_7: 0.12268/0.31729, loss_mask_dice_7: 0.31655/1.10019, loss_spatial_bce_7: 0.07294/0.10675, loss_spatial_dice_7: 0.15187/0.22289, loss_spatial_ce_7: 0.03853/0.15404, loss_grounding_bce_7: 0.04079/0.08475, loss_grounding_dice_7: 0.05038/0.16028, loss_grounding_ce_7: 0.00995/0.31833, loss_mask_ce_8: 0.86861/1.01484, loss_mask_bce_8: 0.11589/0.33332, loss_mask_dice_8: 0.29531/1.17670, loss_spatial_bce_8: 0.07870/0.12344, loss_spatial_dice_8: 0.17044/0.25775, loss_spatial_ce_8: 0.10664/0.19968, loss_grounding_bce_8: 0.04137/0.08887, loss_grounding_dice_8: 0.05714/0.17003, loss_grounding_ce_8: 0.00738/0.41659, loss_mask_ce_9: 2.79286/3.47508, loss_mask_bce_9: 0.12529/0.36017, loss_mask_dice_9: 0.49768/1.75973, loss_spatial_bce_9: 0.43890/0.35451, loss_spatial_dice_9: 0.74619/0.79316, loss_spatial_ce_9: 1.57497/1.38710, loss_grounding_bce_9: 0.05139/0.10099, loss_grounding_dice_9: 0.09417/0.24221, loss_grounding_ce_9: 0.03102/0.66986] items per batch[64] items per second[0.37] total items[4723200] mini batches[ 73800] memory[4999] epoch remaining[0:32:23] INFO:trainer.default_trainer:epochs[ 40] optim steps[73900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.09090/0.75403, loss_mask_bce_0: 0.17912/0.30086, loss_mask_dice_0: 0.23294/1.02016, loss_spatial_bce_0: 0.08837/0.08460, loss_spatial_dice_0: 0.09175/0.17886, loss_spatial_ce_0: 0.00005/0.05587, loss_grounding_bce_0: 0.06792/0.08065, loss_grounding_dice_0: 0.06802/0.15041, loss_grounding_ce_0: 0.16720/0.24887, loss_mask_ce_1: 0.08360/0.75479, loss_mask_bce_1: 0.18577/0.30168, loss_mask_dice_1: 0.23536/1.02449, loss_spatial_bce_1: 0.08477/0.08504, loss_spatial_dice_1: 0.09417/0.18177, loss_spatial_ce_1: 0.00004/0.05963, loss_grounding_bce_1: 0.07270/0.08083, loss_grounding_dice_1: 0.07316/0.15114, loss_grounding_ce_1: 0.17672/0.25024, loss_mask_ce_2: 0.07631/0.76243, loss_mask_bce_2: 0.18970/0.30204, loss_mask_dice_2: 0.23462/1.02540, loss_spatial_bce_2: 0.08135/0.08514, loss_spatial_dice_2: 0.09368/0.18238, loss_spatial_ce_2: 0.00006/0.06177, loss_grounding_bce_2: 0.07036/0.08082, loss_grounding_dice_2: 0.07148/0.15107, loss_grounding_ce_2: 0.16089/0.25296, loss_mask_ce_3: 0.06798/0.76673, loss_mask_bce_3: 0.18908/0.30340, loss_mask_dice_3: 0.24368/1.02331, loss_spatial_bce_3: 0.08068/0.08728, loss_spatial_dice_3: 0.09960/0.18370, loss_spatial_ce_3: 0.00038/0.06662, loss_grounding_bce_3: 0.07130/0.08118, loss_grounding_dice_3: 0.07416/0.15069, loss_grounding_ce_3: 0.16673/0.25412, loss_mask_ce_4: 0.07313/0.77268, loss_mask_bce_4: 0.18716/0.30605, loss_mask_dice_4: 0.24638/1.04264, loss_spatial_bce_4: 0.08429/0.08962, loss_spatial_dice_4: 0.10053/0.19222, loss_spatial_ce_4: 0.00078/0.08035, loss_grounding_bce_4: 0.06350/0.08191, loss_grounding_dice_4: 0.06861/0.15338, loss_grounding_ce_4: 0.17294/0.25850, loss_mask_ce_5: 0.09741/0.79798, loss_mask_bce_5: 0.18699/0.30791, loss_mask_dice_5: 0.25478/1.05069, loss_spatial_bce_5: 0.08904/0.09204, loss_spatial_dice_5: 0.10524/0.19545, loss_spatial_ce_5: 0.00050/0.09388, loss_grounding_bce_5: 0.06654/0.08218, loss_grounding_dice_5: 0.07277/0.15413, loss_grounding_ce_5: 0.20101/0.27641, loss_mask_ce_6: 0.10712/0.82492, loss_mask_bce_6: 0.18518/0.31005, loss_mask_dice_6: 0.24512/1.05469, loss_spatial_bce_6: 0.09351/0.09747, loss_spatial_dice_6: 0.11047/0.19781, loss_spatial_ce_6: 0.03420/0.11843, loss_grounding_bce_6: 0.07112/0.08303, loss_grounding_dice_6: 0.07448/0.15464, loss_grounding_ce_6: 0.17832/0.28495, loss_mask_ce_7: 0.09863/0.88099, loss_mask_bce_7: 0.19024/0.31724, loss_mask_dice_7: 0.24375/1.10017, loss_spatial_bce_7: 0.09384/0.10675, loss_spatial_dice_7: 0.09878/0.22287, loss_spatial_ce_7: 0.05381/0.15401, loss_grounding_bce_7: 0.07757/0.08473, loss_grounding_dice_7: 0.08302/0.16025, loss_grounding_ce_7: 0.18378/0.31848, loss_mask_ce_8: 0.15396/1.01479, loss_mask_bce_8: 0.18010/0.33328, loss_mask_dice_8: 0.25774/1.17664, loss_spatial_bce_8: 0.10256/0.12344, loss_spatial_dice_8: 0.10953/0.25775, loss_spatial_ce_8: 0.12884/0.19965, loss_grounding_bce_8: 0.07410/0.08886, loss_grounding_dice_8: 0.08227/0.17003, loss_grounding_ce_8: 0.22782/0.41660, loss_mask_ce_9: 2.01864/3.47501, loss_mask_bce_9: 0.31034/0.36013, loss_mask_dice_9: 0.60840/1.75984, loss_spatial_bce_9: 0.62542/0.35451, loss_spatial_dice_9: 0.78534/0.79315, loss_spatial_ce_9: 1.45190/1.38704, loss_grounding_bce_9: 0.19961/0.10097, loss_grounding_dice_9: 0.38789/0.24221, loss_grounding_ce_9: 0.35288/0.66983] items per batch[64] items per second[0.36] total items[4729600] mini batches[ 73900] memory[4999] epoch remaining[0:29:27] INFO:trainer.default_trainer:epochs[ 40] optim steps[74000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05757/0.75401, loss_mask_bce_0: 0.13700/0.30089, loss_mask_dice_0: 0.22263/1.02023, loss_spatial_bce_0: 0.06404/0.08462, loss_spatial_dice_0: 0.08176/0.17885, loss_spatial_ce_0: 0.09273/0.05587, loss_grounding_bce_0: 0.10246/0.08068, loss_grounding_dice_0: 0.03988/0.15041, loss_grounding_ce_0: 0.00668/0.24895, loss_mask_ce_1: 0.06038/0.75476, loss_mask_bce_1: 0.14134/0.30172, loss_mask_dice_1: 0.20224/1.02460, loss_spatial_bce_1: 0.06558/0.08506, loss_spatial_dice_1: 0.08014/0.18176, loss_spatial_ce_1: 0.09272/0.05963, loss_grounding_bce_1: 0.09569/0.08085, loss_grounding_dice_1: 0.03894/0.15114, loss_grounding_ce_1: 0.00821/0.25032, loss_mask_ce_2: 0.08624/0.76241, loss_mask_bce_2: 0.13569/0.30207, loss_mask_dice_2: 0.18713/1.02547, loss_spatial_bce_2: 0.06522/0.08515, loss_spatial_dice_2: 0.07528/0.18237, loss_spatial_ce_2: 0.09289/0.06177, loss_grounding_bce_2: 0.10147/0.08085, loss_grounding_dice_2: 0.04055/0.15108, loss_grounding_ce_2: 0.00575/0.25307, loss_mask_ce_3: 0.07642/0.76671, loss_mask_bce_3: 0.13398/0.30343, loss_mask_dice_3: 0.20597/1.02343, loss_spatial_bce_3: 0.06467/0.08730, loss_spatial_dice_3: 0.07521/0.18370, loss_spatial_ce_3: 0.09278/0.06661, loss_grounding_bce_3: 0.10092/0.08121, loss_grounding_dice_3: 0.03986/0.15071, loss_grounding_ce_3: 0.00675/0.25422, loss_mask_ce_4: 0.08152/0.77264, loss_mask_bce_4: 0.13783/0.30608, loss_mask_dice_4: 0.22019/1.04271, loss_spatial_bce_4: 0.06504/0.08964, loss_spatial_dice_4: 0.08874/0.19222, loss_spatial_ce_4: 0.09443/0.08034, loss_grounding_bce_4: 0.10007/0.08194, loss_grounding_dice_4: 0.04163/0.15338, loss_grounding_ce_4: 0.01032/0.25861, loss_mask_ce_5: 0.07646/0.79798, loss_mask_bce_5: 0.14138/0.30795, loss_mask_dice_5: 0.21335/1.05075, loss_spatial_bce_5: 0.07464/0.09204, loss_spatial_dice_5: 0.12588/0.19545, loss_spatial_ce_5: 0.09785/0.09390, loss_grounding_bce_5: 0.10482/0.08221, loss_grounding_dice_5: 0.04427/0.15414, loss_grounding_ce_5: 0.00758/0.27649, loss_mask_ce_6: 0.07989/0.82492, loss_mask_bce_6: 0.14157/0.31009, loss_mask_dice_6: 0.19697/1.05477, loss_spatial_bce_6: 0.07764/0.09749, loss_spatial_dice_6: 0.12308/0.19781, loss_spatial_ce_6: 0.09819/0.11841, loss_grounding_bce_6: 0.10491/0.08306, loss_grounding_dice_6: 0.04126/0.15466, loss_grounding_ce_6: 0.00510/0.28506, loss_mask_ce_7: 0.10045/0.88100, loss_mask_bce_7: 0.13573/0.31728, loss_mask_dice_7: 0.19753/1.10028, loss_spatial_bce_7: 0.11477/0.10676, loss_spatial_dice_7: 0.15247/0.22287, loss_spatial_ce_7: 0.30799/0.15400, loss_grounding_bce_7: 0.10357/0.08475, loss_grounding_dice_7: 0.03822/0.16026, loss_grounding_ce_7: 0.00745/0.31853, loss_mask_ce_8: 0.13330/1.01483, loss_mask_bce_8: 0.14701/0.33332, loss_mask_dice_8: 0.23441/1.17677, loss_spatial_bce_8: 0.08470/0.12345, loss_spatial_dice_8: 0.14470/0.25774, loss_spatial_ce_8: 0.20770/0.19962, loss_grounding_bce_8: 0.11153/0.08888, loss_grounding_dice_8: 0.03851/0.17004, loss_grounding_ce_8: 0.02943/0.41676, loss_mask_ce_9: 1.70038/3.47505, loss_mask_bce_9: 0.11431/0.36018, loss_mask_dice_9: 0.19954/1.76004, loss_spatial_bce_9: 0.56113/0.35452, loss_spatial_dice_9: 0.56472/0.79316, loss_spatial_ce_9: 0.63698/1.38700, loss_grounding_bce_9: 0.09936/0.10102, loss_grounding_dice_9: 0.03822/0.24221, loss_grounding_ce_9: 0.07919/0.66990] items per batch[64] items per second[0.36] total items[4736000] mini batches[ 74000] memory[4999] epoch remaining[0:26:32] INFO:trainer.default_trainer:epochs[ 40] optim steps[74100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17518/0.75390, loss_mask_bce_0: 0.04252/0.30087, loss_mask_dice_0: 0.23625/1.02020, loss_spatial_bce_0: 0.01600/0.08462, loss_spatial_dice_0: 0.09509/0.17885, loss_spatial_ce_0: 0.00177/0.05586, loss_grounding_bce_0: 0.01983/0.08068, loss_grounding_dice_0: 0.10167/0.15043, loss_grounding_ce_0: 0.02830/0.24889, loss_mask_ce_1: 0.18048/0.75466, loss_mask_bce_1: 0.04360/0.30171, loss_mask_dice_1: 0.25517/1.02460, loss_spatial_bce_1: 0.01555/0.08507, loss_spatial_dice_1: 0.09317/0.18176, loss_spatial_ce_1: 0.00538/0.05963, loss_grounding_bce_1: 0.01701/0.08085, loss_grounding_dice_1: 0.10719/0.15116, loss_grounding_ce_1: 0.02772/0.25030, loss_mask_ce_2: 0.17101/0.76232, loss_mask_bce_2: 0.04217/0.30207, loss_mask_dice_2: 0.23249/1.02545, loss_spatial_bce_2: 0.01648/0.08516, loss_spatial_dice_2: 0.09876/0.18237, loss_spatial_ce_2: 0.00085/0.06176, loss_grounding_bce_2: 0.01978/0.08085, loss_grounding_dice_2: 0.11264/0.15109, loss_grounding_ce_2: 0.01924/0.25302, loss_mask_ce_3: 0.20879/0.76661, loss_mask_bce_3: 0.04425/0.30343, loss_mask_dice_3: 0.25961/1.02341, loss_spatial_bce_3: 0.02211/0.08731, loss_spatial_dice_3: 0.11418/0.18369, loss_spatial_ce_3: 0.00531/0.06660, loss_grounding_bce_3: 0.01761/0.08121, loss_grounding_dice_3: 0.10601/0.15072, loss_grounding_ce_3: 0.02739/0.25418, loss_mask_ce_4: 0.18556/0.77256, loss_mask_bce_4: 0.04763/0.30608, loss_mask_dice_4: 0.26561/1.04273, loss_spatial_bce_4: 0.01678/0.08965, loss_spatial_dice_4: 0.08414/0.19221, loss_spatial_ce_4: 0.02474/0.08031, loss_grounding_bce_4: 0.02331/0.08194, loss_grounding_dice_4: 0.12819/0.15339, loss_grounding_ce_4: 0.01557/0.25858, loss_mask_ce_5: 0.16302/0.79792, loss_mask_bce_5: 0.05312/0.30795, loss_mask_dice_5: 0.28300/1.05073, loss_spatial_bce_5: 0.01582/0.09205, loss_spatial_dice_5: 0.08777/0.19544, loss_spatial_ce_5: 0.00327/0.09389, loss_grounding_bce_5: 0.02093/0.08222, loss_grounding_dice_5: 0.10825/0.15416, loss_grounding_ce_5: 0.01228/0.27643, loss_mask_ce_6: 0.16075/0.82484, loss_mask_bce_6: 0.04584/0.31008, loss_mask_dice_6: 0.25202/1.05478, loss_spatial_bce_6: 0.01878/0.09750, loss_spatial_dice_6: 0.12478/0.19781, loss_spatial_ce_6: 0.00602/0.11841, loss_grounding_bce_6: 0.02077/0.08307, loss_grounding_dice_6: 0.11516/0.15466, loss_grounding_ce_6: 0.01706/0.28505, loss_mask_ce_7: 0.27660/0.88091, loss_mask_bce_7: 0.04434/0.31727, loss_mask_dice_7: 0.28488/1.10024, loss_spatial_bce_7: 0.01818/0.10677, loss_spatial_dice_7: 0.10322/0.22287, loss_spatial_ce_7: 0.03136/0.15396, loss_grounding_bce_7: 0.01881/0.08475, loss_grounding_dice_7: 0.11593/0.16027, loss_grounding_ce_7: 0.05137/0.31850, loss_mask_ce_8: 0.68180/1.01475, loss_mask_bce_8: 0.05446/0.33331, loss_mask_dice_8: 0.26052/1.17676, loss_spatial_bce_8: 0.01910/0.12345, loss_spatial_dice_8: 0.14400/0.25773, loss_spatial_ce_8: 0.26390/0.19956, loss_grounding_bce_8: 0.02619/0.08888, loss_grounding_dice_8: 0.12594/0.17004, loss_grounding_ce_8: 0.35024/0.41666, loss_mask_ce_9: 2.58350/3.47503, loss_mask_bce_9: 0.11643/0.36016, loss_mask_dice_9: 0.51089/1.76002, loss_spatial_bce_9: 0.15620/0.35450, loss_spatial_dice_9: 0.66839/0.79314, loss_spatial_ce_9: 1.34775/1.38702, loss_grounding_bce_9: 0.05943/0.10101, loss_grounding_dice_9: 0.26247/0.24220, loss_grounding_ce_9: 0.35588/0.66983] items per batch[64] items per second[0.37] total items[4742400] mini batches[ 74100] memory[4999] epoch remaining[0:23:36] INFO:trainer.default_trainer:epochs[ 40] optim steps[74200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.08086/0.75381, loss_mask_bce_0: 0.05310/0.30089, loss_mask_dice_0: 0.11774/1.02017, loss_spatial_bce_0: 0.22848/0.08462, loss_spatial_dice_0: 0.12769/0.17884, loss_spatial_ce_0: 0.00131/0.05583, loss_grounding_bce_0: 0.00846/0.08067, loss_grounding_dice_0: 0.02179/0.15042, loss_grounding_ce_0: 0.47840/0.24890, loss_mask_ce_1: 1.01749/0.75457, loss_mask_bce_1: 0.06107/0.30172, loss_mask_dice_1: 0.11776/1.02456, loss_spatial_bce_1: 0.24355/0.08506, loss_spatial_dice_1: 0.14350/0.18176, loss_spatial_ce_1: 0.00166/0.05960, loss_grounding_bce_1: 0.01109/0.08084, loss_grounding_dice_1: 0.02231/0.15114, loss_grounding_ce_1: 0.52061/0.25033, loss_mask_ce_2: 1.11922/0.76224, loss_mask_bce_2: 0.05190/0.30208, loss_mask_dice_2: 0.11949/1.02539, loss_spatial_bce_2: 0.28641/0.08516, loss_spatial_dice_2: 0.14585/0.18236, loss_spatial_ce_2: 0.00166/0.06175, loss_grounding_bce_2: 0.00997/0.08084, loss_grounding_dice_2: 0.02244/0.15108, loss_grounding_ce_2: 0.64092/0.25307, loss_mask_ce_3: 1.37345/0.76656, loss_mask_bce_3: 0.05788/0.30344, loss_mask_dice_3: 0.11485/1.02336, loss_spatial_bce_3: 0.23802/0.08730, loss_spatial_dice_3: 0.13431/0.18369, loss_spatial_ce_3: 0.00112/0.06658, loss_grounding_bce_3: 0.01074/0.08120, loss_grounding_dice_3: 0.02048/0.15071, loss_grounding_ce_3: 0.64870/0.25422, loss_mask_ce_4: 1.14858/0.77250, loss_mask_bce_4: 0.06058/0.30609, loss_mask_dice_4: 0.12657/1.04268, loss_spatial_bce_4: 0.25082/0.08967, loss_spatial_dice_4: 0.18352/0.19221, loss_spatial_ce_4: 0.00541/0.08028, loss_grounding_bce_4: 0.01011/0.08193, loss_grounding_dice_4: 0.01816/0.15337, loss_grounding_ce_4: 0.48240/0.25860, loss_mask_ce_5: 1.35676/0.79785, loss_mask_bce_5: 0.05425/0.30796, loss_mask_dice_5: 0.12331/1.05070, loss_spatial_bce_5: 0.21846/0.09204, loss_spatial_dice_5: 0.17801/0.19544, loss_spatial_ce_5: 0.11956/0.09386, loss_grounding_bce_5: 0.00953/0.08221, loss_grounding_dice_5: 0.01949/0.15414, loss_grounding_ce_5: 0.58461/0.27646, loss_mask_ce_6: 0.73774/0.82472, loss_mask_bce_6: 0.43024/0.31010, loss_mask_dice_6: 0.19900/1.05474, loss_spatial_bce_6: 0.22147/0.09749, loss_spatial_dice_6: 0.13846/0.19780, loss_spatial_ce_6: 0.01534/0.11837, loss_grounding_bce_6: 0.01588/0.08306, loss_grounding_dice_6: 0.03323/0.15465, loss_grounding_ce_6: 0.59576/0.28509, loss_mask_ce_7: 1.53595/0.88081, loss_mask_bce_7: 0.07437/0.31727, loss_mask_dice_7: 0.16162/1.10018, loss_spatial_bce_7: 0.21053/0.10676, loss_spatial_dice_7: 0.14717/0.22285, loss_spatial_ce_7: 0.00103/0.15392, loss_grounding_bce_7: 0.01534/0.08475, loss_grounding_dice_7: 0.03209/0.16025, loss_grounding_ce_7: 0.69268/0.31854, loss_mask_ce_8: 0.84451/1.01467, loss_mask_bce_8: 0.39434/0.33333, loss_mask_dice_8: 0.23036/1.17670, loss_spatial_bce_8: 0.26113/0.12344, loss_spatial_dice_8: 0.23102/0.25772, loss_spatial_ce_8: 0.02681/0.19950, loss_grounding_bce_8: 0.02111/0.08888, loss_grounding_dice_8: 0.04362/0.17001, loss_grounding_ce_8: 1.01758/0.41666, loss_mask_ce_9: 2.66333/3.47476, loss_mask_bce_9: 0.34509/0.36017, loss_mask_dice_9: 0.42792/1.76002, loss_spatial_bce_9: 0.44343/0.35451, loss_spatial_dice_9: 0.77518/0.79314, loss_spatial_ce_9: 1.01366/1.38692, loss_grounding_bce_9: 0.18866/0.10101, loss_grounding_dice_9: 0.13293/0.24219, loss_grounding_ce_9: 0.62235/0.66978] items per batch[64] items per second[0.37] total items[4748800] mini batches[ 74200] memory[4999] epoch remaining[0:20:38] INFO:trainer.default_trainer:epochs[ 40] optim steps[74300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.56795/0.75387, loss_mask_bce_0: 0.44186/0.30084, loss_mask_dice_0: 1.19856/1.02028, loss_spatial_bce_0: 0.04455/0.08460, loss_spatial_dice_0: 0.13177/0.17882, loss_spatial_ce_0: 0.00183/0.05582, loss_grounding_bce_0: 0.02485/0.08065, loss_grounding_dice_0: 0.14790/0.15041, loss_grounding_ce_0: 0.14089/0.24885, loss_mask_ce_1: 0.65509/0.75463, loss_mask_bce_1: 0.46074/0.30166, loss_mask_dice_1: 1.18802/1.02464, loss_spatial_bce_1: 0.04230/0.08505, loss_spatial_dice_1: 0.13510/0.18173, loss_spatial_ce_1: 0.00248/0.05959, loss_grounding_bce_1: 0.02370/0.08083, loss_grounding_dice_1: 0.13620/0.15114, loss_grounding_ce_1: 0.13925/0.25029, loss_mask_ce_2: 0.65102/0.76228, loss_mask_bce_2: 0.44594/0.30203, loss_mask_dice_2: 1.22964/1.02544, loss_spatial_bce_2: 0.03798/0.08515, loss_spatial_dice_2: 0.12900/0.18233, loss_spatial_ce_2: 0.00529/0.06174, loss_grounding_bce_2: 0.02562/0.08082, loss_grounding_dice_2: 0.12170/0.15106, loss_grounding_ce_2: 0.16004/0.25301, loss_mask_ce_3: 0.66784/0.76665, loss_mask_bce_3: 0.41697/0.30339, loss_mask_dice_3: 1.19189/1.02348, loss_spatial_bce_3: 0.04192/0.08729, loss_spatial_dice_3: 0.12230/0.18366, loss_spatial_ce_3: 0.02796/0.06660, loss_grounding_bce_3: 0.02168/0.08118, loss_grounding_dice_3: 0.14628/0.15070, loss_grounding_ce_3: 0.21388/0.25417, loss_mask_ce_4: 0.67341/0.77255, loss_mask_bce_4: 0.44583/0.30603, loss_mask_dice_4: 1.20024/1.04278, loss_spatial_bce_4: 0.04327/0.08965, loss_spatial_dice_4: 0.11790/0.19219, loss_spatial_ce_4: 0.03850/0.08026, loss_grounding_bce_4: 0.02590/0.08191, loss_grounding_dice_4: 0.14332/0.15335, loss_grounding_ce_4: 0.19261/0.25856, loss_mask_ce_5: 0.60116/0.79788, loss_mask_bce_5: 0.46883/0.30791, loss_mask_dice_5: 1.21135/1.05077, loss_spatial_bce_5: 0.04690/0.09202, loss_spatial_dice_5: 0.12271/0.19542, loss_spatial_ce_5: 0.04440/0.09385, loss_grounding_bce_5: 0.03083/0.08219, loss_grounding_dice_5: 0.13904/0.15414, loss_grounding_ce_5: 0.19544/0.27639, loss_mask_ce_6: 0.60770/0.82477, loss_mask_bce_6: 0.45978/0.31004, loss_mask_dice_6: 1.45159/1.05484, loss_spatial_bce_6: 0.05044/0.09747, loss_spatial_dice_6: 0.15212/0.19778, loss_spatial_ce_6: 0.02874/0.11836, loss_grounding_bce_6: 0.02424/0.08304, loss_grounding_dice_6: 0.16723/0.15464, loss_grounding_ce_6: 0.22943/0.28501, loss_mask_ce_7: 0.80439/0.88083, loss_mask_bce_7: 0.49243/0.31722, loss_mask_dice_7: 1.51232/1.10030, loss_spatial_bce_7: 0.05909/0.10674, loss_spatial_dice_7: 0.15575/0.22283, loss_spatial_ce_7: 0.01685/0.15391, loss_grounding_bce_7: 0.02756/0.08473, loss_grounding_dice_7: 0.17746/0.16024, loss_grounding_ce_7: 0.18297/0.31846, loss_mask_ce_8: 1.13938/1.01477, loss_mask_bce_8: 0.48982/0.33330, loss_mask_dice_8: 1.46176/1.17678, loss_spatial_bce_8: 0.06492/0.12342, loss_spatial_dice_8: 0.21025/0.25770, loss_spatial_ce_8: 0.23424/0.19945, loss_grounding_bce_8: 0.02996/0.08887, loss_grounding_dice_8: 0.18812/0.17000, loss_grounding_ce_8: 0.24984/0.41651, loss_mask_ce_9: 5.58147/3.47493, loss_mask_bce_9: 0.55714/0.36011, loss_mask_dice_9: 3.05377/1.76005, loss_spatial_bce_9: 0.38419/0.35447, loss_spatial_dice_9: 0.89623/0.79313, loss_spatial_ce_9: 1.29920/1.38690, loss_grounding_bce_9: 0.05340/0.10099, loss_grounding_dice_9: 0.40108/0.24216, loss_grounding_ce_9: 0.64911/0.66966] items per batch[64] items per second[0.37] total items[4755200] mini batches[ 74300] memory[4999] epoch remaining[0:17:42] INFO:trainer.default_trainer:epochs[ 40] optim steps[74400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68317/0.75381, loss_mask_bce_0: 0.36661/0.30081, loss_mask_dice_0: 0.37043/1.02012, loss_spatial_bce_0: 0.13365/0.08462, loss_spatial_dice_0: 0.15968/0.17880, loss_spatial_ce_0: 0.02465/0.05581, loss_grounding_bce_0: 0.17804/0.08066, loss_grounding_dice_0: 0.17572/0.15040, loss_grounding_ce_0: 0.00187/0.24887, loss_mask_ce_1: 0.64730/0.75457, loss_mask_bce_1: 0.37256/0.30163, loss_mask_dice_1: 0.36224/1.02452, loss_spatial_bce_1: 0.14642/0.08506, loss_spatial_dice_1: 0.17556/0.18172, loss_spatial_ce_1: 0.03425/0.05957, loss_grounding_bce_1: 0.18274/0.08083, loss_grounding_dice_1: 0.18410/0.15112, loss_grounding_ce_1: 0.00193/0.25033, loss_mask_ce_2: 0.59830/0.76222, loss_mask_bce_2: 0.37877/0.30200, loss_mask_dice_2: 0.42132/1.02537, loss_spatial_bce_2: 0.14829/0.08516, loss_spatial_dice_2: 0.17951/0.18232, loss_spatial_ce_2: 0.03284/0.06173, loss_grounding_bce_2: 0.19462/0.08082, loss_grounding_dice_2: 0.16400/0.15104, loss_grounding_ce_2: 0.00080/0.25300, loss_mask_ce_3: 0.58735/0.76659, loss_mask_bce_3: 0.37709/0.30335, loss_mask_dice_3: 0.37142/1.02335, loss_spatial_bce_3: 0.14949/0.08729, loss_spatial_dice_3: 0.16828/0.18365, loss_spatial_ce_3: 0.08831/0.06661, loss_grounding_bce_3: 0.18142/0.08119, loss_grounding_dice_3: 0.15891/0.15068, loss_grounding_ce_3: 0.00056/0.25421, loss_mask_ce_4: 0.64156/0.77246, loss_mask_bce_4: 0.38971/0.30600, loss_mask_dice_4: 0.40313/1.04266, loss_spatial_bce_4: 0.14237/0.08966, loss_spatial_dice_4: 0.18055/0.19218, loss_spatial_ce_4: 0.12831/0.08027, loss_grounding_bce_4: 0.19103/0.08192, loss_grounding_dice_4: 0.17504/0.15334, loss_grounding_ce_4: 0.00060/0.25862, loss_mask_ce_5: 0.63839/0.79780, loss_mask_bce_5: 0.39010/0.30788, loss_mask_dice_5: 0.39036/1.05066, loss_spatial_bce_5: 0.14291/0.09204, loss_spatial_dice_5: 0.18450/0.19541, loss_spatial_ce_5: 0.23037/0.09382, loss_grounding_bce_5: 0.19084/0.08219, loss_grounding_dice_5: 0.18598/0.15412, loss_grounding_ce_5: 0.00528/0.27641, loss_mask_ce_6: 0.51242/0.82468, loss_mask_bce_6: 0.38903/0.31002, loss_mask_dice_6: 0.37925/1.05468, loss_spatial_bce_6: 0.16336/0.09749, loss_spatial_dice_6: 0.17757/0.19777, loss_spatial_ce_6: 0.34548/0.11833, loss_grounding_bce_6: 0.20614/0.08304, loss_grounding_dice_6: 0.21835/0.15462, loss_grounding_ce_6: 0.00440/0.28500, loss_mask_ce_7: 0.78197/0.88078, loss_mask_bce_7: 0.38156/0.31719, loss_mask_dice_7: 0.44164/1.10019, loss_spatial_bce_7: 0.21916/0.10675, loss_spatial_dice_7: 0.24684/0.22282, loss_spatial_ce_7: 0.58298/0.15387, loss_grounding_bce_7: 0.17354/0.08473, loss_grounding_dice_7: 0.15124/0.16022, loss_grounding_ce_7: 0.16217/0.31845, loss_mask_ce_8: 0.85690/1.01469, loss_mask_bce_8: 0.42066/0.33328, loss_mask_dice_8: 0.46025/1.17668, loss_spatial_bce_8: 0.36791/0.12342, loss_spatial_dice_8: 0.44751/0.25767, loss_spatial_ce_8: 0.16040/0.19939, loss_grounding_bce_8: 0.18741/0.08886, loss_grounding_dice_8: 0.18538/0.16997, loss_grounding_ce_8: 0.03294/0.41651, loss_mask_ce_9: 3.09791/3.47490, loss_mask_bce_9: 0.57412/0.36008, loss_mask_dice_9: 0.66958/1.75980, loss_spatial_bce_9: 0.51272/0.35449, loss_spatial_dice_9: 0.75954/0.79312, loss_spatial_ce_9: 1.14736/1.38689, loss_grounding_bce_9: 0.19872/0.10099, loss_grounding_dice_9: 0.14852/0.24214, loss_grounding_ce_9: 0.50759/0.66966] items per batch[64] items per second[0.37] total items[4761600] mini batches[ 74400] memory[4999] epoch remaining[0:14:47] INFO:trainer.default_trainer:epochs[ 40] optim steps[74500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42362/0.75380, loss_mask_bce_0: 1.38653/0.30085, loss_mask_dice_0: 1.89156/1.02046, loss_spatial_bce_0: 0.12656/0.08462, loss_spatial_dice_0: 0.17493/0.17880, loss_spatial_ce_0: 0.00340/0.05578, loss_grounding_bce_0: 0.08868/0.08066, loss_grounding_dice_0: 0.40181/0.15040, loss_grounding_ce_0: 0.28014/0.24879, loss_mask_ce_1: 1.31825/0.75456, loss_mask_bce_1: 1.39202/0.30168, loss_mask_dice_1: 1.88935/1.02484, loss_spatial_bce_1: 0.12192/0.08507, loss_spatial_dice_1: 0.18230/0.18172, loss_spatial_ce_1: 0.01067/0.05954, loss_grounding_bce_1: 0.08679/0.08084, loss_grounding_dice_1: 0.39318/0.15113, loss_grounding_ce_1: 0.28830/0.25024, loss_mask_ce_2: 1.32821/0.76221, loss_mask_bce_2: 1.32723/0.30204, loss_mask_dice_2: 1.89827/1.02570, loss_spatial_bce_2: 0.12065/0.08516, loss_spatial_dice_2: 0.17829/0.18232, loss_spatial_ce_2: 0.00582/0.06170, loss_grounding_bce_2: 0.06460/0.08083, loss_grounding_dice_2: 0.32335/0.15105, loss_grounding_ce_2: 0.30996/0.25291, loss_mask_ce_3: 1.44162/0.76657, loss_mask_bce_3: 1.34219/0.30340, loss_mask_dice_3: 1.81145/1.02366, loss_spatial_bce_3: 0.15846/0.08730, loss_spatial_dice_3: 0.22635/0.18366, loss_spatial_ce_3: 0.00133/0.06657, loss_grounding_bce_3: 0.06091/0.08120, loss_grounding_dice_3: 0.34560/0.15069, loss_grounding_ce_3: 0.34907/0.25412, loss_mask_ce_4: 1.62862/0.77244, loss_mask_bce_4: 1.41427/0.30604, loss_mask_dice_4: 1.87606/1.04302, loss_spatial_bce_4: 0.18419/0.08968, loss_spatial_dice_4: 0.23154/0.19219, loss_spatial_ce_4: 0.00363/0.08022, loss_grounding_bce_4: 0.06513/0.08193, loss_grounding_dice_4: 0.30333/0.15335, loss_grounding_ce_4: 0.36965/0.25849, loss_mask_ce_5: 1.88996/0.79778, loss_mask_bce_5: 1.57514/0.30793, loss_mask_dice_5: 2.00859/1.05101, loss_spatial_bce_5: 0.15708/0.09205, loss_spatial_dice_5: 0.26602/0.19543, loss_spatial_ce_5: 0.05030/0.09380, loss_grounding_bce_5: 0.07121/0.08220, loss_grounding_dice_5: 0.30725/0.15413, loss_grounding_ce_5: 0.44950/0.27631, loss_mask_ce_6: 1.52938/0.82466, loss_mask_bce_6: 1.51195/0.31007, loss_mask_dice_6: 1.90982/1.05502, loss_spatial_bce_6: 0.18525/0.09750, loss_spatial_dice_6: 0.26136/0.19779, loss_spatial_ce_6: 0.11539/0.11831, loss_grounding_bce_6: 0.06541/0.08305, loss_grounding_dice_6: 0.33685/0.15463, loss_grounding_ce_6: 0.59968/0.28491, loss_mask_ce_7: 2.07910/0.88078, loss_mask_bce_7: 1.86735/0.31725, loss_mask_dice_7: 2.06269/1.10053, loss_spatial_bce_7: 0.15542/0.10677, loss_spatial_dice_7: 0.25144/0.22284, loss_spatial_ce_7: 0.10876/0.15379, loss_grounding_bce_7: 0.06231/0.08474, loss_grounding_dice_7: 0.47436/0.16023, loss_grounding_ce_7: 1.03414/0.31834, loss_mask_ce_8: 1.80658/1.01464, loss_mask_bce_8: 1.98023/0.33333, loss_mask_dice_8: 2.41235/1.17708, loss_spatial_bce_8: 0.24816/0.12343, loss_spatial_dice_8: 0.29083/0.25768, loss_spatial_ce_8: 0.33873/0.19932, loss_grounding_bce_8: 0.45647/0.08887, loss_grounding_dice_8: 0.81304/0.16998, loss_grounding_ce_8: 0.09076/0.41634, loss_mask_ce_9: 4.92642/3.47489, loss_mask_bce_9: 1.48656/0.36012, loss_mask_dice_9: 2.94672/1.76044, loss_spatial_bce_9: 0.31122/0.35450, loss_spatial_dice_9: 0.91565/0.79314, loss_spatial_ce_9: 1.30363/1.38700, loss_grounding_bce_9: 0.25110/0.10101, loss_grounding_dice_9: 0.75091/0.24214, loss_grounding_ce_9: 0.08047/0.66952] items per batch[64] items per second[0.37] total items[4768000] mini batches[ 74500] memory[4999] epoch remaining[0:11:52] INFO:trainer.default_trainer:epochs[ 40] optim steps[74600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.07252/0.75387, loss_mask_bce_0: 0.34351/0.30085, loss_mask_dice_0: 4.33171/1.02045, loss_spatial_bce_0: 0.03344/0.08463, loss_spatial_dice_0: 0.25127/0.17879, loss_spatial_ce_0: 0.00261/0.05578, loss_grounding_bce_0: 0.02785/0.08066, loss_grounding_dice_0: 0.10286/0.15040, loss_grounding_ce_0: 0.02693/0.24873, loss_mask_ce_1: 1.06394/0.75461, loss_mask_bce_1: 0.34239/0.30167, loss_mask_dice_1: 3.74080/1.02480, loss_spatial_bce_1: 0.03715/0.08507, loss_spatial_dice_1: 0.28607/0.18171, loss_spatial_ce_1: 0.00754/0.05952, loss_grounding_bce_1: 0.02758/0.08084, loss_grounding_dice_1: 0.09711/0.15113, loss_grounding_ce_1: 0.02684/0.25019, loss_mask_ce_2: 1.07500/0.76228, loss_mask_bce_2: 0.36219/0.30203, loss_mask_dice_2: 4.54377/1.02566, loss_spatial_bce_2: 0.03485/0.08516, loss_spatial_dice_2: 0.28832/0.18231, loss_spatial_ce_2: 0.00625/0.06170, loss_grounding_bce_2: 0.02839/0.08083, loss_grounding_dice_2: 0.10395/0.15105, loss_grounding_ce_2: 0.02896/0.25286, loss_mask_ce_3: 1.12290/0.76663, loss_mask_bce_3: 0.35228/0.30339, loss_mask_dice_3: 4.09065/1.02363, loss_spatial_bce_3: 0.03842/0.08730, loss_spatial_dice_3: 0.26363/0.18365, loss_spatial_ce_3: 0.01482/0.06657, loss_grounding_bce_3: 0.02796/0.08120, loss_grounding_dice_3: 0.07945/0.15068, loss_grounding_ce_3: 0.02940/0.25407, loss_mask_ce_4: 1.31236/0.77249, loss_mask_bce_4: 0.36973/0.30604, loss_mask_dice_4: 3.78881/1.04298, loss_spatial_bce_4: 0.04089/0.08968, loss_spatial_dice_4: 0.28588/0.19219, loss_spatial_ce_4: 0.00289/0.08022, loss_grounding_bce_4: 0.02935/0.08193, loss_grounding_dice_4: 0.10218/0.15334, loss_grounding_ce_4: 0.04417/0.25848, loss_mask_ce_5: 1.34507/0.79789, loss_mask_bce_5: 0.37242/0.30793, loss_mask_dice_5: 4.20668/1.05099, loss_spatial_bce_5: 0.04349/0.09205, loss_spatial_dice_5: 0.26286/0.19542, loss_spatial_ce_5: 0.01808/0.09380, loss_grounding_bce_5: 0.02838/0.08220, loss_grounding_dice_5: 0.11889/0.15412, loss_grounding_ce_5: 0.03142/0.27630, loss_mask_ce_6: 1.59784/0.82477, loss_mask_bce_6: 0.35615/0.31007, loss_mask_dice_6: 4.19707/1.05500, loss_spatial_bce_6: 0.04602/0.09749, loss_spatial_dice_6: 0.32613/0.19779, loss_spatial_ce_6: 0.06221/0.11830, loss_grounding_bce_6: 0.03019/0.08305, loss_grounding_dice_6: 0.10506/0.15463, loss_grounding_ce_6: 0.05631/0.28490, loss_mask_ce_7: 1.64227/0.88092, loss_mask_bce_7: 0.40559/0.31724, loss_mask_dice_7: 4.45218/1.10051, loss_spatial_bce_7: 0.05171/0.10677, loss_spatial_dice_7: 0.34455/0.22283, loss_spatial_ce_7: 0.09660/0.15376, loss_grounding_bce_7: 0.03461/0.08475, loss_grounding_dice_7: 0.16095/0.16022, loss_grounding_ce_7: 0.09260/0.31832, loss_mask_ce_8: 2.40024/1.01480, loss_mask_bce_8: 0.34593/0.33332, loss_mask_dice_8: 5.32168/1.17704, loss_spatial_bce_8: 0.05948/0.12343, loss_spatial_dice_8: 0.42036/0.25766, loss_spatial_ce_8: 0.05909/0.19928, loss_grounding_bce_8: 0.02403/0.08888, loss_grounding_dice_8: 0.19809/0.16998, loss_grounding_ce_8: 0.36226/0.41653, loss_mask_ce_9: 6.61371/3.47534, loss_mask_bce_9: 0.39877/0.36014, loss_mask_dice_9: 6.28442/1.76043, loss_spatial_bce_9: 0.21959/0.35451, loss_spatial_dice_9: 0.93777/0.79315, loss_spatial_ce_9: 1.37821/1.38697, loss_grounding_bce_9: 0.03499/0.10103, loss_grounding_dice_9: 0.30103/0.24216, loss_grounding_ce_9: 0.41152/0.66971] items per batch[64] items per second[0.37] total items[4774400] mini batches[ 74600] memory[4999] epoch remaining[0:08:57] INFO:trainer.default_trainer:epochs[ 40] optim steps[74700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.30320/0.75385, loss_mask_bce_0: 0.75727/0.30084, loss_mask_dice_0: 4.99278/1.02056, loss_spatial_bce_0: 0.04497/0.08461, loss_spatial_dice_0: 0.21181/0.17878, loss_spatial_ce_0: 0.08899/0.05576, loss_grounding_bce_0: 0.01778/0.08066, loss_grounding_dice_0: 0.09572/0.15039, loss_grounding_ce_0: 0.25256/0.24862, loss_mask_ce_1: 1.60912/0.75459, loss_mask_bce_1: 0.77562/0.30165, loss_mask_dice_1: 5.16230/1.02493, loss_spatial_bce_1: 0.04507/0.08506, loss_spatial_dice_1: 0.19001/0.18170, loss_spatial_ce_1: 0.18958/0.05950, loss_grounding_bce_1: 0.01886/0.08083, loss_grounding_dice_1: 0.11256/0.15112, loss_grounding_ce_1: 0.13771/0.25009, loss_mask_ce_2: 1.40587/0.76226, loss_mask_bce_2: 0.70212/0.30202, loss_mask_dice_2: 5.21453/1.02579, loss_spatial_bce_2: 0.04505/0.08515, loss_spatial_dice_2: 0.18224/0.18230, loss_spatial_ce_2: 0.20198/0.06168, loss_grounding_bce_2: 0.01773/0.08083, loss_grounding_dice_2: 0.10234/0.15103, loss_grounding_ce_2: 0.12873/0.25276, loss_mask_ce_3: 1.36651/0.76664, loss_mask_bce_3: 0.73811/0.30337, loss_mask_dice_3: 4.96078/1.02373, loss_spatial_bce_3: 0.04160/0.08729, loss_spatial_dice_3: 0.18956/0.18364, loss_spatial_ce_3: 0.01260/0.06655, loss_grounding_bce_3: 0.01784/0.08120, loss_grounding_dice_3: 0.13807/0.15067, loss_grounding_ce_3: 0.13793/0.25397, loss_mask_ce_4: 1.53344/0.77249, loss_mask_bce_4: 0.75412/0.30604, loss_mask_dice_4: 5.01882/1.04310, loss_spatial_bce_4: 0.04346/0.08967, loss_spatial_dice_4: 0.26403/0.19218, loss_spatial_ce_4: 0.02989/0.08021, loss_grounding_bce_4: 0.01609/0.08193, loss_grounding_dice_4: 0.05113/0.15332, loss_grounding_ce_4: 0.21634/0.25841, loss_mask_ce_5: 1.79770/0.79788, loss_mask_bce_5: 0.87279/0.30792, loss_mask_dice_5: 5.40409/1.05112, loss_spatial_bce_5: 0.05719/0.09203, loss_spatial_dice_5: 0.24947/0.19541, loss_spatial_ce_5: 0.08931/0.09378, loss_grounding_bce_5: 0.01646/0.08221, loss_grounding_dice_5: 0.09478/0.15411, loss_grounding_ce_5: 0.12716/0.27619, loss_mask_ce_6: 1.80189/0.82483, loss_mask_bce_6: 0.88009/0.31007, loss_mask_dice_6: 5.23507/1.05511, loss_spatial_bce_6: 0.04679/0.09748, loss_spatial_dice_6: 0.19636/0.19778, loss_spatial_ce_6: 0.15973/0.11827, loss_grounding_bce_6: 0.02098/0.08305, loss_grounding_dice_6: 0.33353/0.15463, loss_grounding_ce_6: 0.21030/0.28480, loss_mask_ce_7: 1.98370/0.88101, loss_mask_bce_7: 0.80855/0.31724, loss_mask_dice_7: 5.39038/1.10067, loss_spatial_bce_7: 0.06067/0.10676, loss_spatial_dice_7: 0.35020/0.22281, loss_spatial_ce_7: 0.30458/0.15372, loss_grounding_bce_7: 0.02057/0.08475, loss_grounding_dice_7: 0.21093/0.16022, loss_grounding_ce_7: 0.22877/0.31823, loss_mask_ce_8: 1.68899/1.01484, loss_mask_bce_8: 0.89140/0.33332, loss_mask_dice_8: 5.85642/1.17722, loss_spatial_bce_8: 0.07879/0.12341, loss_spatial_dice_8: 0.39122/0.25764, loss_spatial_ce_8: 0.20354/0.19922, loss_grounding_bce_8: 0.01781/0.08889, loss_grounding_dice_8: 0.15381/0.16997, loss_grounding_ce_8: 0.24111/0.41641, loss_mask_ce_9: 4.98677/3.47546, loss_mask_bce_9: 0.84670/0.36013, loss_mask_dice_9: 8.29853/1.76061, loss_spatial_bce_9: 0.16003/0.35447, loss_spatial_dice_9: 0.93066/0.79315, loss_spatial_ce_9: 1.39964/1.38691, loss_grounding_bce_9: 0.02964/0.10103, loss_grounding_dice_9: 0.30484/0.24214, loss_grounding_ce_9: 0.62805/0.66969] items per batch[64] items per second[0.37] total items[4780800] mini batches[ 74700] memory[4999] epoch remaining[0:06:02] INFO:trainer.default_trainer:epochs[ 40] optim steps[74800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.75233/0.75376, loss_mask_bce_0: 0.21619/0.30079, loss_mask_dice_0: 1.22643/1.02031, loss_spatial_bce_0: 0.02729/0.08460, loss_spatial_dice_0: 0.22322/0.17875, loss_spatial_ce_0: 0.09857/0.05579, loss_grounding_bce_0: 0.03370/0.08065, loss_grounding_dice_0: 0.25080/0.15038, loss_grounding_ce_0: 1.04600/0.24856, loss_mask_ce_1: 0.77442/0.75452, loss_mask_bce_1: 0.21585/0.30160, loss_mask_dice_1: 1.14590/1.02469, loss_spatial_bce_1: 0.02589/0.08505, loss_spatial_dice_1: 0.23425/0.18168, loss_spatial_ce_1: 0.08119/0.05951, loss_grounding_bce_1: 0.03636/0.08082, loss_grounding_dice_1: 0.24245/0.15112, loss_grounding_ce_1: 1.24075/0.25005, loss_mask_ce_2: 0.80085/0.76221, loss_mask_bce_2: 0.22898/0.30196, loss_mask_dice_2: 1.15834/1.02553, loss_spatial_bce_2: 0.02870/0.08514, loss_spatial_dice_2: 0.23000/0.18228, loss_spatial_ce_2: 0.05543/0.06168, loss_grounding_bce_2: 0.03131/0.08082, loss_grounding_dice_2: 0.25464/0.15103, loss_grounding_ce_2: 0.93280/0.25271, loss_mask_ce_3: 0.81548/0.76658, loss_mask_bce_3: 0.19812/0.30331, loss_mask_dice_3: 1.19069/1.02346, loss_spatial_bce_3: 0.03487/0.08727, loss_spatial_dice_3: 0.23601/0.18362, loss_spatial_ce_3: 0.06610/0.06655, loss_grounding_bce_3: 0.03421/0.08118, loss_grounding_dice_3: 0.26322/0.15067, loss_grounding_ce_3: 1.66314/0.25392, loss_mask_ce_4: 0.80391/0.77237, loss_mask_bce_4: 0.18404/0.30598, loss_mask_dice_4: 1.11274/1.04283, loss_spatial_bce_4: 0.04589/0.08966, loss_spatial_dice_4: 0.26157/0.19215, loss_spatial_ce_4: 0.08471/0.08021, loss_grounding_bce_4: 0.03191/0.08192, loss_grounding_dice_4: 0.26931/0.15332, loss_grounding_ce_4: 0.84115/0.25834, loss_mask_ce_5: 0.99910/0.79780, loss_mask_bce_5: 0.18135/0.30786, loss_mask_dice_5: 1.13531/1.05089, loss_spatial_bce_5: 0.04777/0.09203, loss_spatial_dice_5: 0.27223/0.19540, loss_spatial_ce_5: 0.04008/0.09377, loss_grounding_bce_5: 0.03442/0.08219, loss_grounding_dice_5: 0.23571/0.15411, loss_grounding_ce_5: 0.93039/0.27613, loss_mask_ce_6: 1.09829/0.82471, loss_mask_bce_6: 0.23052/0.31001, loss_mask_dice_6: 1.20516/1.05484, loss_spatial_bce_6: 0.04029/0.09747, loss_spatial_dice_6: 0.28427/0.19777, loss_spatial_ce_6: 0.19933/0.11826, loss_grounding_bce_6: 0.04150/0.08304, loss_grounding_dice_6: 0.22525/0.15462, loss_grounding_ce_6: 0.73629/0.28474, loss_mask_ce_7: 1.07896/0.88087, loss_mask_bce_7: 0.20415/0.31719, loss_mask_dice_7: 1.23095/1.10039, loss_spatial_bce_7: 0.10081/0.10675, loss_spatial_dice_7: 0.32006/0.22281, loss_spatial_ce_7: 0.12858/0.15369, loss_grounding_bce_7: 0.04624/0.08474, loss_grounding_dice_7: 0.28190/0.16021, loss_grounding_ce_7: 0.70326/0.31815, loss_mask_ce_8: 0.95982/1.01470, loss_mask_bce_8: 0.20859/0.33326, loss_mask_dice_8: 1.33135/1.17693, loss_spatial_bce_8: 0.06214/0.12340, loss_spatial_dice_8: 0.35140/0.25762, loss_spatial_ce_8: 0.12212/0.19919, loss_grounding_bce_8: 0.04436/0.08888, loss_grounding_dice_8: 0.25148/0.16997, loss_grounding_ce_8: 1.21648/0.41633, loss_mask_ce_9: 4.21511/3.47509, loss_mask_bce_9: 0.16359/0.36005, loss_mask_dice_9: 1.50436/1.76010, loss_spatial_bce_9: 0.08133/0.35442, loss_spatial_dice_9: 0.78753/0.79313, loss_spatial_ce_9: 0.96035/1.38685, loss_grounding_bce_9: 0.03829/0.10102, loss_grounding_dice_9: 0.35003/0.24213, loss_grounding_ce_9: 1.68933/0.66959] items per batch[64] items per second[0.36] total items[4787200] mini batches[ 74800] memory[4999] epoch remaining[0:03:07] INFO:trainer.default_trainer:epochs[ 40] optim steps[74900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.84248/0.75369, loss_mask_bce_0: 0.21858/0.30079, loss_mask_dice_0: 0.57383/1.01999, loss_spatial_bce_0: 0.04670/0.08462, loss_spatial_dice_0: 0.09659/0.17876, loss_spatial_ce_0: 0.03684/0.05580, loss_grounding_bce_0: 0.04931/0.08067, loss_grounding_dice_0: 0.13086/0.15038, loss_grounding_ce_0: 0.46132/0.24857, loss_mask_ce_1: 0.84024/0.75446, loss_mask_bce_1: 0.21347/0.30161, loss_mask_dice_1: 0.51935/1.02437, loss_spatial_bce_1: 0.04439/0.08507, loss_spatial_dice_1: 0.11294/0.18168, loss_spatial_ce_1: 0.03709/0.05951, loss_grounding_bce_1: 0.05783/0.08084, loss_grounding_dice_1: 0.14026/0.15113, loss_grounding_ce_1: 0.43240/0.25009, loss_mask_ce_2: 1.04395/0.76212, loss_mask_bce_2: 0.21437/0.30197, loss_mask_dice_2: 0.59722/1.02523, loss_spatial_bce_2: 0.04374/0.08516, loss_spatial_dice_2: 0.09677/0.18228, loss_spatial_ce_2: 0.02191/0.06168, loss_grounding_bce_2: 0.05673/0.08083, loss_grounding_dice_2: 0.12790/0.15104, loss_grounding_ce_2: 0.40076/0.25277, loss_mask_ce_3: 1.07050/0.76650, loss_mask_bce_3: 0.20988/0.30333, loss_mask_dice_3: 0.50382/1.02316, loss_spatial_bce_3: 0.05004/0.08730, loss_spatial_dice_3: 0.11367/0.18363, loss_spatial_ce_3: 0.02257/0.06656, loss_grounding_bce_3: 0.04292/0.08120, loss_grounding_dice_3: 0.11361/0.15068, loss_grounding_ce_3: 0.47118/0.25396, loss_mask_ce_4: 1.03146/0.77233, loss_mask_bce_4: 0.21607/0.30599, loss_mask_dice_4: 0.51371/1.04255, loss_spatial_bce_4: 0.05823/0.08969, loss_spatial_dice_4: 0.13272/0.19216, loss_spatial_ce_4: 0.01600/0.08022, loss_grounding_bce_4: 0.04269/0.08193, loss_grounding_dice_4: 0.10901/0.15332, loss_grounding_ce_4: 0.51384/0.25838, loss_mask_ce_5: 1.33468/0.79774, loss_mask_bce_5: 0.18440/0.30787, loss_mask_dice_5: 0.53742/1.05057, loss_spatial_bce_5: 0.07062/0.09206, loss_spatial_dice_5: 0.17125/0.19542, loss_spatial_ce_5: 0.04006/0.09376, loss_grounding_bce_5: 0.04695/0.08221, loss_grounding_dice_5: 0.13569/0.15412, loss_grounding_ce_5: 0.54762/0.27618, loss_mask_ce_6: 1.38110/0.82472, loss_mask_bce_6: 0.19603/0.31002, loss_mask_dice_6: 0.48626/1.05453, loss_spatial_bce_6: 0.08603/0.09752, loss_spatial_dice_6: 0.17003/0.19779, loss_spatial_ce_6: 0.05168/0.11824, loss_grounding_bce_6: 0.04710/0.08305, loss_grounding_dice_6: 0.13850/0.15463, loss_grounding_ce_6: 0.51414/0.28479, loss_mask_ce_7: 1.38976/0.88087, loss_mask_bce_7: 0.20808/0.31719, loss_mask_dice_7: 1.01694/1.10010, loss_spatial_bce_7: 0.09853/0.10679, loss_spatial_dice_7: 0.15276/0.22281, loss_spatial_ce_7: 0.08990/0.15365, loss_grounding_bce_7: 0.04479/0.08475, loss_grounding_dice_7: 0.18066/0.16022, loss_grounding_ce_7: 0.54316/0.31821, loss_mask_ce_8: 1.70862/1.01461, loss_mask_bce_8: 0.18792/0.33326, loss_mask_dice_8: 0.99778/1.17661, loss_spatial_bce_8: 0.08070/0.12342, loss_spatial_dice_8: 0.19701/0.25761, loss_spatial_ce_8: 0.21632/0.19920, loss_grounding_bce_8: 0.04765/0.08889, loss_grounding_dice_8: 0.27383/0.16997, loss_grounding_ce_8: 0.59589/0.41633, loss_mask_ce_9: 4.03505/3.47490, loss_mask_bce_9: 0.37320/0.36006, loss_mask_dice_9: 1.48822/1.75962, loss_spatial_bce_9: 0.55755/0.35446, loss_spatial_dice_9: 0.90183/0.79311, loss_spatial_ce_9: 1.71751/1.38676, loss_grounding_bce_9: 0.09170/0.10104, loss_grounding_dice_9: 0.36318/0.24215, loss_grounding_ce_9: 0.65975/0.66951] items per batch[64] items per second[0.36] total items[4793600] mini batches[ 74900] memory[4999] epoch remaining[0:00:12] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00074907. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0031 s/iter. Inference: 0.3598 s/iter. Eval: 0.1094 s/iter. Total: 0.4723 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0025 s/iter. Inference: 0.3670 s/iter. Eval: 0.0914 s/iter. Total: 0.4611 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0027 s/iter. Inference: 0.3758 s/iter. Eval: 0.0827 s/iter. Total: 0.4613 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0027 s/iter. Inference: 0.3778 s/iter. Eval: 0.0792 s/iter. Total: 0.4599 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3792 s/iter. Eval: 0.0764 s/iter. Total: 0.4585 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0028 s/iter. Inference: 0.3780 s/iter. Eval: 0.0741 s/iter. Total: 0.4550 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalt0u_tk_v ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.804 | 83.109 | 66.291 | 133 | | Things | 61.966 | 84.236 | 73.053 | 80 | | Stuff | 46.502 | 81.408 | 56.084 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.54s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.97 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.43 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.64s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.29 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.460 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.696 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.496 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.267 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.679 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.553 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.571 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.609 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.52 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.986 | 69.608 | 49.599 | 26.709 | 49.962 | 67.936 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.294 | bicycle | 23.210 | car | 42.670 | | motorcycle | 41.388 | airplane | 62.805 | bus | 71.456 | | train | 75.283 | truck | 44.573 | boat | 31.464 | | traffic light | 29.736 | fire hydrant | 71.483 | stop sign | 69.703 | | parking meter | 54.150 | bench | 26.276 | bird | 34.759 | | cat | 77.721 | dog | 70.830 | horse | 51.367 | | sheep | 53.985 | cow | 56.989 | elephant | 66.176 | | bear | 80.085 | zebra | 66.356 | giraffe | 62.626 | | backpack | 25.137 | umbrella | 55.805 | handbag | 24.926 | | tie | 41.909 | suitcase | 51.840 | frisbee | 70.844 | | skis | 8.663 | snowboard | 34.889 | sports ball | 49.357 | | kite | 37.570 | baseball bat | 37.770 | baseball glove | 50.301 | | skateboard | 44.047 | surfboard | 45.004 | tennis racket | 63.879 | | bottle | 41.826 | wine glass | 38.134 | cup | 50.091 | | fork | 25.941 | knife | 25.232 | spoon | 22.377 | | bowl | 41.325 | banana | 22.185 | apple | 27.034 | | sandwich | 50.250 | orange | 31.593 | broccoli | 24.661 | | carrot | 22.715 | hot dog | 34.903 | pizza | 52.967 | | donut | 55.144 | cake | 48.010 | chair | 28.456 | | couch | 42.541 | potted plant | 23.098 | bed | 42.265 | | dining table | 15.393 | toilet | 69.891 | tv | 66.172 | | laptop | 71.704 | mouse | 63.435 | remote | 43.994 | | keyboard | 59.356 | cell phone | 46.192 | microwave | 66.853 | | oven | 33.353 | toaster | 49.245 | sink | 45.005 | | refrigerator | 69.955 | book | 14.636 | clock | 53.967 | | vase | 41.272 | scissors | 38.005 | teddy bear | 58.995 | | hair drier | 36.103 | toothbrush | 28.284 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.24021877592526, 'fwIoU': 71.43347409537846, 'IoU-person': 89.03908067849848, 'IoU-bicycle': 73.736226089278, 'IoU-car': 73.86078718214547, 'IoU-motorcycle': 88.17323966206625, 'IoU-airplane': 84.5948938872396, 'IoU-bus': 87.93444682143014, 'IoU-train': 88.18325058175006, 'IoU-truck': 72.23656094668435, 'IoU-boat': 72.69967923342502, 'IoU-traffic light': 78.64480600648184, 'IoU-fire hydrant': 93.2879801169949, 'IoU-stop sign': 84.63193405126775, 'IoU-parking meter': 85.00429271185997, 'IoU-bench': 63.40383506696637, 'IoU-bird': 77.88077356592561, 'IoU-cat': 90.66896247175083, 'IoU-dog': 85.6900171690818, 'IoU-horse': 89.09956316117379, 'IoU-sheep': 86.27416667300713, 'IoU-cow': 89.52300459768003, 'IoU-elephant': 89.73025778855455, 'IoU-bear': 79.80892735642404, 'IoU-zebra': 87.83937000232727, 'IoU-giraffe': 89.57364080147143, 'IoU-backpack': 54.284197625693196, 'IoU-umbrella': 89.60904044036421, 'IoU-handbag': 51.32665931985777, 'IoU-tie': 76.76410776898372, 'IoU-suitcase': 78.85024534730934, 'IoU-frisbee': 84.79212863033845, 'IoU-skis': 60.67256607965857, 'IoU-snowboard': 74.53827711277192, 'IoU-sports ball': 78.646702277305, 'IoU-kite': 79.56916572763005, 'IoU-baseball bat': 68.01990879928651, 'IoU-baseball glove': 76.72409876827373, 'IoU-skateboard': 86.12973913191765, 'IoU-surfboard': 86.53873062202622, 'IoU-tennis racket': 91.47868973515328, 'IoU-bottle': 71.71014040025189, 'IoU-wine glass': 82.04972194265902, 'IoU-cup': 69.24016270582662, 'IoU-fork': 69.50821593430449, 'IoU-knife': 65.58822766796972, 'IoU-spoon': 61.98808601160268, 'IoU-bowl': 57.99743885864228, 'IoU-banana': 81.6923694453495, 'IoU-apple': 60.78610455252386, 'IoU-sandwich': 69.51505581448667, 'IoU-orange': 77.57382253360353, 'IoU-broccoli': 68.18211304577892, 'IoU-carrot': 64.10536489329486, 'IoU-hot dog': 65.39578085069002, 'IoU-pizza': 69.19838001247763, 'IoU-donut': 65.35013233701399, 'IoU-cake': 73.84788419712467, 'IoU-chair': 62.3278656844515, 'IoU-couch': 70.38738917893724, 'IoU-potted plant': 42.901263639450995, 'IoU-bed': 74.87056123390448, 'IoU-dining table': 51.31884001378745, 'IoU-toilet': 84.16481540187404, 'IoU-tv': 76.51577131120456, 'IoU-laptop': 77.36891104638495, 'IoU-mouse': 70.08038831798046, 'IoU-remote': 65.08038093782537, 'IoU-keyboard': 57.03415196182795, 'IoU-cell phone': 65.89674172778639, 'IoU-microwave': 72.12233850803841, 'IoU-oven': 70.79574289941411, 'IoU-toaster': 81.98710609613488, 'IoU-sink': 75.27078868530212, 'IoU-refrigerator': 83.36090976712684, 'IoU-book': 54.84239971932856, 'IoU-clock': 73.43076072938237, 'IoU-vase': 64.69171740720121, 'IoU-scissors': 58.460389899435164, 'IoU-teddy bear': 83.12862740388496, 'IoU-hair drier': 48.95286156669343, 'IoU-toothbrush': 77.15224430194908, 'IoU-banner': 34.643841308721896, 'IoU-blanket': 16.19071247346832, 'IoU-bridge': 39.14697102878382, 'IoU-cardboard': 50.04424418634855, 'IoU-counter': 33.536178370039444, 'IoU-curtain': 71.58015490437057, 'IoU-door-stuff': 47.22595547538219, 'IoU-floor-wood': 67.20402486473242, 'IoU-flower': 44.105919453492305, 'IoU-fruit': 48.78466458895534, 'IoU-gravel': 27.52501144037895, 'IoU-house': 23.981318067331227, 'IoU-light': 43.30874082940369, 'IoU-mirror-stuff': 65.27168496215816, 'IoU-net': 46.649859215437154, 'IoU-pillow': 27.08334594151659, 'IoU-platform': 29.80351328677181, 'IoU-playingfield': 70.31979411007269, 'IoU-railroad': 63.12038346085443, 'IoU-river': 53.55043091535187, 'IoU-road': 67.34271198646947, 'IoU-roof': 19.16375643611913, 'IoU-sand': 64.08058631946301, 'IoU-sea': 86.38102387520834, 'IoU-shelf': 38.616138138476266, 'IoU-snow': 92.02873494601077, 'IoU-stairs': 36.274010643435204, 'IoU-tent': 10.323901790742612, 'IoU-towel': 46.19818774501191, 'IoU-wall-brick': 50.651685330166366, 'IoU-wall-stone': 30.709261016657834, 'IoU-wall-tile': 71.64898860964404, 'IoU-wall-wood': 45.52240318928168, 'IoU-water-other': 29.297130623339502, 'IoU-window-blind': 50.135770643245706, 'IoU-window-other': 50.62204511065506, 'IoU-tree-merged': 81.71394217220966, 'IoU-fence-merged': 55.6398073021565, 'IoU-ceiling-merged': 68.5963398628463, 'IoU-sky-other-merged': 93.86970958032413, 'IoU-cabinet-merged': 62.795037572761316, 'IoU-table-merged': 41.091979474412085, 'IoU-floor-other-merged': 54.84161947684591, 'IoU-pavement-merged': 58.11198699529362, 'IoU-mountain-merged': 57.178985726445816, 'IoU-grass-merged': 71.7343981020478, 'IoU-dirt-merged': 45.882804275014024, 'IoU-paper-merged': 35.02625731215744, 'IoU-food-other-merged': 42.940399951920796, 'IoU-building-other-merged': 59.464462892151516, 'IoU-rock-merged': 64.40245631165735, 'IoU-wall-other-merged': 68.24262047221082, 'IoU-rug-merged': 68.00728177714474, 'mACC': 76.79625386914735, 'pACC': 82.1164561901238, 'ACC-person': 92.94322872748945, 'ACC-bicycle': 82.68817436900261, 'ACC-car': 86.40569049516336, 'ACC-motorcycle': 92.56844460809891, 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Inference done 11/25. Dataloading: 0.3158 s/iter. Inference: 0.1742 s/iter. Eval: 0.0000 s/iter. Total: 0.4900 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3387 s/iter. Inference: 0.3475 s/iter. Eval: 0.0000 s/iter. Total: 0.6864 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3525 s/iter. Inference: 0.5533 s/iter. Eval: 0.0000 s/iter. Total: 0.9060 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3388937664618086, 'noc@0.8': 2.3748902546093063, 'noc@0.85': 2.80684811237928, 'noc@0.9': 3.5677494878548432, 'miou@iter1': 0.876166896021851} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1484 s/iter. Eval: 0.0010 s/iter. Total: 0.1508 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.20404052734375, 'precision@0.6': 72.28915405273438, 'precision@0.7': 68.51924133300781, 'precision@0.8': 59.23046875, 'precision@0.9': 32.452388763427734, 'cIoU': 62.104644775390625, 'mIoU': 66.76644134521484} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.8037244712534, 'SQ': 83.10894926181021, 'RQ': 66.2911545073578, 'PQ_th': 61.96617176131176, 'SQ_th': 84.23579022914087, 'RQ_th': 73.05308291919793, 'PQ_st': 46.501917240976596, 'SQ_st': 81.40805723565076, 'RQ_st': 56.084470112127335}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 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'ACC-baseball bat': 88.57000501989226, 'ACC-baseball glove': 91.26946383923102, 'ACC-skateboard': 90.68072767750941, 'ACC-surfboard': 92.34379581473719, 'ACC-tennis racket': 95.25962585768136, 'ACC-bottle': 86.41386544072539, 'ACC-wine glass': 90.83706551105148, 'ACC-cup': 86.3690686601048, 'ACC-fork': 80.68317613830483, 'ACC-knife': 78.77592144793728, 'ACC-spoon': 78.56940282864153, 'ACC-bowl': 73.63698259059733, 'ACC-banana': 89.93310833166433, 'ACC-apple': 74.749409621198, 'ACC-sandwich': 80.3587293833036, 'ACC-orange': 85.65600243692343, 'ACC-broccoli': 79.22037588991407, 'ACC-carrot': 75.6370865977851, 'ACC-hot dog': 72.38440118703672, 'ACC-pizza': 73.81752876419708, 'ACC-donut': 73.30488643247033, 'ACC-cake': 81.77473732798093, 'ACC-chair': 80.18292398266041, 'ACC-couch': 77.15534641891975, 'ACC-potted plant': 62.292034284151256, 'ACC-bed': 86.17690391116852, 'ACC-dining table': 73.40643462694482, 'ACC-toilet': 89.06590194166088, 'ACC-tv': 85.65604157277696, 'ACC-laptop': 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62.76168271255216, 'ACC-mirror-stuff': 78.53922828526856, 'ACC-net': 62.2322099620222, 'ACC-pillow': 59.07131335584342, 'ACC-platform': 47.39668983605419, 'ACC-playingfield': 87.44789964251977, 'ACC-railroad': 81.25335744551037, 'ACC-river': 72.85564658393791, 'ACC-road': 85.41613541813406, 'ACC-roof': 27.083287494855906, 'ACC-sand': 68.96023785878002, 'ACC-sea': 91.71413794076214, 'ACC-shelf': 53.90881373241603, 'ACC-snow': 95.77860946082683, 'ACC-stairs': 63.23766434065129, 'ACC-tent': 11.825731564001655, 'ACC-towel': 55.058951598490054, 'ACC-wall-brick': 69.0056607320683, 'ACC-wall-stone': 37.616333874031646, 'ACC-wall-tile': 86.49016672487345, 'ACC-wall-wood': 60.27657715050726, 'ACC-water-other': 48.62294828586042, 'ACC-window-blind': 65.90703248522377, 'ACC-window-other': 73.7495433194319, 'ACC-tree-merged': 89.24067210192152, 'ACC-fence-merged': 72.76825863469692, 'ACC-ceiling-merged': 84.21837273187897, 'ACC-sky-other-merged': 97.0195314223698, 'ACC-cabinet-merged': 79.74932225342526, 'ACC-table-merged': 57.73531923839765, 'ACC-floor-other-merged': 64.99361760422194, 'ACC-pavement-merged': 71.78061540618992, 'ACC-mountain-merged': 70.27050114891613, 'ACC-grass-merged': 84.07281212084467, 'ACC-dirt-merged': 69.1010795931801, 'ACC-paper-merged': 46.531301512641946, 'ACC-food-other-merged': 60.366018632496946, 'ACC-building-other-merged': 75.130292631098, 'ACC-rock-merged': 82.70113735984168, 'ACC-wall-other-merged': 82.19105943612483, 'ACC-rug-merged': 81.75296951423472})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3388937664618086, 'noc@0.8': 2.3748902546093063, 'noc@0.85': 2.80684811237928, 'noc@0.9': 3.5677494878548432, 'miou@iter1': 0.876166896021851}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.20404052734375, 'precision@0.6': 72.28915405273438, 'precision@0.7': 68.51924133300781, 'precision@0.8': 59.23046875, 'precision@0.9': 32.452388763427734, 'cIoU': 62.104644775390625, 'mIoU': 66.76644134521484}}} INFO:trainer.default_trainer:This epoch takes 0:56:48.591105 INFO:trainer.default_trainer:PROGRESS: 82.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 41 training. INFO:trainer.default_trainer:epochs[ 41] optim steps[75000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00618/0.75373, loss_mask_bce_0: 0.02500/0.30075, loss_mask_dice_0: 0.01811/1.01991, loss_spatial_bce_0: 0.03040/0.08461, loss_spatial_dice_0: 0.01883/0.17874, loss_spatial_ce_0: 0.00021/0.05577, loss_grounding_bce_0: 0.03253/0.08066, loss_grounding_dice_0: 0.02342/0.15038, loss_grounding_ce_0: 0.00080/0.24858, loss_mask_ce_1: 0.00532/0.75449, loss_mask_bce_1: 0.02832/0.30157, loss_mask_dice_1: 0.02015/1.02430, loss_spatial_bce_1: 0.02895/0.08506, loss_spatial_dice_1: 0.01921/0.18167, loss_spatial_ce_1: 0.00016/0.05950, loss_grounding_bce_1: 0.03165/0.08083, loss_grounding_dice_1: 0.02231/0.15114, loss_grounding_ce_1: 0.00047/0.25007, loss_mask_ce_2: 0.00596/0.76214, loss_mask_bce_2: 0.02538/0.30194, loss_mask_dice_2: 0.01745/1.02518, loss_spatial_bce_2: 0.03037/0.08516, loss_spatial_dice_2: 0.01972/0.18227, loss_spatial_ce_2: 0.00022/0.06166, loss_grounding_bce_2: 0.03201/0.08083, loss_grounding_dice_2: 0.02132/0.15104, loss_grounding_ce_2: 0.00040/0.25274, loss_mask_ce_3: 0.00730/0.76652, loss_mask_bce_3: 0.02707/0.30331, loss_mask_dice_3: 0.01788/1.02312, loss_spatial_bce_3: 0.03024/0.08729, loss_spatial_dice_3: 0.02002/0.18362, loss_spatial_ce_3: 0.00024/0.06653, loss_grounding_bce_3: 0.03091/0.08119, loss_grounding_dice_3: 0.02134/0.15068, loss_grounding_ce_3: 0.00096/0.25395, loss_mask_ce_4: 0.00537/0.77233, loss_mask_bce_4: 0.02578/0.30596, loss_mask_dice_4: 0.01741/1.04251, loss_spatial_bce_4: 0.03165/0.08968, loss_spatial_dice_4: 0.02040/0.19216, loss_spatial_ce_4: 0.00081/0.08022, loss_grounding_bce_4: 0.03234/0.08193, loss_grounding_dice_4: 0.02271/0.15333, loss_grounding_ce_4: 0.00064/0.25837, loss_mask_ce_5: 0.00683/0.79773, loss_mask_bce_5: 0.02870/0.30784, loss_mask_dice_5: 0.01985/1.05054, loss_spatial_bce_5: 0.03387/0.09206, loss_spatial_dice_5: 0.02406/0.19542, loss_spatial_ce_5: 0.00171/0.09373, loss_grounding_bce_5: 0.03202/0.08220, loss_grounding_dice_5: 0.02219/0.15413, loss_grounding_ce_5: 0.00066/0.27616, loss_mask_ce_6: 0.01222/0.82473, loss_mask_bce_6: 0.03064/0.31000, loss_mask_dice_6: 0.02036/1.05446, loss_spatial_bce_6: 0.03216/0.09751, loss_spatial_dice_6: 0.02068/0.19778, loss_spatial_ce_6: 0.00194/0.11821, loss_grounding_bce_6: 0.03809/0.08305, loss_grounding_dice_6: 0.02543/0.15462, loss_grounding_ce_6: 0.00160/0.28479, loss_mask_ce_7: 0.01572/0.88091, loss_mask_bce_7: 0.03259/0.31717, loss_mask_dice_7: 0.02005/1.10003, loss_spatial_bce_7: 0.03584/0.10679, loss_spatial_dice_7: 0.02608/0.22281, loss_spatial_ce_7: 0.00206/0.15361, loss_grounding_bce_7: 0.03552/0.08474, loss_grounding_dice_7: 0.02290/0.16023, loss_grounding_ce_7: 0.00417/0.31820, loss_mask_ce_8: 0.02166/1.01458, loss_mask_bce_8: 0.03256/0.33323, loss_mask_dice_8: 0.02245/1.17652, loss_spatial_bce_8: 0.03252/0.12342, loss_spatial_dice_8: 0.02280/0.25761, loss_spatial_ce_8: 0.04123/0.19917, loss_grounding_bce_8: 0.03787/0.08888, loss_grounding_dice_8: 0.02701/0.16998, loss_grounding_ce_8: 0.00510/0.41628, loss_mask_ce_9: 1.67855/3.47496, loss_mask_bce_9: 0.03892/0.36003, loss_mask_dice_9: 0.03733/1.75947, loss_spatial_bce_9: 0.34640/0.35446, loss_spatial_dice_9: 0.22052/0.79310, loss_spatial_ce_9: 0.41662/1.38671, loss_grounding_bce_9: 0.05082/0.10103, loss_grounding_dice_9: 0.04534/0.24216, loss_grounding_ce_9: 0.07147/0.66936] items per batch[64] items per second[0.16] total items[4800000] mini batches[ 75000] memory[4999] epoch remaining[0:52:38] INFO:trainer.default_trainer:epochs[ 41] optim steps[75100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.32437/0.75376, loss_mask_bce_0: 0.48184/0.30073, loss_mask_dice_0: 1.03445/1.02004, loss_spatial_bce_0: 0.07204/0.08458, loss_spatial_dice_0: 0.24774/0.17874, loss_spatial_ce_0: 0.00328/0.05576, loss_grounding_bce_0: 0.01577/0.08066, loss_grounding_dice_0: 0.02362/0.15039, loss_grounding_ce_0: 0.02222/0.24865, loss_mask_ce_1: 0.33399/0.75450, loss_mask_bce_1: 0.47081/0.30155, loss_mask_dice_1: 1.01341/1.02442, loss_spatial_bce_1: 0.06663/0.08503, loss_spatial_dice_1: 0.29000/0.18168, loss_spatial_ce_1: 0.00548/0.05949, loss_grounding_bce_1: 0.01689/0.08082, loss_grounding_dice_1: 0.02567/0.15116, loss_grounding_ce_1: 0.02208/0.25016, loss_mask_ce_2: 0.34642/0.76212, loss_mask_bce_2: 0.48870/0.30192, loss_mask_dice_2: 0.95371/1.02531, loss_spatial_bce_2: 0.06175/0.08513, loss_spatial_dice_2: 0.25427/0.18228, loss_spatial_ce_2: 0.01077/0.06164, loss_grounding_bce_2: 0.01725/0.08082, loss_grounding_dice_2: 0.02764/0.15106, loss_grounding_ce_2: 0.02077/0.25284, loss_mask_ce_3: 0.35113/0.76656, loss_mask_bce_3: 0.46797/0.30328, loss_mask_dice_3: 0.94939/1.02324, loss_spatial_bce_3: 0.07084/0.08726, loss_spatial_dice_3: 0.24387/0.18362, loss_spatial_ce_3: 0.02186/0.06653, loss_grounding_bce_3: 0.01463/0.08118, loss_grounding_dice_3: 0.02564/0.15071, loss_grounding_ce_3: 0.03572/0.25404, loss_mask_ce_4: 0.35648/0.77238, loss_mask_bce_4: 0.47327/0.30593, loss_mask_dice_4: 0.95639/1.04263, loss_spatial_bce_4: 0.06319/0.08965, loss_spatial_dice_4: 0.23563/0.19216, loss_spatial_ce_4: 0.03386/0.08021, loss_grounding_bce_4: 0.01539/0.08192, loss_grounding_dice_4: 0.02554/0.15334, loss_grounding_ce_4: 0.02011/0.25847, loss_mask_ce_5: 0.36194/0.79775, loss_mask_bce_5: 0.47068/0.30781, loss_mask_dice_5: 0.96709/1.05067, loss_spatial_bce_5: 0.07220/0.09203, loss_spatial_dice_5: 0.26553/0.19543, loss_spatial_ce_5: 0.06154/0.09372, loss_grounding_bce_5: 0.01720/0.08219, loss_grounding_dice_5: 0.02665/0.15415, loss_grounding_ce_5: 0.01964/0.27621, loss_mask_ce_6: 0.39527/0.82478, loss_mask_bce_6: 0.46572/0.30997, loss_mask_dice_6: 0.93675/1.05457, loss_spatial_bce_6: 0.09328/0.09748, loss_spatial_dice_6: 0.24682/0.19779, loss_spatial_ce_6: 0.06372/0.11820, loss_grounding_bce_6: 0.01352/0.08304, loss_grounding_dice_6: 0.02403/0.15464, loss_grounding_ce_6: 0.04200/0.28485, loss_mask_ce_7: 0.65737/0.88094, loss_mask_bce_7: 0.46256/0.31714, loss_mask_dice_7: 1.14810/1.10015, loss_spatial_bce_7: 0.08418/0.10677, loss_spatial_dice_7: 0.26076/0.22282, loss_spatial_ce_7: 0.13116/0.15361, loss_grounding_bce_7: 0.01360/0.08474, loss_grounding_dice_7: 0.02336/0.16025, loss_grounding_ce_7: 0.15506/0.31826, loss_mask_ce_8: 0.52378/1.01466, loss_mask_bce_8: 0.44978/0.33320, loss_mask_dice_8: 1.33367/1.17666, loss_spatial_bce_8: 0.14171/0.12339, loss_spatial_dice_8: 0.27613/0.25761, loss_spatial_ce_8: 0.24032/0.19916, loss_grounding_bce_8: 0.01588/0.08888, loss_grounding_dice_8: 0.02721/0.17000, loss_grounding_ce_8: 0.01635/0.41639, loss_mask_ce_9: 1.87655/3.47494, loss_mask_bce_9: 0.41608/0.36002, loss_mask_dice_9: 1.34607/1.75959, loss_spatial_bce_9: 0.24347/0.35438, loss_spatial_dice_9: 0.88496/0.79313, loss_spatial_ce_9: 1.21446/1.38680, loss_grounding_bce_9: 0.02783/0.10103, loss_grounding_dice_9: 0.09162/0.24216, loss_grounding_ce_9: 0.70874/0.66946] items per batch[64] items per second[0.37] total items[4806400] mini batches[ 75100] memory[4999] epoch remaining[0:48:16] INFO:trainer.default_trainer:epochs[ 41] optim steps[75200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.13045/0.75352, loss_mask_bce_0: 0.35975/0.30073, loss_mask_dice_0: 0.72430/1.01980, loss_spatial_bce_0: 0.05608/0.08458, loss_spatial_dice_0: 0.09097/0.17871, loss_spatial_ce_0: 0.17431/0.05575, loss_grounding_bce_0: 0.05760/0.08068, loss_grounding_dice_0: 0.03152/0.15040, loss_grounding_ce_0: 0.01260/0.24859, loss_mask_ce_1: 1.20317/0.75426, loss_mask_bce_1: 0.36859/0.30155, loss_mask_dice_1: 0.71185/1.02417, loss_spatial_bce_1: 0.05220/0.08504, loss_spatial_dice_1: 0.08764/0.18165, loss_spatial_ce_1: 0.20303/0.05947, loss_grounding_bce_1: 0.05730/0.08085, loss_grounding_dice_1: 0.02964/0.15117, loss_grounding_ce_1: 0.01079/0.25011, loss_mask_ce_2: 1.22635/0.76190, loss_mask_bce_2: 0.36569/0.30192, loss_mask_dice_2: 0.70516/1.02505, loss_spatial_bce_2: 0.05627/0.08513, loss_spatial_dice_2: 0.10323/0.18225, loss_spatial_ce_2: 0.14917/0.06161, loss_grounding_bce_2: 0.05848/0.08085, loss_grounding_dice_2: 0.02908/0.15108, loss_grounding_ce_2: 0.01850/0.25280, loss_mask_ce_3: 1.22555/0.76633, loss_mask_bce_3: 0.36515/0.30328, loss_mask_dice_3: 0.74727/1.02297, loss_spatial_bce_3: 0.05561/0.08727, loss_spatial_dice_3: 0.09617/0.18360, loss_spatial_ce_3: 0.32677/0.06651, loss_grounding_bce_3: 0.05535/0.08120, loss_grounding_dice_3: 0.02720/0.15071, loss_grounding_ce_3: 0.01262/0.25399, loss_mask_ce_4: 1.33911/0.77214, loss_mask_bce_4: 0.35966/0.30594, loss_mask_dice_4: 0.73896/1.04239, loss_spatial_bce_4: 0.05770/0.08965, loss_spatial_dice_4: 0.11161/0.19213, loss_spatial_ce_4: 0.31291/0.08019, loss_grounding_bce_4: 0.06060/0.08195, loss_grounding_dice_4: 0.03024/0.15336, loss_grounding_ce_4: 0.01183/0.25835, loss_mask_ce_5: 1.39611/0.79754, loss_mask_bce_5: 0.38341/0.30782, loss_mask_dice_5: 0.74611/1.05041, loss_spatial_bce_5: 0.05346/0.09204, loss_spatial_dice_5: 0.09278/0.19541, loss_spatial_ce_5: 0.37262/0.09372, loss_grounding_bce_5: 0.05964/0.08222, loss_grounding_dice_5: 0.03228/0.15416, loss_grounding_ce_5: 0.01484/0.27609, loss_mask_ce_6: 1.29753/0.82458, loss_mask_bce_6: 0.37119/0.30998, loss_mask_dice_6: 0.73376/1.05436, loss_spatial_bce_6: 0.06824/0.09749, loss_spatial_dice_6: 0.13725/0.19777, loss_spatial_ce_6: 0.31983/0.11820, loss_grounding_bce_6: 0.06270/0.08306, loss_grounding_dice_6: 0.03296/0.15465, loss_grounding_ce_6: 0.10541/0.28475, loss_mask_ce_7: 1.48718/0.88074, loss_mask_bce_7: 0.36955/0.31715, loss_mask_dice_7: 0.74933/1.09990, loss_spatial_bce_7: 0.10020/0.10676, loss_spatial_dice_7: 0.23244/0.22279, loss_spatial_ce_7: 0.24632/0.15361, loss_grounding_bce_7: 0.07622/0.08476, loss_grounding_dice_7: 0.04334/0.16026, loss_grounding_ce_7: 0.11579/0.31816, loss_mask_ce_8: 1.27628/1.01435, loss_mask_bce_8: 0.37154/0.33321, loss_mask_dice_8: 0.76513/1.17640, loss_spatial_bce_8: 0.15633/0.12339, loss_spatial_dice_8: 0.24124/0.25757, loss_spatial_ce_8: 0.34605/0.19915, loss_grounding_bce_8: 0.28613/0.08891, loss_grounding_dice_8: 0.08635/0.17000, loss_grounding_ce_8: 0.05692/0.41625, loss_mask_ce_9: 3.70773/3.47464, loss_mask_bce_9: 0.42145/0.36003, loss_mask_dice_9: 2.10916/1.75918, loss_spatial_bce_9: 0.27661/0.35442, loss_spatial_dice_9: 0.83320/0.79307, loss_spatial_ce_9: 1.00120/1.38675, loss_grounding_bce_9: 0.18465/0.10105, loss_grounding_dice_9: 0.05410/0.24214, loss_grounding_ce_9: 1.23096/0.66941] items per batch[64] items per second[0.37] total items[4812800] mini batches[ 75200] memory[4999] epoch remaining[0:44:47] INFO:trainer.default_trainer:epochs[ 41] optim steps[75300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.93706/0.75358, loss_mask_bce_0: 0.66026/0.30074, loss_mask_dice_0: 1.78472/1.01997, loss_spatial_bce_0: 0.00405/0.08458, loss_spatial_dice_0: 0.24810/0.17872, loss_spatial_ce_0: 0.00358/0.05574, loss_grounding_bce_0: 0.02224/0.08069, loss_grounding_dice_0: 0.05195/0.15040, loss_grounding_ce_0: 0.68980/0.24873, loss_mask_ce_1: 1.94191/0.75430, loss_mask_bce_1: 0.62957/0.30156, loss_mask_dice_1: 1.74673/1.02432, loss_spatial_bce_1: 0.00295/0.08503, loss_spatial_dice_1: 0.16787/0.18165, loss_spatial_ce_1: 0.00140/0.05946, loss_grounding_bce_1: 0.01333/0.08086, loss_grounding_dice_1: 0.04910/0.15117, loss_grounding_ce_1: 0.73527/0.25021, loss_mask_ce_2: 2.20370/0.76192, loss_mask_bce_2: 0.65595/0.30193, loss_mask_dice_2: 1.83344/1.02521, loss_spatial_bce_2: 0.00595/0.08512, loss_spatial_dice_2: 0.30407/0.18225, loss_spatial_ce_2: 0.00101/0.06161, loss_grounding_bce_2: 0.02044/0.08085, loss_grounding_dice_2: 0.05509/0.15107, loss_grounding_ce_2: 0.74583/0.25288, loss_mask_ce_3: 2.07675/0.76643, loss_mask_bce_3: 0.61806/0.30328, loss_mask_dice_3: 1.82030/1.02313, loss_spatial_bce_3: 0.00260/0.08726, loss_spatial_dice_3: 0.18386/0.18360, loss_spatial_ce_3: 0.00019/0.06650, loss_grounding_bce_3: 0.02332/0.08121, loss_grounding_dice_3: 0.05457/0.15071, loss_grounding_ce_3: 0.71961/0.25408, loss_mask_ce_4: 1.84978/0.77220, loss_mask_bce_4: 0.68262/0.30596, loss_mask_dice_4: 1.68806/1.04254, loss_spatial_bce_4: 0.00445/0.08964, loss_spatial_dice_4: 0.25262/0.19215, loss_spatial_ce_4: 0.00081/0.08020, loss_grounding_bce_4: 0.02525/0.08196, loss_grounding_dice_4: 0.05013/0.15335, loss_grounding_ce_4: 0.75883/0.25843, loss_mask_ce_5: 2.40652/0.79765, loss_mask_bce_5: 0.74251/0.30783, loss_mask_dice_5: 1.77526/1.05061, loss_spatial_bce_5: 0.00390/0.09203, loss_spatial_dice_5: 0.22257/0.19542, loss_spatial_ce_5: 0.04316/0.09372, loss_grounding_bce_5: 0.01326/0.08223, loss_grounding_dice_5: 0.05028/0.15416, loss_grounding_ce_5: 0.80609/0.27617, loss_mask_ce_6: 2.26939/0.82467, loss_mask_bce_6: 0.76627/0.31000, loss_mask_dice_6: 1.72786/1.05453, loss_spatial_bce_6: 0.00850/0.09748, loss_spatial_dice_6: 0.36983/0.19779, loss_spatial_ce_6: 0.06083/0.11822, loss_grounding_bce_6: 0.01041/0.08307, loss_grounding_dice_6: 0.04432/0.15466, loss_grounding_ce_6: 0.83781/0.28487, loss_mask_ce_7: 2.56912/0.88081, loss_mask_bce_7: 0.75749/0.31717, loss_mask_dice_7: 2.17580/1.10008, loss_spatial_bce_7: 0.02914/0.10676, loss_spatial_dice_7: 0.52766/0.22281, loss_spatial_ce_7: 0.14926/0.15361, loss_grounding_bce_7: 0.02169/0.08477, loss_grounding_dice_7: 0.05374/0.16025, loss_grounding_ce_7: 0.85993/0.31830, loss_mask_ce_8: 3.18135/1.01445, loss_mask_bce_8: 0.73891/0.33322, loss_mask_dice_8: 2.01175/1.17661, loss_spatial_bce_8: 0.09941/0.12339, loss_spatial_dice_8: 0.53635/0.25758, loss_spatial_ce_8: 0.24193/0.19914, loss_grounding_bce_8: 0.01227/0.08892, loss_grounding_dice_8: 0.04376/0.17000, loss_grounding_ce_8: 1.06491/0.41623, loss_mask_ce_9: 9.15328/3.47468, loss_mask_bce_9: 1.47865/0.36004, loss_mask_dice_9: 14.21861/1.75951, loss_spatial_bce_9: 0.14519/0.35440, loss_spatial_dice_9: 0.99641/0.79309, loss_spatial_ce_9: 1.66063/1.38671, loss_grounding_bce_9: 0.02532/0.10107, loss_grounding_dice_9: 0.49744/0.24214, loss_grounding_ce_9: 0.78930/0.66939] items per batch[64] items per second[0.37] total items[4819200] mini batches[ 75300] memory[4999] epoch remaining[0:41:42] INFO:trainer.default_trainer:epochs[ 41] optim steps[75400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53839/0.75359, loss_mask_bce_0: 0.62690/0.30073, loss_mask_dice_0: 0.38511/1.01998, loss_spatial_bce_0: 0.16870/0.08457, loss_spatial_dice_0: 0.19823/0.17872, loss_spatial_ce_0: 0.08299/0.05571, loss_grounding_bce_0: 0.03744/0.08068, loss_grounding_dice_0: 0.15493/0.15042, loss_grounding_ce_0: 0.73865/0.24881, loss_mask_ce_1: 1.49049/0.75431, loss_mask_bce_1: 0.59063/0.30156, loss_mask_dice_1: 0.38701/1.02436, loss_spatial_bce_1: 0.17101/0.08502, loss_spatial_dice_1: 0.20776/0.18165, loss_spatial_ce_1: 0.06602/0.05944, loss_grounding_bce_1: 0.02803/0.08085, loss_grounding_dice_1: 0.13508/0.15119, loss_grounding_ce_1: 0.83283/0.25031, loss_mask_ce_2: 1.46072/0.76193, loss_mask_bce_2: 0.61894/0.30193, loss_mask_dice_2: 0.40515/1.02523, loss_spatial_bce_2: 0.22028/0.08512, loss_spatial_dice_2: 0.22925/0.18225, loss_spatial_ce_2: 0.05862/0.06159, loss_grounding_bce_2: 0.02692/0.08084, loss_grounding_dice_2: 0.11695/0.15109, loss_grounding_ce_2: 0.80483/0.25297, loss_mask_ce_3: 1.54847/0.76643, loss_mask_bce_3: 0.60086/0.30329, loss_mask_dice_3: 0.38632/1.02314, loss_spatial_bce_3: 0.17955/0.08725, loss_spatial_dice_3: 0.16087/0.18360, loss_spatial_ce_3: 0.14555/0.06649, loss_grounding_bce_3: 0.04364/0.08120, loss_grounding_dice_3: 0.14284/0.15074, loss_grounding_ce_3: 0.71800/0.25418, loss_mask_ce_4: 1.56523/0.77223, loss_mask_bce_4: 0.55692/0.30596, loss_mask_dice_4: 0.41422/1.04256, loss_spatial_bce_4: 0.17544/0.08965, loss_spatial_dice_4: 0.21567/0.19215, loss_spatial_ce_4: 0.10427/0.08019, loss_grounding_bce_4: 0.04388/0.08195, loss_grounding_dice_4: 0.13747/0.15337, loss_grounding_ce_4: 0.79353/0.25850, loss_mask_ce_5: 1.69250/0.79771, loss_mask_bce_5: 0.58481/0.30783, loss_mask_dice_5: 0.41803/1.05063, loss_spatial_bce_5: 0.19824/0.09204, loss_spatial_dice_5: 0.20318/0.19542, loss_spatial_ce_5: 0.14989/0.09373, loss_grounding_bce_5: 0.03308/0.08223, loss_grounding_dice_5: 0.15684/0.15419, loss_grounding_ce_5: 0.65287/0.27620, loss_mask_ce_6: 2.10297/0.82469, loss_mask_bce_6: 0.37591/0.31002, loss_mask_dice_6: 0.47981/1.05454, loss_spatial_bce_6: 0.17230/0.09748, loss_spatial_dice_6: 0.17835/0.19779, loss_spatial_ce_6: 0.14091/0.11823, loss_grounding_bce_6: 0.05772/0.08308, loss_grounding_dice_6: 0.14342/0.15468, loss_grounding_ce_6: 0.69475/0.28493, loss_mask_ce_7: 1.88815/0.88085, loss_mask_bce_7: 0.48776/0.31719, loss_mask_dice_7: 0.45443/1.10013, loss_spatial_bce_7: 0.22811/0.10676, loss_spatial_dice_7: 0.23010/0.22283, loss_spatial_ce_7: 0.15822/0.15362, loss_grounding_bce_7: 0.03604/0.08477, loss_grounding_dice_7: 0.15049/0.16027, loss_grounding_ce_7: 0.70247/0.31836, loss_mask_ce_8: 2.02779/1.01452, loss_mask_bce_8: 0.77343/0.33323, loss_mask_dice_8: 0.46922/1.17669, loss_spatial_bce_8: 0.18520/0.12338, loss_spatial_dice_8: 0.26470/0.25758, loss_spatial_ce_8: 0.17973/0.19912, loss_grounding_bce_8: 0.06324/0.08892, loss_grounding_dice_8: 0.19981/0.17002, loss_grounding_ce_8: 0.89513/0.41643, loss_mask_ce_9: 5.79173/3.47490, loss_mask_bce_9: 0.70542/0.36006, loss_mask_dice_9: 1.03605/1.75973, loss_spatial_bce_9: 0.55154/0.35438, loss_spatial_dice_9: 0.84995/0.79311, loss_spatial_ce_9: 1.74140/1.38677, loss_grounding_bce_9: 0.11315/0.10107, loss_grounding_dice_9: 0.75346/0.24221, loss_grounding_ce_9: 1.00584/0.66943] items per batch[64] items per second[0.37] total items[4825600] mini batches[ 75400] memory[4999] epoch remaining[0:38:48] INFO:trainer.default_trainer:epochs[ 41] optim steps[75500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.45068/0.75361, loss_mask_bce_0: 0.17516/0.30076, loss_mask_dice_0: 0.31342/1.02007, loss_spatial_bce_0: 0.20072/0.08456, loss_spatial_dice_0: 0.19047/0.17872, loss_spatial_ce_0: 0.03174/0.05571, loss_grounding_bce_0: 0.02045/0.08068, loss_grounding_dice_0: 0.05333/0.15041, loss_grounding_ce_0: 0.82060/0.24887, loss_mask_ce_1: 1.38071/0.75428, loss_mask_bce_1: 0.18267/0.30160, loss_mask_dice_1: 0.32439/1.02444, loss_spatial_bce_1: 0.21636/0.08501, loss_spatial_dice_1: 0.19325/0.18164, loss_spatial_ce_1: 0.06534/0.05942, loss_grounding_bce_1: 0.02067/0.08085, loss_grounding_dice_1: 0.07127/0.15118, loss_grounding_ce_1: 0.71764/0.25036, loss_mask_ce_2: 1.28735/0.76189, loss_mask_bce_2: 0.18256/0.30197, loss_mask_dice_2: 0.30950/1.02533, loss_spatial_bce_2: 0.33020/0.08511, loss_spatial_dice_2: 0.20662/0.18225, loss_spatial_ce_2: 0.05198/0.06157, loss_grounding_bce_2: 0.01988/0.08083, loss_grounding_dice_2: 0.07110/0.15108, loss_grounding_ce_2: 0.61864/0.25302, loss_mask_ce_3: 1.54803/0.76640, loss_mask_bce_3: 0.17045/0.30332, loss_mask_dice_3: 0.28787/1.02322, loss_spatial_bce_3: 0.27414/0.08724, loss_spatial_dice_3: 0.20507/0.18360, loss_spatial_ce_3: 0.03533/0.06647, loss_grounding_bce_3: 0.02013/0.08120, loss_grounding_dice_3: 0.06396/0.15073, loss_grounding_ce_3: 0.86775/0.25425, loss_mask_ce_4: 1.37134/0.77223, loss_mask_bce_4: 0.19755/0.30600, loss_mask_dice_4: 0.32613/1.04267, loss_spatial_bce_4: 0.12678/0.08964, loss_spatial_dice_4: 0.17846/0.19216, loss_spatial_ce_4: 0.10001/0.08018, loss_grounding_bce_4: 0.01819/0.08195, loss_grounding_dice_4: 0.06666/0.15337, loss_grounding_ce_4: 0.85498/0.25855, loss_mask_ce_5: 1.46194/0.79769, loss_mask_bce_5: 0.18750/0.30788, loss_mask_dice_5: 0.35081/1.05076, loss_spatial_bce_5: 0.16092/0.09203, loss_spatial_dice_5: 0.22768/0.19543, loss_spatial_ce_5: 0.05200/0.09373, loss_grounding_bce_5: 0.01937/0.08222, loss_grounding_dice_5: 0.09016/0.15419, loss_grounding_ce_5: 0.40867/0.27624, loss_mask_ce_6: 1.48425/0.82468, loss_mask_bce_6: 0.20320/0.31005, loss_mask_dice_6: 0.38972/1.05464, loss_spatial_bce_6: 0.11099/0.09748, loss_spatial_dice_6: 0.14837/0.19780, loss_spatial_ce_6: 0.24253/0.11822, loss_grounding_bce_6: 0.02089/0.08307, loss_grounding_dice_6: 0.07038/0.15468, loss_grounding_ce_6: 0.30853/0.28501, loss_mask_ce_7: 1.51391/0.88087, loss_mask_bce_7: 0.26516/0.31723, loss_mask_dice_7: 0.51626/1.10025, loss_spatial_bce_7: 0.16338/0.10675, loss_spatial_dice_7: 0.20677/0.22285, loss_spatial_ce_7: 0.14160/0.15360, loss_grounding_bce_7: 0.01708/0.08476, loss_grounding_dice_7: 0.06992/0.16027, loss_grounding_ce_7: 0.61142/0.31840, loss_mask_ce_8: 2.17115/1.01455, loss_mask_bce_8: 0.28393/0.33327, loss_mask_dice_8: 0.44691/1.17686, loss_spatial_bce_8: 0.17238/0.12338, loss_spatial_dice_8: 0.29971/0.25759, loss_spatial_ce_8: 0.12996/0.19910, loss_grounding_bce_8: 0.02638/0.08891, loss_grounding_dice_8: 0.08360/0.17003, loss_grounding_ce_8: 0.92257/0.41652, loss_mask_ce_9: 5.00117/3.47500, loss_mask_bce_9: 0.32852/0.36011, loss_mask_dice_9: 0.97465/1.75996, loss_spatial_bce_9: 0.39643/0.35433, loss_spatial_dice_9: 0.83779/0.79314, loss_spatial_ce_9: 1.33401/1.38682, loss_grounding_bce_9: 0.02531/0.10106, loss_grounding_dice_9: 0.25936/0.24221, loss_grounding_ce_9: 2.16476/0.66939] items per batch[64] items per second[0.36] total items[4832000] mini batches[ 75500] memory[4999] epoch remaining[0:36:03] INFO:trainer.default_trainer:epochs[ 41] optim steps[75600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.30825/0.75352, loss_mask_bce_0: 0.36945/0.30072, loss_mask_dice_0: 0.84747/1.01990, loss_spatial_bce_0: 0.06900/0.08455, loss_spatial_dice_0: 0.22306/0.17870, loss_spatial_ce_0: 0.00759/0.05568, loss_grounding_bce_0: 0.14469/0.08070, loss_grounding_dice_0: 0.41849/0.15044, loss_grounding_ce_0: 0.38227/0.24890, loss_mask_ce_1: 1.30252/0.75420, loss_mask_bce_1: 0.32842/0.30156, loss_mask_dice_1: 0.84162/1.02425, loss_spatial_bce_1: 0.06161/0.08500, loss_spatial_dice_1: 0.22570/0.18162, loss_spatial_ce_1: 0.00561/0.05940, loss_grounding_bce_1: 0.14488/0.08087, loss_grounding_dice_1: 0.39572/0.15121, loss_grounding_ce_1: 0.36222/0.25036, loss_mask_ce_2: 0.75618/0.76182, loss_mask_bce_2: 0.38923/0.30194, loss_mask_dice_2: 0.96503/1.02514, loss_spatial_bce_2: 0.07027/0.08510, loss_spatial_dice_2: 0.23512/0.18224, loss_spatial_ce_2: 0.03151/0.06156, loss_grounding_bce_2: 0.14814/0.08085, loss_grounding_dice_2: 0.38812/0.15110, loss_grounding_ce_2: 0.35109/0.25305, loss_mask_ce_3: 1.24301/0.76633, loss_mask_bce_3: 0.36617/0.30329, loss_mask_dice_3: 0.88278/1.02306, loss_spatial_bce_3: 0.07592/0.08724, loss_spatial_dice_3: 0.24163/0.18358, loss_spatial_ce_3: 0.05203/0.06645, loss_grounding_bce_3: 0.14315/0.08122, loss_grounding_dice_3: 0.35377/0.15076, loss_grounding_ce_3: 0.52466/0.25428, loss_mask_ce_4: 1.33049/0.77217, loss_mask_bce_4: 0.37833/0.30596, loss_mask_dice_4: 0.89048/1.04249, loss_spatial_bce_4: 0.08154/0.08963, loss_spatial_dice_4: 0.25876/0.19215, loss_spatial_ce_4: 0.04107/0.08017, loss_grounding_bce_4: 0.15654/0.08197, loss_grounding_dice_4: 0.41967/0.15340, loss_grounding_ce_4: 0.32215/0.25856, loss_mask_ce_5: 1.77273/0.79761, loss_mask_bce_5: 0.31447/0.30784, loss_mask_dice_5: 0.82005/1.05058, loss_spatial_bce_5: 0.09069/0.09203, loss_spatial_dice_5: 0.27837/0.19543, loss_spatial_ce_5: 0.07843/0.09372, loss_grounding_bce_5: 0.15373/0.08224, loss_grounding_dice_5: 0.38429/0.15422, loss_grounding_ce_5: 0.36588/0.27625, loss_mask_ce_6: 1.86478/0.82465, loss_mask_bce_6: 0.27620/0.31001, loss_mask_dice_6: 0.86200/1.05444, loss_spatial_bce_6: 0.07591/0.09746, loss_spatial_dice_6: 0.25662/0.19779, loss_spatial_ce_6: 0.18154/0.11822, loss_grounding_bce_6: 0.12668/0.08308, loss_grounding_dice_6: 0.43202/0.15472, loss_grounding_ce_6: 0.40068/0.28501, loss_mask_ce_7: 1.08801/0.88081, loss_mask_bce_7: 0.40927/0.31717, loss_mask_dice_7: 1.05641/1.10001, loss_spatial_bce_7: 0.14206/0.10673, loss_spatial_dice_7: 0.32186/0.22284, loss_spatial_ce_7: 0.13010/0.15357, loss_grounding_bce_7: 0.13469/0.08477, loss_grounding_dice_7: 0.40896/0.16030, loss_grounding_ce_7: 0.66808/0.31843, loss_mask_ce_8: 1.12254/1.01443, loss_mask_bce_8: 0.36211/0.33321, loss_mask_dice_8: 1.00434/1.17663, loss_spatial_bce_8: 0.12477/0.12336, loss_spatial_dice_8: 0.31128/0.25758, loss_spatial_ce_8: 0.21436/0.19906, loss_grounding_bce_8: 0.14554/0.08893, loss_grounding_dice_8: 0.50599/0.17005, loss_grounding_ce_8: 0.24593/0.41659, loss_mask_ce_9: 3.62965/3.47477, loss_mask_bce_9: 0.30029/0.36005, loss_mask_dice_9: 1.21108/1.75957, loss_spatial_bce_9: 0.45492/0.35430, loss_spatial_dice_9: 0.86083/0.79314, loss_spatial_ce_9: 1.09175/1.38680, loss_grounding_bce_9: 0.13132/0.10108, loss_grounding_dice_9: 0.56273/0.24224, loss_grounding_ce_9: 0.29544/0.66929] items per batch[64] items per second[0.36] total items[4838400] mini batches[ 75600] memory[4999] epoch remaining[0:33:09] INFO:trainer.default_trainer:epochs[ 41] optim steps[75700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50646/0.75347, loss_mask_bce_0: 0.16898/0.30070, loss_mask_dice_0: 0.19201/1.01973, loss_spatial_bce_0: 0.04687/0.08453, loss_spatial_dice_0: 0.04634/0.17866, loss_spatial_ce_0: 0.00014/0.05567, loss_grounding_bce_0: 0.02341/0.08069, loss_grounding_dice_0: 0.04669/0.15043, loss_grounding_ce_0: 0.44884/0.24880, loss_mask_ce_1: 0.52908/0.75415, loss_mask_bce_1: 0.17601/0.30154, loss_mask_dice_1: 0.20632/1.02409, loss_spatial_bce_1: 0.05022/0.08498, loss_spatial_dice_1: 0.05119/0.18159, loss_spatial_ce_1: 0.00012/0.05938, loss_grounding_bce_1: 0.02130/0.08086, loss_grounding_dice_1: 0.04115/0.15120, loss_grounding_ce_1: 0.46513/0.25026, loss_mask_ce_2: 0.49793/0.76177, loss_mask_bce_2: 0.17294/0.30192, loss_mask_dice_2: 0.21220/1.02496, loss_spatial_bce_2: 0.04929/0.08509, loss_spatial_dice_2: 0.05016/0.18220, loss_spatial_ce_2: 0.00024/0.06153, loss_grounding_bce_2: 0.02109/0.08085, loss_grounding_dice_2: 0.04216/0.15108, loss_grounding_ce_2: 0.38395/0.25296, loss_mask_ce_3: 0.55914/0.76628, loss_mask_bce_3: 0.17066/0.30326, loss_mask_dice_3: 0.20099/1.02289, loss_spatial_bce_3: 0.05050/0.08723, loss_spatial_dice_3: 0.05243/0.18355, loss_spatial_ce_3: 0.00015/0.06642, loss_grounding_bce_3: 0.02003/0.08121, loss_grounding_dice_3: 0.04322/0.15074, loss_grounding_ce_3: 0.41997/0.25418, loss_mask_ce_4: 0.55158/0.77212, loss_mask_bce_4: 0.18991/0.30594, loss_mask_dice_4: 0.24173/1.04232, loss_spatial_bce_4: 0.04808/0.08961, loss_spatial_dice_4: 0.05760/0.19211, loss_spatial_ce_4: 0.00103/0.08014, loss_grounding_bce_4: 0.01873/0.08197, loss_grounding_dice_4: 0.03699/0.15339, loss_grounding_ce_4: 0.39491/0.25849, loss_mask_ce_5: 0.60079/0.79760, loss_mask_bce_5: 0.18759/0.30782, loss_mask_dice_5: 0.23723/1.05042, loss_spatial_bce_5: 0.05066/0.09201, loss_spatial_dice_5: 0.05692/0.19539, loss_spatial_ce_5: 0.00577/0.09369, loss_grounding_bce_5: 0.02487/0.08224, loss_grounding_dice_5: 0.06012/0.15421, loss_grounding_ce_5: 0.95429/0.27619, loss_mask_ce_6: 0.65047/0.82463, loss_mask_bce_6: 0.18349/0.30999, loss_mask_dice_6: 0.23153/1.05427, loss_spatial_bce_6: 0.04814/0.09744, loss_spatial_dice_6: 0.05546/0.19776, loss_spatial_ce_6: 0.00341/0.11819, loss_grounding_bce_6: 0.02487/0.08308, loss_grounding_dice_6: 0.06318/0.15470, loss_grounding_ce_6: 1.01468/0.28493, loss_mask_ce_7: 0.52592/0.88079, loss_mask_bce_7: 0.19231/0.31717, loss_mask_dice_7: 0.26712/1.09985, loss_spatial_bce_7: 0.05546/0.10671, loss_spatial_dice_7: 0.06101/0.22280, loss_spatial_ce_7: 0.00741/0.15353, loss_grounding_bce_7: 0.02340/0.08477, loss_grounding_dice_7: 0.05759/0.16028, loss_grounding_ce_7: 3.53572/0.31837, loss_mask_ce_8: 0.65443/1.01442, loss_mask_bce_8: 0.19455/0.33321, loss_mask_dice_8: 0.24927/1.17647, loss_spatial_bce_8: 0.06086/0.12333, loss_spatial_dice_8: 0.06337/0.25754, loss_spatial_ce_8: 0.07024/0.19902, loss_grounding_bce_8: 0.02456/0.08893, loss_grounding_dice_8: 0.05703/0.17003, loss_grounding_ce_8: 1.72969/0.41649, loss_mask_ce_9: 4.22238/3.47483, loss_mask_bce_9: 0.26495/0.36006, loss_mask_dice_9: 0.42481/1.75948, loss_spatial_bce_9: 0.47078/0.35430, loss_spatial_dice_9: 0.72080/0.79315, loss_spatial_ce_9: 1.17346/1.38665, loss_grounding_bce_9: 0.03187/0.10108, loss_grounding_dice_9: 0.08247/0.24223, loss_grounding_ce_9: 3.59886/0.66935] items per batch[64] items per second[0.36] total items[4844800] mini batches[ 75700] memory[4999] epoch remaining[0:30:16] INFO:trainer.default_trainer:epochs[ 41] optim steps[75800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.18090/0.75347, loss_mask_bce_0: 0.22975/0.30073, loss_mask_dice_0: 0.87107/1.01978, loss_spatial_bce_0: 0.03631/0.08454, loss_spatial_dice_0: 0.18325/0.17866, loss_spatial_ce_0: 0.00986/0.05565, loss_grounding_bce_0: 0.04214/0.08068, loss_grounding_dice_0: 0.15792/0.15041, loss_grounding_ce_0: 0.59184/0.24887, loss_mask_ce_1: 0.16389/0.75410, loss_mask_bce_1: 0.23056/0.30156, loss_mask_dice_1: 0.84207/1.02409, loss_spatial_bce_1: 0.03671/0.08499, loss_spatial_dice_1: 0.15131/0.18158, loss_spatial_ce_1: 0.00906/0.05936, loss_grounding_bce_1: 0.03752/0.08086, loss_grounding_dice_1: 0.15849/0.15119, loss_grounding_ce_1: 0.64836/0.25026, loss_mask_ce_2: 0.16505/0.76173, loss_mask_bce_2: 0.23447/0.30195, loss_mask_dice_2: 0.90866/1.02500, loss_spatial_bce_2: 0.03543/0.08509, loss_spatial_dice_2: 0.16211/0.18221, loss_spatial_ce_2: 0.00825/0.06153, loss_grounding_bce_2: 0.03885/0.08084, loss_grounding_dice_2: 0.15646/0.15106, loss_grounding_ce_2: 0.70599/0.25303, loss_mask_ce_3: 0.21844/0.76625, loss_mask_bce_3: 0.24845/0.30329, loss_mask_dice_3: 0.90891/1.02295, loss_spatial_bce_3: 0.03523/0.08723, loss_spatial_dice_3: 0.15260/0.18355, loss_spatial_ce_3: 0.01254/0.06642, loss_grounding_bce_3: 0.03848/0.08120, loss_grounding_dice_3: 0.16061/0.15072, loss_grounding_ce_3: 0.69048/0.25419, loss_mask_ce_4: 0.27517/0.77209, loss_mask_bce_4: 0.23755/0.30596, loss_mask_dice_4: 0.87788/1.04234, loss_spatial_bce_4: 0.04031/0.08962, loss_spatial_dice_4: 0.22312/0.19212, loss_spatial_ce_4: 0.11517/0.08015, loss_grounding_bce_4: 0.03319/0.08196, loss_grounding_dice_4: 0.15269/0.15337, loss_grounding_ce_4: 0.69901/0.25849, loss_mask_ce_5: 0.28716/0.79759, loss_mask_bce_5: 0.24463/0.30785, loss_mask_dice_5: 0.97027/1.05045, loss_spatial_bce_5: 0.03609/0.09202, loss_spatial_dice_5: 0.17541/0.19540, loss_spatial_ce_5: 0.06042/0.09370, loss_grounding_bce_5: 0.02860/0.08222, loss_grounding_dice_5: 0.14927/0.15419, loss_grounding_ce_5: 0.68503/0.27624, loss_mask_ce_6: 0.40327/0.82464, loss_mask_bce_6: 0.24000/0.31002, loss_mask_dice_6: 0.94635/1.05433, loss_spatial_bce_6: 0.04133/0.09745, loss_spatial_dice_6: 0.18503/0.19776, loss_spatial_ce_6: 0.05453/0.11823, loss_grounding_bce_6: 0.03297/0.08307, loss_grounding_dice_6: 0.15312/0.15468, loss_grounding_ce_6: 0.52148/0.28498, loss_mask_ce_7: 0.36662/0.88079, loss_mask_bce_7: 0.25286/0.31720, loss_mask_dice_7: 0.93669/1.09988, loss_spatial_bce_7: 0.04378/0.10671, loss_spatial_dice_7: 0.25900/0.22281, loss_spatial_ce_7: 0.20642/0.15353, loss_grounding_bce_7: 0.02842/0.08476, loss_grounding_dice_7: 0.16716/0.16026, loss_grounding_ce_7: 0.98591/0.31843, loss_mask_ce_8: 0.50381/1.01448, loss_mask_bce_8: 0.25757/0.33322, loss_mask_dice_8: 1.28982/1.17649, loss_spatial_bce_8: 0.06226/0.12333, loss_spatial_dice_8: 0.28526/0.25754, loss_spatial_ce_8: 0.15037/0.19899, loss_grounding_bce_8: 0.03848/0.08892, loss_grounding_dice_8: 0.15522/0.17002, loss_grounding_ce_8: 0.85169/0.41646, loss_mask_ce_9: 2.66171/3.47501, loss_mask_bce_9: 0.20982/0.36007, loss_mask_dice_9: 1.43234/1.75947, loss_spatial_bce_9: 0.17205/0.35425, loss_spatial_dice_9: 0.83505/0.79317, loss_spatial_ce_9: 1.11427/1.38673, loss_grounding_bce_9: 0.02926/0.10107, loss_grounding_dice_9: 0.15706/0.24220, loss_grounding_ce_9: 1.18073/0.66956] items per batch[64] items per second[0.36] total items[4851200] mini batches[ 75800] memory[4999] epoch remaining[0:27:21] INFO:trainer.default_trainer:epochs[ 41] optim steps[75900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 3.90693/0.75340, loss_mask_bce_0: 0.04824/0.30066, loss_mask_dice_0: 2.09936/1.01987, loss_spatial_bce_0: 0.00787/0.08452, loss_spatial_dice_0: 0.40607/0.17864, loss_spatial_ce_0: 0.00705/0.05563, loss_grounding_bce_0: 0.01656/0.08066, loss_grounding_dice_0: 0.43780/0.15041, loss_grounding_ce_0: 0.60995/0.24883, loss_mask_ce_1: 4.22255/0.75408, loss_mask_bce_1: 0.04490/0.30149, loss_mask_dice_1: 1.89227/1.02420, loss_spatial_bce_1: 0.00789/0.08497, loss_spatial_dice_1: 0.33985/0.18157, loss_spatial_ce_1: 0.00830/0.05934, loss_grounding_bce_1: 0.01678/0.08084, loss_grounding_dice_1: 0.35063/0.15118, loss_grounding_ce_1: 0.84648/0.25029, loss_mask_ce_2: 4.06271/0.76167, loss_mask_bce_2: 0.04452/0.30188, loss_mask_dice_2: 1.81226/1.02514, loss_spatial_bce_2: 0.00830/0.08507, loss_spatial_dice_2: 0.38288/0.18220, loss_spatial_ce_2: 0.00469/0.06151, loss_grounding_bce_2: 0.01205/0.08082, loss_grounding_dice_2: 0.30599/0.15104, loss_grounding_ce_2: 0.92657/0.25301, loss_mask_ce_3: 4.03488/0.76620, loss_mask_bce_3: 0.04297/0.30322, loss_mask_dice_3: 1.46764/1.02302, loss_spatial_bce_3: 0.01008/0.08721, loss_spatial_dice_3: 0.41626/0.18354, loss_spatial_ce_3: 0.59514/0.06641, loss_grounding_bce_3: 0.01511/0.08119, loss_grounding_dice_3: 0.29773/0.15071, loss_grounding_ce_3: 0.79124/0.25416, loss_mask_ce_4: 4.25938/0.77203, loss_mask_bce_4: 0.03564/0.30589, loss_mask_dice_4: 1.50360/1.04246, loss_spatial_bce_4: 0.00758/0.08959, loss_spatial_dice_4: 0.43483/0.19211, loss_spatial_ce_4: 0.00121/0.08015, loss_grounding_bce_4: 0.02259/0.08194, loss_grounding_dice_4: 0.32616/0.15335, loss_grounding_ce_4: 0.74998/0.25847, loss_mask_ce_5: 4.26253/0.79756, loss_mask_bce_5: 0.03331/0.30778, loss_mask_dice_5: 1.78581/1.05056, loss_spatial_bce_5: 0.00986/0.09200, loss_spatial_dice_5: 0.43737/0.19540, loss_spatial_ce_5: 0.03804/0.09370, loss_grounding_bce_5: 0.01197/0.08220, loss_grounding_dice_5: 0.39749/0.15417, loss_grounding_ce_5: 0.75830/0.27621, loss_mask_ce_6: 5.21380/0.82463, loss_mask_bce_6: 0.03806/0.30996, loss_mask_dice_6: 1.42148/1.05444, loss_spatial_bce_6: 0.01022/0.09742, loss_spatial_dice_6: 0.43665/0.19775, loss_spatial_ce_6: 0.35007/0.11823, loss_grounding_bce_6: 0.01514/0.08305, loss_grounding_dice_6: 0.25569/0.15467, loss_grounding_ce_6: 0.88594/0.28495, loss_mask_ce_7: 4.24884/0.88074, loss_mask_bce_7: 0.04521/0.31714, loss_mask_dice_7: 2.06792/1.10008, loss_spatial_bce_7: 0.01207/0.10668, loss_spatial_dice_7: 0.49841/0.22279, loss_spatial_ce_7: 0.06178/0.15350, loss_grounding_bce_7: 0.02846/0.08474, loss_grounding_dice_7: 0.39788/0.16024, loss_grounding_ce_7: 0.61278/0.31843, loss_mask_ce_8: 4.07485/1.01442, loss_mask_bce_8: 0.05457/0.33316, loss_mask_dice_8: 2.08450/1.17667, loss_spatial_bce_8: 0.01032/0.12329, loss_spatial_dice_8: 0.46544/0.25753, loss_spatial_ce_8: 0.08187/0.19895, loss_grounding_bce_8: 0.02341/0.08890, loss_grounding_dice_8: 0.45298/0.17002, loss_grounding_ce_8: 0.81550/0.41633, loss_mask_ce_9: 5.74341/3.47512, loss_mask_bce_9: 0.03203/0.36002, loss_mask_dice_9: 2.68212/1.75971, loss_spatial_bce_9: 0.03107/0.35424, loss_spatial_dice_9: 0.80425/0.79315, loss_spatial_ce_9: 2.29007/1.38677, loss_grounding_bce_9: 0.02259/0.10104, loss_grounding_dice_9: 0.58311/0.24218, loss_grounding_ce_9: 0.69149/0.66937] items per batch[64] items per second[0.36] total items[4857600] mini batches[ 75900] memory[4999] epoch remaining[0:24:26] INFO:trainer.default_trainer:epochs[ 41] optim steps[76000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.45074/0.75343, loss_mask_bce_0: 0.16616/0.30061, loss_mask_dice_0: 3.87529/1.02012, loss_spatial_bce_0: 0.02051/0.08449, loss_spatial_dice_0: 0.42985/0.17865, loss_spatial_ce_0: 0.06752/0.05562, loss_grounding_bce_0: 0.01107/0.08065, loss_grounding_dice_0: 0.04770/0.15044, loss_grounding_ce_0: 0.00102/0.24876, loss_mask_ce_1: 2.41284/0.75412, loss_mask_bce_1: 0.18824/0.30145, loss_mask_dice_1: 3.96932/1.02448, loss_spatial_bce_1: 0.02213/0.08494, loss_spatial_dice_1: 0.45664/0.18157, loss_spatial_ce_1: 0.37029/0.05933, loss_grounding_bce_1: 0.01048/0.08083, loss_grounding_dice_1: 0.04444/0.15121, loss_grounding_ce_1: 0.00421/0.25022, loss_mask_ce_2: 2.63358/0.76171, loss_mask_bce_2: 0.15603/0.30184, loss_mask_dice_2: 4.04989/1.02539, loss_spatial_bce_2: 0.02083/0.08504, loss_spatial_dice_2: 0.43199/0.18220, loss_spatial_ce_2: 0.21123/0.06150, loss_grounding_bce_2: 0.01021/0.08081, loss_grounding_dice_2: 0.04279/0.15107, loss_grounding_ce_2: 0.00155/0.25294, loss_mask_ce_3: 3.01042/0.76626, loss_mask_bce_3: 0.20733/0.30317, loss_mask_dice_3: 4.30396/1.02328, loss_spatial_bce_3: 0.02023/0.08719, loss_spatial_dice_3: 0.47423/0.18354, loss_spatial_ce_3: 0.06139/0.06640, loss_grounding_bce_3: 0.00971/0.08117, loss_grounding_dice_3: 0.04044/0.15074, loss_grounding_ce_3: 0.00132/0.25411, loss_mask_ce_4: 2.43134/0.77206, loss_mask_bce_4: 0.16569/0.30584, loss_mask_dice_4: 3.62270/1.04270, loss_spatial_bce_4: 0.02392/0.08957, loss_spatial_dice_4: 0.45568/0.19212, loss_spatial_ce_4: 0.30600/0.08014, loss_grounding_bce_4: 0.00840/0.08192, loss_grounding_dice_4: 0.04137/0.15337, loss_grounding_ce_4: 0.00208/0.25840, loss_mask_ce_5: 2.83030/0.79764, loss_mask_bce_5: 0.17442/0.30773, loss_mask_dice_5: 4.20263/1.05080, loss_spatial_bce_5: 0.02768/0.09199, loss_spatial_dice_5: 0.47451/0.19541, loss_spatial_ce_5: 0.50766/0.09373, loss_grounding_bce_5: 0.01244/0.08219, loss_grounding_dice_5: 0.05038/0.15421, loss_grounding_ce_5: 0.00426/0.27614, loss_mask_ce_6: 2.69211/0.82472, loss_mask_bce_6: 0.20731/0.30990, loss_mask_dice_6: 4.79606/1.05473, loss_spatial_bce_6: 0.02910/0.09741, loss_spatial_dice_6: 0.46575/0.19776, loss_spatial_ce_6: 0.12591/0.11825, loss_grounding_bce_6: 0.00817/0.08303, loss_grounding_dice_6: 0.04677/0.15470, loss_grounding_ce_6: 0.00239/0.28492, loss_mask_ce_7: 2.42485/0.88079, loss_mask_bce_7: 0.19344/0.31707, loss_mask_dice_7: 4.35017/1.10041, loss_spatial_bce_7: 0.03101/0.10666, loss_spatial_dice_7: 0.53275/0.22280, loss_spatial_ce_7: 0.22304/0.15352, loss_grounding_bce_7: 0.00978/0.08472, loss_grounding_dice_7: 0.04162/0.16027, loss_grounding_ce_7: 0.00812/0.31843, loss_mask_ce_8: 3.46545/1.01449, loss_mask_bce_8: 0.19136/0.33311, loss_mask_dice_8: 4.43717/1.17699, loss_spatial_bce_8: 0.03640/0.12326, loss_spatial_dice_8: 0.52292/0.25754, loss_spatial_ce_8: 0.21174/0.19896, loss_grounding_bce_8: 0.01183/0.08888, loss_grounding_dice_8: 0.04082/0.17004, loss_grounding_ce_8: 0.13419/0.41632, loss_mask_ce_9: 6.65873/3.47501, loss_mask_bce_9: 0.14435/0.35994, loss_mask_dice_9: 5.54411/1.75991, loss_spatial_bce_9: 0.14728/0.35421, loss_spatial_dice_9: 0.91897/0.79314, loss_spatial_ce_9: 1.50786/1.38683, loss_grounding_bce_9: 0.01881/0.10102, loss_grounding_dice_9: 0.14173/0.24220, loss_grounding_ce_9: 3.17027/0.66932] items per batch[64] items per second[0.37] total items[4864000] mini batches[ 76000] memory[4999] epoch remaining[0:21:29] INFO:trainer.default_trainer:epochs[ 41] optim steps[76100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.48437/0.75341, loss_mask_bce_0: 0.18153/0.30060, loss_mask_dice_0: 0.15859/1.02007, loss_spatial_bce_0: 0.07756/0.08448, loss_spatial_dice_0: 0.06970/0.17863, loss_spatial_ce_0: 0.00057/0.05560, loss_grounding_bce_0: 0.13235/0.08065, loss_grounding_dice_0: 0.05504/0.15043, loss_grounding_ce_0: 0.00201/0.24880, loss_mask_ce_1: 0.50505/0.75409, loss_mask_bce_1: 0.17679/0.30143, loss_mask_dice_1: 0.14869/1.02444, loss_spatial_bce_1: 0.07494/0.08493, loss_spatial_dice_1: 0.06582/0.18156, loss_spatial_ce_1: 0.00067/0.05932, loss_grounding_bce_1: 0.14658/0.08083, loss_grounding_dice_1: 0.05958/0.15120, loss_grounding_ce_1: 0.00195/0.25028, loss_mask_ce_2: 0.49695/0.76169, loss_mask_bce_2: 0.17293/0.30183, loss_mask_dice_2: 0.14519/1.02535, loss_spatial_bce_2: 0.07671/0.08503, loss_spatial_dice_2: 0.06409/0.18218, loss_spatial_ce_2: 0.00185/0.06148, loss_grounding_bce_2: 0.14517/0.08081, loss_grounding_dice_2: 0.05642/0.15107, loss_grounding_ce_2: 0.00269/0.25298, loss_mask_ce_3: 0.50007/0.76622, loss_mask_bce_3: 0.17685/0.30316, loss_mask_dice_3: 0.14484/1.02327, loss_spatial_bce_3: 0.08276/0.08717, loss_spatial_dice_3: 0.08036/0.18353, loss_spatial_ce_3: 0.02721/0.06640, loss_grounding_bce_3: 0.17571/0.08117, loss_grounding_dice_3: 0.06269/0.15074, loss_grounding_ce_3: 0.00378/0.25416, loss_mask_ce_4: 0.49191/0.77204, loss_mask_bce_4: 0.17735/0.30583, loss_mask_dice_4: 0.13939/1.04264, loss_spatial_bce_4: 0.07982/0.08956, loss_spatial_dice_4: 0.06656/0.19211, loss_spatial_ce_4: 0.02100/0.08013, loss_grounding_bce_4: 0.16974/0.08193, loss_grounding_dice_4: 0.06100/0.15337, loss_grounding_ce_4: 0.00292/0.25841, loss_mask_ce_5: 0.53399/0.79765, loss_mask_bce_5: 0.17496/0.30771, loss_mask_dice_5: 0.14445/1.05076, loss_spatial_bce_5: 0.08064/0.09198, loss_spatial_dice_5: 0.06970/0.19540, loss_spatial_ce_5: 0.00173/0.09373, loss_grounding_bce_5: 0.18107/0.08219, loss_grounding_dice_5: 0.05898/0.15421, loss_grounding_ce_5: 0.00609/0.27615, loss_mask_ce_6: 0.67252/0.82470, loss_mask_bce_6: 0.17975/0.30990, loss_mask_dice_6: 0.14163/1.05471, loss_spatial_bce_6: 0.09391/0.09740, loss_spatial_dice_6: 0.07098/0.19775, loss_spatial_ce_6: 0.00558/0.11823, loss_grounding_bce_6: 0.18583/0.08303, loss_grounding_dice_6: 0.05860/0.15471, loss_grounding_ce_6: 0.00655/0.28494, loss_mask_ce_7: 0.71415/0.88079, loss_mask_bce_7: 0.19328/0.31706, loss_mask_dice_7: 0.15163/1.10042, loss_spatial_bce_7: 0.09087/0.10665, loss_spatial_dice_7: 0.08059/0.22279, loss_spatial_ce_7: 0.02050/0.15350, loss_grounding_bce_7: 0.19423/0.08473, loss_grounding_dice_7: 0.05939/0.16026, loss_grounding_ce_7: 0.03672/0.31844, loss_mask_ce_8: 0.66325/1.01449, loss_mask_bce_8: 0.17512/0.33309, loss_mask_dice_8: 0.16823/1.17695, loss_spatial_bce_8: 0.12459/0.12325, loss_spatial_dice_8: 0.10938/0.25753, loss_spatial_ce_8: 0.02727/0.19892, loss_grounding_bce_8: 0.15621/0.08888, loss_grounding_dice_8: 0.06760/0.17004, loss_grounding_ce_8: 0.00044/0.41627, loss_mask_ce_9: 1.77352/3.47501, loss_mask_bce_9: 0.17681/0.35992, loss_mask_dice_9: 0.21324/1.75990, loss_spatial_bce_9: 0.47809/0.35424, loss_spatial_dice_9: 0.56767/0.79311, loss_spatial_ce_9: 0.97910/1.38675, loss_grounding_bce_9: 0.14926/0.10102, loss_grounding_dice_9: 0.05414/0.24220, loss_grounding_ce_9: 0.28061/0.66925] items per batch[64] items per second[0.36] total items[4870400] mini batches[ 76100] memory[4999] epoch remaining[0:18:33] INFO:trainer.default_trainer:epochs[ 41] optim steps[76200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77919/0.75331, loss_mask_bce_0: 0.21131/0.30060, loss_mask_dice_0: 0.42984/1.02003, loss_spatial_bce_0: 0.21015/0.08449, loss_spatial_dice_0: 0.14348/0.17862, loss_spatial_ce_0: 0.09241/0.05559, loss_grounding_bce_0: 0.02522/0.08065, loss_grounding_dice_0: 0.03338/0.15043, loss_grounding_ce_0: 0.00349/0.24880, loss_mask_ce_1: 0.86213/0.75401, loss_mask_bce_1: 0.20241/0.30144, loss_mask_dice_1: 0.54598/1.02442, loss_spatial_bce_1: 0.20861/0.08493, loss_spatial_dice_1: 0.15657/0.18155, loss_spatial_ce_1: 0.07490/0.05931, loss_grounding_bce_1: 0.02728/0.08082, loss_grounding_dice_1: 0.03603/0.15120, loss_grounding_ce_1: 0.00214/0.25028, loss_mask_ce_2: 0.82280/0.76160, loss_mask_bce_2: 0.19129/0.30183, loss_mask_dice_2: 0.58734/1.02531, loss_spatial_bce_2: 0.18328/0.08505, loss_spatial_dice_2: 0.14779/0.18217, loss_spatial_ce_2: 0.06015/0.06147, loss_grounding_bce_2: 0.02867/0.08081, loss_grounding_dice_2: 0.03779/0.15106, loss_grounding_ce_2: 0.00499/0.25297, loss_mask_ce_3: 0.84930/0.76614, loss_mask_bce_3: 0.20580/0.30316, loss_mask_dice_3: 0.57973/1.02325, loss_spatial_bce_3: 0.22826/0.08718, loss_spatial_dice_3: 0.15395/0.18352, loss_spatial_ce_3: 0.06449/0.06640, loss_grounding_bce_3: 0.02365/0.08117, loss_grounding_dice_3: 0.03353/0.15074, loss_grounding_ce_3: 0.00203/0.25417, loss_mask_ce_4: 0.72474/0.77197, loss_mask_bce_4: 0.29737/0.30584, loss_mask_dice_4: 0.57020/1.04262, loss_spatial_bce_4: 0.19670/0.08958, loss_spatial_dice_4: 0.15701/0.19211, loss_spatial_ce_4: 0.07537/0.08011, loss_grounding_bce_4: 0.03815/0.08193, loss_grounding_dice_4: 0.04306/0.15337, loss_grounding_ce_4: 0.00122/0.25841, loss_mask_ce_5: 0.78216/0.79758, loss_mask_bce_5: 0.23933/0.30772, loss_mask_dice_5: 0.67840/1.05073, loss_spatial_bce_5: 0.18280/0.09199, loss_spatial_dice_5: 0.14332/0.19541, loss_spatial_ce_5: 0.06865/0.09373, loss_grounding_bce_5: 0.03174/0.08219, loss_grounding_dice_5: 0.04308/0.15422, loss_grounding_ce_5: 0.04522/0.27614, loss_mask_ce_6: 0.95209/0.82463, loss_mask_bce_6: 0.24432/0.30990, loss_mask_dice_6: 0.65806/1.05465, loss_spatial_bce_6: 0.17281/0.09742, loss_spatial_dice_6: 0.15740/0.19775, loss_spatial_ce_6: 0.08815/0.11823, loss_grounding_bce_6: 0.04149/0.08303, loss_grounding_dice_6: 0.05043/0.15470, loss_grounding_ce_6: 0.01038/0.28494, loss_mask_ce_7: 1.29682/0.88070, loss_mask_bce_7: 0.23234/0.31707, loss_mask_dice_7: 0.47726/1.10034, loss_spatial_bce_7: 0.23730/0.10666, loss_spatial_dice_7: 0.21238/0.22279, loss_spatial_ce_7: 0.08414/0.15347, loss_grounding_bce_7: 0.03226/0.08472, loss_grounding_dice_7: 0.04116/0.16025, loss_grounding_ce_7: 0.06608/0.31845, loss_mask_ce_8: 1.80583/1.01439, loss_mask_bce_8: 0.36693/0.33311, loss_mask_dice_8: 0.76740/1.17693, loss_spatial_bce_8: 0.34945/0.12327, loss_spatial_dice_8: 0.25125/0.25752, loss_spatial_ce_8: 0.12458/0.19888, loss_grounding_bce_8: 0.03397/0.08887, loss_grounding_dice_8: 0.04390/0.17002, loss_grounding_ce_8: 0.56724/0.41621, loss_mask_ce_9: 6.00855/3.47505, loss_mask_bce_9: 0.89558/0.35994, loss_mask_dice_9: 1.52843/1.75982, loss_spatial_bce_9: 0.47931/0.35427, loss_spatial_dice_9: 0.66761/0.79314, loss_spatial_ce_9: 1.15502/1.38671, loss_grounding_bce_9: 0.03957/0.10101, loss_grounding_dice_9: 0.06474/0.24219, loss_grounding_ce_9: 2.60920/0.66932] items per batch[64] items per second[0.36] total items[4876800] mini batches[ 76200] memory[4999] epoch remaining[0:15:39] INFO:trainer.default_trainer:epochs[ 41] optim steps[76300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.68582/0.75325, loss_mask_bce_0: 0.34793/0.30067, loss_mask_dice_0: 3.57587/1.02038, loss_spatial_bce_0: 0.01254/0.08449, loss_spatial_dice_0: 0.21086/0.17862, loss_spatial_ce_0: 0.01509/0.05558, loss_grounding_bce_0: 0.04433/0.08065, loss_grounding_dice_0: 0.35578/0.15043, loss_grounding_ce_0: 0.49495/0.24878, loss_mask_ce_1: 1.01671/0.75396, loss_mask_bce_1: 0.34731/0.30151, loss_mask_dice_1: 3.40284/1.02478, loss_spatial_bce_1: 0.01196/0.08494, loss_spatial_dice_1: 0.25631/0.18155, loss_spatial_ce_1: 0.00981/0.05930, loss_grounding_bce_1: 0.04319/0.08083, loss_grounding_dice_1: 0.24760/0.15119, loss_grounding_ce_1: 0.57676/0.25027, loss_mask_ce_2: 0.71435/0.76157, loss_mask_bce_2: 0.35522/0.30189, loss_mask_dice_2: 3.36334/1.02568, loss_spatial_bce_2: 0.01264/0.08505, loss_spatial_dice_2: 0.23020/0.18216, loss_spatial_ce_2: 0.04940/0.06147, loss_grounding_bce_2: 0.04552/0.08081, loss_grounding_dice_2: 0.34238/0.15106, loss_grounding_ce_2: 0.45835/0.25296, loss_mask_ce_3: 0.74484/0.76608, loss_mask_bce_3: 0.34131/0.30322, loss_mask_dice_3: 2.76422/1.02359, loss_spatial_bce_3: 0.01547/0.08718, loss_spatial_dice_3: 0.30830/0.18352, loss_spatial_ce_3: 0.01605/0.06640, loss_grounding_bce_3: 0.04583/0.08117, loss_grounding_dice_3: 0.24300/0.15074, loss_grounding_ce_3: 0.48692/0.25415, loss_mask_ce_4: 0.83663/0.77192, loss_mask_bce_4: 0.38054/0.30590, loss_mask_dice_4: 3.37198/1.04299, loss_spatial_bce_4: 0.01228/0.08958, loss_spatial_dice_4: 0.30157/0.19211, loss_spatial_ce_4: 0.03991/0.08011, loss_grounding_bce_4: 0.04923/0.08193, loss_grounding_dice_4: 0.32405/0.15336, loss_grounding_ce_4: 0.49961/0.25837, loss_mask_ce_5: 1.17079/0.79755, loss_mask_bce_5: 0.35433/0.30779, loss_mask_dice_5: 3.19118/1.05107, loss_spatial_bce_5: 0.01362/0.09200, loss_spatial_dice_5: 0.26381/0.19541, loss_spatial_ce_5: 0.05664/0.09372, loss_grounding_bce_5: 0.04902/0.08218, loss_grounding_dice_5: 0.32444/0.15421, loss_grounding_ce_5: 0.47982/0.27610, loss_mask_ce_6: 0.71193/0.82463, loss_mask_bce_6: 0.34940/0.30996, loss_mask_dice_6: 3.29181/1.05502, loss_spatial_bce_6: 0.01616/0.09743, loss_spatial_dice_6: 0.24459/0.19775, loss_spatial_ce_6: 0.07621/0.11820, loss_grounding_bce_6: 0.04972/0.08303, loss_grounding_dice_6: 0.30285/0.15470, loss_grounding_ce_6: 0.43455/0.28489, loss_mask_ce_7: 0.87725/0.88066, loss_mask_bce_7: 0.35179/0.31713, loss_mask_dice_7: 3.34716/1.10072, loss_spatial_bce_7: 0.01269/0.10666, loss_spatial_dice_7: 0.29626/0.22279, loss_spatial_ce_7: 0.05462/0.15343, loss_grounding_bce_7: 0.04954/0.08472, loss_grounding_dice_7: 0.35567/0.16024, loss_grounding_ce_7: 0.59450/0.31840, loss_mask_ce_8: 0.89756/1.01438, loss_mask_bce_8: 0.35957/0.33316, loss_mask_dice_8: 4.04720/1.17735, loss_spatial_bce_8: 0.01927/0.12327, loss_spatial_dice_8: 0.54207/0.25752, loss_spatial_ce_8: 0.23487/0.19884, loss_grounding_bce_8: 0.05156/0.08887, loss_grounding_dice_8: 0.43405/0.17002, loss_grounding_ce_8: 0.30446/0.41618, loss_mask_ce_9: 3.81328/3.47504, loss_mask_bce_9: 0.35747/0.35999, loss_mask_dice_9: 6.21103/1.76029, loss_spatial_bce_9: 0.10595/0.35433, loss_spatial_dice_9: 0.95672/0.79317, loss_spatial_ce_9: 1.77765/1.38684, loss_grounding_bce_9: 0.04904/0.10102, loss_grounding_dice_9: 0.51010/0.24219, loss_grounding_ce_9: 0.34521/0.66925] items per batch[64] items per second[0.37] total items[4883200] mini batches[ 76300] memory[4999] epoch remaining[0:12:42] INFO:trainer.default_trainer:epochs[ 41] optim steps[76400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.50080/0.75333, loss_mask_bce_0: 0.17830/0.30067, loss_mask_dice_0: 1.49914/1.02046, loss_spatial_bce_0: 0.03919/0.08447, loss_spatial_dice_0: 0.31778/0.17861, loss_spatial_ce_0: 0.03226/0.05558, loss_grounding_bce_0: 0.02024/0.08064, loss_grounding_dice_0: 0.26937/0.15042, loss_grounding_ce_0: 0.60776/0.24876, loss_mask_ce_1: 0.56525/0.75406, loss_mask_bce_1: 0.18014/0.30152, loss_mask_dice_1: 1.74363/1.02488, loss_spatial_bce_1: 0.03907/0.08491, loss_spatial_dice_1: 0.32796/0.18155, loss_spatial_ce_1: 0.03557/0.05929, loss_grounding_bce_1: 0.02007/0.08082, loss_grounding_dice_1: 0.31939/0.15117, loss_grounding_ce_1: 0.58103/0.25027, loss_mask_ce_2: 0.60649/0.76162, loss_mask_bce_2: 0.17326/0.30189, loss_mask_dice_2: 1.07824/1.02573, loss_spatial_bce_2: 0.03730/0.08502, loss_spatial_dice_2: 0.39019/0.18216, loss_spatial_ce_2: 0.07828/0.06146, loss_grounding_bce_2: 0.01993/0.08080, loss_grounding_dice_2: 0.24864/0.15105, loss_grounding_ce_2: 0.62082/0.25294, loss_mask_ce_3: 0.65623/0.76616, loss_mask_bce_3: 0.19617/0.30323, loss_mask_dice_3: 2.10517/1.02366, loss_spatial_bce_3: 0.05569/0.08715, loss_spatial_dice_3: 0.34979/0.18352, loss_spatial_ce_3: 0.04159/0.06639, loss_grounding_bce_3: 0.02342/0.08116, loss_grounding_dice_3: 0.28041/0.15074, loss_grounding_ce_3: 0.54797/0.25411, loss_mask_ce_4: 0.36978/0.77201, loss_mask_bce_4: 0.17300/0.30590, loss_mask_dice_4: 2.52134/1.04304, loss_spatial_bce_4: 0.03164/0.08955, loss_spatial_dice_4: 0.32320/0.19211, loss_spatial_ce_4: 0.05388/0.08011, loss_grounding_bce_4: 0.01898/0.08191, loss_grounding_dice_4: 0.16763/0.15335, loss_grounding_ce_4: 0.54174/0.25836, loss_mask_ce_5: 0.59019/0.79764, loss_mask_bce_5: 0.16255/0.30778, loss_mask_dice_5: 1.55150/1.05112, loss_spatial_bce_5: 0.03697/0.09198, loss_spatial_dice_5: 0.28349/0.19541, loss_spatial_ce_5: 0.09641/0.09371, loss_grounding_bce_5: 0.01695/0.08217, loss_grounding_dice_5: 0.34755/0.15421, loss_grounding_ce_5: 0.59011/0.27605, loss_mask_ce_6: 0.52774/0.82472, loss_mask_bce_6: 0.17644/0.30995, loss_mask_dice_6: 2.44838/1.05509, loss_spatial_bce_6: 0.10972/0.09741, loss_spatial_dice_6: 0.39670/0.19775, loss_spatial_ce_6: 0.21822/0.11819, loss_grounding_bce_6: 0.01697/0.08302, loss_grounding_dice_6: 0.17669/0.15468, loss_grounding_ce_6: 0.60668/0.28488, loss_mask_ce_7: 0.94385/0.88079, loss_mask_bce_7: 0.15069/0.31713, loss_mask_dice_7: 2.19390/1.10082, loss_spatial_bce_7: 0.07379/0.10664, loss_spatial_dice_7: 0.41714/0.22279, loss_spatial_ce_7: 0.14327/0.15339, loss_grounding_bce_7: 0.01759/0.08470, loss_grounding_dice_7: 0.31478/0.16024, loss_grounding_ce_7: 0.62195/0.31841, loss_mask_ce_8: 0.78105/1.01454, loss_mask_bce_8: 0.15098/0.33316, loss_mask_dice_8: 1.82719/1.17747, loss_spatial_bce_8: 0.07369/0.12324, loss_spatial_dice_8: 0.41928/0.25751, loss_spatial_ce_8: 0.17267/0.19882, loss_grounding_bce_8: 0.01893/0.08886, loss_grounding_dice_8: 0.42817/0.17002, loss_grounding_ce_8: 0.60607/0.41618, loss_mask_ce_9: 5.75791/3.47532, loss_mask_bce_9: 0.15189/0.35999, loss_mask_dice_9: 2.26794/1.76039, loss_spatial_bce_9: 0.15216/0.35429, loss_spatial_dice_9: 0.90111/0.79321, loss_spatial_ce_9: 1.47390/1.38694, loss_grounding_bce_9: 0.01745/0.10100, loss_grounding_dice_9: 0.28888/0.24219, loss_grounding_ce_9: 0.62325/0.66945] items per batch[64] items per second[0.37] total items[4889600] mini batches[ 76400] memory[4999] epoch remaining[0:09:46] INFO:trainer.default_trainer:epochs[ 41] optim steps[76500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.57741/0.75334, loss_mask_bce_0: 0.09418/0.30068, loss_mask_dice_0: 0.68807/1.02070, loss_spatial_bce_0: 0.02517/0.08447, loss_spatial_dice_0: 0.22951/0.17861, loss_spatial_ce_0: 0.07930/0.05555, loss_grounding_bce_0: 0.01491/0.08065, loss_grounding_dice_0: 0.04209/0.15042, loss_grounding_ce_0: 0.00000/0.24887, loss_mask_ce_1: 0.61268/0.75407, loss_mask_bce_1: 0.10019/0.30154, loss_mask_dice_1: 0.78743/1.02510, loss_spatial_bce_1: 0.02413/0.08491, loss_spatial_dice_1: 0.23604/0.18154, loss_spatial_ce_1: 0.03682/0.05926, loss_grounding_bce_1: 0.01566/0.08083, loss_grounding_dice_1: 0.04358/0.15118, loss_grounding_ce_1: 0.00001/0.25036, loss_mask_ce_2: 0.77645/0.76162, loss_mask_bce_2: 0.09133/0.30191, loss_mask_dice_2: 0.79210/1.02595, loss_spatial_bce_2: 0.02575/0.08502, loss_spatial_dice_2: 0.24864/0.18216, loss_spatial_ce_2: 0.03595/0.06144, loss_grounding_bce_2: 0.01365/0.08081, loss_grounding_dice_2: 0.04227/0.15106, loss_grounding_ce_2: 0.00001/0.25300, loss_mask_ce_3: 0.88170/0.76615, loss_mask_bce_3: 0.09819/0.30325, loss_mask_dice_3: 0.80844/1.02389, loss_spatial_bce_3: 0.02793/0.08715, loss_spatial_dice_3: 0.26929/0.18351, loss_spatial_ce_3: 0.03676/0.06637, loss_grounding_bce_3: 0.01545/0.08117, loss_grounding_dice_3: 0.04332/0.15075, loss_grounding_ce_3: 0.00000/0.25418, loss_mask_ce_4: 0.76143/0.77203, loss_mask_bce_4: 0.10495/0.30591, loss_mask_dice_4: 0.95358/1.04327, loss_spatial_bce_4: 0.02855/0.08955, loss_spatial_dice_4: 0.24135/0.19211, loss_spatial_ce_4: 0.03324/0.08011, loss_grounding_bce_4: 0.01397/0.08193, loss_grounding_dice_4: 0.04083/0.15336, loss_grounding_ce_4: 0.00000/0.25843, loss_mask_ce_5: 0.89157/0.79767, loss_mask_bce_5: 0.11015/0.30779, loss_mask_dice_5: 0.93438/1.05135, loss_spatial_bce_5: 0.03015/0.09198, loss_spatial_dice_5: 0.26435/0.19542, loss_spatial_ce_5: 0.04426/0.09369, loss_grounding_bce_5: 0.01790/0.08219, loss_grounding_dice_5: 0.04342/0.15422, loss_grounding_ce_5: 0.00002/0.27614, loss_mask_ce_6: 0.40012/0.82478, loss_mask_bce_6: 0.10748/0.30997, loss_mask_dice_6: 0.95691/1.05531, loss_spatial_bce_6: 0.04571/0.09742, loss_spatial_dice_6: 0.27577/0.19776, loss_spatial_ce_6: 0.07212/0.11816, loss_grounding_bce_6: 0.01544/0.08303, loss_grounding_dice_6: 0.04079/0.15470, loss_grounding_ce_6: 0.00004/0.28502, loss_mask_ce_7: 0.59889/0.88081, loss_mask_bce_7: 0.11141/0.31715, loss_mask_dice_7: 1.02513/1.10103, loss_spatial_bce_7: 0.03388/0.10664, loss_spatial_dice_7: 0.27366/0.22279, loss_spatial_ce_7: 0.12866/0.15335, loss_grounding_bce_7: 0.01935/0.08472, loss_grounding_dice_7: 0.04147/0.16025, loss_grounding_ce_7: 0.00016/0.31846, loss_mask_ce_8: 0.74638/1.01461, loss_mask_bce_8: 0.10865/0.33319, loss_mask_dice_8: 1.04129/1.17769, loss_spatial_bce_8: 0.03877/0.12323, loss_spatial_dice_8: 0.32491/0.25751, loss_spatial_ce_8: 0.44814/0.19878, loss_grounding_bce_8: 0.01711/0.08889, loss_grounding_dice_8: 0.03802/0.17004, loss_grounding_ce_8: 0.01807/0.41625, loss_mask_ce_9: 2.62020/3.47560, loss_mask_bce_9: 0.09852/0.36001, loss_mask_dice_9: 1.16597/1.76063, loss_spatial_bce_9: 0.21357/0.35429, loss_spatial_dice_9: 0.86462/0.79322, loss_spatial_ce_9: 1.28241/1.38690, loss_grounding_bce_9: 0.01817/0.10103, loss_grounding_dice_9: 0.05332/0.24222, loss_grounding_ce_9: 0.14091/0.66950] items per batch[64] items per second[0.37] total items[4896000] mini batches[ 76500] memory[4999] epoch remaining[0:06:50] INFO:trainer.default_trainer:epochs[ 41] optim steps[76600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.81717/0.75325, loss_mask_bce_0: 0.55521/0.30064, loss_mask_dice_0: 0.76210/1.02052, loss_spatial_bce_0: 0.09544/0.08446, loss_spatial_dice_0: 0.15142/0.17859, loss_spatial_ce_0: 0.03148/0.05553, loss_grounding_bce_0: 0.11873/0.08065, loss_grounding_dice_0: 0.15756/0.15042, loss_grounding_ce_0: 0.13342/0.24882, loss_mask_ce_1: 0.78905/0.75395, loss_mask_bce_1: 0.57021/0.30149, loss_mask_dice_1: 0.73958/1.02496, loss_spatial_bce_1: 0.10011/0.08490, loss_spatial_dice_1: 0.18220/0.18152, loss_spatial_ce_1: 0.02558/0.05925, loss_grounding_bce_1: 0.11880/0.08083, loss_grounding_dice_1: 0.15044/0.15118, loss_grounding_ce_1: 0.12338/0.25032, loss_mask_ce_2: 0.83954/0.76151, loss_mask_bce_2: 0.56611/0.30187, loss_mask_dice_2: 0.71910/1.02580, loss_spatial_bce_2: 0.08929/0.08501, loss_spatial_dice_2: 0.14840/0.18213, loss_spatial_ce_2: 0.04278/0.06145, loss_grounding_bce_2: 0.12043/0.08081, loss_grounding_dice_2: 0.15129/0.15106, loss_grounding_ce_2: 0.07298/0.25293, loss_mask_ce_3: 0.92326/0.76603, loss_mask_bce_3: 0.58428/0.30320, loss_mask_dice_3: 0.74551/1.02372, loss_spatial_bce_3: 0.10003/0.08714, loss_spatial_dice_3: 0.18023/0.18349, loss_spatial_ce_3: 0.02675/0.06635, loss_grounding_bce_3: 0.11996/0.08117, loss_grounding_dice_3: 0.14806/0.15074, loss_grounding_ce_3: 0.06265/0.25412, loss_mask_ce_4: 0.85418/0.77194, loss_mask_bce_4: 0.61089/0.30587, loss_mask_dice_4: 0.85405/1.04311, loss_spatial_bce_4: 0.10709/0.08955, loss_spatial_dice_4: 0.16226/0.19209, loss_spatial_ce_4: 0.06616/0.08010, loss_grounding_bce_4: 0.11963/0.08193, loss_grounding_dice_4: 0.15525/0.15335, loss_grounding_ce_4: 0.06292/0.25840, loss_mask_ce_5: 0.49695/0.79758, loss_mask_bce_5: 0.62613/0.30775, loss_mask_dice_5: 0.98329/1.05120, loss_spatial_bce_5: 0.10252/0.09197, loss_spatial_dice_5: 0.14116/0.19540, loss_spatial_ce_5: 0.09267/0.09371, loss_grounding_bce_5: 0.11397/0.08218, loss_grounding_dice_5: 0.14817/0.15422, loss_grounding_ce_5: 0.13983/0.27610, loss_mask_ce_6: 0.52397/0.82466, loss_mask_bce_6: 0.63862/0.30993, loss_mask_dice_6: 0.99279/1.05514, loss_spatial_bce_6: 0.12091/0.09741, loss_spatial_dice_6: 0.16061/0.19774, loss_spatial_ce_6: 0.15753/0.11814, loss_grounding_bce_6: 0.11581/0.08302, loss_grounding_dice_6: 0.14942/0.15469, loss_grounding_ce_6: 0.12896/0.28497, loss_mask_ce_7: 0.86625/0.88068, loss_mask_bce_7: 0.65298/0.31712, loss_mask_dice_7: 0.91367/1.10085, loss_spatial_bce_7: 0.14287/0.10663, loss_spatial_dice_7: 0.21050/0.22277, loss_spatial_ce_7: 0.02544/0.15336, loss_grounding_bce_7: 0.11457/0.08472, loss_grounding_dice_7: 0.15123/0.16025, loss_grounding_ce_7: 0.26176/0.31836, loss_mask_ce_8: 0.62032/1.01446, loss_mask_bce_8: 0.82042/0.33316, loss_mask_dice_8: 1.03888/1.17754, loss_spatial_bce_8: 0.16792/0.12322, loss_spatial_dice_8: 0.21758/0.25747, loss_spatial_ce_8: 0.07014/0.19874, loss_grounding_bce_8: 0.13167/0.08888, loss_grounding_dice_8: 0.15918/0.17002, loss_grounding_ce_8: 0.35729/0.41626, loss_mask_ce_9: 3.77138/3.47520, loss_mask_bce_9: 0.65655/0.35997, loss_mask_dice_9: 1.55825/1.76028, loss_spatial_bce_9: 0.37408/0.35427, loss_spatial_dice_9: 0.74900/0.79320, loss_spatial_ce_9: 1.47759/1.38685, loss_grounding_bce_9: 0.15129/0.10103, loss_grounding_dice_9: 0.18542/0.24221, loss_grounding_ce_9: 1.32330/0.66953] items per batch[64] items per second[0.36] total items[4902400] mini batches[ 76600] memory[4999] epoch remaining[0:03:55] INFO:trainer.default_trainer:epochs[ 41] optim steps[76700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.59278/0.75313, loss_mask_bce_0: 0.22685/0.30066, loss_mask_dice_0: 0.14545/1.02016, loss_spatial_bce_0: 0.13351/0.08449, loss_spatial_dice_0: 0.06432/0.17860, loss_spatial_ce_0: 0.00297/0.05554, loss_grounding_bce_0: 0.01764/0.08067, loss_grounding_dice_0: 0.03035/0.15041, loss_grounding_ce_0: 0.13345/0.24879, loss_mask_ce_1: 0.53207/0.75378, loss_mask_bce_1: 0.22738/0.30151, loss_mask_dice_1: 0.13852/1.02462, loss_spatial_bce_1: 0.14121/0.08494, loss_spatial_dice_1: 0.06583/0.18153, loss_spatial_ce_1: 0.00558/0.05926, loss_grounding_bce_1: 0.01826/0.08086, loss_grounding_dice_1: 0.03148/0.15117, loss_grounding_ce_1: 0.12679/0.25027, loss_mask_ce_2: 0.51813/0.76139, loss_mask_bce_2: 0.23830/0.30188, loss_mask_dice_2: 0.14479/1.02546, loss_spatial_bce_2: 0.13040/0.08505, loss_spatial_dice_2: 0.06507/0.18214, loss_spatial_ce_2: 0.00348/0.06145, loss_grounding_bce_2: 0.01790/0.08083, loss_grounding_dice_2: 0.02996/0.15105, loss_grounding_ce_2: 0.11184/0.25291, loss_mask_ce_3: 0.55583/0.76591, loss_mask_bce_3: 0.25294/0.30322, loss_mask_dice_3: 0.13741/1.02337, loss_spatial_bce_3: 0.12898/0.08718, loss_spatial_dice_3: 0.06405/0.18349, loss_spatial_ce_3: 0.00163/0.06637, loss_grounding_bce_3: 0.01812/0.08119, loss_grounding_dice_3: 0.03078/0.15074, loss_grounding_ce_3: 0.11394/0.25412, loss_mask_ce_4: 0.51374/0.77181, loss_mask_bce_4: 0.21324/0.30588, loss_mask_dice_4: 0.13774/1.04276, loss_spatial_bce_4: 0.11750/0.08959, loss_spatial_dice_4: 0.07702/0.19210, loss_spatial_ce_4: 0.01351/0.08013, loss_grounding_bce_4: 0.01582/0.08195, loss_grounding_dice_4: 0.02796/0.15334, loss_grounding_ce_4: 0.11908/0.25840, loss_mask_ce_5: 0.54829/0.79743, loss_mask_bce_5: 0.21260/0.30777, loss_mask_dice_5: 0.14380/1.05086, loss_spatial_bce_5: 0.10573/0.09202, loss_spatial_dice_5: 0.08131/0.19541, loss_spatial_ce_5: 0.00758/0.09376, loss_grounding_bce_5: 0.01816/0.08221, loss_grounding_dice_5: 0.03063/0.15421, loss_grounding_ce_5: 0.11248/0.27606, loss_mask_ce_6: 0.69618/0.82457, loss_mask_bce_6: 0.21405/0.30994, loss_mask_dice_6: 0.13101/1.05479, loss_spatial_bce_6: 0.11713/0.09746, loss_spatial_dice_6: 0.09754/0.19774, loss_spatial_ce_6: 0.02663/0.11818, loss_grounding_bce_6: 0.01717/0.08305, loss_grounding_dice_6: 0.02976/0.15469, loss_grounding_ce_6: 0.08601/0.28495, loss_mask_ce_7: 0.87264/0.88061, loss_mask_bce_7: 0.23015/0.31714, loss_mask_dice_7: 0.14919/1.10052, loss_spatial_bce_7: 0.12990/0.10669, loss_spatial_dice_7: 0.15574/0.22276, loss_spatial_ce_7: 0.01831/0.15337, loss_grounding_bce_7: 0.01675/0.08475, loss_grounding_dice_7: 0.03479/0.16024, loss_grounding_ce_7: 0.15394/0.31839, loss_mask_ce_8: 0.94332/1.01439, loss_mask_bce_8: 0.28410/0.33317, loss_mask_dice_8: 0.14474/1.17714, loss_spatial_bce_8: 0.10570/0.12327, loss_spatial_dice_8: 0.17089/0.25746, loss_spatial_ce_8: 0.08813/0.19879, loss_grounding_bce_8: 0.01621/0.08892, loss_grounding_dice_8: 0.03094/0.17003, loss_grounding_ce_8: 0.17353/0.41624, loss_mask_ce_9: 4.01633/3.47487, loss_mask_bce_9: 0.28686/0.35997, loss_mask_dice_9: 0.38918/1.75968, loss_spatial_bce_9: 0.66880/0.35431, loss_spatial_dice_9: 0.79590/0.79315, loss_spatial_ce_9: 1.54476/1.38679, loss_grounding_bce_9: 0.03618/0.10108, loss_grounding_dice_9: 0.08339/0.24221, loss_grounding_ce_9: 0.40678/0.66944] items per batch[64] items per second[0.35] total items[4908800] mini batches[ 76700] memory[4999] epoch remaining[0:00:59] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00076734. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0025 s/iter. Inference: 0.3754 s/iter. Eval: 0.0867 s/iter. Total: 0.4647 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0026 s/iter. Inference: 0.3717 s/iter. Eval: 0.0770 s/iter. Total: 0.4515 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0027 s/iter. Inference: 0.3772 s/iter. Eval: 0.0773 s/iter. Total: 0.4573 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3810 s/iter. Eval: 0.0735 s/iter. Total: 0.4574 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3821 s/iter. Eval: 0.0716 s/iter. Total: 0.4566 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3802 s/iter. Eval: 0.0700 s/iter. Total: 0.4532 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval8txou44f ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.752 | 83.328 | 66.195 | 133 | | Things | 61.946 | 84.153 | 73.128 | 80 | | Stuff | 46.403 | 82.084 | 55.730 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.52s) creating index... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.38 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.35s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.26 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.48 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.693 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.552 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.377 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.765 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.616 | 69.326 | 49.215 | 26.293 | 49.743 | 67.385 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.749 | bicycle | 21.867 | car | 43.817 | | motorcycle | 41.036 | airplane | 60.704 | bus | 70.945 | | train | 74.712 | truck | 44.768 | boat | 30.409 | | traffic light | 28.706 | fire hydrant | 71.051 | stop sign | 66.666 | | parking meter | 53.004 | bench | 26.383 | bird | 33.855 | | cat | 77.782 | dog | 70.684 | horse | 49.382 | | sheep | 53.299 | cow | 56.734 | elephant | 65.982 | | bear | 79.650 | zebra | 66.310 | giraffe | 63.112 | | backpack | 23.358 | umbrella | 54.390 | handbag | 25.253 | | tie | 39.998 | suitcase | 50.174 | frisbee | 70.242 | | skis | 8.716 | snowboard | 34.893 | sports ball | 50.577 | | kite | 37.812 | baseball bat | 37.603 | baseball glove | 50.859 | | skateboard | 43.467 | surfboard | 44.815 | tennis racket | 62.961 | | bottle | 41.304 | wine glass | 38.225 | cup | 50.592 | | fork | 26.634 | knife | 24.973 | spoon | 22.295 | | bowl | 39.572 | banana | 22.728 | apple | 26.650 | | sandwich | 49.343 | orange | 30.284 | broccoli | 25.228 | | carrot | 22.783 | hot dog | 33.378 | pizza | 53.397 | | donut | 55.204 | cake | 46.799 | chair | 28.192 | | couch | 45.702 | potted plant | 23.386 | bed | 42.303 | | dining table | 14.989 | toilet | 70.590 | tv | 66.775 | | laptop | 69.838 | mouse | 64.258 | remote | 44.035 | | keyboard | 58.659 | cell phone | 45.547 | microwave | 65.333 | | oven | 32.142 | toaster | 53.891 | sink | 44.304 | | refrigerator | 68.671 | book | 13.482 | clock | 55.239 | | vase | 40.693 | scissors | 37.904 | teddy bear | 56.471 | | hair drier | 33.886 | toothbrush | 28.870 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.87482343401093, 'fwIoU': 71.82641016868354, 'IoU-person': 89.26763512945807, 'IoU-bicycle': 78.00173572869434, 'IoU-car': 72.93613248419757, 'IoU-motorcycle': 86.40153953518337, 'IoU-airplane': 81.67081145462367, 'IoU-bus': 87.56373100432106, 'IoU-train': 88.27198630633517, 'IoU-truck': 67.9360928292956, 'IoU-boat': 72.10389029928712, 'IoU-traffic light': 78.6496481956506, 'IoU-fire hydrant': 93.29180515164104, 'IoU-stop sign': 85.23340071489267, 'IoU-parking meter': 85.05031827637258, 'IoU-bench': 63.90236383571549, 'IoU-bird': 76.40001739919589, 'IoU-cat': 90.62940808893097, 'IoU-dog': 87.17110560025347, 'IoU-horse': 89.53602212032517, 'IoU-sheep': 86.02576941774707, 'IoU-cow': 89.42965094489071, 'IoU-elephant': 91.48093379417037, 'IoU-bear': 73.01787558152408, 'IoU-zebra': 84.91179796016787, 'IoU-giraffe': 89.64933842405843, 'IoU-backpack': 52.63897125889084, 'IoU-umbrella': 86.45195879087876, 'IoU-handbag': 51.275857914565094, 'IoU-tie': 76.3187111709541, 'IoU-suitcase': 79.97158141466576, 'IoU-frisbee': 84.71798031170941, 'IoU-skis': 60.985260360033834, 'IoU-snowboard': 73.46834842850683, 'IoU-sports ball': 77.95069852150476, 'IoU-kite': 79.29321889898681, 'IoU-baseball bat': 68.53150538925313, 'IoU-baseball glove': 78.38694493348983, 'IoU-skateboard': 86.21739797810969, 'IoU-surfboard': 86.45706634181083, 'IoU-tennis racket': 91.31167303167072, 'IoU-bottle': 72.16147067847322, 'IoU-wine glass': 82.70167566314535, 'IoU-cup': 71.94609144160846, 'IoU-fork': 67.1973594409167, 'IoU-knife': 64.74743938726363, 'IoU-spoon': 58.610452291310764, 'IoU-bowl': 60.79027323681845, 'IoU-banana': 82.35047740850749, 'IoU-apple': 59.572999283472925, 'IoU-sandwich': 68.81749577351039, 'IoU-orange': 76.58085228995621, 'IoU-broccoli': 71.0941164564994, 'IoU-carrot': 62.96392599488432, 'IoU-hot dog': 65.19512376637147, 'IoU-pizza': 85.41896594231555, 'IoU-donut': 70.67903382512677, 'IoU-cake': 80.55416020729254, 'IoU-chair': 63.880129127140385, 'IoU-couch': 70.93958591094612, 'IoU-potted plant': 44.081721129025226, 'IoU-bed': 75.9092162216432, 'IoU-dining table': 54.97131926033355, 'IoU-toilet': 82.8170976357413, 'IoU-tv': 82.65113560993585, 'IoU-laptop': 77.92243193334004, 'IoU-mouse': 77.3407890249474, 'IoU-remote': 71.7578127137518, 'IoU-keyboard': 62.605907797829374, 'IoU-cell phone': 76.32927314848331, 'IoU-microwave': 72.2402168601503, 'IoU-oven': 70.72647770473803, 'IoU-toaster': 85.40017925589733, 'IoU-sink': 72.15210173807492, 'IoU-refrigerator': 82.89117162685832, 'IoU-book': 54.259762991949, 'IoU-clock': 70.29130843220473, 'IoU-vase': 66.22711426314908, 'IoU-scissors': 84.60922604821593, 'IoU-teddy bear': 83.46311068720325, 'IoU-hair drier': 50.47151000492622, 'IoU-toothbrush': 76.8439473124128, 'IoU-banner': 34.34525590551181, 'IoU-blanket': 15.878440610988678, 'IoU-bridge': 37.405974448733005, 'IoU-cardboard': 52.11179625302513, 'IoU-counter': 30.823120177436873, 'IoU-curtain': 72.05730643873213, 'IoU-door-stuff': 47.907038174138236, 'IoU-floor-wood': 63.839557863754216, 'IoU-flower': 49.54369704389468, 'IoU-fruit': 48.087252913527784, 'IoU-gravel': 30.540555910004453, 'IoU-house': 24.903353796576088, 'IoU-light': 44.9828523512455, 'IoU-mirror-stuff': 64.06441750490637, 'IoU-net': 50.665657312794195, 'IoU-pillow': 16.97835388563467, 'IoU-platform': 31.46176264469201, 'IoU-playingfield': 68.20270360537187, 'IoU-railroad': 64.15268252270802, 'IoU-river': 51.49667058920075, 'IoU-road': 66.6224822854179, 'IoU-roof': 20.150986076141216, 'IoU-sand': 63.24972401503799, 'IoU-sea': 85.75329677257825, 'IoU-shelf': 40.71625995503787, 'IoU-snow': 92.15135818126976, 'IoU-stairs': 35.286064090882505, 'IoU-tent': 10.763394875062339, 'IoU-towel': 44.36700999841179, 'IoU-wall-brick': 52.037334444682, 'IoU-wall-stone': 32.30053349689441, 'IoU-wall-tile': 69.80905401524701, 'IoU-wall-wood': 44.72677698932234, 'IoU-water-other': 26.453402659987635, 'IoU-window-blind': 52.13672102370522, 'IoU-window-other': 51.244622601879755, 'IoU-tree-merged': 82.02355995794996, 'IoU-fence-merged': 55.60614099138841, 'IoU-ceiling-merged': 67.94612571359184, 'IoU-sky-other-merged': 93.68454078177201, 'IoU-cabinet-merged': 64.18380216044307, 'IoU-table-merged': 42.46633251043128, 'IoU-floor-other-merged': 54.574543225991015, 'IoU-pavement-merged': 57.76673210717955, 'IoU-mountain-merged': 58.06248319734091, 'IoU-grass-merged': 72.44755697418871, 'IoU-dirt-merged': 47.173503617479085, 'IoU-paper-merged': 38.68396174995008, 'IoU-food-other-merged': 44.24403608958985, 'IoU-building-other-merged': 59.75761745790129, 'IoU-rock-merged': 64.24383893661037, 'IoU-wall-other-merged': 68.56468289134658, 'IoU-rug-merged': 68.02994237753263, 'mACC': 77.44692281908559, 'pACC': 82.43114477636298, 'ACC-person': 93.39568352008072, 'ACC-bicycle': 89.31708646632805, 'ACC-car': 86.64918700007061, 'ACC-motorcycle': 90.75508367466189, 'ACC-airplane': 87.40087513746083, 'ACC-bus': 93.48834400036502, 'ACC-train': 96.17646981476278, 'ACC-truck': 78.37846073082835, 'ACC-boat': 81.4102410846141, 'ACC-traffic light': 91.18455359030372, 'ACC-fire hydrant': 95.88635364046681, 'ACC-stop sign': 88.38782493814989, 'ACC-parking meter': 88.22343209130955, 'ACC-bench': 78.97462999069757, 'ACC-bird': 82.68894552582474, 'ACC-cat': 94.33815448440525, 'ACC-dog': 90.5570211527621, 'ACC-horse': 94.32403786876458, 'ACC-sheep': 89.27144780773128, 'ACC-cow': 92.99528681521146, 'ACC-elephant': 93.71147221631256, 'ACC-bear': 74.49914935210138, 'ACC-zebra': 87.0161143815734, 'ACC-giraffe': 93.60179653645406, 'ACC-backpack': 70.49598601239254, 'ACC-umbrella': 90.65656157663382, 'ACC-handbag': 70.62631521351084, 'ACC-tie': 83.94752425013642, 'ACC-suitcase': 86.34029186592784, 'ACC-frisbee': 94.00800000000001, 'ACC-skis': 75.674027332477, 'ACC-snowboard': 81.96909211138596, 'ACC-sports ball': 88.04333219627665, 'ACC-kite': 85.27305631275186, 'ACC-baseball bat': 87.67728835909462, 'ACC-baseball glove': 92.39496191896703, 'ACC-skateboard': 90.76140867449763, 'ACC-surfboard': 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'ACC-mountain-merged': 71.88611619830726, 'ACC-grass-merged': 83.25315912476871, 'ACC-dirt-merged': 73.67336579474745, 'ACC-paper-merged': 49.70459051541575, 'ACC-food-other-merged': 61.60805738093858, 'ACC-building-other-merged': 73.58800836562257, 'ACC-rock-merged': 85.13809069475208, 'ACC-wall-other-merged': 82.54766017440143, 'ACC-rug-merged': 80.478236232518})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3411 s/iter. Inference: 0.4809 s/iter. Eval: 0.0000 s/iter. Total: 0.8221 s/iter. ETA=0:00:11 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3570 s/iter. Inference: 0.4962 s/iter. Eval: 0.0000 s/iter. Total: 0.8533 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3653 s/iter. Inference: 0.6278 s/iter. Eval: 0.0000 s/iter. Total: 0.9933 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.363476733977173, 'noc@0.8': 2.4310798946444248, 'noc@0.85': 2.89640035118525, 'noc@0.9': 3.659057652911911, 'miou@iter1': 0.8641414190196908} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1349 s/iter. Eval: 0.0010 s/iter. Total: 0.1372 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.55382537841797, 'precision@0.6': 72.48348236083984, 'precision@0.7': 68.4803695678711, 'precision@0.8': 60.318695068359375, 'precision@0.9': 32.99650192260742, 'cIoU': 61.671356201171875, 'mIoU': 66.87120819091797} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.75210206299687, 'SQ': 83.32816270990484, 'RQ': 66.19459460653438, 'PQ_th': 61.94610417979176, 'SQ_th': 84.15253437999155, 'RQ_th': 73.12769253675904, 'PQ_st': 46.402664905570575, 'SQ_st': 82.08382811354757, 'RQ_st': 55.72954112694997}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-laptop': 89.21771733213954, 'ACC-mouse': 86.71652074788337, 'ACC-remote': 76.00622492316654, 'ACC-keyboard': 68.63450417291965, 'ACC-cell phone': 86.16691763427107, 'ACC-microwave': 74.7692854469993, 'ACC-oven': 92.14126753439947, 'ACC-toaster': 91.93296746910454, 'ACC-sink': 81.33783173281563, 'ACC-refrigerator': 92.24795573017897, 'ACC-book': 71.5237735003222, 'ACC-clock': 74.64229763990144, 'ACC-vase': 75.16203055051682, 'ACC-scissors': 90.00048205382475, 'ACC-teddy bear': 88.68793974924746, 'ACC-hair drier': 61.24014459340221, 'ACC-toothbrush': 84.6812022237665, 'ACC-banner': 78.59390020099625, 'ACC-blanket': 26.545539365949246, 'ACC-bridge': 61.36939650356044, 'ACC-cardboard': 67.54485967718482, 'ACC-counter': 56.47657489585334, 'ACC-curtain': 83.75420203074076, 'ACC-door-stuff': 70.01555316201991, 'ACC-floor-wood': 79.43384110443917, 'ACC-flower': 69.8570270154923, 'ACC-fruit': 69.08565425750565, 'ACC-gravel': 39.38211632091074, 'ACC-house': 29.444361951922204, 'ACC-light': 64.94336887034414, 'ACC-mirror-stuff': 75.95289217864362, 'ACC-net': 66.3638751987017, 'ACC-pillow': 36.22453424429702, 'ACC-platform': 58.85712797726147, 'ACC-playingfield': 84.28837188641705, 'ACC-railroad': 85.74767485053404, 'ACC-river': 79.82337415283754, 'ACC-road': 86.54434534237583, 'ACC-roof': 27.411735465128125, 'ACC-sand': 68.2434630170535, 'ACC-sea': 91.66070823255356, 'ACC-shelf': 56.00948141015767, 'ACC-snow': 95.43534437831887, 'ACC-stairs': 63.368433890656675, 'ACC-tent': 13.493889867049194, 'ACC-towel': 54.71766237189243, 'ACC-wall-brick': 66.05127075297356, 'ACC-wall-stone': 40.679862337252196, 'ACC-wall-tile': 85.52578559961147, 'ACC-wall-wood': 61.64391876187708, 'ACC-water-other': 34.455995993173296, 'ACC-window-blind': 65.05652177503792, 'ACC-window-other': 73.83292347188237, 'ACC-tree-merged': 90.16427561503632, 'ACC-fence-merged': 74.58259118364727, 'ACC-ceiling-merged': 83.35583092050523, 'ACC-sky-other-merged': 96.91536000357529, 'ACC-cabinet-merged': 77.54257410638051, 'ACC-table-merged': 56.538294926854235, 'ACC-floor-other-merged': 64.1621089851127, 'ACC-pavement-merged': 70.79507136220306, 'ACC-mountain-merged': 71.88611619830726, 'ACC-grass-merged': 83.25315912476871, 'ACC-dirt-merged': 73.67336579474745, 'ACC-paper-merged': 49.70459051541575, 'ACC-food-other-merged': 61.60805738093858, 'ACC-building-other-merged': 73.58800836562257, 'ACC-rock-merged': 85.13809069475208, 'ACC-wall-other-merged': 82.54766017440143, 'ACC-rug-merged': 80.478236232518})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.363476733977173, 'noc@0.8': 2.4310798946444248, 'noc@0.85': 2.89640035118525, 'noc@0.9': 3.659057652911911, 'miou@iter1': 0.8641414190196908}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.55382537841797, 'precision@0.6': 72.48348236083984, 'precision@0.7': 68.4803695678711, 'precision@0.8': 60.318695068359375, 'precision@0.9': 32.99650192260742, 'cIoU': 61.671356201171875, 'mIoU': 66.87120819091797}}} INFO:trainer.default_trainer:This epoch takes 0:57:00.239087 INFO:trainer.default_trainer:PROGRESS: 84.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 42 training. INFO:trainer.default_trainer:epochs[ 42] optim steps[76800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.66838/0.75311, loss_mask_bce_0: 0.11973/0.30068, loss_mask_dice_0: 0.31549/1.02010, loss_spatial_bce_0: 0.02423/0.08449, loss_spatial_dice_0: 0.13644/0.17858, loss_spatial_ce_0: 0.03582/0.05552, loss_grounding_bce_0: 0.10853/0.08068, loss_grounding_dice_0: 0.13145/0.15042, loss_grounding_ce_0: 0.46338/0.24877, loss_mask_ce_1: 1.80193/0.75375, loss_mask_bce_1: 0.11505/0.30153, loss_mask_dice_1: 0.32890/1.02457, loss_spatial_bce_1: 0.02676/0.08494, loss_spatial_dice_1: 0.14990/0.18152, loss_spatial_ce_1: 0.04694/0.05924, loss_grounding_bce_1: 0.11263/0.08086, loss_grounding_dice_1: 0.13223/0.15118, loss_grounding_ce_1: 0.62117/0.25027, loss_mask_ce_2: 1.62739/0.76139, loss_mask_bce_2: 0.12729/0.30189, loss_mask_dice_2: 0.34378/1.02542, loss_spatial_bce_2: 0.03467/0.08505, loss_spatial_dice_2: 0.14756/0.18213, loss_spatial_ce_2: 0.08605/0.06143, loss_grounding_bce_2: 0.10648/0.08083, loss_grounding_dice_2: 0.12370/0.15106, loss_grounding_ce_2: 0.61844/0.25288, loss_mask_ce_3: 1.84608/0.76590, loss_mask_bce_3: 0.12215/0.30323, loss_mask_dice_3: 0.38356/1.02333, loss_spatial_bce_3: 0.02893/0.08718, loss_spatial_dice_3: 0.15763/0.18348, loss_spatial_ce_3: 0.10648/0.06635, loss_grounding_bce_3: 0.10078/0.08119, loss_grounding_dice_3: 0.12949/0.15075, loss_grounding_ce_3: 0.63581/0.25410, loss_mask_ce_4: 1.77744/0.77179, loss_mask_bce_4: 0.10929/0.30590, loss_mask_dice_4: 0.32531/1.04274, loss_spatial_bce_4: 0.03122/0.08959, loss_spatial_dice_4: 0.16204/0.19210, loss_spatial_ce_4: 0.07582/0.08011, loss_grounding_bce_4: 0.09858/0.08196, loss_grounding_dice_4: 0.11938/0.15335, loss_grounding_ce_4: 0.75747/0.25841, loss_mask_ce_5: 1.64821/0.79743, loss_mask_bce_5: 0.09895/0.30779, loss_mask_dice_5: 0.36059/1.05079, loss_spatial_bce_5: 0.02646/0.09202, loss_spatial_dice_5: 0.16887/0.19541, loss_spatial_ce_5: 0.06304/0.09375, loss_grounding_bce_5: 0.09344/0.08222, loss_grounding_dice_5: 0.12520/0.15421, loss_grounding_ce_5: 0.57898/0.27603, loss_mask_ce_6: 1.56206/0.82462, loss_mask_bce_6: 0.10023/0.30996, loss_mask_dice_6: 0.35756/1.05475, loss_spatial_bce_6: 0.02831/0.09746, loss_spatial_dice_6: 0.15782/0.19774, loss_spatial_ce_6: 0.03378/0.11817, loss_grounding_bce_6: 0.08338/0.08305, loss_grounding_dice_6: 0.12314/0.15469, loss_grounding_ce_6: 0.66886/0.28495, loss_mask_ce_7: 1.65601/0.88057, loss_mask_bce_7: 0.11328/0.31716, loss_mask_dice_7: 0.29811/1.10047, loss_spatial_bce_7: 0.05156/0.10669, loss_spatial_dice_7: 0.19703/0.22275, loss_spatial_ce_7: 0.00934/0.15333, loss_grounding_bce_7: 0.10380/0.08476, loss_grounding_dice_7: 0.12637/0.16024, loss_grounding_ce_7: 0.71078/0.31837, loss_mask_ce_8: 2.00329/1.01442, loss_mask_bce_8: 0.10268/0.33318, loss_mask_dice_8: 0.42603/1.17707, loss_spatial_bce_8: 0.09685/0.12327, loss_spatial_dice_8: 0.20839/0.25744, loss_spatial_ce_8: 0.23307/0.19875, loss_grounding_bce_8: 0.08663/0.08892, loss_grounding_dice_8: 0.12738/0.17004, loss_grounding_ce_8: 0.76382/0.41610, loss_mask_ce_9: 3.31061/3.47502, loss_mask_bce_9: 0.09452/0.36000, loss_mask_dice_9: 0.46442/1.75959, loss_spatial_bce_9: 0.25912/0.35435, loss_spatial_dice_9: 0.72954/0.79317, loss_spatial_ce_9: 0.98517/1.38670, loss_grounding_bce_9: 0.07268/0.10108, loss_grounding_dice_9: 0.13100/0.24223, loss_grounding_ce_9: 0.72634/0.66938] items per batch[64] items per second[0.16] total items[4915200] mini batches[ 76800] memory[4999] epoch remaining[0:54:52] INFO:trainer.default_trainer:epochs[ 42] optim steps[76900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53723/0.75288, loss_mask_bce_0: 0.50556/0.30064, loss_mask_dice_0: 0.94745/1.02008, loss_spatial_bce_0: 0.05888/0.08448, loss_spatial_dice_0: 0.13851/0.17857, loss_spatial_ce_0: 0.00135/0.05550, loss_grounding_bce_0: 0.07943/0.08067, loss_grounding_dice_0: 0.07515/0.15040, loss_grounding_ce_0: 0.01341/0.24872, loss_mask_ce_1: 1.56979/0.75356, loss_mask_bce_1: 0.52655/0.30149, loss_mask_dice_1: 0.95868/1.02449, loss_spatial_bce_1: 0.06586/0.08493, loss_spatial_dice_1: 0.17627/0.18151, loss_spatial_ce_1: 0.00452/0.05922, loss_grounding_bce_1: 0.07964/0.08086, loss_grounding_dice_1: 0.07305/0.15116, loss_grounding_ce_1: 0.01773/0.25018, loss_mask_ce_2: 1.52458/0.76123, loss_mask_bce_2: 0.52560/0.30185, loss_mask_dice_2: 0.97204/1.02539, loss_spatial_bce_2: 0.07107/0.08504, loss_spatial_dice_2: 0.17744/0.18211, loss_spatial_ce_2: 0.00720/0.06142, loss_grounding_bce_2: 0.07752/0.08083, loss_grounding_dice_2: 0.07004/0.15105, loss_grounding_ce_2: 0.02024/0.25279, loss_mask_ce_3: 1.51562/0.76572, loss_mask_bce_3: 0.52211/0.30319, loss_mask_dice_3: 0.95134/1.02327, loss_spatial_bce_3: 0.06610/0.08717, loss_spatial_dice_3: 0.16005/0.18347, loss_spatial_ce_3: 0.02530/0.06633, loss_grounding_bce_3: 0.07722/0.08119, loss_grounding_dice_3: 0.07096/0.15073, loss_grounding_ce_3: 0.02185/0.25404, loss_mask_ce_4: 1.60123/0.77160, loss_mask_bce_4: 0.50064/0.30586, loss_mask_dice_4: 0.99610/1.04269, loss_spatial_bce_4: 0.06414/0.08958, loss_spatial_dice_4: 0.16520/0.19209, loss_spatial_ce_4: 0.06591/0.08009, loss_grounding_bce_4: 0.08079/0.08196, loss_grounding_dice_4: 0.07116/0.15334, loss_grounding_ce_4: 0.02756/0.25831, loss_mask_ce_5: 1.63891/0.79725, loss_mask_bce_5: 0.50551/0.30774, loss_mask_dice_5: 0.98843/1.05074, loss_spatial_bce_5: 0.07691/0.09201, loss_spatial_dice_5: 0.19565/0.19540, loss_spatial_ce_5: 0.10625/0.09373, loss_grounding_bce_5: 0.08346/0.08221, loss_grounding_dice_5: 0.07566/0.15420, loss_grounding_ce_5: 0.02467/0.27594, loss_mask_ce_6: 1.66974/0.82444, loss_mask_bce_6: 0.41912/0.30991, loss_mask_dice_6: 0.88909/1.05471, loss_spatial_bce_6: 0.20813/0.09745, loss_spatial_dice_6: 0.20325/0.19773, loss_spatial_ce_6: 0.00652/0.11814, loss_grounding_bce_6: 0.07976/0.08304, loss_grounding_dice_6: 0.06966/0.15469, loss_grounding_ce_6: 0.01793/0.28489, loss_mask_ce_7: 1.60550/0.88037, loss_mask_bce_7: 0.47006/0.31712, loss_mask_dice_7: 0.97779/1.10042, loss_spatial_bce_7: 0.14283/0.10668, loss_spatial_dice_7: 0.19069/0.22274, loss_spatial_ce_7: 0.01313/0.15331, loss_grounding_bce_7: 0.08642/0.08475, loss_grounding_dice_7: 0.07907/0.16022, loss_grounding_ce_7: 0.03709/0.31824, loss_mask_ce_8: 1.79056/1.01428, loss_mask_bce_8: 0.59117/0.33313, loss_mask_dice_8: 1.11922/1.17701, loss_spatial_bce_8: 0.10306/0.12325, loss_spatial_dice_8: 0.17326/0.25742, loss_spatial_ce_8: 0.09401/0.19868, loss_grounding_bce_8: 0.08968/0.08891, loss_grounding_dice_8: 0.08772/0.17001, loss_grounding_ce_8: 0.13831/0.41595, loss_mask_ce_9: 3.53329/3.47469, loss_mask_bce_9: 0.85549/0.35995, loss_mask_dice_9: 4.14812/1.75944, loss_spatial_bce_9: 0.18080/0.35435, loss_spatial_dice_9: 0.83603/0.79315, loss_spatial_ce_9: 1.15089/1.38669, loss_grounding_bce_9: 0.10101/0.10107, loss_grounding_dice_9: 0.05376/0.24221, loss_grounding_ce_9: 0.23457/0.66917] items per batch[64] items per second[0.37] total items[4921600] mini batches[ 76900] memory[4999] epoch remaining[0:49:41] INFO:trainer.default_trainer:epochs[ 42] optim steps[77000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08317/0.75282, loss_mask_bce_0: 0.05260/0.30060, loss_mask_dice_0: 0.24508/1.02008, loss_spatial_bce_0: 0.09401/0.08448, loss_spatial_dice_0: 0.08773/0.17856, loss_spatial_ce_0: 0.00001/0.05552, loss_grounding_bce_0: 0.00353/0.08067, loss_grounding_dice_0: 0.02516/0.15038, loss_grounding_ce_0: 0.00475/0.24884, loss_mask_ce_1: 0.08565/0.75349, loss_mask_bce_1: 0.05200/0.30145, loss_mask_dice_1: 0.19205/1.02451, loss_spatial_bce_1: 0.10884/0.08493, loss_spatial_dice_1: 0.08168/0.18150, loss_spatial_ce_1: 0.00001/0.05921, loss_grounding_bce_1: 0.00481/0.08086, loss_grounding_dice_1: 0.02924/0.15115, loss_grounding_ce_1: 0.00340/0.25032, loss_mask_ce_2: 0.09890/0.76116, loss_mask_bce_2: 0.04700/0.30181, loss_mask_dice_2: 0.17654/1.02542, loss_spatial_bce_2: 0.09965/0.08504, loss_spatial_dice_2: 0.10136/0.18211, loss_spatial_ce_2: 0.00001/0.06141, loss_grounding_bce_2: 0.00263/0.08082, loss_grounding_dice_2: 0.01830/0.15103, loss_grounding_ce_2: 0.00426/0.25286, loss_mask_ce_3: 0.08878/0.76569, loss_mask_bce_3: 0.05824/0.30314, loss_mask_dice_3: 0.20733/1.02330, loss_spatial_bce_3: 0.05978/0.08717, loss_spatial_dice_3: 0.08764/0.18346, loss_spatial_ce_3: 0.00002/0.06633, loss_grounding_bce_3: 0.00466/0.08118, loss_grounding_dice_3: 0.02749/0.15071, loss_grounding_ce_3: 0.00503/0.25412, loss_mask_ce_4: 0.08149/0.77154, loss_mask_bce_4: 0.06294/0.30581, loss_mask_dice_4: 0.17527/1.04272, loss_spatial_bce_4: 0.09242/0.08958, loss_spatial_dice_4: 0.08254/0.19209, loss_spatial_ce_4: 0.00024/0.08009, loss_grounding_bce_4: 0.00453/0.08195, loss_grounding_dice_4: 0.02851/0.15332, loss_grounding_ce_4: 0.00661/0.25841, loss_mask_ce_5: 0.09084/0.79716, loss_mask_bce_5: 0.10107/0.30770, loss_mask_dice_5: 0.21071/1.05076, loss_spatial_bce_5: 0.08640/0.09200, loss_spatial_dice_5: 0.08485/0.19540, loss_spatial_ce_5: 0.00006/0.09373, loss_grounding_bce_5: 0.00352/0.08220, loss_grounding_dice_5: 0.02397/0.15418, loss_grounding_ce_5: 0.00459/0.27601, loss_mask_ce_6: 0.16384/0.82442, loss_mask_bce_6: 0.05599/0.30986, loss_mask_dice_6: 0.19073/1.05472, loss_spatial_bce_6: 0.08819/0.09745, loss_spatial_dice_6: 0.11845/0.19774, loss_spatial_ce_6: 0.05453/0.11814, loss_grounding_bce_6: 0.00303/0.08303, loss_grounding_dice_6: 0.02268/0.15467, loss_grounding_ce_6: 0.00480/0.28497, loss_mask_ce_7: 0.24858/0.88036, loss_mask_bce_7: 0.07411/0.31707, loss_mask_dice_7: 0.22419/1.10046, loss_spatial_bce_7: 0.15573/0.10667, loss_spatial_dice_7: 0.11852/0.22274, loss_spatial_ce_7: 0.02842/0.15330, loss_grounding_bce_7: 0.00429/0.08474, loss_grounding_dice_7: 0.02585/0.16021, loss_grounding_ce_7: 0.00615/0.31833, loss_mask_ce_8: 0.23571/1.01422, loss_mask_bce_8: 0.06645/0.33308, loss_mask_dice_8: 0.25779/1.17701, loss_spatial_bce_8: 0.11526/0.12324, loss_spatial_dice_8: 0.12902/0.25742, loss_spatial_ce_8: 0.03103/0.19868, loss_grounding_bce_8: 0.00304/0.08890, loss_grounding_dice_8: 0.02238/0.16999, loss_grounding_ce_8: 0.00132/0.41606, loss_mask_ce_9: 2.45065/3.47460, loss_mask_bce_9: 0.07476/0.35990, loss_mask_dice_9: 0.51156/1.75928, loss_spatial_bce_9: 0.38438/0.35435, loss_spatial_dice_9: 0.87800/0.79313, loss_spatial_ce_9: 1.45889/1.38666, loss_grounding_bce_9: 0.01064/0.10106, loss_grounding_dice_9: 0.07502/0.24217, loss_grounding_ce_9: 0.04564/0.66935] items per batch[64] items per second[0.36] total items[4928000] mini batches[ 77000] memory[4999] epoch remaining[0:46:18] INFO:trainer.default_trainer:epochs[ 42] optim steps[77100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42379/0.75288, loss_mask_bce_0: 0.05926/0.30059, loss_mask_dice_0: 0.83468/1.02008, loss_spatial_bce_0: 0.00501/0.08447, loss_spatial_dice_0: 0.10539/0.17855, loss_spatial_ce_0: 0.00165/0.05550, loss_grounding_bce_0: 0.02151/0.08066, loss_grounding_dice_0: 0.20133/0.15039, loss_grounding_ce_0: 0.20200/0.24885, loss_mask_ce_1: 1.46252/0.75353, loss_mask_bce_1: 0.06086/0.30144, loss_mask_dice_1: 0.98254/1.02447, loss_spatial_bce_1: 0.00549/0.08492, loss_spatial_dice_1: 0.12599/0.18149, loss_spatial_ce_1: 0.00298/0.05919, loss_grounding_bce_1: 0.01728/0.08086, loss_grounding_dice_1: 0.13958/0.15115, loss_grounding_ce_1: 0.29286/0.25033, loss_mask_ce_2: 1.50321/0.76121, loss_mask_bce_2: 0.04792/0.30180, loss_mask_dice_2: 0.87824/1.02539, loss_spatial_bce_2: 0.00540/0.08504, loss_spatial_dice_2: 0.14180/0.18211, loss_spatial_ce_2: 0.00313/0.06141, loss_grounding_bce_2: 0.01717/0.08083, loss_grounding_dice_2: 0.17890/0.15104, loss_grounding_ce_2: 0.19054/0.25286, loss_mask_ce_3: 1.21637/0.76576, loss_mask_bce_3: 0.05749/0.30314, loss_mask_dice_3: 0.86522/1.02326, loss_spatial_bce_3: 0.00553/0.08717, loss_spatial_dice_3: 0.13658/0.18346, loss_spatial_ce_3: 0.02967/0.06632, loss_grounding_bce_3: 0.01930/0.08118, loss_grounding_dice_3: 0.29873/0.15072, loss_grounding_ce_3: 0.31412/0.25418, loss_mask_ce_4: 1.10980/0.77156, loss_mask_bce_4: 0.06790/0.30581, loss_mask_dice_4: 0.85754/1.04273, loss_spatial_bce_4: 0.00634/0.08958, loss_spatial_dice_4: 0.13978/0.19209, loss_spatial_ce_4: 0.04671/0.08009, loss_grounding_bce_4: 0.01583/0.08195, loss_grounding_dice_4: 0.21673/0.15333, loss_grounding_ce_4: 0.25236/0.25845, loss_mask_ce_5: 1.28232/0.79718, loss_mask_bce_5: 0.05705/0.30769, loss_mask_dice_5: 0.86234/1.05076, loss_spatial_bce_5: 0.00681/0.09200, loss_spatial_dice_5: 0.15262/0.19540, loss_spatial_ce_5: 0.09290/0.09371, loss_grounding_bce_5: 0.01787/0.08220, loss_grounding_dice_5: 0.19105/0.15418, loss_grounding_ce_5: 0.26019/0.27603, loss_mask_ce_6: 1.20708/0.82440, loss_mask_bce_6: 0.05874/0.30986, loss_mask_dice_6: 0.72964/1.05470, loss_spatial_bce_6: 0.00711/0.09745, loss_spatial_dice_6: 0.14535/0.19774, loss_spatial_ce_6: 0.05539/0.11813, loss_grounding_bce_6: 0.01924/0.08304, loss_grounding_dice_6: 0.20037/0.15468, loss_grounding_ce_6: 0.14529/0.28501, loss_mask_ce_7: 1.14141/0.88036, loss_mask_bce_7: 0.04977/0.31706, loss_mask_dice_7: 0.80357/1.10042, loss_spatial_bce_7: 0.00580/0.10667, loss_spatial_dice_7: 0.12153/0.22273, loss_spatial_ce_7: 0.12729/0.15327, loss_grounding_bce_7: 0.01612/0.08475, loss_grounding_dice_7: 0.14369/0.16022, loss_grounding_ce_7: 0.21269/0.31832, loss_mask_ce_8: 0.97963/1.01424, loss_mask_bce_8: 0.06205/0.33308, loss_mask_dice_8: 0.76313/1.17701, loss_spatial_bce_8: 0.00650/0.12323, loss_spatial_dice_8: 0.23325/0.25743, loss_spatial_ce_8: 0.10319/0.19867, loss_grounding_bce_8: 0.01547/0.08890, loss_grounding_dice_8: 0.13354/0.17001, loss_grounding_ce_8: 0.22552/0.41603, loss_mask_ce_9: 4.04387/3.47440, loss_mask_bce_9: 0.03205/0.35985, loss_mask_dice_9: 1.11613/1.75924, loss_spatial_bce_9: 0.05995/0.35431, loss_spatial_dice_9: 0.91126/0.79311, loss_spatial_ce_9: 1.25340/1.38656, loss_grounding_bce_9: 0.00657/0.10105, loss_grounding_dice_9: 0.25367/0.24218, loss_grounding_ce_9: 0.51071/0.66925] items per batch[64] items per second[0.36] total items[4934400] mini batches[ 77100] memory[4999] epoch remaining[0:43:17] INFO:trainer.default_trainer:epochs[ 42] optim steps[77200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.06272/0.75298, loss_mask_bce_0: 0.01594/0.30060, loss_mask_dice_0: 0.05349/1.02027, loss_spatial_bce_0: 0.01156/0.08446, loss_spatial_dice_0: 0.04472/0.17854, loss_spatial_ce_0: 0.00056/0.05550, loss_grounding_bce_0: 0.01096/0.08066, loss_grounding_dice_0: 0.04290/0.15038, loss_grounding_ce_0: 0.00442/0.24884, loss_mask_ce_1: 0.04872/0.75362, loss_mask_bce_1: 0.01535/0.30144, loss_mask_dice_1: 0.06267/1.02467, loss_spatial_bce_1: 0.01149/0.08491, loss_spatial_dice_1: 0.04354/0.18149, loss_spatial_ce_1: 0.00053/0.05918, loss_grounding_bce_1: 0.01239/0.08085, loss_grounding_dice_1: 0.04698/0.15115, loss_grounding_ce_1: 0.00491/0.25033, loss_mask_ce_2: 0.12021/0.76130, loss_mask_bce_2: 0.01467/0.30181, loss_mask_dice_2: 0.05609/1.02559, loss_spatial_bce_2: 0.01175/0.08502, loss_spatial_dice_2: 0.04008/0.18210, loss_spatial_ce_2: 0.00042/0.06142, loss_grounding_bce_2: 0.01158/0.08082, loss_grounding_dice_2: 0.04499/0.15102, loss_grounding_ce_2: 0.00482/0.25286, loss_mask_ce_3: 0.13630/0.76589, loss_mask_bce_3: 0.01371/0.30314, loss_mask_dice_3: 0.05101/1.02345, loss_spatial_bce_3: 0.01170/0.08715, loss_spatial_dice_3: 0.04126/0.18345, loss_spatial_ce_3: 0.00048/0.06632, loss_grounding_bce_3: 0.01247/0.08118, loss_grounding_dice_3: 0.04303/0.15071, loss_grounding_ce_3: 0.00627/0.25419, loss_mask_ce_4: 0.05765/0.77169, loss_mask_bce_4: 0.01455/0.30582, loss_mask_dice_4: 0.05677/1.04291, loss_spatial_bce_4: 0.01234/0.08957, loss_spatial_dice_4: 0.04095/0.19209, loss_spatial_ce_4: 0.00018/0.08008, loss_grounding_bce_4: 0.01156/0.08194, loss_grounding_dice_4: 0.04445/0.15332, loss_grounding_ce_4: 0.01076/0.25850, loss_mask_ce_5: 0.02073/0.79730, loss_mask_bce_5: 0.01596/0.30770, loss_mask_dice_5: 0.05609/1.05098, loss_spatial_bce_5: 0.01485/0.09199, loss_spatial_dice_5: 0.04957/0.19540, loss_spatial_ce_5: 0.00046/0.09371, loss_grounding_bce_5: 0.01227/0.08219, loss_grounding_dice_5: 0.04667/0.15418, loss_grounding_ce_5: 0.00338/0.27608, loss_mask_ce_6: 0.08354/0.82450, loss_mask_bce_6: 0.01368/0.30987, loss_mask_dice_6: 0.06163/1.05491, loss_spatial_bce_6: 0.01408/0.09744, loss_spatial_dice_6: 0.04777/0.19774, loss_spatial_ce_6: 0.00038/0.11813, loss_grounding_bce_6: 0.01209/0.08303, loss_grounding_dice_6: 0.04499/0.15467, loss_grounding_ce_6: 0.00775/0.28506, loss_mask_ce_7: 0.12856/0.88045, loss_mask_bce_7: 0.01551/0.31706, loss_mask_dice_7: 0.05195/1.10064, loss_spatial_bce_7: 0.01657/0.10665, loss_spatial_dice_7: 0.04728/0.22273, loss_spatial_ce_7: 0.00174/0.15326, loss_grounding_bce_7: 0.01145/0.08474, loss_grounding_dice_7: 0.04330/0.16021, loss_grounding_ce_7: 0.00440/0.31835, loss_mask_ce_8: 0.29264/1.01435, loss_mask_bce_8: 0.02261/0.33308, loss_mask_dice_8: 0.09386/1.17727, loss_spatial_bce_8: 0.01822/0.12321, loss_spatial_dice_8: 0.04824/0.25742, loss_spatial_ce_8: 0.00160/0.19861, loss_grounding_bce_8: 0.01669/0.08890, loss_grounding_dice_8: 0.07818/0.17001, loss_grounding_ce_8: 0.03247/0.41609, loss_mask_ce_9: 2.17736/3.47460, loss_mask_bce_9: 0.01430/0.35987, loss_mask_dice_9: 0.08056/1.75970, loss_spatial_bce_9: 0.36052/0.35427, loss_spatial_dice_9: 0.62057/0.79311, loss_spatial_ce_9: 1.93582/1.38651, loss_grounding_bce_9: 0.01300/0.10104, loss_grounding_dice_9: 0.08134/0.24218, loss_grounding_ce_9: 0.14390/0.66928] items per batch[64] items per second[0.36] total items[4940800] mini batches[ 77200] memory[4999] epoch remaining[0:40:25] INFO:trainer.default_trainer:epochs[ 42] optim steps[77300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.17764/0.75298, loss_mask_bce_0: 0.07337/0.30060, loss_mask_dice_0: 0.05274/1.02042, loss_spatial_bce_0: 0.04348/0.08445, loss_spatial_dice_0: 0.02797/0.17856, loss_spatial_ce_0: 0.00037/0.05548, loss_grounding_bce_0: 0.04516/0.08066, loss_grounding_dice_0: 0.03034/0.15043, loss_grounding_ce_0: 0.01834/0.24887, loss_mask_ce_1: 0.17270/0.75365, loss_mask_bce_1: 0.07185/0.30145, loss_mask_dice_1: 0.05223/1.02481, loss_spatial_bce_1: 0.04333/0.08490, loss_spatial_dice_1: 0.02691/0.18150, loss_spatial_ce_1: 0.00045/0.05917, loss_grounding_bce_1: 0.04210/0.08085, loss_grounding_dice_1: 0.02808/0.15118, loss_grounding_ce_1: 0.01226/0.25039, loss_mask_ce_2: 0.18897/0.76133, loss_mask_bce_2: 0.07311/0.30181, loss_mask_dice_2: 0.05349/1.02571, loss_spatial_bce_2: 0.04196/0.08501, loss_spatial_dice_2: 0.02602/0.18212, loss_spatial_ce_2: 0.00013/0.06140, loss_grounding_bce_2: 0.03839/0.08082, loss_grounding_dice_2: 0.02628/0.15106, loss_grounding_ce_2: 0.00599/0.25292, loss_mask_ce_3: 0.17998/0.76592, loss_mask_bce_3: 0.07361/0.30315, loss_mask_dice_3: 0.04956/1.02360, loss_spatial_bce_3: 0.04710/0.08714, loss_spatial_dice_3: 0.02730/0.18347, loss_spatial_ce_3: 0.00009/0.06632, loss_grounding_bce_3: 0.04237/0.08118, loss_grounding_dice_3: 0.02722/0.15075, loss_grounding_ce_3: 0.01198/0.25426, loss_mask_ce_4: 0.19276/0.77174, loss_mask_bce_4: 0.07271/0.30584, loss_mask_dice_4: 0.04931/1.04308, loss_spatial_bce_4: 0.04209/0.08956, loss_spatial_dice_4: 0.02578/0.19211, loss_spatial_ce_4: 0.00012/0.08008, loss_grounding_bce_4: 0.04170/0.08195, loss_grounding_dice_4: 0.02642/0.15337, loss_grounding_ce_4: 0.01562/0.25854, loss_mask_ce_5: 0.22254/0.79732, loss_mask_bce_5: 0.07166/0.30771, loss_mask_dice_5: 0.04981/1.05115, loss_spatial_bce_5: 0.04224/0.09198, loss_spatial_dice_5: 0.02924/0.19543, loss_spatial_ce_5: 0.00012/0.09373, loss_grounding_bce_5: 0.04191/0.08220, loss_grounding_dice_5: 0.02816/0.15422, loss_grounding_ce_5: 0.01180/0.27611, loss_mask_ce_6: 0.20240/0.82456, loss_mask_bce_6: 0.07540/0.30988, loss_mask_dice_6: 0.05119/1.05506, loss_spatial_bce_6: 0.04602/0.09744, loss_spatial_dice_6: 0.03127/0.19776, loss_spatial_ce_6: 0.00037/0.11816, loss_grounding_bce_6: 0.04075/0.08303, loss_grounding_dice_6: 0.02739/0.15472, loss_grounding_ce_6: 0.01134/0.28509, loss_mask_ce_7: 0.21237/0.88056, loss_mask_bce_7: 0.07115/0.31707, loss_mask_dice_7: 0.05075/1.10079, loss_spatial_bce_7: 0.04561/0.10665, loss_spatial_dice_7: 0.03431/0.22276, loss_spatial_ce_7: 0.00014/0.15324, loss_grounding_bce_7: 0.04030/0.08474, loss_grounding_dice_7: 0.02815/0.16026, loss_grounding_ce_7: 0.02536/0.31839, loss_mask_ce_8: 0.23146/1.01443, loss_mask_bce_8: 0.08017/0.33308, loss_mask_dice_8: 0.06027/1.17744, loss_spatial_bce_8: 0.04991/0.12321, loss_spatial_dice_8: 0.04167/0.25745, loss_spatial_ce_8: 0.00316/0.19859, loss_grounding_bce_8: 0.04579/0.08891, loss_grounding_dice_8: 0.03670/0.17007, loss_grounding_ce_8: 0.02109/0.41609, loss_mask_ce_9: 2.54852/3.47467, loss_mask_bce_9: 0.08399/0.35984, loss_mask_dice_9: 0.08316/1.75983, loss_spatial_bce_9: 0.65232/0.35425, loss_spatial_dice_9: 0.66505/0.79312, loss_spatial_ce_9: 1.68789/1.38657, loss_grounding_bce_9: 0.11884/0.10105, loss_grounding_dice_9: 0.10852/0.24225, loss_grounding_ce_9: 0.11705/0.66908] items per batch[64] items per second[0.37] total items[4947200] mini batches[ 77300] memory[4999] epoch remaining[0:37:11] INFO:trainer.default_trainer:epochs[ 42] optim steps[77400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.39069/0.75298, loss_mask_bce_0: 0.52094/0.30061, loss_mask_dice_0: 3.58478/1.02029, loss_spatial_bce_0: 0.06086/0.08444, loss_spatial_dice_0: 0.21087/0.17856, loss_spatial_ce_0: 0.02229/0.05547, loss_grounding_bce_0: 0.03216/0.08066, loss_grounding_dice_0: 0.23517/0.15042, loss_grounding_ce_0: 0.60520/0.24882, loss_mask_ce_1: 1.33847/0.75365, loss_mask_bce_1: 0.51800/0.30145, loss_mask_dice_1: 3.20250/1.02466, loss_spatial_bce_1: 0.06297/0.08489, loss_spatial_dice_1: 0.22005/0.18150, loss_spatial_ce_1: 0.03539/0.05917, loss_grounding_bce_1: 0.02983/0.08085, loss_grounding_dice_1: 0.25102/0.15116, loss_grounding_ce_1: 0.64059/0.25035, loss_mask_ce_2: 1.27947/0.76132, loss_mask_bce_2: 0.49204/0.30181, loss_mask_dice_2: 3.25967/1.02559, loss_spatial_bce_2: 0.05460/0.08500, loss_spatial_dice_2: 0.23665/0.18212, loss_spatial_ce_2: 0.07110/0.06141, loss_grounding_bce_2: 0.03256/0.08082, loss_grounding_dice_2: 0.24606/0.15104, loss_grounding_ce_2: 0.61597/0.25292, loss_mask_ce_3: 1.42691/0.76593, loss_mask_bce_3: 0.50910/0.30316, loss_mask_dice_3: 2.61699/1.02345, loss_spatial_bce_3: 0.04740/0.08713, loss_spatial_dice_3: 0.24760/0.18347, loss_spatial_ce_3: 0.04465/0.06631, loss_grounding_bce_3: 0.03588/0.08118, loss_grounding_dice_3: 0.26100/0.15073, loss_grounding_ce_3: 0.60877/0.25422, loss_mask_ce_4: 1.47335/0.77174, loss_mask_bce_4: 0.53381/0.30584, loss_mask_dice_4: 2.96803/1.04291, loss_spatial_bce_4: 0.03835/0.08954, loss_spatial_dice_4: 0.21386/0.19211, loss_spatial_ce_4: 0.08165/0.08007, loss_grounding_bce_4: 0.03773/0.08195, loss_grounding_dice_4: 0.24571/0.15335, loss_grounding_ce_4: 0.62248/0.25847, loss_mask_ce_5: 1.62607/0.79730, loss_mask_bce_5: 0.52926/0.30770, loss_mask_dice_5: 3.22219/1.05098, loss_spatial_bce_5: 0.04022/0.09197, loss_spatial_dice_5: 0.23313/0.19543, loss_spatial_ce_5: 0.19387/0.09372, loss_grounding_bce_5: 0.04199/0.08220, loss_grounding_dice_5: 0.24862/0.15420, loss_grounding_ce_5: 0.67865/0.27609, loss_mask_ce_6: 1.31191/0.82455, loss_mask_bce_6: 0.69975/0.30987, loss_mask_dice_6: 3.30157/1.05491, loss_spatial_bce_6: 0.05056/0.09743, loss_spatial_dice_6: 0.23177/0.19777, loss_spatial_ce_6: 0.25021/0.11816, loss_grounding_bce_6: 0.03417/0.08303, loss_grounding_dice_6: 0.23056/0.15471, loss_grounding_ce_6: 0.68850/0.28504, loss_mask_ce_7: 1.54890/0.88057, loss_mask_bce_7: 0.46336/0.31706, loss_mask_dice_7: 3.50847/1.10064, loss_spatial_bce_7: 0.11657/0.10664, loss_spatial_dice_7: 0.27090/0.22276, loss_spatial_ce_7: 0.14554/0.15322, loss_grounding_bce_7: 0.03350/0.08474, loss_grounding_dice_7: 0.24081/0.16025, loss_grounding_ce_7: 0.69799/0.31838, loss_mask_ce_8: 1.80518/1.01444, loss_mask_bce_8: 0.55817/0.33306, loss_mask_dice_8: 4.12507/1.17727, loss_spatial_bce_8: 0.05628/0.12320, loss_spatial_dice_8: 0.34806/0.25745, loss_spatial_ce_8: 0.16433/0.19856, loss_grounding_bce_8: 0.03392/0.08891, loss_grounding_dice_8: 0.32922/0.17005, loss_grounding_ce_8: 0.68362/0.41610, loss_mask_ce_9: 5.28958/3.47449, loss_mask_bce_9: 0.47097/0.35981, loss_mask_dice_9: 5.46213/1.75969, loss_spatial_bce_9: 0.28193/0.35422, loss_spatial_dice_9: 0.97210/0.79312, loss_spatial_ce_9: 1.32016/1.38653, loss_grounding_bce_9: 0.03087/0.10105, loss_grounding_dice_9: 0.44591/0.24223, loss_grounding_ce_9: 0.65589/0.66904] items per batch[64] items per second[0.37] total items[4953600] mini batches[ 77400] memory[4999] epoch remaining[0:34:10] INFO:trainer.default_trainer:epochs[ 42] optim steps[77500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.22357/0.75297, loss_mask_bce_0: 0.08820/0.30057, loss_mask_dice_0: 1.22646/1.02017, loss_spatial_bce_0: 0.01599/0.08443, loss_spatial_dice_0: 0.21043/0.17856, loss_spatial_ce_0: 0.03205/0.05547, loss_grounding_bce_0: 0.00973/0.08066, loss_grounding_dice_0: 0.38909/0.15044, loss_grounding_ce_0: 0.06108/0.24870, loss_mask_ce_1: 0.19946/0.75364, loss_mask_bce_1: 0.08194/0.30141, loss_mask_dice_1: 1.11947/1.02453, loss_spatial_bce_1: 0.01618/0.08489, loss_spatial_dice_1: 0.21251/0.18149, loss_spatial_ce_1: 0.03365/0.05917, loss_grounding_bce_1: 0.00526/0.08085, loss_grounding_dice_1: 0.33124/0.15118, loss_grounding_ce_1: 0.04608/0.25022, loss_mask_ce_2: 0.21303/0.76133, loss_mask_bce_2: 0.08660/0.30177, loss_mask_dice_2: 1.14287/1.02548, loss_spatial_bce_2: 0.01575/0.08500, loss_spatial_dice_2: 0.19659/0.18211, loss_spatial_ce_2: 0.01977/0.06140, loss_grounding_bce_2: 0.01364/0.08082, loss_grounding_dice_2: 0.45511/0.15106, loss_grounding_ce_2: 0.04076/0.25281, loss_mask_ce_3: 0.23968/0.76595, loss_mask_bce_3: 0.09346/0.30311, loss_mask_dice_3: 1.23005/1.02335, loss_spatial_bce_3: 0.01864/0.08713, loss_spatial_dice_3: 0.22228/0.18347, loss_spatial_ce_3: 0.07364/0.06629, loss_grounding_bce_3: 0.00745/0.08118, loss_grounding_dice_3: 0.30544/0.15075, loss_grounding_ce_3: 0.04583/0.25413, loss_mask_ce_4: 0.23196/0.77173, loss_mask_bce_4: 0.09271/0.30580, loss_mask_dice_4: 1.12946/1.04284, loss_spatial_bce_4: 0.01723/0.08954, loss_spatial_dice_4: 0.21455/0.19210, loss_spatial_ce_4: 0.69637/0.08007, loss_grounding_bce_4: 0.00420/0.08195, loss_grounding_dice_4: 0.36968/0.15337, loss_grounding_ce_4: 0.03131/0.25834, loss_mask_ce_5: 0.26499/0.79729, loss_mask_bce_5: 0.09123/0.30767, loss_mask_dice_5: 1.17051/1.05086, loss_spatial_bce_5: 0.01918/0.09197, loss_spatial_dice_5: 0.24609/0.19543, loss_spatial_ce_5: 0.17415/0.09370, loss_grounding_bce_5: 0.00845/0.08220, loss_grounding_dice_5: 0.54215/0.15422, loss_grounding_ce_5: 0.03960/0.27598, loss_mask_ce_6: 0.29999/0.82455, loss_mask_bce_6: 0.09069/0.30983, loss_mask_dice_6: 1.14308/1.05479, loss_spatial_bce_6: 0.01719/0.09743, loss_spatial_dice_6: 0.20717/0.19776, loss_spatial_ce_6: 0.16453/0.11814, loss_grounding_bce_6: 0.00997/0.08303, loss_grounding_dice_6: 0.30739/0.15472, loss_grounding_ce_6: 0.06028/0.28495, loss_mask_ce_7: 0.34084/0.88058, loss_mask_bce_7: 0.08942/0.31702, loss_mask_dice_7: 0.97892/1.10051, loss_spatial_bce_7: 0.02152/0.10663, loss_spatial_dice_7: 0.26864/0.22276, loss_spatial_ce_7: 0.27805/0.15318, loss_grounding_bce_7: 0.00881/0.08474, loss_grounding_dice_7: 0.39202/0.16026, loss_grounding_ce_7: 0.04486/0.31825, loss_mask_ce_8: 0.94636/1.01437, loss_mask_bce_8: 0.10711/0.33304, loss_mask_dice_8: 1.37345/1.17715, loss_spatial_bce_8: 0.02976/0.12318, loss_spatial_dice_8: 0.35465/0.25745, loss_spatial_ce_8: 0.40415/0.19854, loss_grounding_bce_8: 0.00447/0.08891, loss_grounding_dice_8: 0.55612/0.17007, loss_grounding_ce_8: 0.19744/0.41596, loss_mask_ce_9: 2.27663/3.47455, loss_mask_bce_9: 0.06730/0.35976, loss_mask_dice_9: 1.22492/1.75945, loss_spatial_bce_9: 0.10081/0.35422, loss_spatial_dice_9: 0.83997/0.79314, loss_spatial_ce_9: 1.11099/1.38670, loss_grounding_bce_9: 0.00326/0.10104, loss_grounding_dice_9: 0.45046/0.24225, loss_grounding_ce_9: 0.04245/0.66891] items per batch[64] items per second[0.36] total items[4960000] mini batches[ 77500] memory[4999] epoch remaining[0:31:15] INFO:trainer.default_trainer:epochs[ 42] optim steps[77600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.30774/0.75300, loss_mask_bce_0: 0.36539/0.30057, loss_mask_dice_0: 0.44145/1.02021, loss_spatial_bce_0: 0.09913/0.08442, loss_spatial_dice_0: 0.12265/0.17855, loss_spatial_ce_0: 0.04418/0.05549, loss_grounding_bce_0: 0.00915/0.08065, loss_grounding_dice_0: 0.14210/0.15044, loss_grounding_ce_0: 1.33516/0.24871, loss_mask_ce_1: 1.33576/0.75368, loss_mask_bce_1: 0.36724/0.30141, loss_mask_dice_1: 0.44755/1.02457, loss_spatial_bce_1: 0.10218/0.08488, loss_spatial_dice_1: 0.13220/0.18149, loss_spatial_ce_1: 0.01788/0.05919, loss_grounding_bce_1: 0.01477/0.08084, loss_grounding_dice_1: 0.22001/0.15118, loss_grounding_ce_1: 1.45232/0.25021, loss_mask_ce_2: 1.36108/0.76135, loss_mask_bce_2: 0.37179/0.30177, loss_mask_dice_2: 0.38982/1.02549, loss_spatial_bce_2: 0.09089/0.08499, loss_spatial_dice_2: 0.10793/0.18211, loss_spatial_ce_2: 0.01822/0.06142, loss_grounding_bce_2: 0.02267/0.08080, loss_grounding_dice_2: 0.30005/0.15106, loss_grounding_ce_2: 1.35058/0.25282, loss_mask_ce_3: 1.30348/0.76601, loss_mask_bce_3: 0.37508/0.30310, loss_mask_dice_3: 0.38435/1.02335, loss_spatial_bce_3: 0.10749/0.08712, loss_spatial_dice_3: 0.11824/0.18346, loss_spatial_ce_3: 0.04307/0.06630, loss_grounding_bce_3: 0.02973/0.08116, loss_grounding_dice_3: 0.38644/0.15075, loss_grounding_ce_3: 1.03929/0.25412, loss_mask_ce_4: 1.29029/0.77177, loss_mask_bce_4: 0.38414/0.30579, loss_mask_dice_4: 0.43205/1.04286, loss_spatial_bce_4: 0.11286/0.08954, loss_spatial_dice_4: 0.12126/0.19210, loss_spatial_ce_4: 0.09999/0.08007, loss_grounding_bce_4: 0.03531/0.08193, loss_grounding_dice_4: 0.22896/0.15337, loss_grounding_ce_4: 1.17993/0.25836, loss_mask_ce_5: 1.40497/0.79735, loss_mask_bce_5: 0.36138/0.30766, loss_mask_dice_5: 0.42291/1.05088, loss_spatial_bce_5: 0.10041/0.09197, loss_spatial_dice_5: 0.11569/0.19543, loss_spatial_ce_5: 0.06477/0.09370, loss_grounding_bce_5: 0.04155/0.08219, loss_grounding_dice_5: 0.37162/0.15423, loss_grounding_ce_5: 1.33276/0.27599, loss_mask_ce_6: 1.34731/0.82462, loss_mask_bce_6: 0.35804/0.30983, loss_mask_dice_6: 0.42953/1.05479, loss_spatial_bce_6: 0.10200/0.09743, loss_spatial_dice_6: 0.13166/0.19776, loss_spatial_ce_6: 0.11585/0.11812, loss_grounding_bce_6: 0.04273/0.08302, loss_grounding_dice_6: 0.46786/0.15472, loss_grounding_ce_6: 0.89147/0.28494, loss_mask_ce_7: 1.57418/0.88064, loss_mask_bce_7: 0.38680/0.31704, loss_mask_dice_7: 0.54176/1.10057, loss_spatial_bce_7: 0.11071/0.10662, loss_spatial_dice_7: 0.15683/0.22275, loss_spatial_ce_7: 0.08120/0.15316, loss_grounding_bce_7: 0.04121/0.08472, loss_grounding_dice_7: 0.38384/0.16027, loss_grounding_ce_7: 0.96077/0.31824, loss_mask_ce_8: 1.45779/1.01443, loss_mask_bce_8: 0.42869/0.33303, loss_mask_dice_8: 0.50936/1.17717, loss_spatial_bce_8: 0.12169/0.12318, loss_spatial_dice_8: 0.14962/0.25744, loss_spatial_ce_8: 0.12860/0.19851, loss_grounding_bce_8: 0.03362/0.08889, loss_grounding_dice_8: 0.42790/0.17007, loss_grounding_ce_8: 0.89356/0.41607, loss_mask_ce_9: 3.49413/3.47463, loss_mask_bce_9: 0.50727/0.35976, loss_mask_dice_9: 1.22931/1.75947, loss_spatial_bce_9: 0.43445/0.35418, loss_spatial_dice_9: 0.82803/0.79312, loss_spatial_ce_9: 1.43247/1.38663, loss_grounding_bce_9: 0.07497/0.10103, loss_grounding_dice_9: 0.98728/0.24225, loss_grounding_ce_9: 0.05430/0.66889] items per batch[64] items per second[0.37] total items[4966400] mini batches[ 77600] memory[4999] epoch remaining[0:28:16] INFO:trainer.default_trainer:epochs[ 42] optim steps[77700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.24342/0.75288, loss_mask_bce_0: 0.24663/0.30057, loss_mask_dice_0: 0.60409/1.02008, loss_spatial_bce_0: 0.11855/0.08443, loss_spatial_dice_0: 0.21604/0.17852, loss_spatial_ce_0: 0.00548/0.05546, loss_grounding_bce_0: 0.02496/0.08065, loss_grounding_dice_0: 0.07218/0.15041, loss_grounding_ce_0: 0.03150/0.24869, loss_mask_ce_1: 1.26812/0.75356, loss_mask_bce_1: 0.24582/0.30141, loss_mask_dice_1: 0.63329/1.02446, loss_spatial_bce_1: 0.11345/0.08488, loss_spatial_dice_1: 0.23323/0.18145, loss_spatial_ce_1: 0.00529/0.05916, loss_grounding_bce_1: 0.02582/0.08085, loss_grounding_dice_1: 0.07116/0.15114, loss_grounding_ce_1: 0.04118/0.25019, loss_mask_ce_2: 1.42742/0.76123, loss_mask_bce_2: 0.23294/0.30177, loss_mask_dice_2: 0.62404/1.02536, loss_spatial_bce_2: 0.11382/0.08499, loss_spatial_dice_2: 0.22279/0.18208, loss_spatial_ce_2: 0.01258/0.06140, loss_grounding_bce_2: 0.03060/0.08081, loss_grounding_dice_2: 0.07136/0.15103, loss_grounding_ce_2: 0.03068/0.25280, loss_mask_ce_3: 1.08425/0.76590, loss_mask_bce_3: 0.31789/0.30311, loss_mask_dice_3: 0.70537/1.02326, loss_spatial_bce_3: 0.11006/0.08712, loss_spatial_dice_3: 0.21123/0.18343, loss_spatial_ce_3: 0.01222/0.06629, loss_grounding_bce_3: 0.03391/0.08117, loss_grounding_dice_3: 0.07524/0.15072, loss_grounding_ce_3: 0.01728/0.25408, loss_mask_ce_4: 1.16984/0.77167, loss_mask_bce_4: 0.28134/0.30579, loss_mask_dice_4: 0.69086/1.04273, loss_spatial_bce_4: 0.10287/0.08955, loss_spatial_dice_4: 0.20529/0.19208, loss_spatial_ce_4: 0.00884/0.08005, loss_grounding_bce_4: 0.02795/0.08194, loss_grounding_dice_4: 0.07052/0.15334, loss_grounding_ce_4: 0.02810/0.25833, loss_mask_ce_5: 1.15506/0.79724, loss_mask_bce_5: 0.28306/0.30766, loss_mask_dice_5: 0.69284/1.05077, loss_spatial_bce_5: 0.12645/0.09197, loss_spatial_dice_5: 0.25163/0.19540, loss_spatial_ce_5: 0.01024/0.09370, loss_grounding_bce_5: 0.03038/0.08219, loss_grounding_dice_5: 0.07136/0.15419, loss_grounding_ce_5: 0.02276/0.27600, loss_mask_ce_6: 1.12450/0.82452, loss_mask_bce_6: 0.31201/0.30983, loss_mask_dice_6: 0.67705/1.05471, loss_spatial_bce_6: 0.09639/0.09745, loss_spatial_dice_6: 0.19433/0.19774, loss_spatial_ce_6: 0.08056/0.11812, loss_grounding_bce_6: 0.03058/0.08302, loss_grounding_dice_6: 0.07279/0.15469, loss_grounding_ce_6: 0.01925/0.28489, loss_mask_ce_7: 1.59914/0.88049, loss_mask_bce_7: 0.25733/0.31704, loss_mask_dice_7: 0.69559/1.10048, loss_spatial_bce_7: 0.11193/0.10664, loss_spatial_dice_7: 0.22829/0.22272, loss_spatial_ce_7: 0.07355/0.15315, loss_grounding_bce_7: 0.03329/0.08473, loss_grounding_dice_7: 0.07240/0.16023, loss_grounding_ce_7: 0.07552/0.31820, loss_mask_ce_8: 1.65712/1.01435, loss_mask_bce_8: 0.26981/0.33303, loss_mask_dice_8: 0.73741/1.17709, loss_spatial_bce_8: 0.15710/0.12318, loss_spatial_dice_8: 0.32367/0.25740, loss_spatial_ce_8: 0.11733/0.19853, loss_grounding_bce_8: 0.02791/0.08889, loss_grounding_dice_8: 0.08222/0.17003, loss_grounding_ce_8: 0.61658/0.41621, loss_mask_ce_9: 2.44304/3.47448, loss_mask_bce_9: 0.26079/0.35979, loss_mask_dice_9: 0.76254/1.75957, loss_spatial_bce_9: 0.48930/0.35418, loss_spatial_dice_9: 0.72756/0.79309, loss_spatial_ce_9: 1.35441/1.38656, loss_grounding_bce_9: 0.02273/0.10104, loss_grounding_dice_9: 0.06017/0.24223, loss_grounding_ce_9: 2.85452/0.66885] items per batch[64] items per second[0.36] total items[4972800] mini batches[ 77700] memory[4999] epoch remaining[0:25:19] INFO:trainer.default_trainer:epochs[ 42] optim steps[77800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20773/0.75284, loss_mask_bce_0: 0.21397/0.30057, loss_mask_dice_0: 0.08825/1.02018, loss_spatial_bce_0: 0.19441/0.08442, loss_spatial_dice_0: 0.06521/0.17851, loss_spatial_ce_0: 0.00029/0.05544, loss_grounding_bce_0: 0.22118/0.08065, loss_grounding_dice_0: 0.09160/0.15042, loss_grounding_ce_0: 0.06664/0.24870, loss_mask_ce_1: 0.21532/0.75354, loss_mask_bce_1: 0.22688/0.30141, loss_mask_dice_1: 0.09597/1.02453, loss_spatial_bce_1: 0.17311/0.08487, loss_spatial_dice_1: 0.06119/0.18145, loss_spatial_ce_1: 0.00006/0.05914, loss_grounding_bce_1: 0.24942/0.08085, loss_grounding_dice_1: 0.10144/0.15116, loss_grounding_ce_1: 0.08073/0.25018, loss_mask_ce_2: 0.19914/0.76124, loss_mask_bce_2: 0.21466/0.30176, loss_mask_dice_2: 0.09041/1.02545, loss_spatial_bce_2: 0.19867/0.08498, loss_spatial_dice_2: 0.07750/0.18207, loss_spatial_ce_2: 0.00033/0.06138, loss_grounding_bce_2: 0.23417/0.08081, loss_grounding_dice_2: 0.09619/0.15104, loss_grounding_ce_2: 0.07604/0.25281, loss_mask_ce_3: 0.22766/0.76589, loss_mask_bce_3: 0.20923/0.30310, loss_mask_dice_3: 0.09020/1.02333, loss_spatial_bce_3: 0.23560/0.08712, loss_spatial_dice_3: 0.09222/0.18344, loss_spatial_ce_3: 0.00045/0.06627, loss_grounding_bce_3: 0.21576/0.08117, loss_grounding_dice_3: 0.09169/0.15073, loss_grounding_ce_3: 0.07042/0.25409, loss_mask_ce_4: 0.24215/0.77167, loss_mask_bce_4: 0.22306/0.30578, loss_mask_dice_4: 0.09375/1.04282, loss_spatial_bce_4: 0.23469/0.08954, loss_spatial_dice_4: 0.09399/0.19208, loss_spatial_ce_4: 0.00308/0.08004, loss_grounding_bce_4: 0.23161/0.08194, loss_grounding_dice_4: 0.09613/0.15335, loss_grounding_ce_4: 0.08414/0.25832, loss_mask_ce_5: 0.22042/0.79721, loss_mask_bce_5: 0.23108/0.30766, loss_mask_dice_5: 0.09222/1.05085, loss_spatial_bce_5: 0.24946/0.09197, loss_spatial_dice_5: 0.10318/0.19541, loss_spatial_ce_5: 0.00090/0.09368, loss_grounding_bce_5: 0.24583/0.08220, loss_grounding_dice_5: 0.09617/0.15420, loss_grounding_ce_5: 0.08281/0.27598, loss_mask_ce_6: 0.30541/0.82453, loss_mask_bce_6: 0.26702/0.30984, loss_mask_dice_6: 0.10352/1.05480, loss_spatial_bce_6: 0.27668/0.09744, loss_spatial_dice_6: 0.11572/0.19775, loss_spatial_ce_6: 0.00157/0.11811, loss_grounding_bce_6: 0.27811/0.08302, loss_grounding_dice_6: 0.10823/0.15469, loss_grounding_ce_6: 0.14099/0.28489, loss_mask_ce_7: 0.43904/0.88048, loss_mask_bce_7: 0.30448/0.31706, loss_mask_dice_7: 0.11365/1.10060, loss_spatial_bce_7: 0.33562/0.10664, loss_spatial_dice_7: 0.13356/0.22272, loss_spatial_ce_7: 0.00353/0.15311, loss_grounding_bce_7: 0.17089/0.08473, loss_grounding_dice_7: 0.07110/0.16023, loss_grounding_ce_7: 0.91965/0.31821, loss_mask_ce_8: 0.12901/1.01432, loss_mask_bce_8: 0.34105/0.33305, loss_mask_dice_8: 0.11904/1.17723, loss_spatial_bce_8: 0.71810/0.12317, loss_spatial_dice_8: 0.22927/0.25740, loss_spatial_ce_8: 0.00644/0.19851, loss_grounding_bce_8: 0.34004/0.08889, loss_grounding_dice_8: 0.11996/0.17004, loss_grounding_ce_8: 0.01945/0.41621, loss_mask_ce_9: 2.43219/3.47444, loss_mask_bce_9: 0.28318/0.35978, loss_mask_dice_9: 0.14677/1.75975, loss_spatial_bce_9: 0.66812/0.35415, loss_spatial_dice_9: 0.57269/0.79308, loss_spatial_ce_9: 1.14554/1.38659, loss_grounding_bce_9: 0.33433/0.10103, loss_grounding_dice_9: 0.14415/0.24223, loss_grounding_ce_9: 0.13363/0.66886] items per batch[64] items per second[0.36] total items[4979200] mini batches[ 77800] memory[4999] epoch remaining[0:22:22] INFO:trainer.default_trainer:epochs[ 42] optim steps[77900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.10356/0.75286, loss_mask_bce_0: 0.10305/0.30055, loss_mask_dice_0: 1.78949/1.02010, loss_spatial_bce_0: 0.01785/0.08441, loss_spatial_dice_0: 0.32525/0.17850, loss_spatial_ce_0: 0.04755/0.05542, loss_grounding_bce_0: 0.00258/0.08064, loss_grounding_dice_0: 0.08854/0.15040, loss_grounding_ce_0: 0.07763/0.24862, loss_mask_ce_1: 1.03662/0.75350, loss_mask_bce_1: 0.10192/0.30139, loss_mask_dice_1: 1.66644/1.02445, loss_spatial_bce_1: 0.01970/0.08487, loss_spatial_dice_1: 0.32408/0.18144, loss_spatial_ce_1: 0.05096/0.05912, loss_grounding_bce_1: 0.00249/0.08083, loss_grounding_dice_1: 0.18023/0.15115, loss_grounding_ce_1: 0.07501/0.25010, loss_mask_ce_2: 0.91948/0.76120, loss_mask_bce_2: 0.10986/0.30175, loss_mask_dice_2: 1.36504/1.02538, loss_spatial_bce_2: 0.01820/0.08498, loss_spatial_dice_2: 0.32667/0.18206, loss_spatial_ce_2: 0.09461/0.06135, loss_grounding_bce_2: 0.00284/0.08080, loss_grounding_dice_2: 0.09397/0.15104, loss_grounding_ce_2: 0.05602/0.25276, loss_mask_ce_3: 0.90548/0.76588, loss_mask_bce_3: 0.11231/0.30308, loss_mask_dice_3: 1.83716/1.02327, loss_spatial_bce_3: 0.01884/0.08711, loss_spatial_dice_3: 0.32933/0.18342, loss_spatial_ce_3: 0.09536/0.06625, loss_grounding_bce_3: 0.00294/0.08116, loss_grounding_dice_3: 0.14710/0.15072, loss_grounding_ce_3: 0.03410/0.25401, loss_mask_ce_4: 1.03080/0.77171, loss_mask_bce_4: 0.11461/0.30577, loss_mask_dice_4: 1.55720/1.04274, loss_spatial_bce_4: 0.02274/0.08954, loss_spatial_dice_4: 0.35170/0.19207, loss_spatial_ce_4: 0.10392/0.08005, loss_grounding_bce_4: 0.00204/0.08193, loss_grounding_dice_4: 0.08251/0.15334, loss_grounding_ce_4: 0.05209/0.25830, loss_mask_ce_5: 0.97690/0.79720, loss_mask_bce_5: 0.12040/0.30766, loss_mask_dice_5: 1.42276/1.05076, loss_spatial_bce_5: 0.01962/0.09196, loss_spatial_dice_5: 0.37047/0.19540, loss_spatial_ce_5: 0.08782/0.09368, loss_grounding_bce_5: 0.00226/0.08219, loss_grounding_dice_5: 0.09480/0.15419, loss_grounding_ce_5: 0.03509/0.27591, loss_mask_ce_6: 1.48057/0.82452, loss_mask_bce_6: 0.12991/0.30984, loss_mask_dice_6: 1.58760/1.05474, loss_spatial_bce_6: 0.02656/0.09744, loss_spatial_dice_6: 0.40381/0.19774, loss_spatial_ce_6: 0.11681/0.11814, loss_grounding_bce_6: 0.00177/0.08301, loss_grounding_dice_6: 0.08499/0.15468, loss_grounding_ce_6: 0.03606/0.28486, loss_mask_ce_7: 1.23001/0.88045, loss_mask_bce_7: 0.12477/0.31707, loss_mask_dice_7: 2.16030/1.10053, loss_spatial_bce_7: 0.04044/0.10662, loss_spatial_dice_7: 0.39580/0.22270, loss_spatial_ce_7: 0.15629/0.15311, loss_grounding_bce_7: 0.00470/0.08472, loss_grounding_dice_7: 0.17479/0.16022, loss_grounding_ce_7: 0.26907/0.31814, loss_mask_ce_8: 1.47617/1.01427, loss_mask_bce_8: 0.11121/0.33303, loss_mask_dice_8: 1.66234/1.17716, loss_spatial_bce_8: 0.05485/0.12315, loss_spatial_dice_8: 0.42296/0.25738, loss_spatial_ce_8: 0.16994/0.19849, loss_grounding_bce_8: 0.00411/0.08888, loss_grounding_dice_8: 0.17750/0.17003, loss_grounding_ce_8: 0.26620/0.41605, loss_mask_ce_9: 4.38368/3.47430, loss_mask_bce_9: 0.10400/0.35976, loss_mask_dice_9: 2.47080/1.75967, loss_spatial_bce_9: 0.07615/0.35416, loss_spatial_dice_9: 0.93330/0.79307, loss_spatial_ce_9: 1.51429/1.38652, loss_grounding_bce_9: 0.00658/0.10102, loss_grounding_dice_9: 0.53252/0.24221, loss_grounding_ce_9: 0.41347/0.66869] items per batch[64] items per second[0.36] total items[4985600] mini batches[ 77900] memory[4999] epoch remaining[0:19:26] INFO:trainer.default_trainer:epochs[ 42] optim steps[78000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.53399/0.75282, loss_mask_bce_0: 0.61575/0.30055, loss_mask_dice_0: 1.77053/1.02007, loss_spatial_bce_0: 0.08639/0.08439, loss_spatial_dice_0: 0.23224/0.17848, loss_spatial_ce_0: 0.00837/0.05541, loss_grounding_bce_0: 0.04664/0.08062, loss_grounding_dice_0: 0.15911/0.15040, loss_grounding_ce_0: 0.48057/0.24868, loss_mask_ce_1: 1.44736/0.75346, loss_mask_bce_1: 0.61886/0.30139, loss_mask_dice_1: 1.56122/1.02442, loss_spatial_bce_1: 0.07767/0.08485, loss_spatial_dice_1: 0.23728/0.18142, loss_spatial_ce_1: 0.01151/0.05910, loss_grounding_bce_1: 0.04893/0.08082, loss_grounding_dice_1: 0.20652/0.15115, loss_grounding_ce_1: 0.49030/0.25014, loss_mask_ce_2: 1.55380/0.76120, loss_mask_bce_2: 0.62486/0.30175, loss_mask_dice_2: 1.61895/1.02535, loss_spatial_bce_2: 0.07706/0.08496, loss_spatial_dice_2: 0.21187/0.18205, loss_spatial_ce_2: 0.01241/0.06134, loss_grounding_bce_2: 0.05033/0.08078, loss_grounding_dice_2: 0.20557/0.15104, loss_grounding_ce_2: 0.50858/0.25283, loss_mask_ce_3: 1.52619/0.76588, loss_mask_bce_3: 0.62963/0.30307, loss_mask_dice_3: 1.48433/1.02323, loss_spatial_bce_3: 0.08789/0.08710, loss_spatial_dice_3: 0.21356/0.18341, loss_spatial_ce_3: 0.01612/0.06623, loss_grounding_bce_3: 0.04623/0.08114, loss_grounding_dice_3: 0.17075/0.15071, loss_grounding_ce_3: 0.47304/0.25404, loss_mask_ce_4: 1.67799/0.77169, loss_mask_bce_4: 0.62596/0.30577, loss_mask_dice_4: 1.59559/1.04271, loss_spatial_bce_4: 0.09408/0.08953, loss_spatial_dice_4: 0.24161/0.19207, loss_spatial_ce_4: 0.05696/0.08002, loss_grounding_bce_4: 0.04959/0.08191, loss_grounding_dice_4: 0.15702/0.15334, loss_grounding_ce_4: 0.47124/0.25835, loss_mask_ce_5: 1.81529/0.79719, loss_mask_bce_5: 0.63659/0.30766, loss_mask_dice_5: 1.55870/1.05074, loss_spatial_bce_5: 0.08838/0.09195, loss_spatial_dice_5: 0.22518/0.19540, loss_spatial_ce_5: 0.05946/0.09367, loss_grounding_bce_5: 0.05047/0.08217, loss_grounding_dice_5: 0.16246/0.15419, loss_grounding_ce_5: 0.43502/0.27595, loss_mask_ce_6: 1.68465/0.82452, loss_mask_bce_6: 0.63593/0.30985, loss_mask_dice_6: 1.72389/1.05473, loss_spatial_bce_6: 0.11422/0.09743, loss_spatial_dice_6: 0.24048/0.19774, loss_spatial_ce_6: 0.09003/0.11812, loss_grounding_bce_6: 0.05289/0.08300, loss_grounding_dice_6: 0.16752/0.15468, loss_grounding_ce_6: 0.45251/0.28487, loss_mask_ce_7: 1.57843/0.88044, loss_mask_bce_7: 0.68375/0.31707, loss_mask_dice_7: 1.70223/1.10051, loss_spatial_bce_7: 0.10507/0.10660, loss_spatial_dice_7: 0.24247/0.22268, loss_spatial_ce_7: 0.02584/0.15306, loss_grounding_bce_7: 0.06118/0.08470, loss_grounding_dice_7: 0.17379/0.16021, loss_grounding_ce_7: 0.45451/0.31817, loss_mask_ce_8: 1.65556/1.01429, loss_mask_bce_8: 0.68820/0.33306, loss_mask_dice_8: 1.90078/1.17719, loss_spatial_bce_8: 0.09281/0.12311, loss_spatial_dice_8: 0.26680/0.25736, loss_spatial_ce_8: 0.07664/0.19844, loss_grounding_bce_8: 0.05911/0.08885, loss_grounding_dice_8: 0.21045/0.17003, loss_grounding_ce_8: 0.51734/0.41608, loss_mask_ce_9: 4.42399/3.47447, loss_mask_bce_9: 0.99202/0.35978, loss_mask_dice_9: 3.40885/1.75979, loss_spatial_bce_9: 0.22337/0.35409, loss_spatial_dice_9: 0.94636/0.79307, loss_spatial_ce_9: 1.01615/1.38650, loss_grounding_bce_9: 0.08200/0.10100, loss_grounding_dice_9: 0.26569/0.24221, loss_grounding_ce_9: 0.52209/0.66869] items per batch[64] items per second[0.37] total items[4992000] mini batches[ 78000] memory[4999] epoch remaining[0:16:28] INFO:trainer.default_trainer:epochs[ 42] optim steps[78100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15527/0.75269, loss_mask_bce_0: 0.07451/0.30053, loss_mask_dice_0: 0.44879/1.02027, loss_spatial_bce_0: 0.03624/0.08438, loss_spatial_dice_0: 0.25233/0.17846, loss_spatial_ce_0: 0.00060/0.05539, loss_grounding_bce_0: 0.03157/0.08060, loss_grounding_dice_0: 0.21343/0.15038, loss_grounding_ce_0: 0.32601/0.24862, loss_mask_ce_1: 0.13564/0.75335, loss_mask_bce_1: 0.08130/0.30136, loss_mask_dice_1: 0.45622/1.02461, loss_spatial_bce_1: 0.03915/0.08483, loss_spatial_dice_1: 0.23627/0.18141, loss_spatial_ce_1: 0.00081/0.05908, loss_grounding_bce_1: 0.03363/0.08080, loss_grounding_dice_1: 0.22266/0.15113, loss_grounding_ce_1: 0.34478/0.25006, loss_mask_ce_2: 1.05742/0.76108, loss_mask_bce_2: 0.06480/0.30173, loss_mask_dice_2: 0.37989/1.02551, loss_spatial_bce_2: 0.03903/0.08494, loss_spatial_dice_2: 0.23509/0.18203, loss_spatial_ce_2: 0.00132/0.06133, loss_grounding_bce_2: 0.03098/0.08077, loss_grounding_dice_2: 0.19602/0.15102, loss_grounding_ce_2: 0.34304/0.25275, loss_mask_ce_3: 0.78555/0.76578, loss_mask_bce_3: 0.05865/0.30305, loss_mask_dice_3: 0.35267/1.02341, loss_spatial_bce_3: 0.04075/0.08708, loss_spatial_dice_3: 0.25118/0.18340, loss_spatial_ce_3: 0.01759/0.06621, loss_grounding_bce_3: 0.03090/0.08113, loss_grounding_dice_3: 0.26997/0.15069, loss_grounding_ce_3: 0.23927/0.25397, loss_mask_ce_4: 0.16542/0.77157, loss_mask_bce_4: 0.08266/0.30575, loss_mask_dice_4: 0.46379/1.04288, loss_spatial_bce_4: 0.03872/0.08952, loss_spatial_dice_4: 0.23507/0.19205, loss_spatial_ce_4: 0.02099/0.08001, loss_grounding_bce_4: 0.02923/0.08190, loss_grounding_dice_4: 0.19354/0.15332, loss_grounding_ce_4: 0.39055/0.25831, loss_mask_ce_5: 0.17992/0.79708, loss_mask_bce_5: 0.08312/0.30764, loss_mask_dice_5: 0.48231/1.05093, loss_spatial_bce_5: 0.03531/0.09194, loss_spatial_dice_5: 0.21112/0.19539, loss_spatial_ce_5: 0.00137/0.09366, loss_grounding_bce_5: 0.03155/0.08216, loss_grounding_dice_5: 0.21640/0.15417, loss_grounding_ce_5: 0.37624/0.27595, loss_mask_ce_6: 0.16955/0.82438, loss_mask_bce_6: 0.07398/0.30982, loss_mask_dice_6: 0.45914/1.05491, loss_spatial_bce_6: 0.05466/0.09741, loss_spatial_dice_6: 0.24854/0.19772, loss_spatial_ce_6: 0.05294/0.11812, loss_grounding_bce_6: 0.03264/0.08298, loss_grounding_dice_6: 0.19616/0.15465, loss_grounding_ce_6: 0.31061/0.28483, loss_mask_ce_7: 0.23406/0.88033, loss_mask_bce_7: 0.07690/0.31705, loss_mask_dice_7: 0.43954/1.10073, loss_spatial_bce_7: 0.04457/0.10658, loss_spatial_dice_7: 0.25611/0.22267, loss_spatial_ce_7: 0.00796/0.15303, loss_grounding_bce_7: 0.03516/0.08469, loss_grounding_dice_7: 0.25006/0.16019, loss_grounding_ce_7: 0.35442/0.31812, loss_mask_ce_8: 0.27904/1.01415, loss_mask_bce_8: 0.09691/0.33303, loss_mask_dice_8: 0.49200/1.17737, loss_spatial_bce_8: 0.06873/0.12308, loss_spatial_dice_8: 0.28109/0.25734, loss_spatial_ce_8: 0.38024/0.19841, loss_grounding_bce_8: 0.03472/0.08884, loss_grounding_dice_8: 0.23714/0.17000, loss_grounding_ce_8: 0.44471/0.41599, loss_mask_ce_9: 2.80051/3.47436, loss_mask_bce_9: 0.05496/0.35975, loss_mask_dice_9: 0.53703/1.76006, loss_spatial_bce_9: 0.34494/0.35406, loss_spatial_dice_9: 0.70361/0.79306, loss_spatial_ce_9: 1.01761/1.38647, loss_grounding_bce_9: 0.02974/0.10098, loss_grounding_dice_9: 0.26714/0.24219, loss_grounding_ce_9: 0.56732/0.66863] items per batch[64] items per second[0.37] total items[4998400] mini batches[ 78100] memory[4999] epoch remaining[0:13:32] INFO:trainer.default_trainer:epochs[ 42] optim steps[78200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00324/0.75280, loss_mask_bce_0: 0.05360/0.30060, loss_mask_dice_0: 0.09854/1.02025, loss_spatial_bce_0: 0.02982/0.08438, loss_spatial_dice_0: 0.06648/0.17846, loss_spatial_ce_0: 0.00001/0.05536, loss_grounding_bce_0: 0.03050/0.08059, loss_grounding_dice_0: 0.04424/0.15038, loss_grounding_ce_0: 0.00080/0.24860, loss_mask_ce_1: 0.00419/0.75346, loss_mask_bce_1: 0.05477/0.30144, loss_mask_dice_1: 0.10470/1.02459, loss_spatial_bce_1: 0.02806/0.08484, loss_spatial_dice_1: 0.04971/0.18141, loss_spatial_ce_1: 0.00000/0.05906, loss_grounding_bce_1: 0.03082/0.08079, loss_grounding_dice_1: 0.05637/0.15113, loss_grounding_ce_1: 0.00101/0.25004, loss_mask_ce_2: 0.00369/0.76118, loss_mask_bce_2: 0.05265/0.30181, loss_mask_dice_2: 0.09006/1.02552, loss_spatial_bce_2: 0.02993/0.08495, loss_spatial_dice_2: 0.05553/0.18203, loss_spatial_ce_2: 0.00000/0.06130, loss_grounding_bce_2: 0.02970/0.08075, loss_grounding_dice_2: 0.05017/0.15102, loss_grounding_ce_2: 0.00099/0.25276, loss_mask_ce_3: 0.00312/0.76588, loss_mask_bce_3: 0.05183/0.30312, loss_mask_dice_3: 0.10703/1.02340, loss_spatial_bce_3: 0.03208/0.08708, loss_spatial_dice_3: 0.05262/0.18340, loss_spatial_ce_3: 0.00001/0.06618, loss_grounding_bce_3: 0.03154/0.08112, loss_grounding_dice_3: 0.05300/0.15070, loss_grounding_ce_3: 0.00180/0.25394, loss_mask_ce_4: 0.00337/0.77168, loss_mask_bce_4: 0.05454/0.30583, loss_mask_dice_4: 0.09465/1.04289, loss_spatial_bce_4: 0.02724/0.08953, loss_spatial_dice_4: 0.04465/0.19206, loss_spatial_ce_4: 0.00001/0.08001, loss_grounding_bce_4: 0.03263/0.08189, loss_grounding_dice_4: 0.05901/0.15332, loss_grounding_ce_4: 0.00269/0.25828, loss_mask_ce_5: 0.00321/0.79718, loss_mask_bce_5: 0.05404/0.30771, loss_mask_dice_5: 0.09654/1.05091, loss_spatial_bce_5: 0.02835/0.09194, loss_spatial_dice_5: 0.05603/0.19540, loss_spatial_ce_5: 0.00027/0.09364, loss_grounding_bce_5: 0.03161/0.08215, loss_grounding_dice_5: 0.05421/0.15419, loss_grounding_ce_5: 0.00206/0.27592, loss_mask_ce_6: 0.00547/0.82449, loss_mask_bce_6: 0.05695/0.30989, loss_mask_dice_6: 0.11988/1.05491, loss_spatial_bce_6: 0.03067/0.09742, loss_spatial_dice_6: 0.05189/0.19773, loss_spatial_ce_6: 0.01694/0.11810, loss_grounding_bce_6: 0.02977/0.08297, loss_grounding_dice_6: 0.06352/0.15465, loss_grounding_ce_6: 0.00095/0.28489, loss_mask_ce_7: 0.00671/0.88040, loss_mask_bce_7: 0.05325/0.31714, loss_mask_dice_7: 0.10265/1.10073, loss_spatial_bce_7: 0.03038/0.10658, loss_spatial_dice_7: 0.05670/0.22267, loss_spatial_ce_7: 0.08305/0.15298, loss_grounding_bce_7: 0.03126/0.08467, loss_grounding_dice_7: 0.04791/0.16019, loss_grounding_ce_7: 0.00115/0.31809, loss_mask_ce_8: 0.00746/1.01429, loss_mask_bce_8: 0.05748/0.33311, loss_mask_dice_8: 0.11295/1.17736, loss_spatial_bce_8: 0.03530/0.12307, loss_spatial_dice_8: 0.07205/0.25733, loss_spatial_ce_8: 0.00146/0.19836, loss_grounding_bce_8: 0.02947/0.08882, loss_grounding_dice_8: 0.05054/0.17000, loss_grounding_ce_8: 0.00116/0.41605, loss_mask_ce_9: 1.47093/3.47458, loss_mask_bce_9: 0.06437/0.35981, loss_mask_dice_9: 0.15099/1.76019, loss_spatial_bce_9: 0.38499/0.35405, loss_spatial_dice_9: 0.64630/0.79310, loss_spatial_ce_9: 1.07613/1.38656, loss_grounding_bce_9: 0.03524/0.10097, loss_grounding_dice_9: 0.07268/0.24220, loss_grounding_ce_9: 0.10309/0.66876] items per batch[64] items per second[0.37] total items[5004800] mini batches[ 78200] memory[4999] epoch remaining[0:10:34] INFO:trainer.default_trainer:epochs[ 42] optim steps[78300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.06467/0.75280, loss_mask_bce_0: 0.17369/0.30059, loss_mask_dice_0: 0.32369/1.02030, loss_spatial_bce_0: 0.10053/0.08438, loss_spatial_dice_0: 0.19722/0.17846, loss_spatial_ce_0: 0.11774/0.05535, loss_grounding_bce_0: 0.13937/0.08059, loss_grounding_dice_0: 0.05960/0.15037, loss_grounding_ce_0: 0.01412/0.24870, loss_mask_ce_1: 2.31868/0.75342, loss_mask_bce_1: 0.15957/0.30142, loss_mask_dice_1: 0.29437/1.02463, loss_spatial_bce_1: 0.09717/0.08484, loss_spatial_dice_1: 0.19987/0.18141, loss_spatial_ce_1: 0.12018/0.05906, loss_grounding_bce_1: 0.14698/0.08079, loss_grounding_dice_1: 0.06262/0.15113, loss_grounding_ce_1: 0.01985/0.25014, loss_mask_ce_2: 2.40253/0.76118, loss_mask_bce_2: 0.16795/0.30179, loss_mask_dice_2: 0.30372/1.02556, loss_spatial_bce_2: 0.11307/0.08495, loss_spatial_dice_2: 0.22119/0.18203, loss_spatial_ce_2: 0.11401/0.06130, loss_grounding_bce_2: 0.13365/0.08075, loss_grounding_dice_2: 0.05940/0.15102, loss_grounding_ce_2: 0.01674/0.25288, loss_mask_ce_3: 2.11888/0.76587, loss_mask_bce_3: 0.17124/0.30310, loss_mask_dice_3: 0.32769/1.02344, loss_spatial_bce_3: 0.10658/0.08708, loss_spatial_dice_3: 0.21442/0.18339, loss_spatial_ce_3: 0.12562/0.06618, loss_grounding_bce_3: 0.13389/0.08112, loss_grounding_dice_3: 0.05797/0.15070, loss_grounding_ce_3: 0.01575/0.25404, loss_mask_ce_4: 1.98484/0.77167, loss_mask_bce_4: 0.15979/0.30582, loss_mask_dice_4: 0.31850/1.04291, loss_spatial_bce_4: 0.10631/0.08952, loss_spatial_dice_4: 0.21496/0.19206, loss_spatial_ce_4: 0.13835/0.07999, loss_grounding_bce_4: 0.12521/0.08189, loss_grounding_dice_4: 0.05354/0.15332, loss_grounding_ce_4: 0.01541/0.25836, loss_mask_ce_5: 2.41367/0.79719, loss_mask_bce_5: 0.17050/0.30771, loss_mask_dice_5: 0.37842/1.05092, loss_spatial_bce_5: 0.11054/0.09194, loss_spatial_dice_5: 0.22204/0.19539, loss_spatial_ce_5: 0.15022/0.09363, loss_grounding_bce_5: 0.13382/0.08215, loss_grounding_dice_5: 0.05018/0.15418, loss_grounding_ce_5: 0.03133/0.27606, loss_mask_ce_6: 3.04334/0.82448, loss_mask_bce_6: 0.18424/0.30990, loss_mask_dice_6: 0.35919/1.05495, loss_spatial_bce_6: 0.11705/0.09742, loss_spatial_dice_6: 0.21125/0.19772, loss_spatial_ce_6: 0.13895/0.11810, loss_grounding_bce_6: 0.12820/0.08297, loss_grounding_dice_6: 0.05246/0.15465, loss_grounding_ce_6: 0.04197/0.28501, loss_mask_ce_7: 2.74207/0.88038, loss_mask_bce_7: 0.14852/0.31713, loss_mask_dice_7: 0.36767/1.10080, loss_spatial_bce_7: 0.11111/0.10658, loss_spatial_dice_7: 0.22911/0.22267, loss_spatial_ce_7: 0.14032/0.15294, loss_grounding_bce_7: 0.12564/0.08467, loss_grounding_dice_7: 0.05073/0.16020, loss_grounding_ce_7: 0.03100/0.31822, loss_mask_ce_8: 2.88529/1.01424, loss_mask_bce_8: 0.18372/0.33311, loss_mask_dice_8: 0.34088/1.17739, loss_spatial_bce_8: 0.13254/0.12306, loss_spatial_dice_8: 0.27272/0.25732, loss_spatial_ce_8: 0.28332/0.19832, loss_grounding_bce_8: 0.15292/0.08884, loss_grounding_dice_8: 0.06362/0.17002, loss_grounding_ce_8: 0.08291/0.41618, loss_mask_ce_9: 5.54740/3.47482, loss_mask_bce_9: 0.20673/0.35982, loss_mask_dice_9: 0.49543/1.76021, loss_spatial_bce_9: 0.36978/0.35405, loss_spatial_dice_9: 0.61532/0.79308, loss_spatial_ce_9: 1.50655/1.38654, loss_grounding_bce_9: 0.14027/0.10098, loss_grounding_dice_9: 0.06766/0.24221, loss_grounding_ce_9: 0.49049/0.66888] items per batch[64] items per second[0.37] total items[5011200] mini batches[ 78300] memory[4999] epoch remaining[0:07:38] INFO:trainer.default_trainer:epochs[ 42] optim steps[78400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37100/0.75274, loss_mask_bce_0: 0.43630/0.30059, loss_mask_dice_0: 1.33684/1.02054, loss_spatial_bce_0: 0.09433/0.08436, loss_spatial_dice_0: 0.26251/0.17846, loss_spatial_ce_0: 0.08485/0.05535, loss_grounding_bce_0: 0.05465/0.08059, loss_grounding_dice_0: 0.28871/0.15037, loss_grounding_ce_0: 0.20488/0.24869, loss_mask_ce_1: 0.38325/0.75339, loss_mask_bce_1: 0.44764/0.30142, loss_mask_dice_1: 1.28770/1.02485, loss_spatial_bce_1: 0.09934/0.08482, loss_spatial_dice_1: 0.25810/0.18141, loss_spatial_ce_1: 0.16078/0.05907, loss_grounding_bce_1: 0.05977/0.08079, loss_grounding_dice_1: 0.27230/0.15113, loss_grounding_ce_1: 0.31596/0.25013, loss_mask_ce_2: 0.39851/0.76114, loss_mask_bce_2: 0.45650/0.30179, loss_mask_dice_2: 1.31054/1.02576, loss_spatial_bce_2: 0.10269/0.08493, loss_spatial_dice_2: 0.28030/0.18202, loss_spatial_ce_2: 0.17329/0.06130, loss_grounding_bce_2: 0.05992/0.08075, loss_grounding_dice_2: 0.27020/0.15103, loss_grounding_ce_2: 0.21958/0.25288, loss_mask_ce_3: 0.40101/0.76583, loss_mask_bce_3: 0.47297/0.30310, loss_mask_dice_3: 1.26626/1.02367, loss_spatial_bce_3: 0.12057/0.08707, loss_spatial_dice_3: 0.27960/0.18339, loss_spatial_ce_3: 0.04030/0.06619, loss_grounding_bce_3: 0.05625/0.08112, loss_grounding_dice_3: 0.27474/0.15071, loss_grounding_ce_3: 0.21163/0.25403, loss_mask_ce_4: 0.37732/0.77161, loss_mask_bce_4: 0.42925/0.30582, loss_mask_dice_4: 1.22431/1.04314, loss_spatial_bce_4: 0.12019/0.08951, loss_spatial_dice_4: 0.28043/0.19207, loss_spatial_ce_4: 0.05137/0.07996, loss_grounding_bce_4: 0.05432/0.08189, loss_grounding_dice_4: 0.26514/0.15332, loss_grounding_ce_4: 0.21101/0.25836, loss_mask_ce_5: 0.37945/0.79716, loss_mask_bce_5: 0.41636/0.30770, loss_mask_dice_5: 1.25408/1.05113, loss_spatial_bce_5: 0.15123/0.09194, loss_spatial_dice_5: 0.30472/0.19540, loss_spatial_ce_5: 0.05225/0.09361, loss_grounding_bce_5: 0.05561/0.08214, loss_grounding_dice_5: 0.27598/0.15419, loss_grounding_ce_5: 0.21253/0.27607, loss_mask_ce_6: 0.46266/0.82443, loss_mask_bce_6: 0.42825/0.30989, loss_mask_dice_6: 1.15266/1.05514, loss_spatial_bce_6: 0.14828/0.09741, loss_spatial_dice_6: 0.30071/0.19773, loss_spatial_ce_6: 0.04994/0.11807, loss_grounding_bce_6: 0.05643/0.08297, loss_grounding_dice_6: 0.29787/0.15466, loss_grounding_ce_6: 0.32105/0.28502, loss_mask_ce_7: 0.34541/0.88034, loss_mask_bce_7: 0.76446/0.31712, loss_mask_dice_7: 1.55501/1.10100, loss_spatial_bce_7: 0.08945/0.10657, loss_spatial_dice_7: 0.24286/0.22268, loss_spatial_ce_7: 0.09187/0.15294, loss_grounding_bce_7: 0.05515/0.08466, loss_grounding_dice_7: 0.28418/0.16020, loss_grounding_ce_7: 0.31490/0.31825, loss_mask_ce_8: 1.23926/1.01417, loss_mask_bce_8: 0.81040/0.33310, loss_mask_dice_8: 1.60428/1.17761, loss_spatial_bce_8: 0.11312/0.12304, loss_spatial_dice_8: 0.27689/0.25731, loss_spatial_ce_8: 0.34366/0.19829, loss_grounding_bce_8: 0.06017/0.08883, loss_grounding_dice_8: 0.29899/0.17001, loss_grounding_ce_8: 0.30292/0.41609, loss_mask_ce_9: 2.29352/3.47461, loss_mask_bce_9: 0.52261/0.35980, loss_mask_dice_9: 1.63055/1.76037, loss_spatial_bce_9: 0.51971/0.35401, loss_spatial_dice_9: 0.91345/0.79309, loss_spatial_ce_9: 1.47142/1.38649, loss_grounding_bce_9: 0.04560/0.10097, loss_grounding_dice_9: 0.30960/0.24221, loss_grounding_ce_9: 0.30430/0.66876] items per batch[64] items per second[0.37] total items[5017600] mini batches[ 78400] memory[4999] epoch remaining[0:04:42] INFO:trainer.default_trainer:epochs[ 42] optim steps[78500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.94078/0.75255, loss_mask_bce_0: 0.34086/0.30060, loss_mask_dice_0: 0.22858/1.02012, loss_spatial_bce_0: 0.15777/0.08438, loss_spatial_dice_0: 0.11128/0.17844, loss_spatial_ce_0: 0.00336/0.05534, loss_grounding_bce_0: 0.20430/0.08061, loss_grounding_dice_0: 0.17347/0.15038, loss_grounding_ce_0: 0.00167/0.24858, loss_mask_ce_1: 0.82256/0.75320, loss_mask_bce_1: 0.36107/0.30143, loss_mask_dice_1: 0.24443/1.02441, loss_spatial_bce_1: 0.15660/0.08484, loss_spatial_dice_1: 0.11328/0.18139, loss_spatial_ce_1: 0.00210/0.05905, loss_grounding_bce_1: 0.20533/0.08081, loss_grounding_dice_1: 0.17437/0.15115, loss_grounding_ce_1: 0.00202/0.25002, loss_mask_ce_2: 0.73966/0.76094, loss_mask_bce_2: 0.34901/0.30179, loss_mask_dice_2: 0.24769/1.02535, loss_spatial_bce_2: 0.17577/0.08495, loss_spatial_dice_2: 0.13008/0.18201, loss_spatial_ce_2: 0.00274/0.06129, loss_grounding_bce_2: 0.20410/0.08077, loss_grounding_dice_2: 0.18179/0.15103, loss_grounding_ce_2: 0.00163/0.25277, loss_mask_ce_3: 0.90209/0.76567, loss_mask_bce_3: 0.33568/0.30309, loss_mask_dice_3: 0.22966/1.02324, loss_spatial_bce_3: 0.24844/0.08709, loss_spatial_dice_3: 0.16502/0.18337, loss_spatial_ce_3: 0.01647/0.06619, loss_grounding_bce_3: 0.20311/0.08113, loss_grounding_dice_3: 0.17810/0.15071, loss_grounding_ce_3: 0.00090/0.25393, loss_mask_ce_4: 0.82247/0.77142, loss_mask_bce_4: 0.33496/0.30582, loss_mask_dice_4: 0.22870/1.04271, loss_spatial_bce_4: 0.21749/0.08953, loss_spatial_dice_4: 0.15906/0.19205, loss_spatial_ce_4: 0.02021/0.07993, loss_grounding_bce_4: 0.20142/0.08192, loss_grounding_dice_4: 0.17496/0.15333, loss_grounding_ce_4: 0.00236/0.25827, loss_mask_ce_5: 0.91109/0.79696, loss_mask_bce_5: 0.35730/0.30770, loss_mask_dice_5: 0.24536/1.05068, loss_spatial_bce_5: 0.21649/0.09195, loss_spatial_dice_5: 0.15303/0.19538, loss_spatial_ce_5: 0.04484/0.09361, loss_grounding_bce_5: 0.21120/0.08218, loss_grounding_dice_5: 0.18219/0.15420, loss_grounding_ce_5: 0.00320/0.27593, loss_mask_ce_6: 1.19617/0.82429, loss_mask_bce_6: 0.34813/0.30988, loss_mask_dice_6: 0.23625/1.05470, loss_spatial_bce_6: 0.16107/0.09743, loss_spatial_dice_6: 0.13152/0.19771, loss_spatial_ce_6: 0.01153/0.11807, loss_grounding_bce_6: 0.21291/0.08299, loss_grounding_dice_6: 0.17563/0.15467, loss_grounding_ce_6: 0.00146/0.28492, loss_mask_ce_7: 1.01156/0.88012, loss_mask_bce_7: 0.34391/0.31712, loss_mask_dice_7: 0.22958/1.10053, loss_spatial_bce_7: 0.23246/0.10660, loss_spatial_dice_7: 0.15012/0.22266, loss_spatial_ce_7: 0.00644/0.15290, loss_grounding_bce_7: 0.22624/0.08468, loss_grounding_dice_7: 0.16400/0.16021, loss_grounding_ce_7: 0.09218/0.31817, loss_mask_ce_8: 1.02186/1.01395, loss_mask_bce_8: 0.37369/0.33309, loss_mask_dice_8: 0.23602/1.17708, loss_spatial_bce_8: 0.16102/0.12307, loss_spatial_dice_8: 0.16866/0.25728, loss_spatial_ce_8: 0.06518/0.19825, loss_grounding_bce_8: 0.23068/0.08885, loss_grounding_dice_8: 0.14109/0.17000, loss_grounding_ce_8: 0.63301/0.41604, loss_mask_ce_9: 3.22814/3.47419, loss_mask_bce_9: 0.46493/0.35980, loss_mask_dice_9: 0.43460/1.75953, loss_spatial_bce_9: 0.45425/0.35405, loss_spatial_dice_9: 0.68628/0.79304, loss_spatial_ce_9: 0.75477/1.38632, loss_grounding_bce_9: 0.18975/0.10100, loss_grounding_dice_9: 0.14026/0.24220, loss_grounding_ce_9: 2.32142/0.66865] items per batch[64] items per second[0.37] total items[5024000] mini batches[ 78500] memory[4999] epoch remaining[0:01:46] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00078561. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0022 s/iter. Inference: 0.3711 s/iter. Eval: 0.1048 s/iter. Total: 0.4781 s/iter. ETA=0:00:32 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0027 s/iter. Inference: 0.3652 s/iter. Eval: 0.0823 s/iter. Total: 0.4503 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0028 s/iter. Inference: 0.3665 s/iter. Eval: 0.0778 s/iter. Total: 0.4472 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0028 s/iter. Inference: 0.3727 s/iter. Eval: 0.0749 s/iter. Total: 0.4505 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 58/79. Dataloading: 0.0028 s/iter. Inference: 0.3748 s/iter. Eval: 0.0739 s/iter. Total: 0.4517 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 70/79. Dataloading: 0.0029 s/iter. Inference: 0.3735 s/iter. Eval: 0.0719 s/iter. Total: 0.4484 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval9drhtlg1 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.528 | 83.049 | 66.110 | 133 | | Things | 61.673 | 83.972 | 72.975 | 80 | | Stuff | 46.252 | 81.657 | 55.749 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 15.06 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Loading and preparing results... DONE (t=4.72s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.04 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.692 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.490 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.495 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.352 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.550 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.569 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.606 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764 INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.52 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.426 | 69.236 | 49.032 | 25.817 | 49.450 | 67.295 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 48.416 | bicycle | 22.439 | car | 42.831 | | motorcycle | 40.856 | airplane | 61.449 | bus | 70.997 | | train | 74.349 | truck | 44.129 | boat | 30.969 | | traffic light | 29.321 | fire hydrant | 70.865 | stop sign | 67.558 | | parking meter | 50.040 | bench | 26.800 | bird | 33.986 | | cat | 76.845 | dog | 70.494 | horse | 48.635 | | sheep | 53.748 | cow | 57.385 | elephant | 66.278 | | bear | 80.259 | zebra | 65.601 | giraffe | 62.571 | | backpack | 24.874 | umbrella | 55.367 | handbag | 23.426 | | tie | 40.515 | suitcase | 49.973 | frisbee | 69.308 | | skis | 8.072 | snowboard | 35.143 | sports ball | 49.787 | | kite | 38.484 | baseball bat | 37.490 | baseball glove | 50.375 | | skateboard | 44.049 | surfboard | 44.909 | tennis racket | 63.329 | | bottle | 42.609 | wine glass | 37.175 | cup | 49.971 | | fork | 26.521 | knife | 23.336 | spoon | 21.992 | | bowl | 37.302 | banana | 22.800 | apple | 25.579 | | sandwich | 49.138 | orange | 30.464 | broccoli | 24.597 | | carrot | 23.702 | hot dog | 36.575 | pizza | 53.305 | | donut | 56.787 | cake | 48.240 | chair | 28.840 | | couch | 44.017 | potted plant | 22.619 | bed | 42.402 | | dining table | 15.598 | toilet | 69.659 | tv | 65.909 | | laptop | 68.134 | mouse | 63.450 | remote | 44.761 | | keyboard | 58.018 | cell phone | 47.116 | microwave | 66.249 | | oven | 30.948 | toaster | 44.657 | sink | 44.429 | | refrigerator | 68.533 | book | 14.035 | clock | 55.312 | | vase | 40.681 | scissors | 36.168 | teddy bear | 56.226 | | hair drier | 34.612 | toothbrush | 29.715 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.63653784842258, 'fwIoU': 71.32371319962026, 'IoU-person': 88.58683356224746, 'IoU-bicycle': 73.39383018941973, 'IoU-car': 71.93896491464467, 'IoU-motorcycle': 87.55478229990385, 'IoU-airplane': 86.8875089421009, 'IoU-bus': 87.26860596902154, 'IoU-train': 87.18244486146783, 'IoU-truck': 68.72510001553286, 'IoU-boat': 72.89642833086903, 'IoU-traffic light': 80.02994077431673, 'IoU-fire hydrant': 93.29144318323563, 'IoU-stop sign': 86.75357504199907, 'IoU-parking meter': 84.2911004076691, 'IoU-bench': 63.05255490647824, 'IoU-bird': 77.10473330548719, 'IoU-cat': 86.08921824829095, 'IoU-dog': 87.17141274648434, 'IoU-horse': 88.23283871000508, 'IoU-sheep': 86.96228631908821, 'IoU-cow': 91.85040416932567, 'IoU-elephant': 90.87134525589032, 'IoU-bear': 85.19271604471015, 'IoU-zebra': 87.74251232052124, 'IoU-giraffe': 89.56290010436612, 'IoU-backpack': 52.06345041305656, 'IoU-umbrella': 82.00056928240677, 'IoU-handbag': 47.57169301088448, 'IoU-tie': 74.52423411673898, 'IoU-suitcase': 78.65680370846687, 'IoU-frisbee': 84.60662129381377, 'IoU-skis': 60.8562707934406, 'IoU-snowboard': 74.374206015495, 'IoU-sports ball': 81.06656209249145, 'IoU-kite': 79.1426302392037, 'IoU-baseball bat': 67.4374590970998, 'IoU-baseball glove': 81.93973093023999, 'IoU-skateboard': 86.20782682874814, 'IoU-surfboard': 86.41551645205267, 'IoU-tennis racket': 91.0742865085785, 'IoU-bottle': 72.15136305596873, 'IoU-wine glass': 82.42440215623212, 'IoU-cup': 71.06143245787618, 'IoU-fork': 68.56530341277048, 'IoU-knife': 62.15598412860843, 'IoU-spoon': 60.63399960549675, 'IoU-bowl': 56.158317523856084, 'IoU-banana': 82.29224522411077, 'IoU-apple': 61.23494723605567, 'IoU-sandwich': 70.52984657998101, 'IoU-orange': 81.36319042515441, 'IoU-broccoli': 68.38738260021125, 'IoU-carrot': 64.61362053401777, 'IoU-hot dog': 68.27679903198181, 'IoU-pizza': 85.0289433050435, 'IoU-donut': 69.36385136737316, 'IoU-cake': 80.05764977576979, 'IoU-chair': 62.848616152826196, 'IoU-couch': 67.64888722963823, 'IoU-potted plant': 47.435626025581385, 'IoU-bed': 70.90518574891263, 'IoU-dining table': 54.73145177402338, 'IoU-toilet': 84.12196793577601, 'IoU-tv': 76.77974829360215, 'IoU-laptop': 76.09392343334392, 'IoU-mouse': 74.76377435049687, 'IoU-remote': 74.22396417184206, 'IoU-keyboard': 59.86784972443752, 'IoU-cell phone': 79.928040101639, 'IoU-microwave': 79.57259331674943, 'IoU-oven': 71.96571060861808, 'IoU-toaster': 85.07360820533755, 'IoU-sink': 69.06303481716468, 'IoU-refrigerator': 82.46899431962231, 'IoU-book': 57.301075424186344, 'IoU-clock': 76.35417228742557, 'IoU-vase': 64.29527780836433, 'IoU-scissors': 84.84779105883166, 'IoU-teddy bear': 84.29926739206003, 'IoU-hair drier': 48.79703038357546, 'IoU-toothbrush': 71.88961793237686, 'IoU-banner': 31.213156318845837, 'IoU-blanket': 16.66599736211747, 'IoU-bridge': 39.14964178660261, 'IoU-cardboard': 51.71991805243772, 'IoU-counter': 31.562893275271875, 'IoU-curtain': 69.74694040834667, 'IoU-door-stuff': 47.039514091211025, 'IoU-floor-wood': 64.24787930679624, 'IoU-flower': 41.347441855358255, 'IoU-fruit': 46.742811965026945, 'IoU-gravel': 28.173663310960578, 'IoU-house': 23.72736155389324, 'IoU-light': 45.76862574290331, 'IoU-mirror-stuff': 63.99215001722737, 'IoU-net': 50.16615293867385, 'IoU-pillow': 21.249932933500357, 'IoU-platform': 30.118957375258205, 'IoU-playingfield': 67.44970437400269, 'IoU-railroad': 62.3335757429312, 'IoU-river': 54.070753373541024, 'IoU-road': 66.80481842492638, 'IoU-roof': 19.809284131465564, 'IoU-sand': 65.91825168390125, 'IoU-sea': 84.1039060730221, 'IoU-shelf': 39.77199427860491, 'IoU-snow': 92.15450467204346, 'IoU-stairs': 33.42742767288254, 'IoU-tent': 12.177528520206812, 'IoU-towel': 44.56983174230511, 'IoU-wall-brick': 51.033243648842785, 'IoU-wall-stone': 30.26106441570971, 'IoU-wall-tile': 69.82170824030607, 'IoU-wall-wood': 44.1788779554088, 'IoU-water-other': 22.11094462959257, 'IoU-window-blind': 50.58619574794776, 'IoU-window-other': 49.6800619304787, 'IoU-tree-merged': 81.92518485857003, 'IoU-fence-merged': 55.17458960359145, 'IoU-ceiling-merged': 68.75145144203822, 'IoU-sky-other-merged': 93.53164472299372, 'IoU-cabinet-merged': 63.813713617675894, 'IoU-table-merged': 41.03205086441592, 'IoU-floor-other-merged': 54.8422674995213, 'IoU-pavement-merged': 55.951928400499575, 'IoU-mountain-merged': 57.963241912280836, 'IoU-grass-merged': 72.71085428623275, 'IoU-dirt-merged': 45.61251230080472, 'IoU-paper-merged': 32.414394288280505, 'IoU-food-other-merged': 42.00192488875655, 'IoU-building-other-merged': 59.453544619298725, 'IoU-rock-merged': 64.7521385550128, 'IoU-wall-other-merged': 68.2134095450255, 'IoU-rug-merged': 68.470138559922, 'mACC': 77.22525615609426, 'pACC': 82.03728223435908, 'ACC-person': 92.97244072896976, 'ACC-bicycle': 83.33461681563715, 'ACC-car': 86.836209766086, 'ACC-motorcycle': 91.97943071942723, 'ACC-airplane': 90.68598022790468, 'ACC-bus': 94.03108907564376, 'ACC-train': 95.4476325257016, 'ACC-truck': 76.7810424158215, 'ACC-boat': 82.0016882727572, 'ACC-traffic light': 91.81493641828374, 'ACC-fire hydrant': 95.81029262939445, 'ACC-stop sign': 90.0966491934771, 'ACC-parking meter': 87.70374421507043, 'ACC-bench': 76.4658737313041, 'ACC-bird': 81.3752396726756, 'ACC-cat': 89.41705232769175, 'ACC-dog': 90.0850166974069, 'ACC-horse': 92.86386986519294, 'ACC-sheep': 89.98857610101042, 'ACC-cow': 95.41734499211874, 'ACC-elephant': 93.02148508211957, 'ACC-bear': 86.91601523328269, 'ACC-zebra': 90.00753362845721, 'ACC-giraffe': 93.52665480095888, 'ACC-backpack': 77.81735041761246, 'ACC-umbrella': 85.87099736601729, 'ACC-handbag': 66.32120541280437, 'ACC-tie': 84.23977036047334, 'ACC-suitcase': 83.67725987440802, 'ACC-frisbee': 93.98072727272726, 'ACC-skis': 75.47436600714829, 'ACC-snowboard': 81.44204158604981, 'ACC-sports ball': 88.6795670915124, 'ACC-kite': 85.53381709996754, 'ACC-baseball bat': 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'ACC-mouse': 86.14222481954049, 'ACC-remote': 78.86467602433747, 'ACC-keyboard': 67.65541323060188, 'ACC-cell phone': 89.02835695561653, 'ACC-microwave': 84.28116675523326, 'ACC-oven': 90.08136537021495, 'ACC-toaster': 91.26647415456624, 'ACC-sink': 77.63387560894287, 'ACC-refrigerator': 92.18876564056468, 'ACC-book': 76.38869042615785, 'ACC-clock': 81.77014492228419, 'ACC-vase': 72.95582565299998, 'ACC-scissors': 90.17390665601556, 'ACC-teddy bear': 89.35893967141958, 'ACC-hair drier': 60.61537585745596, 'ACC-toothbrush': 79.56567060458651, 'ACC-banner': 81.8960134994518, 'ACC-blanket': 37.7511444298991, 'ACC-bridge': 58.45895594404884, 'ACC-cardboard': 69.0145412243711, 'ACC-counter': 57.25962148839204, 'ACC-curtain': 82.38382898661288, 'ACC-door-stuff': 68.25936341290108, 'ACC-floor-wood': 85.19515990707711, 'ACC-flower': 60.50571515363006, 'ACC-fruit': 66.79046727599538, 'ACC-gravel': 36.51754488094465, 'ACC-house': 27.749421710383317, 'ACC-light': 63.92020441756306, 'ACC-mirror-stuff': 75.68866675358147, 'ACC-net': 65.61634663775654, 'ACC-pillow': 48.09698172880702, 'ACC-platform': 46.18428433428005, 'ACC-playingfield': 83.47257808466198, 'ACC-railroad': 84.15766707890543, 'ACC-river': 79.47536168082954, 'ACC-road': 87.22467657129876, 'ACC-roof': 26.844438468465164, 'ACC-sand': 73.30310279933879, 'ACC-sea': 91.48315361269881, 'ACC-shelf': 57.36179043802121, 'ACC-snow': 95.35558403445556, 'ACC-stairs': 58.19092523691394, 'ACC-tent': 14.461327584260031, 'ACC-towel': 54.49118930404438, 'ACC-wall-brick': 70.03657989197963, 'ACC-wall-stone': 36.283668039944686, 'ACC-wall-tile': 85.54096272331635, 'ACC-wall-wood': 65.28873859047701, 'ACC-water-other': 29.439636160627813, 'ACC-window-blind': 63.24552170805051, 'ACC-window-other': 74.40128558256403, 'ACC-tree-merged': 89.81572606343896, 'ACC-fence-merged': 73.29743677094966, 'ACC-ceiling-merged': 83.29487999127102, 'ACC-sky-other-merged': 97.19510015594405, 'ACC-cabinet-merged': 77.77633823599834, 'ACC-table-merged': 55.717045799204634, 'ACC-floor-other-merged': 66.3030491365317, 'ACC-pavement-merged': 67.36059994501724, 'ACC-mountain-merged': 70.45583244494661, 'ACC-grass-merged': 83.9908995410971, 'ACC-dirt-merged': 69.83480348673575, 'ACC-paper-merged': 41.361038615005604, 'ACC-food-other-merged': 56.940285824035584, 'ACC-building-other-merged': 74.64390071088867, 'ACC-rock-merged': 84.1324870845315, 'ACC-wall-other-merged': 81.40443637586937, 'ACC-rug-merged': 81.2062876744692})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3025 s/iter. Inference: 0.1765 s/iter. Eval: 0.0000 s/iter. Total: 0.4791 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3149 s/iter. Inference: 0.3437 s/iter. Eval: 0.0000 s/iter. Total: 0.6588 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3440 s/iter. Inference: 0.4372 s/iter. Eval: 0.0000 s/iter. Total: 0.7813 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3169446883230904, 'noc@0.8': 2.3172373426982733, 'noc@0.85': 2.751829089844893, 'noc@0.9': 3.5191688615744807, 'miou@iter1': 0.8732896954963736} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0016 s/iter. Inference: 0.1426 s/iter. Eval: 0.0011 s/iter. Total: 0.1453 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.74815368652344, 'precision@0.6': 73.33851623535156, 'precision@0.7': 69.17994689941406, 'precision@0.8': 59.65798568725586, 'precision@0.9': 32.49125671386719, 'cIoU': 62.75739288330078, 'mIoU': 67.2593002319336} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.528165541825246, 'SQ': 83.04940071897623, 'RQ': 66.11036369486675, 'PQ_th': 61.67349311272354, 'SQ_th': 83.97172468062175, 'RQ_th': 72.97453966952239, 'PQ_st': 46.25219939707308, 'SQ_st': 81.65721360705855, 'RQ_st': 55.749343355763806}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 45.426289879652956, 'AP50': 69.23562096380621, 'AP75': 49.03215817749898, 'APs': 25.81716907705794, 'APm': 49.450379813293175, 'APl': 67.29511804399321, 'AP-person': 48.41634691836649, 'AP-bicycle': 22.439345539893147, 'AP-car': 42.83077372889928, 'AP-motorcycle': 40.85557838709867, 'AP-airplane': 61.448844852711574, 'AP-bus': 70.99720081404868, 'AP-train': 74.34887429964475, 'AP-truck': 44.128940786317, 'AP-boat': 30.96863783575405, 'AP-traffic light': 29.320769648918233, 'AP-fire hydrant': 70.86521638165983, 'AP-stop sign': 67.55842181360595, 'AP-parking meter': 50.039969446483745, 'AP-bench': 26.800168065977033, 'AP-bird': 33.98644824251709, 'AP-cat': 76.84494755074884, 'AP-dog': 70.49409610792476, 'AP-horse': 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'IoU-umbrella': 82.00056928240677, 'IoU-handbag': 47.57169301088448, 'IoU-tie': 74.52423411673898, 'IoU-suitcase': 78.65680370846687, 'IoU-frisbee': 84.60662129381377, 'IoU-skis': 60.8562707934406, 'IoU-snowboard': 74.374206015495, 'IoU-sports ball': 81.06656209249145, 'IoU-kite': 79.1426302392037, 'IoU-baseball bat': 67.4374590970998, 'IoU-baseball glove': 81.93973093023999, 'IoU-skateboard': 86.20782682874814, 'IoU-surfboard': 86.41551645205267, 'IoU-tennis racket': 91.0742865085785, 'IoU-bottle': 72.15136305596873, 'IoU-wine glass': 82.42440215623212, 'IoU-cup': 71.06143245787618, 'IoU-fork': 68.56530341277048, 'IoU-knife': 62.15598412860843, 'IoU-spoon': 60.63399960549675, 'IoU-bowl': 56.158317523856084, 'IoU-banana': 82.29224522411077, 'IoU-apple': 61.23494723605567, 'IoU-sandwich': 70.52984657998101, 'IoU-orange': 81.36319042515441, 'IoU-broccoli': 68.38738260021125, 'IoU-carrot': 64.61362053401777, 'IoU-hot dog': 68.27679903198181, 'IoU-pizza': 85.0289433050435, 'IoU-donut': 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'ACC-laptop': 86.96298594368274, 'ACC-mouse': 86.14222481954049, 'ACC-remote': 78.86467602433747, 'ACC-keyboard': 67.65541323060188, 'ACC-cell phone': 89.02835695561653, 'ACC-microwave': 84.28116675523326, 'ACC-oven': 90.08136537021495, 'ACC-toaster': 91.26647415456624, 'ACC-sink': 77.63387560894287, 'ACC-refrigerator': 92.18876564056468, 'ACC-book': 76.38869042615785, 'ACC-clock': 81.77014492228419, 'ACC-vase': 72.95582565299998, 'ACC-scissors': 90.17390665601556, 'ACC-teddy bear': 89.35893967141958, 'ACC-hair drier': 60.61537585745596, 'ACC-toothbrush': 79.56567060458651, 'ACC-banner': 81.8960134994518, 'ACC-blanket': 37.7511444298991, 'ACC-bridge': 58.45895594404884, 'ACC-cardboard': 69.0145412243711, 'ACC-counter': 57.25962148839204, 'ACC-curtain': 82.38382898661288, 'ACC-door-stuff': 68.25936341290108, 'ACC-floor-wood': 85.19515990707711, 'ACC-flower': 60.50571515363006, 'ACC-fruit': 66.79046727599538, 'ACC-gravel': 36.51754488094465, 'ACC-house': 27.749421710383317, 'ACC-light': 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77.77633823599834, 'ACC-table-merged': 55.717045799204634, 'ACC-floor-other-merged': 66.3030491365317, 'ACC-pavement-merged': 67.36059994501724, 'ACC-mountain-merged': 70.45583244494661, 'ACC-grass-merged': 83.9908995410971, 'ACC-dirt-merged': 69.83480348673575, 'ACC-paper-merged': 41.361038615005604, 'ACC-food-other-merged': 56.940285824035584, 'ACC-building-other-merged': 74.64390071088867, 'ACC-rock-merged': 84.1324870845315, 'ACC-wall-other-merged': 81.40443637586937, 'ACC-rug-merged': 81.2062876744692})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3169446883230904, 'noc@0.8': 2.3172373426982733, 'noc@0.85': 2.751829089844893, 'noc@0.9': 3.5191688615744807, 'miou@iter1': 0.8732896954963736}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.74815368652344, 'precision@0.6': 73.33851623535156, 'precision@0.7': 69.17994689941406, 'precision@0.8': 59.65798568725586, 'precision@0.9': 32.49125671386719, 'cIoU': 62.75739288330078, 'mIoU': 67.2593002319336}}} INFO:trainer.default_trainer:This epoch takes 0:56:51.526397 INFO:trainer.default_trainer:PROGRESS: 86.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 43 training. INFO:trainer.default_trainer:epochs[ 43] optim steps[78600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.08312/0.75259, loss_mask_bce_0: 0.03467/0.30061, loss_mask_dice_0: 0.02104/1.02017, loss_spatial_bce_0: 0.04145/0.08438, loss_spatial_dice_0: 0.02709/0.17843, loss_spatial_ce_0: 0.12806/0.05533, loss_grounding_bce_0: 0.04414/0.08062, loss_grounding_dice_0: 0.02808/0.15038, loss_grounding_ce_0: 0.00079/0.24858, loss_mask_ce_1: 0.08846/0.75321, loss_mask_bce_1: 0.03351/0.30145, loss_mask_dice_1: 0.02013/1.02444, loss_spatial_bce_1: 0.03883/0.08484, loss_spatial_dice_1: 0.02798/0.18138, loss_spatial_ce_1: 0.12473/0.05903, loss_grounding_bce_1: 0.04065/0.08083, loss_grounding_dice_1: 0.02602/0.15115, loss_grounding_ce_1: 0.00108/0.25001, loss_mask_ce_2: 0.10719/0.76097, loss_mask_bce_2: 0.03079/0.30182, loss_mask_dice_2: 0.01956/1.02538, loss_spatial_bce_2: 0.04138/0.08495, loss_spatial_dice_2: 0.03127/0.18200, loss_spatial_ce_2: 0.16327/0.06129, loss_grounding_bce_2: 0.04504/0.08079, loss_grounding_dice_2: 0.02880/0.15103, loss_grounding_ce_2: 0.00115/0.25276, loss_mask_ce_3: 0.08819/0.76569, loss_mask_bce_3: 0.03247/0.30312, loss_mask_dice_3: 0.02005/1.02330, loss_spatial_bce_3: 0.04072/0.08708, loss_spatial_dice_3: 0.03225/0.18336, loss_spatial_ce_3: 0.16024/0.06617, loss_grounding_bce_3: 0.04288/0.08115, loss_grounding_dice_3: 0.02715/0.15071, loss_grounding_ce_3: 0.00038/0.25391, loss_mask_ce_4: 0.07176/0.77150, loss_mask_bce_4: 0.03471/0.30585, loss_mask_dice_4: 0.02155/1.04274, loss_spatial_bce_4: 0.04452/0.08953, loss_spatial_dice_4: 0.03502/0.19205, loss_spatial_ce_4: 0.12590/0.07991, loss_grounding_bce_4: 0.04324/0.08193, loss_grounding_dice_4: 0.02834/0.15333, loss_grounding_ce_4: 0.00032/0.25829, loss_mask_ce_5: 0.09081/0.79703, loss_mask_bce_5: 0.03648/0.30772, loss_mask_dice_5: 0.01977/1.05073, loss_spatial_bce_5: 0.15399/0.09196, loss_spatial_dice_5: 0.15296/0.19538, loss_spatial_ce_5: 0.14063/0.09358, loss_grounding_bce_5: 0.04241/0.08219, loss_grounding_dice_5: 0.02586/0.15420, loss_grounding_ce_5: 0.00036/0.27592, loss_mask_ce_6: 0.07176/0.82433, loss_mask_bce_6: 0.03405/0.30991, loss_mask_dice_6: 0.01923/1.05474, loss_spatial_bce_6: 0.37673/0.09744, loss_spatial_dice_6: 0.29957/0.19771, loss_spatial_ce_6: 0.18577/0.11803, loss_grounding_bce_6: 0.04403/0.08300, loss_grounding_dice_6: 0.02615/0.15467, loss_grounding_ce_6: 0.00081/0.28491, loss_mask_ce_7: 0.09405/0.88012, loss_mask_bce_7: 0.03662/0.31714, loss_mask_dice_7: 0.01760/1.10057, loss_spatial_bce_7: 0.61305/0.10662, loss_spatial_dice_7: 0.39356/0.22266, loss_spatial_ce_7: 0.29958/0.15286, loss_grounding_bce_7: 0.04817/0.08469, loss_grounding_dice_7: 0.02418/0.16020, loss_grounding_ce_7: 0.00223/0.31816, loss_mask_ce_8: 0.12508/1.01397, loss_mask_bce_8: 0.02933/0.33312, loss_mask_dice_8: 0.02057/1.17711, loss_spatial_bce_8: 0.59674/0.12310, loss_spatial_dice_8: 0.45309/0.25727, loss_spatial_ce_8: 0.54472/0.19823, loss_grounding_bce_8: 0.03576/0.08886, loss_grounding_dice_8: 0.02815/0.16999, loss_grounding_ce_8: 0.00112/0.41608, loss_mask_ce_9: 2.11992/3.47440, loss_mask_bce_9: 0.07277/0.35982, loss_mask_dice_9: 0.11056/1.75960, loss_spatial_bce_9: 0.52247/0.35409, loss_spatial_dice_9: 0.52835/0.79306, loss_spatial_ce_9: 0.48013/1.38634, loss_grounding_bce_9: 0.11312/0.10101, loss_grounding_dice_9: 0.12626/0.24220, loss_grounding_ce_9: 0.14497/0.66865] items per batch[64] items per second[0.16] total items[5030400] mini batches[ 78600] memory[4999] epoch remaining[0:59:28] INFO:trainer.default_trainer:epochs[ 43] optim steps[78700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37906/0.75247, loss_mask_bce_0: 0.46944/0.30060, loss_mask_dice_0: 0.54379/1.02003, loss_spatial_bce_0: 0.06339/0.08437, loss_spatial_dice_0: 0.09420/0.17841, loss_spatial_ce_0: 0.00089/0.05531, loss_grounding_bce_0: 0.11349/0.08061, loss_grounding_dice_0: 0.07259/0.15036, loss_grounding_ce_0: 0.34555/0.24860, loss_mask_ce_1: 0.37187/0.75310, loss_mask_bce_1: 0.47563/0.30143, loss_mask_dice_1: 0.51931/1.02430, loss_spatial_bce_1: 0.06419/0.08483, loss_spatial_dice_1: 0.08860/0.18135, loss_spatial_ce_1: 0.00096/0.05901, loss_grounding_bce_1: 0.11515/0.08082, loss_grounding_dice_1: 0.07298/0.15113, loss_grounding_ce_1: 0.29943/0.25001, loss_mask_ce_2: 0.36805/0.76085, loss_mask_bce_2: 0.46159/0.30181, loss_mask_dice_2: 0.53680/1.02522, loss_spatial_bce_2: 0.06349/0.08494, loss_spatial_dice_2: 0.09710/0.18197, loss_spatial_ce_2: 0.00114/0.06127, loss_grounding_bce_2: 0.11698/0.08078, loss_grounding_dice_2: 0.07364/0.15102, loss_grounding_ce_2: 0.31111/0.25279, loss_mask_ce_3: 0.36197/0.76554, loss_mask_bce_3: 0.45849/0.30311, loss_mask_dice_3: 0.52309/1.02317, loss_spatial_bce_3: 0.06694/0.08708, loss_spatial_dice_3: 0.10559/0.18334, loss_spatial_ce_3: 0.00064/0.06617, loss_grounding_bce_3: 0.11678/0.08114, loss_grounding_dice_3: 0.07486/0.15069, loss_grounding_ce_3: 0.32539/0.25394, loss_mask_ce_4: 0.31908/0.77138, loss_mask_bce_4: 0.54540/0.30584, loss_mask_dice_4: 0.58461/1.04258, loss_spatial_bce_4: 0.06742/0.08953, loss_spatial_dice_4: 0.10316/0.19203, loss_spatial_ce_4: 0.00888/0.07989, loss_grounding_bce_4: 0.11258/0.08192, loss_grounding_dice_4: 0.07108/0.15331, loss_grounding_ce_4: 0.33533/0.25830, loss_mask_ce_5: 0.35511/0.79691, loss_mask_bce_5: 0.57439/0.30771, loss_mask_dice_5: 0.61630/1.05059, loss_spatial_bce_5: 0.06394/0.09195, loss_spatial_dice_5: 0.12811/0.19537, loss_spatial_ce_5: 0.02941/0.09356, loss_grounding_bce_5: 0.10113/0.08217, loss_grounding_dice_5: 0.07314/0.15419, loss_grounding_ce_5: 0.42136/0.27595, loss_mask_ce_6: 0.33996/0.82420, loss_mask_bce_6: 0.52666/0.30990, loss_mask_dice_6: 0.58579/1.05463, loss_spatial_bce_6: 0.06612/0.09744, loss_spatial_dice_6: 0.09486/0.19769, loss_spatial_ce_6: 0.00410/0.11800, loss_grounding_bce_6: 0.10323/0.08299, loss_grounding_dice_6: 0.07428/0.15465, loss_grounding_ce_6: 0.49876/0.28491, loss_mask_ce_7: 0.39326/0.87998, loss_mask_bce_7: 0.60581/0.31712, loss_mask_dice_7: 0.68055/1.10043, loss_spatial_bce_7: 0.07197/0.10662, loss_spatial_dice_7: 0.11648/0.22263, loss_spatial_ce_7: 0.06528/0.15282, loss_grounding_bce_7: 0.09193/0.08468, loss_grounding_dice_7: 0.07768/0.16019, loss_grounding_ce_7: 0.49647/0.31814, loss_mask_ce_8: 0.40980/1.01386, loss_mask_bce_8: 0.65226/0.33311, loss_mask_dice_8: 0.73495/1.17696, loss_spatial_bce_8: 0.08060/0.12309, loss_spatial_dice_8: 0.12388/0.25725, loss_spatial_ce_8: 0.13493/0.19819, loss_grounding_bce_8: 0.12803/0.08886, loss_grounding_dice_8: 0.08831/0.16997, loss_grounding_ce_8: 0.99301/0.41606, loss_mask_ce_9: 2.69276/3.47415, loss_mask_bce_9: 0.68293/0.35980, loss_mask_dice_9: 1.09430/1.75936, loss_spatial_bce_9: 0.36237/0.35413, loss_spatial_dice_9: 0.86970/0.79306, loss_spatial_ce_9: 1.23374/1.38632, loss_grounding_bce_9: 0.12152/0.10101, loss_grounding_dice_9: 0.09929/0.24219, loss_grounding_ce_9: 2.31281/0.66860] items per batch[64] items per second[0.37] total items[5036800] mini batches[ 78700] memory[4999] epoch remaining[0:50:42] INFO:trainer.default_trainer:epochs[ 43] optim steps[78800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.15832/0.75253, loss_mask_bce_0: 0.08549/0.30062, loss_mask_dice_0: 0.50979/1.02004, loss_spatial_bce_0: 0.02174/0.08438, loss_spatial_dice_0: 0.19148/0.17841, loss_spatial_ce_0: 0.00388/0.05531, loss_grounding_bce_0: 0.03059/0.08061, loss_grounding_dice_0: 0.02443/0.15037, loss_grounding_ce_0: 0.00048/0.24869, loss_mask_ce_1: 0.14731/0.75317, loss_mask_bce_1: 0.08404/0.30145, loss_mask_dice_1: 0.47930/1.02430, loss_spatial_bce_1: 0.02220/0.08483, loss_spatial_dice_1: 0.17075/0.18136, loss_spatial_ce_1: 0.00761/0.05903, loss_grounding_bce_1: 0.03676/0.08082, loss_grounding_dice_1: 0.02687/0.15115, loss_grounding_ce_1: 0.00046/0.25011, loss_mask_ce_2: 0.15267/0.76090, loss_mask_bce_2: 0.08449/0.30183, loss_mask_dice_2: 0.42671/1.02521, loss_spatial_bce_2: 0.02353/0.08494, loss_spatial_dice_2: 0.21157/0.18198, loss_spatial_ce_2: 0.00226/0.06127, loss_grounding_bce_2: 0.03586/0.08077, loss_grounding_dice_2: 0.02546/0.15104, loss_grounding_ce_2: 0.00058/0.25287, loss_mask_ce_3: 0.15965/0.76559, loss_mask_bce_3: 0.08467/0.30313, loss_mask_dice_3: 0.46414/1.02316, loss_spatial_bce_3: 0.02211/0.08708, loss_spatial_dice_3: 0.22322/0.18334, loss_spatial_ce_3: 0.00281/0.06617, loss_grounding_bce_3: 0.03705/0.08114, loss_grounding_dice_3: 0.02521/0.15072, loss_grounding_ce_3: 0.00071/0.25404, loss_mask_ce_4: 0.17765/0.77138, loss_mask_bce_4: 0.08342/0.30587, loss_mask_dice_4: 0.51649/1.04261, loss_spatial_bce_4: 0.01846/0.08953, loss_spatial_dice_4: 0.15995/0.19203, loss_spatial_ce_4: 0.00446/0.07992, loss_grounding_bce_4: 0.03412/0.08192, loss_grounding_dice_4: 0.02560/0.15333, loss_grounding_ce_4: 0.00096/0.25838, loss_mask_ce_5: 0.15932/0.79694, loss_mask_bce_5: 0.07888/0.30772, loss_mask_dice_5: 0.49519/1.05060, loss_spatial_bce_5: 0.02532/0.09196, loss_spatial_dice_5: 0.23373/0.19538, loss_spatial_ce_5: 0.07829/0.09358, loss_grounding_bce_5: 0.03629/0.08218, loss_grounding_dice_5: 0.02571/0.15423, loss_grounding_ce_5: 0.00068/0.27602, loss_mask_ce_6: 0.14404/0.82421, loss_mask_bce_6: 0.08305/0.30992, loss_mask_dice_6: 0.40999/1.05462, loss_spatial_bce_6: 0.02198/0.09745, loss_spatial_dice_6: 0.20601/0.19771, loss_spatial_ce_6: 0.04151/0.11799, loss_grounding_bce_6: 0.03452/0.08299, loss_grounding_dice_6: 0.02544/0.15468, loss_grounding_ce_6: 0.00082/0.28498, loss_mask_ce_7: 0.17784/0.88000, loss_mask_bce_7: 0.08192/0.31713, loss_mask_dice_7: 0.51241/1.10041, loss_spatial_bce_7: 0.05418/0.10662, loss_spatial_dice_7: 0.24838/0.22265, loss_spatial_ce_7: 0.14348/0.15282, loss_grounding_bce_7: 0.03589/0.08468, loss_grounding_dice_7: 0.02547/0.16021, loss_grounding_ce_7: 0.00058/0.31817, loss_mask_ce_8: 0.30441/1.01386, loss_mask_bce_8: 0.09661/0.33313, loss_mask_dice_8: 0.45669/1.17692, loss_spatial_bce_8: 0.03383/0.12309, loss_spatial_dice_8: 0.27763/0.25726, loss_spatial_ce_8: 0.10030/0.19820, loss_grounding_bce_8: 0.03463/0.08885, loss_grounding_dice_8: 0.02673/0.16998, loss_grounding_ce_8: 0.00129/0.41613, loss_mask_ce_9: 3.93844/3.47412, loss_mask_bce_9: 0.08094/0.35983, loss_mask_dice_9: 0.55499/1.75936, loss_spatial_bce_9: 0.84413/0.35414, loss_spatial_dice_9: 0.85149/0.79307, loss_spatial_ce_9: 1.72624/1.38630, loss_grounding_bce_9: 0.04182/0.10100, loss_grounding_dice_9: 0.03391/0.24221, loss_grounding_ce_9: 0.01833/0.66856] items per batch[64] items per second[0.37] total items[5043200] mini batches[ 78800] memory[4999] epoch remaining[0:46:41] INFO:trainer.default_trainer:epochs[ 43] optim steps[78900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.28388/0.75241, loss_mask_bce_0: 0.90377/0.30064, loss_mask_dice_0: 1.23271/1.01996, loss_spatial_bce_0: 0.28865/0.08438, loss_spatial_dice_0: 0.42751/0.17840, loss_spatial_ce_0: 0.22881/0.05531, loss_grounding_bce_0: 0.11334/0.08063, loss_grounding_dice_0: 0.07310/0.15037, loss_grounding_ce_0: 0.02988/0.24875, loss_mask_ce_1: 1.29194/0.75308, loss_mask_bce_1: 0.93563/0.30148, loss_mask_dice_1: 1.20134/1.02425, loss_spatial_bce_1: 0.25580/0.08484, loss_spatial_dice_1: 0.40669/0.18135, loss_spatial_ce_1: 0.23142/0.05903, loss_grounding_bce_1: 0.11954/0.08084, loss_grounding_dice_1: 0.07206/0.15115, loss_grounding_ce_1: 0.03299/0.25006, loss_mask_ce_2: 1.21961/0.76080, loss_mask_bce_2: 0.98824/0.30185, loss_mask_dice_2: 1.29971/1.02512, loss_spatial_bce_2: 0.23541/0.08495, loss_spatial_dice_2: 0.41658/0.18197, loss_spatial_ce_2: 0.21943/0.06128, loss_grounding_bce_2: 0.11936/0.08080, loss_grounding_dice_2: 0.07290/0.15104, loss_grounding_ce_2: 0.03695/0.25280, loss_mask_ce_3: 1.30976/0.76550, loss_mask_bce_3: 0.93404/0.30315, loss_mask_dice_3: 1.26813/1.02309, loss_spatial_bce_3: 0.28094/0.08708, loss_spatial_dice_3: 0.44473/0.18334, loss_spatial_ce_3: 0.09578/0.06617, loss_grounding_bce_3: 0.12102/0.08116, loss_grounding_dice_3: 0.07502/0.15072, loss_grounding_ce_3: 0.02864/0.25410, loss_mask_ce_4: 1.25734/0.77128, loss_mask_bce_4: 0.91052/0.30590, loss_mask_dice_4: 1.21783/1.04255, loss_spatial_bce_4: 0.30655/0.08954, loss_spatial_dice_4: 0.42909/0.19204, loss_spatial_ce_4: 0.22673/0.07992, loss_grounding_bce_4: 0.12137/0.08194, loss_grounding_dice_4: 0.07181/0.15334, loss_grounding_ce_4: 0.02670/0.25833, loss_mask_ce_5: 1.20901/0.79680, loss_mask_bce_5: 0.88589/0.30775, loss_mask_dice_5: 1.21231/1.05058, loss_spatial_bce_5: 0.29395/0.09197, loss_spatial_dice_5: 0.43100/0.19538, loss_spatial_ce_5: 0.28975/0.09359, loss_grounding_bce_5: 0.12370/0.08219, loss_grounding_dice_5: 0.07029/0.15422, loss_grounding_ce_5: 0.03264/0.27612, loss_mask_ce_6: 1.26677/0.82409, loss_mask_bce_6: 0.92222/0.30995, loss_mask_dice_6: 1.26362/1.05455, loss_spatial_bce_6: 0.41275/0.09747, loss_spatial_dice_6: 0.45609/0.19770, loss_spatial_ce_6: 0.33450/0.11799, loss_grounding_bce_6: 0.11740/0.08302, loss_grounding_dice_6: 0.06740/0.15468, loss_grounding_ce_6: 0.03837/0.28502, loss_mask_ce_7: 1.21250/0.87986, loss_mask_bce_7: 0.84550/0.31715, loss_mask_dice_7: 1.24213/1.10032, loss_spatial_bce_7: 0.31349/0.10665, loss_spatial_dice_7: 0.44411/0.22264, loss_spatial_ce_7: 0.45202/0.15282, loss_grounding_bce_7: 0.12468/0.08470, loss_grounding_dice_7: 0.06831/0.16020, loss_grounding_ce_7: 0.04025/0.31821, loss_mask_ce_8: 0.97813/1.01370, loss_mask_bce_8: 0.93241/0.33316, loss_mask_dice_8: 1.29032/1.17690, loss_spatial_bce_8: 0.33555/0.12311, loss_spatial_dice_8: 0.48076/0.25726, loss_spatial_ce_8: 0.49960/0.19820, loss_grounding_bce_8: 0.10959/0.08888, loss_grounding_dice_8: 0.07526/0.16997, loss_grounding_ce_8: 0.03670/0.41612, loss_mask_ce_9: 2.91538/3.47403, loss_mask_bce_9: 0.84935/0.35984, loss_mask_dice_9: 1.80477/1.75927, loss_spatial_bce_9: 0.43439/0.35421, loss_spatial_dice_9: 0.87572/0.79306, loss_spatial_ce_9: 2.18742/1.38632, loss_grounding_bce_9: 0.11218/0.10103, loss_grounding_dice_9: 0.08220/0.24218, loss_grounding_ce_9: 0.16406/0.66846] items per batch[64] items per second[0.36] total items[5049600] mini batches[ 78900] memory[4999] epoch remaining[0:43:43] INFO:trainer.default_trainer:epochs[ 43] optim steps[79000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.36554/0.75245, loss_mask_bce_0: 0.21296/0.30065, loss_mask_dice_0: 0.19671/1.02013, loss_spatial_bce_0: 0.14809/0.08440, loss_spatial_dice_0: 0.13446/0.17840, loss_spatial_ce_0: 0.00043/0.05532, loss_grounding_bce_0: 0.23381/0.08062, loss_grounding_dice_0: 0.24345/0.15036, loss_grounding_ce_0: 0.02762/0.24881, loss_mask_ce_1: 0.39830/0.75313, loss_mask_bce_1: 0.20524/0.30148, loss_mask_dice_1: 0.17934/1.02440, loss_spatial_bce_1: 0.15169/0.08486, loss_spatial_dice_1: 0.12499/0.18135, loss_spatial_ce_1: 0.00031/0.05904, loss_grounding_bce_1: 0.21964/0.08083, loss_grounding_dice_1: 0.21123/0.15114, loss_grounding_ce_1: 0.03446/0.25012, loss_mask_ce_2: 0.42321/0.76084, loss_mask_bce_2: 0.21406/0.30185, loss_mask_dice_2: 0.20148/1.02526, loss_spatial_bce_2: 0.14777/0.08497, loss_spatial_dice_2: 0.12208/0.18197, loss_spatial_ce_2: 0.00052/0.06128, loss_grounding_bce_2: 0.23965/0.08079, loss_grounding_dice_2: 0.26261/0.15104, loss_grounding_ce_2: 0.03146/0.25288, loss_mask_ce_3: 0.45641/0.76557, loss_mask_bce_3: 0.20410/0.30315, loss_mask_dice_3: 0.18451/1.02323, loss_spatial_bce_3: 0.15689/0.08711, loss_spatial_dice_3: 0.14833/0.18335, loss_spatial_ce_3: 0.00941/0.06616, loss_grounding_bce_3: 0.22516/0.08115, loss_grounding_dice_3: 0.21763/0.15072, loss_grounding_ce_3: 0.01742/0.25420, loss_mask_ce_4: 0.52834/0.77135, loss_mask_bce_4: 0.25194/0.30589, loss_mask_dice_4: 0.24781/1.04269, loss_spatial_bce_4: 0.17599/0.08956, loss_spatial_dice_4: 0.20298/0.19205, loss_spatial_ce_4: 0.03078/0.07995, loss_grounding_bce_4: 0.33121/0.08194, loss_grounding_dice_4: 0.36566/0.15333, loss_grounding_ce_4: 0.00786/0.25841, loss_mask_ce_5: 0.48128/0.79689, loss_mask_bce_5: 0.26798/0.30775, loss_mask_dice_5: 0.28486/1.05074, loss_spatial_bce_5: 0.17071/0.09200, loss_spatial_dice_5: 0.17567/0.19539, loss_spatial_ce_5: 0.08789/0.09361, loss_grounding_bce_5: 0.37165/0.08218, loss_grounding_dice_5: 0.42978/0.15422, loss_grounding_ce_5: 0.00563/0.27620, loss_mask_ce_6: 0.48382/0.82424, loss_mask_bce_6: 0.25524/0.30995, loss_mask_dice_6: 0.28806/1.05470, loss_spatial_bce_6: 0.15430/0.09749, loss_spatial_dice_6: 0.15342/0.19771, loss_spatial_ce_6: 0.20878/0.11802, loss_grounding_bce_6: 0.34627/0.08301, loss_grounding_dice_6: 0.42955/0.15467, loss_grounding_ce_6: 0.01133/0.28508, loss_mask_ce_7: 0.48215/0.87993, loss_mask_bce_7: 0.27595/0.31716, loss_mask_dice_7: 0.29611/1.10052, loss_spatial_bce_7: 0.21741/0.10667, loss_spatial_dice_7: 0.26070/0.22266, loss_spatial_ce_7: 0.22481/0.15282, loss_grounding_bce_7: 0.39171/0.08470, loss_grounding_dice_7: 0.46818/0.16020, loss_grounding_ce_7: 0.01214/0.31828, loss_mask_ce_8: 0.43197/1.01373, loss_mask_bce_8: 0.27992/0.33316, loss_mask_dice_8: 0.29142/1.17714, loss_spatial_bce_8: 0.24767/0.12310, loss_spatial_dice_8: 0.28731/0.25727, loss_spatial_ce_8: 0.34528/0.19821, loss_grounding_bce_8: 0.39348/0.08888, loss_grounding_dice_8: 0.44708/0.16998, loss_grounding_ce_8: 0.01707/0.41617, loss_mask_ce_9: 2.00202/3.47409, loss_mask_bce_9: 0.21376/0.35986, loss_mask_dice_9: 0.25360/1.75965, loss_spatial_bce_9: 0.63532/0.35422, loss_spatial_dice_9: 0.66429/0.79308, loss_spatial_ce_9: 1.24555/1.38633, loss_grounding_bce_9: 0.23822/0.10104, loss_grounding_dice_9: 0.32557/0.24219, loss_grounding_ce_9: 0.02702/0.66853] items per batch[64] items per second[0.37] total items[5056000] mini batches[ 79000] memory[4999] epoch remaining[0:40:39] INFO:trainer.default_trainer:epochs[ 43] optim steps[79100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.26128/0.75250, loss_mask_bce_0: 0.31646/0.30066, loss_mask_dice_0: 0.26396/1.02014, loss_spatial_bce_0: 0.12653/0.08440, loss_spatial_dice_0: 0.10946/0.17841, loss_spatial_ce_0: 0.00007/0.05532, loss_grounding_bce_0: 0.20611/0.08062, loss_grounding_dice_0: 0.10496/0.15039, loss_grounding_ce_0: 0.00318/0.24888, loss_mask_ce_1: 0.24210/0.75314, loss_mask_bce_1: 0.31643/0.30149, loss_mask_dice_1: 0.24360/1.02437, loss_spatial_bce_1: 0.12577/0.08486, loss_spatial_dice_1: 0.10375/0.18137, loss_spatial_ce_1: 0.00003/0.05904, loss_grounding_bce_1: 0.21868/0.08083, loss_grounding_dice_1: 0.10697/0.15117, loss_grounding_ce_1: 0.00243/0.25018, loss_mask_ce_2: 0.23499/0.76085, loss_mask_bce_2: 0.31006/0.30186, loss_mask_dice_2: 0.24533/1.02525, loss_spatial_bce_2: 0.13589/0.08497, loss_spatial_dice_2: 0.10984/0.18200, loss_spatial_ce_2: 0.00008/0.06130, loss_grounding_bce_2: 0.20750/0.08079, loss_grounding_dice_2: 0.10860/0.15106, loss_grounding_ce_2: 0.00395/0.25291, loss_mask_ce_3: 0.19750/0.76561, loss_mask_bce_3: 0.30196/0.30317, loss_mask_dice_3: 0.24005/1.02322, loss_spatial_bce_3: 0.12848/0.08710, loss_spatial_dice_3: 0.11199/0.18337, loss_spatial_ce_3: 0.00011/0.06616, loss_grounding_bce_3: 0.21257/0.08115, loss_grounding_dice_3: 0.11074/0.15074, loss_grounding_ce_3: 0.00416/0.25426, loss_mask_ce_4: 0.22327/0.77138, loss_mask_bce_4: 0.31802/0.30591, loss_mask_dice_4: 0.24008/1.04270, loss_spatial_bce_4: 0.12961/0.08955, loss_spatial_dice_4: 0.11325/0.19208, loss_spatial_ce_4: 0.00036/0.07997, loss_grounding_bce_4: 0.21373/0.08194, loss_grounding_dice_4: 0.10423/0.15335, loss_grounding_ce_4: 0.00467/0.25847, loss_mask_ce_5: 0.21175/0.79692, loss_mask_bce_5: 0.31596/0.30776, loss_mask_dice_5: 0.24301/1.05074, loss_spatial_bce_5: 0.12994/0.09200, loss_spatial_dice_5: 0.12608/0.19542, loss_spatial_ce_5: 0.00175/0.09363, loss_grounding_bce_5: 0.22096/0.08218, loss_grounding_dice_5: 0.10364/0.15425, loss_grounding_ce_5: 0.00297/0.27626, loss_mask_ce_6: 0.19778/0.82426, loss_mask_bce_6: 0.31727/0.30997, loss_mask_dice_6: 0.23507/1.05471, loss_spatial_bce_6: 0.13227/0.09749, loss_spatial_dice_6: 0.12389/0.19774, loss_spatial_ce_6: 0.02728/0.11805, loss_grounding_bce_6: 0.21267/0.08301, loss_grounding_dice_6: 0.09895/0.15470, loss_grounding_ce_6: 0.00460/0.28511, loss_mask_ce_7: 0.18240/0.87999, loss_mask_bce_7: 0.29666/0.31717, loss_mask_dice_7: 0.26089/1.10052, loss_spatial_bce_7: 0.12051/0.10667, loss_spatial_dice_7: 0.11331/0.22269, loss_spatial_ce_7: 0.06993/0.15282, loss_grounding_bce_7: 0.20386/0.08470, loss_grounding_dice_7: 0.12065/0.16024, loss_grounding_ce_7: 0.00308/0.31833, loss_mask_ce_8: 0.22882/1.01384, loss_mask_bce_8: 0.30868/0.33318, loss_mask_dice_8: 0.27052/1.17710, loss_spatial_bce_8: 0.13321/0.12310, loss_spatial_dice_8: 0.14545/0.25730, loss_spatial_ce_8: 0.14607/0.19823, loss_grounding_bce_8: 0.21521/0.08888, loss_grounding_dice_8: 0.12692/0.17001, loss_grounding_ce_8: 0.00746/0.41619, loss_mask_ce_9: 2.14900/3.47414, loss_mask_bce_9: 0.26001/0.35989, loss_mask_dice_9: 0.37841/1.75969, loss_spatial_bce_9: 0.56241/0.35418, loss_spatial_dice_9: 0.72450/0.79310, loss_spatial_ce_9: 0.99284/1.38647, loss_grounding_bce_9: 0.14089/0.10103, loss_grounding_dice_9: 0.16596/0.24223, loss_grounding_ce_9: 0.03894/0.66845] items per batch[64] items per second[0.36] total items[5062400] mini batches[ 79100] memory[4999] epoch remaining[0:37:44] INFO:trainer.default_trainer:epochs[ 43] optim steps[79200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.03902/0.75250, loss_mask_bce_0: 0.42628/0.30064, loss_mask_dice_0: 0.20822/1.02005, loss_spatial_bce_0: 0.38948/0.08438, loss_spatial_dice_0: 0.12922/0.17840, loss_spatial_ce_0: 0.00004/0.05532, loss_grounding_bce_0: 0.49261/0.08062, loss_grounding_dice_0: 0.23555/0.15038, loss_grounding_ce_0: 0.00897/0.24883, loss_mask_ce_1: 0.04844/0.75315, loss_mask_bce_1: 0.47557/0.30147, loss_mask_dice_1: 0.21853/1.02428, loss_spatial_bce_1: 0.25909/0.08484, loss_spatial_dice_1: 0.10543/0.18135, loss_spatial_ce_1: 0.00012/0.05903, loss_grounding_bce_1: 0.54758/0.08083, loss_grounding_dice_1: 0.24987/0.15116, loss_grounding_ce_1: 0.00648/0.25013, loss_mask_ce_2: 0.05723/0.76086, loss_mask_bce_2: 0.51057/0.30184, loss_mask_dice_2: 0.22495/1.02513, loss_spatial_bce_2: 0.22841/0.08495, loss_spatial_dice_2: 0.09363/0.18199, loss_spatial_ce_2: 0.00010/0.06129, loss_grounding_bce_2: 0.56673/0.08079, loss_grounding_dice_2: 0.24475/0.15105, loss_grounding_ce_2: 0.00557/0.25289, loss_mask_ce_3: 0.05033/0.76557, loss_mask_bce_3: 0.51228/0.30315, loss_mask_dice_3: 0.22025/1.02313, loss_spatial_bce_3: 0.20471/0.08709, loss_spatial_dice_3: 0.15167/0.18336, loss_spatial_ce_3: 0.00041/0.06615, loss_grounding_bce_3: 0.58174/0.08115, loss_grounding_dice_3: 0.24680/0.15073, loss_grounding_ce_3: 0.00709/0.25423, loss_mask_ce_4: 0.04982/0.77132, loss_mask_bce_4: 0.50275/0.30589, loss_mask_dice_4: 0.22136/1.04261, loss_spatial_bce_4: 0.18795/0.08954, loss_spatial_dice_4: 0.15858/0.19207, loss_spatial_ce_4: 0.07589/0.07998, loss_grounding_bce_4: 0.61731/0.08194, loss_grounding_dice_4: 0.24938/0.15334, loss_grounding_ce_4: 0.00309/0.25844, loss_mask_ce_5: 0.05274/0.79689, loss_mask_bce_5: 0.59882/0.30774, loss_mask_dice_5: 0.22129/1.05061, loss_spatial_bce_5: 0.18883/0.09198, loss_spatial_dice_5: 0.15803/0.19542, loss_spatial_ce_5: 0.01652/0.09362, loss_grounding_bce_5: 0.70856/0.08219, loss_grounding_dice_5: 0.24748/0.15423, loss_grounding_ce_5: 0.00289/0.27624, loss_mask_ce_6: 0.05789/0.82428, loss_mask_bce_6: 0.54392/0.30995, loss_mask_dice_6: 0.21017/1.05461, loss_spatial_bce_6: 0.20531/0.09748, loss_spatial_dice_6: 0.16194/0.19773, loss_spatial_ce_6: 0.01587/0.11803, loss_grounding_bce_6: 0.65697/0.08301, loss_grounding_dice_6: 0.24421/0.15469, loss_grounding_ce_6: 0.00545/0.28508, loss_mask_ce_7: 0.05536/0.87995, loss_mask_bce_7: 0.38140/0.31716, loss_mask_dice_7: 0.18891/1.10038, loss_spatial_bce_7: 0.29394/0.10666, loss_spatial_dice_7: 0.17343/0.22268, loss_spatial_ce_7: 0.02551/0.15281, loss_grounding_bce_7: 0.42805/0.08470, loss_grounding_dice_7: 0.21019/0.16022, loss_grounding_ce_7: 0.00443/0.31831, loss_mask_ce_8: 0.12251/1.01377, loss_mask_bce_8: 0.32656/0.33314, loss_mask_dice_8: 0.16156/1.17694, loss_spatial_bce_8: 0.37833/0.12308, loss_spatial_dice_8: 0.13973/0.25728, loss_spatial_ce_8: 0.12587/0.19821, loss_grounding_bce_8: 0.39680/0.08888, loss_grounding_dice_8: 0.18929/0.17000, loss_grounding_ce_8: 0.00614/0.41617, loss_mask_ce_9: 2.19858/3.47392, loss_mask_bce_9: 0.26922/0.35987, loss_mask_dice_9: 0.22451/1.75961, loss_spatial_bce_9: 0.43402/0.35416, loss_spatial_dice_9: 0.49708/0.79310, loss_spatial_ce_9: 0.49459/1.38649, loss_grounding_bce_9: 0.20029/0.10103, loss_grounding_dice_9: 0.21343/0.24222, loss_grounding_ce_9: 0.07454/0.66837] items per batch[64] items per second[0.37] total items[5068800] mini batches[ 79200] memory[4999] epoch remaining[0:34:44] INFO:trainer.default_trainer:epochs[ 43] optim steps[79300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.77694/0.75244, loss_mask_bce_0: 0.40962/0.30065, loss_mask_dice_0: 1.07321/1.02000, loss_spatial_bce_0: 0.09144/0.08439, loss_spatial_dice_0: 0.22763/0.17839, loss_spatial_ce_0: 0.03602/0.05533, loss_grounding_bce_0: 0.01149/0.08061, loss_grounding_dice_0: 0.37431/0.15037, loss_grounding_ce_0: 0.68709/0.24883, loss_mask_ce_1: 0.77772/0.75308, loss_mask_bce_1: 0.42746/0.30148, loss_mask_dice_1: 1.15093/1.02426, loss_spatial_bce_1: 0.08869/0.08484, loss_spatial_dice_1: 0.23680/0.18134, loss_spatial_ce_1: 0.04316/0.05904, loss_grounding_bce_1: 0.01461/0.08083, loss_grounding_dice_1: 0.43381/0.15115, loss_grounding_ce_1: 0.51213/0.25011, loss_mask_ce_2: 0.78416/0.76076, loss_mask_bce_2: 0.41650/0.30185, loss_mask_dice_2: 1.06913/1.02510, loss_spatial_bce_2: 0.09718/0.08495, loss_spatial_dice_2: 0.25726/0.18198, loss_spatial_ce_2: 0.04089/0.06130, loss_grounding_bce_2: 0.01382/0.08079, loss_grounding_dice_2: 0.42152/0.15104, loss_grounding_ce_2: 0.62668/0.25288, loss_mask_ce_3: 0.78394/0.76550, loss_mask_bce_3: 0.41173/0.30315, loss_mask_dice_3: 0.79881/1.02307, loss_spatial_bce_3: 0.10788/0.08709, loss_spatial_dice_3: 0.26635/0.18335, loss_spatial_ce_3: 0.04528/0.06615, loss_grounding_bce_3: 0.01653/0.08115, loss_grounding_dice_3: 0.45748/0.15073, loss_grounding_ce_3: 0.56998/0.25423, loss_mask_ce_4: 0.84411/0.77122, loss_mask_bce_4: 0.38266/0.30590, loss_mask_dice_4: 0.95968/1.04256, loss_spatial_bce_4: 0.08792/0.08955, loss_spatial_dice_4: 0.26416/0.19207, loss_spatial_ce_4: 0.07241/0.07998, loss_grounding_bce_4: 0.01301/0.08193, loss_grounding_dice_4: 0.38499/0.15334, loss_grounding_ce_4: 0.75743/0.25842, loss_mask_ce_5: 0.87533/0.79680, loss_mask_bce_5: 0.34275/0.30775, loss_mask_dice_5: 0.82652/1.05057, loss_spatial_bce_5: 0.07951/0.09199, loss_spatial_dice_5: 0.26178/0.19541, loss_spatial_ce_5: 0.18386/0.09364, loss_grounding_bce_5: 0.01266/0.08219, loss_grounding_dice_5: 0.41041/0.15422, loss_grounding_ce_5: 0.40534/0.27628, loss_mask_ce_6: 0.97671/0.82419, loss_mask_bce_6: 0.35389/0.30996, loss_mask_dice_6: 1.03162/1.05457, loss_spatial_bce_6: 0.08991/0.09749, loss_spatial_dice_6: 0.23317/0.19772, loss_spatial_ce_6: 0.29280/0.11803, loss_grounding_bce_6: 0.01323/0.08301, loss_grounding_dice_6: 0.43277/0.15469, loss_grounding_ce_6: 0.96912/0.28514, loss_mask_ce_7: 1.16899/0.87983, loss_mask_bce_7: 0.42185/0.31718, loss_mask_dice_7: 1.11285/1.10033, loss_spatial_bce_7: 0.14478/0.10666, loss_spatial_dice_7: 0.32498/0.22267, loss_spatial_ce_7: 0.34281/0.15280, loss_grounding_bce_7: 0.01839/0.08470, loss_grounding_dice_7: 0.41971/0.16021, loss_grounding_ce_7: 0.51211/0.31837, loss_mask_ce_8: 1.12672/1.01368, loss_mask_bce_8: 0.38208/0.33316, loss_mask_dice_8: 1.07604/1.17688, loss_spatial_bce_8: 0.19819/0.12307, loss_spatial_dice_8: 0.33156/0.25727, loss_spatial_ce_8: 0.22173/0.19819, loss_grounding_bce_8: 0.01065/0.08888, loss_grounding_dice_8: 0.42446/0.17000, loss_grounding_ce_8: 0.39795/0.41613, loss_mask_ce_9: 5.28188/3.47400, loss_mask_bce_9: 0.41100/0.35987, loss_mask_dice_9: 1.58922/1.75953, loss_spatial_bce_9: 0.26435/0.35417, loss_spatial_dice_9: 0.91598/0.79313, loss_spatial_ce_9: 1.22389/1.38650, loss_grounding_bce_9: 0.00680/0.10102, loss_grounding_dice_9: 0.32814/0.24221, loss_grounding_ce_9: 2.03455/0.66830] items per batch[64] items per second[0.37] total items[5075200] mini batches[ 79300] memory[4999] epoch remaining[0:31:45] INFO:trainer.default_trainer:epochs[ 43] optim steps[79400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.00815/0.75246, loss_mask_bce_0: 0.04895/0.30062, loss_mask_dice_0: 0.03260/1.01992, loss_spatial_bce_0: 0.05383/0.08438, loss_spatial_dice_0: 0.03552/0.17839, loss_spatial_ce_0: 0.00001/0.05535, loss_grounding_bce_0: 0.04861/0.08059, loss_grounding_dice_0: 0.05633/0.15036, loss_grounding_ce_0: 0.00088/0.24877, loss_mask_ce_1: 0.00780/0.75310, loss_mask_bce_1: 0.04749/0.30145, loss_mask_dice_1: 0.03317/1.02421, loss_spatial_bce_1: 0.05762/0.08484, loss_spatial_dice_1: 0.03824/0.18135, loss_spatial_ce_1: 0.00001/0.05904, loss_grounding_bce_1: 0.04564/0.08081, loss_grounding_dice_1: 0.05249/0.15114, loss_grounding_ce_1: 0.00122/0.25007, loss_mask_ce_2: 0.01032/0.76080, loss_mask_bce_2: 0.04736/0.30182, loss_mask_dice_2: 0.03182/1.02503, loss_spatial_bce_2: 0.05612/0.08495, loss_spatial_dice_2: 0.03819/0.18198, loss_spatial_ce_2: 0.00001/0.06129, loss_grounding_bce_2: 0.04498/0.08077, loss_grounding_dice_2: 0.05182/0.15103, loss_grounding_ce_2: 0.00139/0.25283, loss_mask_ce_3: 0.01195/0.76554, loss_mask_bce_3: 0.04766/0.30312, loss_mask_dice_3: 0.03208/1.02304, loss_spatial_bce_3: 0.05927/0.08709, loss_spatial_dice_3: 0.03974/0.18335, loss_spatial_ce_3: 0.00005/0.06615, loss_grounding_bce_3: 0.04427/0.08113, loss_grounding_dice_3: 0.05459/0.15073, loss_grounding_ce_3: 0.00052/0.25417, loss_mask_ce_4: 0.01059/0.77126, loss_mask_bce_4: 0.04479/0.30587, loss_mask_dice_4: 0.03073/1.04249, loss_spatial_bce_4: 0.05968/0.08955, loss_spatial_dice_4: 0.03564/0.19208, loss_spatial_ce_4: 0.00001/0.08000, loss_grounding_bce_4: 0.04239/0.08191, loss_grounding_dice_4: 0.04996/0.15332, loss_grounding_ce_4: 0.00028/0.25837, loss_mask_ce_5: 0.01265/0.79686, loss_mask_bce_5: 0.04479/0.30772, loss_mask_dice_5: 0.03181/1.05052, loss_spatial_bce_5: 0.05301/0.09199, loss_spatial_dice_5: 0.03383/0.19542, loss_spatial_ce_5: 0.00000/0.09366, loss_grounding_bce_5: 0.04483/0.08217, loss_grounding_dice_5: 0.05277/0.15421, loss_grounding_ce_5: 0.00007/0.27620, loss_mask_ce_6: 0.01520/0.82426, loss_mask_bce_6: 0.04364/0.30993, loss_mask_dice_6: 0.03128/1.05446, loss_spatial_bce_6: 0.06679/0.09749, loss_spatial_dice_6: 0.04243/0.19774, loss_spatial_ce_6: 0.00007/0.11803, loss_grounding_bce_6: 0.04176/0.08299, loss_grounding_dice_6: 0.05319/0.15468, loss_grounding_ce_6: 0.00058/0.28505, loss_mask_ce_7: 0.01793/0.87994, loss_mask_bce_7: 0.04682/0.31715, loss_mask_dice_7: 0.03089/1.10025, loss_spatial_bce_7: 0.05368/0.10666, loss_spatial_dice_7: 0.03323/0.22268, loss_spatial_ce_7: 0.06796/0.15278, loss_grounding_bce_7: 0.04351/0.08468, loss_grounding_dice_7: 0.04950/0.16020, loss_grounding_ce_7: 0.00028/0.31830, loss_mask_ce_8: 0.02331/1.01379, loss_mask_bce_8: 0.04445/0.33314, loss_mask_dice_8: 0.03401/1.17679, loss_spatial_bce_8: 0.04930/0.12307, loss_spatial_dice_8: 0.03082/0.25728, loss_spatial_ce_8: 0.02394/0.19820, loss_grounding_bce_8: 0.04234/0.08886, loss_grounding_dice_8: 0.05730/0.16998, loss_grounding_ce_8: 0.00354/0.41609, loss_mask_ce_9: 1.48089/3.47409, loss_mask_bce_9: 0.04753/0.35986, loss_mask_dice_9: 0.03905/1.75956, loss_spatial_bce_9: 0.26900/0.35416, loss_spatial_dice_9: 0.15148/0.79312, loss_spatial_ce_9: 0.15724/1.38652, loss_grounding_bce_9: 0.04355/0.10100, loss_grounding_dice_9: 0.06413/0.24219, loss_grounding_ce_9: 0.07123/0.66821] items per batch[64] items per second[0.36] total items[5081600] mini batches[ 79400] memory[4999] epoch remaining[0:28:55] INFO:trainer.default_trainer:epochs[ 43] optim steps[79500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.31643/0.75232, loss_mask_bce_0: 0.23140/0.30056, loss_mask_dice_0: 0.17379/1.02003, loss_spatial_bce_0: 0.09299/0.08436, loss_spatial_dice_0: 0.08173/0.17836, loss_spatial_ce_0: 0.00093/0.05534, loss_grounding_bce_0: 0.13252/0.08058, loss_grounding_dice_0: 0.09678/0.15035, loss_grounding_ce_0: 0.11200/0.24874, loss_mask_ce_1: 0.30371/0.75297, loss_mask_bce_1: 0.24625/0.30139, loss_mask_dice_1: 0.17596/1.02433, loss_spatial_bce_1: 0.09851/0.08482, loss_spatial_dice_1: 0.08040/0.18133, loss_spatial_ce_1: 0.00122/0.05902, loss_grounding_bce_1: 0.13442/0.08079, loss_grounding_dice_1: 0.09580/0.15114, loss_grounding_ce_1: 0.08417/0.25008, loss_mask_ce_2: 0.32177/0.76067, loss_mask_bce_2: 0.23227/0.30175, loss_mask_dice_2: 0.17353/1.02513, loss_spatial_bce_2: 0.09287/0.08493, loss_spatial_dice_2: 0.08925/0.18196, loss_spatial_ce_2: 0.00894/0.06129, loss_grounding_bce_2: 0.13577/0.08076, loss_grounding_dice_2: 0.09873/0.15103, loss_grounding_ce_2: 0.06463/0.25284, loss_mask_ce_3: 0.37653/0.76541, loss_mask_bce_3: 0.22124/0.30307, loss_mask_dice_3: 0.16597/1.02317, loss_spatial_bce_3: 0.10619/0.08707, loss_spatial_dice_3: 0.10621/0.18334, loss_spatial_ce_3: 0.07930/0.06615, loss_grounding_bce_3: 0.12678/0.08111, loss_grounding_dice_3: 0.09571/0.15072, loss_grounding_ce_3: 0.11192/0.25415, loss_mask_ce_4: 0.46358/0.77117, loss_mask_bce_4: 0.21251/0.30581, loss_mask_dice_4: 0.16084/1.04264, loss_spatial_bce_4: 0.10214/0.08954, loss_spatial_dice_4: 0.09599/0.19208, loss_spatial_ce_4: 0.08208/0.08001, loss_grounding_bce_4: 0.12936/0.08189, loss_grounding_dice_4: 0.09593/0.15333, loss_grounding_ce_4: 0.15341/0.25838, loss_mask_ce_5: 0.44067/0.79672, loss_mask_bce_5: 0.22031/0.30766, loss_mask_dice_5: 0.16989/1.05065, loss_spatial_bce_5: 0.11655/0.09198, loss_spatial_dice_5: 0.10519/0.19541, loss_spatial_ce_5: 0.08533/0.09365, loss_grounding_bce_5: 0.12393/0.08215, loss_grounding_dice_5: 0.09893/0.15420, loss_grounding_ce_5: 0.13386/0.27622, loss_mask_ce_6: 0.38275/0.82414, loss_mask_bce_6: 0.21898/0.30987, loss_mask_dice_6: 0.17370/1.05462, loss_spatial_bce_6: 0.13685/0.09748, loss_spatial_dice_6: 0.16502/0.19773, loss_spatial_ce_6: 0.03065/0.11803, loss_grounding_bce_6: 0.11896/0.08297, loss_grounding_dice_6: 0.09447/0.15468, loss_grounding_ce_6: 0.08842/0.28502, loss_mask_ce_7: 0.35328/0.87985, loss_mask_bce_7: 0.22013/0.31709, loss_mask_dice_7: 0.19209/1.10040, loss_spatial_bce_7: 0.10837/0.10663, loss_spatial_dice_7: 0.11542/0.22266, loss_spatial_ce_7: 0.11123/0.15277, loss_grounding_bce_7: 0.12648/0.08466, loss_grounding_dice_7: 0.10834/0.16020, loss_grounding_ce_7: 0.09005/0.31826, loss_mask_ce_8: 0.21967/1.01365, loss_mask_bce_8: 0.25877/0.33308, loss_mask_dice_8: 0.31288/1.17695, loss_spatial_bce_8: 0.13459/0.12304, loss_spatial_dice_8: 0.11268/0.25726, loss_spatial_ce_8: 0.30115/0.19817, loss_grounding_bce_8: 0.14787/0.08884, loss_grounding_dice_8: 0.17177/0.16998, loss_grounding_ce_8: 0.03594/0.41611, loss_mask_ce_9: 1.03963/3.47395, loss_mask_bce_9: 0.23058/0.35979, loss_mask_dice_9: 0.32504/1.75970, loss_spatial_bce_9: 0.67344/0.35411, loss_spatial_dice_9: 0.83630/0.79312, loss_spatial_ce_9: 0.57128/1.38652, loss_grounding_bce_9: 0.12269/0.10099, loss_grounding_dice_9: 0.18134/0.24219, loss_grounding_ce_9: 0.01813/0.66819] items per batch[64] items per second[0.36] total items[5088000] mini batches[ 79500] memory[4999] epoch remaining[0:25:59] INFO:trainer.default_trainer:epochs[ 43] optim steps[79600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.47548/0.75245, loss_mask_bce_0: 0.38146/0.30053, loss_mask_dice_0: 1.19714/1.02022, loss_spatial_bce_0: 0.05810/0.08434, loss_spatial_dice_0: 0.20165/0.17836, loss_spatial_ce_0: 0.03004/0.05532, loss_grounding_bce_0: 0.02907/0.08056, loss_grounding_dice_0: 0.29264/0.15035, loss_grounding_ce_0: 0.34093/0.24869, loss_mask_ce_1: 1.32106/0.75309, loss_mask_bce_1: 0.30360/0.30137, loss_mask_dice_1: 1.05289/1.02451, loss_spatial_bce_1: 0.05812/0.08481, loss_spatial_dice_1: 0.21430/0.18132, loss_spatial_ce_1: 0.03195/0.05902, loss_grounding_bce_1: 0.02448/0.08078, loss_grounding_dice_1: 0.25303/0.15113, loss_grounding_ce_1: 0.34074/0.25003, loss_mask_ce_2: 0.44339/0.76073, loss_mask_bce_2: 0.37512/0.30173, loss_mask_dice_2: 1.14622/1.02530, loss_spatial_bce_2: 0.05557/0.08491, loss_spatial_dice_2: 0.22210/0.18195, loss_spatial_ce_2: 0.03317/0.06126, loss_grounding_bce_2: 0.02425/0.08075, loss_grounding_dice_2: 0.22615/0.15102, loss_grounding_ce_2: 0.36494/0.25277, loss_mask_ce_3: 0.54665/0.76550, loss_mask_bce_3: 0.37126/0.30304, loss_mask_dice_3: 1.63765/1.02337, loss_spatial_bce_3: 0.06252/0.08705, loss_spatial_dice_3: 0.26045/0.18333, loss_spatial_ce_3: 0.04552/0.06613, loss_grounding_bce_3: 0.02520/0.08110, loss_grounding_dice_3: 0.25289/0.15071, loss_grounding_ce_3: 0.38448/0.25410, loss_mask_ce_4: 0.70397/0.77126, loss_mask_bce_4: 0.29657/0.30579, loss_mask_dice_4: 1.07044/1.04288, loss_spatial_bce_4: 0.06333/0.08952, loss_spatial_dice_4: 0.26884/0.19208, loss_spatial_ce_4: 0.05042/0.07998, loss_grounding_bce_4: 0.02553/0.08188, loss_grounding_dice_4: 0.34322/0.15333, loss_grounding_ce_4: 0.47358/0.25833, loss_mask_ce_5: 0.86417/0.79680, loss_mask_bce_5: 0.29468/0.30764, loss_mask_dice_5: 1.09759/1.05088, loss_spatial_bce_5: 0.06815/0.09197, loss_spatial_dice_5: 0.28066/0.19542, loss_spatial_ce_5: 0.03834/0.09363, loss_grounding_bce_5: 0.02657/0.08213, loss_grounding_dice_5: 0.33323/0.15420, loss_grounding_ce_5: 0.44107/0.27620, loss_mask_ce_6: 0.50544/0.82422, loss_mask_bce_6: 0.35928/0.30985, loss_mask_dice_6: 1.25322/1.05488, loss_spatial_bce_6: 0.06915/0.09747, loss_spatial_dice_6: 0.28400/0.19772, loss_spatial_ce_6: 0.05787/0.11802, loss_grounding_bce_6: 0.03128/0.08296, loss_grounding_dice_6: 0.31504/0.15467, loss_grounding_ce_6: 0.59865/0.28501, loss_mask_ce_7: 1.02853/0.87991, loss_mask_bce_7: 0.30539/0.31706, loss_mask_dice_7: 1.23697/1.10065, loss_spatial_bce_7: 0.06425/0.10661, loss_spatial_dice_7: 0.30031/0.22266, loss_spatial_ce_7: 0.10042/0.15275, loss_grounding_bce_7: 0.04644/0.08465, loss_grounding_dice_7: 0.37885/0.16019, loss_grounding_ce_7: 0.19019/0.31825, loss_mask_ce_8: 0.81117/1.01372, loss_mask_bce_8: 0.36738/0.33305, loss_mask_dice_8: 1.67811/1.17718, loss_spatial_bce_8: 0.08185/0.12302, loss_spatial_dice_8: 0.30695/0.25725, loss_spatial_ce_8: 0.18778/0.19812, loss_grounding_bce_8: 0.03605/0.08883, loss_grounding_dice_8: 0.45287/0.16998, loss_grounding_ce_8: 0.01569/0.41603, loss_mask_ce_9: 2.41544/3.47404, loss_mask_bce_9: 0.30358/0.35979, loss_mask_dice_9: 1.72775/1.75994, loss_spatial_bce_9: 0.34438/0.35409, loss_spatial_dice_9: 0.87126/0.79314, loss_spatial_ce_9: 1.33559/1.38653, loss_grounding_bce_9: 0.04471/0.10098, loss_grounding_dice_9: 0.48157/0.24217, loss_grounding_ce_9: 0.02082/0.66818] items per batch[64] items per second[0.36] total items[5094400] mini batches[ 79600] memory[4999] epoch remaining[0:23:04] INFO:trainer.default_trainer:epochs[ 43] optim steps[79700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.16373/0.75246, loss_mask_bce_0: 0.24568/0.30056, loss_mask_dice_0: 0.20317/1.02009, loss_spatial_bce_0: 0.08071/0.08436, loss_spatial_dice_0: 0.07093/0.17834, loss_spatial_ce_0: 0.00048/0.05529, loss_grounding_bce_0: 0.02429/0.08057, loss_grounding_dice_0: 0.05926/0.15034, loss_grounding_ce_0: 0.05301/0.24867, loss_mask_ce_1: 0.16996/0.75311, loss_mask_bce_1: 0.25357/0.30139, loss_mask_dice_1: 0.21295/1.02439, loss_spatial_bce_1: 0.08485/0.08482, loss_spatial_dice_1: 0.07555/0.18131, loss_spatial_ce_1: 0.00116/0.05902, loss_grounding_bce_1: 0.02653/0.08079, loss_grounding_dice_1: 0.06296/0.15112, loss_grounding_ce_1: 0.06479/0.25004, loss_mask_ce_2: 0.17591/0.76075, loss_mask_bce_2: 0.26878/0.30176, loss_mask_dice_2: 0.22566/1.02518, loss_spatial_bce_2: 0.09599/0.08493, loss_spatial_dice_2: 0.07399/0.18194, loss_spatial_ce_2: 0.00151/0.06124, loss_grounding_bce_2: 0.02085/0.08076, loss_grounding_dice_2: 0.05358/0.15102, loss_grounding_ce_2: 0.04289/0.25280, loss_mask_ce_3: 0.17095/0.76554, loss_mask_bce_3: 0.27540/0.30307, loss_mask_dice_3: 0.21939/1.02325, loss_spatial_bce_3: 0.09001/0.08707, loss_spatial_dice_3: 0.07229/0.18332, loss_spatial_ce_3: 0.00144/0.06612, loss_grounding_bce_3: 0.02667/0.08111, loss_grounding_dice_3: 0.06174/0.15071, loss_grounding_ce_3: 0.04997/0.25413, loss_mask_ce_4: 0.14376/0.77130, loss_mask_bce_4: 0.25953/0.30582, loss_mask_dice_4: 0.20768/1.04274, loss_spatial_bce_4: 0.08188/0.08954, loss_spatial_dice_4: 0.06755/0.19207, loss_spatial_ce_4: 0.00338/0.07996, loss_grounding_bce_4: 0.02482/0.08189, loss_grounding_dice_4: 0.05987/0.15333, loss_grounding_ce_4: 0.04846/0.25834, loss_mask_ce_5: 0.15540/0.79682, loss_mask_bce_5: 0.25907/0.30768, loss_mask_dice_5: 0.20906/1.05077, loss_spatial_bce_5: 0.07937/0.09199, loss_spatial_dice_5: 0.06978/0.19541, loss_spatial_ce_5: 0.00662/0.09364, loss_grounding_bce_5: 0.02226/0.08214, loss_grounding_dice_5: 0.05556/0.15419, loss_grounding_ce_5: 0.06217/0.27615, loss_mask_ce_6: 0.20410/0.82424, loss_mask_bce_6: 0.26088/0.30988, loss_mask_dice_6: 0.20426/1.05477, loss_spatial_bce_6: 0.08553/0.09749, loss_spatial_dice_6: 0.07057/0.19771, loss_spatial_ce_6: 0.01272/0.11801, loss_grounding_bce_6: 0.02216/0.08297, loss_grounding_dice_6: 0.05514/0.15466, loss_grounding_ce_6: 0.11345/0.28497, loss_mask_ce_7: 0.22735/0.87991, loss_mask_bce_7: 0.26938/0.31709, loss_mask_dice_7: 0.21057/1.10051, loss_spatial_bce_7: 0.08859/0.10663, loss_spatial_dice_7: 0.07591/0.22264, loss_spatial_ce_7: 0.03734/0.15271, loss_grounding_bce_7: 0.02235/0.08466, loss_grounding_dice_7: 0.05722/0.16018, loss_grounding_ce_7: 0.10244/0.31818, loss_mask_ce_8: 0.20440/1.01370, loss_mask_bce_8: 0.26752/0.33308, loss_mask_dice_8: 0.21762/1.17700, loss_spatial_bce_8: 0.11142/0.12304, loss_spatial_dice_8: 0.11824/0.25722, loss_spatial_ce_8: 0.04159/0.19812, loss_grounding_bce_8: 0.02259/0.08883, loss_grounding_dice_8: 0.05343/0.16996, loss_grounding_ce_8: 0.02903/0.41597, loss_mask_ce_9: 1.87627/3.47382, loss_mask_bce_9: 0.25534/0.35982, loss_mask_dice_9: 0.25754/1.75969, loss_spatial_bce_9: 0.46651/0.35417, loss_spatial_dice_9: 0.83354/0.79311, loss_spatial_ce_9: 1.34362/1.38655, loss_grounding_bce_9: 0.02015/0.10099, loss_grounding_dice_9: 0.06261/0.24215, loss_grounding_ce_9: 0.13524/0.66813] items per batch[64] items per second[0.37] total items[5100800] mini batches[ 79700] memory[4999] epoch remaining[0:20:07] INFO:trainer.default_trainer:epochs[ 43] optim steps[79800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.86077/0.75251, loss_mask_bce_0: 0.18236/0.30060, loss_mask_dice_0: 2.43678/1.02021, loss_spatial_bce_0: 0.02537/0.08435, loss_spatial_dice_0: 0.20588/0.17832, loss_spatial_ce_0: 0.03958/0.05527, loss_grounding_bce_0: 0.01176/0.08056, loss_grounding_dice_0: 0.23007/0.15034, loss_grounding_ce_0: 0.63222/0.24867, loss_mask_ce_1: 0.90685/0.75317, loss_mask_bce_1: 0.19975/0.30144, loss_mask_dice_1: 2.47889/1.02450, loss_spatial_bce_1: 0.02255/0.08481, loss_spatial_dice_1: 0.24175/0.18129, loss_spatial_ce_1: 0.03805/0.05900, loss_grounding_bce_1: 0.01147/0.08078, loss_grounding_dice_1: 0.18180/0.15112, loss_grounding_ce_1: 0.63898/0.25005, loss_mask_ce_2: 0.93829/0.76083, loss_mask_bce_2: 0.21425/0.30181, loss_mask_dice_2: 2.38706/1.02529, loss_spatial_bce_2: 0.02403/0.08492, loss_spatial_dice_2: 0.20361/0.18192, loss_spatial_ce_2: 0.03611/0.06122, loss_grounding_bce_2: 0.00987/0.08075, loss_grounding_dice_2: 0.22977/0.15102, loss_grounding_ce_2: 0.62372/0.25282, loss_mask_ce_3: 0.90364/0.76558, loss_mask_bce_3: 0.18544/0.30312, loss_mask_dice_3: 2.39802/1.02336, loss_spatial_bce_3: 0.02056/0.08705, loss_spatial_dice_3: 0.19969/0.18330, loss_spatial_ce_3: 0.06583/0.06610, loss_grounding_bce_3: 0.01226/0.08110, loss_grounding_dice_3: 0.21478/0.15071, loss_grounding_ce_3: 0.66898/0.25414, loss_mask_ce_4: 0.87992/0.77138, loss_mask_bce_4: 0.21443/0.30588, loss_mask_dice_4: 2.51642/1.04285, loss_spatial_bce_4: 0.02755/0.08954, loss_spatial_dice_4: 0.26882/0.19206, loss_spatial_ce_4: 0.04462/0.07994, loss_grounding_bce_4: 0.01263/0.08188, loss_grounding_dice_4: 0.20969/0.15333, loss_grounding_ce_4: 0.61966/0.25832, loss_mask_ce_5: 0.70039/0.79691, loss_mask_bce_5: 0.21622/0.30774, loss_mask_dice_5: 2.40131/1.05088, loss_spatial_bce_5: 0.02532/0.09198, loss_spatial_dice_5: 0.25520/0.19540, loss_spatial_ce_5: 0.11630/0.09361, loss_grounding_bce_5: 0.01216/0.08213, loss_grounding_dice_5: 0.20984/0.15419, loss_grounding_ce_5: 0.60592/0.27614, loss_mask_ce_6: 0.96518/0.82431, loss_mask_bce_6: 0.21632/0.30993, loss_mask_dice_6: 2.70698/1.05486, loss_spatial_bce_6: 0.03901/0.09748, loss_spatial_dice_6: 0.28675/0.19770, loss_spatial_ce_6: 0.08320/0.11798, loss_grounding_bce_6: 0.01028/0.08296, loss_grounding_dice_6: 0.20966/0.15466, loss_grounding_ce_6: 0.65932/0.28496, loss_mask_ce_7: 0.90128/0.88002, loss_mask_bce_7: 0.21003/0.31714, loss_mask_dice_7: 2.45750/1.10063, loss_spatial_bce_7: 0.03696/0.10661, loss_spatial_dice_7: 0.29795/0.22263, loss_spatial_ce_7: 0.27962/0.15268, loss_grounding_bce_7: 0.01394/0.08464, loss_grounding_dice_7: 0.21552/0.16018, loss_grounding_ce_7: 0.68635/0.31817, loss_mask_ce_8: 0.93262/1.01385, loss_mask_bce_8: 0.22856/0.33313, loss_mask_dice_8: 2.76600/1.17714, loss_spatial_bce_8: 0.03718/0.12302, loss_spatial_dice_8: 0.38322/0.25721, loss_spatial_ce_8: 0.16216/0.19807, loss_grounding_bce_8: 0.01505/0.08881, loss_grounding_dice_8: 0.29275/0.16997, loss_grounding_ce_8: 0.68713/0.41595, loss_mask_ce_9: 3.48909/3.47410, loss_mask_bce_9: 0.18786/0.35987, loss_mask_dice_9: 4.08439/1.75997, loss_spatial_bce_9: 0.19456/0.35414, loss_spatial_dice_9: 0.97850/0.79311, loss_spatial_ce_9: 1.68709/1.38654, loss_grounding_bce_9: 0.01060/0.10097, loss_grounding_dice_9: 0.39082/0.24215, loss_grounding_ce_9: 0.69060/0.66819] items per batch[64] items per second[0.36] total items[5107200] mini batches[ 79800] memory[4999] epoch remaining[0:17:13] INFO:trainer.default_trainer:epochs[ 43] optim steps[79900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.12807/0.75237, loss_mask_bce_0: 0.43360/0.30057, loss_mask_dice_0: 2.93045/1.02013, loss_spatial_bce_0: 0.07961/0.08434, loss_spatial_dice_0: 0.42669/0.17830, loss_spatial_ce_0: 0.06591/0.05526, loss_grounding_bce_0: 0.00844/0.08056, loss_grounding_dice_0: 0.02363/0.15034, loss_grounding_ce_0: 1.08712/0.24859, loss_mask_ce_1: 1.13686/0.75303, loss_mask_bce_1: 0.42578/0.30141, loss_mask_dice_1: 2.96942/1.02444, loss_spatial_bce_1: 0.08123/0.08481, loss_spatial_dice_1: 0.42014/0.18128, loss_spatial_ce_1: 0.05909/0.05900, loss_grounding_bce_1: 0.00784/0.08078, loss_grounding_dice_1: 0.01877/0.15112, loss_grounding_ce_1: 0.89200/0.24998, loss_mask_ce_2: 1.17526/0.76069, loss_mask_bce_2: 0.41825/0.30177, loss_mask_dice_2: 2.99888/1.02520, loss_spatial_bce_2: 0.06913/0.08492, loss_spatial_dice_2: 0.42727/0.18191, loss_spatial_ce_2: 0.10015/0.06121, loss_grounding_bce_2: 0.01217/0.08075, loss_grounding_dice_2: 0.02335/0.15102, loss_grounding_ce_2: 0.87158/0.25273, loss_mask_ce_3: 1.11656/0.76545, loss_mask_bce_3: 0.45499/0.30308, loss_mask_dice_3: 2.98033/1.02329, loss_spatial_bce_3: 0.07352/0.08705, loss_spatial_dice_3: 0.42144/0.18329, loss_spatial_ce_3: 0.11127/0.06611, loss_grounding_bce_3: 0.01094/0.08110, loss_grounding_dice_3: 0.02096/0.15071, loss_grounding_ce_3: 0.84799/0.25406, loss_mask_ce_4: 1.11076/0.77125, loss_mask_bce_4: 0.43438/0.30584, loss_mask_dice_4: 3.00649/1.04276, loss_spatial_bce_4: 0.08013/0.08954, loss_spatial_dice_4: 0.46353/0.19205, loss_spatial_ce_4: 0.09624/0.07993, loss_grounding_bce_4: 0.01086/0.08188, loss_grounding_dice_4: 0.02443/0.15332, loss_grounding_ce_4: 1.12792/0.25823, loss_mask_ce_5: 1.10546/0.79681, loss_mask_bce_5: 0.41699/0.30770, loss_mask_dice_5: 3.04619/1.05083, loss_spatial_bce_5: 0.13773/0.09198, loss_spatial_dice_5: 0.47293/0.19539, loss_spatial_ce_5: 0.04919/0.09361, loss_grounding_bce_5: 0.01080/0.08213, loss_grounding_dice_5: 0.02211/0.15419, loss_grounding_ce_5: 1.73843/0.27606, loss_mask_ce_6: 1.16433/0.82416, loss_mask_bce_6: 0.43356/0.30989, loss_mask_dice_6: 2.89867/1.05473, loss_spatial_bce_6: 0.09178/0.09748, loss_spatial_dice_6: 0.45622/0.19770, loss_spatial_ce_6: 0.14835/0.11798, loss_grounding_bce_6: 0.00919/0.08296, loss_grounding_dice_6: 0.02055/0.15466, loss_grounding_ce_6: 1.56485/0.28489, loss_mask_ce_7: 1.29239/0.87992, loss_mask_bce_7: 0.39823/0.31710, loss_mask_dice_7: 2.98543/1.10056, loss_spatial_bce_7: 0.09611/0.10660, loss_spatial_dice_7: 0.44282/0.22261, loss_spatial_ce_7: 0.28877/0.15266, loss_grounding_bce_7: 0.01032/0.08464, loss_grounding_dice_7: 0.01953/0.16016, loss_grounding_ce_7: 2.22478/0.31812, loss_mask_ce_8: 1.21829/1.01372, loss_mask_bce_8: 0.45850/0.33309, loss_mask_dice_8: 3.54344/1.17702, loss_spatial_bce_8: 0.08427/0.12302, loss_spatial_dice_8: 0.53213/0.25719, loss_spatial_ce_8: 0.26415/0.19804, loss_grounding_bce_8: 0.01249/0.08881, loss_grounding_dice_8: 0.02020/0.16995, loss_grounding_ce_8: 3.47355/0.41595, loss_mask_ce_9: 3.05813/3.47383, loss_mask_bce_9: 0.53535/0.35984, loss_mask_dice_9: 4.40520/1.75974, loss_spatial_bce_9: 0.29675/0.35416, loss_spatial_dice_9: 0.98080/0.79309, loss_spatial_ce_9: 1.84380/1.38653, loss_grounding_bce_9: 0.04797/0.10099, loss_grounding_dice_9: 0.18885/0.24213, loss_grounding_ce_9: 3.99620/0.66812] items per batch[64] items per second[0.36] total items[5113600] mini batches[ 79900] memory[4999] epoch remaining[0:14:17] INFO:trainer.default_trainer:epochs[ 43] optim steps[80000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.01853/0.75243, loss_mask_bce_0: 0.01521/0.30060, loss_mask_dice_0: 0.13739/1.02025, loss_spatial_bce_0: 0.00660/0.08432, loss_spatial_dice_0: 0.06476/0.17830, loss_spatial_ce_0: 0.11081/0.05524, loss_grounding_bce_0: 0.00679/0.08053, loss_grounding_dice_0: 0.09979/0.15032, loss_grounding_ce_0: 0.07213/0.24861, loss_mask_ce_1: 0.01831/0.75311, loss_mask_bce_1: 0.01399/0.30143, loss_mask_dice_1: 0.08490/1.02460, loss_spatial_bce_1: 0.00698/0.08479, loss_spatial_dice_1: 0.07274/0.18128, loss_spatial_ce_1: 0.04898/0.05897, loss_grounding_bce_1: 0.00745/0.08075, loss_grounding_dice_1: 0.05963/0.15111, loss_grounding_ce_1: 0.07749/0.25002, loss_mask_ce_2: 0.01733/0.76078, loss_mask_bce_2: 0.01502/0.30180, loss_mask_dice_2: 0.09958/1.02536, loss_spatial_bce_2: 0.00567/0.08490, loss_spatial_dice_2: 0.03718/0.18190, loss_spatial_ce_2: 0.11702/0.06118, loss_grounding_bce_2: 0.00849/0.08072, loss_grounding_dice_2: 0.05875/0.15100, loss_grounding_ce_2: 0.07106/0.25275, loss_mask_ce_3: 0.01895/0.76553, loss_mask_bce_3: 0.01388/0.30311, loss_mask_dice_3: 0.10479/1.02344, loss_spatial_bce_3: 0.00714/0.08703, loss_spatial_dice_3: 0.05507/0.18328, loss_spatial_ce_3: 0.07868/0.06607, loss_grounding_bce_3: 0.00744/0.08107, loss_grounding_dice_3: 0.04206/0.15069, loss_grounding_ce_3: 0.07143/0.25407, loss_mask_ce_4: 0.06383/0.77133, loss_mask_bce_4: 0.01238/0.30587, loss_mask_dice_4: 0.07210/1.04292, loss_spatial_bce_4: 0.00739/0.08952, loss_spatial_dice_4: 0.04399/0.19204, loss_spatial_ce_4: 0.00447/0.07991, loss_grounding_bce_4: 0.00768/0.08185, loss_grounding_dice_4: 0.04702/0.15331, loss_grounding_ce_4: 0.07267/0.25824, loss_mask_ce_5: 0.09590/0.79688, loss_mask_bce_5: 0.01479/0.30773, loss_mask_dice_5: 0.07316/1.05101, loss_spatial_bce_5: 0.00773/0.09196, loss_spatial_dice_5: 0.06089/0.19539, loss_spatial_ce_5: 0.00439/0.09360, loss_grounding_bce_5: 0.00848/0.08210, loss_grounding_dice_5: 0.06029/0.15417, loss_grounding_ce_5: 0.08233/0.27609, loss_mask_ce_6: 0.08117/0.82430, loss_mask_bce_6: 0.01791/0.30993, loss_mask_dice_6: 0.11755/1.05491, loss_spatial_bce_6: 0.01009/0.09746, loss_spatial_dice_6: 0.05550/0.19770, loss_spatial_ce_6: 0.05354/0.11794, loss_grounding_bce_6: 0.00888/0.08293, loss_grounding_dice_6: 0.02049/0.15463, loss_grounding_ce_6: 0.07841/0.28491, loss_mask_ce_7: 0.14705/0.88001, loss_mask_bce_7: 0.02137/0.31713, loss_mask_dice_7: 0.07896/1.10075, loss_spatial_bce_7: 0.00993/0.10658, loss_spatial_dice_7: 0.07310/0.22261, loss_spatial_ce_7: 0.00865/0.15262, loss_grounding_bce_7: 0.01069/0.08461, loss_grounding_dice_7: 0.05540/0.16015, loss_grounding_ce_7: 0.14631/0.31813, loss_mask_ce_8: 0.11962/1.01379, loss_mask_bce_8: 0.01904/0.33311, loss_mask_dice_8: 0.52532/1.17722, loss_spatial_bce_8: 0.01376/0.12298, loss_spatial_dice_8: 0.14115/0.25718, loss_spatial_ce_8: 0.03942/0.19799, loss_grounding_bce_8: 0.01027/0.08878, loss_grounding_dice_8: 0.26027/0.16994, loss_grounding_ce_8: 0.13373/0.41592, loss_mask_ce_9: 2.23838/3.47392, loss_mask_bce_9: 0.01550/0.35986, loss_mask_dice_9: 0.20275/1.76019, loss_spatial_bce_9: 0.19763/0.35410, loss_spatial_dice_9: 0.61025/0.79312, loss_spatial_ce_9: 0.61350/1.38656, loss_grounding_bce_9: 0.00736/0.10095, loss_grounding_dice_9: 0.04604/0.24213, loss_grounding_ce_9: 0.30639/0.66812] items per batch[64] items per second[0.37] total items[5120000] mini batches[ 80000] memory[4999] epoch remaining[0:11:21] INFO:trainer.default_trainer:epochs[ 43] optim steps[80100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.42560/0.75245, loss_mask_bce_0: 0.28258/0.30056, loss_mask_dice_0: 0.32461/1.02033, loss_spatial_bce_0: 0.15294/0.08430, loss_spatial_dice_0: 0.20619/0.17830, loss_spatial_ce_0: 0.15468/0.05522, loss_grounding_bce_0: 0.19045/0.08053, loss_grounding_dice_0: 0.17510/0.15033, loss_grounding_ce_0: 0.46583/0.24869, loss_mask_ce_1: 1.45547/0.75310, loss_mask_bce_1: 0.28726/0.30140, loss_mask_dice_1: 0.29731/1.02470, loss_spatial_bce_1: 0.17433/0.08477, loss_spatial_dice_1: 0.24424/0.18127, loss_spatial_ce_1: 0.06334/0.05896, loss_grounding_bce_1: 0.20803/0.08075, loss_grounding_dice_1: 0.18268/0.15111, loss_grounding_ce_1: 0.43830/0.25006, loss_mask_ce_2: 1.43278/0.76075, loss_mask_bce_2: 0.25834/0.30176, loss_mask_dice_2: 0.29655/1.02544, loss_spatial_bce_2: 0.16346/0.08488, loss_spatial_dice_2: 0.21218/0.18190, loss_spatial_ce_2: 0.15321/0.06116, loss_grounding_bce_2: 0.17581/0.08071, loss_grounding_dice_2: 0.16172/0.15100, loss_grounding_ce_2: 0.49240/0.25276, loss_mask_ce_3: 1.48156/0.76552, loss_mask_bce_3: 0.26299/0.30308, loss_mask_dice_3: 0.31820/1.02355, loss_spatial_bce_3: 0.15165/0.08700, loss_spatial_dice_3: 0.21795/0.18328, loss_spatial_ce_3: 0.15666/0.06605, loss_grounding_bce_3: 0.18865/0.08106, loss_grounding_dice_3: 0.18748/0.15069, loss_grounding_ce_3: 0.51187/0.25411, loss_mask_ce_4: 1.81312/0.77131, loss_mask_bce_4: 0.28120/0.30583, loss_mask_dice_4: 0.31832/1.04300, loss_spatial_bce_4: 0.16707/0.08950, loss_spatial_dice_4: 0.19136/0.19204, loss_spatial_ce_4: 0.18502/0.07989, loss_grounding_bce_4: 0.18474/0.08184, loss_grounding_dice_4: 0.18902/0.15330, loss_grounding_ce_4: 0.62935/0.25830, loss_mask_ce_5: 2.03502/0.79689, loss_mask_bce_5: 0.24966/0.30768, loss_mask_dice_5: 0.32641/1.05107, loss_spatial_bce_5: 0.20493/0.09194, loss_spatial_dice_5: 0.26324/0.19539, loss_spatial_ce_5: 0.13625/0.09360, loss_grounding_bce_5: 0.16501/0.08210, loss_grounding_dice_5: 0.16394/0.15416, loss_grounding_ce_5: 0.55720/0.27619, loss_mask_ce_6: 2.05237/0.82429, loss_mask_bce_6: 0.26653/0.30989, loss_mask_dice_6: 0.34822/1.05500, loss_spatial_bce_6: 0.23077/0.09743, loss_spatial_dice_6: 0.32146/0.19770, loss_spatial_ce_6: 0.12326/0.11793, loss_grounding_bce_6: 0.16695/0.08292, loss_grounding_dice_6: 0.17446/0.15463, loss_grounding_ce_6: 0.59077/0.28498, loss_mask_ce_7: 1.92802/0.88000, loss_mask_bce_7: 0.29523/0.31709, loss_mask_dice_7: 0.34015/1.10086, loss_spatial_bce_7: 0.21073/0.10656, loss_spatial_dice_7: 0.32075/0.22261, loss_spatial_ce_7: 0.33294/0.15258, loss_grounding_bce_7: 0.19313/0.08461, loss_grounding_dice_7: 0.18538/0.16015, loss_grounding_ce_7: 0.63292/0.31824, loss_mask_ce_8: 2.47892/1.01377, loss_mask_bce_8: 0.31975/0.33309, loss_mask_dice_8: 0.44358/1.17731, loss_spatial_bce_8: 0.25631/0.12296, loss_spatial_dice_8: 0.39767/0.25717, loss_spatial_ce_8: 0.40618/0.19794, loss_grounding_bce_8: 0.20382/0.08880, loss_grounding_dice_8: 0.21695/0.16996, loss_grounding_ce_8: 0.96805/0.41604, loss_mask_ce_9: 3.14693/3.47428, loss_mask_bce_9: 0.47139/0.35984, loss_mask_dice_9: 0.64343/1.76019, loss_spatial_bce_9: 0.60977/0.35407, loss_spatial_dice_9: 0.85350/0.79314, loss_spatial_ce_9: 1.77167/1.38672, loss_grounding_bce_9: 0.31652/0.10097, loss_grounding_dice_9: 0.34700/0.24214, loss_grounding_ce_9: 0.51509/0.66832] items per batch[64] items per second[0.36] total items[5126400] mini batches[ 80100] memory[4999] epoch remaining[0:08:26] INFO:trainer.default_trainer:epochs[ 43] optim steps[80200] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.05099/0.75232, loss_mask_bce_0: 0.01089/0.30055, loss_mask_dice_0: 0.04199/1.02036, loss_spatial_bce_0: 0.00683/0.08430, loss_spatial_dice_0: 0.02483/0.17829, loss_spatial_ce_0: 0.00000/0.05522, loss_grounding_bce_0: 0.00818/0.08052, loss_grounding_dice_0: 0.01801/0.15033, loss_grounding_ce_0: 0.00013/0.24865, loss_mask_ce_1: 0.04398/0.75299, loss_mask_bce_1: 0.00980/0.30139, loss_mask_dice_1: 0.03969/1.02478, loss_spatial_bce_1: 0.00780/0.08476, loss_spatial_dice_1: 0.02511/0.18126, loss_spatial_ce_1: 0.00000/0.05896, loss_grounding_bce_1: 0.00687/0.08074, loss_grounding_dice_1: 0.01836/0.15110, loss_grounding_ce_1: 0.00010/0.25003, loss_mask_ce_2: 0.03413/0.76065, loss_mask_bce_2: 0.01202/0.30176, loss_mask_dice_2: 0.04231/1.02552, loss_spatial_bce_2: 0.00723/0.08487, loss_spatial_dice_2: 0.02381/0.18189, loss_spatial_ce_2: 0.00000/0.06118, loss_grounding_bce_2: 0.00857/0.08070, loss_grounding_dice_2: 0.01894/0.15101, loss_grounding_ce_2: 0.00011/0.25271, loss_mask_ce_3: 0.05092/0.76542, loss_mask_bce_3: 0.01038/0.30308, loss_mask_dice_3: 0.03956/1.02360, loss_spatial_bce_3: 0.00671/0.08700, loss_spatial_dice_3: 0.02298/0.18327, loss_spatial_ce_3: 0.00000/0.06606, loss_grounding_bce_3: 0.00834/0.08105, loss_grounding_dice_3: 0.02078/0.15068, loss_grounding_ce_3: 0.00010/0.25405, loss_mask_ce_4: 0.04526/0.77117, loss_mask_bce_4: 0.01041/0.30582, loss_mask_dice_4: 0.04050/1.04306, loss_spatial_bce_4: 0.00863/0.08949, loss_spatial_dice_4: 0.02638/0.19204, loss_spatial_ce_4: 0.00000/0.07989, loss_grounding_bce_4: 0.00812/0.08183, loss_grounding_dice_4: 0.02000/0.15330, loss_grounding_ce_4: 0.00012/0.25821, loss_mask_ce_5: 0.04432/0.79681, loss_mask_bce_5: 0.00986/0.30768, loss_mask_dice_5: 0.04367/1.05110, loss_spatial_bce_5: 0.00814/0.09193, loss_spatial_dice_5: 0.02621/0.19539, loss_spatial_ce_5: 0.00937/0.09358, loss_grounding_bce_5: 0.00618/0.08209, loss_grounding_dice_5: 0.01838/0.15416, loss_grounding_ce_5: 0.00009/0.27613, loss_mask_ce_6: 0.06506/0.82419, loss_mask_bce_6: 0.01094/0.30988, loss_mask_dice_6: 0.04005/1.05504, loss_spatial_bce_6: 0.00747/0.09743, loss_spatial_dice_6: 0.02593/0.19770, loss_spatial_ce_6: 0.06662/0.11790, loss_grounding_bce_6: 0.00616/0.08291, loss_grounding_dice_6: 0.01698/0.15464, loss_grounding_ce_6: 0.00159/0.28490, loss_mask_ce_7: 0.21188/0.87986, loss_mask_bce_7: 0.00926/0.31710, loss_mask_dice_7: 0.03425/1.10093, loss_spatial_bce_7: 0.00743/0.10655, loss_spatial_dice_7: 0.02484/0.22260, loss_spatial_ce_7: 0.00138/0.15254, loss_grounding_bce_7: 0.00759/0.08460, loss_grounding_dice_7: 0.01381/0.16016, loss_grounding_ce_7: 0.13254/0.31817, loss_mask_ce_8: 0.14409/1.01364, loss_mask_bce_8: 0.01105/0.33310, loss_mask_dice_8: 0.04435/1.17739, loss_spatial_bce_8: 0.00858/0.12296, loss_spatial_dice_8: 0.03550/0.25715, loss_spatial_ce_8: 0.11930/0.19790, loss_grounding_bce_8: 0.00594/0.08879, loss_grounding_dice_8: 0.01676/0.16996, loss_grounding_ce_8: 0.00262/0.41597, loss_mask_ce_9: 1.87970/3.47417, loss_mask_bce_9: 0.01027/0.35984, loss_mask_dice_9: 0.05332/1.76039, loss_spatial_bce_9: 0.17527/0.35407, loss_spatial_dice_9: 0.70353/0.79315, loss_spatial_ce_9: 0.81224/1.38672, loss_grounding_bce_9: 0.00618/0.10097, loss_grounding_dice_9: 0.02219/0.24215, loss_grounding_ce_9: 0.78782/0.66822] items per batch[64] items per second[0.35] total items[5132800] mini batches[ 80200] memory[4999] epoch remaining[0:05:31] INFO:trainer.default_trainer:epochs[ 43] optim steps[80300] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.83549/0.75230, loss_mask_bce_0: 1.05060/0.30059, loss_mask_dice_0: 1.25447/1.01999, loss_spatial_bce_0: 0.23271/0.08432, loss_spatial_dice_0: 0.24418/0.17827, loss_spatial_ce_0: 0.13028/0.05521, loss_grounding_bce_0: 0.15737/0.08054, loss_grounding_dice_0: 0.27544/0.15033, loss_grounding_ce_0: 0.27451/0.24874, loss_mask_ce_1: 2.47861/0.75297, loss_mask_bce_1: 0.95608/0.30142, loss_mask_dice_1: 1.30120/1.02439, loss_spatial_bce_1: 0.24231/0.08479, loss_spatial_dice_1: 0.23987/0.18125, loss_spatial_ce_1: 0.16498/0.05895, loss_grounding_bce_1: 0.14172/0.08076, loss_grounding_dice_1: 0.25321/0.15111, loss_grounding_ce_1: 0.26774/0.25013, loss_mask_ce_2: 2.50366/0.76063, loss_mask_bce_2: 0.90543/0.30179, loss_mask_dice_2: 1.31522/1.02514, loss_spatial_bce_2: 0.23067/0.08489, loss_spatial_dice_2: 0.25178/0.18187, loss_spatial_ce_2: 0.18035/0.06117, loss_grounding_bce_2: 0.14349/0.08073, loss_grounding_dice_2: 0.25202/0.15102, loss_grounding_ce_2: 0.28910/0.25280, loss_mask_ce_3: 2.60401/0.76541, loss_mask_bce_3: 0.97867/0.30310, loss_mask_dice_3: 1.25236/1.02322, loss_spatial_bce_3: 0.28300/0.08702, loss_spatial_dice_3: 0.32613/0.18326, loss_spatial_ce_3: 0.10600/0.06605, loss_grounding_bce_3: 0.15144/0.08107, loss_grounding_dice_3: 0.24616/0.15069, loss_grounding_ce_3: 0.33302/0.25413, loss_mask_ce_4: 2.65893/0.77115, loss_mask_bce_4: 0.99912/0.30585, loss_mask_dice_4: 1.21816/1.04265, loss_spatial_bce_4: 0.26968/0.08952, loss_spatial_dice_4: 0.27901/0.19203, loss_spatial_ce_4: 0.50632/0.07992, loss_grounding_bce_4: 0.15357/0.08185, loss_grounding_dice_4: 0.26221/0.15331, loss_grounding_ce_4: 0.41313/0.25832, loss_mask_ce_5: 2.80876/0.79680, loss_mask_bce_5: 1.04846/0.30770, loss_mask_dice_5: 1.19699/1.05072, loss_spatial_bce_5: 0.28635/0.09196, loss_spatial_dice_5: 0.29870/0.19538, loss_spatial_ce_5: 0.55462/0.09360, loss_grounding_bce_5: 0.14313/0.08210, loss_grounding_dice_5: 0.22919/0.15416, loss_grounding_ce_5: 0.36695/0.27624, loss_mask_ce_6: 2.50803/0.82416, loss_mask_bce_6: 0.97018/0.30991, loss_mask_dice_6: 1.24392/1.05466, loss_spatial_bce_6: 0.24679/0.09747, loss_spatial_dice_6: 0.22955/0.19770, loss_spatial_ce_6: 0.59780/0.11793, loss_grounding_bce_6: 0.13765/0.08293, loss_grounding_dice_6: 0.21652/0.15464, loss_grounding_ce_6: 0.37296/0.28497, loss_mask_ce_7: 2.56111/0.87980, loss_mask_bce_7: 1.04645/0.31713, loss_mask_dice_7: 1.40325/1.10051, loss_spatial_bce_7: 0.26814/0.10658, loss_spatial_dice_7: 0.29245/0.22259, loss_spatial_ce_7: 0.77985/0.15252, loss_grounding_bce_7: 0.16525/0.08462, loss_grounding_dice_7: 0.26242/0.16016, loss_grounding_ce_7: 0.40875/0.31828, loss_mask_ce_8: 2.32422/1.01356, loss_mask_bce_8: 0.97196/0.33312, loss_mask_dice_8: 1.29729/1.17694, loss_spatial_bce_8: 0.26117/0.12296, loss_spatial_dice_8: 0.35608/0.25712, loss_spatial_ce_8: 0.97517/0.19789, loss_grounding_bce_8: 0.16464/0.08881, loss_grounding_dice_8: 0.26997/0.16997, loss_grounding_ce_8: 0.32017/0.41609, loss_mask_ce_9: 4.50278/3.47385, loss_mask_bce_9: 0.87040/0.35986, loss_mask_dice_9: 1.68297/1.75967, loss_spatial_bce_9: 0.45896/0.35412, loss_spatial_dice_9: 0.80306/0.79311, loss_spatial_ce_9: 1.12665/1.38665, loss_grounding_bce_9: 0.13626/0.10098, loss_grounding_dice_9: 0.37777/0.24215, loss_grounding_ce_9: 0.52627/0.66825] items per batch[64] items per second[0.37] total items[5139200] mini batches[ 80300] memory[4999] epoch remaining[0:02:34] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00080388. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0028 s/iter. Inference: 0.3762 s/iter. Eval: 0.0810 s/iter. Total: 0.4601 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0024 s/iter. Inference: 0.3807 s/iter. Eval: 0.0765 s/iter. Total: 0.4597 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0026 s/iter. Inference: 0.3854 s/iter. Eval: 0.0717 s/iter. Total: 0.4598 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0027 s/iter. Inference: 0.3865 s/iter. Eval: 0.0720 s/iter. Total: 0.4614 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3854 s/iter. Eval: 0.0706 s/iter. Total: 0.4588 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0028 s/iter. Inference: 0.3863 s/iter. Eval: 0.0682 s/iter. Total: 0.4574 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evall0xowxvm ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.607 | 83.026 | 66.115 | 133 | | Things | 62.045 | 84.128 | 73.273 | 80 | | Stuff | 45.889 | 81.363 | 55.311 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.50s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 12.94 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.32 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.01s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.74 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.47 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 45.623 | 69.578 | 49.226 | 26.283 | 49.753 | 67.297 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.236 | bicycle | 24.407 | car | 42.656 | | motorcycle | 41.898 | airplane | 61.480 | bus | 71.027 | | train | 74.365 | truck | 45.633 | boat | 31.473 | | traffic light | 28.904 | fire hydrant | 72.371 | stop sign | 68.785 | | parking meter | 51.226 | bench | 27.511 | bird | 34.632 | | cat | 76.318 | dog | 70.492 | horse | 50.628 | | sheep | 54.498 | cow | 56.815 | elephant | 63.136 | | bear | 79.721 | zebra | 65.870 | giraffe | 62.113 | | backpack | 25.460 | umbrella | 55.327 | handbag | 25.350 | | tie | 39.993 | suitcase | 50.435 | frisbee | 68.863 | | skis | 8.830 | snowboard | 35.398 | sports ball | 49.451 | | kite | 37.983 | baseball bat | 38.199 | baseball glove | 50.452 | | skateboard | 44.224 | surfboard | 45.241 | tennis racket | 63.725 | | bottle | 41.081 | wine glass | 36.391 | cup | 51.346 | | fork | 26.885 | knife | 24.162 | spoon | 22.490 | | bowl | 38.551 | banana | 21.065 | apple | 25.572 | | sandwich | 48.548 | orange | 30.978 | broccoli | 23.466 | | carrot | 21.958 | hot dog | 35.277 | pizza | 51.508 | | donut | 54.834 | cake | 46.840 | chair | 28.968 | | couch | 42.677 | potted plant | 23.503 | bed | 40.693 | | dining table | 15.000 | toilet | 69.455 | tv | 67.075 | | laptop | 70.154 | mouse | 63.975 | remote | 44.623 | | keyboard | 57.786 | cell phone | 46.179 | microwave | 67.804 | | oven | 33.607 | toaster | 47.543 | sink | 45.535 | | refrigerator | 69.260 | book | 14.386 | clock | 54.053 | | vase | 41.966 | scissors | 38.073 | teddy bear | 56.977 | | hair drier | 35.337 | toothbrush | 30.117 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.696 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.492 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.673 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.547 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.564 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.604 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.756 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 66.06751366228147, 'fwIoU': 71.6520703105254, 'IoU-person': 88.81614507220132, 'IoU-bicycle': 73.44266283021777, 'IoU-car': 73.63686200305747, 'IoU-motorcycle': 87.62168970507295, 'IoU-airplane': 89.33182233139799, 'IoU-bus': 88.04530147622621, 'IoU-train': 87.94611535919323, 'IoU-truck': 71.29063906026651, 'IoU-boat': 73.15461595153448, 'IoU-traffic light': 79.35664743800189, 'IoU-fire hydrant': 93.24482226347473, 'IoU-stop sign': 95.8832899233292, 'IoU-parking meter': 84.77399456048548, 'IoU-bench': 61.08400609813176, 'IoU-bird': 72.99483261065822, 'IoU-cat': 90.84827166937937, 'IoU-dog': 86.51463080200318, 'IoU-horse': 88.08661948177163, 'IoU-sheep': 88.63384430852749, 'IoU-cow': 88.15318483597014, 'IoU-elephant': 88.83082510229072, 'IoU-bear': 93.23022456900371, 'IoU-zebra': 84.55032007706801, 'IoU-giraffe': 89.40338898735848, 'IoU-backpack': 54.24356438709593, 'IoU-umbrella': 83.85809997417303, 'IoU-handbag': 50.41600396925839, 'IoU-tie': 77.1058694217431, 'IoU-suitcase': 84.84338778999293, 'IoU-frisbee': 84.82108509595942, 'IoU-skis': 60.96380973019829, 'IoU-snowboard': 75.03040972403451, 'IoU-sports ball': 80.29860297473685, 'IoU-kite': 78.95997708817998, 'IoU-baseball bat': 69.39078927533747, 'IoU-baseball glove': 75.31917882795076, 'IoU-skateboard': 86.30639938647693, 'IoU-surfboard': 86.96446840623165, 'IoU-tennis racket': 91.14148773043091, 'IoU-bottle': 72.41185820291204, 'IoU-wine glass': 82.79969135981527, 'IoU-cup': 71.13370994577667, 'IoU-fork': 71.05094862502665, 'IoU-knife': 65.30618153448094, 'IoU-spoon': 59.1967375840108, 'IoU-bowl': 60.93782048036647, 'IoU-banana': 82.59588155743577, 'IoU-apple': 59.26560415642772, 'IoU-sandwich': 68.68675100876794, 'IoU-orange': 79.56859897480282, 'IoU-broccoli': 70.34358350745417, 'IoU-carrot': 65.30356798233998, 'IoU-hot dog': 64.09960726719792, 'IoU-pizza': 82.8447783080536, 'IoU-donut': 69.55080594586349, 'IoU-cake': 80.32870447227944, 'IoU-chair': 62.30475369125234, 'IoU-couch': 71.04646975747995, 'IoU-potted plant': 43.91922989045291, 'IoU-bed': 72.33798445216067, 'IoU-dining table': 56.532235052815174, 'IoU-toilet': 83.70079484372953, 'IoU-tv': 76.41721939380093, 'IoU-laptop': 79.55318000971666, 'IoU-mouse': 72.16178122644068, 'IoU-remote': 71.53813274714538, 'IoU-keyboard': 70.1852347813287, 'IoU-cell phone': 81.20129067007578, 'IoU-microwave': 79.4931393565688, 'IoU-oven': 73.66430327666792, 'IoU-toaster': 77.45523902563835, 'IoU-sink': 69.09997392430483, 'IoU-refrigerator': 85.16223362973629, 'IoU-book': 56.71945720009563, 'IoU-clock': 79.94244061498623, 'IoU-vase': 66.60951355507942, 'IoU-scissors': 85.72678904825115, 'IoU-teddy bear': 85.1925366488616, 'IoU-hair drier': 49.02081494215491, 'IoU-toothbrush': 76.38720448567513, 'IoU-banner': 34.13092420727817, 'IoU-blanket': 15.881133030299477, 'IoU-bridge': 34.473016119735505, 'IoU-cardboard': 46.456365214786, 'IoU-counter': 32.70130159839486, 'IoU-curtain': 72.14910704616896, 'IoU-door-stuff': 48.173171761403324, 'IoU-floor-wood': 65.27875011816776, 'IoU-flower': 50.77550253325327, 'IoU-fruit': 48.89971431565807, 'IoU-gravel': 31.68474427995106, 'IoU-house': 27.13231446808982, 'IoU-light': 43.212286488700094, 'IoU-mirror-stuff': 61.03551042349862, 'IoU-net': 50.45124549359393, 'IoU-pillow': 18.448680919111965, 'IoU-platform': 28.12637252344784, 'IoU-playingfield': 68.1978778462566, 'IoU-railroad': 64.36321927530348, 'IoU-river': 52.40724968218973, 'IoU-road': 67.11983609355715, 'IoU-roof': 18.239734746260943, 'IoU-sand': 64.48162258810098, 'IoU-sea': 84.52251902503265, 'IoU-shelf': 41.357096971873474, 'IoU-snow': 92.13101773706805, 'IoU-stairs': 35.408843889518124, 'IoU-tent': 10.73838364009393, 'IoU-towel': 45.07834459639108, 'IoU-wall-brick': 52.3938683969396, 'IoU-wall-stone': 27.566961777361165, 'IoU-wall-tile': 69.0405189162145, 'IoU-wall-wood': 44.38727381025412, 'IoU-water-other': 20.4982116536711, 'IoU-window-blind': 49.6351650147177, 'IoU-window-other': 50.019758969876236, 'IoU-tree-merged': 81.51526170330358, 'IoU-fence-merged': 55.1749250023464, 'IoU-ceiling-merged': 67.28293148661926, 'IoU-sky-other-merged': 93.65220297981989, 'IoU-cabinet-merged': 63.763688319184276, 'IoU-table-merged': 41.12682390981855, 'IoU-floor-other-merged': 54.37021480958987, 'IoU-pavement-merged': 56.37510590926868, 'IoU-mountain-merged': 57.782887812186544, 'IoU-grass-merged': 73.17304332805536, 'IoU-dirt-merged': 46.66356512297335, 'IoU-paper-merged': 35.00744763723946, 'IoU-food-other-merged': 45.5786846400896, 'IoU-building-other-merged': 59.20024208978535, 'IoU-rock-merged': 63.80008442579963, 'IoU-wall-other-merged': 68.0464569795277, 'IoU-rug-merged': 68.53343231975559, 'mACC': 77.53554893811112, 'pACC': 82.35385696165964, 'ACC-person': 93.08069854821149, 'ACC-bicycle': 82.02585423678951, 'ACC-car': 84.93564591129626, 'ACC-motorcycle': 92.04890962056018, 'ACC-airplane': 93.48672225795961, 'ACC-bus': 93.74422339633091, 'ACC-train': 96.59896184304824, 'ACC-truck': 82.36567302671108, 'ACC-boat': 81.27039766363649, 'ACC-traffic light': 90.94221809239325, 'ACC-fire hydrant': 95.79481354643939, 'ACC-stop sign': 98.4022080769475, 'ACC-parking meter': 87.69817150218307, 'ACC-bench': 75.97896687453482, 'ACC-bird': 78.16421430161094, 'ACC-cat': 94.62440530871787, 'ACC-dog': 89.10122976757943, 'ACC-horse': 92.78618564898646, 'ACC-sheep': 93.20698636794492, 'ACC-cow': 93.15931538912967, 'ACC-elephant': 90.71423821967201, 'ACC-bear': 95.20401141574091, 'ACC-zebra': 86.57197449720438, 'ACC-giraffe': 93.30729027692225, 'ACC-backpack': 72.92135914672089, 'ACC-umbrella': 86.59530163422131, 'ACC-handbag': 71.82299132250604, 'ACC-tie': 85.95714361906533, 'ACC-suitcase': 90.48241069126396, 'ACC-frisbee': 93.80872727272728, 'ACC-skis': 76.6801704957802, 'ACC-snowboard': 83.08784991795449, 'ACC-sports ball': 88.20096959923922, 'ACC-kite': 84.66029363275518, 'ACC-baseball bat': 87.94738853216852, 'ACC-baseball glove': 92.71092280754849, 'ACC-skateboard': 90.92366837080492, 'ACC-surfboard': 92.80496653708485, 'ACC-tennis racket': 94.93136039432011, 'ACC-bottle': 86.09968989781834, 'ACC-wine glass': 90.57378832659427, 'ACC-cup': 89.5573491366259, 'ACC-fork': 85.5327496942929, 'ACC-knife': 78.5115776361105, 'ACC-spoon': 78.07021870388866, 'ACC-bowl': 69.17714659300152, 'ACC-banana': 89.8826110588752, 'ACC-apple': 73.86744736078195, 'ACC-sandwich': 81.08494137417026, 'ACC-orange': 89.61455053085795, 'ACC-broccoli': 82.63429953044343, 'ACC-carrot': 78.06040988366593, 'ACC-hot dog': 71.27498594642647, 'ACC-pizza': 93.15854908490573, 'ACC-donut': 77.62666885605395, 'ACC-cake': 88.34317177038938, 'ACC-chair': 76.23060278520072, 'ACC-couch': 79.08527216928303, 'ACC-potted plant': 56.11585142196082, 'ACC-bed': 78.65174679568089, 'ACC-dining table': 80.18518746851707, 'ACC-toilet': 88.38482678214523, 'ACC-tv': 82.81097225970753, 'ACC-laptop': 91.02006856186014, 'ACC-mouse': 87.35899628078454, 'ACC-remote': 76.39310911438297, 'ACC-keyboard': 74.91044001813293, 'ACC-cell phone': 90.71293869103198, 'ACC-microwave': 84.58778942907867, 'ACC-oven': 89.42487763669898, 'ACC-toaster': 91.14604004434165, 'ACC-sink': 77.54119508495306, 'ACC-refrigerator': 93.6115490952208, 'ACC-book': 74.69561654198537, 'ACC-clock': 85.50899587706311, 'ACC-vase': 75.44618666299682, 'ACC-scissors': 91.17164329853364, 'ACC-teddy bear': 90.63854202695858, 'ACC-hair drier': 60.061765291890815, 'ACC-toothbrush': 84.25816539263377, 'ACC-banner': 72.04688230593412, 'ACC-blanket': 26.840184328424954, 'ACC-bridge': 54.070629704499055, 'ACC-cardboard': 58.532034317289515, 'ACC-counter': 56.56691560133012, 'ACC-curtain': 83.07805144545377, 'ACC-door-stuff': 66.55605224582834, 'ACC-floor-wood': 86.33616082009478, 'ACC-flower': 75.69554662375386, 'ACC-fruit': 66.629568881337, 'ACC-gravel': 42.00495557415253, 'ACC-house': 32.93962188179741, 'ACC-light': 62.23143877043219, 'ACC-mirror-stuff': 72.3000094896771, 'ACC-net': 65.87568423222991, 'ACC-pillow': 60.18194634008548, 'ACC-platform': 42.314337184208746, 'ACC-playingfield': 83.88808734152568, 'ACC-railroad': 85.53368712149909, 'ACC-river': 82.50903456149386, 'ACC-road': 87.97788246379493, 'ACC-roof': 24.561560056554494, 'ACC-sand': 70.45994526446663, 'ACC-sea': 92.00241838137103, 'ACC-shelf': 59.01131418937581, 'ACC-snow': 95.49918691359358, 'ACC-stairs': 53.2475144045539, 'ACC-tent': 14.384062213783558, 'ACC-towel': 54.32844281487339, 'ACC-wall-brick': 70.78582986745215, 'ACC-wall-stone': 32.646664417744475, 'ACC-wall-tile': 84.69432105387278, 'ACC-wall-wood': 66.62561368516057, 'ACC-water-other': 23.94267736490971, 'ACC-window-blind': 64.6716653345654, 'ACC-window-other': 68.78587162361814, 'ACC-tree-merged': 89.28203120417172, 'ACC-fence-merged': 72.31965090867628, 'ACC-ceiling-merged': 79.24097758005455, 'ACC-sky-other-merged': 97.49288004779541, 'ACC-cabinet-merged': 77.57507262956207, 'ACC-table-merged': 53.55841517116369, 'ACC-floor-other-merged': 61.72690771957641, 'ACC-pavement-merged': 68.59066997189699, 'ACC-mountain-merged': 67.05108647346047, 'ACC-grass-merged': 84.68843384227196, 'ACC-dirt-merged': 70.78900430213902, 'ACC-paper-merged': 47.65565058751121, 'ACC-food-other-merged': 61.29797750071471, 'ACC-building-other-merged': 77.8720333691193, 'ACC-rock-merged': 85.06264584542095, 'ACC-wall-other-merged': 82.12171910678059, 'ACC-rug-merged': 84.37567980706929})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.2694 s/iter. Inference: 0.1776 s/iter. Eval: 0.0000 s/iter. Total: 0.4469 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.2938 s/iter. Inference: 0.3511 s/iter. Eval: 0.0000 s/iter. Total: 0.6450 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 22/25. Dataloading: 0.3201 s/iter. Inference: 0.5338 s/iter. Eval: 0.0000 s/iter. Total: 0.8540 s/iter. ETA=0:00:02 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.366403277729002, 'noc@0.8': 2.422007609013755, 'noc@0.85': 2.8358208955223883, 'noc@0.9': 3.6810067310506294, 'miou@iter1': 0.8725764503735773} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1428 s/iter. Eval: 0.0010 s/iter. Total: 0.1451 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.048583984375, 'precision@0.6': 72.36688995361328, 'precision@0.7': 68.28604888916016, 'precision@0.8': 58.91954803466797, 'precision@0.9': 32.68558120727539, 'cIoU': 62.21101379394531, 'mIoU': 66.73597717285156} INFO:trainer.default_trainer:This epoch takes 0:56:53.622045 INFO:trainer.default_trainer:PROGRESS: 88.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 44 training. INFO:trainer.default_trainer:epochs[ 44] optim steps[80400] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.54310/0.75224, loss_mask_bce_0: 0.18858/0.30061, loss_mask_dice_0: 0.28152/1.01987, loss_spatial_bce_0: 0.08724/0.08433, loss_spatial_dice_0: 0.12881/0.17827, loss_spatial_ce_0: 0.00031/0.05521, loss_grounding_bce_0: 0.15946/0.08056, loss_grounding_dice_0: 0.07140/0.15035, loss_grounding_ce_0: 0.00919/0.24874, loss_mask_ce_1: 0.53308/0.75294, loss_mask_bce_1: 0.18425/0.30144, loss_mask_dice_1: 0.27298/1.02424, loss_spatial_bce_1: 0.09015/0.08480, loss_spatial_dice_1: 0.12617/0.18125, loss_spatial_ce_1: 0.00034/0.05894, loss_grounding_bce_1: 0.16244/0.08077, loss_grounding_dice_1: 0.07183/0.15113, loss_grounding_ce_1: 0.00645/0.25014, loss_mask_ce_2: 0.57307/0.76060, loss_mask_bce_2: 0.18059/0.30181, loss_mask_dice_2: 0.26021/1.02501, loss_spatial_bce_2: 0.09034/0.08490, loss_spatial_dice_2: 0.11006/0.18187, loss_spatial_ce_2: 0.00058/0.06117, loss_grounding_bce_2: 0.15997/0.08074, loss_grounding_dice_2: 0.07086/0.15104, loss_grounding_ce_2: 0.00934/0.25283, loss_mask_ce_3: 0.53133/0.76536, loss_mask_bce_3: 0.18261/0.30312, loss_mask_dice_3: 0.28949/1.02311, loss_spatial_bce_3: 0.09372/0.08703, loss_spatial_dice_3: 0.11555/0.18326, loss_spatial_ce_3: 0.00435/0.06605, loss_grounding_bce_3: 0.15544/0.08109, loss_grounding_dice_3: 0.07131/0.15070, loss_grounding_ce_3: 0.00747/0.25418, loss_mask_ce_4: 0.59182/0.77114, loss_mask_bce_4: 0.18565/0.30587, loss_mask_dice_4: 0.28941/1.04253, loss_spatial_bce_4: 0.09745/0.08955, loss_spatial_dice_4: 0.13105/0.19203, loss_spatial_ce_4: 0.00389/0.07993, loss_grounding_bce_4: 0.15860/0.08187, loss_grounding_dice_4: 0.07018/0.15332, loss_grounding_ce_4: 0.01504/0.25834, loss_mask_ce_5: 0.57031/0.79677, loss_mask_bce_5: 0.18191/0.30772, loss_mask_dice_5: 0.28834/1.05064, loss_spatial_bce_5: 0.10562/0.09198, loss_spatial_dice_5: 0.12633/0.19539, loss_spatial_ce_5: 0.06669/0.09360, loss_grounding_bce_5: 0.15209/0.08212, loss_grounding_dice_5: 0.07106/0.15417, loss_grounding_ce_5: 0.01119/0.27626, loss_mask_ce_6: 0.58787/0.82410, loss_mask_bce_6: 0.18146/0.30993, loss_mask_dice_6: 0.27856/1.05454, loss_spatial_bce_6: 0.09531/0.09749, loss_spatial_dice_6: 0.13177/0.19771, loss_spatial_ce_6: 0.06451/0.11792, loss_grounding_bce_6: 0.16113/0.08294, loss_grounding_dice_6: 0.07619/0.15465, loss_grounding_ce_6: 0.00923/0.28498, loss_mask_ce_7: 0.68074/0.87975, loss_mask_bce_7: 0.18142/0.31715, loss_mask_dice_7: 0.30986/1.10037, loss_spatial_bce_7: 0.10292/0.10662, loss_spatial_dice_7: 0.13111/0.22258, loss_spatial_ce_7: 0.05950/0.15250, loss_grounding_bce_7: 0.16022/0.08463, loss_grounding_dice_7: 0.07304/0.16018, loss_grounding_ce_7: 0.00623/0.31827, loss_mask_ce_8: 0.86711/1.01353, loss_mask_bce_8: 0.20072/0.33315, loss_mask_dice_8: 0.32702/1.17683, loss_spatial_bce_8: 0.12584/0.12297, loss_spatial_dice_8: 0.17895/0.25710, loss_spatial_ce_8: 0.07586/0.19791, loss_grounding_bce_8: 0.16828/0.08882, loss_grounding_dice_8: 0.07920/0.16998, loss_grounding_ce_8: 0.00860/0.41614, loss_mask_ce_9: 2.26768/3.47386, loss_mask_bce_9: 0.23612/0.35990, loss_mask_dice_9: 0.50375/1.75946, loss_spatial_bce_9: 0.48532/0.35411, loss_spatial_dice_9: 0.84250/0.79310, loss_spatial_ce_9: 1.17870/1.38661, loss_grounding_bce_9: 0.19919/0.10101, loss_grounding_dice_9: 0.09026/0.24219, loss_grounding_ce_9: 0.11044/0.66822] items per batch[64] items per second[0.17] total items[5145600] mini batches[ 80400] memory[4999] epoch remaining[1:09:28] INFO:trainer.default_trainer:epochs[ 44] optim steps[80500] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.07592/0.75220, loss_mask_bce_0: 0.26954/0.30061, loss_mask_dice_0: 0.75269/1.01992, loss_spatial_bce_0: 0.04557/0.08432, loss_spatial_dice_0: 0.12547/0.17827, loss_spatial_ce_0: 0.06149/0.05519, loss_grounding_bce_0: 0.04669/0.08057, loss_grounding_dice_0: 0.13157/0.15037, loss_grounding_ce_0: 0.48089/0.24871, loss_mask_ce_1: 0.08743/0.75294, loss_mask_bce_1: 0.24903/0.30143, loss_mask_dice_1: 0.67323/1.02428, loss_spatial_bce_1: 0.04775/0.08479, loss_spatial_dice_1: 0.12180/0.18124, loss_spatial_ce_1: 0.06943/0.05893, loss_grounding_bce_1: 0.04724/0.08078, loss_grounding_dice_1: 0.14193/0.15114, loss_grounding_ce_1: 0.44626/0.25008, loss_mask_ce_2: 0.08327/0.76058, loss_mask_bce_2: 0.26307/0.30180, loss_mask_dice_2: 0.72933/1.02510, loss_spatial_bce_2: 0.05088/0.08489, loss_spatial_dice_2: 0.14713/0.18187, loss_spatial_ce_2: 0.07321/0.06116, loss_grounding_bce_2: 0.04439/0.08075, loss_grounding_dice_2: 0.12575/0.15106, loss_grounding_ce_2: 0.44650/0.25277, loss_mask_ce_3: 0.08768/0.76533, loss_mask_bce_3: 0.25785/0.30312, loss_mask_dice_3: 0.75058/1.02318, loss_spatial_bce_3: 0.04392/0.08702, loss_spatial_dice_3: 0.14569/0.18325, loss_spatial_ce_3: 0.04955/0.06603, loss_grounding_bce_3: 0.04541/0.08110, loss_grounding_dice_3: 0.13451/0.15072, loss_grounding_ce_3: 0.39149/0.25413, loss_mask_ce_4: 0.10263/0.77110, loss_mask_bce_4: 0.24745/0.30586, loss_mask_dice_4: 0.65847/1.04262, loss_spatial_bce_4: 0.05340/0.08954, loss_spatial_dice_4: 0.17080/0.19203, loss_spatial_ce_4: 0.16894/0.07992, loss_grounding_bce_4: 0.04771/0.08188, loss_grounding_dice_4: 0.21098/0.15334, loss_grounding_ce_4: 0.42884/0.25834, loss_mask_ce_5: 0.07658/0.79677, loss_mask_bce_5: 0.25380/0.30771, loss_mask_dice_5: 0.63240/1.05070, loss_spatial_bce_5: 0.05518/0.09197, loss_spatial_dice_5: 0.16926/0.19539, loss_spatial_ce_5: 0.19650/0.09361, loss_grounding_bce_5: 0.04516/0.08213, loss_grounding_dice_5: 0.14016/0.15419, loss_grounding_ce_5: 0.44571/0.27618, loss_mask_ce_6: 0.12860/0.82406, loss_mask_bce_6: 0.24775/0.30992, loss_mask_dice_6: 0.68901/1.05465, loss_spatial_bce_6: 0.05621/0.09748, loss_spatial_dice_6: 0.14122/0.19771, loss_spatial_ce_6: 0.30623/0.11792, loss_grounding_bce_6: 0.04496/0.08295, loss_grounding_dice_6: 0.14321/0.15467, loss_grounding_ce_6: 0.37080/0.28491, loss_mask_ce_7: 0.22035/0.87969, loss_mask_bce_7: 0.24543/0.31714, loss_mask_dice_7: 0.65934/1.10043, loss_spatial_bce_7: 0.06460/0.10660, loss_spatial_dice_7: 0.15810/0.22258, loss_spatial_ce_7: 0.20717/0.15249, loss_grounding_bce_7: 0.04739/0.08464, loss_grounding_dice_7: 0.15118/0.16020, loss_grounding_ce_7: 0.52200/0.31822, loss_mask_ce_8: 0.33716/1.01349, loss_mask_bce_8: 0.24395/0.33314, loss_mask_dice_8: 0.73764/1.17689, loss_spatial_bce_8: 0.05936/0.12295, loss_spatial_dice_8: 0.16740/0.25710, loss_spatial_ce_8: 0.13187/0.19790, loss_grounding_bce_8: 0.04631/0.08883, loss_grounding_dice_8: 0.14278/0.17001, loss_grounding_ce_8: 0.45954/0.41604, loss_mask_ce_9: 4.48113/3.47371, loss_mask_bce_9: 0.22030/0.35990, loss_mask_dice_9: 1.24507/1.75956, loss_spatial_bce_9: 0.52984/0.35410, loss_spatial_dice_9: 0.92085/0.79310, loss_spatial_ce_9: 2.33142/1.38662, loss_grounding_bce_9: 0.03941/0.10102, loss_grounding_dice_9: 0.21987/0.24220, loss_grounding_ce_9: 0.51004/0.66811] items per batch[64] items per second[0.36] total items[5152000] mini batches[ 80500] memory[4999] epoch remaining[0:52:28] INFO:trainer.default_trainer:epochs[ 44] optim steps[80600] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.14636/0.75208, loss_mask_bce_0: 0.09990/0.30061, loss_mask_dice_0: 0.23828/1.01996, loss_spatial_bce_0: 0.01338/0.08431, loss_spatial_dice_0: 0.05856/0.17824, loss_spatial_ce_0: 0.00003/0.05518, loss_grounding_bce_0: 0.00595/0.08056, loss_grounding_dice_0: 0.02692/0.15037, loss_grounding_ce_0: 0.02608/0.24874, loss_mask_ce_1: 0.15968/0.75283, loss_mask_bce_1: 0.09688/0.30143, loss_mask_dice_1: 0.21084/1.02433, loss_spatial_bce_1: 0.01120/0.08478, loss_spatial_dice_1: 0.05460/0.18122, loss_spatial_ce_1: 0.00021/0.05893, loss_grounding_bce_1: 0.01022/0.08078, loss_grounding_dice_1: 0.04270/0.15113, loss_grounding_ce_1: 0.04634/0.25012, loss_mask_ce_2: 0.15077/0.76045, loss_mask_bce_2: 0.08604/0.30180, loss_mask_dice_2: 0.20790/1.02512, loss_spatial_bce_2: 0.01284/0.08488, loss_spatial_dice_2: 0.05537/0.18185, loss_spatial_ce_2: 0.00128/0.06117, loss_grounding_bce_2: 0.00559/0.08074, loss_grounding_dice_2: 0.02811/0.15105, loss_grounding_ce_2: 0.02048/0.25281, loss_mask_ce_3: 0.12970/0.76525, loss_mask_bce_3: 0.09632/0.30311, loss_mask_dice_3: 0.19530/1.02323, loss_spatial_bce_3: 0.01159/0.08701, loss_spatial_dice_3: 0.04696/0.18323, loss_spatial_ce_3: 0.00222/0.06603, loss_grounding_bce_3: 0.00646/0.08109, loss_grounding_dice_3: 0.03158/0.15071, loss_grounding_ce_3: 0.01309/0.25418, loss_mask_ce_4: 0.12932/0.77099, loss_mask_bce_4: 0.07675/0.30586, loss_mask_dice_4: 0.21493/1.04269, loss_spatial_bce_4: 0.01230/0.08953, loss_spatial_dice_4: 0.07570/0.19202, loss_spatial_ce_4: 0.01179/0.07990, loss_grounding_bce_4: 0.00690/0.08187, loss_grounding_dice_4: 0.03573/0.15334, loss_grounding_ce_4: 0.00979/0.25842, loss_mask_ce_5: 0.13380/0.79665, loss_mask_bce_5: 0.07953/0.30771, loss_mask_dice_5: 0.21005/1.05077, loss_spatial_bce_5: 0.01586/0.09196, loss_spatial_dice_5: 0.10981/0.19537, loss_spatial_ce_5: 0.02758/0.09359, loss_grounding_bce_5: 0.00645/0.08212, loss_grounding_dice_5: 0.03433/0.15419, loss_grounding_ce_5: 0.01188/0.27623, loss_mask_ce_6: 0.12164/0.82400, loss_mask_bce_6: 0.08806/0.30991, loss_mask_dice_6: 0.22041/1.05473, loss_spatial_bce_6: 0.01297/0.09747, loss_spatial_dice_6: 0.08159/0.19770, loss_spatial_ce_6: 0.06863/0.11791, loss_grounding_bce_6: 0.00694/0.08294, loss_grounding_dice_6: 0.03799/0.15466, loss_grounding_ce_6: 0.04801/0.28495, loss_mask_ce_7: 0.10484/0.87956, loss_mask_bce_7: 0.10669/0.31713, loss_mask_dice_7: 0.23306/1.10049, loss_spatial_bce_7: 0.03293/0.10659, loss_spatial_dice_7: 0.20499/0.22256, loss_spatial_ce_7: 0.11864/0.15249, loss_grounding_bce_7: 0.00539/0.08464, loss_grounding_dice_7: 0.03033/0.16020, loss_grounding_ce_7: 0.38402/0.31824, loss_mask_ce_8: 0.15308/1.01336, loss_mask_bce_8: 0.08683/0.33313, loss_mask_dice_8: 0.23853/1.17698, loss_spatial_bce_8: 0.06211/0.12294, loss_spatial_dice_8: 0.29591/0.25708, loss_spatial_ce_8: 0.00935/0.19787, loss_grounding_bce_8: 0.01100/0.08882, loss_grounding_dice_8: 0.04701/0.17001, loss_grounding_ce_8: 0.76589/0.41606, loss_mask_ce_9: 2.74878/3.47367, loss_mask_bce_9: 0.04282/0.35990, loss_mask_dice_9: 0.30687/1.75957, loss_spatial_bce_9: 0.30322/0.35411, loss_spatial_dice_9: 0.85974/0.79311, loss_spatial_ce_9: 1.16355/1.38663, loss_grounding_bce_9: 0.01290/0.10101, loss_grounding_dice_9: 0.09791/0.24221, loss_grounding_ce_9: 1.96090/0.66814] items per batch[64] items per second[0.37] total items[5158400] mini batches[ 80600] memory[4999] epoch remaining[0:48:09] INFO:trainer.default_trainer:epochs[ 44] optim steps[80700] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.37014/0.75189, loss_mask_bce_0: 0.42090/0.30058, loss_mask_dice_0: 0.57325/1.01970, loss_spatial_bce_0: 0.07116/0.08430, loss_spatial_dice_0: 0.10789/0.17822, loss_spatial_ce_0: 0.00732/0.05517, loss_grounding_bce_0: 0.17063/0.08057, loss_grounding_dice_0: 0.17758/0.15036, loss_grounding_ce_0: 0.00419/0.24872, loss_mask_ce_1: 0.35546/0.75263, loss_mask_bce_1: 0.42912/0.30140, loss_mask_dice_1: 0.57463/1.02406, loss_spatial_bce_1: 0.07152/0.08478, loss_spatial_dice_1: 0.11612/0.18121, loss_spatial_ce_1: 0.00524/0.05890, loss_grounding_bce_1: 0.16353/0.08078, loss_grounding_dice_1: 0.16795/0.15112, loss_grounding_ce_1: 0.00390/0.25011, loss_mask_ce_2: 0.35242/0.76026, loss_mask_bce_2: 0.42580/0.30177, loss_mask_dice_2: 0.56587/1.02489, loss_spatial_bce_2: 0.06742/0.08488, loss_spatial_dice_2: 0.11354/0.18183, loss_spatial_ce_2: 0.00443/0.06114, loss_grounding_bce_2: 0.15684/0.08075, loss_grounding_dice_2: 0.16762/0.15104, loss_grounding_ce_2: 0.00303/0.25280, loss_mask_ce_3: 0.36997/0.76504, loss_mask_bce_3: 0.43350/0.30308, loss_mask_dice_3: 0.58489/1.02294, loss_spatial_bce_3: 0.07129/0.08701, loss_spatial_dice_3: 0.11890/0.18321, loss_spatial_ce_3: 0.00650/0.06602, loss_grounding_bce_3: 0.15539/0.08110, loss_grounding_dice_3: 0.16188/0.15069, loss_grounding_ce_3: 0.00302/0.25419, loss_mask_ce_4: 0.31248/0.77077, loss_mask_bce_4: 0.43911/0.30584, loss_mask_dice_4: 0.59360/1.04242, loss_spatial_bce_4: 0.07953/0.08953, loss_spatial_dice_4: 0.12906/0.19201, loss_spatial_ce_4: 0.00962/0.07988, loss_grounding_bce_4: 0.16874/0.08188, loss_grounding_dice_4: 0.16660/0.15332, loss_grounding_ce_4: 0.00304/0.25846, loss_mask_ce_5: 0.33088/0.79644, loss_mask_bce_5: 0.47084/0.30768, loss_mask_dice_5: 0.61215/1.05051, loss_spatial_bce_5: 0.08408/0.09196, loss_spatial_dice_5: 0.15695/0.19536, loss_spatial_ce_5: 0.04205/0.09357, loss_grounding_bce_5: 0.17951/0.08213, loss_grounding_dice_5: 0.17612/0.15417, loss_grounding_ce_5: 0.00341/0.27621, loss_mask_ce_6: 0.33258/0.82377, loss_mask_bce_6: 0.44144/0.30989, loss_mask_dice_6: 0.59570/1.05446, loss_spatial_bce_6: 0.07782/0.09747, loss_spatial_dice_6: 0.12110/0.19769, loss_spatial_ce_6: 0.08939/0.11789, loss_grounding_bce_6: 0.16578/0.08295, loss_grounding_dice_6: 0.17193/0.15464, loss_grounding_ce_6: 0.00440/0.28504, loss_mask_ce_7: 0.38569/0.87936, loss_mask_bce_7: 0.44185/0.31710, loss_mask_dice_7: 0.60161/1.10021, loss_spatial_bce_7: 0.09937/0.10658, loss_spatial_dice_7: 0.14032/0.22255, loss_spatial_ce_7: 0.06612/0.15245, loss_grounding_bce_7: 0.17069/0.08464, loss_grounding_dice_7: 0.17312/0.16018, loss_grounding_ce_7: 0.00609/0.31830, loss_mask_ce_8: 0.46682/1.01311, loss_mask_bce_8: 0.44796/0.33309, loss_mask_dice_8: 0.55959/1.17666, loss_spatial_bce_8: 0.14727/0.12292, loss_spatial_dice_8: 0.16643/0.25705, loss_spatial_ce_8: 0.08820/0.19785, loss_grounding_bce_8: 0.17118/0.08883, loss_grounding_dice_8: 0.15866/0.16999, loss_grounding_ce_8: 0.00890/0.41606, loss_mask_ce_9: 2.76855/3.47329, loss_mask_bce_9: 0.46956/0.35986, loss_mask_dice_9: 0.85804/1.75902, loss_spatial_bce_9: 0.45493/0.35411, loss_spatial_dice_9: 0.91159/0.79307, loss_spatial_ce_9: 2.32734/1.38662, loss_grounding_bce_9: 0.17008/0.10102, loss_grounding_dice_9: 0.21166/0.24218, loss_grounding_ce_9: 0.06213/0.66818] items per batch[64] items per second[0.37] total items[5164800] mini batches[ 80700] memory[4999] epoch remaining[0:44:50] INFO:trainer.default_trainer:epochs[ 44] optim steps[80800] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.63196/0.75187, loss_mask_bce_0: 0.14427/0.30064, loss_mask_dice_0: 0.06108/1.01982, loss_spatial_bce_0: 0.15506/0.08431, loss_spatial_dice_0: 0.04347/0.17822, loss_spatial_ce_0: 0.34535/0.05515, loss_grounding_bce_0: 0.09111/0.08059, loss_grounding_dice_0: 0.04140/0.15038, loss_grounding_ce_0: 0.30873/0.24872, loss_mask_ce_1: 0.62097/0.75260, loss_mask_bce_1: 0.13884/0.30147, loss_mask_dice_1: 0.05988/1.02421, loss_spatial_bce_1: 0.15009/0.08478, loss_spatial_dice_1: 0.04268/0.18121, loss_spatial_ce_1: 0.33674/0.05890, loss_grounding_bce_1: 0.08683/0.08081, loss_grounding_dice_1: 0.04008/0.15114, loss_grounding_ce_1: 0.26471/0.25010, loss_mask_ce_2: 0.50828/0.76024, loss_mask_bce_2: 0.13951/0.30183, loss_mask_dice_2: 0.06187/1.02503, loss_spatial_bce_2: 0.14114/0.08488, loss_spatial_dice_2: 0.04705/0.18184, loss_spatial_ce_2: 0.34248/0.06112, loss_grounding_bce_2: 0.08864/0.08078, loss_grounding_dice_2: 0.04178/0.15106, loss_grounding_ce_2: 0.24285/0.25279, loss_mask_ce_3: 0.55175/0.76502, loss_mask_bce_3: 0.15178/0.30314, loss_mask_dice_3: 0.06133/1.02309, loss_spatial_bce_3: 0.13476/0.08701, loss_spatial_dice_3: 0.04937/0.18322, loss_spatial_ce_3: 0.35930/0.06600, loss_grounding_bce_3: 0.09675/0.08112, loss_grounding_dice_3: 0.03990/0.15072, loss_grounding_ce_3: 0.27889/0.25418, loss_mask_ce_4: 0.58789/0.77073, loss_mask_bce_4: 0.13859/0.30590, loss_mask_dice_4: 0.06593/1.04258, loss_spatial_bce_4: 0.13287/0.08954, loss_spatial_dice_4: 0.04897/0.19201, loss_spatial_ce_4: 0.37391/0.07988, loss_grounding_bce_4: 0.09025/0.08190, loss_grounding_dice_4: 0.04296/0.15334, loss_grounding_ce_4: 0.29342/0.25846, loss_mask_ce_5: 0.59811/0.79641, loss_mask_bce_5: 0.15164/0.30775, loss_mask_dice_5: 0.06199/1.05065, loss_spatial_bce_5: 0.12777/0.09197, loss_spatial_dice_5: 0.04982/0.19537, loss_spatial_ce_5: 0.37994/0.09358, loss_grounding_bce_5: 0.09755/0.08216, loss_grounding_dice_5: 0.03992/0.15420, loss_grounding_ce_5: 0.27969/0.27622, loss_mask_ce_6: 0.97526/0.82375, loss_mask_bce_6: 0.15530/0.30994, loss_mask_dice_6: 0.06638/1.05461, loss_spatial_bce_6: 0.12947/0.09748, loss_spatial_dice_6: 0.05238/0.19770, loss_spatial_ce_6: 0.37890/0.11791, loss_grounding_bce_6: 0.09754/0.08297, loss_grounding_dice_6: 0.04279/0.15466, loss_grounding_ce_6: 0.42465/0.28503, loss_mask_ce_7: 0.79140/0.87935, loss_mask_bce_7: 0.15383/0.31715, loss_mask_dice_7: 0.08352/1.10034, loss_spatial_bce_7: 0.11775/0.10659, loss_spatial_dice_7: 0.05982/0.22256, loss_spatial_ce_7: 0.40007/0.15245, loss_grounding_bce_7: 0.10217/0.08466, loss_grounding_dice_7: 0.05247/0.16021, loss_grounding_ce_7: 0.34962/0.31825, loss_mask_ce_8: 0.69330/1.01308, loss_mask_bce_8: 0.16444/0.33316, loss_mask_dice_8: 0.08492/1.17683, loss_spatial_bce_8: 0.14354/0.12292, loss_spatial_dice_8: 0.07076/0.25705, loss_spatial_ce_8: 0.44983/0.19785, loss_grounding_bce_8: 0.11520/0.08885, loss_grounding_dice_8: 0.06370/0.17001, loss_grounding_ce_8: 0.37554/0.41602, loss_mask_ce_9: 3.62804/3.47342, loss_mask_bce_9: 0.35783/0.35992, loss_mask_dice_9: 0.24087/1.75932, loss_spatial_bce_9: 0.56230/0.35414, loss_spatial_dice_9: 0.40598/0.79309, loss_spatial_ce_9: 0.78246/1.38663, loss_grounding_bce_9: 0.23880/0.10104, loss_grounding_dice_9: 0.15812/0.24220, loss_grounding_ce_9: 0.83844/0.66808] items per batch[64] items per second[0.36] total items[5171200] mini batches[ 80800] memory[4999] epoch remaining[0:41:45] INFO:trainer.default_trainer:epochs[ 44] optim steps[80900] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 0.20080/0.75187, loss_mask_bce_0: 0.49473/0.30064, loss_mask_dice_0: 0.61995/1.01990, loss_spatial_bce_0: 0.13026/0.08430, loss_spatial_dice_0: 0.15186/0.17822, loss_spatial_ce_0: 0.01343/0.05514, loss_grounding_bce_0: 0.12434/0.08059, loss_grounding_dice_0: 0.08777/0.15038, loss_grounding_ce_0: 0.00006/0.24868, loss_mask_ce_1: 0.22011/0.75262, loss_mask_bce_1: 0.48148/0.30146, loss_mask_dice_1: 0.62121/1.02430, loss_spatial_bce_1: 0.13492/0.08477, loss_spatial_dice_1: 0.18848/0.18121, loss_spatial_ce_1: 0.00492/0.05888, loss_grounding_bce_1: 0.12730/0.08081, loss_grounding_dice_1: 0.08557/0.15114, loss_grounding_ce_1: 0.00008/0.25008, loss_mask_ce_2: 0.21778/0.76023, loss_mask_bce_2: 0.48780/0.30183, loss_mask_dice_2: 0.64981/1.02510, loss_spatial_bce_2: 0.13407/0.08487, loss_spatial_dice_2: 0.18843/0.18184, loss_spatial_ce_2: 0.00624/0.06111, loss_grounding_bce_2: 0.12070/0.08078, loss_grounding_dice_2: 0.08765/0.15107, loss_grounding_ce_2: 0.00007/0.25277, loss_mask_ce_3: 0.23366/0.76498, loss_mask_bce_3: 0.49547/0.30314, loss_mask_dice_3: 0.60776/1.02316, loss_spatial_bce_3: 0.13515/0.08700, loss_spatial_dice_3: 0.17607/0.18322, loss_spatial_ce_3: 0.01676/0.06599, loss_grounding_bce_3: 0.12371/0.08112, loss_grounding_dice_3: 0.08820/0.15072, loss_grounding_ce_3: 0.00009/0.25417, loss_mask_ce_4: 0.21858/0.77073, loss_mask_bce_4: 0.49248/0.30590, loss_mask_dice_4: 0.56840/1.04263, loss_spatial_bce_4: 0.13881/0.08953, loss_spatial_dice_4: 0.16507/0.19202, loss_spatial_ce_4: 0.06257/0.07987, loss_grounding_bce_4: 0.12713/0.08190, loss_grounding_dice_4: 0.08342/0.15335, loss_grounding_ce_4: 0.00007/0.25851, loss_mask_ce_5: 0.21482/0.79640, loss_mask_bce_5: 0.48800/0.30775, loss_mask_dice_5: 0.56972/1.05074, loss_spatial_bce_5: 0.13097/0.09197, loss_spatial_dice_5: 0.21426/0.19538, loss_spatial_ce_5: 0.06409/0.09359, loss_grounding_bce_5: 0.11948/0.08215, loss_grounding_dice_5: 0.08843/0.15420, loss_grounding_ce_5: 0.00031/0.27626, loss_mask_ce_6: 0.24741/0.82376, loss_mask_bce_6: 0.51583/0.30993, loss_mask_dice_6: 0.61725/1.05470, loss_spatial_bce_6: 0.14386/0.09748, loss_spatial_dice_6: 0.18878/0.19770, loss_spatial_ce_6: 0.22495/0.11792, loss_grounding_bce_6: 0.12404/0.08296, loss_grounding_dice_6: 0.09446/0.15467, loss_grounding_ce_6: 0.00002/0.28504, loss_mask_ce_7: 0.22595/0.87934, loss_mask_bce_7: 0.50076/0.31715, loss_mask_dice_7: 0.54910/1.10043, loss_spatial_bce_7: 0.15061/0.10659, loss_spatial_dice_7: 0.18394/0.22257, loss_spatial_ce_7: 0.36218/0.15246, loss_grounding_bce_7: 0.12489/0.08466, loss_grounding_dice_7: 0.08496/0.16021, loss_grounding_ce_7: 0.00025/0.31825, loss_mask_ce_8: 0.30031/1.01314, loss_mask_bce_8: 0.50684/0.33315, loss_mask_dice_8: 0.59578/1.17693, loss_spatial_bce_8: 0.15380/0.12291, loss_spatial_dice_8: 0.17797/0.25704, loss_spatial_ce_8: 0.61136/0.19785, loss_grounding_bce_8: 0.13459/0.08884, loss_grounding_dice_8: 0.07216/0.17002, loss_grounding_ce_8: 0.01353/0.41614, loss_mask_ce_9: 1.92233/3.47348, loss_mask_bce_9: 0.50006/0.35993, loss_mask_dice_9: 0.68965/1.75936, loss_spatial_bce_9: 0.43441/0.35414, loss_spatial_dice_9: 0.66176/0.79310, loss_spatial_ce_9: 1.61452/1.38671, loss_grounding_bce_9: 0.16307/0.10104, loss_grounding_dice_9: 0.06715/0.24224, loss_grounding_ce_9: 0.19835/0.66807] items per batch[64] items per second[0.36] total items[5177600] mini batches[ 80900] memory[4999] epoch remaining[0:38:51] INFO:trainer.default_trainer:epochs[ 44] optim steps[81000] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 1.99512/0.75193, loss_mask_bce_0: 0.69520/0.30064, loss_mask_dice_0: 1.62099/1.02003, loss_spatial_bce_0: 0.23630/0.08429, loss_spatial_dice_0: 0.54924/0.17822, loss_spatial_ce_0: 0.06040/0.05513, loss_grounding_bce_0: 0.08516/0.08058, loss_grounding_dice_0: 0.22387/0.15038, loss_grounding_ce_0: 0.44498/0.24872, loss_mask_ce_1: 2.04131/0.75268, loss_mask_bce_1: 0.68174/0.30147, loss_mask_dice_1: 1.53259/1.02443, loss_spatial_bce_1: 0.22951/0.08476, loss_spatial_dice_1: 0.55888/0.18121, loss_spatial_ce_1: 0.06082/0.05888, loss_grounding_bce_1: 0.08192/0.08080, loss_grounding_dice_1: 0.22243/0.15114, loss_grounding_ce_1: 0.45946/0.25009, loss_mask_ce_2: 1.94812/0.76029, loss_mask_bce_2: 0.69816/0.30183, loss_mask_dice_2: 1.52187/1.02520, loss_spatial_bce_2: 0.23774/0.08486, loss_spatial_dice_2: 0.54902/0.18184, loss_spatial_ce_2: 0.05477/0.06111, loss_grounding_bce_2: 0.08490/0.08077, loss_grounding_dice_2: 0.22361/0.15107, loss_grounding_ce_2: 0.43210/0.25276, loss_mask_ce_3: 2.09208/0.76506, loss_mask_bce_3: 0.72018/0.30314, loss_mask_dice_3: 1.53949/1.02327, loss_spatial_bce_3: 0.24377/0.08699, loss_spatial_dice_3: 0.55749/0.18321, loss_spatial_ce_3: 0.06190/0.06598, loss_grounding_bce_3: 0.08480/0.08111, loss_grounding_dice_3: 0.22246/0.15072, loss_grounding_ce_3: 0.43143/0.25419, loss_mask_ce_4: 1.98520/0.77077, loss_mask_bce_4: 0.69676/0.30590, loss_mask_dice_4: 1.58043/1.04274, loss_spatial_bce_4: 0.24120/0.08951, loss_spatial_dice_4: 0.56050/0.19202, loss_spatial_ce_4: 0.06682/0.07987, loss_grounding_bce_4: 0.08607/0.08188, loss_grounding_dice_4: 0.22913/0.15335, loss_grounding_ce_4: 0.43876/0.25855, loss_mask_ce_5: 2.09693/0.79642, loss_mask_bce_5: 0.72014/0.30774, loss_mask_dice_5: 1.57404/1.05085, loss_spatial_bce_5: 0.24758/0.09195, loss_spatial_dice_5: 0.57334/0.19538, loss_spatial_ce_5: 0.07168/0.09357, loss_grounding_bce_5: 0.08069/0.08213, loss_grounding_dice_5: 0.22474/0.15420, loss_grounding_ce_5: 0.54036/0.27628, loss_mask_ce_6: 2.41596/0.82383, loss_mask_bce_6: 0.74368/0.30993, loss_mask_dice_6: 1.46797/1.05481, loss_spatial_bce_6: 0.24067/0.09746, loss_spatial_dice_6: 0.56386/0.19770, loss_spatial_ce_6: 0.11052/0.11791, loss_grounding_bce_6: 0.06847/0.08295, loss_grounding_dice_6: 0.21286/0.15467, loss_grounding_ce_6: 0.54611/0.28512, loss_mask_ce_7: 2.28961/0.87940, loss_mask_bce_7: 0.77027/0.31715, loss_mask_dice_7: 1.67308/1.10055, loss_spatial_bce_7: 0.24622/0.10658, loss_spatial_dice_7: 0.57259/0.22257, loss_spatial_ce_7: 0.17216/0.15243, loss_grounding_bce_7: 0.07871/0.08464, loss_grounding_dice_7: 0.21741/0.16021, loss_grounding_ce_7: 0.57449/0.31835, loss_mask_ce_8: 1.93464/1.01315, loss_mask_bce_8: 0.87725/0.33314, loss_mask_dice_8: 1.72253/1.17707, loss_spatial_bce_8: 0.28187/0.12290, loss_spatial_dice_8: 0.61172/0.25705, loss_spatial_ce_8: 0.24787/0.19781, loss_grounding_bce_8: 0.08182/0.08882, loss_grounding_dice_8: 0.22600/0.17002, loss_grounding_ce_8: 0.56135/0.41625, loss_mask_ce_9: 5.95587/3.47373, loss_mask_bce_9: 0.85852/0.35994, loss_mask_dice_9: 2.62999/1.75951, loss_spatial_bce_9: 0.48745/0.35416, loss_spatial_dice_9: 0.85513/0.79311, loss_spatial_ce_9: 1.07898/1.38682, loss_grounding_bce_9: 0.16363/0.10102, loss_grounding_dice_9: 0.36083/0.24226, loss_grounding_ce_9: 0.59497/0.66820] items per batch[64] items per second[0.37] total items[5184000] mini batches[ 81000] memory[4999] epoch remaining[0:35:42] INFO:trainer.default_trainer:epochs[ 44] optim steps[81100] learning rate[default: 1.00000e-04] train loss[loss_mask_ce_0: 2.45317/0.75185, loss_mask_bce_0: 0.30270/0.30063, loss_mask_dice_0: 0.27045/1.01983, loss_spatial_bce_0: 0.17960/0.08430, loss_spatial_dice_0: 0.15453/0.17821, loss_spatial_ce_0: 0.00762/0.05513, loss_grounding_bce_0: 0.05227/0.08058, loss_grounding_dice_0: 0.09657/0.15040, loss_grounding_ce_0: 0.02041/0.24872, loss_mask_ce_1: 2.18953/0.75263, loss_mask_bce_1: 0.29526/0.30146, loss_mask_dice_1: 0.26556/1.02425, loss_spatial_bce_1: 0.18104/0.08477, loss_spatial_dice_1: 0.14466/0.18121, loss_spatial_ce_1: 0.00729/0.05887, loss_grounding_bce_1: 0.05708/0.08080, loss_grounding_dice_1: 0.11242/0.15117, loss_grounding_ce_1: 0.02015/0.25009, loss_mask_ce_2: 2.25961/0.76022, loss_mask_bce_2: 0.30142/0.30182, loss_mask_dice_2: 0.27529/1.02503, loss_spatial_bce_2: 0.18178/0.08487, loss_spatial_dice_2: 0.13615/0.18183, loss_spatial_ce_2: 0.01092/0.06111, loss_grounding_bce_2: 0.06360/0.08077, loss_grounding_dice_2: 0.12600/0.15108, loss_grounding_ce_2: 0.03708/0.25276, loss_mask_ce_3: 2.12979/0.76499, loss_mask_bce_3: 0.31354/0.30313, loss_mask_dice_3: 0.25547/1.02307, loss_spatial_bce_3: 0.19029/0.08700, loss_spatial_dice_3: 0.14030/0.18321, loss_spatial_ce_3: 0.00962/0.06599, loss_grounding_bce_3: 0.06254/0.08111, loss_grounding_dice_3: 0.12326/0.15074, loss_grounding_ce_3: 0.01985/0.25419, loss_mask_ce_4: 1.99272/0.77072, loss_mask_bce_4: 0.32171/0.30590, loss_mask_dice_4: 0.28783/1.04257, loss_spatial_bce_4: 0.17529/0.08953, loss_spatial_dice_4: 0.13589/0.19202, loss_spatial_ce_4: 0.01814/0.07986, loss_grounding_bce_4: 0.06250/0.08189, loss_grounding_dice_4: 0.11099/0.15337, loss_grounding_ce_4: 0.01548/0.25853, loss_mask_ce_5: 1.78549/0.79638, loss_mask_bce_5: 0.31623/0.30773, loss_mask_dice_5: 0.25659/1.05064, loss_spatial_bce_5: 0.18403/0.09197, loss_spatial_dice_5: 0.15606/0.19539, loss_spatial_ce_5: 0.04223/0.09357, loss_grounding_bce_5: 0.06632/0.08214, loss_grounding_dice_5: 0.11892/0.15422, loss_grounding_ce_5: 0.02223/0.27625, loss_mask_ce_6: 2.52092/0.82382, loss_mask_bce_6: 0.31308/0.30992, loss_mask_dice_6: 0.24595/1.05462, loss_spatial_bce_6: 0.18412/0.09749, loss_spatial_dice_6: 0.15299/0.19771, loss_spatial_ce_6: 0.17721/0.11791, loss_grounding_bce_6: 0.05964/0.08296, loss_grounding_dice_6: 0.10418/0.15469, loss_grounding_ce_6: 0.02362/0.28513, loss_mask_ce_7: 2.21033/0.87941, loss_mask_bce_7: 0.31509/0.31715, loss_mask_dice_7: 0.27755/1.10036, loss_spatial_bce_7: 0.19513/0.10660, loss_spatial_dice_7: 0.20973/0.22257, loss_spatial_ce_7: 0.12423/0.15243, loss_grounding_bce_7: 0.05589/0.08465, loss_grounding_dice_7: 0.09020/0.16023, loss_grounding_ce_7: 0.01056/0.31835, loss_mask_ce_8: 2.61228/1.01306, loss_mask_bce_8: 0.30965/0.33312, loss_mask_dice_8: 0.22154/1.17684, loss_spatial_bce_8: 0.20889/0.12292, loss_spatial_dice_8: 0.16523/0.25705, loss_spatial_ce_8: 0.08291/0.19780, loss_grounding_bce_8: 0.07358/0.08883, loss_grounding_dice_8: 0.10270/0.17004, loss_grounding_ce_8: 0.01150/0.41617, loss_mask_ce_9: 2.70599/3.47343, loss_mask_bce_9: 0.30349/0.35991, loss_mask_dice_9: 0.42110/1.75915, loss_spatial_bce_9: 0.44702/0.35418, loss_spatial_dice_9: 0.53490/0.79308, loss_spatial_ce_9: 0.90319/1.38678, loss_grounding_bce_9: 0.06581/0.10103, loss_grounding_dice_9: 0.13340/0.24227, loss_grounding_ce_9: 0.21973/0.66808] items per batch[64] items per second[0.37] total items[5190400] mini batches[ 81100] memory[4999] epoch remaining[0:32:43] INFO:trainer.default_trainer:epochs[ 44] optim steps[81200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.48260/0.75192, loss_mask_bce_0: 0.82765/0.30065, loss_mask_dice_0: 1.34964/1.01974, loss_spatial_bce_0: 0.25886/0.08430, loss_spatial_dice_0: 0.37624/0.17820, loss_spatial_ce_0: 0.14223/0.05515, loss_grounding_bce_0: 0.25418/0.08057, loss_grounding_dice_0: 0.31295/0.15040, loss_grounding_ce_0: 0.12588/0.24865, loss_mask_ce_1: 1.46086/0.75274, loss_mask_bce_1: 0.80721/0.30148, loss_mask_dice_1: 1.35061/1.02415, loss_spatial_bce_1: 0.25710/0.08477, loss_spatial_dice_1: 0.37772/0.18120, loss_spatial_ce_1: 0.14756/0.05887, loss_grounding_bce_1: 0.25315/0.08079, loss_grounding_dice_1: 0.31059/0.15116, loss_grounding_ce_1: 0.12880/0.25004, loss_mask_ce_2: 1.40959/0.76029, loss_mask_bce_2: 0.82031/0.30184, loss_mask_dice_2: 1.32785/1.02493, loss_spatial_bce_2: 0.24894/0.08487, loss_spatial_dice_2: 0.38026/0.18183, loss_spatial_ce_2: 0.12644/0.06110, loss_grounding_bce_2: 0.26013/0.08076, loss_grounding_dice_2: 0.32023/0.15107, loss_grounding_ce_2: 0.13353/0.25269, loss_mask_ce_3: 1.45348/0.76510, loss_mask_bce_3: 0.83525/0.30314, loss_mask_dice_3: 1.31642/1.02296, loss_spatial_bce_3: 0.25345/0.08701, loss_spatial_dice_3: 0.40175/0.18321, loss_spatial_ce_3: 0.15132/0.06599, loss_grounding_bce_3: 0.27150/0.08110, loss_grounding_dice_3: 0.30891/0.15074, loss_grounding_ce_3: 0.12448/0.25411, loss_mask_ce_4: 1.76844/0.77081, loss_mask_bce_4: 0.82939/0.30592, loss_mask_dice_4: 1.31626/1.04247, loss_spatial_bce_4: 0.24845/0.08953, loss_spatial_dice_4: 0.38900/0.19202, loss_spatial_ce_4: 0.37291/0.07984, loss_grounding_bce_4: 0.25333/0.08188, loss_grounding_dice_4: 0.30957/0.15336, loss_grounding_ce_4: 0.12495/0.25849, loss_mask_ce_5: 1.52062/0.79646, loss_mask_bce_5: 0.82732/0.30775, loss_mask_dice_5: 1.30158/1.05055, loss_spatial_bce_5: 0.24166/0.09197, loss_spatial_dice_5: 0.40883/0.19539, loss_spatial_ce_5: 0.31171/0.09359, loss_grounding_bce_5: 0.25100/0.08213, loss_grounding_dice_5: 0.31150/0.15422, loss_grounding_ce_5: 0.14317/0.27618, loss_mask_ce_6: 1.75003/0.82391, loss_mask_bce_6: 0.81506/0.30994, loss_mask_dice_6: 1.31383/1.05451, loss_spatial_bce_6: 0.24672/0.09749, loss_spatial_dice_6: 0.42126/0.19771, loss_spatial_ce_6: 0.30900/0.11790, loss_grounding_bce_6: 0.25209/0.08295, loss_grounding_dice_6: 0.31863/0.15467, loss_grounding_ce_6: 0.14348/0.28507, loss_mask_ce_7: 1.53029/0.87954, loss_mask_bce_7: 0.82345/0.31717, loss_mask_dice_7: 1.33326/1.10024, loss_spatial_bce_7: 0.24790/0.10659, loss_spatial_dice_7: 0.42987/0.22257, loss_spatial_ce_7: 0.43056/0.15240, loss_grounding_bce_7: 0.25323/0.08463, loss_grounding_dice_7: 0.31230/0.16022, loss_grounding_ce_7: 0.13134/0.31831, loss_mask_ce_8: 1.65936/1.01318, loss_mask_bce_8: 0.86390/0.33314, loss_mask_dice_8: 1.35305/1.17674, loss_spatial_bce_8: 0.23082/0.12292, loss_spatial_dice_8: 0.46807/0.25704, loss_spatial_ce_8: 0.52187/0.19776, loss_grounding_bce_8: 0.24245/0.08881, loss_grounding_dice_8: 0.30041/0.17002, loss_grounding_ce_8: 0.11802/0.41605, loss_mask_ce_9: 2.92776/3.47349, loss_mask_bce_9: 0.76478/0.35997, loss_mask_dice_9: 1.70887/1.75935, loss_spatial_bce_9: 0.46146/0.35421, loss_spatial_dice_9: 0.85477/0.79307, loss_spatial_ce_9: 1.34628/1.38660, loss_grounding_bce_9: 0.24344/0.10102, loss_grounding_dice_9: 0.31729/0.24226, loss_grounding_ce_9: 0.40778/0.66808] items per batch[64] items per second[0.37] total items[5196800] mini batches[ 81200] memory[4999] epoch remaining[0:29:44] INFO:trainer.default_trainer:epochs[ 44] optim steps[81300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.58370/0.75197, loss_mask_bce_0: 0.52342/0.30065, loss_mask_dice_0: 1.04631/1.01970, loss_spatial_bce_0: 0.26023/0.08430, loss_spatial_dice_0: 0.30414/0.17819, loss_spatial_ce_0: 0.00087/0.05513, loss_grounding_bce_0: 0.07423/0.08057, loss_grounding_dice_0: 0.11024/0.15038, loss_grounding_ce_0: 2.57953/0.24866, loss_mask_ce_1: 1.48633/0.75280, loss_mask_bce_1: 0.61725/0.30147, loss_mask_dice_1: 1.12390/1.02408, loss_spatial_bce_1: 0.24117/0.08477, loss_spatial_dice_1: 0.33715/0.18119, loss_spatial_ce_1: 0.00131/0.05884, loss_grounding_bce_1: 0.08050/0.08079, loss_grounding_dice_1: 0.11292/0.15115, loss_grounding_ce_1: 2.79845/0.25006, loss_mask_ce_2: 1.58631/0.76035, loss_mask_bce_2: 0.52508/0.30183, loss_mask_dice_2: 1.02699/1.02484, loss_spatial_bce_2: 0.23234/0.08487, loss_spatial_dice_2: 0.33327/0.18182, loss_spatial_ce_2: 0.00229/0.06107, loss_grounding_bce_2: 0.08109/0.08076, loss_grounding_dice_2: 0.11839/0.15106, loss_grounding_ce_2: 2.66325/0.25271, loss_mask_ce_3: 1.54620/0.76515, loss_mask_bce_3: 0.53990/0.30313, loss_mask_dice_3: 1.06281/1.02288, loss_spatial_bce_3: 0.19626/0.08701, loss_spatial_dice_3: 0.35532/0.18320, loss_spatial_ce_3: 0.00396/0.06597, loss_grounding_bce_3: 0.10190/0.08110, loss_grounding_dice_3: 0.13318/0.15073, loss_grounding_ce_3: 2.80231/0.25413, loss_mask_ce_4: 1.48825/0.77089, loss_mask_bce_4: 0.56596/0.30591, loss_mask_dice_4: 1.01865/1.04237, loss_spatial_bce_4: 0.14919/0.08953, loss_spatial_dice_4: 0.28433/0.19201, loss_spatial_ce_4: 0.00235/0.07983, loss_grounding_bce_4: 0.12582/0.08188, loss_grounding_dice_4: 0.16364/0.15334, loss_grounding_ce_4: 0.76181/0.25848, loss_mask_ce_5: 1.79666/0.79651, loss_mask_bce_5: 0.51819/0.30774, loss_mask_dice_5: 0.96300/1.05048, loss_spatial_bce_5: 0.13470/0.09197, loss_spatial_dice_5: 0.25310/0.19538, loss_spatial_ce_5: 0.04319/0.09357, loss_grounding_bce_5: 0.10126/0.08213, loss_grounding_dice_5: 0.11250/0.15420, loss_grounding_ce_5: 2.83498/0.27619, loss_mask_ce_6: 1.79194/0.82400, loss_mask_bce_6: 0.50914/0.30993, loss_mask_dice_6: 0.87761/1.05443, loss_spatial_bce_6: 0.16117/0.09749, loss_spatial_dice_6: 0.26568/0.19771, loss_spatial_ce_6: 0.04969/0.11789, loss_grounding_bce_6: 0.10576/0.08295, loss_grounding_dice_6: 0.13037/0.15466, loss_grounding_ce_6: 2.49855/0.28507, loss_mask_ce_7: 1.84375/0.87964, loss_mask_bce_7: 0.50783/0.31716, loss_mask_dice_7: 0.87278/1.10014, loss_spatial_bce_7: 0.19395/0.10660, loss_spatial_dice_7: 0.30616/0.22255, loss_spatial_ce_7: 0.07322/0.15239, loss_grounding_bce_7: 0.09267/0.08464, loss_grounding_dice_7: 0.15165/0.16020, loss_grounding_ce_7: 2.34220/0.31830, loss_mask_ce_8: 2.03420/1.01333, loss_mask_bce_8: 0.56361/0.33314, loss_mask_dice_8: 1.14927/1.17665, loss_spatial_bce_8: 0.19224/0.12293, loss_spatial_dice_8: 0.33875/0.25704, loss_spatial_ce_8: 0.13683/0.19773, loss_grounding_bce_8: 0.15475/0.08881, loss_grounding_dice_8: 0.21060/0.17001, loss_grounding_ce_8: 3.37144/0.41610, loss_mask_ce_9: 3.41378/3.47350, loss_mask_bce_9: 0.82469/0.35998, loss_mask_dice_9: 1.97873/1.75921, loss_spatial_bce_9: 0.40897/0.35424, loss_spatial_dice_9: 0.86501/0.79308, loss_spatial_ce_9: 1.35181/1.38667, loss_grounding_bce_9: 0.26249/0.10103, loss_grounding_dice_9: 0.43804/0.24224, loss_grounding_ce_9: 2.42113/0.66819] items per batch[64] items per second[0.36] total items[5203200] mini batches[ 81300] memory[4999] epoch remaining[0:26:48] INFO:trainer.default_trainer:epochs[ 44] optim steps[81400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.26268/0.75186, loss_mask_bce_0: 0.03219/0.30062, loss_mask_dice_0: 1.61061/1.02002, loss_spatial_bce_0: 0.00265/0.08429, loss_spatial_dice_0: 0.24080/0.17818, loss_spatial_ce_0: 0.02763/0.05512, loss_grounding_bce_0: 0.00933/0.08058, loss_grounding_dice_0: 0.03556/0.15037, loss_grounding_ce_0: 0.00106/0.24862, loss_mask_ce_1: 0.33873/0.75268, loss_mask_bce_1: 0.02907/0.30144, loss_mask_dice_1: 2.04235/1.02442, loss_spatial_bce_1: 0.00250/0.08476, loss_spatial_dice_1: 0.25798/0.18118, loss_spatial_ce_1: 0.03016/0.05883, loss_grounding_bce_1: 0.00754/0.08080, loss_grounding_dice_1: 0.02764/0.15114, loss_grounding_ce_1: 0.00148/0.25003, loss_mask_ce_2: 0.24484/0.76021, loss_mask_bce_2: 0.03188/0.30180, loss_mask_dice_2: 1.89878/1.02518, loss_spatial_bce_2: 0.00205/0.08486, loss_spatial_dice_2: 0.22542/0.18182, loss_spatial_ce_2: 0.02818/0.06106, loss_grounding_bce_2: 0.00835/0.08077, loss_grounding_dice_2: 0.03327/0.15105, loss_grounding_ce_2: 0.00119/0.25267, loss_mask_ce_3: 0.34261/0.76507, loss_mask_bce_3: 0.02862/0.30310, loss_mask_dice_3: 1.66982/1.02323, loss_spatial_bce_3: 0.00398/0.08700, loss_spatial_dice_3: 0.29511/0.18319, loss_spatial_ce_3: 0.04137/0.06595, loss_grounding_bce_3: 0.00629/0.08111, loss_grounding_dice_3: 0.02569/0.15072, loss_grounding_ce_3: 0.00122/0.25408, loss_mask_ce_4: 0.20621/0.77081, loss_mask_bce_4: 0.03288/0.30587, loss_mask_dice_4: 1.32292/1.04274, loss_spatial_bce_4: 0.00285/0.08952, loss_spatial_dice_4: 0.21798/0.19201, loss_spatial_ce_4: 0.16758/0.07983, loss_grounding_bce_4: 0.01028/0.08189, loss_grounding_dice_4: 0.03672/0.15334, loss_grounding_ce_4: 0.00145/0.25844, loss_mask_ce_5: 0.25360/0.79638, loss_mask_bce_5: 0.03161/0.30770, loss_mask_dice_5: 1.24997/1.05084, loss_spatial_bce_5: 0.00284/0.09196, loss_spatial_dice_5: 0.23824/0.19537, loss_spatial_ce_5: 0.07174/0.09356, loss_grounding_bce_5: 0.00680/0.08214, loss_grounding_dice_5: 0.02823/0.15420, loss_grounding_ce_5: 0.00300/0.27614, loss_mask_ce_6: 0.26772/0.82389, loss_mask_bce_6: 0.03019/0.30989, loss_mask_dice_6: 1.40747/1.05479, loss_spatial_bce_6: 0.00330/0.09748, loss_spatial_dice_6: 0.27502/0.19770, loss_spatial_ce_6: 0.11650/0.11789, loss_grounding_bce_6: 0.00791/0.08296, loss_grounding_dice_6: 0.02933/0.15466, loss_grounding_ce_6: 0.00189/0.28504, loss_mask_ce_7: 0.41174/0.87952, loss_mask_bce_7: 0.03297/0.31712, loss_mask_dice_7: 1.70792/1.10054, loss_spatial_bce_7: 0.00237/0.10658, loss_spatial_dice_7: 0.23387/0.22254, loss_spatial_ce_7: 0.38106/0.15238, loss_grounding_bce_7: 0.00831/0.08465, loss_grounding_dice_7: 0.03552/0.16020, loss_grounding_ce_7: 0.00109/0.31828, loss_mask_ce_8: 0.33462/1.01319, loss_mask_bce_8: 0.03514/0.33310, loss_mask_dice_8: 2.29602/1.17706, loss_spatial_bce_8: 0.00373/0.12290, loss_spatial_dice_8: 0.29187/0.25701, loss_spatial_ce_8: 0.18586/0.19772, loss_grounding_bce_8: 0.00985/0.08883, loss_grounding_dice_8: 0.03501/0.17002, loss_grounding_ce_8: 0.00099/0.41608, loss_mask_ce_9: 2.63041/3.47350, loss_mask_bce_9: 0.02970/0.35993, loss_mask_dice_9: 2.39876/1.75961, loss_spatial_bce_9: 0.06736/0.35425, loss_spatial_dice_9: 0.77232/0.79308, loss_spatial_ce_9: 1.83563/1.38673, loss_grounding_bce_9: 0.00973/0.10105, loss_grounding_dice_9: 0.04322/0.24224, loss_grounding_ce_9: 0.02980/0.66802] items per batch[64] items per second[0.36] total items[5209600] mini batches[ 81400] memory[4999] epoch remaining[0:23:52] INFO:trainer.default_trainer:epochs[ 44] optim steps[81500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.41265/0.75192, loss_mask_bce_0: 0.47290/0.30057, loss_mask_dice_0: 0.37677/1.02020, loss_spatial_bce_0: 0.29888/0.08426, loss_spatial_dice_0: 0.20320/0.17817, loss_spatial_ce_0: 0.06825/0.05511, loss_grounding_bce_0: 0.16641/0.08055, loss_grounding_dice_0: 0.13229/0.15038, loss_grounding_ce_0: 0.34745/0.24856, loss_mask_ce_1: 1.42389/0.75275, loss_mask_bce_1: 0.45104/0.30139, loss_mask_dice_1: 0.33967/1.02454, loss_spatial_bce_1: 0.29306/0.08474, loss_spatial_dice_1: 0.23303/0.18118, loss_spatial_ce_1: 0.03810/0.05880, loss_grounding_bce_1: 0.16646/0.08078, loss_grounding_dice_1: 0.13594/0.15114, loss_grounding_ce_1: 0.36055/0.24995, loss_mask_ce_2: 1.46734/0.76027, loss_mask_bce_2: 0.54953/0.30176, loss_mask_dice_2: 0.37647/1.02531, loss_spatial_bce_2: 0.28857/0.08484, loss_spatial_dice_2: 0.23224/0.18181, loss_spatial_ce_2: 0.03658/0.06103, loss_grounding_bce_2: 0.22327/0.08075, loss_grounding_dice_2: 0.14936/0.15106, loss_grounding_ce_2: 0.37271/0.25261, loss_mask_ce_3: 1.38217/0.76512, loss_mask_bce_3: 0.58472/0.30306, loss_mask_dice_3: 0.39533/1.02339, loss_spatial_bce_3: 0.30900/0.08697, loss_spatial_dice_3: 0.20784/0.18319, loss_spatial_ce_3: 0.04065/0.06592, loss_grounding_bce_3: 0.24424/0.08109, loss_grounding_dice_3: 0.15763/0.15073, loss_grounding_ce_3: 0.42410/0.25400, loss_mask_ce_4: 1.36682/0.77091, loss_mask_bce_4: 0.54153/0.30582, loss_mask_dice_4: 0.38124/1.04290, loss_spatial_bce_4: 0.30570/0.08950, loss_spatial_dice_4: 0.23960/0.19201, loss_spatial_ce_4: 0.07538/0.07980, loss_grounding_bce_4: 0.23113/0.08186, loss_grounding_dice_4: 0.17190/0.15335, loss_grounding_ce_4: 0.38036/0.25836, loss_mask_ce_5: 1.58179/0.79644, loss_mask_bce_5: 0.57164/0.30766, loss_mask_dice_5: 0.41029/1.05099, loss_spatial_bce_5: 0.32880/0.09194, loss_spatial_dice_5: 0.23458/0.19537, loss_spatial_ce_5: 0.03865/0.09354, loss_grounding_bce_5: 0.20537/0.08212, loss_grounding_dice_5: 0.16915/0.15421, loss_grounding_ce_5: 0.49249/0.27605, loss_mask_ce_6: 1.63196/0.82398, loss_mask_bce_6: 0.54122/0.30984, loss_mask_dice_6: 0.41279/1.05494, loss_spatial_bce_6: 0.28783/0.09746, loss_spatial_dice_6: 0.18084/0.19770, loss_spatial_ce_6: 0.13851/0.11786, loss_grounding_bce_6: 0.17892/0.08294, loss_grounding_dice_6: 0.17142/0.15466, loss_grounding_ce_6: 0.47707/0.28497, loss_mask_ce_7: 1.60255/0.87957, loss_mask_bce_7: 0.52651/0.31708, loss_mask_dice_7: 0.49003/1.10074, loss_spatial_bce_7: 0.24652/0.10655, loss_spatial_dice_7: 0.19580/0.22253, loss_spatial_ce_7: 0.18370/0.15234, loss_grounding_bce_7: 0.18110/0.08463, loss_grounding_dice_7: 0.17231/0.16022, loss_grounding_ce_7: 0.45636/0.31818, loss_mask_ce_8: 1.23757/1.01320, loss_mask_bce_8: 0.58339/0.33304, loss_mask_dice_8: 0.64343/1.17728, loss_spatial_bce_8: 0.24551/0.12286, loss_spatial_dice_8: 0.21118/0.25700, loss_spatial_ce_8: 0.05971/0.19766, loss_grounding_bce_8: 0.16147/0.08880, loss_grounding_dice_8: 0.20608/0.17003, loss_grounding_ce_8: 0.44771/0.41598, loss_mask_ce_9: 5.09040/3.47342, loss_mask_bce_9: 0.63781/0.35988, loss_mask_dice_9: 0.84860/1.75984, loss_spatial_bce_9: 0.62136/0.35424, loss_spatial_dice_9: 0.70193/0.79309, loss_spatial_ce_9: 0.97979/1.38683, loss_grounding_bce_9: 0.21681/0.10104, loss_grounding_dice_9: 0.28925/0.24224, loss_grounding_ce_9: 0.77805/0.66799] items per batch[64] items per second[0.37] total items[5216000] mini batches[ 81500] memory[4999] epoch remaining[0:20:54] INFO:trainer.default_trainer:epochs[ 44] optim steps[81600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.11494/0.75178, loss_mask_bce_0: 0.02353/0.30055, loss_mask_dice_0: 0.17458/1.02000, loss_spatial_bce_0: 0.01806/0.08425, loss_spatial_dice_0: 0.07353/0.17813, loss_spatial_ce_0: 0.19572/0.05508, loss_grounding_bce_0: 0.00952/0.08055, loss_grounding_dice_0: 0.07618/0.15035, loss_grounding_ce_0: 0.13819/0.24848, loss_mask_ce_1: 0.11723/0.75261, loss_mask_bce_1: 0.02276/0.30136, loss_mask_dice_1: 0.28561/1.02434, loss_spatial_bce_1: 0.02204/0.08473, loss_spatial_dice_1: 0.06538/0.18114, loss_spatial_ce_1: 0.19446/0.05877, loss_grounding_bce_1: 0.00914/0.08078, loss_grounding_dice_1: 0.15025/0.15112, loss_grounding_ce_1: 0.13856/0.24987, loss_mask_ce_2: 0.10782/0.76013, loss_mask_bce_2: 0.02508/0.30173, loss_mask_dice_2: 0.24663/1.02510, loss_spatial_bce_2: 0.01860/0.08483, loss_spatial_dice_2: 0.09729/0.18177, loss_spatial_ce_2: 0.22815/0.06100, loss_grounding_bce_2: 0.01016/0.08074, loss_grounding_dice_2: 0.11767/0.15103, loss_grounding_ce_2: 0.33666/0.25255, loss_mask_ce_3: 0.12048/0.76497, loss_mask_bce_3: 0.02279/0.30304, loss_mask_dice_3: 0.20033/1.02320, loss_spatial_bce_3: 0.02058/0.08696, loss_spatial_dice_3: 0.10955/0.18315, loss_spatial_ce_3: 0.21233/0.06590, loss_grounding_bce_3: 0.00955/0.08109, loss_grounding_dice_3: 0.09036/0.15070, loss_grounding_ce_3: 0.14292/0.25390, loss_mask_ce_4: 0.10045/0.77081, loss_mask_bce_4: 0.02337/0.30579, loss_mask_dice_4: 0.17373/1.04268, loss_spatial_bce_4: 0.02603/0.08950, loss_spatial_dice_4: 0.10480/0.19197, loss_spatial_ce_4: 0.33111/0.07978, loss_grounding_bce_4: 0.00932/0.08186, loss_grounding_dice_4: 0.08487/0.15333, loss_grounding_ce_4: 0.13270/0.25826, loss_mask_ce_5: 0.11158/0.79632, loss_mask_bce_5: 0.02646/0.30763, loss_mask_dice_5: 0.21674/1.05078, loss_spatial_bce_5: 0.03584/0.09193, loss_spatial_dice_5: 0.07975/0.19534, loss_spatial_ce_5: 0.40509/0.09350, loss_grounding_bce_5: 0.01003/0.08212, loss_grounding_dice_5: 0.07554/0.15418, loss_grounding_ce_5: 0.14339/0.27594, loss_mask_ce_6: 0.12537/0.82387, loss_mask_bce_6: 0.02567/0.30980, loss_mask_dice_6: 0.19994/1.05472, loss_spatial_bce_6: 0.03245/0.09745, loss_spatial_dice_6: 0.08027/0.19766, loss_spatial_ce_6: 0.37501/0.11782, loss_grounding_bce_6: 0.01126/0.08294, loss_grounding_dice_6: 0.10359/0.15463, loss_grounding_ce_6: 0.14482/0.28487, loss_mask_ce_7: 0.16561/0.87947, loss_mask_bce_7: 0.03101/0.31704, loss_mask_dice_7: 0.17781/1.10052, loss_spatial_bce_7: 0.07711/0.10654, loss_spatial_dice_7: 0.09695/0.22248, loss_spatial_ce_7: 0.43323/0.15229, loss_grounding_bce_7: 0.01224/0.08463, loss_grounding_dice_7: 0.06262/0.16019, loss_grounding_ce_7: 0.13246/0.31806, loss_mask_ce_8: 0.13464/1.01306, loss_mask_bce_8: 0.03349/0.33301, loss_mask_dice_8: 0.34248/1.17702, loss_spatial_bce_8: 0.67516/0.12285, loss_spatial_dice_8: 0.22672/0.25695, loss_spatial_ce_8: 0.00223/0.19761, loss_grounding_bce_8: 0.01234/0.08880, loss_grounding_dice_8: 0.06704/0.17000, loss_grounding_ce_8: 0.13259/0.41582, loss_mask_ce_9: 2.47289/3.47328, loss_mask_bce_9: 0.05717/0.35986, loss_mask_dice_9: 0.36740/1.75953, loss_spatial_bce_9: 0.23751/0.35427, loss_spatial_dice_9: 0.82274/0.79308, loss_spatial_ce_9: 2.02574/1.38677, loss_grounding_bce_9: 0.02255/0.10104, loss_grounding_dice_9: 0.11990/0.24219, loss_grounding_ce_9: 0.34790/0.66778] items per batch[64] items per second[0.37] total items[5222400] mini batches[ 81600] memory[4999] epoch remaining[0:17:58] INFO:trainer.default_trainer:epochs[ 44] optim steps[81700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.20664/0.75170, loss_mask_bce_0: 0.35217/0.30058, loss_mask_dice_0: 3.15723/1.01998, loss_spatial_bce_0: 0.02944/0.08426, loss_spatial_dice_0: 0.27985/0.17811, loss_spatial_ce_0: 0.00671/0.05506, loss_grounding_bce_0: 0.06376/0.08056, loss_grounding_dice_0: 0.36491/0.15034, loss_grounding_ce_0: 0.61921/0.24846, loss_mask_ce_1: 1.19639/0.75252, loss_mask_bce_1: 0.34216/0.30140, loss_mask_dice_1: 3.13150/1.02431, loss_spatial_bce_1: 0.02944/0.08474, loss_spatial_dice_1: 0.27910/0.18111, loss_spatial_ce_1: 0.00305/0.05874, loss_grounding_bce_1: 0.07170/0.08078, loss_grounding_dice_1: 0.27723/0.15111, loss_grounding_ce_1: 0.64610/0.24984, loss_mask_ce_2: 1.75184/0.76005, loss_mask_bce_2: 0.34393/0.30176, loss_mask_dice_2: 2.98703/1.02511, loss_spatial_bce_2: 0.02823/0.08483, loss_spatial_dice_2: 0.28297/0.18175, loss_spatial_ce_2: 0.02365/0.06098, loss_grounding_bce_2: 0.07454/0.08075, loss_grounding_dice_2: 0.41179/0.15102, loss_grounding_ce_2: 0.68291/0.25252, loss_mask_ce_3: 1.25973/0.76487, loss_mask_bce_3: 0.34418/0.30308, loss_mask_dice_3: 2.97250/1.02319, loss_spatial_bce_3: 0.03170/0.08698, loss_spatial_dice_3: 0.29130/0.18312, loss_spatial_ce_3: 0.14600/0.06587, loss_grounding_bce_3: 0.07075/0.08109, loss_grounding_dice_3: 0.27930/0.15069, loss_grounding_ce_3: 0.68792/0.25388, loss_mask_ce_4: 1.34904/0.77073, loss_mask_bce_4: 0.32349/0.30583, loss_mask_dice_4: 3.21085/1.04269, loss_spatial_bce_4: 0.03957/0.08952, loss_spatial_dice_4: 0.32586/0.19195, loss_spatial_ce_4: 0.03083/0.07974, loss_grounding_bce_4: 0.07953/0.08187, loss_grounding_dice_4: 0.38298/0.15331, loss_grounding_ce_4: 0.91883/0.25826, loss_mask_ce_5: 1.46556/0.79624, loss_mask_bce_5: 0.37131/0.30767, loss_mask_dice_5: 3.06640/1.05079, loss_spatial_bce_5: 0.03520/0.09195, loss_spatial_dice_5: 0.32666/0.19532, loss_spatial_ce_5: 0.07625/0.09347, loss_grounding_bce_5: 0.06183/0.08212, loss_grounding_dice_5: 0.34032/0.15417, loss_grounding_ce_5: 0.69226/0.27590, loss_mask_ce_6: 1.76098/0.82377, loss_mask_bce_6: 0.34597/0.30984, loss_mask_dice_6: 3.09926/1.05474, loss_spatial_bce_6: 0.03583/0.09746, loss_spatial_dice_6: 0.32806/0.19764, loss_spatial_ce_6: 0.55506/0.11781, loss_grounding_bce_6: 0.04389/0.08294, loss_grounding_dice_6: 0.39499/0.15462, loss_grounding_ce_6: 0.64506/0.28482, loss_mask_ce_7: 1.77544/0.87937, loss_mask_bce_7: 0.35313/0.31709, loss_mask_dice_7: 3.63554/1.10055, loss_spatial_bce_7: 0.05248/0.10656, loss_spatial_dice_7: 0.35742/0.22245, loss_spatial_ce_7: 0.08239/0.15225, loss_grounding_bce_7: 0.07850/0.08464, loss_grounding_dice_7: 0.29263/0.16018, loss_grounding_ce_7: 0.86337/0.31795, loss_mask_ce_8: 2.31672/1.01303, loss_mask_bce_8: 0.34353/0.33306, loss_mask_dice_8: 3.69670/1.17705, loss_spatial_bce_8: 0.05117/0.12285, loss_spatial_dice_8: 0.38722/0.25691, loss_spatial_ce_8: 0.17882/0.19755, loss_grounding_bce_8: 0.04300/0.08881, loss_grounding_dice_8: 0.40550/0.16999, loss_grounding_ce_8: 0.64807/0.41575, loss_mask_ce_9: 6.46587/3.47341, loss_mask_bce_9: 0.38677/0.35993, loss_mask_dice_9: 6.09620/1.75968, loss_spatial_bce_9: 0.15072/0.35431, loss_spatial_dice_9: 0.88612/0.79311, loss_spatial_ce_9: 1.53026/1.38673, loss_grounding_bce_9: 0.02725/0.10104, loss_grounding_dice_9: 0.62193/0.24218, loss_grounding_ce_9: 0.43672/0.66779] items per batch[64] items per second[0.37] total items[5228800] mini batches[ 81700] memory[4999] epoch remaining[0:15:01] INFO:trainer.default_trainer:epochs[ 44] optim steps[81800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.40364/0.75165, loss_mask_bce_0: 0.09685/0.30058, loss_mask_dice_0: 0.52774/1.02019, loss_spatial_bce_0: 0.02291/0.08425, loss_spatial_dice_0: 0.12665/0.17809, loss_spatial_ce_0: 0.04244/0.05502, loss_grounding_bce_0: 0.02187/0.08055, loss_grounding_dice_0: 0.04366/0.15033, loss_grounding_ce_0: 0.04372/0.24844, loss_mask_ce_1: 0.39267/0.75245, loss_mask_bce_1: 0.09156/0.30140, loss_mask_dice_1: 0.50327/1.02452, loss_spatial_bce_1: 0.02514/0.08473, loss_spatial_dice_1: 0.13539/0.18110, loss_spatial_ce_1: 0.05306/0.05871, loss_grounding_bce_1: 0.01982/0.08078, loss_grounding_dice_1: 0.04399/0.15110, loss_grounding_ce_1: 0.03327/0.24983, loss_mask_ce_2: 0.42052/0.75999, loss_mask_bce_2: 0.10066/0.30176, loss_mask_dice_2: 0.54237/1.02531, loss_spatial_bce_2: 0.02480/0.08482, loss_spatial_dice_2: 0.13582/0.18174, loss_spatial_ce_2: 0.04917/0.06095, loss_grounding_bce_2: 0.01668/0.08074, loss_grounding_dice_2: 0.03743/0.15102, loss_grounding_ce_2: 0.00969/0.25251, loss_mask_ce_3: 0.36046/0.76478, loss_mask_bce_3: 0.08725/0.30308, loss_mask_dice_3: 0.49534/1.02339, loss_spatial_bce_3: 0.02098/0.08697, loss_spatial_dice_3: 0.10301/0.18311, loss_spatial_ce_3: 0.06229/0.06586, loss_grounding_bce_3: 0.02122/0.08108, loss_grounding_dice_3: 0.04763/0.15068, loss_grounding_ce_3: 0.00646/0.25386, loss_mask_ce_4: 0.43584/0.77066, loss_mask_bce_4: 0.10137/0.30583, loss_mask_dice_4: 0.57754/1.04293, loss_spatial_bce_4: 0.03273/0.08951, loss_spatial_dice_4: 0.16667/0.19194, loss_spatial_ce_4: 0.02812/0.07972, loss_grounding_bce_4: 0.02663/0.08186, loss_grounding_dice_4: 0.05048/0.15330, loss_grounding_ce_4: 0.01214/0.25823, loss_mask_ce_5: 0.55051/0.79616, loss_mask_bce_5: 0.09420/0.30767, loss_mask_dice_5: 0.50708/1.05101, loss_spatial_bce_5: 0.03982/0.09194, loss_spatial_dice_5: 0.17246/0.19531, loss_spatial_ce_5: 0.03462/0.09346, loss_grounding_bce_5: 0.02122/0.08212, loss_grounding_dice_5: 0.04294/0.15417, loss_grounding_ce_5: 0.01580/0.27586, loss_mask_ce_6: 0.54044/0.82370, loss_mask_bce_6: 0.09851/0.30985, loss_mask_dice_6: 0.57685/1.05496, loss_spatial_bce_6: 0.03910/0.09745, loss_spatial_dice_6: 0.16030/0.19764, loss_spatial_ce_6: 0.02041/0.11778, loss_grounding_bce_6: 0.02094/0.08293, loss_grounding_dice_6: 0.04145/0.15462, loss_grounding_ce_6: 0.02361/0.28482, loss_mask_ce_7: 0.86032/0.87928, loss_mask_bce_7: 0.10149/0.31709, loss_mask_dice_7: 0.57263/1.10080, loss_spatial_bce_7: 0.09045/0.10655, loss_spatial_dice_7: 0.19451/0.22245, loss_spatial_ce_7: 0.00683/0.15219, loss_grounding_bce_7: 0.02392/0.08464, loss_grounding_dice_7: 0.04712/0.16019, loss_grounding_ce_7: 0.05296/0.31794, loss_mask_ce_8: 1.30864/1.01295, loss_mask_bce_8: 0.10775/0.33306, loss_mask_dice_8: 0.55379/1.17730, loss_spatial_bce_8: 0.06002/0.12283, loss_spatial_dice_8: 0.20712/0.25690, loss_spatial_ce_8: 0.09406/0.19750, loss_grounding_bce_8: 0.02398/0.08883, loss_grounding_dice_8: 0.04440/0.16999, loss_grounding_ce_8: 0.06598/0.41563, loss_mask_ce_9: 5.11864/3.47330, loss_mask_bce_9: 0.14903/0.35993, loss_mask_dice_9: 1.51111/1.76017, loss_spatial_bce_9: 0.31154/0.35428, loss_spatial_dice_9: 0.88156/0.79311, loss_spatial_ce_9: 1.32646/1.38659, loss_grounding_bce_9: 0.02134/0.10104, loss_grounding_dice_9: 0.06758/0.24217, loss_grounding_ce_9: 2.25945/0.66772] items per batch[64] items per second[0.36] total items[5235200] mini batches[ 81800] memory[4999] epoch remaining[0:12:07] INFO:trainer.default_trainer:epochs[ 44] optim steps[81900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.85560/0.75162, loss_mask_bce_0: 0.29378/0.30057, loss_mask_dice_0: 0.44470/1.02008, loss_spatial_bce_0: 0.11896/0.08423, loss_spatial_dice_0: 0.17482/0.17807, loss_spatial_ce_0: 0.00505/0.05500, loss_grounding_bce_0: 0.15270/0.08054, loss_grounding_dice_0: 0.17963/0.15033, loss_grounding_ce_0: 0.22829/0.24845, loss_mask_ce_1: 0.81599/0.75242, loss_mask_bce_1: 0.29918/0.30139, loss_mask_dice_1: 0.49739/1.02441, loss_spatial_bce_1: 0.12440/0.08471, loss_spatial_dice_1: 0.17928/0.18108, loss_spatial_ce_1: 0.00377/0.05868, loss_grounding_bce_1: 0.15354/0.08077, loss_grounding_dice_1: 0.17977/0.15110, loss_grounding_ce_1: 0.21457/0.24981, loss_mask_ce_2: 0.81529/0.75998, loss_mask_bce_2: 0.30816/0.30175, loss_mask_dice_2: 0.48715/1.02520, loss_spatial_bce_2: 0.12551/0.08481, loss_spatial_dice_2: 0.19053/0.18172, loss_spatial_ce_2: 0.00176/0.06092, loss_grounding_bce_2: 0.15538/0.08073, loss_grounding_dice_2: 0.18069/0.15101, loss_grounding_ce_2: 0.23094/0.25251, loss_mask_ce_3: 0.88214/0.76474, loss_mask_bce_3: 0.30490/0.30307, loss_mask_dice_3: 0.48069/1.02332, loss_spatial_bce_3: 0.12441/0.08695, loss_spatial_dice_3: 0.17392/0.18310, loss_spatial_ce_3: 0.01068/0.06583, loss_grounding_bce_3: 0.15311/0.08107, loss_grounding_dice_3: 0.17837/0.15068, loss_grounding_ce_3: 0.23109/0.25386, loss_mask_ce_4: 0.80501/0.77064, loss_mask_bce_4: 0.29359/0.30582, loss_mask_dice_4: 0.45772/1.04284, loss_spatial_bce_4: 0.12754/0.08949, loss_spatial_dice_4: 0.19478/0.19192, loss_spatial_ce_4: 0.13523/0.07971, loss_grounding_bce_4: 0.16079/0.08185, loss_grounding_dice_4: 0.18637/0.15331, loss_grounding_ce_4: 0.23190/0.25824, loss_mask_ce_5: 0.82853/0.79612, loss_mask_bce_5: 0.31190/0.30767, loss_mask_dice_5: 0.46981/1.05090, loss_spatial_bce_5: 0.13017/0.09192, loss_spatial_dice_5: 0.22387/0.19529, loss_spatial_ce_5: 0.11653/0.09345, loss_grounding_bce_5: 0.15974/0.08211, loss_grounding_dice_5: 0.19037/0.15418, loss_grounding_ce_5: 0.25285/0.27581, loss_mask_ce_6: 1.05515/0.82370, loss_mask_bce_6: 0.31016/0.30984, loss_mask_dice_6: 0.53895/1.05484, loss_spatial_bce_6: 0.13011/0.09743, loss_spatial_dice_6: 0.20905/0.19763, loss_spatial_ce_6: 0.20006/0.11777, loss_grounding_bce_6: 0.16079/0.08292, loss_grounding_dice_6: 0.18760/0.15462, loss_grounding_ce_6: 0.27841/0.28481, loss_mask_ce_7: 0.71124/0.87926, loss_mask_bce_7: 0.34098/0.31709, loss_mask_dice_7: 0.52557/1.10073, loss_spatial_bce_7: 0.13091/0.10653, loss_spatial_dice_7: 0.19084/0.22243, loss_spatial_ce_7: 0.18330/0.15217, loss_grounding_bce_7: 0.17927/0.08463, loss_grounding_dice_7: 0.19285/0.16020, loss_grounding_ce_7: 0.18606/0.31788, loss_mask_ce_8: 0.99442/1.01296, loss_mask_bce_8: 0.34956/0.33306, loss_mask_dice_8: 0.52952/1.17724, loss_spatial_bce_8: 0.15094/0.12280, loss_spatial_dice_8: 0.21036/0.25687, loss_spatial_ce_8: 0.05046/0.19746, loss_grounding_bce_8: 0.18587/0.08882, loss_grounding_dice_8: 0.20061/0.17000, loss_grounding_ce_8: 0.17845/0.41556, loss_mask_ce_9: 3.60289/3.47357, loss_mask_bce_9: 0.36829/0.35992, loss_mask_dice_9: 0.93014/1.75999, loss_spatial_bce_9: 0.43301/0.35427, loss_spatial_dice_9: 0.82475/0.79310, loss_spatial_ce_9: 0.97007/1.38658, loss_grounding_bce_9: 0.18316/0.10103, loss_grounding_dice_9: 0.30419/0.24218, loss_grounding_ce_9: 0.27891/0.66794] items per batch[64] items per second[0.37] total items[5241600] mini batches[ 81900] memory[4999] epoch remaining[0:09:11] INFO:trainer.default_trainer:epochs[ 44] optim steps[82000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.23162/0.75145, loss_mask_bce_0: 0.02330/0.30056, loss_mask_dice_0: 0.06072/1.02014, loss_spatial_bce_0: 0.09122/0.08422, loss_spatial_dice_0: 0.34084/0.17804, loss_spatial_ce_0: 1.11952/0.05498, loss_grounding_bce_0: 0.00283/0.08054, loss_grounding_dice_0: 0.04785/0.15032, loss_grounding_ce_0: 0.00010/0.24839, loss_mask_ce_1: 0.24334/0.75226, loss_mask_bce_1: 0.02328/0.30138, loss_mask_dice_1: 0.05506/1.02449, loss_spatial_bce_1: 0.04108/0.08470, loss_spatial_dice_1: 0.19250/0.18105, loss_spatial_ce_1: 1.72347/0.05867, loss_grounding_bce_1: 0.00276/0.08077, loss_grounding_dice_1: 0.06729/0.15110, loss_grounding_ce_1: 0.00014/0.24975, loss_mask_ce_2: 0.21439/0.75980, loss_mask_bce_2: 0.02971/0.30174, loss_mask_dice_2: 0.06234/1.02526, loss_spatial_bce_2: 0.06102/0.08479, loss_spatial_dice_2: 0.26537/0.18170, loss_spatial_ce_2: 1.57743/0.06092, loss_grounding_bce_2: 0.00261/0.08074, loss_grounding_dice_2: 0.04934/0.15100, loss_grounding_ce_2: 0.00019/0.25244, loss_mask_ce_3: 0.25662/0.76459, loss_mask_bce_3: 0.02088/0.30307, loss_mask_dice_3: 0.06537/1.02341, loss_spatial_bce_3: 0.05175/0.08694, loss_spatial_dice_3: 0.25683/0.18307, loss_spatial_ce_3: 2.08282/0.06583, loss_grounding_bce_3: 0.00195/0.08108, loss_grounding_dice_3: 0.04019/0.15067, loss_grounding_ce_3: 0.00007/0.25381, loss_mask_ce_4: 0.23105/0.77049, loss_mask_bce_4: 0.02182/0.30581, loss_mask_dice_4: 0.05685/1.04291, loss_spatial_bce_4: 0.02966/0.08947, loss_spatial_dice_4: 0.11087/0.19190, loss_spatial_ce_4: 2.71680/0.07972, loss_grounding_bce_4: 0.00116/0.08185, loss_grounding_dice_4: 0.03168/0.15331, loss_grounding_ce_4: 0.00006/0.25825, loss_mask_ce_5: 0.25660/0.79602, loss_mask_bce_5: 0.02662/0.30766, loss_mask_dice_5: 0.07509/1.05099, loss_spatial_bce_5: 0.05015/0.09190, loss_spatial_dice_5: 0.13821/0.19527, loss_spatial_ce_5: 3.61367/0.09348, loss_grounding_bce_5: 0.00153/0.08212, loss_grounding_dice_5: 0.04378/0.15418, loss_grounding_ce_5: 0.00017/0.27574, loss_mask_ce_6: 0.28274/0.82358, loss_mask_bce_6: 0.02477/0.30983, loss_mask_dice_6: 0.06512/1.05491, loss_spatial_bce_6: 0.02962/0.09741, loss_spatial_dice_6: 0.12803/0.19761, loss_spatial_ce_6: 3.77871/0.11780, loss_grounding_bce_6: 0.00201/0.08293, loss_grounding_dice_6: 0.03447/0.15462, loss_grounding_ce_6: 0.00115/0.28473, loss_mask_ce_7: 0.22329/0.87913, loss_mask_bce_7: 0.02928/0.31709, loss_mask_dice_7: 0.06468/1.10079, loss_spatial_bce_7: 0.03606/0.10651, loss_spatial_dice_7: 0.15945/0.22240, loss_spatial_ce_7: 3.41924/0.15219, loss_grounding_bce_7: 0.00175/0.08464, loss_grounding_dice_7: 0.04632/0.16019, loss_grounding_ce_7: 0.00036/0.31778, loss_mask_ce_8: 0.17898/1.01281, loss_mask_bce_8: 0.02692/0.33306, loss_mask_dice_8: 0.05803/1.17731, loss_spatial_bce_8: 0.03645/0.12277, loss_spatial_dice_8: 0.14274/0.25684, loss_spatial_ce_8: 3.55777/0.19745, loss_grounding_bce_8: 0.00161/0.08883, loss_grounding_dice_8: 0.04374/0.16999, loss_grounding_ce_8: 0.00200/0.41553, loss_mask_ce_9: 1.65470/3.47356, loss_mask_bce_9: 0.01742/0.35992, loss_mask_dice_9: 0.09519/1.76006, loss_spatial_bce_9: 0.02506/0.35425, loss_spatial_dice_9: 0.36210/0.79310, loss_spatial_ce_9: 0.22746/1.38667, loss_grounding_bce_9: 0.00251/0.10104, loss_grounding_dice_9: 0.06651/0.24216, loss_grounding_ce_9: 0.21743/0.66795] items per batch[64] items per second[0.37] total items[5248000] mini batches[ 82000] memory[4999] epoch remaining[0:06:16] INFO:trainer.default_trainer:epochs[ 44] optim steps[82100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.64957/0.75127, loss_mask_bce_0: 0.19003/0.30052, loss_mask_dice_0: 2.06155/1.02001, loss_spatial_bce_0: 0.03402/0.08423, loss_spatial_dice_0: 0.22491/0.17803, loss_spatial_ce_0: 0.01635/0.05497, loss_grounding_bce_0: 0.08213/0.08054, loss_grounding_dice_0: 0.16860/0.15031, loss_grounding_ce_0: 0.01083/0.24837, loss_mask_ce_1: 0.69153/0.75206, loss_mask_bce_1: 0.18083/0.30133, loss_mask_dice_1: 1.69348/1.02435, loss_spatial_bce_1: 0.03476/0.08470, loss_spatial_dice_1: 0.21562/0.18104, loss_spatial_ce_1: 0.00684/0.05866, loss_grounding_bce_1: 0.08885/0.08077, loss_grounding_dice_1: 0.17242/0.15108, loss_grounding_ce_1: 0.01344/0.24972, loss_mask_ce_2: 0.66593/0.75963, loss_mask_bce_2: 0.19260/0.30170, loss_mask_dice_2: 1.96094/1.02511, loss_spatial_bce_2: 0.03202/0.08480, loss_spatial_dice_2: 0.22872/0.18169, loss_spatial_ce_2: 0.22813/0.06091, loss_grounding_bce_2: 0.08807/0.08073, loss_grounding_dice_2: 0.16893/0.15099, loss_grounding_ce_2: 0.01082/0.25247, loss_mask_ce_3: 0.79377/0.76441, loss_mask_bce_3: 0.17898/0.30302, loss_mask_dice_3: 1.70784/1.02328, loss_spatial_bce_3: 0.03762/0.08695, loss_spatial_dice_3: 0.24852/0.18307, loss_spatial_ce_3: 0.01124/0.06582, loss_grounding_bce_3: 0.08787/0.08108, loss_grounding_dice_3: 0.16548/0.15066, loss_grounding_ce_3: 0.00557/0.25378, loss_mask_ce_4: 0.91808/0.77032, loss_mask_bce_4: 0.19120/0.30577, loss_mask_dice_4: 1.73153/1.04281, loss_spatial_bce_4: 0.04241/0.08948, loss_spatial_dice_4: 0.24039/0.19189, loss_spatial_ce_4: 0.24272/0.07970, loss_grounding_bce_4: 0.08419/0.08185, loss_grounding_dice_4: 0.15888/0.15329, loss_grounding_ce_4: 0.00318/0.25823, loss_mask_ce_5: 0.92727/0.79584, loss_mask_bce_5: 0.18901/0.30762, loss_mask_dice_5: 1.63304/1.05089, loss_spatial_bce_5: 0.04228/0.09191, loss_spatial_dice_5: 0.29177/0.19526, loss_spatial_ce_5: 0.03548/0.09348, loss_grounding_bce_5: 0.09119/0.08213, loss_grounding_dice_5: 0.18223/0.15416, loss_grounding_ce_5: 0.00357/0.27565, loss_mask_ce_6: 1.01542/0.82342, loss_mask_bce_6: 0.19059/0.30979, loss_mask_dice_6: 1.84030/1.05478, loss_spatial_bce_6: 0.04555/0.09742, loss_spatial_dice_6: 0.25067/0.19760, loss_spatial_ce_6: 0.05705/0.11781, loss_grounding_bce_6: 0.08629/0.08293, loss_grounding_dice_6: 0.15840/0.15461, loss_grounding_ce_6: 0.00163/0.28462, loss_mask_ce_7: 1.18552/0.87894, loss_mask_bce_7: 0.20571/0.31704, loss_mask_dice_7: 2.18451/1.10066, loss_spatial_bce_7: 0.05411/0.10652, loss_spatial_dice_7: 0.32075/0.22239, loss_spatial_ce_7: 0.04908/0.15218, loss_grounding_bce_7: 0.09515/0.08465, loss_grounding_dice_7: 0.16986/0.16018, loss_grounding_ce_7: 0.00212/0.31767, loss_mask_ce_8: 1.00033/1.01259, loss_mask_bce_8: 0.23739/0.33301, loss_mask_dice_8: 2.61068/1.17714, loss_spatial_bce_8: 0.06451/0.12278, loss_spatial_dice_8: 0.28322/0.25683, loss_spatial_ce_8: 0.25700/0.19744, loss_grounding_bce_8: 0.09432/0.08883, loss_grounding_dice_8: 0.16811/0.16996, loss_grounding_ce_8: 0.00145/0.41545, loss_mask_ce_9: 3.79622/3.47307, loss_mask_bce_9: 0.24238/0.35986, loss_mask_dice_9: 2.67377/1.75975, loss_spatial_bce_9: 0.30695/0.35427, loss_spatial_dice_9: 0.87149/0.79306, loss_spatial_ce_9: 1.39918/1.38664, loss_grounding_bce_9: 0.08965/0.10104, loss_grounding_dice_9: 0.18659/0.24214, loss_grounding_ce_9: 0.01186/0.66771] items per batch[64] items per second[0.37] total items[5254400] mini batches[ 82100] memory[4999] epoch remaining[0:03:21] INFO:trainer.default_trainer:epochs[ 44] optim steps[82200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.34755/0.75120, loss_mask_bce_0: 0.36336/0.30053, loss_mask_dice_0: 0.15906/1.01976, loss_spatial_bce_0: 0.23027/0.08424, loss_spatial_dice_0: 0.10787/0.17803, loss_spatial_ce_0: 0.00061/0.05494, loss_grounding_bce_0: 0.16657/0.08055, loss_grounding_dice_0: 0.08866/0.15031, loss_grounding_ce_0: 0.00884/0.24836, loss_mask_ce_1: 0.33268/0.75198, loss_mask_bce_1: 0.36376/0.30133, loss_mask_dice_1: 0.16305/1.02412, loss_spatial_bce_1: 0.24170/0.08472, loss_spatial_dice_1: 0.11516/0.18104, loss_spatial_ce_1: 0.00017/0.05863, loss_grounding_bce_1: 0.17240/0.08077, loss_grounding_dice_1: 0.09611/0.15109, loss_grounding_ce_1: 0.01029/0.24970, loss_mask_ce_2: 0.34321/0.75954, loss_mask_bce_2: 0.36281/0.30171, loss_mask_dice_2: 0.16692/1.02487, loss_spatial_bce_2: 0.26056/0.08482, loss_spatial_dice_2: 0.12387/0.18168, loss_spatial_ce_2: 0.00050/0.06088, loss_grounding_bce_2: 0.16666/0.08074, loss_grounding_dice_2: 0.09517/0.15099, loss_grounding_ce_2: 0.00871/0.25246, loss_mask_ce_3: 0.41904/0.76433, loss_mask_bce_3: 0.35557/0.30302, loss_mask_dice_3: 0.15834/1.02308, loss_spatial_bce_3: 0.43612/0.08696, loss_spatial_dice_3: 0.16315/0.18306, loss_spatial_ce_3: 0.00012/0.06579, loss_grounding_bce_3: 0.16706/0.08108, loss_grounding_dice_3: 0.09535/0.15067, loss_grounding_ce_3: 0.00593/0.25376, loss_mask_ce_4: 0.45005/0.77024, loss_mask_bce_4: 0.34968/0.30577, loss_mask_dice_4: 0.15804/1.04260, loss_spatial_bce_4: 0.35892/0.08950, loss_spatial_dice_4: 0.15452/0.19189, loss_spatial_ce_4: 0.00071/0.07967, loss_grounding_bce_4: 0.15741/0.08186, loss_grounding_dice_4: 0.09216/0.15330, loss_grounding_ce_4: 0.00541/0.25821, loss_mask_ce_5: 0.53337/0.79576, loss_mask_bce_5: 0.35873/0.30763, loss_mask_dice_5: 0.15832/1.05070, loss_spatial_bce_5: 0.38979/0.09193, loss_spatial_dice_5: 0.16098/0.19526, loss_spatial_ce_5: 0.00132/0.09345, loss_grounding_bce_5: 0.16677/0.08214, loss_grounding_dice_5: 0.08806/0.15417, loss_grounding_ce_5: 0.00725/0.27558, loss_mask_ce_6: 0.48443/0.82337, loss_mask_bce_6: 0.34471/0.30979, loss_mask_dice_6: 0.15980/1.05455, loss_spatial_bce_6: 0.34626/0.09744, loss_spatial_dice_6: 0.15299/0.19759, loss_spatial_ce_6: 0.00172/0.11780, loss_grounding_bce_6: 0.17101/0.08294, loss_grounding_dice_6: 0.08965/0.15462, loss_grounding_ce_6: 0.00610/0.28457, loss_mask_ce_7: 0.45647/0.87887, loss_mask_bce_7: 0.37065/0.31703, loss_mask_dice_7: 0.18447/1.10039, loss_spatial_bce_7: 0.38956/0.10654, loss_spatial_dice_7: 0.17578/0.22239, loss_spatial_ce_7: 0.09780/0.15216, loss_grounding_bce_7: 0.18265/0.08466, loss_grounding_dice_7: 0.11097/0.16019, loss_grounding_ce_7: 0.01049/0.31762, loss_mask_ce_8: 1.07848/1.01252, loss_mask_bce_8: 0.39833/0.33301, loss_mask_dice_8: 0.22069/1.17691, loss_spatial_bce_8: 0.35008/0.12281, loss_spatial_dice_8: 0.18643/0.25683, loss_spatial_ce_8: 0.04680/0.19739, loss_grounding_bce_8: 0.22840/0.08884, loss_grounding_dice_8: 0.15544/0.16997, loss_grounding_ce_8: 0.01037/0.41542, loss_mask_ce_9: 1.82900/3.47288, loss_mask_bce_9: 0.53195/0.35986, loss_mask_dice_9: 0.26495/1.75926, loss_spatial_bce_9: 0.75076/0.35427, loss_spatial_dice_9: 0.65436/0.79304, loss_spatial_ce_9: 0.76978/1.38658, loss_grounding_bce_9: 0.18560/0.10105, loss_grounding_dice_9: 0.10919/0.24215, loss_grounding_ce_9: 0.21621/0.66770] items per batch[64] items per second[0.37] total items[5260800] mini batches[ 82200] memory[4999] epoch remaining[0:00:26] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00082215. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0024 s/iter. Inference: 0.3697 s/iter. Eval: 0.0812 s/iter. Total: 0.4534 s/iter. ETA=0:00:30 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 22/79. Dataloading: 0.0028 s/iter. Inference: 0.3727 s/iter. Eval: 0.0831 s/iter. Total: 0.4587 s/iter. ETA=0:00:26 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 33/79. Dataloading: 0.0029 s/iter. Inference: 0.3764 s/iter. Eval: 0.0781 s/iter. Total: 0.4575 s/iter. ETA=0:00:21 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 44/79. Dataloading: 0.0029 s/iter. Inference: 0.3798 s/iter. Eval: 0.0758 s/iter. Total: 0.4586 s/iter. ETA=0:00:16 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0029 s/iter. Inference: 0.3805 s/iter. Eval: 0.0741 s/iter. Total: 0.4576 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0029 s/iter. Inference: 0.3796 s/iter. Eval: 0.0719 s/iter. Total: 0.4546 s/iter. ETA=0:00:05 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalitk8qe8w ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.996 | 82.980 | 66.655 | 133 | | Things | 62.204 | 83.958 | 73.580 | 80 | | Stuff | 46.625 | 81.504 | 56.203 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* Loading and preparing results... DONE (t=0.53s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.33 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.38 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.68s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.56 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.461 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.699 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.262 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.679 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.353 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.554 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.574 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.381 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.612 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.771 INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.49 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.061 | 69.931 | 49.768 | 26.160 | 49.978 | 67.914 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.260 | bicycle | 23.963 | car | 44.400 | | motorcycle | 42.324 | airplane | 61.568 | bus | 71.145 | | train | 75.622 | truck | 42.499 | boat | 31.890 | | traffic light | 29.449 | fire hydrant | 72.010 | stop sign | 68.276 | | parking meter | 52.813 | bench | 26.884 | bird | 35.248 | | cat | 77.036 | dog | 71.499 | horse | 51.016 | | sheep | 54.415 | cow | 56.945 | elephant | 65.877 | | bear | 80.220 | zebra | 66.751 | giraffe | 62.479 | | backpack | 24.799 | umbrella | 56.696 | handbag | 24.312 | | tie | 41.613 | suitcase | 50.964 | frisbee | 68.506 | | skis | 8.169 | snowboard | 34.108 | sports ball | 51.462 | | kite | 38.080 | baseball bat | 39.558 | baseball glove | 50.614 | | skateboard | 43.640 | surfboard | 44.688 | tennis racket | 63.639 | | bottle | 43.095 | wine glass | 38.483 | cup | 51.576 | | fork | 27.290 | knife | 24.977 | spoon | 23.025 | | bowl | 40.982 | banana | 22.317 | apple | 26.997 | | sandwich | 49.026 | orange | 31.681 | broccoli | 24.765 | | carrot | 22.890 | hot dog | 34.997 | pizza | 55.075 | | donut | 57.352 | cake | 48.761 | chair | 28.948 | | couch | 45.585 | potted plant | 22.633 | bed | 44.450 | | dining table | 15.671 | toilet | 70.280 | tv | 67.210 | | laptop | 70.552 | mouse | 64.288 | remote | 44.438 | | keyboard | 57.914 | cell phone | 47.058 | microwave | 68.229 | | oven | 33.209 | toaster | 50.674 | sink | 45.163 | | refrigerator | 69.938 | book | 15.117 | clock | 54.431 | | vase | 40.982 | scissors | 35.968 | teddy bear | 58.348 | | hair drier | 28.284 | toothbrush | 27.785 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.48586862714838, 'fwIoU': 71.61749079342094, 'IoU-person': 88.68208068666995, 'IoU-bicycle': 73.11513431581406, 'IoU-car': 72.16753353247756, 'IoU-motorcycle': 85.83898010916107, 'IoU-airplane': 83.54923593410386, 'IoU-bus': 87.65025750626124, 'IoU-train': 88.94708184565432, 'IoU-truck': 68.49430149683421, 'IoU-boat': 74.68882738325546, 'IoU-traffic light': 78.54979842639229, 'IoU-fire hydrant': 93.21993271918664, 'IoU-stop sign': 85.96115966224022, 'IoU-parking meter': 85.09144529513706, 'IoU-bench': 60.02221220069246, 'IoU-bird': 76.6732102602208, 'IoU-cat': 88.98957736124619, 'IoU-dog': 82.39550599683115, 'IoU-horse': 88.54646070701476, 'IoU-sheep': 86.01907804955161, 'IoU-cow': 88.26937289457928, 'IoU-elephant': 88.2365370703814, 'IoU-bear': 87.76493609907891, 'IoU-zebra': 83.62250035966048, 'IoU-giraffe': 89.44057281029518, 'IoU-backpack': 50.510206115139745, 'IoU-umbrella': 84.6650493069664, 'IoU-handbag': 49.160618100879205, 'IoU-tie': 73.85353062832381, 'IoU-suitcase': 85.5871556635115, 'IoU-frisbee': 84.60146412308337, 'IoU-skis': 58.76117673032344, 'IoU-snowboard': 71.23862033594051, 'IoU-sports ball': 78.52884085297211, 'IoU-kite': 79.17638199723383, 'IoU-baseball bat': 68.8677610458576, 'IoU-baseball glove': 77.68422777251875, 'IoU-skateboard': 86.23636629236726, 'IoU-surfboard': 86.31698721634166, 'IoU-tennis racket': 91.20699476895999, 'IoU-bottle': 69.79508966805497, 'IoU-wine glass': 82.52716931795217, 'IoU-cup': 70.02088092703046, 'IoU-fork': 71.68473228824112, 'IoU-knife': 66.00683334237215, 'IoU-spoon': 60.940117575511856, 'IoU-bowl': 61.23277683473517, 'IoU-banana': 83.35682060404793, 'IoU-apple': 60.945523426423875, 'IoU-sandwich': 67.86356622853002, 'IoU-orange': 78.70732706747224, 'IoU-broccoli': 69.43665107696458, 'IoU-carrot': 65.46260007125873, 'IoU-hot dog': 57.05290169954078, 'IoU-pizza': 84.83743204873568, 'IoU-donut': 53.97649547657431, 'IoU-cake': 79.51983407854875, 'IoU-chair': 62.184331343960686, 'IoU-couch': 67.14354758824764, 'IoU-potted plant': 45.533274456936084, 'IoU-bed': 73.7929784057283, 'IoU-dining table': 55.09306523367733, 'IoU-toilet': 85.09132527596893, 'IoU-tv': 75.67146756482573, 'IoU-laptop': 81.64672245176968, 'IoU-mouse': 76.68592835162875, 'IoU-remote': 66.88393593738282, 'IoU-keyboard': 63.646525887209485, 'IoU-cell phone': 79.42248422399697, 'IoU-microwave': 79.43703382375978, 'IoU-oven': 73.27745186938913, 'IoU-toaster': 77.28177358141257, 'IoU-sink': 67.79444301188016, 'IoU-refrigerator': 84.54520393137221, 'IoU-book': 56.063494102921474, 'IoU-clock': 71.52300807605666, 'IoU-vase': 62.401154193192646, 'IoU-scissors': 87.99799808437385, 'IoU-teddy bear': 83.4783003038411, 'IoU-hair drier': 48.58772030872019, 'IoU-toothbrush': 76.14752502239358, 'IoU-banner': 31.576932128739738, 'IoU-blanket': 17.409965598106925, 'IoU-bridge': 36.9108728378752, 'IoU-cardboard': 48.305215825463925, 'IoU-counter': 32.06282806286532, 'IoU-curtain': 72.68005302662905, 'IoU-door-stuff': 48.93139760921554, 'IoU-floor-wood': 64.93658822584113, 'IoU-flower': 42.5470694287775, 'IoU-fruit': 50.11174941365015, 'IoU-gravel': 25.25618270388858, 'IoU-house': 27.036761749652232, 'IoU-light': 43.285116728505805, 'IoU-mirror-stuff': 61.10044774525979, 'IoU-net': 39.06546394919401, 'IoU-pillow': 23.61178297015446, 'IoU-platform': 27.91081292926192, 'IoU-playingfield': 71.12561667018336, 'IoU-railroad': 64.44736764338575, 'IoU-river': 53.82174930331626, 'IoU-road': 67.19577541257642, 'IoU-roof': 18.180101436035333, 'IoU-sand': 65.76077233893682, 'IoU-sea': 84.8455500232999, 'IoU-shelf': 37.71621131499482, 'IoU-snow': 92.18697583637454, 'IoU-stairs': 33.36158031090674, 'IoU-tent': 11.082672136872997, 'IoU-towel': 62.4412207088918, 'IoU-wall-brick': 52.7310957715479, 'IoU-wall-stone': 28.315493725104364, 'IoU-wall-tile': 70.01669823513106, 'IoU-wall-wood': 46.111079597862975, 'IoU-water-other': 27.40380788425644, 'IoU-window-blind': 49.71775524392678, 'IoU-window-other': 50.99770804971501, 'IoU-tree-merged': 81.72183073974215, 'IoU-fence-merged': 51.86750357613751, 'IoU-ceiling-merged': 68.15771422514473, 'IoU-sky-other-merged': 93.99525551239584, 'IoU-cabinet-merged': 62.890382643987095, 'IoU-table-merged': 41.70294758122392, 'IoU-floor-other-merged': 55.84451541399391, 'IoU-pavement-merged': 57.72237092052882, 'IoU-mountain-merged': 58.74247058922513, 'IoU-grass-merged': 72.881056250038, 'IoU-dirt-merged': 47.46257431003793, 'IoU-paper-merged': 34.52946758043567, 'IoU-food-other-merged': 44.15010361747551, 'IoU-building-other-merged': 59.50062650771455, 'IoU-rock-merged': 64.89405328916644, 'IoU-wall-other-merged': 68.18287698861329, 'IoU-rug-merged': 68.14774665267447, 'mACC': 77.17215140808102, 'pACC': 82.28919158470126, 'ACC-person': 92.76209268351897, 'ACC-bicycle': 82.34924851390055, 'ACC-car': 86.96394708439665, 'ACC-motorcycle': 90.14357835533902, 'ACC-airplane': 87.78246560398082, 'ACC-bus': 93.97074230548087, 'ACC-train': 95.7116379006635, 'ACC-truck': 79.14296322961933, 'ACC-boat': 84.27853925785882, 'ACC-traffic light': 91.455314839078, 'ACC-fire hydrant': 95.90423465008733, 'ACC-stop sign': 88.45335758237572, 'ACC-parking meter': 88.32244494354065, 'ACC-bench': 73.07688351634425, 'ACC-bird': 81.70024224059713, 'ACC-cat': 93.11510322798519, 'ACC-dog': 84.60615080509109, 'ACC-horse': 92.94857925953605, 'ACC-sheep': 90.53289916801651, 'ACC-cow': 91.62620569916426, 'ACC-elephant': 90.22589879903006, 'ACC-bear': 89.56368563373267, 'ACC-zebra': 85.58997072137116, 'ACC-giraffe': 93.21324243024785, 'ACC-backpack': 69.41733639494834, 'ACC-umbrella': 89.26618679430793, 'ACC-handbag': 70.15216560612394, 'ACC-tie': 82.04320647454368, 'ACC-suitcase': 93.44261143084792, 'ACC-frisbee': 93.83963636363636, 'ACC-skis': 72.54827136222858, 'ACC-snowboard': 81.64237956454654, 'ACC-sports ball': 87.61021697108775, 'ACC-kite': 85.08453331118376, 'ACC-baseball bat': 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'ACC-mouse': 86.84205779754626, 'ACC-remote': 71.07221983136486, 'ACC-keyboard': 68.76646433502498, 'ACC-cell phone': 89.2699090182842, 'ACC-microwave': 84.48227842261473, 'ACC-oven': 91.858711449221, 'ACC-toaster': 91.40675252158918, 'ACC-sink': 77.68146058955587, 'ACC-refrigerator': 94.5511223521004, 'ACC-book': 75.18779244067132, 'ACC-clock': 76.2189941091807, 'ACC-vase': 72.13210339353108, 'ACC-scissors': 93.6378077110248, 'ACC-teddy bear': 88.87345206044002, 'ACC-hair drier': 60.02760937010787, 'ACC-toothbrush': 84.92182070882556, 'ACC-banner': 74.0381055000536, 'ACC-blanket': 24.156872271053036, 'ACC-bridge': 57.20689891612405, 'ACC-cardboard': 68.75486403955213, 'ACC-counter': 54.89112522178659, 'ACC-curtain': 81.92279612381522, 'ACC-door-stuff': 69.12075158344311, 'ACC-floor-wood': 84.22330691215491, 'ACC-flower': 59.4475895432647, 'ACC-fruit': 69.27384120490522, 'ACC-gravel': 31.8940469428726, 'ACC-house': 33.66001158407305, 'ACC-light': 63.55414768594725, 'ACC-mirror-stuff': 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Inference done 11/25. Dataloading: 0.3452 s/iter. Inference: 0.1910 s/iter. Eval: 0.0000 s/iter. Total: 0.5363 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3392 s/iter. Inference: 0.3530 s/iter. Eval: 0.0000 s/iter. Total: 0.6923 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3598 s/iter. Inference: 0.5559 s/iter. Eval: 0.0000 s/iter. Total: 0.9158 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3488440152180274, 'noc@0.8': 2.3195785776997364, 'noc@0.85': 2.6965174129353233, 'noc@0.9': 3.4779045946736904, 'miou@iter1': 0.874482560374076} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1468 s/iter. Eval: 0.0010 s/iter. Total: 0.1493 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.02021026611328, 'precision@0.6': 73.29965209960938, 'precision@0.7': 69.21881103515625, 'precision@0.8': 60.27982711791992, 'precision@0.9': 33.54061508178711, 'cIoU': 62.529624938964844, 'mIoU': 67.5015640258789} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.995687407156346, 'SQ': 82.97976153954761, 'RQ': 66.65538671511648, 'PQ_th': 62.20367632956836, 'SQ_th': 83.95760130131421, 'RQ_th': 73.58024272923355, 'PQ_st': 46.625138090308006, 'SQ_st': 81.50377699348475, 'RQ_st': 56.20277386361897}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-baseball bat': 87.83762521634237, 'ACC-baseball glove': 92.24508765482804, 'ACC-skateboard': 90.75321714072831, 'ACC-surfboard': 92.30352951668281, 'ACC-tennis racket': 94.98988601057074, 'ACC-bottle': 84.39098508974212, 'ACC-wine glass': 90.70379503544814, 'ACC-cup': 87.764747635069, 'ACC-fork': 83.30336811154174, 'ACC-knife': 78.03638162233459, 'ACC-spoon': 77.22495550705516, 'ACC-bowl': 74.03004573291942, 'ACC-banana': 90.12939478481357, 'ACC-apple': 75.41712759363833, 'ACC-sandwich': 78.11065976054941, 'ACC-orange': 88.50135261353985, 'ACC-broccoli': 82.08735827769542, 'ACC-carrot': 78.61937523378157, 'ACC-hot dog': 62.95058589239102, 'ACC-pizza': 91.99545213591547, 'ACC-donut': 60.23772508534638, 'ACC-cake': 88.19166833991883, 'ACC-chair': 79.22617564459583, 'ACC-couch': 78.8083098583851, 'ACC-potted plant': 61.823268404016964, 'ACC-bed': 86.07805600033083, 'ACC-dining table': 77.56366050618834, 'ACC-toilet': 89.98410423978515, 'ACC-tv': 86.80905414164127, 'ACC-laptop': 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'ACC-mirror-stuff': 77.78686096558795, 'ACC-net': 68.3725083305066, 'ACC-pillow': 44.32764069029786, 'ACC-platform': 44.98377757098646, 'ACC-playingfield': 89.74661223291477, 'ACC-railroad': 83.23346280819553, 'ACC-river': 71.31010621511057, 'ACC-road': 86.9266168540475, 'ACC-roof': 23.92792968988742, 'ACC-sand': 70.83645873156665, 'ACC-sea': 91.43302065050798, 'ACC-shelf': 57.35006891447464, 'ACC-snow': 95.59671862984915, 'ACC-stairs': 56.51743071712361, 'ACC-tent': 14.946510198192472, 'ACC-towel': 80.07822302136194, 'ACC-wall-brick': 68.61813610657377, 'ACC-wall-stone': 35.06416605658671, 'ACC-wall-tile': 85.85614655164075, 'ACC-wall-wood': 63.620778223595806, 'ACC-water-other': 45.019124653094664, 'ACC-window-blind': 62.963853409521576, 'ACC-window-other': 72.22678494612911, 'ACC-tree-merged': 89.48568760765129, 'ACC-fence-merged': 69.44478908695903, 'ACC-ceiling-merged': 82.55473823033718, 'ACC-sky-other-merged': 97.13530986591806, 'ACC-cabinet-merged': 78.24039329837676, 'ACC-table-merged': 53.79476605386958, 'ACC-floor-other-merged': 65.5809228119712, 'ACC-pavement-merged': 70.34496943515038, 'ACC-mountain-merged': 71.14001544238778, 'ACC-grass-merged': 84.83203768560031, 'ACC-dirt-merged': 68.40574874953957, 'ACC-paper-merged': 48.58519927688588, 'ACC-food-other-merged': 59.08259070890137, 'ACC-building-other-merged': 74.87892462646765, 'ACC-rock-merged': 82.765994519812, 'ACC-wall-other-merged': 81.64344027924069, 'ACC-rug-merged': 82.58595977647359})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3488440152180274, 'noc@0.8': 2.3195785776997364, 'noc@0.85': 2.6965174129353233, 'noc@0.9': 3.4779045946736904, 'miou@iter1': 0.874482560374076}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 76.02021026611328, 'precision@0.6': 73.29965209960938, 'precision@0.7': 69.21881103515625, 'precision@0.8': 60.27982711791992, 'precision@0.9': 33.54061508178711, 'cIoU': 62.529624938964844, 'mIoU': 67.5015640258789}}} INFO:trainer.default_trainer:This epoch takes 0:56:40.248096 INFO:trainer.default_trainer:PROGRESS: 90.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 45 training. INFO:trainer.default_trainer:epochs[ 45] optim steps[82300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.10327/0.75113, loss_mask_bce_0: 0.01594/0.30048, loss_mask_dice_0: 0.70492/1.01981, loss_spatial_bce_0: 0.00531/0.08422, loss_spatial_dice_0: 0.18225/0.17802, loss_spatial_ce_0: 0.00567/0.05493, loss_grounding_bce_0: 0.00424/0.08053, loss_grounding_dice_0: 0.16496/0.15031, loss_grounding_ce_0: 0.17045/0.24833, loss_mask_ce_1: 0.11050/0.75195, loss_mask_bce_1: 0.01680/0.30128, loss_mask_dice_1: 0.66246/1.02417, loss_spatial_bce_1: 0.00536/0.08470, loss_spatial_dice_1: 0.20896/0.18103, loss_spatial_ce_1: 0.00790/0.05861, loss_grounding_bce_1: 0.00493/0.08076, loss_grounding_dice_1: 0.10379/0.15109, loss_grounding_ce_1: 0.30596/0.24965, loss_mask_ce_2: 0.10975/0.75950, loss_mask_bce_2: 0.01616/0.30166, loss_mask_dice_2: 0.63501/1.02494, loss_spatial_bce_2: 0.00502/0.08480, loss_spatial_dice_2: 0.19189/0.18167, loss_spatial_ce_2: 0.01688/0.06087, loss_grounding_bce_2: 0.00546/0.08073, loss_grounding_dice_2: 0.23028/0.15100, loss_grounding_ce_2: 0.34216/0.25246, loss_mask_ce_3: 0.11531/0.76431, loss_mask_bce_3: 0.01670/0.30297, loss_mask_dice_3: 0.88254/1.02312, loss_spatial_bce_3: 0.00368/0.08694, loss_spatial_dice_3: 0.18791/0.18306, loss_spatial_ce_3: 0.02793/0.06577, loss_grounding_bce_3: 0.00421/0.08106, loss_grounding_dice_3: 0.08793/0.15067, loss_grounding_ce_3: 0.31817/0.25373, loss_mask_ce_4: 0.14413/0.77024, loss_mask_bce_4: 0.01764/0.30572, loss_mask_dice_4: 0.90973/1.04265, loss_spatial_bce_4: 0.00520/0.08948, loss_spatial_dice_4: 0.13758/0.19189, loss_spatial_ce_4: 0.23922/0.07966, loss_grounding_bce_4: 0.00376/0.08184, loss_grounding_dice_4: 0.21591/0.15330, loss_grounding_ce_4: 0.28647/0.25821, loss_mask_ce_5: 0.10755/0.79575, loss_mask_bce_5: 0.01859/0.30756, loss_mask_dice_5: 0.79058/1.05073, loss_spatial_bce_5: 0.00541/0.09191, loss_spatial_dice_5: 0.24559/0.19526, loss_spatial_ce_5: 0.05164/0.09344, loss_grounding_bce_5: 0.00449/0.08212, loss_grounding_dice_5: 0.22796/0.15418, loss_grounding_ce_5: 0.31077/0.27554, loss_mask_ce_6: 0.16127/0.82334, loss_mask_bce_6: 0.01260/0.30973, loss_mask_dice_6: 0.78222/1.05461, loss_spatial_bce_6: 0.00509/0.09742, loss_spatial_dice_6: 0.24511/0.19759, loss_spatial_ce_6: 0.08579/0.11779, loss_grounding_bce_6: 0.00455/0.08292, loss_grounding_dice_6: 0.23947/0.15463, loss_grounding_ce_6: 0.17604/0.28452, loss_mask_ce_7: 0.31830/0.87885, loss_mask_bce_7: 0.01647/0.31698, loss_mask_dice_7: 0.81883/1.10042, loss_spatial_bce_7: 0.00508/0.10651, loss_spatial_dice_7: 0.10105/0.22238, loss_spatial_ce_7: 0.12710/0.15214, loss_grounding_bce_7: 0.00446/0.08464, loss_grounding_dice_7: 0.16761/0.16020, loss_grounding_ce_7: 0.35187/0.31753, loss_mask_ce_8: 0.23676/1.01248, loss_mask_bce_8: 0.01514/0.33295, loss_mask_dice_8: 0.60500/1.17700, loss_spatial_bce_8: 0.00540/0.12278, loss_spatial_dice_8: 0.25422/0.25682, loss_spatial_ce_8: 0.13420/0.19735, loss_grounding_bce_8: 0.00573/0.08882, loss_grounding_dice_8: 0.18511/0.16997, loss_grounding_ce_8: 0.25095/0.41530, loss_mask_ce_9: 3.35957/3.47295, loss_mask_bce_9: 0.01346/0.35978, loss_mask_dice_9: 0.81675/1.75922, loss_spatial_bce_9: 0.18101/0.35424, loss_spatial_dice_9: 0.88079/0.79303, loss_spatial_ce_9: 2.08403/1.38657, loss_grounding_bce_9: 0.00415/0.10102, loss_grounding_dice_9: 0.19947/0.24215, loss_grounding_ce_9: 0.43113/0.66756] items per batch[64] items per second[0.16] total items[5267200] mini batches[ 82300] memory[4999] epoch remaining[0:54:36] INFO:trainer.default_trainer:epochs[ 45] optim steps[82400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.15661/0.75102, loss_mask_bce_0: 0.19705/0.30047, loss_mask_dice_0: 0.36083/1.01989, loss_spatial_bce_0: 0.06468/0.08421, loss_spatial_dice_0: 0.06026/0.17800, loss_spatial_ce_0: 0.00004/0.05490, loss_grounding_bce_0: 0.05112/0.08053, loss_grounding_dice_0: 0.03714/0.15032, loss_grounding_ce_0: 0.35986/0.24834, loss_mask_ce_1: 1.16929/0.75183, loss_mask_bce_1: 0.21161/0.30127, loss_mask_dice_1: 0.40526/1.02424, loss_spatial_bce_1: 0.06459/0.08468, loss_spatial_dice_1: 0.06026/0.18101, loss_spatial_ce_1: 0.00003/0.05860, loss_grounding_bce_1: 0.05232/0.08075, loss_grounding_dice_1: 0.03654/0.15110, loss_grounding_ce_1: 0.52412/0.24969, loss_mask_ce_2: 1.19661/0.75939, loss_mask_bce_2: 0.21108/0.30165, loss_mask_dice_2: 0.38973/1.02499, loss_spatial_bce_2: 0.06192/0.08479, loss_spatial_dice_2: 0.05734/0.18166, loss_spatial_ce_2: 0.00007/0.06085, loss_grounding_bce_2: 0.04842/0.08073, loss_grounding_dice_2: 0.03268/0.15102, loss_grounding_ce_2: 0.30521/0.25250, loss_mask_ce_3: 1.21283/0.76421, loss_mask_bce_3: 0.21248/0.30296, loss_mask_dice_3: 0.41005/1.02322, loss_spatial_bce_3: 0.06816/0.08693, loss_spatial_dice_3: 0.05817/0.18304, loss_spatial_ce_3: 0.00014/0.06575, loss_grounding_bce_3: 0.05053/0.08107, loss_grounding_dice_3: 0.03662/0.15069, loss_grounding_ce_3: 0.29903/0.25373, loss_mask_ce_4: 1.15775/0.77014, loss_mask_bce_4: 0.21972/0.30571, loss_mask_dice_4: 0.43128/1.04276, loss_spatial_bce_4: 0.06658/0.08946, loss_spatial_dice_4: 0.05959/0.19187, loss_spatial_ce_4: 0.00264/0.07964, loss_grounding_bce_4: 0.05133/0.08184, loss_grounding_dice_4: 0.03597/0.15331, loss_grounding_ce_4: 0.28774/0.25822, loss_mask_ce_5: 1.12673/0.79562, loss_mask_bce_5: 0.23643/0.30756, loss_mask_dice_5: 0.48049/1.05085, loss_spatial_bce_5: 0.06940/0.09190, loss_spatial_dice_5: 0.06493/0.19525, loss_spatial_ce_5: 0.00201/0.09344, loss_grounding_bce_5: 0.05219/0.08212, loss_grounding_dice_5: 0.03716/0.15419, loss_grounding_ce_5: 0.29780/0.27551, loss_mask_ce_6: 1.31444/0.82320, loss_mask_bce_6: 0.21160/0.30972, loss_mask_dice_6: 0.42154/1.05471, loss_spatial_bce_6: 0.06838/0.09740, loss_spatial_dice_6: 0.06193/0.19758, loss_spatial_ce_6: 0.01259/0.11778, loss_grounding_bce_6: 0.05142/0.08292, loss_grounding_dice_6: 0.03679/0.15464, loss_grounding_ce_6: 0.18694/0.28448, loss_mask_ce_7: 1.36194/0.87868, loss_mask_bce_7: 0.20078/0.31696, loss_mask_dice_7: 0.40433/1.10054, loss_spatial_bce_7: 0.08245/0.10650, loss_spatial_dice_7: 0.07725/0.22237, loss_spatial_ce_7: 0.04698/0.15210, loss_grounding_bce_7: 0.05232/0.08464, loss_grounding_dice_7: 0.03912/0.16020, loss_grounding_ce_7: 0.14359/0.31751, loss_mask_ce_8: 1.47076/1.01236, loss_mask_bce_8: 0.20778/0.33293, loss_mask_dice_8: 0.36215/1.17710, loss_spatial_bce_8: 0.08621/0.12276, loss_spatial_dice_8: 0.07626/0.25681, loss_spatial_ce_8: 0.04687/0.19730, loss_grounding_bce_8: 0.05509/0.08882, loss_grounding_dice_8: 0.04082/0.16999, loss_grounding_ce_8: 0.56139/0.41533, loss_mask_ce_9: 4.46784/3.47289, loss_mask_bce_9: 0.33374/0.35978, loss_mask_dice_9: 0.84050/1.75928, loss_spatial_bce_9: 0.53070/0.35424, loss_spatial_dice_9: 0.86550/0.79304, loss_spatial_ce_9: 1.78767/1.38650, loss_grounding_bce_9: 0.12345/0.10103, loss_grounding_dice_9: 0.07526/0.24218, loss_grounding_ce_9: 1.60158/0.66750] items per batch[64] items per second[0.37] total items[5273600] mini batches[ 82400] memory[4999] epoch remaining[0:49:25] INFO:trainer.default_trainer:epochs[ 45] optim steps[82500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.83847/0.75079, loss_mask_bce_0: 0.41535/0.30044, loss_mask_dice_0: 0.40107/1.01966, loss_spatial_bce_0: 0.21857/0.08421, loss_spatial_dice_0: 0.20477/0.17798, loss_spatial_ce_0: 0.00029/0.05489, loss_grounding_bce_0: 0.22323/0.08053, loss_grounding_dice_0: 0.21859/0.15030, loss_grounding_ce_0: 0.31463/0.24827, loss_mask_ce_1: 0.85491/0.75163, loss_mask_bce_1: 0.42352/0.30123, loss_mask_dice_1: 0.39177/1.02400, loss_spatial_bce_1: 0.22011/0.08468, loss_spatial_dice_1: 0.18668/0.18099, loss_spatial_ce_1: 0.00016/0.05859, loss_grounding_bce_1: 0.22464/0.08075, loss_grounding_dice_1: 0.21180/0.15108, loss_grounding_ce_1: 0.31877/0.24961, loss_mask_ce_2: 0.95630/0.75921, loss_mask_bce_2: 0.41935/0.30162, loss_mask_dice_2: 0.39980/1.02475, loss_spatial_bce_2: 0.22411/0.08478, loss_spatial_dice_2: 0.19970/0.18164, loss_spatial_ce_2: 0.00024/0.06082, loss_grounding_bce_2: 0.22214/0.08073, loss_grounding_dice_2: 0.21752/0.15100, loss_grounding_ce_2: 0.33568/0.25244, loss_mask_ce_3: 0.98947/0.76402, loss_mask_bce_3: 0.40620/0.30292, loss_mask_dice_3: 0.37692/1.02296, loss_spatial_bce_3: 0.23132/0.08693, loss_spatial_dice_3: 0.18433/0.18302, loss_spatial_ce_3: 0.00015/0.06572, loss_grounding_bce_3: 0.22225/0.08106, loss_grounding_dice_3: 0.21176/0.15067, loss_grounding_ce_3: 0.35193/0.25364, loss_mask_ce_4: 1.00357/0.76997, loss_mask_bce_4: 0.43426/0.30567, loss_mask_dice_4: 0.38181/1.04251, loss_spatial_bce_4: 0.24635/0.08946, loss_spatial_dice_4: 0.19269/0.19185, loss_spatial_ce_4: 0.00021/0.07961, loss_grounding_bce_4: 0.22167/0.08184, loss_grounding_dice_4: 0.20956/0.15328, loss_grounding_ce_4: 0.33126/0.25814, loss_mask_ce_5: 0.96865/0.79543, loss_mask_bce_5: 0.40960/0.30753, loss_mask_dice_5: 0.36794/1.05058, loss_spatial_bce_5: 0.24349/0.09189, loss_spatial_dice_5: 0.20802/0.19523, loss_spatial_ce_5: 0.01314/0.09343, loss_grounding_bce_5: 0.22293/0.08212, loss_grounding_dice_5: 0.21298/0.15417, loss_grounding_ce_5: 0.31217/0.27544, loss_mask_ce_6: 1.00345/0.82304, loss_mask_bce_6: 0.41677/0.30969, loss_mask_dice_6: 0.39617/1.05444, loss_spatial_bce_6: 0.26128/0.09740, loss_spatial_dice_6: 0.20957/0.19756, loss_spatial_ce_6: 0.12082/0.11776, loss_grounding_bce_6: 0.21831/0.08292, loss_grounding_dice_6: 0.22203/0.15462, loss_grounding_ce_6: 0.33770/0.28440, loss_mask_ce_7: 0.93505/0.87850, loss_mask_bce_7: 0.42132/0.31692, loss_mask_dice_7: 0.41638/1.10025, loss_spatial_bce_7: 0.29410/0.10649, loss_spatial_dice_7: 0.27685/0.22234, loss_spatial_ce_7: 0.02000/0.15207, loss_grounding_bce_7: 0.23208/0.08463, loss_grounding_dice_7: 0.24954/0.16018, loss_grounding_ce_7: 0.35491/0.31746, loss_mask_ce_8: 1.27040/1.01217, loss_mask_bce_8: 0.49613/0.33289, loss_mask_dice_8: 0.52964/1.17679, loss_spatial_bce_8: 0.31811/0.12274, loss_spatial_dice_8: 0.26680/0.25677, loss_spatial_ce_8: 0.13364/0.19726, loss_grounding_bce_8: 0.28239/0.08882, loss_grounding_dice_8: 0.33352/0.16997, loss_grounding_ce_8: 0.40657/0.41525, loss_mask_ce_9: 3.17162/3.47259, loss_mask_bce_9: 0.49850/0.35973, loss_mask_dice_9: 0.69799/1.75894, loss_spatial_bce_9: 0.52203/0.35430, loss_spatial_dice_9: 0.80114/0.79303, loss_spatial_ce_9: 1.80554/1.38641, loss_grounding_bce_9: 0.29093/0.10103, loss_grounding_dice_9: 0.48082/0.24216, loss_grounding_ce_9: 0.15222/0.66744] items per batch[64] items per second[0.37] total items[5280000] mini batches[ 82500] memory[4999] epoch remaining[0:45:44] INFO:trainer.default_trainer:epochs[ 45] optim steps[82600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.05814/0.75063, loss_mask_bce_0: 0.08620/0.30042, loss_mask_dice_0: 0.43774/1.01961, loss_spatial_bce_0: 0.04054/0.08420, loss_spatial_dice_0: 0.16182/0.17797, loss_spatial_ce_0: 0.00022/0.05488, loss_grounding_bce_0: 0.02523/0.08053, loss_grounding_dice_0: 0.08431/0.15033, loss_grounding_ce_0: 0.00305/0.24827, loss_mask_ce_1: 0.06229/0.75146, loss_mask_bce_1: 0.09370/0.30122, loss_mask_dice_1: 0.49143/1.02395, loss_spatial_bce_1: 0.03981/0.08467, loss_spatial_dice_1: 0.15564/0.18098, loss_spatial_ce_1: 0.00045/0.05857, loss_grounding_bce_1: 0.02594/0.08075, loss_grounding_dice_1: 0.09176/0.15111, loss_grounding_ce_1: 0.00557/0.24962, loss_mask_ce_2: 0.04383/0.75903, loss_mask_bce_2: 0.08690/0.30160, loss_mask_dice_2: 0.50058/1.02471, loss_spatial_bce_2: 0.04087/0.08477, loss_spatial_dice_2: 0.16621/0.18162, loss_spatial_ce_2: 0.00066/0.06080, loss_grounding_bce_2: 0.02594/0.08073, loss_grounding_dice_2: 0.08795/0.15103, loss_grounding_ce_2: 0.00569/0.25245, loss_mask_ce_3: 0.05228/0.76385, loss_mask_bce_3: 0.08905/0.30291, loss_mask_dice_3: 0.47719/1.02293, loss_spatial_bce_3: 0.03832/0.08691, loss_spatial_dice_3: 0.16042/0.18301, loss_spatial_ce_3: 0.00103/0.06570, loss_grounding_bce_3: 0.02255/0.08107, loss_grounding_dice_3: 0.08812/0.15069, loss_grounding_ce_3: 0.00508/0.25366, loss_mask_ce_4: 0.07604/0.76982, loss_mask_bce_4: 0.08900/0.30565, loss_mask_dice_4: 0.50544/1.04247, loss_spatial_bce_4: 0.04146/0.08945, loss_spatial_dice_4: 0.16351/0.19184, loss_spatial_ce_4: 0.00038/0.07958, loss_grounding_bce_4: 0.02789/0.08184, loss_grounding_dice_4: 0.09780/0.15330, loss_grounding_ce_4: 0.00165/0.25815, loss_mask_ce_5: 0.04694/0.79530, loss_mask_bce_5: 0.09309/0.30751, loss_mask_dice_5: 0.43966/1.05052, loss_spatial_bce_5: 0.04034/0.09188, loss_spatial_dice_5: 0.21914/0.19522, loss_spatial_ce_5: 0.00053/0.09341, loss_grounding_bce_5: 0.02652/0.08212, loss_grounding_dice_5: 0.09243/0.15419, loss_grounding_ce_5: 0.00417/0.27541, loss_mask_ce_6: 0.08024/0.82290, loss_mask_bce_6: 0.08801/0.30968, loss_mask_dice_6: 0.47624/1.05441, loss_spatial_bce_6: 0.03946/0.09739, loss_spatial_dice_6: 0.16453/0.19755, loss_spatial_ce_6: 0.00030/0.11774, loss_grounding_bce_6: 0.02695/0.08292, loss_grounding_dice_6: 0.09255/0.15464, loss_grounding_ce_6: 0.00570/0.28440, loss_mask_ce_7: 0.08339/0.87835, loss_mask_bce_7: 0.09107/0.31691, loss_mask_dice_7: 0.44317/1.10020, loss_spatial_bce_7: 0.04854/0.10647, loss_spatial_dice_7: 0.19714/0.22232, loss_spatial_ce_7: 0.02882/0.15206, loss_grounding_bce_7: 0.02646/0.08463, loss_grounding_dice_7: 0.10273/0.16021, loss_grounding_ce_7: 0.00379/0.31743, loss_mask_ce_8: 0.28524/1.01203, loss_mask_bce_8: 0.09347/0.33287, loss_mask_dice_8: 0.48737/1.17678, loss_spatial_bce_8: 0.04417/0.12273, loss_spatial_dice_8: 0.20245/0.25674, loss_spatial_ce_8: 0.06419/0.19723, loss_grounding_bce_8: 0.03032/0.08881, loss_grounding_dice_8: 0.10503/0.17000, loss_grounding_ce_8: 0.29643/0.41523, loss_mask_ce_9: 2.35272/3.47238, loss_mask_bce_9: 0.08779/0.35971, loss_mask_dice_9: 0.58686/1.75893, loss_spatial_bce_9: 0.31473/0.35428, loss_spatial_dice_9: 0.76916/0.79303, loss_spatial_ce_9: 1.02989/1.38637, loss_grounding_bce_9: 0.02342/0.10103, loss_grounding_dice_9: 0.10514/0.24217, loss_grounding_ce_9: 1.00120/0.66739] items per batch[64] items per second[0.37] total items[5286400] mini batches[ 82600] memory[4999] epoch remaining[0:42:23] INFO:trainer.default_trainer:epochs[ 45] optim steps[82700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.75014/0.75061, loss_mask_bce_0: 0.07740/0.30044, loss_mask_dice_0: 2.49444/1.01981, loss_spatial_bce_0: 0.00261/0.08419, loss_spatial_dice_0: 0.27797/0.17796, loss_spatial_ce_0: 0.25927/0.05488, loss_grounding_bce_0: 0.00672/0.08053, loss_grounding_dice_0: 0.30650/0.15033, loss_grounding_ce_0: 0.61896/0.24832, loss_mask_ce_1: 0.44199/0.75149, loss_mask_bce_1: 0.04611/0.30124, loss_mask_dice_1: 2.11637/1.02416, loss_spatial_bce_1: 0.00527/0.08467, loss_spatial_dice_1: 0.26465/0.18097, loss_spatial_ce_1: 0.04472/0.05857, loss_grounding_bce_1: 0.00666/0.08075, loss_grounding_dice_1: 0.29874/0.15111, loss_grounding_ce_1: 0.62352/0.24968, loss_mask_ce_2: 0.91574/0.75903, loss_mask_bce_2: 0.05374/0.30163, loss_mask_dice_2: 2.50994/1.02492, loss_spatial_bce_2: 0.00288/0.08476, loss_spatial_dice_2: 0.25788/0.18161, loss_spatial_ce_2: 0.03114/0.06081, loss_grounding_bce_2: 0.00843/0.08073, loss_grounding_dice_2: 0.33897/0.15103, loss_grounding_ce_2: 0.68385/0.25247, loss_mask_ce_3: 0.59680/0.76384, loss_mask_bce_3: 0.07404/0.30293, loss_mask_dice_3: 2.57475/1.02314, loss_spatial_bce_3: 0.00512/0.08691, loss_spatial_dice_3: 0.30782/0.18300, loss_spatial_ce_3: 0.02462/0.06570, loss_grounding_bce_3: 0.00649/0.08106, loss_grounding_dice_3: 0.31291/0.15069, loss_grounding_ce_3: 0.58006/0.25371, loss_mask_ce_4: 0.69018/0.76983, loss_mask_bce_4: 0.04439/0.30567, loss_mask_dice_4: 2.09403/1.04265, loss_spatial_bce_4: 0.00503/0.08944, loss_spatial_dice_4: 0.30804/0.19184, loss_spatial_ce_4: 0.09391/0.07957, loss_grounding_bce_4: 0.00656/0.08184, loss_grounding_dice_4: 0.27171/0.15330, loss_grounding_ce_4: 0.61769/0.25816, loss_mask_ce_5: 1.28877/0.79531, loss_mask_bce_5: 0.05336/0.30754, loss_mask_dice_5: 2.31866/1.05072, loss_spatial_bce_5: 0.00277/0.09187, loss_spatial_dice_5: 0.31493/0.19522, loss_spatial_ce_5: 0.06145/0.09343, loss_grounding_bce_5: 0.00689/0.08212, loss_grounding_dice_5: 0.33353/0.15420, loss_grounding_ce_5: 0.64517/0.27542, loss_mask_ce_6: 0.90953/0.82290, loss_mask_bce_6: 0.08596/0.30971, loss_mask_dice_6: 3.03681/1.05461, loss_spatial_bce_6: 0.00639/0.09739, loss_spatial_dice_6: 0.33731/0.19755, loss_spatial_ce_6: 0.28065/0.11775, loss_grounding_bce_6: 0.00565/0.08291, loss_grounding_dice_6: 0.26088/0.15465, loss_grounding_ce_6: 0.64855/0.28441, loss_mask_ce_7: 1.08126/0.87833, loss_mask_bce_7: 0.04574/0.31694, loss_mask_dice_7: 1.87474/1.10039, loss_spatial_bce_7: 0.00449/0.10646, loss_spatial_dice_7: 0.34107/0.22231, loss_spatial_ce_7: 0.25021/0.15205, loss_grounding_bce_7: 0.00705/0.08463, loss_grounding_dice_7: 0.30471/0.16021, loss_grounding_ce_7: 0.66684/0.31742, loss_mask_ce_8: 1.12298/1.01204, loss_mask_bce_8: 0.06230/0.33290, loss_mask_dice_8: 2.32642/1.17703, loss_spatial_bce_8: 0.00403/0.12271, loss_spatial_dice_8: 0.45606/0.25674, loss_spatial_ce_8: 0.46247/0.19721, loss_grounding_bce_8: 0.00656/0.08880, loss_grounding_dice_8: 0.30203/0.17000, loss_grounding_ce_8: 0.72098/0.41525, loss_mask_ce_9: 3.56218/3.47262, loss_mask_bce_9: 0.06285/0.35975, loss_mask_dice_9: 2.96068/1.75931, loss_spatial_bce_9: 0.03597/0.35425, loss_spatial_dice_9: 0.92417/0.79305, loss_spatial_ce_9: 1.77552/1.38641, loss_grounding_bce_9: 0.00733/0.10102, loss_grounding_dice_9: 0.43998/0.24219, loss_grounding_ce_9: 0.72306/0.66740] items per batch[64] items per second[0.36] total items[5292800] mini batches[ 82700] memory[4999] epoch remaining[0:39:27] INFO:trainer.default_trainer:epochs[ 45] optim steps[82800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.39687/0.75051, loss_mask_bce_0: 0.04616/0.30050, loss_mask_dice_0: 0.70882/1.01975, loss_spatial_bce_0: 0.01573/0.08418, loss_spatial_dice_0: 0.32470/0.17794, loss_spatial_ce_0: 0.00204/0.05486, loss_grounding_bce_0: 0.00120/0.08052, loss_grounding_dice_0: 0.03604/0.15033, loss_grounding_ce_0: 0.00111/0.24827, loss_mask_ce_1: 0.74036/0.75138, loss_mask_bce_1: 0.04634/0.30130, loss_mask_dice_1: 1.03924/1.02414, loss_spatial_bce_1: 0.01522/0.08466, loss_spatial_dice_1: 0.30527/0.18095, loss_spatial_ce_1: 0.00174/0.05855, loss_grounding_bce_1: 0.00233/0.08074, loss_grounding_dice_1: 0.04293/0.15110, loss_grounding_ce_1: 0.00040/0.24964, loss_mask_ce_2: 0.32016/0.75893, loss_mask_bce_2: 0.05237/0.30169, loss_mask_dice_2: 0.87987/1.02488, loss_spatial_bce_2: 0.01538/0.08476, loss_spatial_dice_2: 0.30622/0.18160, loss_spatial_ce_2: 0.00661/0.06078, loss_grounding_bce_2: 0.00341/0.08072, loss_grounding_dice_2: 0.05987/0.15103, loss_grounding_ce_2: 0.00154/0.25241, loss_mask_ce_3: 0.43223/0.76375, loss_mask_bce_3: 0.05153/0.30299, loss_mask_dice_3: 0.92401/1.02311, loss_spatial_bce_3: 0.01658/0.08690, loss_spatial_dice_3: 0.29968/0.18298, loss_spatial_ce_3: 0.01089/0.06569, loss_grounding_bce_3: 0.00488/0.08105, loss_grounding_dice_3: 0.10763/0.15069, loss_grounding_ce_3: 0.00239/0.25367, loss_mask_ce_4: 0.37408/0.76974, loss_mask_bce_4: 0.04856/0.30574, loss_mask_dice_4: 0.82407/1.04261, loss_spatial_bce_4: 0.02367/0.08944, loss_spatial_dice_4: 0.32763/0.19183, loss_spatial_ce_4: 0.02343/0.07956, loss_grounding_bce_4: 0.00130/0.08183, loss_grounding_dice_4: 0.06423/0.15330, loss_grounding_ce_4: 0.00466/0.25809, loss_mask_ce_5: 0.44009/0.79520, loss_mask_bce_5: 0.04808/0.30760, loss_mask_dice_5: 0.93367/1.05069, loss_spatial_bce_5: 0.02039/0.09187, loss_spatial_dice_5: 0.33161/0.19520, loss_spatial_ce_5: 0.06476/0.09342, loss_grounding_bce_5: 0.00305/0.08211, loss_grounding_dice_5: 0.05807/0.15420, loss_grounding_ce_5: 0.00320/0.27536, loss_mask_ce_6: 0.55889/0.82282, loss_mask_bce_6: 0.04334/0.30977, loss_mask_dice_6: 0.77207/1.05457, loss_spatial_bce_6: 0.01867/0.09738, loss_spatial_dice_6: 0.32554/0.19753, loss_spatial_ce_6: 0.03251/0.11775, loss_grounding_bce_6: 0.00068/0.08290, loss_grounding_dice_6: 0.01459/0.15465, loss_grounding_ce_6: 0.00182/0.28436, loss_mask_ce_7: 0.79996/0.87820, loss_mask_bce_7: 0.04570/0.31700, loss_mask_dice_7: 1.18326/1.10038, loss_spatial_bce_7: 0.01865/0.10646, loss_spatial_dice_7: 0.34969/0.22229, loss_spatial_ce_7: 0.12478/0.15203, loss_grounding_bce_7: 0.00396/0.08461, loss_grounding_dice_7: 0.09215/0.16022, loss_grounding_ce_7: 0.00350/0.31735, loss_mask_ce_8: 0.63429/1.01188, loss_mask_bce_8: 0.05485/0.33297, loss_mask_dice_8: 1.09112/1.17699, loss_spatial_bce_8: 0.06564/0.12271, loss_spatial_dice_8: 0.41866/0.25673, loss_spatial_ce_8: 0.28054/0.19718, loss_grounding_bce_8: 0.00164/0.08879, loss_grounding_dice_8: 0.06089/0.17001, loss_grounding_ce_8: 0.03071/0.41522, loss_mask_ce_9: 2.18092/3.47267, loss_mask_bce_9: 0.04913/0.35981, loss_mask_dice_9: 1.02972/1.75936, loss_spatial_bce_9: 0.09529/0.35425, loss_spatial_dice_9: 0.78007/0.79307, loss_spatial_ce_9: 0.94347/1.38629, loss_grounding_bce_9: 0.00104/0.10102, loss_grounding_dice_9: 0.05713/0.24222, loss_grounding_ce_9: 0.67049/0.66730] items per batch[64] items per second[0.37] total items[5299200] mini batches[ 82800] memory[4999] epoch remaining[0:36:24] INFO:trainer.default_trainer:epochs[ 45] optim steps[82900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.08236/0.75049, loss_mask_bce_0: 0.19216/0.30047, loss_mask_dice_0: 0.29321/1.01951, loss_spatial_bce_0: 0.08130/0.08418, loss_spatial_dice_0: 0.15719/0.17792, loss_spatial_ce_0: 0.11931/0.05484, loss_grounding_bce_0: 0.07894/0.08051, loss_grounding_dice_0: 0.04945/0.15032, loss_grounding_ce_0: 0.18749/0.24829, loss_mask_ce_1: 1.10818/0.75136, loss_mask_bce_1: 0.19776/0.30128, loss_mask_dice_1: 0.27266/1.02389, loss_spatial_bce_1: 0.08819/0.08466, loss_spatial_dice_1: 0.19211/0.18093, loss_spatial_ce_1: 0.12993/0.05854, loss_grounding_bce_1: 0.07555/0.08074, loss_grounding_dice_1: 0.05137/0.15108, loss_grounding_ce_1: 0.24097/0.24967, loss_mask_ce_2: 1.01781/0.75890, loss_mask_bce_2: 0.19141/0.30166, loss_mask_dice_2: 0.28888/1.02464, loss_spatial_bce_2: 0.08083/0.08476, loss_spatial_dice_2: 0.15715/0.18158, loss_spatial_ce_2: 0.20946/0.06077, loss_grounding_bce_2: 0.07066/0.08072, loss_grounding_dice_2: 0.04880/0.15101, loss_grounding_ce_2: 0.21934/0.25244, loss_mask_ce_3: 1.14608/0.76373, loss_mask_bce_3: 0.19146/0.30296, loss_mask_dice_3: 0.25182/1.02289, loss_spatial_bce_3: 0.08400/0.08690, loss_spatial_dice_3: 0.14973/0.18297, loss_spatial_ce_3: 0.34121/0.06568, loss_grounding_bce_3: 0.07813/0.08105, loss_grounding_dice_3: 0.05111/0.15067, loss_grounding_ce_3: 0.20781/0.25370, loss_mask_ce_4: 0.85629/0.76973, loss_mask_bce_4: 0.19460/0.30571, loss_mask_dice_4: 0.31986/1.04237, loss_spatial_bce_4: 0.10129/0.08945, loss_spatial_dice_4: 0.24010/0.19181, loss_spatial_ce_4: 0.34025/0.07955, loss_grounding_bce_4: 0.07183/0.08182, loss_grounding_dice_4: 0.04867/0.15329, loss_grounding_ce_4: 0.22441/0.25815, loss_mask_ce_5: 1.05015/0.79519, loss_mask_bce_5: 0.19689/0.30757, loss_mask_dice_5: 0.28221/1.05043, loss_spatial_bce_5: 0.15768/0.09188, loss_spatial_dice_5: 0.25038/0.19519, loss_spatial_ce_5: 0.06396/0.09340, loss_grounding_bce_5: 0.08090/0.08211, loss_grounding_dice_5: 0.04735/0.15418, loss_grounding_ce_5: 0.21019/0.27539, loss_mask_ce_6: 0.96907/0.82279, loss_mask_bce_6: 0.19865/0.30974, loss_mask_dice_6: 0.28475/1.05433, loss_spatial_bce_6: 0.18561/0.09739, loss_spatial_dice_6: 0.24788/0.19752, loss_spatial_ce_6: 0.15275/0.11773, loss_grounding_bce_6: 0.07988/0.08290, loss_grounding_dice_6: 0.04297/0.15464, loss_grounding_ce_6: 0.25309/0.28438, loss_mask_ce_7: 0.93326/0.87819, loss_mask_bce_7: 0.18229/0.31697, loss_mask_dice_7: 0.28833/1.10014, loss_spatial_bce_7: 0.23281/0.10645, loss_spatial_dice_7: 0.24451/0.22227, loss_spatial_ce_7: 0.08675/0.15201, loss_grounding_bce_7: 0.08298/0.08461, loss_grounding_dice_7: 0.03977/0.16021, loss_grounding_ce_7: 0.15230/0.31740, loss_mask_ce_8: 1.05196/1.01180, loss_mask_bce_8: 0.19515/0.33292, loss_mask_dice_8: 0.27993/1.17673, loss_spatial_bce_8: 0.19799/0.12270, loss_spatial_dice_8: 0.26156/0.25670, loss_spatial_ce_8: 0.06672/0.19715, loss_grounding_bce_8: 0.08552/0.08879, loss_grounding_dice_8: 0.04200/0.16999, loss_grounding_ce_8: 0.27433/0.41526, loss_mask_ce_9: 2.64032/3.47250, loss_mask_bce_9: 0.24598/0.35975, loss_mask_dice_9: 0.53392/1.75900, loss_spatial_bce_9: 0.88392/0.35427, loss_spatial_dice_9: 0.85059/0.79307, loss_spatial_ce_9: 1.96365/1.38620, loss_grounding_bce_9: 0.11735/0.10101, loss_grounding_dice_9: 0.08791/0.24219, loss_grounding_ce_9: 1.04704/0.66735] items per batch[64] items per second[0.37] total items[5305600] mini batches[ 82900] memory[4999] epoch remaining[0:33:24] INFO:trainer.default_trainer:epochs[ 45] optim steps[83000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.00500/0.75040, loss_mask_bce_0: 0.02509/0.30046, loss_mask_dice_0: 0.15456/1.01944, loss_spatial_bce_0: 0.02519/0.08418, loss_spatial_dice_0: 0.09314/0.17790, loss_spatial_ce_0: 0.00034/0.05483, loss_grounding_bce_0: 0.01521/0.08051, loss_grounding_dice_0: 0.09889/0.15034, loss_grounding_ce_0: 0.00080/0.24824, loss_mask_ce_1: 0.00519/0.75128, loss_mask_bce_1: 0.02337/0.30127, loss_mask_dice_1: 0.16514/1.02382, loss_spatial_bce_1: 0.02378/0.08466, loss_spatial_dice_1: 0.11686/0.18091, loss_spatial_ce_1: 0.00026/0.05852, loss_grounding_bce_1: 0.01463/0.08073, loss_grounding_dice_1: 0.09846/0.15110, loss_grounding_ce_1: 0.00093/0.24962, loss_mask_ce_2: 0.00527/0.75885, loss_mask_bce_2: 0.02546/0.30165, loss_mask_dice_2: 0.17241/1.02456, loss_spatial_bce_2: 0.02944/0.08476, loss_spatial_dice_2: 0.11006/0.18156, loss_spatial_ce_2: 0.00025/0.06075, loss_grounding_bce_2: 0.01691/0.08072, loss_grounding_dice_2: 0.10688/0.15104, loss_grounding_ce_2: 0.00104/0.25237, loss_mask_ce_3: 0.00379/0.76366, loss_mask_bce_3: 0.02342/0.30295, loss_mask_dice_3: 0.16201/1.02280, loss_spatial_bce_3: 0.02617/0.08690, loss_spatial_dice_3: 0.10673/0.18295, loss_spatial_ce_3: 0.00016/0.06566, loss_grounding_bce_3: 0.01651/0.08105, loss_grounding_dice_3: 0.11410/0.15069, loss_grounding_ce_3: 0.00057/0.25367, loss_mask_ce_4: 0.00395/0.76969, loss_mask_bce_4: 0.02661/0.30570, loss_mask_dice_4: 0.18637/1.04230, loss_spatial_bce_4: 0.02454/0.08944, loss_spatial_dice_4: 0.11837/0.19180, loss_spatial_ce_4: 0.00015/0.07954, loss_grounding_bce_4: 0.01432/0.08182, loss_grounding_dice_4: 0.10794/0.15331, loss_grounding_ce_4: 0.00039/0.25810, loss_mask_ce_5: 0.01106/0.79513, loss_mask_bce_5: 0.02496/0.30756, loss_mask_dice_5: 0.16692/1.05035, loss_spatial_bce_5: 0.02465/0.09187, loss_spatial_dice_5: 0.12118/0.19517, loss_spatial_ce_5: 0.00186/0.09337, loss_grounding_bce_5: 0.01738/0.08211, loss_grounding_dice_5: 0.12114/0.15420, loss_grounding_ce_5: 0.00085/0.27533, loss_mask_ce_6: 0.00898/0.82273, loss_mask_bce_6: 0.02431/0.30972, loss_mask_dice_6: 0.15992/1.05421, loss_spatial_bce_6: 0.02236/0.09738, loss_spatial_dice_6: 0.09335/0.19750, loss_spatial_ce_6: 0.00081/0.11771, loss_grounding_bce_6: 0.01872/0.08290, loss_grounding_dice_6: 0.11591/0.15465, loss_grounding_ce_6: 0.00118/0.28433, loss_mask_ce_7: 0.01189/0.87810, loss_mask_bce_7: 0.02218/0.31695, loss_mask_dice_7: 0.14803/1.10006, loss_spatial_bce_7: 0.02418/0.10644, loss_spatial_dice_7: 0.12607/0.22225, loss_spatial_ce_7: 0.02279/0.15196, loss_grounding_bce_7: 0.01506/0.08461, loss_grounding_dice_7: 0.09185/0.16023, loss_grounding_ce_7: 0.00310/0.31732, loss_mask_ce_8: 0.04084/1.01172, loss_mask_bce_8: 0.02344/0.33290, loss_mask_dice_8: 0.13713/1.17663, loss_spatial_bce_8: 0.08056/0.12269, loss_spatial_dice_8: 0.18166/0.25669, loss_spatial_ce_8: 0.07790/0.19711, loss_grounding_bce_8: 0.01753/0.08879, loss_grounding_dice_8: 0.11843/0.17000, loss_grounding_ce_8: 0.00760/0.41519, loss_mask_ce_9: 1.54928/3.47253, loss_mask_bce_9: 0.02562/0.35974, loss_mask_dice_9: 0.19311/1.75897, loss_spatial_bce_9: 0.13773/0.35426, loss_spatial_dice_9: 0.52972/0.79307, loss_spatial_ce_9: 0.52801/1.38615, loss_grounding_bce_9: 0.01658/0.10100, loss_grounding_dice_9: 0.13738/0.24223, loss_grounding_ce_9: 0.10770/0.66724] items per batch[64] items per second[0.37] total items[5312000] mini batches[ 83000] memory[4999] epoch remaining[0:30:24] INFO:trainer.default_trainer:epochs[ 45] optim steps[83100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.56094/0.75045, loss_mask_bce_0: 0.03295/0.30046, loss_mask_dice_0: 0.82852/1.01934, loss_spatial_bce_0: 0.01502/0.08416, loss_spatial_dice_0: 0.25965/0.17789, loss_spatial_ce_0: 0.00053/0.05481, loss_grounding_bce_0: 0.00558/0.08050, loss_grounding_dice_0: 0.05293/0.15034, loss_grounding_ce_0: 0.00189/0.24821, loss_mask_ce_1: 1.51312/0.75132, loss_mask_bce_1: 0.03313/0.30126, loss_mask_dice_1: 0.93501/1.02373, loss_spatial_bce_1: 0.01293/0.08464, loss_spatial_dice_1: 0.24480/0.18089, loss_spatial_ce_1: 0.00147/0.05850, loss_grounding_bce_1: 0.00658/0.08072, loss_grounding_dice_1: 0.06817/0.15110, loss_grounding_ce_1: 0.00119/0.24958, loss_mask_ce_2: 1.65774/0.75889, loss_mask_bce_2: 0.03061/0.30164, loss_mask_dice_2: 0.97599/1.02446, loss_spatial_bce_2: 0.01364/0.08474, loss_spatial_dice_2: 0.27493/0.18155, loss_spatial_ce_2: 0.00281/0.06074, loss_grounding_bce_2: 0.00760/0.08071, loss_grounding_dice_2: 0.07749/0.15104, loss_grounding_ce_2: 0.00113/0.25232, loss_mask_ce_3: 2.02013/0.76370, loss_mask_bce_3: 0.03937/0.30295, loss_mask_dice_3: 1.05555/1.02271, loss_spatial_bce_3: 0.01389/0.08688, loss_spatial_dice_3: 0.29034/0.18293, loss_spatial_ce_3: 0.00388/0.06565, loss_grounding_bce_3: 0.00568/0.08103, loss_grounding_dice_3: 0.06567/0.15069, loss_grounding_ce_3: 0.00179/0.25362, loss_mask_ce_4: 1.56229/0.76973, loss_mask_bce_4: 0.03438/0.30569, loss_mask_dice_4: 1.02725/1.04221, loss_spatial_bce_4: 0.02007/0.08942, loss_spatial_dice_4: 0.27315/0.19178, loss_spatial_ce_4: 0.00820/0.07952, loss_grounding_bce_4: 0.01000/0.08181, loss_grounding_dice_4: 0.09348/0.15331, loss_grounding_ce_4: 0.00849/0.25806, loss_mask_ce_5: 1.66015/0.79515, loss_mask_bce_5: 0.02976/0.30756, loss_mask_dice_5: 0.71543/1.05025, loss_spatial_bce_5: 0.02414/0.09185, loss_spatial_dice_5: 0.30465/0.19516, loss_spatial_ce_5: 0.00958/0.09335, loss_grounding_bce_5: 0.00737/0.08210, loss_grounding_dice_5: 0.08743/0.15421, loss_grounding_ce_5: 0.01990/0.27532, loss_mask_ce_6: 1.83537/0.82276, loss_mask_bce_6: 0.03035/0.30973, loss_mask_dice_6: 0.90548/1.05413, loss_spatial_bce_6: 0.03167/0.09736, loss_spatial_dice_6: 0.30779/0.19749, loss_spatial_ce_6: 0.00617/0.11769, loss_grounding_bce_6: 0.00636/0.08289, loss_grounding_dice_6: 0.06515/0.15465, loss_grounding_ce_6: 0.03451/0.28429, loss_mask_ce_7: 1.42318/0.87808, loss_mask_bce_7: 0.05456/0.31696, loss_mask_dice_7: 1.11320/1.09999, loss_spatial_bce_7: 0.01922/0.10643, loss_spatial_dice_7: 0.31829/0.22224, loss_spatial_ce_7: 0.03703/0.15193, loss_grounding_bce_7: 0.00631/0.08461, loss_grounding_dice_7: 0.07630/0.16023, loss_grounding_ce_7: 0.03104/0.31729, loss_mask_ce_8: 1.50131/1.01171, loss_mask_bce_8: 0.05276/0.33291, loss_mask_dice_8: 1.00547/1.17656, loss_spatial_bce_8: 0.02670/0.12267, loss_spatial_dice_8: 0.38446/0.25667, loss_spatial_ce_8: 0.10550/0.19708, loss_grounding_bce_8: 0.00447/0.08878, loss_grounding_dice_8: 0.06308/0.17001, loss_grounding_ce_8: 0.01519/0.41515, loss_mask_ce_9: 4.01071/3.47263, loss_mask_bce_9: 0.03167/0.35975, loss_mask_dice_9: 1.13767/1.75895, loss_spatial_bce_9: 0.03847/0.35426, loss_spatial_dice_9: 0.86473/0.79310, loss_spatial_ce_9: 2.44007/1.38625, loss_grounding_bce_9: 0.00748/0.10100, loss_grounding_dice_9: 0.10295/0.24223, loss_grounding_ce_9: 0.73173/0.66724] items per batch[64] items per second[0.37] total items[5318400] mini batches[ 83100] memory[4999] epoch remaining[0:27:29] INFO:trainer.default_trainer:epochs[ 45] optim steps[83200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.50797/0.75038, loss_mask_bce_0: 0.55454/0.30044, loss_mask_dice_0: 2.60170/1.01939, loss_spatial_bce_0: 0.08167/0.08415, loss_spatial_dice_0: 0.29338/0.17786, loss_spatial_ce_0: 0.01688/0.05480, loss_grounding_bce_0: 0.13292/0.08050, loss_grounding_dice_0: 0.93372/0.15034, loss_grounding_ce_0: 0.18793/0.24815, loss_mask_ce_1: 0.50524/0.75124, loss_mask_bce_1: 0.55930/0.30125, loss_mask_dice_1: 2.54390/1.02376, loss_spatial_bce_1: 0.08963/0.08464, loss_spatial_dice_1: 0.30068/0.18086, loss_spatial_ce_1: 0.00606/0.05849, loss_grounding_bce_1: 0.12342/0.08072, loss_grounding_dice_1: 0.94241/0.15110, loss_grounding_ce_1: 0.14358/0.24952, loss_mask_ce_2: 0.56667/0.75883, loss_mask_bce_2: 0.56436/0.30163, loss_mask_dice_2: 2.69371/1.02450, loss_spatial_bce_2: 0.09182/0.08473, loss_spatial_dice_2: 0.28166/0.18152, loss_spatial_ce_2: 0.00266/0.06073, loss_grounding_bce_2: 0.07575/0.08071, loss_grounding_dice_2: 0.82576/0.15104, loss_grounding_ce_2: 0.64906/0.25227, loss_mask_ce_3: 0.61504/0.76366, loss_mask_bce_3: 0.55595/0.30294, loss_mask_dice_3: 2.47226/1.02278, loss_spatial_bce_3: 0.09670/0.08688, loss_spatial_dice_3: 0.31466/0.18291, loss_spatial_ce_3: 0.01638/0.06565, loss_grounding_bce_3: 0.07223/0.08104, loss_grounding_dice_3: 0.72467/0.15069, loss_grounding_ce_3: 0.65434/0.25356, loss_mask_ce_4: 0.62467/0.76966, loss_mask_bce_4: 0.55857/0.30566, loss_mask_dice_4: 2.78814/1.04226, loss_spatial_bce_4: 0.08798/0.08943, loss_spatial_dice_4: 0.29512/0.19175, loss_spatial_ce_4: 0.05253/0.07951, loss_grounding_bce_4: 0.10696/0.08181, loss_grounding_dice_4: 0.94539/0.15331, loss_grounding_ce_4: 0.12364/0.25800, loss_mask_ce_5: 0.88496/0.79506, loss_mask_bce_5: 0.55757/0.30755, loss_mask_dice_5: 2.24316/1.05033, loss_spatial_bce_5: 0.09901/0.09186, loss_spatial_dice_5: 0.31795/0.19513, loss_spatial_ce_5: 0.01684/0.09334, loss_grounding_bce_5: 0.30435/0.08211, loss_grounding_dice_5: 0.99525/0.15421, loss_grounding_ce_5: 0.09931/0.27523, loss_mask_ce_6: 0.85807/0.82272, loss_mask_bce_6: 0.57105/0.30971, loss_mask_dice_6: 2.67277/1.05420, loss_spatial_bce_6: 0.09568/0.09738, loss_spatial_dice_6: 0.31573/0.19746, loss_spatial_ce_6: 0.05711/0.11768, loss_grounding_bce_6: 0.11995/0.08290, loss_grounding_dice_6: 0.87575/0.15466, loss_grounding_ce_6: 0.19928/0.28420, loss_mask_ce_7: 1.06709/0.87798, loss_mask_bce_7: 0.60893/0.31695, loss_mask_dice_7: 2.53381/1.10008, loss_spatial_bce_7: 0.12521/0.10645, loss_spatial_dice_7: 0.39375/0.22221, loss_spatial_ce_7: 0.04555/0.15190, loss_grounding_bce_7: 0.10240/0.08461, loss_grounding_dice_7: 0.93814/0.16022, loss_grounding_ce_7: 0.18036/0.31722, loss_mask_ce_8: 1.21096/1.01161, loss_mask_bce_8: 0.57270/0.33289, loss_mask_dice_8: 3.07280/1.17664, loss_spatial_bce_8: 0.15682/0.12268, loss_spatial_dice_8: 0.41103/0.25663, loss_spatial_ce_8: 0.07045/0.19702, loss_grounding_bce_8: 0.23041/0.08879, loss_grounding_dice_8: 0.99399/0.17001, loss_grounding_ce_8: 0.10899/0.41506, loss_mask_ce_9: 3.17579/3.47260, loss_mask_bce_9: 0.52749/0.35974, loss_mask_dice_9: 3.92671/1.75917, loss_spatial_bce_9: 0.27413/0.35425, loss_spatial_dice_9: 0.87852/0.79307, loss_spatial_ce_9: 2.20674/1.38620, loss_grounding_bce_9: 0.09196/0.10099, loss_grounding_dice_9: 0.97032/0.24222, loss_grounding_ce_9: 0.10512/0.66714] items per batch[64] items per second[0.37] total items[5324800] mini batches[ 83200] memory[4999] epoch remaining[0:24:31] INFO:trainer.default_trainer:epochs[ 45] optim steps[83300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.58519/0.75038, loss_mask_bce_0: 0.13048/0.30043, loss_mask_dice_0: 0.64795/1.01939, loss_spatial_bce_0: 0.04641/0.08415, loss_spatial_dice_0: 0.22596/0.17786, loss_spatial_ce_0: 0.00607/0.05478, loss_grounding_bce_0: 0.02802/0.08050, loss_grounding_dice_0: 0.06992/0.15035, loss_grounding_ce_0: 0.03391/0.24811, loss_mask_ce_1: 0.61522/0.75124, loss_mask_bce_1: 0.12987/0.30123, loss_mask_dice_1: 0.58258/1.02373, loss_spatial_bce_1: 0.04771/0.08463, loss_spatial_dice_1: 0.23858/0.18086, loss_spatial_ce_1: 0.01811/0.05848, loss_grounding_bce_1: 0.03024/0.08072, loss_grounding_dice_1: 0.06681/0.15112, loss_grounding_ce_1: 0.04636/0.24946, loss_mask_ce_2: 0.53409/0.75883, loss_mask_bce_2: 0.12528/0.30162, loss_mask_dice_2: 0.62867/1.02441, loss_spatial_bce_2: 0.04934/0.08472, loss_spatial_dice_2: 0.23732/0.18151, loss_spatial_ce_2: 0.01614/0.06072, loss_grounding_bce_2: 0.03133/0.08071, loss_grounding_dice_2: 0.07267/0.15105, loss_grounding_ce_2: 0.04675/0.25221, loss_mask_ce_3: 0.55196/0.76366, loss_mask_bce_3: 0.12723/0.30292, loss_mask_dice_3: 0.62177/1.02275, loss_spatial_bce_3: 0.04788/0.08687, loss_spatial_dice_3: 0.23957/0.18291, loss_spatial_ce_3: 0.01925/0.06563, loss_grounding_bce_3: 0.02801/0.08103, loss_grounding_dice_3: 0.07104/0.15070, loss_grounding_ce_3: 0.04244/0.25351, loss_mask_ce_4: 0.53258/0.76970, loss_mask_bce_4: 0.11715/0.30564, loss_mask_dice_4: 0.52488/1.04223, loss_spatial_bce_4: 0.04780/0.08942, loss_spatial_dice_4: 0.24003/0.19175, loss_spatial_ce_4: 0.02994/0.07950, loss_grounding_bce_4: 0.03055/0.08181, loss_grounding_dice_4: 0.07256/0.15332, loss_grounding_ce_4: 0.03714/0.25794, loss_mask_ce_5: 0.51732/0.79510, loss_mask_bce_5: 0.12804/0.30753, loss_mask_dice_5: 0.63058/1.05030, loss_spatial_bce_5: 0.04674/0.09184, loss_spatial_dice_5: 0.24380/0.19513, loss_spatial_ce_5: 0.09924/0.09334, loss_grounding_bce_5: 0.02818/0.08210, loss_grounding_dice_5: 0.06979/0.15421, loss_grounding_ce_5: 0.06501/0.27517, loss_mask_ce_6: 0.60197/0.82278, loss_mask_bce_6: 0.12767/0.30969, loss_mask_dice_6: 0.63340/1.05415, loss_spatial_bce_6: 0.08004/0.09737, loss_spatial_dice_6: 0.26352/0.19746, loss_spatial_ce_6: 0.05355/0.11768, loss_grounding_bce_6: 0.03121/0.08289, loss_grounding_dice_6: 0.07194/0.15466, loss_grounding_ce_6: 0.05058/0.28414, loss_mask_ce_7: 0.81714/0.87795, loss_mask_bce_7: 0.13431/0.31693, loss_mask_dice_7: 0.64873/1.10003, loss_spatial_bce_7: 0.07619/0.10644, loss_spatial_dice_7: 0.25337/0.22221, loss_spatial_ce_7: 0.05784/0.15190, loss_grounding_bce_7: 0.03389/0.08460, loss_grounding_dice_7: 0.08004/0.16022, loss_grounding_ce_7: 0.05202/0.31715, loss_mask_ce_8: 0.77599/1.01159, loss_mask_bce_8: 0.13044/0.33287, loss_mask_dice_8: 0.62504/1.17661, loss_spatial_bce_8: 0.05663/0.12267, loss_spatial_dice_8: 0.26100/0.25661, loss_spatial_ce_8: 0.21171/0.19700, loss_grounding_bce_8: 0.03563/0.08879, loss_grounding_dice_8: 0.08278/0.17002, loss_grounding_ce_8: 0.17862/0.41497, loss_mask_ce_9: 2.55684/3.47244, loss_mask_bce_9: 0.14918/0.35972, loss_mask_dice_9: 0.72974/1.75920, loss_spatial_bce_9: 0.27677/0.35428, loss_spatial_dice_9: 0.77962/0.79305, loss_spatial_ce_9: 1.00295/1.38628, loss_grounding_bce_9: 0.03069/0.10100, loss_grounding_dice_9: 0.09505/0.24223, loss_grounding_ce_9: 2.40299/0.66710] items per batch[64] items per second[0.37] total items[5331200] mini batches[ 83300] memory[4999] epoch remaining[0:21:36] INFO:trainer.default_trainer:epochs[ 45] optim steps[83400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.61557/0.75034, loss_mask_bce_0: 0.12891/0.30041, loss_mask_dice_0: 5.38504/1.01932, loss_spatial_bce_0: 0.00748/0.08413, loss_spatial_dice_0: 0.26285/0.17783, loss_spatial_ce_0: 0.03578/0.05475, loss_grounding_bce_0: 0.00907/0.08048, loss_grounding_dice_0: 0.26305/0.15034, loss_grounding_ce_0: 0.20266/0.24803, loss_mask_ce_1: 1.84204/0.75120, loss_mask_bce_1: 0.12083/0.30121, loss_mask_dice_1: 5.44915/1.02368, loss_spatial_bce_1: 0.00731/0.08462, loss_spatial_dice_1: 0.26657/0.18083, loss_spatial_ce_1: 0.00859/0.05846, loss_grounding_bce_1: 0.00787/0.08070, loss_grounding_dice_1: 0.24488/0.15110, loss_grounding_ce_1: 0.16620/0.24940, loss_mask_ce_2: 1.87147/0.75879, loss_mask_bce_2: 0.17484/0.30160, loss_mask_dice_2: 5.52194/1.02436, loss_spatial_bce_2: 0.00688/0.08471, loss_spatial_dice_2: 0.20085/0.18148, loss_spatial_ce_2: 0.03549/0.06070, loss_grounding_bce_2: 0.00933/0.08069, loss_grounding_dice_2: 0.31583/0.15103, loss_grounding_ce_2: 0.35197/0.25219, loss_mask_ce_3: 2.28692/0.76365, loss_mask_bce_3: 0.12470/0.30290, loss_mask_dice_3: 4.70985/1.02271, loss_spatial_bce_3: 0.00763/0.08685, loss_spatial_dice_3: 0.27186/0.18288, loss_spatial_ce_3: 0.08402/0.06563, loss_grounding_bce_3: 0.01052/0.08101, loss_grounding_dice_3: 0.32583/0.15068, loss_grounding_ce_3: 0.18503/0.25344, loss_mask_ce_4: 1.80657/0.76965, loss_mask_bce_4: 0.14338/0.30563, loss_mask_dice_4: 5.66987/1.04219, loss_spatial_bce_4: 0.01139/0.08941, loss_spatial_dice_4: 0.30314/0.19172, loss_spatial_ce_4: 0.12680/0.07947, loss_grounding_bce_4: 0.01115/0.08179, loss_grounding_dice_4: 0.32976/0.15330, loss_grounding_ce_4: 0.14369/0.25788, loss_mask_ce_5: 2.07082/0.79509, loss_mask_bce_5: 0.11689/0.30751, loss_mask_dice_5: 4.82547/1.05025, loss_spatial_bce_5: 0.00987/0.09183, loss_spatial_dice_5: 0.31778/0.19510, loss_spatial_ce_5: 0.10626/0.09333, loss_grounding_bce_5: 0.00780/0.08208, loss_grounding_dice_5: 0.33795/0.15420, loss_grounding_ce_5: 0.19727/0.27512, loss_mask_ce_6: 2.15926/0.82275, loss_mask_bce_6: 0.16598/0.30968, loss_mask_dice_6: 6.01770/1.05412, loss_spatial_bce_6: 0.01069/0.09736, loss_spatial_dice_6: 0.31050/0.19743, loss_spatial_ce_6: 0.08482/0.11766, loss_grounding_bce_6: 0.00785/0.08287, loss_grounding_dice_6: 0.27443/0.15464, loss_grounding_ce_6: 0.17303/0.28407, loss_mask_ce_7: 2.78767/0.87791, loss_mask_bce_7: 0.15433/0.31693, loss_mask_dice_7: 6.56346/1.10001, loss_spatial_bce_7: 0.01107/0.10642, loss_spatial_dice_7: 0.37327/0.22217, loss_spatial_ce_7: 0.07237/0.15187, loss_grounding_bce_7: 0.00683/0.08459, loss_grounding_dice_7: 0.24951/0.16020, loss_grounding_ce_7: 0.23039/0.31707, loss_mask_ce_8: 2.27291/1.01156, loss_mask_bce_8: 0.14507/0.33287, loss_mask_dice_8: 5.81621/1.17657, loss_spatial_bce_8: 0.01091/0.12265, loss_spatial_dice_8: 0.38821/0.25658, loss_spatial_ce_8: 0.12839/0.19694, loss_grounding_bce_8: 0.01069/0.08878, loss_grounding_dice_8: 0.33155/0.17000, loss_grounding_ce_8: 0.05036/0.41480, loss_mask_ce_9: 8.82350/3.47244, loss_mask_bce_9: 0.15136/0.35973, loss_mask_dice_9: 8.77298/1.75921, loss_spatial_bce_9: 0.02345/0.35425, loss_spatial_dice_9: 0.89507/0.79302, loss_spatial_ce_9: 2.34509/1.38624, loss_grounding_bce_9: 0.01529/0.10099, loss_grounding_dice_9: 0.33216/0.24222, loss_grounding_ce_9: 0.23857/0.66696] items per batch[64] items per second[0.36] total items[5337600] mini batches[ 83400] memory[4999] epoch remaining[0:18:43] INFO:trainer.default_trainer:epochs[ 45] optim steps[83500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.51709/0.75036, loss_mask_bce_0: 0.08625/0.30041, loss_mask_dice_0: 0.54898/1.01914, loss_spatial_bce_0: 0.01556/0.08414, loss_spatial_dice_0: 0.07693/0.17781, loss_spatial_ce_0: 0.00139/0.05474, loss_grounding_bce_0: 0.03036/0.08048, loss_grounding_dice_0: 0.09838/0.15033, loss_grounding_ce_0: 0.00265/0.24797, loss_mask_ce_1: 0.45254/0.75120, loss_mask_bce_1: 0.08518/0.30121, loss_mask_dice_1: 0.53845/1.02352, loss_spatial_bce_1: 0.01604/0.08462, loss_spatial_dice_1: 0.08383/0.18081, loss_spatial_ce_1: 0.00094/0.05846, loss_grounding_bce_1: 0.03437/0.08070, loss_grounding_dice_1: 0.10738/0.15108, loss_grounding_ce_1: 0.00189/0.24932, loss_mask_ce_2: 0.33200/0.75881, loss_mask_bce_2: 0.08558/0.30160, loss_mask_dice_2: 0.52397/1.02422, loss_spatial_bce_2: 0.01639/0.08471, loss_spatial_dice_2: 0.07753/0.18147, loss_spatial_ce_2: 0.00611/0.06068, loss_grounding_bce_2: 0.03082/0.08068, loss_grounding_dice_2: 0.10442/0.15102, loss_grounding_ce_2: 0.00153/0.25219, loss_mask_ce_3: 0.45117/0.76368, loss_mask_bce_3: 0.08280/0.30290, loss_mask_dice_3: 0.47014/1.02256, loss_spatial_bce_3: 0.01799/0.08686, loss_spatial_dice_3: 0.10299/0.18287, loss_spatial_ce_3: 0.03251/0.06562, loss_grounding_bce_3: 0.02737/0.08101, loss_grounding_dice_3: 0.09458/0.15068, loss_grounding_ce_3: 0.00095/0.25342, loss_mask_ce_4: 0.37948/0.76965, loss_mask_bce_4: 0.08246/0.30564, loss_mask_dice_4: 0.46570/1.04202, loss_spatial_bce_4: 0.01666/0.08941, loss_spatial_dice_4: 0.09337/0.19171, loss_spatial_ce_4: 0.06504/0.07945, loss_grounding_bce_4: 0.03421/0.08179, loss_grounding_dice_4: 0.11800/0.15329, loss_grounding_ce_4: 0.00185/0.25788, loss_mask_ce_5: 0.27178/0.79509, loss_mask_bce_5: 0.08127/0.30752, loss_mask_dice_5: 0.57038/1.05011, loss_spatial_bce_5: 0.01761/0.09183, loss_spatial_dice_5: 0.09605/0.19509, loss_spatial_ce_5: 0.02438/0.09332, loss_grounding_bce_5: 0.03806/0.08208, loss_grounding_dice_5: 0.12649/0.15419, loss_grounding_ce_5: 0.02125/0.27504, loss_mask_ce_6: 0.31097/0.82279, loss_mask_bce_6: 0.08670/0.30969, loss_mask_dice_6: 0.56964/1.05398, loss_spatial_bce_6: 0.01956/0.09736, loss_spatial_dice_6: 0.09836/0.19742, loss_spatial_ce_6: 0.03042/0.11765, loss_grounding_bce_6: 0.03224/0.08287, loss_grounding_dice_6: 0.11038/0.15463, loss_grounding_ce_6: 0.05103/0.28401, loss_mask_ce_7: 0.51634/0.87792, loss_mask_bce_7: 0.10294/0.31693, loss_mask_dice_7: 0.53647/1.09984, loss_spatial_bce_7: 0.02108/0.10641, loss_spatial_dice_7: 0.12196/0.22215, loss_spatial_ce_7: 0.01804/0.15184, loss_grounding_bce_7: 0.03387/0.08459, loss_grounding_dice_7: 0.11476/0.16019, loss_grounding_ce_7: 0.01781/0.31703, loss_mask_ce_8: 0.80289/1.01160, loss_mask_bce_8: 0.09985/0.33288, loss_mask_dice_8: 0.60064/1.17640, loss_spatial_bce_8: 0.02817/0.12265, loss_spatial_dice_8: 0.15310/0.25654, loss_spatial_ce_8: 0.04587/0.19688, loss_grounding_bce_8: 0.02729/0.08878, loss_grounding_dice_8: 0.09498/0.16998, loss_grounding_ce_8: 0.47204/0.41472, loss_mask_ce_9: 3.40342/3.47256, loss_mask_bce_9: 0.13867/0.35976, loss_mask_dice_9: 1.27699/1.75918, loss_spatial_bce_9: 0.16814/0.35426, loss_spatial_dice_9: 0.83524/0.79303, loss_spatial_ce_9: 1.51133/1.38621, loss_grounding_bce_9: 0.05064/0.10099, loss_grounding_dice_9: 0.17748/0.24221, loss_grounding_ce_9: 1.41882/0.66690] items per batch[64] items per second[0.37] total items[5344000] mini batches[ 83500] memory[4999] epoch remaining[0:15:48] INFO:trainer.default_trainer:epochs[ 45] optim steps[83600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.42184/0.75030, loss_mask_bce_0: 0.53856/0.30040, loss_mask_dice_0: 0.72072/1.01924, loss_spatial_bce_0: 0.15510/0.08413, loss_spatial_dice_0: 0.22436/0.17780, loss_spatial_ce_0: 0.00375/0.05472, loss_grounding_bce_0: 0.01508/0.08047, loss_grounding_dice_0: 0.01623/0.15032, loss_grounding_ce_0: 0.01887/0.24793, loss_mask_ce_1: 0.46785/0.75113, loss_mask_bce_1: 0.53255/0.30120, loss_mask_dice_1: 0.70888/1.02368, loss_spatial_bce_1: 0.16014/0.08461, loss_spatial_dice_1: 0.23037/0.18079, loss_spatial_ce_1: 0.00446/0.05843, loss_grounding_bce_1: 0.01508/0.08069, loss_grounding_dice_1: 0.01501/0.15107, loss_grounding_ce_1: 0.02267/0.24927, loss_mask_ce_2: 0.44887/0.75872, loss_mask_bce_2: 0.54423/0.30159, loss_mask_dice_2: 0.72489/1.02435, loss_spatial_bce_2: 0.15115/0.08470, loss_spatial_dice_2: 0.22037/0.18145, loss_spatial_ce_2: 0.00414/0.06065, loss_grounding_bce_2: 0.01495/0.08068, loss_grounding_dice_2: 0.01544/0.15100, loss_grounding_ce_2: 0.01897/0.25214, loss_mask_ce_3: 0.42248/0.76358, loss_mask_bce_3: 0.54743/0.30290, loss_mask_dice_3: 0.72142/1.02270, loss_spatial_bce_3: 0.14837/0.08685, loss_spatial_dice_3: 0.23289/0.18285, loss_spatial_ce_3: 0.00685/0.06560, loss_grounding_bce_3: 0.01441/0.08101, loss_grounding_dice_3: 0.01503/0.15067, loss_grounding_ce_3: 0.02105/0.25338, loss_mask_ce_4: 0.42860/0.76957, loss_mask_bce_4: 0.52935/0.30563, loss_mask_dice_4: 0.69842/1.04214, loss_spatial_bce_4: 0.15339/0.08941, loss_spatial_dice_4: 0.22739/0.19170, loss_spatial_ce_4: 0.00967/0.07942, loss_grounding_bce_4: 0.01418/0.08179, loss_grounding_dice_4: 0.01559/0.15328, loss_grounding_ce_4: 0.01877/0.25781, loss_mask_ce_5: 0.42109/0.79501, loss_mask_bce_5: 0.55040/0.30752, loss_mask_dice_5: 0.70728/1.05023, loss_spatial_bce_5: 0.14996/0.09182, loss_spatial_dice_5: 0.23048/0.19508, loss_spatial_ce_5: 0.06941/0.09328, loss_grounding_bce_5: 0.01532/0.08208, loss_grounding_dice_5: 0.01664/0.15418, loss_grounding_ce_5: 0.02575/0.27498, loss_mask_ce_6: 0.41088/0.82272, loss_mask_bce_6: 0.53595/0.30967, loss_mask_dice_6: 0.70347/1.05413, loss_spatial_bce_6: 0.14294/0.09734, loss_spatial_dice_6: 0.21701/0.19740, loss_spatial_ce_6: 0.12380/0.11763, loss_grounding_bce_6: 0.01490/0.08286, loss_grounding_dice_6: 0.01415/0.15461, loss_grounding_ce_6: 0.03130/0.28394, loss_mask_ce_7: 0.47386/0.87785, loss_mask_bce_7: 0.54308/0.31691, loss_mask_dice_7: 0.71870/1.10001, loss_spatial_bce_7: 0.16915/0.10640, loss_spatial_dice_7: 0.23534/0.22214, loss_spatial_ce_7: 0.07496/0.15179, loss_grounding_bce_7: 0.01531/0.08458, loss_grounding_dice_7: 0.01595/0.16017, loss_grounding_ce_7: 0.02822/0.31697, loss_mask_ce_8: 0.55227/1.01155, loss_mask_bce_8: 0.57488/0.33288, loss_mask_dice_8: 0.75743/1.17660, loss_spatial_bce_8: 0.21516/0.12264, loss_spatial_dice_8: 0.29549/0.25652, loss_spatial_ce_8: 0.18964/0.19682, loss_grounding_bce_8: 0.01895/0.08878, loss_grounding_dice_8: 0.01852/0.16997, loss_grounding_ce_8: 0.10521/0.41463, loss_mask_ce_9: 3.82972/3.47251, loss_mask_bce_9: 0.57358/0.35978, loss_mask_dice_9: 0.93101/1.75949, loss_spatial_bce_9: 0.39666/0.35428, loss_spatial_dice_9: 0.71542/0.79301, loss_spatial_ce_9: 0.95574/1.38610, loss_grounding_bce_9: 0.02358/0.10099, loss_grounding_dice_9: 0.03015/0.24220, loss_grounding_ce_9: 0.50303/0.66690] items per batch[64] items per second[0.37] total items[5350400] mini batches[ 83600] memory[4999] epoch remaining[0:12:53] INFO:trainer.default_trainer:epochs[ 45] optim steps[83700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.58226/0.75048, loss_mask_bce_0: 0.23523/0.30044, loss_mask_dice_0: 0.45331/1.01933, loss_spatial_bce_0: 0.09253/0.08413, loss_spatial_dice_0: 0.16817/0.17781, loss_spatial_ce_0: 0.00421/0.05471, loss_grounding_bce_0: 0.26699/0.08048, loss_grounding_dice_0: 0.12346/0.15032, loss_grounding_ce_0: 0.08158/0.24788, loss_mask_ce_1: 0.58692/0.75131, loss_mask_bce_1: 0.23127/0.30125, loss_mask_dice_1: 0.45505/1.02381, loss_spatial_bce_1: 0.08126/0.08461, loss_spatial_dice_1: 0.13912/0.18080, loss_spatial_ce_1: 0.00216/0.05843, loss_grounding_bce_1: 0.26027/0.08070, loss_grounding_dice_1: 0.12211/0.15107, loss_grounding_ce_1: 0.06745/0.24923, loss_mask_ce_2: 0.56684/0.75889, loss_mask_bce_2: 0.23561/0.30164, loss_mask_dice_2: 0.47757/1.02448, loss_spatial_bce_2: 0.07825/0.08471, loss_spatial_dice_2: 0.13601/0.18146, loss_spatial_ce_2: 0.00860/0.06065, loss_grounding_bce_2: 0.24877/0.08069, loss_grounding_dice_2: 0.11783/0.15101, loss_grounding_ce_2: 0.06660/0.25211, loss_mask_ce_3: 0.57758/0.76376, loss_mask_bce_3: 0.21435/0.30294, loss_mask_dice_3: 0.48362/1.02282, loss_spatial_bce_3: 0.08095/0.08685, loss_spatial_dice_3: 0.14899/0.18286, loss_spatial_ce_3: 0.01046/0.06560, loss_grounding_bce_3: 0.20814/0.08102, loss_grounding_dice_3: 0.09804/0.15067, loss_grounding_ce_3: 0.06804/0.25333, loss_mask_ce_4: 0.60751/0.76974, loss_mask_bce_4: 0.23606/0.30567, loss_mask_dice_4: 0.46156/1.04223, loss_spatial_bce_4: 0.09322/0.08941, loss_spatial_dice_4: 0.17641/0.19172, loss_spatial_ce_4: 0.03031/0.07943, loss_grounding_bce_4: 0.24643/0.08180, loss_grounding_dice_4: 0.12071/0.15329, loss_grounding_ce_4: 0.09952/0.25777, loss_mask_ce_5: 0.69174/0.79517, loss_mask_bce_5: 0.23891/0.30757, loss_mask_dice_5: 0.47311/1.05036, loss_spatial_bce_5: 0.09956/0.09183, loss_spatial_dice_5: 0.17962/0.19509, loss_spatial_ce_5: 0.02295/0.09328, loss_grounding_bce_5: 0.21889/0.08209, loss_grounding_dice_5: 0.10642/0.15419, loss_grounding_ce_5: 0.08388/0.27491, loss_mask_ce_6: 0.76510/0.82288, loss_mask_bce_6: 0.21952/0.30972, loss_mask_dice_6: 0.44948/1.05426, loss_spatial_bce_6: 0.11043/0.09734, loss_spatial_dice_6: 0.20235/0.19741, loss_spatial_ce_6: 0.11363/0.11762, loss_grounding_bce_6: 0.20666/0.08288, loss_grounding_dice_6: 0.09856/0.15463, loss_grounding_ce_6: 0.11320/0.28386, loss_mask_ce_7: 0.86146/0.87802, loss_mask_bce_7: 0.24950/0.31696, loss_mask_dice_7: 0.57864/1.10015, loss_spatial_bce_7: 0.11143/0.10640, loss_spatial_dice_7: 0.17548/0.22215, loss_spatial_ce_7: 0.00980/0.15178, loss_grounding_bce_7: 0.39430/0.08459, loss_grounding_dice_7: 0.19126/0.16018, loss_grounding_ce_7: 0.14811/0.31694, loss_mask_ce_8: 1.06561/1.01180, loss_mask_bce_8: 0.30931/0.33293, loss_mask_dice_8: 0.52608/1.17676, loss_spatial_bce_8: 0.16548/0.12263, loss_spatial_dice_8: 0.24499/0.25652, loss_spatial_ce_8: 0.29603/0.19682, loss_grounding_bce_8: 0.38466/0.08880, loss_grounding_dice_8: 0.16752/0.16999, loss_grounding_ce_8: 0.15159/0.41466, loss_mask_ce_9: 6.23493/3.47279, loss_mask_bce_9: 0.53342/0.35984, loss_mask_dice_9: 1.04452/1.75977, loss_spatial_bce_9: 0.66230/0.35426, loss_spatial_dice_9: 0.83785/0.79303, loss_spatial_ce_9: 1.50333/1.38623, loss_grounding_bce_9: 0.58503/0.10101, loss_grounding_dice_9: 0.27603/0.24221, loss_grounding_ce_9: 0.09616/0.66704] items per batch[64] items per second[0.36] total items[5356800] mini batches[ 83700] memory[4999] epoch remaining[0:09:58] INFO:trainer.default_trainer:epochs[ 45] optim steps[83800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.47936/0.75043, loss_mask_bce_0: 0.26981/0.30043, loss_mask_dice_0: 0.35069/1.01941, loss_spatial_bce_0: 0.05728/0.08413, loss_spatial_dice_0: 0.08522/0.17780, loss_spatial_ce_0: 0.05206/0.05469, loss_grounding_bce_0: 0.01095/0.08048, loss_grounding_dice_0: 0.01567/0.15031, loss_grounding_ce_0: 0.00016/0.24778, loss_mask_ce_1: 0.44086/0.75125, loss_mask_bce_1: 0.25344/0.30123, loss_mask_dice_1: 0.33822/1.02388, loss_spatial_bce_1: 0.05984/0.08461, loss_spatial_dice_1: 0.09661/0.18080, loss_spatial_ce_1: 0.05168/0.05840, loss_grounding_bce_1: 0.00957/0.08070, loss_grounding_dice_1: 0.01505/0.15106, loss_grounding_ce_1: 0.00012/0.24912, loss_mask_ce_2: 0.47837/0.75881, loss_mask_bce_2: 0.26245/0.30162, loss_mask_dice_2: 0.33722/1.02458, loss_spatial_bce_2: 0.05959/0.08470, loss_spatial_dice_2: 0.08802/0.18146, loss_spatial_ce_2: 0.04780/0.06063, loss_grounding_bce_2: 0.01134/0.08069, loss_grounding_dice_2: 0.01695/0.15099, loss_grounding_ce_2: 0.00010/0.25201, loss_mask_ce_3: 0.40759/0.76370, loss_mask_bce_3: 0.26736/0.30293, loss_mask_dice_3: 0.34496/1.02293, loss_spatial_bce_3: 0.05999/0.08685, loss_spatial_dice_3: 0.08960/0.18286, loss_spatial_ce_3: 0.05681/0.06559, loss_grounding_bce_3: 0.00847/0.08101, loss_grounding_dice_3: 0.01373/0.15066, loss_grounding_ce_3: 0.00013/0.25325, loss_mask_ce_4: 0.40292/0.76968, loss_mask_bce_4: 0.25078/0.30566, loss_mask_dice_4: 0.34528/1.04234, loss_spatial_bce_4: 0.06445/0.08941, loss_spatial_dice_4: 0.09411/0.19171, loss_spatial_ce_4: 0.06295/0.07940, loss_grounding_bce_4: 0.00913/0.08179, loss_grounding_dice_4: 0.01373/0.15327, loss_grounding_ce_4: 0.00018/0.25770, loss_mask_ce_5: 0.41259/0.79512, loss_mask_bce_5: 0.27234/0.30756, loss_mask_dice_5: 0.35089/1.05047, loss_spatial_bce_5: 0.11981/0.09182, loss_spatial_dice_5: 0.14154/0.19509, loss_spatial_ce_5: 0.05455/0.09327, loss_grounding_bce_5: 0.00840/0.08209, loss_grounding_dice_5: 0.01329/0.15417, loss_grounding_ce_5: 0.00008/0.27482, loss_mask_ce_6: 0.47238/0.82285, loss_mask_bce_6: 0.25768/0.30971, loss_mask_dice_6: 0.34185/1.05435, loss_spatial_bce_6: 0.12097/0.09734, loss_spatial_dice_6: 0.13397/0.19741, loss_spatial_ce_6: 0.11713/0.11761, loss_grounding_bce_6: 0.00866/0.08287, loss_grounding_dice_6: 0.01426/0.15462, loss_grounding_ce_6: 0.00022/0.28377, loss_mask_ce_7: 0.52636/0.87799, loss_mask_bce_7: 0.26464/0.31694, loss_mask_dice_7: 0.34920/1.10026, loss_spatial_bce_7: 0.08041/0.10639, loss_spatial_dice_7: 0.11189/0.22215, loss_spatial_ce_7: 0.17476/0.15175, loss_grounding_bce_7: 0.00965/0.08459, loss_grounding_dice_7: 0.01417/0.16017, loss_grounding_ce_7: 0.06198/0.31686, loss_mask_ce_8: 0.63629/1.01178, loss_mask_bce_8: 0.29229/0.33291, loss_mask_dice_8: 0.38447/1.17690, loss_spatial_bce_8: 0.10140/0.12262, loss_spatial_dice_8: 0.16114/0.25651, loss_spatial_ce_8: 0.13936/0.19676, loss_grounding_bce_8: 0.01109/0.08880, loss_grounding_dice_8: 0.01800/0.16997, loss_grounding_ce_8: 0.00112/0.41456, loss_mask_ce_9: 2.55796/3.47301, loss_mask_bce_9: 0.43391/0.35983, loss_mask_dice_9: 0.82443/1.75989, loss_spatial_bce_9: 0.34798/0.35425, loss_spatial_dice_9: 0.76849/0.79304, loss_spatial_ce_9: 1.53993/1.38622, loss_grounding_bce_9: 0.01424/0.10101, loss_grounding_dice_9: 0.03446/0.24219, loss_grounding_ce_9: 0.62098/0.66710] items per batch[64] items per second[0.37] total items[5363200] mini batches[ 83800] memory[4999] epoch remaining[0:07:03] INFO:trainer.default_trainer:epochs[ 45] optim steps[83900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.04469/0.75028, loss_mask_bce_0: 0.01236/0.30043, loss_mask_dice_0: 0.02453/1.01933, loss_spatial_bce_0: 0.00745/0.08413, loss_spatial_dice_0: 0.01664/0.17779, loss_spatial_ce_0: 0.00005/0.05467, loss_grounding_bce_0: 0.00477/0.08049, loss_grounding_dice_0: 0.00809/0.15030, loss_grounding_ce_0: 0.00641/0.24772, loss_mask_ce_1: 0.03312/0.75112, loss_mask_bce_1: 0.00911/0.30123, loss_mask_dice_1: 0.01953/1.02380, loss_spatial_bce_1: 0.00713/0.08461, loss_spatial_dice_1: 0.01578/0.18079, loss_spatial_ce_1: 0.00002/0.05838, loss_grounding_bce_1: 0.00394/0.08071, loss_grounding_dice_1: 0.00746/0.15105, loss_grounding_ce_1: 0.00379/0.24906, loss_mask_ce_2: 0.02694/0.75867, loss_mask_bce_2: 0.01048/0.30162, loss_mask_dice_2: 0.02343/1.02449, loss_spatial_bce_2: 0.00732/0.08470, loss_spatial_dice_2: 0.01539/0.18144, loss_spatial_ce_2: 0.00002/0.06061, loss_grounding_bce_2: 0.00418/0.08070, loss_grounding_dice_2: 0.00752/0.15098, loss_grounding_ce_2: 0.01122/0.25200, loss_mask_ce_3: 0.03020/0.76358, loss_mask_bce_3: 0.01003/0.30293, loss_mask_dice_3: 0.02193/1.02287, loss_spatial_bce_3: 0.00820/0.08684, loss_spatial_dice_3: 0.01770/0.18284, loss_spatial_ce_3: 0.00008/0.06557, loss_grounding_bce_3: 0.00546/0.08102, loss_grounding_dice_3: 0.00891/0.15064, loss_grounding_ce_3: 0.00326/0.25325, loss_mask_ce_4: 0.02492/0.76952, loss_mask_bce_4: 0.01029/0.30566, loss_mask_dice_4: 0.02257/1.04228, loss_spatial_bce_4: 0.00706/0.08941, loss_spatial_dice_4: 0.01700/0.19170, loss_spatial_ce_4: 0.00002/0.07938, loss_grounding_bce_4: 0.00482/0.08180, loss_grounding_dice_4: 0.00890/0.15326, loss_grounding_ce_4: 0.00175/0.25765, loss_mask_ce_5: 0.02817/0.79502, loss_mask_bce_5: 0.00922/0.30756, loss_mask_dice_5: 0.02154/1.05040, loss_spatial_bce_5: 0.00759/0.09183, loss_spatial_dice_5: 0.01536/0.19508, loss_spatial_ce_5: 0.00010/0.09324, loss_grounding_bce_5: 0.00385/0.08210, loss_grounding_dice_5: 0.00774/0.15416, loss_grounding_ce_5: 0.00581/0.27484, loss_mask_ce_6: 0.03745/0.82272, loss_mask_bce_6: 0.00936/0.30970, loss_mask_dice_6: 0.02172/1.05429, loss_spatial_bce_6: 0.00698/0.09733, loss_spatial_dice_6: 0.01438/0.19740, loss_spatial_ce_6: 0.00032/0.11758, loss_grounding_bce_6: 0.00481/0.08288, loss_grounding_dice_6: 0.00908/0.15461, loss_grounding_ce_6: 0.01059/0.28377, loss_mask_ce_7: 0.04288/0.87787, loss_mask_bce_7: 0.00948/0.31692, loss_mask_dice_7: 0.02329/1.10014, loss_spatial_bce_7: 0.00888/0.10639, loss_spatial_dice_7: 0.01983/0.22213, loss_spatial_ce_7: 0.00105/0.15173, loss_grounding_bce_7: 0.00421/0.08460, loss_grounding_dice_7: 0.00730/0.16015, loss_grounding_ce_7: 0.00379/0.31683, loss_mask_ce_8: 0.03115/1.01164, loss_mask_bce_8: 0.00932/0.33289, loss_mask_dice_8: 0.02427/1.17680, loss_spatial_bce_8: 0.00802/0.12262, loss_spatial_dice_8: 0.01530/0.25649, loss_spatial_ce_8: 0.06780/0.19672, loss_grounding_bce_8: 0.00428/0.08880, loss_grounding_dice_8: 0.00770/0.16996, loss_grounding_ce_8: 0.00636/0.41444, loss_mask_ce_9: 1.64530/3.47267, loss_mask_bce_9: 0.01116/0.35982, loss_mask_dice_9: 0.02885/1.75969, loss_spatial_bce_9: 0.29755/0.35422, loss_spatial_dice_9: 0.56900/0.79303, loss_spatial_ce_9: 0.58630/1.38611, loss_grounding_bce_9: 0.00556/0.10102, loss_grounding_dice_9: 0.01334/0.24217, loss_grounding_ce_9: 0.01235/0.66697] items per batch[64] items per second[0.38] total items[5369600] mini batches[ 83900] memory[4999] epoch remaining[0:04:08] INFO:trainer.default_trainer:epochs[ 45] optim steps[84000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.30179/0.75011, loss_mask_bce_0: 0.45498/0.30041, loss_mask_dice_0: 0.71514/1.01905, loss_spatial_bce_0: 0.12385/0.08415, loss_spatial_dice_0: 0.18891/0.17779, loss_spatial_ce_0: 0.03824/0.05466, loss_grounding_bce_0: 0.00000/0.08049, loss_grounding_dice_0: 0.00013/0.15031, loss_grounding_ce_0: 0.11301/0.24772, loss_mask_ce_1: 0.94276/0.75097, loss_mask_bce_1: 0.46618/0.30121, loss_mask_dice_1: 0.67884/1.02350, loss_spatial_bce_1: 0.12864/0.08463, loss_spatial_dice_1: 0.16745/0.18079, loss_spatial_ce_1: 0.03941/0.05835, loss_grounding_bce_1: 0.00000/0.08071, loss_grounding_dice_1: 0.00008/0.15105, loss_grounding_ce_1: 0.11320/0.24907, loss_mask_ce_2: 0.96055/0.75850, loss_mask_bce_2: 0.45244/0.30160, loss_mask_dice_2: 0.68537/1.02419, loss_spatial_bce_2: 0.14888/0.08472, loss_spatial_dice_2: 0.17868/0.18144, loss_spatial_ce_2: 0.03942/0.06058, loss_grounding_bce_2: 0.00000/0.08070, loss_grounding_dice_2: 0.00004/0.15099, loss_grounding_ce_2: 0.16200/0.25201, loss_mask_ce_3: 0.92810/0.76343, loss_mask_bce_3: 0.46519/0.30290, loss_mask_dice_3: 0.74247/1.02258, loss_spatial_bce_3: 0.21442/0.08687, loss_spatial_dice_3: 0.19668/0.18284, loss_spatial_ce_3: 0.05146/0.06554, loss_grounding_bce_3: 0.00000/0.08102, loss_grounding_dice_3: 0.00006/0.15065, loss_grounding_ce_3: 0.18282/0.25325, loss_mask_ce_4: 0.93326/0.76937, loss_mask_bce_4: 0.44708/0.30563, loss_mask_dice_4: 0.71886/1.04199, loss_spatial_bce_4: 0.34235/0.08943, loss_spatial_dice_4: 0.25598/0.19170, loss_spatial_ce_4: 0.08303/0.07937, loss_grounding_bce_4: 0.00000/0.08180, loss_grounding_dice_4: 0.00007/0.15326, loss_grounding_ce_4: 0.12507/0.25762, loss_mask_ce_5: 1.05870/0.79484, loss_mask_bce_5: 0.45808/0.30753, loss_mask_dice_5: 0.72501/1.05010, loss_spatial_bce_5: 0.25420/0.09185, loss_spatial_dice_5: 0.25542/0.19508, loss_spatial_ce_5: 0.06842/0.09322, loss_grounding_bce_5: 0.00000/0.08209, loss_grounding_dice_5: 0.00015/0.15417, loss_grounding_ce_5: 0.13347/0.27482, loss_mask_ce_6: 1.06531/0.82254, loss_mask_bce_6: 0.45132/0.30967, loss_mask_dice_6: 0.75341/1.05399, loss_spatial_bce_6: 0.20766/0.09736, loss_spatial_dice_6: 0.25931/0.19741, loss_spatial_ce_6: 0.05659/0.11757, loss_grounding_bce_6: 0.00000/0.08287, loss_grounding_dice_6: 0.00008/0.15462, loss_grounding_ce_6: 0.14198/0.28377, loss_mask_ce_7: 1.09212/0.87768, loss_mask_bce_7: 0.45827/0.31690, loss_mask_dice_7: 0.68493/1.09982, loss_spatial_bce_7: 0.19294/0.10643, loss_spatial_dice_7: 0.29105/0.22213, loss_spatial_ce_7: 0.07284/0.15170, loss_grounding_bce_7: 0.00000/0.08460, loss_grounding_dice_7: 0.00008/0.16015, loss_grounding_ce_7: 0.24857/0.31679, loss_mask_ce_8: 1.30681/1.01145, loss_mask_bce_8: 0.47447/0.33286, loss_mask_dice_8: 0.73683/1.17647, loss_spatial_bce_8: 0.20499/0.12263, loss_spatial_dice_8: 0.33742/0.25648, loss_spatial_ce_8: 0.15951/0.19670, loss_grounding_bce_8: 0.00000/0.08880, loss_grounding_dice_8: 0.00034/0.16996, loss_grounding_ce_8: 0.20719/0.41440, loss_mask_ce_9: 2.98955/3.47244, loss_mask_bce_9: 0.38841/0.35978, loss_mask_dice_9: 0.96011/1.75912, loss_spatial_bce_9: 0.33635/0.35422, loss_spatial_dice_9: 0.76463/0.79298, loss_spatial_ce_9: 1.18114/1.38596, loss_grounding_bce_9: 0.00000/0.10102, loss_grounding_dice_9: 0.00281/0.24216, loss_grounding_ce_9: 0.53763/0.66699] items per batch[64] items per second[0.37] total items[5376000] mini batches[ 84000] memory[4999] epoch remaining[0:01:13] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00084042. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0030 s/iter. Inference: 0.3834 s/iter. Eval: 0.0797 s/iter. Total: 0.4661 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0026 s/iter. Inference: 0.3743 s/iter. Eval: 0.0773 s/iter. Total: 0.4543 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3756 s/iter. Eval: 0.0765 s/iter. Total: 0.4551 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3784 s/iter. Eval: 0.0740 s/iter. Total: 0.4553 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 56/79. Dataloading: 0.0028 s/iter. Inference: 0.3796 s/iter. Eval: 0.0730 s/iter. Total: 0.4556 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 68/79. Dataloading: 0.0029 s/iter. Inference: 0.3790 s/iter. Eval: 0.0715 s/iter. Total: 0.4536 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evaln1kzzdrq ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 56.076 | 82.985 | 66.737 | 133 | | Things | 62.233 | 83.964 | 73.619 | 80 | | Stuff | 46.783 | 81.507 | 56.349 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.90 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.39 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.77s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 19.97 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.50 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.177 | 70.068 | 49.825 | 26.287 | 50.139 | 68.133 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.175 | bicycle | 23.466 | car | 44.168 | | motorcycle | 42.484 | airplane | 62.040 | bus | 71.839 | | train | 75.664 | truck | 43.511 | boat | 31.992 | | traffic light | 29.563 | fire hydrant | 72.192 | stop sign | 68.417 | | parking meter | 52.393 | bench | 27.381 | bird | 35.627 | | cat | 77.551 | dog | 71.308 | horse | 51.329 | | sheep | 54.508 | cow | 57.585 | elephant | 66.299 | | bear | 80.431 | zebra | 66.480 | giraffe | 62.514 | | backpack | 25.428 | umbrella | 56.732 | handbag | 24.961 | | tie | 41.632 | suitcase | 51.629 | frisbee | 69.669 | | skis | 8.746 | snowboard | 34.334 | sports ball | 50.481 | | kite | 37.905 | baseball bat | 39.229 | baseball glove | 50.632 | | skateboard | 43.758 | surfboard | 45.395 | tennis racket | 63.374 | | bottle | 42.944 | wine glass | 38.799 | cup | 51.398 | | fork | 27.052 | knife | 24.747 | spoon | 22.951 | | bowl | 40.370 | banana | 22.135 | apple | 27.722 | | sandwich | 48.634 | orange | 32.749 | broccoli | 24.369 | | carrot | 23.064 | hot dog | 32.021 | pizza | 54.045 | | donut | 56.714 | cake | 48.690 | chair | 29.090 | | couch | 45.977 | potted plant | 23.013 | bed | 43.882 | | dining table | 15.541 | toilet | 70.549 | tv | 67.902 | | laptop | 70.980 | mouse | 64.692 | remote | 44.415 | | keyboard | 58.438 | cell phone | 46.390 | microwave | 67.743 | | oven | 33.598 | toaster | 51.208 | sink | 45.317 | | refrigerator | 69.729 | book | 15.808 | clock | 54.078 | | vase | 41.482 | scissors | 36.654 | teddy bear | 58.188 | | hair drier | 30.426 | toothbrush | 28.866 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.701 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.498 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.681 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.555 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.575 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.385 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.771 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.84129003189891, 'fwIoU': 71.7836135572823, 'IoU-person': 88.7367941698624, 'IoU-bicycle': 71.43499504443986, 'IoU-car': 73.6959443875532, 'IoU-motorcycle': 87.16897000746452, 'IoU-airplane': 86.90817872190586, 'IoU-bus': 87.50635746663639, 'IoU-train': 88.75857970117688, 'IoU-truck': 70.67661791982638, 'IoU-boat': 74.90524999844712, 'IoU-traffic light': 79.12662352800992, 'IoU-fire hydrant': 93.20703761647387, 'IoU-stop sign': 85.49089533280403, 'IoU-parking meter': 85.22059457507413, 'IoU-bench': 60.04823495856871, 'IoU-bird': 76.17294080826886, 'IoU-cat': 87.61789630432398, 'IoU-dog': 81.74935612563065, 'IoU-horse': 88.94533632358566, 'IoU-sheep': 85.75695416203159, 'IoU-cow': 90.26042526093093, 'IoU-elephant': 90.80009148828006, 'IoU-bear': 88.63869940631776, 'IoU-zebra': 83.67836974655134, 'IoU-giraffe': 89.50719129981448, 'IoU-backpack': 51.8556467305577, 'IoU-umbrella': 88.58032780185836, 'IoU-handbag': 48.85713964307409, 'IoU-tie': 76.05554075652637, 'IoU-suitcase': 77.27372589499086, 'IoU-frisbee': 84.70800938973277, 'IoU-skis': 59.2598837418217, 'IoU-snowboard': 71.23688975436438, 'IoU-sports ball': 78.78571263864228, 'IoU-kite': 79.47837197677188, 'IoU-baseball bat': 68.93338270560022, 'IoU-baseball glove': 82.03039217883176, 'IoU-skateboard': 86.2482255293571, 'IoU-surfboard': 86.29640637512045, 'IoU-tennis racket': 91.02230212846727, 'IoU-bottle': 70.00276507696131, 'IoU-wine glass': 82.62303639487905, 'IoU-cup': 71.42325282619414, 'IoU-fork': 71.69026755072801, 'IoU-knife': 65.46607294840325, 'IoU-spoon': 61.10947201088578, 'IoU-bowl': 59.08932368849927, 'IoU-banana': 83.40209392681795, 'IoU-apple': 61.54940076388732, 'IoU-sandwich': 69.92452277405613, 'IoU-orange': 79.08064620309035, 'IoU-broccoli': 68.2447692835893, 'IoU-carrot': 65.58253273393747, 'IoU-hot dog': 61.28359398808939, 'IoU-pizza': 82.66182717562135, 'IoU-donut': 59.998019987352166, 'IoU-cake': 79.84147943227032, 'IoU-chair': 62.53401847803788, 'IoU-couch': 68.60800612943731, 'IoU-potted plant': 43.41654848314213, 'IoU-bed': 74.50838285037733, 'IoU-dining table': 54.422742018348224, 'IoU-toilet': 85.57667835472361, 'IoU-tv': 79.0075026633564, 'IoU-laptop': 81.28331584436515, 'IoU-mouse': 76.25616200529711, 'IoU-remote': 67.48918407722243, 'IoU-keyboard': 63.5440056916731, 'IoU-cell phone': 79.54200788595693, 'IoU-microwave': 79.4548737505984, 'IoU-oven': 72.910551333429, 'IoU-toaster': 86.14392155597845, 'IoU-sink': 70.54324232324505, 'IoU-refrigerator': 84.01882640354378, 'IoU-book': 55.7022488453995, 'IoU-clock': 72.27927072413343, 'IoU-vase': 62.778592627622686, 'IoU-scissors': 86.82061628008536, 'IoU-teddy bear': 83.41029625966887, 'IoU-hair drier': 48.40074075767621, 'IoU-toothbrush': 76.0770302838971, 'IoU-banner': 32.483747760179405, 'IoU-blanket': 17.635816825286863, 'IoU-bridge': 36.458239787872515, 'IoU-cardboard': 49.95478970811472, 'IoU-counter': 30.759215266653804, 'IoU-curtain': 73.08817026495132, 'IoU-door-stuff': 48.686632340295624, 'IoU-floor-wood': 64.74890563691511, 'IoU-flower': 44.077942496978665, 'IoU-fruit': 49.57417874063939, 'IoU-gravel': 29.404005744664968, 'IoU-house': 26.931992687149524, 'IoU-light': 44.33862345087364, 'IoU-mirror-stuff': 62.41689837507993, 'IoU-net': 41.04844388281289, 'IoU-pillow': 17.051114486559044, 'IoU-platform': 28.72805480425855, 'IoU-playingfield': 71.55813084706425, 'IoU-railroad': 65.09535095594265, 'IoU-river': 53.33399607753054, 'IoU-road': 67.31937968869364, 'IoU-roof': 18.51264903819395, 'IoU-sand': 65.33366294567128, 'IoU-sea': 85.1106914682084, 'IoU-shelf': 37.63000818859909, 'IoU-snow': 92.1640691910602, 'IoU-stairs': 33.56967246893651, 'IoU-tent': 11.100580569621693, 'IoU-towel': 64.90184316212519, 'IoU-wall-brick': 52.04730922603983, 'IoU-wall-stone': 27.602618807846575, 'IoU-wall-tile': 70.14980883056259, 'IoU-wall-wood': 45.61468762202379, 'IoU-water-other': 27.21000925203077, 'IoU-window-blind': 49.97910946965737, 'IoU-window-other': 51.24681068906486, 'IoU-tree-merged': 82.02929801826603, 'IoU-fence-merged': 53.313960425667126, 'IoU-ceiling-merged': 68.15339952696809, 'IoU-sky-other-merged': 94.17783363072274, 'IoU-cabinet-merged': 63.090996038558345, 'IoU-table-merged': 40.78558918671346, 'IoU-floor-other-merged': 55.55626149078405, 'IoU-pavement-merged': 57.064898579437305, 'IoU-mountain-merged': 59.200405135364484, 'IoU-grass-merged': 73.03137034432544, 'IoU-dirt-merged': 47.79796452360647, 'IoU-paper-merged': 36.898956392463056, 'IoU-food-other-merged': 43.610069617927074, 'IoU-building-other-merged': 60.05113243230201, 'IoU-rock-merged': 64.41145903026319, 'IoU-wall-other-merged': 68.50061569179127, 'IoU-rug-merged': 68.01406942706049, 'mACC': 77.29214182463198, 'pACC': 82.39099124620708, 'ACC-person': 92.8220185436001, 'ACC-bicycle': 79.109724757941, 'ACC-car': 86.90016038087937, 'ACC-motorcycle': 91.61201855614114, 'ACC-airplane': 91.06005656667179, 'ACC-bus': 93.91851577713162, 'ACC-train': 95.42210526535338, 'ACC-truck': 81.2737732650622, 'ACC-boat': 84.27500822986993, 'ACC-traffic light': 91.20784359626357, 'ACC-fire hydrant': 95.94037030236521, 'ACC-stop sign': 88.60923493092886, 'ACC-parking meter': 88.38504076504276, 'ACC-bench': 72.58810465298552, 'ACC-bird': 81.24957201307929, 'ACC-cat': 90.88766781194843, 'ACC-dog': 84.71316615802266, 'ACC-horse': 93.62017101727125, 'ACC-sheep': 90.00872331498829, 'ACC-cow': 93.67962648092163, 'ACC-elephant': 92.910562942301, 'ACC-bear': 90.43908646409413, 'ACC-zebra': 85.65704265417847, 'ACC-giraffe': 93.32726907526053, 'ACC-backpack': 70.91294116615981, 'ACC-umbrella': 93.38727272854992, 'ACC-handbag': 70.12382068067289, 'ACC-tie': 84.33796067737643, 'ACC-suitcase': 83.84639875802652, 'ACC-frisbee': 94.21563636363636, 'ACC-skis': 73.04708395656515, 'ACC-snowboard': 81.70948543998433, 'ACC-sports ball': 88.2950351970726, 'ACC-kite': 85.64041188122799, 'ACC-baseball bat': 87.8709662917981, 'ACC-baseball glove': 92.32002478689752, 'ACC-skateboard': 90.8108945154876, 'ACC-surfboard': 92.3935206391802, 'ACC-tennis racket': 94.83609313905103, 'ACC-bottle': 84.27742258770104, 'ACC-wine glass': 90.97650087940382, 'ACC-cup': 88.94202345425688, 'ACC-fork': 83.15696880782734, 'ACC-knife': 77.78119733274713, 'ACC-spoon': 77.89594610702146, 'ACC-bowl': 71.36024489688126, 'ACC-banana': 90.25419164555267, 'ACC-apple': 75.643804085367, 'ACC-sandwich': 80.80732615740857, 'ACC-orange': 88.91429816403642, 'ACC-broccoli': 79.6160204065123, 'ACC-carrot': 77.75505659515073, 'ACC-hot dog': 67.58439770087895, 'ACC-pizza': 89.13010485637635, 'ACC-donut': 67.4124960793446, 'ACC-cake': 88.27888460045845, 'ACC-chair': 79.55125634387143, 'ACC-couch': 79.7092116972264, 'ACC-potted plant': 59.884213823825505, 'ACC-bed': 87.56793601826521, 'ACC-dining table': 76.51216006042533, 'ACC-toilet': 90.64335116794989, 'ACC-tv': 89.33741980086639, 'ACC-laptop': 94.59752728144906, 'ACC-mouse': 86.82907120620182, 'ACC-remote': 71.9703127965931, 'ACC-keyboard': 68.6503241179426, 'ACC-cell phone': 88.94571131438238, 'ACC-microwave': 84.51218032728062, 'ACC-oven': 90.88250062021163, 'ACC-toaster': 91.37664399403303, 'ACC-sink': 80.59924427215181, 'ACC-refrigerator': 94.78118050047509, 'ACC-book': 74.85917318091396, 'ACC-clock': 76.7477917385532, 'ACC-vase': 71.03278094085235, 'ACC-scissors': 92.41396486975366, 'ACC-teddy bear': 88.83718388217439, 'ACC-hair drier': 60.25673867873509, 'ACC-toothbrush': 85.82522585128561, 'ACC-banner': 70.58067282838921, 'ACC-blanket': 24.450907412978605, 'ACC-bridge': 53.35403482218136, 'ACC-cardboard': 66.93604769521593, 'ACC-counter': 54.1500750181213, 'ACC-curtain': 83.68322710838657, 'ACC-door-stuff': 69.61041728399012, 'ACC-floor-wood': 83.58749249351148, 'ACC-flower': 64.13112844472005, 'ACC-fruit': 68.9806048923981, 'ACC-gravel': 38.508522699861636, 'ACC-house': 34.41642018176215, 'ACC-light': 63.22463681430157, 'ACC-mirror-stuff': 76.72383975394243, 'ACC-net': 67.17386870279103, 'ACC-pillow': 36.95501236823278, 'ACC-platform': 45.9516260222879, 'ACC-playingfield': 90.3016713987327, 'ACC-railroad': 81.82680587109553, 'ACC-river': 70.4327177649973, 'ACC-road': 87.41252834971898, 'ACC-roof': 24.93625395738855, 'ACC-sand': 70.20126373307023, 'ACC-sea': 91.7940476705232, 'ACC-shelf': 57.36340719988971, 'ACC-snow': 95.67649215070634, 'ACC-stairs': 56.57375309455468, 'ACC-tent': 14.79924595689868, 'ACC-towel': 81.93613260905278, 'ACC-wall-brick': 68.53184527549094, 'ACC-wall-stone': 36.87858571503763, 'ACC-wall-tile': 85.70693541183209, 'ACC-wall-wood': 61.7038846042437, 'ACC-water-other': 45.16731588822023, 'ACC-window-blind': 62.9524747260169, 'ACC-window-other': 71.34685983880341, 'ACC-tree-merged': 89.67334597441949, 'ACC-fence-merged': 69.99998481236376, 'ACC-ceiling-merged': 82.80168627365147, 'ACC-sky-other-merged': 97.2096234836625, 'ACC-cabinet-merged': 77.89779602473219, 'ACC-table-merged': 54.51033998845273, 'ACC-floor-other-merged': 66.46745003937525, 'ACC-pavement-merged': 68.8058583644151, 'ACC-mountain-merged': 70.33649461091132, 'ACC-grass-merged': 84.71494904309573, 'ACC-dirt-merged': 69.21438886602606, 'ACC-paper-merged': 49.32666955518931, 'ACC-food-other-merged': 58.764405898817074, 'ACC-building-other-merged': 75.34762608150632, 'ACC-rock-merged': 82.78210293291095, 'ACC-wall-other-merged': 82.36834119978789, 'ACC-rug-merged': 82.19864518105967})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3183 s/iter. Inference: 0.1740 s/iter. Eval: 0.0000 s/iter. Total: 0.4924 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3100 s/iter. Inference: 0.4574 s/iter. Eval: 0.0000 s/iter. Total: 0.7675 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3383 s/iter. Inference: 0.4993 s/iter. Eval: 0.0000 s/iter. Total: 0.8377 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3687445127304654, 'noc@0.8': 2.3204565408252855, 'noc@0.85': 2.7281240854550775, 'noc@0.9': 3.4779045946736904, 'miou@iter1': 0.8707848242719793} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1515 s/iter. Eval: 0.0011 s/iter. Total: 0.1541 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.9424819946289, 'precision@0.6': 73.22191619873047, 'precision@0.7': 69.14108276367188, 'precision@0.8': 60.27982711791992, 'precision@0.9': 33.696075439453125, 'cIoU': 61.976219177246094, 'mIoU': 67.45757293701172} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 56.076078958332246, 'SQ': 82.98492834439841, 'RQ': 66.73681775859532, 'PQ_th': 62.23261345244319, 'SQ_th': 83.96373013656145, 'RQ_th': 73.6190502365523, 'PQ_st': 46.7831967030704, 'SQ_st': 81.50749167698253, 'RQ_st': 56.348542320169685}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-mouse': 86.82907120620182, 'ACC-remote': 71.9703127965931, 'ACC-keyboard': 68.6503241179426, 'ACC-cell phone': 88.94571131438238, 'ACC-microwave': 84.51218032728062, 'ACC-oven': 90.88250062021163, 'ACC-toaster': 91.37664399403303, 'ACC-sink': 80.59924427215181, 'ACC-refrigerator': 94.78118050047509, 'ACC-book': 74.85917318091396, 'ACC-clock': 76.7477917385532, 'ACC-vase': 71.03278094085235, 'ACC-scissors': 92.41396486975366, 'ACC-teddy bear': 88.83718388217439, 'ACC-hair drier': 60.25673867873509, 'ACC-toothbrush': 85.82522585128561, 'ACC-banner': 70.58067282838921, 'ACC-blanket': 24.450907412978605, 'ACC-bridge': 53.35403482218136, 'ACC-cardboard': 66.93604769521593, 'ACC-counter': 54.1500750181213, 'ACC-curtain': 83.68322710838657, 'ACC-door-stuff': 69.61041728399012, 'ACC-floor-wood': 83.58749249351148, 'ACC-flower': 64.13112844472005, 'ACC-fruit': 68.9806048923981, 'ACC-gravel': 38.508522699861636, 'ACC-house': 34.41642018176215, 'ACC-light': 63.22463681430157, 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'ACC-table-merged': 54.51033998845273, 'ACC-floor-other-merged': 66.46745003937525, 'ACC-pavement-merged': 68.8058583644151, 'ACC-mountain-merged': 70.33649461091132, 'ACC-grass-merged': 84.71494904309573, 'ACC-dirt-merged': 69.21438886602606, 'ACC-paper-merged': 49.32666955518931, 'ACC-food-other-merged': 58.764405898817074, 'ACC-building-other-merged': 75.34762608150632, 'ACC-rock-merged': 82.78210293291095, 'ACC-wall-other-merged': 82.36834119978789, 'ACC-rug-merged': 82.19864518105967})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3687445127304654, 'noc@0.8': 2.3204565408252855, 'noc@0.85': 2.7281240854550775, 'noc@0.9': 3.4779045946736904, 'miou@iter1': 0.8707848242719793}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.9424819946289, 'precision@0.6': 73.22191619873047, 'precision@0.7': 69.14108276367188, 'precision@0.8': 60.27982711791992, 'precision@0.9': 33.696075439453125, 'cIoU': 61.976219177246094, 'mIoU': 67.45757293701172}}} INFO:trainer.default_trainer:This epoch takes 0:56:35.257157 INFO:trainer.default_trainer:PROGRESS: 92.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 46 training. INFO:trainer.default_trainer:epochs[ 46] optim steps[84100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.21926/0.75010, loss_mask_bce_0: 0.14400/0.30040, loss_mask_dice_0: 0.23054/1.01914, loss_spatial_bce_0: 0.04967/0.08415, loss_spatial_dice_0: 0.07023/0.17778, loss_spatial_ce_0: 0.00128/0.05464, loss_grounding_bce_0: 0.05481/0.08049, loss_grounding_dice_0: 0.07016/0.15031, loss_grounding_ce_0: 0.00925/0.24771, loss_mask_ce_1: 0.25229/0.75094, loss_mask_bce_1: 0.14529/0.30120, loss_mask_dice_1: 0.23266/1.02361, loss_spatial_bce_1: 0.05583/0.08463, loss_spatial_dice_1: 0.07264/0.18078, loss_spatial_ce_1: 0.00217/0.05833, loss_grounding_bce_1: 0.05606/0.08071, loss_grounding_dice_1: 0.07257/0.15106, loss_grounding_ce_1: 0.01096/0.24908, loss_mask_ce_2: 0.27285/0.75848, loss_mask_bce_2: 0.14456/0.30159, loss_mask_dice_2: 0.23620/1.02430, loss_spatial_bce_2: 0.05015/0.08472, loss_spatial_dice_2: 0.07097/0.18144, loss_spatial_ce_2: 0.00124/0.06057, loss_grounding_bce_2: 0.05262/0.08069, loss_grounding_dice_2: 0.06625/0.15100, loss_grounding_ce_2: 0.01094/0.25200, loss_mask_ce_3: 0.25126/0.76341, loss_mask_bce_3: 0.13899/0.30289, loss_mask_dice_3: 0.23020/1.02268, loss_spatial_bce_3: 0.05723/0.08687, loss_spatial_dice_3: 0.07933/0.18284, loss_spatial_ce_3: 0.00120/0.06553, loss_grounding_bce_3: 0.05662/0.08102, loss_grounding_dice_3: 0.06766/0.15065, loss_grounding_ce_3: 0.00719/0.25324, loss_mask_ce_4: 0.21718/0.76938, loss_mask_bce_4: 0.12661/0.30563, loss_mask_dice_4: 0.26071/1.04210, loss_spatial_bce_4: 0.07973/0.08943, loss_spatial_dice_4: 0.11852/0.19170, loss_spatial_ce_4: 0.00458/0.07934, loss_grounding_bce_4: 0.05509/0.08179, loss_grounding_dice_4: 0.07433/0.15327, loss_grounding_ce_4: 0.01151/0.25764, loss_mask_ce_5: 0.25721/0.79483, loss_mask_bce_5: 0.11437/0.30752, loss_mask_dice_5: 0.21567/1.05020, loss_spatial_bce_5: 0.05636/0.09185, loss_spatial_dice_5: 0.08421/0.19508, loss_spatial_ce_5: 0.00230/0.09319, loss_grounding_bce_5: 0.05413/0.08209, loss_grounding_dice_5: 0.07773/0.15418, loss_grounding_ce_5: 0.01137/0.27481, loss_mask_ce_6: 0.28230/0.82255, loss_mask_bce_6: 0.12178/0.30966, loss_mask_dice_6: 0.24568/1.05410, loss_spatial_bce_6: 0.05219/0.09736, loss_spatial_dice_6: 0.07665/0.19741, loss_spatial_ce_6: 0.06088/0.11754, loss_grounding_bce_6: 0.06213/0.08287, loss_grounding_dice_6: 0.09785/0.15463, loss_grounding_ce_6: 0.02190/0.28376, loss_mask_ce_7: 0.27580/0.87765, loss_mask_bce_7: 0.13775/0.31690, loss_mask_dice_7: 0.22784/1.09993, loss_spatial_bce_7: 0.07636/0.10643, loss_spatial_dice_7: 0.15670/0.22213, loss_spatial_ce_7: 0.36478/0.15166, loss_grounding_bce_7: 0.06281/0.08459, loss_grounding_dice_7: 0.07875/0.16016, loss_grounding_ce_7: 0.02288/0.31678, loss_mask_ce_8: 0.45648/1.01148, loss_mask_bce_8: 0.12198/0.33285, loss_mask_dice_8: 0.25269/1.17658, loss_spatial_bce_8: 0.06870/0.12263, loss_spatial_dice_8: 0.14630/0.25647, loss_spatial_ce_8: 0.17208/0.19667, loss_grounding_bce_8: 0.05185/0.08879, loss_grounding_dice_8: 0.07785/0.16996, loss_grounding_ce_8: 0.03129/0.41438, loss_mask_ce_9: 3.18866/3.47249, loss_mask_bce_9: 0.16025/0.35977, loss_mask_dice_9: 0.41467/1.75914, loss_spatial_bce_9: 0.47866/0.35420, loss_spatial_dice_9: 0.74792/0.79299, loss_spatial_ce_9: 0.73354/1.38596, loss_grounding_bce_9: 0.07060/0.10103, loss_grounding_dice_9: 0.25727/0.24219, loss_grounding_ce_9: 0.06292/0.66691] items per batch[64] items per second[0.17] total items[5382400] mini batches[ 84100] memory[4999] epoch remaining[0:55:14] INFO:trainer.default_trainer:epochs[ 46] optim steps[84200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.77293/0.74993, loss_mask_bce_0: 0.02457/0.30041, loss_mask_dice_0: 1.30151/1.01897, loss_spatial_bce_0: 0.00819/0.08414, loss_spatial_dice_0: 0.26330/0.17777, loss_spatial_ce_0: 0.01255/0.05461, loss_grounding_bce_0: 0.01282/0.08049, loss_grounding_dice_0: 0.11946/0.15031, loss_grounding_ce_0: 0.00398/0.24765, loss_mask_ce_1: 0.56583/0.75076, loss_mask_bce_1: 0.03463/0.30122, loss_mask_dice_1: 1.45066/1.02344, loss_spatial_bce_1: 0.00769/0.08462, loss_spatial_dice_1: 0.25872/0.18076, loss_spatial_ce_1: 0.09649/0.05831, loss_grounding_bce_1: 0.01231/0.08070, loss_grounding_dice_1: 0.10795/0.15105, loss_grounding_ce_1: 0.00810/0.24903, loss_mask_ce_2: 0.63302/0.75831, loss_mask_bce_2: 0.03172/0.30161, loss_mask_dice_2: 1.16806/1.02415, loss_spatial_bce_2: 0.00708/0.08471, loss_spatial_dice_2: 0.30329/0.18142, loss_spatial_ce_2: 0.06005/0.06055, loss_grounding_bce_2: 0.00910/0.08069, loss_grounding_dice_2: 0.09518/0.15099, loss_grounding_ce_2: 0.00743/0.25195, loss_mask_ce_3: 0.60186/0.76324, loss_mask_bce_3: 0.03031/0.30291, loss_mask_dice_3: 1.52601/1.02251, loss_spatial_bce_3: 0.00674/0.08686, loss_spatial_dice_3: 0.31019/0.18283, loss_spatial_ce_3: 0.09090/0.06551, loss_grounding_bce_3: 0.00982/0.08102, loss_grounding_dice_3: 0.11208/0.15064, loss_grounding_ce_3: 0.00492/0.25319, loss_mask_ce_4: 0.60024/0.76920, loss_mask_bce_4: 0.02692/0.30564, loss_mask_dice_4: 1.39097/1.04192, loss_spatial_bce_4: 0.01035/0.08942, loss_spatial_dice_4: 0.29256/0.19169, loss_spatial_ce_4: 0.07402/0.07931, loss_grounding_bce_4: 0.00835/0.08179, loss_grounding_dice_4: 0.09010/0.15325, loss_grounding_ce_4: 0.00339/0.25761, loss_mask_ce_5: 0.67037/0.79465, loss_mask_bce_5: 0.03523/0.30753, loss_mask_dice_5: 1.28806/1.05001, loss_spatial_bce_5: 0.01105/0.09184, loss_spatial_dice_5: 0.36075/0.19507, loss_spatial_ce_5: 0.35957/0.09316, loss_grounding_bce_5: 0.01069/0.08208, loss_grounding_dice_5: 0.10459/0.15416, loss_grounding_ce_5: 0.00538/0.27481, loss_mask_ce_6: 0.64664/0.82238, loss_mask_bce_6: 0.02773/0.30967, loss_mask_dice_6: 1.38528/1.05391, loss_spatial_bce_6: 0.01215/0.09735, loss_spatial_dice_6: 0.31714/0.19739, loss_spatial_ce_6: 0.03006/0.11752, loss_grounding_bce_6: 0.00901/0.08286, loss_grounding_dice_6: 0.11866/0.15462, loss_grounding_ce_6: 0.00390/0.28370, loss_mask_ce_7: 0.72385/0.87745, loss_mask_bce_7: 0.02789/0.31691, loss_mask_dice_7: 1.18899/1.09974, loss_spatial_bce_7: 0.01277/0.10642, loss_spatial_dice_7: 0.34646/0.22211, loss_spatial_ce_7: 0.08991/0.15162, loss_grounding_bce_7: 0.01274/0.08459, loss_grounding_dice_7: 0.10810/0.16015, loss_grounding_ce_7: 0.01632/0.31676, loss_mask_ce_8: 0.76861/1.01128, loss_mask_bce_8: 0.03862/0.33287, loss_mask_dice_8: 1.46219/1.17638, loss_spatial_bce_8: 0.01186/0.12261, loss_spatial_dice_8: 0.37356/0.25644, loss_spatial_ce_8: 0.08611/0.19662, loss_grounding_bce_8: 0.00997/0.08879, loss_grounding_dice_8: 0.10771/0.16995, loss_grounding_ce_8: 0.46852/0.41433, loss_mask_ce_9: 3.33909/3.47225, loss_mask_bce_9: 0.03195/0.35978, loss_mask_dice_9: 1.43900/1.75884, loss_spatial_bce_9: 0.05448/0.35424, loss_spatial_dice_9: 0.87587/0.79297, loss_spatial_ce_9: 1.84009/1.38594, loss_grounding_bce_9: 0.00882/0.10102, loss_grounding_dice_9: 0.14657/0.24216, loss_grounding_ce_9: 0.65352/0.66681] items per batch[64] items per second[0.37] total items[5388800] mini batches[ 84200] memory[4999] epoch remaining[0:49:32] INFO:trainer.default_trainer:epochs[ 46] optim steps[84300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.72251/0.74984, loss_mask_bce_0: 0.17779/0.30042, loss_mask_dice_0: 0.22350/1.01901, loss_spatial_bce_0: 0.16306/0.08413, loss_spatial_dice_0: 0.10982/0.17775, loss_spatial_ce_0: 0.00425/0.05461, loss_grounding_bce_0: 0.04049/0.08048, loss_grounding_dice_0: 0.05481/0.15030, loss_grounding_ce_0: 0.01305/0.24768, loss_mask_ce_1: 0.75133/0.75068, loss_mask_bce_1: 0.18808/0.30123, loss_mask_dice_1: 0.21644/1.02349, loss_spatial_bce_1: 0.16859/0.08461, loss_spatial_dice_1: 0.11708/0.18074, loss_spatial_ce_1: 0.00202/0.05830, loss_grounding_bce_1: 0.04502/0.08070, loss_grounding_dice_1: 0.06522/0.15105, loss_grounding_ce_1: 0.01369/0.24908, loss_mask_ce_2: 0.72688/0.75822, loss_mask_bce_2: 0.19735/0.30162, loss_mask_dice_2: 0.22406/1.02417, loss_spatial_bce_2: 0.13003/0.08470, loss_spatial_dice_2: 0.11213/0.18140, loss_spatial_ce_2: 0.00366/0.06053, loss_grounding_bce_2: 0.04130/0.08069, loss_grounding_dice_2: 0.06244/0.15098, loss_grounding_ce_2: 0.01610/0.25200, loss_mask_ce_3: 0.67962/0.76316, loss_mask_bce_3: 0.20783/0.30292, loss_mask_dice_3: 0.24804/1.02252, loss_spatial_bce_3: 0.11152/0.08685, loss_spatial_dice_3: 0.10765/0.18281, loss_spatial_ce_3: 0.00641/0.06549, loss_grounding_bce_3: 0.04314/0.08102, loss_grounding_dice_3: 0.06375/0.15063, loss_grounding_ce_3: 0.01806/0.25325, loss_mask_ce_4: 0.85124/0.76912, loss_mask_bce_4: 0.23486/0.30565, loss_mask_dice_4: 0.26772/1.04195, loss_spatial_bce_4: 0.13864/0.08942, loss_spatial_dice_4: 0.13344/0.19167, loss_spatial_ce_4: 0.00886/0.07929, loss_grounding_bce_4: 0.05418/0.08178, loss_grounding_dice_4: 0.08018/0.15323, loss_grounding_ce_4: 0.02304/0.25766, loss_mask_ce_5: 0.79661/0.79460, loss_mask_bce_5: 0.25285/0.30754, loss_mask_dice_5: 0.27149/1.05003, loss_spatial_bce_5: 0.16110/0.09183, loss_spatial_dice_5: 0.13919/0.19505, loss_spatial_ce_5: 0.01151/0.09316, loss_grounding_bce_5: 0.04486/0.08208, loss_grounding_dice_5: 0.06228/0.15415, loss_grounding_ce_5: 0.11642/0.27484, loss_mask_ce_6: 0.93960/0.82235, loss_mask_bce_6: 0.22946/0.30967, loss_mask_dice_6: 0.26153/1.05391, loss_spatial_bce_6: 0.12101/0.09734, loss_spatial_dice_6: 0.12611/0.19738, loss_spatial_ce_6: 0.03057/0.11753, loss_grounding_bce_6: 0.04721/0.08286, loss_grounding_dice_6: 0.06327/0.15460, loss_grounding_ce_6: 0.04459/0.28372, loss_mask_ce_7: 1.41538/0.87738, loss_mask_bce_7: 0.23920/0.31691, loss_mask_dice_7: 0.29878/1.09980, loss_spatial_bce_7: 0.17761/0.10642, loss_spatial_dice_7: 0.17898/0.22210, loss_spatial_ce_7: 0.04622/0.15159, loss_grounding_bce_7: 0.10885/0.08459, loss_grounding_dice_7: 0.14958/0.16015, loss_grounding_ce_7: 0.11105/0.31677, loss_mask_ce_8: 0.62439/1.01117, loss_mask_bce_8: 0.75557/0.33288, loss_mask_dice_8: 0.88206/1.17640, loss_spatial_bce_8: 0.27748/0.12262, loss_spatial_dice_8: 0.21143/0.25642, loss_spatial_ce_8: 0.02557/0.19660, loss_grounding_bce_8: 0.19121/0.08879, loss_grounding_dice_8: 0.20390/0.16994, loss_grounding_ce_8: 0.22760/0.41433, loss_mask_ce_9: 4.66552/3.47231, loss_mask_bce_9: 0.48250/0.35979, loss_mask_dice_9: 0.86914/1.75891, loss_spatial_bce_9: 0.41298/0.35424, loss_spatial_dice_9: 0.73503/0.79295, loss_spatial_ce_9: 1.27976/1.38595, loss_grounding_bce_9: 0.06670/0.10102, loss_grounding_dice_9: 0.08761/0.24215, loss_grounding_ce_9: 2.54689/0.66685] items per batch[64] items per second[0.37] total items[5395200] mini batches[ 84300] memory[4999] epoch remaining[0:46:13] INFO:trainer.default_trainer:epochs[ 46] optim steps[84400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.17052/0.74973, loss_mask_bce_0: 0.53219/0.30039, loss_mask_dice_0: 0.26245/1.01880, loss_spatial_bce_0: 0.25061/0.08413, loss_spatial_dice_0: 0.13348/0.17773, loss_spatial_ce_0: 0.39544/0.05459, loss_grounding_bce_0: 0.29715/0.08049, loss_grounding_dice_0: 0.16256/0.15028, loss_grounding_ce_0: 0.43177/0.24764, loss_mask_ce_1: 0.16897/0.75055, loss_mask_bce_1: 0.53443/0.30121, loss_mask_dice_1: 0.26690/1.02329, loss_spatial_bce_1: 0.27137/0.08461, loss_spatial_dice_1: 0.14246/0.18072, loss_spatial_ce_1: 0.42625/0.05827, loss_grounding_bce_1: 0.28188/0.08071, loss_grounding_dice_1: 0.15367/0.15103, loss_grounding_ce_1: 0.49996/0.24904, loss_mask_ce_2: 0.18019/0.75811, loss_mask_bce_2: 0.51975/0.30159, loss_mask_dice_2: 0.26184/1.02396, loss_spatial_bce_2: 0.26445/0.08470, loss_spatial_dice_2: 0.14441/0.18138, loss_spatial_ce_2: 0.38635/0.06051, loss_grounding_bce_2: 0.27817/0.08070, loss_grounding_dice_2: 0.15896/0.15096, loss_grounding_ce_2: 0.38052/0.25194, loss_mask_ce_3: 0.17312/0.76305, loss_mask_bce_3: 0.54051/0.30290, loss_mask_dice_3: 0.27202/1.02233, loss_spatial_bce_3: 0.24673/0.08684, loss_spatial_dice_3: 0.13217/0.18279, loss_spatial_ce_3: 0.32927/0.06547, loss_grounding_bce_3: 0.28642/0.08103, loss_grounding_dice_3: 0.15714/0.15061, loss_grounding_ce_3: 0.38957/0.25321, loss_mask_ce_4: 0.16911/0.76900, loss_mask_bce_4: 0.55615/0.30562, loss_mask_dice_4: 0.27325/1.04177, loss_spatial_bce_4: 0.24584/0.08941, loss_spatial_dice_4: 0.13683/0.19165, loss_spatial_ce_4: 0.32973/0.07928, loss_grounding_bce_4: 0.30400/0.08179, loss_grounding_dice_4: 0.14970/0.15321, loss_grounding_ce_4: 0.37741/0.25762, loss_mask_ce_5: 0.17384/0.79447, loss_mask_bce_5: 0.54944/0.30752, loss_mask_dice_5: 0.26491/1.04984, loss_spatial_bce_5: 0.25373/0.09183, loss_spatial_dice_5: 0.14885/0.19503, loss_spatial_ce_5: 0.31080/0.09313, loss_grounding_bce_5: 0.28990/0.08209, loss_grounding_dice_5: 0.16348/0.15413, loss_grounding_ce_5: 0.35735/0.27481, loss_mask_ce_6: 0.17453/0.82222, loss_mask_bce_6: 0.53823/0.30965, loss_mask_dice_6: 0.25766/1.05373, loss_spatial_bce_6: 0.24816/0.09734, loss_spatial_dice_6: 0.13672/0.19736, loss_spatial_ce_6: 0.36760/0.11750, loss_grounding_bce_6: 0.30718/0.08286, loss_grounding_dice_6: 0.17100/0.15458, loss_grounding_ce_6: 0.28026/0.28369, loss_mask_ce_7: 0.19077/0.87723, loss_mask_bce_7: 0.52978/0.31689, loss_mask_dice_7: 0.27205/1.09958, loss_spatial_bce_7: 0.24827/0.10641, loss_spatial_dice_7: 0.14990/0.22207, loss_spatial_ce_7: 0.25921/0.15156, loss_grounding_bce_7: 0.28662/0.08459, loss_grounding_dice_7: 0.15971/0.16013, loss_grounding_ce_7: 0.28737/0.31674, loss_mask_ce_8: 0.25429/1.01099, loss_mask_bce_8: 0.57277/0.33285, loss_mask_dice_8: 0.25677/1.17615, loss_spatial_bce_8: 0.26655/0.12261, loss_spatial_dice_8: 0.15137/0.25639, loss_spatial_ce_8: 0.26188/0.19657, loss_grounding_bce_8: 0.30151/0.08879, loss_grounding_dice_8: 0.15371/0.16991, loss_grounding_ce_8: 0.43532/0.41434, loss_mask_ce_9: 1.94120/3.47204, loss_mask_bce_9: 0.53379/0.35977, loss_mask_dice_9: 0.24993/1.75865, loss_spatial_bce_9: 0.57106/0.35422, loss_spatial_dice_9: 0.60383/0.79291, loss_spatial_ce_9: 0.65431/1.38592, loss_grounding_bce_9: 0.33311/0.10103, loss_grounding_dice_9: 0.19287/0.24212, loss_grounding_ce_9: 0.62724/0.66678] items per batch[64] items per second[0.37] total items[5401600] mini batches[ 84400] memory[4999] epoch remaining[0:43:01] INFO:trainer.default_trainer:epochs[ 46] optim steps[84500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.56741/0.74979, loss_mask_bce_0: 0.84308/0.30046, loss_mask_dice_0: 0.66338/1.01894, loss_spatial_bce_0: 0.34013/0.08413, loss_spatial_dice_0: 0.30372/0.17773, loss_spatial_ce_0: 0.20614/0.05457, loss_grounding_bce_0: 0.51351/0.08050, loss_grounding_dice_0: 0.24463/0.15027, loss_grounding_ce_0: 0.00700/0.24771, loss_mask_ce_1: 0.35371/0.75062, loss_mask_bce_1: 0.79348/0.30129, loss_mask_dice_1: 0.61990/1.02340, loss_spatial_bce_1: 0.36022/0.08461, loss_spatial_dice_1: 0.31603/0.18072, loss_spatial_ce_1: 0.13378/0.05826, loss_grounding_bce_1: 0.51631/0.08072, loss_grounding_dice_1: 0.25194/0.15101, loss_grounding_ce_1: 0.00652/0.24911, loss_mask_ce_2: 0.66716/0.75819, loss_mask_bce_2: 0.82423/0.30166, loss_mask_dice_2: 0.66786/1.02409, loss_spatial_bce_2: 0.42642/0.08470, loss_spatial_dice_2: 0.31697/0.18138, loss_spatial_ce_2: 0.10342/0.06050, loss_grounding_bce_2: 0.53311/0.08071, loss_grounding_dice_2: 0.25883/0.15095, loss_grounding_ce_2: 0.01068/0.25202, loss_mask_ce_3: 0.32315/0.76314, loss_mask_bce_3: 0.82952/0.30297, loss_mask_dice_3: 0.59585/1.02244, loss_spatial_bce_3: 0.44305/0.08685, loss_spatial_dice_3: 0.32644/0.18279, loss_spatial_ce_3: 0.07502/0.06546, loss_grounding_bce_3: 0.53380/0.08103, loss_grounding_dice_3: 0.25572/0.15060, loss_grounding_ce_3: 0.00973/0.25328, loss_mask_ce_4: 0.10781/0.76910, loss_mask_bce_4: 0.82715/0.30570, loss_mask_dice_4: 0.85279/1.04186, loss_spatial_bce_4: 0.29788/0.08942, loss_spatial_dice_4: 0.33341/0.19166, loss_spatial_ce_4: 0.65946/0.07929, loss_grounding_bce_4: 0.53274/0.08180, loss_grounding_dice_4: 0.25814/0.15320, loss_grounding_ce_4: 0.00396/0.25769, loss_mask_ce_5: 0.54814/0.79456, loss_mask_bce_5: 0.86651/0.30760, loss_mask_dice_5: 0.61469/1.04998, loss_spatial_bce_5: 0.47544/0.09183, loss_spatial_dice_5: 0.35029/0.19504, loss_spatial_ce_5: 0.10747/0.09314, loss_grounding_bce_5: 0.55886/0.08210, loss_grounding_dice_5: 0.27089/0.15412, loss_grounding_ce_5: 0.00339/0.27492, loss_mask_ce_6: 0.55262/0.82235, loss_mask_bce_6: 0.80731/0.30974, loss_mask_dice_6: 0.59095/1.05385, loss_spatial_bce_6: 0.44391/0.09734, loss_spatial_dice_6: 0.39491/0.19737, loss_spatial_ce_6: 0.42497/0.11750, loss_grounding_bce_6: 0.53300/0.08288, loss_grounding_dice_6: 0.25970/0.15457, loss_grounding_ce_6: 0.00897/0.28379, loss_mask_ce_7: 0.84608/0.87734, loss_mask_bce_7: 0.74929/0.31699, loss_mask_dice_7: 0.64139/1.09968, loss_spatial_bce_7: 0.47797/0.10642, loss_spatial_dice_7: 0.44509/0.22208, loss_spatial_ce_7: 0.76855/0.15157, loss_grounding_bce_7: 0.48485/0.08461, loss_grounding_dice_7: 0.24094/0.16012, loss_grounding_ce_7: 0.02403/0.31689, loss_mask_ce_8: 1.13353/1.01108, loss_mask_bce_8: 0.85777/0.33296, loss_mask_dice_8: 0.62442/1.17632, loss_spatial_bce_8: 0.59188/0.12262, loss_spatial_dice_8: 0.46169/0.25640, loss_spatial_ce_8: 1.07041/0.19656, loss_grounding_bce_8: 0.53502/0.08881, loss_grounding_dice_8: 0.24712/0.16991, loss_grounding_ce_8: 0.00997/0.41453, loss_mask_ce_9: 1.52399/3.47236, loss_mask_bce_9: 0.71755/0.35988, loss_mask_dice_9: 0.76851/1.75918, loss_spatial_bce_9: 0.55573/0.35422, loss_spatial_dice_9: 0.63911/0.79292, loss_spatial_ce_9: 1.21386/1.38594, loss_grounding_bce_9: 0.47297/0.10107, loss_grounding_dice_9: 0.23947/0.24213, loss_grounding_ce_9: 0.09141/0.66690] items per batch[64] items per second[0.37] total items[5408000] mini batches[ 84500] memory[4999] epoch remaining[0:40:03] INFO:trainer.default_trainer:epochs[ 46] optim steps[84600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.13250/0.74972, loss_mask_bce_0: 0.04923/0.30046, loss_mask_dice_0: 0.33342/1.01901, loss_spatial_bce_0: 0.03282/0.08411, loss_spatial_dice_0: 0.13838/0.17773, loss_spatial_ce_0: 0.00037/0.05456, loss_grounding_bce_0: 0.01393/0.08048, loss_grounding_dice_0: 0.16183/0.15028, loss_grounding_ce_0: 0.01448/0.24769, loss_mask_ce_1: 0.12025/0.75054, loss_mask_bce_1: 0.04302/0.30128, loss_mask_dice_1: 0.24832/1.02345, loss_spatial_bce_1: 0.03850/0.08460, loss_spatial_dice_1: 0.12694/0.18072, loss_spatial_ce_1: 0.00370/0.05824, loss_grounding_bce_1: 0.01552/0.08070, loss_grounding_dice_1: 0.13307/0.15103, loss_grounding_ce_1: 0.01148/0.24908, loss_mask_ce_2: 0.14183/0.75816, loss_mask_bce_2: 0.04566/0.30166, loss_mask_dice_2: 0.21725/1.02412, loss_spatial_bce_2: 0.03826/0.08469, loss_spatial_dice_2: 0.12061/0.18138, loss_spatial_ce_2: 0.00433/0.06048, loss_grounding_bce_2: 0.01442/0.08069, loss_grounding_dice_2: 0.10340/0.15097, loss_grounding_ce_2: 0.01073/0.25200, loss_mask_ce_3: 0.15971/0.76307, loss_mask_bce_3: 0.04752/0.30296, loss_mask_dice_3: 0.27030/1.02249, loss_spatial_bce_3: 0.03742/0.08683, loss_spatial_dice_3: 0.13010/0.18279, loss_spatial_ce_3: 0.00652/0.06544, loss_grounding_bce_3: 0.01481/0.08101, loss_grounding_dice_3: 0.23227/0.15062, loss_grounding_ce_3: 0.01516/0.25324, loss_mask_ce_4: 0.17712/0.76903, loss_mask_bce_4: 0.05400/0.30570, loss_mask_dice_4: 0.27390/1.04195, loss_spatial_bce_4: 0.03992/0.08940, loss_spatial_dice_4: 0.11364/0.19166, loss_spatial_ce_4: 0.00880/0.07927, loss_grounding_bce_4: 0.01342/0.08178, loss_grounding_dice_4: 0.17357/0.15322, loss_grounding_ce_4: 0.01097/0.25764, loss_mask_ce_5: 0.22352/0.79450, loss_mask_bce_5: 0.07541/0.30761, loss_mask_dice_5: 0.21323/1.05010, loss_spatial_bce_5: 0.03417/0.09182, loss_spatial_dice_5: 0.09526/0.19504, loss_spatial_ce_5: 0.00592/0.09312, loss_grounding_bce_5: 0.01304/0.08208, loss_grounding_dice_5: 0.12199/0.15413, loss_grounding_ce_5: 0.00728/0.27490, loss_mask_ce_6: 0.23997/0.82227, loss_mask_bce_6: 0.07002/0.30974, loss_mask_dice_6: 0.23854/1.05395, loss_spatial_bce_6: 0.03421/0.09732, loss_spatial_dice_6: 0.13597/0.19737, loss_spatial_ce_6: 0.00431/0.11749, loss_grounding_bce_6: 0.01172/0.08286, loss_grounding_dice_6: 0.14309/0.15458, loss_grounding_ce_6: 0.01859/0.28379, loss_mask_ce_7: 0.37611/0.87724, loss_mask_bce_7: 0.08464/0.31699, loss_mask_dice_7: 0.26920/1.09976, loss_spatial_bce_7: 0.04263/0.10640, loss_spatial_dice_7: 0.13296/0.22208, loss_spatial_ce_7: 0.04469/0.15153, loss_grounding_bce_7: 0.01489/0.08459, loss_grounding_dice_7: 0.13321/0.16013, loss_grounding_ce_7: 0.08923/0.31687, loss_mask_ce_8: 1.10067/1.01108, loss_mask_bce_8: 0.07303/0.33295, loss_mask_dice_8: 0.18328/1.17642, loss_spatial_bce_8: 0.04980/0.12260, loss_spatial_dice_8: 0.12495/0.25640, loss_spatial_ce_8: 0.09860/0.19652, loss_grounding_bce_8: 0.01271/0.08879, loss_grounding_dice_8: 0.17998/0.16994, loss_grounding_ce_8: 0.57192/0.41449, loss_mask_ce_9: 2.24957/3.47243, loss_mask_bce_9: 0.07356/0.35988, loss_mask_dice_9: 0.30203/1.75919, loss_spatial_bce_9: 0.51678/0.35421, loss_spatial_dice_9: 0.72734/0.79294, loss_spatial_ce_9: 0.95655/1.38600, loss_grounding_bce_9: 0.01081/0.10106, loss_grounding_dice_9: 0.22190/0.24218, loss_grounding_ce_9: 0.47994/0.66677] items per batch[64] items per second[0.37] total items[5414400] mini batches[ 84600] memory[4999] epoch remaining[0:37:01] INFO:trainer.default_trainer:epochs[ 46] optim steps[84700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.21172/0.74959, loss_mask_bce_0: 0.33544/0.30045, loss_mask_dice_0: 0.62980/1.01884, loss_spatial_bce_0: 0.08455/0.08410, loss_spatial_dice_0: 0.12520/0.17771, loss_spatial_ce_0: 0.00034/0.05454, loss_grounding_bce_0: 0.12632/0.08047, loss_grounding_dice_0: 0.29817/0.15025, loss_grounding_ce_0: 2.20268/0.24770, loss_mask_ce_1: 0.21651/0.75039, loss_mask_bce_1: 0.33035/0.30128, loss_mask_dice_1: 0.58011/1.02330, loss_spatial_bce_1: 0.08432/0.08459, loss_spatial_dice_1: 0.12586/0.18071, loss_spatial_ce_1: 0.00115/0.05822, loss_grounding_bce_1: 0.12646/0.08069, loss_grounding_dice_1: 0.28883/0.15101, loss_grounding_ce_1: 2.17119/0.24908, loss_mask_ce_2: 0.21791/0.75801, loss_mask_bce_2: 0.32975/0.30165, loss_mask_dice_2: 0.57842/1.02396, loss_spatial_bce_2: 0.08885/0.08468, loss_spatial_dice_2: 0.13864/0.18136, loss_spatial_ce_2: 0.00083/0.06046, loss_grounding_bce_2: 0.13767/0.08068, loss_grounding_dice_2: 0.32365/0.15094, loss_grounding_ce_2: 2.40617/0.25202, loss_mask_ce_3: 0.20428/0.76293, loss_mask_bce_3: 0.32899/0.30296, loss_mask_dice_3: 0.54396/1.02233, loss_spatial_bce_3: 0.09118/0.08683, loss_spatial_dice_3: 0.13058/0.18278, loss_spatial_ce_3: 0.00083/0.06542, loss_grounding_bce_3: 0.13848/0.08100, loss_grounding_dice_3: 0.31955/0.15060, loss_grounding_ce_3: 2.41023/0.25330, loss_mask_ce_4: 0.20175/0.76888, loss_mask_bce_4: 0.33570/0.30568, loss_mask_dice_4: 0.56526/1.04178, loss_spatial_bce_4: 0.08584/0.08939, loss_spatial_dice_4: 0.15402/0.19165, loss_spatial_ce_4: 0.00221/0.07925, loss_grounding_bce_4: 0.13996/0.08177, loss_grounding_dice_4: 0.32522/0.15319, loss_grounding_ce_4: 2.67087/0.25765, loss_mask_ce_5: 0.24620/0.79434, loss_mask_bce_5: 0.33716/0.30759, loss_mask_dice_5: 0.60043/1.04992, loss_spatial_bce_5: 0.09125/0.09181, loss_spatial_dice_5: 0.15655/0.19502, loss_spatial_ce_5: 0.00337/0.09309, loss_grounding_bce_5: 0.11097/0.08206, loss_grounding_dice_5: 0.27953/0.15410, loss_grounding_ce_5: 2.76304/0.27492, loss_mask_ce_6: 0.24450/0.82212, loss_mask_bce_6: 0.34618/0.30973, loss_mask_dice_6: 0.60489/1.05378, loss_spatial_bce_6: 0.09205/0.09731, loss_spatial_dice_6: 0.17288/0.19735, loss_spatial_ce_6: 0.00943/0.11746, loss_grounding_bce_6: 0.13705/0.08285, loss_grounding_dice_6: 0.32857/0.15455, loss_grounding_ce_6: 1.79312/0.28382, loss_mask_ce_7: 0.27070/0.87708, loss_mask_bce_7: 0.33193/0.31698, loss_mask_dice_7: 0.59520/1.09957, loss_spatial_bce_7: 0.08352/0.10638, loss_spatial_dice_7: 0.14939/0.22207, loss_spatial_ce_7: 0.01479/0.15150, loss_grounding_bce_7: 0.13240/0.08458, loss_grounding_dice_7: 0.33657/0.16011, loss_grounding_ce_7: 2.39210/0.31692, loss_mask_ce_8: 0.70160/1.01089, loss_mask_bce_8: 0.36652/0.33293, loss_mask_dice_8: 0.58233/1.17623, loss_spatial_bce_8: 0.08322/0.12259, loss_spatial_dice_8: 0.16274/0.25638, loss_spatial_ce_8: 0.14992/0.19648, loss_grounding_bce_8: 0.11887/0.08878, loss_grounding_dice_8: 0.30624/0.16990, loss_grounding_ce_8: 3.21756/0.41458, loss_mask_ce_9: 3.57832/3.47212, loss_mask_bce_9: 0.41918/0.35986, loss_mask_dice_9: 1.06202/1.75891, loss_spatial_bce_9: 0.57143/0.35423, loss_spatial_dice_9: 0.78774/0.79296, loss_spatial_ce_9: 1.21108/1.38596, loss_grounding_bce_9: 0.11133/0.10104, loss_grounding_dice_9: 0.33504/0.24215, loss_grounding_ce_9: 2.87981/0.66683] items per batch[64] items per second[0.37] total items[5420800] mini batches[ 84700] memory[4999] epoch remaining[0:34:03] INFO:trainer.default_trainer:epochs[ 46] optim steps[84800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.40520/0.74966, loss_mask_bce_0: 0.07621/0.30048, loss_mask_dice_0: 0.90766/1.01860, loss_spatial_bce_0: 0.08769/0.08411, loss_spatial_dice_0: 0.24368/0.17769, loss_spatial_ce_0: 0.03434/0.05452, loss_grounding_bce_0: 0.01754/0.08048, loss_grounding_dice_0: 0.09563/0.15025, loss_grounding_ce_0: 0.28686/0.24770, loss_mask_ce_1: 0.20715/0.75045, loss_mask_bce_1: 0.08878/0.30131, loss_mask_dice_1: 1.19886/1.02307, loss_spatial_bce_1: 0.09441/0.08459, loss_spatial_dice_1: 0.24284/0.18069, loss_spatial_ce_1: 0.06137/0.05819, loss_grounding_bce_1: 0.01953/0.08069, loss_grounding_dice_1: 0.11610/0.15100, loss_grounding_ce_1: 0.28764/0.24905, loss_mask_ce_2: 0.22081/0.75804, loss_mask_bce_2: 0.08670/0.30168, loss_mask_dice_2: 0.94811/1.02375, loss_spatial_bce_2: 0.07799/0.08468, loss_spatial_dice_2: 0.22474/0.18135, loss_spatial_ce_2: 0.05007/0.06044, loss_grounding_bce_2: 0.02084/0.08068, loss_grounding_dice_2: 0.10601/0.15093, loss_grounding_ce_2: 0.29265/0.25202, loss_mask_ce_3: 0.21391/0.76298, loss_mask_bce_3: 0.10186/0.30299, loss_mask_dice_3: 1.25481/1.02211, loss_spatial_bce_3: 0.06217/0.08683, loss_spatial_dice_3: 0.21791/0.18276, loss_spatial_ce_3: 0.04243/0.06540, loss_grounding_bce_3: 0.02079/0.08101, loss_grounding_dice_3: 0.11294/0.15060, loss_grounding_ce_3: 0.29185/0.25328, loss_mask_ce_4: 0.85818/0.76898, loss_mask_bce_4: 0.08252/0.30571, loss_mask_dice_4: 1.15775/1.04154, loss_spatial_bce_4: 0.05803/0.08940, loss_spatial_dice_4: 0.24033/0.19163, loss_spatial_ce_4: 0.04973/0.07923, loss_grounding_bce_4: 0.01831/0.08177, loss_grounding_dice_4: 0.10394/0.15319, loss_grounding_ce_4: 0.28802/0.25762, loss_mask_ce_5: 0.19833/0.79442, loss_mask_bce_5: 0.08231/0.30762, loss_mask_dice_5: 1.09295/1.04969, loss_spatial_bce_5: 0.04803/0.09181, loss_spatial_dice_5: 0.25250/0.19500, loss_spatial_ce_5: 0.15838/0.09307, loss_grounding_bce_5: 0.01636/0.08206, loss_grounding_dice_5: 0.09909/0.15410, loss_grounding_ce_5: 0.30045/0.27491, loss_mask_ce_6: 0.59065/0.82220, loss_mask_bce_6: 0.08047/0.30975, loss_mask_dice_6: 1.26812/1.05355, loss_spatial_bce_6: 0.02935/0.09732, loss_spatial_dice_6: 0.20400/0.19734, loss_spatial_ce_6: 0.20187/0.11744, loss_grounding_bce_6: 0.01898/0.08284, loss_grounding_dice_6: 0.10106/0.15456, loss_grounding_ce_6: 0.28900/0.28382, loss_mask_ce_7: 0.59571/0.87714, loss_mask_bce_7: 0.09282/0.31701, loss_mask_dice_7: 1.16563/1.09933, loss_spatial_bce_7: 0.02221/0.10639, loss_spatial_dice_7: 0.19889/0.22205, loss_spatial_ce_7: 0.29540/0.15146, loss_grounding_bce_7: 0.01926/0.08458, loss_grounding_dice_7: 0.10860/0.16010, loss_grounding_ce_7: 0.32172/0.31688, loss_mask_ce_8: 0.67023/1.01092, loss_mask_bce_8: 0.09780/0.33297, loss_mask_dice_8: 1.10860/1.17598, loss_spatial_bce_8: 0.03120/0.12259, loss_spatial_dice_8: 0.23512/0.25636, loss_spatial_ce_8: 0.59748/0.19644, loss_grounding_bce_8: 0.02345/0.08879, loss_grounding_dice_8: 0.11053/0.16991, loss_grounding_ce_8: 0.33745/0.41446, loss_mask_ce_9: 2.89949/3.47219, loss_mask_bce_9: 0.06684/0.35990, loss_mask_dice_9: 1.53900/1.75865, loss_spatial_bce_9: 0.24935/0.35424, loss_spatial_dice_9: 0.85323/0.79297, loss_spatial_ce_9: 1.10434/1.38599, loss_grounding_bce_9: 0.01509/0.10104, loss_grounding_dice_9: 0.16312/0.24215, loss_grounding_ce_9: 0.51621/0.66675] items per batch[64] items per second[0.37] total items[5427200] mini batches[ 84800] memory[4999] epoch remaining[0:31:07] INFO:trainer.default_trainer:epochs[ 46] optim steps[84900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.91774/0.74961, loss_mask_bce_0: 0.03274/0.30048, loss_mask_dice_0: 1.47343/1.01874, loss_spatial_bce_0: 0.00783/0.08411, loss_spatial_dice_0: 0.28548/0.17769, loss_spatial_ce_0: 0.00771/0.05451, loss_grounding_bce_0: 0.00266/0.08048, loss_grounding_dice_0: 0.11545/0.15025, loss_grounding_ce_0: 0.11287/0.24771, loss_mask_ce_1: 0.48823/0.75042, loss_mask_bce_1: 0.07552/0.30130, loss_mask_dice_1: 1.91326/1.02322, loss_spatial_bce_1: 0.00804/0.08460, loss_spatial_dice_1: 0.24952/0.18069, loss_spatial_ce_1: 0.09039/0.05818, loss_grounding_bce_1: 0.00164/0.08069, loss_grounding_dice_1: 0.09718/0.15100, loss_grounding_ce_1: 0.05698/0.24907, loss_mask_ce_2: 0.79943/0.75800, loss_mask_bce_2: 0.02833/0.30167, loss_mask_dice_2: 1.23565/1.02388, loss_spatial_bce_2: 0.00721/0.08469, loss_spatial_dice_2: 0.32201/0.18135, loss_spatial_ce_2: 0.05340/0.06043, loss_grounding_bce_2: 0.00115/0.08068, loss_grounding_dice_2: 0.08686/0.15093, loss_grounding_ce_2: 0.02603/0.25204, loss_mask_ce_3: 0.49663/0.76295, loss_mask_bce_3: 0.07664/0.30297, loss_mask_dice_3: 1.57869/1.02222, loss_spatial_bce_3: 0.01466/0.08683, loss_spatial_dice_3: 0.29436/0.18277, loss_spatial_ce_3: 0.04447/0.06539, loss_grounding_bce_3: 0.00164/0.08101, loss_grounding_dice_3: 0.08454/0.15059, loss_grounding_ce_3: 0.04557/0.25331, loss_mask_ce_4: 0.69396/0.76897, loss_mask_bce_4: 0.02957/0.30570, loss_mask_dice_4: 1.40873/1.04167, loss_spatial_bce_4: 0.01105/0.08939, loss_spatial_dice_4: 0.29408/0.19164, loss_spatial_ce_4: 0.00826/0.07924, loss_grounding_bce_4: 0.00245/0.08177, loss_grounding_dice_4: 0.11847/0.15318, loss_grounding_ce_4: 0.06751/0.25767, loss_mask_ce_5: 0.42259/0.79444, loss_mask_bce_5: 0.07457/0.30761, loss_mask_dice_5: 1.54257/1.04984, loss_spatial_bce_5: 0.01153/0.09181, loss_spatial_dice_5: 0.30895/0.19502, loss_spatial_ce_5: 0.01083/0.09309, loss_grounding_bce_5: 0.00175/0.08207, loss_grounding_dice_5: 0.09285/0.15409, loss_grounding_ce_5: 0.09254/0.27495, loss_mask_ce_6: 0.96397/0.82221, loss_mask_bce_6: 0.02523/0.30975, loss_mask_dice_6: 1.37718/1.05368, loss_spatial_bce_6: 0.01426/0.09731, loss_spatial_dice_6: 0.33997/0.19736, loss_spatial_ce_6: 0.01653/0.11743, loss_grounding_bce_6: 0.00254/0.08285, loss_grounding_dice_6: 0.17300/0.15455, loss_grounding_ce_6: 0.29921/0.28383, loss_mask_ce_7: 0.74436/0.87712, loss_mask_bce_7: 0.05825/0.31701, loss_mask_dice_7: 2.18292/1.09949, loss_spatial_bce_7: 0.01026/0.10639, loss_spatial_dice_7: 0.37048/0.22206, loss_spatial_ce_7: 0.10179/0.15143, loss_grounding_bce_7: 0.00197/0.08458, loss_grounding_dice_7: 0.09900/0.16010, loss_grounding_ce_7: 0.46185/0.31690, loss_mask_ce_8: 0.95257/1.01089, loss_mask_bce_8: 0.03244/0.33295, loss_mask_dice_8: 1.36012/1.17612, loss_spatial_bce_8: 0.00841/0.12258, loss_spatial_dice_8: 0.37079/0.25637, loss_spatial_ce_8: 0.64722/0.19646, loss_grounding_bce_8: 0.00353/0.08879, loss_grounding_dice_8: 0.05938/0.16991, loss_grounding_ce_8: 2.44228/0.41454, loss_mask_ce_9: 3.82108/3.47235, loss_mask_bce_9: 0.02489/0.35990, loss_mask_dice_9: 1.47898/1.75879, loss_spatial_bce_9: 0.02093/0.35421, loss_spatial_dice_9: 0.77399/0.79297, loss_spatial_ce_9: 1.78049/1.38595, loss_grounding_bce_9: 0.00227/0.10105, loss_grounding_dice_9: 0.23525/0.24213, loss_grounding_ce_9: 1.96673/0.66682] items per batch[64] items per second[0.37] total items[5433600] mini batches[ 84900] memory[4999] epoch remaining[0:28:11] INFO:trainer.default_trainer:epochs[ 46] optim steps[85000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.24779/0.74951, loss_mask_bce_0: 0.25540/0.30043, loss_mask_dice_0: 0.47740/1.01863, loss_spatial_bce_0: 0.05886/0.08410, loss_spatial_dice_0: 0.09008/0.17766, loss_spatial_ce_0: 0.00181/0.05449, loss_grounding_bce_0: 0.12790/0.08047, loss_grounding_dice_0: 0.13103/0.15024, loss_grounding_ce_0: 0.01080/0.24765, loss_mask_ce_1: 0.25480/0.75034, loss_mask_bce_1: 0.25891/0.30125, loss_mask_dice_1: 0.48035/1.02314, loss_spatial_bce_1: 0.06119/0.08458, loss_spatial_dice_1: 0.09457/0.18066, loss_spatial_ce_1: 0.00124/0.05816, loss_grounding_bce_1: 0.12900/0.08069, loss_grounding_dice_1: 0.12372/0.15099, loss_grounding_ce_1: 0.01064/0.24901, loss_mask_ce_2: 0.25075/0.75790, loss_mask_bce_2: 0.25596/0.30163, loss_mask_dice_2: 0.44956/1.02378, loss_spatial_bce_2: 0.06297/0.08467, loss_spatial_dice_2: 0.08497/0.18132, loss_spatial_ce_2: 0.00277/0.06041, loss_grounding_bce_2: 0.12910/0.08068, loss_grounding_dice_2: 0.12873/0.15092, loss_grounding_ce_2: 0.00900/0.25198, loss_mask_ce_3: 0.23921/0.76284, loss_mask_bce_3: 0.24207/0.30292, loss_mask_dice_3: 0.45948/1.02216, loss_spatial_bce_3: 0.06221/0.08682, loss_spatial_dice_3: 0.09149/0.18273, loss_spatial_ce_3: 0.00346/0.06538, loss_grounding_bce_3: 0.13046/0.08101, loss_grounding_dice_3: 0.13546/0.15058, loss_grounding_ce_3: 0.01070/0.25326, loss_mask_ce_4: 0.23233/0.76890, loss_mask_bce_4: 0.25143/0.30566, loss_mask_dice_4: 0.46239/1.04159, loss_spatial_bce_4: 0.06859/0.08939, loss_spatial_dice_4: 0.10385/0.19161, loss_spatial_ce_4: 0.01554/0.07921, loss_grounding_bce_4: 0.12839/0.08177, loss_grounding_dice_4: 0.13360/0.15317, loss_grounding_ce_4: 0.01031/0.25761, loss_mask_ce_5: 0.21924/0.79434, loss_mask_bce_5: 0.26270/0.30757, loss_mask_dice_5: 0.49691/1.04978, loss_spatial_bce_5: 0.07311/0.09180, loss_spatial_dice_5: 0.10162/0.19498, loss_spatial_ce_5: 0.01337/0.09306, loss_grounding_bce_5: 0.13315/0.08207, loss_grounding_dice_5: 0.13944/0.15409, loss_grounding_ce_5: 0.00856/0.27486, loss_mask_ce_6: 0.18331/0.82210, loss_mask_bce_6: 0.26081/0.30970, loss_mask_dice_6: 0.45170/1.05360, loss_spatial_bce_6: 0.07102/0.09731, loss_spatial_dice_6: 0.09095/0.19733, loss_spatial_ce_6: 0.02079/0.11738, loss_grounding_bce_6: 0.13130/0.08285, loss_grounding_dice_6: 0.13252/0.15455, loss_grounding_ce_6: 0.00866/0.28377, loss_mask_ce_7: 0.25246/0.87702, loss_mask_bce_7: 0.26182/0.31695, loss_mask_dice_7: 0.49221/1.09943, loss_spatial_bce_7: 0.07741/0.10637, loss_spatial_dice_7: 0.13984/0.22202, loss_spatial_ce_7: 0.05340/0.15139, loss_grounding_bce_7: 0.12935/0.08457, loss_grounding_dice_7: 0.13669/0.16009, loss_grounding_ce_7: 0.01156/0.31683, loss_mask_ce_8: 0.29128/1.01078, loss_mask_bce_8: 0.26651/0.33289, loss_mask_dice_8: 0.46798/1.17609, loss_spatial_bce_8: 0.22073/0.12257, loss_spatial_dice_8: 0.21528/0.25633, loss_spatial_ce_8: 0.11469/0.19641, loss_grounding_bce_8: 0.13166/0.08878, loss_grounding_dice_8: 0.14343/0.16991, loss_grounding_ce_8: 0.01289/0.41448, loss_mask_ce_9: 3.28604/3.47212, loss_mask_bce_9: 0.29045/0.35986, loss_mask_dice_9: 0.94785/1.75879, loss_spatial_bce_9: 0.36978/0.35424, loss_spatial_dice_9: 0.77005/0.79295, loss_spatial_ce_9: 1.76758/1.38595, loss_grounding_bce_9: 0.13124/0.10104, loss_grounding_dice_9: 0.18337/0.24213, loss_grounding_ce_9: 0.13353/0.66673] items per batch[64] items per second[0.37] total items[5440000] mini batches[ 85000] memory[4999] epoch remaining[0:25:15] INFO:trainer.default_trainer:epochs[ 46] optim steps[85100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.26360/0.74947, loss_mask_bce_0: 0.34315/0.30041, loss_mask_dice_0: 0.54573/1.01885, loss_spatial_bce_0: 0.14226/0.08408, loss_spatial_dice_0: 0.19959/0.17765, loss_spatial_ce_0: 0.00308/0.05447, loss_grounding_bce_0: 0.10804/0.08046, loss_grounding_dice_0: 0.24668/0.15023, loss_grounding_ce_0: 0.00801/0.24755, loss_mask_ce_1: 0.26207/0.75031, loss_mask_bce_1: 0.33840/0.30123, loss_mask_dice_1: 0.54848/1.02338, loss_spatial_bce_1: 0.14300/0.08457, loss_spatial_dice_1: 0.21785/0.18064, loss_spatial_ce_1: 0.00336/0.05814, loss_grounding_bce_1: 0.10487/0.08068, loss_grounding_dice_1: 0.23465/0.15098, loss_grounding_ce_1: 0.00849/0.24892, loss_mask_ce_2: 0.28496/0.75789, loss_mask_bce_2: 0.33585/0.30161, loss_mask_dice_2: 0.52266/1.02401, loss_spatial_bce_2: 0.14773/0.08466, loss_spatial_dice_2: 0.20360/0.18130, loss_spatial_ce_2: 0.00228/0.06040, loss_grounding_bce_2: 0.10948/0.08066, loss_grounding_dice_2: 0.24070/0.15091, loss_grounding_ce_2: 0.00685/0.25189, loss_mask_ce_3: 0.26579/0.76281, loss_mask_bce_3: 0.33920/0.30290, loss_mask_dice_3: 0.54002/1.02241, loss_spatial_bce_3: 0.14420/0.08681, loss_spatial_dice_3: 0.21289/0.18272, loss_spatial_ce_3: 0.00157/0.06535, loss_grounding_bce_3: 0.10388/0.08099, loss_grounding_dice_3: 0.21803/0.15057, loss_grounding_ce_3: 0.00727/0.25318, loss_mask_ce_4: 0.25467/0.76891, loss_mask_bce_4: 0.34090/0.30564, loss_mask_dice_4: 0.53392/1.04180, loss_spatial_bce_4: 0.13656/0.08937, loss_spatial_dice_4: 0.20843/0.19160, loss_spatial_ce_4: 0.01004/0.07919, loss_grounding_bce_4: 0.10412/0.08176, loss_grounding_dice_4: 0.22206/0.15317, loss_grounding_ce_4: 0.00743/0.25751, loss_mask_ce_5: 0.27197/0.79435, loss_mask_bce_5: 0.34120/0.30755, loss_mask_dice_5: 0.54114/1.05002, loss_spatial_bce_5: 0.13783/0.09179, loss_spatial_dice_5: 0.21116/0.19498, loss_spatial_ce_5: 0.00311/0.09302, loss_grounding_bce_5: 0.11129/0.08206, loss_grounding_dice_5: 0.24509/0.15409, loss_grounding_ce_5: 0.00787/0.27475, loss_mask_ce_6: 0.25776/0.82210, loss_mask_bce_6: 0.34628/0.30968, loss_mask_dice_6: 0.53895/1.05385, loss_spatial_bce_6: 0.14370/0.09730, loss_spatial_dice_6: 0.20670/0.19732, loss_spatial_ce_6: 0.06686/0.11735, loss_grounding_bce_6: 0.11023/0.08283, loss_grounding_dice_6: 0.22373/0.15454, loss_grounding_ce_6: 0.00852/0.28366, loss_mask_ce_7: 0.23277/0.87703, loss_mask_bce_7: 0.35643/0.31693, loss_mask_dice_7: 0.56310/1.09969, loss_spatial_bce_7: 0.15369/0.10635, loss_spatial_dice_7: 0.22496/0.22202, loss_spatial_ce_7: 0.00936/0.15134, loss_grounding_bce_7: 0.11330/0.08456, loss_grounding_dice_7: 0.24187/0.16009, loss_grounding_ce_7: 0.00590/0.31670, loss_mask_ce_8: 0.30430/1.01087, loss_mask_bce_8: 0.34510/0.33286, loss_mask_dice_8: 0.54007/1.17637, loss_spatial_bce_8: 0.17712/0.12254, loss_spatial_dice_8: 0.23921/0.25632, loss_spatial_ce_8: 0.03835/0.19637, loss_grounding_bce_8: 0.11597/0.08877, loss_grounding_dice_8: 0.24011/0.16990, loss_grounding_ce_8: 0.03941/0.41444, loss_mask_ce_9: 1.51757/3.47234, loss_mask_bce_9: 0.35252/0.35985, loss_mask_dice_9: 0.55849/1.75913, loss_spatial_bce_9: 0.49295/0.35424, loss_spatial_dice_9: 0.75968/0.79296, loss_spatial_ce_9: 0.91356/1.38597, loss_grounding_bce_9: 0.11554/0.10103, loss_grounding_dice_9: 0.25023/0.24213, loss_grounding_ce_9: 0.08308/0.66677] items per batch[64] items per second[0.37] total items[5446400] mini batches[ 85100] memory[4999] epoch remaining[0:22:20] INFO:trainer.default_trainer:epochs[ 46] optim steps[85200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.26136/0.74943, loss_mask_bce_0: 0.34943/0.30033, loss_mask_dice_0: 0.85639/1.01886, loss_spatial_bce_0: 0.07627/0.08406, loss_spatial_dice_0: 0.07022/0.17763, loss_spatial_ce_0: 0.00014/0.05446, loss_grounding_bce_0: 0.06593/0.08046, loss_grounding_dice_0: 0.06527/0.15023, loss_grounding_ce_0: 0.18133/0.24746, loss_mask_ce_1: 1.25820/0.75026, loss_mask_bce_1: 0.35735/0.30116, loss_mask_dice_1: 0.84050/1.02340, loss_spatial_bce_1: 0.07686/0.08455, loss_spatial_dice_1: 0.07006/0.18063, loss_spatial_ce_1: 0.00004/0.05813, loss_grounding_bce_1: 0.06804/0.08067, loss_grounding_dice_1: 0.07051/0.15098, loss_grounding_ce_1: 0.18343/0.24883, loss_mask_ce_2: 1.26800/0.75784, loss_mask_bce_2: 0.35250/0.30153, loss_mask_dice_2: 0.85997/1.02397, loss_spatial_bce_2: 0.07814/0.08464, loss_spatial_dice_2: 0.06812/0.18128, loss_spatial_ce_2: 0.00005/0.06039, loss_grounding_bce_2: 0.06599/0.08066, loss_grounding_dice_2: 0.06800/0.15090, loss_grounding_ce_2: 0.19364/0.25180, loss_mask_ce_3: 0.66158/0.76276, loss_mask_bce_3: 0.35061/0.30282, loss_mask_dice_3: 0.90127/1.02240, loss_spatial_bce_3: 0.07702/0.08679, loss_spatial_dice_3: 0.07111/0.18270, loss_spatial_ce_3: 0.00017/0.06534, loss_grounding_bce_3: 0.06554/0.08099, loss_grounding_dice_3: 0.07145/0.15056, loss_grounding_ce_3: 0.20749/0.25309, loss_mask_ce_4: 1.21911/0.76885, loss_mask_bce_4: 0.35592/0.30556, loss_mask_dice_4: 0.90354/1.04178, loss_spatial_bce_4: 0.08209/0.08936, loss_spatial_dice_4: 0.06960/0.19159, loss_spatial_ce_4: 0.00036/0.07917, loss_grounding_bce_4: 0.07062/0.08175, loss_grounding_dice_4: 0.08071/0.15316, loss_grounding_ce_4: 0.18052/0.25740, loss_mask_ce_5: 0.69487/0.79427, loss_mask_bce_5: 0.34925/0.30747, loss_mask_dice_5: 0.90399/1.05002, loss_spatial_bce_5: 0.07929/0.09178, loss_spatial_dice_5: 0.07296/0.19497, loss_spatial_ce_5: 0.00344/0.09301, loss_grounding_bce_5: 0.07107/0.08206, loss_grounding_dice_5: 0.06952/0.15408, loss_grounding_ce_5: 0.20254/0.27464, loss_mask_ce_6: 0.55717/0.82206, loss_mask_bce_6: 0.37858/0.30959, loss_mask_dice_6: 0.96906/1.05382, loss_spatial_bce_6: 0.08228/0.09729, loss_spatial_dice_6: 0.07932/0.19731, loss_spatial_ce_6: 0.00252/0.11732, loss_grounding_bce_6: 0.07303/0.08283, loss_grounding_dice_6: 0.08142/0.15453, loss_grounding_ce_6: 0.18077/0.28354, loss_mask_ce_7: 0.62399/0.87692, loss_mask_bce_7: 0.37965/0.31686, loss_mask_dice_7: 0.97120/1.09967, loss_spatial_bce_7: 0.08100/0.10634, loss_spatial_dice_7: 0.07598/0.22201, loss_spatial_ce_7: 0.00725/0.15132, loss_grounding_bce_7: 0.06702/0.08456, loss_grounding_dice_7: 0.07643/0.16009, loss_grounding_ce_7: 0.18634/0.31656, loss_mask_ce_8: 0.68867/1.01074, loss_mask_bce_8: 0.36988/0.33279, loss_mask_dice_8: 1.04164/1.17631, loss_spatial_bce_8: 0.08400/0.12253, loss_spatial_dice_8: 0.08983/0.25630, loss_spatial_ce_8: 0.03966/0.19635, loss_grounding_bce_8: 0.06524/0.08876, loss_grounding_dice_8: 0.07241/0.16990, loss_grounding_ce_8: 0.47156/0.41426, loss_mask_ce_9: 2.98670/3.47215, loss_mask_bce_9: 0.42980/0.35976, loss_mask_dice_9: 1.29312/1.75900, loss_spatial_bce_9: 0.42051/0.35427, loss_spatial_dice_9: 0.81947/0.79293, loss_spatial_ce_9: 1.35462/1.38599, loss_grounding_bce_9: 0.12784/0.10103, loss_grounding_dice_9: 0.20421/0.24212, loss_grounding_ce_9: 0.36977/0.66663] items per batch[64] items per second[0.37] total items[5452800] mini batches[ 85200] memory[4999] epoch remaining[0:19:23] INFO:trainer.default_trainer:epochs[ 46] optim steps[85300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.50791/0.74945, loss_mask_bce_0: 0.27181/0.30035, loss_mask_dice_0: 0.25691/1.01894, loss_spatial_bce_0: 0.15151/0.08405, loss_spatial_dice_0: 0.12043/0.17762, loss_spatial_ce_0: 0.00951/0.05444, loss_grounding_bce_0: 0.17433/0.08044, loss_grounding_dice_0: 0.13788/0.15023, loss_grounding_ce_0: 0.05655/0.24742, loss_mask_ce_1: 0.45239/0.75028, loss_mask_bce_1: 0.27370/0.30118, loss_mask_dice_1: 0.24865/1.02349, loss_spatial_bce_1: 0.15528/0.08454, loss_spatial_dice_1: 0.12412/0.18062, loss_spatial_ce_1: 0.00553/0.05811, loss_grounding_bce_1: 0.16230/0.08066, loss_grounding_dice_1: 0.12976/0.15097, loss_grounding_ce_1: 0.06984/0.24880, loss_mask_ce_2: 0.51477/0.75787, loss_mask_bce_2: 0.26056/0.30156, loss_mask_dice_2: 0.23103/1.02406, loss_spatial_bce_2: 0.14801/0.08464, loss_spatial_dice_2: 0.12331/0.18127, loss_spatial_ce_2: 0.00722/0.06037, loss_grounding_bce_2: 0.16716/0.08064, loss_grounding_dice_2: 0.12258/0.15091, loss_grounding_ce_2: 0.16892/0.25175, loss_mask_ce_3: 0.53359/0.76278, loss_mask_bce_3: 0.26182/0.30285, loss_mask_dice_3: 0.23253/1.02248, loss_spatial_bce_3: 0.15644/0.08679, loss_spatial_dice_3: 0.11633/0.18269, loss_spatial_ce_3: 0.00421/0.06531, loss_grounding_bce_3: 0.17575/0.08097, loss_grounding_dice_3: 0.13459/0.15056, loss_grounding_ce_3: 0.20711/0.25304, loss_mask_ce_4: 0.64142/0.76888, loss_mask_bce_4: 0.27488/0.30559, loss_mask_dice_4: 0.23556/1.04185, loss_spatial_bce_4: 0.16342/0.08936, loss_spatial_dice_4: 0.11507/0.19158, loss_spatial_ce_4: 0.00506/0.07914, loss_grounding_bce_4: 0.18625/0.08174, loss_grounding_dice_4: 0.14152/0.15315, loss_grounding_ce_4: 0.20336/0.25737, loss_mask_ce_5: 0.63111/0.79427, loss_mask_bce_5: 0.26371/0.30749, loss_mask_dice_5: 0.23097/1.05010, loss_spatial_bce_5: 0.15060/0.09178, loss_spatial_dice_5: 0.12421/0.19496, loss_spatial_ce_5: 0.01245/0.09299, loss_grounding_bce_5: 0.18815/0.08204, loss_grounding_dice_5: 0.13325/0.15408, loss_grounding_ce_5: 0.27640/0.27459, loss_mask_ce_6: 0.68165/0.82208, loss_mask_bce_6: 0.28144/0.30962, loss_mask_dice_6: 0.23525/1.05390, loss_spatial_bce_6: 0.16474/0.09728, loss_spatial_dice_6: 0.18841/0.19730, loss_spatial_ce_6: 0.05335/0.11732, loss_grounding_bce_6: 0.19863/0.08281, loss_grounding_dice_6: 0.13767/0.15452, loss_grounding_ce_6: 0.09031/0.28351, loss_mask_ce_7: 0.80622/0.87691, loss_mask_bce_7: 0.27769/0.31689, loss_mask_dice_7: 0.26469/1.09977, loss_spatial_bce_7: 0.16822/0.10633, loss_spatial_dice_7: 0.16103/0.22200, loss_spatial_ce_7: 0.06911/0.15128, loss_grounding_bce_7: 0.19072/0.08454, loss_grounding_dice_7: 0.13625/0.16007, loss_grounding_ce_7: 0.10810/0.31655, loss_mask_ce_8: 0.73380/1.01072, loss_mask_bce_8: 0.29411/0.33281, loss_mask_dice_8: 0.28980/1.17641, loss_spatial_bce_8: 0.19283/0.12253, loss_spatial_dice_8: 0.17106/0.25630, loss_spatial_ce_8: 0.15039/0.19629, loss_grounding_bce_8: 0.18968/0.08874, loss_grounding_dice_8: 0.13011/0.16990, loss_grounding_ce_8: 0.44183/0.41425, loss_mask_ce_9: 4.93119/3.47238, loss_mask_bce_9: 0.29831/0.35981, loss_mask_dice_9: 0.44589/1.75916, loss_spatial_bce_9: 0.50101/0.35427, loss_spatial_dice_9: 0.64459/0.79294, loss_spatial_ce_9: 1.52093/1.38591, loss_grounding_bce_9: 0.18761/0.10100, loss_grounding_dice_9: 0.14801/0.24210, loss_grounding_ce_9: 1.95061/0.66669] items per batch[64] items per second[0.37] total items[5459200] mini batches[ 85300] memory[4999] epoch remaining[0:16:30] INFO:trainer.default_trainer:epochs[ 46] optim steps[85400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.69568/0.74944, loss_mask_bce_0: 0.06377/0.30037, loss_mask_dice_0: 3.13513/1.01903, loss_spatial_bce_0: 0.00091/0.08405, loss_spatial_dice_0: 0.27592/0.17761, loss_spatial_ce_0: 0.27642/0.05441, loss_grounding_bce_0: 0.00140/0.08046, loss_grounding_dice_0: 0.35381/0.15024, loss_grounding_ce_0: 0.54618/0.24734, loss_mask_ce_1: 0.67250/0.75024, loss_mask_bce_1: 0.06571/0.30120, loss_mask_dice_1: 4.13242/1.02361, loss_spatial_bce_1: 0.00073/0.08454, loss_spatial_dice_1: 0.33694/0.18061, loss_spatial_ce_1: 0.25160/0.05809, loss_grounding_bce_1: 0.00064/0.08067, loss_grounding_dice_1: 0.24064/0.15098, loss_grounding_ce_1: 0.54300/0.24872, loss_mask_ce_2: 0.73867/0.75785, loss_mask_bce_2: 0.06154/0.30157, loss_mask_dice_2: 3.93749/1.02414, loss_spatial_bce_2: 0.00117/0.08464, loss_spatial_dice_2: 0.44201/0.18127, loss_spatial_ce_2: 0.32467/0.06034, loss_grounding_bce_2: 0.00110/0.08066, loss_grounding_dice_2: 0.27651/0.15091, loss_grounding_ce_2: 0.53808/0.25167, loss_mask_ce_3: 0.70597/0.76275, loss_mask_bce_3: 0.05746/0.30285, loss_mask_dice_3: 3.36168/1.02258, loss_spatial_bce_3: 0.00139/0.08679, loss_spatial_dice_3: 0.43457/0.18269, loss_spatial_ce_3: 0.20994/0.06529, loss_grounding_bce_3: 0.00124/0.08099, loss_grounding_dice_3: 0.30153/0.15056, loss_grounding_ce_3: 0.53835/0.25296, loss_mask_ce_4: 0.78139/0.76885, loss_mask_bce_4: 0.06170/0.30560, loss_mask_dice_4: 3.39456/1.04194, loss_spatial_bce_4: 0.00082/0.08935, loss_spatial_dice_4: 0.29563/0.19158, loss_spatial_ce_4: 0.39065/0.07912, loss_grounding_bce_4: 0.00156/0.08175, loss_grounding_dice_4: 0.34661/0.15315, loss_grounding_ce_4: 0.56123/0.25729, loss_mask_ce_5: 0.68578/0.79428, loss_mask_bce_5: 0.05236/0.30750, loss_mask_dice_5: 3.08566/1.05018, loss_spatial_bce_5: 0.00058/0.09177, loss_spatial_dice_5: 0.25034/0.19495, loss_spatial_ce_5: 0.39241/0.09296, loss_grounding_bce_5: 0.00180/0.08205, loss_grounding_dice_5: 0.32777/0.15408, loss_grounding_ce_5: 0.62868/0.27450, loss_mask_ce_6: 0.77205/0.82204, loss_mask_bce_6: 0.05828/0.30963, loss_mask_dice_6: 3.94845/1.05401, loss_spatial_bce_6: 0.00115/0.09728, loss_spatial_dice_6: 0.27632/0.19729, loss_spatial_ce_6: 0.47358/0.11731, loss_grounding_bce_6: 0.00074/0.08283, loss_grounding_dice_6: 0.25761/0.15453, loss_grounding_ce_6: 0.55594/0.28342, loss_mask_ce_7: 1.07209/0.87691, loss_mask_bce_7: 0.05405/0.31690, loss_mask_dice_7: 3.81080/1.09987, loss_spatial_bce_7: 0.00093/0.10633, loss_spatial_dice_7: 0.32112/0.22199, loss_spatial_ce_7: 0.64466/0.15125, loss_grounding_bce_7: 0.00128/0.08455, loss_grounding_dice_7: 0.35800/0.16008, loss_grounding_ce_7: 0.62623/0.31644, loss_mask_ce_8: 1.03964/1.01072, loss_mask_bce_8: 0.05436/0.33284, loss_mask_dice_8: 4.28497/1.17652, loss_spatial_bce_8: 0.00244/0.12252, loss_spatial_dice_8: 0.49703/0.25629, loss_spatial_ce_8: 0.37020/0.19624, loss_grounding_bce_8: 0.00097/0.08876, loss_grounding_dice_8: 0.28931/0.16990, loss_grounding_ce_8: 0.64776/0.41422, loss_mask_ce_9: 4.38223/3.47235, loss_mask_bce_9: 0.03155/0.35984, loss_mask_dice_9: 2.65471/1.75930, loss_spatial_bce_9: 0.00264/0.35426, loss_spatial_dice_9: 0.50294/0.79292, loss_spatial_ce_9: 3.05699/1.38588, loss_grounding_bce_9: 0.00107/0.10103, loss_grounding_dice_9: 0.29853/0.24210, loss_grounding_ce_9: 0.67532/0.66649] items per batch[64] items per second[0.36] total items[5465600] mini batches[ 85400] memory[4999] epoch remaining[0:13:37] INFO:trainer.default_trainer:epochs[ 46] optim steps[85500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.29474/0.74944, loss_mask_bce_0: 0.30547/0.30035, loss_mask_dice_0: 0.22303/1.01903, loss_spatial_bce_0: 0.20726/0.08404, loss_spatial_dice_0: 0.11289/0.17760, loss_spatial_ce_0: 0.07073/0.05441, loss_grounding_bce_0: 0.02488/0.08044, loss_grounding_dice_0: 0.03753/0.15024, loss_grounding_ce_0: 0.15250/0.24730, loss_mask_ce_1: 0.26645/0.75023, loss_mask_bce_1: 0.29046/0.30118, loss_mask_dice_1: 0.23348/1.02362, loss_spatial_bce_1: 0.17334/0.08453, loss_spatial_dice_1: 0.10841/0.18060, loss_spatial_ce_1: 0.05583/0.05808, loss_grounding_bce_1: 0.02376/0.08066, loss_grounding_dice_1: 0.03865/0.15097, loss_grounding_ce_1: 0.15893/0.24869, loss_mask_ce_2: 0.26192/0.75787, loss_mask_bce_2: 0.24775/0.30155, loss_mask_dice_2: 0.21988/1.02416, loss_spatial_bce_2: 0.20346/0.08462, loss_spatial_dice_2: 0.10484/0.18125, loss_spatial_ce_2: 0.06874/0.06032, loss_grounding_bce_2: 0.02017/0.08064, loss_grounding_dice_2: 0.03280/0.15090, loss_grounding_ce_2: 0.12131/0.25164, loss_mask_ce_3: 0.26391/0.76277, loss_mask_bce_3: 0.29481/0.30283, loss_mask_dice_3: 0.25660/1.02259, loss_spatial_bce_3: 0.16179/0.08677, loss_spatial_dice_3: 0.10562/0.18267, loss_spatial_ce_3: 0.06455/0.06527, loss_grounding_bce_3: 0.02452/0.08097, loss_grounding_dice_3: 0.03649/0.15056, loss_grounding_ce_3: 0.08284/0.25291, loss_mask_ce_4: 0.38707/0.76887, loss_mask_bce_4: 0.28729/0.30557, loss_mask_dice_4: 0.23102/1.04192, loss_spatial_bce_4: 0.08671/0.08934, loss_spatial_dice_4: 0.07422/0.19156, loss_spatial_ce_4: 0.06309/0.07911, loss_grounding_bce_4: 0.02472/0.08174, loss_grounding_dice_4: 0.03690/0.15315, loss_grounding_ce_4: 0.02929/0.25727, loss_mask_ce_5: 0.40258/0.79435, loss_mask_bce_5: 0.29195/0.30748, loss_mask_dice_5: 0.22713/1.05014, loss_spatial_bce_5: 0.09703/0.09176, loss_spatial_dice_5: 0.07504/0.19493, loss_spatial_ce_5: 0.07515/0.09292, loss_grounding_bce_5: 0.02008/0.08204, loss_grounding_dice_5: 0.03270/0.15408, loss_grounding_ce_5: 0.13034/0.27446, loss_mask_ce_6: 0.45203/0.82212, loss_mask_bce_6: 0.26065/0.30961, loss_mask_dice_6: 0.25994/1.05398, loss_spatial_bce_6: 0.11068/0.09726, loss_spatial_dice_6: 0.09157/0.19728, loss_spatial_ce_6: 0.05000/0.11728, loss_grounding_bce_6: 0.02239/0.08281, loss_grounding_dice_6: 0.03374/0.15453, loss_grounding_ce_6: 0.07297/0.28336, loss_mask_ce_7: 0.37164/0.87697, loss_mask_bce_7: 0.32415/0.31687, loss_mask_dice_7: 0.30903/1.09985, loss_spatial_bce_7: 0.12321/0.10631, loss_spatial_dice_7: 0.09351/0.22196, loss_spatial_ce_7: 0.05340/0.15122, loss_grounding_bce_7: 0.02107/0.08453, loss_grounding_dice_7: 0.03634/0.16007, loss_grounding_ce_7: 0.57532/0.31641, loss_mask_ce_8: 0.65448/1.01078, loss_mask_bce_8: 0.42598/0.33281, loss_mask_dice_8: 0.34572/1.17652, loss_spatial_bce_8: 0.12400/0.12250, loss_spatial_dice_8: 0.09849/0.25627, loss_spatial_ce_8: 0.04219/0.19619, loss_grounding_bce_8: 0.02214/0.08874, loss_grounding_dice_8: 0.03302/0.16990, loss_grounding_ce_8: 0.02676/0.41418, loss_mask_ce_9: 3.32535/3.47246, loss_mask_bce_9: 0.34450/0.35979, loss_mask_dice_9: 0.32316/1.75927, loss_spatial_bce_9: 0.41529/0.35425, loss_spatial_dice_9: 0.78954/0.79294, loss_spatial_ce_9: 2.02450/1.38587, loss_grounding_bce_9: 0.02448/0.10100, loss_grounding_dice_9: 0.05053/0.24209, loss_grounding_ce_9: 1.10149/0.66663] items per batch[64] items per second[0.37] total items[5472000] mini batches[ 85500] memory[4999] epoch remaining[0:10:42] INFO:trainer.default_trainer:epochs[ 46] optim steps[85600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.21216/0.74932, loss_mask_bce_0: 0.50799/0.30032, loss_mask_dice_0: 0.37543/1.01898, loss_spatial_bce_0: 0.18483/0.08402, loss_spatial_dice_0: 0.15768/0.17757, loss_spatial_ce_0: 0.11279/0.05438, loss_grounding_bce_0: 0.13611/0.08045, loss_grounding_dice_0: 0.23462/0.15023, loss_grounding_ce_0: 0.00872/0.24732, loss_mask_ce_1: 0.20290/0.75014, loss_mask_bce_1: 0.54715/0.30115, loss_mask_dice_1: 0.38345/1.02356, loss_spatial_bce_1: 0.15972/0.08452, loss_spatial_dice_1: 0.14971/0.18058, loss_spatial_ce_1: 0.11287/0.05805, loss_grounding_bce_1: 0.13703/0.08066, loss_grounding_dice_1: 0.23347/0.15096, loss_grounding_ce_1: 0.00652/0.24872, loss_mask_ce_2: 0.19800/0.75776, loss_mask_bce_2: 0.57243/0.30152, loss_mask_dice_2: 0.39153/1.02413, loss_spatial_bce_2: 0.16682/0.08461, loss_spatial_dice_2: 0.16044/0.18123, loss_spatial_ce_2: 0.11336/0.06029, loss_grounding_bce_2: 0.13623/0.08064, loss_grounding_dice_2: 0.24102/0.15089, loss_grounding_ce_2: 0.00630/0.25165, loss_mask_ce_3: 0.21166/0.76268, loss_mask_bce_3: 0.57628/0.30280, loss_mask_dice_3: 0.38038/1.02256, loss_spatial_bce_3: 0.17877/0.08675, loss_spatial_dice_3: 0.15780/0.18265, loss_spatial_ce_3: 0.11449/0.06524, loss_grounding_bce_3: 0.14466/0.08098, loss_grounding_dice_3: 0.23702/0.15055, loss_grounding_ce_3: 0.01019/0.25291, loss_mask_ce_4: 0.21791/0.76877, loss_mask_bce_4: 0.51879/0.30554, loss_mask_dice_4: 0.38462/1.04185, loss_spatial_bce_4: 0.18325/0.08932, loss_spatial_dice_4: 0.15786/0.19154, loss_spatial_ce_4: 0.12275/0.07908, loss_grounding_bce_4: 0.13864/0.08174, loss_grounding_dice_4: 0.23151/0.15314, loss_grounding_ce_4: 0.01053/0.25729, loss_mask_ce_5: 0.22171/0.79424, loss_mask_bce_5: 0.55186/0.30745, loss_mask_dice_5: 0.39971/1.05008, loss_spatial_bce_5: 0.22028/0.09174, loss_spatial_dice_5: 0.17383/0.19491, loss_spatial_ce_5: 0.15045/0.09289, loss_grounding_bce_5: 0.13674/0.08204, loss_grounding_dice_5: 0.24262/0.15406, loss_grounding_ce_5: 0.00997/0.27448, loss_mask_ce_6: 0.22163/0.82203, loss_mask_bce_6: 0.56809/0.30958, loss_mask_dice_6: 0.38748/1.05394, loss_spatial_bce_6: 0.23627/0.09724, loss_spatial_dice_6: 0.18456/0.19725, loss_spatial_ce_6: 0.38502/0.11725, loss_grounding_bce_6: 0.14578/0.08282, loss_grounding_dice_6: 0.25843/0.15451, loss_grounding_ce_6: 0.01705/0.28338, loss_mask_ce_7: 0.24637/0.87687, loss_mask_bce_7: 0.54740/0.31684, loss_mask_dice_7: 0.40425/1.09981, loss_spatial_bce_7: 0.25713/0.10628, loss_spatial_dice_7: 0.19375/0.22194, loss_spatial_ce_7: 0.10590/0.15118, loss_grounding_bce_7: 0.11698/0.08453, loss_grounding_dice_7: 0.27099/0.16005, loss_grounding_ce_7: 0.01480/0.31647, loss_mask_ce_8: 0.25696/1.01071, loss_mask_bce_8: 0.59236/0.33278, loss_mask_dice_8: 0.40293/1.17649, loss_spatial_bce_8: 0.31245/0.12247, loss_spatial_dice_8: 0.21402/0.25624, loss_spatial_ce_8: 0.34461/0.19614, loss_grounding_bce_8: 0.14001/0.08875, loss_grounding_dice_8: 0.26524/0.16990, loss_grounding_ce_8: 0.01441/0.41424, loss_mask_ce_9: 1.50095/3.47268, loss_mask_bce_9: 0.46949/0.35978, loss_mask_dice_9: 0.50769/1.75922, loss_spatial_bce_9: 0.48940/0.35426, loss_spatial_dice_9: 0.80755/0.79295, loss_spatial_ce_9: 1.52462/1.38585, loss_grounding_bce_9: 0.12645/0.10103, loss_grounding_dice_9: 0.36136/0.24210, loss_grounding_ce_9: 0.02004/0.66671] items per batch[64] items per second[0.37] total items[5478400] mini batches[ 85600] memory[4999] epoch remaining[0:07:48] INFO:trainer.default_trainer:epochs[ 46] optim steps[85700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.08126/0.74920, loss_mask_bce_0: 0.03252/0.30030, loss_mask_dice_0: 0.50885/1.01909, loss_spatial_bce_0: 0.00578/0.08401, loss_spatial_dice_0: 0.08295/0.17755, loss_spatial_ce_0: 0.04829/0.05437, loss_grounding_bce_0: 0.00455/0.08044, loss_grounding_dice_0: 0.04269/0.15022, loss_grounding_ce_0: 0.00009/0.24726, loss_mask_ce_1: 0.08942/0.75001, loss_mask_bce_1: 0.03283/0.30112, loss_mask_dice_1: 0.52050/1.02368, loss_spatial_bce_1: 0.00761/0.08450, loss_spatial_dice_1: 0.12301/0.18056, loss_spatial_ce_1: 0.04087/0.05804, loss_grounding_bce_1: 0.00335/0.08065, loss_grounding_dice_1: 0.03619/0.15095, loss_grounding_ce_1: 0.00010/0.24867, loss_mask_ce_2: 0.09186/0.75762, loss_mask_bce_2: 0.03351/0.30150, loss_mask_dice_2: 0.55356/1.02423, loss_spatial_bce_2: 0.00681/0.08460, loss_spatial_dice_2: 0.12433/0.18121, loss_spatial_ce_2: 0.04131/0.06028, loss_grounding_bce_2: 0.00362/0.08064, loss_grounding_dice_2: 0.03665/0.15089, loss_grounding_ce_2: 0.00007/0.25155, loss_mask_ce_3: 0.09175/0.76257, loss_mask_bce_3: 0.03626/0.30278, loss_mask_dice_3: 0.61701/1.02264, loss_spatial_bce_3: 0.00624/0.08674, loss_spatial_dice_3: 0.08844/0.18263, loss_spatial_ce_3: 0.04243/0.06523, loss_grounding_bce_3: 0.00535/0.08097, loss_grounding_dice_3: 0.04865/0.15054, loss_grounding_ce_3: 0.00010/0.25282, loss_mask_ce_4: 0.08637/0.76865, loss_mask_bce_4: 0.03458/0.30552, loss_mask_dice_4: 0.45776/1.04193, loss_spatial_bce_4: 0.00789/0.08930, loss_spatial_dice_4: 0.12400/0.19152, loss_spatial_ce_4: 0.04888/0.07909, loss_grounding_bce_4: 0.00336/0.08173, loss_grounding_dice_4: 0.03369/0.15312, loss_grounding_ce_4: 0.00031/0.25722, loss_mask_ce_5: 0.09292/0.79416, loss_mask_bce_5: 0.04312/0.30742, loss_mask_dice_5: 0.51055/1.05017, loss_spatial_bce_5: 0.00732/0.09173, loss_spatial_dice_5: 0.16411/0.19489, loss_spatial_ce_5: 0.06249/0.09289, loss_grounding_bce_5: 0.00356/0.08203, loss_grounding_dice_5: 0.03978/0.15406, loss_grounding_ce_5: 0.00027/0.27441, loss_mask_ce_6: 0.09484/0.82188, loss_mask_bce_6: 0.03465/0.30956, loss_mask_dice_6: 0.51064/1.05405, loss_spatial_bce_6: 0.00962/0.09723, loss_spatial_dice_6: 0.15542/0.19724, loss_spatial_ce_6: 0.06566/0.11725, loss_grounding_bce_6: 0.00434/0.08280, loss_grounding_dice_6: 0.05265/0.15451, loss_grounding_ce_6: 0.00010/0.28336, loss_mask_ce_7: 0.24049/0.87671, loss_mask_bce_7: 0.03094/0.31681, loss_mask_dice_7: 0.39274/1.09990, loss_spatial_bce_7: 0.00788/0.10627, loss_spatial_dice_7: 0.15317/0.22192, loss_spatial_ce_7: 0.06018/0.15115, loss_grounding_bce_7: 0.00516/0.08452, loss_grounding_dice_7: 0.05054/0.16004, loss_grounding_ce_7: 0.00015/0.31638, loss_mask_ce_8: 0.26556/1.01057, loss_mask_bce_8: 0.03480/0.33275, loss_mask_dice_8: 0.52586/1.17658, loss_spatial_bce_8: 0.01553/0.12246, loss_spatial_dice_8: 0.16852/0.25621, loss_spatial_ce_8: 0.08188/0.19614, loss_grounding_bce_8: 0.00288/0.08874, loss_grounding_dice_8: 0.03996/0.16988, loss_grounding_ce_8: 0.00029/0.41414, loss_mask_ce_9: 1.80574/3.47249, loss_mask_bce_9: 0.02792/0.35973, loss_mask_dice_9: 0.63144/1.75926, loss_spatial_bce_9: 0.09183/0.35425, loss_spatial_dice_9: 0.82644/0.79295, loss_spatial_ce_9: 1.98867/1.38586, loss_grounding_bce_9: 0.00503/0.10101, loss_grounding_dice_9: 0.05530/0.24209, loss_grounding_ce_9: 0.01402/0.66656] items per batch[64] items per second[0.37] total items[5484800] mini batches[ 85700] memory[4999] epoch remaining[0:04:54] INFO:trainer.default_trainer:epochs[ 46] optim steps[85800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.14916/0.74903, loss_mask_bce_0: 0.09989/0.30030, loss_mask_dice_0: 0.70305/1.01880, loss_spatial_bce_0: 0.03787/0.08401, loss_spatial_dice_0: 0.26740/0.17754, loss_spatial_ce_0: 0.07762/0.05437, loss_grounding_bce_0: 0.03012/0.08045, loss_grounding_dice_0: 0.19975/0.15022, loss_grounding_ce_0: 0.26703/0.24722, loss_mask_ce_1: 1.12588/0.74984, loss_mask_bce_1: 0.09792/0.30113, loss_mask_dice_1: 0.69982/1.02337, loss_spatial_bce_1: 0.03268/0.08450, loss_spatial_dice_1: 0.23585/0.18054, loss_spatial_ce_1: 0.11012/0.05804, loss_grounding_bce_1: 0.03277/0.08067, loss_grounding_dice_1: 0.21151/0.15095, loss_grounding_ce_1: 0.22473/0.24861, loss_mask_ce_2: 1.23940/0.75745, loss_mask_bce_2: 0.09785/0.30150, loss_mask_dice_2: 0.69724/1.02392, loss_spatial_bce_2: 0.03552/0.08460, loss_spatial_dice_2: 0.25030/0.18119, loss_spatial_ce_2: 0.15020/0.06026, loss_grounding_bce_2: 0.02707/0.08065, loss_grounding_dice_2: 0.17013/0.15089, loss_grounding_ce_2: 0.31744/0.25151, loss_mask_ce_3: 1.13734/0.76238, loss_mask_bce_3: 0.09144/0.30279, loss_mask_dice_3: 0.74886/1.02233, loss_spatial_bce_3: 0.03193/0.08674, loss_spatial_dice_3: 0.25158/0.18261, loss_spatial_ce_3: 0.12674/0.06523, loss_grounding_bce_3: 0.02511/0.08098, loss_grounding_dice_3: 0.18441/0.15055, loss_grounding_ce_3: 0.19323/0.25278, loss_mask_ce_4: 1.24688/0.76848, loss_mask_bce_4: 0.09176/0.30552, loss_mask_dice_4: 0.77204/1.04163, loss_spatial_bce_4: 0.03787/0.08931, loss_spatial_dice_4: 0.25736/0.19151, loss_spatial_ce_4: 0.03625/0.07907, loss_grounding_bce_4: 0.02344/0.08175, loss_grounding_dice_4: 0.17774/0.15313, loss_grounding_ce_4: 0.22449/0.25717, loss_mask_ce_5: 1.33872/0.79397, loss_mask_bce_5: 0.09134/0.30743, loss_mask_dice_5: 0.82428/1.04986, loss_spatial_bce_5: 0.02520/0.09174, loss_spatial_dice_5: 0.27066/0.19488, loss_spatial_ce_5: 0.14822/0.09287, loss_grounding_bce_5: 0.02327/0.08205, loss_grounding_dice_5: 0.16454/0.15406, loss_grounding_ce_5: 0.23082/0.27437, loss_mask_ce_6: 1.32307/0.82172, loss_mask_bce_6: 0.09587/0.30956, loss_mask_dice_6: 0.67278/1.05375, loss_spatial_bce_6: 0.02559/0.09724, loss_spatial_dice_6: 0.24370/0.19723, loss_spatial_ce_6: 0.17031/0.11724, loss_grounding_bce_6: 0.02012/0.08282, loss_grounding_dice_6: 0.15335/0.15451, loss_grounding_ce_6: 0.23118/0.28331, loss_mask_ce_7: 1.42263/0.87654, loss_mask_bce_7: 0.10005/0.31681, loss_mask_dice_7: 0.65921/1.09957, loss_spatial_bce_7: 0.02339/0.10628, loss_spatial_dice_7: 0.28838/0.22190, loss_spatial_ce_7: 0.07327/0.15115, loss_grounding_bce_7: 0.02346/0.08453, loss_grounding_dice_7: 0.20986/0.16004, loss_grounding_ce_7: 0.26221/0.31634, loss_mask_ce_8: 1.40934/1.01037, loss_mask_bce_8: 0.10841/0.33273, loss_mask_dice_8: 0.83102/1.17619, loss_spatial_bce_8: 0.03005/0.12247, loss_spatial_dice_8: 0.35214/0.25619, loss_spatial_ce_8: 0.40107/0.19610, loss_grounding_bce_8: 0.03116/0.08875, loss_grounding_dice_8: 0.19377/0.16987, loss_grounding_ce_8: 0.24872/0.41409, loss_mask_ce_9: 2.90382/3.47212, loss_mask_bce_9: 0.08660/0.35973, loss_mask_dice_9: 1.26094/1.75867, loss_spatial_bce_9: 0.10803/0.35429, loss_spatial_dice_9: 0.80620/0.79294, loss_spatial_ce_9: 1.31307/1.38587, loss_grounding_bce_9: 0.05363/0.10102, loss_grounding_dice_9: 0.32398/0.24209, loss_grounding_ce_9: 0.02326/0.66646] items per batch[64] items per second[0.37] total items[5491200] mini batches[ 85800] memory[4999] epoch remaining[0:02:00] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00085869. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0031 s/iter. Inference: 0.3672 s/iter. Eval: 0.0911 s/iter. Total: 0.4613 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0027 s/iter. Inference: 0.3671 s/iter. Eval: 0.0807 s/iter. Total: 0.4506 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0028 s/iter. Inference: 0.3686 s/iter. Eval: 0.0776 s/iter. Total: 0.4490 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0028 s/iter. Inference: 0.3754 s/iter. Eval: 0.0753 s/iter. Total: 0.4535 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3773 s/iter. Eval: 0.0738 s/iter. Total: 0.4540 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3751 s/iter. Eval: 0.0728 s/iter. Total: 0.4509 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval3ihe8trt ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 55.999 | 83.004 | 66.622 | 133 | | Things | 62.182 | 83.965 | 73.531 | 80 | | Stuff | 46.665 | 81.553 | 56.193 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.55s) creating index... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.35 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.82s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.71 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.52 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.204 | 70.169 | 49.865 | 26.450 | 50.201 | 68.003 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.686 | bicycle | 23.410 | car | 44.322 | | motorcycle | 42.651 | airplane | 62.269 | bus | 71.253 | | train | 75.656 | truck | 43.907 | boat | 31.799 | | traffic light | 29.907 | fire hydrant | 72.737 | stop sign | 68.392 | | parking meter | 52.325 | bench | 27.269 | bird | 35.484 | | cat | 76.805 | dog | 71.630 | horse | 51.577 | | sheep | 54.890 | cow | 57.590 | elephant | 66.399 | | bear | 80.310 | zebra | 66.660 | giraffe | 62.568 | | backpack | 24.833 | umbrella | 56.799 | handbag | 24.612 | | tie | 41.788 | suitcase | 52.065 | frisbee | 69.985 | | skis | 9.010 | snowboard | 34.682 | sports ball | 50.837 | | kite | 37.229 | baseball bat | 39.212 | baseball glove | 51.185 | | skateboard | 43.870 | surfboard | 45.631 | tennis racket | 63.307 | | bottle | 42.985 | wine glass | 38.970 | cup | 51.827 | | fork | 26.835 | knife | 24.862 | spoon | 22.524 | | bowl | 40.105 | banana | 22.480 | apple | 27.919 | | sandwich | 48.870 | orange | 31.915 | broccoli | 24.786 | | carrot | 23.184 | hot dog | 32.518 | pizza | 54.361 | | donut | 57.070 | cake | 49.451 | chair | 29.464 | | couch | 45.376 | potted plant | 23.431 | bed | 44.483 | | dining table | 15.370 | toilet | 70.265 | tv | 68.307 | | laptop | 71.115 | mouse | 64.602 | remote | 44.898 | | keyboard | 58.395 | cell phone | 46.551 | microwave | 67.557 | | oven | 33.456 | toaster | 48.868 | sink | 45.553 | | refrigerator | 70.289 | book | 15.217 | clock | 54.440 | | vase | 41.435 | scissors | 36.879 | teddy bear | 58.169 | | hair drier | 28.001 | toothbrush | 29.018 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.702 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.499 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.264 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.502 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.680 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.556 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.577 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.388 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.613 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.771 INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.78553390010254, 'fwIoU': 71.6650883401141, 'IoU-person': 88.77293772396571, 'IoU-bicycle': 70.47103471369573, 'IoU-car': 72.47508948741975, 'IoU-motorcycle': 85.80217055422142, 'IoU-airplane': 86.80750906580485, 'IoU-bus': 87.37137872992231, 'IoU-train': 88.38924706579088, 'IoU-truck': 69.28884266299107, 'IoU-boat': 74.72256409186674, 'IoU-traffic light': 79.04331222576889, 'IoU-fire hydrant': 93.22788712920479, 'IoU-stop sign': 85.99157024520265, 'IoU-parking meter': 84.86796297338009, 'IoU-bench': 59.60721685607403, 'IoU-bird': 74.44901704855353, 'IoU-cat': 88.01166994250521, 'IoU-dog': 83.59224241164536, 'IoU-horse': 88.41560609726591, 'IoU-sheep': 85.39128108045277, 'IoU-cow': 88.36873873243579, 'IoU-elephant': 87.73897295566776, 'IoU-bear': 92.35466622614364, 'IoU-zebra': 83.70986364759531, 'IoU-giraffe': 89.39677356864959, 'IoU-backpack': 53.15623702172583, 'IoU-umbrella': 88.45570317174813, 'IoU-handbag': 49.2303473423098, 'IoU-tie': 76.41510130726999, 'IoU-suitcase': 80.09334579482386, 'IoU-frisbee': 84.69873331610845, 'IoU-skis': 58.738375757175454, 'IoU-snowboard': 71.01514220355764, 'IoU-sports ball': 78.53901312091257, 'IoU-kite': 79.19054629427484, 'IoU-baseball bat': 68.52682749906403, 'IoU-baseball glove': 81.19553928770455, 'IoU-skateboard': 86.21696568490108, 'IoU-surfboard': 86.27362810173109, 'IoU-tennis racket': 91.06529507601267, 'IoU-bottle': 70.49126867057961, 'IoU-wine glass': 82.6255674330063, 'IoU-cup': 71.22209407342042, 'IoU-fork': 70.09969665437542, 'IoU-knife': 65.77823758557606, 'IoU-spoon': 61.78425328675231, 'IoU-bowl': 58.313339879240345, 'IoU-banana': 82.43601350919835, 'IoU-apple': 61.1466635840252, 'IoU-sandwich': 71.91074738074525, 'IoU-orange': 79.10713469439064, 'IoU-broccoli': 69.39768977738899, 'IoU-carrot': 65.43111790232722, 'IoU-hot dog': 60.866747591341884, 'IoU-pizza': 80.82771778989638, 'IoU-donut': 58.21524129412675, 'IoU-cake': 78.80663681016716, 'IoU-chair': 63.11287981456876, 'IoU-couch': 69.75415217930664, 'IoU-potted plant': 43.40579139533024, 'IoU-bed': 73.76441426818103, 'IoU-dining table': 53.542190152005084, 'IoU-toilet': 87.10300550529298, 'IoU-tv': 77.46899852359394, 'IoU-laptop': 80.71685267270642, 'IoU-mouse': 75.7255340256935, 'IoU-remote': 67.62144709162958, 'IoU-keyboard': 64.82323073642273, 'IoU-cell phone': 79.0962475033051, 'IoU-microwave': 79.51954217295742, 'IoU-oven': 73.44928767736761, 'IoU-toaster': 86.40772931864038, 'IoU-sink': 68.57595094301632, 'IoU-refrigerator': 82.89492967705814, 'IoU-book': 55.858323536791474, 'IoU-clock': 72.59170633325837, 'IoU-vase': 62.71699930940468, 'IoU-scissors': 86.15430922042741, 'IoU-teddy bear': 83.57773590304971, 'IoU-hair drier': 48.95360665668469, 'IoU-toothbrush': 76.14642309737559, 'IoU-banner': 33.025933533863906, 'IoU-blanket': 18.611192429867355, 'IoU-bridge': 35.58636187991876, 'IoU-cardboard': 49.476703877733854, 'IoU-counter': 31.766577694604976, 'IoU-curtain': 71.48705285450163, 'IoU-door-stuff': 48.57409497290052, 'IoU-floor-wood': 63.398159165072535, 'IoU-flower': 43.673486430358125, 'IoU-fruit': 49.181110916985624, 'IoU-gravel': 27.718707025561578, 'IoU-house': 26.80719061526039, 'IoU-light': 44.01585694643686, 'IoU-mirror-stuff': 63.97866688311642, 'IoU-net': 44.04975655697502, 'IoU-pillow': 19.17119996046666, 'IoU-platform': 28.079647846814442, 'IoU-playingfield': 71.2359810344297, 'IoU-railroad': 64.86506924631695, 'IoU-river': 52.99544977741159, 'IoU-road': 66.84411797156152, 'IoU-roof': 18.68642995389395, 'IoU-sand': 65.83149683451661, 'IoU-sea': 85.82062865122487, 'IoU-shelf': 38.3261891178675, 'IoU-snow': 92.20967569185319, 'IoU-stairs': 33.289357632510885, 'IoU-tent': 11.135541588935803, 'IoU-towel': 65.50641588555743, 'IoU-wall-brick': 52.344018528152205, 'IoU-wall-stone': 28.325103344402073, 'IoU-wall-tile': 70.70414733181049, 'IoU-wall-wood': 46.319139750443355, 'IoU-water-other': 28.71336246971269, 'IoU-window-blind': 49.830371815039854, 'IoU-window-other': 51.36052449177932, 'IoU-tree-merged': 81.93894387449829, 'IoU-fence-merged': 53.866322647937295, 'IoU-ceiling-merged': 68.04227496146764, 'IoU-sky-other-merged': 94.12417477542978, 'IoU-cabinet-merged': 63.36117253029978, 'IoU-table-merged': 41.35807307099058, 'IoU-floor-other-merged': 55.23923639009879, 'IoU-pavement-merged': 56.390696748111914, 'IoU-mountain-merged': 58.73654800862247, 'IoU-grass-merged': 72.84288888988083, 'IoU-dirt-merged': 46.4344358699135, 'IoU-paper-merged': 35.6590634561441, 'IoU-food-other-merged': 43.398372397208675, 'IoU-building-other-merged': 60.04429626565406, 'IoU-rock-merged': 66.45302906667337, 'IoU-wall-other-merged': 68.30969113639993, 'IoU-rug-merged': 67.8122560702788, 'mACC': 77.29184741644495, 'pACC': 82.32557017040264, 'ACC-person': 92.99786915740265, 'ACC-bicycle': 79.21169670592103, 'ACC-car': 87.14548512694297, 'ACC-motorcycle': 90.10810672482404, 'ACC-airplane': 90.98412014334887, 'ACC-bus': 93.92290367959711, 'ACC-train': 95.2819547116476, 'ACC-truck': 79.50424068254871, 'ACC-boat': 84.32389089859144, 'ACC-traffic light': 91.27126407403117, 'ACC-fire hydrant': 95.94939087139764, 'ACC-stop sign': 88.5806912417356, 'ACC-parking meter': 87.9858790047394, 'ACC-bench': 72.65043630096434, 'ACC-bird': 78.20931342338177, 'ACC-cat': 91.29668324556552, 'ACC-dog': 86.42651072018973, 'ACC-horse': 93.01277309743668, 'ACC-sheep': 89.94587043733921, 'ACC-cow': 91.66495280411769, 'ACC-elephant': 89.75037403736268, 'ACC-bear': 94.24282539587401, 'ACC-zebra': 85.73832411830502, 'ACC-giraffe': 93.18768565868221, 'ACC-backpack': 73.47492572370093, 'ACC-umbrella': 93.14604870983084, 'ACC-handbag': 70.53168263012205, 'ACC-tie': 84.89510183484607, 'ACC-suitcase': 86.30102072532418, 'ACC-frisbee': 94.17236363636363, 'ACC-skis': 72.71965301178547, 'ACC-snowboard': 81.50522887022116, 'ACC-sports ball': 88.82066548826248, 'ACC-kite': 85.36505251712187, 'ACC-baseball bat': 87.76757149600282, 'ACC-baseball glove': 92.38703587615198, 'ACC-skateboard': 90.81426089922843, 'ACC-surfboard': 92.29143611508594, 'ACC-tennis racket': 94.8841785081489, 'ACC-bottle': 85.0125238300411, 'ACC-wine glass': 91.05591920364091, 'ACC-cup': 89.15986327973715, 'ACC-fork': 83.5241265124965, 'ACC-knife': 77.92060526122957, 'ACC-spoon': 77.81394756203323, 'ACC-bowl': 70.51733839177933, 'ACC-banana': 90.67839077616209, 'ACC-apple': 74.69907730114961, 'ACC-sandwich': 81.05447461433643, 'ACC-orange': 89.37291585553562, 'ACC-broccoli': 80.58180974415698, 'ACC-carrot': 78.09126541139005, 'ACC-hot dog': 67.32740250741898, 'ACC-pizza': 87.64265961075843, 'ACC-donut': 65.44446895313865, 'ACC-cake': 88.3804583289493, 'ACC-chair': 80.38870643237487, 'ACC-couch': 78.22554638305337, 'ACC-potted plant': 59.711145193204906, 'ACC-bed': 86.75345011345202, 'ACC-dining table': 75.5262242676202, 'ACC-toilet': 92.24185594483403, 'ACC-tv': 89.87493935548837, 'ACC-laptop': 93.24547056530854, 'ACC-mouse': 87.01160496232085, 'ACC-remote': 72.10391100794199, 'ACC-keyboard': 71.46960197563837, 'ACC-cell phone': 89.0211548323693, 'ACC-microwave': 84.47518739950824, 'ACC-oven': 90.59325127503176, 'ACC-toaster': 91.43001820197347, 'ACC-sink': 78.11503077311718, 'ACC-refrigerator': 92.17394418169653, 'ACC-book': 76.79922809671906, 'ACC-clock': 77.1335427041549, 'ACC-vase': 71.08230567990812, 'ACC-scissors': 91.65805856265322, 'ACC-teddy bear': 89.00483247404016, 'ACC-hair drier': 60.954088748470106, 'ACC-toothbrush': 84.58391243919388, 'ACC-banner': 75.24133513398098, 'ACC-blanket': 25.55513997414361, 'ACC-bridge': 53.90790863748565, 'ACC-cardboard': 66.76667151374146, 'ACC-counter': 55.09851170801784, 'ACC-curtain': 83.74568632061616, 'ACC-door-stuff': 68.9795884064556, 'ACC-floor-wood': 82.03668164617568, 'ACC-flower': 61.97094347106774, 'ACC-fruit': 68.51629479268504, 'ACC-gravel': 35.8561905760469, 'ACC-house': 33.516690818826014, 'ACC-light': 63.60941507483826, 'ACC-mirror-stuff': 79.69284487215987, 'ACC-net': 67.34990123111918, 'ACC-pillow': 36.870269513956174, 'ACC-platform': 45.30102904196349, 'ACC-playingfield': 90.4806702280419, 'ACC-railroad': 81.61804628105483, 'ACC-river': 70.58481837136024, 'ACC-road': 87.3712804906793, 'ACC-roof': 24.909362050630126, 'ACC-sand': 71.35687589416352, 'ACC-sea': 91.71919517339414, 'ACC-shelf': 56.73619341501873, 'ACC-snow': 95.5957303553093, 'ACC-stairs': 56.86900092571968, 'ACC-tent': 14.850216008464686, 'ACC-towel': 83.02246316766228, 'ACC-wall-brick': 68.96750687391001, 'ACC-wall-stone': 34.127125111847995, 'ACC-wall-tile': 85.72522301198859, 'ACC-wall-wood': 62.228733764391286, 'ACC-water-other': 47.73421382838959, 'ACC-window-blind': 62.55298199506862, 'ACC-window-other': 73.7683773256702, 'ACC-tree-merged': 90.00892246784974, 'ACC-fence-merged': 71.58125724187386, 'ACC-ceiling-merged': 82.93269442799232, 'ACC-sky-other-merged': 97.13794739185725, 'ACC-cabinet-merged': 77.78384299106035, 'ACC-table-merged': 54.90451403806226, 'ACC-floor-other-merged': 66.16994525625269, 'ACC-pavement-merged': 67.48812933104297, 'ACC-mountain-merged': 70.36867825964165, 'ACC-grass-merged': 84.85514776121956, 'ACC-dirt-merged': 66.83679034945472, 'ACC-paper-merged': 47.37373828007791, 'ACC-food-other-merged': 60.344641265980215, 'ACC-building-other-merged': 74.64334006820124, 'ACC-rock-merged': 83.64959769805483, 'ACC-wall-other-merged': 82.33328386254833, 'ACC-rug-merged': 82.84639167179324})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3121 s/iter. Inference: 0.2117 s/iter. Eval: 0.0000 s/iter. Total: 0.5238 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3579 s/iter. Inference: 0.3595 s/iter. Eval: 0.0000 s/iter. Total: 0.7176 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3645 s/iter. Inference: 0.5596 s/iter. Eval: 0.0000 s/iter. Total: 0.9242 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3362598770851624, 'noc@0.8': 2.2724612232952883, 'noc@0.85': 2.675153643546971, 'noc@0.9': 3.3927421714954638, 'miou@iter1': 0.8732884618878051} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0014 s/iter. Inference: 0.1438 s/iter. Eval: 0.0010 s/iter. Total: 0.1463 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.44772338867188, 'precision@0.6': 73.53284454345703, 'precision@0.7': 69.76292419433594, 'precision@0.8': 60.39642333984375, 'precision@0.9': 33.7738037109375, 'cIoU': 62.884605407714844, 'mIoU': 67.80730438232422} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 55.99873341914685, 'SQ': 83.00381837874768, 'RQ': 66.6220643001094, 'PQ_th': 62.182303005017104, 'SQ_th': 83.96484657051738, 'RQ_th': 73.5311259162707, 'PQ_st': 46.66504347821066, 'SQ_st': 81.55320978739728, 'RQ_st': 56.193292049299934}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 46.20427755083464, 'AP50': 70.16920322849013, 'AP75': 49.86486264095613, 'APs': 26.44975416125882, 'APm': 50.20108272918446, 'APl': 68.00306245298079, 'AP-person': 49.686308910690215, 'AP-bicycle': 23.41025084601775, 'AP-car': 44.321929177647256, 'AP-motorcycle': 42.650903949144514, 'AP-airplane': 62.26918538970877, 'AP-bus': 71.25286043572567, 'AP-train': 75.65613644445047, 'AP-truck': 43.90730880950861, 'AP-boat': 31.798667208708686, 'AP-traffic light': 29.907373295866574, 'AP-fire hydrant': 72.73742629018992, 'AP-stop sign': 68.39177941258768, 'AP-parking meter': 52.324521371332224, 'AP-bench': 27.26910702974094, 'AP-bird': 35.48435068670623, 'AP-cat': 76.80532232577265, 'AP-dog': 71.63024696279777, 'AP-horse': 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'ACC-cabinet-merged': 77.78384299106035, 'ACC-table-merged': 54.90451403806226, 'ACC-floor-other-merged': 66.16994525625269, 'ACC-pavement-merged': 67.48812933104297, 'ACC-mountain-merged': 70.36867825964165, 'ACC-grass-merged': 84.85514776121956, 'ACC-dirt-merged': 66.83679034945472, 'ACC-paper-merged': 47.37373828007791, 'ACC-food-other-merged': 60.344641265980215, 'ACC-building-other-merged': 74.64334006820124, 'ACC-rock-merged': 83.64959769805483, 'ACC-wall-other-merged': 82.33328386254833, 'ACC-rug-merged': 82.84639167179324})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3362598770851624, 'noc@0.8': 2.2724612232952883, 'noc@0.85': 2.675153643546971, 'noc@0.9': 3.3927421714954638, 'miou@iter1': 0.8732884618878051}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 76.44772338867188, 'precision@0.6': 73.53284454345703, 'precision@0.7': 69.76292419433594, 'precision@0.8': 60.39642333984375, 'precision@0.9': 33.7738037109375, 'cIoU': 62.884605407714844, 'mIoU': 67.80730438232422}}} INFO:trainer.default_trainer:This epoch takes 0:56:30.266592 INFO:trainer.default_trainer:PROGRESS: 94.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 47 training. INFO:trainer.default_trainer:epochs[ 47] optim steps[85900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.60389/0.74899, loss_mask_bce_0: 0.04391/0.30028, loss_mask_dice_0: 1.68209/1.01887, loss_spatial_bce_0: 0.00594/0.08400, loss_spatial_dice_0: 0.40509/0.17753, loss_spatial_ce_0: 0.01014/0.05436, loss_grounding_bce_0: 0.00279/0.08044, loss_grounding_dice_0: 0.17753/0.15022, loss_grounding_ce_0: 0.35486/0.24722, loss_mask_ce_1: 1.45741/0.74979, loss_mask_bce_1: 0.03662/0.30111, loss_mask_dice_1: 1.68976/1.02344, loss_spatial_bce_1: 0.00524/0.08449, loss_spatial_dice_1: 0.37324/0.18053, loss_spatial_ce_1: 0.15549/0.05805, loss_grounding_bce_1: 0.00529/0.08066, loss_grounding_dice_1: 0.22615/0.15096, loss_grounding_ce_1: 0.44911/0.24862, loss_mask_ce_2: 1.67835/0.75741, loss_mask_bce_2: 0.04216/0.30148, loss_mask_dice_2: 1.83038/1.02399, loss_spatial_bce_2: 0.00462/0.08458, loss_spatial_dice_2: 0.41385/0.18119, loss_spatial_ce_2: 0.05270/0.06028, loss_grounding_bce_2: 0.00519/0.08064, loss_grounding_dice_2: 0.31095/0.15089, loss_grounding_ce_2: 0.53925/0.25151, loss_mask_ce_3: 1.65684/0.76232, loss_mask_bce_3: 0.03153/0.30277, loss_mask_dice_3: 1.41653/1.02236, loss_spatial_bce_3: 0.00530/0.08673, loss_spatial_dice_3: 0.37735/0.18261, loss_spatial_ce_3: 0.06777/0.06523, loss_grounding_bce_3: 0.00390/0.08098, loss_grounding_dice_3: 0.21655/0.15055, loss_grounding_ce_3: 0.42981/0.25278, loss_mask_ce_4: 2.06890/0.76846, loss_mask_bce_4: 0.03212/0.30550, loss_mask_dice_4: 1.48930/1.04171, loss_spatial_bce_4: 0.00655/0.08930, loss_spatial_dice_4: 0.43413/0.19152, loss_spatial_ce_4: 0.04493/0.07906, loss_grounding_bce_4: 0.00226/0.08174, loss_grounding_dice_4: 0.16732/0.15313, loss_grounding_ce_4: 0.85695/0.25714, loss_mask_ce_5: 1.53319/0.79395, loss_mask_bce_5: 0.03270/0.30742, loss_mask_dice_5: 1.44936/1.04993, loss_spatial_bce_5: 0.00737/0.09173, loss_spatial_dice_5: 0.40606/0.19488, loss_spatial_ce_5: 0.07925/0.09287, loss_grounding_bce_5: 0.00280/0.08204, loss_grounding_dice_5: 0.19656/0.15407, loss_grounding_ce_5: 0.77530/0.27431, loss_mask_ce_6: 1.61775/0.82167, loss_mask_bce_6: 0.03188/0.30955, loss_mask_dice_6: 1.65037/1.05384, loss_spatial_bce_6: 0.01130/0.09722, loss_spatial_dice_6: 0.51741/0.19724, loss_spatial_ce_6: 0.12452/0.11724, loss_grounding_bce_6: 0.00376/0.08282, loss_grounding_dice_6: 0.18233/0.15452, loss_grounding_ce_6: 0.67834/0.28330, loss_mask_ce_7: 1.87464/0.87647, loss_mask_bce_7: 0.03153/0.31680, loss_mask_dice_7: 1.42745/1.09963, loss_spatial_bce_7: 0.01144/0.10627, loss_spatial_dice_7: 0.54076/0.22191, loss_spatial_ce_7: 0.08808/0.15112, loss_grounding_bce_7: 0.00302/0.08453, loss_grounding_dice_7: 0.16761/0.16005, loss_grounding_ce_7: 0.67261/0.31630, loss_mask_ce_8: 2.16296/1.01038, loss_mask_bce_8: 0.04014/0.33273, loss_mask_dice_8: 1.66901/1.17627, loss_spatial_bce_8: 0.00679/0.12246, loss_spatial_dice_8: 0.46237/0.25622, loss_spatial_ce_8: 0.17215/0.19608, loss_grounding_bce_8: 0.00387/0.08876, loss_grounding_dice_8: 0.31780/0.16988, loss_grounding_ce_8: 2.84999/0.41408, loss_mask_ce_9: 2.88890/3.47217, loss_mask_bce_9: 0.02134/0.35973, loss_mask_dice_9: 1.86702/1.75867, loss_spatial_bce_9: 0.02199/0.35425, loss_spatial_dice_9: 0.90365/0.79295, loss_spatial_ce_9: 2.38077/1.38586, loss_grounding_bce_9: 0.00466/0.10102, loss_grounding_dice_9: 0.38063/0.24210, loss_grounding_ce_9: 1.79552/0.66645] items per batch[64] items per second[0.16] total items[5497600] mini batches[ 85900] memory[4999] epoch remaining[0:59:43] INFO:trainer.default_trainer:epochs[ 47] optim steps[86000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.42086/0.74883, loss_mask_bce_0: 0.02699/0.30023, loss_mask_dice_0: 0.25887/1.01879, loss_spatial_bce_0: 0.00907/0.08398, loss_spatial_dice_0: 0.09219/0.17751, loss_spatial_ce_0: 0.00011/0.05434, loss_grounding_bce_0: 0.01410/0.08044, loss_grounding_dice_0: 0.10441/0.15021, loss_grounding_ce_0: 0.75281/0.24719, loss_mask_ce_1: 0.39544/0.74963, loss_mask_bce_1: 0.02776/0.30106, loss_mask_dice_1: 0.27929/1.02337, loss_spatial_bce_1: 0.00759/0.08447, loss_spatial_dice_1: 0.07973/0.18051, loss_spatial_ce_1: 0.00019/0.05803, loss_grounding_bce_1: 0.01102/0.08065, loss_grounding_dice_1: 0.09681/0.15095, loss_grounding_ce_1: 0.70889/0.24857, loss_mask_ce_2: 0.38137/0.75726, loss_mask_bce_2: 0.03012/0.30143, loss_mask_dice_2: 0.28907/1.02394, loss_spatial_bce_2: 0.00907/0.08456, loss_spatial_dice_2: 0.08826/0.18116, loss_spatial_ce_2: 0.00038/0.06026, loss_grounding_bce_2: 0.01230/0.08063, loss_grounding_dice_2: 0.08149/0.15089, loss_grounding_ce_2: 0.41283/0.25148, loss_mask_ce_3: 0.41146/0.76216, loss_mask_bce_3: 0.02530/0.30272, loss_mask_dice_3: 0.25687/1.02233, loss_spatial_bce_3: 0.00821/0.08671, loss_spatial_dice_3: 0.08322/0.18259, loss_spatial_ce_3: 0.00188/0.06521, loss_grounding_bce_3: 0.01091/0.08097, loss_grounding_dice_3: 0.08587/0.15054, loss_grounding_ce_3: 0.37791/0.25273, loss_mask_ce_4: 0.39949/0.76829, loss_mask_bce_4: 0.02537/0.30545, loss_mask_dice_4: 0.26188/1.04167, loss_spatial_bce_4: 0.00815/0.08928, loss_spatial_dice_4: 0.08310/0.19149, loss_spatial_ce_4: 0.00193/0.07903, loss_grounding_bce_4: 0.01339/0.08173, loss_grounding_dice_4: 0.09729/0.15313, loss_grounding_ce_4: 0.49102/0.25710, loss_mask_ce_5: 0.38741/0.79381, loss_mask_bce_5: 0.02686/0.30736, loss_mask_dice_5: 0.26832/1.04988, loss_spatial_bce_5: 0.00984/0.09172, loss_spatial_dice_5: 0.09330/0.19485, loss_spatial_ce_5: 0.05551/0.09284, loss_grounding_bce_5: 0.01059/0.08203, loss_grounding_dice_5: 0.10511/0.15406, loss_grounding_ce_5: 0.29795/0.27426, loss_mask_ce_6: 0.38533/0.82151, loss_mask_bce_6: 0.02324/0.30949, loss_mask_dice_6: 0.23685/1.05380, loss_spatial_bce_6: 0.01119/0.09720, loss_spatial_dice_6: 0.10604/0.19722, loss_spatial_ce_6: 0.02985/0.11722, loss_grounding_bce_6: 0.01338/0.08281, loss_grounding_dice_6: 0.10825/0.15451, loss_grounding_ce_6: 0.71684/0.28323, loss_mask_ce_7: 0.52726/0.87632, loss_mask_bce_7: 0.02833/0.31675, loss_mask_dice_7: 0.25995/1.09959, loss_spatial_bce_7: 0.01329/0.10625, loss_spatial_dice_7: 0.13129/0.22188, loss_spatial_ce_7: 0.00657/0.15109, loss_grounding_bce_7: 0.01067/0.08453, loss_grounding_dice_7: 0.10153/0.16004, loss_grounding_ce_7: 0.91257/0.31624, loss_mask_ce_8: 0.64944/1.01019, loss_mask_bce_8: 0.03363/0.33268, loss_mask_dice_8: 0.27392/1.17626, loss_spatial_bce_8: 0.01600/0.12244, loss_spatial_dice_8: 0.15268/0.25619, loss_spatial_ce_8: 0.04435/0.19602, loss_grounding_bce_8: 0.01361/0.08875, loss_grounding_dice_8: 0.10192/0.16987, loss_grounding_ce_8: 0.53811/0.41397, loss_mask_ce_9: 3.79064/3.47191, loss_mask_bce_9: 0.04209/0.35968, loss_mask_dice_9: 0.76849/1.75853, loss_spatial_bce_9: 0.17564/0.35425, loss_spatial_dice_9: 0.86878/0.79293, loss_spatial_ce_9: 1.36722/1.38573, loss_grounding_bce_9: 0.04791/0.10101, loss_grounding_dice_9: 0.24255/0.24209, loss_grounding_ce_9: 1.65782/0.66630] items per batch[64] items per second[0.37] total items[5504000] mini batches[ 86000] memory[4999] epoch remaining[0:50:13] INFO:trainer.default_trainer:epochs[ 47] optim steps[86100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 2.19574/0.74874, loss_mask_bce_0: 0.79572/0.30019, loss_mask_dice_0: 2.28818/1.01883, loss_spatial_bce_0: 0.07901/0.08396, loss_spatial_dice_0: 0.31338/0.17749, loss_spatial_ce_0: 0.03690/0.05434, loss_grounding_bce_0: 0.06827/0.08041, loss_grounding_dice_0: 0.06201/0.15021, loss_grounding_ce_0: 0.80379/0.24720, loss_mask_ce_1: 2.20880/0.74954, loss_mask_bce_1: 0.82270/0.30101, loss_mask_dice_1: 2.24237/1.02342, loss_spatial_bce_1: 0.09464/0.08445, loss_spatial_dice_1: 0.32062/0.18049, loss_spatial_ce_1: 0.03780/0.05801, loss_grounding_bce_1: 0.06825/0.08063, loss_grounding_dice_1: 0.06190/0.15094, loss_grounding_ce_1: 0.76355/0.24856, loss_mask_ce_2: 2.20392/0.75717, loss_mask_bce_2: 0.84758/0.30139, loss_mask_dice_2: 2.25917/1.02399, loss_spatial_bce_2: 0.09996/0.08454, loss_spatial_dice_2: 0.32577/0.18115, loss_spatial_ce_2: 0.04813/0.06025, loss_grounding_bce_2: 0.06519/0.08061, loss_grounding_dice_2: 0.06221/0.15088, loss_grounding_ce_2: 0.81616/0.25146, loss_mask_ce_3: 2.46555/0.76209, loss_mask_bce_3: 0.78582/0.30268, loss_mask_dice_3: 2.13596/1.02238, loss_spatial_bce_3: 0.09907/0.08668, loss_spatial_dice_3: 0.34679/0.18258, loss_spatial_ce_3: 0.09367/0.06519, loss_grounding_bce_3: 0.07181/0.08095, loss_grounding_dice_3: 0.06638/0.15054, loss_grounding_ce_3: 0.84717/0.25273, loss_mask_ce_4: 2.35413/0.76821, loss_mask_bce_4: 0.87162/0.30541, loss_mask_dice_4: 2.27787/1.04174, loss_spatial_bce_4: 0.10902/0.08926, loss_spatial_dice_4: 0.40530/0.19149, loss_spatial_ce_4: 0.13736/0.07901, loss_grounding_bce_4: 0.07057/0.08171, loss_grounding_dice_4: 0.06546/0.15312, loss_grounding_ce_4: 0.83814/0.25718, loss_mask_ce_5: 2.31432/0.79374, loss_mask_bce_5: 0.93903/0.30731, loss_mask_dice_5: 2.30828/1.04993, loss_spatial_bce_5: 0.12773/0.09170, loss_spatial_dice_5: 0.41611/0.19484, loss_spatial_ce_5: 0.20372/0.09282, loss_grounding_bce_5: 0.06959/0.08201, loss_grounding_dice_5: 0.05904/0.15405, loss_grounding_ce_5: 0.89066/0.27434, loss_mask_ce_6: 2.30662/0.82141, loss_mask_bce_6: 0.98371/0.30944, loss_mask_dice_6: 2.23845/1.05385, loss_spatial_bce_6: 0.13914/0.09718, loss_spatial_dice_6: 0.40105/0.19721, loss_spatial_ce_6: 0.19777/0.11719, loss_grounding_bce_6: 0.07302/0.08279, loss_grounding_dice_6: 0.05942/0.15450, loss_grounding_ce_6: 1.02549/0.28329, loss_mask_ce_7: 2.18173/0.87623, loss_mask_bce_7: 0.92219/0.31670, loss_mask_dice_7: 2.09495/1.09965, loss_spatial_bce_7: 0.18483/0.10622, loss_spatial_dice_7: 0.48838/0.22187, loss_spatial_ce_7: 0.16563/0.15105, loss_grounding_bce_7: 0.07188/0.08451, loss_grounding_dice_7: 0.05901/0.16004, loss_grounding_ce_7: 0.95329/0.31623, loss_mask_ce_8: 1.96817/1.01012, loss_mask_bce_8: 1.07153/0.33262, loss_mask_dice_8: 2.38089/1.17633, loss_spatial_bce_8: 0.17217/0.12240, loss_spatial_dice_8: 0.50720/0.25617, loss_spatial_ce_8: 0.32592/0.19599, loss_grounding_bce_8: 0.06309/0.08873, loss_grounding_dice_8: 0.05196/0.16988, loss_grounding_ce_8: 1.29758/0.41399, loss_mask_ce_9: 5.98221/3.47179, loss_mask_bce_9: 1.31350/0.35963, loss_mask_dice_9: 3.96831/1.75855, loss_spatial_bce_9: 0.29236/0.35418, loss_spatial_dice_9: 0.97588/0.79294, loss_spatial_ce_9: 1.39941/1.38567, loss_grounding_bce_9: 0.15807/0.10098, loss_grounding_dice_9: 0.20484/0.24209, loss_grounding_ce_9: 1.91403/0.66632] items per batch[64] items per second[0.38] total items[5510400] mini batches[ 86100] memory[4999] epoch remaining[0:46:15] INFO:trainer.default_trainer:epochs[ 47] optim steps[86200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.68994/0.74867, loss_mask_bce_0: 0.07200/0.30013, loss_mask_dice_0: 0.53696/1.01873, loss_spatial_bce_0: 0.01109/0.08394, loss_spatial_dice_0: 0.14041/0.17748, loss_spatial_ce_0: 0.04664/0.05432, loss_grounding_bce_0: 0.00646/0.08042, loss_grounding_dice_0: 0.08991/0.15021, loss_grounding_ce_0: 0.02963/0.24719, loss_mask_ce_1: 0.79353/0.74947, loss_mask_bce_1: 0.06494/0.30095, loss_mask_dice_1: 0.55649/1.02331, loss_spatial_bce_1: 0.01156/0.08444, loss_spatial_dice_1: 0.13083/0.18048, loss_spatial_ce_1: 0.04039/0.05800, loss_grounding_bce_1: 0.00506/0.08063, loss_grounding_dice_1: 0.08554/0.15094, loss_grounding_ce_1: 0.03757/0.24858, loss_mask_ce_2: 0.89463/0.75708, loss_mask_bce_2: 0.06688/0.30133, loss_mask_dice_2: 0.71879/1.02390, loss_spatial_bce_2: 0.01127/0.08453, loss_spatial_dice_2: 0.11743/0.18113, loss_spatial_ce_2: 0.04533/0.06022, loss_grounding_bce_2: 0.00675/0.08062, loss_grounding_dice_2: 0.09435/0.15088, loss_grounding_ce_2: 0.04314/0.25147, loss_mask_ce_3: 0.76615/0.76200, loss_mask_bce_3: 0.06448/0.30262, loss_mask_dice_3: 0.52447/1.02229, loss_spatial_bce_3: 0.01221/0.08667, loss_spatial_dice_3: 0.15287/0.18256, loss_spatial_ce_3: 0.11342/0.06516, loss_grounding_bce_3: 0.00576/0.08095, loss_grounding_dice_3: 0.07330/0.15054, loss_grounding_ce_3: 0.03501/0.25272, loss_mask_ce_4: 0.90711/0.76814, loss_mask_bce_4: 0.07370/0.30535, loss_mask_dice_4: 0.73869/1.04165, loss_spatial_bce_4: 0.01311/0.08924, loss_spatial_dice_4: 0.15709/0.19147, loss_spatial_ce_4: 0.20282/0.07899, loss_grounding_bce_4: 0.00552/0.08171, loss_grounding_dice_4: 0.08086/0.15312, loss_grounding_ce_4: 0.02822/0.25715, loss_mask_ce_5: 0.92544/0.79365, loss_mask_bce_5: 0.06409/0.30725, loss_mask_dice_5: 0.45233/1.04985, loss_spatial_bce_5: 0.01276/0.09168, loss_spatial_dice_5: 0.14571/0.19482, loss_spatial_ce_5: 0.10636/0.09280, loss_grounding_bce_5: 0.00631/0.08201, loss_grounding_dice_5: 0.07896/0.15405, loss_grounding_ce_5: 0.02037/0.27431, loss_mask_ce_6: 0.71774/0.82131, loss_mask_bce_6: 0.07548/0.30939, loss_mask_dice_6: 0.54483/1.05377, loss_spatial_bce_6: 0.01296/0.09717, loss_spatial_dice_6: 0.12741/0.19719, loss_spatial_ce_6: 0.14134/0.11716, loss_grounding_bce_6: 0.00493/0.08278, loss_grounding_dice_6: 0.07346/0.15450, loss_grounding_ce_6: 0.03945/0.28331, loss_mask_ce_7: 1.14739/0.87610, loss_mask_bce_7: 0.06850/0.31664, loss_mask_dice_7: 0.60027/1.09958, loss_spatial_bce_7: 0.04213/0.10620, loss_spatial_dice_7: 0.19235/0.22185, loss_spatial_ce_7: 0.17866/0.15100, loss_grounding_bce_7: 0.00625/0.08450, loss_grounding_dice_7: 0.08216/0.16004, loss_grounding_ce_7: 0.09799/0.31619, loss_mask_ce_8: 1.61518/1.00997, loss_mask_bce_8: 0.07748/0.33256, loss_mask_dice_8: 0.58631/1.17629, loss_spatial_bce_8: 0.04574/0.12238, loss_spatial_dice_8: 0.26166/0.25615, loss_spatial_ce_8: 0.13968/0.19594, loss_grounding_bce_8: 0.00439/0.08873, loss_grounding_dice_8: 0.08003/0.16988, loss_grounding_ce_8: 0.17215/0.41392, loss_mask_ce_9: 4.04402/3.47168, loss_mask_bce_9: 0.06937/0.35955, loss_mask_dice_9: 0.75767/1.75839, loss_spatial_bce_9: 0.09280/0.35416, loss_spatial_dice_9: 0.83301/0.79292, loss_spatial_ce_9: 1.41033/1.38567, loss_grounding_bce_9: 0.00408/0.10098, loss_grounding_dice_9: 0.09237/0.24209, loss_grounding_ce_9: 0.52385/0.66627] items per batch[64] items per second[0.38] total items[5516800] mini batches[ 86200] memory[4999] epoch remaining[0:43:00] INFO:trainer.default_trainer:epochs[ 47] optim steps[86300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.19904/0.74865, loss_mask_bce_0: 0.03523/0.30008, loss_mask_dice_0: 2.42843/1.01892, loss_spatial_bce_0: 0.00252/0.08393, loss_spatial_dice_0: 0.31854/0.17748, loss_spatial_ce_0: 0.01190/0.05430, loss_grounding_bce_0: 0.00302/0.08041, loss_grounding_dice_0: 0.22228/0.15022, loss_grounding_ce_0: 0.46076/0.24720, loss_mask_ce_1: 0.24190/0.74945, loss_mask_bce_1: 0.02954/0.30090, loss_mask_dice_1: 1.64187/1.02351, loss_spatial_bce_1: 0.00392/0.08443, loss_spatial_dice_1: 0.22499/0.18048, loss_spatial_ce_1: 0.02404/0.05800, loss_grounding_bce_1: 0.00300/0.08062, loss_grounding_dice_1: 0.24578/0.15095, loss_grounding_ce_1: 0.46369/0.24859, loss_mask_ce_2: 0.25743/0.75707, loss_mask_bce_2: 0.02439/0.30128, loss_mask_dice_2: 1.45726/1.02409, loss_spatial_bce_2: 0.00325/0.08452, loss_spatial_dice_2: 0.18475/0.18113, loss_spatial_ce_2: 0.01670/0.06021, loss_grounding_bce_2: 0.00334/0.08060, loss_grounding_dice_2: 0.22269/0.15089, loss_grounding_ce_2: 0.43646/0.25148, loss_mask_ce_3: 0.17360/0.76198, loss_mask_bce_3: 0.02911/0.30257, loss_mask_dice_3: 1.86725/1.02250, loss_spatial_bce_3: 0.00320/0.08666, loss_spatial_dice_3: 0.28053/0.18256, loss_spatial_ce_3: 0.18734/0.06515, loss_grounding_bce_3: 0.00363/0.08094, loss_grounding_dice_3: 0.24387/0.15054, loss_grounding_ce_3: 0.45579/0.25272, loss_mask_ce_4: 0.15094/0.76812, loss_mask_bce_4: 0.02998/0.30530, loss_mask_dice_4: 1.49048/1.04185, loss_spatial_bce_4: 0.00358/0.08924, loss_spatial_dice_4: 0.16340/0.19148, loss_spatial_ce_4: 0.00836/0.07899, loss_grounding_bce_4: 0.00361/0.08170, loss_grounding_dice_4: 0.23155/0.15313, loss_grounding_ce_4: 0.50717/0.25715, loss_mask_ce_5: 0.56480/0.79363, loss_mask_bce_5: 0.02639/0.30721, loss_mask_dice_5: 1.92498/1.05007, loss_spatial_bce_5: 0.00325/0.09167, loss_spatial_dice_5: 0.20482/0.19483, loss_spatial_ce_5: 0.04621/0.09280, loss_grounding_bce_5: 0.00408/0.08200, loss_grounding_dice_5: 0.30189/0.15406, loss_grounding_ce_5: 0.43752/0.27430, loss_mask_ce_6: 0.18269/0.82128, loss_mask_bce_6: 0.04938/0.30935, loss_mask_dice_6: 2.36053/1.05402, loss_spatial_bce_6: 0.00472/0.09717, loss_spatial_dice_6: 0.31171/0.19720, loss_spatial_ce_6: 0.09994/0.11715, loss_grounding_bce_6: 0.00444/0.08278, loss_grounding_dice_6: 0.23523/0.15452, loss_grounding_ce_6: 0.53004/0.28330, loss_mask_ce_7: 0.17361/0.87611, loss_mask_bce_7: 0.03343/0.31661, loss_mask_dice_7: 1.94855/1.09983, loss_spatial_bce_7: 0.00352/0.10619, loss_spatial_dice_7: 0.30002/0.22186, loss_spatial_ce_7: 0.10111/0.15098, loss_grounding_bce_7: 0.00679/0.08450, loss_grounding_dice_7: 0.34795/0.16004, loss_grounding_ce_7: 0.51410/0.31619, loss_mask_ce_8: 0.48514/1.00996, loss_mask_bce_8: 0.04125/0.33253, loss_mask_dice_8: 1.93704/1.17657, loss_spatial_bce_8: 0.00449/0.12237, loss_spatial_dice_8: 0.28426/0.25615, loss_spatial_ce_8: 0.09348/0.19592, loss_grounding_bce_8: 0.00818/0.08872, loss_grounding_dice_8: 0.41203/0.16989, loss_grounding_ce_8: 0.52664/0.41399, loss_mask_ce_9: 5.50677/3.47175, loss_mask_bce_9: 0.03121/0.35952, loss_mask_dice_9: 1.95948/1.75872, loss_spatial_bce_9: 0.05853/0.35415, loss_spatial_dice_9: 0.70509/0.79293, loss_spatial_ce_9: 5.07115/1.38571, loss_grounding_bce_9: 0.00429/0.10099, loss_grounding_dice_9: 0.41651/0.24211, loss_grounding_ce_9: 0.57967/0.66633] items per batch[64] items per second[0.37] total items[5523200] mini batches[ 86300] memory[4999] epoch remaining[0:40:09] INFO:trainer.default_trainer:epochs[ 47] optim steps[86400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.37514/0.74863, loss_mask_bce_0: 0.48501/0.30010, loss_mask_dice_0: 1.05483/1.01893, loss_spatial_bce_0: 0.06452/0.08393, loss_spatial_dice_0: 0.10613/0.17748, loss_spatial_ce_0: 0.00007/0.05430, loss_grounding_bce_0: 0.18886/0.08043, loss_grounding_dice_0: 0.22196/0.15025, loss_grounding_ce_0: 0.00241/0.24719, loss_mask_ce_1: 0.38357/0.74942, loss_mask_bce_1: 0.46007/0.30092, loss_mask_dice_1: 1.06900/1.02351, loss_spatial_bce_1: 0.06694/0.08442, loss_spatial_dice_1: 0.10301/0.18048, loss_spatial_ce_1: 0.00009/0.05798, loss_grounding_bce_1: 0.17816/0.08064, loss_grounding_dice_1: 0.21187/0.15097, loss_grounding_ce_1: 0.00186/0.24856, loss_mask_ce_2: 0.40739/0.75706, loss_mask_bce_2: 0.47249/0.30130, loss_mask_dice_2: 1.02832/1.02407, loss_spatial_bce_2: 0.07095/0.08451, loss_spatial_dice_2: 0.10908/0.18112, loss_spatial_ce_2: 0.00034/0.06020, loss_grounding_bce_2: 0.16708/0.08063, loss_grounding_dice_2: 0.20709/0.15092, loss_grounding_ce_2: 0.00222/0.25146, loss_mask_ce_3: 0.38350/0.76198, loss_mask_bce_3: 0.44974/0.30260, loss_mask_dice_3: 0.95071/1.02251, loss_spatial_bce_3: 0.06347/0.08665, loss_spatial_dice_3: 0.10612/0.18256, loss_spatial_ce_3: 0.00380/0.06513, loss_grounding_bce_3: 0.16114/0.08096, loss_grounding_dice_3: 0.21372/0.15056, loss_grounding_ce_3: 0.00381/0.25271, loss_mask_ce_4: 0.39211/0.76809, loss_mask_bce_4: 0.49246/0.30533, loss_mask_dice_4: 1.06885/1.04186, loss_spatial_bce_4: 0.06741/0.08923, loss_spatial_dice_4: 0.11603/0.19148, loss_spatial_ce_4: 0.02329/0.07896, loss_grounding_bce_4: 0.17097/0.08172, loss_grounding_dice_4: 0.21996/0.15316, loss_grounding_ce_4: 0.00316/0.25715, loss_mask_ce_5: 0.42823/0.79363, loss_mask_bce_5: 0.47758/0.30723, loss_mask_dice_5: 1.01583/1.05008, loss_spatial_bce_5: 0.07771/0.09166, loss_spatial_dice_5: 0.12007/0.19483, loss_spatial_ce_5: 0.01530/0.09279, loss_grounding_bce_5: 0.17625/0.08202, loss_grounding_dice_5: 0.20022/0.15409, loss_grounding_ce_5: 0.00162/0.27429, loss_mask_ce_6: 0.42694/0.82125, loss_mask_bce_6: 0.46490/0.30937, loss_mask_dice_6: 0.94669/1.05401, loss_spatial_bce_6: 0.06803/0.09715, loss_spatial_dice_6: 0.09646/0.19720, loss_spatial_ce_6: 0.01908/0.11712, loss_grounding_bce_6: 0.18572/0.08280, loss_grounding_dice_6: 0.21770/0.15454, loss_grounding_ce_6: 0.00071/0.28328, loss_mask_ce_7: 0.48228/0.87611, loss_mask_bce_7: 0.48050/0.31663, loss_mask_dice_7: 1.03196/1.09984, loss_spatial_bce_7: 0.09186/0.10618, loss_spatial_dice_7: 0.13680/0.22185, loss_spatial_ce_7: 0.02061/0.15093, loss_grounding_bce_7: 0.16643/0.08451, loss_grounding_dice_7: 0.21245/0.16007, loss_grounding_ce_7: 0.00231/0.31619, loss_mask_ce_8: 0.93305/1.00995, loss_mask_bce_8: 0.49850/0.33256, loss_mask_dice_8: 1.29054/1.17662, loss_spatial_bce_8: 0.11027/0.12236, loss_spatial_dice_8: 0.13201/0.25615, loss_spatial_ce_8: 0.09884/0.19589, loss_grounding_bce_8: 0.16776/0.08874, loss_grounding_dice_8: 0.19410/0.16992, loss_grounding_ce_8: 0.00197/0.41394, loss_mask_ce_9: 3.34898/3.47184, loss_mask_bce_9: 0.53899/0.35953, loss_mask_dice_9: 3.49484/1.75875, loss_spatial_bce_9: 0.27789/0.35414, loss_spatial_dice_9: 0.91512/0.79296, loss_spatial_ce_9: 1.10960/1.38571, loss_grounding_bce_9: 0.19874/0.10100, loss_grounding_dice_9: 0.33373/0.24216, loss_grounding_ce_9: 0.17266/0.66630] items per batch[64] items per second[0.37] total items[5529600] mini batches[ 86400] memory[4999] epoch remaining[0:37:21] INFO:trainer.default_trainer:epochs[ 47] optim steps[86500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.26827/0.74863, loss_mask_bce_0: 0.06199/0.30011, loss_mask_dice_0: 0.52045/1.01880, loss_spatial_bce_0: 0.00546/0.08392, loss_spatial_dice_0: 0.07385/0.17745, loss_spatial_ce_0: 0.02798/0.05428, loss_grounding_bce_0: 0.00349/0.08044, loss_grounding_dice_0: 0.08025/0.15025, loss_grounding_ce_0: 0.48590/0.24716, loss_mask_ce_1: 0.25821/0.74945, loss_mask_bce_1: 0.06631/0.30093, loss_mask_dice_1: 0.52965/1.02339, loss_spatial_bce_1: 0.00545/0.08441, loss_spatial_dice_1: 0.06667/0.18045, loss_spatial_ce_1: 0.02788/0.05796, loss_grounding_bce_1: 0.00474/0.08065, loss_grounding_dice_1: 0.08821/0.15097, loss_grounding_ce_1: 0.40186/0.24857, loss_mask_ce_2: 0.28963/0.75707, loss_mask_bce_2: 0.06951/0.30131, loss_mask_dice_2: 0.65757/1.02394, loss_spatial_bce_2: 0.00580/0.08450, loss_spatial_dice_2: 0.08099/0.18110, loss_spatial_ce_2: 0.02798/0.06017, loss_grounding_bce_2: 0.00464/0.08063, loss_grounding_dice_2: 0.09064/0.15093, loss_grounding_ce_2: 0.48304/0.25146, loss_mask_ce_3: 0.26558/0.76200, loss_mask_bce_3: 0.06050/0.30260, loss_mask_dice_3: 0.50595/1.02238, loss_spatial_bce_3: 0.00687/0.08665, loss_spatial_dice_3: 0.06531/0.18253, loss_spatial_ce_3: 0.02823/0.06511, loss_grounding_bce_3: 0.00426/0.08097, loss_grounding_dice_3: 0.09613/0.15055, loss_grounding_ce_3: 0.39485/0.25268, loss_mask_ce_4: 0.29274/0.76814, loss_mask_bce_4: 0.06077/0.30533, loss_mask_dice_4: 0.45928/1.04175, loss_spatial_bce_4: 0.00610/0.08922, loss_spatial_dice_4: 0.06024/0.19146, loss_spatial_ce_4: 0.03170/0.07894, loss_grounding_bce_4: 0.00351/0.08172, loss_grounding_dice_4: 0.07521/0.15316, loss_grounding_ce_4: 0.53565/0.25715, loss_mask_ce_5: 0.28066/0.79364, loss_mask_bce_5: 0.05559/0.30724, loss_mask_dice_5: 0.36966/1.04996, loss_spatial_bce_5: 0.00559/0.09166, loss_spatial_dice_5: 0.05947/0.19481, loss_spatial_ce_5: 0.03051/0.09279, loss_grounding_bce_5: 0.00386/0.08202, loss_grounding_dice_5: 0.09446/0.15408, loss_grounding_ce_5: 0.39720/0.27429, loss_mask_ce_6: 0.32748/0.82128, loss_mask_bce_6: 0.06090/0.30938, loss_mask_dice_6: 0.44056/1.05389, loss_spatial_bce_6: 0.00623/0.09715, loss_spatial_dice_6: 0.06151/0.19718, loss_spatial_ce_6: 0.03629/0.11710, loss_grounding_bce_6: 0.00393/0.08280, loss_grounding_dice_6: 0.07815/0.15453, loss_grounding_ce_6: 0.39897/0.28327, loss_mask_ce_7: 0.40525/0.87609, loss_mask_bce_7: 0.06208/0.31664, loss_mask_dice_7: 0.51328/1.09971, loss_spatial_bce_7: 0.00679/0.10617, loss_spatial_dice_7: 0.06926/0.22183, loss_spatial_ce_7: 0.05404/0.15090, loss_grounding_bce_7: 0.00439/0.08452, loss_grounding_dice_7: 0.10204/0.16007, loss_grounding_ce_7: 0.51411/0.31612, loss_mask_ce_8: 0.62997/1.00992, loss_mask_bce_8: 0.06794/0.33255, loss_mask_dice_8: 0.49671/1.17647, loss_spatial_bce_8: 0.00788/0.12234, loss_spatial_dice_8: 0.08598/0.25612, loss_spatial_ce_8: 0.06071/0.19584, loss_grounding_bce_8: 0.00438/0.08874, loss_grounding_dice_8: 0.10200/0.16992, loss_grounding_ce_8: 0.53801/0.41391, loss_mask_ce_9: 2.92157/3.47179, loss_mask_bce_9: 0.10335/0.35956, loss_mask_dice_9: 1.11406/1.75865, loss_spatial_bce_9: 0.24943/0.35413, loss_spatial_dice_9: 0.84665/0.79294, loss_spatial_ce_9: 2.22545/1.38562, loss_grounding_bce_9: 0.00451/0.10099, loss_grounding_dice_9: 0.16679/0.24214, loss_grounding_ce_9: 0.63282/0.66627] items per batch[64] items per second[0.37] total items[5536000] mini batches[ 86500] memory[4999] epoch remaining[0:34:28] INFO:trainer.default_trainer:epochs[ 47] optim steps[86600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.74936/0.74852, loss_mask_bce_0: 0.26922/0.30010, loss_mask_dice_0: 0.36712/1.01871, loss_spatial_bce_0: 0.09374/0.08391, loss_spatial_dice_0: 0.15753/0.17744, loss_spatial_ce_0: 0.05001/0.05427, loss_grounding_bce_0: 0.12859/0.08043, loss_grounding_dice_0: 0.18094/0.15024, loss_grounding_ce_0: 0.34996/0.24715, loss_mask_ce_1: 0.81892/0.74935, loss_mask_bce_1: 0.26869/0.30092, loss_mask_dice_1: 0.35833/1.02332, loss_spatial_bce_1: 0.09880/0.08441, loss_spatial_dice_1: 0.15422/0.18043, loss_spatial_ce_1: 0.05272/0.05794, loss_grounding_bce_1: 0.12675/0.08064, loss_grounding_dice_1: 0.17633/0.15095, loss_grounding_ce_1: 0.36836/0.24859, loss_mask_ce_2: 0.92262/0.75697, loss_mask_bce_2: 0.27654/0.30130, loss_mask_dice_2: 0.41818/1.02385, loss_spatial_bce_2: 0.10684/0.08450, loss_spatial_dice_2: 0.17625/0.18109, loss_spatial_ce_2: 0.05091/0.06014, loss_grounding_bce_2: 0.12136/0.08063, loss_grounding_dice_2: 0.18607/0.15092, loss_grounding_ce_2: 0.32811/0.25149, loss_mask_ce_3: 0.74095/0.76190, loss_mask_bce_3: 0.27258/0.30259, loss_mask_dice_3: 0.40108/1.02227, loss_spatial_bce_3: 0.10494/0.08664, loss_spatial_dice_3: 0.17421/0.18251, loss_spatial_ce_3: 0.07728/0.06509, loss_grounding_bce_3: 0.12693/0.08096, loss_grounding_dice_3: 0.18833/0.15054, loss_grounding_ce_3: 0.30301/0.25266, loss_mask_ce_4: 0.85174/0.76803, loss_mask_bce_4: 0.25964/0.30533, loss_mask_dice_4: 0.37910/1.04163, loss_spatial_bce_4: 0.13990/0.08921, loss_spatial_dice_4: 0.18788/0.19144, loss_spatial_ce_4: 0.10192/0.07891, loss_grounding_bce_4: 0.12796/0.08171, loss_grounding_dice_4: 0.17636/0.15314, loss_grounding_ce_4: 0.40002/0.25716, loss_mask_ce_5: 1.05679/0.79355, loss_mask_bce_5: 0.27496/0.30723, loss_mask_dice_5: 0.38470/1.04985, loss_spatial_bce_5: 0.19466/0.09165, loss_spatial_dice_5: 0.21014/0.19480, loss_spatial_ce_5: 0.07702/0.09276, loss_grounding_bce_5: 0.12375/0.08201, loss_grounding_dice_5: 0.17389/0.15407, loss_grounding_ce_5: 0.40418/0.27432, loss_mask_ce_6: 1.20788/0.82118, loss_mask_bce_6: 0.26181/0.30938, loss_mask_dice_6: 0.36667/1.05377, loss_spatial_bce_6: 0.17958/0.09713, loss_spatial_dice_6: 0.20261/0.19716, loss_spatial_ce_6: 0.10614/0.11707, loss_grounding_bce_6: 0.12717/0.08279, loss_grounding_dice_6: 0.17592/0.15453, loss_grounding_ce_6: 0.47910/0.28326, loss_mask_ce_7: 0.99300/0.87597, loss_mask_bce_7: 0.25261/0.31664, loss_mask_dice_7: 0.38609/1.09960, loss_spatial_bce_7: 0.21276/0.10615, loss_spatial_dice_7: 0.21706/0.22180, loss_spatial_ce_7: 0.11059/0.15085, loss_grounding_bce_7: 0.12689/0.08451, loss_grounding_dice_7: 0.18576/0.16006, loss_grounding_ce_7: 0.38875/0.31613, loss_mask_ce_8: 1.03729/1.00979, loss_mask_bce_8: 0.30627/0.33255, loss_mask_dice_8: 0.47443/1.17636, loss_spatial_bce_8: 0.25926/0.12232, loss_spatial_dice_8: 0.27918/0.25610, loss_spatial_ce_8: 0.21755/0.19579, loss_grounding_bce_8: 0.14131/0.08873, loss_grounding_dice_8: 0.22144/0.16992, loss_grounding_ce_8: 0.40682/0.41391, loss_mask_ce_9: 4.04281/3.47175, loss_mask_bce_9: 0.33801/0.35955, loss_mask_dice_9: 0.56912/1.75845, loss_spatial_bce_9: 0.49796/0.35417, loss_spatial_dice_9: 0.85806/0.79296, loss_spatial_ce_9: 1.39659/1.38568, loss_grounding_bce_9: 0.16102/0.10098, loss_grounding_dice_9: 0.27036/0.24214, loss_grounding_ce_9: 0.76884/0.66621] items per batch[64] items per second[0.37] total items[5542400] mini batches[ 86600] memory[4999] epoch remaining[0:31:38] INFO:trainer.default_trainer:epochs[ 47] optim steps[86700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.85201/0.74847, loss_mask_bce_0: 0.36058/0.30012, loss_mask_dice_0: 0.33860/1.01857, loss_spatial_bce_0: 0.26788/0.08392, loss_spatial_dice_0: 0.20578/0.17741, loss_spatial_ce_0: 0.03779/0.05424, loss_grounding_bce_0: 0.11766/0.08043, loss_grounding_dice_0: 0.05844/0.15023, loss_grounding_ce_0: 0.37624/0.24707, loss_mask_ce_1: 0.87252/0.74928, loss_mask_bce_1: 0.34961/0.30095, loss_mask_dice_1: 0.32090/1.02319, loss_spatial_bce_1: 0.25432/0.08441, loss_spatial_dice_1: 0.19347/0.18041, loss_spatial_ce_1: 0.04344/0.05790, loss_grounding_bce_1: 0.10920/0.08064, loss_grounding_dice_1: 0.05476/0.15094, loss_grounding_ce_1: 0.37881/0.24850, loss_mask_ce_2: 0.92300/0.75689, loss_mask_bce_2: 0.34771/0.30133, loss_mask_dice_2: 0.31493/1.02370, loss_spatial_bce_2: 0.25702/0.08450, loss_spatial_dice_2: 0.21316/0.18106, loss_spatial_ce_2: 0.03122/0.06011, loss_grounding_bce_2: 0.10539/0.08062, loss_grounding_dice_2: 0.05381/0.15091, loss_grounding_ce_2: 0.32706/0.25140, loss_mask_ce_3: 0.91352/0.76183, loss_mask_bce_3: 0.36048/0.30262, loss_mask_dice_3: 0.32424/1.02213, loss_spatial_bce_3: 0.26703/0.08664, loss_spatial_dice_3: 0.21993/0.18249, loss_spatial_ce_3: 0.02864/0.06506, loss_grounding_bce_3: 0.11146/0.08096, loss_grounding_dice_3: 0.05545/0.15053, loss_grounding_ce_3: 0.38203/0.25258, loss_mask_ce_4: 0.81660/0.76795, loss_mask_bce_4: 0.36036/0.30536, loss_mask_dice_4: 0.31823/1.04149, loss_spatial_bce_4: 0.23857/0.08922, loss_spatial_dice_4: 0.19260/0.19141, loss_spatial_ce_4: 0.07173/0.07887, loss_grounding_bce_4: 0.12025/0.08171, loss_grounding_dice_4: 0.05896/0.15314, loss_grounding_ce_4: 0.37416/0.25708, loss_mask_ce_5: 0.90138/0.79347, loss_mask_bce_5: 0.36423/0.30725, loss_mask_dice_5: 0.34066/1.04973, loss_spatial_bce_5: 0.27547/0.09165, loss_spatial_dice_5: 0.22162/0.19477, loss_spatial_ce_5: 0.09367/0.09273, loss_grounding_bce_5: 0.11535/0.08201, loss_grounding_dice_5: 0.06085/0.15406, loss_grounding_ce_5: 0.32723/0.27423, loss_mask_ce_6: 1.28759/0.82112, loss_mask_bce_6: 0.32134/0.30941, loss_mask_dice_6: 0.29003/1.05364, loss_spatial_bce_6: 0.26152/0.09714, loss_spatial_dice_6: 0.23255/0.19714, loss_spatial_ce_6: 0.10472/0.11702, loss_grounding_bce_6: 0.11123/0.08279, loss_grounding_dice_6: 0.06098/0.15451, loss_grounding_ce_6: 0.49743/0.28317, loss_mask_ce_7: 0.80502/0.87591, loss_mask_bce_7: 0.36840/0.31667, loss_mask_dice_7: 0.32947/1.09945, loss_spatial_bce_7: 0.24641/0.10615, loss_spatial_dice_7: 0.22086/0.22177, loss_spatial_ce_7: 0.10124/0.15079, loss_grounding_bce_7: 0.12747/0.08451, loss_grounding_dice_7: 0.06198/0.16005, loss_grounding_ce_7: 0.49859/0.31602, loss_mask_ce_8: 1.14225/1.00973, loss_mask_bce_8: 0.35107/0.33257, loss_mask_dice_8: 0.30701/1.17624, loss_spatial_bce_8: 0.25342/0.12231, loss_spatial_dice_8: 0.17122/0.25606, loss_spatial_ce_8: 0.06875/0.19573, loss_grounding_bce_8: 0.11785/0.08872, loss_grounding_dice_8: 0.06248/0.16990, loss_grounding_ce_8: 0.35340/0.41378, loss_mask_ce_9: 3.78034/3.47171, loss_mask_bce_9: 0.43226/0.35960, loss_mask_dice_9: 0.55197/1.75839, loss_spatial_bce_9: 0.57236/0.35419, loss_spatial_dice_9: 0.77177/0.79294, loss_spatial_ce_9: 1.04977/1.38558, loss_grounding_bce_9: 0.11698/0.10098, loss_grounding_dice_9: 0.07604/0.24212, loss_grounding_ce_9: 0.39814/0.66611] items per batch[64] items per second[0.36] total items[5548800] mini batches[ 86700] memory[4999] epoch remaining[0:28:48] INFO:trainer.default_trainer:epochs[ 47] optim steps[86800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.16404/0.74837, loss_mask_bce_0: 0.02401/0.30010, loss_mask_dice_0: 0.15243/1.01859, loss_spatial_bce_0: 0.03456/0.08391, loss_spatial_dice_0: 0.20098/0.17739, loss_spatial_ce_0: 0.04135/0.05422, loss_grounding_bce_0: 0.00535/0.08042, loss_grounding_dice_0: 0.06433/0.15021, loss_grounding_ce_0: 0.00605/0.24703, loss_mask_ce_1: 0.17887/0.74916, loss_mask_bce_1: 0.02317/0.30094, loss_mask_dice_1: 0.13152/1.02321, loss_spatial_bce_1: 0.02277/0.08440, loss_spatial_dice_1: 0.20700/0.18039, loss_spatial_ce_1: 0.03461/0.05788, loss_grounding_bce_1: 0.00623/0.08063, loss_grounding_dice_1: 0.07291/0.15092, loss_grounding_ce_1: 0.00768/0.24848, loss_mask_ce_2: 0.19467/0.75676, loss_mask_bce_2: 0.01932/0.30132, loss_mask_dice_2: 0.11484/1.02369, loss_spatial_bce_2: 0.02294/0.08449, loss_spatial_dice_2: 0.20613/0.18104, loss_spatial_ce_2: 0.05324/0.06008, loss_grounding_bce_2: 0.00511/0.08062, loss_grounding_dice_2: 0.05857/0.15089, loss_grounding_ce_2: 0.00795/0.25135, loss_mask_ce_3: 0.14258/0.76171, loss_mask_bce_3: 0.02637/0.30261, loss_mask_dice_3: 0.15368/1.02210, loss_spatial_bce_3: 0.02641/0.08664, loss_spatial_dice_3: 0.21990/0.18247, loss_spatial_ce_3: 0.11774/0.06502, loss_grounding_bce_3: 0.00636/0.08095, loss_grounding_dice_3: 0.06074/0.15051, loss_grounding_ce_3: 0.00596/0.25254, loss_mask_ce_4: 0.09033/0.76785, loss_mask_bce_4: 0.02371/0.30534, loss_mask_dice_4: 0.12792/1.04147, loss_spatial_bce_4: 0.03154/0.08921, loss_spatial_dice_4: 0.22153/0.19139, loss_spatial_ce_4: 0.09598/0.07884, loss_grounding_bce_4: 0.00564/0.08172, loss_grounding_dice_4: 0.05395/0.15312, loss_grounding_ce_4: 0.01183/0.25705, loss_mask_ce_5: 0.12997/0.79335, loss_mask_bce_5: 0.02222/0.30724, loss_mask_dice_5: 0.13675/1.04975, loss_spatial_bce_5: 0.05039/0.09165, loss_spatial_dice_5: 0.24810/0.19475, loss_spatial_ce_5: 0.07291/0.09269, loss_grounding_bce_5: 0.00450/0.08202, loss_grounding_dice_5: 0.04696/0.15404, loss_grounding_ce_5: 0.03401/0.27417, loss_mask_ce_6: 0.10668/0.82096, loss_mask_bce_6: 0.02261/0.30939, loss_mask_dice_6: 0.14276/1.05364, loss_spatial_bce_6: 0.04094/0.09713, loss_spatial_dice_6: 0.24281/0.19712, loss_spatial_ce_6: 0.08947/0.11699, loss_grounding_bce_6: 0.00715/0.08279, loss_grounding_dice_6: 0.05307/0.15450, loss_grounding_ce_6: 0.04749/0.28310, loss_mask_ce_7: 0.21493/0.87575, loss_mask_bce_7: 0.02787/0.31667, loss_mask_dice_7: 0.13864/1.09948, loss_spatial_bce_7: 0.04607/0.10614, loss_spatial_dice_7: 0.24260/0.22174, loss_spatial_ce_7: 0.10791/0.15075, loss_grounding_bce_7: 0.01923/0.08452, loss_grounding_dice_7: 0.07900/0.16004, loss_grounding_ce_7: 0.04257/0.31593, loss_mask_ce_8: 0.67085/1.00958, loss_mask_bce_8: 0.03235/0.33257, loss_mask_dice_8: 0.16718/1.17624, loss_spatial_bce_8: 0.05838/0.12231, loss_spatial_dice_8: 0.26498/0.25602, loss_spatial_ce_8: 0.25283/0.19567, loss_grounding_bce_8: 0.00540/0.08873, loss_grounding_dice_8: 0.08075/0.16989, loss_grounding_ce_8: 0.03701/0.41370, loss_mask_ce_9: 2.03746/3.47163, loss_mask_bce_9: 0.04359/0.35960, loss_mask_dice_9: 0.24268/1.75851, loss_spatial_bce_9: 0.09226/0.35419, loss_spatial_dice_9: 0.60554/0.79292, loss_spatial_ce_9: 1.02653/1.38558, loss_grounding_bce_9: 0.00509/0.10098, loss_grounding_dice_9: 0.10284/0.24210, loss_grounding_ce_9: 0.07739/0.66604] items per batch[64] items per second[0.36] total items[5555200] mini batches[ 86800] memory[4999] epoch remaining[0:25:57] INFO:trainer.default_trainer:epochs[ 47] optim steps[86900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.71330/0.74831, loss_mask_bce_0: 0.38228/0.30011, loss_mask_dice_0: 1.77923/1.01869, loss_spatial_bce_0: 0.05066/0.08390, loss_spatial_dice_0: 0.15944/0.17737, loss_spatial_ce_0: 0.06332/0.05420, loss_grounding_bce_0: 0.01466/0.08042, loss_grounding_dice_0: 0.02513/0.15021, loss_grounding_ce_0: 0.07306/0.24704, loss_mask_ce_1: 0.70660/0.74911, loss_mask_bce_1: 0.36787/0.30094, loss_mask_dice_1: 1.69144/1.02331, loss_spatial_bce_1: 0.07718/0.08439, loss_spatial_dice_1: 0.17420/0.18036, loss_spatial_ce_1: 0.03840/0.05786, loss_grounding_bce_1: 0.01581/0.08063, loss_grounding_dice_1: 0.02811/0.15091, loss_grounding_ce_1: 0.05036/0.24849, loss_mask_ce_2: 0.87276/0.75670, loss_mask_bce_2: 0.38998/0.30133, loss_mask_dice_2: 1.77611/1.02380, loss_spatial_bce_2: 0.08002/0.08448, loss_spatial_dice_2: 0.17190/0.18101, loss_spatial_ce_2: 0.08522/0.06006, loss_grounding_bce_2: 0.01415/0.08062, loss_grounding_dice_2: 0.02735/0.15089, loss_grounding_ce_2: 0.04285/0.25135, loss_mask_ce_3: 0.77943/0.76166, loss_mask_bce_3: 0.39174/0.30261, loss_mask_dice_3: 1.66259/1.02224, loss_spatial_bce_3: 0.07087/0.08663, loss_spatial_dice_3: 0.19218/0.18244, loss_spatial_ce_3: 0.03678/0.06500, loss_grounding_bce_3: 0.01548/0.08095, loss_grounding_dice_3: 0.03101/0.15051, loss_grounding_ce_3: 0.05175/0.25255, loss_mask_ce_4: 0.78718/0.76781, loss_mask_bce_4: 0.35551/0.30534, loss_mask_dice_4: 1.64663/1.04161, loss_spatial_bce_4: 0.09356/0.08920, loss_spatial_dice_4: 0.18828/0.19137, loss_spatial_ce_4: 0.03608/0.07881, loss_grounding_bce_4: 0.01601/0.08172, loss_grounding_dice_4: 0.03065/0.15312, loss_grounding_ce_4: 0.07523/0.25707, loss_mask_ce_5: 0.78991/0.79331, loss_mask_bce_5: 0.44688/0.30725, loss_mask_dice_5: 1.81932/1.04987, loss_spatial_bce_5: 0.14447/0.09164, loss_spatial_dice_5: 0.22803/0.19473, loss_spatial_ce_5: 0.05061/0.09268, loss_grounding_bce_5: 0.02881/0.08202, loss_grounding_dice_5: 0.04777/0.15404, loss_grounding_ce_5: 0.26163/0.27415, loss_mask_ce_6: 1.09840/0.82094, loss_mask_bce_6: 0.39803/0.30940, loss_mask_dice_6: 1.66988/1.05378, loss_spatial_bce_6: 0.14456/0.09712, loss_spatial_dice_6: 0.23158/0.19709, loss_spatial_ce_6: 0.04205/0.11698, loss_grounding_bce_6: 0.03567/0.08279, loss_grounding_dice_6: 0.05312/0.15450, loss_grounding_ce_6: 0.25811/0.28311, loss_mask_ce_7: 0.88849/0.87572, loss_mask_bce_7: 0.44519/0.31667, loss_mask_dice_7: 1.83195/1.09960, loss_spatial_bce_7: 0.23529/0.10613, loss_spatial_dice_7: 0.26463/0.22171, loss_spatial_ce_7: 0.05122/0.15071, loss_grounding_bce_7: 0.02741/0.08452, loss_grounding_dice_7: 0.04759/0.16003, loss_grounding_ce_7: 0.32954/0.31593, loss_mask_ce_8: 1.43607/1.00957, loss_mask_bce_8: 0.40312/0.33258, loss_mask_dice_8: 1.80161/1.17637, loss_spatial_bce_8: 0.19825/0.12230, loss_spatial_dice_8: 0.29815/0.25600, loss_spatial_ce_8: 0.25042/0.19562, loss_grounding_bce_8: 0.06810/0.08873, loss_grounding_dice_8: 0.10553/0.16988, loss_grounding_ce_8: 0.35060/0.41368, loss_mask_ce_9: 6.17842/3.47179, loss_mask_bce_9: 0.76196/0.35962, loss_mask_dice_9: 3.63266/1.75865, loss_spatial_bce_9: 0.26601/0.35421, loss_spatial_dice_9: 0.87846/0.79293, loss_spatial_ce_9: 1.56392/1.38560, loss_grounding_bce_9: 0.12441/0.10099, loss_grounding_dice_9: 0.23507/0.24209, loss_grounding_ce_9: 2.32663/0.66608] items per batch[64] items per second[0.37] total items[5561600] mini batches[ 86900] memory[4999] epoch remaining[0:23:01] INFO:trainer.default_trainer:epochs[ 47] optim steps[87000] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.83282/0.74824, loss_mask_bce_0: 0.96048/0.30009, loss_mask_dice_0: 4.25760/1.01863, loss_spatial_bce_0: 0.07839/0.08388, loss_spatial_dice_0: 0.17810/0.17734, loss_spatial_ce_0: 0.00015/0.05418, loss_grounding_bce_0: 0.17690/0.08042, loss_grounding_dice_0: 0.18835/0.15020, loss_grounding_ce_0: 0.03050/0.24692, loss_mask_ce_1: 0.86945/0.74907, loss_mask_bce_1: 0.98199/0.30092, loss_mask_dice_1: 4.36882/1.02323, loss_spatial_bce_1: 0.07848/0.08437, loss_spatial_dice_1: 0.18125/0.18034, loss_spatial_ce_1: 0.00013/0.05783, loss_grounding_bce_1: 0.17301/0.08063, loss_grounding_dice_1: 0.16785/0.15090, loss_grounding_ce_1: 0.03279/0.24840, loss_mask_ce_2: 0.86596/0.75664, loss_mask_bce_2: 0.95769/0.30130, loss_mask_dice_2: 4.42066/1.02375, loss_spatial_bce_2: 0.07579/0.08446, loss_spatial_dice_2: 0.18793/0.18099, loss_spatial_ce_2: 0.00026/0.06004, loss_grounding_bce_2: 0.17277/0.08062, loss_grounding_dice_2: 0.17706/0.15089, loss_grounding_ce_2: 0.03061/0.25124, loss_mask_ce_3: 0.80437/0.76159, loss_mask_bce_3: 0.97356/0.30259, loss_mask_dice_3: 4.32556/1.02217, loss_spatial_bce_3: 0.07321/0.08661, loss_spatial_dice_3: 0.16937/0.18242, loss_spatial_ce_3: 0.00127/0.06497, loss_grounding_bce_3: 0.15849/0.08095, loss_grounding_dice_3: 0.16097/0.15050, loss_grounding_ce_3: 0.05999/0.25246, loss_mask_ce_4: 0.80646/0.76773, loss_mask_bce_4: 0.96550/0.30532, loss_mask_dice_4: 4.36133/1.04155, loss_spatial_bce_4: 0.08072/0.08918, loss_spatial_dice_4: 0.18477/0.19135, loss_spatial_ce_4: 0.00472/0.07879, loss_grounding_bce_4: 0.15372/0.08171, loss_grounding_dice_4: 0.13785/0.15310, loss_grounding_ce_4: 0.04640/0.25696, loss_mask_ce_5: 0.92483/0.79323, loss_mask_bce_5: 0.94198/0.30723, loss_mask_dice_5: 4.43439/1.04982, loss_spatial_bce_5: 0.07572/0.09162, loss_spatial_dice_5: 0.19692/0.19470, loss_spatial_ce_5: 0.01623/0.09264, loss_grounding_bce_5: 0.16198/0.08201, loss_grounding_dice_5: 0.19553/0.15403, loss_grounding_ce_5: 0.03479/0.27403, loss_mask_ce_6: 0.94766/0.82083, loss_mask_bce_6: 0.97862/0.30939, loss_mask_dice_6: 4.36299/1.05371, loss_spatial_bce_6: 0.07748/0.09711, loss_spatial_dice_6: 0.19384/0.19708, loss_spatial_ce_6: 0.05356/0.11697, loss_grounding_bce_6: 0.14697/0.08278, loss_grounding_dice_6: 0.13888/0.15449, loss_grounding_ce_6: 0.07368/0.28301, loss_mask_ce_7: 1.06718/0.87563, loss_mask_bce_7: 1.12056/0.31666, loss_mask_dice_7: 4.87733/1.09955, loss_spatial_bce_7: 0.07367/0.10611, loss_spatial_dice_7: 0.23149/0.22169, loss_spatial_ce_7: 0.04831/0.15068, loss_grounding_bce_7: 0.13919/0.08451, loss_grounding_dice_7: 0.12522/0.16001, loss_grounding_ce_7: 0.07167/0.31584, loss_mask_ce_8: 1.56398/1.00945, loss_mask_bce_8: 0.98780/0.33256, loss_mask_dice_8: 5.02823/1.17627, loss_spatial_bce_8: 0.08836/0.12228, loss_spatial_dice_8: 0.25712/0.25597, loss_spatial_ce_8: 0.04748/0.19559, loss_grounding_bce_8: 0.16004/0.08872, loss_grounding_dice_8: 0.14821/0.16987, loss_grounding_ce_8: 0.48621/0.41348, loss_mask_ce_9: 5.87019/3.47148, loss_mask_bce_9: 1.57618/0.35961, loss_mask_dice_9: 8.43945/1.75853, loss_spatial_bce_9: 0.19063/0.35421, loss_spatial_dice_9: 0.95180/0.79292, loss_spatial_ce_9: 1.23262/1.38554, loss_grounding_bce_9: 0.38373/0.10098, loss_grounding_dice_9: 0.34097/0.24208, loss_grounding_ce_9: 0.47813/0.66599] items per batch[64] items per second[0.36] total items[5568000] mini batches[ 87000] memory[4999] epoch remaining[0:20:09] INFO:trainer.default_trainer:epochs[ 47] optim steps[87100] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.31360/0.74827, loss_mask_bce_0: 0.32407/0.30007, loss_mask_dice_0: 0.39563/1.01866, loss_spatial_bce_0: 0.08081/0.08386, loss_spatial_dice_0: 0.11048/0.17733, loss_spatial_ce_0: 0.00032/0.05416, loss_grounding_bce_0: 0.04167/0.08040, loss_grounding_dice_0: 0.07397/0.15019, loss_grounding_ce_0: 0.96196/0.24690, loss_mask_ce_1: 0.31885/0.74912, loss_mask_bce_1: 0.32912/0.30089, loss_mask_dice_1: 0.39673/1.02326, loss_spatial_bce_1: 0.07953/0.08436, loss_spatial_dice_1: 0.10237/0.18032, loss_spatial_ce_1: 0.00047/0.05782, loss_grounding_bce_1: 0.04289/0.08061, loss_grounding_dice_1: 0.07294/0.15089, loss_grounding_ce_1: 0.99279/0.24836, loss_mask_ce_2: 0.32120/0.75667, loss_mask_bce_2: 0.32847/0.30128, loss_mask_dice_2: 0.39620/1.02377, loss_spatial_bce_2: 0.08086/0.08445, loss_spatial_dice_2: 0.09642/0.18098, loss_spatial_ce_2: 0.00076/0.06001, loss_grounding_bce_2: 0.03857/0.08060, loss_grounding_dice_2: 0.06244/0.15088, loss_grounding_ce_2: 0.90333/0.25120, loss_mask_ce_3: 0.34719/0.76163, loss_mask_bce_3: 0.32180/0.30257, loss_mask_dice_3: 0.39402/1.02219, loss_spatial_bce_3: 0.08525/0.08660, loss_spatial_dice_3: 0.11959/0.18240, loss_spatial_ce_3: 0.00174/0.06495, loss_grounding_bce_3: 0.03816/0.08092, loss_grounding_dice_3: 0.06147/0.15049, loss_grounding_ce_3: 0.84530/0.25243, loss_mask_ce_4: 0.31176/0.76779, loss_mask_bce_4: 0.33495/0.30530, loss_mask_dice_4: 0.40421/1.04157, loss_spatial_bce_4: 0.08620/0.08917, loss_spatial_dice_4: 0.11148/0.19133, loss_spatial_ce_4: 0.01258/0.07877, loss_grounding_bce_4: 0.04083/0.08168, loss_grounding_dice_4: 0.06860/0.15310, loss_grounding_ce_4: 0.69009/0.25692, loss_mask_ce_5: 0.31523/0.79330, loss_mask_bce_5: 0.32700/0.30721, loss_mask_dice_5: 0.39297/1.04984, loss_spatial_bce_5: 0.08843/0.09161, loss_spatial_dice_5: 0.11812/0.19469, loss_spatial_ce_5: 0.04465/0.09261, loss_grounding_bce_5: 0.03668/0.08198, loss_grounding_dice_5: 0.05689/0.15402, loss_grounding_ce_5: 0.82592/0.27399, loss_mask_ce_6: 0.33047/0.82087, loss_mask_bce_6: 0.34754/0.30938, loss_mask_dice_6: 0.41839/1.05380, loss_spatial_bce_6: 0.11706/0.09708, loss_spatial_dice_6: 0.15988/0.19706, loss_spatial_ce_6: 0.07729/0.11694, loss_grounding_bce_6: 0.02969/0.08276, loss_grounding_dice_6: 0.05468/0.15447, loss_grounding_ce_6: 3.00141/0.28299, loss_mask_ce_7: 0.47073/0.87568, loss_mask_bce_7: 0.34874/0.31665, loss_mask_dice_7: 0.39116/1.09963, loss_spatial_bce_7: 0.11654/0.10609, loss_spatial_dice_7: 0.24763/0.22167, loss_spatial_ce_7: 0.16764/0.15063, loss_grounding_bce_7: 0.03485/0.08448, loss_grounding_dice_7: 0.05599/0.16000, loss_grounding_ce_7: 0.82804/0.31580, loss_mask_ce_8: 0.37683/1.00951, loss_mask_bce_8: 0.36277/0.33255, loss_mask_dice_8: 0.39411/1.17632, loss_spatial_bce_8: 0.15647/0.12226, loss_spatial_dice_8: 0.28737/0.25595, loss_spatial_ce_8: 0.16114/0.19554, loss_grounding_bce_8: 0.04698/0.08870, loss_grounding_dice_8: 0.06018/0.16985, loss_grounding_ce_8: 1.21732/0.41345, loss_mask_ce_9: 3.35260/3.47172, loss_mask_bce_9: 0.36303/0.35960, loss_mask_dice_9: 0.62672/1.75873, loss_spatial_bce_9: 0.48101/0.35419, loss_spatial_dice_9: 0.81143/0.79291, loss_spatial_ce_9: 1.50649/1.38552, loss_grounding_bce_9: 0.04843/0.10096, loss_grounding_dice_9: 0.13434/0.24208, loss_grounding_ce_9: 1.12068/0.66594] items per batch[64] items per second[0.36] total items[5574400] mini batches[ 87100] memory[4999] epoch remaining[0:17:18] INFO:trainer.default_trainer:epochs[ 47] optim steps[87200] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.07683/0.74817, loss_mask_bce_0: 0.10026/0.30005, loss_mask_dice_0: 0.10984/1.01848, loss_spatial_bce_0: 0.11092/0.08387, loss_spatial_dice_0: 0.17304/0.17730, loss_spatial_ce_0: 0.19081/0.05417, loss_grounding_bce_0: 0.07528/0.08041, loss_grounding_dice_0: 0.08205/0.15019, loss_grounding_ce_0: 0.04929/0.24688, loss_mask_ce_1: 0.07568/0.74903, loss_mask_bce_1: 0.10609/0.30088, loss_mask_dice_1: 0.11120/1.02306, loss_spatial_bce_1: 0.10257/0.08436, loss_spatial_dice_1: 0.15153/0.18029, loss_spatial_ce_1: 0.19191/0.05780, loss_grounding_bce_1: 0.07753/0.08062, loss_grounding_dice_1: 0.08426/0.15089, loss_grounding_ce_1: 0.03636/0.24834, loss_mask_ce_2: 0.07219/0.75661, loss_mask_bce_2: 0.10315/0.30127, loss_mask_dice_2: 0.10541/1.02360, loss_spatial_bce_2: 0.09434/0.08445, loss_spatial_dice_2: 0.12661/0.18095, loss_spatial_ce_2: 0.20247/0.05999, loss_grounding_bce_2: 0.07945/0.08061, loss_grounding_dice_2: 0.08270/0.15088, loss_grounding_ce_2: 0.04303/0.25116, loss_mask_ce_3: 0.08491/0.76154, loss_mask_bce_3: 0.10289/0.30256, loss_mask_dice_3: 0.10623/1.02201, loss_spatial_bce_3: 0.10197/0.08660, loss_spatial_dice_3: 0.13654/0.18238, loss_spatial_ce_3: 0.18930/0.06492, loss_grounding_bce_3: 0.07378/0.08094, loss_grounding_dice_3: 0.07911/0.15049, loss_grounding_ce_3: 0.05652/0.25239, loss_mask_ce_4: 0.05313/0.76769, loss_mask_bce_4: 0.10181/0.30529, loss_mask_dice_4: 0.11068/1.04138, loss_spatial_bce_4: 0.13376/0.08917, loss_spatial_dice_4: 0.21070/0.19130, loss_spatial_ce_4: 0.19612/0.07875, loss_grounding_bce_4: 0.07883/0.08170, loss_grounding_dice_4: 0.08605/0.15309, loss_grounding_ce_4: 0.03138/0.25689, loss_mask_ce_5: 0.09140/0.79322, loss_mask_bce_5: 0.10324/0.30719, loss_mask_dice_5: 0.10144/1.04963, loss_spatial_bce_5: 0.13621/0.09161, loss_spatial_dice_5: 0.18122/0.19466, loss_spatial_ce_5: 0.21464/0.09258, loss_grounding_bce_5: 0.07703/0.08200, loss_grounding_dice_5: 0.07601/0.15402, loss_grounding_ce_5: 0.05042/0.27397, loss_mask_ce_6: 0.12999/0.82075, loss_mask_bce_6: 0.09732/0.30936, loss_mask_dice_6: 0.10336/1.05362, loss_spatial_bce_6: 0.12127/0.09709, loss_spatial_dice_6: 0.21328/0.19704, loss_spatial_ce_6: 0.22526/0.11691, loss_grounding_bce_6: 0.07670/0.08277, loss_grounding_dice_6: 0.07563/0.15447, loss_grounding_ce_6: 0.08105/0.28297, loss_mask_ce_7: 0.17462/0.87557, loss_mask_bce_7: 0.11047/0.31663, loss_mask_dice_7: 0.12991/1.09943, loss_spatial_bce_7: 0.14625/0.10609, loss_spatial_dice_7: 0.23006/0.22164, loss_spatial_ce_7: 0.19790/0.15060, loss_grounding_bce_7: 0.08003/0.08450, loss_grounding_dice_7: 0.10278/0.16001, loss_grounding_ce_7: 0.13844/0.31578, loss_mask_ce_8: 0.16922/1.00944, loss_mask_bce_8: 0.13637/0.33254, loss_mask_dice_8: 0.20002/1.17612, loss_spatial_bce_8: 0.20029/0.12226, loss_spatial_dice_8: 0.36130/0.25591, loss_spatial_ce_8: 0.27903/0.19549, loss_grounding_bce_8: 0.09905/0.08871, loss_grounding_dice_8: 0.13899/0.16985, loss_grounding_ce_8: 0.11236/0.41349, loss_mask_ce_9: 2.67787/3.47147, loss_mask_bce_9: 0.24001/0.35960, loss_mask_dice_9: 0.36824/1.75842, loss_spatial_bce_9: 0.55411/0.35427, loss_spatial_dice_9: 0.48473/0.79290, loss_spatial_ce_9: 0.53534/1.38542, loss_grounding_bce_9: 0.17647/0.10097, loss_grounding_dice_9: 0.27183/0.24206, loss_grounding_ce_9: 0.57742/0.66588] items per batch[64] items per second[0.37] total items[5580800] mini batches[ 87200] memory[4999] epoch remaining[0:14:24] INFO:trainer.default_trainer:epochs[ 47] optim steps[87300] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.55037/0.74813, loss_mask_bce_0: 0.32162/0.30007, loss_mask_dice_0: 0.30352/1.01885, loss_spatial_bce_0: 0.04747/0.08385, loss_spatial_dice_0: 0.06819/0.17730, loss_spatial_ce_0: 0.05991/0.05417, loss_grounding_bce_0: 0.02694/0.08041, loss_grounding_dice_0: 0.03029/0.15020, loss_grounding_ce_0: 0.18518/0.24690, loss_mask_ce_1: 0.50563/0.74901, loss_mask_bce_1: 0.34673/0.30090, loss_mask_dice_1: 0.31651/1.02341, loss_spatial_bce_1: 0.05253/0.08434, loss_spatial_dice_1: 0.06451/0.18029, loss_spatial_ce_1: 0.06653/0.05780, loss_grounding_bce_1: 0.02909/0.08061, loss_grounding_dice_1: 0.02726/0.15090, loss_grounding_ce_1: 0.16508/0.24836, loss_mask_ce_2: 0.54492/0.75658, loss_mask_bce_2: 0.30709/0.30129, loss_mask_dice_2: 0.28923/1.02397, loss_spatial_bce_2: 0.04531/0.08444, loss_spatial_dice_2: 0.05977/0.18095, loss_spatial_ce_2: 0.08320/0.05999, loss_grounding_bce_2: 0.02879/0.08061, loss_grounding_dice_2: 0.02518/0.15088, loss_grounding_ce_2: 0.19985/0.25117, loss_mask_ce_3: 0.58784/0.76152, loss_mask_bce_3: 0.33391/0.30258, loss_mask_dice_3: 0.31804/1.02239, loss_spatial_bce_3: 0.04873/0.08658, loss_spatial_dice_3: 0.06757/0.18239, loss_spatial_ce_3: 0.06209/0.06491, loss_grounding_bce_3: 0.02665/0.08094, loss_grounding_dice_3: 0.02732/0.15050, loss_grounding_ce_3: 0.21460/0.25240, loss_mask_ce_4: 0.50375/0.76766, loss_mask_bce_4: 0.31694/0.30530, loss_mask_dice_4: 0.31066/1.04173, loss_spatial_bce_4: 0.04639/0.08915, loss_spatial_dice_4: 0.06688/0.19131, loss_spatial_ce_4: 0.05998/0.07874, loss_grounding_bce_4: 0.02827/0.08169, loss_grounding_dice_4: 0.02965/0.15309, loss_grounding_ce_4: 0.19605/0.25692, loss_mask_ce_5: 0.51768/0.79319, loss_mask_bce_5: 0.57465/0.30721, loss_mask_dice_5: 0.49043/1.05000, loss_spatial_bce_5: 0.04461/0.09159, loss_spatial_dice_5: 0.06310/0.19468, loss_spatial_ce_5: 0.07206/0.09258, loss_grounding_bce_5: 0.02723/0.08199, loss_grounding_dice_5: 0.02849/0.15402, loss_grounding_ce_5: 0.21165/0.27398, loss_mask_ce_6: 0.61690/0.82074, loss_mask_bce_6: 0.31858/0.30937, loss_mask_dice_6: 0.39590/1.05401, loss_spatial_bce_6: 0.05543/0.09707, loss_spatial_dice_6: 0.06839/0.19705, loss_spatial_ce_6: 0.07857/0.11690, loss_grounding_bce_6: 0.02811/0.08277, loss_grounding_dice_6: 0.02671/0.15447, loss_grounding_ce_6: 0.19422/0.28300, loss_mask_ce_7: 0.69251/0.87556, loss_mask_bce_7: 0.30043/0.31664, loss_mask_dice_7: 0.31990/1.09981, loss_spatial_bce_7: 0.05378/0.10607, loss_spatial_dice_7: 0.07021/0.22165, loss_spatial_ce_7: 0.07584/0.15057, loss_grounding_bce_7: 0.02989/0.08449, loss_grounding_dice_7: 0.02987/0.16002, loss_grounding_ce_7: 0.22076/0.31585, loss_mask_ce_8: 0.68284/1.00944, loss_mask_bce_8: 0.30317/0.33255, loss_mask_dice_8: 0.35168/1.17648, loss_spatial_bce_8: 0.07163/0.12223, loss_spatial_dice_8: 0.09755/0.25592, loss_spatial_ce_8: 0.12356/0.19544, loss_grounding_bce_8: 0.04256/0.08870, loss_grounding_dice_8: 0.02854/0.16987, loss_grounding_ce_8: 0.28446/0.41349, loss_mask_ce_9: 5.59338/3.47157, loss_mask_bce_9: 0.49026/0.35961, loss_mask_dice_9: 1.20584/1.75910, loss_spatial_bce_9: 0.38011/0.35421, loss_spatial_dice_9: 0.75981/0.79292, loss_spatial_ce_9: 1.17280/1.38545, loss_grounding_bce_9: 0.06012/0.10097, loss_grounding_dice_9: 0.21279/0.24209, loss_grounding_ce_9: 0.57078/0.66591] items per batch[64] items per second[0.37] total items[5587200] mini batches[ 87300] memory[4999] epoch remaining[0:11:30] INFO:trainer.default_trainer:epochs[ 47] optim steps[87400] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 1.01014/0.74820, loss_mask_bce_0: 0.24427/0.30006, loss_mask_dice_0: 0.19325/1.01882, loss_spatial_bce_0: 0.07447/0.08385, loss_spatial_dice_0: 0.06196/0.17729, loss_spatial_ce_0: 0.01097/0.05416, loss_grounding_bce_0: 0.04677/0.08040, loss_grounding_dice_0: 0.05118/0.15019, loss_grounding_ce_0: 0.65248/0.24684, loss_mask_ce_1: 1.02683/0.74909, loss_mask_bce_1: 0.24067/0.30089, loss_mask_dice_1: 0.18147/1.02337, loss_spatial_bce_1: 0.08528/0.08434, loss_spatial_dice_1: 0.06059/0.18028, loss_spatial_ce_1: 0.00999/0.05779, loss_grounding_bce_1: 0.04801/0.08060, loss_grounding_dice_1: 0.05471/0.15088, loss_grounding_ce_1: 0.64640/0.24831, loss_mask_ce_2: 1.01628/0.75663, loss_mask_bce_2: 0.22873/0.30128, loss_mask_dice_2: 0.17705/1.02396, loss_spatial_bce_2: 0.10423/0.08444, loss_spatial_dice_2: 0.07400/0.18093, loss_spatial_ce_2: 0.01291/0.05997, loss_grounding_bce_2: 0.04649/0.08060, loss_grounding_dice_2: 0.05343/0.15087, loss_grounding_ce_2: 0.61718/0.25111, loss_mask_ce_3: 1.02669/0.76161, loss_mask_bce_3: 0.22190/0.30257, loss_mask_dice_3: 0.17675/1.02236, loss_spatial_bce_3: 0.18590/0.08658, loss_spatial_dice_3: 0.11864/0.18238, loss_spatial_ce_3: 0.04511/0.06489, loss_grounding_bce_3: 0.04864/0.08093, loss_grounding_dice_3: 0.05120/0.15048, loss_grounding_ce_3: 0.61747/0.25233, loss_mask_ce_4: 1.03766/0.76779, loss_mask_bce_4: 0.24216/0.30529, loss_mask_dice_4: 0.20138/1.04170, loss_spatial_bce_4: 0.12372/0.08915, loss_spatial_dice_4: 0.09363/0.19130, loss_spatial_ce_4: 0.07178/0.07871, loss_grounding_bce_4: 0.05223/0.08168, loss_grounding_dice_4: 0.05998/0.15308, loss_grounding_ce_4: 0.64244/0.25686, loss_mask_ce_5: 1.04340/0.79326, loss_mask_bce_5: 0.25281/0.30721, loss_mask_dice_5: 0.20395/1.05001, loss_spatial_bce_5: 0.14910/0.09159, loss_spatial_dice_5: 0.12131/0.19467, loss_spatial_ce_5: 0.20784/0.09256, loss_grounding_bce_5: 0.05114/0.08199, loss_grounding_dice_5: 0.05705/0.15400, loss_grounding_ce_5: 0.67293/0.27392, loss_mask_ce_6: 1.09247/0.82081, loss_mask_bce_6: 0.26235/0.30937, loss_mask_dice_6: 0.21136/1.05400, loss_spatial_bce_6: 0.13824/0.09707, loss_spatial_dice_6: 0.12351/0.19704, loss_spatial_ce_6: 0.22425/0.11686, loss_grounding_bce_6: 0.05670/0.08276, loss_grounding_dice_6: 0.06488/0.15446, loss_grounding_ce_6: 0.58457/0.28291, loss_mask_ce_7: 1.38773/0.87565, loss_mask_bce_7: 0.26775/0.31663, loss_mask_dice_7: 0.22399/1.09981, loss_spatial_bce_7: 0.26608/0.10607, loss_spatial_dice_7: 0.18670/0.22164, loss_spatial_ce_7: 0.22350/0.15052, loss_grounding_bce_7: 0.06146/0.08448, loss_grounding_dice_7: 0.07247/0.16000, loss_grounding_ce_7: 0.73516/0.31580, loss_mask_ce_8: 1.23863/1.00955, loss_mask_bce_8: 0.47546/0.33255, loss_mask_dice_8: 0.34399/1.17649, loss_spatial_bce_8: 0.45713/0.12223, loss_spatial_dice_8: 0.21929/0.25590, loss_spatial_ce_8: 0.33105/0.19540, loss_grounding_bce_8: 0.10020/0.08870, loss_grounding_dice_8: 0.10715/0.16987, loss_grounding_ce_8: 0.67657/0.41345, loss_mask_ce_9: 3.19495/3.47190, loss_mask_bce_9: 0.46168/0.35963, loss_mask_dice_9: 0.46417/1.75910, loss_spatial_bce_9: 0.63203/0.35422, loss_spatial_dice_9: 0.79538/0.79294, loss_spatial_ce_9: 0.93634/1.38567, loss_grounding_bce_9: 0.24279/0.10098, loss_grounding_dice_9: 0.21432/0.24210, loss_grounding_ce_9: 0.55167/0.66601] items per batch[64] items per second[0.37] total items[5593600] mini batches[ 87400] memory[4999] epoch remaining[0:08:35] INFO:trainer.default_trainer:epochs[ 47] optim steps[87500] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.16215/0.74824, loss_mask_bce_0: 0.05204/0.30004, loss_mask_dice_0: 0.13559/1.01893, loss_spatial_bce_0: 0.02354/0.08384, loss_spatial_dice_0: 0.10962/0.17728, loss_spatial_ce_0: 0.00006/0.05414, loss_grounding_bce_0: 0.04768/0.08039, loss_grounding_dice_0: 0.04869/0.15018, loss_grounding_ce_0: 0.02839/0.24678, loss_mask_ce_1: 0.17638/0.74914, loss_mask_bce_1: 0.04910/0.30086, loss_mask_dice_1: 0.13189/1.02346, loss_spatial_bce_1: 0.02178/0.08433, loss_spatial_dice_1: 0.07687/0.18027, loss_spatial_ce_1: 0.00004/0.05777, loss_grounding_bce_1: 0.04968/0.08060, loss_grounding_dice_1: 0.05135/0.15087, loss_grounding_ce_1: 0.03122/0.24825, loss_mask_ce_2: 0.18662/0.75668, loss_mask_bce_2: 0.04412/0.30125, loss_mask_dice_2: 0.13632/1.02406, loss_spatial_bce_2: 0.02157/0.08443, loss_spatial_dice_2: 0.08617/0.18093, loss_spatial_ce_2: 0.00007/0.05995, loss_grounding_bce_2: 0.04506/0.08059, loss_grounding_dice_2: 0.05000/0.15086, loss_grounding_ce_2: 0.03362/0.25105, loss_mask_ce_3: 0.18382/0.76162, loss_mask_bce_3: 0.04786/0.30254, loss_mask_dice_3: 0.12148/1.02249, loss_spatial_bce_3: 0.02364/0.08657, loss_spatial_dice_3: 0.09217/0.18237, loss_spatial_ce_3: 0.00004/0.06487, loss_grounding_bce_3: 0.05266/0.08092, loss_grounding_dice_3: 0.05181/0.15047, loss_grounding_ce_3: 0.04340/0.25228, loss_mask_ce_4: 0.23125/0.76783, loss_mask_bce_4: 0.04734/0.30527, loss_mask_dice_4: 0.15270/1.04179, loss_spatial_bce_4: 0.02609/0.08914, loss_spatial_dice_4: 0.07561/0.19130, loss_spatial_ce_4: 0.00296/0.07869, loss_grounding_bce_4: 0.04875/0.08167, loss_grounding_dice_4: 0.04954/0.15307, loss_grounding_ce_4: 0.05502/0.25678, loss_mask_ce_5: 0.19357/0.79334, loss_mask_bce_5: 0.04382/0.30718, loss_mask_dice_5: 0.12523/1.05011, loss_spatial_bce_5: 0.02224/0.09158, loss_spatial_dice_5: 0.06219/0.19467, loss_spatial_ce_5: 0.03269/0.09255, loss_grounding_bce_5: 0.04785/0.08198, loss_grounding_dice_5: 0.05064/0.15399, loss_grounding_ce_5: 0.06715/0.27385, loss_mask_ce_6: 0.22318/0.82089, loss_mask_bce_6: 0.04963/0.30934, loss_mask_dice_6: 0.15260/1.05409, loss_spatial_bce_6: 0.02470/0.09706, loss_spatial_dice_6: 0.08213/0.19704, loss_spatial_ce_6: 0.07301/0.11684, loss_grounding_bce_6: 0.04578/0.08275, loss_grounding_dice_6: 0.04809/0.15445, loss_grounding_ce_6: 0.15423/0.28285, loss_mask_ce_7: 0.20381/0.87568, loss_mask_bce_7: 0.04310/0.31660, loss_mask_dice_7: 0.14465/1.09992, loss_spatial_bce_7: 0.02412/0.10606, loss_spatial_dice_7: 0.09421/0.22163, loss_spatial_ce_7: 0.05192/0.15050, loss_grounding_bce_7: 0.04430/0.08447, loss_grounding_dice_7: 0.04723/0.15999, loss_grounding_ce_7: 0.04532/0.31571, loss_mask_ce_8: 0.19536/1.00961, loss_mask_bce_8: 0.04447/0.33253, loss_mask_dice_8: 0.11273/1.17657, loss_spatial_bce_8: 0.03201/0.12222, loss_spatial_dice_8: 0.10513/0.25589, loss_spatial_ce_8: 0.05919/0.19538, loss_grounding_bce_8: 0.05182/0.08869, loss_grounding_dice_8: 0.05483/0.16985, loss_grounding_ce_8: 0.10877/0.41337, loss_mask_ce_9: 2.54652/3.47194, loss_mask_bce_9: 0.04854/0.35962, loss_mask_dice_9: 0.19748/1.75923, loss_spatial_bce_9: 0.18762/0.35420, loss_spatial_dice_9: 0.70895/0.79294, loss_spatial_ce_9: 0.66302/1.38571, loss_grounding_bce_9: 0.04860/0.10098, loss_grounding_dice_9: 0.05620/0.24208, loss_grounding_ce_9: 0.13868/0.66598] items per batch[64] items per second[0.37] total items[5600000] mini batches[ 87500] memory[4999] epoch remaining[0:05:41] INFO:trainer.default_trainer:epochs[ 47] optim steps[87600] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.57852/0.74806, loss_mask_bce_0: 0.15278/0.30006, loss_mask_dice_0: 0.24970/1.01856, loss_spatial_bce_0: 0.05129/0.08385, loss_spatial_dice_0: 0.09747/0.17726, loss_spatial_ce_0: 0.00611/0.05413, loss_grounding_bce_0: 0.08746/0.08040, loss_grounding_dice_0: 0.11216/0.15016, loss_grounding_ce_0: 0.00351/0.24669, loss_mask_ce_1: 0.61284/0.74897, loss_mask_bce_1: 0.14575/0.30088, loss_mask_dice_1: 0.24831/1.02310, loss_spatial_bce_1: 0.04981/0.08434, loss_spatial_dice_1: 0.09392/0.18025, loss_spatial_ce_1: 0.01412/0.05775, loss_grounding_bce_1: 0.08215/0.08060, loss_grounding_dice_1: 0.10359/0.15085, loss_grounding_ce_1: 0.00271/0.24816, loss_mask_ce_2: 0.64650/0.75650, loss_mask_bce_2: 0.14806/0.30127, loss_mask_dice_2: 0.25668/1.02371, loss_spatial_bce_2: 0.05235/0.08444, loss_spatial_dice_2: 0.09068/0.18091, loss_spatial_ce_2: 0.00775/0.05993, loss_grounding_bce_2: 0.08650/0.08060, loss_grounding_dice_2: 0.11152/0.15083, loss_grounding_ce_2: 0.00385/0.25099, loss_mask_ce_3: 0.68222/0.76141, loss_mask_bce_3: 0.17053/0.30256, loss_mask_dice_3: 0.28440/1.02215, loss_spatial_bce_3: 0.05482/0.08658, loss_spatial_dice_3: 0.11785/0.18235, loss_spatial_ce_3: 0.02562/0.06485, loss_grounding_bce_3: 0.09728/0.08093, loss_grounding_dice_3: 0.11889/0.15045, loss_grounding_ce_3: 0.00322/0.25220, loss_mask_ce_4: 0.67741/0.76765, loss_mask_bce_4: 0.14559/0.30528, loss_mask_dice_4: 0.25860/1.04143, loss_spatial_bce_4: 0.06134/0.08915, loss_spatial_dice_4: 0.13464/0.19128, loss_spatial_ce_4: 0.00905/0.07867, loss_grounding_bce_4: 0.08227/0.08168, loss_grounding_dice_4: 0.10622/0.15304, loss_grounding_ce_4: 0.00204/0.25669, loss_mask_ce_5: 0.71570/0.79315, loss_mask_bce_5: 0.15340/0.30719, loss_mask_dice_5: 0.27113/1.04974, loss_spatial_bce_5: 0.05598/0.09159, loss_spatial_dice_5: 0.12480/0.19465, loss_spatial_ce_5: 0.02074/0.09254, loss_grounding_bce_5: 0.09114/0.08198, loss_grounding_dice_5: 0.11186/0.15397, loss_grounding_ce_5: 0.00272/0.27378, loss_mask_ce_6: 0.69906/0.82068, loss_mask_bce_6: 0.14749/0.30936, loss_mask_dice_6: 0.27665/1.05372, loss_spatial_bce_6: 0.06289/0.09707, loss_spatial_dice_6: 0.13486/0.19701, loss_spatial_ce_6: 0.03999/0.11683, loss_grounding_bce_6: 0.08371/0.08276, loss_grounding_dice_6: 0.10184/0.15443, loss_grounding_ce_6: 0.00319/0.28277, loss_mask_ce_7: 0.76430/0.87548, loss_mask_bce_7: 0.12975/0.31663, loss_mask_dice_7: 0.26230/1.09954, loss_spatial_bce_7: 0.05650/0.10606, loss_spatial_dice_7: 0.13287/0.22160, loss_spatial_ce_7: 0.15389/0.15045, loss_grounding_bce_7: 0.07505/0.08448, loss_grounding_dice_7: 0.09887/0.15997, loss_grounding_ce_7: 0.00219/0.31563, loss_mask_ce_8: 0.68148/1.00937, loss_mask_bce_8: 0.16454/0.33254, loss_mask_dice_8: 0.27318/1.17616, loss_spatial_bce_8: 0.12444/0.12222, loss_spatial_dice_8: 0.19077/0.25585, loss_spatial_ce_8: 0.08243/0.19534, loss_grounding_bce_8: 0.08078/0.08870, loss_grounding_dice_8: 0.09933/0.16983, loss_grounding_ce_8: 0.01811/0.41332, loss_mask_ce_9: 2.49407/3.47148, loss_mask_bce_9: 0.20773/0.35964, loss_mask_dice_9: 0.51167/1.75863, loss_spatial_bce_9: 0.39804/0.35426, loss_spatial_dice_9: 0.74589/0.79291, loss_spatial_ce_9: 0.98883/1.38565, loss_grounding_bce_9: 0.10402/0.10099, loss_grounding_dice_9: 0.19364/0.24205, loss_grounding_ce_9: 0.09573/0.66591] items per batch[64] items per second[0.37] total items[5606400] mini batches[ 87600] memory[4999] epoch remaining[0:02:47] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00087696. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0030 s/iter. Inference: 0.3795 s/iter. Eval: 0.0824 s/iter. Total: 0.4650 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0027 s/iter. Inference: 0.3750 s/iter. Eval: 0.0753 s/iter. Total: 0.4531 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0028 s/iter. Inference: 0.3772 s/iter. Eval: 0.0771 s/iter. Total: 0.4572 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0028 s/iter. Inference: 0.3815 s/iter. Eval: 0.0734 s/iter. Total: 0.4578 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0028 s/iter. Inference: 0.3811 s/iter. Eval: 0.0720 s/iter. Total: 0.4561 s/iter. ETA=0:00:10 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0029 s/iter. Inference: 0.3791 s/iter. Eval: 0.0712 s/iter. Total: 0.4533 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval1kzld_hz ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 56.114 | 83.038 | 66.740 | 133 | | Things | 62.312 | 84.073 | 73.602 | 80 | | Stuff | 46.758 | 81.475 | 56.381 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json Loading and preparing results... DONE (t=0.56s) creating index... index created! INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 14.88 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... DONE (t=4.42s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 18.97 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.462 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.701 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.266 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.503 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.679 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.578 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.771 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.234 | 70.147 | 50.065 | 26.605 | 50.337 | 67.906 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.820 | bicycle | 22.525 | car | 44.000 | | motorcycle | 42.377 | airplane | 61.963 | bus | 71.482 | | train | 75.964 | truck | 44.121 | boat | 32.202 | | traffic light | 29.465 | fire hydrant | 71.119 | stop sign | 69.594 | | parking meter | 53.129 | bench | 27.470 | bird | 35.350 | | cat | 77.228 | dog | 71.258 | horse | 50.955 | | sheep | 54.442 | cow | 57.451 | elephant | 66.514 | | bear | 80.072 | zebra | 66.433 | giraffe | 62.329 | | backpack | 25.099 | umbrella | 56.736 | handbag | 24.442 | | tie | 41.964 | suitcase | 52.269 | frisbee | 70.132 | | skis | 8.661 | snowboard | 34.671 | sports ball | 50.898 | | kite | 38.476 | baseball bat | 38.831 | baseball glove | 50.060 | | skateboard | 43.293 | surfboard | 45.517 | tennis racket | 63.629 | | bottle | 43.034 | wine glass | 38.832 | cup | 51.493 | | fork | 27.295 | knife | 25.011 | spoon | 22.872 | | bowl | 40.882 | banana | 22.616 | apple | 28.423 | | sandwich | 49.238 | orange | 31.723 | broccoli | 25.062 | | carrot | 23.180 | hot dog | 32.319 | pizza | 53.205 | | donut | 56.931 | cake | 49.532 | chair | 29.210 | | couch | 45.876 | potted plant | 23.561 | bed | 44.036 | | dining table | 15.551 | toilet | 70.348 | tv | 67.772 | | laptop | 71.230 | mouse | 64.288 | remote | 44.964 | | keyboard | 58.200 | cell phone | 46.272 | microwave | 67.529 | | oven | 33.166 | toaster | 46.819 | sink | 44.805 | | refrigerator | 70.659 | book | 15.207 | clock | 54.738 | | vase | 41.945 | scissors | 35.871 | teddy bear | 58.019 | | hair drier | 36.814 | toothbrush | 28.269 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.82526160394913, 'fwIoU': 71.67588732723542, 'IoU-person': 88.82245535021862, 'IoU-bicycle': 70.54884273837773, 'IoU-car': 74.2424616134919, 'IoU-motorcycle': 86.71290410630456, 'IoU-airplane': 86.95440693012839, 'IoU-bus': 87.79888389267994, 'IoU-train': 88.0657212466619, 'IoU-truck': 71.2312284768383, 'IoU-boat': 75.22780433301396, 'IoU-traffic light': 79.06071638066685, 'IoU-fire hydrant': 93.11647590093031, 'IoU-stop sign': 85.9319666356891, 'IoU-parking meter': 85.31298736353821, 'IoU-bench': 60.42965255694074, 'IoU-bird': 74.5954631495639, 'IoU-cat': 89.74567965782455, 'IoU-dog': 84.76988721827269, 'IoU-horse': 88.92705146285206, 'IoU-sheep': 88.54434462415938, 'IoU-cow': 90.45412142984736, 'IoU-elephant': 86.81817094525096, 'IoU-bear': 88.7939204917452, 'IoU-zebra': 82.43891521877272, 'IoU-giraffe': 89.43969952135782, 'IoU-backpack': 53.80749318272052, 'IoU-umbrella': 81.88121941557999, 'IoU-handbag': 49.222034330628325, 'IoU-tie': 76.26222802651357, 'IoU-suitcase': 80.458264230513, 'IoU-frisbee': 84.80456885224953, 'IoU-skis': 58.005064063952204, 'IoU-snowboard': 71.13425004600683, 'IoU-sports ball': 79.21221746195893, 'IoU-kite': 79.61593823606385, 'IoU-baseball bat': 69.17519369708064, 'IoU-baseball glove': 77.47317371099265, 'IoU-skateboard': 86.23082269467257, 'IoU-surfboard': 86.38660155993307, 'IoU-tennis racket': 90.92457395157089, 'IoU-bottle': 70.28440547509449, 'IoU-wine glass': 82.62669873086553, 'IoU-cup': 71.06485207590131, 'IoU-fork': 71.5344046888555, 'IoU-knife': 65.2229850892923, 'IoU-spoon': 60.82733159351217, 'IoU-bowl': 55.450323785643285, 'IoU-banana': 83.37014004523627, 'IoU-apple': 57.75604499123199, 'IoU-sandwich': 69.73860934775985, 'IoU-orange': 78.92676123549434, 'IoU-broccoli': 67.57275745984819, 'IoU-carrot': 64.87843045347962, 'IoU-hot dog': 61.88162072819957, 'IoU-pizza': 80.92841509726816, 'IoU-donut': 58.03820861586373, 'IoU-cake': 80.25867169072609, 'IoU-chair': 63.70144393124379, 'IoU-couch': 69.82530996193485, 'IoU-potted plant': 43.90732570022826, 'IoU-bed': 75.0680004767938, 'IoU-dining table': 53.51410724857671, 'IoU-toilet': 87.2661419686114, 'IoU-tv': 77.34833523603282, 'IoU-laptop': 79.60056216890258, 'IoU-mouse': 76.46235975905678, 'IoU-remote': 67.65619583116657, 'IoU-keyboard': 62.438750742434166, 'IoU-cell phone': 76.38479423760542, 'IoU-microwave': 79.15769908432686, 'IoU-oven': 73.52434572137719, 'IoU-toaster': 85.78189921460084, 'IoU-sink': 72.05869102428126, 'IoU-refrigerator': 83.85416070556445, 'IoU-book': 56.470872748002876, 'IoU-clock': 72.58155190523362, 'IoU-vase': 66.09447186566696, 'IoU-scissors': 86.69789682175605, 'IoU-teddy bear': 83.50577656571994, 'IoU-hair drier': 49.11369473491102, 'IoU-toothbrush': 76.09097647563688, 'IoU-banner': 34.689497666862586, 'IoU-blanket': 17.89395856000896, 'IoU-bridge': 37.96589015210321, 'IoU-cardboard': 50.16493927875351, 'IoU-counter': 30.692781847801033, 'IoU-curtain': 73.05870374299954, 'IoU-door-stuff': 47.12931863921513, 'IoU-floor-wood': 63.57957268089185, 'IoU-flower': 44.54882237555802, 'IoU-fruit': 48.18072248196547, 'IoU-gravel': 31.074215443888413, 'IoU-house': 26.53220486815587, 'IoU-light': 44.28271676808966, 'IoU-mirror-stuff': 61.454586894989596, 'IoU-net': 42.11639598804025, 'IoU-pillow': 23.15707661852821, 'IoU-platform': 28.49931351862713, 'IoU-playingfield': 71.7015006465304, 'IoU-railroad': 65.18641506745537, 'IoU-river': 54.05305142643788, 'IoU-road': 67.0446924971973, 'IoU-roof': 20.510650016163602, 'IoU-sand': 65.43465451351369, 'IoU-sea': 85.38302921381037, 'IoU-shelf': 37.529501704269784, 'IoU-snow': 92.04400128254518, 'IoU-stairs': 33.50941436151228, 'IoU-tent': 11.118235631394906, 'IoU-towel': 64.82806262131396, 'IoU-wall-brick': 52.207630778286806, 'IoU-wall-stone': 28.377150955102998, 'IoU-wall-tile': 71.69495043291622, 'IoU-wall-wood': 47.15618169953183, 'IoU-water-other': 27.315339804800992, 'IoU-window-blind': 49.48129407222256, 'IoU-window-other': 50.86093125258654, 'IoU-tree-merged': 81.74780023252691, 'IoU-fence-merged': 54.29550716434696, 'IoU-ceiling-merged': 67.69454707646472, 'IoU-sky-other-merged': 93.8837496211791, 'IoU-cabinet-merged': 62.86619110010576, 'IoU-table-merged': 40.28563766578208, 'IoU-floor-other-merged': 55.05835102844699, 'IoU-pavement-merged': 56.83325055807733, 'IoU-mountain-merged': 58.86227432569163, 'IoU-grass-merged': 73.15916922902235, 'IoU-dirt-merged': 47.118679729906184, 'IoU-paper-merged': 38.07262160906202, 'IoU-food-other-merged': 43.07829227626337, 'IoU-building-other-merged': 59.42970485009052, 'IoU-rock-merged': 64.80659175840196, 'IoU-wall-other-merged': 68.63341713373578, 'IoU-rug-merged': 67.43317252255943, 'mACC': 77.4000534697948, 'pACC': 82.33511826033862, 'ACC-person': 92.96962904447774, 'ACC-bicycle': 79.28615310069135, 'ACC-car': 86.91082722238474, 'ACC-motorcycle': 91.00793455590798, 'ACC-airplane': 91.04550539864236, 'ACC-bus': 93.88086897251232, 'ACC-train': 95.49846940723343, 'ACC-truck': 82.03338598909768, 'ACC-boat': 84.79377509190788, 'ACC-traffic light': 91.27389166444715, 'ACC-fire hydrant': 95.93060246725908, 'ACC-stop sign': 88.56334243981766, 'ACC-parking meter': 88.47588894490401, 'ACC-bench': 73.97273124115684, 'ACC-bird': 78.25863891599472, 'ACC-cat': 93.28455523320295, 'ACC-dog': 87.39226729960568, 'ACC-horse': 93.54202863727326, 'ACC-sheep': 93.29904198925874, 'ACC-cow': 93.8424999240376, 'ACC-elephant': 88.80591846476239, 'ACC-bear': 90.62852275425497, 'ACC-zebra': 84.41138949153621, 'ACC-giraffe': 93.24641528064312, 'ACC-backpack': 74.38355053855794, 'ACC-umbrella': 86.25058216192636, 'ACC-handbag': 70.10390747355221, 'ACC-tie': 84.65017306385244, 'ACC-suitcase': 86.95930558461026, 'ACC-frisbee': 94.11672727272727, 'ACC-skis': 72.5118144308119, 'ACC-snowboard': 81.41510127109304, 'ACC-sports ball': 88.68008393545654, 'ACC-kite': 85.7585848441602, 'ACC-baseball bat': 87.19627778735138, 'ACC-baseball glove': 92.31462066679637, 'ACC-skateboard': 90.77128340013735, 'ACC-surfboard': 92.3680501788337, 'ACC-tennis racket': 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84.55728948631945, 'ACC-oven': 90.5291741613665, 'ACC-toaster': 91.40469966743763, 'ACC-sink': 81.26722966037954, 'ACC-refrigerator': 93.99864960041423, 'ACC-book': 75.0754391863495, 'ACC-clock': 77.32546323661563, 'ACC-vase': 75.39824635343551, 'ACC-scissors': 92.18900641820235, 'ACC-teddy bear': 88.93826356537049, 'ACC-hair drier': 60.96120456550821, 'ACC-toothbrush': 84.7133425990271, 'ACC-banner': 75.58521799218177, 'ACC-blanket': 24.671611633749908, 'ACC-bridge': 55.117872779171705, 'ACC-cardboard': 67.9001017885706, 'ACC-counter': 56.00001125740878, 'ACC-curtain': 83.6038805386731, 'ACC-door-stuff': 66.96202854223469, 'ACC-floor-wood': 81.99304617371726, 'ACC-flower': 64.4965066390177, 'ACC-fruit': 68.82595048862119, 'ACC-gravel': 41.919757521646865, 'ACC-house': 33.42992231405953, 'ACC-light': 63.24252095854228, 'ACC-mirror-stuff': 77.11956815761023, 'ACC-net': 66.10453760422833, 'ACC-pillow': 46.014841879337794, 'ACC-platform': 45.86927517876136, 'ACC-playingfield': 91.5250615874427, 'ACC-railroad': 82.2789614720139, 'ACC-river': 71.4741265054644, 'ACC-road': 86.71410250861416, 'ACC-roof': 27.91982952160928, 'ACC-sand': 70.65363872887356, 'ACC-sea': 92.53179458520981, 'ACC-shelf': 54.8525760181221, 'ACC-snow': 95.73953088935528, 'ACC-stairs': 56.93447039602677, 'ACC-tent': 14.39456988595255, 'ACC-towel': 82.84473280871735, 'ACC-wall-brick': 69.8835819645278, 'ACC-wall-stone': 34.672907425749685, 'ACC-wall-tile': 85.6963030861597, 'ACC-wall-wood': 64.2068959731792, 'ACC-water-other': 42.76841127629898, 'ACC-window-blind': 62.56247952525196, 'ACC-window-other': 73.39223459564279, 'ACC-tree-merged': 89.80904784157588, 'ACC-fence-merged': 72.15632540515357, 'ACC-ceiling-merged': 82.80091040090896, 'ACC-sky-other-merged': 97.1560861362648, 'ACC-cabinet-merged': 78.94692860564679, 'ACC-table-merged': 55.60102403390891, 'ACC-floor-other-merged': 65.43793321868245, 'ACC-pavement-merged': 68.8768296299105, 'ACC-mountain-merged': 70.71861239997801, 'ACC-grass-merged': 84.66374578180185, 'ACC-dirt-merged': 66.22029983660435, 'ACC-paper-merged': 51.732938693616205, 'ACC-food-other-merged': 57.7858510520199, 'ACC-building-other-merged': 73.89728930094297, 'ACC-rock-merged': 83.57490911224947, 'ACC-wall-other-merged': 82.33522997407117, 'ACC-rug-merged': 82.90593692326046})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3253 s/iter. Inference: 0.1967 s/iter. Eval: 0.0000 s/iter. Total: 0.5220 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 18/25. Dataloading: 0.3245 s/iter. Inference: 0.4597 s/iter. Eval: 0.0000 s/iter. Total: 0.7844 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 23/25. Dataloading: 0.3488 s/iter. Inference: 0.5038 s/iter. Eval: 0.0000 s/iter. Total: 0.8526 s/iter. ETA=0:00:01 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3254316652033948, 'noc@0.8': 2.311969563944981, 'noc@0.85': 2.635937957272461, 'noc@0.9': 3.446883230904302, 'miou@iter1': 0.874179600064504} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1443 s/iter. Eval: 0.0010 s/iter. Total: 0.1466 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 76.13680267333984, 'precision@0.6': 73.1441879272461, 'precision@0.7': 69.21881103515625, 'precision@0.8': 60.4352912902832, 'precision@0.9': 33.929264068603516, 'cIoU': 62.51947784423828, 'mIoU': 67.56260681152344} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 56.11376560976842, 'SQ': 83.03784610136529, 'RQ': 66.73961414337867, 'PQ_th': 62.31187270017122, 'SQ_th': 84.0729306206448, 'RQ_th': 73.6024616073664, 'PQ_st': 46.758132265764154, 'SQ_st': 81.47545437415081, 'RQ_st': 56.38059910339721}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 'AP-bench': 0.0, 'AP-bird': 0.0, 'AP-cat': 0.0, 'AP-dog': 0.0, 'AP-horse': 0.0, 'AP-sheep': 0.0, 'AP-cow': 0.0, 'AP-elephant': 0.0, 'AP-bear': 0.0, 'AP-zebra': 0.0, 'AP-giraffe': 0.0, 'AP-backpack': 0.0, 'AP-umbrella': 0.0, 'AP-handbag': 0.0, 'AP-tie': 0.0, 'AP-suitcase': 0.0, 'AP-frisbee': 0.0, 'AP-skis': 0.0, 'AP-snowboard': 0.0, 'AP-sports ball': 0.0, 'AP-kite': 0.0, 'AP-baseball bat': 0.0, 'AP-baseball glove': 0.0, 'AP-skateboard': 0.0, 'AP-surfboard': 0.0, 'AP-tennis racket': 0.0, 'AP-bottle': 0.0, 'AP-wine glass': 0.0, 'AP-cup': 0.0, 'AP-fork': 0.0, 'AP-knife': 0.0, 'AP-spoon': 0.0, 'AP-bowl': 0.0, 'AP-banana': 0.0, 'AP-apple': 0.0, 'AP-sandwich': 0.0, 'AP-orange': 0.0, 'AP-broccoli': 0.0, 'AP-carrot': 0.0, 'AP-hot dog': 0.0, 'AP-pizza': 0.0, 'AP-donut': 0.0, 'AP-cake': 0.0, 'AP-chair': 0.0, 'AP-couch': 0.0, 'AP-potted plant': 0.0, 'AP-bed': 0.0, 'AP-dining table': 0.0, 'AP-toilet': 0.0, 'AP-tv': 0.0, 'AP-laptop': 0.0, 'AP-mouse': 0.0, 'AP-remote': 0.0, 'AP-keyboard': 0.0, 'AP-cell phone': 0.0, 'AP-microwave': 0.0, 'AP-oven': 0.0, 'AP-toaster': 0.0, 'AP-sink': 0.0, 'AP-refrigerator': 0.0, 'AP-book': 0.0, 'AP-clock': 0.0, 'AP-vase': 0.0, 'AP-scissors': 0.0, 'AP-teddy bear': 0.0, 'AP-hair drier': 0.0, 'AP-toothbrush': 0.0}), ('segm', {'AP': 46.23421086196592, 'AP50': 70.14717713827538, 'AP75': 50.06498343206407, 'APs': 26.605269588672957, 'APm': 50.33715508372701, 'APl': 67.90572089899177, 'AP-person': 49.819754023648194, 'AP-bicycle': 22.525467748060688, 'AP-car': 44.000419865568006, 'AP-motorcycle': 42.3774611367643, 'AP-airplane': 61.96337155389489, 'AP-bus': 71.48154715392405, 'AP-train': 75.96426097206256, 'AP-truck': 44.12132142932098, 'AP-boat': 32.201593742312355, 'AP-traffic light': 29.464638523958737, 'AP-fire hydrant': 71.11949435344668, 'AP-stop sign': 69.5936257382229, 'AP-parking meter': 53.128918299060146, 'AP-bench': 27.4699439803758, 'AP-bird': 35.35015366021758, 'AP-cat': 77.22771302771918, 'AP-dog': 71.25794603266068, 'AP-horse': 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67.56260681152344}}} INFO:trainer.default_trainer:This epoch takes 0:56:20.482394 INFO:trainer.default_trainer:PROGRESS: 96.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 48 training. INFO:trainer.default_trainer:epochs[ 48] optim steps[87700] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.00161/0.74801, loss_mask_bce_0: 0.02832/0.30003, loss_mask_dice_0: 0.01898/1.01844, loss_spatial_bce_0: 0.03670/0.08384, loss_spatial_dice_0: 0.02104/0.17725, loss_spatial_ce_0: 0.00005/0.05411, loss_grounding_bce_0: 0.03281/0.08038, loss_grounding_dice_0: 0.02175/0.15016, loss_grounding_ce_0: 0.00003/0.24668, loss_mask_ce_1: 0.00157/0.74890, loss_mask_bce_1: 0.02775/0.30085, loss_mask_dice_1: 0.01817/1.02300, loss_spatial_bce_1: 0.03548/0.08433, loss_spatial_dice_1: 0.02126/0.18024, loss_spatial_ce_1: 0.00004/0.05773, loss_grounding_bce_1: 0.03219/0.08058, loss_grounding_dice_1: 0.02049/0.15086, loss_grounding_ce_1: 0.00001/0.24814, loss_mask_ce_2: 0.00198/0.75644, loss_mask_bce_2: 0.02649/0.30124, loss_mask_dice_2: 0.01846/1.02359, loss_spatial_bce_2: 0.03542/0.08443, loss_spatial_dice_2: 0.02241/0.18090, loss_spatial_ce_2: 0.00010/0.05990, loss_grounding_bce_2: 0.03147/0.08058, loss_grounding_dice_2: 0.02258/0.15083, loss_grounding_ce_2: 0.00002/0.25097, loss_mask_ce_3: 0.00282/0.76134, loss_mask_bce_3: 0.02846/0.30254, loss_mask_dice_3: 0.02008/1.02204, loss_spatial_bce_3: 0.03565/0.08657, loss_spatial_dice_3: 0.02164/0.18234, loss_spatial_ce_3: 0.00015/0.06483, loss_grounding_bce_3: 0.03311/0.08090, loss_grounding_dice_3: 0.02347/0.15045, loss_grounding_ce_3: 0.00005/0.25218, loss_mask_ce_4: 0.00335/0.76761, loss_mask_bce_4: 0.02795/0.30525, loss_mask_dice_4: 0.01794/1.04131, loss_spatial_bce_4: 0.03559/0.08914, loss_spatial_dice_4: 0.02183/0.19128, loss_spatial_ce_4: 0.00022/0.07863, loss_grounding_bce_4: 0.03370/0.08166, loss_grounding_dice_4: 0.02183/0.15305, loss_grounding_ce_4: 0.00003/0.25668, loss_mask_ce_5: 0.00274/0.79309, loss_mask_bce_5: 0.02961/0.30717, loss_mask_dice_5: 0.01842/1.04965, loss_spatial_bce_5: 0.03755/0.09158, loss_spatial_dice_5: 0.02362/0.19465, loss_spatial_ce_5: 0.00047/0.09252, loss_grounding_bce_5: 0.03802/0.08196, loss_grounding_dice_5: 0.02473/0.15397, loss_grounding_ce_5: 0.00009/0.27377, loss_mask_ce_6: 0.00551/0.82062, loss_mask_bce_6: 0.03098/0.30933, loss_mask_dice_6: 0.02001/1.05360, loss_spatial_bce_6: 0.03597/0.09705, loss_spatial_dice_6: 0.02242/0.19702, loss_spatial_ce_6: 0.00437/0.11680, loss_grounding_bce_6: 0.03486/0.08273, loss_grounding_dice_6: 0.02353/0.15443, loss_grounding_ce_6: 0.00323/0.28275, loss_mask_ce_7: 0.00768/0.87541, loss_mask_bce_7: 0.03197/0.31659, loss_mask_dice_7: 0.02008/1.09939, loss_spatial_bce_7: 0.03605/0.10606, loss_spatial_dice_7: 0.02276/0.22161, loss_spatial_ce_7: 0.00132/0.15042, loss_grounding_bce_7: 0.03569/0.08446, loss_grounding_dice_7: 0.02259/0.15997, loss_grounding_ce_7: 0.00364/0.31559, loss_mask_ce_8: 0.01011/1.00935, loss_mask_bce_8: 0.03071/0.33250, loss_mask_dice_8: 0.01923/1.17602, loss_spatial_bce_8: 0.03392/0.12221, loss_spatial_dice_8: 0.02107/0.25585, loss_spatial_ce_8: 0.00174/0.19532, loss_grounding_bce_8: 0.03701/0.08868, loss_grounding_dice_8: 0.02421/0.16983, loss_grounding_ce_8: 0.00207/0.41327, loss_mask_ce_9: 1.50826/3.47148, loss_mask_bce_9: 0.03919/0.35960, loss_mask_dice_9: 0.03148/1.75831, loss_spatial_bce_9: 0.50550/0.35423, loss_spatial_dice_9: 0.54044/0.79291, loss_spatial_ce_9: 0.36027/1.38563, loss_grounding_bce_9: 0.04607/0.10098, loss_grounding_dice_9: 0.03773/0.24206, loss_grounding_ce_9: 0.14943/0.66586] items per batch[64] items per second[0.17] total items[5612800] mini batches[ 87700] memory[4999] epoch remaining[1:48:36] INFO:trainer.default_trainer:epochs[ 48] optim steps[87800] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.99355/0.74798, loss_mask_bce_0: 0.18984/0.30003, loss_mask_dice_0: 0.14688/1.01835, loss_spatial_bce_0: 0.05310/0.08383, loss_spatial_dice_0: 0.03881/0.17724, loss_spatial_ce_0: 0.01475/0.05411, loss_grounding_bce_0: 0.00000/0.08039, loss_grounding_dice_0: 0.00000/0.15017, loss_grounding_ce_0: 0.01801/0.24659, loss_mask_ce_1: 0.98873/0.74888, loss_mask_bce_1: 0.18667/0.30085, loss_mask_dice_1: 0.14448/1.02289, loss_spatial_bce_1: 0.05612/0.08432, loss_spatial_dice_1: 0.04167/0.18022, loss_spatial_ce_1: 0.01511/0.05773, loss_grounding_bce_1: 0.00000/0.08060, loss_grounding_dice_1: 0.00000/0.15087, loss_grounding_ce_1: 0.02584/0.24803, loss_mask_ce_2: 1.06782/0.75642, loss_mask_bce_2: 0.19321/0.30124, loss_mask_dice_2: 0.14531/1.02348, loss_spatial_bce_2: 0.05266/0.08442, loss_spatial_dice_2: 0.03896/0.18089, loss_spatial_ce_2: 0.01046/0.05990, loss_grounding_bce_2: 0.00000/0.08059, loss_grounding_dice_2: 0.00000/0.15084, loss_grounding_ce_2: 0.01146/0.25087, loss_mask_ce_3: 1.08049/0.76131, loss_mask_bce_3: 0.18981/0.30253, loss_mask_dice_3: 0.13938/1.02193, loss_spatial_bce_3: 0.05420/0.08657, loss_spatial_dice_3: 0.03950/0.18233, loss_spatial_ce_3: 0.00709/0.06482, loss_grounding_bce_3: 0.00000/0.08092, loss_grounding_dice_3: 0.00000/0.15046, loss_grounding_ce_3: 0.01540/0.25208, loss_mask_ce_4: 1.21837/0.76759, loss_mask_bce_4: 0.20212/0.30526, loss_mask_dice_4: 0.17048/1.04121, loss_spatial_bce_4: 0.05520/0.08914, loss_spatial_dice_4: 0.04018/0.19127, loss_spatial_ce_4: 0.00296/0.07862, loss_grounding_bce_4: 0.00000/0.08168, loss_grounding_dice_4: 0.00000/0.15305, loss_grounding_ce_4: 0.01326/0.25657, loss_mask_ce_5: 0.94459/0.79305, loss_mask_bce_5: 0.22590/0.30717, loss_mask_dice_5: 0.22614/1.04956, loss_spatial_bce_5: 0.05686/0.09157, loss_spatial_dice_5: 0.04142/0.19464, loss_spatial_ce_5: 0.00530/0.09251, loss_grounding_bce_5: 0.00000/0.08198, loss_grounding_dice_5: 0.00000/0.15398, loss_grounding_ce_5: 0.02862/0.27366, loss_mask_ce_6: 0.92136/0.82057, loss_mask_bce_6: 0.21441/0.30933, loss_mask_dice_6: 0.19755/1.05353, loss_spatial_bce_6: 0.05303/0.09705, loss_spatial_dice_6: 0.03896/0.19701, loss_spatial_ce_6: 0.00149/0.11678, loss_grounding_bce_6: 0.00000/0.08275, loss_grounding_dice_6: 0.00000/0.15443, loss_grounding_ce_6: 0.06408/0.28266, loss_mask_ce_7: 0.87120/0.87537, loss_mask_bce_7: 0.21784/0.31659, loss_mask_dice_7: 0.21751/1.09931, loss_spatial_bce_7: 0.05658/0.10606, loss_spatial_dice_7: 0.04717/0.22159, loss_spatial_ce_7: 0.00252/0.15041, loss_grounding_bce_7: 0.00000/0.08446, loss_grounding_dice_7: 0.00001/0.15997, loss_grounding_ce_7: 0.13029/0.31550, loss_mask_ce_8: 1.35371/1.00924, loss_mask_bce_8: 0.21220/0.33250, loss_mask_dice_8: 0.22100/1.17595, loss_spatial_bce_8: 0.05624/0.12220, loss_spatial_dice_8: 0.05506/0.25583, loss_spatial_ce_8: 0.01831/0.19532, loss_grounding_bce_8: 0.00000/0.08868, loss_grounding_dice_8: 0.00003/0.16983, loss_grounding_ce_8: 0.03676/0.41311, loss_mask_ce_9: 3.04126/3.47133, loss_mask_bce_9: 0.49735/0.35960, loss_mask_dice_9: 0.65768/1.75817, loss_spatial_bce_9: 0.68709/0.35427, loss_spatial_dice_9: 0.77991/0.79289, loss_spatial_ce_9: 1.72063/1.38558, loss_grounding_bce_9: 0.00000/0.10098, loss_grounding_dice_9: 0.00079/0.24208, loss_grounding_ce_9: 0.59955/0.66570] items per batch[64] items per second[0.37] total items[5619200] mini batches[ 87800] memory[4999] epoch remaining[0:52:11] INFO:trainer.default_trainer:epochs[ 48] optim steps[87900] learning rate[default: 1.00000e-05] train loss[loss_mask_ce_0: 0.84368/0.74788, loss_mask_bce_0: 0.00487/0.30002, loss_mask_dice_0: 0.28107/1.01835, loss_spatial_bce_0: 0.00269/0.08381, loss_spatial_dice_0: 0.04791/0.17722, loss_spatial_ce_0: 0.00057/0.05409, loss_grounding_bce_0: 0.00151/0.08039, loss_grounding_dice_0: 0.02975/0.15017, loss_grounding_ce_0: 0.01850/0.24656, loss_mask_ce_1: 0.85093/0.74877, loss_mask_bce_1: 0.00247/0.30083, loss_mask_dice_1: 0.14785/1.02287, loss_spatial_bce_1: 0.00259/0.08430, loss_spatial_dice_1: 0.13810/0.18021, loss_spatial_ce_1: 0.00082/0.05771, loss_grounding_bce_1: 0.00163/0.08060, loss_grounding_dice_1: 0.04456/0.15088, loss_grounding_ce_1: 0.00880/0.24800, loss_mask_ce_2: 0.85673/0.75630, loss_mask_bce_2: 0.00237/0.30122, loss_mask_dice_2: 0.22092/1.02346, loss_spatial_bce_2: 0.00241/0.08440, loss_spatial_dice_2: 0.14403/0.18087, loss_spatial_ce_2: 0.00071/0.05988, loss_grounding_bce_2: 0.00303/0.08059, loss_grounding_dice_2: 0.22305/0.15085, loss_grounding_ce_2: 0.01845/0.25089, loss_mask_ce_3: 0.93235/0.76119, loss_mask_bce_3: 0.00415/0.30251, loss_mask_dice_3: 0.12366/1.02190, loss_spatial_bce_3: 0.00338/0.08655, loss_spatial_dice_3: 0.04408/0.18232, loss_spatial_ce_3: 0.00158/0.06479, loss_grounding_bce_3: 0.00260/0.08092, loss_grounding_dice_3: 0.20464/0.15046, loss_grounding_ce_3: 0.01583/0.25205, loss_mask_ce_4: 0.99225/0.76750, loss_mask_bce_4: 0.00306/0.30523, loss_mask_dice_4: 0.07921/1.04124, loss_spatial_bce_4: 0.00489/0.08912, loss_spatial_dice_4: 0.09812/0.19126, loss_spatial_ce_4: 0.01580/0.07858, loss_grounding_bce_4: 0.00245/0.08168, loss_grounding_dice_4: 0.05425/0.15306, loss_grounding_ce_4: 0.01570/0.25653, loss_mask_ce_5: 1.00070/0.79294, loss_mask_bce_5: 0.00500/0.30715, loss_mask_dice_5: 0.44384/1.04954, loss_spatial_bce_5: 0.00217/0.09155, loss_spatial_dice_5: 0.10013/0.19463, loss_spatial_ce_5: 0.00221/0.09247, loss_grounding_bce_5: 0.00152/0.08198, loss_grounding_dice_5: 0.03382/0.15400, loss_grounding_ce_5: 0.00943/0.27360, loss_mask_ce_6: 1.03514/0.82047, loss_mask_bce_6: 0.00355/0.30931, loss_mask_dice_6: 0.46631/1.05354, loss_spatial_bce_6: 0.00401/0.09702, loss_spatial_dice_6: 0.15155/0.19700, loss_spatial_ce_6: 0.00045/0.11674, loss_grounding_bce_6: 0.00223/0.08276, loss_grounding_dice_6: 0.05068/0.15443, loss_grounding_ce_6: 0.00716/0.28258, loss_mask_ce_7: 1.05059/0.87526, loss_mask_bce_7: 0.00316/0.31656, loss_mask_dice_7: 0.07949/1.09929, loss_spatial_bce_7: 0.00532/0.10602, loss_spatial_dice_7: 0.08495/0.22157, loss_spatial_ce_7: 0.04212/0.15036, loss_grounding_bce_7: 0.00273/0.08447, loss_grounding_dice_7: 0.05996/0.15998, loss_grounding_ce_7: 0.00528/0.31543, loss_mask_ce_8: 1.12842/1.00909, loss_mask_bce_8: 0.00618/0.33247, loss_mask_dice_8: 0.48064/1.17595, loss_spatial_bce_8: 0.01433/0.12217, loss_spatial_dice_8: 0.30782/0.25580, loss_spatial_ce_8: 0.14010/0.19524, loss_grounding_bce_8: 0.00190/0.08868, loss_grounding_dice_8: 0.04619/0.16985, loss_grounding_ce_8: 0.05523/0.41303, loss_mask_ce_9: 2.93361/3.47133, loss_mask_bce_9: 0.00549/0.35958, loss_mask_dice_9: 0.50239/1.75819, loss_spatial_bce_9: 0.00873/0.35424, loss_spatial_dice_9: 0.42903/0.79289, loss_spatial_ce_9: 1.06204/1.38557, loss_grounding_bce_9: 0.00240/0.10099, loss_grounding_dice_9: 0.10571/0.24209, loss_grounding_ce_9: 0.07184/0.66566] items per batch[64] items per second[0.37] total items[5625600] mini batches[ 87900] memory[4999] epoch remaining[0:47:45] INFO:trainer.default_trainer:epochs[ 48] optim steps[88000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.10039/0.74774, loss_mask_bce_0: 0.02312/0.30002, loss_mask_dice_0: 0.07975/1.01813, loss_spatial_bce_0: 0.02211/0.08381, loss_spatial_dice_0: 0.25635/0.17721, loss_spatial_ce_0: 0.09249/0.05406, loss_grounding_bce_0: 0.02163/0.08039, loss_grounding_dice_0: 0.00934/0.15016, loss_grounding_ce_0: 0.00148/0.24652, loss_mask_ce_1: 0.10411/0.74862, loss_mask_bce_1: 0.02254/0.30084, loss_mask_dice_1: 0.12065/1.02266, loss_spatial_bce_1: 0.01653/0.08430, loss_spatial_dice_1: 0.15764/0.18020, loss_spatial_ce_1: 0.09250/0.05769, loss_grounding_bce_1: 0.02231/0.08060, loss_grounding_dice_1: 0.00897/0.15086, loss_grounding_ce_1: 0.00103/0.24796, loss_mask_ce_2: 0.11520/0.75617, loss_mask_bce_2: 0.02139/0.30122, loss_mask_dice_2: 0.09681/1.02323, loss_spatial_bce_2: 0.01839/0.08440, loss_spatial_dice_2: 0.15010/0.18086, loss_spatial_ce_2: 0.09271/0.05985, loss_grounding_bce_2: 0.02153/0.08059, loss_grounding_dice_2: 0.00854/0.15084, loss_grounding_ce_2: 0.00156/0.25086, loss_mask_ce_3: 0.10420/0.76105, loss_mask_bce_3: 0.02280/0.30252, loss_mask_dice_3: 0.09098/1.02168, loss_spatial_bce_3: 0.01628/0.08655, loss_spatial_dice_3: 0.13231/0.18231, loss_spatial_ce_3: 0.09270/0.06478, loss_grounding_bce_3: 0.02403/0.08092, loss_grounding_dice_3: 0.00945/0.15046, loss_grounding_ce_3: 0.00139/0.25201, loss_mask_ce_4: 0.10063/0.76735, loss_mask_bce_4: 0.02022/0.30524, loss_mask_dice_4: 0.08634/1.04103, loss_spatial_bce_4: 0.02060/0.08912, loss_spatial_dice_4: 0.28747/0.19125, loss_spatial_ce_4: 0.10895/0.07856, loss_grounding_bce_4: 0.02372/0.08168, loss_grounding_dice_4: 0.00947/0.15305, loss_grounding_ce_4: 0.00441/0.25649, loss_mask_ce_5: 0.11577/0.79281, loss_mask_bce_5: 0.02358/0.30715, loss_mask_dice_5: 0.10785/1.04931, loss_spatial_bce_5: 0.03009/0.09154, loss_spatial_dice_5: 0.26626/0.19461, loss_spatial_ce_5: 0.13036/0.09244, loss_grounding_bce_5: 0.02358/0.08198, loss_grounding_dice_5: 0.00989/0.15399, loss_grounding_ce_5: 0.01121/0.27355, loss_mask_ce_6: 0.09115/0.82032, loss_mask_bce_6: 0.02296/0.30931, loss_mask_dice_6: 0.08455/1.05332, loss_spatial_bce_6: 0.02553/0.09702, loss_spatial_dice_6: 0.25279/0.19699, loss_spatial_ce_6: 0.12342/0.11672, loss_grounding_bce_6: 0.02352/0.08275, loss_grounding_dice_6: 0.00991/0.15442, loss_grounding_ce_6: 0.00997/0.28252, loss_mask_ce_7: 0.12080/0.87508, loss_mask_bce_7: 0.02350/0.31656, loss_mask_dice_7: 0.07855/1.09909, loss_spatial_bce_7: 0.02102/0.10602, loss_spatial_dice_7: 0.05135/0.22155, loss_spatial_ce_7: 0.35495/0.15032, loss_grounding_bce_7: 0.02231/0.08446, loss_grounding_dice_7: 0.00927/0.15996, loss_grounding_ce_7: 0.01927/0.31542, loss_mask_ce_8: 0.11886/1.00890, loss_mask_bce_8: 0.02561/0.33247, loss_mask_dice_8: 0.10898/1.17574, loss_spatial_bce_8: 0.02286/0.12216, loss_spatial_dice_8: 0.09235/0.25579, loss_spatial_ce_8: 0.03690/0.19520, loss_grounding_bce_8: 0.02231/0.08868, loss_grounding_dice_8: 0.00855/0.16983, loss_grounding_ce_8: 0.06849/0.41300, loss_mask_ce_9: 1.81541/3.47105, loss_mask_bce_9: 0.03140/0.35958, loss_mask_dice_9: 0.11933/1.75795, loss_spatial_bce_9: 0.76442/0.35420, loss_spatial_dice_9: 0.70826/0.79286, loss_spatial_ce_9: 1.60481/1.38551, loss_grounding_bce_9: 0.04014/0.10098, loss_grounding_dice_9: 0.02244/0.24208, loss_grounding_ce_9: 0.08038/0.66569] items per batch[64] items per second[0.37] total items[5632000] mini batches[ 88000] memory[4999] epoch remaining[0:44:33] INFO:trainer.default_trainer:epochs[ 48] optim steps[88100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.78183/0.74765, loss_mask_bce_0: 0.15000/0.30005, loss_mask_dice_0: 0.27726/1.01822, loss_spatial_bce_0: 0.05574/0.08381, loss_spatial_dice_0: 0.13029/0.17720, loss_spatial_ce_0: 0.00066/0.05405, loss_grounding_bce_0: 0.01590/0.08040, loss_grounding_dice_0: 0.09275/0.15018, loss_grounding_ce_0: 0.00952/0.24648, loss_mask_ce_1: 0.74958/0.74850, loss_mask_bce_1: 0.14994/0.30088, loss_mask_dice_1: 0.29191/1.02273, loss_spatial_bce_1: 0.06338/0.08430, loss_spatial_dice_1: 0.15511/0.18019, loss_spatial_ce_1: 0.00049/0.05768, loss_grounding_bce_1: 0.01641/0.08061, loss_grounding_dice_1: 0.09905/0.15088, loss_grounding_ce_1: 0.01348/0.24792, loss_mask_ce_2: 0.53818/0.75608, loss_mask_bce_2: 0.19344/0.30126, loss_mask_dice_2: 0.29087/1.02331, loss_spatial_bce_2: 0.05629/0.08439, loss_spatial_dice_2: 0.13782/0.18085, loss_spatial_ce_2: 0.00115/0.05984, loss_grounding_bce_2: 0.01826/0.08060, loss_grounding_dice_2: 0.10324/0.15086, loss_grounding_ce_2: 0.01315/0.25083, loss_mask_ce_3: 0.76655/0.76092, loss_mask_bce_3: 0.14364/0.30255, loss_mask_dice_3: 0.27340/1.02177, loss_spatial_bce_3: 0.05815/0.08654, loss_spatial_dice_3: 0.12981/0.18230, loss_spatial_ce_3: 0.00910/0.06475, loss_grounding_bce_3: 0.01546/0.08093, loss_grounding_dice_3: 0.09232/0.15048, loss_grounding_ce_3: 0.01112/0.25197, loss_mask_ce_4: 0.78191/0.76723, loss_mask_bce_4: 0.15210/0.30528, loss_mask_dice_4: 0.26677/1.04109, loss_spatial_bce_4: 0.06046/0.08912, loss_spatial_dice_4: 0.13281/0.19124, loss_spatial_ce_4: 0.12764/0.07853, loss_grounding_bce_4: 0.01627/0.08168, loss_grounding_dice_4: 0.08883/0.15306, loss_grounding_ce_4: 0.00349/0.25644, loss_mask_ce_5: 0.71982/0.79272, loss_mask_bce_5: 0.14041/0.30719, loss_mask_dice_5: 0.28480/1.04940, loss_spatial_bce_5: 0.07636/0.09154, loss_spatial_dice_5: 0.14272/0.19461, loss_spatial_ce_5: 0.13686/0.09242, loss_grounding_bce_5: 0.01431/0.08198, loss_grounding_dice_5: 0.09570/0.15401, loss_grounding_ce_5: 0.00498/0.27350, loss_mask_ce_6: 0.10036/0.82021, loss_mask_bce_6: 0.27741/0.30936, loss_mask_dice_6: 0.44619/1.05343, loss_spatial_bce_6: 0.07012/0.09702, loss_spatial_dice_6: 0.15306/0.19698, loss_spatial_ce_6: 0.15954/0.11670, loss_grounding_bce_6: 0.01616/0.08276, loss_grounding_dice_6: 0.10407/0.15444, loss_grounding_ce_6: 0.00420/0.28245, loss_mask_ce_7: 0.14160/0.87501, loss_mask_bce_7: 0.21910/0.31660, loss_mask_dice_7: 0.46288/1.09916, loss_spatial_bce_7: 0.10094/0.10601, loss_spatial_dice_7: 0.17702/0.22154, loss_spatial_ce_7: 0.09773/0.15028, loss_grounding_bce_7: 0.01486/0.08447, loss_grounding_dice_7: 0.08498/0.15998, loss_grounding_ce_7: 0.00409/0.31534, loss_mask_ce_8: 0.12674/1.00878, loss_mask_bce_8: 0.28609/0.33253, loss_mask_dice_8: 0.45255/1.17583, loss_spatial_bce_8: 0.09315/0.12217, loss_spatial_dice_8: 0.18701/0.25577, loss_spatial_ce_8: 0.22135/0.19514, loss_grounding_bce_8: 0.01441/0.08869, loss_grounding_dice_8: 0.08365/0.16985, loss_grounding_ce_8: 0.00102/0.41291, loss_mask_ce_9: 1.82326/3.47107, loss_mask_bce_9: 0.16859/0.35966, loss_mask_dice_9: 0.40022/1.75820, loss_spatial_bce_9: 0.41935/0.35418, loss_spatial_dice_9: 0.83283/0.79286, loss_spatial_ce_9: 0.80544/1.38541, loss_grounding_bce_9: 0.01552/0.10099, loss_grounding_dice_9: 0.15441/0.24209, loss_grounding_ce_9: 0.36625/0.66561] items per batch[64] items per second[0.38] total items[5638400] mini batches[ 88100] memory[4999] epoch remaining[0:41:20] INFO:trainer.default_trainer:epochs[ 48] optim steps[88200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.27818/0.74765, loss_mask_bce_0: 0.19064/0.30007, loss_mask_dice_0: 0.45846/1.01822, loss_spatial_bce_0: 0.06257/0.08381, loss_spatial_dice_0: 0.11579/0.17720, loss_spatial_ce_0: 0.15087/0.05404, loss_grounding_bce_0: 0.01704/0.08041, loss_grounding_dice_0: 0.18925/0.15018, loss_grounding_ce_0: 0.01573/0.24659, loss_mask_ce_1: 0.28732/0.74851, loss_mask_bce_1: 0.20144/0.30090, loss_mask_dice_1: 0.47313/1.02272, loss_spatial_bce_1: 0.06950/0.08430, loss_spatial_dice_1: 0.12443/0.18019, loss_spatial_ce_1: 0.19436/0.05766, loss_grounding_bce_1: 0.01447/0.08062, loss_grounding_dice_1: 0.19206/0.15088, loss_grounding_ce_1: 0.01867/0.24802, loss_mask_ce_2: 0.29708/0.75609, loss_mask_bce_2: 0.19983/0.30127, loss_mask_dice_2: 0.44501/1.02333, loss_spatial_bce_2: 0.06826/0.08439, loss_spatial_dice_2: 0.11899/0.18085, loss_spatial_ce_2: 0.18306/0.05982, loss_grounding_bce_2: 0.02080/0.08060, loss_grounding_dice_2: 0.22435/0.15086, loss_grounding_ce_2: 0.01947/0.25098, loss_mask_ce_3: 0.26743/0.76095, loss_mask_bce_3: 0.20498/0.30256, loss_mask_dice_3: 0.43676/1.02177, loss_spatial_bce_3: 0.07010/0.08654, loss_spatial_dice_3: 0.12005/0.18230, loss_spatial_ce_3: 0.17624/0.06474, loss_grounding_bce_3: 0.02085/0.08094, loss_grounding_dice_3: 0.21849/0.15047, loss_grounding_ce_3: 0.02025/0.25208, loss_mask_ce_4: 0.27845/0.76725, loss_mask_bce_4: 0.21318/0.30530, loss_mask_dice_4: 0.50955/1.04107, loss_spatial_bce_4: 0.06740/0.08912, loss_spatial_dice_4: 0.12103/0.19124, loss_spatial_ce_4: 0.15669/0.07852, loss_grounding_bce_4: 0.02458/0.08169, loss_grounding_dice_4: 0.23141/0.15305, loss_grounding_ce_4: 0.02330/0.25655, loss_mask_ce_5: 0.32978/0.79274, loss_mask_bce_5: 0.21549/0.30720, loss_mask_dice_5: 0.49622/1.04939, loss_spatial_bce_5: 0.07696/0.09154, loss_spatial_dice_5: 0.13778/0.19460, loss_spatial_ce_5: 0.21131/0.09241, loss_grounding_bce_5: 0.02637/0.08199, loss_grounding_dice_5: 0.23223/0.15400, loss_grounding_ce_5: 0.02286/0.27367, loss_mask_ce_6: 0.29165/0.82022, loss_mask_bce_6: 0.22520/0.30938, loss_mask_dice_6: 0.47632/1.05339, loss_spatial_bce_6: 0.09501/0.09702, loss_spatial_dice_6: 0.14322/0.19698, loss_spatial_ce_6: 0.37318/0.11669, loss_grounding_bce_6: 0.02366/0.08277, loss_grounding_dice_6: 0.25064/0.15444, loss_grounding_ce_6: 0.01726/0.28261, loss_mask_ce_7: 0.29938/0.87503, loss_mask_bce_7: 0.21998/0.31662, loss_mask_dice_7: 0.52171/1.09913, loss_spatial_bce_7: 0.08309/0.10601, loss_spatial_dice_7: 0.16138/0.22154, loss_spatial_ce_7: 0.50484/0.15027, loss_grounding_bce_7: 0.03077/0.08448, loss_grounding_dice_7: 0.31891/0.15998, loss_grounding_ce_7: 0.02458/0.31550, loss_mask_ce_8: 0.35398/1.00882, loss_mask_bce_8: 0.23247/0.33254, loss_mask_dice_8: 0.54513/1.17581, loss_spatial_bce_8: 0.06955/0.12216, loss_spatial_dice_8: 0.17402/0.25576, loss_spatial_ce_8: 0.30625/0.19511, loss_grounding_bce_8: 0.03224/0.08870, loss_grounding_dice_8: 0.32829/0.16985, loss_grounding_ce_8: 0.05792/0.41297, loss_mask_ce_9: 2.66189/3.47120, loss_mask_bce_9: 0.23721/0.35968, loss_mask_dice_9: 0.59786/1.75823, loss_spatial_bce_9: 0.39294/0.35421, loss_spatial_dice_9: 0.82085/0.79287, loss_spatial_ce_9: 2.10541/1.38546, loss_grounding_bce_9: 0.03257/0.10101, loss_grounding_dice_9: 0.39121/0.24212, loss_grounding_ce_9: 0.01136/0.66572] items per batch[64] items per second[0.37] total items[5644800] mini batches[ 88200] memory[4999] epoch remaining[0:38:28] INFO:trainer.default_trainer:epochs[ 48] optim steps[88300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.71529/0.74765, loss_mask_bce_0: 0.09142/0.30012, loss_mask_dice_0: 2.92205/1.01827, loss_spatial_bce_0: 0.00727/0.08380, loss_spatial_dice_0: 0.18882/0.17718, loss_spatial_ce_0: 0.03767/0.05404, loss_grounding_bce_0: 0.00310/0.08041, loss_grounding_dice_0: 0.03117/0.15017, loss_grounding_ce_0: 0.00085/0.24660, loss_mask_ce_1: 0.66503/0.74849, loss_mask_bce_1: 0.08696/0.30095, loss_mask_dice_1: 2.82259/1.02277, loss_spatial_bce_1: 0.00807/0.08429, loss_spatial_dice_1: 0.19459/0.18017, loss_spatial_ce_1: 0.03647/0.05765, loss_grounding_bce_1: 0.00543/0.08062, loss_grounding_dice_1: 0.04785/0.15087, loss_grounding_ce_1: 0.00118/0.24803, loss_mask_ce_2: 0.84944/0.75611, loss_mask_bce_2: 0.08723/0.30132, loss_mask_dice_2: 2.58868/1.02337, loss_spatial_bce_2: 0.00811/0.08439, loss_spatial_dice_2: 0.22130/0.18083, loss_spatial_ce_2: 0.04538/0.05981, loss_grounding_bce_2: 0.00507/0.08061, loss_grounding_dice_2: 0.05217/0.15086, loss_grounding_ce_2: 0.00387/0.25100, loss_mask_ce_3: 0.75753/0.76097, loss_mask_bce_3: 0.09084/0.30261, loss_mask_dice_3: 2.61014/1.02182, loss_spatial_bce_3: 0.00895/0.08653, loss_spatial_dice_3: 0.25544/0.18228, loss_spatial_ce_3: 0.07087/0.06473, loss_grounding_bce_3: 0.00465/0.08094, loss_grounding_dice_3: 0.04531/0.15047, loss_grounding_ce_3: 0.00356/0.25208, loss_mask_ce_4: 0.74884/0.76724, loss_mask_bce_4: 0.08689/0.30535, loss_mask_dice_4: 2.66171/1.04113, loss_spatial_bce_4: 0.00920/0.08911, loss_spatial_dice_4: 0.23568/0.19123, loss_spatial_ce_4: 0.07663/0.07850, loss_grounding_bce_4: 0.00288/0.08169, loss_grounding_dice_4: 0.02929/0.15305, loss_grounding_ce_4: 0.00366/0.25654, loss_mask_ce_5: 0.68589/0.79276, loss_mask_bce_5: 0.08951/0.30725, loss_mask_dice_5: 3.03284/1.04947, loss_spatial_bce_5: 0.00885/0.09154, loss_spatial_dice_5: 0.20808/0.19459, loss_spatial_ce_5: 0.29354/0.09240, loss_grounding_bce_5: 0.00598/0.08199, loss_grounding_dice_5: 0.04885/0.15399, loss_grounding_ce_5: 0.01170/0.27372, loss_mask_ce_6: 0.74515/0.82024, loss_mask_bce_6: 0.09488/0.30943, loss_mask_dice_6: 2.67739/1.05347, loss_spatial_bce_6: 0.01196/0.09702, loss_spatial_dice_6: 0.22838/0.19697, loss_spatial_ce_6: 0.16720/0.11667, loss_grounding_bce_6: 0.00406/0.08277, loss_grounding_dice_6: 0.03711/0.15443, loss_grounding_ce_6: 0.06357/0.28262, loss_mask_ce_7: 0.93935/0.87499, loss_mask_bce_7: 0.08472/0.31668, loss_mask_dice_7: 2.27775/1.09919, loss_spatial_bce_7: 0.01195/0.10601, loss_spatial_dice_7: 0.26644/0.22153, loss_spatial_ce_7: 0.08176/0.15024, loss_grounding_bce_7: 0.00301/0.08448, loss_grounding_dice_7: 0.03433/0.15998, loss_grounding_ce_7: 0.02290/0.31552, loss_mask_ce_8: 1.25414/1.00884, loss_mask_bce_8: 0.10367/0.33261, loss_mask_dice_8: 2.74467/1.17593, loss_spatial_bce_8: 0.02012/0.12216, loss_spatial_dice_8: 0.30372/0.25575, loss_spatial_ce_8: 0.23897/0.19506, loss_grounding_bce_8: 0.00524/0.08869, loss_grounding_dice_8: 0.03984/0.16984, loss_grounding_ce_8: 0.70779/0.41300, loss_mask_ce_9: 3.41211/3.47136, loss_mask_bce_9: 0.09335/0.35973, loss_mask_dice_9: 4.67419/1.75846, loss_spatial_bce_9: 0.06131/0.35421, loss_spatial_dice_9: 0.86775/0.79290, loss_spatial_ce_9: 1.57906/1.38546, loss_grounding_bce_9: 0.00569/0.10101, loss_grounding_dice_9: 0.06610/0.24213, loss_grounding_ce_9: 1.97220/0.66578] items per batch[64] items per second[0.37] total items[5651200] mini batches[ 88300] memory[4999] epoch remaining[0:35:33] INFO:trainer.default_trainer:epochs[ 48] optim steps[88400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.44546/0.74761, loss_mask_bce_0: 0.36798/0.30010, loss_mask_dice_0: 1.91727/1.01800, loss_spatial_bce_0: 0.06416/0.08380, loss_spatial_dice_0: 0.16889/0.17715, loss_spatial_ce_0: 0.00039/0.05402, loss_grounding_bce_0: 0.04972/0.08041, loss_grounding_dice_0: 0.21496/0.15016, loss_grounding_ce_0: 0.02175/0.24662, loss_mask_ce_1: 0.44624/0.74840, loss_mask_bce_1: 0.40107/0.30092, loss_mask_dice_1: 1.95638/1.02249, loss_spatial_bce_1: 0.06438/0.08429, loss_spatial_dice_1: 0.16316/0.18014, loss_spatial_ce_1: 0.00122/0.05762, loss_grounding_bce_1: 0.05250/0.08062, loss_grounding_dice_1: 0.25392/0.15086, loss_grounding_ce_1: 0.02220/0.24800, loss_mask_ce_2: 0.45367/0.75604, loss_mask_bce_2: 0.38276/0.30129, loss_mask_dice_2: 1.76202/1.02309, loss_spatial_bce_2: 0.06090/0.08439, loss_spatial_dice_2: 0.15699/0.18080, loss_spatial_ce_2: 0.00736/0.05978, loss_grounding_bce_2: 0.05363/0.08060, loss_grounding_dice_2: 0.28723/0.15086, loss_grounding_ce_2: 0.02256/0.25098, loss_mask_ce_3: 0.46378/0.76091, loss_mask_bce_3: 0.39931/0.30258, loss_mask_dice_3: 1.91093/1.02156, loss_spatial_bce_3: 0.06286/0.08654, loss_spatial_dice_3: 0.17016/0.18225, loss_spatial_ce_3: 0.03709/0.06471, loss_grounding_bce_3: 0.05222/0.08094, loss_grounding_dice_3: 0.24205/0.15047, loss_grounding_ce_3: 0.02755/0.25206, loss_mask_ce_4: 0.56474/0.76717, loss_mask_bce_4: 0.40047/0.30532, loss_mask_dice_4: 1.86860/1.04084, loss_spatial_bce_4: 0.05941/0.08912, loss_spatial_dice_4: 0.16912/0.19121, loss_spatial_ce_4: 0.09079/0.07849, loss_grounding_bce_4: 0.05239/0.08169, loss_grounding_dice_4: 0.23692/0.15305, loss_grounding_ce_4: 0.03374/0.25655, loss_mask_ce_5: 0.61883/0.79271, loss_mask_bce_5: 0.40263/0.30723, loss_mask_dice_5: 2.03664/1.04920, loss_spatial_bce_5: 0.07211/0.09154, loss_spatial_dice_5: 0.20581/0.19457, loss_spatial_ce_5: 0.00580/0.09237, loss_grounding_bce_5: 0.05285/0.08199, loss_grounding_dice_5: 0.22908/0.15398, loss_grounding_ce_5: 0.04477/0.27369, loss_mask_ce_6: 0.71676/0.82015, loss_mask_bce_6: 0.41461/0.30941, loss_mask_dice_6: 2.02303/1.05322, loss_spatial_bce_6: 0.07149/0.09703, loss_spatial_dice_6: 0.20786/0.19695, loss_spatial_ce_6: 0.01022/0.11664, loss_grounding_bce_6: 0.05175/0.08277, loss_grounding_dice_6: 0.25742/0.15442, loss_grounding_ce_6: 0.05349/0.28258, loss_mask_ce_7: 0.74876/0.87489, loss_mask_bce_7: 0.41131/0.31666, loss_mask_dice_7: 2.34762/1.09891, loss_spatial_bce_7: 0.07536/0.10600, loss_spatial_dice_7: 0.22277/0.22150, loss_spatial_ce_7: 0.03965/0.15019, loss_grounding_bce_7: 0.05158/0.08447, loss_grounding_dice_7: 0.25915/0.15998, loss_grounding_ce_7: 0.04163/0.31545, loss_mask_ce_8: 1.05663/1.00870, loss_mask_bce_8: 0.48432/0.33259, loss_mask_dice_8: 2.68426/1.17566, loss_spatial_bce_8: 0.07652/0.12215, loss_spatial_dice_8: 0.21549/0.25572, loss_spatial_ce_8: 0.02822/0.19501, loss_grounding_bce_8: 0.05159/0.08869, loss_grounding_dice_8: 0.28965/0.16984, loss_grounding_ce_8: 0.05849/0.41292, loss_mask_ce_9: 3.19381/3.47106, loss_mask_bce_9: 0.75857/0.35970, loss_mask_dice_9: 4.42589/1.75804, loss_spatial_bce_9: 0.34512/0.35422, loss_spatial_dice_9: 0.93519/0.79290, loss_spatial_ce_9: 1.72124/1.38539, loss_grounding_bce_9: 0.06624/0.10102, loss_grounding_dice_9: 0.41186/0.24213, loss_grounding_ce_9: 0.16831/0.66561] items per batch[64] items per second[0.37] total items[5657600] mini batches[ 88400] memory[4999] epoch remaining[0:32:37] INFO:trainer.default_trainer:epochs[ 48] optim steps[88500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.25199/0.74753, loss_mask_bce_0: 1.15603/0.30013, loss_mask_dice_0: 2.75434/1.01799, loss_spatial_bce_0: 0.15852/0.08380, loss_spatial_dice_0: 0.34074/0.17713, loss_spatial_ce_0: 0.03364/0.05401, loss_grounding_bce_0: 0.06783/0.08041, loss_grounding_dice_0: 0.15334/0.15015, loss_grounding_ce_0: 0.41011/0.24659, loss_mask_ce_1: 1.27010/0.74832, loss_mask_bce_1: 1.15162/0.30094, loss_mask_dice_1: 2.69470/1.02247, loss_spatial_bce_1: 0.17005/0.08429, loss_spatial_dice_1: 0.36892/0.18012, loss_spatial_ce_1: 0.03744/0.05761, loss_grounding_bce_1: 0.06652/0.08062, loss_grounding_dice_1: 0.15013/0.15085, loss_grounding_ce_1: 0.41157/0.24797, loss_mask_ce_2: 1.35124/0.75598, loss_mask_bce_2: 1.16188/0.30131, loss_mask_dice_2: 2.58507/1.02307, loss_spatial_bce_2: 0.14887/0.08439, loss_spatial_dice_2: 0.36087/0.18079, loss_spatial_ce_2: 0.03569/0.05976, loss_grounding_bce_2: 0.06915/0.08060, loss_grounding_dice_2: 0.16237/0.15085, loss_grounding_ce_2: 0.40618/0.25096, loss_mask_ce_3: 1.33666/0.76087, loss_mask_bce_3: 1.14242/0.30260, loss_mask_dice_3: 2.60070/1.02155, loss_spatial_bce_3: 0.15186/0.08653, loss_spatial_dice_3: 0.34945/0.18223, loss_spatial_ce_3: 0.06896/0.06469, loss_grounding_bce_3: 0.06932/0.08093, loss_grounding_dice_3: 0.15426/0.15046, loss_grounding_ce_3: 0.40525/0.25202, loss_mask_ce_4: 1.36160/0.76712, loss_mask_bce_4: 1.21473/0.30535, loss_mask_dice_4: 2.92805/1.04082, loss_spatial_bce_4: 0.16473/0.08912, loss_spatial_dice_4: 0.34918/0.19120, loss_spatial_ce_4: 0.06465/0.07847, loss_grounding_bce_4: 0.06987/0.08169, loss_grounding_dice_4: 0.15188/0.15303, loss_grounding_ce_4: 0.46801/0.25652, loss_mask_ce_5: 1.73970/0.79267, loss_mask_bce_5: 1.19796/0.30726, loss_mask_dice_5: 2.95248/1.04917, loss_spatial_bce_5: 0.15166/0.09154, loss_spatial_dice_5: 0.35323/0.19456, loss_spatial_ce_5: 0.07451/0.09235, loss_grounding_bce_5: 0.06987/0.08199, loss_grounding_dice_5: 0.15882/0.15397, loss_grounding_ce_5: 0.40117/0.27361, loss_mask_ce_6: 1.85547/0.82011, loss_mask_bce_6: 1.14687/0.30945, loss_mask_dice_6: 2.89279/1.05319, loss_spatial_bce_6: 0.14907/0.09702, loss_spatial_dice_6: 0.34367/0.19693, loss_spatial_ce_6: 0.06448/0.11663, loss_grounding_bce_6: 0.06807/0.08277, loss_grounding_dice_6: 0.14527/0.15442, loss_grounding_ce_6: 0.46503/0.28251, loss_mask_ce_7: 1.73275/0.87484, loss_mask_bce_7: 1.21467/0.31669, loss_mask_dice_7: 3.26257/1.09891, loss_spatial_bce_7: 0.16977/0.10600, loss_spatial_dice_7: 0.35078/0.22148, loss_spatial_ce_7: 0.08420/0.15016, loss_grounding_bce_7: 0.06591/0.08448, loss_grounding_dice_7: 0.14461/0.15997, loss_grounding_ce_7: 0.49530/0.31536, loss_mask_ce_8: 1.76607/1.00864, loss_mask_bce_8: 1.26976/0.33262, loss_mask_dice_8: 3.45629/1.17565, loss_spatial_bce_8: 0.17891/0.12214, loss_spatial_dice_8: 0.39423/0.25569, loss_spatial_ce_8: 0.11131/0.19496, loss_grounding_bce_8: 0.07308/0.08870, loss_grounding_dice_8: 0.16410/0.16983, loss_grounding_ce_8: 0.48098/0.41279, loss_mask_ce_9: 4.83598/3.47122, loss_mask_bce_9: 1.22903/0.35976, loss_mask_dice_9: 5.00161/1.75823, loss_spatial_bce_9: 0.26829/0.35424, loss_spatial_dice_9: 0.93766/0.79288, loss_spatial_ce_9: 1.51256/1.38534, loss_grounding_bce_9: 0.08241/0.10103, loss_grounding_dice_9: 0.27463/0.24212, loss_grounding_ce_9: 0.52736/0.66551] items per batch[64] items per second[0.37] total items[5664000] mini batches[ 88500] memory[4999] epoch remaining[0:29:40] INFO:trainer.default_trainer:epochs[ 48] optim steps[88600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.84372/0.74748, loss_mask_bce_0: 0.33368/0.30016, loss_mask_dice_0: 0.24193/1.01790, loss_spatial_bce_0: 0.23758/0.08380, loss_spatial_dice_0: 0.16088/0.17711, loss_spatial_ce_0: 0.05090/0.05399, loss_grounding_bce_0: 0.37202/0.08042, loss_grounding_dice_0: 0.22077/0.15013, loss_grounding_ce_0: 0.02498/0.24659, loss_mask_ce_1: 0.81427/0.74829, loss_mask_bce_1: 0.35210/0.30097, loss_mask_dice_1: 0.25334/1.02235, loss_spatial_bce_1: 0.24056/0.08429, loss_spatial_dice_1: 0.16509/0.18010, loss_spatial_ce_1: 0.06267/0.05759, loss_grounding_bce_1: 0.37324/0.08062, loss_grounding_dice_1: 0.22380/0.15083, loss_grounding_ce_1: 0.02164/0.24797, loss_mask_ce_2: 0.22349/0.75594, loss_mask_bce_2: 0.51000/0.30135, loss_mask_dice_2: 0.30563/1.02296, loss_spatial_bce_2: 0.25299/0.08439, loss_spatial_dice_2: 0.17480/0.18077, loss_spatial_ce_2: 0.05795/0.05973, loss_grounding_bce_2: 0.36764/0.08061, loss_grounding_dice_2: 0.21987/0.15083, loss_grounding_ce_2: 0.01503/0.25095, loss_mask_ce_3: 0.81957/0.76083, loss_mask_bce_3: 0.37356/0.30264, loss_mask_dice_3: 0.27002/1.02143, loss_spatial_bce_3: 0.25408/0.08653, loss_spatial_dice_3: 0.17309/0.18222, loss_spatial_ce_3: 0.03967/0.06467, loss_grounding_bce_3: 0.40353/0.08094, loss_grounding_dice_3: 0.22670/0.15045, loss_grounding_ce_3: 0.01723/0.25201, loss_mask_ce_4: 0.76573/0.76709, loss_mask_bce_4: 0.35989/0.30538, loss_mask_dice_4: 0.24018/1.04067, loss_spatial_bce_4: 0.27057/0.08912, loss_spatial_dice_4: 0.17497/0.19118, loss_spatial_ce_4: 0.03303/0.07844, loss_grounding_bce_4: 0.27001/0.08170, loss_grounding_dice_4: 0.16525/0.15302, loss_grounding_ce_4: 0.39569/0.25650, loss_mask_ce_5: 0.99737/0.79265, loss_mask_bce_5: 0.35176/0.30729, loss_mask_dice_5: 0.21075/1.04903, loss_spatial_bce_5: 0.25190/0.09154, loss_spatial_dice_5: 0.17060/0.19454, loss_spatial_ce_5: 0.11077/0.09233, loss_grounding_bce_5: 0.27567/0.08200, loss_grounding_dice_5: 0.15155/0.15396, loss_grounding_ce_5: 0.39619/0.27359, loss_mask_ce_6: 1.02705/0.82005, loss_mask_bce_6: 0.34946/0.30949, loss_mask_dice_6: 0.21738/1.05304, loss_spatial_bce_6: 0.22763/0.09703, loss_spatial_dice_6: 0.15944/0.19692, loss_spatial_ce_6: 0.15315/0.11660, loss_grounding_bce_6: 0.27729/0.08278, loss_grounding_dice_6: 0.14987/0.15441, loss_grounding_ce_6: 0.44936/0.28250, loss_mask_ce_7: 0.29326/0.87479, loss_mask_bce_7: 0.44362/0.31673, loss_mask_dice_7: 0.26259/1.09876, loss_spatial_bce_7: 0.24714/0.10600, loss_spatial_dice_7: 0.16610/0.22146, loss_spatial_ce_7: 0.14865/0.15011, loss_grounding_bce_7: 0.32817/0.08449, loss_grounding_dice_7: 0.19073/0.15995, loss_grounding_ce_7: 0.04177/0.31536, loss_mask_ce_8: 0.30438/1.00865, loss_mask_bce_8: 0.49984/0.33265, loss_mask_dice_8: 0.28319/1.17547, loss_spatial_bce_8: 0.22587/0.12214, loss_spatial_dice_8: 0.16010/0.25566, loss_spatial_ce_8: 0.20252/0.19491, loss_grounding_bce_8: 0.37335/0.08871, loss_grounding_dice_8: 0.20928/0.16982, loss_grounding_ce_8: 0.02885/0.41282, loss_mask_ce_9: 2.13046/3.47115, loss_mask_bce_9: 0.36146/0.35980, loss_mask_dice_9: 0.30407/1.75800, loss_spatial_bce_9: 0.45392/0.35425, loss_spatial_dice_9: 0.69214/0.79289, loss_spatial_ce_9: 1.42753/1.38537, loss_grounding_bce_9: 0.27093/0.10104, loss_grounding_dice_9: 0.22615/0.24210, loss_grounding_ce_9: 0.13369/0.66559] items per batch[64] items per second[0.36] total items[5670400] mini batches[ 88600] memory[4999] epoch remaining[0:26:48] INFO:trainer.default_trainer:epochs[ 48] optim steps[88700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.13701/0.74738, loss_mask_bce_0: 0.57867/0.30013, loss_mask_dice_0: 1.69957/1.01804, loss_spatial_bce_0: 0.10556/0.08379, loss_spatial_dice_0: 0.20195/0.17709, loss_spatial_ce_0: 0.01100/0.05397, loss_grounding_bce_0: 0.02780/0.08041, loss_grounding_dice_0: 0.16117/0.15013, loss_grounding_ce_0: 0.56885/0.24656, loss_mask_ce_1: 1.11557/0.74817, loss_mask_bce_1: 0.61219/0.30095, loss_mask_dice_1: 1.73738/1.02250, loss_spatial_bce_1: 0.09738/0.08428, loss_spatial_dice_1: 0.18910/0.18008, loss_spatial_ce_1: 0.02607/0.05757, loss_grounding_bce_1: 0.02983/0.08062, loss_grounding_dice_1: 0.17619/0.15082, loss_grounding_ce_1: 0.56712/0.24795, loss_mask_ce_2: 1.55146/0.75584, loss_mask_bce_2: 0.52790/0.30132, loss_mask_dice_2: 1.65372/1.02310, loss_spatial_bce_2: 0.09196/0.08438, loss_spatial_dice_2: 0.19824/0.18075, loss_spatial_ce_2: 0.04182/0.05972, loss_grounding_bce_2: 0.02631/0.08060, loss_grounding_dice_2: 0.15438/0.15083, loss_grounding_ce_2: 0.62770/0.25094, loss_mask_ce_3: 1.11367/0.76073, loss_mask_bce_3: 0.59976/0.30260, loss_mask_dice_3: 1.78416/1.02159, loss_spatial_bce_3: 0.09671/0.08652, loss_spatial_dice_3: 0.20842/0.18220, loss_spatial_ce_3: 0.07080/0.06464, loss_grounding_bce_3: 0.02950/0.08094, loss_grounding_dice_3: 0.19264/0.15044, loss_grounding_ce_3: 0.55417/0.25200, loss_mask_ce_4: 1.15889/0.76701, loss_mask_bce_4: 0.60121/0.30535, loss_mask_dice_4: 1.74010/1.04078, loss_spatial_bce_4: 0.11245/0.08911, loss_spatial_dice_4: 0.21212/0.19116, loss_spatial_ce_4: 0.01997/0.07840, loss_grounding_bce_4: 0.03375/0.08169, loss_grounding_dice_4: 0.17496/0.15302, loss_grounding_ce_4: 0.60577/0.25647, loss_mask_ce_5: 1.44046/0.79255, loss_mask_bce_5: 0.53582/0.30726, loss_mask_dice_5: 1.89051/1.04917, loss_spatial_bce_5: 0.11983/0.09153, loss_spatial_dice_5: 0.21964/0.19452, loss_spatial_ce_5: 0.03632/0.09231, loss_grounding_bce_5: 0.03882/0.08199, loss_grounding_dice_5: 0.22176/0.15395, loss_grounding_ce_5: 0.67044/0.27357, loss_mask_ce_6: 1.72818/0.81994, loss_mask_bce_6: 0.58314/0.30946, loss_mask_dice_6: 1.81371/1.05318, loss_spatial_bce_6: 0.15854/0.09702, loss_spatial_dice_6: 0.25195/0.19690, loss_spatial_ce_6: 0.04774/0.11656, loss_grounding_bce_6: 0.03913/0.08278, loss_grounding_dice_6: 0.22927/0.15440, loss_grounding_ce_6: 0.68163/0.28249, loss_mask_ce_7: 1.99303/0.87468, loss_mask_bce_7: 0.62540/0.31670, loss_mask_dice_7: 1.90991/1.09892, loss_spatial_bce_7: 0.17805/0.10598, loss_spatial_dice_7: 0.26594/0.22144, loss_spatial_ce_7: 0.13054/0.15005, loss_grounding_bce_7: 0.04798/0.08448, loss_grounding_dice_7: 0.27828/0.15995, loss_grounding_ce_7: 0.59774/0.31540, loss_mask_ce_8: 1.63768/1.00859, loss_mask_bce_8: 0.68811/0.33262, loss_mask_dice_8: 2.35151/1.17565, loss_spatial_bce_8: 0.17071/0.12212, loss_spatial_dice_8: 0.29140/0.25563, loss_spatial_ce_8: 0.25606/0.19486, loss_grounding_bce_8: 0.05009/0.08870, loss_grounding_dice_8: 0.26652/0.16980, loss_grounding_ce_8: 0.78380/0.41276, loss_mask_ce_9: 4.55500/3.47122, loss_mask_bce_9: 0.68153/0.35977, loss_mask_dice_9: 4.35492/1.75830, loss_spatial_bce_9: 0.27817/0.35422, loss_spatial_dice_9: 0.91441/0.79287, loss_spatial_ce_9: 1.26534/1.38539, loss_grounding_bce_9: 0.04487/0.10102, loss_grounding_dice_9: 0.42861/0.24210, loss_grounding_ce_9: 0.66153/0.66550] items per batch[64] items per second[0.37] total items[5676800] mini batches[ 88700] memory[4999] epoch remaining[0:23:51] INFO:trainer.default_trainer:epochs[ 48] optim steps[88800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.94042/0.74730, loss_mask_bce_0: 0.22098/0.30007, loss_mask_dice_0: 2.37398/1.01819, loss_spatial_bce_0: 0.01258/0.08376, loss_spatial_dice_0: 0.22112/0.17709, loss_spatial_ce_0: 0.00464/0.05395, loss_grounding_bce_0: 0.02120/0.08038, loss_grounding_dice_0: 0.20298/0.15012, loss_grounding_ce_0: 0.66701/0.24645, loss_mask_ce_1: 0.99773/0.74812, loss_mask_bce_1: 0.23212/0.30088, loss_mask_dice_1: 2.33947/1.02267, loss_spatial_bce_1: 0.01119/0.08425, loss_spatial_dice_1: 0.22922/0.18007, loss_spatial_ce_1: 0.01338/0.05755, loss_grounding_bce_1: 0.02128/0.08059, loss_grounding_dice_1: 0.28734/0.15082, loss_grounding_ce_1: 0.54299/0.24784, loss_mask_ce_2: 0.82153/0.75579, loss_mask_bce_2: 0.24534/0.30125, loss_mask_dice_2: 2.31916/1.02322, loss_spatial_bce_2: 0.01228/0.08435, loss_spatial_dice_2: 0.23098/0.18074, loss_spatial_ce_2: 0.00421/0.05969, loss_grounding_bce_2: 0.02501/0.08058, loss_grounding_dice_2: 0.28065/0.15082, loss_grounding_ce_2: 0.55656/0.25084, loss_mask_ce_3: 0.79565/0.76069, loss_mask_bce_3: 0.24606/0.30253, loss_mask_dice_3: 3.14592/1.02172, loss_spatial_bce_3: 0.01166/0.08650, loss_spatial_dice_3: 0.23517/0.18219, loss_spatial_ce_3: 0.01503/0.06461, loss_grounding_bce_3: 0.02209/0.08091, loss_grounding_dice_3: 0.26640/0.15043, loss_grounding_ce_3: 0.57147/0.25188, loss_mask_ce_4: 0.84850/0.76695, loss_mask_bce_4: 0.23460/0.30528, loss_mask_dice_4: 2.28785/1.04091, loss_spatial_bce_4: 0.01301/0.08908, loss_spatial_dice_4: 0.27861/0.19116, loss_spatial_ce_4: 0.01530/0.07838, loss_grounding_bce_4: 0.02155/0.08166, loss_grounding_dice_4: 0.21178/0.15301, loss_grounding_ce_4: 0.58599/0.25635, loss_mask_ce_5: 1.12197/0.79253, loss_mask_bce_5: 0.22413/0.30719, loss_mask_dice_5: 2.62608/1.04931, loss_spatial_bce_5: 0.01326/0.09150, loss_spatial_dice_5: 0.21786/0.19452, loss_spatial_ce_5: 0.11545/0.09228, loss_grounding_bce_5: 0.02490/0.08197, loss_grounding_dice_5: 0.29663/0.15395, loss_grounding_ce_5: 0.62080/0.27345, loss_mask_ce_6: 0.87135/0.81991, loss_mask_bce_6: 0.22961/0.30939, loss_mask_dice_6: 2.57110/1.05334, loss_spatial_bce_6: 0.01734/0.09699, loss_spatial_dice_6: 0.26997/0.19689, loss_spatial_ce_6: 0.12377/0.11653, loss_grounding_bce_6: 0.01995/0.08275, loss_grounding_dice_6: 0.23788/0.15440, loss_grounding_ce_6: 0.65462/0.28237, loss_mask_ce_7: 1.28211/0.87461, loss_mask_bce_7: 0.22976/0.31663, loss_mask_dice_7: 2.51253/1.09908, loss_spatial_bce_7: 0.03759/0.10595, loss_spatial_dice_7: 0.41724/0.22143, loss_spatial_ce_7: 0.10547/0.15002, loss_grounding_bce_7: 0.01958/0.08445, loss_grounding_dice_7: 0.25549/0.15993, loss_grounding_ce_7: 0.68068/0.31529, loss_mask_ce_8: 1.34114/1.00852, loss_mask_bce_8: 0.25795/0.33254, loss_mask_dice_8: 3.02780/1.17579, loss_spatial_bce_8: 0.03061/0.12208, loss_spatial_dice_8: 0.42999/0.25562, loss_spatial_ce_8: 0.14827/0.19484, loss_grounding_bce_8: 0.02355/0.08867, loss_grounding_dice_8: 0.28973/0.16979, loss_grounding_ce_8: 0.74244/0.41266, loss_mask_ce_9: 4.06411/3.47104, loss_mask_bce_9: 0.28969/0.35969, loss_mask_dice_9: 4.46553/1.75847, loss_spatial_bce_9: 0.11613/0.35418, loss_spatial_dice_9: 0.85400/0.79287, loss_spatial_ce_9: 1.49094/1.38539, loss_grounding_bce_9: 0.02004/0.10099, loss_grounding_dice_9: 0.41488/0.24208, loss_grounding_ce_9: 0.65894/0.66542] items per batch[64] items per second[0.37] total items[5683200] mini batches[ 88800] memory[4999] epoch remaining[0:20:57] INFO:trainer.default_trainer:epochs[ 48] optim steps[88900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.31271/0.74730, loss_mask_bce_0: 0.19541/0.30000, loss_mask_dice_0: 0.72680/1.01800, loss_spatial_bce_0: 0.02673/0.08374, loss_spatial_dice_0: 0.09982/0.17706, loss_spatial_ce_0: 0.12053/0.05394, loss_grounding_bce_0: 0.03945/0.08038, loss_grounding_dice_0: 0.15138/0.15011, loss_grounding_ce_0: 0.01064/0.24640, loss_mask_ce_1: 0.31341/0.74810, loss_mask_bce_1: 0.19524/0.30082, loss_mask_dice_1: 0.67475/1.02252, loss_spatial_bce_1: 0.03063/0.08424, loss_spatial_dice_1: 0.10898/0.18005, loss_spatial_ce_1: 0.11682/0.05754, loss_grounding_bce_1: 0.04282/0.08058, loss_grounding_dice_1: 0.15562/0.15081, loss_grounding_ce_1: 0.01269/0.24778, loss_mask_ce_2: 0.31792/0.75576, loss_mask_bce_2: 0.19540/0.30119, loss_mask_dice_2: 0.71051/1.02304, loss_spatial_bce_2: 0.03118/0.08434, loss_spatial_dice_2: 0.11782/0.18073, loss_spatial_ce_2: 0.12562/0.05966, loss_grounding_bce_2: 0.04938/0.08057, loss_grounding_dice_2: 0.15023/0.15081, loss_grounding_ce_2: 0.00904/0.25077, loss_mask_ce_3: 0.30983/0.76069, loss_mask_bce_3: 0.19075/0.30246, loss_mask_dice_3: 0.73751/1.02157, loss_spatial_bce_3: 0.02985/0.08648, loss_spatial_dice_3: 0.12271/0.18218, loss_spatial_ce_3: 0.08149/0.06458, loss_grounding_bce_3: 0.04756/0.08090, loss_grounding_dice_3: 0.15204/0.15042, loss_grounding_ce_3: 0.01178/0.25184, loss_mask_ce_4: 0.29670/0.76695, loss_mask_bce_4: 0.19908/0.30522, loss_mask_dice_4: 0.75167/1.04074, loss_spatial_bce_4: 0.03193/0.08906, loss_spatial_dice_4: 0.11823/0.19114, loss_spatial_ce_4: 0.31390/0.07834, loss_grounding_bce_4: 0.04287/0.08166, loss_grounding_dice_4: 0.15155/0.15300, loss_grounding_ce_4: 0.00920/0.25628, loss_mask_ce_5: 0.39172/0.79253, loss_mask_bce_5: 0.20354/0.30712, loss_mask_dice_5: 0.79389/1.04913, loss_spatial_bce_5: 0.03089/0.09149, loss_spatial_dice_5: 0.15617/0.19449, loss_spatial_ce_5: 0.08545/0.09224, loss_grounding_bce_5: 0.04087/0.08196, loss_grounding_dice_5: 0.15598/0.15394, loss_grounding_ce_5: 0.00610/0.27337, loss_mask_ce_6: 0.27173/0.81989, loss_mask_bce_6: 0.20813/0.30933, loss_mask_dice_6: 0.84947/1.05317, loss_spatial_bce_6: 0.03323/0.09697, loss_spatial_dice_6: 0.13044/0.19687, loss_spatial_ce_6: 0.41353/0.11649, loss_grounding_bce_6: 0.04532/0.08274, loss_grounding_dice_6: 0.16738/0.15438, loss_grounding_ce_6: 0.00395/0.28230, loss_mask_ce_7: 0.42652/0.87460, loss_mask_bce_7: 0.19690/0.31656, loss_mask_dice_7: 0.85197/1.09892, loss_spatial_bce_7: 0.03389/0.10593, loss_spatial_dice_7: 0.15371/0.22140, loss_spatial_ce_7: 0.35400/0.14997, loss_grounding_bce_7: 0.03774/0.08445, loss_grounding_dice_7: 0.17069/0.15992, loss_grounding_ce_7: 0.00493/0.31519, loss_mask_ce_8: 0.39366/1.00847, loss_mask_bce_8: 0.18855/0.33247, loss_mask_dice_8: 0.81157/1.17563, loss_spatial_bce_8: 0.03430/0.12205, loss_spatial_dice_8: 0.16845/0.25559, loss_spatial_ce_8: 0.25227/0.19479, loss_grounding_bce_8: 0.02778/0.08866, loss_grounding_dice_8: 0.16755/0.16978, loss_grounding_ce_8: 0.00754/0.41250, loss_mask_ce_9: 3.70835/3.47093, loss_mask_bce_9: 0.16763/0.35961, loss_mask_dice_9: 1.03036/1.75827, loss_spatial_bce_9: 0.18575/0.35415, loss_spatial_dice_9: 0.87154/0.79284, loss_spatial_ce_9: 1.42285/1.38527, loss_grounding_bce_9: 0.02866/0.10098, loss_grounding_dice_9: 0.20188/0.24206, loss_grounding_ce_9: 0.05852/0.66529] items per batch[64] items per second[0.38] total items[5689600] mini batches[ 88900] memory[4999] epoch remaining[0:18:02] INFO:trainer.default_trainer:epochs[ 48] optim steps[89000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.01975/0.74728, loss_mask_bce_0: 0.03845/0.29996, loss_mask_dice_0: 0.13132/1.01797, loss_spatial_bce_0: 0.01676/0.08373, loss_spatial_dice_0: 0.06238/0.17705, loss_spatial_ce_0: 0.00004/0.05392, loss_grounding_bce_0: 0.00912/0.08037, loss_grounding_dice_0: 0.06354/0.15010, loss_grounding_ce_0: 0.09388/0.24636, loss_mask_ce_1: 0.02096/0.74806, loss_mask_bce_1: 0.03674/0.30077, loss_mask_dice_1: 0.14322/1.02248, loss_spatial_bce_1: 0.01645/0.08422, loss_spatial_dice_1: 0.06291/0.18004, loss_spatial_ce_1: 0.00003/0.05751, loss_grounding_bce_1: 0.00863/0.08057, loss_grounding_dice_1: 0.06762/0.15079, loss_grounding_ce_1: 0.09468/0.24774, loss_mask_ce_2: 0.02743/0.75575, loss_mask_bce_2: 0.03975/0.30115, loss_mask_dice_2: 0.15263/1.02303, loss_spatial_bce_2: 0.01779/0.08432, loss_spatial_dice_2: 0.07073/0.18071, loss_spatial_ce_2: 0.00003/0.05964, loss_grounding_bce_2: 0.00867/0.08056, loss_grounding_dice_2: 0.06391/0.15080, loss_grounding_ce_2: 0.09380/0.25074, loss_mask_ce_3: 0.02324/0.76065, loss_mask_bce_3: 0.03798/0.30242, loss_mask_dice_3: 0.13758/1.02155, loss_spatial_bce_3: 0.01690/0.08646, loss_spatial_dice_3: 0.05891/0.18216, loss_spatial_ce_3: 0.00011/0.06456, loss_grounding_bce_3: 0.00947/0.08089, loss_grounding_dice_3: 0.06487/0.15041, loss_grounding_ce_3: 0.09380/0.25181, loss_mask_ce_4: 0.02230/0.76690, loss_mask_bce_4: 0.03826/0.30518, loss_mask_dice_4: 0.14067/1.04069, loss_spatial_bce_4: 0.01520/0.08905, loss_spatial_dice_4: 0.05200/0.19113, loss_spatial_ce_4: 0.00050/0.07832, loss_grounding_bce_4: 0.00676/0.08164, loss_grounding_dice_4: 0.06246/0.15299, loss_grounding_ce_4: 0.09374/0.25627, loss_mask_ce_5: 0.01483/0.79249, loss_mask_bce_5: 0.03935/0.30708, loss_mask_dice_5: 0.15125/1.04911, loss_spatial_bce_5: 0.01570/0.09147, loss_spatial_dice_5: 0.06332/0.19449, loss_spatial_ce_5: 0.03589/0.09221, loss_grounding_bce_5: 0.00806/0.08195, loss_grounding_dice_5: 0.06578/0.15392, loss_grounding_ce_5: 0.09464/0.27331, loss_mask_ce_6: 0.01047/0.81988, loss_mask_bce_6: 0.03879/0.30929, loss_mask_dice_6: 0.13850/1.05316, loss_spatial_bce_6: 0.01828/0.09696, loss_spatial_dice_6: 0.05944/0.19686, loss_spatial_ce_6: 0.01667/0.11645, loss_grounding_bce_6: 0.00958/0.08273, loss_grounding_dice_6: 0.06139/0.15437, loss_grounding_ce_6: 0.10012/0.28223, loss_mask_ce_7: 0.02321/0.87455, loss_mask_bce_7: 0.03911/0.31652, loss_mask_dice_7: 0.14906/1.09888, loss_spatial_bce_7: 0.01853/0.10591, loss_spatial_dice_7: 0.06190/0.22139, loss_spatial_ce_7: 0.00204/0.14993, loss_grounding_bce_7: 0.00708/0.08443, loss_grounding_dice_7: 0.06877/0.15990, loss_grounding_ce_7: 0.09926/0.31516, loss_mask_ce_8: 0.06449/1.00840, loss_mask_bce_8: 0.03871/0.33244, loss_mask_dice_8: 0.13099/1.17563, loss_spatial_bce_8: 0.02234/0.12204, loss_spatial_dice_8: 0.06869/0.25558, loss_spatial_ce_8: 0.00022/0.19473, loss_grounding_bce_8: 0.00743/0.08865, loss_grounding_dice_8: 0.06633/0.16976, loss_grounding_ce_8: 0.12915/0.41250, loss_mask_ce_9: 2.39102/3.47099, loss_mask_bce_9: 0.04090/0.35960, loss_mask_dice_9: 0.21275/1.75831, loss_spatial_bce_9: 0.31875/0.35413, loss_spatial_dice_9: 0.82065/0.79283, loss_spatial_ce_9: 1.55472/1.38523, loss_grounding_bce_9: 0.00677/0.10097, loss_grounding_dice_9: 0.09048/0.24204, loss_grounding_ce_9: 0.38680/0.66528] items per batch[64] items per second[0.37] total items[5696000] mini batches[ 89000] memory[4999] epoch remaining[0:15:08] INFO:trainer.default_trainer:epochs[ 48] optim steps[89100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.23985/0.74718, loss_mask_bce_0: 0.10365/0.29995, loss_mask_dice_0: 0.93094/1.01821, loss_spatial_bce_0: 0.02375/0.08370, loss_spatial_dice_0: 0.21493/0.17703, loss_spatial_ce_0: 0.00073/0.05389, loss_grounding_bce_0: 0.04651/0.08034, loss_grounding_dice_0: 0.04627/0.15009, loss_grounding_ce_0: 0.00032/0.24631, loss_mask_ce_1: 0.22065/0.74798, loss_mask_bce_1: 0.10083/0.30076, loss_mask_dice_1: 0.73243/1.02273, loss_spatial_bce_1: 0.02734/0.08420, loss_spatial_dice_1: 0.23070/0.18002, loss_spatial_ce_1: 0.00042/0.05747, loss_grounding_bce_1: 0.04686/0.08055, loss_grounding_dice_1: 0.04825/0.15079, loss_grounding_ce_1: 0.00029/0.24771, loss_mask_ce_2: 0.23887/0.75568, loss_mask_bce_2: 0.10663/0.30113, loss_mask_dice_2: 0.67175/1.02327, loss_spatial_bce_2: 0.02418/0.08430, loss_spatial_dice_2: 0.23448/0.18069, loss_spatial_ce_2: 0.00033/0.05961, loss_grounding_bce_2: 0.04582/0.08053, loss_grounding_dice_2: 0.04969/0.15079, loss_grounding_ce_2: 0.00030/0.25072, loss_mask_ce_3: 0.22137/0.76056, loss_mask_bce_3: 0.10970/0.30241, loss_mask_dice_3: 0.80908/1.02179, loss_spatial_bce_3: 0.02678/0.08644, loss_spatial_dice_3: 0.24670/0.18215, loss_spatial_ce_3: 0.31576/0.06452, loss_grounding_bce_3: 0.04357/0.08086, loss_grounding_dice_3: 0.04590/0.15040, loss_grounding_ce_3: 0.00024/0.25178, loss_mask_ce_4: 0.19061/0.76683, loss_mask_bce_4: 0.10382/0.30516, loss_mask_dice_4: 0.79271/1.04095, loss_spatial_bce_4: 0.02798/0.08903, loss_spatial_dice_4: 0.23420/0.19112, loss_spatial_ce_4: 0.00024/0.07827, loss_grounding_bce_4: 0.04373/0.08162, loss_grounding_dice_4: 0.04556/0.15299, loss_grounding_ce_4: 0.00024/0.25623, loss_mask_ce_5: 0.22574/0.79242, loss_mask_bce_5: 0.11361/0.30707, loss_mask_dice_5: 1.02755/1.04932, loss_spatial_bce_5: 0.02754/0.09145, loss_spatial_dice_5: 0.22814/0.19447, loss_spatial_ce_5: 0.00083/0.09216, loss_grounding_bce_5: 0.04601/0.08192, loss_grounding_dice_5: 0.04799/0.15391, loss_grounding_ce_5: 0.00014/0.27325, loss_mask_ce_6: 0.19884/0.81978, loss_mask_bce_6: 0.10148/0.30928, loss_mask_dice_6: 0.76947/1.05339, loss_spatial_bce_6: 0.03078/0.09694, loss_spatial_dice_6: 0.25705/0.19686, loss_spatial_ce_6: 0.05320/0.11640, loss_grounding_bce_6: 0.04352/0.08270, loss_grounding_dice_6: 0.04712/0.15436, loss_grounding_ce_6: 0.00018/0.28216, loss_mask_ce_7: 0.35086/0.87442, loss_mask_bce_7: 0.10675/0.31651, loss_mask_dice_7: 1.26419/1.09913, loss_spatial_bce_7: 0.03539/0.10589, loss_spatial_dice_7: 0.25901/0.22137, loss_spatial_ce_7: 0.00606/0.14988, loss_grounding_bce_7: 0.04553/0.08441, loss_grounding_dice_7: 0.04823/0.15990, loss_grounding_ce_7: 0.00049/0.31506, loss_mask_ce_8: 0.43676/1.00831, loss_mask_bce_8: 0.10312/0.33243, loss_mask_dice_8: 0.60303/1.17590, loss_spatial_bce_8: 0.04729/0.12201, loss_spatial_dice_8: 0.32509/0.25556, loss_spatial_ce_8: 0.04281/0.19469, loss_grounding_bce_8: 0.04589/0.08863, loss_grounding_dice_8: 0.04582/0.16976, loss_grounding_ce_8: 0.00040/0.41259, loss_mask_ce_9: 2.09211/3.47092, loss_mask_bce_9: 0.11092/0.35960, loss_mask_dice_9: 1.15067/1.75881, loss_spatial_bce_9: 0.29864/0.35409, loss_spatial_dice_9: 0.92395/0.79287, loss_spatial_ce_9: 1.32993/1.38525, loss_grounding_bce_9: 0.04931/0.10096, loss_grounding_dice_9: 0.05111/0.24203, loss_grounding_ce_9: 0.01660/0.66522] items per batch[64] items per second[0.37] total items[5702400] mini batches[ 89100] memory[4999] epoch remaining[0:12:14] INFO:trainer.default_trainer:epochs[ 48] optim steps[89200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.92603/0.74728, loss_mask_bce_0: 0.33033/0.29998, loss_mask_dice_0: 0.52019/1.01810, loss_spatial_bce_0: 0.11990/0.08370, loss_spatial_dice_0: 0.23112/0.17702, loss_spatial_ce_0: 0.05386/0.05388, loss_grounding_bce_0: 0.03154/0.08035, loss_grounding_dice_0: 0.22655/0.15009, loss_grounding_ce_0: 0.49212/0.24624, loss_mask_ce_1: 0.93709/0.74807, loss_mask_bce_1: 0.34177/0.30080, loss_mask_dice_1: 0.56159/1.02264, loss_spatial_bce_1: 0.11791/0.08420, loss_spatial_dice_1: 0.22144/0.18001, loss_spatial_ce_1: 0.06216/0.05745, loss_grounding_bce_1: 0.02691/0.08055, loss_grounding_dice_1: 0.22412/0.15078, loss_grounding_ce_1: 0.52569/0.24764, loss_mask_ce_2: 0.99556/0.75578, loss_mask_bce_2: 0.33487/0.30117, loss_mask_dice_2: 0.56389/1.02318, loss_spatial_bce_2: 0.11840/0.08430, loss_spatial_dice_2: 0.21798/0.18068, loss_spatial_ce_2: 0.06094/0.05958, loss_grounding_bce_2: 0.02595/0.08054, loss_grounding_dice_2: 0.20252/0.15079, loss_grounding_ce_2: 0.56556/0.25064, loss_mask_ce_3: 0.92300/0.76065, loss_mask_bce_3: 0.33557/0.30245, loss_mask_dice_3: 0.58604/1.02168, loss_spatial_bce_3: 0.11717/0.08644, loss_spatial_dice_3: 0.22680/0.18214, loss_spatial_ce_3: 0.01637/0.06450, loss_grounding_bce_3: 0.02460/0.08087, loss_grounding_dice_3: 0.18616/0.15041, loss_grounding_ce_3: 0.46995/0.25171, loss_mask_ce_4: 1.05945/0.76694, loss_mask_bce_4: 0.34310/0.30520, loss_mask_dice_4: 0.57015/1.04084, loss_spatial_bce_4: 0.12961/0.08902, loss_spatial_dice_4: 0.24741/0.19111, loss_spatial_ce_4: 0.04888/0.07825, loss_grounding_bce_4: 0.02841/0.08162, loss_grounding_dice_4: 0.22674/0.15299, loss_grounding_ce_4: 0.55390/0.25616, loss_mask_ce_5: 0.98884/0.79252, loss_mask_bce_5: 0.34536/0.30711, loss_mask_dice_5: 0.66055/1.04918, loss_spatial_bce_5: 0.14409/0.09145, loss_spatial_dice_5: 0.25266/0.19446, loss_spatial_ce_5: 0.03316/0.09212, loss_grounding_bce_5: 0.02914/0.08193, loss_grounding_dice_5: 0.22080/0.15391, loss_grounding_ce_5: 0.58268/0.27321, loss_mask_ce_6: 0.92814/0.81987, loss_mask_bce_6: 0.34649/0.30931, loss_mask_dice_6: 0.59824/1.05326, loss_spatial_bce_6: 0.13907/0.09694, loss_spatial_dice_6: 0.22571/0.19685, loss_spatial_ce_6: 0.09794/0.11637, loss_grounding_bce_6: 0.02951/0.08270, loss_grounding_dice_6: 0.23150/0.15436, loss_grounding_ce_6: 0.57092/0.28213, loss_mask_ce_7: 0.76804/0.87451, loss_mask_bce_7: 0.32846/0.31654, loss_mask_dice_7: 0.52342/1.09901, loss_spatial_bce_7: 0.12280/0.10589, loss_spatial_dice_7: 0.22567/0.22136, loss_spatial_ce_7: 0.10451/0.14986, loss_grounding_bce_7: 0.02934/0.08441, loss_grounding_dice_7: 0.24606/0.15989, loss_grounding_ce_7: 0.49519/0.31502, loss_mask_ce_8: 1.01158/1.00840, loss_mask_bce_8: 0.43541/0.33247, loss_mask_dice_8: 0.69786/1.17575, loss_spatial_bce_8: 0.16120/0.12201, loss_spatial_dice_8: 0.31140/0.25554, loss_spatial_ce_8: 0.04994/0.19465, loss_grounding_bce_8: 0.03573/0.08863, loss_grounding_dice_8: 0.33006/0.16975, loss_grounding_ce_8: 0.51614/0.41257, loss_mask_ce_9: 2.53634/3.47116, loss_mask_bce_9: 0.47273/0.35965, loss_mask_dice_9: 1.14157/1.75870, loss_spatial_bce_9: 0.48604/0.35407, loss_spatial_dice_9: 0.72761/0.79288, loss_spatial_ce_9: 1.18906/1.38523, loss_grounding_bce_9: 0.06542/0.10096, loss_grounding_dice_9: 0.68489/0.24203, loss_grounding_ce_9: 0.02033/0.66535] items per batch[64] items per second[0.37] total items[5708800] mini batches[ 89200] memory[4999] epoch remaining[0:09:20] INFO:trainer.default_trainer:epochs[ 48] optim steps[89300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.01407/0.74728, loss_mask_bce_0: 0.05078/0.30001, loss_mask_dice_0: 0.04996/1.01819, loss_spatial_bce_0: 0.02395/0.08370, loss_spatial_dice_0: 0.02707/0.17701, loss_spatial_ce_0: 0.00001/0.05387, loss_grounding_bce_0: 0.01562/0.08037, loss_grounding_dice_0: 0.03196/0.15008, loss_grounding_ce_0: 0.00024/0.24627, loss_mask_ce_1: 0.01416/0.74808, loss_mask_bce_1: 0.04318/0.30083, loss_mask_dice_1: 0.04752/1.02268, loss_spatial_bce_1: 0.02647/0.08420, loss_spatial_dice_1: 0.03234/0.17999, loss_spatial_ce_1: 0.00000/0.05744, loss_grounding_bce_1: 0.01750/0.08058, loss_grounding_dice_1: 0.02024/0.15077, loss_grounding_ce_1: 0.00020/0.24766, loss_mask_ce_2: 0.01273/0.75577, loss_mask_bce_2: 0.05011/0.30120, loss_mask_dice_2: 0.06837/1.02324, loss_spatial_bce_2: 0.02593/0.08430, loss_spatial_dice_2: 0.02439/0.18067, loss_spatial_ce_2: 0.00001/0.05956, loss_grounding_bce_2: 0.01769/0.08056, loss_grounding_dice_2: 0.02657/0.15078, loss_grounding_ce_2: 0.00023/0.25066, loss_mask_ce_3: 0.00804/0.76063, loss_mask_bce_3: 0.04909/0.30248, loss_mask_dice_3: 0.05114/1.02174, loss_spatial_bce_3: 0.02446/0.08644, loss_spatial_dice_3: 0.02409/0.18212, loss_spatial_ce_3: 0.00001/0.06448, loss_grounding_bce_3: 0.01668/0.08089, loss_grounding_dice_3: 0.02203/0.15039, loss_grounding_ce_3: 0.00045/0.25172, loss_mask_ce_4: 0.01596/0.76694, loss_mask_bce_4: 0.04572/0.30523, loss_mask_dice_4: 0.07277/1.04090, loss_spatial_bce_4: 0.02829/0.08902, loss_spatial_dice_4: 0.03451/0.19110, loss_spatial_ce_4: 0.00001/0.07823, loss_grounding_bce_4: 0.01908/0.08165, loss_grounding_dice_4: 0.03145/0.15298, loss_grounding_ce_4: 0.00040/0.25621, loss_mask_ce_5: 0.01515/0.79251, loss_mask_bce_5: 0.05225/0.30714, loss_mask_dice_5: 0.06045/1.04922, loss_spatial_bce_5: 0.02390/0.09145, loss_spatial_dice_5: 0.02534/0.19445, loss_spatial_ce_5: 0.00003/0.09209, loss_grounding_bce_5: 0.01687/0.08195, loss_grounding_dice_5: 0.02931/0.15390, loss_grounding_ce_5: 0.00040/0.27326, loss_mask_ce_6: 0.01629/0.81987, loss_mask_bce_6: 0.04974/0.30934, loss_mask_dice_6: 0.07336/1.05331, loss_spatial_bce_6: 0.02678/0.09694, loss_spatial_dice_6: 0.03525/0.19683, loss_spatial_ce_6: 0.00007/0.11634, loss_grounding_bce_6: 0.01442/0.08273, loss_grounding_dice_6: 0.02081/0.15434, loss_grounding_ce_6: 0.00065/0.28217, loss_mask_ce_7: 0.02150/0.87448, loss_mask_bce_7: 0.04741/0.31657, loss_mask_dice_7: 0.05808/1.09903, loss_spatial_bce_7: 0.02814/0.10589, loss_spatial_dice_7: 0.03910/0.22134, loss_spatial_ce_7: 0.03019/0.14983, loss_grounding_bce_7: 0.01609/0.08444, loss_grounding_dice_7: 0.02113/0.15989, loss_grounding_ce_7: 0.00024/0.31501, loss_mask_ce_8: 0.07125/1.00839, loss_mask_bce_8: 0.04526/0.33250, loss_mask_dice_8: 0.07637/1.17581, loss_spatial_bce_8: 0.03272/0.12201, loss_spatial_dice_8: 0.03776/0.25552, loss_spatial_ce_8: 0.01373/0.19460, loss_grounding_bce_8: 0.01742/0.08867, loss_grounding_dice_8: 0.02617/0.16975, loss_grounding_ce_8: 0.00078/0.41253, loss_mask_ce_9: 2.89188/3.47138, loss_mask_bce_9: 0.06769/0.35967, loss_mask_dice_9: 0.18060/1.75880, loss_spatial_bce_9: 1.30115/0.35411, loss_spatial_dice_9: 0.66435/0.79288, loss_spatial_ce_9: 1.94628/1.38528, loss_grounding_bce_9: 0.03094/0.10100, loss_grounding_dice_9: 0.06653/0.24203, loss_grounding_ce_9: 0.14873/0.66530] items per batch[64] items per second[0.36] total items[5715200] mini batches[ 89300] memory[4999] epoch remaining[0:06:27] INFO:trainer.default_trainer:epochs[ 48] optim steps[89400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.43835/0.74718, loss_mask_bce_0: 0.45523/0.30001, loss_mask_dice_0: 0.77152/1.01797, loss_spatial_bce_0: 0.11783/0.08371, loss_spatial_dice_0: 0.20740/0.17698, loss_spatial_ce_0: 0.13164/0.05385, loss_grounding_bce_0: 0.04368/0.08036, loss_grounding_dice_0: 0.11846/0.15006, loss_grounding_ce_0: 0.01160/0.24625, loss_mask_ce_1: 1.36078/0.74798, loss_mask_bce_1: 0.45023/0.30082, loss_mask_dice_1: 0.78786/1.02245, loss_spatial_bce_1: 0.12936/0.08420, loss_spatial_dice_1: 0.20530/0.17997, loss_spatial_ce_1: 0.10579/0.05742, loss_grounding_bce_1: 0.04068/0.08056, loss_grounding_dice_1: 0.11986/0.15074, loss_grounding_ce_1: 0.01072/0.24761, loss_mask_ce_2: 1.43578/0.75566, loss_mask_bce_2: 0.45833/0.30119, loss_mask_dice_2: 0.83332/1.02301, loss_spatial_bce_2: 0.11984/0.08430, loss_spatial_dice_2: 0.20703/0.18064, loss_spatial_ce_2: 0.12027/0.05954, loss_grounding_bce_2: 0.04175/0.08055, loss_grounding_dice_2: 0.12771/0.15075, loss_grounding_ce_2: 0.01540/0.25065, loss_mask_ce_3: 1.44236/0.76054, loss_mask_bce_3: 0.48151/0.30247, loss_mask_dice_3: 0.81601/1.02151, loss_spatial_bce_3: 0.11927/0.08645, loss_spatial_dice_3: 0.21302/0.18210, loss_spatial_ce_3: 0.13034/0.06446, loss_grounding_bce_3: 0.04136/0.08088, loss_grounding_dice_3: 0.11884/0.15038, loss_grounding_ce_3: 0.01431/0.25169, loss_mask_ce_4: 1.42291/0.76684, loss_mask_bce_4: 0.55298/0.30522, loss_mask_dice_4: 0.87148/1.04066, loss_spatial_bce_4: 0.11162/0.08903, loss_spatial_dice_4: 0.23212/0.19107, loss_spatial_ce_4: 0.17490/0.07821, loss_grounding_bce_4: 0.04157/0.08164, loss_grounding_dice_4: 0.13307/0.15296, loss_grounding_ce_4: 0.02445/0.25620, loss_mask_ce_5: 1.48941/0.79238, loss_mask_bce_5: 0.50101/0.30713, loss_mask_dice_5: 0.83634/1.04898, loss_spatial_bce_5: 0.11452/0.09146, loss_spatial_dice_5: 0.24372/0.19442, loss_spatial_ce_5: 0.17956/0.09208, loss_grounding_bce_5: 0.04107/0.08193, loss_grounding_dice_5: 0.12780/0.15387, loss_grounding_ce_5: 0.01975/0.27322, loss_mask_ce_6: 1.51641/0.81974, loss_mask_bce_6: 0.54983/0.30933, loss_mask_dice_6: 0.88143/1.05307, loss_spatial_bce_6: 0.17470/0.09694, loss_spatial_dice_6: 0.25830/0.19681, loss_spatial_ce_6: 0.09052/0.11631, loss_grounding_bce_6: 0.04469/0.08272, loss_grounding_dice_6: 0.15031/0.15432, loss_grounding_ce_6: 0.03683/0.28213, loss_mask_ce_7: 1.42141/0.87437, loss_mask_bce_7: 0.47993/0.31656, loss_mask_dice_7: 0.81821/1.09875, loss_spatial_bce_7: 0.13917/0.10589, loss_spatial_dice_7: 0.23561/0.22131, loss_spatial_ce_7: 0.12016/0.14979, loss_grounding_bce_7: 0.04421/0.08443, loss_grounding_dice_7: 0.13300/0.15986, loss_grounding_ce_7: 0.02307/0.31496, loss_mask_ce_8: 1.39324/1.00822, loss_mask_bce_8: 0.50286/0.33248, loss_mask_dice_8: 0.91119/1.17555, loss_spatial_bce_8: 0.11544/0.12202, loss_spatial_dice_8: 0.25184/0.25548, loss_spatial_ce_8: 0.18592/0.19455, loss_grounding_bce_8: 0.04527/0.08866, loss_grounding_dice_8: 0.14153/0.16973, loss_grounding_ce_8: 0.00812/0.41249, loss_mask_ce_9: 2.12106/3.47103, loss_mask_bce_9: 0.47927/0.35966, loss_mask_dice_9: 1.07722/1.75839, loss_spatial_bce_9: 0.36764/0.35414, loss_spatial_dice_9: 0.87721/0.79284, loss_spatial_ce_9: 1.53797/1.38518, loss_grounding_bce_9: 0.06221/0.10099, loss_grounding_dice_9: 0.20522/0.24198, loss_grounding_ce_9: 0.29676/0.66519] items per batch[64] items per second[0.37] total items[5721600] mini batches[ 89400] memory[4999] epoch remaining[0:03:33] INFO:trainer.default_trainer:epochs[ 48] optim steps[89500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.22707/0.74704, loss_mask_bce_0: 0.63213/0.29999, loss_mask_dice_0: 1.06861/1.01773, loss_spatial_bce_0: 0.11432/0.08373, loss_spatial_dice_0: 0.17682/0.17697, loss_spatial_ce_0: 0.03295/0.05382, loss_grounding_bce_0: 0.07729/0.08037, loss_grounding_dice_0: 0.15186/0.15005, loss_grounding_ce_0: 0.45071/0.24620, loss_mask_ce_1: 0.23362/0.74781, loss_mask_bce_1: 0.62101/0.30080, loss_mask_dice_1: 1.06906/1.02220, loss_spatial_bce_1: 0.11785/0.08422, loss_spatial_dice_1: 0.18372/0.17995, loss_spatial_ce_1: 0.03568/0.05740, loss_grounding_bce_1: 0.07943/0.08057, loss_grounding_dice_1: 0.14994/0.15073, loss_grounding_ce_1: 0.45132/0.24755, loss_mask_ce_2: 0.23920/0.75550, loss_mask_bce_2: 0.62543/0.30116, loss_mask_dice_2: 1.08984/1.02276, loss_spatial_bce_2: 0.10899/0.08432, loss_spatial_dice_2: 0.17600/0.18064, loss_spatial_ce_2: 0.03449/0.05953, loss_grounding_bce_2: 0.07615/0.08056, loss_grounding_dice_2: 0.14711/0.15074, loss_grounding_ce_2: 0.44463/0.25060, loss_mask_ce_3: 0.22537/0.76039, loss_mask_bce_3: 0.63320/0.30245, loss_mask_dice_3: 1.06179/1.02126, loss_spatial_bce_3: 0.11360/0.08646, loss_spatial_dice_3: 0.17191/0.18209, loss_spatial_ce_3: 0.04033/0.06444, loss_grounding_bce_3: 0.07843/0.08089, loss_grounding_dice_3: 0.15355/0.15036, loss_grounding_ce_3: 0.43852/0.25165, loss_mask_ce_4: 0.25071/0.76667, loss_mask_bce_4: 0.62793/0.30520, loss_mask_dice_4: 1.09999/1.04042, loss_spatial_bce_4: 0.11261/0.08905, loss_spatial_dice_4: 0.15975/0.19106, loss_spatial_ce_4: 0.05592/0.07819, loss_grounding_bce_4: 0.07497/0.08164, loss_grounding_dice_4: 0.14171/0.15294, loss_grounding_ce_4: 0.43143/0.25617, loss_mask_ce_5: 0.25360/0.79221, loss_mask_bce_5: 0.63720/0.30712, loss_mask_dice_5: 1.10280/1.04873, loss_spatial_bce_5: 0.10909/0.09147, loss_spatial_dice_5: 0.19646/0.19441, loss_spatial_ce_5: 0.15181/0.09205, loss_grounding_bce_5: 0.07770/0.08194, loss_grounding_dice_5: 0.15181/0.15386, loss_grounding_ce_5: 0.42464/0.27317, loss_mask_ce_6: 0.30451/0.81958, loss_mask_bce_6: 0.63399/0.30932, loss_mask_dice_6: 1.06126/1.05282, loss_spatial_bce_6: 0.12449/0.09696, loss_spatial_dice_6: 0.18774/0.19680, loss_spatial_ce_6: 0.17846/0.11629, loss_grounding_bce_6: 0.07608/0.08273, loss_grounding_dice_6: 0.14672/0.15431, loss_grounding_ce_6: 0.42502/0.28205, loss_mask_ce_7: 0.32933/0.87421, loss_mask_bce_7: 0.62525/0.31655, loss_mask_dice_7: 1.08119/1.09848, loss_spatial_bce_7: 0.15199/0.10590, loss_spatial_dice_7: 0.23024/0.22129, loss_spatial_ce_7: 0.05984/0.14977, loss_grounding_bce_7: 0.07497/0.08444, loss_grounding_dice_7: 0.15363/0.15985, loss_grounding_ce_7: 0.45132/0.31490, loss_mask_ce_8: 0.44118/1.00805, loss_mask_bce_8: 0.64575/0.33247, loss_mask_dice_8: 1.28294/1.17528, loss_spatial_bce_8: 0.18310/0.12204, loss_spatial_dice_8: 0.23750/0.25546, loss_spatial_ce_8: 0.06832/0.19452, loss_grounding_bce_8: 0.08540/0.08867, loss_grounding_dice_8: 0.19371/0.16973, loss_grounding_ce_8: 0.44064/0.41241, loss_mask_ce_9: 2.23388/3.47076, loss_mask_bce_9: 0.85207/0.35963, loss_mask_dice_9: 2.12990/1.75795, loss_spatial_bce_9: 0.33864/0.35416, loss_spatial_dice_9: 0.89617/0.79284, loss_spatial_ce_9: 1.35419/1.38515, loss_grounding_bce_9: 0.11436/0.10099, loss_grounding_dice_9: 0.32404/0.24198, loss_grounding_ce_9: 0.49393/0.66516] items per batch[64] items per second[0.37] total items[5728000] mini batches[ 89500] memory[4999] epoch remaining[0:00:39] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00089523. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0027 s/iter. Inference: 0.3720 s/iter. Eval: 0.0807 s/iter. Total: 0.4555 s/iter. ETA=0:00:30 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0025 s/iter. Inference: 0.3594 s/iter. Eval: 0.0752 s/iter. Total: 0.4373 s/iter. ETA=0:00:24 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 34/79. Dataloading: 0.0027 s/iter. Inference: 0.3666 s/iter. Eval: 0.0759 s/iter. Total: 0.4452 s/iter. ETA=0:00:20 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 45/79. Dataloading: 0.0027 s/iter. Inference: 0.3729 s/iter. Eval: 0.0733 s/iter. Total: 0.4490 s/iter. ETA=0:00:15 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0027 s/iter. Inference: 0.3757 s/iter. Eval: 0.0714 s/iter. Total: 0.4499 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0028 s/iter. Inference: 0.3742 s/iter. Eval: 0.0708 s/iter. Total: 0.4479 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_eval2h34llt2 ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 56.124 | 83.038 | 66.772 | 133 | | Things | 62.299 | 84.002 | 73.647 | 80 | | Stuff | 46.804 | 81.584 | 56.394 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* DONE (t=0.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.74 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.88s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 21.77 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.51 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.702 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.504 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.683 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.354 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.578 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.388 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.617 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.325 | 70.201 | 50.113 | 26.495 | 50.380 | 68.267 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.782 | bicycle | 22.883 | car | 44.286 | | motorcycle | 42.657 | airplane | 61.922 | bus | 71.682 | | train | 75.491 | truck | 43.801 | boat | 31.749 | | traffic light | 30.086 | fire hydrant | 72.161 | stop sign | 69.445 | | parking meter | 53.683 | bench | 27.292 | bird | 35.544 | | cat | 77.057 | dog | 71.405 | horse | 51.276 | | sheep | 54.797 | cow | 57.780 | elephant | 66.079 | | bear | 80.064 | zebra | 66.622 | giraffe | 62.524 | | backpack | 25.257 | umbrella | 56.949 | handbag | 24.599 | | tie | 42.058 | suitcase | 51.855 | frisbee | 69.874 | | skis | 8.462 | snowboard | 35.054 | sports ball | 51.068 | | kite | 38.906 | baseball bat | 39.228 | baseball glove | 50.693 | | skateboard | 43.767 | surfboard | 45.923 | tennis racket | 63.605 | | bottle | 43.276 | wine glass | 39.142 | cup | 51.751 | | fork | 27.539 | knife | 25.345 | spoon | 23.133 | | bowl | 40.636 | banana | 22.686 | apple | 28.739 | | sandwich | 49.419 | orange | 32.101 | broccoli | 25.351 | | carrot | 24.000 | hot dog | 31.635 | pizza | 54.035 | | donut | 57.002 | cake | 49.569 | chair | 29.330 | | couch | 46.175 | potted plant | 23.394 | bed | 44.324 | | dining table | 15.670 | toilet | 70.486 | tv | 68.260 | | laptop | 71.263 | mouse | 64.449 | remote | 44.923 | | keyboard | 58.350 | cell phone | 46.398 | microwave | 67.536 | | oven | 33.623 | toaster | 49.983 | sink | 44.882 | | refrigerator | 70.530 | book | 15.512 | clock | 55.035 | | vase | 41.663 | scissors | 36.088 | teddy bear | 57.951 | | hair drier | 29.568 | toothbrush | 27.912 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.82640581492018, 'fwIoU': 71.69029476012182, 'IoU-person': 88.80594834191557, 'IoU-bicycle': 71.37569637096365, 'IoU-car': 73.78712395545634, 'IoU-motorcycle': 86.8339493426804, 'IoU-airplane': 86.89506397817158, 'IoU-bus': 87.70432505107183, 'IoU-train': 87.8813903765162, 'IoU-truck': 70.27294858495895, 'IoU-boat': 75.23777219161094, 'IoU-traffic light': 78.8028224161168, 'IoU-fire hydrant': 93.20794440987346, 'IoU-stop sign': 85.85589066158647, 'IoU-parking meter': 85.18276925537711, 'IoU-bench': 60.69517224681044, 'IoU-bird': 74.52642940023308, 'IoU-cat': 89.76859099535078, 'IoU-dog': 83.717685370214, 'IoU-horse': 88.9593459297647, 'IoU-sheep': 85.88879243312132, 'IoU-cow': 90.4638973201496, 'IoU-elephant': 90.7853126317948, 'IoU-bear': 88.56222969997403, 'IoU-zebra': 82.84670378451044, 'IoU-giraffe': 89.4899816820533, 'IoU-backpack': 53.8821140990611, 'IoU-umbrella': 81.75274209338102, 'IoU-handbag': 49.57386021091857, 'IoU-tie': 76.27886903660958, 'IoU-suitcase': 78.97079627969012, 'IoU-frisbee': 84.82110127775451, 'IoU-skis': 58.462398014937264, 'IoU-snowboard': 71.0558788743099, 'IoU-sports ball': 78.9022527330953, 'IoU-kite': 79.73747692896043, 'IoU-baseball bat': 68.84924374499364, 'IoU-baseball glove': 77.86453593486465, 'IoU-skateboard': 86.27003238837938, 'IoU-surfboard': 86.27314061368516, 'IoU-tennis racket': 90.84736503979659, 'IoU-bottle': 69.9115957553207, 'IoU-wine glass': 82.68071071601223, 'IoU-cup': 71.0894680179762, 'IoU-fork': 71.34152317231771, 'IoU-knife': 65.32729902200602, 'IoU-spoon': 60.85067922786579, 'IoU-bowl': 59.78460081394591, 'IoU-banana': 83.14555474366577, 'IoU-apple': 58.274571221568664, 'IoU-sandwich': 69.24920614446123, 'IoU-orange': 79.81721072565281, 'IoU-broccoli': 69.30780731586422, 'IoU-carrot': 65.2127873989282, 'IoU-hot dog': 61.73819476302893, 'IoU-pizza': 80.51572131977237, 'IoU-donut': 58.152926593243315, 'IoU-cake': 80.04376762243105, 'IoU-chair': 63.072377939675384, 'IoU-couch': 69.65084977648043, 'IoU-potted plant': 43.365405720159416, 'IoU-bed': 73.66504341048827, 'IoU-dining table': 53.49873095185508, 'IoU-toilet': 87.15988041806312, 'IoU-tv': 76.87822665106324, 'IoU-laptop': 80.23766767306087, 'IoU-mouse': 75.99663588141166, 'IoU-remote': 67.50720930611448, 'IoU-keyboard': 62.51760885813423, 'IoU-cell phone': 80.0959971695623, 'IoU-microwave': 79.49119631608588, 'IoU-oven': 74.39659195593455, 'IoU-toaster': 85.87604750398438, 'IoU-sink': 70.62659756962843, 'IoU-refrigerator': 83.83723568509325, 'IoU-book': 55.7459367453565, 'IoU-clock': 72.26634729878214, 'IoU-vase': 65.90259785785078, 'IoU-scissors': 87.64120561608466, 'IoU-teddy bear': 83.54565310271938, 'IoU-hair drier': 48.53855507376053, 'IoU-toothbrush': 76.59869596564371, 'IoU-banner': 34.09576757495611, 'IoU-blanket': 17.484212339394983, 'IoU-bridge': 37.36453218674552, 'IoU-cardboard': 45.988827365993174, 'IoU-counter': 31.53664836129291, 'IoU-curtain': 73.53145777238966, 'IoU-door-stuff': 48.278640130508194, 'IoU-floor-wood': 63.79435800355899, 'IoU-flower': 44.238974243312114, 'IoU-fruit': 49.37039863483121, 'IoU-gravel': 30.254525207561322, 'IoU-house': 25.85833060411697, 'IoU-light': 44.41923938447512, 'IoU-mirror-stuff': 60.93755775973485, 'IoU-net': 43.98788761269338, 'IoU-pillow': 24.368305114029027, 'IoU-platform': 29.078374235136685, 'IoU-playingfield': 71.60456128340643, 'IoU-railroad': 64.8746949953674, 'IoU-river': 53.96157235589689, 'IoU-road': 67.17611043918063, 'IoU-roof': 18.930382111945093, 'IoU-sand': 66.17028282037643, 'IoU-sea': 85.09745520406533, 'IoU-shelf': 38.11420729382656, 'IoU-snow': 92.27512350733161, 'IoU-stairs': 32.82768484571483, 'IoU-tent': 11.189524274787619, 'IoU-towel': 65.46752911411082, 'IoU-wall-brick': 51.93088975286314, 'IoU-wall-stone': 27.933023585462852, 'IoU-wall-tile': 71.1496824623092, 'IoU-wall-wood': 46.691158183879125, 'IoU-water-other': 25.826430902649406, 'IoU-window-blind': 49.95543240115832, 'IoU-window-other': 51.1904954757879, 'IoU-tree-merged': 81.99346516382204, 'IoU-fence-merged': 54.54478685785736, 'IoU-ceiling-merged': 67.59760879565019, 'IoU-sky-other-merged': 93.85674404677124, 'IoU-cabinet-merged': 63.0868160084156, 'IoU-table-merged': 41.32456795517761, 'IoU-floor-other-merged': 55.25231161928757, 'IoU-pavement-merged': 57.021155088274526, 'IoU-mountain-merged': 58.85911356528285, 'IoU-grass-merged': 73.15586918635017, 'IoU-dirt-merged': 47.47522379749428, 'IoU-paper-merged': 35.60605086728511, 'IoU-food-other-merged': 42.58835681115194, 'IoU-building-other-merged': 59.20465769957096, 'IoU-rock-merged': 64.43571686984411, 'IoU-wall-other-merged': 68.42588543224673, 'IoU-rug-merged': 67.88385335728596, 'mACC': 77.43101516326483, 'pACC': 82.36673950115326, 'ACC-person': 93.01660757125838, 'ACC-bicycle': 79.27525071168373, 'ACC-car': 86.98447381958664, 'ACC-motorcycle': 91.18183795649287, 'ACC-airplane': 91.18692207809764, 'ACC-bus': 93.96147701342225, 'ACC-train': 95.58677649827109, 'ACC-truck': 80.9661237275522, 'ACC-boat': 85.1749789885444, 'ACC-traffic light': 91.34388111825471, 'ACC-fire hydrant': 95.93946290784714, 'ACC-stop sign': 88.60072419791251, 'ACC-parking meter': 88.34305102142643, 'ACC-bench': 73.8454553645743, 'ACC-bird': 78.24087745878487, 'ACC-cat': 94.347847554674, 'ACC-dog': 86.30172278709331, 'ACC-horse': 93.56012610703827, 'ACC-sheep': 90.49539105688747, 'ACC-cow': 93.88358240213468, 'ACC-elephant': 92.90253131677325, 'ACC-bear': 90.40130870680771, 'ACC-zebra': 84.84868229604675, 'ACC-giraffe': 93.35907424952795, 'ACC-backpack': 73.38794138526393, 'ACC-umbrella': 86.23309096196682, 'ACC-handbag': 70.07044969762423, 'ACC-tie': 84.59875306502852, 'ACC-suitcase': 85.71054817971641, 'ACC-frisbee': 94.23963636363636, 'ACC-skis': 72.73055601931195, 'ACC-snowboard': 81.51404570057065, 'ACC-sports ball': 88.62995007287499, 'ACC-kite': 86.06742025527375, 'ACC-baseball bat': 87.58213517745693, 'ACC-baseball glove': 92.4760237204845, 'ACC-skateboard': 90.8928098531808, 'ACC-surfboard': 92.4027089219577, 'ACC-tennis racket': 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Inference done 11/25. Dataloading: 0.3015 s/iter. Inference: 0.1992 s/iter. Eval: 0.0000 s/iter. Total: 0.5008 s/iter. ETA=0:00:07 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3159 s/iter. Inference: 0.3544 s/iter. Eval: 0.0000 s/iter. Total: 0.6704 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3270 s/iter. Inference: 0.5235 s/iter. Eval: 0.0000 s/iter. Total: 0.8506 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3453321627158326, 'noc@0.8': 2.3069944395668713, 'noc@0.85': 2.648814749780509, 'noc@0.9': 3.440152180275095, 'miou@iter1': 0.8734129413652741} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0015 s/iter. Inference: 0.1449 s/iter. Eval: 0.0011 s/iter. Total: 0.1475 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.82588195800781, 'precision@0.6': 73.06645965576172, 'precision@0.7': 69.29653930664062, 'precision@0.8': 60.47415542602539, 'precision@0.9': 33.73493957519531, 'cIoU': 62.32347869873047, 'mIoU': 67.51370239257812} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 56.12442126352003, 'SQ': 83.03827128897665, 'RQ': 66.77157727332978, 'PQ_th': 62.29935907073963, 'SQ_th': 84.00165014892397, 'RQ_th': 73.64667805164416, 'PQ_st': 46.80376042243382, 'SQ_st': 81.5841145192448, 'RQ_st': 56.394066664553335}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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'ACC-tv': 89.43835332250248, 'ACC-laptop': 93.43085090039351, 'ACC-mouse': 87.03361001987669, 'ACC-remote': 72.04783453528253, 'ACC-keyboard': 68.90060655851279, 'ACC-cell phone': 88.80754292158983, 'ACC-microwave': 84.53499120884003, 'ACC-oven': 92.27887913251695, 'ACC-toaster': 91.44096675744844, 'ACC-sink': 80.22962405824794, 'ACC-refrigerator': 94.31880460146935, 'ACC-book': 75.8764464430809, 'ACC-clock': 76.97815514368085, 'ACC-vase': 74.89385454122392, 'ACC-scissors': 93.2663967165248, 'ACC-teddy bear': 88.97055344506173, 'ACC-hair drier': 60.21831326672928, 'ACC-toothbrush': 84.90531619179987, 'ACC-banner': 75.11547598991325, 'ACC-blanket': 23.935609048110777, 'ACC-bridge': 55.96614284408237, 'ACC-cardboard': 62.452319325287185, 'ACC-counter': 55.84497172201455, 'ACC-curtain': 83.92240331267527, 'ACC-door-stuff': 68.77784733068214, 'ACC-floor-wood': 81.67877403230608, 'ACC-flower': 64.21206697010246, 'ACC-fruit': 67.9603483453135, 'ACC-gravel': 40.160123451945914, 'ACC-house': 31.996007021207863, 'ACC-light': 63.4097586418099, 'ACC-mirror-stuff': 77.48602933285797, 'ACC-net': 67.31038259361982, 'ACC-pillow': 47.660859625177864, 'ACC-platform': 47.39672600243255, 'ACC-playingfield': 91.44984952014096, 'ACC-railroad': 82.9156442387346, 'ACC-river': 73.05226849412996, 'ACC-road': 86.97647955415647, 'ACC-roof': 25.470873212808698, 'ACC-sand': 70.76609169447306, 'ACC-sea': 91.78449074318866, 'ACC-shelf': 56.077587503867875, 'ACC-snow': 95.68108652923384, 'ACC-stairs': 56.452130637425334, 'ACC-tent': 14.260845381433729, 'ACC-towel': 81.70171628493904, 'ACC-wall-brick': 70.00385746024588, 'ACC-wall-stone': 33.945088018986645, 'ACC-wall-tile': 85.50775651090268, 'ACC-wall-wood': 62.349277345475194, 'ACC-water-other': 41.48534576283385, 'ACC-window-blind': 62.43419404509446, 'ACC-window-other': 72.96750802250575, 'ACC-tree-merged': 89.83000108123592, 'ACC-fence-merged': 72.31688208576247, 'ACC-ceiling-merged': 82.65690025282018, 'ACC-sky-other-merged': 97.16828111131846, 'ACC-cabinet-merged': 78.20889793287058, 'ACC-table-merged': 55.09531857778821, 'ACC-floor-other-merged': 65.84578463038237, 'ACC-pavement-merged': 68.87858187035306, 'ACC-mountain-merged': 70.14434989132486, 'ACC-grass-merged': 84.8050891708587, 'ACC-dirt-merged': 66.95468213751896, 'ACC-paper-merged': 48.020310095934136, 'ACC-food-other-merged': 57.41462561609747, 'ACC-building-other-merged': 73.89653899846165, 'ACC-rock-merged': 83.16183797447218, 'ACC-wall-other-merged': 82.41084933146514, 'ACC-rug-merged': 82.77203685987027})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3453321627158326, 'noc@0.8': 2.3069944395668713, 'noc@0.85': 2.648814749780509, 'noc@0.9': 3.440152180275095, 'miou@iter1': 0.8734129413652741}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.82588195800781, 'precision@0.6': 73.06645965576172, 'precision@0.7': 69.29653930664062, 'precision@0.8': 60.47415542602539, 'precision@0.9': 33.73493957519531, 'cIoU': 62.32347869873047, 'mIoU': 67.51370239257812}}} INFO:trainer.default_trainer:This epoch takes 0:56:24.288553 INFO:trainer.default_trainer:PROGRESS: 98.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml'] INFO:trainer.default_trainer:Start epoch: 49 training. INFO:trainer.default_trainer:epochs[ 49] optim steps[89600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.05465/0.74704, loss_mask_bce_0: 0.23930/0.29998, loss_mask_dice_0: 0.14942/1.01772, loss_spatial_bce_0: 0.03469/0.08371, loss_spatial_dice_0: 0.03765/0.17696, loss_spatial_ce_0: 0.00031/0.05380, loss_grounding_bce_0: 0.10346/0.08037, loss_grounding_dice_0: 0.06225/0.15007, loss_grounding_ce_0: 0.07464/0.24621, loss_mask_ce_1: 0.05619/0.74782, loss_mask_bce_1: 0.23492/0.30080, loss_mask_dice_1: 0.14996/1.02220, loss_spatial_bce_1: 0.03723/0.08420, loss_spatial_dice_1: 0.04033/0.17994, loss_spatial_ce_1: 0.00079/0.05737, loss_grounding_bce_1: 0.08775/0.08057, loss_grounding_dice_1: 0.05802/0.15074, loss_grounding_ce_1: 0.07384/0.24755, loss_mask_ce_2: 0.04828/0.75548, loss_mask_bce_2: 0.29140/0.30116, loss_mask_dice_2: 0.15560/1.02276, loss_spatial_bce_2: 0.03607/0.08431, loss_spatial_dice_2: 0.03596/0.18062, loss_spatial_ce_2: 0.00078/0.05950, loss_grounding_bce_2: 0.09833/0.08056, loss_grounding_dice_2: 0.06128/0.15075, loss_grounding_ce_2: 0.07426/0.25059, loss_mask_ce_3: 0.05400/0.76038, loss_mask_bce_3: 0.26745/0.30244, loss_mask_dice_3: 0.15039/1.02124, loss_spatial_bce_3: 0.05186/0.08645, loss_spatial_dice_3: 0.04821/0.18208, loss_spatial_ce_3: 0.00158/0.06443, loss_grounding_bce_3: 0.08823/0.08089, loss_grounding_dice_3: 0.06087/0.15039, loss_grounding_ce_3: 0.07677/0.25161, loss_mask_ce_4: 0.05743/0.76667, loss_mask_bce_4: 0.21372/0.30520, loss_mask_dice_4: 0.14986/1.04041, loss_spatial_bce_4: 0.03945/0.08904, loss_spatial_dice_4: 0.04139/0.19105, loss_spatial_ce_4: 0.00068/0.07817, loss_grounding_bce_4: 0.07661/0.08164, loss_grounding_dice_4: 0.05694/0.15296, loss_grounding_ce_4: 0.08146/0.25615, loss_mask_ce_5: 0.07417/0.79223, loss_mask_bce_5: 0.19494/0.30712, loss_mask_dice_5: 0.14474/1.04876, loss_spatial_bce_5: 0.03494/0.09146, loss_spatial_dice_5: 0.04321/0.19440, loss_spatial_ce_5: 0.00085/0.09203, loss_grounding_bce_5: 0.06640/0.08194, loss_grounding_dice_5: 0.05878/0.15388, loss_grounding_ce_5: 0.09173/0.27311, loss_mask_ce_6: 0.05551/0.81958, loss_mask_bce_6: 0.20743/0.30932, loss_mask_dice_6: 0.14743/1.05283, loss_spatial_bce_6: 0.03473/0.09695, loss_spatial_dice_6: 0.04335/0.19679, loss_spatial_ce_6: 0.01113/0.11626, loss_grounding_bce_6: 0.07192/0.08273, loss_grounding_dice_6: 0.05859/0.15432, loss_grounding_ce_6: 0.06921/0.28206, loss_mask_ce_7: 0.04618/0.87419, loss_mask_bce_7: 0.13582/0.31655, loss_mask_dice_7: 0.13451/1.09852, loss_spatial_bce_7: 0.04162/0.10589, loss_spatial_dice_7: 0.06278/0.22128, loss_spatial_ce_7: 0.05552/0.14972, loss_grounding_bce_7: 0.05030/0.08445, loss_grounding_dice_7: 0.04801/0.15987, loss_grounding_ce_7: 0.06983/0.31485, loss_mask_ce_8: 0.07035/1.00807, loss_mask_bce_8: 0.13773/0.33247, loss_mask_dice_8: 0.13879/1.17530, loss_spatial_bce_8: 0.06292/0.12202, loss_spatial_dice_8: 0.04942/0.25545, loss_spatial_ce_8: 0.07071/0.19449, loss_grounding_bce_8: 0.05880/0.08868, loss_grounding_dice_8: 0.05679/0.16975, loss_grounding_ce_8: 0.06292/0.41241, loss_mask_ce_9: 2.97207/3.47086, loss_mask_bce_9: 0.11144/0.35962, loss_mask_dice_9: 0.36791/1.75786, loss_spatial_bce_9: 0.53469/0.35414, loss_spatial_dice_9: 0.68677/0.79284, loss_spatial_ce_9: 0.78123/1.38517, loss_grounding_bce_9: 0.04883/0.10099, loss_grounding_dice_9: 0.14491/0.24200, loss_grounding_ce_9: 0.39595/0.66504] items per batch[64] items per second[0.16] total items[5734400] mini batches[ 89600] memory[4999] epoch remaining[0:53:12] INFO:trainer.default_trainer:epochs[ 49] optim steps[89700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 2.34988/0.74693, loss_mask_bce_0: 1.29648/0.29994, loss_mask_dice_0: 1.98175/1.01772, loss_spatial_bce_0: 0.19011/0.08369, loss_spatial_dice_0: 0.32327/0.17694, loss_spatial_ce_0: 0.06820/0.05378, loss_grounding_bce_0: 0.52221/0.08036, loss_grounding_dice_0: 0.28661/0.15007, loss_grounding_ce_0: 0.19798/0.24614, loss_mask_ce_1: 2.38564/0.74773, loss_mask_bce_1: 1.27152/0.30075, loss_mask_dice_1: 2.02185/1.02218, loss_spatial_bce_1: 0.18022/0.08418, loss_spatial_dice_1: 0.32427/0.17992, loss_spatial_ce_1: 0.05869/0.05736, loss_grounding_bce_1: 0.53414/0.08056, loss_grounding_dice_1: 0.29326/0.15072, loss_grounding_ce_1: 0.23759/0.24748, loss_mask_ce_2: 2.35368/0.75535, loss_mask_bce_2: 1.27077/0.30112, loss_mask_dice_2: 2.02094/1.02273, loss_spatial_bce_2: 0.17421/0.08428, loss_spatial_dice_2: 0.30866/0.18061, loss_spatial_ce_2: 0.05538/0.05948, loss_grounding_bce_2: 0.50351/0.08054, loss_grounding_dice_2: 0.27356/0.15074, loss_grounding_ce_2: 0.17354/0.25052, loss_mask_ce_3: 2.42659/0.76028, loss_mask_bce_3: 1.45368/0.30240, loss_mask_dice_3: 2.17534/1.02122, loss_spatial_bce_3: 0.17926/0.08643, loss_spatial_dice_3: 0.30756/0.18206, loss_spatial_ce_3: 0.05222/0.06440, loss_grounding_bce_3: 0.53181/0.08088, loss_grounding_dice_3: 0.28872/0.15037, loss_grounding_ce_3: 0.16572/0.25155, loss_mask_ce_4: 2.49859/0.76653, loss_mask_bce_4: 1.30306/0.30516, loss_mask_dice_4: 2.04111/1.04040, loss_spatial_bce_4: 0.17545/0.08902, loss_spatial_dice_4: 0.34281/0.19104, loss_spatial_ce_4: 0.08225/0.07814, loss_grounding_bce_4: 0.54591/0.08163, loss_grounding_dice_4: 0.30506/0.15295, loss_grounding_ce_4: 0.07044/0.25609, loss_mask_ce_5: 2.42845/0.79209, loss_mask_bce_5: 1.33475/0.30709, loss_mask_dice_5: 2.07206/1.04874, loss_spatial_bce_5: 0.18284/0.09144, loss_spatial_dice_5: 0.34965/0.19439, loss_spatial_ce_5: 0.18023/0.09200, loss_grounding_bce_5: 0.52596/0.08193, loss_grounding_dice_5: 0.32978/0.15387, loss_grounding_ce_5: 0.13211/0.27305, loss_mask_ce_6: 2.37928/0.81945, loss_mask_bce_6: 1.34875/0.30929, loss_mask_dice_6: 2.11281/1.05283, loss_spatial_bce_6: 0.18266/0.09693, loss_spatial_dice_6: 0.35271/0.19677, loss_spatial_ce_6: 0.15936/0.11622, loss_grounding_bce_6: 0.52026/0.08271, loss_grounding_dice_6: 0.32916/0.15431, loss_grounding_ce_6: 0.11538/0.28198, loss_mask_ce_7: 2.49425/0.87406, loss_mask_bce_7: 1.49582/0.31651, loss_mask_dice_7: 2.36896/1.09853, loss_spatial_bce_7: 0.17008/0.10587, loss_spatial_dice_7: 0.33281/0.22126, loss_spatial_ce_7: 0.10603/0.14967, loss_grounding_bce_7: 0.50848/0.08444, loss_grounding_dice_7: 0.34141/0.15987, loss_grounding_ce_7: 0.30152/0.31477, loss_mask_ce_8: 2.81618/1.00795, loss_mask_bce_8: 1.48557/0.33244, loss_mask_dice_8: 2.52220/1.17531, loss_spatial_bce_8: 0.16290/0.12199, loss_spatial_dice_8: 0.36133/0.25544, loss_spatial_ce_8: 0.17947/0.19441, loss_grounding_bce_8: 0.51342/0.08867, loss_grounding_dice_8: 0.29977/0.16974, loss_grounding_ce_8: 0.30626/0.41230, loss_mask_ce_9: 4.64773/3.47071, loss_mask_bce_9: 1.50661/0.35960, loss_mask_dice_9: 4.61610/1.75795, loss_spatial_bce_9: 0.29046/0.35410, loss_spatial_dice_9: 0.93955/0.79283, loss_spatial_ce_9: 1.37834/1.38513, loss_grounding_bce_9: 0.47926/0.10099, loss_grounding_dice_9: 0.24259/0.24198, loss_grounding_ce_9: 0.80699/0.66494] items per batch[64] items per second[0.37] total items[5740800] mini batches[ 89700] memory[4999] epoch remaining[0:48:53] INFO:trainer.default_trainer:epochs[ 49] optim steps[89800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.20402/0.74674, loss_mask_bce_0: 0.00645/0.29990, loss_mask_dice_0: 0.73223/1.01759, loss_spatial_bce_0: 0.00794/0.08368, loss_spatial_dice_0: 0.20551/0.17692, loss_spatial_ce_0: 0.00305/0.05376, loss_grounding_bce_0: 0.00387/0.08036, loss_grounding_dice_0: 0.15068/0.15004, loss_grounding_ce_0: 0.06501/0.24617, loss_mask_ce_1: 0.10606/0.74756, loss_mask_bce_1: 0.00492/0.30071, loss_mask_dice_1: 0.38148/1.02204, loss_spatial_bce_1: 0.00585/0.08417, loss_spatial_dice_1: 0.25903/0.17990, loss_spatial_ce_1: 0.00237/0.05733, loss_grounding_bce_1: 0.00380/0.08057, loss_grounding_dice_1: 0.24789/0.15071, loss_grounding_ce_1: 0.07945/0.24747, loss_mask_ce_2: 0.09813/0.75520, loss_mask_bce_2: 0.00538/0.30108, loss_mask_dice_2: 0.50644/1.02257, loss_spatial_bce_2: 0.00607/0.08428, loss_spatial_dice_2: 0.21200/0.18059, loss_spatial_ce_2: 0.00433/0.05946, loss_grounding_bce_2: 0.00421/0.08055, loss_grounding_dice_2: 0.31925/0.15072, loss_grounding_ce_2: 0.06806/0.25056, loss_mask_ce_3: 0.20690/0.76012, loss_mask_bce_3: 0.00615/0.30236, loss_mask_dice_3: 0.67117/1.02110, loss_spatial_bce_3: 0.00650/0.08642, loss_spatial_dice_3: 0.29570/0.18205, loss_spatial_ce_3: 0.01263/0.06438, loss_grounding_bce_3: 0.00314/0.08088, loss_grounding_dice_3: 0.14917/0.15036, loss_grounding_ce_3: 0.05624/0.25157, loss_mask_ce_4: 0.08176/0.76635, loss_mask_bce_4: 0.00939/0.30512, loss_mask_dice_4: 0.43897/1.04026, loss_spatial_bce_4: 0.00624/0.08901, loss_spatial_dice_4: 0.20863/0.19102, loss_spatial_ce_4: 0.00349/0.07811, loss_grounding_bce_4: 0.00509/0.08164, loss_grounding_dice_4: 0.17016/0.15293, loss_grounding_ce_4: 0.02481/0.25613, loss_mask_ce_5: 0.07729/0.79191, loss_mask_bce_5: 0.00703/0.30704, loss_mask_dice_5: 0.53475/1.04861, loss_spatial_bce_5: 0.00709/0.09143, loss_spatial_dice_5: 0.24845/0.19437, loss_spatial_ce_5: 0.01102/0.09195, loss_grounding_bce_5: 0.00651/0.08194, loss_grounding_dice_5: 0.24574/0.15385, loss_grounding_ce_5: 0.06231/0.27304, loss_mask_ce_6: 0.08430/0.81925, loss_mask_bce_6: 0.01023/0.30924, loss_mask_dice_6: 0.66789/1.05266, loss_spatial_bce_6: 0.00668/0.09692, loss_spatial_dice_6: 0.28097/0.19676, loss_spatial_ce_6: 0.08415/0.11619, loss_grounding_bce_6: 0.00512/0.08272, loss_grounding_dice_6: 0.11736/0.15429, loss_grounding_ce_6: 0.11157/0.28198, loss_mask_ce_7: 0.23812/0.87384, loss_mask_bce_7: 0.01420/0.31646, loss_mask_dice_7: 0.66694/1.09837, loss_spatial_bce_7: 0.00376/0.10585, loss_spatial_dice_7: 0.31232/0.22124, loss_spatial_ce_7: 0.12472/0.14963, loss_grounding_bce_7: 0.00527/0.08444, loss_grounding_dice_7: 0.14759/0.15985, loss_grounding_ce_7: 0.02749/0.31474, loss_mask_ce_8: 0.12114/1.00774, loss_mask_bce_8: 0.01153/0.33239, loss_mask_dice_8: 0.61882/1.17513, loss_spatial_bce_8: 0.00579/0.12197, loss_spatial_dice_8: 0.31509/0.25541, loss_spatial_ce_8: 0.10624/0.19436, loss_grounding_bce_8: 0.00437/0.08867, loss_grounding_dice_8: 0.28241/0.16973, loss_grounding_ce_8: 0.12176/0.41225, loss_mask_ce_9: 2.02726/3.47046, loss_mask_bce_9: 0.01833/0.35955, loss_mask_dice_9: 0.44475/1.75768, loss_spatial_bce_9: 0.01793/0.35410, loss_spatial_dice_9: 0.61997/0.79281, loss_spatial_ce_9: 1.19912/1.38514, loss_grounding_bce_9: 0.01202/0.10098, loss_grounding_dice_9: 0.30159/0.24196, loss_grounding_ce_9: 0.06348/0.66504] items per batch[64] items per second[0.36] total items[5747200] mini batches[ 89800] memory[4999] epoch remaining[0:45:56] INFO:trainer.default_trainer:epochs[ 49] optim steps[89900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.52118/0.74670, loss_mask_bce_0: 0.32241/0.29989, loss_mask_dice_0: 0.89048/1.01751, loss_spatial_bce_0: 0.02496/0.08368, loss_spatial_dice_0: 0.09508/0.17691, loss_spatial_ce_0: 0.00031/0.05374, loss_grounding_bce_0: 0.11310/0.08036, loss_grounding_dice_0: 0.09211/0.15001, loss_grounding_ce_0: 0.04041/0.24620, loss_mask_ce_1: 0.53146/0.74753, loss_mask_bce_1: 0.32691/0.30069, loss_mask_dice_1: 0.92142/1.02195, loss_spatial_bce_1: 0.02527/0.08417, loss_spatial_dice_1: 0.10038/0.17989, loss_spatial_ce_1: 0.00115/0.05730, loss_grounding_bce_1: 0.12950/0.08056, loss_grounding_dice_1: 0.10752/0.15068, loss_grounding_ce_1: 0.05439/0.24751, loss_mask_ce_2: 0.51198/0.75516, loss_mask_bce_2: 0.33632/0.30106, loss_mask_dice_2: 0.92319/1.02251, loss_spatial_bce_2: 0.02811/0.08427, loss_spatial_dice_2: 0.12717/0.18058, loss_spatial_ce_2: 0.00773/0.05943, loss_grounding_bce_2: 0.14083/0.08055, loss_grounding_dice_2: 0.10926/0.15069, loss_grounding_ce_2: 0.03373/0.25060, loss_mask_ce_3: 0.51631/0.76009, loss_mask_bce_3: 0.33336/0.30235, loss_mask_dice_3: 0.90809/1.02104, loss_spatial_bce_3: 0.02850/0.08642, loss_spatial_dice_3: 0.10744/0.18203, loss_spatial_ce_3: 0.02997/0.06435, loss_grounding_bce_3: 0.13007/0.08088, loss_grounding_dice_3: 0.10685/0.15033, loss_grounding_ce_3: 0.06101/0.25161, loss_mask_ce_4: 0.60574/0.76632, loss_mask_bce_4: 0.35421/0.30510, loss_mask_dice_4: 0.91131/1.04018, loss_spatial_bce_4: 0.02529/0.08901, loss_spatial_dice_4: 0.12741/0.19101, loss_spatial_ce_4: 0.02446/0.07808, loss_grounding_bce_4: 0.12505/0.08163, loss_grounding_dice_4: 0.09990/0.15290, loss_grounding_ce_4: 0.09949/0.25616, loss_mask_ce_5: 0.65257/0.79186, loss_mask_bce_5: 0.36389/0.30703, loss_mask_dice_5: 0.93168/1.04856, loss_spatial_bce_5: 0.02872/0.09143, loss_spatial_dice_5: 0.13795/0.19436, loss_spatial_ce_5: 0.01396/0.09192, loss_grounding_bce_5: 0.10834/0.08193, loss_grounding_dice_5: 0.09957/0.15381, loss_grounding_ce_5: 0.46813/0.27308, loss_mask_ce_6: 0.67615/0.81921, loss_mask_bce_6: 0.38784/0.30922, loss_mask_dice_6: 0.91870/1.05262, loss_spatial_bce_6: 0.03860/0.09691, loss_spatial_dice_6: 0.13697/0.19675, loss_spatial_ce_6: 0.02345/0.11616, loss_grounding_bce_6: 0.16652/0.08271, loss_grounding_dice_6: 0.11395/0.15425, loss_grounding_ce_6: 0.43685/0.28198, loss_mask_ce_7: 0.64962/0.87382, loss_mask_bce_7: 0.53540/0.31645, loss_mask_dice_7: 0.99557/1.09829, loss_spatial_bce_7: 0.04003/0.10585, loss_spatial_dice_7: 0.18713/0.22123, loss_spatial_ce_7: 0.04045/0.14959, loss_grounding_bce_7: 0.14597/0.08444, loss_grounding_dice_7: 0.10863/0.15982, loss_grounding_ce_7: 0.11194/0.31474, loss_mask_ce_8: 1.59509/1.00772, loss_mask_bce_8: 0.51577/0.33237, loss_mask_dice_8: 1.37385/1.17506, loss_spatial_bce_8: 0.03582/0.12196, loss_spatial_dice_8: 0.18066/0.25538, loss_spatial_ce_8: 0.05446/0.19433, loss_grounding_bce_8: 0.20473/0.08867, loss_grounding_dice_8: 0.20327/0.16970, loss_grounding_ce_8: 2.09445/0.41221, loss_mask_ce_9: 5.54558/3.47033, loss_mask_bce_9: 0.58993/0.35955, loss_mask_dice_9: 2.93966/1.75764, loss_spatial_bce_9: 0.25311/0.35409, loss_spatial_dice_9: 0.93379/0.79281, loss_spatial_ce_9: 1.24467/1.38511, loss_grounding_bce_9: 0.34130/0.10100, loss_grounding_dice_9: 0.25506/0.24193, loss_grounding_ce_9: 1.29855/0.66500] items per batch[64] items per second[0.37] total items[5753600] mini batches[ 89900] memory[4999] epoch remaining[0:42:39] INFO:trainer.default_trainer:epochs[ 49] optim steps[90000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.92824/0.74670, loss_mask_bce_0: 0.40610/0.29990, loss_mask_dice_0: 2.94628/1.01765, loss_spatial_bce_0: 0.01686/0.08366, loss_spatial_dice_0: 0.18989/0.17689, loss_spatial_ce_0: 0.02995/0.05373, loss_grounding_bce_0: 0.05511/0.08037, loss_grounding_dice_0: 0.26301/0.15004, loss_grounding_ce_0: 0.67838/0.24626, loss_mask_ce_1: 0.90191/0.74753, loss_mask_bce_1: 0.40295/0.30071, loss_mask_dice_1: 3.35446/1.02207, loss_spatial_bce_1: 0.01859/0.08416, loss_spatial_dice_1: 0.22177/0.17988, loss_spatial_ce_1: 0.03265/0.05729, loss_grounding_bce_1: 0.07076/0.08057, loss_grounding_dice_1: 0.26960/0.15071, loss_grounding_ce_1: 0.63014/0.24758, loss_mask_ce_2: 0.78417/0.75517, loss_mask_bce_2: 0.36764/0.30108, loss_mask_dice_2: 3.31832/1.02263, loss_spatial_bce_2: 0.02130/0.08426, loss_spatial_dice_2: 0.25002/0.18057, loss_spatial_ce_2: 0.03901/0.05941, loss_grounding_bce_2: 0.06738/0.08056, loss_grounding_dice_2: 0.26297/0.15072, loss_grounding_ce_2: 0.67715/0.25065, loss_mask_ce_3: 0.72496/0.76010, loss_mask_bce_3: 0.40633/0.30236, loss_mask_dice_3: 3.43524/1.02117, loss_spatial_bce_3: 0.02438/0.08641, loss_spatial_dice_3: 0.23275/0.18202, loss_spatial_ce_3: 0.03466/0.06433, loss_grounding_bce_3: 0.05048/0.08089, loss_grounding_dice_3: 0.26948/0.15034, loss_grounding_ce_3: 0.73071/0.25167, loss_mask_ce_4: 0.96758/0.76631, loss_mask_bce_4: 0.43589/0.30513, loss_mask_dice_4: 3.41147/1.04033, loss_spatial_bce_4: 0.01978/0.08900, loss_spatial_dice_4: 0.27302/0.19100, loss_spatial_ce_4: 0.04414/0.07805, loss_grounding_bce_4: 0.04250/0.08164, loss_grounding_dice_4: 0.26894/0.15292, loss_grounding_ce_4: 0.70286/0.25621, loss_mask_ce_5: 0.87025/0.79187, loss_mask_bce_5: 0.45235/0.30706, loss_mask_dice_5: 3.41847/1.04870, loss_spatial_bce_5: 0.02692/0.09142, loss_spatial_dice_5: 0.26809/0.19435, loss_spatial_ce_5: 0.05645/0.09191, loss_grounding_bce_5: 0.06377/0.08194, loss_grounding_dice_5: 0.29936/0.15384, loss_grounding_ce_5: 0.74636/0.27322, loss_mask_ce_6: 0.93952/0.81919, loss_mask_bce_6: 0.48187/0.30926, loss_mask_dice_6: 3.35909/1.05278, loss_spatial_bce_6: 0.02150/0.09690, loss_spatial_dice_6: 0.27220/0.19674, loss_spatial_ce_6: 0.09477/0.11615, loss_grounding_bce_6: 0.02999/0.08273, loss_grounding_dice_6: 0.32880/0.15428, loss_grounding_ce_6: 0.79414/0.28206, loss_mask_ce_7: 0.92914/0.87383, loss_mask_bce_7: 0.47683/0.31648, loss_mask_dice_7: 3.74462/1.09846, loss_spatial_bce_7: 0.01764/0.10584, loss_spatial_dice_7: 0.29679/0.22122, loss_spatial_ce_7: 0.14877/0.14958, loss_grounding_bce_7: 0.06736/0.08445, loss_grounding_dice_7: 0.32861/0.15984, loss_grounding_ce_7: 0.71136/0.31479, loss_mask_ce_8: 1.34996/1.00774, loss_mask_bce_8: 0.40176/0.33240, loss_mask_dice_8: 4.01279/1.17522, loss_spatial_bce_8: 0.02563/0.12195, loss_spatial_dice_8: 0.39284/0.25538, loss_spatial_ce_8: 0.11971/0.19430, loss_grounding_bce_8: 0.05327/0.08869, loss_grounding_dice_8: 0.35156/0.16972, loss_grounding_ce_8: 0.89166/0.41232, loss_mask_ce_9: 7.82688/3.47060, loss_mask_bce_9: 0.37789/0.35956, loss_mask_dice_9: 5.34133/1.75796, loss_spatial_bce_9: 0.09506/0.35406, loss_spatial_dice_9: 0.90568/0.79280, loss_spatial_ce_9: 1.19536/1.38517, loss_grounding_bce_9: 0.03911/0.10101, loss_grounding_dice_9: 0.49693/0.24196, loss_grounding_ce_9: 0.67238/0.66497] items per batch[64] items per second[0.36] total items[5760000] mini batches[ 90000] memory[4999] epoch remaining[0:39:44] INFO:trainer.default_trainer:epochs[ 49] optim steps[90100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.39447/0.74668, loss_mask_bce_0: 0.65133/0.29988, loss_mask_dice_0: 1.58445/1.01774, loss_spatial_bce_0: 0.05935/0.08364, loss_spatial_dice_0: 0.13307/0.17689, loss_spatial_ce_0: 0.00469/0.05372, loss_grounding_bce_0: 0.03489/0.08036, loss_grounding_dice_0: 0.09331/0.15003, loss_grounding_ce_0: 0.00271/0.24627, loss_mask_ce_1: 0.39240/0.74748, loss_mask_bce_1: 0.66357/0.30069, loss_mask_dice_1: 1.54929/1.02214, loss_spatial_bce_1: 0.06120/0.08413, loss_spatial_dice_1: 0.14564/0.17988, loss_spatial_ce_1: 0.03193/0.05725, loss_grounding_bce_1: 0.03387/0.08056, loss_grounding_dice_1: 0.08597/0.15071, loss_grounding_ce_1: 0.00509/0.24757, loss_mask_ce_2: 0.39440/0.75514, loss_mask_bce_2: 0.66666/0.30106, loss_mask_dice_2: 1.57248/1.02271, loss_spatial_bce_2: 0.06204/0.08424, loss_spatial_dice_2: 0.13806/0.18057, loss_spatial_ce_2: 0.04737/0.05938, loss_grounding_bce_2: 0.04262/0.08055, loss_grounding_dice_2: 0.09275/0.15072, loss_grounding_ce_2: 0.00628/0.25064, loss_mask_ce_3: 0.39979/0.76007, loss_mask_bce_3: 0.66717/0.30234, loss_mask_dice_3: 1.51970/1.02123, loss_spatial_bce_3: 0.06869/0.08638, loss_spatial_dice_3: 0.14687/0.18202, loss_spatial_ce_3: 0.06154/0.06430, loss_grounding_bce_3: 0.03173/0.08088, loss_grounding_dice_3: 0.08211/0.15035, loss_grounding_ce_3: 0.00485/0.25162, loss_mask_ce_4: 0.44073/0.76626, loss_mask_bce_4: 0.69289/0.30511, loss_mask_dice_4: 1.64163/1.04041, loss_spatial_bce_4: 0.07630/0.08898, loss_spatial_dice_4: 0.15527/0.19100, loss_spatial_ce_4: 0.16460/0.07802, loss_grounding_bce_4: 0.03825/0.08163, loss_grounding_dice_4: 0.08625/0.15292, loss_grounding_ce_4: 0.00700/0.25616, loss_mask_ce_5: 0.43516/0.79183, loss_mask_bce_5: 0.70369/0.30704, loss_mask_dice_5: 1.69534/1.04879, loss_spatial_bce_5: 0.07279/0.09139, loss_spatial_dice_5: 0.20411/0.19435, loss_spatial_ce_5: 0.10995/0.09187, loss_grounding_bce_5: 0.04268/0.08193, loss_grounding_dice_5: 0.08988/0.15384, loss_grounding_ce_5: 0.13385/0.27322, loss_mask_ce_6: 0.46101/0.81917, loss_mask_bce_6: 0.74176/0.30925, loss_mask_dice_6: 1.76326/1.05288, loss_spatial_bce_6: 0.09195/0.09687, loss_spatial_dice_6: 0.19314/0.19674, loss_spatial_ce_6: 0.17641/0.11612, loss_grounding_bce_6: 0.03579/0.08271, loss_grounding_dice_6: 0.10088/0.15429, loss_grounding_ce_6: 0.49691/0.28203, loss_mask_ce_7: 0.55983/0.87378, loss_mask_bce_7: 0.72426/0.31646, loss_mask_dice_7: 1.92776/1.09859, loss_spatial_bce_7: 0.09900/0.10580, loss_spatial_dice_7: 0.23820/0.22122, loss_spatial_ce_7: 0.09794/0.14954, loss_grounding_bce_7: 0.03347/0.08444, loss_grounding_dice_7: 0.08991/0.15984, loss_grounding_ce_7: 0.12367/0.31473, loss_mask_ce_8: 0.68145/1.00770, loss_mask_bce_8: 0.77648/0.33238, loss_mask_dice_8: 2.01718/1.17535, loss_spatial_bce_8: 0.10280/0.12191, loss_spatial_dice_8: 0.27433/0.25538, loss_spatial_ce_8: 0.29186/0.19425, loss_grounding_bce_8: 0.04121/0.08868, loss_grounding_dice_8: 0.08406/0.16973, loss_grounding_ce_8: 0.46653/0.41230, loss_mask_ce_9: 4.87319/3.47061, loss_mask_bce_9: 1.14191/0.35954, loss_mask_dice_9: 4.54778/1.75811, loss_spatial_bce_9: 0.24484/0.35400, loss_spatial_dice_9: 0.93983/0.79282, loss_spatial_ce_9: 1.27465/1.38512, loss_grounding_bce_9: 0.03532/0.10099, loss_grounding_dice_9: 0.11415/0.24198, loss_grounding_ce_9: 2.25885/0.66485] items per batch[64] items per second[0.36] total items[5766400] mini batches[ 90100] memory[4999] epoch remaining[0:36:46] INFO:trainer.default_trainer:epochs[ 49] optim steps[90200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 1.02073/0.74661, loss_mask_bce_0: 0.17104/0.29985, loss_mask_dice_0: 1.14671/1.01755, loss_spatial_bce_0: 0.02147/0.08363, loss_spatial_dice_0: 0.18865/0.17687, loss_spatial_ce_0: 0.02741/0.05369, loss_grounding_bce_0: 0.03730/0.08035, loss_grounding_dice_0: 0.11982/0.15001, loss_grounding_ce_0: 0.08154/0.24624, loss_mask_ce_1: 1.08530/0.74741, loss_mask_bce_1: 0.17170/0.30066, loss_mask_dice_1: 1.14646/1.02193, loss_spatial_bce_1: 0.02270/0.08413, loss_spatial_dice_1: 0.18766/0.17986, loss_spatial_ce_1: 0.03065/0.05722, loss_grounding_bce_1: 0.03347/0.08055, loss_grounding_dice_1: 0.10454/0.15069, loss_grounding_ce_1: 0.04516/0.24754, loss_mask_ce_2: 1.05847/0.75506, loss_mask_bce_2: 0.17424/0.30103, loss_mask_dice_2: 1.15829/1.02249, loss_spatial_bce_2: 0.02201/0.08423, loss_spatial_dice_2: 0.21227/0.18055, loss_spatial_ce_2: 0.02498/0.05935, loss_grounding_bce_2: 0.03412/0.08054, loss_grounding_dice_2: 0.09796/0.15070, loss_grounding_ce_2: 0.07502/0.25062, loss_mask_ce_3: 1.19339/0.76000, loss_mask_bce_3: 0.17718/0.30230, loss_mask_dice_3: 1.28115/1.02103, loss_spatial_bce_3: 0.02101/0.08638, loss_spatial_dice_3: 0.19051/0.18200, loss_spatial_ce_3: 0.01993/0.06428, loss_grounding_bce_3: 0.03502/0.08087, loss_grounding_dice_3: 0.09628/0.15033, loss_grounding_ce_3: 0.06932/0.25163, loss_mask_ce_4: 1.29951/0.76623, loss_mask_bce_4: 0.16231/0.30507, loss_mask_dice_4: 1.27116/1.04019, loss_spatial_bce_4: 0.02249/0.08897, loss_spatial_dice_4: 0.20570/0.19098, loss_spatial_ce_4: 0.01903/0.07801, loss_grounding_bce_4: 0.03243/0.08163, loss_grounding_dice_4: 0.10802/0.15290, loss_grounding_ce_4: 0.03713/0.25613, loss_mask_ce_5: 1.39886/0.79177, loss_mask_bce_5: 0.15369/0.30700, loss_mask_dice_5: 1.32846/1.04858, loss_spatial_bce_5: 0.02529/0.09138, loss_spatial_dice_5: 0.21612/0.19433, loss_spatial_ce_5: 0.04620/0.09185, loss_grounding_bce_5: 0.03624/0.08192, loss_grounding_dice_5: 0.09683/0.15381, loss_grounding_ce_5: 0.08167/0.27317, loss_mask_ce_6: 1.94577/0.81911, loss_mask_bce_6: 0.14998/0.30922, loss_mask_dice_6: 1.37123/1.05266, loss_spatial_bce_6: 0.02264/0.09687, loss_spatial_dice_6: 0.19281/0.19672, loss_spatial_ce_6: 0.05724/0.11609, loss_grounding_bce_6: 0.04094/0.08271, loss_grounding_dice_6: 0.11785/0.15426, loss_grounding_ce_6: 0.17296/0.28202, loss_mask_ce_7: 1.82248/0.87370, loss_mask_bce_7: 0.15853/0.31642, loss_mask_dice_7: 1.47854/1.09837, loss_spatial_bce_7: 0.02261/0.10579, loss_spatial_dice_7: 0.26804/0.22119, loss_spatial_ce_7: 0.05487/0.14951, loss_grounding_bce_7: 0.04528/0.08443, loss_grounding_dice_7: 0.16259/0.15982, loss_grounding_ce_7: 0.63291/0.31467, loss_mask_ce_8: 1.89939/1.00754, loss_mask_bce_8: 0.20731/0.33234, loss_mask_dice_8: 1.57977/1.17510, loss_spatial_bce_8: 0.02548/0.12191, loss_spatial_dice_8: 0.29966/0.25535, loss_spatial_ce_8: 0.08252/0.19420, loss_grounding_bce_8: 0.09566/0.08867, loss_grounding_dice_8: 0.23822/0.16970, loss_grounding_ce_8: 0.83523/0.41221, loss_mask_ce_9: 4.91128/3.47045, loss_mask_bce_9: 0.27988/0.35950, loss_mask_dice_9: 2.37108/1.75788, loss_spatial_bce_9: 0.06071/0.35400, loss_spatial_dice_9: 0.90262/0.79281, loss_spatial_ce_9: 1.42387/1.38506, loss_grounding_bce_9: 0.17331/0.10097, loss_grounding_dice_9: 0.33504/0.24193, loss_grounding_ce_9: 0.10747/0.66483] items per batch[64] items per second[0.37] total items[5772800] mini batches[ 90200] memory[4999] epoch remaining[0:33:43] INFO:trainer.default_trainer:epochs[ 49] optim steps[90300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.88375/0.74666, loss_mask_bce_0: 0.62447/0.29979, loss_mask_dice_0: 2.85729/1.01755, loss_spatial_bce_0: 0.06509/0.08362, loss_spatial_dice_0: 0.19843/0.17687, loss_spatial_ce_0: 0.00070/0.05367, loss_grounding_bce_0: 0.02762/0.08033, loss_grounding_dice_0: 0.19267/0.15002, loss_grounding_ce_0: 0.69493/0.24626, loss_mask_ce_1: 0.83376/0.74746, loss_mask_bce_1: 0.61038/0.30061, loss_mask_dice_1: 2.78466/1.02192, loss_spatial_bce_1: 0.06495/0.08411, loss_spatial_dice_1: 0.21483/0.17985, loss_spatial_ce_1: 0.00123/0.05720, loss_grounding_bce_1: 0.02745/0.08053, loss_grounding_dice_1: 0.14444/0.15069, loss_grounding_ce_1: 0.63549/0.24756, loss_mask_ce_2: 0.60227/0.75509, loss_mask_bce_2: 0.65627/0.30098, loss_mask_dice_2: 3.64444/1.02251, loss_spatial_bce_2: 0.06303/0.08422, loss_spatial_dice_2: 0.20230/0.18054, loss_spatial_ce_2: 0.00053/0.05932, loss_grounding_bce_2: 0.02831/0.08052, loss_grounding_dice_2: 0.14739/0.15071, loss_grounding_ce_2: 0.73140/0.25065, loss_mask_ce_3: 0.62454/0.76005, loss_mask_bce_3: 0.65173/0.30226, loss_mask_dice_3: 3.46930/1.02105, loss_spatial_bce_3: 0.06722/0.08636, loss_spatial_dice_3: 0.21814/0.18199, loss_spatial_ce_3: 0.00048/0.06427, loss_grounding_bce_3: 0.02749/0.08085, loss_grounding_dice_3: 0.16341/0.15033, loss_grounding_ce_3: 0.65764/0.25164, loss_mask_ce_4: 0.79773/0.76627, loss_mask_bce_4: 0.64534/0.30503, loss_mask_dice_4: 2.80342/1.04019, loss_spatial_bce_4: 0.07146/0.08896, loss_spatial_dice_4: 0.22453/0.19098, loss_spatial_ce_4: 0.00158/0.07798, loss_grounding_bce_4: 0.02717/0.08161, loss_grounding_dice_4: 0.17246/0.15290, loss_grounding_ce_4: 0.67010/0.25614, loss_mask_ce_5: 0.92299/0.79183, loss_mask_bce_5: 0.65229/0.30696, loss_mask_dice_5: 2.79363/1.04857, loss_spatial_bce_5: 0.07298/0.09137, loss_spatial_dice_5: 0.25588/0.19433, loss_spatial_ce_5: 0.02154/0.09182, loss_grounding_bce_5: 0.03219/0.08190, loss_grounding_dice_5: 0.17234/0.15382, loss_grounding_ce_5: 0.78228/0.27319, loss_mask_ce_6: 1.15438/0.81920, loss_mask_bce_6: 0.67756/0.30918, loss_mask_dice_6: 2.84324/1.05264, loss_spatial_bce_6: 0.07468/0.09686, loss_spatial_dice_6: 0.29790/0.19672, loss_spatial_ce_6: 0.03029/0.11606, loss_grounding_bce_6: 0.03262/0.08269, loss_grounding_dice_6: 0.16853/0.15427, loss_grounding_ce_6: 0.64923/0.28201, loss_mask_ce_7: 0.82990/0.87375, loss_mask_bce_7: 0.71793/0.31637, loss_mask_dice_7: 2.86766/1.09836, loss_spatial_bce_7: 0.11086/0.10577, loss_spatial_dice_7: 0.33854/0.22120, loss_spatial_ce_7: 0.03815/0.14949, loss_grounding_bce_7: 0.04095/0.08441, loss_grounding_dice_7: 0.16301/0.15983, loss_grounding_ce_7: 0.70960/0.31469, loss_mask_ce_8: 0.79839/1.00761, loss_mask_bce_8: 0.74534/0.33229, loss_mask_dice_8: 3.18359/1.17506, loss_spatial_bce_8: 0.16185/0.12189, loss_spatial_dice_8: 0.34121/0.25537, loss_spatial_ce_8: 0.07313/0.19419, loss_grounding_bce_8: 0.04249/0.08864, loss_grounding_dice_8: 0.15728/0.16971, loss_grounding_ce_8: 0.65829/0.41228, loss_mask_ce_9: 5.78487/3.47063, loss_mask_bce_9: 1.44298/0.35945, loss_mask_dice_9: 10.39664/1.75788, loss_spatial_bce_9: 0.25073/0.35395, loss_spatial_dice_9: 0.93469/0.79281, loss_spatial_ce_9: 1.17408/1.38501, loss_grounding_bce_9: 0.06207/0.10095, loss_grounding_dice_9: 0.53838/0.24194, loss_grounding_ce_9: 0.60753/0.66483] items per batch[64] items per second[0.37] total items[5779200] mini batches[ 90300] memory[4999] epoch remaining[0:30:41] INFO:trainer.default_trainer:epochs[ 49] optim steps[90400] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.39976/0.74662, loss_mask_bce_0: 0.71776/0.29979, loss_mask_dice_0: 0.81473/1.01743, loss_spatial_bce_0: 0.10076/0.08360, loss_spatial_dice_0: 0.12537/0.17684, loss_spatial_ce_0: 0.03295/0.05365, loss_grounding_bce_0: 0.21915/0.08033, loss_grounding_dice_0: 0.15280/0.15002, loss_grounding_ce_0: 3.42706/0.24632, loss_mask_ce_1: 0.29407/0.74740, loss_mask_bce_1: 0.78796/0.30060, loss_mask_dice_1: 0.82420/1.02179, loss_spatial_bce_1: 0.10668/0.08410, loss_spatial_dice_1: 0.12497/0.17983, loss_spatial_ce_1: 0.02988/0.05719, loss_grounding_bce_1: 0.20240/0.08053, loss_grounding_dice_1: 0.14337/0.15069, loss_grounding_ce_1: 3.44879/0.24763, loss_mask_ce_2: 0.26098/0.75503, loss_mask_bce_2: 0.79325/0.30097, loss_mask_dice_2: 0.84465/1.02235, loss_spatial_bce_2: 0.10373/0.08420, loss_spatial_dice_2: 0.12476/0.18052, loss_spatial_ce_2: 0.02951/0.05931, loss_grounding_bce_2: 0.19162/0.08052, loss_grounding_dice_2: 0.13365/0.15071, loss_grounding_ce_2: 3.15683/0.25069, loss_mask_ce_3: 0.46114/0.75999, loss_mask_bce_3: 0.71241/0.30224, loss_mask_dice_3: 0.81791/1.02090, loss_spatial_bce_3: 0.10491/0.08635, loss_spatial_dice_3: 0.12558/0.18197, loss_spatial_ce_3: 0.03351/0.06426, loss_grounding_bce_3: 0.19037/0.08085, loss_grounding_dice_3: 0.14117/0.15033, loss_grounding_ce_3: 3.32951/0.25168, loss_mask_ce_4: 0.42276/0.76623, loss_mask_bce_4: 0.69687/0.30502, loss_mask_dice_4: 0.83248/1.04006, loss_spatial_bce_4: 0.11130/0.08894, loss_spatial_dice_4: 0.13465/0.19096, loss_spatial_ce_4: 0.05044/0.07797, loss_grounding_bce_4: 0.18314/0.08160, loss_grounding_dice_4: 0.13725/0.15291, loss_grounding_ce_4: 3.93456/0.25618, loss_mask_ce_5: 0.38171/0.79175, loss_mask_bce_5: 0.67511/0.30696, loss_mask_dice_5: 0.82507/1.04841, loss_spatial_bce_5: 0.11988/0.09135, loss_spatial_dice_5: 0.14595/0.19431, loss_spatial_ce_5: 0.06417/0.09179, loss_grounding_bce_5: 0.20400/0.08190, loss_grounding_dice_5: 0.13985/0.15382, loss_grounding_ce_5: 5.00937/0.27325, loss_mask_ce_6: 0.41659/0.81918, loss_mask_bce_6: 0.63720/0.30917, loss_mask_dice_6: 0.79870/1.05250, loss_spatial_bce_6: 0.11800/0.09685, loss_spatial_dice_6: 0.15369/0.19671, loss_spatial_ce_6: 0.07324/0.11603, loss_grounding_bce_6: 0.19990/0.08269, loss_grounding_dice_6: 0.14597/0.15426, loss_grounding_ce_6: 4.47052/0.28209, loss_mask_ce_7: 0.43213/0.87372, loss_mask_bce_7: 0.65438/0.31637, loss_mask_dice_7: 0.83259/1.09823, loss_spatial_bce_7: 0.12582/0.10577, loss_spatial_dice_7: 0.16344/0.22118, loss_spatial_ce_7: 0.06859/0.14945, loss_grounding_bce_7: 0.20823/0.08441, loss_grounding_dice_7: 0.13857/0.15983, loss_grounding_ce_7: 4.35695/0.31472, loss_mask_ce_8: 0.74688/1.00762, loss_mask_bce_8: 0.66706/0.33229, loss_mask_dice_8: 0.86197/1.17489, loss_spatial_bce_8: 0.14326/0.12188, loss_spatial_dice_8: 0.17214/0.25534, loss_spatial_ce_8: 0.18200/0.19414, loss_grounding_bce_8: 0.20529/0.08864, loss_grounding_dice_8: 0.14929/0.16971, loss_grounding_ce_8: 0.94834/0.41230, loss_mask_ce_9: 3.19725/3.47061, loss_mask_bce_9: 0.93044/0.35945, loss_mask_dice_9: 1.35541/1.75775, loss_spatial_bce_9: 0.38065/0.35392, loss_spatial_dice_9: 0.82949/0.79282, loss_spatial_ce_9: 1.06753/1.38504, loss_grounding_bce_9: 0.19229/0.10095, loss_grounding_dice_9: 0.21649/0.24196, loss_grounding_ce_9: 3.12839/0.66494] items per batch[64] items per second[0.37] total items[5785600] mini batches[ 90400] memory[4999] epoch remaining[0:27:41] INFO:trainer.default_trainer:epochs[ 49] optim steps[90500] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.18944/0.74649, loss_mask_bce_0: 0.51940/0.29977, loss_mask_dice_0: 0.47513/1.01753, loss_spatial_bce_0: 0.14293/0.08360, loss_spatial_dice_0: 0.13045/0.17682, loss_spatial_ce_0: 0.02147/0.05365, loss_grounding_bce_0: 0.21046/0.08033, loss_grounding_dice_0: 0.20816/0.15003, loss_grounding_ce_0: 0.00605/0.24632, loss_mask_ce_1: 0.19496/0.74730, loss_mask_bce_1: 0.50631/0.30058, loss_mask_dice_1: 0.47878/1.02189, loss_spatial_bce_1: 0.15468/0.08410, loss_spatial_dice_1: 0.14064/0.17981, loss_spatial_ce_1: 0.01932/0.05717, loss_grounding_bce_1: 0.20974/0.08054, loss_grounding_dice_1: 0.20653/0.15070, loss_grounding_ce_1: 0.00580/0.24764, loss_mask_ce_2: 0.18698/0.75491, loss_mask_bce_2: 0.50602/0.30096, loss_mask_dice_2: 0.47272/1.02244, loss_spatial_bce_2: 0.17873/0.08421, loss_spatial_dice_2: 0.16227/0.18050, loss_spatial_ce_2: 0.04400/0.05931, loss_grounding_bce_2: 0.21440/0.08052, loss_grounding_dice_2: 0.20723/0.15072, loss_grounding_ce_2: 0.00778/0.25063, loss_mask_ce_3: 0.19119/0.75989, loss_mask_bce_3: 0.50196/0.30223, loss_mask_dice_3: 0.46535/1.02099, loss_spatial_bce_3: 0.14446/0.08635, loss_spatial_dice_3: 0.14198/0.18195, loss_spatial_ce_3: 0.02566/0.06424, loss_grounding_bce_3: 0.21567/0.08086, loss_grounding_dice_3: 0.20041/0.15034, loss_grounding_ce_3: 0.00811/0.25162, loss_mask_ce_4: 0.19038/0.76611, loss_mask_bce_4: 0.48839/0.30501, loss_mask_dice_4: 0.44921/1.04018, loss_spatial_bce_4: 0.14757/0.08895, loss_spatial_dice_4: 0.13784/0.19094, loss_spatial_ce_4: 0.01517/0.07797, loss_grounding_bce_4: 0.20177/0.08160, loss_grounding_dice_4: 0.19913/0.15291, loss_grounding_ce_4: 0.00528/0.25612, loss_mask_ce_5: 0.18755/0.79166, loss_mask_bce_5: 0.49307/0.30694, loss_mask_dice_5: 0.46665/1.04849, loss_spatial_bce_5: 0.14205/0.09136, loss_spatial_dice_5: 0.14327/0.19430, loss_spatial_ce_5: 0.02302/0.09179, loss_grounding_bce_5: 0.20063/0.08190, loss_grounding_dice_5: 0.20086/0.15383, loss_grounding_ce_5: 0.00694/0.27318, loss_mask_ce_6: 0.20162/0.81904, loss_mask_bce_6: 0.49559/0.30916, loss_mask_dice_6: 0.45330/1.05256, loss_spatial_bce_6: 0.14897/0.09686, loss_spatial_dice_6: 0.12553/0.19669, loss_spatial_ce_6: 0.02195/0.11602, loss_grounding_bce_6: 0.20076/0.08269, loss_grounding_dice_6: 0.19180/0.15426, loss_grounding_ce_6: 0.01235/0.28201, loss_mask_ce_7: 0.23841/0.87361, loss_mask_bce_7: 0.50086/0.31636, loss_mask_dice_7: 0.47217/1.09829, loss_spatial_bce_7: 0.15508/0.10578, loss_spatial_dice_7: 0.15224/0.22117, loss_spatial_ce_7: 0.03126/0.14941, loss_grounding_bce_7: 0.20988/0.08441, loss_grounding_dice_7: 0.21835/0.15983, loss_grounding_ce_7: 0.00646/0.31466, loss_mask_ce_8: 0.31111/1.00750, loss_mask_bce_8: 0.49689/0.33228, loss_mask_dice_8: 0.49127/1.17503, loss_spatial_bce_8: 0.14458/0.12188, loss_spatial_dice_8: 0.14807/0.25532, loss_spatial_ce_8: 0.05093/0.19415, loss_grounding_bce_8: 0.18840/0.08865, loss_grounding_dice_8: 0.21185/0.16972, loss_grounding_ce_8: 0.00862/0.41224, loss_mask_ce_9: 3.31383/3.47057, loss_mask_bce_9: 0.50443/0.35943, loss_mask_dice_9: 0.58871/1.75798, loss_spatial_bce_9: 0.46874/0.35393, loss_spatial_dice_9: 0.83628/0.79281, loss_spatial_ce_9: 2.06115/1.38501, loss_grounding_bce_9: 0.17499/0.10095, loss_grounding_dice_9: 0.24422/0.24196, loss_grounding_ce_9: 0.03838/0.66479] items per batch[64] items per second[0.36] total items[5792000] mini batches[ 90500] memory[4999] epoch remaining[0:24:47] INFO:trainer.default_trainer:epochs[ 49] optim steps[90600] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.71974/0.74644, loss_mask_bce_0: 0.19485/0.29971, loss_mask_dice_0: 0.18220/1.01760, loss_spatial_bce_0: 0.22903/0.08358, loss_spatial_dice_0: 0.16797/0.17681, loss_spatial_ce_0: 0.00004/0.05364, loss_grounding_bce_0: 0.12354/0.08032, loss_grounding_dice_0: 0.10559/0.15003, loss_grounding_ce_0: 0.10264/0.24627, loss_mask_ce_1: 0.65969/0.74725, loss_mask_bce_1: 0.19462/0.30052, loss_mask_dice_1: 0.17983/1.02192, loss_spatial_bce_1: 0.24247/0.08408, loss_spatial_dice_1: 0.16944/0.17980, loss_spatial_ce_1: 0.00006/0.05717, loss_grounding_bce_1: 0.12164/0.08053, loss_grounding_dice_1: 0.10205/0.15070, loss_grounding_ce_1: 0.09213/0.24758, loss_mask_ce_2: 0.60986/0.75485, loss_mask_bce_2: 0.20581/0.30091, loss_mask_dice_2: 0.18972/1.02249, loss_spatial_bce_2: 0.24208/0.08419, loss_spatial_dice_2: 0.17952/0.18049, loss_spatial_ce_2: 0.00011/0.05930, loss_grounding_bce_2: 0.11959/0.08051, loss_grounding_dice_2: 0.10470/0.15073, loss_grounding_ce_2: 0.08490/0.25056, loss_mask_ce_3: 0.61122/0.75986, loss_mask_bce_3: 0.20309/0.30217, loss_mask_dice_3: 0.17930/1.02103, loss_spatial_bce_3: 0.25194/0.08633, loss_spatial_dice_3: 0.19719/0.18194, loss_spatial_ce_3: 0.00025/0.06423, loss_grounding_bce_3: 0.12113/0.08084, loss_grounding_dice_3: 0.09874/0.15034, loss_grounding_ce_3: 0.06495/0.25155, loss_mask_ce_4: 0.62493/0.76610, loss_mask_bce_4: 0.19956/0.30496, loss_mask_dice_4: 0.18579/1.04025, loss_spatial_bce_4: 0.25986/0.08893, loss_spatial_dice_4: 0.23647/0.19094, loss_spatial_ce_4: 0.00047/0.07795, loss_grounding_bce_4: 0.12061/0.08159, loss_grounding_dice_4: 0.09964/0.15292, loss_grounding_ce_4: 0.05529/0.25606, loss_mask_ce_5: 0.70366/0.79168, loss_mask_bce_5: 0.20531/0.30690, loss_mask_dice_5: 0.18198/1.04858, loss_spatial_bce_5: 0.26154/0.09134, loss_spatial_dice_5: 0.21261/0.19430, loss_spatial_ce_5: 0.00292/0.09177, loss_grounding_bce_5: 0.11976/0.08189, loss_grounding_dice_5: 0.09690/0.15384, loss_grounding_ce_5: 0.08564/0.27311, loss_mask_ce_6: 0.67897/0.81902, loss_mask_bce_6: 0.19912/0.30912, loss_mask_dice_6: 0.17768/1.05262, loss_spatial_bce_6: 0.28783/0.09684, loss_spatial_dice_6: 0.25868/0.19669, loss_spatial_ce_6: 0.00134/0.11599, loss_grounding_bce_6: 0.12255/0.08268, loss_grounding_dice_6: 0.09885/0.15427, loss_grounding_ce_6: 0.10016/0.28195, loss_mask_ce_7: 0.85008/0.87359, loss_mask_bce_7: 0.20566/0.31632, loss_mask_dice_7: 0.19194/1.09839, loss_spatial_bce_7: 0.27855/0.10577, loss_spatial_dice_7: 0.25206/0.22116, loss_spatial_ce_7: 0.00167/0.14937, loss_grounding_bce_7: 0.13153/0.08440, loss_grounding_dice_7: 0.11041/0.15985, loss_grounding_ce_7: 0.12718/0.31459, loss_mask_ce_8: 1.29735/1.00751, loss_mask_bce_8: 0.21956/0.33223, loss_mask_dice_8: 0.19001/1.17514, loss_spatial_bce_8: 0.26326/0.12186, loss_spatial_dice_8: 0.19825/0.25531, loss_spatial_ce_8: 0.00824/0.19412, loss_grounding_bce_8: 0.13090/0.08864, loss_grounding_dice_8: 0.10312/0.16974, loss_grounding_ce_8: 0.23175/0.41217, loss_mask_ce_9: 2.75490/3.47051, loss_mask_bce_9: 0.27119/0.35937, loss_mask_dice_9: 0.31689/1.75799, loss_spatial_bce_9: 0.45775/0.35392, loss_spatial_dice_9: 0.70370/0.79282, loss_spatial_ce_9: 0.39229/1.38500, loss_grounding_bce_9: 0.15141/0.10094, loss_grounding_dice_9: 0.14186/0.24197, loss_grounding_ce_9: 0.48095/0.66478] items per batch[64] items per second[0.38] total items[5798400] mini batches[ 90600] memory[4999] epoch remaining[0:21:48] INFO:trainer.default_trainer:epochs[ 49] optim steps[90700] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.12247/0.74644, loss_mask_bce_0: 0.23407/0.29973, loss_mask_dice_0: 0.25294/1.01757, loss_spatial_bce_0: 0.11851/0.08358, loss_spatial_dice_0: 0.14746/0.17681, loss_spatial_ce_0: 0.01486/0.05363, loss_grounding_bce_0: 0.06577/0.08031, loss_grounding_dice_0: 0.14589/0.15000, loss_grounding_ce_0: 0.04375/0.24620, loss_mask_ce_1: 0.12042/0.74723, loss_mask_bce_1: 0.23220/0.30055, loss_mask_dice_1: 0.24060/1.02193, loss_spatial_bce_1: 0.11099/0.08408, loss_spatial_dice_1: 0.17376/0.17980, loss_spatial_ce_1: 0.01001/0.05717, loss_grounding_bce_1: 0.06951/0.08051, loss_grounding_dice_1: 0.15807/0.15068, loss_grounding_ce_1: 0.04939/0.24751, loss_mask_ce_2: 0.13470/0.75485, loss_mask_bce_2: 0.22767/0.30093, loss_mask_dice_2: 0.25631/1.02248, loss_spatial_bce_2: 0.11421/0.08419, loss_spatial_dice_2: 0.22970/0.18049, loss_spatial_ce_2: 0.02960/0.05929, loss_grounding_bce_2: 0.06677/0.08050, loss_grounding_dice_2: 0.15996/0.15071, loss_grounding_ce_2: 0.07321/0.25049, loss_mask_ce_3: 0.13584/0.75988, loss_mask_bce_3: 0.23746/0.30219, loss_mask_dice_3: 0.25287/1.02104, loss_spatial_bce_3: 0.12107/0.08633, loss_spatial_dice_3: 0.19580/0.18194, loss_spatial_ce_3: 0.04763/0.06423, loss_grounding_bce_3: 0.06287/0.08083, loss_grounding_dice_3: 0.15434/0.15031, loss_grounding_ce_3: 0.06959/0.25149, loss_mask_ce_4: 0.13351/0.76610, loss_mask_bce_4: 0.22998/0.30498, loss_mask_dice_4: 0.26037/1.04026, loss_spatial_bce_4: 0.10585/0.08893, loss_spatial_dice_4: 0.18584/0.19093, loss_spatial_ce_4: 0.13841/0.07794, loss_grounding_bce_4: 0.06466/0.08158, loss_grounding_dice_4: 0.16152/0.15290, loss_grounding_ce_4: 0.05570/0.25598, loss_mask_ce_5: 0.14035/0.79169, loss_mask_bce_5: 0.23159/0.30692, loss_mask_dice_5: 0.26397/1.04858, loss_spatial_bce_5: 0.11451/0.09134, loss_spatial_dice_5: 0.15895/0.19429, loss_spatial_ce_5: 0.12806/0.09177, loss_grounding_bce_5: 0.06441/0.08188, loss_grounding_dice_5: 0.15860/0.15381, loss_grounding_ce_5: 0.05747/0.27302, loss_mask_ce_6: 0.13459/0.81901, loss_mask_bce_6: 0.22356/0.30914, loss_mask_dice_6: 0.26057/1.05260, loss_spatial_bce_6: 0.13277/0.09683, loss_spatial_dice_6: 0.26984/0.19669, loss_spatial_ce_6: 0.07185/0.11600, loss_grounding_bce_6: 0.06015/0.08267, loss_grounding_dice_6: 0.14810/0.15424, loss_grounding_ce_6: 0.05844/0.28187, loss_mask_ce_7: 0.14259/0.87357, loss_mask_bce_7: 0.21939/0.31635, loss_mask_dice_7: 0.25845/1.09837, loss_spatial_bce_7: 0.14554/0.10576, loss_spatial_dice_7: 0.28516/0.22115, loss_spatial_ce_7: 0.16243/0.14938, loss_grounding_bce_7: 0.06504/0.08438, loss_grounding_dice_7: 0.15525/0.15982, loss_grounding_ce_7: 0.07004/0.31453, loss_mask_ce_8: 0.19872/1.00741, loss_mask_bce_8: 0.23547/0.33225, loss_mask_dice_8: 0.26537/1.17511, loss_spatial_bce_8: 0.16947/0.12186, loss_spatial_dice_8: 0.29273/0.25530, loss_spatial_ce_8: 0.14078/0.19410, loss_grounding_bce_8: 0.06744/0.08862, loss_grounding_dice_8: 0.15483/0.16972, loss_grounding_ce_8: 0.14676/0.41201, loss_mask_ce_9: 2.69803/3.47042, loss_mask_bce_9: 0.34142/0.35938, loss_mask_dice_9: 0.63817/1.75802, loss_spatial_bce_9: 0.71612/0.35394, loss_spatial_dice_9: 0.86646/0.79282, loss_spatial_ce_9: 1.53440/1.38503, loss_grounding_bce_9: 0.12723/0.10093, loss_grounding_dice_9: 0.46200/0.24196, loss_grounding_ce_9: 0.56897/0.66468] items per batch[64] items per second[0.38] total items[5804800] mini batches[ 90700] memory[4999] epoch remaining[0:18:49] INFO:trainer.default_trainer:epochs[ 49] optim steps[90800] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.04385/0.74641, loss_mask_bce_0: 0.05667/0.29972, loss_mask_dice_0: 0.15638/1.01761, loss_spatial_bce_0: 0.06328/0.08357, loss_spatial_dice_0: 0.22376/0.17680, loss_spatial_ce_0: 0.19358/0.05362, loss_grounding_bce_0: 0.02932/0.08030, loss_grounding_dice_0: 0.09898/0.15001, loss_grounding_ce_0: 0.00362/0.24624, loss_mask_ce_1: 0.04151/0.74721, loss_mask_bce_1: 0.05784/0.30055, loss_mask_dice_1: 0.18082/1.02197, loss_spatial_bce_1: 0.08402/0.08407, loss_spatial_dice_1: 0.24151/0.17978, loss_spatial_ce_1: 0.08232/0.05715, loss_grounding_bce_1: 0.02911/0.08050, loss_grounding_dice_1: 0.09849/0.15069, loss_grounding_ce_1: 0.00303/0.24754, loss_mask_ce_2: 0.04008/0.75481, loss_mask_bce_2: 0.05667/0.30093, loss_mask_dice_2: 0.15618/1.02253, loss_spatial_bce_2: 0.08157/0.08417, loss_spatial_dice_2: 0.28913/0.18047, loss_spatial_ce_2: 0.07312/0.05929, loss_grounding_bce_2: 0.02998/0.08049, loss_grounding_dice_2: 0.10158/0.15071, loss_grounding_ce_2: 0.00182/0.25050, loss_mask_ce_3: 0.03579/0.75984, loss_mask_bce_3: 0.05952/0.30219, loss_mask_dice_3: 0.17953/1.02110, loss_spatial_bce_3: 0.10837/0.08632, loss_spatial_dice_3: 0.31414/0.18193, loss_spatial_ce_3: 0.08005/0.06421, loss_grounding_bce_3: 0.02756/0.08082, loss_grounding_dice_3: 0.10505/0.15031, loss_grounding_ce_3: 0.00190/0.25153, loss_mask_ce_4: 0.03862/0.76606, loss_mask_bce_4: 0.05869/0.30498, loss_mask_dice_4: 0.18652/1.04026, loss_spatial_bce_4: 0.08184/0.08892, loss_spatial_dice_4: 0.23666/0.19092, loss_spatial_ce_4: 0.13226/0.07793, loss_grounding_bce_4: 0.02617/0.08157, loss_grounding_dice_4: 0.09888/0.15291, loss_grounding_ce_4: 0.00260/0.25601, loss_mask_ce_5: 0.03483/0.79168, loss_mask_bce_5: 0.06062/0.30691, loss_mask_dice_5: 0.19103/1.04860, loss_spatial_bce_5: 0.09394/0.09133, loss_spatial_dice_5: 0.25209/0.19428, loss_spatial_ce_5: 0.22992/0.09176, loss_grounding_bce_5: 0.02894/0.08187, loss_grounding_dice_5: 0.09726/0.15382, loss_grounding_ce_5: 0.00358/0.27305, loss_mask_ce_6: 0.03316/0.81897, loss_mask_bce_6: 0.05905/0.30914, loss_mask_dice_6: 0.17802/1.05261, loss_spatial_bce_6: 0.10282/0.09683, loss_spatial_dice_6: 0.24663/0.19667, loss_spatial_ce_6: 0.11384/0.11598, loss_grounding_bce_6: 0.03092/0.08266, loss_grounding_dice_6: 0.10279/0.15425, loss_grounding_ce_6: 0.00642/0.28187, loss_mask_ce_7: 0.03786/0.87352, loss_mask_bce_7: 0.05947/0.31635, loss_mask_dice_7: 0.18414/1.09843, loss_spatial_bce_7: 0.04674/0.10575, loss_spatial_dice_7: 0.24536/0.22114, loss_spatial_ce_7: 0.41523/0.14935, loss_grounding_bce_7: 0.02966/0.08437, loss_grounding_dice_7: 0.09927/0.15983, loss_grounding_ce_7: 0.00535/0.31457, loss_mask_ce_8: 0.05938/1.00739, loss_mask_bce_8: 0.06994/0.33224, loss_mask_dice_8: 0.21181/1.17513, loss_spatial_bce_8: 0.07609/0.12184, loss_spatial_dice_8: 0.30170/0.25527, loss_spatial_ce_8: 0.38805/0.19406, loss_grounding_bce_8: 0.03296/0.08861, loss_grounding_dice_8: 0.11053/0.16973, loss_grounding_ce_8: 0.00692/0.41203, loss_mask_ce_9: 1.92294/3.47064, loss_mask_bce_9: 0.06913/0.35938, loss_mask_dice_9: 0.30132/1.75807, loss_spatial_bce_9: 0.72850/0.35394, loss_spatial_dice_9: 0.91150/0.79283, loss_spatial_ce_9: 1.73037/1.38504, loss_grounding_bce_9: 0.03451/0.10093, loss_grounding_dice_9: 0.17975/0.24198, loss_grounding_ce_9: 0.20523/0.66475] items per batch[64] items per second[0.37] total items[5811200] mini batches[ 90800] memory[4999] epoch remaining[0:15:55] INFO:trainer.default_trainer:epochs[ 49] optim steps[90900] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.20509/0.74629, loss_mask_bce_0: 0.27417/0.29971, loss_mask_dice_0: 0.09842/1.01772, loss_spatial_bce_0: 0.51115/0.08357, loss_spatial_dice_0: 0.22240/0.17679, loss_spatial_ce_0: 1.44872/0.05363, loss_grounding_bce_0: 0.17605/0.08031, loss_grounding_dice_0: 0.07050/0.14999, loss_grounding_ce_0: 0.04810/0.24617, loss_mask_ce_1: 0.22108/0.74711, loss_mask_bce_1: 0.26623/0.30054, loss_mask_dice_1: 0.09221/1.02209, loss_spatial_bce_1: 0.40312/0.08407, loss_spatial_dice_1: 0.17924/0.17978, loss_spatial_ce_1: 1.38790/0.05715, loss_grounding_bce_1: 0.16847/0.08051, loss_grounding_dice_1: 0.06344/0.15067, loss_grounding_ce_1: 0.06436/0.24748, loss_mask_ce_2: 0.21934/0.75468, loss_mask_bce_2: 0.25405/0.30092, loss_mask_dice_2: 0.08886/1.02265, loss_spatial_bce_2: 0.23772/0.08417, loss_spatial_dice_2: 0.10989/0.18046, loss_spatial_ce_2: 1.08370/0.05929, loss_grounding_bce_2: 0.15558/0.08050, loss_grounding_dice_2: 0.06034/0.15070, loss_grounding_ce_2: 0.07107/0.25042, loss_mask_ce_3: 0.21279/0.75972, loss_mask_bce_3: 0.26164/0.30217, loss_mask_dice_3: 0.08657/1.02119, loss_spatial_bce_3: 0.18816/0.08632, loss_spatial_dice_3: 0.08225/0.18192, loss_spatial_ce_3: 0.96617/0.06421, loss_grounding_bce_3: 0.15701/0.08083, loss_grounding_dice_3: 0.05908/0.15030, loss_grounding_ce_3: 0.06547/0.25146, loss_mask_ce_4: 0.22177/0.76597, loss_mask_bce_4: 0.25024/0.30496, loss_mask_dice_4: 0.08877/1.04037, loss_spatial_bce_4: 0.17693/0.08891, loss_spatial_dice_4: 0.07498/0.19091, loss_spatial_ce_4: 1.03582/0.07793, loss_grounding_bce_4: 0.14535/0.08158, loss_grounding_dice_4: 0.05301/0.15290, loss_grounding_ce_4: 0.07087/0.25595, loss_mask_ce_5: 0.27839/0.79156, loss_mask_bce_5: 0.25988/0.30689, loss_mask_dice_5: 0.09584/1.04871, loss_spatial_bce_5: 0.18355/0.09133, loss_spatial_dice_5: 0.15359/0.19426, loss_spatial_ce_5: 0.85861/0.09176, loss_grounding_bce_5: 0.15216/0.08188, loss_grounding_dice_5: 0.05915/0.15380, loss_grounding_ce_5: 0.07921/0.27301, loss_mask_ce_6: 0.30119/0.81883, loss_mask_bce_6: 0.24074/0.30912, loss_mask_dice_6: 0.08306/1.05275, loss_spatial_bce_6: 0.20125/0.09683, loss_spatial_dice_6: 0.12665/0.19666, loss_spatial_ce_6: 1.07575/0.11597, loss_grounding_bce_6: 0.14333/0.08267, loss_grounding_dice_6: 0.05374/0.15424, loss_grounding_ce_6: 0.12438/0.28179, loss_mask_ce_7: 0.29017/0.87336, loss_mask_bce_7: 0.25784/0.31632, loss_mask_dice_7: 0.10711/1.09856, loss_spatial_bce_7: 0.29833/0.10575, loss_spatial_dice_7: 0.14122/0.22113, loss_spatial_ce_7: 0.92165/0.14931, loss_grounding_bce_7: 0.15394/0.08438, loss_grounding_dice_7: 0.05849/0.15981, loss_grounding_ce_7: 0.10815/0.31450, loss_mask_ce_8: 0.20771/1.00724, loss_mask_bce_8: 0.23328/0.33222, loss_mask_dice_8: 0.09283/1.17525, loss_spatial_bce_8: 0.20628/0.12184, loss_spatial_dice_8: 0.07888/0.25526, loss_spatial_ce_8: 0.22990/0.19403, loss_grounding_bce_8: 0.13131/0.08862, loss_grounding_dice_8: 0.05282/0.16972, loss_grounding_ce_8: 0.04447/0.41196, loss_mask_ce_9: 1.65078/3.47047, loss_mask_bce_9: 0.23512/0.35935, loss_mask_dice_9: 0.11236/1.75822, loss_spatial_bce_9: 1.89891/0.35393, loss_spatial_dice_9: 0.50606/0.79281, loss_spatial_ce_9: 2.07870/1.38507, loss_grounding_bce_9: 0.14518/0.10093, loss_grounding_dice_9: 0.06524/0.24196, loss_grounding_ce_9: 0.08936/0.66463] items per batch[64] items per second[0.36] total items[5817600] mini batches[ 90900] memory[4999] epoch remaining[0:13:02] INFO:trainer.default_trainer:epochs[ 49] optim steps[91000] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.60759/0.74631, loss_mask_bce_0: 0.09110/0.29973, loss_mask_dice_0: 3.76847/1.01778, loss_spatial_bce_0: 0.00477/0.08355, loss_spatial_dice_0: 0.30869/0.17677, loss_spatial_ce_0: 0.01048/0.05361, loss_grounding_bce_0: 0.01183/0.08031, loss_grounding_dice_0: 0.16004/0.15000, loss_grounding_ce_0: 0.05817/0.24613, loss_mask_ce_1: 0.71344/0.74712, loss_mask_bce_1: 0.06869/0.30056, loss_mask_dice_1: 3.36979/1.02215, loss_spatial_bce_1: 0.00660/0.08405, loss_spatial_dice_1: 0.38499/0.17975, loss_spatial_ce_1: 0.18538/0.05713, loss_grounding_bce_1: 0.00871/0.08051, loss_grounding_dice_1: 0.19279/0.15066, loss_grounding_ce_1: 0.07018/0.24744, loss_mask_ce_2: 0.78115/0.75470, loss_mask_bce_2: 0.07824/0.30094, loss_mask_dice_2: 3.51733/1.02273, loss_spatial_bce_2: 0.00528/0.08415, loss_spatial_dice_2: 0.38300/0.18044, loss_spatial_ce_2: 0.04161/0.05926, loss_grounding_bce_2: 0.01136/0.08050, loss_grounding_dice_2: 0.22371/0.15070, loss_grounding_ce_2: 0.07409/0.25037, loss_mask_ce_3: 0.64538/0.75973, loss_mask_bce_3: 0.08282/0.30220, loss_mask_dice_3: 3.39292/1.02126, loss_spatial_bce_3: 0.00557/0.08630, loss_spatial_dice_3: 0.43809/0.18190, loss_spatial_ce_3: 0.01407/0.06420, loss_grounding_bce_3: 0.00950/0.08083, loss_grounding_dice_3: 0.17531/0.15031, loss_grounding_ce_3: 0.05352/0.25142, loss_mask_ce_4: 0.90993/0.76596, loss_mask_bce_4: 0.07851/0.30499, loss_mask_dice_4: 3.24291/1.04045, loss_spatial_bce_4: 0.00481/0.08889, loss_spatial_dice_4: 0.31235/0.19089, loss_spatial_ce_4: 0.01206/0.07790, loss_grounding_bce_4: 0.00759/0.08158, loss_grounding_dice_4: 0.14238/0.15289, loss_grounding_ce_4: 0.07500/0.25589, loss_mask_ce_5: 0.61869/0.79160, loss_mask_bce_5: 0.08593/0.30692, loss_mask_dice_5: 3.89013/1.04880, loss_spatial_bce_5: 0.00538/0.09130, loss_spatial_dice_5: 0.33341/0.19424, loss_spatial_ce_5: 0.02516/0.09175, loss_grounding_bce_5: 0.00869/0.08188, loss_grounding_dice_5: 0.18121/0.15380, loss_grounding_ce_5: 0.10199/0.27294, loss_mask_ce_6: 0.78277/0.81883, loss_mask_bce_6: 0.08128/0.30915, loss_mask_dice_6: 3.11591/1.05281, loss_spatial_bce_6: 0.00719/0.09680, loss_spatial_dice_6: 0.37426/0.19665, loss_spatial_ce_6: 0.05768/0.11595, loss_grounding_bce_6: 0.00740/0.08267, loss_grounding_dice_6: 0.13988/0.15424, loss_grounding_ce_6: 0.11259/0.28172, loss_mask_ce_7: 1.36757/0.87339, loss_mask_bce_7: 0.08038/0.31634, loss_mask_dice_7: 3.29796/1.09862, loss_spatial_bce_7: 0.00574/0.10573, loss_spatial_dice_7: 0.39432/0.22111, loss_spatial_ce_7: 0.23686/0.14928, loss_grounding_bce_7: 0.01053/0.08438, loss_grounding_dice_7: 0.23227/0.15981, loss_grounding_ce_7: 0.12743/0.31444, loss_mask_ce_8: 0.93511/1.00726, loss_mask_bce_8: 0.10895/0.33226, loss_mask_dice_8: 4.36369/1.17537, loss_spatial_bce_8: 0.00638/0.12180, loss_spatial_dice_8: 0.46906/0.25524, loss_spatial_ce_8: 0.14696/0.19397, loss_grounding_bce_8: 0.01142/0.08862, loss_grounding_dice_8: 0.20108/0.16972, loss_grounding_ce_8: 0.22366/0.41190, loss_mask_ce_9: 2.37735/3.47068, loss_mask_bce_9: 0.05883/0.35939, loss_mask_dice_9: 4.01221/1.75839, loss_spatial_bce_9: 0.01698/0.35390, loss_spatial_dice_9: 0.91675/0.79282, loss_spatial_ce_9: 1.76024/1.38509, loss_grounding_bce_9: 0.00672/0.10094, loss_grounding_dice_9: 0.16354/0.24198, loss_grounding_ce_9: 0.22089/0.66470] items per batch[64] items per second[0.37] total items[5824000] mini batches[ 91000] memory[4999] epoch remaining[0:10:08] INFO:trainer.default_trainer:epochs[ 49] optim steps[91100] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 3.92105/0.74632, loss_mask_bce_0: 0.72500/0.29967, loss_mask_dice_0: 12.78415/1.01801, loss_spatial_bce_0: 0.02812/0.08353, loss_spatial_dice_0: 0.41574/0.17675, loss_spatial_ce_0: 0.22120/0.05360, loss_grounding_bce_0: 0.01964/0.08030, loss_grounding_dice_0: 0.43243/0.14998, loss_grounding_ce_0: 0.68215/0.24619, loss_mask_ce_1: 3.60418/0.74713, loss_mask_bce_1: 0.74860/0.30050, loss_mask_dice_1: 12.21930/1.02235, loss_spatial_bce_1: 0.02253/0.08403, loss_spatial_dice_1: 0.45110/0.17974, loss_spatial_ce_1: 0.29022/0.05712, loss_grounding_bce_1: 0.02086/0.08049, loss_grounding_dice_1: 0.44695/0.15064, loss_grounding_ce_1: 0.64332/0.24753, loss_mask_ce_2: 3.53351/0.75470, loss_mask_bce_2: 0.71637/0.30088, loss_mask_dice_2: 11.89184/1.02292, loss_spatial_bce_2: 0.02256/0.08413, loss_spatial_dice_2: 0.50788/0.18043, loss_spatial_ce_2: 0.28147/0.05926, loss_grounding_bce_2: 0.01725/0.08049, loss_grounding_dice_2: 0.41794/0.15068, loss_grounding_ce_2: 0.67334/0.25043, loss_mask_ce_3: 3.44186/0.75977, loss_mask_bce_3: 0.72555/0.30214, loss_mask_dice_3: 12.32892/1.02149, loss_spatial_bce_3: 0.02961/0.08627, loss_spatial_dice_3: 0.53996/0.18189, loss_spatial_ce_3: 0.28200/0.06420, loss_grounding_bce_3: 0.01890/0.08082, loss_grounding_dice_3: 0.42392/0.15029, loss_grounding_ce_3: 0.75232/0.25149, loss_mask_ce_4: 3.46053/0.76597, loss_mask_bce_4: 0.74911/0.30493, loss_mask_dice_4: 12.28304/1.04064, loss_spatial_bce_4: 0.04587/0.08887, loss_spatial_dice_4: 0.52340/0.19088, loss_spatial_ce_4: 0.28102/0.07788, loss_grounding_bce_4: 0.01833/0.08157, loss_grounding_dice_4: 0.44128/0.15287, loss_grounding_ce_4: 0.67473/0.25595, loss_mask_ce_5: 3.81848/0.79162, loss_mask_bce_5: 0.70983/0.30686, loss_mask_dice_5: 12.52111/1.04902, loss_spatial_bce_5: 0.04413/0.09128, loss_spatial_dice_5: 0.50559/0.19424, loss_spatial_ce_5: 0.32631/0.09173, loss_grounding_bce_5: 0.01720/0.08187, loss_grounding_dice_5: 0.44844/0.15378, loss_grounding_ce_5: 0.73986/0.27297, loss_mask_ce_6: 3.66537/0.81886, loss_mask_bce_6: 0.68538/0.30909, loss_mask_dice_6: 12.91446/1.05304, loss_spatial_bce_6: 0.06460/0.09677, loss_spatial_dice_6: 0.53307/0.19664, loss_spatial_ce_6: 0.11986/0.11593, loss_grounding_bce_6: 0.01755/0.08266, loss_grounding_dice_6: 0.45823/0.15422, loss_grounding_ce_6: 0.79027/0.28176, loss_mask_ce_7: 3.81069/0.87338, loss_mask_bce_7: 0.72319/0.31629, loss_mask_dice_7: 13.78360/1.09887, loss_spatial_bce_7: 0.06862/0.10570, loss_spatial_dice_7: 0.52968/0.22110, loss_spatial_ce_7: 0.16281/0.14926, loss_grounding_bce_7: 0.01821/0.08437, loss_grounding_dice_7: 0.46880/0.15979, loss_grounding_ce_7: 0.74769/0.31450, loss_mask_ce_8: 3.01304/1.00734, loss_mask_bce_8: 0.94436/0.33220, loss_mask_dice_8: 14.10379/1.17560, loss_spatial_bce_8: 0.05383/0.12177, loss_spatial_dice_8: 0.64760/0.25523, loss_spatial_ce_8: 0.32068/0.19399, loss_grounding_bce_8: 0.02551/0.08861, loss_grounding_dice_8: 0.54742/0.16970, loss_grounding_ce_8: 0.81065/0.41198, loss_mask_ce_9: 7.78024/3.47104, loss_mask_bce_9: 1.26272/0.35934, loss_mask_dice_9: 17.29132/1.75861, loss_spatial_bce_9: 0.13005/0.35385, loss_spatial_dice_9: 0.98322/0.79282, loss_spatial_ce_9: 2.04778/1.38520, loss_grounding_bce_9: 0.02828/0.10094, loss_grounding_dice_9: 0.68884/0.24197, loss_grounding_ce_9: 0.68166/0.66483] items per batch[64] items per second[0.37] total items[5830400] mini batches[ 91100] memory[4999] epoch remaining[0:07:14] INFO:trainer.default_trainer:epochs[ 49] optim steps[91200] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.23103/0.74620, loss_mask_bce_0: 0.45704/0.29969, loss_mask_dice_0: 0.41546/1.01788, loss_spatial_bce_0: 0.11510/0.08354, loss_spatial_dice_0: 0.09870/0.17673, loss_spatial_ce_0: 0.00039/0.05359, loss_grounding_bce_0: 0.04950/0.08032, loss_grounding_dice_0: 0.06292/0.14995, loss_grounding_ce_0: 0.00010/0.24609, loss_mask_ce_1: 0.21130/0.74701, loss_mask_bce_1: 0.44693/0.30052, loss_mask_dice_1: 0.42012/1.02219, loss_spatial_bce_1: 0.11715/0.08404, loss_spatial_dice_1: 0.09901/0.17972, loss_spatial_ce_1: 0.00019/0.05712, loss_grounding_bce_1: 0.05082/0.08052, loss_grounding_dice_1: 0.06363/0.15062, loss_grounding_ce_1: 0.00009/0.24743, loss_mask_ce_2: 0.19738/0.75460, loss_mask_bce_2: 0.43780/0.30090, loss_mask_dice_2: 0.41488/1.02276, loss_spatial_bce_2: 0.11288/0.08414, loss_spatial_dice_2: 0.10139/0.18041, loss_spatial_ce_2: 0.00022/0.05926, loss_grounding_bce_2: 0.05186/0.08051, loss_grounding_dice_2: 0.06618/0.15066, loss_grounding_ce_2: 0.00016/0.25034, loss_mask_ce_3: 0.19881/0.75965, loss_mask_bce_3: 0.44908/0.30215, loss_mask_dice_3: 0.37510/1.02134, loss_spatial_bce_3: 0.11678/0.08629, loss_spatial_dice_3: 0.10485/0.18186, loss_spatial_ce_3: 0.00049/0.06419, loss_grounding_bce_3: 0.05120/0.08084, loss_grounding_dice_3: 0.06875/0.15026, loss_grounding_ce_3: 0.00017/0.25140, loss_mask_ce_4: 0.22852/0.76588, loss_mask_bce_4: 0.45462/0.30495, loss_mask_dice_4: 0.44077/1.04050, loss_spatial_bce_4: 0.12613/0.08889, loss_spatial_dice_4: 0.12381/0.19086, loss_spatial_ce_4: 0.00038/0.07787, loss_grounding_bce_4: 0.05475/0.08159, loss_grounding_dice_4: 0.07211/0.15286, loss_grounding_ce_4: 0.00048/0.25582, loss_mask_ce_5: 0.16521/0.79152, loss_mask_bce_5: 0.45096/0.30688, loss_mask_dice_5: 0.43838/1.04887, loss_spatial_bce_5: 0.13179/0.09129, loss_spatial_dice_5: 0.13646/0.19422, loss_spatial_ce_5: 0.00173/0.09171, loss_grounding_bce_5: 0.04893/0.08189, loss_grounding_dice_5: 0.06589/0.15376, loss_grounding_ce_5: 0.00041/0.27285, loss_mask_ce_6: 0.16034/0.81875, loss_mask_bce_6: 0.45332/0.30912, loss_mask_dice_6: 0.42020/1.05289, loss_spatial_bce_6: 0.12928/0.09679, loss_spatial_dice_6: 0.12371/0.19662, loss_spatial_ce_6: 0.00245/0.11591, loss_grounding_bce_6: 0.04993/0.08267, loss_grounding_dice_6: 0.06806/0.15420, loss_grounding_ce_6: 0.00082/0.28166, loss_mask_ce_7: 0.20281/0.87326, loss_mask_bce_7: 0.46451/0.31630, loss_mask_dice_7: 0.42137/1.09875, loss_spatial_bce_7: 0.12680/0.10571, loss_spatial_dice_7: 0.12071/0.22107, loss_spatial_ce_7: 0.00580/0.14922, loss_grounding_bce_7: 0.05382/0.08439, loss_grounding_dice_7: 0.07065/0.15977, loss_grounding_ce_7: 0.35597/0.31438, loss_mask_ce_8: 0.25906/1.00722, loss_mask_bce_8: 0.45316/0.33221, loss_mask_dice_8: 0.38599/1.17543, loss_spatial_bce_8: 0.14190/0.12179, loss_spatial_dice_8: 0.16338/0.25519, loss_spatial_ce_8: 0.05603/0.19396, loss_grounding_bce_8: 0.04917/0.08862, loss_grounding_dice_8: 0.06902/0.16967, loss_grounding_ce_8: 0.15156/0.41187, loss_mask_ce_9: 1.91119/3.47064, loss_mask_bce_9: 0.47019/0.35935, loss_mask_dice_9: 0.59354/1.75836, loss_spatial_bce_9: 0.56654/0.35389, loss_spatial_dice_9: 0.76434/0.79280, loss_spatial_ce_9: 1.61774/1.38507, loss_grounding_bce_9: 0.04633/0.10097, loss_grounding_dice_9: 0.07987/0.24194, loss_grounding_ce_9: 0.88004/0.66475] items per batch[64] items per second[0.37] total items[5836800] mini batches[ 91200] memory[4999] epoch remaining[0:04:20] INFO:trainer.default_trainer:epochs[ 49] optim steps[91300] learning rate[default: 1.00000e-06] train loss[loss_mask_ce_0: 0.16440/0.74604, loss_mask_bce_0: 0.17842/0.29969, loss_mask_dice_0: 0.40141/1.01750, loss_spatial_bce_0: 0.05964/0.08354, loss_spatial_dice_0: 0.09646/0.17672, loss_spatial_ce_0: 0.00154/0.05360, loss_grounding_bce_0: 0.10424/0.08032, loss_grounding_dice_0: 0.14768/0.14995, loss_grounding_ce_0: 0.00004/0.24607, loss_mask_ce_1: 0.17375/0.74685, loss_mask_bce_1: 0.17637/0.30051, loss_mask_dice_1: 0.38790/1.02181, loss_spatial_bce_1: 0.05965/0.08404, loss_spatial_dice_1: 0.10056/0.17971, loss_spatial_ce_1: 0.00117/0.05712, loss_grounding_bce_1: 0.10655/0.08052, loss_grounding_dice_1: 0.15251/0.15062, loss_grounding_ce_1: 0.00003/0.24743, loss_mask_ce_2: 0.17567/0.75446, loss_mask_bce_2: 0.18838/0.30090, loss_mask_dice_2: 0.41058/1.02239, loss_spatial_bce_2: 0.05683/0.08415, loss_spatial_dice_2: 0.09447/0.18039, loss_spatial_ce_2: 0.00256/0.05926, loss_grounding_bce_2: 0.09876/0.08051, loss_grounding_dice_2: 0.14774/0.15065, loss_grounding_ce_2: 0.00012/0.25034, loss_mask_ce_3: 0.17134/0.75949, loss_mask_bce_3: 0.17817/0.30214, loss_mask_dice_3: 0.40581/1.02098, loss_spatial_bce_3: 0.05761/0.08630, loss_spatial_dice_3: 0.09435/0.18185, loss_spatial_ce_3: 0.00185/0.06418, loss_grounding_bce_3: 0.09328/0.08084, loss_grounding_dice_3: 0.14410/0.15025, loss_grounding_ce_3: 0.00008/0.25140, loss_mask_ce_4: 0.18188/0.76571, loss_mask_bce_4: 0.19011/0.30494, loss_mask_dice_4: 0.42193/1.04011, loss_spatial_bce_4: 0.05871/0.08890, loss_spatial_dice_4: 0.10381/0.19085, loss_spatial_ce_4: 0.00192/0.07786, loss_grounding_bce_4: 0.10227/0.08160, loss_grounding_dice_4: 0.16196/0.15285, loss_grounding_ce_4: 0.00011/0.25583, loss_mask_ce_5: 0.15467/0.79137, loss_mask_bce_5: 0.17718/0.30687, loss_mask_dice_5: 0.38672/1.04846, loss_spatial_bce_5: 0.05517/0.09131, loss_spatial_dice_5: 0.09784/0.19421, loss_spatial_ce_5: 0.00951/0.09170, loss_grounding_bce_5: 0.09775/0.08190, loss_grounding_dice_5: 0.15820/0.15376, loss_grounding_ce_5: 0.00010/0.27283, loss_mask_ce_6: 0.16054/0.81859, loss_mask_bce_6: 0.18069/0.30911, loss_mask_dice_6: 0.39024/1.05251, loss_spatial_bce_6: 0.06351/0.09681, loss_spatial_dice_6: 0.11202/0.19661, loss_spatial_ce_6: 0.01777/0.11590, loss_grounding_bce_6: 0.10555/0.08268, loss_grounding_dice_6: 0.15398/0.15420, loss_grounding_ce_6: 0.00005/0.28161, loss_mask_ce_7: 0.15660/0.87309, loss_mask_bce_7: 0.17579/0.31629, loss_mask_dice_7: 0.40907/1.09834, loss_spatial_bce_7: 0.06459/0.10574, loss_spatial_dice_7: 0.10121/0.22106, loss_spatial_ce_7: 0.03653/0.14919, loss_grounding_bce_7: 0.10839/0.08439, loss_grounding_dice_7: 0.16928/0.15977, loss_grounding_ce_7: 0.00150/0.31434, loss_mask_ce_8: 0.25524/1.00703, loss_mask_bce_8: 0.18620/0.33218, loss_mask_dice_8: 0.38736/1.17498, loss_spatial_bce_8: 0.06924/0.12180, loss_spatial_dice_8: 0.10454/0.25517, loss_spatial_ce_8: 0.04223/0.19392, loss_grounding_bce_8: 0.11792/0.08863, loss_grounding_dice_8: 0.16710/0.16967, loss_grounding_ce_8: 0.03798/0.41184, loss_mask_ce_9: 2.04633/3.47038, loss_mask_bce_9: 0.20477/0.35934, loss_mask_dice_9: 0.55121/1.75773, loss_spatial_bce_9: 0.39665/0.35390, loss_spatial_dice_9: 0.88923/0.79278, loss_spatial_ce_9: 1.39687/1.38497, loss_grounding_bce_9: 0.06741/0.10097, loss_grounding_dice_9: 0.18998/0.24193, loss_grounding_ce_9: 4.06454/0.66472] items per batch[64] items per second[0.37] total items[5843200] mini batches[ 91300] memory[4999] epoch remaining[0:01:26] WARNING:trainer.utils_trainer:Saving checkpoint... WARNING:trainer.utils_trainer:Finished saving checkpoint and model to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/focall_unicl_lang_v1.yaml_conf~/run_7/00091350. INFO:trainer.default_trainer:Evaluation start ... INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 11/79. Dataloading: 0.0017 s/iter. Inference: 0.3791 s/iter. Eval: 0.0793 s/iter. Total: 0.4602 s/iter. ETA=0:00:31 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 23/79. Dataloading: 0.0023 s/iter. Inference: 0.3707 s/iter. Eval: 0.0746 s/iter. Total: 0.4478 s/iter. ETA=0:00:25 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 35/79. Dataloading: 0.0025 s/iter. Inference: 0.3717 s/iter. Eval: 0.0732 s/iter. Total: 0.4475 s/iter. ETA=0:00:19 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 46/79. Dataloading: 0.0026 s/iter. Inference: 0.3774 s/iter. Eval: 0.0708 s/iter. Total: 0.4509 s/iter. ETA=0:00:14 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 57/79. Dataloading: 0.0026 s/iter. Inference: 0.3789 s/iter. Eval: 0.0701 s/iter. Total: 0.4518 s/iter. ETA=0:00:09 INFO:base_dir.pipeline.XDecoderPipeline:Task coco_2017_val_panoptic_with_sem_seg. Inference done 69/79. Dataloading: 0.0027 s/iter. Inference: 0.3740 s/iter. Eval: 0.0699 s/iter. Total: 0.4467 s/iter. ETA=0:00:04 INFO:datasets.evaluation.panoptic_evaluation:Writing all panoptic predictions to /tmp/panoptic_evalvflr2dbf ... INFO:datasets.evaluation.panoptic_evaluation:Panoptic Evaluation Results: | | PQ | SQ | RQ | #categories | |:------:|:------:|:------:|:------:|:-------------:| | All | 56.127 | 83.042 | 66.772 | 133 | | Things | 62.304 | 84.000 | 73.651 | 80 | | Stuff | 46.803 | 81.596 | 56.389 | 53 | INFO:detectron2.evaluation.coco_evaluation:Preparing results for COCO format ... INFO:detectron2.evaluation.coco_evaluation:Saving results to /mnt/output/exp_seem/mainzvision/seem_v1_focalt_enc6_fpn_dec10_lang_bs64_ep50_scw5.0_sdw2.0_smw2.0_nsm32_lr0.0001_ts10_fbTrue_flTrue_feTrue_spaTrue_grdTrue_iterbase_pn_v2_maxi5_qsn3_mc10/coco_instances_results.json INFO:detectron2.evaluation.coco_evaluation:Evaluating predictions with unofficial COCO API... Loading and preparing results... DONE (t=0.55s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *bbox* INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 13.76 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.40 seconds. INFO:detectron2.evaluation.coco_evaluation:Evaluation results for bbox: | AP | AP50 | AP75 | APs | APm | APl | |:-----:|:------:|:------:|:-----:|:-----:|:-----:| | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | INFO:detectron2.evaluation.coco_evaluation:Per-category bbox AP: | category | AP | category | AP | category | AP | |:--------------|:------|:-------------|:------|:---------------|:------| | person | 0.000 | bicycle | 0.000 | car | 0.000 | | motorcycle | 0.000 | airplane | 0.000 | bus | 0.000 | | train | 0.000 | truck | 0.000 | boat | 0.000 | | traffic light | 0.000 | fire hydrant | 0.000 | stop sign | 0.000 | | parking meter | 0.000 | bench | 0.000 | bird | 0.000 | | cat | 0.000 | dog | 0.000 | horse | 0.000 | | sheep | 0.000 | cow | 0.000 | elephant | 0.000 | | bear | 0.000 | zebra | 0.000 | giraffe | 0.000 | | backpack | 0.000 | umbrella | 0.000 | handbag | 0.000 | | tie | 0.000 | suitcase | 0.000 | frisbee | 0.000 | | skis | 0.000 | snowboard | 0.000 | sports ball | 0.000 | | kite | 0.000 | baseball bat | 0.000 | baseball glove | 0.000 | | skateboard | 0.000 | surfboard | 0.000 | tennis racket | 0.000 | | bottle | 0.000 | wine glass | 0.000 | cup | 0.000 | | fork | 0.000 | knife | 0.000 | spoon | 0.000 | | bowl | 0.000 | banana | 0.000 | apple | 0.000 | | sandwich | 0.000 | orange | 0.000 | broccoli | 0.000 | | carrot | 0.000 | hot dog | 0.000 | pizza | 0.000 | | donut | 0.000 | cake | 0.000 | chair | 0.000 | | couch | 0.000 | potted plant | 0.000 | bed | 0.000 | | dining table | 0.000 | toilet | 0.000 | tv | 0.000 | | laptop | 0.000 | mouse | 0.000 | remote | 0.000 | | keyboard | 0.000 | cell phone | 0.000 | microwave | 0.000 | | oven | 0.000 | toaster | 0.000 | sink | 0.000 | | refrigerator | 0.000 | book | 0.000 | clock | 0.000 | | vase | 0.000 | scissors | 0.000 | teddy bear | 0.000 | | hair drier | 0.000 | toothbrush | 0.000 | | | Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Loading and preparing results... INFO:detectron2.evaluation.fast_eval_api:Evaluate annotation type *segm* DONE (t=4.89s) creating index... index created! INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.evaluate() finished in 20.69 seconds. INFO:detectron2.evaluation.fast_eval_api:Accumulating evaluation results... INFO:detectron2.evaluation.fast_eval_api:COCOeval_opt.accumulate() finished in 1.52 seconds. Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.701 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.501 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.265 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.504 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.682 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.355 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.557 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.578 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.389 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.615 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772 INFO:detectron2.evaluation.coco_evaluation:Evaluation results for segm: | AP | AP50 | AP75 | APs | APm | APl | |:------:|:------:|:------:|:------:|:------:|:------:| | 46.326 | 70.122 | 50.133 | 26.453 | 50.363 | 68.152 | INFO:detectron2.evaluation.coco_evaluation:Per-category segm AP: | category | AP | category | AP | category | AP | |:--------------|:-------|:-------------|:-------|:---------------|:-------| | person | 49.779 | bicycle | 23.316 | car | 44.172 | | motorcycle | 42.855 | airplane | 61.919 | bus | 71.397 | | train | 75.358 | truck | 43.923 | boat | 31.856 | | traffic light | 29.687 | fire hydrant | 72.344 | stop sign | 69.531 | | parking meter | 54.036 | bench | 27.226 | bird | 35.685 | | cat | 77.155 | dog | 71.784 | horse | 51.160 | | sheep | 55.044 | cow | 57.657 | elephant | 66.492 | | bear | 80.043 | zebra | 66.433 | giraffe | 62.466 | | backpack | 25.306 | umbrella | 56.793 | handbag | 24.583 | | tie | 41.667 | suitcase | 51.641 | frisbee | 69.746 | | skis | 8.529 | snowboard | 34.559 | sports ball | 51.040 | | kite | 38.387 | baseball bat | 38.796 | baseball glove | 50.787 | | skateboard | 43.556 | surfboard | 45.467 | tennis racket | 63.828 | | bottle | 43.138 | wine glass | 38.904 | cup | 51.958 | | fork | 27.306 | knife | 25.344 | spoon | 22.949 | | bowl | 40.639 | banana | 22.806 | apple | 28.987 | | sandwich | 49.410 | orange | 32.058 | broccoli | 25.189 | | carrot | 23.792 | hot dog | 32.367 | pizza | 54.266 | | donut | 56.996 | cake | 49.592 | chair | 29.301 | | couch | 46.085 | potted plant | 23.606 | bed | 44.481 | | dining table | 15.824 | toilet | 70.578 | tv | 68.033 | | laptop | 71.590 | mouse | 64.546 | remote | 44.774 | | keyboard | 58.068 | cell phone | 46.498 | microwave | 67.850 | | oven | 33.561 | toaster | 49.983 | sink | 44.896 | | refrigerator | 70.578 | book | 15.443 | clock | 54.549 | | vase | 41.686 | scissors | 35.916 | teddy bear | 58.140 | | hair drier | 29.568 | toothbrush | 28.800 | | | INFO:datasets.evaluation.segmentation_evaluation:OrderedDict([('sem_seg', {'mIoU': 65.83206022719531, 'fwIoU': 71.72147034071547, 'IoU-person': 88.79282929470298, 'IoU-bicycle': 70.62701468955258, 'IoU-car': 73.53579980471467, 'IoU-motorcycle': 87.45308755288673, 'IoU-airplane': 86.94681073671633, 'IoU-bus': 87.47371677931554, 'IoU-train': 87.80683592633966, 'IoU-truck': 69.99848158901457, 'IoU-boat': 75.12946302913782, 'IoU-traffic light': 78.92107046944206, 'IoU-fire hydrant': 93.2171779904728, 'IoU-stop sign': 85.80805409813404, 'IoU-parking meter': 85.15422373159308, 'IoU-bench': 61.037368552690694, 'IoU-bird': 73.97669811759802, 'IoU-cat': 89.51176472988246, 'IoU-dog': 83.55967328833735, 'IoU-horse': 88.69097145203956, 'IoU-sheep': 87.44568025175941, 'IoU-cow': 90.4465987741394, 'IoU-elephant': 89.49703078707381, 'IoU-bear': 88.51871469325341, 'IoU-zebra': 85.108465220943, 'IoU-giraffe': 89.44967195977233, 'IoU-backpack': 52.915967729051836, 'IoU-umbrella': 81.77664973492004, 'IoU-handbag': 49.72139813968013, 'IoU-tie': 76.1538860205925, 'IoU-suitcase': 78.0727973463154, 'IoU-frisbee': 84.78605751145682, 'IoU-skis': 58.62264301408211, 'IoU-snowboard': 71.10847192614459, 'IoU-sports ball': 78.81204695146631, 'IoU-kite': 79.70054355550067, 'IoU-baseball bat': 68.8154427162992, 'IoU-baseball glove': 77.89186217120152, 'IoU-skateboard': 86.27760689149811, 'IoU-surfboard': 86.25574296742802, 'IoU-tennis racket': 90.88255020317987, 'IoU-bottle': 70.03065712966541, 'IoU-wine glass': 82.61685219085741, 'IoU-cup': 71.07990584478414, 'IoU-fork': 71.35858340299647, 'IoU-knife': 65.2639306568332, 'IoU-spoon': 60.99070459552429, 'IoU-bowl': 59.90758235200615, 'IoU-banana': 83.27303483788627, 'IoU-apple': 58.432776470643766, 'IoU-sandwich': 70.32608251881612, 'IoU-orange': 79.88283803274192, 'IoU-broccoli': 69.64261049606284, 'IoU-carrot': 65.20106815739182, 'IoU-hot dog': 61.33703304601242, 'IoU-pizza': 81.05279346920052, 'IoU-donut': 57.99120883410216, 'IoU-cake': 79.72938461493915, 'IoU-chair': 62.96073428950576, 'IoU-couch': 69.85652317377799, 'IoU-potted plant': 43.712226236869014, 'IoU-bed': 73.70452097473303, 'IoU-dining table': 54.10699173396527, 'IoU-toilet': 86.98187239798943, 'IoU-tv': 76.91471508868402, 'IoU-laptop': 80.57545176435681, 'IoU-mouse': 76.28415093025978, 'IoU-remote': 67.43126114656597, 'IoU-keyboard': 63.465470664616944, 'IoU-cell phone': 80.17755058690975, 'IoU-microwave': 79.42644692855836, 'IoU-oven': 74.4820565658571, 'IoU-toaster': 85.85110007005997, 'IoU-sink': 68.71751342553046, 'IoU-refrigerator': 83.7232319841939, 'IoU-book': 55.469459368345476, 'IoU-clock': 72.55771380209652, 'IoU-vase': 64.3370797807479, 'IoU-scissors': 87.88632575900587, 'IoU-teddy bear': 82.68242280353975, 'IoU-hair drier': 48.46149441740624, 'IoU-toothbrush': 76.66158871752991, 'IoU-banner': 34.90877229987912, 'IoU-blanket': 17.616283334322397, 'IoU-bridge': 38.019693227102, 'IoU-cardboard': 45.970281029759434, 'IoU-counter': 31.721131994300265, 'IoU-curtain': 73.1837138671486, 'IoU-door-stuff': 48.35458630025208, 'IoU-floor-wood': 63.532484499776906, 'IoU-flower': 44.55855562591901, 'IoU-fruit': 48.43482735865472, 'IoU-gravel': 29.580525894964392, 'IoU-house': 25.32802550467815, 'IoU-light': 44.30518559706187, 'IoU-mirror-stuff': 60.385768899319594, 'IoU-net': 43.251617610131674, 'IoU-pillow': 22.32997137978566, 'IoU-platform': 28.779851204829164, 'IoU-playingfield': 71.9056683993638, 'IoU-railroad': 64.98171848124284, 'IoU-river': 54.05187049833229, 'IoU-road': 67.1587896084882, 'IoU-roof': 19.500575035943555, 'IoU-sand': 65.9379410086135, 'IoU-sea': 85.1464423269655, 'IoU-shelf': 38.59444959375231, 'IoU-snow': 92.23485919726609, 'IoU-stairs': 34.00114999422039, 'IoU-tent': 11.055692421160167, 'IoU-towel': 65.70970289318501, 'IoU-wall-brick': 52.25399067071993, 'IoU-wall-stone': 27.72960136317905, 'IoU-wall-tile': 70.21536678889656, 'IoU-wall-wood': 46.88036567770696, 'IoU-water-other': 26.595797308238193, 'IoU-window-blind': 49.42037346731613, 'IoU-window-other': 51.17564239066073, 'IoU-tree-merged': 82.01490025647256, 'IoU-fence-merged': 54.763683781678566, 'IoU-ceiling-merged': 67.68523332071537, 'IoU-sky-other-merged': 93.87084602010778, 'IoU-cabinet-merged': 63.273144375009615, 'IoU-table-merged': 41.488317269893635, 'IoU-floor-other-merged': 55.214313396862046, 'IoU-pavement-merged': 56.97238947136963, 'IoU-mountain-merged': 59.07731339089703, 'IoU-grass-merged': 73.16899257421923, 'IoU-dirt-merged': 47.61307447304567, 'IoU-paper-merged': 37.21964172988619, 'IoU-food-other-merged': 44.188902369459655, 'IoU-building-other-merged': 59.08086348300326, 'IoU-rock-merged': 64.73982035058526, 'IoU-wall-other-merged': 68.34075945048966, 'IoU-rug-merged': 67.69472209024708, 'mACC': 77.41030386949984, 'pACC': 82.39562087984439, 'ACC-person': 93.03197582108986, 'ACC-bicycle': 79.38687040866654, 'ACC-car': 86.97065483815652, 'ACC-motorcycle': 91.83013709536395, 'ACC-airplane': 91.18875091342373, 'ACC-bus': 93.92981768527481, 'ACC-train': 95.31537139171267, 'ACC-truck': 80.53501100489514, 'ACC-boat': 85.11818828838935, 'ACC-traffic light': 91.36227425116658, 'ACC-fire hydrant': 95.9372744857742, 'ACC-stop sign': 88.58894010604374, 'ACC-parking meter': 88.29082303785427, 'ACC-bench': 73.74769959190256, 'ACC-bird': 78.23590211083149, 'ACC-cat': 93.99199941477418, 'ACC-dog': 86.27030195071026, 'ACC-horse': 93.22597198175694, 'ACC-sheep': 91.82880440752456, 'ACC-cow': 93.83973765472788, 'ACC-elephant': 91.5678990995594, 'ACC-bear': 90.3430139676846, 'ACC-zebra': 87.15734460671901, 'ACC-giraffe': 93.29994236988239, 'ACC-backpack': 72.57331224090937, 'ACC-umbrella': 86.22405267122468, 'ACC-handbag': 69.98066086731885, 'ACC-tie': 84.39785950579363, 'ACC-suitcase': 84.8856650336575, 'ACC-frisbee': 94.25527272727273, 'ACC-skis': 72.63549542244043, 'ACC-snowboard': 81.53118953736131, 'ACC-sports ball': 88.56120982830444, 'ACC-kite': 86.10228065008974, 'ACC-baseball bat': 87.64844270954303, 'ACC-baseball glove': 92.37910983333694, 'ACC-skateboard': 90.89875713112289, 'ACC-surfboard': 92.36507749911156, 'ACC-tennis racket': 94.81310451791657, 'ACC-bottle': 84.8987857520847, 'ACC-wine glass': 90.79082881543398, 'ACC-cup': 89.07722272530746, 'ACC-fork': 83.268430157329, 'ACC-knife': 77.68282406389683, 'ACC-spoon': 77.83141592374751, 'ACC-bowl': 72.53771486259556, 'ACC-banana': 90.80828419743911, 'ACC-apple': 74.7153719371365, 'ACC-sandwich': 81.02367357970817, 'ACC-orange': 90.23439249395308, 'ACC-broccoli': 80.78207863720866, 'ACC-carrot': 77.61205868954245, 'ACC-hot dog': 67.77847864074708, 'ACC-pizza': 89.31967464977176, 'ACC-donut': 65.20527586847483, 'ACC-cake': 88.50871123296149, 'ACC-chair': 79.23682868017664, 'ACC-couch': 79.2375480831923, 'ACC-potted plant': 60.29363113294707, 'ACC-bed': 86.60477450335632, 'ACC-dining table': 75.31551661266849, 'ACC-toilet': 92.13498990363811, 'ACC-tv': 89.43465067218531, 'ACC-laptop': 93.55796352392841, 'ACC-mouse': 86.95532973316162, 'ACC-remote': 71.98886005370011, 'ACC-keyboard': 69.59002885230663, 'ACC-cell phone': 88.68873342745471, 'ACC-microwave': 84.52320131500034, 'ACC-oven': 92.21162671803191, 'ACC-toaster': 91.39854110498297, 'ACC-sink': 77.77312085238331, 'ACC-refrigerator': 94.19303284197835, 'ACC-book': 75.54768709198397, 'ACC-clock': 77.25156477183297, 'ACC-vase': 73.03966460796461, 'ACC-scissors': 93.51121119466711, 'ACC-teddy bear': 88.02514748190116, 'ACC-hair drier': 60.226852247175025, 'ACC-toothbrush': 84.80455177206393, 'ACC-banner': 77.53283644347465, 'ACC-blanket': 24.28767877905795, 'ACC-bridge': 55.6422244344443, 'ACC-cardboard': 62.11443943580049, 'ACC-counter': 55.98274989727614, 'ACC-curtain': 83.5139213452517, 'ACC-door-stuff': 68.88851145368251, 'ACC-floor-wood': 81.62709157483602, 'ACC-flower': 64.19283653882363, 'ACC-fruit': 68.23439016733317, 'ACC-gravel': 39.943831680296, 'ACC-house': 31.74506594795565, 'ACC-light': 63.48261110898442, 'ACC-mirror-stuff': 76.54801466199456, 'ACC-net': 66.63701383207739, 'ACC-pillow': 43.70777707085401, 'ACC-platform': 46.921499790777496, 'ACC-playingfield': 91.58418919433133, 'ACC-railroad': 82.72881669177481, 'ACC-river': 72.64071458883208, 'ACC-road': 86.64713762601444, 'ACC-roof': 26.07365937733012, 'ACC-sand': 70.68839626186848, 'ACC-sea': 91.80102901503138, 'ACC-shelf': 56.09135243477601, 'ACC-snow': 95.69190923352808, 'ACC-stairs': 58.296201500292625, 'ACC-tent': 14.324727846063126, 'ACC-towel': 81.94344401538825, 'ACC-wall-brick': 69.99804353900271, 'ACC-wall-stone': 33.68750867270526, 'ACC-wall-tile': 84.97551479636137, 'ACC-wall-wood': 62.56441812851501, 'ACC-water-other': 43.02782459553684, 'ACC-window-blind': 62.098981699589906, 'ACC-window-other': 72.81618102276127, 'ACC-tree-merged': 89.83083655150052, 'ACC-fence-merged': 72.75704314947649, 'ACC-ceiling-merged': 82.6107235740194, 'ACC-sky-other-merged': 97.17457747715945, 'ACC-cabinet-merged': 78.1970398520475, 'ACC-table-merged': 55.133392892333376, 'ACC-floor-other-merged': 65.7619339816924, 'ACC-pavement-merged': 69.09659485235426, 'ACC-mountain-merged': 70.28352075737276, 'ACC-grass-merged': 84.92526878597737, 'ACC-dirt-merged': 66.65427456229473, 'ACC-paper-merged': 50.13320002938202, 'ACC-food-other-merged': 59.96968265024329, 'ACC-building-other-merged': 73.57441121732222, 'ACC-rock-merged': 83.60871409185154, 'ACC-wall-other-merged': 82.51023425204335, 'ACC-rug-merged': 82.7999454984784})]) INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 11/25. Dataloading: 0.3185 s/iter. Inference: 0.1737 s/iter. Eval: 0.0000 s/iter. Total: 0.4923 s/iter. ETA=0:00:06 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 17/25. Dataloading: 0.3368 s/iter. Inference: 0.3415 s/iter. Eval: 0.0000 s/iter. Total: 0.6785 s/iter. ETA=0:00:05 INFO:base_dir.pipeline.XDecoderPipeline:Task pascalvoc_val_Point. Inference done 21/25. Dataloading: 0.3563 s/iter. Inference: 0.5350 s/iter. Eval: 0.0000 s/iter. Total: 0.8914 s/iter. ETA=0:00:03 INFO:datasets.evaluation.interactive_evaluation:{'noc@0.5': 1.3362598770851624, 'noc@0.8': 2.279484928299678, 'noc@0.85': 2.6619841966637403, 'noc@0.9': 3.4375182908984487, 'miou@iter1': 0.8724096944048142} INFO:base_dir.pipeline.XDecoderPipeline:Task refcocog_val_umd. Inference done 11/21. Dataloading: 0.0013 s/iter. Inference: 0.1446 s/iter. Eval: 0.0010 s/iter. Total: 0.1469 s/iter. ETA=0:00:01 INFO:datasets.evaluation.grounding_evaluation:{'precision@0.5': 75.9813461303711, 'precision@0.6': 73.33851623535156, 'precision@0.7': 69.52973175048828, 'precision@0.8': 60.979400634765625, 'precision@0.9': 34.006996154785156, 'cIoU': 62.41725158691406, 'mIoU': 67.75566864013672} INFO:trainer.default_trainer:{'coco_2017_val_panoptic_with_sem_seg/coco_panoptic_seg': OrderedDict([('panoptic_seg', {'PQ': 56.12689072399445, 'SQ': 83.04200955844327, 'RQ': 66.77178807468887, 'PQ_th': 62.30398602583751, 'SQ_th': 84.00027721412495, 'RQ_th': 73.65071400134501, 'PQ_st': 46.802973287250204, 'SQ_st': 81.59556781401807, 'RQ_st': 56.38850365709472}), ('bbox', {'AP': 0.0, 'AP50': 0.0, 'AP75': 0.0, 'APs': 0.0, 'APm': 0.0, 'APl': 0.0, 'AP-person': 0.0, 'AP-bicycle': 0.0, 'AP-car': 0.0, 'AP-motorcycle': 0.0, 'AP-airplane': 0.0, 'AP-bus': 0.0, 'AP-train': 0.0, 'AP-truck': 0.0, 'AP-boat': 0.0, 'AP-traffic light': 0.0, 'AP-fire hydrant': 0.0, 'AP-stop sign': 0.0, 'AP-parking meter': 0.0, 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78.1970398520475, 'ACC-table-merged': 55.133392892333376, 'ACC-floor-other-merged': 65.7619339816924, 'ACC-pavement-merged': 69.09659485235426, 'ACC-mountain-merged': 70.28352075737276, 'ACC-grass-merged': 84.92526878597737, 'ACC-dirt-merged': 66.65427456229473, 'ACC-paper-merged': 50.13320002938202, 'ACC-food-other-merged': 59.96968265024329, 'ACC-building-other-merged': 73.57441121732222, 'ACC-rock-merged': 83.60871409185154, 'ACC-wall-other-merged': 82.51023425204335, 'ACC-rug-merged': 82.7999454984784})]), 'pascalvoc_val_Point/interactive': {'interactive': {'noc@0.5': 1.3362598770851624, 'noc@0.8': 2.279484928299678, 'noc@0.85': 2.6619841966637403, 'noc@0.9': 3.4375182908984487, 'miou@iter1': 0.8724096944048142}}, 'refcocog_val_umd/grounding_refcoco': {'grounding': {'precision@0.5': 75.9813461303711, 'precision@0.6': 73.33851623535156, 'precision@0.7': 69.52973175048828, 'precision@0.8': 60.979400634765625, 'precision@0.9': 34.006996154785156, 'cIoU': 62.41725158691406, 'mIoU': 67.75566864013672}}} INFO:trainer.default_trainer:This epoch takes 0:56:21.175555 INFO:trainer.default_trainer:PROGRESS: 100.00% INFO:trainer.default_trainer:Config files are at ['configs/seem/focall_unicl_lang_v1.yaml']