| 2023-04-02 19:19:00,193 INFO **********************Start logging********************** | |
| 2023-04-02 19:19:00,193 INFO CUDA_VISIBLE_DEVICES=ALL | |
| 2023-04-02 19:19:00,194 INFO total_batch_size: 16 | |
| 2023-04-02 19:19:00,194 INFO cfg_file cfgs/scannet_models/CAGroup3D.yaml | |
| 2023-04-02 19:19:00,195 INFO batch_size 16 | |
| 2023-04-02 19:19:00,196 INFO epochs 9 | |
| 2023-04-02 19:19:00,196 INFO workers 4 | |
| 2023-04-02 19:19:00,197 INFO extra_tag cagroup3d-win10-scannet-train | |
| 2023-04-02 19:19:00,197 INFO ckpt ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth | |
| 2023-04-02 19:19:00,198 INFO pretrained_model ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth | |
| 2023-04-02 19:19:00,199 INFO launcher pytorch | |
| 2023-04-02 19:19:00,199 INFO tcp_port 18888 | |
| 2023-04-02 19:19:00,200 INFO sync_bn False | |
| 2023-04-02 19:19:00,200 INFO fix_random_seed True | |
| 2023-04-02 19:19:00,201 INFO ckpt_save_interval 1 | |
| 2023-04-02 19:19:00,201 INFO max_ckpt_save_num 30 | |
| 2023-04-02 19:19:00,202 INFO merge_all_iters_to_one_epoch False | |
| 2023-04-02 19:19:00,202 INFO set_cfgs None | |
| 2023-04-02 19:19:00,203 INFO max_waiting_mins 0 | |
| 2023-04-02 19:19:00,203 INFO start_epoch 0 | |
| 2023-04-02 19:19:00,204 INFO num_epochs_to_eval 0 | |
| 2023-04-02 19:19:00,204 INFO save_to_file False | |
| 2023-04-02 19:19:00,205 INFO cfg.ROOT_DIR: C:\CITYU\CS5182\proj\CAGroup3D | |
| 2023-04-02 19:19:00,205 INFO cfg.LOCAL_RANK: 0 | |
| 2023-04-02 19:19:00,206 INFO cfg.CLASS_NAMES: ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'garbagebin'] | |
| 2023-04-02 19:19:00,207 INFO | |
| cfg.DATA_CONFIG = edict() | |
| 2023-04-02 19:19:00,207 INFO cfg.DATA_CONFIG.DATASET: ScannetDataset | |
| 2023-04-02 19:19:00,208 INFO cfg.DATA_CONFIG.DATA_PATH: ../data/scannet_data/ScanNetV2 | |
| 2023-04-02 19:19:00,208 INFO cfg.DATA_CONFIG.PROCESSED_DATA_TAG: scannet_processed_data_v0_5_0 | |
| 2023-04-02 19:19:00,209 INFO cfg.DATA_CONFIG.POINT_CLOUD_RANGE: [-40, -40, -10, 40, 40, 10] | |
| 2023-04-02 19:19:00,209 INFO | |
| cfg.DATA_CONFIG.DATA_SPLIT = edict() | |
| 2023-04-02 19:19:00,210 INFO cfg.DATA_CONFIG.DATA_SPLIT.train: train | |
| 2023-04-02 19:19:00,210 INFO cfg.DATA_CONFIG.DATA_SPLIT.test: val | |
| 2023-04-02 19:19:00,211 INFO | |
| cfg.DATA_CONFIG.REPEAT = edict() | |
| 2023-04-02 19:19:00,211 INFO cfg.DATA_CONFIG.REPEAT.train: 10 | |
| 2023-04-02 19:19:00,212 INFO cfg.DATA_CONFIG.REPEAT.test: 1 | |
| 2023-04-02 19:19:00,213 INFO | |
| cfg.DATA_CONFIG.INFO_PATH = edict() | |
| 2023-04-02 19:19:00,213 INFO cfg.DATA_CONFIG.INFO_PATH.train: ['scannet_infos_train.pkl'] | |
| 2023-04-02 19:19:00,214 INFO cfg.DATA_CONFIG.INFO_PATH.test: ['scannet_infos_val.pkl'] | |
| 2023-04-02 19:19:00,214 INFO cfg.DATA_CONFIG.GET_ITEM_LIST: ['points', 'instance_mask', 'semantic_mask'] | |
| 2023-04-02 19:19:00,215 INFO cfg.DATA_CONFIG.FILTER_EMPTY_BOXES_FOR_TRAIN: True | |
| 2023-04-02 19:19:00,215 INFO | |
| cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN = edict() | |
| 2023-04-02 19:19:00,216 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.DISABLE_AUG_LIST: ['placeholder'] | |
| 2023-04-02 19:19:00,216 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TRAIN.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}, {'NAME': 'point_seg_class_mapping', 'valid_cat_ids': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39], 'max_cat_id': 40}, {'NAME': 'random_world_flip', 'ALONG_AXIS_LIST': ['x', 'y']}, {'NAME': 'random_world_rotation', 'WORLD_ROT_ANGLE': [-0.087266, 0.087266]}, {'NAME': 'random_world_scaling', 'WORLD_SCALE_RANGE': [0.9, 1.1]}, {'NAME': 'random_world_translation', 'ALONG_AXIS_LIST': ['x', 'y', 'z'], 'NOISE_TRANSLATE_STD': 0.1}] | |
| 2023-04-02 19:19:00,217 INFO | |
| cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST = edict() | |
| 2023-04-02 19:19:00,218 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.DISABLE_AUG_LIST: ['placeholder'] | |
| 2023-04-02 19:19:00,218 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR_TEST.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}] | |
| 2023-04-02 19:19:00,219 INFO | |
| cfg.DATA_CONFIG.DATA_AUGMENTOR = edict() | |
| 2023-04-02 19:19:00,219 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.DISABLE_AUG_LIST: ['placeholder'] | |
| 2023-04-02 19:19:00,220 INFO cfg.DATA_CONFIG.DATA_AUGMENTOR.AUG_CONFIG_LIST: [{'NAME': 'global_alignment', 'rotation_axis': 2}] | |
| 2023-04-02 19:19:00,220 INFO | |
| cfg.DATA_CONFIG.POINT_FEATURE_ENCODING = edict() | |
| 2023-04-02 19:19:00,221 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.encoding_type: absolute_coordinates_encoding | |
| 2023-04-02 19:19:00,222 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.used_feature_list: ['x', 'y', 'z', 'r', 'g', 'b'] | |
| 2023-04-02 19:19:00,222 INFO cfg.DATA_CONFIG.POINT_FEATURE_ENCODING.src_feature_list: ['x', 'y', 'z', 'r', 'g', 'b'] | |
| 2023-04-02 19:19:00,223 INFO cfg.DATA_CONFIG.DATA_PROCESSOR: [{'NAME': 'mask_points_and_boxes_outside_range', 'REMOVE_OUTSIDE_BOXES': True}] | |
| 2023-04-02 19:19:00,223 INFO cfg.DATA_CONFIG._BASE_CONFIG_: cfgs/dataset_configs/scannet_dataset.yaml | |
| 2023-04-02 19:19:00,224 INFO cfg.VOXEL_SIZE: 0.02 | |
| 2023-04-02 19:19:00,224 INFO cfg.N_CLASSES: 18 | |
| 2023-04-02 19:19:00,224 INFO cfg.SEMANTIC_THR: 0.15 | |
| 2023-04-02 19:19:00,225 INFO | |
| cfg.MODEL = edict() | |
| 2023-04-02 19:19:00,225 INFO cfg.MODEL.NAME: CAGroup3D | |
| 2023-04-02 19:19:00,226 INFO cfg.MODEL.VOXEL_SIZE: 0.02 | |
| 2023-04-02 19:19:00,226 INFO cfg.MODEL.SEMANTIC_MIN_THR: 0.05 | |
| 2023-04-02 19:19:00,227 INFO cfg.MODEL.SEMANTIC_ITER_VALUE: 0.02 | |
| 2023-04-02 19:19:00,227 INFO cfg.MODEL.SEMANTIC_THR: 0.15 | |
| 2023-04-02 19:19:00,227 INFO | |
| cfg.MODEL.BACKBONE_3D = edict() | |
| 2023-04-02 19:19:00,228 INFO cfg.MODEL.BACKBONE_3D.NAME: BiResNet | |
| 2023-04-02 19:19:00,228 INFO cfg.MODEL.BACKBONE_3D.IN_CHANNELS: 3 | |
| 2023-04-02 19:19:00,229 INFO cfg.MODEL.BACKBONE_3D.OUT_CHANNELS: 64 | |
| 2023-04-02 19:19:00,229 INFO | |
| cfg.MODEL.DENSE_HEAD = edict() | |
| 2023-04-02 19:19:00,230 INFO cfg.MODEL.DENSE_HEAD.NAME: CAGroup3DHead | |
| 2023-04-02 19:19:00,230 INFO cfg.MODEL.DENSE_HEAD.IN_CHANNELS: [64, 128, 256, 512] | |
| 2023-04-02 19:19:00,231 INFO cfg.MODEL.DENSE_HEAD.OUT_CHANNELS: 64 | |
| 2023-04-02 19:19:00,231 INFO cfg.MODEL.DENSE_HEAD.SEMANTIC_THR: 0.15 | |
| 2023-04-02 19:19:00,232 INFO cfg.MODEL.DENSE_HEAD.VOXEL_SIZE: 0.02 | |
| 2023-04-02 19:19:00,233 INFO cfg.MODEL.DENSE_HEAD.N_CLASSES: 18 | |
| 2023-04-02 19:19:00,233 INFO cfg.MODEL.DENSE_HEAD.N_REG_OUTS: 6 | |
| 2023-04-02 19:19:00,233 INFO cfg.MODEL.DENSE_HEAD.CLS_KERNEL: 9 | |
| 2023-04-02 19:19:00,234 INFO cfg.MODEL.DENSE_HEAD.WITH_YAW: False | |
| 2023-04-02 19:19:00,234 INFO cfg.MODEL.DENSE_HEAD.USE_SEM_SCORE: False | |
| 2023-04-02 19:19:00,235 INFO cfg.MODEL.DENSE_HEAD.EXPAND_RATIO: 3 | |
| 2023-04-02 19:19:00,235 INFO | |
| cfg.MODEL.DENSE_HEAD.ASSIGNER = edict() | |
| 2023-04-02 19:19:00,236 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.NAME: CAGroup3DAssigner | |
| 2023-04-02 19:19:00,237 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.LIMIT: 27 | |
| 2023-04-02 19:19:00,237 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.TOPK: 18 | |
| 2023-04-02 19:19:00,237 INFO cfg.MODEL.DENSE_HEAD.ASSIGNER.N_SCALES: 4 | |
| 2023-04-02 19:19:00,238 INFO | |
| cfg.MODEL.DENSE_HEAD.LOSS_OFFSET = edict() | |
| 2023-04-02 19:19:00,239 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.NAME: SmoothL1Loss | |
| 2023-04-02 19:19:00,239 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.BETA: 0.04 | |
| 2023-04-02 19:19:00,240 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.REDUCTION: sum | |
| 2023-04-02 19:19:00,240 INFO cfg.MODEL.DENSE_HEAD.LOSS_OFFSET.LOSS_WEIGHT: 1.0 | |
| 2023-04-02 19:19:00,240 INFO | |
| cfg.MODEL.DENSE_HEAD.LOSS_BBOX = edict() | |
| 2023-04-02 19:19:00,241 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.NAME: IoU3DLoss | |
| 2023-04-02 19:19:00,242 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.WITH_YAW: False | |
| 2023-04-02 19:19:00,242 INFO cfg.MODEL.DENSE_HEAD.LOSS_BBOX.LOSS_WEIGHT: 1.0 | |
| 2023-04-02 19:19:00,243 INFO | |
| cfg.MODEL.DENSE_HEAD.NMS_CONFIG = edict() | |
| 2023-04-02 19:19:00,243 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.SCORE_THR: 0.01 | |
| 2023-04-02 19:19:00,244 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.NMS_PRE: 1000 | |
| 2023-04-02 19:19:00,244 INFO cfg.MODEL.DENSE_HEAD.NMS_CONFIG.IOU_THR: 0.5 | |
| 2023-04-02 19:19:00,245 INFO | |
| cfg.MODEL.ROI_HEAD = edict() | |
| 2023-04-02 19:19:00,246 INFO cfg.MODEL.ROI_HEAD.NAME: CAGroup3DRoIHead | |
| 2023-04-02 19:19:00,246 INFO cfg.MODEL.ROI_HEAD.NUM_CLASSES: 18 | |
| 2023-04-02 19:19:00,247 INFO cfg.MODEL.ROI_HEAD.MIDDLE_FEATURE_SOURCE: [3] | |
| 2023-04-02 19:19:00,247 INFO cfg.MODEL.ROI_HEAD.GRID_SIZE: 7 | |
| 2023-04-02 19:19:00,247 INFO cfg.MODEL.ROI_HEAD.VOXEL_SIZE: 0.02 | |
| 2023-04-02 19:19:00,248 INFO cfg.MODEL.ROI_HEAD.COORD_KEY: 2 | |
| 2023-04-02 19:19:00,248 INFO cfg.MODEL.ROI_HEAD.MLPS: [[64, 128, 128]] | |
| 2023-04-02 19:19:00,249 INFO cfg.MODEL.ROI_HEAD.CODE_SIZE: 6 | |
| 2023-04-02 19:19:00,249 INFO cfg.MODEL.ROI_HEAD.ENCODE_SINCOS: False | |
| 2023-04-02 19:19:00,250 INFO cfg.MODEL.ROI_HEAD.ROI_PER_IMAGE: 128 | |
| 2023-04-02 19:19:00,250 INFO cfg.MODEL.ROI_HEAD.ROI_FG_RATIO: 0.9 | |
| 2023-04-02 19:19:00,251 INFO cfg.MODEL.ROI_HEAD.REG_FG_THRESH: 0.3 | |
| 2023-04-02 19:19:00,251 INFO cfg.MODEL.ROI_HEAD.ROI_CONV_KERNEL: 5 | |
| 2023-04-02 19:19:00,251 INFO cfg.MODEL.ROI_HEAD.ENLARGE_RATIO: False | |
| 2023-04-02 19:19:00,252 INFO cfg.MODEL.ROI_HEAD.USE_IOU_LOSS: False | |
| 2023-04-02 19:19:00,252 INFO cfg.MODEL.ROI_HEAD.USE_GRID_OFFSET: False | |
| 2023-04-02 19:19:00,253 INFO cfg.MODEL.ROI_HEAD.USE_SIMPLE_POOLING: True | |
| 2023-04-02 19:19:00,253 INFO cfg.MODEL.ROI_HEAD.USE_CENTER_POOLING: True | |
| 2023-04-02 19:19:00,254 INFO | |
| cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS = edict() | |
| 2023-04-02 19:19:00,254 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_CLS_WEIGHT: 1.0 | |
| 2023-04-02 19:19:00,254 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_REG_WEIGHT: 1.0 | |
| 2023-04-02 19:19:00,255 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.RCNN_IOU_WEIGHT: 1.0 | |
| 2023-04-02 19:19:00,255 INFO cfg.MODEL.ROI_HEAD.LOSS_WEIGHTS.CODE_WEIGHT: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0] | |
| 2023-04-02 19:19:00,256 INFO | |
| cfg.MODEL.POST_PROCESSING = edict() | |
| 2023-04-02 19:19:00,256 INFO cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST: [0.25, 0.5] | |
| 2023-04-02 19:19:00,257 INFO cfg.MODEL.POST_PROCESSING.EVAL_METRIC: scannet | |
| 2023-04-02 19:19:00,257 INFO | |
| cfg.OPTIMIZATION = edict() | |
| 2023-04-02 19:19:00,258 INFO cfg.OPTIMIZATION.BATCH_SIZE_PER_GPU: 16 | |
| 2023-04-02 19:19:00,258 INFO cfg.OPTIMIZATION.NUM_EPOCHS: 1 | |
| 2023-04-02 19:19:00,259 INFO cfg.OPTIMIZATION.OPTIMIZER: adamW | |
| 2023-04-02 19:19:00,259 INFO cfg.OPTIMIZATION.LR: 0.001 | |
| 2023-04-02 19:19:00,259 INFO cfg.OPTIMIZATION.WEIGHT_DECAY: 0.0001 | |
| 2023-04-02 19:19:00,260 INFO cfg.OPTIMIZATION.DECAY_STEP_LIST: [7, 9] | |
| 2023-04-02 19:19:00,261 INFO cfg.OPTIMIZATION.LR_DECAY: 0.1 | |
| 2023-04-02 19:19:00,261 INFO cfg.OPTIMIZATION.GRAD_NORM_CLIP: 10 | |
| 2023-04-02 19:19:00,261 INFO cfg.OPTIMIZATION.PCT_START: 0.4 | |
| 2023-04-02 19:19:00,262 INFO cfg.OPTIMIZATION.DIV_FACTOR: 10 | |
| 2023-04-02 19:19:00,262 INFO cfg.OPTIMIZATION.LR_CLIP: 1e-07 | |
| 2023-04-02 19:19:00,263 INFO cfg.OPTIMIZATION.LR_WARMUP: False | |
| 2023-04-02 19:19:00,263 INFO cfg.OPTIMIZATION.WARMUP_EPOCH: 1 | |
| 2023-04-02 19:19:00,264 INFO cfg.TAG: CAGroup3D | |
| 2023-04-02 19:19:00,264 INFO cfg.EXP_GROUP_PATH: scannet_models | |
| 2023-04-02 19:19:00,295 INFO Loading SCANNET dataset | |
| 2023-04-02 19:19:00,413 INFO Total samples for SCANNET dataset: 1201 | |
| 2023-04-02 19:19:03,525 INFO ==> Loading parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU | |
| 2023-04-02 19:19:04,589 INFO ==> Checkpoint trained from version: pcdet+0.5.2+0000000 | |
| 2023-04-02 19:19:04,732 INFO ==> Done (loaded 838/838) | |
| 2023-04-02 19:19:04,914 INFO ==> Loading parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU | |
| 2023-04-02 19:19:06,073 INFO ==> Loading optimizer parameters from checkpoint ../output/scannet_models/CAGroup3D/cagroup3d-win10-scannet-train-good/ckpt/checkpoint_epoch_8.pth to CPU | |
| 2023-04-02 19:19:06,413 INFO ==> Done | |
| 2023-04-02 19:19:06,797 INFO DistributedDataParallel( | |
| (module): CAGroup3D( | |
| (vfe): None | |
| (backbone_3d): BiResNet( | |
| (conv1): Sequential( | |
| (0): MinkowskiConvolution(in=3, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (5): MinkowskiReLU() | |
| ) | |
| (relu): MinkowskiReLU() | |
| (layer1): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (layer2): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (layer3): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (layer4): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=256, out=512, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (compression3): Sequential( | |
| (0): MinkowskiConvolution(in=256, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (compression4): Sequential( | |
| (0): MinkowskiConvolution(in=512, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (down3): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (down4): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=256, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (4): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| (layer3_): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (layer4_): Sequential( | |
| (0): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| (1): BasicBlock( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| ) | |
| ) | |
| (layer5_): Sequential( | |
| (0): Bottleneck( | |
| (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv3): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm3): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| ) | |
| (layer5): Sequential( | |
| (0): Bottleneck( | |
| (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (conv3): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (norm3): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (relu): MinkowskiReLU() | |
| (downsample): Sequential( | |
| (0): MinkowskiConvolution(in=512, out=1024, kernel_size=[1, 1, 1], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| ) | |
| (spp): DAPPM( | |
| (scale1): Sequential( | |
| (0): MinkowskiAvgPooling(kernel_size=[5, 5, 5], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (scale2): Sequential( | |
| (0): MinkowskiAvgPooling(kernel_size=[9, 9, 9], stride=[4, 4, 4], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (scale3): Sequential( | |
| (0): MinkowskiAvgPooling(kernel_size=[17, 17, 17], stride=[8, 8, 8], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (scale4): Sequential( | |
| (0): MinkowskiAvgPooling(kernel_size=[33, 33, 33], stride=[16, 16, 16], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (scale0): Sequential( | |
| (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=1024, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (process1): Sequential( | |
| (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (process2): Sequential( | |
| (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (process3): Sequential( | |
| (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (process4): Sequential( | |
| (0): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (compression): Sequential( | |
| (0): MinkowskiBatchNorm(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=640, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (shortcut): Sequential( | |
| (0): MinkowskiBatchNorm(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiReLU() | |
| (2): MinkowskiConvolution(in=1024, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| ) | |
| (out): Sequential( | |
| (0): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiReLU() | |
| (3): MinkowskiConvolution(in=256, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (5): MinkowskiReLU() | |
| ) | |
| ) | |
| (map_to_bev_module): None | |
| (pfe): None | |
| (backbone_2d): None | |
| (dense_head): CAGroup3DHead( | |
| (loss_centerness): CrossEntropy() | |
| (loss_bbox): IoU3DLoss() | |
| (loss_cls): FocalLoss() | |
| (loss_sem): FocalLoss() | |
| (loss_offset): SmoothL1Loss() | |
| (offset_block): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| (3): MinkowskiConvolution(in=64, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (4): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (5): MinkowskiELU() | |
| (6): MinkowskiConvolution(in=64, out=3, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| ) | |
| (feature_offset): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (semantic_conv): MinkowskiConvolution(in=64, out=18, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (centerness_conv): MinkowskiConvolution(in=64, out=1, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (reg_conv): MinkowskiConvolution(in=64, out=6, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (cls_conv): MinkowskiConvolution(in=64, out=18, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (scales): ModuleList( | |
| (0): Scale() | |
| (1): Scale() | |
| (2): Scale() | |
| (3): Scale() | |
| (4): Scale() | |
| (5): Scale() | |
| (6): Scale() | |
| (7): Scale() | |
| (8): Scale() | |
| (9): Scale() | |
| (10): Scale() | |
| (11): Scale() | |
| (12): Scale() | |
| (13): Scale() | |
| (14): Scale() | |
| (15): Scale() | |
| (16): Scale() | |
| (17): Scale() | |
| ) | |
| (cls_individual_out): ModuleList( | |
| (0): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (1): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (2): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (3): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (4): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (5): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (6): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (7): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (8): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (9): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (10): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (11): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (12): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (13): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (14): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (15): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (16): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (17): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[9, 9, 9], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| ) | |
| (cls_individual_up): ModuleList( | |
| (0): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (1): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (2): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (3): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (4): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (5): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (6): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (7): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (8): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (9): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (10): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (11): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (12): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (13): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (14): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (15): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (16): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| (17): ModuleList( | |
| (0): MinkowskiGenerativeConvolutionTranspose(in=64, out=64, kernel_size=[3, 3, 3], stride=[3, 3, 3], dilation=[1, 1, 1]) | |
| (1): Sequential( | |
| (0): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (1): MinkowskiELU() | |
| ) | |
| ) | |
| ) | |
| (cls_individual_fuse): ModuleList( | |
| (0): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (1): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (2): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (3): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (4): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (5): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (6): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (7): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (8): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (9): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (10): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (11): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (12): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (13): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (14): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (15): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (16): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (17): Sequential( | |
| (0): MinkowskiConvolution(in=128, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| ) | |
| (cls_individual_expand_out): ModuleList( | |
| (0): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (1): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (2): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (3): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (4): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (5): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (6): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (7): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (8): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (9): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (10): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (11): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (12): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (13): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (14): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (15): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (16): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| (17): Sequential( | |
| (0): MinkowskiConvolution(in=64, out=64, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): MinkowskiELU() | |
| ) | |
| ) | |
| ) | |
| (point_head): None | |
| (roi_head): CAGroup3DRoIHead( | |
| (proposal_target_layer): ProposalTargetLayer() | |
| (reg_loss_func): WeightedSmoothL1Loss() | |
| (roi_grid_pool_layers): ModuleList( | |
| (0): SimplePoolingLayer( | |
| (grid_conv): MinkowskiConvolution(in=64, out=128, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (grid_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (grid_relu): MinkowskiELU() | |
| (pooling_conv): MinkowskiConvolution(in=128, out=128, kernel_size=[7, 7, 7], stride=[1, 1, 1], dilation=[1, 1, 1]) | |
| (pooling_bn): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| ) | |
| ) | |
| (reg_fc_layers): Sequential( | |
| (0): Linear(in_features=128, out_features=256, bias=False) | |
| (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (2): ReLU() | |
| (3): Dropout(p=0.3, inplace=False) | |
| (4): Linear(in_features=256, out_features=256, bias=False) | |
| (5): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) | |
| (6): ReLU() | |
| ) | |
| (reg_pred_layer): Linear(in_features=256, out_features=6, bias=True) | |
| ) | |
| ) | |
| ) | |
| 2023-04-02 19:19:06,861 INFO **********************Start training scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)********************** | |
| 2023-04-03 02:48:23,235 INFO Epoch [ 9][ 50]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6437654900550842, loss_bbox: 0.24313656240701675, loss_cls: 0.12552095234394073, loss_sem: 0.0908023527264595, loss_vote: 0.4054640585184097, one_stage_loss: 1.5086894202232362, rcnn_loss_reg: 0.18907397538423537, loss_two_stage: 0.18907397538423537, | |
| 2023-04-03 10:39:11,381 INFO Epoch [ 9][ 100]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6452190101146698, loss_bbox: 0.2506902211904526, loss_cls: 0.13058093473315238, loss_sem: 0.09085427075624466, loss_vote: 0.3957407087087631, one_stage_loss: 1.5130851411819457, rcnn_loss_reg: 0.18459385246038437, loss_two_stage: 0.18459385246038437, | |
| 2023-04-03 18:37:23,817 INFO Epoch [ 9][ 150]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6455395030975342, loss_bbox: 0.26197255998849867, loss_cls: 0.13516157254576683, loss_sem: 0.09203423753380775, loss_vote: 0.3918899363279343, one_stage_loss: 1.5265977954864502, rcnn_loss_reg: 0.19318852871656417, loss_two_stage: 0.19318852871656417, | |
| 2023-04-04 02:50:19,460 INFO Epoch [ 9][ 200]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6455828678607941, loss_bbox: 0.26617022305727006, loss_cls: 0.13564770326018333, loss_sem: 0.09159276977181435, loss_vote: 0.3895376515388489, one_stage_loss: 1.5285312223434449, rcnn_loss_reg: 0.19834407955408095, loss_two_stage: 0.19834407955408095, | |
| 2023-04-04 10:54:09,210 INFO Epoch [ 9][ 250]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6433031773567199, loss_bbox: 0.2684298923611641, loss_cls: 0.1351591469347477, loss_sem: 0.0898670071363449, loss_vote: 0.38588442504405973, one_stage_loss: 1.5226436424255372, rcnn_loss_reg: 0.19586090356111527, loss_two_stage: 0.19586090356111527, | |
| 2023-04-04 18:54:23,838 INFO Epoch [ 9][ 300]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6456489896774292, loss_bbox: 0.26939463675022124, loss_cls: 0.13399980276823042, loss_sem: 0.08941386982798577, loss_vote: 0.38932142794132235, one_stage_loss: 1.5277787280082702, rcnn_loss_reg: 0.19743700653314591, loss_two_stage: 0.19743700653314591, | |
| 2023-04-05 03:03:32,943 INFO Epoch [ 9][ 350]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6423018944263458, loss_bbox: 0.2762458199262619, loss_cls: 0.13613657861948014, loss_sem: 0.08952155798673629, loss_vote: 0.3827694195508957, one_stage_loss: 1.526975281238556, rcnn_loss_reg: 0.2028840947151184, loss_two_stage: 0.2028840947151184, | |
| 2023-04-05 11:14:13,601 INFO Epoch [ 9][ 400]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6456181585788727, loss_bbox: 0.27737378895282744, loss_cls: 0.1374327675998211, loss_sem: 0.08987010061740876, loss_vote: 0.3778589928150177, one_stage_loss: 1.5281538224220277, rcnn_loss_reg: 0.19922083646059036, loss_two_stage: 0.19922083646059036, | |
| 2023-04-05 19:27:24,824 INFO Epoch [ 9][ 450]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6425622916221618, loss_bbox: 0.27846161276102066, loss_cls: 0.1391485698521137, loss_sem: 0.09056971445679665, loss_vote: 0.3740858173370361, one_stage_loss: 1.524828016757965, rcnn_loss_reg: 0.2066230583190918, loss_two_stage: 0.2066230583190918, | |
| 2023-04-06 03:42:50,486 INFO Epoch [ 9][ 500]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6431539249420166, loss_bbox: 0.29076395750045775, loss_cls: 0.14162053808569908, loss_sem: 0.09268154501914978, loss_vote: 0.37324252247810363, one_stage_loss: 1.5414625024795532, rcnn_loss_reg: 0.21089414656162261, loss_two_stage: 0.21089414656162261, | |
| 2023-04-06 12:02:31,235 INFO Epoch [ 9][ 550]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6435322630405426, loss_bbox: 0.2865727955102921, loss_cls: 0.14131027206778526, loss_sem: 0.08974318355321884, loss_vote: 0.36962947726249695, one_stage_loss: 1.5307879900932313, rcnn_loss_reg: 0.21048259049654006, loss_two_stage: 0.21048259049654006, | |
| 2023-04-06 20:02:56,351 INFO Epoch [ 9][ 600]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6414503383636475, loss_bbox: 0.29402961790561677, loss_cls: 0.14405513793230057, loss_sem: 0.09276293635368348, loss_vote: 0.34642710983753205, one_stage_loss: 1.5187251472473144, rcnn_loss_reg: 0.21440828204154969, loss_two_stage: 0.21440828204154969, | |
| 2023-04-07 04:17:17,115 INFO Epoch [ 9][ 650]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6445023834705352, loss_bbox: 0.29507197201251983, loss_cls: 0.1420440413057804, loss_sem: 0.08918493255972862, loss_vote: 0.36039295852184294, one_stage_loss: 1.5311962819099427, rcnn_loss_reg: 0.21248085170984268, loss_two_stage: 0.21248085170984268, | |
| 2023-04-07 12:27:17,390 INFO Epoch [ 9][ 700]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6422169864177704, loss_bbox: 0.29913002490997315, loss_cls: 0.1449529528617859, loss_sem: 0.09117808878421783, loss_vote: 0.35617750346660615, one_stage_loss: 1.5336555647850036, rcnn_loss_reg: 0.21863267749547957, loss_two_stage: 0.21863267749547957, | |
| 2023-04-07 20:27:53,668 INFO Epoch [ 9][ 750]/[ 751] : lr: 1.000e-04, sem_thr: 0.05, loss_centerness: 0.6417906320095063, loss_bbox: 0.30550686359405516, loss_cls: 0.14678879588842392, loss_sem: 0.09153819352388382, loss_vote: 0.33501013278961184, one_stage_loss: 1.5206346249580383, rcnn_loss_reg: 0.21843280464410783, loss_two_stage: 0.21843280464410783, | |
| 2023-04-07 20:40:13,681 INFO **********************End training scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)********************** | |
| 2023-04-07 20:40:13,682 INFO **********************Start evaluation scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)********************** | |
| 2023-04-07 20:40:13,683 INFO Loading SCANNET dataset | |
| 2023-04-07 20:40:13,727 INFO Total samples for SCANNET dataset: 312 | |
| 2023-04-07 20:40:13,734 INFO ==> Loading parameters from checkpoint C:\CITYU\CS5182\proj\CAGroup3D\output\scannet_models\CAGroup3D\cagroup3d-win10-scannet-train\ckpt\checkpoint_epoch_9.pth to CPU | |
| 2023-04-07 20:40:15,066 INFO ==> Checkpoint trained from version: pcdet+0.5.2+4ae8a35+pyde9d900 | |
| 2023-04-07 20:40:15,285 INFO ==> Done (loaded 838/838) | |
| 2023-04-07 20:40:15,536 INFO *************** EPOCH 9 EVALUATION ***************** | |
| 2023-04-07 23:42:31,883 INFO *************** Performance of EPOCH 9 ***************** | |
| 2023-04-07 23:42:31,884 INFO Generate label finished(sec_per_example: 35.0515 second). | |
| 2023-04-07 23:42:31,884 INFO recall_roi_0.25: 0.000000 | |
| 2023-04-07 23:42:31,885 INFO recall_rcnn_0.25: 0.000000 | |
| 2023-04-07 23:42:31,885 INFO recall_roi_0.5: 0.000000 | |
| 2023-04-07 23:42:31,886 INFO recall_rcnn_0.5: 0.000000 | |
| 2023-04-07 23:42:31,886 INFO Average predicted number of objects(312 samples): 615.288 | |
| 2023-04-07 23:42:50,866 INFO {'cabinet_AP_0.25': 0.48540377616882324, 'bed_AP_0.25': 0.8844786882400513, 'chair_AP_0.25': 0.9513316750526428, 'sofa_AP_0.25': 0.897523820400238, 'table_AP_0.25': 0.6726281046867371, 'door_AP_0.25': 0.6615963578224182, 'window_AP_0.25': 0.6129509806632996, 'bookshelf_AP_0.25': 0.6147690415382385, 'picture_AP_0.25': 0.3527411222457886, 'counter_AP_0.25': 0.6677877902984619, 'desk_AP_0.25': 0.8030293583869934, 'curtain_AP_0.25': 0.6958670020103455, 'refrigerator_AP_0.25': 0.5268049240112305, 'showercurtrain_AP_0.25': 0.7306337356567383, 'toilet_AP_0.25': 0.9988548159599304, 'sink_AP_0.25': 0.7495102286338806, 'bathtub_AP_0.25': 0.8837810754776001, 'garbagebin_AP_0.25': 0.6311063170433044, 'mAP_0.25': 0.7122666239738464, 'cabinet_rec_0.25': 0.9005376344086021, 'bed_rec_0.25': 0.9135802469135802, 'chair_rec_0.25': 0.9692982456140351, 'sofa_rec_0.25': 0.979381443298969, 'table_rec_0.25': 0.8514285714285714, 'door_rec_0.25': 0.9079229122055674, 'window_rec_0.25': 0.900709219858156, 'bookshelf_rec_0.25': 0.8701298701298701, 'picture_rec_0.25': 0.6576576576576577, 'counter_rec_0.25': 0.9423076923076923, 'desk_rec_0.25': 0.9606299212598425, 'curtain_rec_0.25': 0.8656716417910447, 'refrigerator_rec_0.25': 0.8947368421052632, 'showercurtrain_rec_0.25': 0.9642857142857143, 'toilet_rec_0.25': 1.0, 'sink_rec_0.25': 0.8367346938775511, 'bathtub_rec_0.25': 0.9032258064516129, 'garbagebin_rec_0.25': 0.8622641509433963, 'mAR_0.25': 0.8989167924742847, 'cabinet_AP_0.50': 0.3409128785133362, 'bed_AP_0.50': 0.8349310159683228, 'chair_AP_0.50': 0.8949430584907532, 'sofa_AP_0.50': 0.8081077337265015, 'table_AP_0.50': 0.6181142330169678, 'door_AP_0.50': 0.5156012773513794, 'window_AP_0.50': 0.31114932894706726, 'bookshelf_AP_0.50': 0.5378049612045288, 'picture_AP_0.50': 0.22183111310005188, 'counter_AP_0.50': 0.3777454197406769, 'desk_AP_0.50': 0.6232985854148865, 'curtain_AP_0.50': 0.40846431255340576, 'refrigerator_AP_0.50': 0.44400057196617126, 'showercurtrain_AP_0.50': 0.43062731623649597, 'toilet_AP_0.50': 0.9467570781707764, 'sink_AP_0.50': 0.5182515382766724, 'bathtub_AP_0.50': 0.8415195941925049, 'garbagebin_AP_0.50': 0.5481723546981812, 'mAP_0.50': 0.5679017901420593, 'cabinet_rec_0.50': 0.6935483870967742, 'bed_rec_0.50': 0.8641975308641975, 'chair_rec_0.50': 0.9232456140350878, 'sofa_rec_0.50': 0.9175257731958762, 'table_rec_0.50': 0.7571428571428571, 'door_rec_0.50': 0.734475374732334, 'window_rec_0.50': 0.5921985815602837, 'bookshelf_rec_0.50': 0.7662337662337663, 'picture_rec_0.50': 0.42342342342342343, 'counter_rec_0.50': 0.6153846153846154, 'desk_rec_0.50': 0.8346456692913385, 'curtain_rec_0.50': 0.6119402985074627, 'refrigerator_rec_0.50': 0.7719298245614035, 'showercurtrain_rec_0.50': 0.6071428571428571, 'toilet_rec_0.50': 0.9482758620689655, 'sink_rec_0.50': 0.6428571428571429, 'bathtub_rec_0.50': 0.8709677419354839, 'garbagebin_rec_0.50': 0.7169811320754716, 'mAR_0.50': 0.7384509140060744} | |
| 2023-04-07 23:42:50,872 INFO Result is save to C:\CITYU\CS5182\proj\CAGroup3D\output\scannet_models\CAGroup3D\cagroup3d-win10-scannet-train\eval\eval_with_train\epoch_9\val | |
| 2023-04-07 23:42:50,873 INFO ****************Evaluation done.***************** | |
| 2023-04-07 23:42:51,201 INFO Epoch 9 has been evaluated | |
| 2023-04-07 23:43:21,209 INFO **********************End evaluation scannet_models/CAGroup3D(cagroup3d-win10-scannet-train)********************** | |