| model = dict( | |
| type='DETR', | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(3, ), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | |
| bbox_head=dict( | |
| type='DETRHead', | |
| num_classes=20, | |
| in_channels=2048, | |
| transformer=dict( | |
| type='Transformer', | |
| encoder=dict( | |
| type='DetrTransformerEncoder', | |
| num_layers=6, | |
| transformerlayers=dict( | |
| type='BaseTransformerLayer', | |
| attn_cfgs=[ | |
| dict( | |
| type='MultiheadAttention', | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1) | |
| ], | |
| feedforward_channels=2048, | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'ffn', 'norm'))), | |
| decoder=dict( | |
| type='DetrTransformerDecoder', | |
| return_intermediate=True, | |
| num_layers=6, | |
| transformerlayers=dict( | |
| type='DetrTransformerDecoderLayer', | |
| attn_cfgs=dict( | |
| type='MultiheadAttention', | |
| embed_dims=256, | |
| num_heads=8, | |
| dropout=0.1), | |
| feedforward_channels=2048, | |
| ffn_dropout=0.1, | |
| operation_order=('self_attn', 'norm', 'cross_attn', 'norm', | |
| 'ffn', 'norm')))), | |
| positional_encoding=dict( | |
| type='SinePositionalEncoding', num_feats=128, normalize=True), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', | |
| bg_cls_weight=0.1, | |
| use_sigmoid=False, | |
| loss_weight=1.0, | |
| class_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=5.0), | |
| loss_iou=dict(type='GIoULoss', loss_weight=2.0)), | |
| train_cfg=dict( | |
| assigner=dict( | |
| type='HungarianAssigner', | |
| cls_cost=dict(type='ClassificationCost', weight=1.0), | |
| reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), | |
| iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), | |
| test_cfg=dict(max_per_img=100)) | |
| dataset_type = 'VOCDataset' | |
| data_root = 'data/VOCdevkit/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='Resize', | |
| img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576), | |
| (1333, 608), (1333, 640), (1333, 672), (1333, 704), | |
| (1333, 736), (1333, 768), (1333, 800)], | |
| multiscale_mode='value', | |
| keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=2, | |
| workers_per_gpu=2, | |
| train=dict( | |
| type='VOCDataset', | |
| ann_file=[ | |
| 'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt', | |
| 'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt' | |
| ], | |
| img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'], | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='Resize', | |
| img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576), | |
| (1333, 608), (1333, 640), (1333, 672), (1333, 704), | |
| (1333, 736), (1333, 768), (1333, 800)], | |
| multiscale_mode='value', | |
| keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) | |
| ]), | |
| val=dict( | |
| type='VOCDataset', | |
| ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', | |
| img_prefix='data/VOCdevkit/VOC2007/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ]), | |
| test=dict( | |
| type='VOCDataset', | |
| ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', | |
| img_prefix='data/VOCdevkit/VOC2007/', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ])) | |
| evaluation = dict(interval=1, metric='mAP', save_best='auto') | |
| checkpoint_config = dict(interval=1) | |
| log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) | |
| custom_hooks = [ | |
| dict(type='NumClassCheckHook'), | |
| dict( | |
| type='MMDetWandbHook', | |
| init_kwargs=dict(project='I2B', group='finetune'), | |
| interval=50, | |
| num_eval_images=0, | |
| log_checkpoint=False) | |
| ] | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = 'pretrain/selfsup_detr_clusters-as-classes_add-contrastive-temp0.5-weight1.0/final_model.pth' | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| opencv_num_threads = 0 | |
| mp_start_method = 'fork' | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| custom_imports = None | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| optimizer = dict( | |
| type='AdamW', | |
| lr=0.0001, | |
| weight_decay=0.0001, | |
| paramwise_cfg=dict( | |
| custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0)))) | |
| optimizer_config = dict(grad_clip=None) | |
| lr_config = dict(policy='step', step=[70]) | |
| runner = dict(type='EpochBasedRunner', max_epochs=100) | |
| work_dir = 'work_dirs/finetune_detr_100e_voc0712' | |
| auto_resume = False | |
| gpu_ids = range(0, 8) | |