Spaces:
Build error
Build error
| model = dict( | |
| type='PSGTr', | |
| backbone=dict(type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict(type='Pretrained', | |
| checkpoint='torchvision://resnet50')), | |
| bbox_head=dict( | |
| type='PSGFormerHead', | |
| num_classes=80, | |
| num_relations=117, | |
| in_channels=2048, | |
| transformer=dict( | |
| type='DualTransformer', | |
| 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'))), | |
| decoder1=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'))), | |
| decoder2=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), | |
| rel_loss_cls=dict(type='CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=2.0, | |
| class_weight=1.0), | |
| sub_id_loss=dict(type='MultilabelCrossEntropy', loss_weight=2.0), | |
| obj_id_loss=dict(type='MultilabelCrossEntropy', loss_weight=2.0), | |
| loss_cls=dict(type='CrossEntropyLoss', | |
| use_sigmoid=False, | |
| loss_weight=4.0, | |
| class_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=3.0), | |
| loss_iou=dict(type='GIoULoss', loss_weight=2.0), | |
| focal_loss=dict(type='BCEFocalLoss', loss_weight=1.0), | |
| dice_loss=dict(type='psgtrDiceLoss', loss_weight=1.0)), | |
| # training and testing settings | |
| train_cfg=dict(id_assigner=dict(type='IdMatcher', | |
| sub_id_cost=dict(type='ClassificationCost', | |
| weight=1.), | |
| obj_id_cost=dict(type='ClassificationCost', | |
| weight=1.), | |
| r_cls_cost=dict(type='ClassificationCost', | |
| weight=1.)), | |
| bbox_assigner=dict(type='HungarianAssigner', | |
| cls_cost=dict(type='ClassificationCost', | |
| weight=4.0), | |
| reg_cost=dict(type='BBoxL1Cost', | |
| weight=3.0), | |
| iou_cost=dict(type='IoUCost', | |
| iou_mode='giou', | |
| weight=2.0))), | |
| test_cfg=dict(max_per_img=100)) | |