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IDEA-Research / DN-DETR

[CVPR 2022 Oral] Official implementation of DN-DETR
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TypeError: cannot unpack non-iterable NoneType object #40

Open jiaowanger opened 1 year ago

jiaowanger commented 1 year ago

D:\anaconda3.9\envs\zj\python.exe F:/1chen/DETR/jin/dn/DN-DETR/main.py Not using distributed mode

sha: a59a5de5bf784f196e15bffed3145d05d5a9126a, status: has uncommited changes, branch: main

08/13 08:31:54.869: Command: F:/1chen/DETR/jin/dn/DN-DETR/main.py 08/13 08:31:54.869: Full config saved to log/r50\config.json 08/13 08:31:54.869: world size: 1 08/13 08:31:54.869: rank: 0 08/13 08:31:54.869: local_rank: 0 [08/13 08:31:54.870]: args: Namespace(amp=False, aux_loss=True, backbone='resnet50', backbone_freeze_keywords=None, batch_norm_type='FrozenBatchNorm2d', batch_size=2, bbox_loss_coef=5, box_noise_scale=0.4, clip_max_norm=0.1, cls_loss_coef=1, coco_panoptic_path=None, coco_path='COCODIR', dataset_file='coco', debug=False, dec_layers=6, dec_n_points=4, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_url='env://', distributed=False, drop_lr_now=False, dropout=0.0, enc_layers=6, enc_n_points=4, eos_coef=0.1, epochs=10, eval=False, find_unused_params=False, finetune_ignore=None, fix_size=False, focal_alpha=0.25, frozen_weights=None, giou_loss_coef=2, hidden_dim=256, label_noise_scale=0.2, local_rank=0, lr=0.0001, lr_backbone=1e-05, lr_drop=40, mask_loss_coef=1, masks=False, modelname='dn_dab_deformable_detr', nheads=8, note='', num_feature_levels=4, num_patterns=0, num_queries=300, num_select=300, num_workers=10, output_dir='log/r50', pe_temperatureH=20, pe_temperatureW=20, position_embedding='sine', pre_norm=False, pretrain_model_path=None, random_refpoints_xy=False, rank=0, remove_difficult=False, resume='', return_interm_layers=False, save_checkpoint_interval=10, save_log=False, save_results=False, scalar=5, seed=42, set_cost_bbox=5, set_cost_class=2, set_cost_giou=2, start_epoch=0, transformer_activation='prelu', two_stage=False, use_dn=False, weight_decay=0.0001, world_size=1)

Namespace(amp=False, aux_loss=True, backbone='resnet50', backbone_freeze_keywords=None, batch_norm_type='FrozenBatchNorm2d', batch_size=2, bbox_loss_coef=5, box_noise_scale=0.4, clip_max_norm=0.1, cls_loss_coef=1, coco_panoptic_path=None, coco_path='COCODIR', dataset_file='coco', debug=False, dec_layers=6, dec_n_points=4, device='cuda', dice_loss_coef=1, dilation=False, dim_feedforward=2048, dist_url='env://', distributed=False, drop_lr_now=False, dropout=0.0, enc_layers=6, enc_n_points=4, eos_coef=0.1, epochs=10, eval=False, find_unused_params=False, finetune_ignore=None, fix_size=False, focal_alpha=0.25, frozen_weights=None, giou_loss_coef=2, hidden_dim=256, label_noise_scale=0.2, local_rank=0, lr=0.0001, lr_backbone=1e-05, lr_drop=40, mask_loss_coef=1, masks=False, modelname='dn_dab_deformable_detr', nheads=8, note='', num_feature_levels=4, num_patterns=0, num_queries=300, num_select=300, num_workers=10, output_dir='log/r50', pe_temperatureH=20, pe_temperatureW=20, position_embedding='sine', pre_norm=False, pretrain_model_path=None, random_refpoints_xy=False, rank=0, remove_difficult=False, resume='', return_interm_layers=False, save_checkpoint_interval=10, save_log=False, save_results=False, scalar=5, seed=42, set_cost_bbox=5, set_cost_class=2, set_cost_giou=2, start_epoch=0, transformer_activation='prelu', two_stage=False, use_dn=False, weight_decay=0.0001, world_size=1) [08/13 08:31:55.431]: number of params:47206754

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Done (t=0.03s) creating index... index created! loading annotations into memory... Done (t=0.00s) creating index... index created! Start training F:\1chen\DETR\jin\dn\DN-DETR\models\dn_dab_deformable_detr\position_encoding.py:53: UserWarning: floordiv is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). dim_t = self.temperature (2 (dim_t // 2) / self.num_pos_feats) Traceback (most recent call last): File "F:/1chen/DETR/jin/dn/DN-DETR/main.py", line 426, in main(args) File "F:/1chen/DETR/jin/dn/DN-DETR/main.py", line 352, in main train_stats = train_one_epoch( File "F:\1chen\DETR\jin\dn\DN-DETR\engine.py", line 52, in train_one_epoch outputs = model(samples) File "D:\anaconda3.9\envs\zj\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(input, kwargs) File "F:\1chen\DETR\jin\dn\DN-DETR\models\dn_dab_deformable_detr\dab_deformable_detr.py", line 206, in forward prepare_for_dn(dn_args, tgt_all_embed, refanchor, src.size(0), self.training, self.num_queries, self.num_classes, File "F:\1chen\DETR\jin\dn\DN-DETR\models\dn_dab_deformable_detr\dn_components.py", line 61, in prepare_for_dn targets, scalar, label_noise_scale, box_noise_scale, num_patterns = dn_args TypeError: cannot unpack non-iterable NoneType object

Process finished with exit code 1

FengLi-ust commented 1 year ago

It seems you set args.use_dn to false so that dn_args does not go into the forward function. You need to set args.use_dn to true by adding --use_dn.

llljjj88 commented 1 year ago

Hello, I also encountered the same problem and I printed out the --use_ dn is already true, but this error cannot be solved

AkimotoAyako commented 11 months ago

I think you might have forgotten to remove "# replace the args to your COCO path," which is preventing the command-line parser from interpreting the --use_dn parameter correctly.