NotImplementedError: Caught NotImplementedError in DataLoader worker process 0. #33
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I have encountered the same problem. Have you solved it now? |
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I'm trying to train using
deim_rtdetrv2_r18vd_coco_120e.pthmodel.Dataset is COCO2017.
I used this command to train
But I met this issue.
Not init distributed mode. cfg: {'task': 'detection', '_model': None, '_postprocessor': None, '_criterion': None, '_optimizer': None, '_lr_scheduler': None, '_lr_warmup_scheduler': None, '_train_dataloader': None, '_val_dataloader': None, '_ema': None, '_scaler': None, '_train_dataset': None, '_val_dataset': None, '_collate_fn': None, '_evaluator': None, '_writer': None, 'num_workers': 0, 'batch_size': None, '_train_batch_size': None, '_val_batch_size': None, '_train_shuffle': None, '_val_shuffle': None, 'resume': 'models/deim_rtdetrv2_r18vd_coco_120e.pth', 'tuning': None, 'epoches': 120, 'last_epoch': -1, 'lrsheduler': 'flatcosine', 'lr_gamma': 0.5, 'no_aug_epoch': 3, 'warmup_iter': 2000, 'flat_epoch': 64, 'use_amp': False, 'use_ema': True, 'ema_decay': 0.9999, 'ema_warmups': 2000, 'sync_bn': True, 'clip_max_norm': 0.1, 'find_unused_parameters': False, 'seed': None, 'print_freq': 100, 'checkpoint_freq': 4, 'output_dir': './output/deim_rtdetrv2_r18vd_120e_coco', 'summary_dir': None, 'device': '', 'yaml_cfg': {'task': 'detection', 'evaluator': {'type': 'CocoEvaluator', 'iou_types': ['bbox']}, 'num_classes': 80, 'remap_mscoco_category': True, 'train_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': 'datasets\\coco8\\images\\train2017', 'ann_file': 'datasets\\coco8\\labels\\instances_train2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Mosaic', 'output_size': 320, 'rotation_range': 10, 'translation_range': [0.1, 0.1], 'scaling_range': [0.5, 1.5], 'probability': 1.0, 'fill_value': 0, 'use_cache': False, 'max_cached_images': 50, 'random_pop': True}, {'type': 'RandomPhotometricDistort', 'p': 0.5}, {'type': 'RandomZoomOut', 'fill': 0}, {'type': 'RandomIoUCrop', 'p': 0.8}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'RandomHorizontalFlip'}, {'type': 'Resize', 'size': [640, 640]}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}, {'type': 'ConvertBoxes', 'fmt': 'cxcywh', 'normalize': True}], 'policy': {'name': 'stop_epoch', 'epoch': [4, 64, 117], 'ops': ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']}, 'mosaic_prob': 0.5}}, 'shuffle': True, 'num_workers': 4, 'drop_last': True, 'collate_fn': {'type': 'BatchImageCollateFunction', 'base_size': 640, 'base_size_repeat': 3, 'stop_epoch': 117, 'scales': None, 'mixup_prob': 0.5, 'mixup_epochs': [4, 64]}, 'total_batch_size': 16}, 'val_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': 'datasets\\coco8\\images\\val2017', 'ann_file': 'datasets\\coco8\\labels\\instances_val2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Resize', 'size': [640, 640]}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}]}}, 'shuffle': False, 'num_workers': 4, 'drop_last': False, 'collate_fn': {'type': 'BatchImageCollateFunction'}, 'total_batch_size': 64}, 'print_freq': 100, 'output_dir': './output/deim_rtdetrv2_r18vd_120e_coco', 'checkpoint_freq': 4, 'sync_bn': True, 'find_unused_parameters': False, 'use_amp': False, 'scaler': {'type': 'GradScaler', 'enabled': True}, 'use_ema': True, 'ema': {'type': 'ModelEMA', 'decay': 0.9999, 'warmups': 2000, 'start': 0}, 'epoches': 120, 'clip_max_norm': 0.1, 'optimizer': {'type': 'AdamW', 'params': [{'params': '^(?=.*(?:norm|bn)).*$', 'weight_decay': 0.0}], 'lr': 0.0002, 'betas': [0.9, 0.999], 'weight_decay': 0.0001}, 'lr_scheduler': {'type': 'MultiStepLR', 'milestones': [1000], 'gamma': 0.1}, 'lr_warmup_scheduler': {'type': 'LinearWarmup', 'warmup_duration': 2000}, 'model': 'DEIM', 'criterion': 'DEIMCriterion', 'postprocessor': 'PostProcessor', 'use_focal_loss': True, 'eval_spatial_size': [640, 640], 'DEIM': {'backbone': 'PResNet', 'encoder': 'HybridEncoder', 'decoder': 'RTDETRTransformerv2'}, 'lrsheduler': 'flatcosine', 'lr_gamma': 0.5, 'warmup_iter': 2000, 'flat_epoch': 64, 'no_aug_epoch': 3, 'PResNet': {'depth': 18, 'variant': 'd', 'freeze_at': -1, 'return_idx': [1, 2, 3], 'num_stages': 4, 'freeze_norm': False, 'pretrained': True, 'local_model_dir': '../RT-DETR-main/rtdetrv2_pytorch/INK1k/'}, 'HybridEncoder': {'in_channels': [128, 256, 512], 'feat_strides': [8, 16, 32], 'hidden_dim': 256, 'use_encoder_idx': [2], 'num_encoder_layers': 1, 'nhead': 8, 'dim_feedforward': 1024, 'dropout': 0.0, 'enc_act': 'gelu', 'expansion': 0.5, 'depth_mult': 1, 'act': 'silu', 'version': 'rt_detrv2'}, 'RTDETRTransformerv2': {'feat_channels': [256, 256, 256], 'feat_strides': [8, 16, 32], 'hidden_dim': 256, 'num_levels': 3, 'num_layers': 3, 'num_queries': 300, 'num_denoising': 100, 'label_noise_ratio': 0.5, 'box_noise_scale': 1.0, 'eval_idx': -1, 'num_points': [4, 4, 4], 'cross_attn_method': 'default', 'query_select_method': 'default', 'query_pos_method': 'as_reg', 'activation': 'silu', 'mlp_act': 'silu'}, 'PostProcessor': {'num_top_queries': 300}, 'DEIMCriterion': {'weight_dict': {'loss_vfl': 1, 'loss_bbox': 5, 'loss_giou': 2, 'loss_mal': 1}, 'losses': ['mal', 'boxes'], 'alpha': 0.75, 'gamma': 1.5, 'use_uni_set': False, 'matcher': {'type': 'HungarianMatcher', 'weight_dict': {'cost_class': 2, 'cost_bbox': 5, 'cost_giou': 2}, 'alpha': 0.25, 'gamma': 2.0}}, '__include__': ['./rtdetrv2_r18vd_120e_coco.yml', '../base/rt_deim.yml'], 'config': 'configs/deim_rtdetrv2/deim_r18vd_120e_coco.yml', 'resume': 'models/deim_rtdetrv2_r18vd_coco_120e.pth', 'test_only': False, 'print_method': 'builtin', 'print_rank': 0}} Loaded PResNet18 from local file@../RT-DETR-main/rtdetrv2_pytorch/INK1k/ResNet18_vd_pretrained_from_paddle.pth. Load PResNet18 state_dict ### Query Position Embedding@as_reg ### Initial lr: [0.0002, 0.0002] building train_dataloader with batch_size=16... ### Transform @Mosaic ### ### Transform @RandomPhotometricDistort ### ### Transform @RandomZoomOut ### ### Transform @RandomIoUCrop ### ### Transform @SanitizeBoundingBoxes ### ### Transform @RandomHorizontalFlip ### ### Transform @Resize ### ### Transform @SanitizeBoundingBoxes ### ### Transform @ConvertPILImage ### ### Transform @ConvertBoxes ### ### Mosaic with [email protected] and ZoomOut/IoUCrop existed ### ### ImgTransforms Epochs: [4, 64, 117] ### ### Policy_ops@['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop'] ### Traceback (most recent call last): File "E:\Data Research\DEIM\train.py", line 84, in <module> main(args) File "E:\Data Research\DEIM\train.py", line 54, in main solver.fit() File "E:\Data Research\DEIM\engine\solver\det_solver.py", line 25, in fit self.train() File "E:\Data Research\DEIM\engine\solver\_solver.py", line 87, in train self.cfg.train_dataloader, shuffle=self.cfg.train_dataloader.shuffle ^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\core\yaml_config.py", line 76, in train_dataloader self._train_dataloader = self.build_dataloader('train_dataloader') ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\core\yaml_config.py", line 172, in build_dataloader loader = create(name, global_cfg, batch_size=bs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\core\workspace.py", line 119, in create return create(name, global_cfg) ^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\core\workspace.py", line 167, in create module_kwargs[k] = create(name, global_cfg) ^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\core\workspace.py", line 185, in create return module(**module_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^ TypeError: BatchImageCollateFunction.__init__() got an unexpected keyword argument 'scales'So that I added this code snippet to
workspace.pyfile at line 179 beforereturn module(**module_kwargs).Then I met this issue.
Not init distributed mode. cfg: {'task': 'detection', '_model': None, '_postprocessor': None, '_criterion': None, '_optimizer': None, '_lr_scheduler': None, '_lr_warmup_scheduler': None, '_train_dataloader': None, '_val_dataloader': None, '_ema': None, '_scaler': None, '_train_dataset': None, '_val_dataset': None, '_collate_fn': None, '_evaluator': None, '_writer': None, 'num_workers': 0, 'batch_size': None, '_train_batch_size': None, '_val_batch_size': None, '_train_shuffle': None, '_val_shuffle': None, 'resume': 'models/deim_rtdetrv2_r18vd_coco_120e.pth', 'tuning': None, 'epoches': 120, 'last_epoch': -1, 'lrsheduler': 'flatcosine', 'lr_gamma': 0.5, 'no_aug_epoch': 3, 'warmup_iter': 2000, 'flat_epoch': 64, 'use_amp': False, 'use_ema': True, 'ema_decay': 0.9999, 'ema_warmups': 2000, 'sync_bn': True, 'clip_max_norm': 0.1, 'find_unused_parameters': False, 'seed': None, 'print_freq': 100, 'checkpoint_freq': 4, 'output_dir': './output/deim_rtdetrv2_r18vd_120e_coco', 'summary_dir': None, 'device': '', 'yaml_cfg': {'task': 'detection', 'evaluator': {'type': 'CocoEvaluator', 'iou_types': ['bbox']}, 'num_classes': 80, 'remap_mscoco_category': True, 'train_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': 'datasets\\coco8\\images\\train2017', 'ann_file': 'datasets\\coco8\\labels\\instances_train2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Mosaic', 'output_size': 320, 'rotation_range': 10, 'translation_range': [0.1, 0.1], 'scaling_range': [0.5, 1.5], 'probability': 1.0, 'fill_value': 0, 'use_cache': False, 'max_cached_images': 50, 'random_pop': True}, {'type': 'RandomPhotometricDistort', 'p': 0.5}, {'type': 'RandomZoomOut', 'fill': 0}, {'type': 'RandomIoUCrop', 'p': 0.8}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'RandomHorizontalFlip'}, {'type': 'Resize', 'size': [640, 640]}, {'type': 'SanitizeBoundingBoxes', 'min_size': 1}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}, {'type': 'ConvertBoxes', 'fmt': 'cxcywh', 'normalize': True}], 'policy': {'name': 'stop_epoch', 'epoch': [4, 64, 117], 'ops': ['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop']}, 'mosaic_prob': 0.5}}, 'shuffle': True, 'num_workers': 4, 'drop_last': True, 'collate_fn': {'type': 'BatchImageCollateFunction', 'base_size': 640, 'base_size_repeat': 3, 'stop_epoch': 117, 'scales': None, 'mixup_prob': 0.5, 'mixup_epochs': [4, 64]}, 'total_batch_size': 16}, 'val_dataloader': {'type': 'DataLoader', 'dataset': {'type': 'CocoDetection', 'img_folder': 'datasets\\coco8\\images\\val2017', 'ann_file': 'datasets\\coco8\\labels\\instances_val2017.json', 'return_masks': False, 'transforms': {'type': 'Compose', 'ops': [{'type': 'Resize', 'size': [640, 640]}, {'type': 'ConvertPILImage', 'dtype': 'float32', 'scale': True}]}}, 'shuffle': False, 'num_workers': 4, 'drop_last': False, 'collate_fn': {'type': 'BatchImageCollateFunction'}, 'total_batch_size': 64}, 'print_freq': 100, 'output_dir': './output/deim_rtdetrv2_r18vd_120e_coco', 'checkpoint_freq': 4, 'sync_bn': True, 'find_unused_parameters': False, 'use_amp': False, 'scaler': {'type': 'GradScaler', 'enabled': True}, 'use_ema': True, 'ema': {'type': 'ModelEMA', 'decay': 0.9999, 'warmups': 2000, 'start': 0}, 'epoches': 120, 'clip_max_norm': 0.1, 'optimizer': {'type': 'AdamW', 'params': [{'params': '^(?=.*(?:norm|bn)).*$', 'weight_decay': 0.0}], 'lr': 0.0002, 'betas': [0.9, 0.999], 'weight_decay': 0.0001}, 'lr_scheduler': {'type': 'MultiStepLR', 'milestones': [1000], 'gamma': 0.1}, 'lr_warmup_scheduler': {'type': 'LinearWarmup', 'warmup_duration': 2000}, 'model': 'DEIM', 'criterion': 'DEIMCriterion', 'postprocessor': 'PostProcessor', 'use_focal_loss': True, 'eval_spatial_size': [640, 640], 'DEIM': {'backbone': 'PResNet', 'encoder': 'HybridEncoder', 'decoder': 'RTDETRTransformerv2'}, 'lrsheduler': 'flatcosine', 'lr_gamma': 0.5, 'warmup_iter': 2000, 'flat_epoch': 64, 'no_aug_epoch': 3, 'PResNet': {'depth': 18, 'variant': 'd', 'freeze_at': -1, 'return_idx': [1, 2, 3], 'num_stages': 4, 'freeze_norm': False, 'pretrained': True, 'local_model_dir': '../RT-DETR-main/rtdetrv2_pytorch/INK1k/'}, 'HybridEncoder': {'in_channels': [128, 256, 512], 'feat_strides': [8, 16, 32], 'hidden_dim': 256, 'use_encoder_idx': [2], 'num_encoder_layers': 1, 'nhead': 8, 'dim_feedforward': 1024, 'dropout': 0.0, 'enc_act': 'gelu', 'expansion': 0.5, 'depth_mult': 1, 'act': 'silu', 'version': 'rt_detrv2'}, 'RTDETRTransformerv2': {'feat_channels': [256, 256, 256], 'feat_strides': [8, 16, 32], 'hidden_dim': 256, 'num_levels': 3, 'num_layers': 3, 'num_queries': 300, 'num_denoising': 100, 'label_noise_ratio': 0.5, 'box_noise_scale': 1.0, 'eval_idx': -1, 'num_points': [4, 4, 4], 'cross_attn_method': 'default', 'query_select_method': 'default', 'query_pos_method': 'as_reg', 'activation': 'silu', 'mlp_act': 'silu'}, 'PostProcessor': {'num_top_queries': 300}, 'DEIMCriterion': {'weight_dict': {'loss_vfl': 1, 'loss_bbox': 5, 'loss_giou': 2, 'loss_mal': 1}, 'losses': ['mal', 'boxes'], 'alpha': 0.75, 'gamma': 1.5, 'use_uni_set': False, 'matcher': {'type': 'HungarianMatcher', 'weight_dict': {'cost_class': 2, 'cost_bbox': 5, 'cost_giou': 2}, 'alpha': 0.25, 'gamma': 2.0}}, '__include__': ['./rtdetrv2_r18vd_120e_coco.yml', '../base/rt_deim.yml'], 'config': 'configs/deim_rtdetrv2/deim_r18vd_120e_coco.yml', 'resume': 'models/deim_rtdetrv2_r18vd_coco_120e.pth', 'test_only': False, 'print_method': 'builtin', 'print_rank': 0}} Loaded PResNet18 from local file@../RT-DETR-main/rtdetrv2_pytorch/INK1k/ResNet18_vd_pretrained_from_paddle.pth. Load PResNet18 state_dict ### Query Position Embedding@as_reg ### Initial lr: [0.0002, 0.0002] building train_dataloader with batch_size=16... ### Transform @Mosaic ### ### Transform @RandomPhotometricDistort ### ### Transform @RandomZoomOut ### ### Transform @RandomIoUCrop ### ### Transform @SanitizeBoundingBoxes ### ### Transform @RandomHorizontalFlip ### ### Transform @Resize ### ### Transform @SanitizeBoundingBoxes ### ### Transform @ConvertPILImage ### ### Transform @ConvertBoxes ### ### Mosaic with [email protected] and ZoomOut/IoUCrop existed ### ### ImgTransforms Epochs: [4, 64, 117] ### ### Policy_ops@['Mosaic', 'RandomPhotometricDistort', 'RandomZoomOut', 'RandomIoUCrop'] ### ### Using MixUp with [email protected] in [4, 64] epochs ### ### Multi-scale Training until 117 epochs ### ### Multi-scales@ [480, 512, 544, 576, 608, 640, 640, 640, 800, 768, 736, 704, 672] ### building val_dataloader with batch_size=64... ### Transform @Resize ### ### Transform @ConvertPILImage ### Resume checkpoint from models/deim_rtdetrv2_r18vd_coco_120e.pth Load model.state_dict Not load criterion.state_dict Not load postprocessor.state_dict Load ema.state_dict from model.state_dict Not load optimizer.state_dict Not load lr_scheduler.state_dict Not load lr_warmup_scheduler.state_dict ------------------------------------- Calculate Flops Results ------------------------------------- Notations: number of parameters (Params), number of multiply-accumulate operations(MACs), number of floating-point operations (FLOPs), floating-point operations per second (FLOPS), fwd FLOPs (model forward propagation FLOPs), bwd FLOPs (model backward propagation FLOPs), default model backpropagation takes 2.00 times as much computation as forward propagation. Total Training Params: 19.98 M fwd MACs: 30.0313 GMACs fwd FLOPs: 60.2087 GFLOPS fwd+bwd MACs: 90.0938 GMACs fwd+bwd FLOPs: 180.626 GFLOPS --------------------------------------------------------------------------------------------------- {'Model FLOPs:60.2087 GFLOPS MACs:30.0313 GMACs Params:19979152'} ------------------------------------------Start training------------------------------------------- ## Using Self-defined Scheduler-flatcosine ## [0.0002, 0.0002] [0.0001, 0.0001] 0 2000 0 0 number of trainable parameters: 20183440 >>>>>> iterable : 0 Traceback (most recent call last): File "E:\Data Research\DEIM\train.py", line 84, in <module> main(args) File "E:\Data Research\DEIM\train.py", line 54, in main solver.fit() File "E:\Data Research\DEIM\engine\solver\det_solver.py", line 108, in fit test_stats, coco_evaluator = evaluate( ^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\solver\det_engine.py", line 140, in evaluate for samples, targets in metric_logger.log_every(data_loader, 10, header): File "E:\Data Research\DEIM\engine\misc\logger.py", line 215, in log_every for obj in iterable: File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\dataloader.py", line 708, in __next__ data = self._next_data() ^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\dataloader.py", line 1480, in _next_data return self._process_data(data) ^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\dataloader.py", line 1505, in _process_data data.reraise() File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\_utils.py", line 733, in reraise raise exception NotImplementedError: Caught NotImplementedError in DataLoader worker process 0. Original Traceback (most recent call last): File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\_utils\worker.py", line 349, in _worker_loop data = fetcher.fetch(index) # type: ignore[possibly-undefined] ^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 52, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\utils\data\_utils\fetch.py", line 52, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] ~~~~~~~~~~~~^^^^^ File "E:\Data Research\DEIM\engine\data\dataset\coco_dataset.py", line 45, in __getitem__ img, target, _ = self._transforms(img, target, self) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1739, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1750, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\data\transforms\container.py", line 58, in forward return self.get_forward(self.policy['name'])(*inputs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\engine\data\transforms\container.py", line 71, in default_forward sample = transform(sample) ^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1739, in _wrapped_call_impl return self._call_impl(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torch\nn\modules\module.py", line 1750, in _call_impl return forward_call(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torchvision\transforms\v2\_transform.py", line 68, in forward flat_outputs = [ ^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torchvision\transforms\v2\_transform.py", line 69, in <listcomp> self.transform(inpt, params) if needs_transform else inpt ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "E:\Data Research\DEIM\.venv\Lib\site-packages\torchvision\transforms\v2\_transform.py", line 55, in transform raise NotImplementedError NotImplementedErrorPlease help me this problem asap.
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