num_samples should be a positive integer value, but got num_samples=0

[MSadriAghdam]

I ran the code and got many errors but handled to solve them somehow! here is a problem that I have. I only want to train the model on the bottle dataset and it is already in the dataset address. the original dataset has broken_large, broken_small, contaminated, and good but I do not know how to feed those 4 groups as a train/test dataset.

I also have got an error while training only on good images.
I will appreciate it if you could help me

$ python train.py --phase train --dataset_path C:\\projects\\interview\\mvtec --category bottle --project_root_path C:\projects\interview\PatchCore_anomaly_detection\results --coreset_sampling_ratio 0.01 --n_neighbors 9 C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\detection\anchor_utils.py:63: UserWarning: Failed to initialize NumPy: module compiled against API version 0x10 but this version of numpy is 0xf (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:68.) device: torch.device = torch.device("cpu"), GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs Using cache found in C:\Users\user/.cache\torch\hub\pytorch_vision_v0.9.0 C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead. warnings.warn( C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\models\_utils.py:223: UserWarning: Arguments other than a weight enum or Nonefor 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passingweights=Wide_ResNet50_2_Weights.IMAGENET1K_V1. You can also use weights=Wide_ResNet50_2_Weights.DEFAULTto get the most up-to-date weights. warnings.warn(msg) C:\projects\interview\PatchCore_anomaly_detection_2\train.py:259: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. transforms.Resize((args.load_size, args.load_size), Image.ANTIALIAS), C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torchvision\transforms\transforms.py:332: UserWarning: Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum. warnings.warn( C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\optimizers.py:37: UserWarning:LightningModule.configure_optimizersreturnedNone`, this fit will run with no optimizer
rank_zero_warn(

| Name | Type | Params

0 | model | ResNet | 68.9 M
1 | criterion | MSELoss | 0
2 | inv_normalize | Normalize | 0

0 Trainable params
68.9 M Non-trainable params
68.9 M Total params
275.533 Total estimated model params size (MB)
Traceback (most recent call last):
File "C:\projects\interview\PatchCore_anomaly_detection_2\train.py", line 437, in
trainer.fit(model)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 685, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1199, in _run
self._dispatch()
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1279, in _dispatch
self.training_type_plugin.start_training(self)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\plugins\training_type\training_type_plugin.py", line 202, in start_training
self._results = trainer.run_stage()
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1289, in run_stage
return self._run_train()
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1319, in _run_train
self.fit_loop.run()
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\loops\base.py", line 140, in run
self.on_run_start(*args, **kwargs)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\loops\fit_loop.py", line 197, in on_run_start
self.trainer.reset_train_val_dataloaders(self.trainer.lightning_module)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 595, in reset_train_val_dataloaders
self.reset_train_dataloader(model=model)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 365, in reset_train_dataloader
self.train_dataloader = self.request_dataloader(RunningStage.TRAINING, model=model)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\data_loading.py", line 611, in request_dataloader
dataloader = source.dataloader()
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\connectors\data_connector.py", line 296, in dataloader
return self.instance.trainer.call_hook(self.name, pl_module=self.instance)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\pytorch_lightning\trainer\trainer.py", line 1501, in call_hook
output = model_fx(*args, **kwargs)
File "C:\projects\interview\PatchCore_anomaly_detection_2\train.py", line 304, in train_dataloader
train_loader = DataLoader(image_datasets, batch_size=args.batch_size, shuffle=True, num_workers=0) #, pin_memory=True)
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torch\utils\data\dataloader.py", line 347, in init
sampler = RandomSampler(dataset, generator=generator) # type: ignore[arg-type]
File "C:\Users\user\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\torch\utils\data\sampler.py", line 107, in init
raise ValueError("num_samples should be a positive integer "
ValueError: num_samples should be a positive integer value, but got num_samples=0
`

[PauloFavero]

The dataset loader in the train script has the file extension hard coded to ".png". If you change it to the file extension that you have it will work.

        for defect_type in defect_types:
            if defect_type == 'good':
                img_paths = glob.glob(os.path.join(self.img_path, defect_type) + "/*.png")
                img_tot_paths.extend(img_paths)
                gt_tot_paths.extend([0]*len(img_paths))
                tot_labels.extend([0]*len(img_paths))
                tot_types.extend(['good']*len(img_paths))
            else:
                img_paths = glob.glob(os.path.join(self.img_path, defect_type) + "/*.png")
                gt_paths = glob.glob(os.path.join(self.gt_path, defect_type) + "/*.png)
                img_paths.sort()
                gt_paths.sort()
                img_tot_paths.extend(img_paths)
                gt_tot_paths.extend(gt_paths)
                tot_labels.extend([1]*len(img_paths))
                tot_types.extend([defect_type]*len(img_paths))