code: def test_image_dataset(self):
      image_files = []
      inputs = []
      targets = {}
      dataloader = self.get_test_image_dataset().iterator()
      for i, sample in enumerate(dataloader):
          input = sample["input"]
          target = sample["target"]
          image = transforms.ToPILImage()(input.squeeze())
          path = f"{self.torchvision_dir}/{target.item()}/{i}.png"
          image_files.append(path)
          image.save(path)
          path = f"{self.other_dir}/{i}.png"
          image.save(path)
          inputs.append(input)
          targets[input] = target
  
      config = self.get_dataset_config()
  
      config["image_files"] = image_files
      # test the dataset using image_files
      dataset = ImagePathDataset.from_config(config)
      dataloader = dataset.iterator()
      # the samples should be in the same order
      for sample, expected_input in zip(dataloader, inputs):
          self.assertTrue(
              torch.allclose(sample["input"], expected_input)
          )
      config.pop("image_files")
  
      # test the dataset with image_folder of type torchvision.ImageFolder
      config["image_folder"] = self.torchvision_dir
      dataset = ImagePathDataset.from_config(config)
      dataloader = dataset.iterator()
      # the order doesn't matter, so we sort the results
      # note that this test assumes that the target for directory 0 will be 0
      for (input, target), (expected_input, expected_target) in zip(
          sorted(
              (
                  (sample["input"], sample["target"])
                  for sample in dataloader
              ),
              key=_sort_key,
          ),
          sorted(targets.items(), key=_sort_key),
      ):
          self.assertTrue(torch.allclose(input, expected_input))
          self.assertEqual(target, expected_target)
  
      # test the dataset with image_folder of the second type
      config["image_folder"] = self.other_dir
      dataset = ImagePathDataset.from_config(config)
      dataloader = dataset.iterator()
      # the order doesn't matter, so we sort the results
      for input, expected_input in zip(
          sorted(
              (sample["input"] for sample in dataloader), key=_sort_key
          ),
          sorted(inputs, key=_sort_key),
      ):
          self.assertTrue(torch.allclose(input, expected_input))
  
file_path: ClassyVision/test/dataset_image_path_dataset_test.py
imports: import os
  import shutil
  import tempfile
  import unittest
  import torch
  from classy_vision.dataset import build_dataset, ClassyDataset
  from classy_vision.dataset.image_path_dataset import ImagePathDataset
  from torchvision import transforms
label_desc: Use image node_id 6 for calling the TestImageDataset obj's underlying member method code with example usage: obj.test_image_dataset() without return types