Add a generic test for the datasets (#1015)

* added a generic test for the datasets

* addressed requested changes

- renamed generic*() to generic_classification*()
- moved function inside Tester
- test class_to_idx attribute outside of generic_classification*()

Author: Philip Meier

test/fakedata_generation.py

@@ -29,7 +29,7 @@ def _make_image_file(filename, num_images):
             f.write(img.numpy().tobytes())
 
     def _make_label_file(filename, num_images):
-        labels = torch.randint(0, 10, size=(num_images,), dtype=torch.uint8)
+        labels = torch.zeros((num_images,), dtype=torch.uint8)
         with open(filename, "wb") as f:
             f.write(_encode(2049))  # magic header
             f.write(_encode(num_images))

test/test_datasets.py

@@ -10,6 +10,12 @@
 
 
 class Tester(unittest.TestCase):
+    def generic_classification_dataset_test(self, dataset, num_images=1):
+        self.assertEqual(len(dataset), num_images)
+        img, target = dataset[0]
+        self.assertTrue(isinstance(img, PIL.Image.Image))
+        self.assertTrue(isinstance(target, int))
+
     def test_imagefolder(self):
         # TODO: create the fake data on-the-fly
         FAKEDATA_DIR = get_file_path_2(
@@ -64,47 +70,36 @@ def test_mnist(self, mock_download_extract):
         num_examples = 30
         with mnist_root(num_examples, "MNIST") as root:
             dataset = torchvision.datasets.MNIST(root, download=True)
-            self.assertEqual(len(dataset), num_examples)
+            self.generic_classification_dataset_test(dataset, num_images=num_examples)
             img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
     @mock.patch('torchvision.datasets.mnist.download_and_extract_archive')
     def test_kmnist(self, mock_download_extract):
         num_examples = 30
         with mnist_root(num_examples, "KMNIST") as root:
             dataset = torchvision.datasets.KMNIST(root, download=True)
+            self.generic_classification_dataset_test(dataset, num_images=num_examples)
             img, target = dataset[0]
-            self.assertEqual(len(dataset), num_examples)
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
     @mock.patch('torchvision.datasets.mnist.download_and_extract_archive')
     def test_fashionmnist(self, mock_download_extract):
         num_examples = 30
         with mnist_root(num_examples, "FashionMNIST") as root:
             dataset = torchvision.datasets.FashionMNIST(root, download=True)
+            self.generic_classification_dataset_test(dataset, num_images=num_examples)
             img, target = dataset[0]
-            self.assertEqual(len(dataset), num_examples)
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
     @mock.patch('torchvision.datasets.utils.download_url')
     def test_imagenet(self, mock_download):
         with imagenet_root() as root:
             dataset = torchvision.datasets.ImageNet(root, split='train', download=True)
-            self.assertEqual(len(dataset), 1)
-            img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.generic_classification_dataset_test(dataset)
 
             dataset = torchvision.datasets.ImageNet(root, split='val', download=True)
-            self.assertEqual(len(dataset), 1)
-            img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.generic_classification_dataset_test(dataset)
 
     @mock.patch('torchvision.datasets.cifar.check_integrity')
     @mock.patch('torchvision.datasets.cifar.CIFAR10._check_integrity')
@@ -113,18 +108,14 @@ def test_cifar10(self, mock_ext_check, mock_int_check):
         mock_int_check.return_value = True
         with cifar_root('CIFAR10') as root:
             dataset = torchvision.datasets.CIFAR10(root, train=True, download=True)
-            self.assertEqual(len(dataset), 5)
+            self.generic_classification_dataset_test(dataset, num_images=5)
             img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
             dataset = torchvision.datasets.CIFAR10(root, train=False, download=True)
-            self.assertEqual(len(dataset), 1)
+            self.generic_classification_dataset_test(dataset)
             img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
     @mock.patch('torchvision.datasets.cifar.check_integrity')
     @mock.patch('torchvision.datasets.cifar.CIFAR10._check_integrity')
@@ -133,18 +124,14 @@ def test_cifar100(self, mock_ext_check, mock_int_check):
         mock_int_check.return_value = True
         with cifar_root('CIFAR100') as root:
             dataset = torchvision.datasets.CIFAR100(root, train=True, download=True)
-            self.assertEqual(len(dataset), 1)
+            self.generic_classification_dataset_test(dataset)
             img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
             dataset = torchvision.datasets.CIFAR100(root, train=False, download=True)
-            self.assertEqual(len(dataset), 1)
+            self.generic_classification_dataset_test(dataset)
             img, target = dataset[0]
-            self.assertTrue(isinstance(img, PIL.Image.Image))
-            self.assertTrue(isinstance(target, int))
-            self.assertEqual(dataset.class_to_idx['fakedata'], target)
+            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
 
 
 if __name__ == '__main__':