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Merged
merged 2 commits into from
Jun 15, 2019
Merged

Add a generic test for the datasets #1015

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4a5e036
added a generic test for the datasets
Jun 13, 2019
20e6922
addressed requested changes
Jun 15, 2019
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2 changes: 1 addition & 1 deletion test/fakedata_generation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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))
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57 changes: 22 additions & 35 deletions test/test_datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand Down Expand Up @@ -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')
Expand All @@ -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')
Expand All @@ -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__':
Expand Down