remove old CIFAR tests and fake data generation (#3447)

test/fakedata_generation.py

@@ -88,61 +88,6 @@ def mnist_root(num_images, cls_name):
         yield tmp_dir
 
 
-@contextlib.contextmanager
-def cifar_root(version):
-    def _get_version_params(version):
-        if version == 'CIFAR10':
-            return {
-                'base_folder': 'cifar-10-batches-py',
-                'train_files': ['data_batch_{}'.format(batch) for batch in range(1, 6)],
-                'test_file': 'test_batch',
-                'target_key': 'labels',
-                'meta_file': 'batches.meta',
-                'classes_key': 'label_names',
-            }
-        elif version == 'CIFAR100':
-            return {
-                'base_folder': 'cifar-100-python',
-                'train_files': ['train'],
-                'test_file': 'test',
-                'target_key': 'fine_labels',
-                'meta_file': 'meta',
-                'classes_key': 'fine_label_names',
-            }
-        else:
-            raise ValueError
-
-    def _make_pickled_file(obj, file):
-        with open(file, 'wb') as fh:
-            pickle.dump(obj, fh, 2)
-
-    def _make_data_file(file, target_key):
-        obj = {
-            'data': np.zeros((1, 32 * 32 * 3), dtype=np.uint8),
-            target_key: [0]
-        }
-        _make_pickled_file(obj, file)
-
-    def _make_meta_file(file, classes_key):
-        obj = {
-            classes_key: ['fakedata'],
-        }
-        _make_pickled_file(obj, file)
-
-    params = _get_version_params(version)
-    with get_tmp_dir() as root:
-        base_folder = os.path.join(root, params['base_folder'])
-        os.mkdir(base_folder)
-
-        for file in list(params['train_files']) + [params['test_file']]:
-            _make_data_file(os.path.join(base_folder, file), params['target_key'])
-
-        _make_meta_file(os.path.join(base_folder, params['meta_file']),
-                        params['classes_key'])
-
-        yield root
-
-
 @contextlib.contextmanager
 def imagenet_root():
     import scipy.io as sio

test/test_datasets.py

@@ -10,7 +10,7 @@ from torch._utils_internal import get_file_path_2
 import torchvision
 from torchvision.datasets import utils
 from common_utils import get_tmp_dir
-from fakedata_generation import mnist_root, cifar_root, imagenet_root, \
+from fakedata_generation import mnist_root, imagenet_root, \
     cityscapes_root, svhn_root, places365_root, widerface_root, stl10_root
 import xml.etree.ElementTree as ET
 from urllib.request import Request, urlopen
@@ -173,38 +173,6 @@ class Tester(DatasetTestcase):
             img, target = dataset[0]
             self.assertTrue(isinstance(img, PIL.Image.Image))
 
-    @mock.patch('torchvision.datasets.cifar.check_integrity')
-    @mock.patch('torchvision.datasets.cifar.CIFAR10._check_integrity')
-    def test_cifar10(self, mock_ext_check, mock_int_check):
-        mock_ext_check.return_value = True
-        mock_int_check.return_value = True
-        with cifar_root('CIFAR10') as root:
-            dataset = torchvision.datasets.CIFAR10(root, train=True, download=True)
-            self.generic_classification_dataset_test(dataset, num_images=5)
-            img, target = dataset[0]
-            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
-
-            dataset = torchvision.datasets.CIFAR10(root, train=False, download=True)
-            self.generic_classification_dataset_test(dataset)
-            img, target = dataset[0]
-            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')
-    def test_cifar100(self, mock_ext_check, mock_int_check):
-        mock_ext_check.return_value = True
-        mock_int_check.return_value = True
-        with cifar_root('CIFAR100') as root:
-            dataset = torchvision.datasets.CIFAR100(root, train=True, download=True)
-            self.generic_classification_dataset_test(dataset)
-            img, target = dataset[0]
-            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
-
-            dataset = torchvision.datasets.CIFAR100(root, train=False, download=True)
-            self.generic_classification_dataset_test(dataset)
-            img, target = dataset[0]
-            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
-
     @unittest.skipIf('win' in sys.platform, 'temporarily disabled on Windows')
     def test_cityscapes(self):
         with cityscapes_root() as root: