Test for SVHN (#1086)

* cast images to PIL at instantiation instead of runtime

* add test for svhn

* added tests for remaining SVHN splits

* flake8

* rolled back changes to datasets

test/fakedata_generation.py

@@ -241,3 +241,20 @@ def cityscapes_root():
                                          '{city}_000000_000000_leftImg8bit.png'.format(city=city)))
 
         yield tmp_dir
+
+
+@contextlib.contextmanager
+def svhn_root():
+    import scipy.io as sio
+
+    def _make_mat(file):
+        images = np.zeros((32, 32, 3, 2), dtype=np.uint8)
+        targets = np.zeros((2,), dtype=np.uint8)
+        sio.savemat(file, {'X': images, 'y': targets})
+
+    with get_tmp_dir() as root:
+        _make_mat(os.path.join(root, "train_32x32.mat"))
+        _make_mat(os.path.join(root, "test_32x32.mat"))
+        _make_mat(os.path.join(root, "extra_32x32.mat"))
+
+        yield root

test/test_datasets.py

@@ -7,7 +7,8 @@ from PIL import Image
 from torch._utils_internal import get_file_path_2
 import torchvision
 from common_utils import get_tmp_dir
-from fakedata_generation import mnist_root, cifar_root, imagenet_root, cityscapes_root
+from fakedata_generation import mnist_root, cifar_root, imagenet_root, \
+    cityscapes_root, svhn_root
 
 
 class Tester(unittest.TestCase):
@@ -185,6 +186,19 @@ class Tester(unittest.TestCase):
                     self.assertTrue(isinstance(output[1][1], dict))  # polygon
                     self.assertTrue(isinstance(output[1][2], PIL.Image.Image))  # color
 
+    @mock.patch('torchvision.datasets.SVHN._check_integrity')
+    def test_svhn(self, mock_check):
+        mock_check.return_value = True
+        with svhn_root() as root:
+            dataset = torchvision.datasets.SVHN(root, split="train")
+            self.generic_classification_dataset_test(dataset, num_images=2)
+
+            dataset = torchvision.datasets.SVHN(root, split="test")
+            self.generic_classification_dataset_test(dataset, num_images=2)
+
+            dataset = torchvision.datasets.SVHN(root, split="extra")
+            self.generic_classification_dataset_test(dataset, num_images=2)
+
 
 if __name__ == '__main__':
     unittest.main()