Ported SVHN dataset to new test framework (#3661)

* Ported SVHN dataset to new test framework

* Fixed flake8 error and added REQUIRED_PACKAGES=scipy

Author: prabhat00155

test/fakedata_generation.py

@@ -210,23 +210,6 @@ def _make_annotations_archive(root):
         yield root
 
 
-@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
-
-
 @contextlib.contextmanager
 def places365_root(split="train-standard", small=False):
     VARIANTS = {

test/test_datasets.py

@@ -2,15 +2,14 @@
 import sys
 import os
 import unittest
-from unittest import mock
 import numpy as np
 import PIL
 from PIL import Image
 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 svhn_root, places365_root, widerface_root, stl10_root
+from fakedata_generation import places365_root, widerface_root, stl10_root
 import xml.etree.ElementTree as ET
 from urllib.request import Request, urlopen
 import itertools
@@ -57,20 +56,6 @@ def generic_segmentation_dataset_test(self, dataset, num_images=1):
 
 
 class Tester(DatasetTestcase):
-    @mock.patch('torchvision.datasets.SVHN._check_integrity')
-    @unittest.skipIf(not HAS_SCIPY, "scipy unavailable")
-    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)
-
     def test_places365(self):
         for split, small in itertools.product(("train-standard", "train-challenge", "val"), (False, True)):
             with places365_root(split=split, small=small) as places365:
@@ -1737,5 +1722,27 @@ def inject_fake_data(self, tmpdir, config):
         return split_to_num_examples[config["train"]]
 
 
+class SvhnTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.SVHN
+    REQUIRED_PACKAGES = ("scipy",)
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(split=("train", "test", "extra"))
+
+    def inject_fake_data(self, tmpdir, config):
+        import scipy.io as sio
+
+        split = config["split"]
+        num_examples = {
+            "train": 2,
+            "test": 3,
+            "extra": 4,
+        }.get(split)
+
+        file = f"{split}_32x32.mat"
+        images = np.zeros((32, 32, 3, num_examples), dtype=np.uint8)
+        targets = np.zeros((num_examples,), dtype=np.uint8)
+        sio.savemat(os.path.join(tmpdir, file), {'X': images, 'y': targets})
+        return num_examples
+
+
 if __name__ == "__main__":
     unittest.main()