add tests for MNIST and variants

test/test_datasets.py

@@ -10,8 +10,8 @@
 import torchvision
 from torchvision.datasets import utils
 from common_utils import get_tmp_dir
-from fakedata_generation import mnist_root, \
-    cityscapes_root, svhn_root, places365_root, widerface_root, stl10_root
+
+from fakedata_generation import cityscapes_root, svhn_root, places365_root, widerface_root, stl10_root
 import xml.etree.ElementTree as ET
 from urllib.request import Request, urlopen
 import itertools
@@ -119,37 +119,6 @@ def test_imagefolder_empty(self):
                     root, loader=lambda x: x, is_valid_file=lambda x: False
                 )
 
-    @mock.patch('torchvision.datasets.mnist.download_and_extract_archive')
-    @mock.patch('torchvision.datasets.mnist.check_integrity', return_value=True)
-    def test_mnist(self, mock_download_extract, mock_check_integrity):
-        num_examples = 30
-        with mnist_root(num_examples, "MNIST") as root:
-            dataset = torchvision.datasets.MNIST(root, download=True)
-            self.generic_classification_dataset_test(dataset, num_images=num_examples)
-            img, target = dataset[0]
-            self.assertEqual(dataset.class_to_idx[dataset.classes[0]], target)
-
-    @mock.patch('torchvision.datasets.mnist.download_and_extract_archive')
-    @mock.patch('torchvision.datasets.mnist.check_integrity', return_value=True)
-    def test_kmnist(self, mock_download_extract, mock_check_integrity):
-        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(dataset.class_to_idx[dataset.classes[0]], target)
-
-    @mock.patch('torchvision.datasets.mnist.download_and_extract_archive')
-    @mock.patch('torchvision.datasets.mnist.check_integrity', return_value=True)
-    def test_fashionmnist(self, mock_download_extract, mock_check_integrity):
-        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(dataset.class_to_idx[dataset.classes[0]], target)
-
-    @unittest.skipIf(sys.platform in ('win32', 'cygwin'), 'temporarily disabled on Windows')
     def test_cityscapes(self):
         with cityscapes_root() as root:
 
@@ -1499,5 +1468,131 @@ def _create_annotations_file(self, root, name, images, num_captions_per_image):
                 fh.write(f"{image.name}#{idx}\t{caption}\n")
 
 
+class MNISTTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.MNIST
+
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(train=(True, False))
+
+    _MAGIC_DTYPES = {
+        torch.uint8: 8,
+        torch.int8: 9,
+        torch.int16: 11,
+        torch.int32: 12,
+        torch.float32: 13,
+        torch.float64: 14,
+    }
+
+    _IMAGES_SIZE = (28, 28)
+    _IMAGES_DTYPE = torch.uint8
+
+    _LABELS_SIZE = ()
+    _LABELS_DTYPE = torch.uint8
+
+    def inject_fake_data(self, tmpdir, config):
+        raw_dir = pathlib.Path(tmpdir) / self.DATASET_CLASS.__name__ / "raw"
+        os.makedirs(raw_dir, exist_ok=True)
+
+        num_images = self._num_images(config)
+        self._create_binary_file(
+            raw_dir, self._images_file(config), (num_images, *self._IMAGES_SIZE), self._IMAGES_DTYPE
+        )
+        self._create_binary_file(
+            raw_dir, self._labels_file(config), (num_images, *self._LABELS_SIZE), self._LABELS_DTYPE
+        )
+        return num_images
+
+    def _num_images(self, config):
+        return 2 if config["train"] else 1
+
+    def _images_file(self, config):
+        return f"{self._prefix(config)}-images-idx3-ubyte"
+
+    def _labels_file(self, config):
+        return f"{self._prefix(config)}-labels-idx1-ubyte"
+
+    def _prefix(self, config):
+        return "train" if config["train"] else "t10k"
+
+    def _create_binary_file(self, root, filename, size, dtype):
+        with open(pathlib.Path(root) / filename, "wb") as fh:
+            for meta in (self._magic(dtype, len(size)), *size):
+                fh.write(self._encode(meta))
+
+            # If ever an MNIST variant is added that uses floating point data, this should be adapted.
+            data = torch.randint(0, torch.iinfo(dtype).max + 1, size, dtype=dtype)
+            fh.write(data.numpy().tobytes())
+
+    def _magic(self, dtype, dims):
+        return self._MAGIC_DTYPES[dtype] * 256 + dims
+
+    def _encode(self, v):
+        return torch.tensor(v, dtype=torch.int32).numpy().tobytes()[::-1]
+
+
+class FashionMNISTTestCase(MNISTTestCase):
+    DATASET_CLASS = datasets.FashionMNIST
+
+
+class KMNISTTestCase(MNISTTestCase):
+    DATASET_CLASS = datasets.KMNIST
+
+
+class EMNISTTestCase(MNISTTestCase):
+    DATASET_CLASS = datasets.EMNIST
+
+    DEFAULT_CONFIG = dict(split="byclass")
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(
+        split=("byclass", "bymerge", "balanced", "letters", "digits", "mnist"), train=(True, False)
+    )
+
+    def _prefix(self, config):
+        return f"emnist-{config['split']}-{'train' if config['train'] else 'test'}"
+
+
+class QMNISTTestCase(MNISTTestCase):
+    DATASET_CLASS = datasets.QMNIST
+
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(what=("train", "test", "test10k", "nist"))
+
+    _LABELS_SIZE = (8,)
+    _LABELS_DTYPE = torch.int32
+
+    def _num_images(self, config):
+        if config["what"] == "nist":
+            return 3
+        elif config["what"] == "train":
+            return 2
+        elif config["what"] == "test50k":
+            # The split 'test50k' is defined as the last 50k images beginning at index 10000. Thus, we need to create
+            # more than 10000 images for the dataset to not be empty. Since this takes significantly longer than the
+            # creation of all other splits, this is excluded from the 'ADDITIONAL_CONFIGS' and is tested only once in
+            # 'test_num_examples_test50k'.
+            return 10001
+        else:
+            return 1
+
+    def _labels_file(self, config):
+        return f"{self._prefix(config)}-labels-idx2-int"
+
+    def _prefix(self, config):
+        if config["what"] == "nist":
+            return "xnist"
+
+        if config["what"] is None:
+            what = "train" if config["train"] else "test"
+        elif config["what"].startswith("test"):
+            what = "test"
+        else:
+            what = config["what"]
+
+        return f"qmnist-{what}"
+
+    def test_num_examples_test50k(self):
+        with self.create_dataset(what="test50k") as (dataset, info):
+            # Since the split 'test50k' selects all images beginning from the index 10000, we subtract the number of
+            # created examples by this.
+            self.assertEqual(len(dataset), info["num_examples"] - 10000)
+
+
 if __name__ == "__main__":
     unittest.main()