Add tests for the STL10 dataset (#3345)

* extract some functionality from places365 fakedata for common use

* add a common DatasetTestcase

* add fakedata generation and tests for STL10

* lint
Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

test/fakedata_generation.py

@@ -13,6 +13,48 @@ from torchvision.io.video import write_video
 import unittest.mock
 import hashlib
 from distutils import dir_util
+import re
+
+
+def mock_class_attribute(stack, target, new):
+    mock = unittest.mock.patch(target, new_callable=unittest.mock.PropertyMock, return_value=new)
+    stack.enter_context(mock)
+    return mock
+
+
+def compute_md5(file):
+    with open(file, "rb") as fh:
+        return hashlib.md5(fh.read()).hexdigest()
+
+
+def make_tar(root, name, *files, compression=None):
+    ext = ".tar"
+    mode = "w"
+    if compression is not None:
+        ext = f"{ext}.{compression}"
+        mode = f"{mode}:{compression}"
+
+    name = os.path.splitext(name)[0] + ext
+    archive = os.path.join(root, name)
+
+    with tarfile.open(archive, mode) as fh:
+        for file in files:
+            fh.add(os.path.join(root, file), arcname=file)
+
+    return name, compute_md5(archive)
+
+
+def clean_dir(root, *keep):
+    pattern = re.compile(f"({f')|('.join(keep)})")
+    for file_or_dir in os.listdir(root):
+        if pattern.search(file_or_dir):
+            continue
+
+        file_or_dir = os.path.join(root, file_or_dir)
+        if os.path.isfile(file_or_dir):
+            os.remove(file_or_dir)
+        else:
+            dir_util.remove_tree(file_or_dir)
 
 
 @contextlib.contextmanager
@@ -385,7 +427,7 @@ def ucf101_root():
 
 
 @contextlib.contextmanager
-def places365_root(split="train-standard", small=False, extract_images=True):
+def places365_root(split="train-standard", small=False):
     VARIANTS = {
         "train-standard": "standard",
         "train-challenge": "challenge",
@@ -425,15 +467,6 @@ def places365_root(split="train-standard", small=False, extract_images=True):
     def mock_target(attr, partial="torchvision.datasets.places365.Places365"):
         return f"{partial}.{attr}"
 
-    def mock_class_attribute(stack, attr, new):
-        mock = unittest.mock.patch(mock_target(attr), new_callable=unittest.mock.PropertyMock, return_value=new)
-        stack.enter_context(mock)
-        return mock
-
-    def compute_md5(file):
-        with open(file, "rb") as fh:
-            return hashlib.md5(fh.read()).hexdigest()
-
     def make_txt(root, name, seq):
         file = os.path.join(root, name)
         with open(file, "w") as fh:
@@ -451,37 +484,20 @@ def places365_root(split="train-standard", small=False, extract_images=True):
         os.makedirs(os.path.dirname(file), exist_ok=True)
         PIL.Image.fromarray(np.zeros((*size, 3), dtype=np.uint8)).save(file)
 
-    def make_tar(root, name, *files, remove_files=True):
-        name = f"{os.path.splitext(name)[0]}.tar"
-        archive = os.path.join(root, name)
-
-        with tarfile.open(archive, "w") as fh:
-            for file in files:
-                fh.add(os.path.join(root, file), arcname=file)
-
-        if remove_files:
-            for file in [os.path.join(root, file) for file in files]:
-                if os.path.isdir(file):
-                    dir_util.remove_tree(file)
-                else:
-                    os.remove(file)
-
-        return name, compute_md5(archive)
-
     def make_devkit_archive(stack, root, split):
         archive = DEVKITS[split]
         files = []
 
         meta = make_categories_txt(root, CATEGORIES)
-        mock_class_attribute(stack, "_CATEGORIES_META", meta)
+        mock_class_attribute(stack, mock_target("_CATEGORIES_META"), meta)
         files.append(meta[0])
 
         meta = {split: make_file_list_txt(root, FILE_LISTS[split])}
-        mock_class_attribute(stack, "_FILE_LIST_META", meta)
+        mock_class_attribute(stack, mock_target("_FILE_LIST_META"), meta)
         files.extend([item[0] for item in meta.values()])
 
         meta = {VARIANTS[split]: make_tar(root, archive, *files)}
-        mock_class_attribute(stack, "_DEVKIT_META", meta)
+        mock_class_attribute(stack, mock_target("_DEVKIT_META"), meta)
 
     def make_images_archive(stack, root, split, small):
         archive, folder_default, folder_renamed = IMAGES[(split, small)]
@@ -493,7 +509,7 @@ def places365_root(split="train-standard", small=False, extract_images=True):
             make_image(os.path.join(root, folder_default, image), image_size)
 
         meta = {(split, small): make_tar(root, archive, folder_default)}
-        mock_class_attribute(stack, "_IMAGES_META", meta)
+        mock_class_attribute(stack, mock_target("_IMAGES_META"), meta)
 
         return [(os.path.join(root, folder_renamed, image), idx) for image, idx in zip(images, idcs)]
 
@@ -501,12 +517,89 @@ def places365_root(split="train-standard", small=False, extract_images=True):
         make_devkit_archive(stack, root, split)
         class_to_idx = dict(CATEGORIES_CONTENT)
         classes = list(class_to_idx.keys())
+
         data = {"class_to_idx": class_to_idx, "classes": classes}
+        data["imgs"] = make_images_archive(stack, root, split, small)
 
-        if extract_images:
-            data["imgs"] = make_images_archive(stack, root, split, small)
-        else:
-            stack.enter_context(unittest.mock.patch(mock_target("download_images")))
-            data["imgs"] = None
+        clean_dir(root, ".tar$")
+
+        yield root, data
+
+
+@contextlib.contextmanager
+def stl10_root(_extracted=False):
+    CLASS_NAMES = ("airplane", "bird")
+    ARCHIVE_NAME = "stl10_binary"
+    NUM_FOLDS = 10
+
+    def mock_target(attr, partial="torchvision.datasets.stl10.STL10"):
+        return f"{partial}.{attr}"
+
+    def make_binary_file(num_elements, root, name):
+        file = os.path.join(root, name)
+        np.zeros(num_elements, dtype=np.uint8).tofile(file)
+        return name, compute_md5(file)
+
+    def make_image_file(num_images, root, name, num_channels=3, height=96, width=96):
+        return make_binary_file(num_images * num_channels * height * width, root, name)
+
+    def make_label_file(num_images, root, name):
+        return make_binary_file(num_images, root, name)
+
+    def make_class_names_file(root, name="class_names.txt"):
+        with open(os.path.join(root, name), "w") as fh:
+            for name in CLASS_NAMES:
+                fh.write(f"{name}\n")
+
+    def make_fold_indices_file(root):
+        offset = 0
+        with open(os.path.join(root, "fold_indices.txt"), "w") as fh:
+            for fold in range(NUM_FOLDS):
+                line = " ".join([str(idx) for idx in range(offset, offset + fold + 1)])
+                fh.write(f"{line}\n")
+                offset += fold + 1
+
+        return tuple(range(1, NUM_FOLDS + 1))
+
+    def make_train_files(stack, root, num_unlabeled_images=1):
+        num_images_in_fold = make_fold_indices_file(root)
+        num_train_images = sum(num_images_in_fold)
+
+        train_list = [
+            list(make_image_file(num_train_images, root, "train_X.bin")),
+            list(make_label_file(num_train_images, root, "train_y.bin")),
+            list(make_image_file(1, root, "unlabeled_X.bin"))
+        ]
+        mock_class_attribute(stack, target=mock_target("train_list"), new=train_list)
+
+        return num_images_in_fold, dict(train=num_train_images, unlabeled=num_unlabeled_images)
+
+    def make_test_files(stack, root, num_images=2):
+        test_list = [
+            list(make_image_file(num_images, root, "test_X.bin")),
+            list(make_label_file(num_images, root, "test_y.bin")),
+        ]
+        mock_class_attribute(stack, target=mock_target("test_list"), new=test_list)
+
+        return dict(test=num_images)
+
+    def make_archive(stack, root, name):
+        archive, md5 = make_tar(root, name, name, compression="gz")
+        mock_class_attribute(stack, target=mock_target("tgz_md5"), new=md5)
+        return archive
+
+    with contextlib.ExitStack() as stack, get_tmp_dir() as root:
+        archive_folder = os.path.join(root, ARCHIVE_NAME)
+        os.mkdir(archive_folder)
+
+        num_images_in_folds, num_images_in_split = make_train_files(stack, archive_folder)
+        num_images_in_split.update(make_test_files(stack, archive_folder))
+
+        make_class_names_file(archive_folder)
+
+        archive = make_archive(stack, root, ARCHIVE_NAME)
+
+        dir_util.remove_tree(archive_folder)
+        data = dict(num_images_in_folds=num_images_in_folds, num_images_in_split=num_images_in_split, archive=archive)
 
         yield root, data

test/test_datasets.py

+import contextlib
 import sys
 import os
 import unittest
@@ -7,9 +8,10 @@ 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 mnist_root, cifar_root, imagenet_root, \
-    cityscapes_root, svhn_root, voc_root, ucf101_root, places365_root, widerface_root
+    cityscapes_root, svhn_root, voc_root, ucf101_root, places365_root, widerface_root, stl10_root
 import xml.etree.ElementTree as ET
 from urllib.request import Request, urlopen
 import itertools
@@ -28,7 +30,7 @@ except ImportError:
     HAS_PYAV = False
 
 
-class Tester(unittest.TestCase):
+class DatasetTestcase(unittest.TestCase):
     def generic_classification_dataset_test(self, dataset, num_images=1):
         self.assertEqual(len(dataset), num_images)
         img, target = dataset[0]
@@ -41,6 +43,8 @@ class Tester(unittest.TestCase):
         self.assertTrue(isinstance(img, PIL.Image.Image))
         self.assertTrue(isinstance(target, PIL.Image.Image))
 
+
+class Tester(DatasetTestcase):
     def test_imagefolder(self):
         # TODO: create the fake data on-the-fly
         FAKEDATA_DIR = get_file_path_2(
@@ -354,7 +358,7 @@ class Tester(unittest.TestCase):
     def test_places365_devkit_no_download(self):
         for split in ("train-standard", "train-challenge", "val"):
             with self.subTest(split=split):
-                with places365_root(split=split, extract_images=False) as places365:
+                with places365_root(split=split) as places365:
                     root, data = places365
 
                     with self.assertRaises(RuntimeError):
@@ -383,12 +387,84 @@ class Tester(unittest.TestCase):
                 torchvision.datasets.Places365(root, split=split, small=small, download=True)
 
     def test_places365_repr_smoke(self):
-        with places365_root(extract_images=False) as places365:
+        with places365_root() as places365:
             root, data = places365
 
             dataset = torchvision.datasets.Places365(root, download=True)
             self.assertIsInstance(repr(dataset), str)
 
 
+class STL10Tester(DatasetTestcase):
+    @contextlib.contextmanager
+    def mocked_root(self):
+        with stl10_root() as (root, data):
+            yield root, data
+
+    @contextlib.contextmanager
+    def mocked_dataset(self, pre_extract=False, download=True, **kwargs):
+        with self.mocked_root() as (root, data):
+            if pre_extract:
+                utils.extract_archive(os.path.join(root, data["archive"]))
+            dataset = torchvision.datasets.STL10(root, download=download, **kwargs)
+            yield dataset, data
+
+    def test_not_found(self):
+        with self.assertRaises(RuntimeError):
+            with self.mocked_dataset(download=False):
+                pass
+
+    def test_splits(self):
+        for split in ('train', 'train+unlabeled', 'unlabeled', 'test'):
+            with self.mocked_dataset(split=split) as (dataset, data):
+                num_images = sum([data["num_images_in_split"][part] for part in split.split("+")])
+                self.generic_classification_dataset_test(dataset, num_images=num_images)
+
+    def test_folds(self):
+        for fold in range(10):
+            with self.mocked_dataset(split="train", folds=fold) as (dataset, data):
+                num_images = data["num_images_in_folds"][fold]
+                self.assertEqual(len(dataset), num_images)
+
+    def test_invalid_folds1(self):
+        with self.assertRaises(ValueError):
+            with self.mocked_dataset(folds=10):
+                pass
+
+    def test_invalid_folds2(self):
+        with self.assertRaises(ValueError):
+            with self.mocked_dataset(folds="0"):
+                pass
+
+    def test_transforms(self):
+        expected_image = "image"
+        expected_target = "target"
+
+        def transform(image):
+            return expected_image
+
+        def target_transform(target):
+            return expected_target
+
+        with self.mocked_dataset(transform=transform, target_transform=target_transform) as (dataset, _):
+            actual_image, actual_target = dataset[0]
+
+            self.assertEqual(actual_image, expected_image)
+            self.assertEqual(actual_target, expected_target)
+
+    def test_unlabeled(self):
+        with self.mocked_dataset(split="unlabeled") as (dataset, _):
+            labels = [dataset[idx][1] for idx in range(len(dataset))]
+            self.assertTrue(all([label == -1 for label in labels]))
+
+    @unittest.mock.patch("torchvision.datasets.stl10.download_and_extract_archive")
+    def test_download_preexisting(self, mock):
+        with self.mocked_dataset(pre_extract=True) as (dataset, data):
+            mock.assert_not_called()
+
+    def test_repr_smoke(self):
+        with self.mocked_dataset() as (dataset, _):
+            self.assertIsInstance(repr(dataset), str)
+
+
 if __name__ == '__main__':
     unittest.main()