import os import shutil import contextlib import tempfile import unittest import mock import PIL import torch import torchvision FAKEDATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets', 'fakedata') @contextlib.contextmanager def tmp_dir(src=None, **kwargs): tmp_dir = tempfile.mkdtemp(**kwargs) if src is not None: os.rmdir(tmp_dir) shutil.copytree(src, tmp_dir) try: yield tmp_dir finally: shutil.rmtree(tmp_dir) @contextlib.contextmanager def get_mnist_data(num_images, cls_name, **kwargs): def _encode(v): return torch.tensor(v, dtype=torch.int32).numpy().tobytes()[::-1] def _make_image_file(filename, num_images): img = torch.randint(0, 255, size=(28 * 28 * num_images,), dtype=torch.uint8) with open(filename, "wb") as f: f.write(_encode(2051)) # magic header f.write(_encode(num_images)) f.write(_encode(28)) f.write(_encode(28)) f.write(img.numpy().tobytes()) def _make_label_file(filename, num_images): labels = torch.randint(0, 10, size=(num_images,), dtype=torch.uint8) with open(filename, "wb") as f: f.write(_encode(2049)) # magic header f.write(_encode(num_images)) f.write(labels.numpy().tobytes()) tmp_dir = tempfile.mkdtemp(**kwargs) raw_dir = os.path.join(tmp_dir, cls_name, "raw") os.makedirs(raw_dir) _make_image_file(os.path.join(raw_dir, "train-images-idx3-ubyte"), num_images) _make_label_file(os.path.join(raw_dir, "train-labels-idx1-ubyte"), num_images) _make_image_file(os.path.join(raw_dir, "t10k-images-idx3-ubyte"), num_images) _make_label_file(os.path.join(raw_dir, "t10k-labels-idx1-ubyte"), num_images) try: yield tmp_dir finally: shutil.rmtree(tmp_dir) class Tester(unittest.TestCase): def test_imagefolder(self): with tmp_dir(src=os.path.join(FAKEDATA_DIR, 'imagefolder')) as root: classes = sorted(['a', 'b']) class_a_image_files = [os.path.join(root, 'a', file) for file in ('a1.png', 'a2.png', 'a3.png')] class_b_image_files = [os.path.join(root, 'b', file) for file in ('b1.png', 'b2.png', 'b3.png', 'b4.png')] dataset = torchvision.datasets.ImageFolder(root, loader=lambda x: x) # test if all classes are present self.assertEqual(classes, sorted(dataset.classes)) # test if combination of classes and class_to_index functions correctly for cls in classes: self.assertEqual(cls, dataset.classes[dataset.class_to_idx[cls]]) # test if all images were detected correctly class_a_idx = dataset.class_to_idx['a'] class_b_idx = dataset.class_to_idx['b'] imgs_a = [(img_file, class_a_idx) for img_file in class_a_image_files] imgs_b = [(img_file, class_b_idx) for img_file in class_b_image_files] imgs = sorted(imgs_a + imgs_b) self.assertEqual(imgs, dataset.imgs) # test if the datasets outputs all images correctly outputs = sorted([dataset[i] for i in range(len(dataset))]) self.assertEqual(imgs, outputs) # redo all tests with specified valid image files dataset = torchvision.datasets.ImageFolder(root, loader=lambda x: x, is_valid_file=lambda x: '3' in x) self.assertEqual(classes, sorted(dataset.classes)) class_a_idx = dataset.class_to_idx['a'] class_b_idx = dataset.class_to_idx['b'] imgs_a = [(img_file, class_a_idx) for img_file in class_a_image_files if '3' in img_file] imgs_b = [(img_file, class_b_idx) for img_file in class_b_image_files if '3' in img_file] imgs = sorted(imgs_a + imgs_b) self.assertEqual(imgs, dataset.imgs) outputs = sorted([dataset[i] for i in range(len(dataset))]) self.assertEqual(imgs, outputs) @mock.patch('torchvision.datasets.mnist.download_and_extract_archive') def test_mnist(self, mock_download_extract): num_examples = 30 with get_mnist_data(num_examples, "MNIST") as root: dataset = torchvision.datasets.MNIST(root, download=True) self.assertEqual(len(dataset), num_examples) img, target = dataset[0] self.assertTrue(isinstance(img, PIL.Image.Image)) self.assertTrue(isinstance(target, int)) @mock.patch('torchvision.datasets.mnist.download_and_extract_archive') def test_kmnist(self, mock_download_extract): num_examples = 30 with get_mnist_data(num_examples, "KMNIST") as root: dataset = torchvision.datasets.KMNIST(root, download=True) img, target = dataset[0] self.assertEqual(len(dataset), num_examples) self.assertTrue(isinstance(img, PIL.Image.Image)) self.assertTrue(isinstance(target, int)) @mock.patch('torchvision.datasets.mnist.download_and_extract_archive') def test_fashionmnist(self, mock_download_extract): num_examples = 30 with get_mnist_data(num_examples, "FashionMNIST") as root: dataset = torchvision.datasets.FashionMNIST(root, download=True) img, target = dataset[0] self.assertEqual(len(dataset), num_examples) self.assertTrue(isinstance(img, PIL.Image.Image)) self.assertTrue(isinstance(target, int)) @mock.patch('torchvision.datasets.utils.download_url') def test_imagenet(self, mock_download): with tmp_dir(src=os.path.join(FAKEDATA_DIR, 'imagenet')) as root: dataset = torchvision.datasets.ImageNet(root, split='train', download=True) self.assertEqual(len(dataset), 3) img, target = dataset[0] self.assertTrue(isinstance(img, PIL.Image.Image)) self.assertTrue(isinstance(target, int)) self.assertEqual(dataset.class_to_idx['Tinca tinca'], target) dataset = torchvision.datasets.ImageNet(root, split='val', download=True) self.assertEqual(len(dataset), 3) img, target = dataset[0] self.assertTrue(isinstance(img, PIL.Image.Image)) self.assertTrue(isinstance(target, int)) self.assertEqual(dataset.class_to_idx['Tinca tinca'], target) if __name__ == '__main__': unittest.main()