test_datasets.py

import os
import sys
import shutil
import contextlib
import tempfile
import unittest
import mock
import numpy as np
import PIL
import torch
import torchvision

PYTHON2 = sys.version_info[0] == 2
if PYTHON2:
    import cPickle as pickle
else:
    import pickle

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)


@contextlib.contextmanager
def cifar_root(version):
    def _get_version_params(version):
        if version == 'CIFAR10':
            return {
                'base_folder': 'cifar-10-batches-py',
                'train_files': ['data_batch_{}'.format(batch) for batch in range(1, 6)],
                'test_file': 'test_batch',
                'target_key': 'labels',
                'meta_file': 'batches.meta',
                'classes_key': 'label_names',
            }
        elif version == 'CIFAR100':
            return {
                'base_folder': 'cifar-100-python',
                'train_files': ['train'],
                'test_file': 'test',
                'target_key': 'fine_labels',
                'meta_file': 'meta',
                'classes_key': 'fine_label_names',
            }
        else:
            raise ValueError

    def _make_pickled_file(obj, file):
        with open(file, 'wb') as fh:
            pickle.dump(obj, fh, 2)

    def _make_data_file(file, target_key):
        obj = {
            'data': np.zeros((1, 32 * 32 * 3), dtype=np.uint8),
            target_key: [0]
        }
        _make_pickled_file(obj, file)

    def _make_meta_file(file, classes_key):
        obj = {
            classes_key: ['fakedata'],
        }
        _make_pickled_file(obj, file)

    params = _get_version_params(version)
    with tmp_dir() as root:
        base_folder = os.path.join(root, params['base_folder'])
        os.mkdir(base_folder)

        for file in list(params['train_files']) + [params['test_file']]:
            _make_data_file(os.path.join(base_folder, file), params['target_key'])

        _make_meta_file(os.path.join(base_folder, params['meta_file']),
                        params['classes_key'])

        yield root


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)

    @mock.patch('torchvision.datasets.cifar.check_integrity')
    @mock.patch('torchvision.datasets.cifar.CIFAR10._check_integrity')
    def test_cifar10(self, mock_ext_check, mock_int_check):
        mock_ext_check.return_value = True
        mock_int_check.return_value = True
        with cifar_root('CIFAR10') as root:
            dataset = torchvision.datasets.CIFAR10(root, train=True, download=True)
            self.assertEqual(len(dataset), 5)
            img, target = dataset[0]
            self.assertTrue(isinstance(img, PIL.Image.Image))
            self.assertTrue(isinstance(target, int))
            self.assertEqual(dataset.class_to_idx['fakedata'], target)

            dataset = torchvision.datasets.CIFAR10(root, train=False, download=True)
            self.assertEqual(len(dataset), 1)
            img, target = dataset[0]
            self.assertTrue(isinstance(img, PIL.Image.Image))
            self.assertTrue(isinstance(target, int))
            self.assertEqual(dataset.class_to_idx['fakedata'], target)

    @mock.patch('torchvision.datasets.cifar.check_integrity')
    @mock.patch('torchvision.datasets.cifar.CIFAR10._check_integrity')
    def test_cifar100(self, mock_ext_check, mock_int_check):
        mock_ext_check.return_value = True
        mock_int_check.return_value = True
        with cifar_root('CIFAR100') as root:
            dataset = torchvision.datasets.CIFAR100(root, train=True, download=True)
            self.assertEqual(len(dataset), 1)
            img, target = dataset[0]
            self.assertTrue(isinstance(img, PIL.Image.Image))
            self.assertTrue(isinstance(target, int))
            self.assertEqual(dataset.class_to_idx['fakedata'], target)

            dataset = torchvision.datasets.CIFAR100(root, train=False, download=True)
            self.assertEqual(len(dataset), 1)
            img, target = dataset[0]
            self.assertTrue(isinstance(img, PIL.Image.Image))
            self.assertTrue(isinstance(target, int))
            self.assertEqual(dataset.class_to_idx['fakedata'], target)


if __name__ == '__main__':
    unittest.main()