def test_can_save_and_load(self):
        source_dataset = Dataset.from_iterable([
            DatasetItem(id='1',
                image=np.ones((8, 8, 3)),
                annotations=[Label(0)]
            ),
            DatasetItem(id='2',
                image=np.ones((10, 10, 3)),
                annotations=[Label(1)]
            ),
            DatasetItem(id='3',
                image=np.ones((10, 10, 3)),
                annotations=[Label(0)]
            ),
            DatasetItem(id='4',
                image=np.ones((8, 8, 3)),
                annotations=[Label(2)]
            ),
        ], categories={
            AnnotationType.label: LabelCategories.from_iterable(
                'label_' + str(label) for label in range(3)),
        })

        with TestDir() as test_dir:
            ImagenetConverter.convert(source_dataset, test_dir, save_images=True)

            parsed_dataset = ImagenetImporter()(test_dir).make_dataset()

            compare_datasets(self, source_dataset, parsed_dataset,
                require_images=True)
 def test_can_detect_imagenet(self):
     self.assertTrue(ImagenetImporter.detect(DUMMY_DATASET_DIR))