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))