def test_can_save_and_load_with_multiple_labels(self): source_dataset = Dataset.from_iterable( [ DatasetItem(id='1', subset='train', annotations=[Label(1), Label(3)]), DatasetItem(id='2', subset='train', image=np.zeros((8, 6, 3)), annotations=[Label(0)]), DatasetItem( id='3', subset='train', image=np.zeros((2, 8, 3)), ), ], categories={ AnnotationType.label: LabelCategories.from_iterable('label_' + str(label) for label in range(10)), }) with TestDir() as test_dir: ImagenetTxtConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset() compare_datasets(self, source_dataset, parsed_dataset, require_images=True)
def test_can_save_dataset_with_no_subsets(self): source_dataset = Dataset.from_iterable([ DatasetItem(id='a/b/c', image=np.zeros((8, 4, 3)), annotations=[Label(1)] ), ], categories={ AnnotationType.label: LabelCategories.from_iterable( 'label_' + str(label) for label in range(10)), }) with TestDir() as test_dir: ImagenetTxtConverter.convert(source_dataset, test_dir, save_images=True) parsed_dataset = ImagenetTxtImporter()(test_dir).make_dataset() compare_datasets(self, source_dataset, parsed_dataset, require_images=True)
def test_can_detect_imagenet(self): self.assertTrue(ImagenetTxtImporter.detect(DUMMY_DATASET_DIR))