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))