def test_can_save_and_load_with_meta_file(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='image_2',
subset='test',
image=np.ones((32, 32, 3)),
annotations=[Label(0)]),
DatasetItem(
id='image_3', subset='test', image=np.ones((32, 32, 3))),
DatasetItem(id='image_4',
subset='test',
image=np.ones((32, 32, 3)),
annotations=[Label(1)])
],
categories=['label_0', 'label_1'])
with TestDir() as test_dir:
CifarConverter.convert(source_dataset,
test_dir,
save_images=True,
save_dataset_meta=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
self.assertTrue(osp.isfile(osp.join(test_dir,
'dataset_meta.json')))
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_cifar100(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='image_2',
subset='test',
image=np.ones((32, 32, 3)),
annotations=[Label(0)]),
DatasetItem(
id='image_3', subset='test', image=np.ones((32, 32, 3))),
DatasetItem(id='image_4',
subset='test',
image=np.ones((32, 32, 3)),
annotations=[Label(1)])
],
categories=[['class_0', 'superclass_0'],
['class_1', 'superclass_0']])
with TestDir() as test_dir:
CifarConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_empty_image(self):
dataset = Dataset.from_iterable(
[DatasetItem(id='a', annotations=[Label(0)]),
DatasetItem(id='b')],
categories=['label_0'])
with TestDir() as test_dir:
CifarConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_without_saving_images(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='a', subset='train_1', annotations=[Label(0)]),
DatasetItem(id='b', subset='train_first', annotations=[Label(1)]),
],
categories=['x', 'y'])
with TestDir() as test_dir:
CifarConverter.convert(source_dataset, test_dir, save_images=False)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id="кириллица с пробелом",
image=np.ones((32, 32, 3)),
annotations=[Label(0)]),
],
categories=['label_0'])
with TestDir() as test_dir:
CifarConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_with_different_image_size(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='image_1',
image=np.ones((10, 8, 3)),
annotations=[Label(0)]),
DatasetItem(id='image_2',
image=np.ones((32, 32, 3)),
annotations=[Label(1)]),
],
categories=['dog', 'cat'])
with TestDir() as test_dir:
CifarConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_and_load_image_with_arbitrary_extension(self):
dataset = Dataset.from_iterable([
DatasetItem(id='q/1',
image=Image(path='q/1.JPEG',
data=np.zeros((32, 32, 3)))),
DatasetItem(id='a/b/c/2',
image=Image(path='a/b/c/2.bmp',
data=np.zeros((32, 32, 3)))),
],
categories=[])
with TestDir() as test_dir:
CifarConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'cifar')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)