def transform(dataset: IDataset,
method: Union[str, Type[Transform]],
*,
env: Optional[Environment] = None,
**kwargs) -> IDataset:
"""
Applies some function to dataset items.
Results are computed lazily, if the transform supports this.
Args:
dataset - The dataset to be transformed
method - The transformation to be applied to the dataset.
If a string is passed, it is treated as a plugin name,
which is searched for in the environment
set by the 'env' argument
env - A plugin collection. If not set, the built-in plugins are used
**kwargs - Parameters for the transformation
Returns: a wrapper around the input dataset
"""
if isinstance(method, str):
if env is None:
env = Environment()
method = env.transforms[method]
if not (inspect.isclass(method) and issubclass(method, Transform)):
raise TypeError("Unexpected 'method' argument type: %s" % \
type(method))
produced = method(dataset, **kwargs)
return Dataset(source=produced, env=env)
def test_can_extract_coco(self):
tfds_example = {
'image': encode_image(np.ones((20, 10)), '.png'),
'image/filename': 'test.png',
'image/id': 123,
'objects': {
'bbox': [[0.1, 0.2, 0.3, 0.4]],
'label': [5],
'is_crowd': [True],
}
}
with mock_tfds_data(example=tfds_example):
tfds_info = tfds.builder('coco/2014').info
expected_dataset = Dataset.from_iterable(
[
DatasetItem(
id='test',
subset='train',
image=np.ones((20, 10)),
annotations=[
Bbox(2,
2,
2,
4,
label=5,
attributes={'is_crowd': True}),
],
attributes={'id': 123},
),
],
categories=tfds_info.features['objects'].feature['label'].names
)
extractor = make_tfds_extractor('coco/2014')
actual_dataset = Dataset(extractor)
compare_datasets(self,
expected_dataset,
actual_dataset,
require_images=True)
