def test_parse_args_from_docstring_normal():
args_help = parse_args_from_docstring(
"""Constrain image dataset
Args:
root: Root directory of dataset where ``MNIST/processed/training.pt``
and ``MNIST/processed/test.pt`` exist.
train: If ``True``, creates dataset from ``training.pt``,
otherwise from ``test.pt``.
normalize: mean and std deviation of the MNIST dataset.
download: If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.
num_samples: number of examples per selected class/digit
digits: list selected MNIST digits/classes
Examples:
>>> dataset = TrialMNIST(download=True)
>>> len(dataset)
300
>>> sorted(set([d.item() for d in dataset.targets]))
[0, 1, 2]
>>> torch.bincount(dataset.targets)
tensor([100, 100, 100])
"""
)
expected_args = ['root', 'train', 'normalize', 'download', 'num_samples', 'digits']
assert len(args_help.keys()) == len(expected_args)
assert all([x == y for x, y in zip(args_help.keys(), expected_args)])
assert args_help['root'] == 'Root directory of dataset where ``MNIST/processed/training.pt``' \
' and ``MNIST/processed/test.pt`` exist.'
assert args_help['normalize'] == 'mean and std deviation of the MNIST dataset.'
def test_parse_args_from_docstring_empty():
args_help = parse_args_from_docstring("""Constrain image dataset
Args:
Returns:
Examples:
""")
assert len(args_help.keys()) == 0