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
