def __init__(
self,
keys: KeysCollection,
k: Union[Sequence[int], int],
mode: NumpyPadModeSequence = NumpyPadMode.CONSTANT,
allow_missing_keys: bool = False,
) -> None:
"""
Args:
keys: keys of the corresponding items to be transformed.
See also: :py:class:`monai.transforms.compose.MapTransform`
k: the target k for each spatial dimension.
if `k` is negative or 0, the original size is preserved.
if `k` is an int, the same `k` be applied to all the input spatial dimensions.
mode: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``,
``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
One of the listed string values or a user supplied function. Defaults to ``"constant"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
It also can be a sequence of string, each element corresponds to a key in ``keys``.
allow_missing_keys: don't raise exception if key is missing.
See also :py:class:`monai.transforms.SpatialPad`
"""
super().__init__(keys, allow_missing_keys)
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.padder = DivisiblePad(k=k)
def __init__(self, keys: KeysCollection, k, mode="constant"):
"""
Args:
k (int or sequence of int): the target k for each spatial dimension.
if `k` is negative or 0, the original size is preserved.
if `k` is an int, the same `k` be applied to all the input spatial dimensions.
mode (str or sequence of str): padding mode for SpatialPad.
See also :py:class:`monai.transforms.SpatialPad`
"""
super().__init__(keys)
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.padder = DivisiblePad(k=k)