Class DivisiblePad

monai/transforms/croppad/array.py:298–342

Pad the input data, so that the spatial sizes are divisible by `k`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information.

class DivisiblePad(Pad):
    """
    Pad the input data, so that the spatial sizes are divisible by `k`.

    This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic<lazy_resampling>`
    for more information.
    """

    backend = SpatialPad.backend

    def __init__(
        self,
        k: Sequence[int] | int,
        mode: str = PytorchPadMode.CONSTANT,
        method: str = Method.SYMMETRIC,
        lazy: bool = False,
        **kwargs,
    ) -> None:
        """
        Args:
            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: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,
                ``"mean"``, ``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
                available modes for PyTorch Tensor: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}.
                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
                https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
            method: {``"symmetric"``, ``"end"``}
                Pad image symmetrically on every side or only pad at the end sides. Defaults to ``"symmetric"``.
            lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.
            kwargs: other arguments for the `np.pad` or `torch.pad` function.
                note that `np.pad` treats channel dimension as the first dimension.

        See also :py:class:`monai.transforms.SpatialPad`
        """
        self.k = k
        self.method: Method = Method(method)
        super().__init__(mode=mode, lazy=lazy, **kwargs)

    def compute_pad_width(self, spatial_shape: Sequence[int]) -> tuple[tuple[int, int]]:
        new_size = compute_divisible_spatial_size(spatial_shape=spatial_shape, k=self.k)
        spatial_pad = SpatialPad(spatial_size=new_size, method=self.method)
        return spatial_pad.compute_pad_width(spatial_shape)


class Crop(InvertibleTransform, LazyTransform):