Class BorderPad

monai/transforms/croppad/array.py:240–295

Pad the input data by adding specified borders to every dimension. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: spatial_border: specified size for every spatial border. Any -ve values will be

class BorderPad(Pad):
    """
    Pad the input data by adding specified borders to every dimension.

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

    Args:
        spatial_border: specified size for every spatial border. Any -ve values will be set to 0. It can be 3 shapes:

            - single int number, pad all the borders with the same size.
            - length equals the length of image shape, pad every spatial dimension separately.
              for example, image shape(CHW) is [1, 4, 4], spatial_border is [2, 1],
              pad every border of H dim with 2, pad every border of W dim with 1, result shape is [1, 8, 6].
            - length equals 2 x (length of image shape), pad every border of every dimension separately.
              for example, image shape(CHW) is [1, 4, 4], spatial_border is [1, 2, 3, 4], pad top of H dim with 1,
              pad bottom of H dim with 2, pad left of W dim with 3, pad right of W dim with 4.
              the result shape is [1, 7, 11].
        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
        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.

    """

    def __init__(
        self, spatial_border: Sequence[int] | int, mode: str = PytorchPadMode.CONSTANT, lazy: bool = False, **kwargs
    ) -> None:
        self.spatial_border = spatial_border
        super().__init__(mode=mode, lazy=lazy, **kwargs)

    def compute_pad_width(self, spatial_shape: Sequence[int]) -> tuple[tuple[int, int]]:
        spatial_border = ensure_tuple(self.spatial_border)
        if not all(isinstance(b, int) for b in spatial_border):
            raise ValueError(f"self.spatial_border must contain only ints, got {spatial_border}.")
        spatial_border = tuple(max(0, b) for b in spatial_border)

        if len(spatial_border) == 1:
            data_pad_width = [(int(spatial_border[0]), int(spatial_border[0])) for _ in spatial_shape]
        elif len(spatial_border) == len(spatial_shape):
            data_pad_width = [(int(sp), int(sp)) for sp in spatial_border[: len(spatial_shape)]]
        elif len(spatial_border) == len(spatial_shape) * 2:
            data_pad_width = [
                (int(spatial_border[2 * i]), int(spatial_border[2 * i + 1])) for i in range(len(spatial_shape))
            ]
        else:
            raise ValueError(
                f"Unsupported spatial_border length: {len(spatial_border)}, available options are "
                f"[1, len(spatial_shape)={len(spatial_shape)}, 2*len(spatial_shape)={2 * len(spatial_shape)}]."
            )
        return tuple([(0, 0)] + data_pad_width)  # type: ignore