Class ResizeWithPadOrCropd

monai/transforms/croppad/dictionary.py:1297–1338

Dictionary-based wrapper of :py:class:`monai.transforms.ResizeWithPadOrCrop`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: keys: keys of the corresponding items to be transformed.

class ResizeWithPadOrCropd(Padd):
    """
    Dictionary-based wrapper of :py:class:`monai.transforms.ResizeWithPadOrCrop`.

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

    Args:
        keys: keys of the corresponding items to be transformed.
            See also: monai.transforms.MapTransform
        spatial_size: the spatial size of output data after padding or crop.
            If has non-positive values, the corresponding size of input image will be used (no padding).
        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
            It also can be a sequence of string, each element corresponds to a key in ``keys``.
        allow_missing_keys: don&#x27;t raise exception if key is missing.
        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.
        pad_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,
        keys: KeysCollection,
        spatial_size: Sequence[int] | int,
        mode: SequenceStr = PytorchPadMode.CONSTANT,
        allow_missing_keys: bool = False,
        method: str = Method.SYMMETRIC,
        lazy: bool = False,
        **pad_kwargs,
    ) -> None:
        padcropper = ResizeWithPadOrCrop(spatial_size=spatial_size, method=method, **pad_kwargs, lazy=lazy)
        super().__init__(
            keys, padder=padcropper, mode=mode, allow_missing_keys=allow_missing_keys, lazy=lazy  # type: ignore
        )


class BoundingRectd(MapTransform):