Method __init__

monai/transforms/croppad/dictionary.py:123–151

Args: keys: keys of the corresponding items to be transformed. See also: :py:class:`monai.transforms.compose.MapTransform` padder: pad transform for the input image. mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"lin

(
        self,
        keys: KeysCollection,
        padder: Pad,
        mode: SequenceStr = PytorchPadMode.CONSTANT,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    )

    backend = Pad.backend

    def __init__(
        self,
        keys: KeysCollection,
        padder: Pad,
        mode: SequenceStr = PytorchPadMode.CONSTANT,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ) -> None:
        """
        Args:
            keys: keys of the corresponding items to be transformed.
                See also: :py:class:`monai.transforms.compose.MapTransform`
            padder: pad transform for the input image.
            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't raise exception if key is missing.
            lazy: a flag to indicate whether this transform should execute lazily or not. Defaults to False.
        """
        MapTransform.__init__(self, keys, allow_missing_keys)
        LazyTransform.__init__(self, lazy)
        if lazy is True and not isinstance(padder, LazyTrait):
            raise ValueError("'padder' must inherit LazyTrait if lazy is True " f"'padder' is of type({type(padder)})")
        self.padder = padder
        self.mode = ensure_tuple_rep(mode, len(self.keys))

    @LazyTransform.lazy.setter  # type: ignore
    def lazy(self, value: bool) -> None: