Method __init__

monai/transforms/spatial/dictionary.py:277–324

Args: keys: keys of the corresponding items to be transformed. key_dst: key of image to resample to match. mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers). Interpolation mode to calculate output values. D

(
        self,
        keys: KeysCollection,
        key_dst: str,
        mode: SequenceStr = GridSampleMode.BILINEAR,
        padding_mode: SequenceStr = GridSamplePadMode.BORDER,
        align_corners: Sequence[bool] | bool = False,
        dtype: Sequence[DtypeLike] | DtypeLike = np.float64,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    )

    backend = ResampleToMatch.backend

    def __init__(
        self,
        keys: KeysCollection,
        key_dst: str,
        mode: SequenceStr = GridSampleMode.BILINEAR,
        padding_mode: SequenceStr = GridSamplePadMode.BORDER,
        align_corners: Sequence[bool] | bool = False,
        dtype: Sequence[DtypeLike] | DtypeLike = np.float64,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ):
        """
        Args:
            keys: keys of the corresponding items to be transformed.
            key_dst: key of image to resample to match.
            mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                When it's an integer, the numpy (cpu tensor)/cupy (cuda tensor) backends will be used
                and the value represents the order of the spline interpolation.
                See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
                It also can be a sequence, each element corresponds to a key in ``keys``.
            padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                Padding mode for outside grid values. Defaults to ``"border"``.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                When `mode` is an integer, using numpy/cupy backends, this argument accepts
                {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}.
                See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
                It also can be a sequence, each element corresponds to a key in ``keys``.
            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
                It also can be a sequence of bool, each element corresponds to a key in ``keys``.
            dtype: data type for resampling computation. Defaults to ``float64`` for best precision.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``float32``.
                It also can be a sequence of dtypes, 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=lazy)
        self.key_dst = key_dst
        self.mode = ensure_tuple_rep(mode, len(self.keys))
        self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
        self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))
        self.dtype = ensure_tuple_rep(dtype, len(self.keys))
        self.resampler = ResampleToMatch(lazy=lazy)

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