Method __call__

monai/transforms/spatial/array.py:268–338

Args: img: input image to be resampled to match ``img_dst``. It currently supports channel-first arrays with at most three spatial dimensions. mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers). Interpol

(  # type: ignore
        self,
        img: torch.Tensor,
        img_dst: torch.Tensor,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike = None,
        lazy: bool | None = None,
    )

    """

    def __call__(  # type: ignore
        self,
        img: torch.Tensor,
        img_dst: torch.Tensor,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike = None,
        lazy: bool | None = None,
    ) -> torch.Tensor:
        """
        Args:
            img: input image to be resampled to match ``img_dst``. It currently supports channel-first arrays with
                at most three spatial dimensions.
            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
            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
            align_corners: Geometrically, we consider the pixels of the input as squares rather than points.
                Defaults to ``None``, effectively using the value of `self.align_corners`.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
            dtype: data type for resampling computation. Defaults to ``self.dtype`` or
                ``np.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`.
            lazy: a flag to indicate whether this transform should execute lazily or not
                during this call. Setting this to False or True overrides the ``lazy`` flag set
                during initialization for this call. Defaults to None.

        Raises:
            ValueError: When the affine matrix of the source image is not invertible.
        Returns:
            Resampled input tensor or MetaTensor.
        """
        if img_dst is None:
            raise RuntimeError("`img_dst` is missing.")
        dst_affine = img_dst.peek_pending_affine() if isinstance(img_dst, MetaTensor) else torch.eye(4)
        lazy_ = self.lazy if lazy is None else lazy
        img = super().__call__(
            img=img,
            dst_affine=dst_affine,
            spatial_size=img_dst.peek_pending_shape() if isinstance(img_dst, MetaTensor) else img_dst.shape[1:],
            mode=mode,
            padding_mode=padding_mode,
            align_corners=align_corners,
            dtype=dtype,
            lazy=lazy_,
        )
        if not lazy_:
            if isinstance(img, MetaTensor):
                img.affine = dst_affine