Class SpatialResample

monai/transforms/spatial/array.py:125–256

Resample input image from the orientation/spacing defined by ``src_affine`` affine matrix into the ones specified by ``dst_affine`` affine matrix. Internally this transform computes the affine transform matrix from ``src_affine`` to ``dst_affine``, by ``xform = linalg.solve(src_aff

class SpatialResample(InvertibleTransform, LazyTransform):
    """
    Resample input image from the orientation/spacing defined by ``src_affine`` affine matrix into
    the ones specified by ``dst_affine`` affine matrix.

    Internally this transform computes the affine transform matrix from ``src_affine`` to ``dst_affine``,
    by ``xform = linalg.solve(src_affine, dst_affine)``, and call ``monai.transforms.Affine`` with ``xform``.

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

    backend = [TransformBackends.TORCH, TransformBackends.NUMPY, TransformBackends.CUPY]

    def __init__(
        self,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: DtypeLike = np.float64,
        lazy: bool = False,
    ):
        """
        Args:
            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&#x27;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
            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``.
            lazy: a flag to indicate whether this transform should execute lazily or not.
                Defaults to False
        """
        LazyTransform.__init__(self, lazy=lazy)
        self.mode = mode
        self.padding_mode = padding_mode
        self.align_corners = align_corners
        self.dtype = dtype

    def __call__(
        self,
        img: torch.Tensor,
        dst_affine: torch.Tensor | None = None,
        spatial_size: Sequence[int] | torch.Tensor | int | None = None,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike = None,
        lazy: bool | None = None,