Class Resample

monai/transforms/spatial/array.py:2006–2176

class Resample(Transform):
    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]

    def __init__(
        self,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        norm_coords: bool = True,
        device: torch.device | None = None,
        align_corners: bool = False,
        dtype: DtypeLike = np.float64,
    ) -> None:
        """
        computes output image using values from `img`, locations from `grid` using pytorch.
        supports spatially 2D or 3D (num_channels, H, W[, D]).

        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 `USE_COMPILED` is `True`, this argument uses
                ``"nearest"``, ``"bilinear"``, ``"bicubic"`` to indicate 0, 1, 3 order interpolations.
                See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
                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 `USE_COMPILED` is `True`, this argument uses an integer to represent the padding mode.
                See also: https://monai.readthedocs.io/en/stable/networks.html#grid-pull (experimental).
                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
            norm_coords: whether to normalize the coordinates from `[-(size-1)/2, (size-1)/2]` to
                `[0, size - 1]` (for ``monai/csrc`` implementation) or
                `[-1, 1]` (for torch ``grid_sample`` implementation) to be compatible with the underlying
                resampling API.
            device: device on which the tensor will be allocated.
            align_corners: Defaults to False.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.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`.

        """
        self.mode = mode
        self.padding_mode = padding_mode
        self.norm_coords = norm_coords
        self.device = device
        self.align_corners = align_corners
        self.dtype = dtype

    def __call__(
        self,
        img: torch.Tensor,
        grid: torch.Tensor | None = None,
        mode: str | int | None = None,