Method __call__

monai/transforms/spatial/array.py:440–547

Args: data_array: in shape (num_channels, H[, W, ...]). mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers). Interpolation mode to calculate output values. Defaults to ``"self.mode"``. See also: https://p

(
        self,
        data_array: torch.Tensor,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike = None,
        scale_extent: bool | None = None,
        output_spatial_shape: Sequence[int] | np.ndarray | int | None = None,
        lazy: bool | None = None,
    )

        self.sp_resample.lazy = val

    def __call__(
        self,
        data_array: torch.Tensor,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike = None,
        scale_extent: bool | None = None,
        output_spatial_shape: Sequence[int] | np.ndarray | int | None = None,
        lazy: bool | None = None,
    ) -> torch.Tensor:
        """
        Args:
            data_array: in shape (num_channels, H[, W, ...]).
            mode: {``"bilinear"``, ``"nearest"``} or spline interpolation order 0-5 (integers).
                Interpolation mode to calculate output values. Defaults to ``"self.mode"``.
                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 ``"self.padding_mode"``.
                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.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
                Defaults to ``None``, effectively using the value of `self.align_corners`.
            dtype: data type for resampling computation. Defaults to ``self.dtype``.
                If None, use the data type of input data. To be compatible with other modules,
                the output data type is always ``float32``.
            scale_extent: whether the scale is computed based on the spacing or the full extent of voxels,
                The option is ignored if output spatial size is specified when calling this transform.
                See also: :py:func:`monai.data.utils.compute_shape_offset`. When this is True, `align_corners`
                should be `True` because `compute_shape_offset` already provides the corner alignment shift/scaling.
            output_spatial_shape: specify the shape of the output data_array. This is typically useful for
                the inverse of `Spacingd` where sometimes we could not compute the exact shape due to the quantization
                error with the affine.
            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 ``data_array`` has no spatial dimensions.
            ValueError: When ``pixdim`` is nonpositive.

        Returns:
            data tensor or MetaTensor (resampled into `self.pixdim`).

        """
        original_spatial_shape = (
            data_array.peek_pending_shape() if isinstance(data_array, MetaTensor) else data_array.shape[1:]
        )
        sr = len(original_spatial_shape)
        if sr <= 0:
            raise ValueError(f"data_array must have at least one spatial dimension, got {original_spatial_shape}.")
        affine_: np.ndarray