Method __call__

monai/transforms/spatial/array.py:2059–2176

Args: img: shape must be (num_channels, H, W[, D]). grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D. if ``norm_coords`` is True, the grid values must be in `[-(size-1)/2, (size-1)/2]`. if ``USE_COMPILED=True`` and ``norm_co

(
        self,
        img: torch.Tensor,
        grid: torch.Tensor | None = None,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        dtype: DtypeLike = None,
        align_corners: bool | None = None,
    )

        self.dtype = dtype

    def __call__(
        self,
        img: torch.Tensor,
        grid: torch.Tensor | None = None,
        mode: str | int | None = None,
        padding_mode: str | None = None,
        dtype: DtypeLike = None,
        align_corners: bool | None = None,
    ) -> torch.Tensor:
        """
        Args:
            img: shape must be (num_channels, H, W[, D]).
            grid: shape must be (3, H, W) for 2D or (4, H, W, D) for 3D.
                if ``norm_coords`` is True, the grid values must be in `[-(size-1)/2, (size-1)/2]`.
                if ``USE_COMPILED=True`` and ``norm_coords=False``, grid values must be in `[0, size-1]`.
                if ``USE_COMPILED=False`` and ``norm_coords=False``, grid values must be in `[-1, 1]`.
            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 `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 ``self.padding_mode``.
                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
            dtype: data type for resampling computation. Defaults to ``self.dtype``.
                To be compatible with other modules, the output data type is always `float32`.
            align_corners: Defaults to ``self.align_corners``.
                See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html

        See also:
            :py:const:`monai.config.USE_COMPILED`
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        if grid is None:
            return img

        _device = img.device if isinstance(img, torch.Tensor) else self.device
        _dtype = dtype or self.dtype or img.dtype
        _align_corners = self.align_corners if align_corners is None else align_corners
        img_t, *_ = convert_data_type(img, torch.Tensor, dtype=_dtype, device=_device)
        sr = min(len(img_t.peek_pending_shape() if isinstance(img_t, MetaTensor) else img_t.shape[1:]), 3)
        _use_compiled = USE_COMPILED and not _compiled_unsupported(img_t.device)
        backend, _interp_mode, _padding_mode, _ = resolves_modes(
            self.mode if mode is None else mode,
            self.padding_mode if padding_mode is None else padding_mode,
            backend=None,
            use_compiled=_use_compiled,
        )