Class AffineGrid

monai/transforms/spatial/array.py:1704–1827

Affine transforms on the coordinates. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D. D

class AffineGrid(LazyTransform):
    """
    Affine transforms on the coordinates.

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

    Args:
        rotate_params: a rotation angle in radians, a scalar for 2D image, a tuple of 3 floats for 3D.
            Defaults to no rotation.
        shear_params: shearing factors for affine matrix, take a 3D affine as example::

            [
                [1.0, params[0], params[1], 0.0],
                [params[2], 1.0, params[3], 0.0],
                [params[4], params[5], 1.0, 0.0],
                [0.0, 0.0, 0.0, 1.0],
            ]

            a tuple of 2 floats for 2D, a tuple of 6 floats for 3D. Defaults to no shearing.
        translate_params: a tuple of 2 floats for 2D, a tuple of 3 floats for 3D. Translation is in
            pixel/voxel relative to the center of the input image. Defaults to no translation.
        scale_params: scale factor for every spatial dims. a tuple of 2 floats for 2D,
            a tuple of 3 floats for 3D. Defaults to `1.0`.
        dtype: data type for the grid computation. Defaults to ``float32``.
            If ``None``, use the data type of input data (if `grid` is provided).
        device: device on which the tensor will be allocated, if a new grid is generated.
        align_corners: Defaults to False.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
        affine: If applied, ignore the params (`rotate_params`, etc.) and use the
            supplied matrix. Should be square with each side = num of image spatial
            dimensions + 1.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
    """

    backend = [TransformBackends.TORCH]

    def __init__(
        self,
        rotate_params: Sequence[float] | float | None = None,
        shear_params: Sequence[float] | float | None = None,
        translate_params: Sequence[float] | float | None = None,
        scale_params: Sequence[float] | float | None = None,
        device: torch.device | None = None,
        dtype: DtypeLike = np.float32,
        align_corners: bool = False,
        affine: NdarrayOrTensor | None = None,
        lazy: bool = False,
    ) -> None:
        LazyTransform.__init__(self, lazy=lazy)
        self.rotate_params = rotate_params
        self.shear_params = shear_params
        self.translate_params = translate_params
        self.scale_params = scale_params
        self.device = device
        _dtype = get_equivalent_dtype(dtype, torch.Tensor)
        self.dtype = _dtype if _dtype in (torch.float16, torch.float64, None) else torch.float32