Class RandAffineGrid

monai/transforms/spatial/array.py:1830–1959

Generate randomised affine grid. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information.

class RandAffineGrid(Randomizable, LazyTransform):
    """
    Generate randomised affine grid.

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

    backend = AffineGrid.backend

    def __init__(
        self,
        rotate_range: RandRange = None,
        shear_range: RandRange = None,
        translate_range: RandRange = None,
        scale_range: RandRange = None,
        device: torch.device | None = None,
        dtype: DtypeLike = np.float32,
        lazy: bool = False,
    ) -> None:
        """
        Args:
            rotate_range: angle range in radians. If element `i` is a pair of (min, max) values, then
                `uniform[rotate_range[i][0], rotate_range[i][1])` will be used to generate the rotation parameter
                for the `i`th spatial dimension. If not, `uniform[-rotate_range[i], rotate_range[i])` will be used.
                This can be altered on a per-dimension basis. E.g., `((0,3), 1, ...)`: for dim0, rotation will be
                in range `[0, 3]`, and for dim1 `[-1, 1]` will be used. Setting a single value will use `[-x, x]`
                for dim0 and nothing for the remaining dimensions.
            shear_range: shear range with format matching `rotate_range`, it defines the range to randomly select
                shearing factors(a tuple of 2 floats for 2D, a tuple of 6 floats for 3D) 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],
                    ]

            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                select voxels to translate for every spatial dims.
            scale_range: scaling range with format matching `rotate_range`. it defines the range to randomly select
                the scale factor to translate for every spatial dims. A value of 1.0 is added to the result.
                This allows 0 to correspond to no change (i.e., a scaling of 1.0).
            device: device to store the output grid data.
            dtype: data type for the grid computation. Defaults to ``np.float32``.
                If ``None``, use the data type of input data (if `grid` is provided).
            lazy: a flag to indicate whether this transform should execute lazily or not.
                Defaults to False

        See also:
            - :py:meth:`monai.transforms.utils.create_rotate`
            - :py:meth:`monai.transforms.utils.create_shear`
            - :py:meth:`monai.transforms.utils.create_translate`
            - :py:meth:`monai.transforms.utils.create_scale`

        """
        LazyTransform.__init__(self, lazy=lazy)