Method __init__

monai/transforms/spatial/array.py:2664–2745

Args: spacing : distance in between the control points. magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``. prob: probability of returning a randomized elastic transform. defaults to 0.1, wit

(
        self,
        spacing: tuple[float, float] | float,
        magnitude_range: tuple[float, float],
        prob: float = 0.1,
        rotate_range: RandRange = None,
        shear_range: RandRange = None,
        translate_range: RandRange = None,
        scale_range: RandRange = None,
        spatial_size: tuple[int, int] | int | None = None,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.REFLECTION,
        device: torch.device | None = None,
    )

    backend = Resample.backend

    def __init__(
        self,
        spacing: tuple[float, float] | float,
        magnitude_range: tuple[float, float],
        prob: float = 0.1,
        rotate_range: RandRange = None,
        shear_range: RandRange = None,
        translate_range: RandRange = None,
        scale_range: RandRange = None,
        spatial_size: tuple[int, int] | int | None = None,
        mode: str | int = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.REFLECTION,
        device: torch.device | None = None,
    ) -> None:
        """
        Args:
            spacing : distance in between the control points.
            magnitude_range: the random offsets will be generated from ``uniform[magnitude[0], magnitude[1])``.
            prob: probability of returning a randomized elastic transform.
                defaults to 0.1, with 10% chance returns a randomized elastic transform,
                otherwise returns a ``spatial_size`` centered area extracted from the input image.
            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) for affine matrix, take a 2D affine as example::

                    [
                        [1.0, params[0], 0.0],
                        [params[1], 1.0, 0.0],
                        [0.0, 0.0, 1.0],
                    ]

            translate_range: translate range with format matching `rotate_range`, it defines the range to randomly
                select pixel 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).
            spatial_size: specifying output image spatial size [h, w].
                if `spatial_size` and `self.spatial_size` are not defined, or smaller than 1,
                the transform will use the spatial size of `img`.
                if some components of the `spatial_size` are non-positive values, the transform will use the
                corresponding components of img size. For example, `spatial_size=(32, -1)` will be adapted
                to `(32, 64)` if the second spatial dimension size of img is `64`.
            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 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 ``"reflection"``.
                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'}.