Class RandRotated

monai/transforms/spatial/dictionary.py:1815–1938

Dictionary-based version :py:class:`monai.transforms.RandRotate` Randomly rotates the input arrays. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: keys: Keys to pick data for transformation

class RandRotated(RandomizableTransform, MapTransform, InvertibleTransform, LazyTransform):
    """
    Dictionary-based version :py:class:`monai.transforms.RandRotate`
    Randomly rotates the input arrays.

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

    Args:
        keys: Keys to pick data for transformation.
        range_x: Range of rotation angle in radians in the plane defined by the first and second axes.
            If single number, angle is uniformly sampled from (-range_x, range_x).
        range_y: Range of rotation angle in radians in the plane defined by the first and third axes.
            If single number, angle is uniformly sampled from (-range_y, range_y). only work for 3D data.
        range_z: Range of rotation angle in radians in the plane defined by the second and third axes.
            If single number, angle is uniformly sampled from (-range_z, range_z). only work for 3D data.
        prob: Probability of rotation.
        keep_size: If it is False, the output shape is adapted so that the
            input array is contained completely in the output.
            If it is True, the output shape is the same as the input. Default is True.
        mode: {``"bilinear"``, ``"nearest"``}
            Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
            It also can be a sequence of string, each element corresponds to a key in ``keys``.
        padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
            Padding mode for outside grid values. Defaults to ``"border"``.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
            It also can be a sequence of string, each element corresponds to a key in ``keys``.
        align_corners: Defaults to False.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
            It also can be a sequence of bool, each element corresponds to a key in ``keys``.
        dtype: data type for resampling computation. Defaults to ``float64`` for best precision.
            If None, use the data type of input data. To be compatible with other modules,
            the output data type is always ``float32``.
            It also can be a sequence of dtype or None, each element corresponds to a key in ``keys``.
        allow_missing_keys: don&#x27;t raise exception if key is missing.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
    """

    backend = RandRotate.backend

    def __init__(
        self,
        keys: KeysCollection,
        range_x: tuple[float, float] | float = 0.0,
        range_y: tuple[float, float] | float = 0.0,
        range_z: tuple[float, float] | float = 0.0,
        prob: float = 0.1,
        keep_size: bool = True,
        mode: SequenceStr = GridSampleMode.BILINEAR,
        padding_mode: SequenceStr = GridSamplePadMode.BORDER,
        align_corners: Sequence[bool] | bool = False,
        dtype: Sequence[DtypeLike | torch.dtype] | DtypeLike | torch.dtype = np.float32,
        allow_missing_keys: bool = False,
        lazy: bool = False,
    ) -> None:
        MapTransform.__init__(self, keys, allow_missing_keys)