Class Rotate

monai/transforms/spatial/array.py:912–1041

Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information. Args: angle: Rotation angle(s) in radians. should a float

class Rotate(InvertibleTransform, LazyTransform):
    """
    Rotates an input image by given angle using :py:class:`monai.networks.layers.AffineTransform`.

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

    Args:
        angle: Rotation angle(s) in radians. should a float for 2D, three floats for 3D.
        keep_size: If it is True, the output shape is kept the same as the input.
            If it is False, the output shape is adapted so that the
            input array is contained completely in the output. 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
        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
        align_corners: Defaults to False.
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
        dtype: data type for resampling computation. Defaults to ``float32``.
            If None, use the data type of input data. To be compatible with other modules,
            the output data type is always ``float32``.
        lazy: a flag to indicate whether this transform should execute lazily or not.
            Defaults to False
    """

    backend = [TransformBackends.TORCH]

    def __init__(
        self,
        angle: Sequence[float] | float,
        keep_size: bool = True,
        mode: str = GridSampleMode.BILINEAR,
        padding_mode: str = GridSamplePadMode.BORDER,
        align_corners: bool = False,
        dtype: DtypeLike | torch.dtype = torch.float32,
        lazy: bool = False,
    ) -> None:
        LazyTransform.__init__(self, lazy=lazy)
        self.angle = angle
        self.keep_size = keep_size
        self.mode: str = mode
        self.padding_mode: str = padding_mode
        self.align_corners = align_corners
        self.dtype = dtype

    def __call__(
        self,
        img: torch.Tensor,
        mode: str | None = None,
        padding_mode: str | None = None,
        align_corners: bool | None = None,
        dtype: DtypeLike | torch.dtype = None,
        lazy: bool | None = None,
    ) -> torch.Tensor:
        """
        Args: