Class Rotate90

monai/transforms/spatial/array.py:1181–1233

Rotate an array by 90 degrees in the plane specified by `axes`. See `torch.rot90` for additional details: https://pytorch.org/docs/stable/generated/torch.rot90.html#torch-rot90. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` f

class Rotate90(InvertibleTransform, LazyTransform):
    """
    Rotate an array by 90 degrees in the plane specified by `axes`.
    See `torch.rot90` for additional details:
    https://pytorch.org/docs/stable/generated/torch.rot90.html#torch-rot90.

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

    backend = [TransformBackends.TORCH]

    def __init__(self, k: int = 1, spatial_axes: tuple[int, int] = (0, 1), lazy: bool = False) -> None:
        """
        Args:
            k: number of times to rotate by 90 degrees.
            spatial_axes: 2 int numbers, defines the plane to rotate with 2 spatial axes.
                Default: (0, 1), this is the first two axis in spatial dimensions.
                If axis is negative it counts from the last to the first axis.
            lazy: a flag to indicate whether this transform should execute lazily or not.
                Defaults to False
        """
        LazyTransform.__init__(self, lazy=lazy)
        self.k = (4 + (k % 4)) % 4  # 0, 1, 2, 3
        spatial_axes_: tuple[int, int] = ensure_tuple(spatial_axes)
        if len(spatial_axes_) != 2:
            raise ValueError(f"spatial_axes must be 2 numbers to define the plane to rotate, got {spatial_axes_}.")
        self.spatial_axes = spatial_axes_

    def __call__(self, img: torch.Tensor, lazy: bool | None = None) -> torch.Tensor:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
            lazy: a flag to indicate whether this transform should execute lazily or not
                during this call. Setting this to False or True overrides the ``lazy`` flag set
                during initialization for this call. Defaults to None.
        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        axes = map_spatial_axes(img.ndim, self.spatial_axes)
        lazy_ = self.lazy if lazy is None else lazy
        return rotate90(img, axes, self.k, lazy=lazy_, transform_info=self.get_transform_info())  # type: ignore

    def inverse(self, data: torch.Tensor) -> torch.Tensor:
        transform = self.pop_transform(data)
        return self.inverse_transform(data, transform)

    def inverse_transform(self, data: torch.Tensor, transform) -> torch.Tensor:
        axes = transform[TraceKeys.EXTRA_INFO]["axes"]
        k = transform[TraceKeys.EXTRA_INFO]["k"]
        inv_k = 4 - k % 4
        xform = Rotate90(k=inv_k, spatial_axes=axes)
        with xform.trace_transform(False):
            return xform(data)


class RandRotate90(RandomizableTransform, InvertibleTransform, LazyTransform):