Class RandRotate90

monai/transforms/spatial/array.py:1236–1301

With probability `prob`, input arrays are rotated by 90 degrees in the plane specified by `spatial_axes`. This transform is capable of lazy execution. See the :ref:`Lazy Resampling topic ` for more information.

class RandRotate90(RandomizableTransform, InvertibleTransform, LazyTransform):
    """
    With probability `prob`, input arrays are rotated by 90 degrees
    in the plane specified by `spatial_axes`.

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

    backend = Rotate90.backend

    def __init__(
        self, prob: float = 0.1, max_k: int = 3, spatial_axes: tuple[int, int] = (0, 1), lazy: bool = False
    ) -> None:
        """
        Args:
            prob: probability of rotating.
                (Default 0.1, with 10% probability it returns a rotated array)
            max_k: number of rotations will be sampled from `np.random.randint(max_k) + 1`, (Default 3).
            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.
            lazy: a flag to indicate whether this transform should execute lazily or not.
                Defaults to False
        """
        RandomizableTransform.__init__(self, prob)
        LazyTransform.__init__(self, lazy=lazy)
        self.max_k = max_k
        self.spatial_axes = spatial_axes

        self._rand_k = 0

    def randomize(self, data: Any | None = None) -> None:
        super().randomize(None)
        if not self._do_transform:
            return None
        self._rand_k = self.R.randint(self.max_k) + 1

    def __call__(self, img: torch.Tensor, randomize: bool = True, lazy: bool | None = None) -> torch.Tensor:
        """
        Args:
            img: channel first array, must have shape: (num_channels, H[, W, ..., ]),
            randomize: whether to execute `randomize()` function first, default to True.
            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.
        """

        if randomize:
            self.randomize()

        lazy_ = self.lazy if lazy is None else lazy
        if self._do_transform:
            xform = Rotate90(self._rand_k, self.spatial_axes, lazy=lazy_)
            out = xform(img)
        else:
            out = convert_to_tensor(img, track_meta=get_track_meta())

        self.push_transform(out, replace=True, lazy=lazy_)