# Source code for kornia.augmentation._2d.geometric.rotation

```from typing import Any, Dict, List, Optional, Tuple, Union

from kornia.augmentation import random_generator as rg
from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
from kornia.constants import Resample
from kornia.core import Tensor, as_tensor
from kornia.geometry.transform import affine
from kornia.geometry.transform.affwarp import _compute_rotation_matrix, _compute_tensor_center
from kornia.utils.misc import eye_like

[docs]class RandomRotation(GeometricAugmentationBase2D):
r"""Apply a random rotation to a tensor image or a batch of tensor images given an amount of degrees.

.. image:: _static/img/RandomRotation.png

Args:
degrees: range of degrees to select from. If degrees is a number the
range of degrees to select from will be (-degrees, +degrees).
resample: Default: the interpolation mode.
same_on_batch: apply the same transformation across the batch.
align_corners: interpolation flag.
p: probability of applying the transformation.
keepdim: whether to keep the output shape the same as input (True) or broadcast it
to the batch form (False).

Shape:
- Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
- Output: :math:`(B, C, H, W)`

.. note::
This function internally uses :func:`kornia.geometry.transform.affine`.

Examples:
>>> rng = torch.manual_seed(0)
>>> input = torch.tensor([[1., 0., 0., 2.],
...                       [0., 0., 0., 0.],
...                       [0., 1., 2., 0.],
...                       [0., 0., 1., 2.]])
>>> aug = RandomRotation(degrees=45.0, p=1.)
>>> out = aug(input)
>>> out
tensor([[[[0.9824, 0.0088, 0.0000, 1.9649],
[0.0000, 0.0029, 0.0000, 0.0176],
[0.0029, 1.0000, 1.9883, 0.0000],
[0.0000, 0.0088, 1.0117, 1.9649]]]])
>>> aug.transform_matrix
tensor([[[ 1.0000, -0.0059,  0.0088],
[ 0.0059,  1.0000, -0.0088],
[ 0.0000,  0.0000,  1.0000]]])
>>> inv = aug.inverse(out)

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomRotation(degrees=45.0, p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""

# Note: Extra params, center=None, fill=0 in TorchVision

def __init__(
self,
degrees: Union[Tensor, float, Tuple[float, float], List[float]],
resample: Union[str, int, Resample] = Resample.BILINEAR.name,
same_on_batch: bool = False,
align_corners: bool = True,
p: float = 0.5,
keepdim: bool = False,
return_transform: Optional[bool] = None,
) -> None:
super().__init__(p=p, return_transform=return_transform, same_on_batch=same_on_batch, keepdim=keepdim)
self._param_generator = rg.PlainUniformGenerator((degrees, "degrees", 0.0, (-360.0, 360.0)))

self.flags = dict(resample=Resample.get(resample), align_corners=align_corners)

def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor:
# TODO: Update to use `get_rotation_matrix2d`
angles: Tensor = params["degrees"].to(input)

center: Tensor = _compute_tensor_center(input)
rotation_mat: Tensor = _compute_rotation_matrix(angles, center.expand(angles.shape[0], -1))

# rotation_mat is B x 2 x 3 and we need a B x 3 x 3 matrix
trans_mat: Tensor = eye_like(3, input, shared_memory=False)
trans_mat[:, 0] = rotation_mat[:, 0]
trans_mat[:, 1] = rotation_mat[:, 1]

return trans_mat

def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
if not isinstance(transform, Tensor):
raise TypeError(f'Expected the transform be a Tensor. Gotcha {type(transform)}')

return affine(input, transform[..., :2, :3], flags["resample"].name.lower(), "zeros", flags["align_corners"])

def inverse_transform(
self,
input: Tensor,
flags: Dict[str, Any],
transform: Optional[Tensor] = None,
size: Optional[Tuple[int, int]] = None,
) -> Tensor:
return self.apply_transform(
input,
params=self._params,
transform=as_tensor(transform, device=input.device, dtype=input.dtype),
flags=flags,
)
```