Source code for kornia.augmentation._2d.geometric.vertical_flip

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

import torch
from torch import Tensor

from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
from kornia.geometry.transform import vflip

[docs]class RandomVerticalFlip(GeometricAugmentationBase2D):
r"""Apply a random vertical flip to a tensor image or a batch of tensor images with a given probability.

.. image:: _static/img/RandomVerticalFlip.png

Args:
p: probability of the image being flipped.
same_on_batch: apply the same transformation across the batch.
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.vflip`.

Examples:
>>> input = torch.tensor([[[[0., 0., 0.],
...                         [0., 0., 0.],
...                         [0., 1., 1.]]]])
>>> seq = RandomVerticalFlip(p=1.0)
>>> seq(input), seq.transform_matrix
(tensor([[[[0., 1., 1.],
[0., 0., 0.],
[0., 0., 0.]]]]), tensor([[[ 1.,  0.,  0.],
[ 0., -1.,  2.],
[ 0.,  0.,  1.]]]))
>>> seq.inverse(seq(input)).equal(input)
True

To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> seq = RandomVerticalFlip(p=1.0)
>>> (seq(input) == seq(input, params=seq._params)).all()
tensor(True)
"""

def compute_transformation(self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any]) -> Tensor:
h: int = int(params["forward_input_shape"][-2])
flip_mat: Tensor = torch.tensor([[1, 0, 0], [0, -1, h - 1], [0, 0, 1]], device=input.device, dtype=input.dtype)

return flip_mat.expand(input.shape[0], 3, 3)

def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
return vflip(input)

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=torch.as_tensor(transform, device=input.device, dtype=input.dtype),
flags=flags,
)
```