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, )