from typing import Any, Dict, Optional, Tuple
import torch
from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
from kornia.core import Tensor
[docs]class RandomChannelShuffle(IntensityAugmentationBase2D):
r"""Shuffle the channels of a batch of multi-dimensional images.
.. image:: _static/img/RandomChannelShuffle.png
Args:
same_on_batch: apply the same transformation across the batch.
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``.
Examples:
>>> rng = torch.manual_seed(0)
>>> img = torch.arange(1*2*2*2.).view(1,2,2,2)
>>> RandomChannelShuffle()(img)
tensor([[[[4., 5.],
[6., 7.]],
<BLANKLINE>
[[0., 1.],
[2., 3.]]]])
To apply the exact augmenation again, you may take the advantage of the previous parameter state:
>>> input = torch.randn(1, 3, 32, 32)
>>> aug = RandomChannelShuffle(p=1.)
>>> (aug(input) == aug(input, params=aug._params)).all()
tensor(True)
"""
def __init__(self, same_on_batch: bool = False, p: float = 0.5, keepdim: bool = False) -> None:
super().__init__(p=p, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim)
def generate_parameters(self, shape: Tuple[int, ...]) -> Dict[str, Tensor]:
B, C, _, _ = shape
channels = torch.rand(B, C).argsort(dim=1)
return {"channels": channels}
def apply_transform(
self, input: Tensor, params: Dict[str, Tensor], flags: Dict[str, Any], transform: Optional[Tensor] = None
) -> Tensor:
out = torch.empty_like(input)
for i in range(out.shape[0]):
out[i] = input[i, params["channels"][i]]
return out