Source code for kornia.augmentation._2d.intensity.channel_shuffle

from typing import Any, Dict, Optional

import torch
from torch import Tensor

from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D


[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, return_transform: Optional[bool] = None, ) -> None: super().__init__( p=p, return_transform=return_transform, same_on_batch=same_on_batch, p_batch=1.0, keepdim=keepdim ) def generate_parameters(self, shape: torch.Size) -> Dict[str, Tensor]: B, C, _, _ = shape channels = torch.rand(B, C).argsort(dim=1) return dict(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