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Add keepdim to RandomChannelShuffle

See original GitHub issue

Describe the bug

K.augmentation.RandomChannelShuffle inconsistency with others kornia augmentations. Keepdim is not in argument of the RandomChannelShuffle class, and so always broadcast it to the batch form (eg, broadcast a torch.Size([6, 64, 64]), CHW to torch.Size([1, 6, 64, 64]) BCH*W)

Reproduction steps

>>> import torch
>>> import kornia as K
>>> image = torch.rand((6,64,64))
>>> image.shape
torch.Size([6, 64, 64])
>>> transform = K.augmentation.RandomChannelShuffle(p=0.8)
>>> transform(image).shape
torch.Size([1, 6, 64, 64])

Expected behavior

>>> import torch
>>> import kornia as K
>>> image = torch.rand((6,64,64))
>>> image.shape
torch.Size([6, 64, 64])
>>> transform = K.augmentation.RandomChannelShuffle(p=0.8, keepdim=True)
>>> transform(image).shape
torch.Size([6, 64, 64])

Environment

PyTorch Version (e.g., 1.0): 1.10.0
OS (e.g., Linux): Ubuntu 20.04.3 LTS (x86_64)
How you installed PyTorch (conda, pip, source): conda
Python version: 3.9.7
CUDA/cuDNN version: 11.4.152
GPU models and configuration: GPU 0: NVIDIA TITAN X (Pascal)

Issue Analytics

State:
Created 2 years ago
Comments:5 (5 by maintainers)

Top GitHub Comments

2reactions

julien-blanchoncommented, Dec 8, 2021

@julien-blanchon could you send a PR with the patch ? Not sure why some are missing @shijianjian ?

Yes, waiting for review https://github.com/kornia/kornia/pull/1486

0reactions

edgarribacommented, Dec 8, 2021

@julien-blanchon could you send a PR with the patch ? Not sure why some are missing @shijianjian ?

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