feat: Added ChannelShuffle transformation and fixes RandomCrop

[fg-mindee]

Following up on #730, this PR introduces the following modifications:

• adds a new transformation ChannelShuffle
• adds the entry in the documentation and corresponding unittests
• fixes RandomCrop which was making the wrong static assumption of channels_last (cf.

  doctr/doctr/transforms/modules/base.py

  Line 177 in f2f1f21

  h, w = img.shape[:2]

  )
• fixes its unittests as well
• fixes GaussianNoise for torch.uint8 (from feat: Added random noise augmentation to object detection #654)

The following snippet:

from doctr.io import read_img_as_tensor
from doctr.transforms import ChannelShuffle
import matplotlib.pyplot as plt

img = read_img_as_tensor('/path/to/img.png')
plt.imshow(transfo(img).permute(1, 2, 0).numpy()); plt.axis('off'); plt.show()

with the following image

produces:

(since it's a PNG, the background, which was transparent, is by default cast to black upon reading the image)

Any feedback is welcome!

[codecov]

Codecov Report

Merging #768 (6a776c9) into main (8e7b0ee) will decrease coverage by 0.02%.
The diff coverage is 93.33%.

@@            Coverage Diff             @@
##             main     #768      +/-   ##
==========================================
- Coverage   95.99%   95.97%   -0.03%     
==========================================
  Files         129      129              
  Lines        4818     4839      +21     
==========================================
+ Hits         4625     4644      +19     
- Misses        193      195       +2     

Flag	Coverage Δ
unittests	95.97% <93.33%> (-0.03%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
doctr/transforms/functional/pytorch.py	97.77% <80.00%> (-2.23%)	⬇️
doctr/transforms/functional/tensorflow.py	98.03% <80.00%> (-1.97%)	⬇️
doctr/transforms/modules/base.py	94.52% <100.00%> (+0.07%)	⬆️
doctr/transforms/modules/pytorch.py	98.00% <100.00%> (+0.32%)	⬆️
doctr/transforms/modules/tensorflow.py	92.50% <100.00%> (+0.32%)	⬆️

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[charlesmindee]

Thanks !

[charlesmindee]

Thanks for the fix!

[fg-mindee]

Sorry I had to solve some conflicts after the last commits on main 😅

[charlesmindee]

Thanks

[SiddhantBahuguna]

Given the range of scale and ratio could it not be that if the maximum value of crop_w = math.sqrt(scale / ratio) for instance is chosen, it will be more than 1 and we might end up getting a negative value for the argument: random.uniform(0, 1 - crop_w) ?

[fg-mindee]

Yes, but that's why it's clipped later on!
We clip it at the end to minimize the constraints on the distribution/randomness

[SiddhantBahuguna]

Its clear now. Thanks!