refactor: Introduces img + target transforms in Datasets

[fg-mindee]

Following up on #352, this PR introduces the following modifications:

• renames "sample_transforms" into "img_transforms" to be more explicit (hesitated with "input_transforms" but we only cover Vision, so I don't think it's necessary)
• adds "sample_transforms" which are performed after "img_transforms" on both the image and the target
• reflected changes in datasets, unittests and training scripts
• added SampleCompose (to compose sample_transforms) and ImgTransformation (to wrap an img_transform as a sample_transform) utils
• added dedicated unittests

With this PR, the way to pass thransforms becomes the following:

• if you only have transformations that don't modify the target, pass them as img_transforms
• if you have both, there are two options:
  • passing the image transformations that have to be performed before the first one to modify the target & pass the rest as sample_transforms:
  • passing all transformations as sample_transforms and when there is one that is only implemented for image, wrap it with ImageTransform

Any feedback is welcome!

[codecov]

Codecov Report

Merging #750 (b02b39f) into main (efe41da) will increase coverage by 0.10%.
The diff coverage is 90.66%.

@@            Coverage Diff             @@
##             main     #750      +/-   ##
==========================================
+ Coverage   96.09%   96.20%   +0.10%     
==========================================
  Files         129      129              
  Lines        4794     4799       +5     
==========================================
+ Hits         4607     4617      +10     
+ Misses        187      182       -5     

Flag	Coverage Δ
unittests	96.20% <90.66%> (+0.10%)	⬆️

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

Impacted Files	Coverage Δ
doctr/models/predictor/base.py	100.00% <ø> (ø)
...dels/detection/differentiable_binarization/base.py	90.76% <73.91%> (+0.38%)	⬆️
doctr/datasets/detection.py	94.28% <90.90%> (-2.78%)	⬇️
doctr/datasets/classification/base.py	93.75% <100.00%> (+0.20%)	⬆️
doctr/datasets/cord.py	97.36% <100.00%> (-0.07%)	⬇️
doctr/datasets/datasets/base.py	95.74% <100.00%> (+0.29%)	⬆️
doctr/datasets/doc_artefacts.py	93.93% <100.00%> (-0.18%)	⬇️
doctr/datasets/funsd.py	96.77% <100.00%> (-0.11%)	⬇️
doctr/datasets/ic03.py	97.14% <100.00%> (-0.08%)	⬇️
doctr/datasets/ic13.py	97.22% <100.00%> (-0.08%)	⬇️
... and 10 more

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[charlesmindee]

Thanks for the PR