[Fix] TensorFlow SAR_Resnet31 implementation

[felixdittrich92]

This PR: (Still in progress :))

• fix SARDecoder forward step

• check train

• check inference

• cleanup / improve code

Any feedback is welcome 🤗

Issue:
#802

[felixdittrich92]

@frgfm
training: Overall it looks good with same config as used for PT implementation it reaches after 17 epochs an exact match from ~70%
inference: i have some problems to translate the torch.scatter_ functionality could you help with this ? :)

[codecov]

Codecov Report

Merging #925 (19b287f) into main (0c8dd60) will increase coverage by 0.04%.
The diff coverage is 97.56%.

@@            Coverage Diff             @@
##             main     #925      +/-   ##
==========================================
+ Coverage   94.68%   94.72%   +0.04%     
==========================================
  Files         134      134              
  Lines        5491     5501      +10     
==========================================
+ Hits         5199     5211      +12     
+ Misses        292      290       -2     

Flag	Coverage Δ
unittests	94.72% <97.56%> (+0.04%)	⬆️

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

Impacted Files	Coverage Δ
doctr/models/classification/resnet/tensorflow.py	100.00% <ø> (ø)
doctr/models/recognition/sar/tensorflow.py	99.25% <97.56%> (+0.87%)	⬆️
doctr/models/recognition/sar/pytorch.py	98.50% <0.00%> (-0.02%)	⬇️
doctr/transforms/functional/base.py	97.10% <0.00%> (+1.44%)	⬆️

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[frgfm]

@frgfm training: Overall it looks good with same config as used for PT implementation it reaches after 17 epochs an exact match from ~70% inference: i have some problems to translate the torch.scatter_ functionality could you help with this ? :)

Could you check the test perf against FUNSD or CORD ? :)
cf. bench https://mindee.github.io/doctr/using_doctr/using_models.html#id5

[frgfm]

Thanks for the PR Felix 🙏
I added a few comments!

[felixdittrich92]

@frgfm training: Overall it looks good with same config as used for PT implementation it reaches after 17 epochs an exact match from ~70% inference: i have some problems to translate the torch.scatter_ functionality could you help with this ? :)

Could you check the test perf against FUNSD or CORD ? :) cf. bench https://mindee.github.io/doctr/using_doctr/using_models.html#id5

@frgfm i think this would not be comparable without a full dataset (like mindee intern) :/ I test the implementations currently on a difficult toy dataset 10k train and 2.5k val (contains extrem blurred, rotated images, ~ 30 different fonts and some corrupted characters). Maybe if i found a bit more time i could train it on MJSynth

[felixdittrich92]

@frgfm ok now with kwargs and check 👍 😅

[frgfm]

FYI: this PR introduces the deprecation of resnet31 as pretrained model for TF

[felixdittrich92]

FYI: this PR introduces the deprecation of resnet31 as pretrained model for TF

@frgfm
Yes i know, but (correct me if i'm wrong) it is currently only used as backbone for SAR (and master which will change before next release also) so i would say lets include this for next training iteration

trained 1 Epoch on MJSynth Subset : 500k train / 200k val
Validation loss decreased inf --> 0.215485: saving state...
Epoch 1/100 - Validation loss: 0.215485 (Exact: 72.99% | Partial: 77.53%)

FUNSD:
Validation loss: 1.89441 (Exact: 44.94% | Partial: 47.41%)
CORD:
Validation loss: 2.34539 (Exact: 36.30% | Partial: 36.77%)

I would say for a toy run this looks really not bad and we can close the SAR issue with this PR wdyt ? :)

[frgfm]

Thanks Felix, I added some comments!

[frgfm]

FYI: this PR introduces the deprecation of resnet31 as pretrained model for TF

@frgfm Yes i know, but (correct me if i'm wrong) it is currently only used as backbone for SAR (and master which will change before next release also) so i would say lets include this for next training iteration

trained 1 Epoch on MJSynth Subset : 500k train / 200k val Validation loss decreased inf --> 0.215485: saving state... Epoch 1/100 - Validation loss: 0.215485 (Exact: 72.99% | Partial: 77.53%)

FUNSD: Validation loss: 1.89441 (Exact: 44.94% | Partial: 47.41%) CORD: Validation loss: 2.34539 (Exact: 36.30% | Partial: 36.77%)

I would say for a toy run this looks really not bad and we can close the SAR issue with this PR wdyt ? :)

I agree, that's quite good 👍

[frgfm]

Just a few typos left!

[felixdittrich92]

@frgfm
Are we good ? :)

[frgfm]

Thanks Felix 🙏