[Feat] Predictor precision PT backend

[felixdittrich92]

This PR:

• adds precision dtype to predictor / det predictor and reco predictor (effects only PT)

Any feedback is welcome 🤗

Revert for TF which would need to convert the weights while loading (takes a lot of time)

tests depends on: #1201
issue: #1112

[felixdittrich92]

@odulcy-mindee ready for review if tests done :)

[codecov]

Codecov Report

Merging #1204 (ad0aaa7) into main (fdd00a3) will decrease coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main    #1204      +/-   ##
==========================================
- Coverage   93.68%   93.67%   -0.01%     
==========================================
  Files         154      154              
  Lines        6903     6911       +8     
==========================================
+ Hits         6467     6474       +7     
- Misses        436      437       +1     

Flag	Coverage Δ
unittests	93.67% <100.00%> (-0.01%)	⬇️

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

Impacted Files	Coverage Δ
doctr/models/detection/zoo.py	96.96% <ø> (ø)
doctr/models/zoo.py	100.00% <ø> (ø)
doctr/models/classification/predictor/pytorch.py	95.45% <100.00%> (+0.45%)	⬆️
doctr/models/detection/predictor/pytorch.py	95.23% <100.00%> (+0.50%)	⬆️
doctr/models/recognition/predictor/pytorch.py	91.66% <100.00%> (+0.49%)	⬆️
doctr/models/utils/pytorch.py	100.00% <100.00%> (ø)

... and 2 files with indirect coverage changes

[felixdittrich92]

I am not an expert of this model and I am new to doctr but is the problem here:

147        if conf.expanded_channels != conf.input_channels:
148            _layers.extend(conv_sequence(conf.expanded_channels, act_fn, kernel_size=1, bn=True, **_kwargs))

https://github.com/felixdittrich92/doctr/blob/1bf12a3ec73ddb463420d4243133b2d423a602d3/doctr/models/utils/tensorflow.py#L87

Hey @nikokks this is not related to the PR so please keep it clean ^^ Feel free to contact me on Linkedin about universial issues

About the problem: It seems to be a issue with your Tensorflow Installation -> DNN lib cannot be found :)

[felixdittrich92]

Now without the extra kwarg which would only effect PyTorch:
simply do:

predictor = (
    ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="db_mobilenet_v3_large", pretrained=True).cuda().half()
)

or

predictor = (
    ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="db_mobilenet_v3_large", pretrained=True).to(device="cuda", dtype=torch.bfloat16)
)

In tensorflow you can do:

policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_global_policy(policy)

We cannot automate this because all these settings are global in TF

[odulcy-mindee]

Definitely better, thanks Felix !