[references] Add FP16 support for training

[fg-mindee]

The reference training script should have an option to switch from a FP32 training to a FP16 training.
It would raise a question: how to harmonize model loading from both FP?

A few suggestions:

• simply cast the FP16 checkpoint to FP32 before hashing + uploading
• for a given model, each checkpoint is marked with its FP. The user may choose how the model needs to be instantiated: by default the output FP is FP32, but this can be changed by the user. Depending on the checkpoint FP, it will be cast to its counterpart FP for loading.

The second option would be cleaner and avoid unnecessary large checkpoints when they can be kept in FP16

Here is a proposition:

• add support of FP16 to doctr.datasets (feat: Added FP16 support to doctr.datasets #367)
• add support of FP16 to doctr.models (feat: Added FP16 support for doctr.models #382)
• add support of FP16 to doctr.transforms (feat: Added support of fp16 to doctr.transforms #388)
• add support of FP16 to references with PyTorch (feat: Added support of AMP to all PyTorch training scripts #604)
• add support of FP16 to references with TensorFlow (feat: Added option to use AMP with TF scripts #682)

[fg-mindee]

Quick update: this design was good on paper but ML frameworks actually uses AMP instead of full fp16. And other data types yield issues with post processing (cv2 doesn't like things that are not in fp32 among others)

• Adding AMP support to PyTorch trainings (feat: Added support of AMP to all PyTorch training scripts #604)
• Adding AMP support to TensorFlow trainings (feat: Added option to use AMP with TF scripts #682)

[fg-mindee]

Moving this to a later release for TensorFlow support of AMP