16-bit Support and Dynamic Loss Scaling

[jasonkena]

This utilizes Apex's AMP, to enable 16-bit computation, increasing performance, and lowering GPU memory consumption (it saved me 1 GB with batch size 4). Unfortunately, it doesn't work with torch.jit.

The dynamic loss scaling will potentially fix #359, #340, #318, #316, #222, #186, and #56.

I also patched dcn_v2.py to support it, so that YOLACT++ will work.

I am very sorry for the awful commit history, it was because the Black code-formatter literally modified all of the code.

Cheers

[breznak]

Thanks @jasonkena ! The issue description is interesting, I'd especially welcome the fp16 support! But it is quite impossible to review with all the (false possitive) code changes due to your editor re-formatting. Can you avoid that and submit a PR with only your changes? (no unnecessary whitespace)