Modified from official RetinaFace

This is only the testing code. Not for training.

The official code takes too much CPU computation (which happens in generate_anchor:anchors_plane -> cython:anchors_cython). When running 4 processes, all cores went up to 100%. And, it can only support single image prediction.

It is obvious that we don't need to render all anchors before selecting proposals. Therefore, I change the pipeline to:

• Select proposal indices where (conf > threshold) to produce (image_idx, row_idx, col_idx)

• Generate anchors for selected indices only (which is much light-weighted)

• Generate landmarks for selected indices only

• Batched_nms (from torchvision)

I move all operations to GPU which should be much faster.

See test_all.py

pip install torchsul 
pip install opencv-python

Other packages are already included in anaconda.

If you want to use RetinaFace-R50, download from here