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Making DensePose fast and light

Code for Making DensePose fast and light.

Original DensePose project Quick Start

See Getting Started

Training and Evaluation

  1. The project dependencies:
  • detectron2 Install it the following way:
git clone https://github.com/facebookresearch/detectron2.git && cd detectron2
git checkout b1fe5127e41b506cd3769180ad774dec0bfd56b0
pip install -e .
  • timm == 0.1.16
  • pytorch >= 1.4.0
  1. You can train a network from scratch using configs in ./configs folder and train_net.py script.
  • s0_bv2_bifpn_f64_s3x.yaml config corresponds to the Mobile-RCNN (B s3x) model,
  • s0_bv2_bifpn_f64.yaml config corresponds to the Mobile-RCNN (B s1x) model,
  • densepose_parsing_rcnn_spnasnet_100_FPN_s3x.yaml config corresponds to the Mobile-RCNN (A s3x) model,
  • densepose_parsing_rcnn_R_50_FPN_s1x.yaml config corresponds to the Parsing RCNN model

Then evaluate the model with --eval_only flag.

  1. You can run QAT of the Mobile-RCNN (B s3x) using train_net.py with --qat flag then evaluate it with --quant-eval flag. To use proposed hooks preserving mechanism it is needed to modify PyTorch source code according to files inside modify_pytorch directroy OR Use PyTorch nightly build (it is now containing the following commit https://github.com/pytorch/pytorch/pull/37233/commits/c8de10d2a394484ac58dd131878950b8ab7ac7a9)

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