This is an unoffical implemention for paper Fast Human Pose Estimation, Feng Zhang, Xiatian Zhu, Mao Ye. Most of code comes from pytorch implementation for stacked hourglass network pytorch-pose. In this repo, we followed Fast Pose Distillation approach proposed by Fast Human Pose Estimation to improve accuracy of a lightweight network. We first trained a deep teacher network (stacks=8, standard convolution, 88.33@Mpii pckh), and used it to teach a student network (stacks=2, depthwise convolution, 84.69%@Mpii pckh). Our experiment shows 0.7% gain from knowledge distillation.

I benchmarked the light student model hg_s2_b1_mobile_fpd and got 43fps on i7-8700K via OpenVino. Details can be found from Fast_Stacked_Hourglass_Network_OpenVino

Please check the offical implementation by fast-human-pose-estimation.pytorch

• Model trained by using extra unlabeled images uploaded, hg_s2_b1_mobile_fpd_unlabeled shows 0.28% extra gain from knowledge transfered from teacher on unlabeled data.
• The key idea is inserting unlabeled images into mpii dataset. For unlabeled samples, loss comes from difference b/w teacher and student. For labeled samples, loss is the sum of teacher-vs-student and student-vs-groundtruth.

hg_s8_b1: teacher model, hg_s2_b1_mobile:student model, hg_s2_b1_mobile_kd: student model trained with FPD. hg_s2_b1_mobile_fpd_unlabeled: student model trained with FPD with extral unlabeled samples.

1. Create a virtualenv

   virtualenv -p /usr/bin/python2.7 pose_venv
   

2. Clone the repository with submodule

   git clone --recursive https://github.com/yuanyuanli85/Fast_Human_Pose_Estimation_Pytorch.git
   

3. Install all dependencies in virtualenv

   source posevenv/bin/activate
   pip install -r requirements.txt
   

4. Create a symbolic link to the images directory of the MPII dataset:

   ln -s PATH_TO_MPII_IMAGES_DIR data/mpii/images
   

5. Disable cudnn for batchnorm layer to solve bug in pytorch0.4.0

   sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ./pose_venv/lib/python2.7/site-packages/torch/nn/functional.py
   

• Download pre-trained modelhg_s2_b1_mobile_fpd) and save it to somewhere, i.e checkpoint/mpii_hg_s2_b1_mobile_fpd/
• Run demo on sample image

python tools/mpii_demo.py -a hg -s 2 -b 1 --mobile True --checkpoint checkpoint/mpii_hg_s2_b1_mobile_fpd/model_best.pth.tar --in_res 256 --device cuda 

• You will see the detected keypoints drawn on image on your screen

• In our experiments, we used stack=8 input resolution=256 as teacher network

python example/mpii.py -a hg --stacks 8 --blocks 1 --checkpoint checkpoint/hg_s8_b1/ 

• Run evaluation to get val score.

python tools/mpii.py -a hg --stacks 8 --blocks 1 --checkpoint checkpoint/hg_s8_b1/preds_best.mat 

• Download teacher model's checkpoint or you can train from scratch. In our experiments, we used hg_s8_b1 as teacher.

• Train student network with knowledge distillation from teacher

python example/mpii_kd.py -a hg --stacks 2 --blocks 1 --checkpoint checkpoint/hg_s2_b1_mobile/ mobile=True --teacher_stack 8 --teacher_checkpoint 
checkpoint/hg_s8_b1/model_best.pth.tar  

Run evaluation to generate mat file

python example/mpii.py -a hg --stacks 2 --blocks 1 --checkpoint checkpoint/hg_s2_b1/ --resume checkpoint/hg_s2_b1/model_best.pth.tar -e

• --resume_checkpoint is the checkpoint want to evaluate

Run tools/eval_PCKh.py to get val score

python tools/mpii_export_to_onxx.py -a hg -s 2 -b 1 --num-classes 16 --mobile True --in_res 256  --checkpoint checkpoint/model_best.pth.tar 
--out_onnx checkpoint/model_best.onnx 

Here

• --checkpoint is the checkpoint want to export
• --out_onnx is the exported onnx file

• OpenVino: https://github.com/opencv/dldt
• Pytorch-pose: https://github.com/yuanyuanli85/pytorch-pose
• Fast Human Pose Estimation https://github.com/yuanyuanli85/Fast_Human_Pose_Estimation_Pytorch