Pytorch-PyraNet
Introduction
This is a pytorch version reproduce code of 'Learning Feature Pyramids for Human Pose Estimation', ICCV 2017. Link of paper: https://arxiv.org/pdf/1708.01101.pdf.
The network
The network designed based on stacked hourglass network.
Pyranet replace the Residual module with Pyramid Residual module. The network:
The authors design a Pyramid Residual module to use features and information of multi-scale:
In this repo, PRM-A, PRM-B and PRM-C has been realized. Following the comment you can choose these three Pyramid Residual Module. See the definition of Pyramid Residual Module in models/prm.py and the network architecture in models/network.py.
Requirements
The code has been tested with Ubuntu 16.04 and CUDA 8.
- python 2.7
- pytorch == 0.4.1 (must be pytorch 0.4.1)
- opencv-python
- numpy
- progress
Datasets
Mpii human pose dataset. Details of path setting: see ref.py. You can set the path in this file.
Train
For example, if you train the PRM network with 300 epochs, batch 6 and 2 stacked hourglass module with one gpu:
CUDA_VISIBLE_DEVICES = 0 python main.py -nEpochs 300 -trainBatch 6 -nStack 2
If you have a multi-gpu server, you can uncomment line27 in train.py to get a parallel speed-up:
output = torch.nn.parallel.data_parallel(model,input_var,device_ids=[0,1,2,3,4,5])
and then set CUDA_VISIBLE_DEVICES=0,1,2,3,4,5.
Finally, you can see more usage of flags in opts.py, such as -expID for specifing the path to save models and predictions.
Tools
network_visual.py : Make network architecture visualization
tools/eval_pckh.py : Get the result of [email protected]
Evaluation
Result: using tools/eval_pckh.py for evaluation.You can get a result like that(after 160 epochs):
Model, Head, Shoulder, Elbow, Wrist, Hip, Knee, Ankle, Mean
hg 96.69 95.06 88.38 83.30 86.31 82.81 78.86 87.43
Acknowledgement
Thanks for the authors of 'Learning Feature Pyramids for Human Pose Estimation'.