Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Training code for the paper Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation, CVPR 2018
Traditional random augmentation has two limitations. It doesn't consider the individual difference of training samples when doing augmentation. And it is also independent of the training status of the target network. To tackle these problems, we design an agent to learn more effective data augmentation.
This package has the following requirements:
Python 2.7Pytorch 0.3.0.post4
Install pytorch:
pip install http://download.pytorch.org/whl/cu90/torch-0.3.0.post4-cp27-cp27mu-linux_x86_64.whl
Install torchvision, scipy, matplotlib, dominate and visdom:
pip install torchvision scipy matplotlib dominate visdom
The training is divided into three stages. First, we pretrain the pose network for 10 epochs. Then we use the fixed pose network to pretrain the augmentation agent. Finally, we jointly optimize these two.
python stack-hg.py --gpu_id 0 --exp_id stack-2-hgs --vis_env stack-2-hgs --is_train true --bs 24
Use the pose network to collect the scale and rotation distributions to train the agent:
python collect-scale-ditri.py --gpu_id 0 --exp_id stack-2-hgs --load_prefix_pose lr-0.00025-10.pth.tar --bs 10
python collect-rotation-ditri.py --gpu_id 0 --exp_id stack-2-hgs --load_prefix_pose lr-0.00025-10.pth.tar --bs 10
Pretrain the agent:
python pretrain-s-r-agent.py --gpu_id 0 --exp_id stack-2-hgs --load_prefix_pose lr-0.00025-10.pth.tar --bs 24
python joint-train-pose-s-r-agent.py --gpu_id 0 --exp_id stack-2-hgs --load_prefix_pose lr-0.00025-10.pth.tar --load_prefix_sr lr-0.00025-1.pth.tar --vis_env stack-2-hgs-joint --is_train true --bs 24
If you find this code useful in your research, please consider citing:
@inproceedings{peng2018jointly,
title={Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation},
author={Peng, Xi and Tang, Zhiqiang and Yang, Fei and Feris, Rogerio S and Metaxas, Dimitris},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2226--2234},
year={2018}
}
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