Code for Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search, ECCV 2020.

We formulate the GAN architecture search problem as a Markov decision process (MDP) inspired by the success of human-designed Progressive GAN. This new formulation enables us to discover competitive GAN architectures on a single 2080TI in 7 hours using off-policy RL.

conda create --name e2ganrl python=3.6
conda activate e2ganrl

conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.0 -c pytorch

python3 -m pip install imageio
python3 -m pip install scipy
python3 -m pip install six
python3 -m pip install numpy==1.18.1
python3 -m pip install python-dateutil==2.7.3
python3 -m pip install tensorboardX==1.6
# For the reward calculation, external tf code
python3 -m pip install tensorflow-gpu==1.13.1
python3 -m pip install tqdm==4.29.1

Code was tested on a RTX2080TI with 11GB RAM.

Download the pre-calculated statistics from AutoGAN (Google Drive) to ./search/fid_statand ./eval/fid_stat .

cd search
bash exps/search.sh

You will find the architectures in the log file ./search/search.log after running the above script.

To train from scratch and get the performance of your discovered architecture, run the following command (you should replace the architecture vector following "--arch" in the script with best-performing candidate architectures in the exploitation stage in search.log):

cd eval
# Train the discovered GAN on CIFAR-10
bash exps/train_derived.sh
# Train the discovered GAN on STL
bash exps/train_derived_stl.sh

Run the following script:

cd eval
# Testing the pretrained CIFAR-10 Model
bash exps/test.sh
# Testing the pretrained STL Model
bash exps/test_stl.sh

Pre-trained models (both CIFAR and STL) are provided (Google Drive). Please put them in eval/checkpoints/ .

Please cite our work if you find it useful.

@InProceedings{Tian_2020_ECCV,
author = {Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink},
title = {Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search},
booktitle = {The European Conference on Computer Vision (ECCV)},
month = {September},
year = {2020}
}

PDF

1. Inception Score code from OpenAI's Improved GAN (official).
2. FID code and CIFAR-10 statistics file from https://github.com/bioinf-jku/TTUR (official).
3. SAC code from https://github.com/pranz24/pytorch-soft-actor-critic.
4. GAN training/eval code is heavily borrowed from AutoGAN https://github.com/TAMU-VITA/AutoGAN

For questions regarding the code, please open an issue or contact Yuan and Qin via email {yutian, qwang} AT ethz.ch