This is the official implementation of EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs. We introduce a novel NAS framework, namely EAGAN, to alleviate the instability when searching GANs. Our EAGAN decouples the search into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training strategy and the weight-resetting strategy to enhance the stability of GAN training.

The framework of the proposed method:

git clone https://github.com/marsggbo/EAGAN
cd EAGAN

We recommend using Anaconda to manage the python environment:

conda create -n EAGAN python=3.8
conda activate EAGAN
pip install -r requirements.txt

In our work, we use CIFAR-10 and STL-10 datasets. The default path for both datasets is ~/datasets/cifar10 and ~/datasets/stl10. Therefore, you should move or download both datasets in this path.

bash ./scripts/train_search_gen.sh

or run the following commands

python search_gen_arch.py \
--gpu_ids 0 \
--num_workers 16 \
--gen_bs 80 \
--dis_bs 40 \
--dataset cifar10 \
--bottom_width 4 \
--img_size 32 \
--max_epoch_G 120 \
--n_critic 1 \
--arch arch_cifar10 \
--draw_arch False \
--latent_dim 120 \
--gf_dim 256 \
--df_dim 128 \
--g_lr 0.0002 \
--d_lr 0.00004 \
--beta1 0.0 \
--beta2 0.9 \
--init_type xavier_uniform \
--val_freq 5 \
--num_eval_imgs 5000 \
--exp_name arch_train_cifar10 \
--data_path ~/datasets/cifar10

• genotypes_exp: specify the genotype path of initial discriminator.
  • exps/cifar10_D.npy is used by default.
  • if you want to specify a different initial discriminator, you can modify dis_genotype variable in search_gen_arch.py, e.g., dis_genotype=np.load('./exps/cifar10_D.npy')

bash ./scripts/train_search_dis.sh

• ./exps/best_G.npy is the best searched generator genotype that can be trained on cifar10 and stl10 datasets, presented in the paper Fig.3.
• ./exps/cifar10_D.npy and ./exps/stl10_D.npy are the best searched discriminator genotypes for cifar10 and stl10 datasets respectively, and their details can be found in the paper Fig.4.

bash ./scripts/train_arch_cifar10.sh

or run the following commands

python fully_train_arch.py \
--gpu_ids 0 \
--num_workers 16 \
--gen_bs 80 \
--dis_bs 40 \
--dataset cifar10 \
--bottom_width 4 \
--img_size 32 \
--max_epoch_G 120 \
--n_critic 5 \
--arch arch_cifar10 \
--draw_arch False \
--genotypes_exp cifar10_D1.npy \
--latent_dim 120 \
--gf_dim 256 \
--df_dim 128 \
--g_lr 0.0002 \
--d_lr 0.0002 \
--beta1 0.0 \
--beta2 0.9 \
--init_type xavier_uniform \
--val_freq 5 \
--num_eval_imgs 50000 \
--exp_name arch_train_cifar10 \
--data_path ~/datasets/cifar10

bash ./scripts/train_arch_stl10.sh

bash ./scripts/test_arch_cifar10.sh
bash ./scripts/test_arch_stl10.sh

If you find this work useful for your research, please kindly cite our paper:

@inproceedings{ECCV2022EAGAN,
  title={EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs},
  author={Guohao Ying, Xin He, Bin Gao, Bo Han, and Xiaowen Chu.},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2022}
}

Thanks NVIDIA AI TECHNOLOGY CENTER (NVAITC) for providing GPU clusters to support our work.