Omni-GAN-PyTorch: A Python repository from PeterouZh

Omni-GAN

This repository contains code for the paper, Omni-GAN: On the Secrets of cGANs and Beyond.
In particular, it contains the code for the CIFAR experiments for your quick reference.
For the ImageNet and DGP experiments, please refer to Omni-GAN-DGP.

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Omni-INR-GAN_input_GT_colorization.mp4

Omni-INR-GAN256x256-SRx60+_compressed.mp4

Omni-INR-GAN256x256_generated_images.mp4

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Comparision of several cGANs

Results on CIFAR100

Let's go!

Environments

git clone --recursive https://github.com/PeterouZh/Omni-GAN-PyTorch.git
cd Omni-GAN-PyTorch
conda create -y --name omnigan python=3.6.7 
conda activate omnigan
pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html
pip install -r requirements.txt

cuda 10.0
cudnn 7.6.5

Cuda and cudnn are needed by the TensorFlow which is only utilized for the sake of evaluation (monitoring the training process). We recommend downloading cuda and cudnn from the official website and installing them manually.

Prepare FID statistics files

We provide the FID statistics files calculated on CIFAR10 and CIFAR100 at OneDrive respectively. Download them and put them into the datasets dir. Of course, you can calculate these files by yourself. Below is the command.

CIFAR10

export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64
export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./
python template_lib/v2/GAN/evaluation/tf_FID_IS_score.py \
  --tl_config_file configs/prepare_files.yaml \
  --tl_command calculate_fid_stat_CIFAR \
  --tl_outdir results/calculate_fid_stat_CIFAR10 \
  --tl_opts dataset_name cifar10_train \
            dataset_mapper_cfg.name CIFAR10DatasetMapper \
            GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar10_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model

CIFAR100

export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64
export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./
python template_lib/v2/GAN/evaluation/tf_FID_IS_score.py \
  --tl_config_file configs/prepare_files.yaml \
  --tl_command calculate_fid_stat_CIFAR \
  --tl_outdir results/calculate_fid_stat_CIFAR100 \
  --tl_opts dataset_name cifar100_train \
            dataset_mapper_cfg.name CIFAR100DatasetMapper \
            GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar100_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model

Evaluation

We provide trained models on CIFAR10 and CIFAR100 at OneDrive. Download them and put them in datasets dir.

CIFAR10

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./:./BigGAN_PyTorch_1_lib 
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python exp/omni_loss/train.py \
  --tl_config_file configs/omni_gan_cifar10.yaml \
  --tl_command eval_trained_model \
  --tl_opts eval_cfg.path datasets/G_ema_best_FID_cifar10.pth \
            GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar10_train_32.npz \
  --tl_outdir results/eval_trained_model_cifar10

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./:./BigGAN_PyTorch_1_lib 
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python exp/omni_loss/train.py \
  --tl_config_file configs/omni_gan_cifar10.yaml \
  --tl_command eval_trained_model \
  --tl_opts eval_cfg.path datasets/G_ema_best_IS_cifar10.pth \
            GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar10_train_32.npz \
  --tl_outdir results/eval_trained_model_cifar10

CIFAR100

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./:./BigGAN_PyTorch_1_lib 
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python exp/omni_loss/train.py \
  --tl_config_file configs/omni_gan_cifar100.yaml \
  --tl_command eval_trained_model \
  --tl_opts eval_cfg.path datasets/G_ema_best_FID_cifar100.pth \
            GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar100_train_32.npz \
  --tl_outdir results/eval_trained_model_cifar100

Train Omni-GAN

CIFAR10

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./:./BigGAN_PyTorch_1_lib 
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python exp/omni_loss/train.py \
  --tl_config_file configs/omni_gan_cifar10.yaml \
  --tl_command train_Omni_GAN \
  --tl_outdir results/train_Omni_GAN_cifar10 \
  --tl_opts GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar10_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model \
            args.data_root datasets/cifar10

The metrics are saved in results/train_Omni_GAN_cifar10/textdir.

CIFAR100

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./:./BigGAN_PyTorch_1_lib 
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python exp/omni_loss/train.py \
  --tl_config_file configs/omni_gan_cifar100.yaml \
  --tl_command train_Omni_GAN \
  --tl_opts GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar100_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model \
            args.data_root datasets/cifar100 \
  --tl_outdir results/train_Omni_GAN_cifar100

The metrics are saved in results/train_Omni_GAN_cifar100/textdir.

Train BigGAN

CIFAR10

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./exp:./:./BigGAN_PyTorch_1_lib
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python BigGAN_PyTorch_1_lib/train.py \
  --tl_config_file configs/biggan_cifar.yaml \
  --tl_command train_BigGAN_c10 \
  --tl_outdir results/train_BigGAN_cifar10 \
  --tl_opts GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar10_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model \
            args.data_root datasets/cifar10

CIFAR100

export CUDA_VISIBLE_DEVICES=0
export PYTHONPATH=./exp:./:./BigGAN_PyTorch_1_lib
export LD_LIBRARY_PATH=$HOME/.keras/envs/cuda-10.0/lib64:$HOME/.keras/envs/cudnn-10.0-linux-x64-v7.6.5.32/lib64 
python BigGAN_PyTorch_1_lib/train.py \
  --tl_config_file configs/biggan_cifar.yaml \
  --tl_command train_BigGAN_c100 \
  --tl_outdir results/train_BigGAN_cifar100 \
  --tl_opts GAN_metric.tf_fid_stat datasets/fid_stats_tf_cifar100_train_32.npz \
            GAN_metric.tf_inception_model_dir datasets/tf_inception_model \
            args.data_root datasets/cifar100

Acknowledgments

BigGAN implemented from https://github.com/ajbrock/BigGAN-PyTorch.
Multi-label classification loss derived by Jianlin Su.
Detectron2 library https://github.com/facebookresearch/detectron2.