GANs with spectral normalization and projection discriminator

NOTE: The setup and example code in this README are for training GANs on single GPU. The models are smaller than the ones used in the papers. Please go to link if you are looking for how to reproduce the results in the papers.

Official Chainer implementation for conditional image generation on ILSVRC2012 dataset (ImageNet) with spectral normalization and projection discrimiantor.

Demo movies

Consecutive category morphing movies:

(5x5 panels 128px images) https://www.youtube.com/watch?v=q3yy5Fxs7Lc
(10x10 panels 128px images) https://www.youtube.com/watch?v=83D_3WXpPjQ

Other materials

Generated images
- from the model trained on all ImageNet images (1K categories): [128px]
- from the model trained on dog and cat images (143 categories): [64px] [128px] [256px]
Pretrained models
Movies
4 corners category morph.

References

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida. Spectral Normalization for Generative Adversarial Networks. ICLR2018. OpenReview
Takeru Miyato, Masanori Koyama. cGANs with Projection Discriminator. ICLR2018. OpenReview

Setup

Install required python libraries:

pip install -r requirements.txt

Download ImageNet dataset:

Please download ILSVRC2012 dataset from http://image-net.org/download-images

Preprocess dataset:

cd datasets
IMAGENET_TRAIN_DIR=/path/to/imagenet/train/ # path to the parent directory of category directories named "n0*******".
PREPROCESSED_DATA_DIR=/path/to/save_dir/
bash preprocess.sh $IMAGENET_TRAIN_DIR $PREPROCESSED_DATA_DIR
# Make the list of image-label pairs for all images (1000 categories, 1281167 images).
python imagenet.py $PREPROCESSED_DATA_DIR
# Make the list of image-label pairs for dog and cat images (143 categories, 180373 images). 
python imagenet_dog_and_cat.py $PREPROCESSED_DATA_DIR

Download inception model:

python source/inception/download.py --outfile=datasets/inception_model

Training examples

Spectral normalization + projection discriminator for 64x64 dog and cat images:

LOGDIR=/path/to/logdir
CONFIG=configs/sn_projection_dog_and_cat_64.yml
python train.py --config=$CONFIG --results_dir=$LOGDIR --data_dir=$PREPROCESSED_DATA_DIR

pretrained model
generated images at 250K iterations
Examples of 64x64 generated images:

Spectral normalization + projection discriminator for 64x64 all ImageNet images:

LOGDIR=/path/to/logdir
CONFIG=configs/sn_projection_64.yml
python train.py --config=$CONFIG --results_dir=$LOGDIR --data_dir=$PREPROCESSED_DATA_DIR

Evaluation examples

(If you want to use pretrained models for the image generation, please download the model from link and set the snapshot argument to the path to the downloaded pretrained model file (.npz).)

Generate images

python evaluations/gen_images.py --config=$CONFIG --snapshot=${LOGDIR}/ResNetGenerator_<iterations>.npz --results_dir=${LOGDIR}/gen_images

Generate category morphing images

Regarding the index-category correspondence, please see 1K ImageNet or 143 dog and cat ImageNet.

python evaluations/gen_interpolated_images.py --n_zs=10 --n_intp=10 --classes $CATEGORY1 $CATEGORY2 --config=$CONFIG --snapshot=${LOGDIR}/ResNetGenerator_<iterations>.npz --results_dir=${LOGDIR}/gen_morphing_images

Calculate inception score (with the original OpenAI implementation)

python evaluations/calc_inception_score.py --config=$CONFIG --snapshot=${LOGDIR}/ResNetGenerator_<iterations>.npz --results_dir=${LOGDIR}/inception_score --splits=10 --tf

AceChuse/sngan_projection