Image Generation from Small Datasets via Batch Statistics Adaptation

The author's official minimal implementation of Image Generation from Small Datasets via Batch Statistics Adaptation.

Clean codes and optimal hyperparameters will be available soon.

Requirements

chainer>=5.0.0
opencv_python
numpy
scipy
Pillow
PyYAML

Dataset preparation

data_path
├── dataset1
├── dataset2
...

• Place all training sample in the same directory for each dataset.
• Specify the root path to data_path in configs/default.yml.
• Specify the dataset directory name to dataset in configs/default.yml.

Run

• Download SNGAN-128 or BigGAN-256 generator pre-trained on ImageNet.
  • SNGAN generator can be downloaded from https://drive.google.com/drive/folders/1m04Db3HbN10Tz5XHqiPpIg8kw23fkbSi (ResNetGenerator_850000.npz)
  • BigGAN generator can be saved by https://github.com/nogu-atsu/chainer-BIGGAN (Code to save model is abailable soon.)
• Specify paths for them to snapshot in configs/default.yml
• Specify gan_type in configs/default.yml

For single GPU training, run

python ./train.py --config_path configs/default.yml

For Multiple GPU training, run

mpirun python ./train.py --config_path configs/default.yml

Multiple GPU training is supported only for BigGAN. For BigGAN ,we used 4 GPUs for training.

Acknowledgement

Pytorch re-implementation from Satoshi Tsutsui and Minjun Li.