AutoInfo GAN: Towards a better image synthesis framework of GAN on high-fidelity few-shot datasets via NAS and contrastive learning - pytorch

0. Data

The datasets used in the paper can be found at link.

Please feel free to try with your own datasets.

Please put these datasets to 'data' directory created by yourself.

1. Description

The code is structured as follows:

• models_search: the definition of shared GAN and controller used in searh.

• operation.py: the helper functions and data loading methods during training.

• search_mixed_2stage.py: hybrid 2-stage method.

• train_search.py: training derived GAN from the scratch.

• benchmarking: the functions we used to compute FID are located here, it automatically downloads the pytorch official inception model.

• lpips: this folder contains the code to compute the LPIPS score, the inception model is also automatically download from official location.

• scripts: this folder contains many scripts you can use to play around the trained model. Including:

  1. style_mix.py: style-mixing as introduced in the paper;
  2. generate_video.py: generating a continuous video from the interpolation of generated images;
  3. find_nearest_neighbor.py: given a generated image, find the closest real-image from the training set;
  4. train_backtracking_one.py: given a real-image, find the latent vector of this image from a trained Generator.

2. How to run

seach GAN, please call

sh ./exps/autogan_search_2stage.sh

train derived GAN, please call:

sh ./exps/derive.sh

Project will automatically generate a directiory 'log', you can find models and logs in it.

3. Acknowledgement

Our project thanks to the FastGAN [link] (https://github.com/odegeasslbc/FastGAN-pytorch) and AutoGAN [link] (https://github.com/TAMU-VITA/AutoGAN)