RANLP2021

Submission #70 repository to the RANLP 2021 conference

Build and run the project

Build

docker build -t "SADPGAN" . | tee log

Train SADPGAN

docker run --gpus all --rm -v <projecthome>/Anonymous/src:/app SADPGAN bash -c "cd /app/run && python -m run_sa_dpgan.py 1 2"

Train DPGAN

docker run --gpus all --rm -v <projecthome>/Anonymous/src:/app DPGAN bash -c "cd /app/run && python -m run_dpgan.py 1 2"

Produce visuals

After training both SADPGAN and DPGAN, two log files should be present in the log directory. To produce the visuals their name has to be passed as an argument (without the .txt) as well as the metric ('NLL_gen' or 'NLL_div') in the following way: docker run --gpus all --rm -v <projecthome>/Anonymous/src:/app bash -c "cd /app/visual && python visual_metric.py 'metric' 'image_name' 'dpgan_log_filename' 'sa_dpgan_log_filename'

Original repo

https://github.com/williamSYSU/TextGAN-PyTorch

Licence

The code included from the TextGAN-PyTorch repository is owned by William Lam and licensed under MIT standard. I only own my further contributions which are also lincensed under MIT standard.