Create environment
conda env create -f env.yml
Set up the gyafc data directory. Data path follows this convention:
data_pth = os.path.join(args.hard_disk_dir, "data", args.data_name, "processed")
where args.hard_disk_dir and args.data_name are specified in the script.
Training with CP-VAE. Current model is CP-VAE with shared BERT encoder, GPT2 decoder, and style loss.
data_name=gyafc
save=checkpoint/exp_name
hard_disk_dir=/hdd2/lannliat/CP-VAE # change to your data directory
python run.py --hard_disk_dir $hard_disk_dir
--data_name $data_name \
--save $save \
--subset \ # trains with only subset of data for faster experiments
--to_plot # plots simplex p and umap of z1.
Apply style transfer on trained model. Currently z1 is taken as the mean of the z1 of the input validation examples.
data_name=gyafc
path_to_checkpoint="/hdd2/lannliat/CP-VAE/checkpoint/subset-cpvae-styleloss-gyafc-fm/20221003-091150/"
python transfer.py --data_name $data_name \
--load_path $path_to_checkpoint