This repository provides the implementation code for our preprint.

Clone this repository and go into the root directory. Set up the package by running python setup.py install. This would automatically install dependencies needed for the code, including logging packages like tensorboard and wandb.

We provide scripts that we use for downloading and cleaning SCOPe dataset. To download, run

chmod  x scripts/install_dataset.sh
./scripts/install_dataset.sh

To train Genie, create a directory runs/[RUN_NAME] and go into the directory. Create a configuration file with name configuration. An example of configuration file is provided in example_configuration and a complete list of configurable parameters could be found in genie/config.py. Note that in the configuration file, name should match with RUN_NAME in order to log into the correct directory. To start training, run

python genie/train.py -c runs/RUN_NAME/configuration -g0 &

for example, to run in the background on GPU 0.

To sample domains using Genie, run

python genie/sample.py -n RUN_NAME -g0

By default, it uses the checkpoint with the latest version and epoch. You could also specify the version and epoch by using the -v and -e flag respectively. This would sample 10 domains per sequence length between 50 and 128, with a sampling batch size of 5. The output are stored in the directory runs/[RUN_NAME]/version_[VERSION]/samples/epoch_[EPOCH].