nashid/code2seq-1

PyTorch's implementation of the code2seq model.

code2seq

PyTorch's implementation of code2seq model.

Installation

You can easily install model through the PIP:

pip install code2seq

Usage

Minimal code example to run the model:

from argparse import ArgumentParser

from omegaconf import DictConfig, OmegaConf
from pytorch_lightning import Trainer

from code2seq.data.path_context_data_module import PathContextDataModule
from code2seq.model import Code2Seq


def train(config: DictConfig):
    # Load data module
    data_module = PathContextDataModule(config.data_folder, config.data)
    data_module.prepare_data()
    data_module.setup()

    # Load model
    model = Code2Seq(
        config.model,
        config.optimizer,
        data_module.vocabulary,
        config.train.teacher_forcing
    )

    trainer = Trainer(max_epochs=config.hyper_parameters.n_epochs)
    trainer.fit(model, datamodule=data_module)


if __name__ == "__main__":
    __arg_parser = ArgumentParser()
    __arg_parser.add_argument("config", help="Path to YAML configuration file", type=str)
    __args = __arg_parser.parse_args()

    __config = OmegaConf.load(__args.config)
    train(__config)

Navigate to config directory to see examples of configs. If you have any questions, then feel free to open the issue.