PyTorch's implementation of code2seq model.
You can easily install model through the PIP:
pip install code2seqMinimal 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.