main.py

import sys
import numpy as np
import tensorflow as tf

from trainer import Trainer
from config import get_config
from utils import prepare_dirs_and_logger, save_config

config = None

def main(_):
  prepare_dirs_and_logger(config)

  if not config.task.lower().startswith('tsp'):
    raise Exception("[!] Task should starts with TSP")

  if config.max_enc_length is None:
    config.max_enc_length = config.max_data_length
  if config.max_dec_length is None:
    config.max_dec_length = config.max_data_length

  rng = np.random.RandomState(config.random_seed)
  tf.set_random_seed(config.random_seed)

  trainer = Trainer(config, rng)
  save_config(config.model_dir, config)

  if config.is_train:
    trainer.train()
  else:
    if not config.load_path:
      raise Exception("[!] You should specify `load_path` to load a pretrained model")
    trainer.test()

  tf.logging.info("Run finished.")

if __name__ == "__main__":
  config, unparsed = get_config()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)