main.py

#! /usr/bin/env python3

import sys
import os
import argparse
import random
import torch
import json

import Util
import Train
import Decode
from Transition import Transition

################################################################################
if __name__ == "__main__" :
  parser = argparse.ArgumentParser()
  parser.add_argument("mode", type=str,
    help="What to do : train | decode")
  parser.add_argument("type", type=str,
    help="Type of train or decode. random | oracle")
  parser.add_argument("corpus", type=str,
    help="Name of the CoNLL-U file. Train file for train mode and input file for decode mode.")
  parser.add_argument("model", type=str,
    help="Path to the model directory.")
  parser.add_argument("--iter", "-n", default=5,
    help="Number of training epoch.")
  parser.add_argument("--batchSize", default=64,
    help="Size of each batch.")
  parser.add_argument("--seed", default=100,
    help="Random seed.")
  parser.add_argument("--bootstrap", default=None,
    help="If not none, extract examples in bootstrap mode (oracle train only).")
  parser.add_argument("--dev", default=None,
    help="Name of the CoNLL-U file of the dev corpus.")
  parser.add_argument("--incr", "-i", default=False, action="store_true",
    help="If true, the neural network will be 'incremenal' i.e. will not see right context words if they have never been the word index.")
  parser.add_argument("--debug", "-d", default=False, action="store_true",
    help="Print debug infos on stderr.")
  parser.add_argument("--silent", "-s", default=False, action="store_true",
    help="Don't print advancement infos.")
  parser.add_argument("--ts", default="",
    help="Comma sepaarated list of supplementary transitions. Example \"BACK 1,BACK 2\"")
  args = parser.parse_args()

  if args.debug :
    args.silent = True

  os.makedirs(args.model, exist_ok=True)

  Util.setDevice(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
  print("Using device : %s"%Util.getDevice(), file=sys.stderr)
  random.seed(args.seed)
  torch.manual_seed(args.seed)

  if args.bootstrap is not None :
    args.bootstrap = int(args.bootstrap)

  transitionSet = [Transition(elem) for elem in (["SHIFT","REDUCE","LEFT","RIGHT"]+args.ts.split(',')) if len(elem) > 0]
  strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}

  if args.mode == "train" :
    json.dump([t.name for t in transitionSet], open(args.model+"/transitions.json", "w"))
    json.dump(strategy, open(args.model+"/strategy.json", "w"))
    print("Transition Set :", [trans.name for trans in transitionSet], file=sys.stderr)
    Train.trainMode(args.debug, args.corpus, args.type, transitionSet, strategy, args.model, int(args.iter), int(args.batchSize), args.dev, args.bootstrap, args.incr, args.silent)
  elif args.mode == "decode" :
    transNames = json.load(open(args.model+"/transitions.json", "r"))
    transitionSet = [Transition(elem) for elem in transNames]
    strategy = json.load(open(args.model+"/strategy.json", "r"))
    print("Transition Set :", [trans.name for trans in transitionSet], file=sys.stderr)
    Decode.decodeMode(args.debug, args.corpus, args.type, transitionSet, strategy, args.model)
  else :
    print("ERROR : unknown mode '%s'"%args.mode, file=sys.stderr)
    exit(1)
################################################################################