nl2sql/test.py

import torch
from sqlnet.utils import *
from sqlnet.model.sqlnet import SQLNet
import argparse

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--gpu', action='store_true', help='Whether use gpu')
    parser.add_argument('--toy', action='store_true', help='Small batchsize for fast debugging.')
    parser.add_argument('--ca', action='store_true', help='Whether use column attention.')
    parser.add_argument('--train_emb', action='store_true', help='Use trained word embedding for SQLNet.')
    parser.add_argument('--output_dir', type=str, default='', help='Output path of prediction result')
    args = parser.parse_args()

    n_word=300
    if args.toy:
        use_small=True
        gpu=args.gpu
        batch_size=16
    else:
        use_small=False
        gpu=args.gpu
        batch_size=64

    dev_sql, dev_table, dev_db, test_sql, test_table, test_db = load_dataset(use_small=use_small, mode='test')

    word_emb = load_word_emb('data/char_embedding.json')
    model = SQLNet(word_emb, N_word=n_word, use_ca=args.ca, gpu=gpu, trainable_emb=args.train_emb)

    model_path = 'saved_model/best_model'
    print ("Loading from %s" % model_path)
    model.load_state_dict(torch.load(model_path))
    print ("Loaded model from %s" % model_path)

    dev_acc = epoch_acc(model, batch_size, dev_sql, dev_table, dev_db)
    print ('Dev Logic Form Accuracy: %.3f, Execution Accuracy: %.3f' % (dev_acc[1], dev_acc[2]))

    print ("Start to predict test set")
    predict_test(model, batch_size, test_sql, test_table, args.output_dir)
    print ("Output path of prediction result is %s" % args.output_dir)