eval.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

# -*- coding: utf-8 -*-
"""
# @Time    : 2019/5/27
# @Author  : Jiaqi&Zecheng
# @File    : eval.py
# @Software: PyCharm
"""


import torch
from src import args as arg
from src import utils
from src.models.model import IRNet
from src.rule import semQL


def evaluate(args):
    """
    :param args:
    :return:
    """

    grammar = semQL.Grammar()
    sql_data, table_data, val_sql_data,\
    val_table_data= utils.load_dataset(args.dataset, use_small=args.toy)

    model = IRNet(args, grammar)

    if args.cuda: model.cuda()

    print('load pretrained model from %s'% (args.load_model))
    pretrained_model = torch.load(args.load_model,
                                     map_location=lambda storage, loc: storage)
    import copy
    pretrained_modeled = copy.deepcopy(pretrained_model)
    for k in pretrained_model.keys():
        if k not in model.state_dict().keys():
            del pretrained_modeled[k]

    model.load_state_dict(pretrained_modeled)

    model.word_emb = utils.load_word_emb(args.glove_embed_path)

    json_datas = utils.epoch_acc(model, args.batch_size, val_sql_data, val_table_data,
                           beam_size=args.beam_size)
    # utils.eval_acc(json_datas, val_sql_data)
    import json
    with open('./predict_lf.json', 'w') as f:
        json.dump(json_datas, f)

if __name__ == '__main__':
    arg_parser = arg.init_arg_parser()
    args = arg.init_config(arg_parser)
    print(args)
    evaluate(args)