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test_input.py
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test_input.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'han'
import os
import torch
import logging
import spacy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from models import *
from dataset import Dataset, DocTextEn, DocTextCh
from utils.load_config import init_logging, read_config
from utils.functions import to_long_tensor, count_parameters, draw_heatmap_sea, beam_search
init_logging()
logger = logging.getLogger(__name__)
def main():
logger.info('------------MODEL TEST INPUT--------------')
logger.info('loading config file...')
# manual set
global_config = read_config('config/global_config.yaml')
# set random seed
seed = global_config['global']['random_seed']
torch.manual_seed(seed)
torch.set_grad_enabled(False) # make sure all tensors below have require_grad=False
logger.info('reading dataset...')
dataset = Dataset(global_config)
logger.info('constructing model...')
dataset_h5_path = global_config['data']['dataset_h5']
model = MatchLSTMPlus(dataset_h5_path)
model.eval() # let training = False, make sure right dropout
logging.info('model parameters count: %d' % count_parameters(model))
model_rerank = None
rank_k = global_config['global']['rank_k']
if global_config['global']['enable_rerank']:
model_rerank = ReRanker(dataset_h5_path)
model_rerank.eval()
logging.info('rerank model parameters count: %d' % count_parameters(model_rerank))
# load model weight
logger.info('loading model weight...')
model_weight_path = global_config['data']['model_path']
is_exist_model_weight = os.path.exists(model_weight_path)
assert is_exist_model_weight, "not found model weight file on '%s'" % model_weight_path
weight = torch.load(model_weight_path, map_location=lambda storage, loc: storage)
model.load_state_dict(weight, strict=False)
if global_config['global']['enable_rerank']:
rerank_weight_path = global_config['data']['rerank_model_path']
assert os.path.exists(rerank_weight_path), "not found rerank model weight file on '%s'" % rerank_weight_path
logger.info('loading rerank model weight...')
weight = torch.load(rerank_weight_path, map_location=lambda storage, loc: storage)
model_rerank.load_state_dict(weight, strict=False)
context = "《战国无双3》（）是由光荣和ω-force开发的战国无双系列的正统第三续作。本作以三大故事为主轴，分别是以武田信玄等人为主的《关东三国志》，织田信长等人为主的《战国三杰》，石田三成等人为主的《关原的年轻武者》，丰富游戏内的剧情。此部份专门介绍角色，欲知武器情报、奥义字或擅长攻击类型等，请至战国无双系列1.由于乡里大辅先生因故去世，不得不寻找其他声优接手。从猛将传 and Z开始。2.战国无双 编年史的原创男女主角亦有专属声优。此模式是任天堂游戏谜之村雨城改编的新增模式。本作中共有20张战场地图（不含村雨城），后来发行的猛将传再新增3张战场地图。但游戏内战役数量繁多，部分地图会有兼用的状况，战役虚实则是以光荣发行的2本「战国无双3 人物真书」内容为主，以下是相关介绍。（注：前方加☆者为猛将传新增关卡及地图。）合并本篇和猛将传的内容，村雨城模式剔除，战国史模式可直接游玩。主打两大模式「战史演武」&「争霸演武」。系列作品外传作品"
question1 = "《战国无双3》是由哪两个公司合作开发的？"
answer1 = ['光荣和ω-force']
question2 = '男女主角亦有专属声优这一模式是由谁改编的？'
answer2 = ['村雨城', '任天堂游戏谜之村雨城']
question3 = '战国史模式主打哪两个模式？'
answer3 = ['「战史演武」&「争霸演武」']
# change here to select questions
question = question2
answer = answer2[0]
# preprocess
preprocess_config = global_config['preprocess']
context_doc = DocTextCh(context, preprocess_config)
question_doc = DocTextCh(question, preprocess_config)
link_char = ''
# mpl.rcParams['font.sans-serif'] = ['Microsoft YaHei']
mpl.rcParams['font.sans-serif'] = ['SimHei']
context_doc.update_em(question_doc)
question_doc.update_em(context_doc)
context_token = context_doc.token
question_token = question_doc.token
context_id_char = None
question_id_char = None
if preprocess_config['use_char']:
context_id_char = to_long_tensor(dataset.sentence_char2id(context_token))
question_id_char = to_long_tensor(dataset.sentence_char2id(question_token))
context_id, context_f = context_doc.to_id(dataset.meta_data)
question_id, question_f = question_doc.to_id(dataset.meta_data)
bat_input = [context_id, question_id, context_id_char, question_id_char, context_f, question_f]
bat_input = [x.unsqueeze(0) if x is not None else x for x in bat_input]
# predict
out_ans_prop, out_ans_range, vis_param = model.forward(*bat_input)
if model_rerank is not None:
cand_ans_range = beam_search(out_ans_prop, k=rank_k)
cand_score, out_ans_range = model_rerank(bat_input[0], bat_input[1], cand_ans_range)
out_ans_range = out_ans_range.numpy()
start = out_ans_range[0][0]
end = out_ans_range[0][1] + 1
out_answer_id = context_id[start:end]
out_answer = dataset.sentence_id2word(out_answer_id)
logging.info('Predict Answer: ' + link_char.join(out_answer))
# to show on visdom
s = 0
e = 48
x_left = vis_param['match']['left']['alpha'][0, :, s:e].numpy()
x_right = vis_param['match']['right']['alpha'][0, :, s:e].numpy()
x_left_gated = vis_param['match']['left']['gated'][0, :, s:e].numpy()
x_right_gated = vis_param['match']['right']['gated'][0, :, s:e].numpy()
draw_heatmap_sea(x_left,
xlabels=context_token[s:e],
ylabels=question_token,
answer=answer,
save_path='data/test-left.png',
bottom=0.2)
draw_heatmap_sea(x_right,
xlabels=context_token[s:e],
ylabels=question_token,
answer=answer,
save_path='data/test-right.png',
bottom=0.2)
# plt.show()
if __name__ == '__main__':
main()