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Main.py
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Main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 17-4-27 下午8:44
# @Author : Tianyu Liu
import sys
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
import time
from SeqUnit import *
from DataLoader import DataLoader
import numpy as np
from PythonROUGE import PythonROUGE
from nltk.translate.bleu_score import corpus_bleu, SmoothingFunction
from preprocess import *
from util import *
import logging
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
tf.app.flags.DEFINE_integer("hidden_size", 500, "Size of each layer.")
tf.app.flags.DEFINE_integer("emb_size", 400, "Size of embedding.")
tf.app.flags.DEFINE_integer("field_size", 50, "Size of embedding.")
tf.app.flags.DEFINE_integer("pos_size", 5, "Size of embedding.")
tf.app.flags.DEFINE_integer("batch_size", 1, "Batch size of train set.")
tf.app.flags.DEFINE_integer("epoch", 50, "Number of training epoch.")
tf.app.flags.DEFINE_integer("source_vocab", 20003,'vocabulary size')
tf.app.flags.DEFINE_integer("field_vocab", 1480,'vocabulary size')
tf.app.flags.DEFINE_integer("position_vocab", 31,'vocabulary size')
tf.app.flags.DEFINE_integer("target_vocab", 20003,'vocabulary size')
tf.app.flags.DEFINE_integer("report", 1000,'report valid results after some steps')
tf.app.flags.DEFINE_float("learning_rate", 0.0003,'learning rate')
tf.app.flags.DEFINE_string("mode",'test','train')
# tf.app.flags.DEFINE_string('train')
# tf.app.flags.DEFINE_string("load",'0','load directory') # BBBBBESTOFAll
tf.app.flags.DEFINE_string("load",'1592984863135','load directory') # BBBBBESTOFAll
tf.app.flags.DEFINE_string("dir",'processed_data','data set directory')
tf.app.flags.DEFINE_integer("limits", 0,'max data set size')
tf.app.flags.DEFINE_boolean("dual_attention", True,'dual attention layer or normal attention')
tf.app.flags.DEFINE_boolean("fgate_encoder", True,'add field gate in encoder lstm')
tf.app.flags.DEFINE_boolean("field", False,'concat field information to word embedding')
tf.app.flags.DEFINE_boolean("position", False,'concat position information to word embedding')
tf.app.flags.DEFINE_boolean("encoder_pos", True,'position information in field-gated encoder')
tf.app.flags.DEFINE_boolean("decoder_pos", True,'position information in dual attention decoder')
FLAGS = tf.app.flags.FLAGS
last_best = 0.0
gold_path_test = 'processed_data/test/test_split_for_rouge/gold_summary_'
gold_path_valid = 'processed_data/valid/valid_split_for_rouge/gold_summary_'
# test phase
if FLAGS.load != "0":
save_dir = 'results/res/' + FLAGS.load + '/'
save_file_dir = save_dir + 'files/'
pred_dir = 'results/evaluation/' + FLAGS.load + '/'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
if not os.path.exists(save_file_dir):
os.mkdir(save_file_dir)
pred_path = pred_dir + 'pred_summary_'
pred_beam_path = pred_dir + 'beam_summary_'
# train phase
else:
prefix = str(int(time.time() * 1000))
save_dir = 'results/res/' + prefix + '/'
save_file_dir = save_dir + 'files/'
pred_dir = 'results/evaluation/' + prefix + '/'
os.mkdir(save_dir)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
if not os.path.exists(save_file_dir):
os.mkdir(save_file_dir)
pred_path = pred_dir + 'pred_summary_'
pred_beam_path = pred_dir + 'beam_summary_'
log_file = save_dir + 'log.txt'
def train(sess, dataloader, model):
write_log("#######################################################")
for flag in FLAGS.__flags:
write_log(flag + " = " + str(FLAGS.__flags[flag]))
write_log("#######################################################")
trainset = dataloader.train_set
k = 0
loss, start_time = 0.0, time.time()
for _ in range(FLAGS.epoch):
for x in dataloader.batch_iter(trainset, FLAGS.batch_size, True):
loss += model(x, sess)
k += 1
progress_bar(k%FLAGS.report, FLAGS.report)
if (k % FLAGS.report == 0):
cost_time = time.time() - start_time
write_log("%d : loss = %.3f, time = %.3f " % (k // FLAGS.report, loss, cost_time))
loss, start_time = 0.0, time.time()
if k // FLAGS.report >= 1:
ksave_dir = save_model(model, save_dir, k // FLAGS.report)
write_log(evaluate(sess, dataloader, model, ksave_dir, 'valid'))
def test(sess, dataloader, model):
logging.info("test = %s", model)
# evaluate(sess, dataloader, model, save_dir, 'test')
output(sess, dataloader, model, save_dir, 'test')
def save_model(model, save_dir, cnt):
new_dir = save_dir + 'loads' + '/'
if not os.path.exists(new_dir):
os.mkdir(new_dir)
nnew_dir = new_dir + str(cnt) + '/'
if not os.path.exists(nnew_dir):
os.mkdir(nnew_dir)
model.save(nnew_dir)
return nnew_dir
class Vocab(object):
def __init__(self):
vocab = dict()
vocab['PAD'] = 0
vocab['START_TOKEN'] = 1
vocab['END_TOKEN'] = 2
vocab['UNK_TOKEN'] = 3
cnt = 4
with open("original_data/word_vocab.txt", "r") as v:
for line in v:
word = line.strip().split()[0]
vocab[word] = cnt
cnt += 1
self._word2id = vocab
self._id2word = {value: key for key, value in vocab.items()}
key_map = dict()
key_map['PAD'] = 0
key_map['START_TOKEN'] = 1
key_map['END_TOKEN'] = 2
key_map['UNK_TOKEN'] = 3
cnt = 4
with open("original_data/field_vocab.txt", "r") as v:
for line in v:
key = line.strip().split()[0]
key_map[key] = cnt
cnt += 1
self._key2id = key_map
self._id2key = {value: key for key, value in key_map.items()}
def word2id(self, word):
ans = self._word2id[word] if word in self._word2id else 3
return ans
def id2word(self, id):
ans = self._id2word[int(id)]
return ans
def key2id(self, key):
ans = self._key2id[key] if key in self._key2id else 3
return ans
def id2key(self, id):
ans = self._id2key[int(id)]
return ans
def output(sess, dataloader, model, ksave_dir, mode='valid'):
if mode == 'valid':
# texts_path = "original_data/valid.summary"
texts_path = "processed_data/valid/valid.box.val"
gold_path = gold_path_valid
evalset = dataloader.dev_set
else:
# texts_path = "original_data/test.summary"
texts_path = "processed_data/test/test.box.val"
gold_path = gold_path_test
evalset = dataloader.test_set
# for copy words from the infoboxes
v = Vocab()
# with copy
pred_list, pred_list_copy, gold_list = [], [], []
pred_unk, pred_mask = [], []
print('..............Begin test iteration...........')
print('Total bacth number is:', len(list(dataloader.batch_iter(evalset, FLAGS.batch_size, False))))
k = 3
fboxes = "original_data/test.box"
box = open(fboxes, "r").read().strip().split('\n')
mixb_word, mixb_label, mixb_pos = [], [], []
box_word, box_label, box_pos = [], [], []
ib = box[k]
item = ib.split('\t')
box_single_word, box_single_label, box_single_pos = [], [], []
for it in item:
if len(it.split(':')) > 2:
continue
# print it
prefix, word = it.split(':')
if '<none>' in word or word.strip()=='' or prefix.strip()=='':
continue
new_label = re.sub("_[1-9]\d*$", "", prefix)
if new_label.strip() == "":
continue
box_single_word.append(word)
box_single_label.append(new_label)
if re.search("_[1-9]\d*$", prefix):
field_id = int(prefix.split('_')[-1])
box_single_pos.append(field_id if field_id<=30 else 30)
else:
box_single_pos.append(1)
box_word.append(box_single_word)
box_label.append(box_single_label)
box_pos.append(box_single_pos)
######################## reverse box #############################
box = box_pos
tmp_pos = []
single_pos = []
reverse_pos = []
for pos in box:
tmp_pos = []
single_pos = []
for p in pos:
if int(p) == 1 and len(tmp_pos) != 0:
single_pos.extend(tmp_pos[::-1])
tmp_pos = []
tmp_pos.append(p)
single_pos.extend(tmp_pos[::-1])
reverse_pos = single_pos
vocab = Vocab()
textss = (" ".join([str(vocab.word2id(word)) for word in box_word[0]]) + '\n')
text = list(map(int,textss.strip().split(' ')))
fields = (" ".join([str(vocab.key2id(word)) for word in box_label[0]]) + '\n')
field = list(map(int,fields.strip().split(' ')))
pos = box_pos[0]
rpos = reverse_pos
text_len = len(text)
pos_len = len(pos)
rpos_len = len(rpos)
batch_data = {'enc_in':[], 'enc_fd':[], 'enc_pos':[], 'enc_rpos':[], 'enc_len':[],
'dec_in':[], 'dec_len':[], 'dec_out':[]}
batch_data['enc_in'].append(text)
batch_data['enc_len'].append(text_len)
batch_data['enc_fd'].append(field)
batch_data['enc_pos'].append(pos)
batch_data['enc_rpos'].append(rpos)
print('.......................input data........................', batch_data)
predictions, atts = model.generate(batch_data, sess)
print('.......................predict........................',predictions)
atts = np.squeeze(atts)
idx = 0
# print('path is:', pred_path + str(k))
# print('x is:',x)
texts = open(texts_path, 'r').read().strip().split('\n')
texts = [list(t.strip().split()) for t in texts]
print('input is', ib)
print(texts[k])
for summary in np.array(predictions):
with open(pred_path + str(k), 'w') as sw:
summary = list(summary)
if 2 in summary:
summary = summary[:summary.index(2)] if summary[0] != 2 else [2]
real_sum, unk_sum, mask_sum = [], [], []
for tk, tid in enumerate(summary):
if tid == 3:
sub = item[np.argmax(atts[tk,: len(item)])]
real_sum.append(sub)
# mask_sum.append("**" + str(sub) + "**")
else:
real_sum.append(v.id2word(tid))
# mask_sum.append(v.id2word(tid))
# unk_sum.append(v.id2word(tid))
print('pred set is:', ' '.join(real_sum))
return pred_list
'''
for x in dataloader.single_test():
print('.......................single test........................',x)
# def generate(self, x, sess):
# predictions, atts = sess.run([self.g_tokens, self.atts],
# {self.encoder_input: x['enc_in'], self.encoder_field: x['enc_fd'],
# self.encoder_len: x['enc_len'], self.encoder_pos: x['enc_pos'],
# self.encoder_rpos: x['enc_rpos']})
# return predictions, atts
predictions, atts = model.generate(x, sess)
atts = np.squeeze(atts)
idx = 0
print('path is:', pred_path + str(k))
# print('x is:',x)
for summary in np.array(predictions):
with open(pred_path + str(k), 'w') as sw:
summary = list(summary)
if 2 in summary:
summary = summary[:summary.index(2)] if summary[0] != 2 else [2]
real_sum, unk_sum, mask_sum = [], [], []
for tk, tid in enumerate(summary):
if tid == 3:
print('npmax',np.argmax(atts[tk,: len(texts[k])]))
print('mm',np.argmax(atts[tk,: len(texts[k])]))
print('texts',np.shape(texts))
print('texts_full',texts[k])
# sub = texts[k]
# real_sum=texts[k]
# mask_sum.append("**" + str(sub) + "**")
else:
real_sum.append(v.id2word(tid))
# mask_sum.append(v.id2word(tid))
# unk_sum.append(v.id2word(tid))
# sw.write(" ".join([str(x) for x in real_sum]) + '\n')
# pred_list.append([str(x) for x in real_sum])
# k += 1
# idx += 1
break
# write_word(pred_mask, ksave_dir, mode + "_summary_copy.txt")
# write_word(pred_unk, ksave_dir, mode + "_summary_unk.txt")
# for tk in range(k):
# with open(gold_path + str(tk), 'r') as g:
# gold_list.append([g.read().strip()])
# gold_set = [[gold_path + str(i)] for i in range(k)]
# pred_set = [pred_path + str(i) for i in range(k)]
# print('gold set is:', ','.join(str(gold_list[0])))
print(real_sum)
print('pred set is:', ' '.join(real_sum))
# print('pred set is:', [i for p in real_sum for i in p])
return pred_list
'''
def evaluate(sess, dataloader, model, ksave_dir, mode='valid'):
if mode == 'valid':
# texts_path = "original_data/valid.summary"
texts_path = "processed_data/valid/valid.box.val"
gold_path = gold_path_valid
evalset = dataloader.dev_set
else:
# texts_path = "original_data/test.summary"
texts_path = "processed_data/test/test.box.val"
gold_path = gold_path_test
evalset = dataloader.test_set
# for copy words from the infoboxes
texts = open(texts_path, 'r').read().strip().split('\n')
print('test', texts[0])
texts = [list(t.strip().split()) for t in texts]
# print('test split', texts)
v = Vocab()
# with copy
pred_list, pred_list_copy, gold_list = [], [], []
pred_unk, pred_mask = [], []
print('..............Begin test iteration...........')
print('Total bacth number is:', len(list(dataloader.batch_iter(evalset, FLAGS.batch_size, False))))
k = 0
# x = dataloader.batch_iter(evalset, FLAGS.batch_size, False)[0]
# predictions, atts = model.generate(x, sess)
# atts = np.squeeze(atts)
# idx = 0
# print('path is:', pred_path + str(k))
# print('x is:',x)
# for summary in np.array(predictions):
# with open(pred_path + str(k), 'w') as sw:
# summary = list(summary)
# if 2 in summary:
# summary = summary[:summary.index(2)] if summary[0] != 2 else [2]
# real_sum, unk_sum, mask_sum = [], [], []
# for tk, tid in enumerate(summary):
# if tid == 3:
# sub = texts[k][np.argmax(atts[tk,: len(texts[k]),idx])]
# real_sum.append(sub)
# mask_sum.append("**" + str(sub) + "**")
# else:
# real_sum.append(v.id2word(tid))
# mask_sum.append(v.id2word(tid))
# unk_sum.append(v.id2word(tid))
# sw.write(" ".join([str(x) for x in real_sum]) + '\n')
# pred_list.append([str(x) for x in real_sum])
# pred_unk.append([str(x) for x in unk_sum])
# pred_mask.append([str(x) for x in mask_sum])
# k += 1
# idx += 1
for x in dataloader.batch_iter(evalset, FLAGS.batch_size, False):
predictions, atts = model.generate(x, sess)
atts = np.squeeze(atts)
idx = 0
print('path is:', pred_path + str(k))
# print('x is:',x)
for summary in np.array(predictions):
with open(pred_path + str(k), 'w') as sw:
summary = list(summary)
if 2 in summary:
summary = summary[:summary.index(2)] if summary[0] != 2 else [2]
real_sum, unk_sum, mask_sum = [], [], []
for tk, tid in enumerate(summary):
if tid == 3:
sub = texts[k][np.argmax(atts[tk,: len(texts[k])])]
real_sum.append(sub)
mask_sum.append("**" + str(sub) + "**")
else:
real_sum.append(v.id2word(tid))
mask_sum.append(v.id2word(tid))
unk_sum.append(v.id2word(tid))
sw.write(" ".join([str(x) for x in real_sum]) + '\n')
pred_list.append([str(x) for x in real_sum])
pred_unk.append([str(x) for x in unk_sum])
pred_mask.append([str(x) for x in mask_sum])
k += 1
idx += 1
break
write_word(pred_mask, ksave_dir, mode + "_summary_copy.txt")
write_word(pred_unk, ksave_dir, mode + "_summary_unk.txt")
for tk in range(k):
with open(gold_path + str(tk), 'r') as g:
gold_list.append([g.read().strip().split()])
gold_set = [[gold_path + str(i)] for i in range(k)]
pred_set = [pred_path + str(i) for i in range(k)]
print('pred set is:', ' '.join(real_sum))
# print('gold set is:', ','.join(str(gold_list[0])))
# print('pred set is:', ','.join(str(pred_list[0])))
# print('pred set is:', pred_unk[0],'\n \n \n')
# print('pred set is:', pred_mask[0])
recall, precision, F_measure = PythonROUGE(pred_set, gold_set, ngram_order=4)
bleu = corpus_bleu(gold_list, pred_list)
copy_result = "with copy F_measure: %s Recall: %s Precision: %s BLEU: %s\n" % \
(str(F_measure), str(recall), str(precision), str(bleu))
# print copy_result
for tk in range(k):
with open(pred_path + str(tk), 'w') as sw:
sw.write(" ".join(pred_unk[tk]) + '\n')
recall, precision, F_measure = PythonROUGE(pred_set, gold_set, ngram_order=4)
bleu = corpus_bleu(gold_list, pred_unk)
nocopy_result = "without copy F_measure: %s Recall: %s Precision: %s BLEU: %s\n" % \
(str(F_measure), str(recall), str(precision), str(bleu))
# print nocopy_result
result = copy_result + nocopy_result
# print result
if mode == 'valid':
print result
return result
def write_log(s):
print s
with open(log_file, 'a') as f:
f.write(s+'\n')
def main():
config = tf.ConfigProto(allow_soft_placement=True, device_count={'cpu':0})
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
copy_file(save_file_dir)
dataloader = DataLoader(FLAGS.dir, FLAGS.limits)
# print('dataloader',dataloader.test_set[0])
model = SeqUnit(batch_size=FLAGS.batch_size, hidden_size=FLAGS.hidden_size, emb_size=FLAGS.emb_size,
field_size=FLAGS.field_size, pos_size=FLAGS.pos_size, field_vocab=FLAGS.field_vocab,
source_vocab=FLAGS.source_vocab, position_vocab=FLAGS.position_vocab,
target_vocab=FLAGS.target_vocab, scope_name="seq2seq", name="seq2seq",
field_concat=FLAGS.field, position_concat=FLAGS.position,
fgate_enc=FLAGS.fgate_encoder, dual_att=FLAGS.dual_attention, decoder_add_pos=FLAGS.decoder_pos,
encoder_add_pos=FLAGS.encoder_pos, learning_rate=FLAGS.learning_rate)
print '........start.............'
sess.run(tf.global_variables_initializer())
# copy_file(save_file_dir)
if FLAGS.load != '0':
model.load(save_dir)
if FLAGS.mode == 'train':
train(sess, dataloader, model)
else:
test(sess, dataloader, model)
if __name__=='__main__':
# with tf.device('/gpu:' + FLAGS.gpu):
main()