-
Notifications
You must be signed in to change notification settings - Fork 0
/
component_bert_multi_label_predict.py
192 lines (160 loc) · 6.3 KB
/
component_bert_multi_label_predict.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
#!/usr/bin/python
# coding:utf8
"""
@author: Cong Yu
@time: 2019-06-06 15:18
"""
import tensorflow as tf
from sklearn.externals import joblib
import numpy as np
import pandas as pd
import json, time
# model_folder = "./bin"
labels = []
for i in range(20):
labels.append("DV" + str(i + 1))
config = {
"in_1": "./out/tokenizer.m", # 第一个输入为 tokenizer 序列化模型(由上一次传递过来)
"in_2": "./bin_divorce_1/",
"file": "./data/dev_divorce.csv", # 第二个输入为 训练/测试 文件
"column_name_x1": "input_x",
"column_name_x2": "",
"column_name_y": "label_tag",
"label_list": labels, # 整个样本空间的 标签集
"split": "", # 标签的分割符，默认为空，表示单标签，不为空的化，按分隔符进行分割出多标签
"max_seq_len": 128, # 输入文本片段的最大 char级别 长度
"out": "./data/predict_divorce.csv" # 输出为 tf_record 的二进制文件
}
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def process_one_example(tokenizer, text_a, text_b=None, max_seq_len=256):
"""
处理 单个样本
"""
tokens_a = tokenizer.tokenize(text_a)
tokens_b = None
if text_b:
tokens_b = tokenizer.tokenize(text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_len - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_len - 2:
tokens_a = tokens_a[0:(max_seq_len - 2)]
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_len:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_len
assert len(input_mask) == max_seq_len
assert len(segment_ids) == max_seq_len
feature = (input_ids, input_mask, segment_ids)
return feature
def load_model(model_folder):
# We retrieve our checkpoint fullpath
try:
checkpoint = tf.train.get_checkpoint_state(model_folder)
input_checkpoint = checkpoint.model_checkpoint_path
print("[INFO] input_checkpoint:", input_checkpoint)
except Exception as e:
input_checkpoint = model_folder
print("[INFO] Model folder", model_folder, repr(e))
# We clear devices to allow TensorFlow to control on which device it will load operations
clear_devices = True
# We import the meta graph and retrieve a Saver
saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)
# We retrieve the protobuf graph definition
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
# We start a session and restore the graph weights
sess = tf.Session()
saver.restore(sess, input_checkpoint)
return sess
def main():
tokenizer = joblib.load(config["in_1"])
sess = load_model(config["in_2"])
input_ids = sess.graph.get_tensor_by_name("input_ids:0")
input_mask = sess.graph.get_tensor_by_name("input_mask:0") # is_training
segment_ids = sess.graph.get_tensor_by_name("segment_ids:0") # fc/dense/Relu cnn_block/Reshape
keep_prob = sess.graph.get_tensor_by_name("keep_prob:0")
p = sess.graph.get_tensor_by_name("loss/Sigmoid:0")
df = pd.read_csv(config["file"], index_col=0)
questions = []
predicts = []
count = 0
t1 = time.time()
for index, row in df.iterrows():
# label = label2id[row["topic"].strip()]
if not (row[config["column_name_x1"]]):
continue
if not isinstance(row[config["column_name_x1"]], str):
print(row[config["column_name_x1"]])
continue
feature = process_one_example(tokenizer, row[config["column_name_x1"]],
row[config["column_name_x2"]] if config["column_name_x2"] != "" else None,
max_seq_len=config["max_seq_len"])
q = row[config["column_name_x1"]] if config["column_name_x2"] == "" else \
row[config["column_name_x1"]] + "###" + row[config["column_name_x2"]]
if count < 5:
print(feature[0])
print(feature[1])
print(feature[2])
questions.append(q)
feed = {input_ids: [feature[0]],
input_mask: [feature[1]],
segment_ids: [feature[2]],
keep_prob: 1.0
}
probs = sess.run([p], feed)[0][0]
result = []
for ii, v in enumerate(probs):
if v > 0.5:
result.append((config["label_list"][ii], float(v)))
predicts.append(json.dumps(result, ensure_ascii=False))
count += 1
if count == 100:
break
t2 = time.time()
print("predict cost time:", t2 - t1)
df_out = pd.DataFrame()
df_out["question"] = questions
df_out["predict"] = predicts
df_out.to_csv(config["out"])
if __name__ == "__main__":
print("********* component_bert_multi_label_predict start *********")
main()