-
Notifications
You must be signed in to change notification settings - Fork 57
/
dataset.py
152 lines (129 loc) · 6.31 KB
/
dataset.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
# -*-coding:utf-8 -*-
import os
import pickle
import tensorflow as tf
import numpy as np
from data.base_preprocess import get_feature_poroto, extract_prefix_surfix
from data.word_enhance import SoftWord, SoftLexicon, ExSoftWord
class NerDataset(object):
def __init__(self, data_dir, batch_size, epoch_size, model_name):
self.surfix, self.prefix = extract_prefix_surfix(model_name)
self.data_dir = data_dir
self.batch_size = batch_size
self.epoch_size = epoch_size
self._params = None
self.init_params()
self.proto = get_feature_poroto(self.params['max_seq_len'], self.surfix)
def parser(self, line):
features = tf.parse_single_example(line, features=self.proto)
features['token_ids'] = tf.cast(features['token_ids'], tf.int32)
features['mask'] = tf.cast(features['mask'], tf.int32)
features['segment_ids'] = tf.cast(features['segment_ids'], tf.int32)
features['label_ids'] = tf.cast(features['label_ids'], tf.int32)
features['seq_len'] = tf.squeeze(tf.cast(features['seq_len'], tf.int32))
# adjust parser when word enhance method is used
if self.surfix == SoftWord:
features['softword_ids'] = tf.cast(features['softword_ids'], tf.int32)
elif self.surfix == ExSoftWord:
# cast to float and reshape to original 2 dimension
features['ex_softword_ids'] = tf.cast(features['ex_softword_ids'], tf.float32)
elif self.surfix == SoftLexicon:
features['softlexicon_ids'] = tf.cast(features['softlexicon_ids'], tf.int32)
features['softlexicon_weights'] = tf.cast(features['softlexicon_weights'], tf.float32)
return features
def build_input_fn(self, file_name, is_predict=0, unbatch=False):
def input_fn():
dataset = tf.data.TFRecordDataset(
os.path.join(self.data_dir, '_'.join(filter(None, [self.prefix, file_name, self.surfix])) + '.tfrecord')). \
map(lambda x: self.parser(x), num_parallel_calls=tf.data.experimental.AUTOTUNE)
if not is_predict:
dataset = dataset.shuffle(64). \
repeat(self.epoch_size)
if not unbatch:
# For performace issue, not to use unbatch in mutitask
dataset = dataset. \
batch(self.batch_size).prefetch(tf.data.experimental.AUTOTUNE)
return dataset
return input_fn
def init_params(self):
"""
Inherit max_seq_len, label_size, n_sample from data_preprocess per dataset
"""
with open(os.path.join(self.data_dir, '_'.join(filter(None, [self.prefix, self.surfix, 'data_params.pkl']))), 'rb') as f:
self._params = pickle.load(f)
self._params['step_per_epoch'] = int(self._params['n_sample']/self.batch_size)
self._params['num_train_steps'] = int(self.epoch_size * self._params['step_per_epoch'])
@property
def params(self):
return self._params
class MultiDataset(object):
"""
Used for Multi-Task & Adversarial task. Each batch will include samples from all tasks with same size
For now only 2 task are supported
"""
def __init__(self, root_dir, data_list, batch_size, epoch_size, model_name):
self._params = {}
self.batch_size = batch_size
self.epoch_size = epoch_size
self.data_list = data_list
self.dataset_dict = dict([(dir, NerDataset(os.path.join(root_dir, dir), batch_size, epoch_size, model_name)) \
for dir in data_list])
self.init_params()
def add_discriminator(self, features, task_id):
features['task_ids'] = np.ones_like(features['token_ids']) * task_id
return features
def build_input_fn(self, file_name):
def input_fn():
dataset_list = [dataset.build_input_fn(file_name, is_predict=0, unbatch=True)().\
map(lambda x: self.add_discriminator(x, i))
for i, dataset in enumerate(self.dataset_dict.values())]
choice_dataset = tf.data.Dataset.range(2).repeat()
dataset = tf.contrib.data.choose_from_datasets(dataset_list,
choice_dataset)
dataset = dataset.repeat(self.epoch_size).batch(self.batch_size)
return dataset
return input_fn
def build_predict_fn(self, data):
"""
For prediction, return input_fn for data each time
"""
def input_fn():
dataset = self.dataset_dict[data]
dataset = dataset.build_input_fn('predict', is_predict=True, unbatch=True)(). \
map(lambda x: self.add_discriminator(x, self.data_list.index(data)))
dataset = dataset.batch(self.batch_size)
return dataset
return input_fn
def init_params(self):
for data, dataset in self.dataset_dict.items():
self._params.update({
data: dataset.params
})
# use smaller step_per_epoch between 2 dataset
self._params['step_per_epoch'] = int(max([p['step_per_epoch'] for p in self._params.values()]))
self._params['num_train_steps'] = int(self.epoch_size * self._params['step_per_epoch'])
self._params['task_list'] = self.data_list
self._params['max_seq_len'] = self._params[self.data_list[0]]['max_seq_len']
@property
def params(self):
return self._params
if __name__ == '__main__':
prep = NerDataset('./data/msra', 100, 10, model_name='bilstm_crf_softlexicon')
train_input = prep.build_input_fn('train')
sess = tf.Session()
iterator = tf.data.make_initializable_iterator(train_input())
sess.run( iterator.initializer )
sess.run( tf.tables_initializer() )
sess.run( tf.global_variables_initializer() )
features = sess.run( iterator.get_next() )
print(features)
prep = MultiDataset('./data', ['msr','msra'], 4 , 2,'bert_bilstm_crf_mtl')
train_input = prep.build_predict_fn('msra')
sess = tf.Session()
iterator = tf.data.make_initializable_iterator(train_input())
sess.run( iterator.initializer )
sess.run( tf.tables_initializer() )
sess.run( tf.global_variables_initializer() )
features = sess.run( iterator.get_next() )
print(features['labels'])
print(features['task_ids'])