-
Notifications
You must be signed in to change notification settings - Fork 109
/
TaskForSingleSentenceClassification.py
148 lines (136 loc) · 7.37 KB
/
TaskForSingleSentenceClassification.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
import sys
sys.path.append('../')
from model import BertForSentenceClassification
from model import BertConfig
from utils import LoadSingleSentenceClassificationDataset
from utils import logger_init
from transformers import BertTokenizer
import logging
import torch
import os
import time
class ModelConfig:
def __init__(self):
self.project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.dataset_dir = os.path.join(self.project_dir, 'data', 'SingleSentenceClassification')
self.pretrained_model_dir = os.path.join(self.project_dir, "bert_base_chinese")
self.vocab_path = os.path.join(self.pretrained_model_dir, 'vocab.txt')
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.train_file_path = os.path.join(self.dataset_dir, 'toutiao_train.txt')
self.val_file_path = os.path.join(self.dataset_dir, 'toutiao_val.txt')
self.test_file_path = os.path.join(self.dataset_dir, 'toutiao_test.txt')
self.model_save_dir = os.path.join(self.project_dir, 'cache')
self.logs_save_dir = os.path.join(self.project_dir, 'logs')
self.split_sep = '_!_'
self.is_sample_shuffle = True
self.batch_size = 64
self.max_sen_len = None
self.num_labels = 15
self.epochs = 10
self.model_val_per_epoch = 2
logger_init(log_file_name='single', log_level=logging.INFO,
log_dir=self.logs_save_dir)
if not os.path.exists(self.model_save_dir):
os.makedirs(self.model_save_dir)
# 把原始bert中的配置参数也导入进来
bert_config_path = os.path.join(self.pretrained_model_dir, "config.json")
bert_config = BertConfig.from_json_file(bert_config_path)
for key, value in bert_config.__dict__.items():
self.__dict__[key] = value
# 将当前配置打印到日志文件中
logging.info(" ### 将当前配置打印到日志文件中 ")
for key, value in self.__dict__.items():
logging.info(f"### {key} = {value}")
def train(config):
model = BertForSentenceClassification(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型，进行追加训练......")
model = model.to(config.device)
optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)
model.train()
bert_tokenize = BertTokenizer.from_pretrained(config.pretrained_model_dir).tokenize
data_loader = LoadSingleSentenceClassificationDataset(vocab_path=config.vocab_path,
tokenizer=bert_tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
split_sep=config.split_sep,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle)
train_iter, test_iter, val_iter = data_loader.load_train_val_test_data(config.train_file_path,
config.val_file_path,
config.test_file_path)
max_acc = 0
for epoch in range(config.epochs):
losses = 0
start_time = time.time()
for idx, (sample, label) in enumerate(train_iter):
sample = sample.to(config.device) # [src_len, batch_size]
label = label.to(config.device)
padding_mask = (sample == data_loader.PAD_IDX).transpose(0, 1)
loss, logits = model(
input_ids=sample,
attention_mask=padding_mask,
token_type_ids=None,
position_ids=None,
labels=label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses += loss.item()
acc = (logits.argmax(1) == label).float().mean()
if idx % 10 == 0:
logging.info(f"Epoch: {epoch}, Batch[{idx}/{len(train_iter)}], "
f"Train loss :{loss.item():.3f}, Train acc: {acc:.3f}")
end_time = time.time()
train_loss = losses / len(train_iter)
logging.info(f"Epoch: {epoch}, Train loss: {train_loss:.3f}, Epoch time = {(end_time - start_time):.3f}s")
if (epoch + 1) % config.model_val_per_epoch == 0:
acc = evaluate(val_iter, model, config.device, data_loader.PAD_IDX)
logging.info(f"Accuracy on val {acc:.3f}")
if acc > max_acc:
max_acc = acc
torch.save(model.state_dict(), model_save_path)
def inference(config):
model = BertForSentenceClassification(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型，进行预测......")
model = model.to(config.device)
data_loader = LoadSingleSentenceClassificationDataset(vocab_path=config.vocab_path,
tokenizer=BertTokenizer.from_pretrained(
config.pretrained_model_dir).tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
split_sep=config.split_sep,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle)
train_iter, test_iter, val_iter = data_loader.load_train_val_test_data(config.train_file_path,
config.val_file_path,
config.test_file_path)
acc = evaluate(test_iter, model, device=config.device, PAD_IDX=data_loader.PAD_IDX)
logging.info(f"Acc on test:{acc:.3f}")
def evaluate(data_iter, model, device, PAD_IDX):
model.eval()
with torch.no_grad():
acc_sum, n = 0.0, 0
for x, y in data_iter:
x, y = x.to(device), y.to(device)
padding_mask = (x == PAD_IDX).transpose(0, 1)
logits = model(x, attention_mask=padding_mask)
acc_sum += (logits.argmax(1) == y).float().sum().item()
n += len(y)
model.train()
return acc_sum / n
if __name__ == '__main__':
model_config = ModelConfig()
train(model_config)
inference(model_config)