-
Notifications
You must be signed in to change notification settings - Fork 12
/
trainer.py
193 lines (153 loc) · 7.52 KB
/
trainer.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
193
import os
import logging
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.optim import Adam, RMSprop
from data_loader import TweetProcessor, load_word_matrix
from utils import load_vocab, compute_metrics, report
from model import ACN
logger = logging.getLogger(__name__)
OPTIMIZER_LIST = {
"adam": Adam,
"rmsprop": RMSprop
}
class Trainer(object):
def __init__(self, args, train_dataset=None, dev_dataset=None, test_dataset=None):
self.args = args
self.train_dataset = train_dataset
self.dev_dataset = dev_dataset
self.test_dataset = test_dataset
self.label_lst = TweetProcessor.get_labels()
self.num_labels = len(self.label_lst)
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
self.pad_token_label_id = args.ignore_index
self.word_vocab, self.char_vocab, self.word_ids_to_tokens, self.char_ids_to_tokens = load_vocab(args)
self.pretrained_word_matrix = None
if not args.no_w2v:
self.pretrained_word_matrix = load_word_matrix(args, self.word_vocab)
self.model = ACN(args, self.pretrained_word_matrix)
# GPU or CPU
self.device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"
self.model.to(self.device)
def train(self):
train_sampler = RandomSampler(self.train_dataset)
train_dataloader = DataLoader(self.train_dataset, sampler=train_sampler, batch_size=self.args.train_batch_size)
# optimizer and schedule (linear warmup and decay)
if self.args.optimizer not in OPTIMIZER_LIST.keys():
raise ValueError("Please choose the optimizer selected in the list: " + ", ".join(OPTIMIZER_LIST.keys()))
optimizer = OPTIMIZER_LIST[self.args.optimizer](self.model.parameters(), lr=self.args.learning_rate)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(self.train_dataset))
logger.info(" Num Epochs = %d", self.args.num_train_epochs)
logger.info(" Batch size = %d", self.args.train_batch_size)
global_step = 0
tr_loss = 0.0
self.model.zero_grad()
train_iterator = trange(int(self.args.num_train_epochs), desc="Epoch")
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
self.model.train()
batch = tuple(t.to(self.device) for t in batch) # GPU or CPU
inputs = {'word_ids': batch[0],
'char_ids': batch[1],
'img_feature': batch[2],
'mask': batch[3],
'label_ids': batch[4]}
outputs = self.model(**inputs)
loss = outputs[0]
loss.backward()
tr_loss += loss.item()
optimizer.step()
self.model.zero_grad()
global_step += 1
if self.args.logging_steps > 0 and global_step % self.args.logging_steps == 0:
self.evaluate("dev")
if self.args.save_steps > 0 and global_step % self.args.save_steps == 0:
self.save_model()
return global_step, tr_loss / global_step
def evaluate(self, mode):
if mode == 'test':
dataset = self.test_dataset
elif mode == 'dev':
dataset = self.dev_dataset
elif mode == 'train':
dataset = self.train_dataset
else:
raise Exception("Only train, dev and test dataset available")
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=self.args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation on %s dataset *****", mode)
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", self.args.eval_batch_size)
eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
self.model.eval()
batch = tuple(t.to(self.device) for t in batch)
with torch.no_grad():
inputs = {'word_ids': batch[0],
'char_ids': batch[1],
'img_feature': batch[2],
'mask': batch[3],
'label_ids': batch[4]}
outputs = self.model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
# Slot prediction
if preds is None:
# decode() in `torchcrf` returns list with best index directly
preds = np.array(self.model.crf.decode(logits, mask=inputs['mask'].byte()))
out_label_ids = inputs["label_ids"].detach().cpu().numpy()
else:
preds = np.append(preds, np.array(self.model.crf.decode(logits, mask=inputs['mask'].byte())), axis=0)
out_label_ids = np.append(out_label_ids, inputs["label_ids"].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
results = {
"loss": eval_loss
}
# Slot result
slot_label_map = {i: label for i, label in enumerate(self.label_lst)}
out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]
for i in range(out_label_ids.shape[0]):
for j in range(out_label_ids.shape[1]):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(slot_label_map[out_label_ids[i][j]])
preds_list[i].append(slot_label_map[preds[i][j]])
result = compute_metrics(out_label_list, preds_list)
results.update(result)
logger.info("***** Eval results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
logger.info("\n" + report(out_label_list, preds_list)) # Get the report for each tag result
return results
def save_model(self):
# Save model checkpoint (Overwrite)
if not os.path.exists(self.args.model_dir):
os.mkdir(self.args.model_dir)
# Save argument
torch.save(self.args, os.path.join(self.args.model_dir, 'args.pt'))
# Save model for inference
torch.save(self.model.state_dict(), os.path.join(self.args.model_dir, 'model.pt'))
logger.info("Saving model checkpoint to {}".format(os.path.join(self.args.model_dir, 'model.pt')))
def load_model(self):
# Check whether model exists
if not os.path.exists(self.args.model_dir):
raise Exception("Model doesn't exists! Train first!")
try:
# self.bert_config = self.config_class.from_pretrained(self.args.model_dir)
self.args = torch.load(os.path.join(self.args.model_dir, 'args.pt'))
logger.info("***** Args loaded *****")
self.model.load_state_dict(torch.load(os.path.join(self.args.model_dir, 'model.pt')))
self.model.to(self.device)
logger.info("***** Model Loaded *****")
except:
raise Exception("Some model files might be missing...")