-
Notifications
You must be signed in to change notification settings - Fork 4
/
main.py
163 lines (145 loc) · 6.45 KB
/
main.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
import numpy as np
from tqdm import tqdm
import os
import torch
import torch.nn as nn
from models.common_layer import NoamOpt
from models import ELMoEncoder, LstmModel, UTransformer
from utils import constant
from utils.data_reader import prepare_data, prepare_data_loaders
from utils.utils import evaluate, getMetrics, predict
def save_model(model, split):
model_save_path = os.path.join(constant.save_path, 'model_{}'.format(split) )
args = {'model':model.state_dict(), 'config':constant.arg}
torch.save(args, model_save_path)
print("Model saved in:",model_save_path)
def load_model():
model_save_path = constant.load_model_path
state = torch.load(model_save_path, map_location= lambda storage, location: storage)
constant.arg = state['config']
load_settings()
if constant.model == "LSTM":
model = LstmModel(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.n_layers,
is_bidirectional=constant.bidirec,
input_dropout=constant.drop,
layer_dropout=constant.drop,
attentive=constant.attn)
elif constant.model == "UTRS":
model = UTransformer(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.hop,
num_heads=constant.heads,
total_key_depth=constant.depth,
total_value_depth=constant.depth,
filter_size=constant.filter,
act=constant.act)
elif constant.model == "ELMO":
model = ELMoEncoder(C=4)
else:
print("Model is not defined")
exit(0)
model = model.load_state_dict(state['model'])
return model
def train(model, data_loader_train, data_loader_val, data_loader_test, vocab, patient=10, split=0):
"""
Training loop
Inputs:
model: the model to be trained
data_loader_train: training data loader
data_loader_val: validation data loader
vocab: vocabulary list
Output:
avg_best: best f1 score on validation data
"""
if(constant.USE_CUDA): model.cuda()
criterion = nn.CrossEntropyLoss()
if(constant.noam):
opt = NoamOpt(constant.emb_dim, 1, 4000, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
else:
opt = torch.optim.Adam(model.parameters(),lr=constant.lr)
avg_best = 0
cnt = 0
for e in range(constant.max_epochs):
model.train()
loss_log = []
f1_log = 0
pbar = tqdm(enumerate(data_loader_train),total=len(data_loader_train))
for i, (X, x_len, y, ind, X_text) in pbar:
if constant.noam:
opt.optimizer.zero_grad()
else:
opt.zero_grad()
if x_len is None: pred_prob = model(X)
else: pred_prob = model(X, x_len)
loss = criterion(pred_prob[0], y)
loss.backward()
opt.step()
## logging
loss_log.append(loss.item())
accuracy, microPrecision, microRecall, microF1 = getMetrics(pred_prob[0].detach().cpu().numpy(),y.cpu().numpy())
f1_log += microF1
pbar.set_description("(Epoch {}) TRAIN MICRO:{:.4f} TRAIN LOSS:{:.4f}".format((e+1), f1_log/float(i+1), np.mean(loss_log)))
## LOG
if(e % 1 == 0):
microF1 = evaluate(model, criterion, data_loader_val)
if(microF1 > avg_best):
avg_best = microF1
save_model(model, split)
predict(model, criterion, data_loader_test, "", split=split) ## print the prediction with the highest Micro-F1
cnt = 0
else:
cnt += 1
if(cnt == patient): break
if(avg_best == 1.0): break
correct = 0
loss_nb = 0
return avg_best
if __name__ == "__main__":
data_loaders_train, data_loaders_val, data_loaders_test, vocab = prepare_data_loaders(num_split=constant.num_split, batch_size=constant.batch_size, hier=False, elmo=constant.elmo, dev_with_label=constant.dev_with_label, include_test=constant.include_test)
results = []
for i in range(constant.num_split):
data_loader_train = data_loaders_train[i]
data_loader_val = data_loaders_val[i]
data_loader_test = data_loaders_test[i]
print("###### EXPERIMENT {} ######".format(i+1))
print("(EXPERIMENT %d) Create the model" % (i+1))
if constant.model == "LSTM":
model = LstmModel(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.n_layers,
is_bidirectional=constant.bidirec,
input_dropout=constant.drop,
layer_dropout=constant.drop,
attentive=constant.attn)
elif constant.model == "UTRS":
model = UTransformer(vocab=vocab,
embedding_size=constant.emb_dim,
hidden_size=constant.hidden_dim,
num_layers=constant.hop,
num_heads=constant.heads,
total_key_depth=constant.depth,
total_value_depth=constant.depth,
filter_size=constant.filter,
act=constant.act)
elif constant.model == "ELMO":
model = ELMoEncoder(C=4)
else:
print("Model is not defined")
exit(0)
print(model)
if not os.path.exists(constant.save_path):
os.makedirs(constant.save_path)
avg_best = train(model, data_loader_train, data_loader_val, data_loader_test, vocab, patient=constant.patient, split=i)
results.append(avg_best)
print("(EXPERIMENT %d) Best F1 VAL: %3.5f" % ((i+1), avg_best))
file_summary = constant.save_path+"summary.txt"
with open(file_summary, 'w') as the_file:
header = "\t".join(["SPLIT_{}".format(i) for i, _ in enumerate(results)])
the_file.write(header+"\tAVG\n")
ris = "\t".join(["{:.4f}".format(e) for i, e in enumerate(results)])
the_file.write(ris+"\t{:.4f}\n".format(np.mean(results)))