-
Notifications
You must be signed in to change notification settings - Fork 15
/
train_ALL_CNN.py
182 lines (167 loc) · 7.7 KB
/
train_ALL_CNN.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
import os
import sys
import torch
import torch.autograd as autograd
import torch.nn.functional as F
import torch.nn.utils as utils
import torch.optim.lr_scheduler as lr_scheduler
import shutil
import re
import random
import hyperparams
import time
torch.manual_seed(hyperparams.seed_num)
random.seed(hyperparams.seed_num)
def train(train_iter, dev_iter, test_iter, model, args):
if args.cuda:
model.cuda()
if args.Adam is True:
print("Adam Training......")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
elif args.SGD is True:
print("SGD Training.......")
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay,
momentum=args.momentum_value)
elif args.Adadelta is True:
print("Adadelta Training.......")
optimizer = torch.optim.Adadelta(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
'''
lambda1 = lambda epoch: epoch // 30
lambda2 = lambda epoch: 0.99 ** epoch
print("lambda1 {} lambda2 {} ".format(lambda1, lambda2))
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda2])
scheduler = lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.9)
'''
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
lambda2 = lambda epoch: args.learning_rate_decay ** epoch
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda2])
steps = 0
epoch_step = 0
model_count = 0
model.train()
time_list = []
for epoch in range(1, args.epochs+1):
print("\n## 第{} 轮迭代，共计迭代 {} 次 ！##\n".format(epoch, args.epochs))
scheduler.step()
# print("now lr is {} \n".format(scheduler.get_lr()))
print("now lr is {} \n".format(optimizer.param_groups[0].get("lr")))
for batch in train_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda is True:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logit = model(feature)
loss = F.cross_entropy(logit, target)
start_time = time.time()
loss.backward()
end_time = time.time()
time_list.append(end_time -start_time)
# print("Backward Time is {} ".format(end_time - start_time))
if args.init_clip_max_norm is not None:
utils.clip_grad_norm(model.parameters(), max_norm=args.init_clip_max_norm)
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
train_size = len(train_iter.dataset)
corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
accuracy = float(corrects)/batch.batch_size * 100.0
sys.stdout.write(
'\rBatch[{}/{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(steps,
train_size,
loss.data[0],
accuracy,
corrects,
batch.batch_size))
if steps % args.test_interval == 0:
eval(dev_iter, model, args, scheduler)
if steps % args.save_interval == 0:
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
save_prefix = os.path.join(args.save_dir, 'snapshot')
save_path = '{}_steps{}.pt'.format(save_prefix, steps)
torch.save(model, save_path)
print("\n", save_path, end=" ")
test_model = torch.load(save_path)
model_count += 1
test_eval(test_iter, test_model, save_path, args, model_count)
# calculate the time
sum = 0
for index, value in enumerate(time_list):
if index != 0:
sum += value
avg = sum / len(time_list)
print("Time is {} ".format(avg))
return model_count
def eval(data_iter, model, args, scheduler):
model.eval()
corrects, avg_loss = 0, 0
for batch in data_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit = model(feature)
loss = F.cross_entropy(logit, target, size_average=False)
avg_loss += loss.data[0]
corrects += (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
size = len(data_iter.dataset)
avg_loss = loss.data[0]/size
# accuracy = float(corrects)/size * 100.0
accuracy = 100.0 * corrects/size
model.train()
print('\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n'.format(avg_loss,
accuracy,
corrects,
size))
def test_eval(data_iter, model, save_path, args, model_count):
model.eval()
corrects, avg_loss = 0, 0
for batch in data_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
logit = model(feature)
loss = F.cross_entropy(logit, target, size_average=False)
avg_loss += loss.data[0]
corrects += (torch.max(logit, 1)
[1].view(target.size()).data == target.data).sum()
size = len(data_iter.dataset)
avg_loss = loss.data[0]/size
# accuracy = float(corrects)/size * 100.0
accuracy = 100.0 * corrects/size
model.train()
print('\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n'.format(avg_loss,
accuracy,
corrects,
size))
print("model_count {}".format(model_count))
# test result
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt", "a")
else:
file = open("./Test_Result.txt", "w")
file.write("model " + save_path + "\n")
file.write("Evaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \n".format(avg_loss, accuracy, corrects, size))
file.write("model_count {} \n".format(model_count))
file.write("\n")
file.close()
# calculate the best score in current file
resultlist = []
if os.path.exists("./Test_Result.txt"):
file = open("./Test_Result.txt")
for line in file.readlines():
if line[:10] == "Evaluation":
resultlist.append(float(line[34:41]))
result = sorted(resultlist)
file.close()
file = open("./Test_Result.txt", "a")
file.write("\nThe Current Best Result is : " + str(result[len(result) - 1]))
file.write("\n\n")
file.close()
# shutil.copy("./Test_Result.txt", "./snapshot/" + args.mulu + "/Test_Result.txt")
shutil.copy("./Test_Result.txt", "./snapshot/" + args.mulu)
# whether to delete the model after test acc so that to save space
if os.path.isfile(save_path) and args.rm_model is True:
os.remove(save_path)