-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathmain.py
180 lines (167 loc) · 7.18 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
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
from __future__ import print_function
import argparse
import os
import shutil
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import transforms
import utils
import YeNet
parser = argparse.ArgumentParser(description='PyTorch implementation of YeNet')
parser.add_argument('train_cover_dir', type=str, metavar='PATH',
help='path of directory containing all ' +
'training cover images')
parser.add_argument('train_stego_dir', type=str, metavar='PATH',
help='path of directory containing all ' +
'training stego images or beta maps')
parser.add_argument('valid_cover_dir', type=str, metavar='PATH',
help='path of directory containing all ' +
'validation cover images')
parser.add_argument('valid_stego_dir', type=str, metavar='PATH',
help='path of directory containing all ' +
'validation stego images or beta maps')
parser.add_argument('--batch-size', type=int, default=32, metavar='N',
help='input batch size for training (default: 32)')
parser.add_argument('--test-batch-size', type=int, default=32, metavar='N',
help='input batch size for testing (default: 32)')
parser.add_argument('--epochs', type=int, default=1000, metavar='N',
help='number of epochs to train (default: 1000)')
parser.add_argument('--lr', type=float, default=4e-1, metavar='LR',
help='learning rate (default: 4e-1)')
parser.add_argument('--use-batch-norm', action='store_true', default=False,
help='use batch normalization after each activation,' +
' also disable pair constraint (default: False)')
parser.add_argument('--embed-otf', action='store_true', default=False,
help='use beta maps and embed on the fly instead' +
' of use stego images (default: False)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--gpu', type=int, default=0,
help='index of gpu used (default: 0)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=200, metavar='N',
help='how many batches to wait before logging training status')
# TODO: use a format to store logs (tensorboard ?)
# parser.add_argument('--log-path', type=str, default='logs/training.log',
# metavar='PATH', help='path to generated log file')
args = parser.parse_args()
arch = 'YeNet_with_bn' if args.use_batch_norm else 'YeNet'
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.cuda.set_device(args.gpu)
else:
args.gpu = None
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
train_transform = transforms.Compose([
utils.RandomRot(),
utils.RandomFlip(),
utils.ToTensor()
])
valid_transform = transforms.Compose([
utils.ToTensor()
])
print("Generate loaders...")
train_loader = utils.DataLoaderStego(args.train_cover_dir, args.train_stego_dir,
embedding_otf=args.embed_otf, shuffle=True,
pair_constraint=not(args.use_batch_norm),
batch_size=args.batch_size,
transform=train_transform,
num_workers=kwargs['num_workers'],
pin_memory=kwargs['pin_memory'])
valid_loader = utils.DataLoaderStego(args.valid_cover_dir, args.valid_stego_dir,
embedding_otf=False, shuffle=False,
pair_constraint=True,
batch_size=args.test_batch_size,
transform=valid_transform,
num_workers=kwargs['num_workers'],
pin_memory=kwargs['pin_memory'])
print('train_loader have {} iterations, valid_loader have {} iterations'.format(
len(train_loader), len(valid_loader)))
# valid_loader = train_loader
print("Generate model")
net = YeNet.YeNet(with_bn=args.use_batch_norm)
print(net)
print("Generate loss and optimizer")
if args.cuda:
net.cuda()
criterion = nn.CrossEntropyLoss().cuda()
else:
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.Adadelta(net.parameters(), lr=args.lr, rho=0.95, eps=1e-8,
weight_decay=5e-4)
_time = time.time()
def train(epoch):
net.train()
running_loss = 0.
running_accuracy = 0.
for batch_idx, data in enumerate(train_loader):
images, labels = Variable(data['images']), Variable(data['labels'])
if args.cuda:
images, labels = images.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = net(images)
accuracy = YeNet.accuracy(outputs, labels).data[0]
running_accuracy += accuracy
loss = criterion(outputs, labels)
running_loss += loss.data[0]
loss.backward()
optimizer.step()
if (batch_idx + 1) % args.log_interval == 0:
running_accuracy /= args.log_interval
running_loss /= args.log_interval
print(('\nTrain epoch: {} [{}/{}]\tAccuracy: ' +
'{:.2f}%\tLoss: {:.6f}').format(
epoch, batch_idx + 1, len(train_loader),
100 * running_accuracy, running_loss))
running_loss = 0.
running_accuracy = 0.
net.train()
def valid():
net.eval()
valid_loss = 0.
valid_accuracy = 0.
correct = 0
for data in valid_loader:
# break
images, labels = Variable(data['images']), Variable(data['labels'])
if args.cuda:
images, labels = images.cuda(), labels.cuda()
outputs = net(images)
valid_loss += criterion(outputs, labels).data[0]
valid_accuracy += YeNet.accuracy(outputs, labels).data[0]
valid_loss /= len(valid_loader)
valid_accuracy /= len(valid_loader)
print('\nTest set: Loss: {:.4f}, Accuracy: {:.2f}%)\n'.format(
valid_loss, 100 * valid_accuracy))
return valid_loss, valid_accuracy
def save_checkpoint(state, is_best, filename='checkpoints/checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'checkpoints/model_best.pth.tar')
best_accuracy = 0.
for epoch in range(1, args.epochs + 1):
print("Epoch:", epoch)
print("Train")
train(epoch)
print("Time:", time.time() - _time)
print("Test")
_, accuracy = valid()
if accuracy > best_accuracy:
best_accuracy = accuracy
is_best = True
else:
is_best = False
print("Time:", time.time() - _time)
save_checkpoint({
'epoch': epoch,
'arch': arch,
'state_dict': net.state_dict(),
'best_prec1': accuracy,
'optimizer': optimizer.state_dict(),
}, is_best)