-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathContrastive-Loss.py
274 lines (256 loc) · 9.16 KB
/
Contrastive-Loss.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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import Dataset,DataLoader
import torchvision.transforms as transforms
from torch.autograd import Variable
import cv2
import torch.nn.functional as F
import numpy as np
import os
import random
import sys
if sys.argv[1]== '--load':
weightname=sys.argv[2]
tempna='./'
name=tempna+weightname
test_only=1
else:
weightname=sys.argv[2]
tempna='./'
name=tempna+weightname
test_only=0
N=16
class custom_dset(Dataset):
def __init__(self,
img_path,
txt_path,
img_transform1,
img_transform2,
):
with open(txt_path, 'r') as f:
lines = f.readlines()
self.img1_list = [
os.path.join(img_path, i.split()[0]) for i in lines
]
self.img2_list = [
os.path.join(img_path, i.split()[1]) for i in lines
]
self.label_list = [i.split()[2] for i in lines]
self.img_transform1 = img_transform1
self.img_transform2 = img_transform2
def __getitem__(self, index):
img1_path = self.img1_list[index]
img2_path = self.img2_list[index]
label = self.label_list[index]
label=int(label)
img1 = cv2.imread(img1_path)
img2 = cv2.imread(img2_path)
img1 = img1.astype(np.float)/255
img2 = img2.astype(np.float)/255
img1 = cv2.resize(img1,(128,128), interpolation = cv2.INTER_AREA)
img2 = cv2.resize(img2,(128,128), interpolation = cv2.INTER_AREA)
img1 = self.img_transform1(img1)
img2 = self.img_transform2(img2)
return img1,img2,label
def __len__(self):
return len(self.label_list)
class Rescale(object):
def __call__(self, img):
if random.random()<0.7:
f = round(0.1*random.randint(7, 13),2)
if f>1:
img = cv2.resize(img,None,fx=f, fy=f, interpolation = cv2.INTER_CUBIC)
a = int(round((f*128-128)/2))
img = img[a:a+128,a:a+128]
else:
img = cv2.resize(img,None,fx=f, fy=f, interpolation = cv2.INTER_AREA)
a= int(round((128-f*128)/2))
temp=np.zeros([128,128,3],dtype=np.uint8)
temp.fill(0)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
temp[i+a,j+a]=img[i,j]
img=temp
return img
class Flip(object):
def __call__(self,img):
if random.random()<0.7:
return cv2.flip(img,1)
return img
class Rotate(object):
def __call__(self,img):
if random.random()<0.7:
angle=random.random()*60-30
rows,cols,cn = img.shape
M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)
img = cv2.warpAffine(img,M,(cols,rows))
return img
return img
class Translate(object):
def __call__(self,img):
if random.random()<0.7:
x=random.random()*20-10
y=random.random()*20-10
rows,cols,cn = img.shape
M= np.float32([[1,0,x],[0,1,y]])
img = cv2.warpAffine(img,M,(cols,rows))
return img
transform1 = transforms.Compose([Rescale(),Flip(),Translate(),Rotate(),transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
transform2 = transforms.Compose([Rescale(),Flip(),Translate(),Rotate(),transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_set = custom_dset('./lfw', './train.txt',transform1,transform2)
train_loader = DataLoader(train_set, batch_size=N, shuffle=True, num_workers=2)
lr = 1e-6
num_epoches = 100
class Cnn(nn.Module):
def __init__(self):
super(Cnn, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, 5, 1, 2),
nn.ReLU(),
nn.BatchNorm2d(64),
nn.MaxPool2d(2, 2),
)
self.conv2 = nn.Sequential(
nn.Conv2d(64, 128, 5, 1, 2),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.MaxPool2d(2, 2),
)
self.conv3 = nn.Sequential(
nn.Conv2d(128, 256, 3, 1, 1),
nn.ReLU(),
nn.BatchNorm2d(256),
nn.MaxPool2d(2, 2),
)
self.conv4 =nn.Sequential(
nn.Conv2d(256, 512, 3, 1, 1),
nn.ReLU(),
nn.BatchNorm2d(512),
)
self.fc = nn.Sequential(
nn.Linear(131072, 1024),
nn.ReLU(),
nn.BatchNorm2d(1024),
)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
net=Cnn()
if torch.cuda.is_available() :
net = net.cuda()
optimizer = torch.optim.Adam(net.parameters(), lr)
class ContrastiveLoss(nn.Module):
def __init__(self, margin=1.0):
super(ContrastiveLoss, self).__init__()
self.margin = margin
def forward(self, output1, output2, label):
euclidean_distance = F.pairwise_distance(output1, output2)
loss_contrastive = torch.mean((label) * torch.pow(euclidean_distance, 2) + (1-label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
return loss_contrastive
loss_func = ContrastiveLoss()
l_his=[]
if test_only==0:
for epoch in range(num_epoches):
print('Epoch:', epoch + 1, 'Training...')
running_loss = 0.0
for i,data in enumerate(train_loader, 0):
image1s,image2s,labels=data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
optimizer.zero_grad()
f1=net(image1s)
f2=net(image2s)
loss = loss_func(f1,f2,labels)
loss.backward()
optimizer.step()
if i % 5 == 4:
l_his.append(loss.data[0])
# print statistics
running_loss += loss.data[0]
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(l_his)
plt.xlabel('Steps')
plt.ylabel('Loss')
fig.savefig('plott2.png')
torch.save(net.state_dict(), name)
else:
net.load_state_dict(torch.load(name))
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
test_set = custom_dset('./lfw', './train.txt',transform,transform)
test_loader = DataLoader(test_set, batch_size=N, shuffle=True, num_workers=2)
correct = 0
total = 0
for data in test_loader:
image1s,image2s,labels = data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
f1=net(image1s)
f2=net(image2s)
dist = F.pairwise_distance(f1, f2)
dist = dist.cpu()
for j in range(dist.size()[0]):
if ((dist.data.numpy()[j]<0.8)):
if labels.cpu().data.numpy()[j]==1:
correct +=1
total+=1
else:
total+=1
else:
if labels.cpu().data.numpy()[j]==0:
correct+=1
total+=1
else:
total+=1
print('Accuracy of the network on the train images: %d %%' % (
100 * correct / total))
test_set = custom_dset('./lfw', './test.txt',transform,transform)
test_loader = DataLoader(test_set, batch_size=N, shuffle=True, num_workers=2)
correct = 0
total = 0
for data in test_loader:
image1s,image2s,labels = data
if torch.cuda.is_available():
image1s = image1s.cuda()
image2s = image2s.cuda()
labels = labels.cuda()
image1s, image2s, labels = Variable(image1s), Variable(image2s), Variable(labels.float())
f1=net(image1s)
f2=net(image2s)
dist = F.pairwise_distance(f1, f2)
dist = dist.cpu()
for j in range(dist.size()[0]):
if ((dist.data.numpy()[j]<0.8)):
if labels.cpu().data.numpy()[j]==1:
correct +=1
total+=1
else:
total+=1
else:
if labels.cpu().data.numpy()[j]==0:
correct+=1
total+=1
else:
total+=1
print('Accuracy of the network on the test images: %d %%' % (
100 * correct / total))