-
Notifications
You must be signed in to change notification settings - Fork 16
/
model_main.py
198 lines (157 loc) · 6.48 KB
/
model_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
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
from torch.autograd import Variable
from resnet import resnet50, resnet18
import torch.nn.functional as F
import math
from attention import GraphAttentionLayer, IWPA
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm)
return out
# #####################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal_(m.weight.data, a=0, mode='fan_out')
init.zeros_(m.bias.data)
elif classname.find('BatchNorm1d') != -1:
init.normal_(m.weight.data, 1.0, 0.01)
init.zeros_(m.bias.data)
def weights_init_classifier(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
init.normal_(m.weight.data, 0, 0.001)
if m.bias:
init.zeros_(m.bias.data)
# Defines the new fc layer and classification layer
# |--Linear--|--bn--|--relu--|--Linear--|
class FeatureBlock(nn.Module):
def __init__(self, input_dim, low_dim, dropout=0.5, relu=True):
super(FeatureBlock, self).__init__()
feat_block = []
feat_block += [nn.Linear(input_dim, low_dim)]
feat_block += [nn.BatchNorm1d(low_dim)]
feat_block = nn.Sequential(*feat_block)
feat_block.apply(weights_init_kaiming)
self.feat_block = feat_block
def forward(self, x):
x = self.feat_block(x)
return x
class ClassBlock(nn.Module):
def __init__(self, input_dim, class_num, dropout=0.5, relu=True):
super(ClassBlock, self).__init__()
classifier = []
if relu:
classifier += [nn.LeakyReLU(0.1)]
if dropout:
classifier += [nn.Dropout(p=dropout)]
classifier += [nn.Linear(input_dim, class_num)]
classifier = nn.Sequential(*classifier)
classifier.apply(weights_init_classifier)
self.classifier = classifier
def forward(self, x):
x = self.classifier(x)
return x
class visible_module(nn.Module):
def __init__(self, arch='resnet50'):
super(visible_module, self).__init__()
model_v = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.visible = model_v
def forward(self, x):
x = self.visible.conv1(x)
x = self.visible.bn1(x)
x = self.visible.relu(x)
x = self.visible.maxpool(x)
return x
class thermal_module(nn.Module):
def __init__(self, arch='resnet50'):
super(thermal_module, self).__init__()
model_t = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
self.thermal = model_t
def forward(self, x):
x = self.thermal.conv1(x)
x = self.thermal.bn1(x)
x = self.thermal.relu(x)
x = self.thermal.maxpool(x)
return x
class base_resnet(nn.Module):
def __init__(self, arch='resnet50'):
super(base_resnet, self).__init__()
model_base = resnet50(pretrained=True,
last_conv_stride=1, last_conv_dilation=1)
# avg pooling to global pooling
model_base.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.base = model_base
def forward(self, x):
x = self.base.layer1(x)
x = self.base.layer2(x)
x = self.base.layer3(x)
x = self.base.layer4(x)
return x
class embed_net(nn.Module):
def __init__(self, low_dim, class_num, drop=0.2, part = 3, alpha=0.2, nheads=4, arch='resnet50', wpa = False):
super(embed_net, self).__init__()
self.thermal_module = thermal_module(arch=arch)
self.visible_module = visible_module(arch=arch)
self.base_resnet = base_resnet(arch=arch)
pool_dim = 2048
self.dropout = drop
self.part = part
self.lpa = wpa
self.l2norm = Normalize(2)
self.bottleneck = nn.BatchNorm1d(pool_dim)
self.bottleneck.bias.requires_grad_(False) # no shift
self.classifier = nn.Linear(pool_dim, class_num, bias=False)
self.classifier1 = nn.Linear(pool_dim, class_num, bias=False)
self.classifier2 = nn.Linear(pool_dim, class_num, bias=False)
self.bottleneck.apply(weights_init_kaiming)
self.classifier.apply(weights_init_classifier)
self.classifier1.apply(weights_init_classifier)
self.classifier2.apply(weights_init_classifier)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.wpa = IWPA(pool_dim, part)
self.attentions = [GraphAttentionLayer(pool_dim, low_dim, dropout=drop, alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(low_dim * nheads, class_num, dropout=drop, alpha=alpha, concat=False)
def forward(self, x1, x2, adj, modal=0, cpa = False):
# domain specific block
if modal == 0:
x1 = self.visible_module(x1)
x2 = self.thermal_module(x2)
x = torch.cat((x1, x2), 0)
elif modal == 1:
x = self.visible_module(x1)
elif modal == 2:
x = self.thermal_module(x2)
# shared four blocks
x = self.base_resnet(x)
x_pool = self.avgpool(x)
x_pool = x_pool.view(x_pool.size(0), x_pool.size(1))
feat = self.bottleneck(x_pool)
if self.lpa:
# intra-modality weighted part attention
feat_att = self.wpa(x, feat, 1, self.part)
if self.training:
# cross-modality graph attention
x_g = F.dropout(x_pool, self.dropout, training=self.training)
x_g = torch.cat([att(x_g, adj) for att in self.attentions], dim=1)
x_g = F.dropout(x_g, self.dropout, training=self.training)
x_g = F.elu(self.out_att(x_g, adj))
return x_pool, self.classifier(feat), self.classifier(feat_att), F.log_softmax(x_g, dim=1)
else:
return self.l2norm(feat), self.l2norm(feat_att)