-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathresnet.py
178 lines (153 loc) · 6.35 KB
/
resnet.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
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1, is_last=False):
super(BasicBlock, self).__init__()
self.is_last = is_last
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
preact = out
out = F.relu(out)
if self.is_last:
return out, preact
else:
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, is_last=False):
super(Bottleneck, self).__init__()
self.is_last = is_last
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
preact = out
out = F.relu(out)
if self.is_last:
return out, preact
else:
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves
# like an identity. This improves the model by 0.2~0.3% according to:
# https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for i in range(num_blocks):
stride = strides[i]
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.avgpool(out)
out = torch.flatten(out, 1)
return out
def resnet18(**kwargs):
return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
def resnet34(**kwargs):
return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
def resnet50(**kwargs):
return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
def resnet101(**kwargs):
return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
model_dict = {
'resnet18': [resnet18, 512],
'resnet34': [resnet34, 512],
'resnet50': [resnet50, 2048],
'resnet101': [resnet101, 2048],
}
class ResNetWithHead(nn.Module):
"""backbone + projection head"""
def __init__(self, arch='resnet50', head='mlp', feat_dim=128):
super(ResNetWithHead, self).__init__()
model_fun, dim_in = model_dict[arch]
self.encoder = model_fun()
if head == 'linear':
self.head = nn.Linear(dim_in, feat_dim)
elif head == 'mlp':
self.head = nn.Sequential(
nn.Linear(dim_in, dim_in),
nn.ReLU(inplace=True),
nn.Linear(dim_in, feat_dim)
)
else:
raise NotImplementedError(
'head not supported: {}'.format(head))
def forward(self, x):
feat = self.encoder(x)
feat = F.normalize(self.head(feat), dim=1)
return feat
class ResNetNoHead(nn.Module):
"""only backbone"""
def __init__(self, arch='resnet50'):
super(ResNetNoHead, self).__init__()
model_fun, dim_in = model_dict[arch]
self.encoder = model_fun()
def forward(self, x):
feat = self.encoder(x)
return feat
class LinearClassifier(nn.Module):
"""Linear classifier"""
def __init__(self, arch='resnet50', num_classes=10):
super(LinearClassifier, self).__init__()
_, feat_dim = model_dict[arch]
self.fc = nn.Linear(feat_dim, num_classes)
def forward(self, features):
return self.fc(features)