-
Notifications
You must be signed in to change notification settings - Fork 0
/
resnet3d.py
158 lines (107 loc) · 5.25 KB
/
resnet3d.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
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
#__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm3d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm3d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv3d(inplanes, planes, kernel_size = 1, bias = False)
self.bn1 = nn.BatchNorm3d(planes)
self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm3d(planes)
self.conv3 = nn.Conv3d(planes, planes*self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(planes*self.expansion)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
# class ResNet(nn.Module):
# def __init__(self, block, layers, num_classes=1000, channel_size=[64,64,128,256,512],dropout=False):
# c = channel_size
# self.inplanes = c[0]
# super(ResNet, self).__init__()
# net = nn.Sequential()
# net.add_module('conv1', nn.Conv3d(1, c[0],kernel_size=7, stride=2, padding=0, bias=False))
# net.add_module('bn1', nn.BatchNorm3d(c[0]))
# net.add_module('relu', nn.ReLU(inplace=True))
# net.add_module('maxpool',nn.MaxPool3d(kernel_size=3, stride=2, padding=1))
# net.add_module('layer1', self._make_layer(block, c[1], layers[0]))
# net.add_module('layer2', self._make_layer(block, c[2], layers[1], stride=2))
# net.add_module('layer3', self._make_layer(block, c[3], layers[2], stride=2))
# net.add_module('layer4', self._make_layer(block, c[4], layers[3], stride=2))
# net.add_module('avgpool', nn.AvgPool3d([5,6,5], stride=1))
# if dropout is True:
# net.add_module('dropout', nn.Dropout(0.5))
# self.feature_extractor = net
# self.classifier = nn.Linear(c[4] * block.expansion, num_classes)
# for m in self.modules():
# if isinstance(m, nn.Conv3d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
# elif isinstance(m, nn.BatchNorm3d):
# nn.init.constant_(m.weight,1)
# nn.init.constant_(m.bias, 0)
# def _make_layer(self, block, planes, blocks, stride=1):
# downsample = None
# if stride != 1 or self.inplanes != planes * block.expansion:
# downsample = nn.Sequential(nn.Conv3d(self.inplanes, planes*block.expansion,kernel_size=1, stride=stride, bias=False),
# nn.BatchNorm3d(planes*block.expansion))
# layers = []
# layers.append(block(self.inplanes, planes, stride, downsample))
# self.inplanes = planes * block.expansion
# for i in range(1, blocks):
# layers.append(block(self.inplanes, planes))
# return nn.Sequential(*layers)
# def forward(self, x):
# x = self.feature_extractor(x)
# x = x.view(x.size(0),-1)
# x = self.classifier(x)
# x = F.log_softmax(x)
# return x
# def resnet18(**kwargs):
# model = ResNet(BasicBlock, [2,2,2,2], **kwargs)
# return model
# def resnet34(**kwargs):
# model = ResNet(BasicBlock, [3,4,6,3], **kwargs)
# return model
# def resnet50(**kwargs):
# model = ResNet(BasicBlock, [3,4,6,3], **kwargs)
# return model