-
Notifications
You must be signed in to change notification settings - Fork 0
/
UNet.py
executable file
·180 lines (154 loc) · 5.88 KB
/
UNet.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
import torch.nn as nn
import torch
from torch import autograd
from functools import partial
import torch.nn.functional as F
from torchvision import models
#基本卷积块(Conv+ReLU)
class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1), #需要看conv2d是什么用法,什么功能
nn.BatchNorm2d(out_ch), #需要看batchnorm2d是什么功能,什么用法
#防止过拟合
nn.ReLU(inplace=True), #是防止过拟合吗?需要看ReLu啥意思
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
#防止过拟合
nn.ReLU(inplace=True)
)
def forward(self, input):
return self.conv(input)
#Unet分割算法
class Unet(nn.Module):
#初始化
def __init__(self, in_ch, out_ch):
super(Unet, self).__init__()
#下采样
self.conv1 = DoubleConv(in_ch, 32)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = DoubleConv(32, 64)
self.pool2 = nn.MaxPool2d(2)
self.conv3 = DoubleConv(64, 128)
self.pool3 = nn.MaxPool2d(2)
self.conv4 = DoubleConv(128, 256)
self.pool4 = nn.MaxPool2d(2)
#中间
self.conv5 = DoubleConv(256, 512)
#上采样
self.up6 = nn.ConvTranspose2d(512, 256, 2, stride=2)
self.conv6 = DoubleConv(512, 256)
self.up7 = nn.ConvTranspose2d(256, 128, 2, stride=2)
self.conv7 = DoubleConv(256, 128)
self.up8 = nn.ConvTranspose2d(128, 64, 2, stride=2)
self.conv8 = DoubleConv(128, 64)
self.up9 = nn.ConvTranspose2d(64, 32, 2, stride=2)
self.conv9 = DoubleConv(64, 32)
#输出
self.conv10 = nn.Conv2d(32, out_ch, 1)
#实现U-net网络进程
def forward(self, x):
#print(x.shape)
c1 = self.conv1(x)
p1 = self.pool1(c1)
#print(p1.shape)
c2 = self.conv2(p1)
p2 = self.pool2(c2)
#print(p2.shape)
c3 = self.conv3(p2)
p3 = self.pool3(c3)
#print(p3.shape)
c4 = self.conv4(p3)
p4 = self.pool4(c4)
#print(p4.shape)
c5 = self.conv5(p4)
up_6 = self.up6(c5)
#使用torch.cat进行跳层链接
merge6 = torch.cat([up_6, c4], dim=1)
c6 = self.conv6(merge6)
up_7 = self.up7(c6)
merge7 = torch.cat([up_7, c3], dim=1)
c7 = self.conv7(merge7)
up_8 = self.up8(c7)
merge8 = torch.cat([up_8, c2], dim=1)
c8 = self.conv8(merge8)
up_9 = self.up9(c8)
merge9 = torch.cat([up_9, c1], dim=1)
c9 = self.conv9(merge9)
c10 = self.conv10(c9)
#作为1*1的卷积核
out = nn.Sigmoid()(c10)
return out
nonlinearity = partial(F.relu, inplace=True)
class DecoderBlock(nn.Module):
def __init__(self, in_channels, n_filters):
super(DecoderBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
self.norm1 = nn.BatchNorm2d(in_channels // 4)
self.relu1 = nonlinearity
self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 3, stride=2, padding=1, output_padding=1)
self.norm2 = nn.BatchNorm2d(in_channels // 4)
self.relu2 = nonlinearity
self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1)
self.norm3 = nn.BatchNorm2d(n_filters)
self.relu3 = nonlinearity
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
x = self.deconv2(x)
x = self.norm2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.norm3(x)
x = self.relu3(x)
return x
class resnet34_unet(nn.Module):
def __init__(self, num_classes=1, num_channels=3,pretrained=True):
super(resnet34_unet, self).__init__()
filters = [64, 128, 256, 512]
resnet = models.resnet34(pretrained=pretrained)
self.firstconv = resnet.conv1
self.firstbn = resnet.bn1
self.firstrelu = resnet.relu
self.firstmaxpool = resnet.maxpool
self.encoder1 = resnet.layer1
self.encoder2 = resnet.layer2
self.encoder3 = resnet.layer3
self.encoder4 = resnet.layer4
self.decoder4 = DecoderBlock(512, filters[2])
self.decoder3 = DecoderBlock(filters[2], filters[1])
self.decoder2 = DecoderBlock(filters[1], filters[0])
self.decoder1 = DecoderBlock(filters[0], filters[0])
self.decoder4 = DecoderBlock(512, filters[2])
self.decoder3 = DecoderBlock(filters[2], filters[1])
self.decoder2 = DecoderBlock(filters[1], filters[0])
self.decoder1 = DecoderBlock(filters[0], filters[0])
self.finaldeconv1 = nn.ConvTranspose2d(filters[0], 32, 4, 2, 1)
self.finalrelu1 = nonlinearity
self.finalconv2 = nn.Conv2d(32, 32, 3, padding=1)
self.finalrelu2 = nonlinearity
self.finalconv3 = nn.Conv2d(32, num_classes, 3, padding=1)
def forward(self, x):
# Encoder
x = self.firstconv(x)
x = self.firstbn(x)
x = self.firstrelu(x)
x = self.firstmaxpool(x)
e1 = self.encoder1(x)
e2 = self.encoder2(e1)
e3 = self.encoder3(e2)
e4 = self.encoder4(e3)
# Center
# Decoder
d4 = self.decoder4(e4) + e3
d3 = self.decoder3(d4) + e2
d2 = self.decoder2(d3) + e1
d1 = self.decoder1(d2)
out = self.finaldeconv1(d1)
out = self.finalrelu1(out)
out = self.finalconv2(out)
out = self.finalrelu2(out)
out = self.finalconv3(out)
return nn.Sigmoid()(out)