Pytorch实现InceptionV1

import torch
import torch.nn as nn
import torchvision

def ConvBNReLU(in_channels,out_channels,kernel_size):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1,padding=kernel_size//2),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

class InceptionV1Module(nn.Module):
    def __init__(self, in_channels,out_channels1, out_channels2reduce,out_channels2, out_channels3reduce, out_channels3, out_channels4):
        super(InceptionV1Module, self).__init__()

        self.branch1_conv = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1)

        self.branch2_conv1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels2reduce,kernel_size=1)
        self.branch2_conv2 = ConvBNReLU(in_channels=out_channels2reduce,out_channels=out_channels2,kernel_size=3)

        self.branch3_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=out_channels3reduce, kernel_size=1)
        self.branch3_conv2 = ConvBNReLU(in_channels=out_channels3reduce, out_channels=out_channels3, kernel_size=5)

        self.branch4_pool = nn.MaxPool2d(kernel_size=3,stride=1,padding=1)
        self.branch4_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1)

    def forward(self,x):
        out1 = self.branch1_conv(x)
        out2 = self.branch2_conv2(self.branch2_conv1(x))
        out3 = self.branch3_conv2(self.branch3_conv1(x))
        out4 = self.branch4_conv1(self.branch4_pool(x))
        out = torch.cat([out1, out2, out3, out4], dim=1)
        return out

class InceptionAux(nn.Module):
    def __init__(self, in_channels,out_channels):
        super(InceptionAux, self).__init__()

        self.auxiliary_avgpool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.auxiliary_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=128, kernel_size=1)
        self.auxiliary_linear1 = nn.Linear(in_features=128 * 4 * 4, out_features=1024)
        self.auxiliary_relu = nn.ReLU6(inplace=True)
        self.auxiliary_dropout = nn.Dropout(p=0.7)
        self.auxiliary_linear2 = nn.Linear(in_features=1024, out_features=out_channels)

    def forward(self, x):
        x = self.auxiliary_conv1(self.auxiliary_avgpool(x))
        x = x.view(x.size(0), -1)
        x= self.auxiliary_relu(self.auxiliary_linear1(x))
        out = self.auxiliary_linear2(self.auxiliary_dropout(x))
        return out

class InceptionV1(nn.Module):
    def __init__(self, num_classes=1000, stage='train'):
        super(InceptionV1, self).__init__()
        self.stage = stage

        self.block1 = nn.Sequential(
            nn.Conv2d(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=3),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(kernel_size=3,stride=2, padding=1),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=1, stride=1),
            nn.BatchNorm2d(64),
        )
        self.block2 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(192),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )

        self.block3 = nn.Sequential(
            InceptionV1Module(in_channels=192,out_channels1=64, out_channels2reduce=96, out_channels2=128, out_channels3reduce = 16, out_channels3=32, out_channels4=32),
            InceptionV1Module(in_channels=256, out_channels1=128, out_channels2reduce=128, out_channels2=192,out_channels3reduce=32, out_channels3=96, out_channels4=64),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )

        self.block4_1 = InceptionV1Module(in_channels=480, out_channels1=192, out_channels2reduce=96, out_channels2=208,out_channels3reduce=16, out_channels3=48, out_channels4=64)

        if self.stage == 'train':
            self.aux_logits1 = InceptionAux(in_channels=512,out_channels=num_classes)

        self.block4_2 = nn.Sequential(
            InceptionV1Module(in_channels=512, out_channels1=160, out_channels2reduce=112, out_channels2=224,
                              out_channels3reduce=24, out_channels3=64, out_channels4=64),
            InceptionV1Module(in_channels=512, out_channels1=128, out_channels2reduce=128, out_channels2=256,
                              out_channels3reduce=24, out_channels3=64, out_channels4=64),
            InceptionV1Module(in_channels=512, out_channels1=112, out_channels2reduce=144, out_channels2=288,
                              out_channels3reduce=32, out_channels3=64, out_channels4=64),
        )

        if self.stage == 'train':
            self.aux_logits2 = InceptionAux(in_channels=528,out_channels=num_classes)

        self.block4_3 = nn.Sequential(
            InceptionV1Module(in_channels=528, out_channels1=256, out_channels2reduce=160, out_channels2=320,
                              out_channels3reduce=32, out_channels3=128, out_channels4=128),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )

        self.block5 = nn.Sequential(
            InceptionV1Module(in_channels=832, out_channels1=256, out_channels2reduce=160, out_channels2=320,out_channels3reduce=32, out_channels3=128, out_channels4=128),
            InceptionV1Module(in_channels=832, out_channels1=384, out_channels2reduce=192, out_channels2=384,out_channels3reduce=48, out_channels3=128, out_channels4=128),
        )

        self.avgpool = nn.AvgPool2d(kernel_size=7,stride=1)
        self.dropout = nn.Dropout(p=0.4)
        self.linear = nn.Linear(in_features=1024,out_features=num_classes)

    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        aux1 = x = self.block4_1(x)
        aux2 = x = self.block4_2(x)
        x = self.block4_3(x)
        out = self.block5(x)
        out = self.avgpool(out)
        out = self.dropout(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        if self.stage == 'train':
            aux1 = self.aux_logits1(aux1)
            aux2 = self.aux_logits2(aux2)
            return aux1, aux2, out
        else:
            return out

if __name__=='__main__':
    model = InceptionV1()
    print(model)

    input = torch.randn(1, 3, 224, 224)
    aux1, aux2, out = model(input)
    print(aux1.shape)
    print(aux2.shape)
    print(out.shape)


  

 

  
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
 

文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/121573916