Tutorial on GoogleNet based image classification --- focus on Inception module and save/load models

 Tutorial on GoogleNet based image classification 

2018-06-26 15:50:29 

本文旨在通过案例来学习 GoogleNet 及其 Inception 结构的定义。针对这种复杂模型的保存以及读取。

1. GoogleNet 的结构：

 1 class Inception(nn.Module):
 2     def __init__(self, in_planes, kernel_1_x, kernel_3_in, kernel_3_x, kernel_5_in, kernel_5_x, pool_planes):
 3         super(Inception, self).__init__()
 4         # 1x1 conv branch
 5         self.b1 = nn.Sequential(
 6             nn.Conv2d(in_planes, kernel_1_x, kernel_size=1),
 7             nn.BatchNorm2d(kernel_1_x),
 8             nn.ReLU(True),
 9         )
10 
11         # 1x1 conv -> 3x3 conv branch
12         self.b2 = nn.Sequential(
13             nn.Conv2d(in_planes, kernel_3_in, kernel_size=1),
14             nn.BatchNorm2d(kernel_3_in),
15             nn.ReLU(True),
16             nn.Conv2d(kernel_3_in, kernel_3_x, kernel_size=3, padding=1),
17             nn.BatchNorm2d(kernel_3_x),
18             nn.ReLU(True),
19         )
20 
21         # 1x1 conv -> 5x5 conv branch
22         self.b3 = nn.Sequential(
23             nn.Conv2d(in_planes, kernel_5_in, kernel_size=1),
24             nn.BatchNorm2d(kernel_5_in),
25             nn.ReLU(True),
26             nn.Conv2d(kernel_5_in, kernel_5_x, kernel_size=3, padding=1),
27             nn.BatchNorm2d(kernel_5_x),
28             nn.ReLU(True),
29             nn.Conv2d(kernel_5_x, kernel_5_x, kernel_size=3, padding=1),
30             nn.BatchNorm2d(kernel_5_x),
31             nn.ReLU(True),
32         )
33 
34         # 3x3 pool -> 1x1 conv branch
35         self.b4 = nn.Sequential(
36             nn.MaxPool2d(3, stride=1, padding=1),
37             nn.Conv2d(in_planes, pool_planes, kernel_size=1),
38             nn.BatchNorm2d(pool_planes),
39             nn.ReLU(True),
40         )
41 
42     def forward(self, x):
43         y1 = self.b1(x)
44         y2 = self.b2(x)
45         y3 = self.b3(x)
46         y4 = self.b4(x)
47         return torch.cat([y1,y2,y3,y4], 1)

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