王权富贵：GoogLeNet的pytorch实现

 感谢kuangliu，参考自这个项目里面的一篇叫googlenet.py的文档。

先构建一个基本的Inception模块。构造如上图所示，具体代码如下所示。

[code]'''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F

class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
nn.Conv2d(in_planes, n1x1, kernel_size=1),
nn.BatchNorm2d(n1x1),
nn.ReLU(True),
)

# 1x1 conv -> 3x3 conv branch
self.b2 = nn.Sequential(
nn.Conv2d(in_planes, n3x3red, kernel_size=1),
nn.BatchNorm2d(n3x3red),
nn.ReLU(True),
nn.Conv2d(n3x3red, n3x3, kernel_size=3, padding=1),
nn.BatchNorm2d(n3x3),
nn.ReLU(True),
)

# 1x1 conv -> 5x5 conv branch
self.b3 = nn.Sequential(
nn.Conv2d(in_planes, n5x5red, kernel_size=1),
nn.BatchNorm2d(n5x5red),
nn.ReLU(True),
nn.Conv2d(n5x5red, n5x5, kernel_size=3, padding=1),
nn.BatchNorm2d(n5x5),
nn.ReLU(True),
nn.Conv2d(n5x5, n5x5, kernel_size=3, padding=1),
nn.BatchNorm2d(n5x5),
nn.ReLU(True),
)

# 3x3 pool -> 1x1 conv branch
self.b4 = nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
nn.Conv2d(in_planes, pool_planes, kernel_size=1),
nn.BatchNorm2d(pool_planes),
nn.ReLU(True),
)

GooLeNet的构建如下代码所示，对应结构图，在代码下面显示： 

[code]class GoogLeNet(nn.Module):
def __init__(self):
super(GoogLeNet, self).__init__()
self.pre_layers = nn.Sequential(
nn.Conv2d(3, 192, kernel_size=3, padding=1),
nn.BatchNorm2d(192),
nn.ReLU(True),
)

self.a3 = Inception(192,  64,  96, 128, 16, 32, 32)
self.b3 = Inception(256, 128, 128, 192, 32, 96, 64)

self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)

self.a4 = Inception(480, 192,  96, 208, 16,  48,  64)
self.b4 = Inception(512, 160, 112, 224, 24,  64,  64)
self.c4 = Inception(512, 128, 128, 256, 24,  64,  64)
self.d4 = Inception(512, 112, 144, 288, 32,  64,  64)
self.e4 = Inception(528, 256, 160, 320, 32, 128, 128)

self.a5 = Inception(832, 256, 160, 320, 32, 128, 128)
self.b5 = Inception(832, 384, 192, 384, 48, 128, 128)

self.avgpool = nn.AvgPool2d(8, stride=1)
self.linear = nn.Linear(1024, 10)

def forward(self, x):
out = self.pre_layers(x)
out = self.a3(out)
out = self.b3(out)
out = self.maxpool(out)
out = self.a4(out)
out = self.b4(out)
out = self.c4(out)
out = self.d4(out)
out = self.e4(out)
out = self.maxpool(out)
out = self.a5(out)
out = self.b5(out)
out = self.avgpool(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out

Inception模块中通道的选取参考如下：

上面代码是简化版，它在开始部分没有经过一个7*7和1*1的卷积，直接从3*3开始。辅助分类器也没有写上。