InceptionV3代码实现（Pytorch）

文章目录

- Inception介绍
- InceptionV3代码实现
- - 第一步：定义基础卷积模块
  - 第二步：定义Inceptionv3模块
  - - InceptionA
    - InceptionB
    - InceptionC
    - InceptionD
    - InceptionE
  - 第三步：定义辅助分类器InceptionAux
  - 第四步：搭建GoogLeNet网络
  - 第五步*：网络结构参数初始化
  - 完整代码
  - 论文复现代码
  - - 论文中结构
    - 代码

Inception介绍

Inception网络是CNN发展史上一个重要的里程碑。在Inception出现之前，大部分流行CNN仅仅是把卷积层堆叠得越来越多，使网络越来越深，以此希望能够得到更好的性能。但是存在以下问题:

图像中突出部分的大小差别很大。
由于信息位置的巨大差异，为卷积操作选择合适的卷积核大小就比较困难。信息分布更全
局性的图像偏好较大的卷积核，信息分布比较局部的图像偏好较小的卷积核。
非常深的网络更容易过拟合。将梯度更新传输到整个网络是很困难的。
简单地堆叠较大的卷积层非常消耗计算资源。

Inception module
解决方案:
为什么不在同一层级上运行具备多个尺寸的滤波器呢?网络本质上会变得稍微「宽一些」，而不是「更深」。作者因此设计了Inception 模块。
Inception模块( Inception module) : 它使用3个不同大小的滤波器(1x1、 3x3、 5x5)对输入执行卷积操作，此外它还会执行最大池化。所有子层的输出最后会被级联起来，并传送至下一个Inception模块。

方面增加了网络的宽度，另一方面增加了网络对尺度的适应性

实现降维的Inception模块:如前所述，深度神经网络需要耗费大量计算资源。为了降低算力成
本，作者在3x3和5x5卷积层之前添加额外的1x1卷积层，来限制输入通道的数量。尽管添加额
外的卷积操作似乎是反直觉的，但是1x1卷积比5x5卷积要廉价很多，而且输入通道数量减少也
有利于降低算力成本。
在这里插入图片描述
InceptionV1–Googlenet

Googl eNet采用了Inception模块化(9个)的结构，共22层;
为了避免梯度消失，网络额外增加了2个辅助的softmax用于向前传导梯度(只用于训练)。

Inception V2在输入的时候增加了BatchNormalization:
所有输出保证在0~1之间。

所有输出数据的均值接近0，标准差接近1的正太分布。使其落入激活函数的敏感区，避免梯度消失，加快收敛。
加快模型收敛速度，并且具有-定的泛化能力。
可以减少dropout的使用。
作者提出可以用2个连续的3x3卷积层(stride= 1)组成的小网络来代替单个的5x5卷积层，这便是Inception V2结构。
5x5卷积核参数是3x3卷积核的25/9=2.78倍。
此外，作者将 n * n的卷积核尺寸分解为 1 * n 和 n * 1 两个卷积
并联比串联计算效率要高
前面三个原则用来构建三种不同类型的 Inception 模块

InceptionV3-网络结构图

InceptionV3整合了前面Inception v2中提到的所有升级，还使用了7x7卷积
目前，InceptionV3是最常用的网络模型

Inception V3设计思想和Trick:
(1) 分解成小卷积很有效，可以降低参数量，减轻过拟合，增加网络非线性的表达能力。
(2) 卷积网络从输入到输出，应该让图片尺寸逐渐减小，输出通道数逐渐增加，即让空间结
构化，将空间信息转化为高阶抽象的特征信息。
(3) InceptionModule用多个分支提取不同抽象程度的高阶特征的思路很有效，可以丰富网络
的表达能力

InceptionV4
在这里插入图片描述

左图是基本的Inception v2/v3模块，使用两个3x3卷积代替5x5卷积，并且使用average pooling,该模
块主要处理尺寸为35x35的feature map;
中图模块使用1xn和nx1卷积代替nxn卷积，同样使用average pooling,该模块主要处理尺寸为17x17
的feature map;
右图将3x3卷积用1x3卷积和3x1卷积代替。

总的来说，Inception v4中基本的Inception module还是沿袭了Inception v2/v3的结构，只是结构看起来更加简洁统一，并且使用更多的Inception modules实验效果也更好。
在这里插入图片描述
Inception模型优势:

采用了1x1卷积核，性价比高，用很少的计算量既可以增加一层的特征变换和非线性变换。
提出Batch Normalization,通过一定的手段，把每层神经元的输入值分布拉到均值0方差1的正态分布，使其落入激活函数的敏感区，避免梯度消失，加快收敛。
引入Inception module, 4个分支结合的结构。
卷积神经网络迁移学习
现在在工程中最为常用的还是vgg、 resnet、 inception这几种结构，设计者通常会先直接套用原版的模型对数据进行训练一次，然后选择效果较为好的模型进行微调与模型缩减。
工程上使用的模型必须在精度高的同时速度要快。
常用的模型缩减的方法是减少卷积的个数与减少resnet的模块数。

InceptionV3代码实现

第一个示例参考文章：

原文链接：GoogLeNet InceptionV3代码复现＋超详细注释（PyTorch）
感谢大佬！

第一步：定义基础卷积模块

BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)

作用：卷积层之后总会添加BatchNorm2d进行数据的归一化处理，这使得数据在进行Relu之前不会因为数据过大而导致网络性能的不稳定

num_features：一般输入参数的shape为batch_size * num_features * height*width，即为其中特征的数量，即为输入BN层的通道数；
eps：分母中添加的一个值，目的是为了计算的稳定性，默认为：1e-5,避免分母为0；
momentum：一个用于运行过程中均值和方差的一个估计参数（可以理解是一个稳定系数，类似于SGD中的momentum的系数）；
affine：当设为true时，会给定可以学习的系数矩阵gamma和beta

class BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x):x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)

第二步：定义Inceptionv3模块

PyTorch提供的有六种基本的Inception模块，分别是InceptionA——InceptionE。

InceptionA

InceptionA
得到输入大小不变，通道数为224+pool_features的特征图。

假如输入为(35, 35, 192)的数据：

第一个branch：
经过branch1x1为带有64个11的卷积核，所以生成第一张特征图(35, 35, 64)；
第二个branch：
首先经过branch5x5_1为带有48个11的卷积核，所以第二张特征图(35, 35, 48)，
然后经过branch5x5_2为带有64个55大小且填充为2的卷积核，特征图大小依旧不变，因此第二张特征图最终为(35, 35, 64)；
第三个branch：
首先经过branch3x3dbl_1为带有64个11的卷积核，所以第三张特征图(35, 35, 64)，
然后经过branch3x3dbl_2为带有96个33大小且填充为1的卷积核，特征图大小依旧不变，因此进一步生成第三张特征图(35, 35, 96)，
最后经过branch3x3dbl_3为带有96个33大小且填充为1的卷积核，特征图大小和通道数不变，因此第三张特征图最终为(35, 35, 96)；
第四个branch：
首先经过avg_pool2d，其中池化核33，步长为1，填充为1，所以第四张特征图大小不变，通道数不变，第四张特征图为(35, 35, 192)，
然后经过branch_pool为带有pool_features个的11卷积，因此第四张特征图最终为(35, 35, pool_features)；
最后将四张特征图进行拼接，最终得到(35，35，64+64+96+pool_features)的特征图。

'''---InceptionA---'''
class InceptionA(nn.Module):def __init__(self, in_channels, pool_features, conv_block=None):super(InceptionA, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 64, kernel_size=1)self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch5x5 = self.branch5x5_1(x)branch5x5 = self.branch5x5_2(branch5x5)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)

InceptionB

InceptionB
得到输入大小减半，通道数为480的特征图，

假如输入为(35, 35, 288)的数据：

第一个branch：
经过branch1x1为带有384个33大小且步长2的卷积核，(35-3+20)/2+1=17所以生成第一张特征图(17, 17, 384)；
第二个branch：
首先经过branch3x3dbl_1为带有64个11的卷积核，特征图大小不变，即(35, 35, 64)；
然后经过branch3x3dbl_2为带有96个33大小填充1的卷积核，特征图大小不变，即(35, 35, 96)，
再经过branch3x3dbl_3为带有96个33大小步长2的卷积核，(35-3+20)/2+1=17，即第二张特征图为(17, 17, 96)；
第三个branch：
经过max_pool2d，池化核大小3*3，步长为2，所以是二倍最大值下采样，通道数保持不变，第三张特征图为(17, 17, 288)；
最后将三张特征图进行拼接，最终得到(17(即Hin/2)，17(即Win/2)，384+96+288(Cin)=768)的特征图。

'''---InceptionB---'''
class InceptionB(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionB, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)def _forward(self, x):branch3x3 = self.branch3x3(x)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)

InceptionC

InceptionC
得到输入大小不变，通道数为768的特征图。

假如输入为(17,17, 768)的数据：

第一个branch：
首先经过branch1x1为带有192个1*1的卷积核，所以生成第一张特征图(17,17, 192)；

第二个branch：
首先经过branch7x7_1为带有c7个11的卷积核，所以第二张特征图(17,17, c7)，
然后经过branch7x7_2为带有c7个17大小且填充为03的卷积核，特征图大小不变，进一步生成第二张特征图(17,17, c7)，
然后经过branch7x7_3为带有192个71大小且填充为30的卷积核，特征图大小不变，进一步生成第二张特征图(17,17, 192)，因此第二张特征图最终为(17,17, 192)；
第三个branch：
首先经过branch7x7dbl_1为带有c7个11的卷积核，所以第三张特征图(17,17, c7)，
然后经过branch7x7dbl_2为带有c7个71大小且填充为30的卷积核，特征图大小不变，进一步生成第三张特征图(17,17, c7)，
然后经过branch7x7dbl_3为带有c7个17大小且填充为03的卷积核，特征图大小不变，进一步生成第三张特征图(17,17, c7)，
然后经过branch7x7dbl_4为带有c7个71大小且填充为30的卷积核，特征图大小不变，进一步生成第三张特征图(17,17, c7)，
然后经过branch7x7dbl_5为带有192个17大小且填充为03的卷积核，特征图大小不变，因此第二张特征图最终为(17,17, 192)；
第四个branch：
首先经过avg_pool2d，其中池化核33，步长为1，填充为1，所以第四张特征图大小不变，通道数不变，第四张特征图为(17,17, 768)，
然后经过branch_pool为带有192个的11卷积，因此第四张特征图最终为(17,17, 192)；
最后将四张特征图进行拼接，最终得到(17, 17, 192+192+192+192=768)的特征图。

'''---InceptionC---'''
class InceptionC(nn.Module):def __init__(self, in_channels, channels_7x7, conv_block=None):super(InceptionC, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 192, kernel_size=1)c7 = channels_7x7self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))self.branch_pool = conv_block(in_channels, 192, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch7x7 = self.branch7x7_1(x)branch7x7 = self.branch7x7_2(branch7x7)branch7x7 = self.branch7x7_3(branch7x7)branch7x7dbl = self.branch7x7dbl_1(x)branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)

InceptionD

InceptionD
得到输入大小减半，通道数512的特征图，

假如输入为(17, 17, 768)的数据：

第一个branch：
首先经过branch3x3_1为带有192个11的卷积核，所以生成第一张特征图(17, 17, 192)；
然后经过branch3x3_2为带有320个33大小步长为2的卷积核，(17-3+20)/2+1=8，最终第一张特征图(8, 8, 320)；
第二个branch：
首先经过branch7x7x3_1为带有192个11的卷积核，特征图大小不变，即(17, 17, 192)；
然后经过branch7x7x3_2为带有192个17大小且填充为03的卷积核，特征图大小不变，进一步生成第三张特征图(17,17, 192)；
再经过branch7x7x3_3为带有192个71大小且填充为30的卷积核，特征图大小不变，进一步生成第三张特征图(17,17, 192)；
最后经过branch7x7x3_4为带有192个3*3大小步长为2的卷积核，最终第一张特征图(8, 8, 192)；
第三个branch：

首先经过max_pool2d，池化核大小3*3，步长为2，所以是二倍最大值下采样，通道数保持不变，第三张特征图为(8, 8, 768)；
最后将三张特征图进行拼接，最终得到(8(即Hin/2)，8(即Win/2)，320+192+768(Cin)=1280)的特征图。

'''---InceptionD---'''
class InceptionD(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionD, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)def _forward(self, x):branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch7x7x3 = self.branch7x7x3_1(x)branch7x7x3 = self.branch7x7x3_2(branch7x7x3)branch7x7x3 = self.branch7x7x3_3(branch7x7x3)branch7x7x3 = self.branch7x7x3_4(branch7x7x3)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch7x7x3, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)

InceptionE

InceptionE
最终得到输入大小不变，通道数为2048的特征图。

假如输入为(8,8, 1280)的数据：

第一个branch：
首先经过branch1x1为带有320个11的卷积核，所以生成第一张特征图(8, 8, 320)；
第二个branch：
首先经过branch3x3_1为带有384个11的卷积核，所以第二张特征图(8, 8, 384)，
经过分支branch3x3_2a为带有384个13大小且填充为01的卷积核，特征图大小不变，进一步生成特征图(8,8, 384)，
经过分支branch3x3_2b为带有192个31大小且填充为10的卷积核，特征图大小不变，进一步生成特征图(8,8, 384)，
因此第二张特征图最终为两个分支拼接(8,8, 384+384=768)；
第三个branch：
首先经过branch3x3dbl_1为带有448个11的卷积核，所以第三张特征图(8,8, 448)，
然后经过branch3x3dbl_2为带有384个33大小且填充为1的卷积核，特征图大小不变，进一步生成第三张特征图(8,8, 384)，
经过分支branch3x3dbl_3a为带有384个13大小且填充为01的卷积核，特征图大小不变，进一步生成特征图(8,8, 384)，
经过分支branch3x3dbl_3b为带有384个31大小且填充为10的卷积核，特征图大小不变，进一步生成特征图(8,8, 384)，
因此第三张特征图最终为两个分支拼接(8,8, 384+384=768)；
第四个branch：
首先经过avg_pool2d，其中池化核33，步长为1，填充为1，所以第四张特征图大小不变，通道数不变，第四张特征图为(8,8, 1280)，
然后经过branch_pool为带有192个的11卷积，因此第四张特征图最终为(8,8, 192)；
最后将四张特征图进行拼接，最终得到(8, 8, 320+768+768+192=2048)的特征图。

'''---InceptionE---'''
class InceptionE(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionE, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 320, kernel_size=1)self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch_pool = conv_block(in_channels, 192, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch3x3 = self.branch3x3_1(x)branch3x3 = [self.branch3x3_2a(branch3x3),self.branch3x3_2b(branch3x3),]branch3x3 = torch.cat(branch3x3, 1)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = [self.branch3x3dbl_3a(branch3x3dbl),self.branch3x3dbl_3b(branch3x3dbl),]branch3x3dbl = torch.cat(branch3x3dbl, 1)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)

第三步：定义辅助分类器InceptionAux

辅助分类器

class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes, conv_block=None):super(InceptionAux, self).__init__()if conv_block is None:conv_block = BasicConv2dself.conv0 = conv_block(in_channels, 128, kernel_size=1)self.conv1 = conv_block(128, 768, kernel_size=5)self.conv1.stddev = 0.01self.fc = nn.Linear(768, num_classes)self.fc.stddev = 0.001def forward(self, x):# N x 768 x 17 x 17x = F.avg_pool2d(x, kernel_size=5, stride=3)# N x 768 x 5 x 5x = self.conv0(x)# N x 128 x 5 x 5x = self.conv1(x)# N x 768 x 1 x 1# Adaptive average poolingx = F.adaptive_avg_pool2d(x, (1, 1))# N x 768 x 1 x 1x = torch.flatten(x, 1)# N x 768x = self.fc(x)# N x 1000return x

第四步：搭建GoogLeNet网络

'''-----------------------搭建GoogLeNet网络--------------------------'''
class GoogLeNet(nn.Module):def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,inception_blocks=None):super(GoogLeNet, self).__init__()if inception_blocks is None:inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC,InceptionD, InceptionE, InceptionAux]assert len(inception_blocks) == 7conv_block = inception_blocks[0]inception_a = inception_blocks[1]inception_b = inception_blocks[2]inception_c = inception_blocks[3]inception_d = inception_blocks[4]inception_e = inception_blocks[5]inception_aux = inception_blocks[6]self.aux_logits = aux_logitsself.transform_input = transform_inputself.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)self.Mixed_5b = inception_a(192, pool_features=32)self.Mixed_5c = inception_a(256, pool_features=64)self.Mixed_5d = inception_a(288, pool_features=64)self.Mixed_6a = inception_b(288)self.Mixed_6b = inception_c(768, channels_7x7=128)self.Mixed_6c = inception_c(768, channels_7x7=160)self.Mixed_6d = inception_c(768, channels_7x7=160)self.Mixed_6e = inception_c(768, channels_7x7=192)if aux_logits:self.AuxLogits = inception_aux(768, num_classes)self.Mixed_7a = inception_d(768)self.Mixed_7b = inception_e(1280)self.Mixed_7c = inception_e(2048)self.fc = nn.Linear(2048, num_classes)
'''输入(229,229,3)的数据，首先归一化输入，经过5个卷积，2个最大池化层。'''def _forward(self, x):# N x 3 x 299 x 299x = self.Conv2d_1a_3x3(x)# N x 32 x 149 x 149x = self.Conv2d_2a_3x3(x)# N x 32 x 147 x 147x = self.Conv2d_2b_3x3(x)# N x 64 x 147 x 147x = F.max_pool2d(x, kernel_size=3, stride=2)# N x 64 x 73 x 73x = self.Conv2d_3b_1x1(x)# N x 80 x 73 x 73x = self.Conv2d_4a_3x3(x)# N x 192 x 71 x 71x = F.max_pool2d(x, kernel_size=3, stride=2)'''然后经过3个InceptionA结构，1个InceptionB，3个InceptionC，1个InceptionD，2个InceptionE，其中InceptionA，辅助分类器AuxLogits以经过最后一个InceptionC的输出为输入。'''# 35 x 35 x 192x = self.Mixed_5b(x)  # InceptionA(192, pool_features=32)# 35 x 35 x 256x = self.Mixed_5c(x)  # InceptionA(256, pool_features=64)# 35 x 35 x 288x = self.Mixed_5d(x)  # InceptionA(288, pool_features=64)# 35 x 35 x 288x = self.Mixed_6a(x)  # InceptionB(288)# 17 x 17 x 768x = self.Mixed_6b(x)  # InceptionC(768, channels_7x7=128)# 17 x 17 x 768x = self.Mixed_6c(x)  # InceptionC(768, channels_7x7=160)# 17 x 17 x 768x = self.Mixed_6d(x)  # InceptionC(768, channels_7x7=160)# 17 x 17 x 768x = self.Mixed_6e(x)  # InceptionC(768, channels_7x7=192)# 17 x 17 x 768if self.training and self.aux_logits:aux = self.AuxLogits(x)  # InceptionAux(768, num_classes)# 17 x 17 x 768x = self.Mixed_7a(x)  # InceptionD(768)# 8 x 8 x 1280x = self.Mixed_7b(x)  # InceptionE(1280)# 8 x 8 x 2048x = self.Mixed_7c(x)  # InceptionE(2048)'''进入分类部分。经过平均池化层+dropout+打平+全连接层输出'''x = F.adaptive_avg_pool2d(x, (1, 1))# N x 2048 x 1 x 1x = F.dropout(x, training=self.training)# N x 2048 x 1 x 1x = torch.flatten(x, 1)#Flatten（）就是将2D的特征图压扁为1D的特征向量，是展平操作，进入全连接层之前使用,类才能写进nn.Sequential# N x 2048x = self.fc(x)# N x 1000 (num_classes)return x, auxdef forward(self, x):x, aux = self._forward(x)return x, aux

第五步*：网络结构参数初始化

    '''-----------------------网络结构参数初始化--------------------------'''# 目的：使网络更好收敛，准确率更高def _initialize_weights(self):  # 将各种初始化方法定义为一个initialize_weights()的函数并在模型初始后进行使用。# 遍历网络中的每一层for m in self.modules():# isinstance(object, type)，如果指定的对象拥有指定的类型，则isinstance()函数返回True'''如果是卷积层Conv2d'''if isinstance(m, nn.Conv2d):# Kaiming正态分布方式的权重初始化nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')'''判断是否有偏置：'''# 如果偏置不是0，将偏置置成0，对偏置进行初始化if m.bias is not None:# torch.nn.init.constant_(tensor, val)，初始化整个矩阵为常数valnn.init.constant_(m.bias, 0)'''如果是全连接层'''elif isinstance(m, nn.Linear):# init.normal_(tensor, mean=0.0, std=1.0)，使用从正态分布中提取的值填充输入张量# 参数：tensor：一个n维Tensor，mean：正态分布的平均值，std：正态分布的标准差nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)

完整代码

from __future__ import divisionimport torch
import torch.nn as nn
import torch.nn.functional as F'''-------------------------第一步：定义基础卷积模块-------------------------------'''
class BasicConv2d(nn.Module):def __init__(self, in_channels, out_channels, **kwargs):super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x):x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)'''-----------------第二步：定义Inceptionv3模块---------------------''''''---InceptionA---'''
class InceptionA(nn.Module):def __init__(self, in_channels, pool_features, conv_block=None):super(InceptionA, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 64, kernel_size=1)self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch5x5 = self.branch5x5_1(x)branch5x5 = self.branch5x5_2(branch5x5)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)'''---InceptionB---'''
class InceptionB(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionB, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)def _forward(self, x):branch3x3 = self.branch3x3(x)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)'''---InceptionC---'''
class InceptionC(nn.Module):def __init__(self, in_channels, channels_7x7, conv_block=None):super(InceptionC, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 192, kernel_size=1)c7 = channels_7x7self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))self.branch_pool = conv_block(in_channels, 192, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch7x7 = self.branch7x7_1(x)branch7x7 = self.branch7x7_2(branch7x7)branch7x7 = self.branch7x7_3(branch7x7)branch7x7dbl = self.branch7x7dbl_1(x)branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)'''---InceptionD---'''
class InceptionD(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionD, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)def _forward(self, x):branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch7x7x3 = self.branch7x7x3_1(x)branch7x7x3 = self.branch7x7x3_2(branch7x7x3)branch7x7x3 = self.branch7x7x3_3(branch7x7x3)branch7x7x3 = self.branch7x7x3_4(branch7x7x3)branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)outputs = [branch3x3, branch7x7x3, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)'''---InceptionE---'''
class InceptionE(nn.Module):def __init__(self, in_channels, conv_block=None):super(InceptionE, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1x1 = conv_block(in_channels, 320, kernel_size=1)self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))self.branch_pool = conv_block(in_channels, 192, kernel_size=1)def _forward(self, x):branch1x1 = self.branch1x1(x)branch3x3 = self.branch3x3_1(x)branch3x3 = [self.branch3x3_2a(branch3x3),self.branch3x3_2b(branch3x3),]branch3x3 = torch.cat(branch3x3, 1)branch3x3dbl = self.branch3x3dbl_1(x)branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)branch3x3dbl = [self.branch3x3dbl_3a(branch3x3dbl),self.branch3x3dbl_3b(branch3x3dbl),]branch3x3dbl = torch.cat(branch3x3dbl, 1)branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]return outputsdef forward(self, x):outputs = self._forward(x)return torch.cat(outputs, 1)'''-------------------第三步：定义辅助分类器InceptionAux-----------------------'''
class InceptionAux(nn.Module):def __init__(self, in_channels, num_classes, conv_block=None):super(InceptionAux, self).__init__()if conv_block is None:conv_block = BasicConv2dself.conv0 = conv_block(in_channels, 128, kernel_size=1)self.conv1 = conv_block(128, 768, kernel_size=5)self.conv1.stddev = 0.01self.fc = nn.Linear(768, num_classes)self.fc.stddev = 0.001def forward(self, x):# N x 768 x 17 x 17x = F.avg_pool2d(x, kernel_size=5, stride=3)# N x 768 x 5 x 5x = self.conv0(x)# N x 128 x 5 x 5x = self.conv1(x)# N x 768 x 1 x 1# Adaptive average poolingx = F.adaptive_avg_pool2d(x, (1, 1))# N x 768 x 1 x 1x = torch.flatten(x, 1)# N x 768x = self.fc(x)# N x 1000return x'''-----------------------第四步：搭建GoogLeNet网络--------------------------'''
class GoogLeNet(nn.Module):def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,inception_blocks=None):super(GoogLeNet, self).__init__()if inception_blocks is None:inception_blocks = [BasicConv2d, InceptionA, InceptionB, InceptionC,InceptionD, InceptionE, InceptionAux]assert len(inception_blocks) == 7conv_block = inception_blocks[0]inception_a = inception_blocks[1]inception_b = inception_blocks[2]inception_c = inception_blocks[3]inception_d = inception_blocks[4]inception_e = inception_blocks[5]inception_aux = inception_blocks[6]self.aux_logits = aux_logitsself.transform_input = transform_inputself.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)self.Mixed_5b = inception_a(192, pool_features=32)self.Mixed_5c = inception_a(256, pool_features=64)self.Mixed_5d = inception_a(288, pool_features=64)self.Mixed_6a = inception_b(288)self.Mixed_6b = inception_c(768, channels_7x7=128)self.Mixed_6c = inception_c(768, channels_7x7=160)self.Mixed_6d = inception_c(768, channels_7x7=160)self.Mixed_6e = inception_c(768, channels_7x7=192)if aux_logits:self.AuxLogits = inception_aux(768, num_classes)self.Mixed_7a = inception_d(768)self.Mixed_7b = inception_e(1280)self.Mixed_7c = inception_e(2048)self.fc = nn.Linear(2048, num_classes)'''输入(229,229,3)的数据，首先归一化输入，经过5个卷积，2个最大池化层。'''def _forward(self, x):# N x 3 x 299 x 299x = self.Conv2d_1a_3x3(x)# N x 32 x 149 x 149x = self.Conv2d_2a_3x3(x)# N x 32 x 147 x 147x = self.Conv2d_2b_3x3(x)# N x 64 x 147 x 147x = F.max_pool2d(x, kernel_size=3, stride=2)# N x 64 x 73 x 73x = self.Conv2d_3b_1x1(x)# N x 80 x 73 x 73x = self.Conv2d_4a_3x3(x)# N x 192 x 71 x 71x = F.max_pool2d(x, kernel_size=3, stride=2)'''然后经过3个InceptionA结构，1个InceptionB，3个InceptionC，1个InceptionD，2个InceptionE，其中InceptionA，辅助分类器AuxLogits以经过最后一个InceptionC的输出为输入。'''# 35 x 35 x 192x = self.Mixed_5b(x)  # InceptionA(192, pool_features=32)# 35 x 35 x 256x = self.Mixed_5c(x)  # InceptionA(256, pool_features=64)# 35 x 35 x 288x = self.Mixed_5d(x)  # InceptionA(288, pool_features=64)# 35 x 35 x 288x = self.Mixed_6a(x)  # InceptionB(288)# 17 x 17 x 768x = self.Mixed_6b(x)  # InceptionC(768, channels_7x7=128)# 17 x 17 x 768x = self.Mixed_6c(x)  # InceptionC(768, channels_7x7=160)# 17 x 17 x 768x = self.Mixed_6d(x)  # InceptionC(768, channels_7x7=160)# 17 x 17 x 768x = self.Mixed_6e(x)  # InceptionC(768, channels_7x7=192)# 17 x 17 x 768if self.training and self.aux_logits:aux = self.AuxLogits(x)  # InceptionAux(768, num_classes)# 17 x 17 x 768x = self.Mixed_7a(x)  # InceptionD(768)# 8 x 8 x 1280x = self.Mixed_7b(x)  # InceptionE(1280)# 8 x 8 x 2048x = self.Mixed_7c(x)  # InceptionE(2048)'''进入分类部分。经过平均池化层+dropout+打平+全连接层输出'''x = F.adaptive_avg_pool2d(x, (1, 1))# N x 2048 x 1 x 1x = F.dropout(x, training=self.training)# N x 2048 x 1 x 1x = torch.flatten(x, 1)#Flatten（）就是将2D的特征图压扁为1D的特征向量，是展平操作，进入全连接层之前使用,类才能写进nn.Sequential# N x 2048x = self.fc(x)# N x 1000 (num_classes)return x, auxdef forward(self, x):x, aux = self._forward(x)return x, aux'''-----------------------第五步：网络结构参数初始化--------------------------'''# 目的：使网络更好收敛，准确率更高def _initialize_weights(self):  # 将各种初始化方法定义为一个initialize_weights()的函数并在模型初始后进行使用。# 遍历网络中的每一层for m in self.modules():# isinstance(object, type)，如果指定的对象拥有指定的类型，则isinstance()函数返回True'''如果是卷积层Conv2d'''if isinstance(m, nn.Conv2d):# Kaiming正态分布方式的权重初始化nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')'''判断是否有偏置：'''# 如果偏置不是0，将偏置置成0，对偏置进行初始化if m.bias is not None:# torch.nn.init.constant_(tensor, val)，初始化整个矩阵为常数valnn.init.constant_(m.bias, 0)'''如果是全连接层'''elif isinstance(m, nn.Linear):# init.normal_(tensor, mean=0.0, std=1.0)，使用从正态分布中提取的值填充输入张量# 参数：tensor：一个n维Tensor，mean：正态分布的平均值，std：正态分布的标准差nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)
'''---------------------------------------显示网络结构-------------------------------'''
if __name__ == '__main__':net = GoogLeNet(1000).cuda()from torchsummary import summarysummary(net, (3, 299, 299))

论文复现代码

上面实现的是torchvision中的Inception v3结构，和论文中不太一样。
GITHUB论文复现代码链接

论文中结构

代码

import torch
import torch.nn as nn
from functools import partial
# functools.partial()：减少某个函数的参数个数。 partial() 函数允许你给一个或多个参数设置固定的值，减少接下来被调用时的参数个数'''-----------------------第一步：定义卷积模块-----------------------'''
#基础卷积模块
class Conv2d(nn.Module):def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, output=False):super(Conv2d, self).__init__()'''卷积层'''self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)'''输出层'''self.output = outputif self.output == False:'''bn层'''self.bn = nn.BatchNorm2d(out_channels)'''relu层'''self.relu = nn.ReLU(inplace=True)def forward(self, x):x = self.conv(x)if self.output:return xelse:x = self.bn(x)x = self.relu(x)return xclass Separable_Conv2d(nn.Module):def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):super(Separable_Conv2d, self).__init__()self.conv_h = nn.Conv2d(in_channels, in_channels, (kernel_size, 1), stride=(stride, 1), padding=(padding, 0))self.conv_w = nn.Conv2d(in_channels, out_channels, (1, kernel_size), stride=(1, stride), padding=(0, padding))self.bn = nn.BatchNorm2d(out_channels)self.relu = nn.ReLU(inplace=True)def forward(self, x):x = self.conv_h(x)x = self.conv_w(x)x = self.bn(x)x = self.relu(x)return xclass Concat_Separable_Conv2d(nn.Module):def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):super(Concat_Separable_Conv2d, self).__init__()self.conv_h = nn.Conv2d(in_channels, out_channels, (kernel_size, 1), stride=(stride, 1), padding=(padding, 0))self.conv_w = nn.Conv2d(in_channels, out_channels, (1, kernel_size), stride=(1, stride), padding=(0, padding))self.bn = nn.BatchNorm2d(out_channels * 2)self.relu = nn.ReLU(inplace=True)def forward(self, x):x_h = self.conv_h(x)x_w = self.conv_w(x)x = torch.cat([x_h, x_w], dim=1)x = self.bn(x)x = self.relu(x)return x#Flatten（）就是将2D的特征图压扁为1D的特征向量，是展平操作，进入全连接层之前使用,类才能写进nn.Sequential
class Flatten(nn.Module):# 传入输入维度和输出维度def __init__(self):# 调用父类构造函数super(Flatten, self).__init__()# 实现forward函数def forward(self, x):# 保存batch维度，后面的维度全部压平return torch.flatten(x, 1)#Squeeze（）降维
class Squeeze(nn.Module):def __init__(self):super(Squeeze, self).__init__()def forward(self, x):return torch.squeeze(x)'''-----------------------搭建GoogLeNet网络--------------------------'''
class GoogLeNet(nn.Module):def __init__(self, num_classes, mode='train'):super(GoogLeNet, self).__init__()self.num_classes = num_classesself.mode = modeself.layers = nn.Sequential(Conv2d(3, 32, 3, stride=2),Conv2d(32, 32, 3, stride=1),Conv2d(32, 64, 3, stride=1, padding=1),nn.MaxPool2d(kernel_size=3, stride=2),Conv2d(64, 80, kernel_size=3),Conv2d(80, 192, kernel_size=3, stride=2),Conv2d(192, 288, kernel_size=3, stride=1, padding=1),#输入：35*35*288。将5*5用两个3*3代替Inceptionv3(288, 64, 48, 64, 64, 96, 64, mode='1'),  # 3aInceptionv3(288, 64, 48, 64, 64, 96, 64, mode='1'),  # 3bInceptionv3(288, 0, 128, 384, 64, 96, 0, stride=2, pool_type='MAX', mode='1'),  # 3c#输入：17*17*768。Inceptionv3(768, 192, 128, 192, 128, 192, 192, mode='2'),  # 4aInceptionv3(768, 192, 160, 192, 160, 192, 192, mode='2'),  # 4bInceptionv3(768, 192, 160, 192, 160, 192, 192, mode='2'),  # 4cInceptionv3(768, 192, 192, 192, 192, 192, 192, mode='2'),  # 4dInceptionv3(768, 0, 192, 320, 192, 192, 0, stride=2, pool_type='MAX', mode='2'),  # 4e#8*8*1280Inceptionv3(1280, 320, 384, 384, 448, 384, 192, mode='3'),  # 5aInceptionv3(2048, 320, 384, 384, 448, 384, 192, pool_type='MAX', mode='3'),  # 5bnn.AvgPool2d(8, 1),Conv2d(2048, num_classes, kernel_size=1, output=True),Squeeze(),)if mode == 'train':self.aux = InceptionAux(768, num_classes)def forward(self, x):for idx, layer in enumerate(self.layers):if (idx == 14 and self.mode == 'train'):aux = self.aux(x)x = layer(x)if self.mode == 'train':return x, auxelse:return x'''-----------------------网络结构参数初始化--------------------------'''# 目的：使网络更好收敛，准确率更高def _initialize_weights(self):  # 将各种初始化方法定义为一个initialize_weights()的函数并在模型初始后进行使用。# 遍历网络中的每一层for m in self.modules():# isinstance(object, type)，如果指定的对象拥有指定的类型，则isinstance()函数返回True'''如果是卷积层Conv2d'''if isinstance(m, nn.Conv2d):# Kaiming正态分布方式的权重初始化nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')'''判断是否有偏置：'''# 如果偏置不是0，将偏置置成0，对偏置进行初始化if m.bias is not None:# torch.nn.init.constant_(tensor, val)，初始化整个矩阵为常数valnn.init.constant_(m.bias, 0)'''如果是全连接层'''elif isinstance(m, nn.Linear):# init.normal_(tensor, mean=0.0, std=1.0)，使用从正态分布中提取的值填充输入张量# 参数：tensor：一个n维Tensor，mean：正态分布的平均值，std：正态分布的标准差nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)'''---------------------Inceptionv3-------------------------------------'''
'''
Inceptionv3由三个连续的Inception模块组组成
'''
class Inceptionv3(nn.Module):def __init__(self, input_channel, conv1_channel, conv3_reduce_channel,conv3_channel, conv3_double_reduce_channel, conv3_double_channel, pool_reduce_channel, stride=1,pool_type='AVG', mode='1'):super(Inceptionv3, self).__init__()self.stride = strideif stride == 2:padding_conv3 = 0padding_conv7 = 2else:padding_conv3 = 1padding_conv7 = 3if conv1_channel != 0:self.conv1 = Conv2d(input_channel, conv1_channel, kernel_size=1)else:self.conv1 = Noneself.conv3_reduce = Conv2d(input_channel, conv3_reduce_channel, kernel_size=1)#第一种Inception模式：输入的特征图尺寸为35x35x288,采用了论文中图5中的架构，将5x5以两个3x3代替。if mode == '1':self.conv3 = Conv2d(conv3_reduce_channel, conv3_channel, kernel_size=3, stride=stride,padding=padding_conv3)self.conv3_double1 = Conv2d(conv3_double_reduce_channel, conv3_double_channel, kernel_size=3, padding=1)self.conv3_double2 = Conv2d(conv3_double_channel, conv3_double_channel, kernel_size=3, stride=stride,padding=padding_conv3)#第二种Inception模块：输入特征图尺寸为17x17x768，采用了论文中图6中nx1+1xn的不对称卷积结构elif mode == '2':self.conv3 = Separable_Conv2d(conv3_reduce_channel, conv3_channel, kernel_size=7, stride=stride,padding=padding_conv7)self.conv3_double1 = Separable_Conv2d(conv3_double_reduce_channel, conv3_double_channel, kernel_size=7,padding=3)self.conv3_double2 = Separable_Conv2d(conv3_double_channel, conv3_double_channel, kernel_size=7,stride=stride, padding=padding_conv7)#第三种Inception模块：输入特征图尺寸为8x8x1280, 采用了论文图7中所示的并行模块的结构elif mode == '3':self.conv3 = Concat_Separable_Conv2d(conv3_reduce_channel, conv3_channel, kernel_size=3, stride=stride,padding=1)self.conv3_double1 = Conv2d(conv3_double_reduce_channel, conv3_double_channel, kernel_size=3, padding=1)self.conv3_double2 = Concat_Separable_Conv2d(conv3_double_channel, conv3_double_channel, kernel_size=3,stride=stride, padding=1)self.conv3_double_reduce = Conv2d(input_channel, conv3_double_reduce_channel, kernel_size=1)if pool_type == 'MAX':self.pool = nn.MaxPool2d(kernel_size=3, stride=stride, padding=padding_conv3)elif pool_type == 'AVG':self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=padding_conv3)if pool_reduce_channel != 0:self.pool_reduce = Conv2d(input_channel, pool_reduce_channel, kernel_size=1)else:self.pool_reduce = Nonedef forward(self, x):output_conv3 = self.conv3(self.conv3_reduce(x))output_conv3_double = self.conv3_double2(self.conv3_double1(self.conv3_double_reduce(x)))if self.pool_reduce != None:output_pool = self.pool_reduce(self.pool(x))else:output_pool = self.pool(x)if self.conv1 != None:output_conv1 = self.conv1(x)outputs = torch.cat([output_conv1, output_conv3, output_conv3_double, output_pool], dim=1)else:outputs = torch.cat([output_conv3, output_conv3_double, output_pool], dim=1)return outputs'''------------辅助分类器---------------------------'''
class InceptionAux(nn.Module):def __init__(self, input_channel, num_classes):super(InceptionAux, self).__init__()self.layers = nn.Sequential(nn.AvgPool2d(5, 3),Conv2d(input_channel, 128, 1),Conv2d(128, 1024, kernel_size=5),Conv2d(1024, num_classes, kernel_size=1, output=True),Squeeze())def forward(self, x):x = self.layers(x)return x'''-------------------显示网络结构-------------------------------'''
if __name__ == '__main__':net = GoogLeNet(1000).cuda()from torchsummary import summarysummary(net, (3, 299, 299))