GoogLeNet有意思的是设计了辅助分类器，还有一个就是卷积拆分，这个在VGG中实际已经提出，这个主题梳理了GoogLeNet的分类技术，顺便实现了多辅助分类器的训练，效果在ImageNet2012数据集上还不错（我只是用4类没有打乱的图像训练）。
要想跟上最新的技术，现在看书不顶用了，得看最新学术论文。

关于GoogLeNet网络

2014年，GoogLeNet和VGG是当年ImageNet挑战赛(ILSVRC14)的双雄，GoogLeNet获得了第一名、VGG获得了第二名，这两类模型结构的共同特点是层次更深了。
- VGG继承了LeNet以及AlexNet的一些框架结构，而GoogLeNet则做了更加大胆的网络结构尝试
- 内存或计算资源有限时，GoogleNet是比较好的选择；从模型结果来看，GoogLeNet的性能却更加优越
  - GoogLeNet虽然深度只有22层，但大小却比AlexNet和VGG小很多；
  - GoogleNet参数为500万个，AlexNet参数个数是GoogleNet的12倍，VGGNet参数又是AlexNet的3倍。
GoogLeNet是谷歌（Google）研究出来的深度网络结构，为什么不叫"GoogleNet"，而叫"GoogLeNet"，据说是为了向"LeNet"致敬，因此取名为"GoogLeNet"
参考资料：
- https://my.oschina.net/u/876354/blog/1637819

GoogLeNet的设计思想

GoogLeNet的核心是提出了Inception（开端）的概念与设计，并且逐步优化设计了4个版本。
1. Inception V1
2. Inception V2
3. Inception V3
4. Inception V4

Inception的核心思想

Inception的核心思想是堆叠
- 通过设计一个稀疏网络结构，但是能够产生稠密的数据；
- 既能增加神经网络表现，又能保证计算资源的使用效率。

Inception的设计思想示意图

Inception使用3个卷积 + 1个池化运算堆叠而成
1. 卷积： $(1 \times 1, 3 \times 3, 5 \times 5)$ ；
2. 池化： $(3 \times 3)$ 最大池化；
3. 每个卷积都使用ReLU函数激活；
4. 输出后叠加，会增加你输出的通道（深度）
Inception设计的分析
- 增加了网络的宽度：
  - 3个卷积运算构成的稀疏网络能够提取输入的每一个细节信息，增加提供网络；
- 降低过度拟合：
  - 池化运算减少空间大小，降低过度拟合（有点类似Dropout，只是不是随机的）
- 增加了网络对尺度的适应性：
  - 三个不同大小的卷积可以增加网络对尺度的适应性；

Inception V1

Inception V1设计

V1在上面Inception基础上，做了运算优化，增加了一个 $1 \times 1$ 的卷积运算。

Inception V1

理解堆叠的概念

GoogLeNet堆叠的示意图

$1 \times 1$ 卷积运算的分析
1. 可以提供一个ReLU运算，增加非线性性。
2. 因为上层叠加，所以每层的深度或者通道数会越来越大，可以增加 $1 \times 1$ 用来减少深度或者通道，这样在做 $3 \times 3$ 与 $5 \times 5$ 的卷积运算时，可以降低运算量。
注意：
- 注意理解增加一个卷积为什么会减少运算量？
  1. 假设输入： $100 \times 100 \times 128$ ，假设卷积核为： $5 \time 5 \times 256$ ，步长为1。
  2. 不增加卷积：
    - 权重参数： $128 \times 5 \times 5 \times 256 = 819200$ ；
  3. 增加卷积(层数假设为32)：
    - 权重参数： $128 \times 1 \times 1 \times 32 + 32 \times 5 \times 5 \times 256 = 204800$ ；
  4. 说明：
    - 增加 $1 \times 1$ 卷积后计算量是原来的 $\dfrac{1}{4}$ ；

GoogLeNet 22层网络结构

GoogLet 22层网络结构

Torch的GoogLeNet网络实现

Torch实现了GoogLeNet网络
- 从源代码与输出的结构看，官方的实现应该是Inception V1, 增加了BatchNorm2d，算是增强V1

from torchvision.models import GoogLeNet
GoogLeNet?

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�[1;31mDocstring:�[0m     
Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::

    import torch.nn as nn
    import torch.nn.functional as F

    class Model(nn.Module):
        def __init__(self):
            super(Model, self).__init__()
            self.conv1 = nn.Conv2d(1, 20, 5)
            self.conv2 = nn.Conv2d(20, 20, 5)

        def forward(self, x):
            x = F.relu(self.conv1(x))
            return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:`to`, etc.
�[1;31mInit docstring:�[0m Initializes internal Module state, shared by both nn.Module and ScriptModule.
�[1;31mFile:�[0m           c:\program files\python36\lib\site-packages\torchvision\models\googlenet.py
�[1;31mType:�[0m           type
�[1;31mSubclasses:�[0m     QuantizableGoogLeNet

aux_logits用来控制中间的两个分类输出
GoogLeNet网络的输入仍然是
- $224 \times 224 \times 3$

from torchvision.models import GoogLeNet
from torchsummary import summary
net = GoogLeNet()
# print(net)
# print("=========================================================")
# 输出网络结构
print(summary(net,input_size=(3, 224, 224), device='cpu'))
# print("=========================================================")
# # 输出网络结构
# print(summary(net.cuda(),input_size=(3, 224, 224)))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 112, 112]           9,408
       BatchNorm2d-2         [-1, 64, 112, 112]             128
       BasicConv2d-3         [-1, 64, 112, 112]               0
         MaxPool2d-4           [-1, 64, 56, 56]               0
            Conv2d-5           [-1, 64, 56, 56]           4,096
       BatchNorm2d-6           [-1, 64, 56, 56]             128
       BasicConv2d-7           [-1, 64, 56, 56]               0
            Conv2d-8          [-1, 192, 56, 56]         110,592
       BatchNorm2d-9          [-1, 192, 56, 56]             384
      BasicConv2d-10          [-1, 192, 56, 56]               0
        MaxPool2d-11          [-1, 192, 28, 28]               0
           Conv2d-12           [-1, 64, 28, 28]          12,288
      BatchNorm2d-13           [-1, 64, 28, 28]             128
      BasicConv2d-14           [-1, 64, 28, 28]               0
           Conv2d-15           [-1, 96, 28, 28]          18,432
      BatchNorm2d-16           [-1, 96, 28, 28]             192
      BasicConv2d-17           [-1, 96, 28, 28]               0
           Conv2d-18          [-1, 128, 28, 28]         110,592
      BatchNorm2d-19          [-1, 128, 28, 28]             256
      BasicConv2d-20          [-1, 128, 28, 28]               0
           Conv2d-21           [-1, 16, 28, 28]           3,072
      BatchNorm2d-22           [-1, 16, 28, 28]              32
      BasicConv2d-23           [-1, 16, 28, 28]               0
           Conv2d-24           [-1, 32, 28, 28]           4,608
      BatchNorm2d-25           [-1, 32, 28, 28]              64
      BasicConv2d-26           [-1, 32, 28, 28]               0
        MaxPool2d-27          [-1, 192, 28, 28]               0
           Conv2d-28           [-1, 32, 28, 28]           6,144
      BatchNorm2d-29           [-1, 32, 28, 28]              64
      BasicConv2d-30           [-1, 32, 28, 28]               0
        Inception-31          [-1, 256, 28, 28]               0
           Conv2d-32          [-1, 128, 28, 28]          32,768
      BatchNorm2d-33          [-1, 128, 28, 28]             256
      BasicConv2d-34          [-1, 128, 28, 28]               0
           Conv2d-35          [-1, 128, 28, 28]          32,768
      BatchNorm2d-36          [-1, 128, 28, 28]             256
      BasicConv2d-37          [-1, 128, 28, 28]               0
           Conv2d-38          [-1, 192, 28, 28]         221,184
      BatchNorm2d-39          [-1, 192, 28, 28]             384
      BasicConv2d-40          [-1, 192, 28, 28]               0
           Conv2d-41           [-1, 32, 28, 28]           8,192
      BatchNorm2d-42           [-1, 32, 28, 28]              64
      BasicConv2d-43           [-1, 32, 28, 28]               0
           Conv2d-44           [-1, 96, 28, 28]          27,648
      BatchNorm2d-45           [-1, 96, 28, 28]             192
      BasicConv2d-46           [-1, 96, 28, 28]               0
        MaxPool2d-47          [-1, 256, 28, 28]               0
           Conv2d-48           [-1, 64, 28, 28]          16,384
      BatchNorm2d-49           [-1, 64, 28, 28]             128
      BasicConv2d-50           [-1, 64, 28, 28]               0
        Inception-51          [-1, 480, 28, 28]               0
        MaxPool2d-52          [-1, 480, 14, 14]               0
           Conv2d-53          [-1, 192, 14, 14]          92,160
      BatchNorm2d-54          [-1, 192, 14, 14]             384
      BasicConv2d-55          [-1, 192, 14, 14]               0
           Conv2d-56           [-1, 96, 14, 14]          46,080
      BatchNorm2d-57           [-1, 96, 14, 14]             192
      BasicConv2d-58           [-1, 96, 14, 14]               0
           Conv2d-59          [-1, 208, 14, 14]         179,712
      BatchNorm2d-60          [-1, 208, 14, 14]             416
      BasicConv2d-61          [-1, 208, 14, 14]               0
           Conv2d-62           [-1, 16, 14, 14]           7,680
      BatchNorm2d-63           [-1, 16, 14, 14]              32
      BasicConv2d-64           [-1, 16, 14, 14]               0
           Conv2d-65           [-1, 48, 14, 14]           6,912
      BatchNorm2d-66           [-1, 48, 14, 14]              96
      BasicConv2d-67           [-1, 48, 14, 14]               0
        MaxPool2d-68          [-1, 480, 14, 14]               0
           Conv2d-69           [-1, 64, 14, 14]          30,720
      BatchNorm2d-70           [-1, 64, 14, 14]             128
      BasicConv2d-71           [-1, 64, 14, 14]               0
        Inception-72          [-1, 512, 14, 14]               0
           Conv2d-73            [-1, 128, 4, 4]          65,536
      BatchNorm2d-74            [-1, 128, 4, 4]             256
      BasicConv2d-75            [-1, 128, 4, 4]               0
           Linear-76                 [-1, 1024]       2,098,176
           Linear-77                 [-1, 1000]       1,025,000
     InceptionAux-78                 [-1, 1000]               0
           Conv2d-79          [-1, 160, 14, 14]          81,920
      BatchNorm2d-80          [-1, 160, 14, 14]             320
      BasicConv2d-81          [-1, 160, 14, 14]               0
           Conv2d-82          [-1, 112, 14, 14]          57,344
      BatchNorm2d-83          [-1, 112, 14, 14]             224
      BasicConv2d-84          [-1, 112, 14, 14]               0
           Conv2d-85          [-1, 224, 14, 14]         225,792
      BatchNorm2d-86          [-1, 224, 14, 14]             448
      BasicConv2d-87          [-1, 224, 14, 14]               0
           Conv2d-88           [-1, 24, 14, 14]          12,288
      BatchNorm2d-89           [-1, 24, 14, 14]              48
      BasicConv2d-90           [-1, 24, 14, 14]               0
           Conv2d-91           [-1, 64, 14, 14]          13,824
      BatchNorm2d-92           [-1, 64, 14, 14]             128
      BasicConv2d-93           [-1, 64, 14, 14]               0
        MaxPool2d-94          [-1, 512, 14, 14]               0
           Conv2d-95           [-1, 64, 14, 14]          32,768
      BatchNorm2d-96           [-1, 64, 14, 14]             128
      BasicConv2d-97           [-1, 64, 14, 14]               0
        Inception-98          [-1, 512, 14, 14]               0
           Conv2d-99          [-1, 128, 14, 14]          65,536
     BatchNorm2d-100          [-1, 128, 14, 14]             256
     BasicConv2d-101          [-1, 128, 14, 14]               0
          Conv2d-102          [-1, 128, 14, 14]          65,536
     BatchNorm2d-103          [-1, 128, 14, 14]             256
     BasicConv2d-104          [-1, 128, 14, 14]               0
          Conv2d-105          [-1, 256, 14, 14]         294,912
     BatchNorm2d-106          [-1, 256, 14, 14]             512
     BasicConv2d-107          [-1, 256, 14, 14]               0
          Conv2d-108           [-1, 24, 14, 14]          12,288
     BatchNorm2d-109           [-1, 24, 14, 14]              48
     BasicConv2d-110           [-1, 24, 14, 14]               0
          Conv2d-111           [-1, 64, 14, 14]          13,824
     BatchNorm2d-112           [-1, 64, 14, 14]             128
     BasicConv2d-113           [-1, 64, 14, 14]               0
       MaxPool2d-114          [-1, 512, 14, 14]               0
          Conv2d-115           [-1, 64, 14, 14]          32,768
     BatchNorm2d-116           [-1, 64, 14, 14]             128
     BasicConv2d-117           [-1, 64, 14, 14]               0
       Inception-118          [-1, 512, 14, 14]               0
          Conv2d-119          [-1, 112, 14, 14]          57,344
     BatchNorm2d-120          [-1, 112, 14, 14]             224
     BasicConv2d-121          [-1, 112, 14, 14]               0
          Conv2d-122          [-1, 144, 14, 14]          73,728
     BatchNorm2d-123          [-1, 144, 14, 14]             288
     BasicConv2d-124          [-1, 144, 14, 14]               0
          Conv2d-125          [-1, 288, 14, 14]         373,248
     BatchNorm2d-126          [-1, 288, 14, 14]             576
     BasicConv2d-127          [-1, 288, 14, 14]               0
          Conv2d-128           [-1, 32, 14, 14]          16,384
     BatchNorm2d-129           [-1, 32, 14, 14]              64
     BasicConv2d-130           [-1, 32, 14, 14]               0
          Conv2d-131           [-1, 64, 14, 14]          18,432
     BatchNorm2d-132           [-1, 64, 14, 14]             128
     BasicConv2d-133           [-1, 64, 14, 14]               0
       MaxPool2d-134          [-1, 512, 14, 14]               0
          Conv2d-135           [-1, 64, 14, 14]          32,768
     BatchNorm2d-136           [-1, 64, 14, 14]             128
     BasicConv2d-137           [-1, 64, 14, 14]               0
       Inception-138          [-1, 528, 14, 14]               0
          Conv2d-139            [-1, 128, 4, 4]          67,584
     BatchNorm2d-140            [-1, 128, 4, 4]             256
     BasicConv2d-141            [-1, 128, 4, 4]               0
          Linear-142                 [-1, 1024]       2,098,176
          Linear-143                 [-1, 1000]       1,025,000
    InceptionAux-144                 [-1, 1000]               0
          Conv2d-145          [-1, 256, 14, 14]         135,168
     BatchNorm2d-146          [-1, 256, 14, 14]             512
     BasicConv2d-147          [-1, 256, 14, 14]               0
          Conv2d-148          [-1, 160, 14, 14]          84,480
     BatchNorm2d-149          [-1, 160, 14, 14]             320
     BasicConv2d-150          [-1, 160, 14, 14]               0
          Conv2d-151          [-1, 320, 14, 14]         460,800
     BatchNorm2d-152          [-1, 320, 14, 14]             640
     BasicConv2d-153          [-1, 320, 14, 14]               0
          Conv2d-154           [-1, 32, 14, 14]          16,896
     BatchNorm2d-155           [-1, 32, 14, 14]              64
     BasicConv2d-156           [-1, 32, 14, 14]               0
          Conv2d-157          [-1, 128, 14, 14]          36,864
     BatchNorm2d-158          [-1, 128, 14, 14]             256
     BasicConv2d-159          [-1, 128, 14, 14]               0
       MaxPool2d-160          [-1, 528, 14, 14]               0
          Conv2d-161          [-1, 128, 14, 14]          67,584
     BatchNorm2d-162          [-1, 128, 14, 14]             256
     BasicConv2d-163          [-1, 128, 14, 14]               0
       Inception-164          [-1, 832, 14, 14]               0
       MaxPool2d-165            [-1, 832, 7, 7]               0
          Conv2d-166            [-1, 256, 7, 7]         212,992
     BatchNorm2d-167            [-1, 256, 7, 7]             512
     BasicConv2d-168            [-1, 256, 7, 7]               0
          Conv2d-169            [-1, 160, 7, 7]         133,120
     BatchNorm2d-170            [-1, 160, 7, 7]             320
     BasicConv2d-171            [-1, 160, 7, 7]               0
          Conv2d-172            [-1, 320, 7, 7]         460,800
     BatchNorm2d-173            [-1, 320, 7, 7]             640
     BasicConv2d-174            [-1, 320, 7, 7]               0
          Conv2d-175             [-1, 32, 7, 7]          26,624
     BatchNorm2d-176             [-1, 32, 7, 7]              64
     BasicConv2d-177             [-1, 32, 7, 7]               0
          Conv2d-178            [-1, 128, 7, 7]          36,864
     BatchNorm2d-179            [-1, 128, 7, 7]             256
     BasicConv2d-180            [-1, 128, 7, 7]               0
       MaxPool2d-181            [-1, 832, 7, 7]               0
          Conv2d-182            [-1, 128, 7, 7]         106,496
     BatchNorm2d-183            [-1, 128, 7, 7]             256
     BasicConv2d-184            [-1, 128, 7, 7]               0
       Inception-185            [-1, 832, 7, 7]               0
          Conv2d-186            [-1, 384, 7, 7]         319,488
     BatchNorm2d-187            [-1, 384, 7, 7]             768
     BasicConv2d-188            [-1, 384, 7, 7]               0
          Conv2d-189            [-1, 192, 7, 7]         159,744
     BatchNorm2d-190            [-1, 192, 7, 7]             384
     BasicConv2d-191            [-1, 192, 7, 7]               0
          Conv2d-192            [-1, 384, 7, 7]         663,552
     BatchNorm2d-193            [-1, 384, 7, 7]             768
     BasicConv2d-194            [-1, 384, 7, 7]               0
          Conv2d-195             [-1, 48, 7, 7]          39,936
     BatchNorm2d-196             [-1, 48, 7, 7]              96
     BasicConv2d-197             [-1, 48, 7, 7]               0
          Conv2d-198            [-1, 128, 7, 7]          55,296
     BatchNorm2d-199            [-1, 128, 7, 7]             256
     BasicConv2d-200            [-1, 128, 7, 7]               0
       MaxPool2d-201            [-1, 832, 7, 7]               0
          Conv2d-202            [-1, 128, 7, 7]         106,496
     BatchNorm2d-203            [-1, 128, 7, 7]             256
     BasicConv2d-204            [-1, 128, 7, 7]               0
       Inception-205           [-1, 1024, 7, 7]               0
AdaptiveAvgPool2d-206           [-1, 1024, 1, 1]               0
         Dropout-207                 [-1, 1024]               0
          Linear-208                 [-1, 1000]       1,025,000
================================================================
Total params: 13,004,888
Trainable params: 13,004,888
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 94.25
Params size (MB): 49.61
Estimated Total Size (MB): 144.43
----------------------------------------------------------------
None

手工实现GoogLeNet网络结构

GoogLeNet施工参数细节

下面略掉了ReLU与BatchNorm2d，这两个操作对数据大小与格式没有影响。

输入图像
- $\color{red}{3 \times 224 \times 224}$
- 要求图像去中心化处理（均值化为0处理）
- 格式： $NCHW$
卷积层-1
- 输入: $\color{red}{3 \times 224 \times 224}$
- 卷积核： $kernel=(7 \times 7), stride = 2, channels = 64, padding=3$
- 输出： $\color{red}{64 \times 112 \times 112}$
- 最大池化核： $kernel=(3 \times 3), stride = 2$
- 输出： $\color{red}{64 \times 56 \times 56}$
卷积层-2
- 输入： $\color{red}{64 \times 56 \times 56}$
- 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
- 输出： $\color{red}{64 \times 56 \times 56}$
- 卷积核： $kernel=(3 \times 3), stride = 1, channels = 192, padding=1$
- 输出： $\color{red}{192 \times 56 \times 56}$
- 最大池化核： $kernel=(3 \times 3), stride = 2$
- 输出： $\color{red}{192 \times 28 \times 28}$
Inception(3a)
- 输入： $\color{red}{192 \times 28 \times 28}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 28 \times 28}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 96, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 128, padding=1$
  - 输出： $\color{red}{128 \times 28 \times 28}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 16, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 32, padding=1$
  - 输出： $\color{red}{32 \times 28 \times 28}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 32, padding=0$
  - 输出： $\color{red}{32 \times 28 \times 28}$
- 输出：
  - $256 = 64 + 128 + 32 + 32$
Inception(3b)
- 输入： $\color{red}{256 \times 28 \times 28}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 输出： $\color{red}{128 \times 28 \times 28}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 192, padding=1$
  - 输出： $\color{red}{192 \times 28 \times 28}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 32, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 96, padding=1$
  - 输出： $\color{red}{96 \times 28 \times 28}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 28 \times 28}$
- 输出：
  - $480 = 128 + 192 + 96 + 64$
- 最大池化核： $kernel=(3 \times 3), stride = 2, padding=0$
- 输出： $\color{red}{480 \times 14 \times 14}$
Inception(4a)
- 输入： $\color{red}{480 \times 14 \times 14}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 192, padding=0$
  - 输出： $\color{red}{192 \times 14 \times 14}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 96, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 208, padding=1$
  - 输出： $\color{red}{208 \times 14 \times 14}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 16, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 48, padding=1$
  - 输出： $\color{red}{48 \times 14 \times 14}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 输出：
  - $512 = 192 + 208 + 48 + 64$
辅助分类器-1
- 输入： $\color{red}{512 \times 14 \times 14}$
- AdaptiveAvgPool2d： $output_size=(4 \times 4)$
- 输出： $\color{red}{512 \times 4 \times 4}$
- 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
- 输出： $\color{red}{128 \times 4 \times 4 = 2048}$
- 全连接： $2048 \to 1024$
- 全连接： $1024 \to n$ ， $n$ 是分类的类别；
- 输出： $\color{red}{n}$
Inception(4b)
- 输入： $\color{red}{512 \times 14 \times 14}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 160, padding=0$
  - 输出： $\color{red}{160 \times 14 \times 14}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 112, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 224, padding=1$
  - 输出： $\color{red}{224 \times 14 \times 14}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 24, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 64, padding=1$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 输出：
  - $512 = 160 + 224 + 64 + 64$
Inception(4c)
- 输入： $\color{red}{512 \times 14 \times 14}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 输出： $\color{red}{128 \times 14 \times 14}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 256, padding=1$
  - 输出： $\color{red}{256 \times 14 \times 14}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 24, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 64, padding=1$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 输出：
  - $512 = 128 + 256 + 64 + 64$
Inception(4d)
- 输入： $\color{red}{512 \times 14 \times 14}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 112, padding=0$
  - 输出： $\color{red}{112 \times 14 \times 14}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 144, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 288, padding=1$
  - 输出： $\color{red}{288 \times 14 \times 14}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 32, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 64, padding=1$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 64, padding=0$
  - 输出： $\color{red}{64 \times 14 \times 14}$
- 输出：
  - $528 = 112 + 288 + 64 + 64$
辅助分类器-2
- 输入： $\color{red}{528 \times 14 \times 14}$
- AdaptiveAvgPool2d： $output_size=(4 \times 4)$
- 输出： $\color{red}{512 \times 4 \times 4}$
- 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
- 输出： $\color{red}{128 \times 4 \times 4 = 2048}$
- 全连接： $2048 \to 1024$
- 全连接： $1024 \to n$ ， $n$ 是分类的类别；
- 输出： $\color{red}{n}$
Inception(4e)
- 输入： $\color{red}{528 \times 14 \times 14}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 256, padding=0$
  - 输出： $\color{red}{256 \times 14 \times 14}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 160, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 320, padding=1$
  - 输出： $\color{red}{320 \times 14 \times 14}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 32, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 128, padding=1$
  - 输出： $\color{red}{128 \times 14 \times 14}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 输出： $\color{red}{128 \times 14 \times 14}$
- 输出：
  - $832 = 256 + 320 + 128 + 128$
- 最大池化核： $kernel=(2 \times 2), stride = 2, padding=0$
- 输出： $\color{red}{832 \times 7 \times 7}$
Inception(5a)
- 输入： $\color{red}{832 \times 7 \times 7}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 256, padding=0$
  - 输出： $\color{red}{256 \times 7 \times 7}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 160, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 320, padding=1$
  - 输出： $\color{red}{320 \times 7 \times 7}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 32, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 128, padding=1$
  - 输出： $\color{red}{128 \times 7 \times 7}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 输出： $\color{red}{128 \times 7 \times 7}$
- 输出：
  - $832 = 256 + 320 + 128 + 128$

Inception(5b)
- 输入： $\color{red}{832 \times 7 \times 7}$
- 分支1
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 384, padding=0$
  - 输出： $\color{red}{256 \times 7 \times 7}$
- 分支2
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 192, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 384, padding=1$
  - 输出： $\color{red}{320 \times 7 \times 7}$
- 分支3
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 48, padding=0$
  - 卷积核： $kernel=(3 \times 3), stride = 1, channels = 128, padding=1$
  - 输出： $\color{red}{128 \times 7 \times 7}$
- 分支4
  - 池化核： $kernel=(3 \times 3), stride = 1, padding=1$
  - 卷积核： $kernel=(1 \times 1), stride = 1, channels = 128, padding=0$
  - 输出： $\color{red}{128 \times 7 \times 7}$
- 输出：
  - $1024 = 384 + 384 + 128 + 128$
- AdaptiveAvgPool2d：
- 输出： $\color{red}{1024 \times 1 \times 1}$
全连接层
- - $n$ 表示类别数

基本的卷积封装

Inception V1的增强核心是BatchNorm2d，每个卷积运算都会包含如下两个运算，所以做一个封装
1. BatchNorm2d
2. relu

import torch
from torch.nn import Conv2d, BatchNorm2d, Module, ReLU

class YQConv2d(Module):
    
    # 构造器，初始化Conv2d， BatchNorm2d与ReLU
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
        super(YQConv2d, self).__init__()
        # 卷积层
        self.conv = Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
        # BatchNorm2d
        self.bn = BatchNorm2d(out_channels, eps=0.001)
        # 激活
        self.relu = ReLU(inplace=True)

    def forward(self, x):
        y_ = self.conv(x)
        y_ = self.bn(y_)
        y_ = self.relu(y_)
        return y_

卷积的封装的结构

from torchsummary import summary
conv = YQConv2d(3, 64)
# 输出网络结构
print(summary(conv,input_size=(3, 224, 224), device='cpu'))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 224, 224]             192
       BatchNorm2d-2         [-1, 64, 224, 224]             128
              ReLU-3         [-1, 64, 224, 224]               0
================================================================
Total params: 320
Trainable params: 320
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 73.50
Params size (MB): 0.00
Estimated Total Size (MB): 74.08
----------------------------------------------------------------
None

Inception封装

使用上面的卷积作为核心单元，封装Inception

Inception结构

import torch
from torch.nn import Module, Sequential, MaxPool2d
class YQInception(Module):
    # 构造器设置4个分支的参数(输出的通道数)
    def __init__(self, in_channels, ch1x1, ch3x3_1, ch3x3_2, ch5x5_1, ch5x5_2, ch_pool):
        super(YQInception, self).__init__()
        # YQConv2d(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
        # 分支-1
        self.branch_1 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch1x1, kernel_size=1, stride=1, padding=0)
        )
        # 分支-2
        self.branch_2 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch3x3_1, kernel_size=1, stride=1, padding=0),
            YQConv2d(in_channels=ch3x3_1,     out_channels=ch3x3_2, kernel_size=3, stride=1, padding=1)
        )
        # 分支-3
        self.branch_3 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch5x5_1, kernel_size=1, stride=1, padding=0),
            YQConv2d(in_channels=ch5x5_1,     out_channels=ch5x5_2, kernel_size=3, stride=1, padding=1)
        )
        # 分支-4
        self.branch_4 = Sequential(
            MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
            YQConv2d(in_channels=in_channels, out_channels=ch_pool, kernel_size=1, stride=1, padding=0)
        )
    
    def forward(self, x):
        b_y1 = self.branch_1(x)
        b_y2 = self.branch_2(x)
        b_y3 = self.branch_3(x)
        b_y4 = self.branch_4(x)
        
        y_ = torch.cat([b_y1, b_y2, b_y3, b_y4],  1)   # 1 表示按照列链接，就是行堆叠在一起。
        return y_

输出下网络结构

from torchsummary import summary
conv = YQInception(3, 64, 96, 128, 16, 32, 32)   # 最后输出的深度是64 + 128 + 32 + 32
# 输出网络结构
print(summary(conv,input_size=(3, 224, 224), device='cpu'))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 224, 224]             192
       BatchNorm2d-2         [-1, 64, 224, 224]             128
              ReLU-3         [-1, 64, 224, 224]               0
          YQConv2d-4         [-1, 64, 224, 224]               0
            Conv2d-5         [-1, 96, 224, 224]             288
       BatchNorm2d-6         [-1, 96, 224, 224]             192
              ReLU-7         [-1, 96, 224, 224]               0
          YQConv2d-8         [-1, 96, 224, 224]               0
            Conv2d-9        [-1, 128, 224, 224]         110,592
      BatchNorm2d-10        [-1, 128, 224, 224]             256
             ReLU-11        [-1, 128, 224, 224]               0
         YQConv2d-12        [-1, 128, 224, 224]               0
           Conv2d-13         [-1, 16, 224, 224]              48
      BatchNorm2d-14         [-1, 16, 224, 224]              32
             ReLU-15         [-1, 16, 224, 224]               0
         YQConv2d-16         [-1, 16, 224, 224]               0
           Conv2d-17         [-1, 32, 224, 224]           4,608
      BatchNorm2d-18         [-1, 32, 224, 224]              64
             ReLU-19         [-1, 32, 224, 224]               0
         YQConv2d-20         [-1, 32, 224, 224]               0
        MaxPool2d-21          [-1, 3, 224, 224]               0
           Conv2d-22         [-1, 32, 224, 224]              96
      BatchNorm2d-23         [-1, 32, 224, 224]              64
             ReLU-24         [-1, 32, 224, 224]               0
         YQConv2d-25         [-1, 32, 224, 224]               0
================================================================
Total params: 116,560
Trainable params: 116,560
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 564.65
Params size (MB): 0.44
Estimated Total Size (MB): 565.67
----------------------------------------------------------------
None

辅助(auxiliary)分类器封装

辅助分类器一共可训练层是3层.

辅助分类器

import torch
from torch.nn import Module, AdaptiveAvgPool2d, Linear, ReLU, Dropout

class YQAuxClassifier(Module):
    def __init__(self, in_channels, num_classes):
        super(YQAuxClassifier, self).__init__()
        # 池化/卷积/全连接/全连接
        self.pool = AdaptiveAvgPool2d((4, 4))
        self.conv = YQConv2d(in_channels=in_channels, out_channels=128, kernel_size=1, stride=1, padding=0)
        self.fc_1 = Linear(2048, 1024)
        self.relu = ReLU(inplace=True)
        self.drop = Dropout(p=0.7, inplace=False)
        self.fc_2 = Linear(1024, num_classes)
    
    def forward(self, x):
        y_ = self.pool(x)
        y_ = self.conv(y_)
        y_ = torch.flatten(y_, 1)    # 卷积到连接层的数据维度转换。
        y_ = self.fc_1(y_)
        y_ = self.relu(y_)
        y_ = self.drop(y_)
        y_ = self.fc_2(y_)
        return y_

辅助分类器可视化

from torchsummary import summary
classifier = YQAuxClassifier(512, 10)   # 最后输出的深度是64 + 128 + 32 + 32
# 输出网络结构
print(summary(classifier, input_size=(512, 14, 14), device='cpu'))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
 AdaptiveAvgPool2d-1            [-1, 512, 4, 4]               0
            Conv2d-2            [-1, 128, 4, 4]          65,536
       BatchNorm2d-3            [-1, 128, 4, 4]             256
              ReLU-4            [-1, 128, 4, 4]               0
          YQConv2d-5            [-1, 128, 4, 4]               0
            Linear-6                 [-1, 1024]       2,098,176
              ReLU-7                 [-1, 1024]               0
           Dropout-8                 [-1, 1024]               0
            Linear-9                   [-1, 10]          10,250
================================================================
Total params: 2,174,218
Trainable params: 2,174,218
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.38
Forward/backward pass size (MB): 0.15
Params size (MB): 8.29
Estimated Total Size (MB): 8.83
----------------------------------------------------------------
None

GoogLeNet实现

按照下面几个阶段实现：
1. 前面几个卷积
2. Inception（包含两个辅助分类器）
3. 分类器

import torch
from torch.nn import Module, AdaptiveAvgPool2d, Linear, Dropout, MaxPool2d
class YQGoogLeNet(Module):
    def __init__(self, num_classes=1000):
        super(YQGoogLeNet, self).__init__()
        # 定义层
        self.conv_1  = YQConv2d(in_channels=3,  out_channels=64,  kernel_size=7, stride=2, padding=3)
        self.pool_1  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.conv_2  = YQConv2d(in_channels=64, out_channels=64,  kernel_size=1, stride=1, padding=0)
        
        self.conv_3  = YQConv2d(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1)
        self.pool_2  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.ince_3a = YQInception(192, 64, 96, 128, 16, 32, 32)
        self.ince_3b = YQInception(256, 128, 128, 192, 32, 96, 64)
        self.pool_3  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.ince_4a = YQInception(480, 192, 96, 208, 16, 48, 64)
        
        self.ince_4b = YQInception(512, 160, 112, 224, 24, 64, 64)
        self.ince_4c = YQInception(512, 128, 128, 256, 24, 64, 64)
        self.ince_4d = YQInception(512, 112, 144, 288, 32, 64, 64)
        self.ince_4e = YQInception(528, 256, 160, 320, 32, 128, 128)
        self.pool_4  = MaxPool2d(2, stride=2, ceil_mode=True)
        
        self.ince_5a = YQInception(832, 256, 160, 320, 32, 128, 128)
        self.ince_5b = YQInception(832, 384, 192, 384, 48, 128, 128)
        
        # 两个辅助分类器
        self.auxi_1  = YQAuxClassifier(512, num_classes)
        self.auxi_2  = YQAuxClassifier(528, num_classes)
        
        # 结尾的分类层
        self.pool_5  = AdaptiveAvgPool2d((1, 1))
        self.drop = Dropout(0.2)
        self.full = Linear(1024, num_classes)
        
    def forward(self, x):
        # -----------------------------
        y_ = self.conv_1(x)
        y_ = self.pool_1(y_)
        # -----------------------------
        y_ = self.conv_2(y_)
        y_ = self.conv_3(y_)
        y_ = self.pool_2(y_)
        # -----------------------------
        y_ = self.ince_3a(y_)
        y_ = self.ince_3b(y_)
        y_ = self.pool_3(y_)
        # -----------------------------
        y_ = self.ince_4a(y_)
        # -----------------------------
        a1 = self.auxi_1(y_)
        # -----------------------------
        y_ = self.ince_4b(y_)
        y_ = self.ince_4c(y_)
        y_ = self.ince_4d(y_)
        # -----------------------------
        a2 = self.auxi_2(y_)
        # -----------------------------
        y_ = self.ince_4e(y_)
        y_ = self.pool_4(y_)
        # -----------------------------
        y_ = self.ince_5a(y_)
        y_ = self.ince_5b(y_)
        # -----------------------------
        y_ = self.pool_5(y_)
        y_ = torch.flatten(y_, 1)
        y_ = self.drop(y_)
        y_ = self.full(y_)
        
        return y_, a1, a2

GoogLeNet网络结构

from torchsummary import summary
net = YQGoogLeNet(1000) 
# 输出网络结构
print(summary(net,input_size=(3, 244, 244), device='cpu'))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 122, 122]           9,408
       BatchNorm2d-2         [-1, 64, 122, 122]             128
              ReLU-3         [-1, 64, 122, 122]               0
          YQConv2d-4         [-1, 64, 122, 122]               0
         MaxPool2d-5           [-1, 64, 61, 61]               0
            Conv2d-6           [-1, 64, 61, 61]           4,096
       BatchNorm2d-7           [-1, 64, 61, 61]             128
              ReLU-8           [-1, 64, 61, 61]               0
          YQConv2d-9           [-1, 64, 61, 61]               0
           Conv2d-10          [-1, 192, 61, 61]         110,592
      BatchNorm2d-11          [-1, 192, 61, 61]             384
             ReLU-12          [-1, 192, 61, 61]               0
         YQConv2d-13          [-1, 192, 61, 61]               0
        MaxPool2d-14          [-1, 192, 30, 30]               0
           Conv2d-15           [-1, 64, 30, 30]          12,288
      BatchNorm2d-16           [-1, 64, 30, 30]             128
             ReLU-17           [-1, 64, 30, 30]               0
         YQConv2d-18           [-1, 64, 30, 30]               0
           Conv2d-19           [-1, 96, 30, 30]          18,432
      BatchNorm2d-20           [-1, 96, 30, 30]             192
             ReLU-21           [-1, 96, 30, 30]               0
         YQConv2d-22           [-1, 96, 30, 30]               0
           Conv2d-23          [-1, 128, 30, 30]         110,592
      BatchNorm2d-24          [-1, 128, 30, 30]             256
             ReLU-25          [-1, 128, 30, 30]               0
         YQConv2d-26          [-1, 128, 30, 30]               0
           Conv2d-27           [-1, 16, 30, 30]           3,072
      BatchNorm2d-28           [-1, 16, 30, 30]              32
             ReLU-29           [-1, 16, 30, 30]               0
         YQConv2d-30           [-1, 16, 30, 30]               0
           Conv2d-31           [-1, 32, 30, 30]           4,608
      BatchNorm2d-32           [-1, 32, 30, 30]              64
             ReLU-33           [-1, 32, 30, 30]               0
         YQConv2d-34           [-1, 32, 30, 30]               0
        MaxPool2d-35          [-1, 192, 30, 30]               0
           Conv2d-36           [-1, 32, 30, 30]           6,144
      BatchNorm2d-37           [-1, 32, 30, 30]              64
             ReLU-38           [-1, 32, 30, 30]               0
         YQConv2d-39           [-1, 32, 30, 30]               0
      YQInception-40          [-1, 256, 30, 30]               0
           Conv2d-41          [-1, 128, 30, 30]          32,768
      BatchNorm2d-42          [-1, 128, 30, 30]             256
             ReLU-43          [-1, 128, 30, 30]               0
         YQConv2d-44          [-1, 128, 30, 30]               0
           Conv2d-45          [-1, 128, 30, 30]          32,768
      BatchNorm2d-46          [-1, 128, 30, 30]             256
             ReLU-47          [-1, 128, 30, 30]               0
         YQConv2d-48          [-1, 128, 30, 30]               0
           Conv2d-49          [-1, 192, 30, 30]         221,184
      BatchNorm2d-50          [-1, 192, 30, 30]             384
             ReLU-51          [-1, 192, 30, 30]               0
         YQConv2d-52          [-1, 192, 30, 30]               0
           Conv2d-53           [-1, 32, 30, 30]           8,192
      BatchNorm2d-54           [-1, 32, 30, 30]              64
             ReLU-55           [-1, 32, 30, 30]               0
         YQConv2d-56           [-1, 32, 30, 30]               0
           Conv2d-57           [-1, 96, 30, 30]          27,648
      BatchNorm2d-58           [-1, 96, 30, 30]             192
             ReLU-59           [-1, 96, 30, 30]               0
         YQConv2d-60           [-1, 96, 30, 30]               0
        MaxPool2d-61          [-1, 256, 30, 30]               0
           Conv2d-62           [-1, 64, 30, 30]          16,384
      BatchNorm2d-63           [-1, 64, 30, 30]             128
             ReLU-64           [-1, 64, 30, 30]               0
         YQConv2d-65           [-1, 64, 30, 30]               0
      YQInception-66          [-1, 480, 30, 30]               0
        MaxPool2d-67          [-1, 480, 15, 15]               0
           Conv2d-68          [-1, 192, 15, 15]          92,160
      BatchNorm2d-69          [-1, 192, 15, 15]             384
             ReLU-70          [-1, 192, 15, 15]               0
         YQConv2d-71          [-1, 192, 15, 15]               0
           Conv2d-72           [-1, 96, 15, 15]          46,080
      BatchNorm2d-73           [-1, 96, 15, 15]             192
             ReLU-74           [-1, 96, 15, 15]               0
         YQConv2d-75           [-1, 96, 15, 15]               0
           Conv2d-76          [-1, 208, 15, 15]         179,712
      BatchNorm2d-77          [-1, 208, 15, 15]             416
             ReLU-78          [-1, 208, 15, 15]               0
         YQConv2d-79          [-1, 208, 15, 15]               0
           Conv2d-80           [-1, 16, 15, 15]           7,680
      BatchNorm2d-81           [-1, 16, 15, 15]              32
             ReLU-82           [-1, 16, 15, 15]               0
         YQConv2d-83           [-1, 16, 15, 15]               0
           Conv2d-84           [-1, 48, 15, 15]           6,912
      BatchNorm2d-85           [-1, 48, 15, 15]              96
             ReLU-86           [-1, 48, 15, 15]               0
         YQConv2d-87           [-1, 48, 15, 15]               0
        MaxPool2d-88          [-1, 480, 15, 15]               0
           Conv2d-89           [-1, 64, 15, 15]          30,720
      BatchNorm2d-90           [-1, 64, 15, 15]             128
             ReLU-91           [-1, 64, 15, 15]               0
         YQConv2d-92           [-1, 64, 15, 15]               0
      YQInception-93          [-1, 512, 15, 15]               0
AdaptiveAvgPool2d-94            [-1, 512, 4, 4]               0
           Conv2d-95            [-1, 128, 4, 4]          65,536
      BatchNorm2d-96            [-1, 128, 4, 4]             256
             ReLU-97            [-1, 128, 4, 4]               0
         YQConv2d-98            [-1, 128, 4, 4]               0
           Linear-99                 [-1, 1024]       2,098,176
            ReLU-100                 [-1, 1024]               0
         Dropout-101                 [-1, 1024]               0
          Linear-102                 [-1, 1000]       1,025,000
 YQAuxClassifier-103                 [-1, 1000]               0
          Conv2d-104          [-1, 160, 15, 15]          81,920
     BatchNorm2d-105          [-1, 160, 15, 15]             320
            ReLU-106          [-1, 160, 15, 15]               0
        YQConv2d-107          [-1, 160, 15, 15]               0
          Conv2d-108          [-1, 112, 15, 15]          57,344
     BatchNorm2d-109          [-1, 112, 15, 15]             224
            ReLU-110          [-1, 112, 15, 15]               0
        YQConv2d-111          [-1, 112, 15, 15]               0
          Conv2d-112          [-1, 224, 15, 15]         225,792
     BatchNorm2d-113          [-1, 224, 15, 15]             448
            ReLU-114          [-1, 224, 15, 15]               0
        YQConv2d-115          [-1, 224, 15, 15]               0
          Conv2d-116           [-1, 24, 15, 15]          12,288
     BatchNorm2d-117           [-1, 24, 15, 15]              48
            ReLU-118           [-1, 24, 15, 15]               0
        YQConv2d-119           [-1, 24, 15, 15]               0
          Conv2d-120           [-1, 64, 15, 15]          13,824
     BatchNorm2d-121           [-1, 64, 15, 15]             128
            ReLU-122           [-1, 64, 15, 15]               0
        YQConv2d-123           [-1, 64, 15, 15]               0
       MaxPool2d-124          [-1, 512, 15, 15]               0
          Conv2d-125           [-1, 64, 15, 15]          32,768
     BatchNorm2d-126           [-1, 64, 15, 15]             128
            ReLU-127           [-1, 64, 15, 15]               0
        YQConv2d-128           [-1, 64, 15, 15]               0
     YQInception-129          [-1, 512, 15, 15]               0
          Conv2d-130          [-1, 128, 15, 15]          65,536
     BatchNorm2d-131          [-1, 128, 15, 15]             256
            ReLU-132          [-1, 128, 15, 15]               0
        YQConv2d-133          [-1, 128, 15, 15]               0
          Conv2d-134          [-1, 128, 15, 15]          65,536
     BatchNorm2d-135          [-1, 128, 15, 15]             256
            ReLU-136          [-1, 128, 15, 15]               0
        YQConv2d-137          [-1, 128, 15, 15]               0
          Conv2d-138          [-1, 256, 15, 15]         294,912
     BatchNorm2d-139          [-1, 256, 15, 15]             512
            ReLU-140          [-1, 256, 15, 15]               0
        YQConv2d-141          [-1, 256, 15, 15]               0
          Conv2d-142           [-1, 24, 15, 15]          12,288
     BatchNorm2d-143           [-1, 24, 15, 15]              48
            ReLU-144           [-1, 24, 15, 15]               0
        YQConv2d-145           [-1, 24, 15, 15]               0
          Conv2d-146           [-1, 64, 15, 15]          13,824
     BatchNorm2d-147           [-1, 64, 15, 15]             128
            ReLU-148           [-1, 64, 15, 15]               0
        YQConv2d-149           [-1, 64, 15, 15]               0
       MaxPool2d-150          [-1, 512, 15, 15]               0
          Conv2d-151           [-1, 64, 15, 15]          32,768
     BatchNorm2d-152           [-1, 64, 15, 15]             128
            ReLU-153           [-1, 64, 15, 15]               0
        YQConv2d-154           [-1, 64, 15, 15]               0
     YQInception-155          [-1, 512, 15, 15]               0
          Conv2d-156          [-1, 112, 15, 15]          57,344
     BatchNorm2d-157          [-1, 112, 15, 15]             224
            ReLU-158          [-1, 112, 15, 15]               0
        YQConv2d-159          [-1, 112, 15, 15]               0
          Conv2d-160          [-1, 144, 15, 15]          73,728
     BatchNorm2d-161          [-1, 144, 15, 15]             288
            ReLU-162          [-1, 144, 15, 15]               0
        YQConv2d-163          [-1, 144, 15, 15]               0
          Conv2d-164          [-1, 288, 15, 15]         373,248
     BatchNorm2d-165          [-1, 288, 15, 15]             576
            ReLU-166          [-1, 288, 15, 15]               0
        YQConv2d-167          [-1, 288, 15, 15]               0
          Conv2d-168           [-1, 32, 15, 15]          16,384
     BatchNorm2d-169           [-1, 32, 15, 15]              64
            ReLU-170           [-1, 32, 15, 15]               0
        YQConv2d-171           [-1, 32, 15, 15]               0
          Conv2d-172           [-1, 64, 15, 15]          18,432
     BatchNorm2d-173           [-1, 64, 15, 15]             128
            ReLU-174           [-1, 64, 15, 15]               0
        YQConv2d-175           [-1, 64, 15, 15]               0
       MaxPool2d-176          [-1, 512, 15, 15]               0
          Conv2d-177           [-1, 64, 15, 15]          32,768
     BatchNorm2d-178           [-1, 64, 15, 15]             128
            ReLU-179           [-1, 64, 15, 15]               0
        YQConv2d-180           [-1, 64, 15, 15]               0
     YQInception-181          [-1, 528, 15, 15]               0
AdaptiveAvgPool2d-182            [-1, 528, 4, 4]               0
          Conv2d-183            [-1, 128, 4, 4]          67,584
     BatchNorm2d-184            [-1, 128, 4, 4]             256
            ReLU-185            [-1, 128, 4, 4]               0
        YQConv2d-186            [-1, 128, 4, 4]               0
          Linear-187                 [-1, 1024]       2,098,176
            ReLU-188                 [-1, 1024]               0
         Dropout-189                 [-1, 1024]               0
          Linear-190                 [-1, 1000]       1,025,000
 YQAuxClassifier-191                 [-1, 1000]               0
          Conv2d-192          [-1, 256, 15, 15]         135,168
     BatchNorm2d-193          [-1, 256, 15, 15]             512
            ReLU-194          [-1, 256, 15, 15]               0
        YQConv2d-195          [-1, 256, 15, 15]               0
          Conv2d-196          [-1, 160, 15, 15]          84,480
     BatchNorm2d-197          [-1, 160, 15, 15]             320
            ReLU-198          [-1, 160, 15, 15]               0
        YQConv2d-199          [-1, 160, 15, 15]               0
          Conv2d-200          [-1, 320, 15, 15]         460,800
     BatchNorm2d-201          [-1, 320, 15, 15]             640
            ReLU-202          [-1, 320, 15, 15]               0
        YQConv2d-203          [-1, 320, 15, 15]               0
          Conv2d-204           [-1, 32, 15, 15]          16,896
     BatchNorm2d-205           [-1, 32, 15, 15]              64
            ReLU-206           [-1, 32, 15, 15]               0
        YQConv2d-207           [-1, 32, 15, 15]               0
          Conv2d-208          [-1, 128, 15, 15]          36,864
     BatchNorm2d-209          [-1, 128, 15, 15]             256
            ReLU-210          [-1, 128, 15, 15]               0
        YQConv2d-211          [-1, 128, 15, 15]               0
       MaxPool2d-212          [-1, 528, 15, 15]               0
          Conv2d-213          [-1, 128, 15, 15]          67,584
     BatchNorm2d-214          [-1, 128, 15, 15]             256
            ReLU-215          [-1, 128, 15, 15]               0
        YQConv2d-216          [-1, 128, 15, 15]               0
     YQInception-217          [-1, 832, 15, 15]               0
       MaxPool2d-218            [-1, 832, 8, 8]               0
          Conv2d-219            [-1, 256, 8, 8]         212,992
     BatchNorm2d-220            [-1, 256, 8, 8]             512
            ReLU-221            [-1, 256, 8, 8]               0
        YQConv2d-222            [-1, 256, 8, 8]               0
          Conv2d-223            [-1, 160, 8, 8]         133,120
     BatchNorm2d-224            [-1, 160, 8, 8]             320
            ReLU-225            [-1, 160, 8, 8]               0
        YQConv2d-226            [-1, 160, 8, 8]               0
          Conv2d-227            [-1, 320, 8, 8]         460,800
     BatchNorm2d-228            [-1, 320, 8, 8]             640
            ReLU-229            [-1, 320, 8, 8]               0
        YQConv2d-230            [-1, 320, 8, 8]               0
          Conv2d-231             [-1, 32, 8, 8]          26,624
     BatchNorm2d-232             [-1, 32, 8, 8]              64
            ReLU-233             [-1, 32, 8, 8]               0
        YQConv2d-234             [-1, 32, 8, 8]               0
          Conv2d-235            [-1, 128, 8, 8]          36,864
     BatchNorm2d-236            [-1, 128, 8, 8]             256
            ReLU-237            [-1, 128, 8, 8]               0
        YQConv2d-238            [-1, 128, 8, 8]               0
       MaxPool2d-239            [-1, 832, 8, 8]               0
          Conv2d-240            [-1, 128, 8, 8]         106,496
     BatchNorm2d-241            [-1, 128, 8, 8]             256
            ReLU-242            [-1, 128, 8, 8]               0
        YQConv2d-243            [-1, 128, 8, 8]               0
     YQInception-244            [-1, 832, 8, 8]               0
          Conv2d-245            [-1, 384, 8, 8]         319,488
     BatchNorm2d-246            [-1, 384, 8, 8]             768
            ReLU-247            [-1, 384, 8, 8]               0
        YQConv2d-248            [-1, 384, 8, 8]               0
          Conv2d-249            [-1, 192, 8, 8]         159,744
     BatchNorm2d-250            [-1, 192, 8, 8]             384
            ReLU-251            [-1, 192, 8, 8]               0
        YQConv2d-252            [-1, 192, 8, 8]               0
          Conv2d-253            [-1, 384, 8, 8]         663,552
     BatchNorm2d-254            [-1, 384, 8, 8]             768
            ReLU-255            [-1, 384, 8, 8]               0
        YQConv2d-256            [-1, 384, 8, 8]               0
          Conv2d-257             [-1, 48, 8, 8]          39,936
     BatchNorm2d-258             [-1, 48, 8, 8]              96
            ReLU-259             [-1, 48, 8, 8]               0
        YQConv2d-260             [-1, 48, 8, 8]               0
          Conv2d-261            [-1, 128, 8, 8]          55,296
     BatchNorm2d-262            [-1, 128, 8, 8]             256
            ReLU-263            [-1, 128, 8, 8]               0
        YQConv2d-264            [-1, 128, 8, 8]               0
       MaxPool2d-265            [-1, 832, 8, 8]               0
          Conv2d-266            [-1, 128, 8, 8]         106,496
     BatchNorm2d-267            [-1, 128, 8, 8]             256
            ReLU-268            [-1, 128, 8, 8]               0
        YQConv2d-269            [-1, 128, 8, 8]               0
     YQInception-270           [-1, 1024, 8, 8]               0
AdaptiveAvgPool2d-271           [-1, 1024, 1, 1]               0
         Dropout-272                 [-1, 1024]               0
          Linear-273                 [-1, 1000]       1,025,000
================================================================
Total params: 13,004,888
Trainable params: 13,004,888
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.68
Forward/backward pass size (MB): 139.36
Params size (MB): 49.61
Estimated Total Size (MB): 189.65
----------------------------------------------------------------
None

我们使用1000个类别，输出的参数大小与官方的大小一致。
上面模型有个不足的地方就是：
- 辅助分类器只对训练有用，实际上对预测分类的时候是没有用处的，可以使用一个逻辑变量打开/关闭辅助分类器的计算。

数据集加载与训练

由于GoogLeNet有两个辅助分类器，鼓励在浅层分类，所以训练的方式与原来的单纯的分类训练方式有差别。
- 在训练过程中，辅助分类器的损失会根据折扣后的权重（折扣权重为0.3）叠加到总损失中。
为了代码清晰，下面代码都几种在一起。

import torch
from torch.nn import Conv2d, BatchNorm2d, Module, ReLU, Sequential, MaxPool2d, AdaptiveAvgPool2d, Linear, Dropout
from torchvision.datasets import ImageFolder
from torchvision.transforms import *
from torchvision.transforms.functional import *
from torch.utils.data import random_split
from torch.utils.data import DataLoader
import torch
import torchvision
import numpy as np
import cv2

# 卷积与BN，ReLU的合并封装，实际上使用Sequential也可以更加简洁的实现。
# --------------------------------------------------------------
class YQConv2d(Module):
    
    # 构造器，初始化Conv2d， BatchNorm2d与ReLU
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
        super(YQConv2d, self).__init__()
        # 卷积层
        self.conv = Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
        # BatchNorm2d
        self.bn = BatchNorm2d(out_channels, eps=0.001)
        # 激活
        self.relu = ReLU(inplace=True)

    def forward(self, x):
        y_ = self.conv(x)
        y_ = self.bn(y_)
        y_ = self.relu(y_)
        return y_

    
# Inception V2实现
# --------------------------------------------------------------
class YQInception(Module):
    # 构造器设置4个分支的参数(输出的通道数)
    def __init__(self, in_channels, ch1x1, ch3x3_1, ch3x3_2, ch5x5_1, ch5x5_2, ch_pool):
        super(YQInception, self).__init__()
        # YQConv2d(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
        # 分支-1
        self.branch_1 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch1x1, kernel_size=1, stride=1, padding=0)
        )
        # 分支-2
        self.branch_2 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch3x3_1, kernel_size=1, stride=1, padding=0),
            YQConv2d(in_channels=ch3x3_1,     out_channels=ch3x3_2, kernel_size=3, stride=1, padding=1)
        )
        # 分支-3
        self.branch_3 = Sequential(
            YQConv2d(in_channels=in_channels, out_channels=ch5x5_1, kernel_size=1, stride=1, padding=0),
            YQConv2d(in_channels=ch5x5_1,     out_channels=ch5x5_2, kernel_size=3, stride=1, padding=1)
        )
        # 分支-4
        self.branch_4 = Sequential(
            MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
            YQConv2d(in_channels=in_channels, out_channels=ch_pool, kernel_size=1, stride=1, padding=0)
        )
    
    def forward(self, x):
        b_y1 = self.branch_1(x)
        b_y2 = self.branch_2(x)
        b_y3 = self.branch_3(x)
        b_y4 = self.branch_4(x)
        
        y_ = torch.cat([b_y1, b_y2, b_y3, b_y4],  1)   # 1 表示按照列链接，就是行堆叠在一起。
        return y_
    

# 辅助分类器实现
# --------------------------------------------------------------
class YQAuxClassifier(Module):
    def __init__(self, in_channels, num_classes):
        super(YQAuxClassifier, self).__init__()
        # 池化/卷积/全连接/全连接
        self.pool = AdaptiveAvgPool2d((4, 4))
        self.conv = YQConv2d(in_channels=in_channels, out_channels=128, kernel_size=1, stride=1, padding=0)
        self.fc_1 = Linear(2048, 1024)
        self.relu = ReLU(inplace=True)
        self.drop = Dropout(p=0.7, inplace=False)
        self.fc_2 = Linear(1024, num_classes)
    
    def forward(self, x):
        y_ = self.pool(x)
        y_ = self.conv(y_)
        y_ = torch.flatten(y_, 1)    # 卷积到连接层的数据维度转换。
        y_ = self.fc_1(y_)
        y_ = self.relu(y_)
        y_ = self.drop(y_)
        y_ = self.fc_2(y_)
        return y_



# GoogLeNet网络实现
# --------------------------------------------------------------
class YQGoogLeNet(Module):
    def __init__(self, num_classes=1000):
        super(YQGoogLeNet, self).__init__()
        # 定义层
        self.conv_1  = YQConv2d(in_channels=3,  out_channels=64,  kernel_size=7, stride=2, padding=3)
        self.pool_1  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.conv_2  = YQConv2d(in_channels=64, out_channels=64,  kernel_size=1, stride=1, padding=0)
        
        self.conv_3  = YQConv2d(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1)
        self.pool_2  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.ince_3a = YQInception(192, 64, 96, 128, 16, 32, 32)
        self.ince_3b = YQInception(256, 128, 128, 192, 32, 96, 64)
        self.pool_3  = MaxPool2d(3, stride=2, ceil_mode=True)
        
        self.ince_4a = YQInception(480, 192, 96, 208, 16, 48, 64)
        
        self.ince_4b = YQInception(512, 160, 112, 224, 24, 64, 64)
        self.ince_4c = YQInception(512, 128, 128, 256, 24, 64, 64)
        self.ince_4d = YQInception(512, 112, 144, 288, 32, 64, 64)
        self.ince_4e = YQInception(528, 256, 160, 320, 32, 128, 128)
        self.pool_4  = MaxPool2d(2, stride=2, ceil_mode=True)
        
        self.ince_5a = YQInception(832, 256, 160, 320, 32, 128, 128)
        self.ince_5b = YQInception(832, 384, 192, 384, 48, 128, 128)
        
        # 两个辅助分类器
        self.auxi_1  = YQAuxClassifier(512, num_classes)
        self.auxi_2  = YQAuxClassifier(528, num_classes)
        
        # 结尾的分类层
        self.pool_5  = AdaptiveAvgPool2d((1, 1))
        self.drop = Dropout(0.2)
        self.full = Linear(1024, num_classes)
        
    def forward(self, x):
        # -----------------------------
        y_ = self.conv_1(x)
        y_ = self.pool_1(y_)
        # -----------------------------
        y_ = self.conv_2(y_)
        y_ = self.conv_3(y_)
        y_ = self.pool_2(y_)
        # -----------------------------
        y_ = self.ince_3a(y_)
        y_ = self.ince_3b(y_)
        y_ = self.pool_3(y_)
        # -----------------------------
        y_ = self.ince_4a(y_)
        # -----------------------------
        a1 = self.auxi_1(y_)
        # -----------------------------
        y_ = self.ince_4b(y_)
        y_ = self.ince_4c(y_)
        y_ = self.ince_4d(y_)
        # -----------------------------
        a2 = self.auxi_2(y_)
        # -----------------------------
        y_ = self.ince_4e(y_)
        y_ = self.pool_4(y_)
        # -----------------------------
        y_ = self.ince_5a(y_)
        y_ = self.ince_5b(y_)
        # -----------------------------
        y_ = self.pool_5(y_)
        y_ = torch.flatten(y_, 1)
        y_ = self.drop(y_)
        y_ = self.full(y_)
        
        return y_, a1, a2


# 数据加载
# --------------------------------------------------------------
def load_data(img_dir, rate=0.8):
    transform = Compose(
        [
            Resize((224, 224)),          #RandomResizedCrop(224),
    #         RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0]),   # 均值与方差,(这个运算输入必须是Tensor图像，所以需要在ToTensor后)
        ]
    )
    ds = ImageFolder(img_dir, transform=transform)

    l = len(ds)
    l_train = int(l * rate)
    train, test = random_split(ds, [l_train, l - l_train])
    
    train_loader = torch.utils.data.DataLoader(dataset=train, shuffle=True, batch_size=50)   # 100，因为每个类的图像是1300个
    test_loader = torch.utils.data.DataLoader(dataset=test, shuffle=True, batch_size=50)  # 一个批次直接预测

    return train_loader, test_loader


# 训练实现
# ==============================================================
# 1. 加载数据集
print("1. 加载数据集")
train_loader, test_loader = load_data("./imagenet2012", 0.8)

CUDA = torch.cuda.is_available()
# 2. 网络搭建
print("2. 网络搭建")
net=YQGoogLeNet(4)
if CUDA:
    net.cuda()

# 3. 训练
print("3. 训练")
optimizer=torch.optim.Adam(net.parameters(),lr=0.001)
loss_F=torch.nn.CrossEntropyLoss()

epoch = 10


for n in range(epoch): # 数据集只迭代一次
    for step, input_data in enumerate(train_loader):
        x_, y_=input_data
        if CUDA:
            # GPU运算 -----------------------------------------------
            x_ = x_.cuda()
            y_ = y_.cuda()
        pred, pred_aux_1, pred_aux_2 =net(x_.view(-1, 3, 224, 224))  
        loss = loss_F(pred, y_) # 计算loss
        loss_aux_1 = loss_F(pred_aux_1, y_)
        loss_aux_2 = loss_F(pred_aux_2, y_) 
        
        last_loss = loss + 0.3 * (loss_aux_1 + loss_aux_2)   # 辅助分类器的误差按照30%的比例使用
        optimizer.zero_grad()
        last_loss.backward()
        optimizer.step()
        
        with torch.no_grad():
            all_num = 0.0 
            acc = 0.0
            for t_x, t_y in  test_loader:
                all_num  += len(t_y)
                if CUDA:
                    t_x = t_x.cuda()
                    t_y = t_y.cuda()

                # 
                test_pred, _, _=net(t_x.view(-1, 3, 224, 224))    # 辅助分类器的返回结果不需要
                prob=torch.nn.functional.softmax(test_pred, dim=1)
                pred_cls=torch.argmax(prob, dim=1)
                acc += (pred_cls == t_y).float().sum()
            print(f"轮数/批次：{n:02d}/{step:02d}: \t识别正确率:{acc/all_num *100:6.4f}, 损失值：{last_loss:6.4f}")
        # print(f"轮数：{n+1:02d}: \t识别正确率:{acc/all_num *100:6.4f}, \t损失值：{loss:6.4f}")

# 保存模型
torch.save(net.state_dict(), "./googlenet.models")  # GPU保存

1. 加载数据集
2. 网络搭建
3. 训练
轮数/批次：00/00:    识别正确率:34.5566, 损失值：2.2265
轮数/批次：00/01:    识别正确率:33.2314, 损失值：2.1856
轮数/批次：00/02:    识别正确率:44.8522, 损失值：3.0660
轮数/批次：00/03:    识别正确率:40.4689, 损失值：2.3589
......
轮数/批次：09/75:    识别正确率:80.8359, 损失值：0.8001
轮数/批次：09/76:    识别正确率:79.6126, 损失值：0.3436
轮数/批次：09/77:    识别正确率:81.0397, 损失值：0.7516
轮数/批次：09/78:    识别正确率:81.2436, 损失值：0.3100

GoogLeNet等几个网络的测试分析

Inception V2

输入图像为：
- $3 \times 299 \times 299$

卷积分解

卷积分解设计
Inception V2使用小核卷积替代大核卷积，并减少计算量
- $5 \times 5$ 卷积使用2个 $3 \times 3$ 的卷积替代。
- V2早期与V1的区别是就是增加了BatchNorm2d，官方的说法是V1增强，后来发布了卷积分解，才正式称呼为V2
卷积分解示意图

卷积分解示意图

深度分解

深度分解设计
深度分解的原因是传统的并联池化处理存在问题：（输出的通道数保持一个相等值。）
1. 先卷积，再池化
  - 增加卷积的计算量。
2. 先池化，再卷积
  - 会产生特征缺失的情况。

卷积与池化的两种串联方式

深度分解的设计如下：
- 可以保持特征，同时降低计算量。

卷积与池化串联的深度分解设计

Inception V2的GoogLeNet网络结构

下图实际上是也可以成为去掉BatchNorm2d的V3版本。实际上也是V3（包含不对成的卷积分解）

Inception V2 GoogLeNet网络结构

注：
- 上表中的Figure 5指没有进化的Inception
- 上表中的Figure 6是指小卷积版的Inception（用3x3卷积核代替5x5卷积核），
- 上表中的Figure 7是指不对称版的Inception（用1xn、nx1卷积核代替nxn卷积核）。

附录

Figure-5

Figure-5

Figure-6

Figure-6

Figure-7

Figure-7

Inception V3

不对称卷积分解设计
引入了不对称卷积分解： $n \times n$ 的卷积，并分解成 $n \times 1$ 与 $1 \times n$ ， $n=3, 5$ ，具体的网络结构见V2中的网络结构。
图像的输入提升为 $3 \times 299 \times 299$

卷积不对称分解

Inception V4

Inception残差设计

使用Inception做残差，改进Inception-v3,得到如下残差方式
1. Inception-ResNet-v1，
2. Inception-ResNet-v2，
3. Inception-v4

Inception残差设计

Inception残差

Inception V4 GoogLeNet网络结构

附录

关于Google发表的关于GoogLetNet的系列文章，在这些论文中对Inception v1、Inception v2、Inception v3、Inception v4 等思想和技术原理进行了详细的介绍：
- 《Going deeper with convolutions》
- 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》
- 《Rethinking the Inception Architecture for Computer Vision》
- 《Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning》
官方的源代码还做了很多细节的处理，比如：
- 结构更加良好的代码组织；
- 权重提供初始化；
- 对不同结构的数据提供transform转换
V2, V3，V4的代码花点时间也可以撸出来。
- 其中V4可以参考我们实现的ResNet网络。

TORCH03-09GoogLeNet网络

TORCH03-09GoogLeNet网络

关于GoogLeNet网络

GoogLeNet的设计思想

Inception的核心思想

Inception V1

Inception V1设计

GoogLeNet 22层网络结构

Torch的GoogLeNet网络实现

手工实现GoogLeNet网络结构

GoogLeNet施工参数细节

基本的卷积封装

Inception封装

辅助(auxiliary)分类器封装

GoogLeNet实现

数据集加载与训练

Inception V2

卷积分解

深度分解

Inception V2的GoogLeNet网络结构

附录

Inception V3

Inception V4

Inception残差设计

Inception V4 GoogLeNet网络结构

附录