注：书中对代码的讲解并不详细，本文对很多细节做了详细注释。另外，书上的源代码是在Jupyter Notebook上运行的，较为分散，本文将代码集中起来，并加以完善，全部用vscode在python 3.9.18下测试通过，同时对于书上部分章节也做了整合。

Chapter7 Modern Convolutional Neural Networks

7.4 Networks with Parallel Connections: GoogLeNet

在GoogLeNet中，基本的卷积块被称为Inception块（Inception block），如下图所示。Inception块由四条并行路径组成，前三条路径使用窗口大小为$1\times 1$、$3\times 3$和$5\times 5$的卷积层，从不同空间大小中提取信息，中间的两条路径先在输入上执行$1\times 1$卷积，以减少通道数，降低模型的复杂性，第四条路径使用$3\times 3$最大汇聚层，然后使用$1\times 1$卷积层来改变通道数，这四条路径都使用合适的填充来使输入与输出的高和宽一致。最后我们将每条线路的输出在通道维度上连结并构成Inception块的输出。在Inception块中，通常调整的超参数是每层输出通道数。

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
import matplotlib.pyplot as plt

class Inception(nn.Module):
    # c1--c4是每条路径的输出通道数
    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
        super(Inception, self).__init__(**kwargs)
        # 线路1，单1x1卷积层
        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
        # 线路2，1x1卷积层后接3x3卷积层
        self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
        # 线路3，1x1卷积层后接5x5卷积层
        self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
        # 线路4，3x3最大汇聚层后接1x1卷积层
        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)

    def forward(self, x):
        p1 = F.relu(self.p1_1(x))
        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
        p4 = F.relu(self.p4_2(self.p4_1(x)))
        # 在通道维度上连结输出
        return torch.cat((p1, p2, p3, p4), dim=1)

#实现各个模块    
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
                nn.ReLU(),
                nn.Conv2d(64, 192, kernel_size=3, padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
                Inception(256, 128, (128, 192), (32, 96), 64),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
                Inception(512, 160, (112, 224), (24, 64), 64),
                Inception(512, 128, (128, 256), (24, 64), 64),
                Inception(512, 112, (144, 288), (32, 64), 64),
                Inception(528, 256, (160, 320), (32, 128), 128),
                nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
                Inception(832, 384, (192, 384), (48, 128), 128),
                nn.AdaptiveAvgPool2d((1,1)),
                nn.Flatten())

net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))
X = torch.rand(size=(1, 1, 96, 96))#为了使Fashion-MNIST上的训练更简洁，将输入的高和宽从224降到96
for layer in net:
    X = layer(X)
    print(layer.__class__.__name__,'output shape:\t', X.shape)
    
#训练
lr, num_epochs, batch_size = 0.1, 10, 128
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)
d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
plt.show()

训练结果: