GoogLeNet

Concept and Principle

  • Inception块
    • 4个路径从不同层面抽取信息，然后再输出通道合并，最终输出高宽与输入相等，要把更多的通道数留给比较重要的通道
    • 要达到相同的输出通道数，Inception块与直接的3x3或5x5卷积相比，参数和计算复杂度更低
  • GoogLeNet
    • 5个stage（高宽减半一次就是一个stage），9个Inception块
  • Inception后续具有多个变种
    • Inception-BN(v2)：使用batch normalization
    • Inception-v3：修改了inception块
    • Inception-v4：使用了残差连接

Implementation

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import torch
from torch import nn,optim
import d2l

class Inception(nn.Module):
    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
        super(Inception, self).__init__(**kwargs)
        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
        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)
        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)
        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)

        self.relu=nn.ReLU()
    def forward(self, x):
        p1 = self.relu(self.p1_1(x))
        p2 = self.relu(self.p2_2(self.relu(self.p2_1(x))))
        p3 = self.relu(self.p3_2(self.relu(self.p3_1(x))))
        p4 = self.relu(self.p4_2(self.p4_1(x)))
        # 以通道维拼接张量
        return torch.cat((p1, p2, p3, p4), dim=1)

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.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))

loss_f=nn.CrossEntropyLoss()
opt=optim.Adam(net.parameters())
train_iter,test_iter=d2l.load_data_fashion_mnist(128,resize=96)

# d2l.train(
#     10,loss_f,opt,net,train_iter,
#     device=torch.device("cuda:0"),
#     save_name="GoogLeNet"
# )

d2l.evaluate(
    net,test_iter,loss_f,
    "D:/code/machine_learning/limu_d2l/params/GoogLeNet_5",
    device=torch.device("cuda:0")
)