GoogLeNet 是 2014 年 Christian Szegedy 提出的一种全新的深度学习结构，inception 的提出则从另一种角度来提升训练结果：能更高效的利用计算资源，在相同的计算量下能提取到更多的特征，从而提升训练结果。GoogleNet 在 2014 年由 Google 团队提出，斩获当年 ImageNet 竞赛中 Classification Task (分类任务)第一名。

GoogLeNet 亮点

（1）引入了 Inception 结构( 融合不同尺度的特征信息)
（2）使用 1*1 的卷积核进行降维以及映射处理
（3）添加两个辅助分类器帮助训练
（4）丢弃全连接层，使用平均池化层(大大减少模型参数)

GoogLeNet 网络结构表格

  该表清晰记录了各卷积层的卷积核大小、步长、卷积核个数/通道数、输出图像的大小等信息。注：上表中的 “#3x3 reduce”，“#5x5 reduce” 表示在 3x3，5x5 卷积操作之前使用了 1x1 卷积的数量，起到降维的作用。

第一层（卷积层）
使用 7*7 的卷积核，步长 2，64 通道，输出为 112x112x64，卷积后进行 ReLU 操作
经过 3*3 的 max pooling，步长为 2，输出为 56x56x64，再进行 ReLU 操作
第二层（卷积层）
使用 3*3 的卷积核，步长为 1，192 通道，输出为 56x56x192，卷积后进行 ReLU 操作
经过 3*3 的 max pooling，步长为 2，输出为 28x28x192，再进行 ReLU 操作
第三层（Inception 3a层）
分为四个分支，采用不同尺度的卷积核来进行处理
（1）64 个 1*1 的卷积核，然后 ReLU，输出 28x28x64
（2）96 个 1*1 的卷积核，作为 3*3 卷积核之前的降维，变成 28x28x96，然后进行ReLU 计算，再进行 128个3*3 的卷积，输出 28x28x128
（3）16 个 1*1 的卷积核，作为 5*5 卷积核之前的降维，变成 28x28x16，进行 ReLU 计算后，再进行 32 个 5*5 的卷积，输出 28x28x32
（4）pool 层，使用 3*3 的核，输出 28x28x192，然后进行 32 个 1*1 的卷积，输出 28x28x32
将四个结果进行连接，对这四部分输出结果的第三维并联，即 64+128+32+32 = 256，最终输出 28x28x256
按表分析，同理其它层…
第三层（Inception 3b层）
第四层（Inception 4a/b/c/d/e层）
第五层（Inception 5a/b层）

  图中右侧淡黄色框即 1*1 卷积核，使用 1*1 卷积核降维。将上表蓝色框起来的列名和上图右侧部分标注的内容进行对应，更好理解 GoogLeNet 网络的 inception 结构。

接下来介绍一下，它到底是如何达到降维的效果的呢？
  如果一个 channel 为 512 的图像使用 64 个 5*5 的卷积核进行卷积，需要使用 512*5*5*64 = 819200 个参数；
  如果加入 1*1 卷积核，图像先使用 24 个 1*1 卷积核，再使用 64 个 5*5 的卷积核进行卷积，那么参数个数为 512*1*1*24 + 24*5*5*64 = 50688；
  由此可见，使用 1*1 卷积核可以达到降维的效果。

GoogLeNet 网络结构图

将 GoogLeNet 网络结构表和网络结构图对比起来分析，会使我们更好得理解 GoogLeNet 网络各层的内部结构以及层与层之间的连接。

代码

这里使用 pytorch 框架构建 GoogLeNet 模型，训练花卉数据集
model.py

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

class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
        super(GoogLeNet, self).__init__()
        self.aux_logits = aux_logits

        self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)  # BasicConv2d类
        self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.conv2 = BasicConv2d(64, 64, kernel_size=1)
        self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
        self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)           # Inception类
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

        if self.aux_logits:
            self.aux1 = InceptionAux(512, num_classes)                      # InceptionAux类
            self.aux2 = InceptionAux(528, num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024, num_classes)
        if init_weights:
            self._initialize_weights()

    # 正向传播
    def forward(self, x):
                             # N x 3 x 224 x 224
        x = self.conv1(x)    # N x 64 x 112 x 112
        x = self.maxpool1(x) # N x 64 x 56 x 56

        x = self.conv2(x)    # N x 64 x 56 x 56
        x = self.conv3(x)    # N x 192 x 56 x 56
        x = self.maxpool2(x) # N x 192 x 28 x 28 

        x = self.inception3a(x)  # N x 256 x 28 x 28
        x = self.inception3b(x)  # N x 480 x 28 x 28
        x = self.maxpool3(x)     # N x 480 x 14 x 14

        x = self.inception4a(x)  # N x 512 x 14 x 14
        if self.training and self.aux_logits:  # eval model不执行该部分
            aux1 = self.aux1(x)
        x = self.inception4b(x)  # N x 512 x 14 x 14
        x = self.inception4c(x)  # N x 512 x 14 x 14
        x = self.inception4d(x)  # N x 528 x 14 x 14
        if self.training and self.aux_logits:  # eval model不执行该部分
            aux2 = self.aux2(x)
        x = self.inception4e(x)  # N x 832 x 14 x 14
        x = self.maxpool4(x)     # N x 832 x 7 x 7
        
        x = self.inception5a(x)  # N x 832 x 7 x 7
        x = self.inception5b(x)  # N x 1024 x 7 x 7
        
        x = self.avgpool(x)      # N x 1024 x 1 x 1
        x = torch.flatten(x, 1)  # N x 1024
        x = self.dropout(x)
        x = self.fc(x)           # N x 1000 (num_classes)   

        if self.training and self.aux_logits:  # eval model不执行该部分
            return x, aux2, aux1
        return x

    # 初始化权重
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)

# 类Inception，有四个分支
class Inception(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(Inception, self).__init__()

        self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)

        self.branch2 = nn.Sequential(
            BasicConv2d(in_channels, ch3x3red, kernel_size=1),
            BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)   # 保证输出大小等于输入大小
        )

        self.branch3 = nn.Sequential(
            BasicConv2d(in_channels, ch5x5red, kernel_size=1),
            BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)   # 保证输出大小等于输入大小
        )

        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            BasicConv2d(in_channels, pool_proj, kernel_size=1)
        )

    def forward(self, x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)
        # 四个分支连接起来
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)

# 辅助分类器：类InceptionAux，包括avepool+conv+fc1+fc2
class InceptionAux(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(InceptionAux, self).__init__()
        self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = BasicConv2d(in_channels, 128, kernel_size=1)  # output[batch, 128, 4, 4]
        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)

    def forward(self, x):
                                 # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
        x = self.averagePool(x)  # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
        x = self.conv(x)         # N x 128 x 4 x 4
        x = torch.flatten(x, 1)
        x = F.dropout(x, 0.5, training=self.training)  # N x 2048
        x = F.relu(self.fc1(x), inplace=True)
        x = F.dropout(x, 0.5, training=self.training)  # N x 1024
        x = self.fc2(x)  # N x num_classes
        return x

# 类BasicConv2d，包括conv+relu
class BasicConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
        self.relu = nn.ReLU(inplace=True)

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

train.py

import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torchvision
import json
import matplotlib.pyplot as plt
import os
import torch.optim as optim
from model import GoogLeNet

# 检测使用 gpu or cpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

data_transform = {
    "train": transforms.Compose([transforms.RandomResizedCrop(224),
                                 transforms.RandomHorizontalFlip(),
                                 transforms.ToTensor(),
                                 transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),
    "val": transforms.Compose([transforms.Resize((224, 224)),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}

image_path = "data_set/flower_data/"  # 数据集路径
train_dataset = datasets.ImageFolder(root=image_path + "/train",
                                     transform=data_transform["train"])
train_num = len(train_dataset)

# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx
cla_dict = dict((val, key) for key, val in flower_list.items())
# 将字典写入json文件中
json_str = json.dumps(cla_dict, indent=4)
with open('class_indices.json', 'w') as json_file:
    json_file.write(json_str)

batch_size = 32
train_loader = torch.utils.data.DataLoader(train_dataset,
                                           batch_size=batch_size, shuffle=True,
                                           num_workers=0)

validate_dataset = datasets.ImageFolder(root=image_path + "val",
                                        transform=data_transform["val"])
val_num = len(validate_dataset)
validate_loader = torch.utils.data.DataLoader(validate_dataset,
                                              batch_size=batch_size, shuffle=False,
                                              num_workers=0)

# 模型，花数据集5个类别
net = GoogLeNet(num_classes=5, aux_logits=True, init_weights=True)
net.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0003)

# 开始训练
print('Start training...')
best_acc = 0.0
save_path = './googleNet.pth'
for epoch in range(30):
    # train
    net.train()
    running_loss = 0.0
    for step, data in enumerate(train_loader, start=0):
        images, labels = data
        optimizer.zero_grad()  # 清空过往梯度
        # 将输入图片载入模型中，得到输出图像，有三个参数：主分类器 + 2个辅助分类器
        logits, aux_logits2, aux_logits1 = net(images.to(device))
        # 计算三个分类器的损失
        loss0 = loss_function(logits, labels.to(device))
        loss1 = loss_function(aux_logits1, labels.to(device))
        loss2 = loss_function(aux_logits2, labels.to(device))
        loss = loss0 + loss1 * 0.3 + loss2 * 0.3  #乘以权重0.3是论文中提出的
        loss.backward()  # 反向传播，计算当前梯度
        optimizer.step()  # 根据梯度更新网络参数

        running_loss += loss.item()  # 累加损失值
        # 打印训练过程
        rate = (step + 1) / len(train_loader)
        a = "*" * int(rate * 50)
        b = "." * int((1 - rate) * 50)
        print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")
    print()

    # validate
    net.eval()
    acc = 0.0  # accumulate accurate number / epoch
    with torch.no_grad():
        for val_data in validate_loader:
            val_images, val_labels = val_data
            outputs = net(val_images.to(device))  # eval model只需要主分类器的输出
            predict_y = torch.max(outputs, dim=1)[1]
            acc += (predict_y == val_labels.to(device)).sum().item()
        val_accurate = acc / val_num
        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)
        print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %
              (epoch + 1, running_loss / step, val_accurate))

print('Finished Training')

predict.py

import torch
from model import GoogLeNet
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json

data_transform = transforms.Compose(
    [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

img = Image.open("data_set/flower_data/predict_flower.jpg") # 载入图像
plt.imshow(img)
img = data_transform(img) # [C, H, W]，转换图像为tensor
img = torch.unsqueeze(img, dim=0) # [N, C, H, W]，增加一个维度N

# 读取类别
try:
    json_file = open('./class_indices.json', 'r')
    class_indict = json.load(json_file)
except Exception as e:
    print(e)
    exit(-1)

# 建立模型
model = GoogLeNet(num_classes=5, aux_logits=False)
# 载入保存的权重文件
model_weight_path = "./googleNet.pth"
missing_keys, unexpected_keys = model.load_state_dict(torch.load(model_weight_path), strict=False)
model.eval()
with torch.no_grad():
    output = torch.squeeze(model(img))
    predict = torch.softmax(output, dim=0)
    predict_cla = torch.argmax(predict).numpy()
print(class_indict[str(predict_cla)], predict[predict_cla].item())
# 画图
name1 = class_indict[str(predict_cla)]
name2 =predict[predict_cla].numpy()
plt.title("This is %s. The accuracy is %s"%(name1, name2),color='red')
plt.show()