深度学习_GoogLeNet_4

目标

• 知道GoogLeNet网络结构的特点
• 能够利用GoogLeNet完成图像分类

一、开发背景
GoogLeNet在2014年由Google团队提出， 斩获当年ImageNet(ILSVRC14)竞赛中Classification Task (分类任务) 第一名，VGG获得了第二名，为了向“LeNet”致敬，因此取名为“GoogLeNet”。

GoogLeNet做了更加大胆的网络结构尝试，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，因此在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的性能也更加优越。

GoogLeNet的名字不是GoogleNet，而是GoogLeNet，这是为了致敬LeNet。GoogLeNet和AlexNet/VGGNet这类依靠加深网络结构的深度的思想不完全一样。GoogLeNet在加深度的同时做了结构上的创新，引入了一个叫做Inception的结构来代替之前的卷积加激活的经典组件。GoogLeNet在ImageNet分类比赛上的Top-5错误率降低到了6.7%。

1.Inception 块

GoogLeNet中的基础卷积块叫作Inception块，得名于同名电影《盗梦空间》（Inception）。Inception块在结构比较复杂，如下图所示：

Inception块里有4条并行的线路。前3条线路使用窗口大小分别是1×11×1、3×33×3和5×55×5的卷积层来抽取不同空间尺寸下的信息，其中中间2个线路会对输入先做1×11×1卷积来减少输入通道数，以降低模型复杂度。第4条线路则使用3×33×3最大池化层，后接1×11×1卷积层来改变通道数。4条线路都使用了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上连结,并向后进行传输。

1×11×1卷积：

它的计算方法和其他卷积核一样，唯一不同的是它的大小是1×11×1，没有考虑在特征图局部信息之间的关系。

它的作用主要是：

• 实现跨通道的交互和信息整合

• 卷积核通道数的降维和升维，减少网络参数

在tf.keras中实现Inception模块，各个卷积层卷积核的个数通过输入参数来控制，如下所示

# 定义Inception模块
class Inception(tf.keras.layers.Layer):
    # 输入参数为各个卷积的卷积核个数
    def __init__(self, c1, c2, c3, c4):
        super().__init__()
        # 线路1：1 x 1卷积层，激活函数是RELU，padding是same
        self.p1_1 = tf.keras.layers.Conv2D(
            c1, kernel_size=1, activation='relu', padding='same')
        # 线路2，1 x 1卷积层后接3 x 3卷积层,激活函数是RELU，padding是same
        self.p2_1 = tf.keras.layers.Conv2D(
            c2[0], kernel_size=1, padding='same', activation='relu')
        self.p2_2 = tf.keras.layers.Conv2D(c2[1], kernel_size=3, padding='same',
                                           activation='relu')
        # 线路3，1 x 1卷积层后接5 x 5卷积层,激活函数是RELU，padding是same
        self.p3_1 = tf.keras.layers.Conv2D(
            c3[0], kernel_size=1, padding='same', activation='relu')
        self.p3_2 = tf.keras.layers.Conv2D(c3[1], kernel_size=5, padding='same',
                                           activation='relu')
        # 线路4，3 x 3最大池化层后接1 x 1卷积层,激活函数是RELU，padding是same
        self.p4_1 = tf.keras.layers.MaxPool2D(
            pool_size=3, padding='same', strides=1)
        self.p4_2 = tf.keras.layers.Conv2D(
            c4, kernel_size=1, padding='same', activation='relu')
    # 完成前向传播过程
    def call(self, x):
        # 线路1
        p1 = self.p1_1(x)
        # 线路2
        p2 = self.p2_2(self.p2_1(x))
        # 线路3
        p3 = self.p3_2(self.p3_1(x))
        # 线路4
        p4 = self.p4_2(self.p4_1(x))
        # 在通道维上concat输出
        outputs = tf.concat([p1, p2, p3, p4], axis=-1)
        return outputs  

指定通道数，对Inception模块进行实例化：

Inception(64, (96, 128), (16, 32), 32)

2.GoogLeNet模型

GoogLeNet主要由Inception模块构成，如下图所示：

整个网络架构我们分为五个模块，每个模块之间使用步幅为2的3×33×3最大池化层来减小输出高宽。

2.1 B1模块

第一模块使用一个64通道的7×77×7卷积层。

# 定义模型的输入
inputs = tf.keras.Input(shape=(224,224,3),name = "input")
# b1 模块
# 卷积层7*7的卷积核，步长为2，pad是same，激活函数RELU
x = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding='same', activation='relu')(inputs)
# 最大池化：窗口大小为3*3，步长为2，pad是same
x = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
# b2 模块

2.2 B2模块

第二模块使用2个卷积层：首先是64通道的1×11×1卷积层，然后是将通道增大3倍的3×33×3卷积层。

# b2 模块
# 卷积层1*1的卷积核，步长为2，pad是same，激活函数RELU
x = tf.keras.layers.Conv2D(64, kernel_size=1, padding='same', activation='relu')(x)
# 卷积层3*3的卷积核，步长为2，pad是same，激活函数RELU
x = tf.keras.layers.Conv2D(192, kernel_size=3, padding='same', activation='relu')(x)
# 最大池化：窗口大小为3*3，步长为2，pad是same
x = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)

2.3 B3模块

第三模块串联2个完整的Inception块。第一个Inception块的输出通道数为64+128+32+32=25664+128+32+32=256。第二个Inception块输出通道数增至128+192+96+64=480

# b3 模块
# Inception
x = Inception(64, (96, 128), (16, 32), 32)(x)
# Inception
x = Inception(128, (128, 192), (32, 96), 64)(x)
# 最大池化：窗口大小为3*3，步长为2，pad是same
x = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)

2.4 B4模块

第四模块更加复杂。它串联了5个Inception块，其输出通道数分别是192+208+48+64=512192+208+48+64=512、160+224+64+64=512160+224+64+64=512、128+256+64+64=512128+256+64+64=512、112+288+64+64=528112+288+64+64=528和256+320+128+128=832256+320+128+128=832。并且增加了辅助分类器，根据实验发现网络的中间层具有很强的识别能力，为了利用中间层抽象的特征，在某些中间层中添加含有多层的分类器，如下图所示：

实现如下所示：

def aux_classifier(x, filter_size):
    #x:输入数据，filter_size:卷积层卷积核个数，全连接层神经元个数
    # 池化层
    x = tf.keras.layers.AveragePooling2D(
        pool_size=5, strides=3, padding='same')(x)
    # 1x1 卷积层
    x = tf.keras.layers.Conv2D(filters=filter_size[0], kernel_size=1, strides=1,
                               padding='valid', activation='relu')(x)
    # 展平
    x = tf.keras.layers.Flatten()(x)
    # 全连接层1
    x = tf.keras.layers.Dense(units=filter_size[1], activation='relu')(x)
    # softmax输出层
    x = tf.keras.layers.Dense(units=10, activation='softmax')(x)
    return x

b4模块的实现：

# b4 模块
# Inception
x = Inception(192, (96, 208), (16, 48), 64)(x)
# 辅助输出1
aux_output_1 = aux_classifier(x, [128, 1024])
# Inception
x = Inception(160, (112, 224), (24, 64), 64)(x)
# Inception
x = Inception(128, (128, 256), (24, 64), 64)(x)
# Inception
x = Inception(112, (144, 288), (32, 64), 64)(x)
# 辅助输出2
aux_output_2 = aux_classifier(x, [128, 1024])
# Inception
x = Inception(256, (160, 320), (32, 128), 128)(x)
# 最大池化
x = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)

2.5 B5模块

第五模块有输出通道数为256+320+128+128=832256+320+128+128=832和384+384+128+128=1024384+384+128+128=1024的两个Inception块。后面紧跟输出层，该模块使用全局平均池化层（GAP）来将每个通道的高和宽变成1。最后输出变成二维数组后接输出个数为标签类别数的全连接层。

全局平均池化层（GAP）

用来替代全连接层，将特征图每一通道中所有像素值相加后求平均，得到就是GAP的结果，在将其送入后续网络中进行计算

实现过程是：

# b5 模块
# Inception
x = Inception(256, (160, 320), (32, 128), 128)(x)
# Inception
x = Inception(384, (192, 384), (48, 128), 128)(x)
# GAP
x = tf.keras.layers.GlobalAvgPool2D()(x)
# 输出层
main_outputs = tf.keras.layers.Dense(10,activation='softmax')(x)
# 使用Model来创建模型，指明输入和输出

构建GoogLeNet模型并通过summary来看下模型的结构：

# 使用Model来创建模型，指明输入和输出
model = tf.keras.Model(inputs=inputs, outputs=[main_outputs,aux_output_1，aux_output_2]) 
model.summary()

Model: "functional_3" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input (InputLayer) [(None, 224, 224, 3)] 0 _________________________________________________________________ conv2d_122 (Conv2D) (None, 112, 112, 64) 9472 _________________________________________________________________ max_pooling2d_27 (MaxPooling (None, 56, 56, 64) 0 _________________________________________________________________ conv2d_123 (Conv2D) (None, 56, 56, 64) 4160 _________________________________________________________________ conv2d_124 (Conv2D) (None, 56, 56, 192) 110784 _________________________________________________________________ max_pooling2d_28 (MaxPooling (None, 28, 28, 192) 0 _________________________________________________________________ inception_19 (Inception) (None, 28, 28, 256) 163696 _________________________________________________________________ inception_20 (Inception) (None, 28, 28, 480) 388736 _________________________________________________________________ max_pooling2d_31 (MaxPooling (None, 14, 14, 480) 0 _________________________________________________________________ inception_21 (Inception) (None, 14, 14, 512) 376176 _________________________________________________________________ inception_22 (Inception) (None, 14, 14, 512) 449160 _________________________________________________________________ inception_23 (Inception) (None, 14, 14, 512) 510104 _________________________________________________________________ inception_24 (Inception) (None, 14, 14, 528) 605376 _________________________________________________________________ inception_25 (Inception) (None, 14, 14, 832) 868352 _________________________________________________________________ max_pooling2d_37 (MaxPooling (None, 7, 7, 832) 0 _________________________________________________________________ inception_26 (Inception) (None, 7, 7, 832) 1043456 _________________________________________________________________ inception_27 (Inception) (None, 7, 7, 1024) 1444080 _________________________________________________________________ global_average_pooling2d_2 ( (None, 1024) 0 _________________________________________________________________ dense_10 (Dense) (None, 10) 10250 ================================================================= Total params: 5,983,802 Trainable params: 5,983,802 Non-trainable params: 0 ___________________________________________________________

3.手写数字识别

因为ImageNet数据集较大训练时间较长，我们仍用前面的MNIST数据集来演示GoogLeNet。读取数据的时将图像高和宽扩大到图像高和宽224。这个通过tf.image.resize_with_pad来实现。

2.1 数据读取

首先获取数据,并进行维度调整：

import numpy as np
# 获取手写数字数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# 训练集数据维度的调整：N H W C
train_images = np.reshape(train_images,(train_images.shape[0],train_images.shape[1],train_images.shape[2],1))
# 测试集数据维度的调整：N H W C
test_images = np.reshape(test_images,(test_images.shape[0],test_images.shape[1],test_images.shape[2],1))

由于使用全部数据训练时间较长，我们定义两个方法获取部分数据，并将图像调整为224*224大小，进行模型训练：(与VGG中是一样的)

# 定义两个方法随机抽取部分样本演示
# 获取训练集数据
def get_train(size):
    # 随机生成要抽样的样本的索引
    index = np.random.randint(0, np.shape(train_images)[0], size)
    # 将这些数据resize成22*227大小
    resized_images = tf.image.resize_with_pad(train_images[index],224,224,)
    # 返回抽取的
    return resized_images.numpy(), train_labels[index]
# 获取测试集数据 
def get_test(size):
    # 随机生成要抽样的样本的索引
    index = np.random.randint(0, np.shape(test_images)[0], size)
    # 将这些数据resize成224*224大小
    resized_images = tf.image.resize_with_pad(test_images[index],224,224,)
    # 返回抽样的测试样本
    return resized_images.numpy(), test_labels[index]

调用上述两个方法，获取参与模型训练和测试的数据集：

# 获取训练样本和测试样本
train_images,train_labels = get_train(256)
test_images,test_labels = get_test(128)

3.2 模型编译

# 指定优化器，损失函数和评价指标
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.0)
# 模型有3个输出，所以指定损失函数对应的权重系数
net.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'],loss_weights=[1,0.3,0.3])

3.3 模型训练

# 模型训练：指定训练数据，batchsize,epoch,验证集
net.fit(train_images,train_labels,batch_size=128,epochs=3,verbose=1,validation_split=0.1)

训练过程：

Epoch 1/3 2/2 [==============================] - 8s 4s/step - loss: 2.9527 - accuracy: 0.1174 - val_loss: 3.3254 - val_accuracy: 0.1154 Epoch 2/3 2/2 [==============================] - 7s 4s/step - loss: 2.8111 - accuracy: 0.0957 - val_loss: 2.2718 - val_accuracy: 0.2308 Epoch 3/3 2/2 [==============================] - 7s 4s/step - loss: 2.3055 - accuracy: 0.0957 - val_loss: 2.2669 - val_accuracy: 0.2308

2.4 模型评估

# 指定测试数据
net.evaluate(test_images,test_labels,verbose=1)

输出为：

4/4 [==============================] - 1s 338ms/step - loss: 2.3110 - accuracy: 0.0781 [2.310971260070801, 0.078125]

4.延伸版本

GoogLeNet是以InceptionV1为基础进行构建的，所以GoogLeNet也叫做InceptionNet,在随后的⼏年⾥，研究⼈员对GoogLeNet进⾏了数次改进， 就又产生了InceptionV2，V3,V4等版本。

4.1 InceptionV2

在InceptionV2中将大卷积核拆分为小卷积核，将V1中的5×55×5的卷积用两个3×33×3的卷积替代，从而增加网络的深度，减少了参数。

4.2 InceptionV3

将n×n卷积分割为1×n和n×1两个卷积，例如，一个的3×33×3卷积首先执行一个1×31×3的卷积，然后执行一个3×13×1的卷积,这种方法的参数量和计算量都比原来降低。

总结

• 知道GoogLeNet的网络架构：有基础模块Inception构成
• 能够利用GoogleNet完成图像分类

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

下面是另一个代码实现GooLeNet网络模型构建和之前代码不冲突

GooLeNet代码实现

展示模型搭建代码

import torch
import torch.nn as nn
import torch.nn.functional as F
 
#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()
 
    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x
 
#前部
class Front(nn.Module):
    def __init__(self):
        super(Front, self).__init__()
 
        self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
        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)
 
    def forward(self,input):
        #输入：(N,3,224,224)
        x = self.conv1(input)#(N,64,112,112)
        x = self.maxpool1(x)#(N,64,56,56)
        x = self.conv2(x)#(N,64,56,56)
        x = self.conv3(x)#(N,192,56,56)
        x = self.maxpool2(x)#(N,192,28,28)
        return x
 
class Inception(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3_1_1, ch3x3_1, ch3x3_2_1, ch3x3_2, pool_ch):
        super(Inception, self).__init__()
 
        self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
 
        self.branch2 = nn.Sequential(
            BasicConv2d(in_channels, ch3x3_1_1, kernel_size=1),
            BasicConv2d(ch3x3_1_1, ch3x3_1, kernel_size=3, padding=1)
        )
 
        self.branch3 = nn.Sequential(
            BasicConv2d(in_channels, ch3x3_2_1, kernel_size=1),
            BasicConv2d(ch3x3_2_1, ch3x3_2, kernel_size=3, padding=1)
        )
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            BasicConv2d(in_channels, pool_ch, kernel_size=1)
        )
 
    def forward(self, x):
        #输入(N,Cin,Hin,Win)
        branch1 = self.branch1(x)#(N,C1,Hin,Win)
        branch2 = self.branch2(x)#(N,C2,Hin,Win)
        branch3 = self.branch3(x)#(N,C3,Hin,Win)
        branch4 = self.branch4(x)#(N,C4,Hin,Win)
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)#(N,C1+C2+C3+C4,Hin,Win)
 
#辅助分类器
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)
 
        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)
 
    def forward(self, x):
        # 输入：aux1:(N,512,14,14), aux2: (N,528,14,14)
        x = self.averagePool(x)# aux1:(N,512,4,4), aux2: (N,528,4,4)
        x = self.conv(x)# (N,128,4,4)
        x = torch.flatten(x, 1)# (N,2048)
        x = F.dropout(x, 0.5, training=self.training)
        x = F.relu(self.fc1(x))# (N,1024)
        x = F.dropout(x, 0.5, training=self.training)
        x = self.fc2(x)# (N,num_classes)
        return x
 
# GooLeNet网络主体
class GoogLeNet(nn.Module):
    def __init__(self, num_classes=1000, aux_logits=True):
        super(GoogLeNet, self).__init__()
        self.aux_logits = aux_logits
 
        self.front = Front()
 
        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        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)
            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)
 
    def forward(self, x):
        #输入：(N,3,224,224)
        x = self.front(x)#(N,192,28,28)
        x = self.inception3a(x)#(N,256,28,28)
        x = self.inception3b(x)#(N,480,28,28)
        x = self.maxpool3(x)#(N,480,14,14)
        x = self.inception4a(x)#(N,512,14,14)
        if self.training and self.aux_logits:
            aux1 = self.aux1(x)
 
        x = self.inception4b(x)#(N,512,14,14)
        x = self.inception4c(x)#(N,512,14,14)
        x = self.inception4d(x)#(N,528,14,14)
        if self.training and self.aux_logits:
            aux2 = self.aux2(x)
 
        x = self.inception4e(x)#(N,832,14,14)
        x = self.maxpool4(x)#(N,832,7,7)
        x = self.inception5a(x)#(N,832,7,7)
        x = self.inception5b(x)#(N,1024,7,7)
 
        x = self.avgpool(x)#(N,1024,1,1)
        x = torch.flatten(x, 1)#(N,1024)
        x = self.dropout(x)
        x = self.fc(x)#(N,num_classes)
        if self.training and self.aux_logits:
            return x, aux2, aux1
        return x

使用 Pytorch 搭建 GoogleNet 网络

本代码使用的数据集来自 “花分类” 数据集，→ 传送门 ←（具体内容看 data_set文件夹下的 README.md）

• model.py （ 搭建 GoogleNet 网络模型 ）

import torch.nn as nn
import torch
import torch.nn.functional as F
 
 
class GoogleNet(nn.Module):
    # aux_logits: 是否使用辅助分类器（训练的时候为True, 验证的时候为False)
    def __init__(self, num_classes=1000, aux_logits=True, init_weight=False):
        super(GoogleNet, self).__init__()
        self.aux_logits = aux_logits
 
        self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)   # 当结构为小数时，ceil_mode=True向上取整，=False向下取整
        # nn.LocalResponseNorm （此处省略）
        self.conv2 = nn.Sequential(
            BasicConv2d(64, 64, kernel_size=1),
            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)
        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(2, 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 aux_logits:      # 使用辅助分类器
            self.aux1 = InceptionAux(512, num_classes)
            self.aux2 = InceptionAux(528, num_classes)
 
        self.avgpool = nn.AdaptiveAvgPool1d((1, 1))
        self.dropout = nn.Dropout(0.4)
        self.fc = nn.Linear(1024, num_classes)
 
        if init_weight:
            self._initialize_weight()
 
    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
 
        x = self.inception3a(x)
        x = self.inception3b(x)
        x =self.maxpool3(x)
 
        x =self.inception4a(x)
        if self.training and self.aux_logits:
            aux1 = self.aux1(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        if self.training and self.aux_logits:
            aux2 = self.aux2(x)
        x = self.inception4e(x)
        x =self.maxpool4(x)
 
        x = self.inception5a(x)
        x = self.inception5b(x)
 
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.dropout(x)
        x = self.fc(x)
 
        if self.training and self.aux_logits:
            return x, aux1, aux2
        return x
 
 
    def _initialize_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='')
                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)
 
 
 
 
 
 
# 创建 Inception 结构函数（模板）
class Inception(nn.Module):
    # 参数为 Inception 结构的那几个卷积核的数量（详细见表）
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(Inception, self).__init__()
        # 四个并联结构
        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)
        )
        self.branch3 = nn.Sequential(
            BasicConv2d(in_channels, ch5x5red, kernel_size=1),
            BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)
        )
        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)
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)
 
 
# 创建辅助分类器结构函数（模板）
class InceptionAux(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(InceptionAux, self).__init__()
        self.avgPool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = BasicConv2d(in_channels, 128, kernel_size=1)
 
        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（输入）
        x = self.avgPool(x)
        # aux1: N x 512 x 4 x 4  aux2: N x 528 x 4 x 4（输出） 4 = (14 - 5)/3 + 1
        x = self.conv(x)
        x = torch.flatten(x, 1)     # 展平
        x = F.dropout(x, 0.5, training=self.training)
        x = F.relu(self.fc1(x), inplace=True)
        x = F.dropout(x, 0.5, training=self.training)
        x = self.fc2(x)
        return x
 
 
# 创建卷积层函数（模板）
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(True)
 
    def forward(self, x):
        x = self.conv(x)
        x = self.relu(x)
        return x
 
 

• train.py （ 训练网络 ）

import os
import json
 
import torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdm
 
from model import GoogleNet
 
 
def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print("using {} device.".format(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))])}
 
    data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root path
    image_path = os.path.join(data_root, "data_set", "flower_data")  # flower data set path
    assert os.path.exists(image_path), "{} path does not exist.".format(image_path)
    train_dataset = datasets.ImageFolder(root=os.path.join(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())
    # write dict into json file
    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
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using {} dataloader workers every process'.format(nw))
 
    train_loader = torch.utils.data.DataLoader(train_dataset,
                                               batch_size=batch_size, shuffle=True,
                                               num_workers=nw)
 
    validate_dataset = datasets.ImageFolder(root=os.path.join(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=nw)
 
    print("using {} images for training, {} images for validation.".format(train_num,
                                                                           val_num))
 
 
    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)
 
    epochs = 30
    best_acc = 0.0
    save_path = './googleNet.pth'
    train_steps = len(train_loader)
    for epoch in range(epochs):
        # train
        net.train()
        running_loss = 0.0
        train_bar = tqdm(train_loader)
        for step, data in enumerate(train_bar):
            images, labels = data
            optimizer.zero_grad()
            logits, aux_logits2, aux_logits1 = net(images.to(device))   # 由于训练的时候会使用辅助分类器，所有相当于有三个返回结果
            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
            loss.backward()
            optimizer.step()
 
            # print statistics
            running_loss += loss.item()
 
            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                     epochs,
                                                                     loss)
 
        # validate
        net.eval()
        acc = 0.0  # accumulate accurate number / epoch
        with torch.no_grad():
            val_bar = tqdm(validate_loader)
            for val_data in val_bar:
                val_images, val_labels = val_data
                outputs = net(val_images.to(device))  # eval model only have last output layer
                predict_y = torch.max(outputs, dim=1)[1]
                acc += torch.eq(predict_y, val_labels.to(device)).sum().item()
 
        val_accurate = acc / val_num
        print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
              (epoch + 1, running_loss / train_steps, val_accurate))
 
        if val_accurate > best_acc:
            best_acc = val_accurate
            torch.save(net.state_dict(), save_path)
 
    print('Finished Training')
 
 
if __name__ == '__main__':
    main()

• predict.py （ 使用训练好的模型网络对图像分类 ）

import os
import json
 
import torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
 
from model import GoogleNet
 
 
def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
    data_transform = transforms.Compose(
        [transforms.Resize((224, 224)),
         transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
 
    # load image
    img_path = "../tulip.jpg"
    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)
 
    # read class_indict
    json_path = './class_indices.json'
    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
 
    json_file = open(json_path, "r")
    class_indict = json.load(json_file)
 
    # create model
    model = GoogleNet(num_classes=5, aux_logits=False).to(device)
 
    # load model weights
    weights_path = "./googleNet.pth"
    assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)
    missing_keys, unexpected_keys = model.load_state_dict(torch.load(weights_path, map_location=device),
                                                          strict=False)
 
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img.to(device))).cpu()
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()
 
    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
                                                 predict[predict_cla].numpy())
    plt.title(print_res)
    print(print_res)
    plt.show()
 
 
if __name__ == '__main__':
    main()

参考文章：【学习笔记】GoogleNet 网络结构_googlenet特点-CSDN博客

参考文章：GoogLeNet详解-CSDN博客

参考文章：CNN经典网络模型（四）：GoogLeNet简介及代码实现（PyTorch超详细注释版）_googlenet代码-CSDN博客