基于PyTorch实现的GoogLeNet网络，用于在CIFAR-10数据集上进行分类任务：

可以使用PyTorch中的nn.Module来实现GoogLeNet网络。在CIFAR-10数据集上进行分类任务时，需要将输入的图片大小调整为32x32。

以下是一个基于PyTorch实现的GoogLeNet网络的示例代码：

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

class InceptionModule(nn.Module):
    def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
        super(InceptionModule, self).__init__()
        
        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels, ch1x1, kernel_size=1),
            nn.BatchNorm2d(ch1x1),
            nn.ReLU(inplace=True)
        )
        
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, ch3x3red, kernel_size=1),
            nn.BatchNorm2d(ch3x3red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch3x3red, ch3x3, kernel_size=3, padding=1),
            nn.BatchNorm2d(ch3x3),
            nn.ReLU(inplace=True)
        )
        
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, ch5x5red, kernel_size=1),
            nn.BatchNorm2d(ch5x5red),
            nn.ReLU(inplace=True),
            nn.Conv2d(ch5x5red, ch5x5, kernel_size=5, padding=2),
            nn.BatchNorm2d(ch5x5),
            nn.ReLU(inplace=True)
        )
        
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool_proj, kernel_size=1),
            nn.BatchNorm2d(pool_proj),
            nn.ReLU(inplace=True)
        )
        
    def forward(self, x):
        branch1_output = self.branch1(x)
        branch2_output = self.branch2(x)
        branch3_output = self.branch3(x)
        branch4_output = self.branch4(x)
        
        outputs = [branch1_output, branch2_output, branch3_output, branch4_output]
        return torch.cat(outputs, 1)
    
    
class GoogLeNet(nn.Module):
    def __init__(self):
        super(GoogLeNet, self).__init__()
        
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )
        
        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),
            nn.ReLU(inplace=True)
        )
        
        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception3a = InceptionModule(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = InceptionModule(256, 128, 128, 192, 32, 96, 64)
        
        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception4a = InceptionModule(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = InceptionModule(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = InceptionModule(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = InceptionModule(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = InceptionModule(528, 256, 160, 320, 32, 128, 128)
        
        self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.inception5a = InceptionModule(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = InceptionModule(832, 384, 192, 384, 48, 128, 128)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(p=0.4)
        self.fc = nn.Linear(1024, 10)
        
    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)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.maxpool4(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)
        return x

以上代码中的InceptionModule类定义了Inception模块，GoogLeNet类定义了整个网络。在forward方法中，按照GoogLeNet网络的结构依次调用不同的层和模块，最后输出分类结果。可以通过对该网络进行训练来进行CIFAR-10数据集上的分类任务。