inception模块

以下是一个简单的Inception模块的PyTorch代码示例：

import torch
import torch.nn as nn

class InceptionModule(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(InceptionModule, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
        self.conv2 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
        self.conv3 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
        self.conv4 = nn.Conv2d(in_channels, out_channels // 4, kernel_size=1)
        self.conv5 = nn.Conv2d(out_channels // 4, out_channels, kernel_size=3, padding=1)
        self.conv6 = nn.Conv2d(out_channels // 4, out_channels, kernel_size=5, padding=2)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        
    def forward(self, x):
        out1 = self.conv1(x)
        out1 = nn.ReLU()(out1)
        
        out2 = self.conv2(x)
        out2 = nn.ReLU()(out2)
        out2 = self.conv5(out2)
        out2 = nn.ReLU()(out2)
        
        out3 = self.conv3(x)
        out3 = nn.ReLU()(out3)
        out3 = self.conv6(out3)
        out3 = nn.ReLU()(out3)
        
        out4 = self.pool(x)
        out4 = self.conv4(out4)
        out4 = nn.ReLU()(out4)
        
        out = torch.cat((out1, out2, out3, out4), dim=1)
        
        return out

这个Inception模块包含四个分支，分别使用不同的卷积核大小和池化操作来提取特征。最后，这些特征被拼接在一起，形成输出特征图。这个模块可以被用于构建更大的神经网络，例如GoogleNet。