深度可分离卷积代码实现

深度可分离卷积的代码实现可以参考以下步骤：

1. 导入所需的库和模块：

import torch
import torch.nn as nn

2. 定义深度可分离卷积的类：

class DepthwiseSeparableConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(DepthwiseSeparableConv, self).__init__()
        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels)
        self.pointwise = nn.Conv2d(in_channels, out_channels, 1)
    
    def forward(self, x):
        x = self.depthwise(x)
        x = self.pointwise(x)
        return x

3. 创建模型实例并进行使用：

model = DepthwiseSeparableConv(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1)
input = torch.randn(1, 3, 224, 224)
output = model(input)

这样就完成了深度可分离卷积的代码实现。