用pytorch写深度可分离卷积

可以使用PyTorch的nn模块中的DepthwiseConv2d和PointwiseConv2d来实现深度可分离卷积。具体实现可以参考以下代码：

import torch.nn as nn

class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=, dilation=1, bias=False):
super(SeparableConv2d, self).__init__()
self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias)
self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, , 1, 1, bias=bias)

def forward(self, x):
x = self.depthwise(x)
x = self.pointwise(x)
return x

# 使用示例
model = nn.Sequential(
SeparableConv2d(3, 32, 3, padding=1),
nn.ReLU(),
SeparableConv2d(32, 64, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
SeparableConv2d(64, 128, 3, padding=1),
nn.ReLU(),
SeparableConv2d(128, 128, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
SeparableConv2d(128, 256, 3, padding=1),
nn.ReLU(),
SeparableConv2d(256, 256, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
SeparableConv2d(256, 512, 3, padding=1),
nn.ReLU(),
SeparableConv2d(512, 512, 3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Flatten(),
nn.Linear(512 * 4 * 4, 10)
)