将resnet-3d中333卷积替换为深度可分离卷积代码

在将ResNet-3D中的3x3x3卷积替换为深度可分离卷积的代码示例如下：
import torch
import torch.nn as nn

# 定义深度可分离卷积模块
class DepthwiseSeparableConv3d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
super(DepthwiseSeparableConv3d, self).__init__()
self.depthwise = nn.Conv3d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels)
self.pointwise = nn.Conv3d(in_channels, out_channels, 1)

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

# 定义ResNet-3D模型
class ResNet3D(nn.Module):
def __init__(self, in_channels, num_classes):
super(ResNet3D, self).__init__()
self.conv1 = nn.Conv3d(in_channels, 64, kernel_size=7, stride=2, padding=3)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)

# 使用深度可分离卷积替换ResNet中的3x3x3卷积
self.layer1 = self._make_layer(64, 64, 3)
self.layer2 = self._make_layer(64, 128, 4, stride=2)
self.layer3 = self._make_layer(128, 256, 6, stride=2)
self.layer4 = self._make_layer(256, 512, 3, stride=2)

self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
self.fc = nn.Linear(512, num_classes)

def _make_layer(self, in_channels, out_channels, blocks, stride=1):
layers = []
layers.append(nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=stride))
layers.append(nn.BatchNorm3d(out_channels))
layers.append(self.relu)

for _ in range(blocks):
layers.append(DepthwiseSeparableConv3d(out_channels, out_channels, kernel_size=3, padding=1))
layers.append(nn.BatchNorm3d(out_channels))
layers.append(self.relu)

return nn.Sequential(*layers)

def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.maxpool(x)

x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)

x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)

return x

这是一个简化的代码示例，仅展示了深度可分离卷积替换ResNet-3D中的3x3x3卷积的部分。你可以根据自己的需求进行修改和扩展。