写一个pytorch代码：使用resnet18加SENet对故障进行特征分类，其中，resnet18的第一层通道·从16开始

这里是一个使用PyTorch实现ResNet18和SENet的代码，用于故障特征分类：

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

class SEBlock(nn.Module):
    def __init__(self, channels, reduction=16):
        super(SEBlock, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Linear(channels, channels // reduction)
        self.fc2 = nn.Linear(channels // reduction, channels)
        
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc1(y)
        y = F.relu(y)
        y = self.fc2(y)
        y = F.sigmoid(y).view(b, c, 1, 1)
        return x * y.expand_as(x)

class ResNet18(nn.Module):
    def __init__(self, in_channels=16, num_classes=2):
        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.layer1 = self._make_layer(64, 2)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        
    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or planes != 64:
            downsample = nn.Sequential(
                nn.Conv2d(64, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes)
            )
        layers = []
        layers.append(nn.Conv2d(64, planes, kernel_size=3, stride=stride, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(planes))
        layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(planes))
        layers.append(SEBlock(planes))
        layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(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 = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

使用时，可以像这样创建一个ResNet18和SENet的结合模型：

model = ResNet18(in_channels=16, num_classes=10)

其中，`in_channels=16`表示输入图片的通道数为16，`num_classes=10`表示分类的类别数为10。可以根据需要进行修改。