生成处理一维数据的RESNET代码

以下是一个简单的基于PyTorch的一维RESNET代码：

import torch
import torch.nn as nn

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size, stride, padding)
        self.bn2 = nn.BatchNorm1d(out_channels)
        self.stride = stride
        self.downsample = nn.Sequential(
            nn.Conv1d(in_channels, out_channels, 1, stride),
            nn.BatchNorm1d(out_channels))
        
    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, in_channels, out_channels, block_sizes, num_classes=1):
        super(ResNet, self).__init__()
        self.in_channels = in_channels
        self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(out_channels, block_sizes[0])
        self.layer2 = self._make_layer(out_channels * 2, block_sizes[1], stride=2)
        self.layer3 = self._make_layer(out_channels * 4, block_sizes[2], stride=2)
        self.layer4 = self._make_layer(out_channels * 8, block_sizes[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool1d((1,))
        self.fc = nn.Linear(out_channels * 8, num_classes)
        
    def _make_layer(self, out_channels, block_size, stride=1):
        layers = []
        layers.append(ResBlock(self.in_channels, out_channels, stride=stride))
        self.in_channels = out_channels
        for i in range(1, block_size):
            layers.append(ResBlock(out_channels, out_channels))
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        return out

在这个例子中，我们定义了ResBlock和ResNet两个类。ResBlock是一维RESNET的基本单元，由两个1D卷积层和批归一化层组成。ResNet由四个不同大小的ResBlock层组成，每个层有多个ResBlock单元。在ResNet的最后，我们有一个全连接层将输出映射到我们的目标类别。