用pytorch写一个一维残差卷积神经网络代码

以下是一个简单的一维残差卷积神经网络的 PyTorch 代码示例：

import torch
import torch.nn as nn

class ResNet1D(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
        super(ResNet1D, 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.in_channels = in_channels
        self.out_channels = out_channels
        self.padding = padding

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.stride != 1 or self.in_channels != self.out_channels:
            residual = nn.functional.conv1d(x, self.out_channels, kernel_size=1, stride=self.stride, padding=)
            residual = nn.BatchNorm1d(self.out_channels)

        out += residual
        out = self.relu(out)

        return out

这是一个基本的 ResNet1D 类，它包含两个卷积层和一个残差块。在前向传递期间，输入通过第一个卷积层，然后通过批量标准化和 ReLU 激活函数。然后，输出通过第二个卷积层和批量标准化。如果输入和输出通道数不同，或者步幅不为 1，则使用 1x1 卷积层和批量标准化来调整残差。最后，输出通过 ReLU 激活函数并返回。