给我一段resnet18处理17*1600格式数据的pytorch代码

可以参考以下代码实现：

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

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResBlock, self).__init__()

        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(out_channels)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNet18(nn.Module):
    def __init__(self, num_classes):
        super(ResNet18, self).__init__()

        self.in_channels = 17

        self.conv1 = nn.Conv1d(17, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.maxpool = nn.MaxPool1d(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.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, out_channels, blocks, stride=1):
        layers = []
        layers.append(ResBlock(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        for i in range(1, blocks):
            layers.append(ResBlock(out_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        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 = out.view(out.size(0), -1)
        out = self.fc(out)

        return out

这是一个基于PyTorch的ResNet18模型，可以处理输入数据为大小为17x1600的格式。