帮我用pytorch写一个resnet18的模型，模型中不要引用BasicBlock，模型的第一层layer都是用nn.sequential。最后将该模型转为onnx

以下是使用PyTorch实现ResNet18模型的代码，其中使用了nn.Sequential代替BasicBlock：

import torch
import torch.nn as nn

class ResNet18(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )

        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )

        self.conv4 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )

        self.conv5 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x) + x
        x = self.conv3(x) + x
        x = self.conv4(x) + x
        x = self.conv5(x) + x
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 将模型转为ONNX格式
model = ResNet18()
dummy_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, dummy_input, 'resnet18.onnx', verbose=True)

转换完成后，可以在当前目录下找到名为resnet18.onnx的文件，这就是我们的ONNX模型。