AttributeError: module 'd2l.torch' has no attribute 'resnet18'

这个错误是因为在 'd2l.torch' 模块中并没有名为 'resnet18' 的属性。可能是你在尝试使用 'd2l.torch.resnet18' 时出错了。

要解决这个问题，你可以检查一下是否导入了正确的模块，并且确认模块中是否包含 'resnet18' 这个属性。你可以查看官方文档或者源代码来确认该模块是否包含所需属性。另外，也可以尝试使用其他的模块或库来获取 'resnet18' 模型。

相关问题

AttributeError: module 'torch.nn.modules' has no attribute 'resnet50'

这个错误通常是由于PyTorch版本问题引起的。在较旧的版本中，`resnet50`是在`torchvision.models`中定义的，而在较新的版本中，它被移动到了`torchvision.models.resnet`中。因此，如果您使用的是较旧的版本，请使用以下代码：

import torchvision.models as models

resnet50 = models.resnet50(pretrained=True)

如果您使用的是较新的版本，请使用以下代码：

import torch.nn as nn
import torch.utils.model_zoo as model_zoo

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               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(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        for m in self.modules():
            if isinstance(m, Bottleneck):
                nn.init.constant_(m.bn3.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        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

def resnet50(pretrained=False, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        state_dict = model_zoo.load_url(model_urls['resnet50'])
        model.load_state_dict(state_dict)
    return model

resnet50 = resnet50(pretrained=True)

AttributeError: module itorchvision.models' has no attribute 'ResNet18_Weights

在您的问题中，您遇到了一个AttributeError: module 'torchvision.models' has no attribute 'ResNet18_Weights'的错误。这意味着torchvision.models模块中没有名为'ResNet18_Weights'的属性。

要解决这个问题，您可以尝试以下方法之一：

1. 检查torchvision的版本：确保您使用的是最新版本的torchvision。您可以使用pip install torchvision --upgrade命令来更新torchvision到最新版本。

2. 检查模型名称：确保您使用的是正确的模型名称。ResNet18_Weights可能不是torchvision.models中可用的模型之一。您可以查看torchvision.models模块的文档，以确定可用的模型名称。

3. 自定义模型：如果您要使用的模型不是torchvision中提供的预训练模型之一，您可以考虑自定义一个模型。您可以使用torch.nn模块来构建自己的模型。

请注意，在上述解决方法中，第一个方法是最常见的解决方法，确保您使用的是最新版本的torchvision，并查看可用的模型名称。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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