下载resnet50的模型权重的pytorch代码

以下是使用PyTorch下载ResNet50模型权重的代码示例：

import torch
import torchvision.models as models

# 下载ResNet50模型权重
model = models.resnet50(pretrained=True)

# 保存模型权重
torch.save(model.state_dict(), 'resnet50.pth')

请注意，上述代码中的`pretrained=True`参数将自动下载并加载ResNet50模型的预训练权重。下载完成后，可以使用`torch.save()`函数将权重保存到指定的文件中（此处为'resnet50.pth'）。

相关问题

resnet50预训练权重pytorch

你可以在PyTorch官方的模型库中找到ResNet-50的预训练权重。你可以使用以下代码来加载ResNet-50的预训练权重：

import torch
import torchvision.models as models

# 加载预训练权重
resnet = models.resnet50(pretrained=True)

# 保存权重
torch.save(resnet.state_dict(), 'resnet50_weights.pth')

在上面的代码中，我们使用`torchvision.models`模块中的`resnet50`函数来加载ResNet-50的预训练模型。然后，我们可以使用`state_dict()`方法来获取模型的权重，并使用`torch.save()`函数将权重保存到一个.pth文件中。

希望这能帮到你！如果你还有其他问题，请随时问。

给出ResNet50的pytorch代码，并详细注释

以下是使用PyTorch实现ResNet50模型的完整代码，注释已经添加在代码中：

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

# 定义ResNet50模型
class ResNet50(nn.Module):

    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        self.inplanes = 64
        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(Bottleneck, 64, 3)
        self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
        self.layer3 = self._make_layer(Bottleneck, 256, 6, stride=2)
        self.layer4 = self._make_layer(Bottleneck, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)

        # 初始化权重
        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.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    # 定义Bottleneck块
    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

# 定义Bottleneck块
class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    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)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

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

        return out

# 加载ImageNet预训练模型
def resnet50(pretrained=True, **kwargs):
    model = ResNet50(**kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model

在上面的代码中，我们首先定义了ResNet50模型，包括卷积层、批归一化层、Bottleneck块、全局平均池化层和全连接层。然后，我们定义了Bottleneck块，包括三个卷积层和批归一化层。最后，我们提供了加载ImageNet预训练模型的函数resnet50。