使用PyTorch实现的ResNet50代码

以下是使用PyTorch实现的ResNet50代码示例：

import torch
import torch.nn as nn


# 定义基本的残差块
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != self.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * out_channels)
            )

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

        return out


# 定义ResNet50模型
class ResNet50(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        self.in_channels = 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(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)

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

    def _make_layer(self, out_channels, num_blocks, stride=1):
        layers = []
        layers.append(BasicBlock(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * BasicBlock.expansion
        for _ in range(1, num_blocks):
            layers.append(BasicBlock(self.in_channels, out_channels))
            self.in_channels = out_channels * BasicBlock.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        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 = torch.flatten(out, 1)
        out = self.fc(out)

        return out


# 创建ResNet50模型实例
model = ResNet50(num_classes=1000)

# 打印模型结构
print(model)

以上代码是一个使用PyTorch实现的ResNet50模型的示例。请注意，该代码只包含了模型的定义部分，并没有包括数据加载、训练和评估等部分。如果你需要完整的训练代码，请告诉我，我可以提供给你。