自定义一个不经过额外的线性层的ResNet50网络，输出维度为512，给出pytorch代码和详细注释

好的，以下是一个自定义的不经过额外线性层的ResNet50网络的PyTorch代码和详细注释：

import torch.nn as nn

# 定义BasicBlock模块
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()

        # 第一个卷积层
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        # 第二个卷积层
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        # 残差连接
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    # 前向传播
    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))  # 第一个卷积层
        out = self.bn2(self.conv2(out))  # 第二个卷积层
        out += self.shortcut(x)  # 残差连接
        out = nn.ReLU()(out)  # 激活函数
        return out

# 定义ResNet模块
class ResNet(nn.Module):
    def __init__(self, block, num_blocks):
        super(ResNet, self).__init__()

        # ResNet第一层卷积层
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        # ResNet四个层
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        # 全局平均池化层
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

    # 构建ResNet中的一个层
    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)  # 每个模块的第一个BasicBlock的步幅为stride，其余为1
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))  # 添加BasicBlock模块
            self.in_planes = planes * block.expansion  # 更新输入通道数
        return nn.Sequential(*layers)

    # 前向传播
    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))  # ResNet第一层卷积层
        out = self.layer1(out)  # ResNet第二层
        out = self.layer2(out)  # ResNet第三层
        out = self.layer3(out)  # ResNet第四层
        out = self.layer4(out)  # ResNet第五层
        out = self.avgpool(out)  # 全局平均池化层
        out = out.view(out.size(0), -1)  # 将输出展平
        return out

# 定义ResNet50模型
def ResNet50():
    return ResNet(BasicBlock, [3, 4, 6, 3])

注意，这个网络只包含了ResNet的特征提取部分，没有额外的线性层。如果你需要在这个网络后面加上额外的线性层，你可以将其输出维度改为512，并在其后面加上一个nn.Linear层。