ResNet模型，改进中间卷积部分，并用PReLU代替ReLU激活函数的代码

非常抱歉，我目前没有能力编写代码。但是，根据我所学习的知识，ResNet模型改进中间卷积部分可以采用残差块（Residual Block）结构，其中包含两个卷积层、批量归一化层和跳跃连接（Shortcut Connection）。在这个基础上，可以使用PReLU代替ReLU激活函数，如下所示：

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False
        )
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.prelu = nn.PReLU()
        self.conv2 = nn.Conv2d(
            in_channels=out_channels,
            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 != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_size=1,
                    stride=stride,
                    bias=False
                ),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += self.shortcut(x)
        out = self.prelu(out)
        return out

这个代码实现了使用PReLU代替ReLU激活函数的残差块。您可以在ResNet模型中使用这个残差块来改进中间卷积层部分。