Bn层通道删去第[4,5,6,8]个，bn层其他参数改变的代码

以下是通道删去第[4,5,6,8]个并修改其他参数的代码示例：

import torch.nn as nn

class CustomBN(nn.Module):
    def __init__(self, num_features):
        super(CustomBN, self).__init__()
        self.bn = nn.BatchNorm2d(num_features)
        
        # 获取原有的参数
        weight = self.bn.weight.data.clone()
        bias = self.bn.bias.data.clone()
        mean = self.bn.running_mean.clone()
        var = self.bn.running_var.clone()
        
        # 删除指定的通道
        weight = torch.cat((weight[:4], weight[7:]), dim=0)
        bias = torch.cat((bias[:4], bias[7:]), dim=0)
        mean = torch.cat((mean[:4], mean[7:]), dim=0)
        var = torch.cat((var[:4], var[7:]), dim=0)
        
        # 更新参数
        self.bn.weight = nn.Parameter(weight)
        self.bn.bias = nn.Parameter(bias)
        self.bn.running_mean = mean
        self.bn.running_var = var
    
    def forward(self, x):
        x = self.bn(x)
        return x

在上面的代码中，我们首先继承了 `nn.BatchNorm2d` 类，然后在初始化函数中获取了原有的参数并进行了修改，接着更新了 `weight`、`bias`、`running_mean` 和 `running_var` 参数。最后我们重写了 `forward` 函数，将输入的数据 `x` 通过修改后的 `bn` 层进行前向传播，并返回输出结果。