去除refinedet网络的conv6-1，conv6-2卷积层，对conv4-3以及conv5-3进行BN归一化处理代码

假设你使用的是PyTorch框架，以下是可能的代码实现：

import torch.nn as nn

class RefineDet(nn.Module):
    def __init__(self, num_classes):
        super(RefineDet, self).__init__()
        # 网络结构代码省略...
        
        # 去除 conv6-1 和 conv6-2
        self.loc_layers = nn.ModuleList([
            nn.Conv2d(512, 4 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(1024, 6 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(512, 6 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 6 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 4 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 4 * num_anchors, kernel_size=3, padding=1)
        ])
        self.conf_layers = nn.ModuleList([
            nn.Conv2d(512, 2 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(1024, 3 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(512, 3 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 3 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 2 * num_anchors, kernel_size=3, padding=1),
            nn.Conv2d(256, 2 * num_anchors, kernel_size=3, padding=1)
        ])
        
        # 对 conv4-3 和 conv5-3 进行 BN 归一化处理
        self.conv4_3_norm = nn.BatchNorm2d(512)
        self.conv5_3_norm = nn.BatchNorm2d(512)
        
    def forward(self, x):
        # 网络前向传播代码省略...
        
        # 对 conv4-3 和 conv5-3 进行 BN 归一化处理
        conv4_3_norm = self.conv4_3_norm(x[23])
        conv5_3_norm = self.conv5_3_norm(x[-2])
        
        # 网络后续处理代码省略...

需要注意的是，去除 conv6-1 和 conv6-2 两个卷积层后，网络的输出通道数会发生变化，需要相应地修改后续的定位层和分类层。同时，对 conv4-3 和 conv5-3 进行 BN 归一化处理时，需要根据网络结构中这两个卷积层的索引进行选择。