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前言

 在上一篇博文RepVGG中，介绍了RepVGG网络。RepVGG 作为一种高效的重参数化网络，通过训练时的多分支结构（3x3卷积、1x1卷积、恒等映射）和推理时的单分支合并，在精度与速度间取得了优秀平衡。然而，其在低精度（如INT8）量化后常出现显著精度损失。
 本文将要介绍的QARepVGG（Make RepVGG Greater Again: A Quantization-aware Approach）的提出正是为了解决这一问题。其核心贡献在于基础的Block设计：

引入

 文章做了详细的消融实验来一步一步的推理出这种结构，本文在此不多做赘述。只大概提一下：RepVGG其实是由三个单元构成：权重、BN和ReLU。卷积操作一般不会影响权重值的改变，基本服从0~1分布；而根据BN层的公式，会出现一个乘法项，导致方差可能发生改变；另外，如果输入的数值范围很大，经过ReLU也会产生大的方差项，导致量化困难。
 因此，QARepVGG去掉了BN层，并在三个分支后新加了一个BN层来将分布改成一个量化友好的分布。
 当然，建议读者阅读原论文，好多实验的设计跟分析很透彻。

Demo实现

 本文旨在复现一个QARepVGG Block，读者可一键运行：

import torch
import torch.nn as nnclass QARepVGGBlock(nn.Module):def __init__(self, in_channels, out_channels):super().__init__()assert in_channels == out_channels, "输入输出通道必须相同！"# 分支1：3x3卷积 + BNself.conv3x3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)self.bn3x3 = nn.BatchNorm2d(out_channels)# 分支2：1x1卷积（无BN）self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)# 分支3：恒等映射（无BN）self.identity = nn.Identity()  # 直接传递输入# 合并后的BN层self.final_bn = nn.BatchNorm2d(out_channels)# 初始化权重（关键！）self._init_weights()def _init_weights(self):"""显式初始化权重"""nn.init.kaiming_normal_(self.conv3x3.weight, mode='fan_out', nonlinearity='relu')nn.init.zeros_(self.conv1x1.weight)  # 初始化为零，与恒等映射互补def forward(self, x):# 分支1：3x3卷积 + BNbranch3x3 = self.bn3x3(self.conv3x3(x))# 分支2：1x1卷积branch1x1 = self.conv1x1(x)# 分支3：恒等映射branch_id = self.identity(x)# 合并后通过最终BNout = self.final_bn(branch3x3 + branch1x1 + branch_id)return outdef reparameterize(self):"""将多分支合并为单一3x3卷积，并融合BN参数"""# 1. 将各分支转换为等效3x3卷积# 分支1：3x3卷积 + BN3x3kernel3x3, bias3x3 = self._fuse_conv_bn(self.conv3x3, self.bn3x3)# 分支2：1x1卷积（无BN），填充为3x3kernel1x1 = self._pad_1x1_to_3x3(self.conv1x1.weight)bias1x1 = torch.zeros_like(bias3x3)  # 无偏置# 分支3：恒等映射（视为1x1单位矩阵卷积，填充为3x3）identity_kernel = torch.eye(self.conv3x3.in_channels, device=self.conv3x3.weight.device)identity_kernel = identity_kernel.view(self.conv3x3.in_channels, self.conv3x3.in_channels, 1, 1)kernel_id = self._pad_1x1_to_3x3(identity_kernel)bias_id = torch.zeros_like(bias3x3)# 2. 合并所有分支的权重和偏置merged_kernel = kernel3x3 + kernel1x1 + kernel_idmerged_bias = bias3x3 + bias1x1 + bias_id# 3. 融合最终BN层参数scale = self.final_bn.weight / (self.final_bn.running_var + self.final_bn.eps).sqrt()merged_kernel = merged_kernel * scale.view(-1, 1, 1, 1)merged_bias = scale * (merged_bias - self.final_bn.running_mean) + self.final_bn.bias# 4. 构建合并后的卷积层merged_conv = nn.Conv2d(self.conv3x3.in_channels,self.conv3x3.out_channels,kernel_size=3,padding=1,bias=True)merged_conv.weight.data = merged_kernelmerged_conv.bias.data = merged_biasreturn merged_convdef _fuse_conv_bn(self, conv, bn):"""融合卷积和BN的权重与偏置"""kernel = conv.weightrunning_mean = bn.running_meanrunning_var = bn.running_vargamma = bn.weightbeta = bn.biaseps = bn.epsstd = (running_var + eps).sqrt()scale_factor = gamma / stdfused_kernel = kernel * scale_factor.view(-1, 1, 1, 1)fused_bias = beta - running_mean * scale_factorreturn fused_kernel, fused_biasdef _pad_1x1_to_3x3(self, kernel):"""将1x1卷积核填充为3x3（中心为原权重，其余为0）"""if kernel.size(-1) == 1:padded = torch.zeros(kernel.size(0), kernel.size(1), 3, 3, device=kernel.device)padded[:, :, 1, 1] = kernel.squeeze()return paddedreturn kernel def test_qarepvgg():torch.manual_seed(42)# 输入数据（小方差加速BN收敛）x = torch.randn(2, 3, 4, 4) * 0.1# 初始化模块block = QARepVGGBlock(3, 3)# 训练模式：更新BN统计量block.train()for _ in range(100):  # 充分训练y = block(x)y.sum().backward()  # 伪反向传播# 推理模式：合并权重block.eval()with torch.no_grad():# 原始输出orig_out = block(x)# 合并后的卷积merged_conv = block.reparameterize()merged_out = merged_conv(x)# 打印关键数据print("out:", orig_out.mean().item())print("merge:", merged_out.mean().item())print("diff:", torch.abs(orig_out - merged_out).max().item())# 验证一致性（容差1e-6）assert torch.allclose(orig_out, merged_out, atol=1e-6), f"合并失败！最大差值：{torch.abs(orig_out - merged_out).max().item()}"print("✅ 测试通过！")test_qarepvgg()

总结

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