RepVGG中

结构重参数化分为两步，第一步是将卷积和BN进行融合以及将只有BN的分支转换成一个Conv2d算子。第二步是将每个分支上的3X3卷积层融合成一个卷积层。

1.1融合Conv和BN

BN中包含四个参数均值、方差、尺度、偏执，对于第i层特征的BN计算公式为：

                                              

卷积的形式，这里借用https://blog.csdn.net/qq_37541097/article/details/125692507博主中的图片演示

卷积之后BN的形式是对应位置卷积后减去对应层的均值，然后除以标准差，乘以对应尺度加上偏执，公式大概如:

                             

其中 pad后的特征图，为对应的卷积核，可以将上述公式分解成 

                                          

Conv2d+BN融合实验：

from collections import OrderedDict

import numpy as np
import torch
import torch.nn as nn


def main():
    torch.random.manual_seed(0)

    f1 = torch.randn(1, 2, 3, 3)

    module = nn.Sequential(OrderedDict(
        conv=nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, stride=1, padding=1, bias=False),
        bn=nn.BatchNorm2d(num_features=2)
    ))

    module.eval()

    with torch.no_grad():
        output1 = module(f1)
        print(output1)

    # fuse conv + bn
    kernel = module.conv.weight 
    running_mean = module.bn.running_mean
    running_var = module.bn.running_var
    gamma = module.bn.weight
    beta = module.bn.bias
    eps = module.bn.eps
    std = (running_var + eps).sqrt()
    t = (gamma / std).reshape(-1, 1, 1, 1)  # [ch] -> [ch, 1, 1, 1]
    kernel = kernel * t
    bias = beta - running_mean * gamma / std
    fused_conv = nn.Conv2d(in_channels=2, out_channels=2, kernel_size=3, stride=1, padding=1, bias=True)
    fused_conv.load_state_dict(OrderedDict(weight=kernel, bias=bias))

    with torch.no_grad():
        output2 = fused_conv(f1)
        print(output2)

    np.testing.assert_allclose(output1.numpy(), output2.numpy(), rtol=1e-03, atol=1e-05)
    print("convert module has been tested, and the result looks good!")


if __name__ == '__main__':
    main()

实验结果：

 

1.2 将1x1卷积转化成3x3卷积

只需要将原来的权重周围补一圈零，变成和3x3卷积一样的形状，此时padding要从0变为1。

1.3 将BN转化为3x3卷积

对于只有BN层的分支，要构造和3x3卷积一样的形式，才能用上述的形式，这种卷积层是做的恒等映射，输入和输出特征图不变。 输出多少特征图，就用多少个卷积核，每个卷积核只在对应输入通道，对应卷积和通道有值且全为一。

这样就可以将BN转化为Conv2d,然后就可以利用上面的将Conv和BN融合

2、多分枝融合 

融合方式很简单，直接将三个分支的参数直接相加

3、结构重参数化实验

import time
import torch.nn as nn
import numpy as np
import torch


def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
    result = nn.Sequential()
    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                        kernel_size=kernel_size, stride=stride, padding=padding,
                                        groups=groups, bias=False))
    result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
    return result


class RepVGGBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False):
        super(RepVGGBlock, self).__init__()
        self.deploy = deploy
        self.groups = groups
        self.in_channels = in_channels
        self.nonlinearity = nn.ReLU()

        if deploy:
            self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                         kernel_size=kernel_size, stride=stride,
                                         padding=padding, dilation=dilation, groups=groups,
                                         bias=True, padding_mode=padding_mode)

        else:
            self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) \
                if out_channels == in_channels and stride == 1 else None
            self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                                     stride=stride, padding=padding, groups=groups)
            self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
                                   stride=stride, padding=0, groups=groups)

    def forward(self, inputs):
        if hasattr(self, 'rbr_reparam'):
            return self.nonlinearity(self.rbr_reparam(inputs))

        if self.rbr_identity is None:
            id_out = 0
        else:
            id_out = self.rbr_identity(inputs)

        return self.nonlinearity(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)

    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid

    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])

    def _fuse_bn_tensor(self, branch):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, 'id_tensor'):
                input_dim = self.in_channels // self.groups
                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, 1, 1] = 1
                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std

    def switch_to_deploy(self):
        if hasattr(self, 'rbr_reparam'):
            return
        kernel, bias = self.get_equivalent_kernel_bias()
        self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels,
                                     out_channels=self.rbr_dense.conv.out_channels,
                                     kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
                                     padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation,
                                     groups=self.rbr_dense.conv.groups, bias=True)
        self.rbr_reparam.weight.data = kernel
        self.rbr_reparam.bias.data = bias
        for para in self.parameters():
            para.detach_()
        self.__delattr__('rbr_dense')
        self.__delattr__('rbr_1x1')
        if hasattr(self, 'rbr_identity'):
            self.__delattr__('rbr_identity')
        if hasattr(self, 'id_tensor'):
            self.__delattr__('id_tensor')
        self.deploy = True

def main():
    f1 = torch.randn(1, 64, 64, 64)
    block = RepVGGBlock(in_channels=64, out_channels=64)
    block.eval()
    with torch.no_grad():
        output1 = block(f1)
        start_time = time.time()
        for _ in range(100):
            block(f1)
        print(f"consume time: {time.time() - start_time}")

        # re-parameterization
        block.switch_to_deploy()
        output2 = block(f1)
        start_time = time.time()
        for _ in range(100):
            block(f1)
        print(f"consume time: {time.time() - start_time}")

        np.testing.assert_allclose(output1.numpy(), output2.numpy(), rtol=1e-03, atol=1e-05)
        print("convert module has been tested, and the result looks good!")


if __name__ == '__main__':
    main()

终端输出结果：