RepVGG：算法简介及repvgg.py代码解析
论文链接：RepVGG: Making VGG-style ConvNets Great Again
代码链接：https://github.com/DingXiaoH/RepVGG
一、论文亮点
在ResNet提出之前，计算机视觉模型大多是以VGG为代表的单分支模型，ResNet及其变种的多分支结构，一定程度上解决了梯度消失、模型退化等问题，但是，多分支结构对于训练阶段十分有益，但是在推理/部署时我们希望模型能更快、更灵活、更省内存。于是，这篇论文提出了一种称为“结构重参数化”(Structural Re-parameterization)，它在推理阶段时将训练阶段的模型进行等效融合，使其变成单分支结构，以便更加适应推理场景的需求。
对于论文的算法具体思路，可以参考一下这篇博文：RepVGG网络简介，本文侧重于逐句解析官方源码内容。
二、官方源码解析(repvgg.py)
import torch.nn as nn
import numpy as np
import torch
import copy
from se_block import SEBlock
def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
"""带BN的卷积层"""
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,
stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
super(RepVGGBlock, self).__init__()
self.deploy = deploy # 推理部署
self.groups = groups
self.in_channels = in_channels
assert kernel_size == 3
assert padding == 1
padding_11 = padding - kernel_size // 2 # //为对商进行向下取整；padding_11应该是1×1卷积的padding数
self.nonlinearity = nn.ReLU()
if use_se: # 是否将identity分支换成SE
self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
else:
self.se = nn.Identity()
if deploy: # 定义推理模型时，基本block就是一个简单的 conv2d
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: # 训练时
# identity分支，仅有一个BN层；当输入输出channel不同时，不采用identity分支，即只有3×3卷积分支和1×1卷积分支
self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
# 3×3卷积层+BN
self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
# 1×1卷积+BN，这里padding_11应该为0
self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding_11, groups=groups)
print('RepVGG Block, identity = ', self.rbr_identity)
def forward(self, inputs):
if hasattr(self, 'rbr_reparam'): # 如果在推理阶段，会定义self.rbr_reparam这个属性，这里是判断是否在推理阶段
# 注意：执行return语句后就会退出函数，之后的语句不再执行
return self.nonlinearity(self.se(self.rbr_reparam(inputs))) # self.se()要看use_se是否为True，决定是否用SE模块
if self.rbr_identity is None:
id_out = 0
else:
id_out = self.rbr_identity(inputs)
# 训练阶段：3×3卷积、1×1卷积、identity三个分支的结果相加后进行ReLU
return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))
# Optional. This improves the accuracy and facilitates quantization.
# 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
# 2. Use like this.
# loss = criterion(....)
# for every RepVGGBlock blk:
# loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
# optimizer.zero_grad()
# loss.backward()
def get_custom_L2(self):
K3 = self.rbr_dense.conv.weight
K1 = self.rbr_1x1.conv.weight
t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel.
l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2.
return l2_loss_eq_kernel + l2_loss_circle
# This func derives the equivalent kernel and bias in a DIFFERENTIABLE way.
# You can get the equivalent kernel and bias at any time and do whatever you want,
# for example, apply some penalties or constraints during training, just like you do to the other models.
# May be useful for quantization or pruning.
def get_equivalent_kernel_bias(self):
"""获取带BN的3×3卷积、带BN的1×1卷积和带BN的identity分支的等效卷积核、偏置，论文Fig4的第二个箭头"""
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)
# 返回三个等效卷积层的叠加，即三个卷积核相加、三个偏置相加
return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
def _pad_1x1_to_3x3_tensor(self, kernel1x1):
""" [0 0 0]
[1] >>>padding>>> [0 1 0]
[0 0 0] """
if kernel1x1 is None:
return 0
else:
return torch.nn.functional.pad(kernel1x1, [1,1,1,1])
def _fuse_bn_tensor(self, branch):
"""融合BN层，既可以实现卷积层和BN层融合成等效的3×3卷积层，也可以实现单独BN层等效成3×3卷积层，论文Fig4的第一个箭头"""
if branch is None: # 若branch不是3x3、1x1、BN，那就返回 W=0, b=0
return 0, 0
if isinstance(branch, nn.Sequential): # 若branch是nn.Sequential()类型，即一个卷积层+BN层
kernel = branch.conv.weight # 卷积核权重
running_mean = branch.bn.running_mean # BN的均值
running_var = branch.bn.running_var # BN的方差
gamma = branch.bn.weight # BN的伽马
beta = branch.bn.bias # BN的偏置
eps = branch.bn.eps # BN的小参数，为了防止分母为0
else: # 如果传进来的仅仅是一个BN层
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // self.groups # self.group如果不等于1，即为分组卷积
# 由于BN层不改变特征层维度，所以这里在自定义kernel时：batch=in_channels，那么输出特征层的深度依然是in_channels
# np.zeros((self.in_channels, input_dim, 3, 3)即为in_channels个(input_dim, 3, 3)大小的卷积核
# 每个卷积核生成一个channel的feature map，故生成输出feature map的通道数依然是in_channels
kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32) # [batch,C,H,W]
for i in range(self.in_channels):
# 这个地方口头表达不太好说，总之就是将BN等效成3×3卷积核的过程，可以去看一下论文理解
# 值得注意的是，i是从0算起的
kernel_value[i, i % input_dim, 1, 1] = 1
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) # 转成tensor并指定给device
# 获取参数
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() # 标准差
t = (gamma / std).reshape(-1, 1, 1, 1) # reshape成与[batch,1,1,1]方便在kernel * t时进行广播
return kernel * t, beta - running_mean * gamma / std # 返回加权后的kernel和偏置
def switch_to_deploy(self):
"""转换成推理所需的VGG结构"""
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
class RepVGG(nn.Module):
def __init__(self, num_blocks, num_classes=1000, width_multiplier=None, override_groups_map=None,
deploy=False, use_se=False):
super(RepVGG, self).__init__()
# len(width_multiplier) == 4对应论文3.4节第三段第一句
assert len(width_multiplier) == 4 # 宽度因子，改变输入/输出通道数
self.deploy = deploy
self.override_groups_map = override_groups_map or dict() # 用于分组卷积的字典，在L215处有定义
self.use_se = use_se
assert 0 not in self.override_groups_map # 确保0不会成为分组数
self.in_planes = min(64, int(64 * width_multiplier[0]))
self.stage0 = RepVGGBlock(in_channels=3, out_channels=self.in_planes, kernel_size=3, stride=2, padding=1, deploy=self.deploy, use_se=self.use_se)
self.cur_layer_idx = 1 # 当前layer的索引，用于对特定layer设置分组卷积
self.stage1 = self._make_stage(int(64 * width_multiplier[0]), num_blocks[0], stride=2)
self.stage2 = self._make_stage(int(128 * width_multiplier[1]), num_blocks[1], stride=2)
self.stage3 = self._make_stage(int(256 * width_multiplier[2]), num_blocks[2], stride=2)
self.stage4 = self._make_stage(int(512 * width_multiplier[3]), num_blocks[3], stride=2)
self.gap = nn.AdaptiveAvgPool2d(output_size=1)
self.linear = nn.Linear(int(512 * width_multiplier[3]), num_classes)
def _make_stage(self, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1) # 论文中提到：每个stage仅在第一个layer进行步长为2
blocks = [] # 创建空列表
for stride in strides:
# 获取override_groups_map中键self.cur_layer_idx对应的值，若未设置值，则返回默认值1
cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1) # 分组数
blocks.append(RepVGGBlock(in_channels=self.in_planes, out_channels=planes, kernel_size=3,
stride=stride, padding=1, groups=cur_groups, deploy=self.deploy, use_se=self.use_se))
self.in_planes = planes # RepVGG中实例属性self.in_planes随传入planes而改变
self.cur_layer_idx += 1 # 每次循环即创建一个layer，那么相应的layer的索引就要+1
return nn.Sequential(*blocks)
def forward(self, x): # 前向传播
out = self.stage0(x)
out = self.stage1(out)
out = self.stage2(out)
out = self.stage3(out)
out = self.stage4(out)
out = self.gap(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
# 特定layer的索引分组卷积
optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26] # 论文3.4节第四段
g2_map = {l: 2 for l in optional_groupwise_layers} # {2: 2, 4: 2, 6: 2,...,20: 2, 22: 2, 24: 2, 26: 2}
g4_map = {l: 4 for l in optional_groupwise_layers}
def create_RepVGG_A0(deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
width_multiplier=[0.75, 0.75, 0.75, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_A1(deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_A2(deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=1000,
width_multiplier=[1.5, 1.5, 1.5, 2.75], override_groups_map=None, deploy=deploy)
def create_RepVGG_B0(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B1(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2, 2, 2, 4], override_groups_map=None, deploy=deploy)
def create_RepVGG_B1g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2, 2, 2, 4], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B1g4(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2, 2, 2, 4], override_groups_map=g4_map, deploy=deploy)
def create_RepVGG_B2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B2g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B2g4(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map, deploy=deploy)
def create_RepVGG_B3(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[3, 3, 3, 5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B3g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[3, 3, 3, 5], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B3g4(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[3, 3, 3, 5], override_groups_map=g4_map, deploy=deploy)
def create_RepVGG_D2se(deploy=False):
return RepVGG(num_blocks=[8, 14, 24, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None, deploy=deploy, use_se=True)
func_dict = {
'RepVGG-A0': create_RepVGG_A0,
'RepVGG-A1': create_RepVGG_A1,
'RepVGG-A2': create_RepVGG_A2,
'RepVGG-B0': create_RepVGG_B0,
'RepVGG-B1': create_RepVGG_B1,
'RepVGG-B1g2': create_RepVGG_B1g2,
'RepVGG-B1g4': create_RepVGG_B1g4,
'RepVGG-B2': create_RepVGG_B2,
'RepVGG-B2g2': create_RepVGG_B2g2,
'RepVGG-B2g4': create_RepVGG_B2g4,
'RepVGG-B3': create_RepVGG_B3,
'RepVGG-B3g2': create_RepVGG_B3g2,
'RepVGG-B3g4': create_RepVGG_B3g4,
'RepVGG-D2se': create_RepVGG_D2se, # Updated at April 25, 2021. This is not reported in the CVPR paper.
}
def get_RepVGG_func_by_name(name): # 传入所需构建的model名（key），返回构建model的函数
return func_dict[name]
# Use this for converting a RepVGG model or a bigger model with RepVGG as its component
# Use like this
# model = create_RepVGG_A0(deploy=False)
# train model or load weights
# repvgg_model_convert(model, save_path='repvgg_deploy.pth')
# If you want to preserve the original model, call with do_copy=True
# ====================== for using RepVGG as the backbone of a bigger model, e.g., PSPNet, the pseudo code will be like
# train_backbone = create_RepVGG_B2(deploy=False)
# train_backbone.load_state_dict(torch.load('RepVGG-B2-train.pth'))
# train_pspnet = build_pspnet(backbone=train_backbone)
# segmentation_train(train_pspnet)
# deploy_pspnet = repvgg_model_convert(train_pspnet)
# segmentation_test(deploy_pspnet)
# ===================== example_pspnet.py shows an example
def repvgg_model_convert(model:torch.nn.Module, save_path=None, do_copy=True):
"""模型转换"""
if do_copy:
model = copy.deepcopy(model)
for module in model.modules():
if hasattr(module, 'switch_to_deploy'):
module.switch_to_deploy()
if save_path is not None:
torch.save(model.state_dict(), save_path)
return model
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