本系列文章记录本人硕士阶段YOLO系列目标检测算法自学及其代码实现的过程。其中算法具体实现借鉴于ultralytics YOLO源码Github，删减了源码中部分内容，满足个人科研需求。
  本系列文章在YOLOv5算法实现的基础上，进一步完成YOLOv7算法的实现。YOLOv7相比于YOLOv5，最主要的不同之处如下：

• 模型结构：引进了更为高效的特征提取模块(ELAN)、下采样模块(MP)，不同的空间池化层(SPPCSPC)，重参数卷积(RepConv)
• 损失计算：采用YOLO-X中的样本匹配方法(SimOTA)

文章地址：
YOLOv5算法实现(一)：模型搭建
[YOLOv7算法实现(二)：损失计算]

1 模型结构

  YOLOv7的模型结构如图1所示，其包含以下几个模块：

• CBS：卷积层、批标准化(BN)和SiLU激活函数
• ELAN：多梯度融合特征提取模块，根据融合的梯度信息的不同分为ELAB-B和ELAB-H
• MP：最大池化和CBS结合的下采样模块
• SPPCSPC：跨阶段特征金字塔池化模块
• RepConv：重参数化卷积
• Detect：检测头
  

图1 YOLOv7网络结构

2 模型模块实现(common.py)

2.1 CBS模块

Class Conv(nn.Module):'''卷积块:conv-BN-Activation'''default_act = nn.SiLU()  # 默认激活函数def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True, b=False):''':param c1: 输入通道数:param c2: 输出通道数:param k: 卷积核大小:param s: 步长:param p: 填充 默认为None则表示填充至与输入分辨率相同:param g: 分组卷积,默认为1时为标准卷积:param d: 间隙卷积,默认为1时为标准卷积;不为1表示点之间有空隙的过滤器,对卷积核进行膨胀:param act: 是否使用激活函数:param b: 卷积偏置,默认使用无偏置卷积'''super(Conv, self).__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=b)self.bn = nn.BatchNorm2d(c2)# 若act为True:使用默认激活函数;若act为其他激活函数模块:则使用该激活函数;反之:使用nn.Identity,表示不对输入进行操作,直接输出输入self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):# 前向传播return self.act(self.bn(self.conv(x)))

2.2 ELAN模块

ELAN_B

class ELAN_B(nn.Module):'''yolov7中特征提取模块(backbone部分)使用了密集的残差结构, 通过增加相当的深度来提高准确率; 内部的残差块使用跳跃连接,缓解了深度神经网络中增加深度带来的梯度消失问题。'''def __init__(self, c1, c2, e=0.5):super().__init__()c_ = int(c1 * e)self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = nn.Sequential(Conv(c_, c_, 3, 1),Conv(c_, c_, 3, 1))self.cv4 = nn.Sequential(Conv(c_, c_, 3, 1),Conv(c_, c_, 3, 1))self.cv5 = Conv(c_ * 4, c2, 1, 1)def forward(self, x):y1 = self.cv1(x)  # c1 // 2y2 = self.cv2(x)  # c1 // 2y3 = self.cv3(y2)  # c1y4 = self.cv4(y3)  # c1return self.cv5(torch.cat([y1, y2, y3, y4], dim=1))

ELAN-H

class ELAN_H(nn.Module):def __init__(self, c1, c2, e=0.5):'''Head部分'''super().__init__()c_ = int(c1 * e)c__ = c_ // 2self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(c_, c__, 3, 1)self.cv4 = Conv(c__, c__, 3, 1)self.cv5 = Conv(c__, c__, 3, 1)self.cv6 = Conv(c__, c__, 3, 1)self.cv7 = Conv(c_ * 2 + c__ * 4, c2, 1, 1)def forward(self, x):y1 = self.cv1(x)y2 = self.cv2(x)y3 = self.cv3(y2)y4 = self.cv4(y3)y5 = self.cv5(y4)y6 = self.cv6(y5)return self.cv7(torch.cat([y1, y2, y3, y4, y5, y6], dim=1))

2.3 MP模块

class MP(nn.Module):'''yolov7中下采样模块'''def __init__(self, c1, c2):super().__init__()# MP-1if c1 == c2:c_ = c1 // 2# MP-2else:c_ = c1# 第一个分支self.maxpool = MaxPool(k=2, s=2)self.cv1 = Conv(c1, c_, 1, 1)# 第二个分支self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(c_, c_, 3, 2)def forward(self, x):o1 = self.cv1(self.maxpool(x))o2 = self.cv3(self.cv2(x))return torch.cat([o1, o2], dim=1)

2.4 SPPCSPC模块

class SPPCSPC(nn.Module):def __init__(self, c1, c2, k=(5, 9, 13), e=0.5):super().__init__()c_ = int(2 * c2 * e)self.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(c_, c_, 3, 1)self.cv4 = Conv(c_, c_, 1, 1)self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])self.cv5 = Conv(4 * c_, c_, 1, 1)self.cv6 = Conv(c_, c_, 3, 1)self.cv7 = Conv(2 * c_, c2, 1, 1)def forward(self, x):x1 = self.cv4(self.cv3(self.cv1(x)))y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))y2 = self.cv2(x)return self.cv7(torch.cat((y1, y2), dim=1))

2.5 RepConv模块

2.5.1 参数融合

  RepConv模块的具体结构如图1中所示，在推理阶段其将多个卷积模块融为一个卷积模块，提高模型计算的高效性。

卷积层+BN层 → 卷积层

  卷积层的计算公式为：
$$Conv(x)=W(x)+b$$
在pytorch中，卷积层权重为conv.weight，偏置为conv.bias。
  BN层的计算公式为：
$$BN(x)=\gamma \frac{x-mean}{\sqrt{\mathrm{var}}}+\beta$$
在pytorch中，γ = bn.weight，mean = bn.running_mean, var = bn.running_var, β = bn.bias。
  卷积层和BN层融合公式为：

$$BN(Conv(x))=\gamma \frac{(W(x)+b)-mean}{\sqrt{\mathrm{var}}}+\beta =new\_W(x)+new\_b$$
$$new\_W(x)=\frac{\gamma W(x)}{\sqrt{\mathrm{var}}},new\_b=\frac{\gamma (b-mean)}{\sqrt{\mathrm{var}}}+\beta$$

BN层->卷积层：构造一个参数为0的卷积层(1×1)，实现卷积层+BN层融合

卷积层(3×3)+卷积层(1×1) →卷积层(3×3)：将卷积层(1×1)填充为3×3，再将卷积层权重和偏置相加实现融合

2.5.2 模块实现

class RepConv(nn.Module):def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):'''重参数卷积训练时:deploy = Falserbr_dense(3x3卷积) + rbr_1x1(1x1卷积) + rbr_identity(c2==c1时)相加rbr_reparam = None推理时:deploy = Truerbr_param = Conv2drbr_dense, rbr_1x1, rbr_identity = None, None, None'''super().__init__()self.deploy = deployself.groups = gself.in_channels = c1self.out_channels = c2assert k == 3assert autopad(k, p) == 1padding_11 = autopad(k, p) - k // 2self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())# 推理阶段, 仅有一个3x3卷积if self.deploy:self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)else:# 输入输出通道数相同时, identity层(BN层)self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)# 3×3卷积 + BN层self.rbr_dense = nn.Sequential(nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),nn.BatchNorm2d(num_features=c2),)# 1×1卷积 + BN层self.rbr_1x1 = nn.Sequential(nn.Conv2d(c1, c2, 1, s, padding_11, groups=g, bias=False),nn.BatchNorm2d(num_features=c2),)def forward(self, x):# 推理阶段if hasattr(self, 'rbr_reparam'):return self.act(self.rbr_reparam(x))# 训练阶段if self.rbr_identity is None:id_out = 0else:id_out = self.rbr_identity(x)return self.act(self.rbr_dense(x) + self.rbr_1x1(x) + id_out)#融合卷积层和BN层: Conv2D+BN=Conv2Ddef fuse_conv_bn(self, conv, bn):std = (bn.running_var + bn.eps).sqrt()bias = bn.bias - bn.running_mean * bn.weight / stdt = (bn.weight / std).reshape(-1, 1, 1, 1)weights = conv.weight * tbn = nn.Identity()conv = nn.Conv2d(in_channels=conv.in_channels,out_channels=conv.out_channels,kernel_size=conv.kernel_size,stride=conv.stride,padding=conv.padding,dilation=conv.dilation,groups=conv.groups,bias=True,padding_mode=conv.padding_mode)conv.weight = torch.nn.Parameter(weights)conv.bias = torch.nn.Parameter(bias)return conv# 重参数操作(在推理阶段执行)def fuse_repvgg_block(self):if self.deploy:returnprint(f"RepConv.fuse_repvgg_block")# 融合3x3的卷积层和BN层为一个3x3卷积(有偏置)self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])# 融合1x1的卷积层和BN层为一个1x1卷积(有偏置)self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])rbr_1x1_bias = self.rbr_1x1.bias# 填充卷积核大小与3x3卷积大小相同weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])# 融合identity的BN层为一个1x1卷积(无偏置)if isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm):identity_conv_1x1 = nn.Conv2d(in_channels=self.in_channels,out_channels=self.out_channels,kernel_size=1,stride=1,padding=0,groups=self.groups,bias=False)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()identity_conv_1x1.weight.data.fill_(0.0)identity_conv_1x1.weight.data.fill_diagonal_(1.0)identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)# 融合该1x1卷积和Identity的BN层identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)bias_identity_expanded = identity_conv_1x1.biasweight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1])else:bias_identity_expanded = torch.nn.Parameter(torch.zeros_like(rbr_1x1_bias))weight_identity_expanded = torch.nn.Parameter(torch.zeros_like(weight_1x1_expanded))# 融合3x3卷积和扩充的1x1卷积的权重和偏置self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)self.rbr_reparam = self.rbr_denseself.deploy = Trueif self.rbr_identity is not None:del self.rbr_identityself.rbr_identity = Noneif self.rbr_1x1 is not None:del self.rbr_1x1self.rbr_1x1 = Noneif self.rbr_dense is not None:del self.rbr_denseself.rbr_dense = None

2.6 Detect模块

Detect模块的具体实现过程可见文章YOLOv5算法实现(二)：模型搭建

3 模型配置文件构建(model.yaml)

基于图1所示的模型结构和模型模块所需的参数，构建模型配置文件。其中结构解析包含四个参数[from，number，module，args]：

• from：当前层的输入来自于哪一层
• number：当前层数量
• module：当前层所有模块(在common.py中实现，需与类名对应)
• args：第一个参数为当前层输出通道数，其余参数为模块特有参数；当前层的输入通道数由“from”参数指向的层决定，在结构解析时加入该参数。

# Parameters
nc: 80  # number of classes 类别数
depth_multiple: 1.0  # model depth multiple 模型深度(模块个数系数)
width_multiple: 1.0  # layer channel multiple 模型宽度(模块通道数系数)
anchors: - [10,13, 16,30, 33,23]  # P3/8 (stride=8 feature_map所用Anchor,小目标检测)- [30,61, 62,45, 59,119]  # P4/16 (stride=16 feature_map所用Anchor)- [116,90, 156,198, 373,326]  # P5/32 (stride=32 feature_map所用Anchor,大目标检测)backbone:# [from, number, module, args][[-1, 1, Conv, [32, 3, 1]],  # 0[-1, 1, Conv, [64, 3, 2]],  # 1-P1/2[-1, 1, Conv, [64, 3, 1]],  # 2[-1, 1, Conv, [128, 3, 2]], # 3-P2/4[-1, 1, ELAN_B, [256]],       # 4[-1, 1, MP, [256]],         # 5-P3/8[-1, 1, ELAN_B, [512]],       # 6[ -1, 1, MP, [512]],        # 7-P4/16[ -1, 1, ELAN_B, [1024]],     # 8[ -1, 1, MP, [1024]],        # 9-P5/32[ -1, 1, ELAN_B, [1024, 0.25]],# 10]head:[[-1, 1, SPPCSPC, [512]],     # 11[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[8, 1, Conv, [256, 1, 1]],  # route backbone P4[[-1, -2], 1, Concat, [1]],[-1, 1, ELAN_H, [256]],  # 16[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[6, 1, Conv, [128, 1, 1]], # route backbone P3[[-1, -2], 1, Concat, [1]],[-1, 1, ELAN_H, [128]],  # 21 (P3/8-samll)[-1, 1, MP, [256]],[[-1, 16], 1, Concat, [1]],[-1, 1, ELAN_H, [256]],  # 24 (P4/16-medium)[-1, 1, MP, [512]],[[-1, 11], 1, Concat, [1]],[-1, 1, ELAN_H, [512]],  # 27 (P5/32-large)[21, 1, RepConv, [256, 3, 1]], # 28[24, 1, RepConv, [512, 3, 1]], # 29[27, 1, RepConv, [1024, 3, 1]], # 30[[28, 29, 30], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

4 模型搭建(yolo.py)

模型搭建的具体实现方法可见文章YOLOv5算法实现(二)：模型搭建，在YOLOv7中，在模型类中额外添加一个如下函数实现RepConv模块的融合即可。

    def fuse(self):print('Fusing layers...')for m in self.model.modules():if isinstance(m, RepConv):m.fuse_repvgg_block()return self