yolov5网络结构（【YOLOv7_0.1】网络结构与源码解析）
导读：前言 论文地址 YOLOv7源码...
前言
论文地址
YOLOv7源码下面对v0.1版本的整体网络结构及各个组件 ，结合源码和train文件夹中的yolov7.yaml配置文件进行解析 。
整体网络结构
分解的yolov7.yaml
# parameters nc: 80 # number of classes depth_multiple: 1.0 # model depth multiple width_multiple: 1.0 # layer channel multiple # anchors anchors: - [12,16, 19,36, 40,28] # P3/8 - [36,75, 76,55, 72,146] # P4/16 - [142,110, 192,243, 459,401] # P5/32 # yolov7 backbone 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]], [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 # ELAN1 [-1, 1, Conv, [64, 1, 1]], [-2, 1, Conv, [64, 1, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1]], # 11 # MPConv [-1, 1, MP, []], [-1, 1, Conv, [128, 1, 1]], [-3, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [128, 3, 2]], [[-1, -3], 1, Concat, [1]], # 16-P3/8 # ELAN1 [-1, 1, Conv, [128, 1, 1]], [-2, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [512, 1, 1]], # 24 # MPConv [-1, 1, MP, []], [-1, 1, Conv, [256, 1, 1]], [-3, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 3, 2]], [[-1, -3], 1, Concat, [1]], # 29-P4/16 # ELAN1 [-1, 1, Conv, [256, 1, 1]], [-2, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [1024, 1, 1]], # 37 # MPConv [-1, 1, MP, []], [-1, 1, Conv, [512, 1, 1]], [-3, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [512, 3, 2]], [[-1, -3], 1, Concat, [1]], # 42-P5/32 # ELAN1 [-1, 1, Conv, [256, 1, 1]], [-2, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [1024, 1, 1]], # 50 ] # yolov7 head head: [[-1, 1, SPPCSPC, [512]], # 51 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, nearest]], [37, 1, Conv, [256, 1, 1]], # route backbone P4 [[-1, -2], 1, Concat, [1]], # ELAN2 [-1, 1, Conv, [256, 1, 1]], [-2, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1]], # 63 [-1, 1, Conv, [128, 1, 1]], [-1, 1, nn.Upsample, [None, 2, nearest]], [24, 1, Conv, [128, 1, 1]], # route backbone P3 [[-1, -2], 1, Concat, [1]], # ELAN2 [-1, 1, Conv, [128, 1, 1]], [-2, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1]], # 75 # MPConv Channel × 2 [-1, 1, MP, []], [-1, 1, Conv, [128, 1, 1]], [-3, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [128, 3, 2]], [[-1, -3, 63], 1, Concat, [1]], # ELAN2 [-1, 1, Conv, [256, 1, 1]], [-2, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1]], # 88 # MPConv Channel × 2 [-1, 1, MP, []], [-1, 1, Conv, [256, 1, 1]], [-3, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 3, 2]], [[-1, -3, 51], 1, Concat, [1]], # ELAN2 [-1, 1, Conv, [512, 1, 1]], [-2, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [-1, 1, Conv, [256, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [512, 1, 1]], # 101 [75, 1, RepConv, [256, 3, 1]], [88, 1, RepConv, [512, 3, 1]], [101, 1, RepConv, [1024, 3, 1]], [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) ]各组件结构
ELAN1 (backbone)
yolov7.yaml中对应部分： # ELAN1 [-1, 1, Conv, [64, 1, 1]], [-2, 1, Conv, [64, 1, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [-1, 1, Conv, [64, 3, 1]], [[-1, -3, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1]], # 11ELAN2 (head)
yolov7.yaml中对应部分： # ELAN2 [-1, 1, Conv, [256, 1, 1]], [-2, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [-1, 1, Conv, [128, 3, 1]], [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1]], # 63MPConv
在backnone中的对应部分 要注意相比于MP函数之前 ，通道数减少一半 [-1, 1, Conv, [256, 1, 1]], # 11 # MPConv [-1, 1, MP, []], [-1, 1, Conv, [128, 1, 1]], [-3, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [128, 3, 2]], [[-1, -3], 1, Concat, [1]], # 16-P3/8 在head中的对应部分 要注意相比于MP函数之前 ，通道数不变 [-1, 1, Conv, [128, 1, 1]], # 75 # MPConv Channel × 2 [-1, 1, MP, []], [-1, 1, Conv, [128, 1, 1]], [-3, 1, Conv, [128, 1, 1]], [-1, 1, Conv, [128, 3, 2]], [[-1, -3, 63], 1, Concat, [1]],SPPCSPC
类似于yolov5中的SPPF ，不同的是 ，使用了5×5 、9×9 、13×13最大池化 。
common.py中对应部分： class SPPCSPC(nn.Module): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): super(SPPCSPC, self).__init__() c_ = int(2 * c2 * e) # hidden channels 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))RepConv（重参数卷积）
原理理解层面
训练时：一个3*3卷积 、一个1*1卷积和一个BN层（当输入输出通道相同时）相加得到输出 推理时：将以上三部分重参数化 ，合并为一个3*3的卷积输出代码实现层面
训练时：不执行Model类的fuse函数 推理时：在attempt_load函数加载训练好的模型时 ，会执行Model类的fuse函数 ，进而调用fuse_repvgg_block函数 ，实现将三个卷积重参数化 ，合并为一个卷积输出 common.py中对应部分： # Represented convolution https://arxiv.org/abs/2101.03697 class RepConv(nn.Module): 重参数卷积 训练时： deploy = False rbr_dense(3*3卷积) + rbr_1x1(1*1卷积) + rbr_identity(c2 == c1时) 三者相加 rbr_reparam = None 推理时： deploy = True rbr_reparam = Conv2d rbr_dense = None rbr_1x1 = None rbr_identity = None def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False): super(RepConv, self).__init__() self.deploy = deploy self.groups = g self.in_channels = c1 self.out_channels = c2 assert k == 3 assert autopad(k, p) == 1 padding_11 = autopad(k, p) - k // 2 self.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3×3的卷积来替换 if deploy: self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True) else: # 训练阶段 ，当输入和输出的通道数相同时 ，会在加一个BN层 self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None) # 3×3的卷积(padding=1) 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的卷积 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, inputs): if hasattr(self, "rbr_reparam"): return self.act(self.rbr_reparam(inputs)) if self.rbr_identity is None: id_out = 0 else: id_out = self.rbr_identity(inputs) return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) # Conv2D + BN -> Conv2D def fuse_conv_bn(self, conv, bn): std = (bn.running_var + bn.eps).sqrt() bias = bn.bias - bn.running_mean * bn.weight / std t = (bn.weight / std).reshape(-1, 1, 1, 1) weights = conv.weight * t bn = 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 # 在推理阶段才执行重参数操作 def fuse_repvgg_block(self): if self.deploy: return print(f"RepConv.fuse_repvgg_block") self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1]) self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1]) rbr_1x1_bias = self.rbr_1x1.bias # self.rbr_1x1.weight [256, 128, 1, 1] # weight_1x1_expanded [256, 128, 3, 3] weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1]) # Fuse self.rbr_identity if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)): # print(f"fuse: rbr_identity == BatchNorm2d or 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() # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") 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) # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity) bias_identity_expanded = identity_conv_1x1.bias weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1]) else: # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}") bias_identity_expanded = torch.nn.Parameter(torch.zeros_like(rbr_1x1_bias)) weight_identity_expanded = torch.nn.Parameter(torch.zeros_like(weight_1x1_expanded)) # print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ") # print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ") # print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ") 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_dense # 前向推理时 ，使用重参数化后的 rbr_reparam 函数 self.deploy = True if self.rbr_identity is not None: del self.rbr_identity self.rbr_identity = None if self.rbr_1x1 is not None: del self.rbr_1x1 self.rbr_1x1 = None if self.rbr_dense is not None: del self.rbr_dense self.rbr_dense = NoneImpConv（隐性知识学习）
这一部分直接继承自YOLOR中的显隐性知识学习 。一般情况下 ，将神经网络的浅层特征称为显性知识 ，深层特征称为隐性知识 。而YOLOR的作者（同时也是YOLOv7的作者）则直接把神经网络最终观察到的知识称为显性知识 ，那些观察不到 、与观察无关的知识称为隐性知识 。
在model/common.py文件中 ，定义了两类隐性知识：ImplicitA和ImplicitM ，分别对输入 相加 和 相乘：
# Add class ImplicitA(nn.Module): def __init__(self, channel, mean=0., std=.02): super(ImplicitA, self).__init__() self.channel = channel self.mean = mean self.std = std # 全0矩阵 self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) nn.init.normal_(self.implicit, mean=self.mean, std=self.std) def forward(self, x): # 全0矩阵 与 输入 相加 return self.implicit + x # Multiply class ImplicitM(nn.Module): def __init__(self, channel, mean=0., std=.02): super(ImplicitM, self).__init__() self.channel = channel self.mean = mean self.std = std # 全1矩阵 self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) nn.init.normal_(self.implicit, mean=self.mean, std=self.std) def forward(self, x): # 全1矩阵 与 输入相乘 return self.implicit * x训练时
在模型训练阶段 ，先对输入进行ImplicitA操作 ， 在进行1*1卷积 ，最后进行ImplicitM操作：
class IDetect(nn.Module): stride = None # strides computed during build export = False # onnx export end2end = False include_nms = False def __init__(self, nc=80, anchors=(), ch=()): # detection layer super(IDetect, self).__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid a = torch.tensor(anchors).float().view(self.nl, -1, 2) self.register_buffer(anchors, a) # shape(nl,na,2) self.register_buffer(anchor_grid, a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv # 初始化隐性知识 self.ia = nn.ModuleList(ImplicitA(x) for x in ch) self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch) def forward(self, x): # x = x.copy() # for profiling z = [] # inference output self.training |= self.export for i in range(self.nl): # 加入隐性知识 x[i] = self.m[i](self.ia[i](x[i])) # conv x[i] = self.im[i](x[i]) bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i] = self._make_grid(nx, ny).to(x[i].device) y = x[i].sigmoid() y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh z.append(y.view(bs, -1, self.no)) return x if self.training else (torch.cat(z, 1), x)推理时
在模型推理阶段 ，将ImplicitA-Conv-ImplicitM融合为一个1*1的Conv操作：
# 将隐性知识与Detect层的1*1卷积进行融合 def fuse(self): print("IDetect.fuse") # fuse ImplicitA and Convolution for i in range(len(self.m)): c1, c2, _, _ = self.m[i].weight.shape c1_, c2_, _, _ = self.ia[i].implicit.shape self.m[i].bias += torch.matmul(self.m[i].weight.reshape(c1, c2), self.ia[i].implicit.reshape(c2_, c1_)).squeeze(1) # fuse ImplicitM and Convolution for i in range(len(self.m)): c1, c2, _, _ = self.im[i].implicit.shape self.m[i].bias *= self.im[i].implicit.reshape(c2) self.m[i].weight *= self.im[i].implicit.transpose(0, 1)References
[1] 深入浅出 Yolo 系列之 Yolov7 基础网络结构详解
[2] 【yolov7系列】网络框架细节拆解
[3] yolov7-GradCAM
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