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专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，助力高效涨点！！！
1. RepNCSPELAN4详图
RepNCSPELAN4是YOLOv9中的特征提取-融合模块，类似前几代YOLO中的C3、C2f等模块。作者通过结合两种神经网络架构，即带有梯度路径规划的 CSPNet 和 ELAN，考虑轻量化、推理速度和准确性设计出的一种广义高效层聚合网络（GELAN），作者使用带有 CSPNet 块的 GELAN 替换了 ELAN，并 RepConv作为计算块。RepNCSPELAN4可拆分为RepN-CSP-ELAN4 ，代码及模块图如下：
RepNCSPELAN4主要由Conv与ReoNCSP组成，其中的ReoNCSP结构上形似C3与C2f模块，ReoNCSP由Conv与数量不等的RepNBottleneck模块组成，RepNBottleneck的个数由模型的宽度因子决定，RepNBottleneck是一个具有残差结构的基础模块，如下图。
class RepConvN(nn.Module):"""RepConv is a basic rep-style block, including training and deploy statusThis code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py"""default_act = nn.SiLU() # default activationdef __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):super().__init__()assert k == 3 and p == 1self.g = gself.c1 = c1self.c2 = c2self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()self.bn = Noneself.conv1 = Conv(c1, c2, k, s, p=p, g=g, act=False)self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)def forward(self, x):"""Forward process"""id_out = 0 if self.bn is None else self.bn(x)return self.act(self.conv1(x) + self.conv2(x) + id_out)class RepNBottleneck(nn.Module):# Standard bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expandsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = RepConvN(c1, c_, k[0], 1)self.cv2 = Conv(c_, c2, k[1], 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class RepNCSP(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e) # hidden channelsself.cv1 = Conv(c1, c_, 1, 1)self.cv2 = Conv(c1, c_, 1, 1)self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)self.m = nn.Sequential(*(RepNBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))class RepNCSPELAN4(nn.Module):# csp-elandef __init__(self, c1, c2, c3, c4, c5=1): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()self.c = c3//2self.cv1 = Conv(c1, c3, 1, 1)self.cv2 = nn.Sequential(RepNCSP(c3//2, c4, c5), Conv(c4, c4, 3, 1))self.cv3 = nn.Sequential(RepNCSP(c4, c4, c5), Conv(c4, c4, 3, 1))self.cv4 = Conv(c3+(2*c4), c2, 1, 1)def forward(self, x):y = list(self.cv1(x).chunk(2, 1))y.extend((m(y[-1])) for m in [self.cv2, self.cv3])return self.cv4(torch.cat(y, 1))def forward_split(self, x):y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in [self.cv2, self.cv3])return self.cv4(torch.cat(y, 1))
