YOLOV8源码解读-C2f模块-以及总结c2模块、Bottleneck

c2f模块是对c2模块的改进

c2模块图解解读

先给出YOLOV8中卷积的定义模块一键三连-卷积-BN-激活函数

def autopad(k, p=None, d=1):  # kernel, padding, dilation"""Pad to 'same' shape outputs."""if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-padreturn pclass Conv(nn.Module):"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initialize Conv layer with given arguments including activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):"""Apply convolution, batch normalization and activation to input tensor."""return self.act(self.bn(self.conv(x)))

C2f模块中使用了模块Bottleneck：

class Bottleneck(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, k[0], 1)self.cv2 = Conv(c_, c2, k[1], 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

他的解读在C2模块解读哪里已经有了：

c2模块中的bottleneck

C2f模块：Faster Implementation of CSP Bottleneck with 2 convolutions

代码：

class C2f(nn.Module):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__()self.c = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, 2 * self.c, 1, 1)self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))def forward(self, x):"""Forward pass through C2f layer."""y = list(self.cv1(x).chunk(2, 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def forward_split(self, x):"""Forward pass using split() instead of chunk()."""y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))

在c2模块的末尾说过chunk和split，并举例说明了它们的区别，在c2f模块中，它的传播函数，使用了split和chunk的2个版本。

c2f也非常好理解，头就是将原来c2模块中的Bottleneck组成的Sequential，换成了ModuleList，由串行变成并行。

图解：首先经过cv1模块，然后一分为2，分别为x1，x2，存储在元组y中。这里源码里面提供了2种方式chunk和split，在c2模块的讲解中，我们已经举例说明它们的用法以及区别了。

    def forward(self, x):"""Forward pass through C2f layer."""y = list(self.cv1(x).chunk(2, 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def forward_split(self, x):"""Forward pass using split() instead of chunk()."""y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))

然后将y里面的最后一个元素拿出来，放进ModuleList中的每一个Bottleneck，假设里面有n个Bottleneck，那么结果也就有n个，最后和y中原来的2个进行拼接，这就是为什么cv2这个卷积模块输入通道数是（2+n）*c的原因。

展开ModuleList这一部分如下图所示。