YOLOV5-CSP组件 VS C3组件

1. CSP组件

 1. 绿色代表输入图像

 2. 蓝色代表CBL组件 = CONV + BN + (x * sigmoid)

 3. 红色代表跳跃组件 = CONV1 + CONV2 + (inputs add CONV2)

 4. 黄色代表拼接组件 = concat

 5. 橘黄色代表BN层

 6. 闪电符号代表激活函数

2. C3组件

 C3组件相比CSP组件，结构上看上去简单了许多，其实和标准CSP组件效果类似，只是删除了标   准CSP组件在残差连接之后的一次卷积操作，直接和输入图经过一次卷积操作的另一分支进行拼   接。

3. 代码部分

class Bottleneck(nn.Module):
    """
        残差组件 = conv1 -> conv2 -> (inputs + conv2)
    """
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2

    def forward(self, x):
        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))

class BottleneckCSP(nn.Module):
    """
        csp结构 如上图
    """
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
        self.cv4 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.ReLU()
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        y1 = self.cv3(self.m(self.cv1(x)))
        y2 = self.cv2(x)
        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))

class C3(nn.Module):
    """
        c3结构 如上图
    """
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.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(*(Bottleneck(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))