yolox-backbone详解之CSPLayer（含代码

densenet

densenet评价
优点：1.更强的梯度流动
2.减少了参数数量
3.保存了低维度的特征
缺点：进行了多次concat操作，导致显存占用过大
cspdensenet评价：
优点：1.加强CNN的学习能力
2.消除计算瓶颈
3.降低内存成本


在此进行对比，
densenet:64*64*1*1+64*64*3*3+64*64*1*1=45056
cspdensenet:(64*32*1*1)*2+32*32*1*1+32*32*3*3+64*64*1*1=18432
所以，可见参数量大大减少

class CSPLayer(nn.Module):
    """C3 in yolov5, CSP Bottleneck with 3 convolutions"""

    def __init__(self, in_channels,out_channels,n=1,shortcut=True, expansion=0.5,depthwise=False,act="silu",):
        """
        Args:
            in_channels (int): input channels.
            out_channels (int): output channels.
            n (int): number of Bottlenecks. Default value: 1.
        """
        # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        hidden_channels = int(out_channels * expansion)  # hidden channels
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv3 = BaseConv(2 * hidden_channels, out_channels, 1, stride=1, act=act)
        module_list = [
            Bottleneck(
                hidden_channels, hidden_channels, shortcut, 1.0, depthwise, act=act
            )
            for _ in range(n)
        ]
        self.m = nn.Sequential(*module_list)

    def forward(self, x):
        x_1 = self.conv1(x)
        x_2 = self.conv2(x)
        x_1 = self.m(x_1)
        x = torch.cat((x_1, x_2), dim=1)
        return self.conv3(x)

class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(
        self,
        in_channels,
        out_channels,
        shortcut=True,
        expansion=0.5,
        depthwise=False,
        act="silu",
    ):
        super().__init__()
        hidden_channels = int(out_channels * expansion)
        Conv = DWConv if depthwise else BaseConv
        self.conv1 = BaseConv(in_channels, hidden_channels, 1, stride=1, act=act)
        self.conv2 = Conv(hidden_channels, out_channels, 3, stride=1, act=act)
        self.use_add = shortcut and in_channels == out_channels

    def forward(self, x):
        y = self.conv2(self.conv1(x))
        if self.use_add:
            y = y + x
        return y