目标检测算法——YOLOv5/YOLOv7改进|结合轻量型网络ShuffleNetV2_flownet与yolov5

深度学习Tricks，第一时间送达

论文题目：《ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design》
论文地址：  https://arxiv.org/abs/1807.11164

FLOPS：每秒浮点运算次数，这个由硬件决定。
GFLOPS：每秒10亿次的浮点运算数，1GFlops = 1,000MFlops。
FLOPs：指计算量的大小，理解为计算量。可以用来衡量算法/模型的复杂度。具体指的是multiply-add数量，计算模型中乘法和加法的运算次数。对于普通卷积层而言：FLOPs = 2*H*W( CinK2+1)Cout。
maccs：是multiply-accumulate operations，指点积运算，一个 macc = 2FLOPs。
MAC：memory access cost 内存访问消耗，这里举个例子更好理解，比如1x1的卷积：MAC=H*W*Cin+H*W*Cout+1*1*Cin*Cout，第一项是输入特征层的内存访问消耗。第二项是输出特征层内存访问消耗，第三项是卷积核的内存访问消耗。

ShuffleNetV2中提出了一个关键点，之前的轻量级网络都是通过计算网络复杂度的一个间接度量，即FLOPs为指导。通过计算浮点运算量来描述轻量级网络的快慢。但是从来不直接考虑运行的速度。在移动设备中的运行速度不仅仅需要考虑FLOPs，还需要考虑其他的因素，比如内存访问成本(memory access cost)和平台特点(platform characterics)。为了衡量计算复杂度，一个广泛使用的指标是浮点运算次数，即FLOPs。然而，FLOPs是一种非直接的指标，是对直接指标例如速度和时延的近似，且并不是等价的，而直接指标才是真正需要关心的。

小海带有创新地将YOLOv5算法与轻量级网络ShuffleNetV2相结合，在保证模型检测精度基本不变的情况下，大大降低计算量FLOPS，并可以明显提高网络推理速度！！！近期较忙，代码咨询的小伙伴请私聊~

ShuffleNetV2代码：

def split(x, groups):
    out = x.chunk(groups, dim=1)
 
    return out
 
 
def shuffle( x, groups):
    N, C, H, W = x.size()
    out = x.view(N, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous().view(N, C, H, W)
 
    return out
class ShuffleUnit(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super().__init__()
        mid_channels = out_channels // 2
        if stride > 1:
            self.branch1 = nn.Sequential(
                nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels, bias=False),
                nn.BatchNorm2d(in_channels),
                nn.Conv2d(in_channels, mid_channels, 1, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.ReLU(inplace=True)
            )
            self.branch2 = nn.Sequential(
                nn.Conv2d(in_channels, mid_channels, 1, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, groups=mid_channels, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.Conv2d(mid_channels, mid_channels, 1, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.ReLU(inplace=True)
            )
        else:
            self.branch1 = nn.Sequential()
            self.branch2 = nn.Sequential(
                nn.Conv2d(mid_channels, mid_channels, 1, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, groups=mid_channels, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.Conv2d(mid_channels, mid_channels, 1, bias=False),
                nn.BatchNorm2d(mid_channels),
                nn.ReLU(inplace=True)
            )
        self.stride = stride
    def forward(self, x):
        if self.stride == 1:
            x1, x2 = split(x, 2)
            out = torch.cat((self.branch1(x1), self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
        out = shuffle(out, 2)
        return out
class ShuffleNetV2(nn.Module):
    def __init__(self, channel_num, class_num=settings.CLASSES_NUM):
        super().__init__()
        self.conv1 = nn.Sequential(
                nn.Conv2d(3, 24, 3, stride=2, padding=1, bias=False),
                nn.BatchNorm2d(24),
                nn.ReLU(inplace=True)
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.stage2 = self.make_layers(24, channel_num[0], 4, 2)
        self.stage3 = self.make_layers(channel_num[0], channel_num[1], 8, 2)
        self.stage4 = self.make_layers(channel_num[1], channel_num[2], 4, 2)
        self.conv5 = nn.Sequential(
                nn.Conv2d(channel_num[2], 1024, 1, bias=False),
                nn.BatchNorm2d(1024),
                nn.ReLU(inplace=True)
        )
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(1024, class_num)
    def make_layers(self, in_channels, out_channels, layers_num, stride):
        layers = []
        layers.append(ShuffleUnit(in_channels, out_channels, stride))
        in_channels = out_channels
        for i in range(layers_num - 1):
            ShuffleUnit(in_channels, out_channels, 1)
        return nn.Sequential(*layers)
    def forward(self, x):
        out = self.conv1(x)
        out = self.maxpool(out)
        out = self.stage2(out)
        out = self.stage3(out)
        out = self.stage4(out)
        out = self.conv5(out)
        out = self.avgpool(out)
        out = out.flatten(1)
        out = self.fc(out)
        return out

亲测，YOLOv5-ShuffleNetV2的参数量和算力是碾压式的小，并且具备神一样的检测速度。

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