YOLOv5s-ShuffleNetV2

对YOLOV5进行轻量化：
一、backbone部分
yaml配置文件：

backbone:
  # [from, number, module, args]
  [[-1, 1, conv_bn_relu_maxpool, [32]],    # 0-P2/4
   [-1, 1, Shuffle_Block, [116, 2]], # 1-P3/8
   [-1, 3, Shuffle_Block, [116, 1]], # 2
   [-1, 1, Shuffle_Block, [232, 2]], # 3-P4/16
   [-1, 7, Shuffle_Block, [232, 1]], # 4
   [-1, 1, Shuffle_Block, [464, 2]], # 5-P5/32
   [-1, 1, Shuffle_Block, [464, 1]], # 6
  ]

1.1、Focus替换
原始的YOLOv5s-5.0的stem是一个Focus切片操作，而v6是一个6x6Conv，这里是仿照v6对Focus进行改进，改为1个3x3卷积（因为我的任务本身不复杂，改为3x3后可以降低参数）

class conv_bn_relu_maxpool(nn.Module):
    def __init__(self, c1, c2):  # ch_in, ch_out
        super(conv_bn_relu_maxpool, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(c2),
            nn.ReLU(inplace=True),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

    def forward(self, x):
        return self.maxpool(self.conv(x))

1.2、所有Conv+C3替换为Shuffle_Block
在这里插入图片描述

def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()  # bs c h w
    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups, channels_per_group, height, width)  # [bs,c,h,w] to [bs,group,channels_per_group,h,w]

    x = torch.transpose(x, 1, 2).contiguous()  # channel shuffle [bs,channels_per_group,group,h,w]

    # flatten
    x = x.view(batchsize, -1, height, width)  # [bs,c,h,w]

    return x

在这里插入图片描述

class Shuffle_Block(nn.Module):
    def __init__(self, inp, oup, stride):
        super(Shuffle_Block, self).__init__()

        if not (1 <= stride <= 3):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = oup // 2  # channel split to 2 feature map
        assert (self.stride != 1) or (inp == branch_features << 1)

        # stride=2 图d 左侧分支=3x3DW Conv + 1x1Conv
        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(inp),
                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
            )

        # 右侧分支=1x1Conv + 3x3DW Conv + 1x1Conv
        self.branch2 = nn.Sequential(
            nn.Conv2d(inp if (self.stride > 1) else branch_features,
                      branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
            self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
            nn.BatchNorm2d(branch_features),
            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
        )

    @staticmethod
    def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
        return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)

    def forward(self, x):
        # x/out: [bs, c, h, w]
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)  # channel split to 2 feature map
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = channel_shuffle(out, 2)

        return out

1.3、砍掉SPP
砍掉了SPP结构和后面的一个C3结构，因为SPP的并行操作会影响速度。

二、head部分

head:
  [[-1, 1, Conv, [96, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[ -1, 4 ], 1, Concat, [1]],  # cat backbone P4
   [-1, 1, DWConvblock, [96, 3, 1]],  # 10

   [-1, 1, Conv, [96, 1, 1 ]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, Concat, [1]],  # cat backbone P3
   [-1, 1, DWConvblock, [96, 3, 1]],  # 14 (P3/8-small)

   [-1, 1, DWConvblock, [96, 3, 2]],
   [[-1, 11], 1, ADD, [1]],  # cat head P4
   [-1, 1, DWConvblock, [96, 3, 1]],  # 17 (P4/16-medium)

   [-1, 1, DWConvblock, [ 96, 3, 2]],
   [[-1, 7], 1, ADD, [1]],  # cat head P5
   [-1, 1, DWConvblock, [96, 3, 1]],  # 20 (P5/32-large)

   [[14, 17, 20], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

2.1、所有层结构输入输出channel相等
2.2、所有C3结构全部替换为DWConv
2.3、PAN的两个Concat改为ADD

三、、总结
ShuffleNeckV2提出的设计轻量化网络的四条准则：
G1、卷积层的输入特征channel和输出特征channel要尽量相等；
G2、尽量不要使用组卷积，或者组卷积g尽量小；
G3、网络分支要尽量少，避免并行结构；
G4、 Element-Wise的操作要尽量少，如：ReLU、ADD、逐点卷积等；

YOLOv5s-ShuffleNetV2改进点总结：

backbone的Focus替换为一个3x3Conv（c=32），因为v5-6.0就替换为了一个6x6Conv，这里为了进一步降低参数量，替换为3x3Conv；
backbone所有Conv和C3替换为Shuffle Block；
砍掉SPP和后面的一个C3结构，SPP并行操作太多了（G3）
head所有层输入输出channel=96（G1）
head所有C3改为DWConv
PAN的两个Concat改为ADD（channel太大，计算量太大，虽然违反了G4，但是计算量更小）

四、实验结果
GFLOPs=值/10^9
参数量（M）=值*4/1024/1024
在这里插入图片描述
参数量、计算量、权重文件大小都压缩到YOLOv5s的1/10，精度[email protected]掉了1%左右（96.7%->95.5%），[email protected]~0.95掉了5个点左右（88.5%->84%）。

参考文献：https://blog.csdn.net/qq_38253797/article/details/124803531

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