Yolov5更换主干网络之《旷视轻量化卷积神经网络ShuffleNetv2》

《ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design》

这篇是2018年发表在ECCV上的论文,同时本篇论文还获得了VALSE年度杰出论文奖
原文地址
官方代码

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ShuffleNet V2属于比较经典的轻量化网络，通过大量实验提出四条轻量化网络设计准则，对输入输出通道、分组卷积组数、网络碎片化程度、逐元素操作对不同硬件上的速度和内存访问量MAC的影响进行了详细分析。

提出ShuffleNet V2模型，通过Channel Split替代分组卷积，满足四条设计准则，达到了速度和精度的最优权衡。

Channel Shuffle原理

（a）（b）为ShuffleNet V1原理图，（c）(d)为ShuffleNet V2原理图（d为降采样层）

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YOLOv5更换方法,三步搞定
第一步；添加如下代码到common.py

# 通道重排，跨group信息交流
def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()
    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups,
               channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class CBRM(nn.Module):           #conv BN ReLU Maxpool2d
    def __init__(self, c1, c2):  # ch_in, ch_out
        super(CBRM, 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))


class Shuffle_Block(nn.Module):
    def __init__(self, ch_in, ch_out, stride):
        super(Shuffle_Block, self).__init__()

        if not (1 <= stride <= 2):
            raise ValueError('illegal stride value')
        self.stride = stride

        branch_features = ch_out // 2
        assert (self.stride != 1) or (ch_in == branch_features << 1)

        if self.stride > 1:
            self.branch1 = nn.Sequential(
                self.depthwise_conv(ch_in, ch_in, kernel_size=3, stride=self.stride, padding=1),
                nn.BatchNorm2d(ch_in),

                nn.Conv2d(ch_in, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
            )

        self.branch2 = nn.Sequential(
            nn.Conv2d(ch_in 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):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)  # 按照维度1进行split
            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


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第二步；yolo.py里加上CBRM和Shuffle_Block

第三步；修改配置文件

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 20  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  # Shuffle_Block: [out, stride]
  [[ -1, 1, CBRM, [ 32 ] ], # 0-P2/4
   [ -1, 1, Shuffle_Block, [ 128, 2 ] ],  # 1-P3/8
   [ -1, 3, Shuffle_Block, [ 128, 1 ] ],  # 2
   [ -1, 1, Shuffle_Block, [ 256, 2 ] ],  # 3-P4/16
   [ -1, 7, Shuffle_Block, [ 256, 1 ] ],  # 4
   [ -1, 1, Shuffle_Block, [ 512, 2 ] ],  # 5-P5/32
   [ -1, 3, Shuffle_Block, [ 512, 1 ] ],  # 6
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P4
   [-1, 1, C3, [256, False]],  # 10

   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, Concat, [1]],  # cat backbone P3
   [-1, 1, C3, [128, False]],  # 14 (P3/8-small)

   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 11], 1, Concat, [1]],  # cat head P4
   [-1, 1, C3, [256, False]],  # 17 (P4/16-medium)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 7], 1, Concat, [1]],  # cat head P5
   [-1, 1, C3, [512, False]],  # 20 (P5/32-large)

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

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更详细的网络结构复现请看ShuffleNet v2网络结构复现（Pytorch版）

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本人更多Yolov5(v6.1)实战内容导航🍀

1.手把手带你调参Yolo v5 (v6.1)（一）🌟强烈推荐

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5.手把手带你Yolov5 (v6.1)添加注意力机制(二)（在C3模块中加入注意力机制）

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8.Yolov5更换上采样方式( 最近邻 / 双线性 / 双立方 / 三线性 / 转置卷积)🍀

9.Yolov5如何更换EIOU / alpha IOU / SIoU？🍀

10.持续更新中

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