yolov7-tiny结合mobilenetV3（降参提速）

在各处看到关于yolo的魔改都是基于yolov5版本的，于是借鉴学习一下用在yolov7-tiny版本上，做一下学习记录。

1、配置yaml文件

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
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

# yolov7-tiny backbone
backbone:
  # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True

  [ [ -1, 1, conv_bn_hswish, [ 16, 2 ] ],                 # 0-p1/2
    [ -1, 1, MobileNet_Block, [ 16,  16, 3, 2, 1, 0 ] ],  # 1-p2/4
    [ -1, 1, MobileNet_Block, [ 24,  72, 3, 2, 0, 0 ] ],  # 2-p3/8
    [ -1, 1, MobileNet_Block, [ 24,  88, 3, 1, 0, 0 ] ],  # 3-p3/8
    [ -1, 1, MobileNet_Block, [ 40,  96, 5, 2, 1, 1 ] ],  # 4-p4/16
    [ -1, 1, MobileNet_Block, [ 40, 240, 5, 1, 1, 1 ] ],  # 5-p4/16
    [ -1, 1, MobileNet_Block, [ 40, 240, 5, 1, 1, 1 ] ],  # 6-p4/16
    [ -1, 1, MobileNet_Block, [ 48, 120, 5, 1, 1, 1 ] ],  # 7-p4/16
    [ -1, 1, MobileNet_Block, [ 48, 144, 5, 1, 1, 1 ] ],  # 8-p4/16
    [ -1, 1, MobileNet_Block, [ 96, 288, 5, 2, 1, 1 ] ],  # 9-p5/32
    [ -1, 1, MobileNet_Block, [ 96, 576, 5, 1, 1, 1 ] ],  # 10-p5/32
    [ -1, 1, MobileNet_Block, [ 96, 576, 5, 1, 1, 1 ] ],  # 11-p5/32
  ]

# yolov7-tiny head
head:
  [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, SP, [5]],
   [-2, 1, SP, [9]],
   [-3, 1, SP, [13]],
   [[-1, -2, -3, -4], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, -7], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 20
  
   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [8, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, -2, -3, -4], 1, Concat, [1]],
   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 30
  
   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],   
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [3, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, -2, -3, -4], 1, Concat, [1]],
   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 40
   
   [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, 30], 1, Concat, [1]],                           
   
   [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, -2, -3, -4], 1, Concat, [1]],
   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 48
   
   [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, 20], 1, Concat, [1]],
   
   [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  
   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [[-1, -2, -3, -4], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],  # 56
      
   [40, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [48, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]],
   [56, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]],

   [[57,58,59], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
  ]

2、配置common.py

把以下代码添加至/models/common.py中即可

#——————MobileNetV3-small——————
 
class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)
 
    def forward(self, x):
        return self.relu(x + 3) / 6
 
 
class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)
 
    def forward(self, x):
        return x * self.sigmoid(x)
 
 
class SELayer(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel),
            h_sigmoid()
        )
 
    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x)
        y = y.view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y
 
 
class conv_bn_hswish(nn.Module):
 
    def __init__(self, c1, c2, stride):
        super(conv_bn_hswish, self).__init__()
        self.conv = nn.Conv2d(c1, c2, 3, stride, 1, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = h_swish()
 
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
 
    def fuseforward(self, x):
        return self.act(self.conv(x))
 
 
class MobileNet_Block(nn.Module):
    def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):
        super(MobileNet_Block, self).__init__()
        assert stride in [1, 2]
 
        self.identity = stride == 1 and inp == oup
        if inp == hidden_dim:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Sequential(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Sequential(),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
 
    def forward(self, x):
        y = self.conv(x)
        if self.identity:
            return x + y
        else:
            return y

3、去yolo.py中加载添加的类

找到parse_model中最长那一段，加入所添加的h_sigmoid， h_swish，SELayer，conv_bn_hswish， MobileNet_Block模块即可，如图所示

4、训练即可，注意train.py时将cfg文件改成自己的yaml，如下所示

python train.py --workers 16 --device 0,1,2,3 --batch-size 32 --data data/data.yaml --cfg cfg/training/yolov7-tiny-mb3s.yaml --weights '' --name yolov7-tiny-mb3s --hyp data/hyp.scratch.p5.yaml

参考blog：

(111条消息) 目标检测算法——YOLOv5/YOLOv7改进之结合轻量化网络MobileNetV3（降参提速）_加勒比海带66的博客-CSDN博客_conv_bn_hswish

标签：人工智能深度学习计算机视觉

本文转载自: https://blog.csdn.net/yjcccccc/article/details/128478652
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