发布时间：2023-04-21 文章分类：电脑百科投稿人：李佳字号：默认 | 大 | 超大打印

yolov5修改骨干网络–原网络说明
yolov5修改骨干网络-使用pytorch自带的网络-以Mobilenet和efficientnet为例
yolov5修改骨干网络-使用自己搭建的网络-以efficientnetv2为例

通过 yolov5修改骨干网络–原网络说明我们知道：yolov5.yaml中存放的是我们模型构建参数，具体构建过程在yolo.py中的parse_model函数，通过循环遍历yolov5.yaml给的参数，去寻找网络名称，并将args的参数传入网络，下面先用pytorch自带的mobile网络进行修改并替换原有yolov5网络。

网络都是分层次的，比如如果把某个网络模型Net按层次从外到内进行划分的话，features和classifier是Net的子层，而conv2d, ReLU, BatchNorm, Maxpool2d等基础操作可能是features的子层， Linear, Dropout, ReLU等可能是classifier的子层。

model.modules()不但会遍历模型的子层，还会遍历子层的子层，以及所有子层。
model.children()只会遍历模型的子层，这里即是features和classifier。

比如先调出pytorch中的Mobilnetv3网络，输出一些相关信息，

from torchvision import models
m = models.mobilenet_v3_small()
print(list(m.children())[0])
print("---"*30)
print(list(m.children())[1])
print("---"*30)
print(list(m.children())[2])
print("len(m.modules()): ",len(list(m.modules())))
print("len(m.children()): ",len(list(m.children())))
print("len(m.children()[0]): ",len(list(m.children())[0]))
--------------------------------------------------------------------
AdaptiveAvgPool2d(output_size=1)
--------------------------------------------------------------------
Sequential(
  (0): Linear(in_features=576, out_features=1024, bias=True)
  (1): Hardswish()
  (2): Dropout(p=0.2, inplace=True)
  (3): Linear(in_features=1024, out_features=1000, bias=True)
)
len(m.modules()):  209
len(m.children()):  3
len(m.children()[0]):  13

可以看到网络共三层，分别是0层：特征提取，1层AdaptiveAvgPool2d，2层：分类层，特征提取部分共13层，里面包含卷积、BN、激活等，都加起来共209个这种小结构。

list(m.children())[0]是网络结构，下面来具体分析。

内容比较多，重点关注哪里进行了下采样，卷积步长为2的地方，特征图就缩小一半。
在yolov5中是对尺寸为20、40、80的特征图进行concat，这里也是如此（当然其他尺寸也可以）。

Ctrl F以下可以看到共5个地方使特征图缩小一半，第一次 $320 * 320$ ；第二次： $160 * 160$ ；第三次： $80 * 80$ ；第四次： $40 * 40$ ；第五次： $20 * 20$ 。

这样就可以划分了，到特征图为 $40 * 40$ 时，卷积在第五个操作中，所以我们要讲前四个作为第一组，其输出与后面的特征图concat。

同样分析后面是[4:9]，最后一块是[9:13]

分析出来后就可以开始改代码了。

--------------------------------------------------------------------
Sequential(
  (0): Conv2dNormActivation(
    (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
    (2): Hardswish()
  )
  (1): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=16, bias=False)
        (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(16, 8, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (2): Conv2dNormActivation(
        (0): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (2): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(16, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(72, 72, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=72, bias=False)
        (1): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (2): Conv2dNormActivation(
        (0): Conv2d(72, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (3): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(24, 88, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(88, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(88, 88, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=88, bias=False)
        (1): BatchNorm2d(88, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU(inplace=True)
      )
      (2): Conv2dNormActivation(
        (0): Conv2d(88, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (4): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(96, 96, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=96, bias=False)
        (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(96, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (5): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)
        (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(240, 64, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(64, 240, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (6): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)
        (1): BatchNorm2d(240, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(240, 64, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(64, 240, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(40, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (7): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(40, 120, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(120, 120, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=120, bias=False)
        (1): BatchNorm2d(120, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(120, 32, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(32, 120, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (8): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(144, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(144, 144, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=144, bias=False)
        (1): BatchNorm2d(144, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(40, 144, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (9): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(288, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(288, 288, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=288, bias=False)
        (1): BatchNorm2d(288, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(72, 288, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(288, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (10): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=576, bias=False)
        (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(144, 576, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (11): InvertedResidual(
    (block): Sequential(
      (0): Conv2dNormActivation(
        (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (1): Conv2dNormActivation(
        (0): Conv2d(576, 576, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=576, bias=False)
        (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): Hardswish()
      )
      (2): SqueezeExcitation(
        (avgpool): AdaptiveAvgPool2d(output_size=1)
        (fc1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1))
        (fc2): Conv2d(144, 576, kernel_size=(1, 1), stride=(1, 1))
        (activation): ReLU()
        (scale_activation): Hardsigmoid()
      )
      (3): Conv2dNormActivation(
        (0): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (12): Conv2dNormActivation(
    (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (1): BatchNorm2d(576, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
    (2): Hardswish()
  )
)

修改代码

先写一下common.py中的MobileNet函数

class MobileNet(nn.Module):
    def __init__(self, *args) -> None:
        super().__init__()
        model = models.mobilenet_v3_small(pretrained=True)
        modules = list(model.children())
        modules = modules[0][args[1]:args[2]]
        self.model = nn.Sequential(*modules)
    def forward(self, x):
        return self.model(x)

*args传入的是前面我们数的模型块

然后修改yolov5.yaml中的backbone，注意第一个的output channel是第4个模型块的输出channel；第二个的output channel是第9个模型块的输出channel；第三个的output channel是第13个也就是最后一个模型块的输出channel，

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, MobileNet, [24, 0, 4]],  # 0
   [-1, 1, MobileNet, [48, 4, 9]],  # 1
   [-1, 1, MobileNet, [576, 9, 13]],  # 2
   [-1, 1, SPPF, [1024, 5]],  # 3
  ]

然后是改yolo.py中的parse_model 函数，很简单，在后面加一个

        elif m is MobileNet:
            c2 = args[0]

同理修改efficientnet_b0


class effb0(nn.Module):
    def __init__(self, *args) -> None:
        super().__init__()
        model = models.efficientnet_b0(pretrained=True)
        modules = list(model.children())
        modules = modules[0][args[1]:args[2]]
        self.model = nn.Sequential(*modules)
    def forward(self, x):
        return self.model(x)

然后修改yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.25  # 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]
  [[-1, 1, effb0, [40,0,4]],  # 0
   [-1, 1, effb0, [112,4,6]],  # 1
   [-1, 1, effb0, [1280,6,9]],  # 2
   [-1, 1, SPPF, [1024, 5]],  # 3
  ]
# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 1], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 7
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 0], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 11 (P3/8-small)
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 7], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 14 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 3], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 17 (P5/32-large)
   [[11, 14, 17], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

添加parse_model

        elif m is effb0:
            c2 = args[0]

其他的可以看看resnet、shufflenet，与上面两个有些许差别，我没弄出来。

标签 Tor, 使用, 修改