YOLOv8改进 更换轻量化模型MobileNetV3-large

1.更换结果

模型参数下降了，但是精度同样下降了，可相比于把backbone换成mobilenetv3-small，还是好一点

2.更换步骤

2.1 创建文件MobileNetV3 .py

• 路径为ultralytics/nn
• 在nn文件夹目录中创建MobileNetV3 .py

• MobileNetV3 的代码为：

 
from torch import nn
 
# ######  Mobilenetv3
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 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()
        self.act = nn.Hardswish(inplace=True)
 
    def forward(self, x):
        return self.act(self.bn(self.conv(x)))
 
    def fuseforward(self, x):
        return self.act(self.conv(x))
 
 
class MobileNetV3_InvertedResidual(nn.Module):
    def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):
        super(MobileNetV3_InvertedResidual, 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),
                nn.Hardswish(inplace=True) 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),
				nn.Hardswish(inplace=True) 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),
                nn.Hardswish(inplace=True) 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

2.2 在ultralytics\ultralytics\nn\tasks.py文件中加入该模块

在task.py文件开头写上以下代码：

from ultralytics.nn.backbone.MobileNetV3 import *

将task.py文件中的def _predict_once函数模块要替换为更换网络结构后的预测模块

换成以下代码：

    def _predict_once(self, x, profile=False, visualize=False):
        """
        Perform a forward pass through the network.
        Args:
            x (torch.Tensor): The input tensor to the model.
            profile (bool):  Print the computation time of each layer if True, defaults to False.
            visualize (bool): Save the feature maps of the model if True, defaults to False.
        Returns:
            (torch.Tensor): The last output of the model.
        """
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            if hasattr(m, 'backbone'):
                x = m(x)
                for _ in range(5 - len(x)):
                    x.insert(0, None)
                for i_idx, i in enumerate(x):
                    if i_idx in self.save:
                        y.append(i)
                    else:
                        y.append(None)
                # for i in x:
                #     if i is not None:
                #         print(i.size())
                x = x[-1]
            else:
                x = m(x)  # run
                y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

值得注意的是，这一步换完后可能会报错，报错如下：

这时只要在_predict_once这个函数的上一行代码，也就是如下图所示，把embed去掉，就不会报错了，也可以把源代码注释，直接换成下图中我注释的代码

2.3 在task.py文件中的def parse_model函数模块中加入MobileNetV3：

        elif m in {conv_bn_hswish, MobileNetV3_InvertedResidual}:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if not output
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, c2, *args[1:]]

2.4 创建yolov8-MobileNetV3.yaml文件

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect

# Parameters
nc: 3 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
  # [depth, width, max_channels]
  n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPs
  s: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPs
  m: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPs
  l: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
  x: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs


# inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs
backbone:
  # [from, repeats, module, args]
 - [-1, 1, conv_bn_hswish, [16, 2]]  # 0-P1/2
 - [-1, 1, MobileNetV3_InvertedResidual, [16, 16, 3, 1, 0, 0]]

 - [-1, 1, MobileNetV3_InvertedResidual, [24, 64, 3, 2, 0, 0]]  # 2-p2/4
 - [-1, 1, MobileNetV3_InvertedResidual, [24, 72, 3, 1, 0, 0]]

 - [-1, 1, MobileNetV3_InvertedResidual, [40, 72, 5, 2, 1, 0]]  # 4-p3/8
 - [-1, 1, MobileNetV3_InvertedResidual, [40, 120, 5, 1, 1, 0]]
 - [-1, 1, MobileNetV3_InvertedResidual, [40, 120, 5, 1, 1, 0]]

 - [-1, 1, MobileNetV3_InvertedResidual, [80, 240, 3, 2, 0, 1]]  # 7-p4/16
 - [-1, 1, MobileNetV3_InvertedResidual, [80, 200, 3, 1, 0, 1]]
 - [-1, 1, MobileNetV3_InvertedResidual, [80, 184, 3, 1, 0, 1]]
 - [-1, 1, MobileNetV3_InvertedResidual, [80, 184, 3, 1, 0, 1]]
 - [-1, 1, MobileNetV3_InvertedResidual, [112, 480, 3, 1, 1, 1]]
 - [-1, 1, MobileNetV3_InvertedResidual, [112, 672, 3, 1, 1, 1]]

 - [-1, 1, MobileNetV3_InvertedResidual, [160, 672, 5, 2, 1, 1]]  # 13-p5/32
 - [-1, 1, MobileNetV3_InvertedResidual, [160, 960, 5, 1, 1, 1]]
 - [-1, 1, MobileNetV3_InvertedResidual, [160, 960, 5, 1, 1, 1]]


# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 12], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [256]]  # 18

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [128]]  # 21 (P3/8-small)

  - [-1, 1, Conv, [128, 3, 2]]
  - [[-1, 18], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [256]]  # 24 (P4/16-medium)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 15], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [512]]  # 27 (P5/32-large)

  - [[21, 24, 27], 1, Detect, [nc]]  # Detect(P3, P4, P5)

ok，到此结束，谢谢

创作此博客的过程中借鉴了以下博客：

YOLOv8改进 更换轻量化模型MobileNetV3_yolov8轻量化-优快云博客

解决yolov8替换骨干网络efficientVit出现的报错TypeError: _predict_once() takes_yolov8改进efficientvit-优快云博客