轻量化YOLOv7系列：结合G-GhostNet | 适配GPU，华为诺亚提出G-Ghost方案升级GhostNet

一休哥※2024-07-25 16:22

轻量化YOLOv7系列：结合G-GhostNet | 适配GPU，华为诺亚提出G-Ghost方案升级GhostNet

本文提供了改进 YOLOv7注意力系列包含不同的注意力机制以及多种加入方式，在本文中具有完整的代码和包含多种更有效加入YOLOv8中的yaml结构，读者可以获取到注意力加入的代码和使用经验，总有一种适合你和你的数据集。

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YOLOv7注意力系列包含不同的注意力机制

需要修改的代码

models/GGhostRegNet.py代码

  1. 新建这个文件，放入网络代码
python 复制代码 
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class GHOSTBottleneck(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, group_width=1,
                 dilation=1, norm_layer=None):
        super(GHOSTBottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = planes * self.expansion
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, width // min(width, group_width), dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes)
        self.bn3 = norm_layer(planes)
        self.relu = nn.SiLU(inplace=True)

        self.downsample = downsample
        self.stride = stride


    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)



        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


# class LambdaLayer(nn.Module):
#     def __init__(self, lambd):
#         super(LambdaLayer, self).__init__()
#         self.lambd = lambd
#
#     def forward(self, x):
#         return self.lambd(x)


class Stage(nn.Module):

    def __init__(self, block, inplanes, planes, group_width, blocks, stride=1, dilate=False, cheap_ratio=0.5):
        super(Stage, self).__init__()
        norm_layer = nn.BatchNorm2d
        downsample = None
        self.dilation = 1
        previous_dilation = self.dilation
        self.inplanes = inplanes
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                conv1x1(inplanes, planes, stride),
                norm_layer(planes),
            )

        self.base = block(inplanes, planes, stride, downsample, group_width,
                          previous_dilation, norm_layer)
        self.end = block(planes, planes, group_width=group_width,
                         dilation=self.dilation,
                         norm_layer=norm_layer)

        group_width = int(group_width * 0.75)
        raw_planes = int(planes * (1 - cheap_ratio) / group_width) * group_width
        cheap_planes = planes - raw_planes
        self.cheap_planes = cheap_planes
        self.raw_planes = raw_planes

        self.merge = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(planes + raw_planes * (blocks - 2), cheap_planes,
                      kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(cheap_planes),
            nn.SiLU(inplace=True),
            nn.Conv2d(cheap_planes, cheap_planes, kernel_size=1, bias=False),
            nn.BatchNorm2d(cheap_planes),
        )
        self.cheap = nn.Sequential(
            nn.Conv2d(cheap_planes, cheap_planes,
                      kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(cheap_planes),
        )
        self.cheap_relu = nn.SiLU(inplace=True)

        layers = []
        # downsample = nn.Sequential(
        #     LambdaLayer(lambda x: x[:, :raw_planes])
        # )

        layers = []
        layers.append(block(raw_planes, raw_planes, 1, downsample, group_width,
                            self.dilation, norm_layer))
        inplanes = raw_planes
        for _ in range(2, blocks - 1):
            layers.append(block(inplanes, raw_planes, group_width=group_width,
                                dilation=self.dilation,
                                norm_layer=norm_layer))

        self.layers = nn.Sequential(*layers)

    def forward(self, input):
        x0 = self.base(input)

        m_list = [x0]
        e = x0[:, :self.raw_planes]
        for l in self.layers:
            e = l(e)
            m_list.append(e)
        m = torch.cat(m_list, 1)
        m = self.merge(m)

        c = x0[:, self.raw_planes:]
        c = self.cheap_relu(self.cheap(c) + m)

        x = torch.cat((e, c), 1)
        x = self.end(x)
        return x


class GGhostRegNet(nn.Module):

    def __init__(self, block, layers, widths, layer_number, num_classes=1000, zero_init_residual=True,
                 group_width=8, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(GGhostRegNet, self).__init__()
        # ---------------------------------
        self.layer_number = layer_number
        # --------------------------------------
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = widths[0]
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False, False]
        if len(replace_stride_with_dilation) != 4:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 4-element tuple, got {}".format(replace_stride_with_dilation))
        self.group_width = group_width
        # self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=1,
        #                        bias=False)
        # self.bn1 = norm_layer(self.inplanes)
        # self.relu = nn.ReLU(inplace=True)
        if self.layer_number in [0]:
            self.layer1 = self._make_layer(block, widths[0], layers[0], stride=1,
                                           dilate=replace_stride_with_dilation[0])


        if self.layer_number in [1]:
            self.inplanes = widths[0]
            if layers[1] > 2:
                self.layer2 = Stage(block, self.inplanes, widths[1], group_width, layers[1], stride=1,
                                    dilate=replace_stride_with_dilation[1], cheap_ratio=0.5)
            else:
                self.layer2 = self._make_layer(block, widths[1], layers[1], stride=1,
                                               dilate=replace_stride_with_dilation[1])
        if self.layer_number in [2]:
            self.inplanes = widths[1]
            self.layer3 = Stage(block, self.inplanes, widths[2], group_width, layers[2], stride=1,
                                dilate=replace_stride_with_dilation[2], cheap_ratio=0.5)

        if self.layer_number in [3]:
            self.inplanes = widths[2]
            if layers[3] > 2:
                self.layer4 = Stage(block, self.inplanes, widths[3], group_width, layers[3], stride=1,
                                    dilate=replace_stride_with_dilation[3], cheap_ratio=0.5)
            else:
                self.layer4 = self._make_layer(block, widths[3], layers[3], stride=1,
                                               dilate=replace_stride_with_dilation[3])
        # self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        # self.dropout = nn.Dropout(0.2)
        # self.fc = nn.Linear(widths[-1] * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes, stride),
                norm_layer(planes),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.group_width,
                            previous_dilation, norm_layer))
        self.inplanes = planes
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, group_width=self.group_width,
                                dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):

        if self.layer_number in [0]:
            x = self.layer1(x)
        if self.layer_number in [1]:
            x = self.layer2(x)
        if self.layer_number in [2]:
            x = self.layer3(x)
        if self.layer_number in [3]:
            x = self.layer4(x)

        return x

    def forward(self, x):
        return self._forward_impl(x)
  1. yolo里引用

创建yaml文件

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

# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32

# yolov7_MY backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [32, 3, 1]],  # 0
  
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
   [-1, 1, Conv, [64, 3, 1]],
   
   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4  
   [-1, 1, Conv, [48, 1, 1]],
#   [-2, 1, Conv, [64, 1, 1]],
#   [-1, 1, Conv, [64, 3, 1]],
#   [-1, 1, Conv, [64, 3, 1]],
#   [-1, 1, Conv, [64, 3, 1]],
#   [-1, 1, Conv, [64, 3, 1]],
#   [[-1, -3, -5, -6], 1, Concat, [1]],
#   [-1, 1, Conv, [256, 1, 1]],  # 11
   [-1, 1, GGhostRegNet, [48, 0]], # 5

   [-1, 1, MP, []],
   [-1, 1, Conv, [48, 1, 1]],
   [-3, 1, Conv, [48, 1, 1]],
   [-1, 1, Conv, [48, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 16-P3/8  
   [-1, 1, Conv, [96, 1, 1]],
#   [-2, 1, Conv, [128, 1, 1]],
#   [-1, 1, Conv, [128, 3, 1]],
#   [-1, 1, Conv, [128, 3, 1]],
#   [-1, 1, Conv, [128, 3, 1]],
#   [-1, 1, Conv, [128, 3, 1]],
#   [[-1, -3, -5, -6], 1, Concat, [1]],
#   [-1, 1, Conv, [512, 1, 1]],  # 24
   [-1, 3, GGhostRegNet, [96, 1]], # 12

   [-1, 1, MP, []],
   [-1, 1, Conv, [96, 1, 1]],
   [-3, 1, Conv, [96, 1, 1]],
   [-1, 1, Conv, [96, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 29-P4/16  
   [-1, 1, Conv, [240, 1, 1]],
#   [-2, 1, Conv, [256, 1, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [[-1, -3, -5, -6], 1, Concat, [1]],
#   [-1, 1, Conv, [1024, 1, 1]],  # 37
   [-1, 5, GGhostRegNet, [240, 2]], # 19

   [-1, 1, MP, []],
   [-1, 1, Conv, [240, 1, 1]],
   [-3, 1, Conv, [240, 1, 1]],
   [-1, 1, Conv, [240, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 42-P5/32  
   [-1, 1, Conv, [528, 1, 1]],
#   [-2, 1, Conv, [256, 1, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [-1, 1, Conv, [256, 3, 1]],
#   [[-1, -3, -5, -6], 1, Concat, [1]],
#   [-1, 1, Conv, [1024, 1, 1]],  # 50
   [-1, 7, GGhostRegNet, [528, 3]], # 26
  ]

# yolov7_MY head
head:
  [[-1, 1, SPPCSPC, [512]], # 27
  
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [19, 1, Conv, [256, 1, 1]], # route backbone P4
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 39
   
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [12, 1, Conv, [128, 1, 1]], # route backbone P3
   [[-1, -2], 1, Concat, [1]],
   
   [-1, 1, Conv, [128, 1, 1]],
   [-2, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [128, 1, 1]], # 51
      
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3, 39], 1, Concat, [1]],
   
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 64
      
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3, 27], 1, Concat, [1]],
   
   [-1, 1, Conv, [512, 1, 1]],
   [-2, 1, Conv, [512, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [512, 1, 1]], # 77
   
   [51, 1, RepConv, [256, 3, 1]],
   [64, 1, RepConv, [512, 3, 1]],
   [77, 1, RepConv, [1024, 3, 1]],

   [[78,79,80], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
  ]

测试是否创建成功

这里是引用

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