做交易平台的网站需要哪些技术,北京网站制做的公司,应聘网站运营建设面试,云端+文明实践活动前言
关于网络中注意力机制的改进有很多种#xff0c;本篇内容从EMA注意力机制开始#xff01;
往期回顾
YOLOv5-7.0改进#xff08;一#xff09;MobileNetv3替换主干网络
YOLOv5-7.0改进#xff08;二#xff09;BiFPN替换Neck网络
YOLOv5-7.0改进#xff08;三本篇内容从EMA注意力机制开始
往期回顾
YOLOv5-7.0改进一MobileNetv3替换主干网络
YOLOv5-7.0改进二BiFPN替换Neck网络
YOLOv5-7.0改进三添加损失函数EIoU、AlphaIoU、SIoU、WIoU、MPDIoU、NWD 目录 一、EMA简介二、Neck端添加EMA第一步在common.py中添加EMA模块第二步在yolo.py中的parse_model函数加入类名第三步制作模型配置文件第四步验证新加入的Neck网络 三、C3中添加EMA第一步在common.py中添加EMA模块第二步在yolo.py中的parse_model函数加入类名第三步制作模型配置文件第四步验证新加入的Neck网络 一、EMA简介
论文题目Efficient Multi-Scale Attention Module with Cross-Spatial Learning
EMA注意力机制基于跨空间学习的高效多尺度注意力机制该模块首先将部分通道维度重塑为批量维度以避免通用卷积进行某种形式的降维接着在每个并行子网络中构建局部的跨通道交互利用一种新的跨空间学习方法融合两个并行子网络的输出特征图设计了一个多尺度并行子网络来建立长短依赖关系。
网络结构 二、Neck端添加EMA
第一步在common.py中添加EMA模块
代码如下
#EMA
class EMA(nn.Module):def __init__(self, channels, factor8):super(EMA, self).__init__()self.groups factor # 分组因子assert channels // self.groups 0self.softmax nn.Softmax(-1) #softmax操作self.agp nn.AdaptiveAvgPool2d((1, 1)) # 1×1平均池化层self.pool_h nn.AdaptiveAvgPool2d((None, 1)) # X平均池化层 h1self.pool_w nn.AdaptiveAvgPool2d((1, None)) # Y平均池化层 w1self.gn nn.GroupNorm(channels // self.groups, channels // self.groups) # 分组操作self.conv1x1 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size1, stride1, padding0) # 1×1卷积分支 self.conv3x3 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size3, stride1, padding1) # 3×3卷积分支def forward(self, x):b, c, h, w x.size()group_x x.reshape(b * self.groups, -1, h, w) # b*g,c//g,h,wx_h self.pool_h(group_x) # 得到平均池化之后的hx_w self.pool_w(group_x).permute(0, 1, 3, 2) # 得到平均池化之后的whw self.conv1x1(torch.cat([x_h, x_w], dim2)) # 先拼接然后送入1×1卷积x_h, x_w torch.split(hw, [h, w], dim2)x1 self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())x2 self.conv3x3(group_x) # 3×3卷积分支x11 self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x12 x2.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwx21 self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x22 x1.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwweights (torch.matmul(x11, x12) torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)return (group_x * weights.sigmoid()).reshape(b, c, h, w)插入效果 第二步在yolo.py中的parse_model函数加入类名
将EMA类名添加到注册表中效果如下 第三步制作模型配置文件
1、复制models/yolov5s.yaml文件并重命名 2、将以下代码复制到新创建的yaml文件
# YOLOv5 by Ultralytics, GPL-3.0 license# Parameters
nc: 12 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # 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, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]], # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, EMA, [256]], # 加入到小目标层后[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, EMA, [512]], # 加入到中目标层后[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[-1, 1, EMA, [1024]], # 加入到大目标层后[[18, 22, 26], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]第四步验证新加入的Neck网络
1、修改yolo.py中以下两个地方
1DetectionModel函数下的cfg 2parser argparse.ArgumentParser()下的cfg 2、运行yolo.py
(1)yolov5s_EMA.yaml 好了到这一步在Neck端添加EMA基本完成接下就可以开始训练~
三、C3中添加EMA
第一步在common.py中添加EMA模块
代码如下
#EMA
class EMA(nn.Module):def __init__(self, channels, factor8):super(EMA, self).__init__()self.groups factor # 分组率assert channels // self.groups 0self.softmax nn.Softmax(-1) # Softmaxself.agp nn.AdaptiveAvgPool2d((1, 1)) # 平均池化层self.pool_h nn.AdaptiveAvgPool2d((None, 1)) # x平均池化层 h1self.pool_w nn.AdaptiveAvgPool2d((1, None)) # y平均池化层 w1self.gn nn.GroupNorm(channels // self.groups, channels // self.groups) # 分组操作self.conv1x1 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size1, stride1, padding0) # 1×1卷积分支self.conv3x3 nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size3, stride1, padding1) # 3×3卷积分支def forward(self, x):b, c, h, w x.size()group_x x.reshape(b * self.groups, -1, h, w) # b*g,c//g,h,wx_h self.pool_h(group_x)x_w self.pool_w(group_x).permute(0, 1, 3, 2)hw self.conv1x1(torch.cat([x_h, x_w], dim2))x_h, x_w torch.split(hw, [h, w], dim2)x1 self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())x2 self.conv3x3(group_x)x11 self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x12 x2.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwx21 self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x22 x1.reshape(b * self.groups, c // self.groups, -1) # b*g, c//g, hwweights (torch.matmul(x11, x12) torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)return (group_x * weights.sigmoid()).reshape(b, c, h, w)class C3_EMA3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, 1, 1)self.cv2 Conv(c1, c_, 1, 1)self.cv3 Conv(2 * c_, c2, 1) # optional actFReLU(c2)self.m nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n)))self.m1 nn.ModuleList([EMA(2 * c_)]) # 添加在最后一个卷积之前def forward(self, x):return self.cv3(self.m1[0](torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)))class C3_EMA2(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, 1, 1)self.cv2 Conv(c1, c_, 1, 1)self.cv3 Conv(2 * c_, c2, 1) # optional actFReLU(c2)self.m nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n)))self.m1 nn.ModuleList([EMA(c1)]) # 添加在最后一个卷积之前def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(self.m1[0](x))), 1))class C3_EMA1(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, 1, 1)self.cv2 Conv(c1, c_, 1, 1)self.cv3 Conv(2 * c_, c2, 1) # optional actFReLU(c2)self.m nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n)))self.m1 nn.ModuleList([EMA(c_)]) # 添加在最后一个卷积之前def forward(self, x):return self.cv3(torch.cat((self.m(self.m1[0](self.cv1(x))), self.cv2(x)), 1))效果如下 第二步在yolo.py中的parse_model函数加入类名
将以下类名添加到注册表中
EMA, C3_EMA1, C3_EMA2, C3_EMA3效果如下
第三步制作模型配置文件
将以下代码复制到yaml文件中
# YOLOv5 by Ultralytics, GPL-3.0 license# Parameters
nc: 12 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # 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, Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, Conv, [128, 3, 2]], # 1-P2/4[-1, 3, C3_EMA1, [128]],[-1, 1, Conv, [256, 3, 2]], # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]], # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]], # 9]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 6], 1, Concat, [1]], # cat backbone P4[-1, 3, C3, [512, False]], # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]第四步验证新加入的Neck网络
1、运行yolo.py 接下来也是对这个模型进行训练需要注意的是这是在主干网络部分改进~
