泰安网站建设定制公司,多语言网站建设费用,《网站开发技术》模板,wordpress 添加新页面系列文章目录
yolov7训练添加spd-conv 文章目录 系列文章目录一、spd-conv是什么#xff1f;二、使用步骤1.第一步#xff1a;先在models/common.py加上2.第二步#xff1a;models/yolo.py加上2.第三步#xff1a;修改yolov7的yaml文件 总结 提示#xff1a;以下是本篇文…系列文章目录
yolov7训练添加spd-conv 文章目录 系列文章目录一、spd-conv是什么二、使用步骤1.第一步先在models/common.py加上2.第二步models/yolo.py加上2.第三步修改yolov7的yaml文件 总结 提示以下是本篇文章正文内容下面案例可供参考
一、spd-conv是什么
SPD-Conv是一种新的构建块用于替代现有的CNN体系结构中的步长卷积和池化层。它由一个空间到深度(SPD)层和一个非步长卷积(Conv)层组成。 空间到深度SPD层的作用是将输入特征图的每个空间维度降低到通道维度同时保留通道内的信息。这可以通过将输入特征图的每个像素或特征映射到一个通道来实现。在这个过程中空间维度的大小会减小而通道维度的大小会增加。 非步长卷积(Conv)层是一种标准的卷积操作它在SPD层之后进行。与步长卷积不同非步长卷积不会在特征图上移动而是对每个像素或特征映射进行卷积操作。这有助于减少在SPD层中可能出现的过度下采样问题并保留更多的细粒度信息。 SPD-Conv的组合方式是将SPD层和Conv层串联起来。具体来说输入特征图首先通过SPD层进行转换然后输出结果再通过Conv层进行卷积操作。这种组合方式可以在不丢失信息的情况下减少空间维度的尺寸同时保留通道内的信息有助于提高CNN对低分辨率图像和小型物体的检测性能。 总结起来SPD-Conv是一种新的构建块旨在解决现有CNN体系结构中步长卷积和池化层的问题。它由一个空间到深度(SPD)层和一个非步长卷积(Conv)层组成能够提高模型对低分辨率图像和小型物体的检测性能并降低对“良好质量输入的依赖。
二、使用步骤
1.第一步先在models/common.py加上 class space_to_depth(nn.Module):# Changing the dimension of the Tensordef __init__(self, dimension1):super().__init__()self.d dimensiondef forward(self, x):return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)2.第二步models/yolo.py加上 elif m is space_to_depth:c2 4 * ch[f]同时在769行里面加入space to death 全部代码 if m in [nn.Conv2d, Conv, RobustConv, RobustConv2, DWConv, GhostConv, RepConv, RepConv_OREPA, DownC,SPP, SPPF, SPPCSPC, GhostSPPCSPC, MixConv2d, Focus, Stem, GhostStem, CrossConv,Bottleneck, BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,RepBottleneck, RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,Res, ResCSPA, ResCSPB, ResCSPC,RepRes, RepResCSPA, RepResCSPB, RepResCSPC,ResX, ResXCSPA, ResXCSPB, ResXCSPC,RepResX, RepResXCSPA, RepResXCSPB, RepResXCSPC,Ghost, GhostCSPA, GhostCSPB, GhostCSPC,SwinTransformerBlock, STCSPA, STCSPB, STCSPC,SwinTransformer2Block, ST2CSPA, ST2CSPB, ST2CSPC,Conv_ATT,SPPCSPC_ATT,CBAM]:c1, c2 ch[f], args[0]if c2 ! no: # if not outputc2 make_divisible(c2 * gw, 8)args [c1, c2, *args[1:]]if m in [DownC, SPPCSPC, GhostSPPCSPC,BottleneckCSPA, BottleneckCSPB, BottleneckCSPC,RepBottleneckCSPA, RepBottleneckCSPB, RepBottleneckCSPC,ResCSPA, ResCSPB, ResCSPC,RepResCSPA, RepResCSPB, RepResCSPC,ResXCSPA, ResXCSPB, ResXCSPC,RepResXCSPA, RepResXCSPB, RepResXCSPC,GhostCSPA, GhostCSPB, GhostCSPC,STCSPA, STCSPB, STCSPC,ST2CSPA, ST2CSPB, ST2CSPC,space_to_depth]:args.insert(2, n) # number of repeatsn 1elif m is nn.BatchNorm2d:args [ch[f]]elif m is Concat:c2 sum([ch[x] for x in f])elif m is Chuncat:c2 sum([ch[x] for x in f])elif m is Shortcut:c2 ch[f[0]]elif m is Foldcut:c2 ch[f] // 2elif m in [Detect, IDetect, IAuxDetect, IBin, IKeypoint]:args.append([ch[x] for x in f])if isinstance(args[1], int): # number of anchorsargs[1] [list(range(args[1] * 2))] * len(f)elif m is ReOrg:c2 ch[f] * 4elif m is Contract:c2 ch[f] * args[0] ** 2elif m is Expand:c2 ch[f] // args[0] ** 2elif m is space_to_depth:c2 4 * ch[f]else:c2 ch[f]2.第三步修改yolov7的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:- [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 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, [64, 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, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3], 1, Concat, [1]], # 16-P3/8[-1, 1, Conv, [128, 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, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]],[[-1, -3], 1, Concat, [1]], # 29-P4/16[-1, 1, Conv, [256, 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, 1, MP, []],[-1, 1, Conv, [512, 1, 1]],[-3, 1, Conv, [512, 1, 1]],[-1, 1, Conv, [512, 3, 2]],[[-1, -3], 1, Concat, [1]], # 42-P5/32[-1, 1, Conv, [256, 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]# yolov7 head
head:[[-1, 1, SPPCSPC, [512]], # 51[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[37, 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]], # 63[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[24, 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]], # 75[-1, 1, MP, []],[-1, 1, Conv, [128, 1, 1]],[-3, 1, Conv, [128, 1, 1]],[-1, 1, Conv, [128, 3, 2]],[[-1, -3, 63], 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]], # 88[-1, 1, MP, []],[-1, 1, Conv, [256, 1, 1]],[-3, 1, Conv, [256, 1, 1]],[-1, 1, Conv, [256, 3, 2]],[[-1, -3, 51], 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]], # 101[-1,1,space_to_depth,[1]], # 2 -P2/4[-1, 1, Conv, [512, 1, 1]], # 103[75, 1, RepConv, [256, 3, 1]],[88, 1, RepConv, [512, 3, 1]],[103, 1, RepConv, [1024, 3, 1]],[[104,105,106], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)]
总结
以上只是·简单添加了一层spd需要添加多层spd-con可以直接修改yolov7的yaml配置文件不需要修改其他。
