在各处看到关于yolo的魔改都是基于yolov5版本的，于是借鉴学习一下用在yolov7-tiny版本上，做一下学习记录。

1、配置yaml文件

# parametersnc: 80# number of classesdepth_multiple: 1.0# model depth multiplewidth_multiple: 1.0# layer channel multiple# anchorsanchors:- [10,13, 16,30, 33,23]# P3/8- [30,61, 62,45, 59,119]# P4/16- [116,90, 156,198, 373,326]# P5/32# yolov7-tiny backbonebackbone:# [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True[ [ -1, 1, conv_bn_hswish, [ 16, 2 ] ], # 0-p1/2[ -1, 1, MobileNet_Block, [ 16,16, 3, 2, 1, 0 ] ],# 1-p2/4[ -1, 1, MobileNet_Block, [ 24,72, 3, 2, 0, 0 ] ],# 2-p3/8[ -1, 1, MobileNet_Block, [ 24,88, 3, 1, 0, 0 ] ],# 3-p3/8[ -1, 1, MobileNet_Block, [ 40,96, 5, 2, 1, 1 ] ],# 4-p4/16[ -1, 1, MobileNet_Block, [ 40, 240, 5, 1, 1, 1 ] ],# 5-p4/16[ -1, 1, MobileNet_Block, [ 40, 240, 5, 1, 1, 1 ] ],# 6-p4/16[ -1, 1, MobileNet_Block, [ 48, 120, 5, 1, 1, 1 ] ],# 7-p4/16[ -1, 1, MobileNet_Block, [ 48, 144, 5, 1, 1, 1 ] ],# 8-p4/16[ -1, 1, MobileNet_Block, [ 96, 288, 5, 2, 1, 1 ] ],# 9-p5/32[ -1, 1, MobileNet_Block, [ 96, 576, 5, 1, 1, 1 ] ],# 10-p5/32[ -1, 1, MobileNet_Block, [ 96, 576, 5, 1, 1, 1 ] ],# 11-p5/32]# yolov7-tiny headhead:[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, SP, [5]], [-2, 1, SP, [9]], [-3, 1, SP, [13]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -7], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 20 [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']], [8, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 [[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 30 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],[-1, 1, nn.Upsample, [None, 2, 'nearest']], [3, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 [[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 40[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 30], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 48[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 20], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 56 [40, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [48, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [56, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[57,58,59], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)]

2、配置common.py

把以下代码添加至/models/common.py中即可

#——————MobileNetV3-small—————— 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) / 6class h_swish(nn.Module):def __init__(self, inplace=True):super(h_swish, self).__init__()self.sigmoid = h_sigmoid(inplace=inplace) def forward(self, x):return x * self.sigmoid(x)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 * yclass 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() def forward(self, x):return self.act(self.bn(self.conv(x))) def fuseforward(self, x):return self.act(self.conv(x))class MobileNet_Block(nn.Module):def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):super(MobileNet_Block, self).__init__()assert stride in [1, 2] self.identity = stride == 1 and inp == oupif inp == hidden_dim:self.conv = nn.Sequential(# dwnn.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),# Squeeze-and-ExciteSELayer(hidden_dim) if use_se else nn.Sequential(),# pw-linearnn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),nn.BatchNorm2d(oup),)else:self.conv = nn.Sequential(# pwnn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),nn.BatchNorm2d(hidden_dim),h_swish() if use_hs else nn.ReLU(inplace=True),# dwnn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,bias=False),nn.BatchNorm2d(hidden_dim),# Squeeze-and-ExciteSELayer(hidden_dim) if use_se else nn.Sequential(),h_swish() if use_hs else nn.ReLU(inplace=True),# pw-linearnn.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 + yelse:return y

3、去yolo.py中加载添加的类

找到parse_model中最长那一段，加入所添加的h_sigmoid， h_swish，SELayer，conv_bn_hswish， MobileNet_Block模块即可，如图所示

4、训练即可，注意train.py时将cfg文件改成自己的yaml， 如下所示

python train.py --workers 16 --device 0,1,2,3 --batch-size 32 --data data/data.yaml --cfg cfg/training/yolov7-tiny-mb3s.yaml --weights '' --name yolov7-tiny-mb3s --hyp data/hyp.scratch.p5.yaml

参考blog：

(111条消息) 目标检测算法——YOLOv5/YOLOv7改进之结合轻量化网络MobileNetV3（降参提速）_加勒比海带66的博客-CSDN博客_conv_bn_hswish