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论文题目：Searching for MobileNetV3
论文地址：https://arxiv.org/abs/1905.02244
源代码：https://github.com/xiaolai-sqlai/mobilenetv3
一、论文简介
MobileNetV3，是谷歌在2019年3月21日提出的轻量化网络架构，在前两个版本的基础上，加入神经网络架构搜索（NAS）和h-swish激活函数，并引入SE通道注意力机制，性能和速度都表现优异，受到学术界和工业界的追捧。
相比于 MobileNetV2 版本而言，具体 MobileNetV3 在性能上有哪些提升呢？在原论文摘要中，作者提到在 ImageNet 分类任务中正确率上升了 3.2%，计算延时还降低了 20%。
小海带结合YOLOv5做实验时的参数：343 layers, 1376844 parameters, 1376844 gradients, 2.3 GFLOPS。参数量及浮点运算量大幅下降，且推理速度提升飞快！
总结其主要特点：
1.论文推出两个版本：Large 和 Small，分别适用于不同的场景
2.网络的架构基于NAS实现的MnasNet（效果比MobileNetV2好），由NAS搜索获取参数
3.引入MobileNetV1的深度可分离卷积
4.引入MobileNetV2的具有线性瓶颈的倒残差结构
5.引入基于squeeze and excitation结构的轻量级注意力模型(SE)
6.使用了一种新的激活函数h-swish(x)
7.网络结构搜索中，结合两种技术：资源受限的NAS（platform-aware NAS）与NetAdapt
8.修改了MobileNetV2网络端部最后阶段
二、YOLOv5结合轻量化网络MobileNetV3
1.配置.yaml文件
# anchors
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 backbone
backbone:# [from, number, module, args][ [ -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]# YOLOv5 head
head:[[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 8], 1, Concat, [1]], # cat backbone P4[-1, 1, C3, [256, False]], # 15[-1, 1, Conv, [128, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 3], 1, Concat, [1]], # cat backbone P3[-1, 1, C3, [128, False]], # 19 (P3/8-small)[-1, 1, Conv, [128, 3, 2]],[[-1, 16], 1, Concat, [1]], # cat head P4[-1, 1, C3, [256, False]], # 22 (P4/16-medium)[-1, 1, Conv, [256, 3, 2]],[[-1, 12], 1, Concat, [1]], # cat head P5[-1, 1, C3, [512, False]], # 25 (P5/32-large)[[19, 22, 25], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)]2.配置common.py
#——————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) / 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__()# Squeeze操作self.avg_pool = nn.AdaptiveAvgPool2d(1)# Excitation操作(FC+ReLU+FC+Sigmoid)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) # 学习到的每一channel的权重return x * yclass conv_bn_hswish(nn.Module):"""This equals todef conv_3x3_bn(inp, oup, stride):return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False),nn.BatchNorm2d(oup),h_swish())"""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 == oup# 输入通道数=扩张通道数 则不进行通道扩张if 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:# 否则 先进行通道扩张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 y3.修改yolo.py
找到parse_model函数，加入h_sigmoid， h_swish，SELayer，conv_bn_hswish， MobileNet_Block等5个模块即可。
