YOLOv5更换骨干网络之 MobileNetV3_办公生活

YOLOv5更换骨干网络之 MobileNetV3

创始人

2024-05-04 15:30:01

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论文地址：https://arxiv.org/abs/1905.02244
代码地址：https://github.com/xiaolai-sqlai/mobilenetv3

我们展示了基于互补搜索技术和新颖架构设计相结合的下一代 MobileNets。MobileNetV3通过结合硬件感知网络架构搜索（NAS）和 NetAdapt算法对移动设计如何协同工作，利用互补的方法来提高移动端CPU推理整体水平。通过这个过程，创建了两个新的发布的 MobileNet模型：MobileNetV3-Large 和 MobileNetV3-Small，分别针对高资源和低资源用例。然后将这些模型应用于目标检测和语义分割。针对语义分割（或任何密集像素预测）任务，提出了一种新的高效分割解码器 Lite reduce Atrous Spatial Pyramid Pooling（LR-ASPP）。实现了移动端分类，检测和分割的最新SOTA成果。与 MobileNetV2 相比，MobileNetV3-Large 在 ImageNet 分类上的准确率提高了 3.2%，同时延迟降低了 20%。与 MobileNetV2 相比，MobileNetV3-Small 的准确率高 6.6%，同时延迟相当。 MobileNetV3-Large 检测速度比 MobileNetV2 快 25%，在COCO检测上的精度大致相等。MobileNetV3-Large LR-ASPP 的速度比 MobileNetV2 R-ASPP 快 30%，在 Cityscapes segmentation分割数据集上，MobileNetV3-Large LR-ASPP 比 MobileNet V2 R-ASPP 快 34%。

MobileNet V3 主要特点

论文推出两个版本：Large和Small，分别适用于不同的资源用例;
使用NetAdapt算法获得卷积核和通道的最佳数量;
继承V1的深度可分离卷积;
继承V2的具有线性瓶颈的残差结构;
引入SE通道注意力结构;
使用了一种新的激活函数h-swish(x)代替Relu6，h的意思表示hard;
使用了Relu6(x + 3)/6来近似SE模块中的sigmoid;
修改了MobileNetV2后端输出head。

MobileNetV3网络结构

MobileNetV3-Large结构

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将YOLOv5主干网络替换为MobileNetV3-large：
yolov5-mobilenetv3large-backbone.yaml

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  
width_multiple: 1.0  # 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# MobileNetV3-large backbone# MobileNetV3_InvertedResidual [out_ch, hid_ch, k_s, stride, SE, HardSwish]
backbone:# [from, number, module, args][[-1, 1, Conv_BN_HSwish, [16, 2]],                               # 0-p1/2[-1, 1, MobileNetV3_InvertedResidual, [ 16,  16, 3, 1, 0, 0]],  # 1-p1/2[-1, 1, MobileNetV3_InvertedResidual, [ 24,  64, 3, 2, 0, 0]],  # 2-p2/4[-1, 1, MobileNetV3_InvertedResidual, [ 24,  72, 3, 1, 0, 0]],  # 3-p2/4[-1, 1, MobileNetV3_InvertedResidual, [ 40,  72, 5, 2, 1, 0]],  # 4-p3/8[-1, 1, MobileNetV3_InvertedResidual, [ 40, 120, 5, 1, 1, 0]],  # 5-p3/8[-1, 1, MobileNetV3_InvertedResidual, [ 40, 120, 5, 1, 1, 0]],  # 6-p3/8[-1, 1, MobileNetV3_InvertedResidual, [ 80, 240, 3, 2, 0, 1]],  # 7-p4/16[-1, 1, MobileNetV3_InvertedResidual, [ 80, 200, 3, 1, 0, 1]],  # 8-p4/16[-1, 1, MobileNetV3_InvertedResidual, [ 80, 184, 3, 1, 0, 1]],  # 9-p4/16[-1, 1, MobileNetV3_InvertedResidual, [ 80, 184, 3, 1, 0, 1]],  # 10-p4/16[-1, 1, MobileNetV3_InvertedResidual, [112, 480, 3, 1, 1, 1]],  # 11-p4/16[-1, 1, MobileNetV3_InvertedResidual, [112, 672, 3, 1, 1, 1]],  # 12-p4/16[-1, 1, MobileNetV3_InvertedResidual, [160, 672, 5, 1, 1, 1]],  # 13-p4/16[-1, 1, MobileNetV3_InvertedResidual, [160, 672, 5, 2, 1, 1]],  # 14-p5/32[-1, 1, MobileNetV3_InvertedResidual, [160, 960, 5, 1, 1, 1]],  # 15-p5/32]head:[[-1, 1, Conv, [160, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 13], 1, Concat, [1]],  # cat backbone P4[-1, 1, C3, [320, False]],   # 19[-1, 1, Conv, [320, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],   # cat backbone P3[-1, 1, C3, [360, False]],   # 23 (P3/8-small)[-1, 1, Conv, [360, 3, 2]],[[-1, 20], 1, Concat, [1]],  # cat head P4[-1, 1, C3, [680, False]],   # 26 (P4/16-medium)[-1, 1, Conv, [680, 3, 2]],[[-1, 16], 1, Concat, [1]],  # cat head P5[-1, 1, C3, [840, False]],   # 29 (P5/32-large)[[23, 26, 29], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

MobileNetV3-Small结构

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将YOLOv5主干网络替换为MobileNetV3-Small：

yolov5-mobilenetv3small-backbone.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # 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# Mobilenetv3-small backbone# MobileNetV3_InvertedResidual [out_ch, hid_ch, k_s, stride, SE, HardSwish]
backbone:# [from, number, module, args][[-1, 1, Conv_BN_HSwish, [16, 2]],                              # 0-p1/2[-1, 1, MobileNetV3_InvertedResidual, [16,  16, 3, 2, 1, 0]],  # 1-p2/4[-1, 1, MobileNetV3_InvertedResidual, [24,  72, 3, 2, 0, 0]],  # 2-p3/8[-1, 1, MobileNetV3_InvertedResidual, [24,  88, 3, 1, 0, 0]],  # 3[-1, 1, MobileNetV3_InvertedResidual, [40,  96, 5, 2, 1, 1]],  # 4-p4/16[-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 5[-1, 1, MobileNetV3_InvertedResidual, [40, 240, 5, 1, 1, 1]],  # 6[-1, 1, MobileNetV3_InvertedResidual, [48, 120, 5, 1, 1, 1]],  # 7[-1, 1, MobileNetV3_InvertedResidual, [48, 144, 5, 1, 1, 1]],  # 8[-1, 1, MobileNetV3_InvertedResidual, [96, 288, 5, 2, 1, 1]],  # 9-p5/32[-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 10[-1, 1, MobileNetV3_InvertedResidual, [96, 576, 5, 1, 1, 1]],  # 11]# YOLOv5 v6.0 head
head:[[-1, 1, Conv, [96, 1, 1]],  # 12[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 8], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [144, False]],  # 15[-1, 1, Conv, [144, 1, 1]], # 16[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 3], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [168, False]],  # 19 (P3/8-small)[-1, 1, Conv, [168, 3, 2]],[[-1, 16], 1, Concat, [1]], # cat head P4[-1, 3, C3, [312, False]],  # 22 (P4/16-medium)[-1, 1, Conv, [312, 3, 2]],[[-1, 12], 1, Concat, [1]], # cat head P5[-1, 3, C3, [408, False]],  # 25 (P5/32-large)[[19, 22, 25], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

在YOLOv5项目中添加方式：

common.py中加入以下代码：

# Mobilenetv3Small
class SeBlock(nn.Module):def __init__(self, in_channel, reduction=4):super().__init__()self.Squeeze = nn.AdaptiveAvgPool2d(1)self.Excitation = nn.Sequential()self.Excitation.add_module('FC1', nn.Conv2d(in_channel, in_channel // reduction, kernel_size=1))  # 1*1卷积与此效果相同self.Excitation.add_module('ReLU', nn.ReLU())self.Excitation.add_module('FC2', nn.Conv2d(in_channel // reduction, in_channel, kernel_size=1))self.Excitation.add_module('Sigmoid', nn.Sigmoid())def forward(self, x):y = self.Squeeze(x)ouput = self.Excitation(y)return x * (ouput.expand_as(x))class 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 = nn.Hardswish()def forward(self, x):return self.act(self.bn(self.conv(x)))class MobileNetV3_InvertedResidual(nn.Module):def __init__(self, inp, oup, hidden_dim, kernel_size, stride, use_se, use_hs):super(MobileNetV3_InvertedResidual, 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),nn.Hardswish() if use_hs else nn.ReLU(),# Squeeze-and-ExciteSeBlock(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),nn.Hardswish() if use_hs else nn.ReLU(),# dwnn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,bias=False),nn.BatchNorm2d(hidden_dim),# Squeeze-and-ExciteSeBlock(hidden_dim) if use_se else nn.Sequential(),nn.Hardswish() if use_hs else nn.ReLU(),# 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

yolo.py中添加如下代码：
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