MobileNetV2原理说明及实践落地

本文参考：

轻量级网络——MobileNetV2_Clichong的博客-CSDN博客_mobilenetv2

1、MobileNetV2介绍

MobileNetV1主要是提出了可分离卷积的概念，大大减少了模型的参数个数，从而缩小了计算量。但是在CenterNet算法中作为BackBone效果并不佳，模型收敛效果不好导致目标检测的准确率不高。

MobileNetV2在MobileNetV1的DW和PW的基础上进行了优化，使得准确率更高，作为CenterNet算法的BackBone效果也可以。它的两个亮点是：

• Inverted Residuals：倒残差结构
• Linear Bottlenecks：结构的最后一层采用线性层

2、MobileNetV2的结构

（1）倒残差结构

ResNet网络：残差结构是先用1*1卷积降维 再升维 的操作，所以两头大中间小。

MobileNetV2中，残差结构是先用1*1卷积升维 再降维 的操作，所以两头小中间大。

在MobileNetV2中采用了新的激活函数：ReLU6，它的定义如下：

（2）线性Bottlenecks

针对倒残差结构最后一层的卷积层，采用了线性的激活函数（f(x)=x，可以认为没有激活函数），而不是使用ReLU6激活函数。

原因解释：

 ReLU激活函数对于低维的信息可能会造成比较大的损失，而对于高维的特征信息造成的损失很小。而且由于倒残差结构是两头小中间大，所以输出的是一个低维的特征信息。所以使用一个线性的激活函数避免特征损失。

低维解释：低维针对的是channel，低维意味着[batch, channel, height, width]中的height和width还较大。

（3）整体结构

当stride=1且输入特征矩阵与输出特征矩阵shape相同时才有shortcut链接。

 3、MobileNetV2的pytorch实现

import torch
import torch.nn as nn
import torchvision

# 分类个数
num_class = 5

# DW卷积
def Conv3x3BNReLU(in_channels, out_channels, stride, groups):
    return nn.Sequential(
        # stride=2,wh减半; stride=1,wh不变
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1, groups=groups),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

# PW卷积
def Conv1x1BNReLU(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

# PW卷积(Linear)没有使用激活函数
def Conv1x1BN(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
        nn.BatchNorm2d(out_channels)
    )


class InvertedResidual(nn.Module):
    # t为扩展因子
    def __init__(self, in_channels, out_channels, expansion_factor, stride):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        self.in_channels = in_channels
        self.out_channels = out_channels
        mid_channels = (in_channels * expansion_factor)

        # 先1*1卷积升维，再1*1卷积降维
        self.bottleneck = nn.Sequential(
            # 升维操作
            Conv1x1BNReLU(in_channels, mid_channels),
            # DW卷积，降低参数量
            Conv3x3BNReLU(mid_channels, mid_channels, stride, groups=mid_channels),
            # 降维操作
            Conv1x1BN(mid_channels, out_channels)
        )

        # stride=1才有shortcut，此方法让原本不相同的channels相同
        if self.stride == 1:
            self.shortcut = Conv1x1BN(in_channels, out_channels)

    def forward(self, x):
        out = self.bottleneck(x)
        out = (out + self.shortcut(x)) if self.stride == 1 else out
        return out

class MobileNetV2(nn.Module):
    def make_layer(self, in_channels, out_channels, stride, factor, block_num):
        layers = []
        layers.append(InvertedResidual(in_channels, out_channels, factor, stride))
        for i in range(block_num):
            layers.append(InvertedResidual(out_channels, out_channels, factor, 1))
        return nn.Sequential(*layers)

    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def __init__(self, num_classes=num_class, t=6):
        super(MobileNetV2, self).__init__()

        self.first_conv = Conv3x3BNReLU(3, 32, 2, groups=1)
        # 32 -> 16 stride=1 wh不变
        self.layer1 = self.make_layer(in_channels=32, out_channels=16, stride=1, factor=1, block_num=1)
        # 16 -> 24 stride=2 wh减半
        self.layer2 = self.make_layer(in_channels=16, out_channels=24, stride=2, factor=t, block_num=2)
        # 24 -> 32 stride=2 wh减半
        self.layer3 = self.make_layer(in_channels=24, out_channels=32, stride=2, factor=t, block_num=3)
        # 32 -> 64 stride=2 wh减半
        self.layer4 = self.make_layer(in_channels=32, out_channels=64, stride=2, factor=t, block_num=4)
        # 64 -> 96 stride=1 wh不变
        self.layer5 = self.make_layer(in_channels=64, out_channels=96, stride=1, factor=t, block_num=3)
        # 96 -> 160 stride=2 wh减半
        self.layer6 = self.make_layer(in_channels=96, out_channels=160, stride=2, factor=t, block_num=3)
        # 160 -> 320 stride=1 wh不变
        self.layer7 = self.make_layer(in_channels=160, out_channels=320, stride=1, factor=t, block_num=1)
        # 320 -> 1280 单纯的升维操作
        self.last_conv = Conv1x1BNReLU(320, 1280)

        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)
        self.dropout = nn.Dropout2d(p=0.2)
        self.linear = nn.Linear(in_features=1280, out_features=num_classes)
        self.init_params()

    def forward(self, x):
        x = self.first_conv(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.layer6(x)
        x = self.layer7(x)
        x = self.last_conv(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.linear(x)
        return x

if __name__ == '__main__':
    model = MobileNetV2()

    input = torch.randn(1, 3, 224, 224)
    out = model(input)
    print(out.shape)

4、MobileNetV2作为CenterNet的BackBone

import torch
import torch.nn as nn

# DW卷积
def Conv3x3BNReLU(in_channels, out_channels, stride, groups):
    return nn.Sequential(
        # stride=2,wh减半; stride=1,wh不变
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1, groups=groups),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

# PW卷积
def Conv1x1BNReLU(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )

# PW卷积(Linear)没有使用激活函数
def Conv1x1BN(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
        nn.BatchNorm2d(out_channels)
    )


class InvertedResidual(nn.Module):
    # t为扩展因子
    def __init__(self, in_channels, out_channels, expansion_factor, stride):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        self.in_channels = in_channels
        self.out_channels = out_channels
        mid_channels = (in_channels * expansion_factor)

        # 先1*1卷积升维，再1*1卷积降维
        self.bottleneck = nn.Sequential(
            # 升维操作
            Conv1x1BNReLU(in_channels, mid_channels),
            # DW卷积，降低参数量
            Conv3x3BNReLU(mid_channels, mid_channels, stride, groups=mid_channels),
            # 降维操作
            Conv1x1BN(mid_channels, out_channels)
        )

        # stride=1才有shortcut，此方法让原本不相同的channels相同
        if self.stride == 1:
            self.shortcut = Conv1x1BN(in_channels, out_channels)

    def forward(self, x):
        out = self.bottleneck(x)
        out = (out + self.shortcut(x)) if self.stride == 1 else out
        return out

class MobileNetV2(nn.Module):
    def make_layer(self, in_channels, out_channels, stride, factor, block_num):
        layers = []
        layers.append(InvertedResidual(in_channels, out_channels, factor, stride))
        for i in range(block_num):
            layers.append(InvertedResidual(out_channels, out_channels, factor, 1))
        return nn.Sequential(*layers)

    def init_params(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear) or isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def __init__(self, heads, t=6):
        super(MobileNetV2, self).__init__()

        num_classes = heads['hm']

        self.first_conv = Conv3x3BNReLU(3, 32, 2, groups=1)
        # 32 -> 16 stride=1 wh不变
        self.layer1 = self.make_layer(in_channels=32, out_channels=16, stride=1, factor=1, block_num=1)
        # 16 -> 24 stride=2 wh减半
        self.layer2 = self.make_layer(in_channels=16, out_channels=24, stride=2, factor=t, block_num=2)
        # 24 -> 32 stride=2 wh减半
        self.layer3 = self.make_layer(in_channels=24, out_channels=32, stride=2, factor=t, block_num=3)
        # 32 -> 64 stride=2 wh减半
        self.layer4 = self.make_layer(in_channels=32, out_channels=64, stride=2, factor=t, block_num=4)
        # 64 -> 96 stride=1 wh不变
        self.layer5 = self.make_layer(in_channels=64, out_channels=96, stride=1, factor=t, block_num=3)
        # 96 -> 160 stride=2 wh减半
        self.layer6 = self.make_layer(in_channels=96, out_channels=160, stride=2, factor=t, block_num=3)
        # 160 -> 320 stride=1 wh不变
        self.layer7 = self.make_layer(in_channels=160, out_channels=320, stride=1, factor=t, block_num=1)
        # 320 -> 1280 单纯的升维操作
        self.last_conv = Conv1x1BNReLU(320, 1280)
        self.init_params()

        self.hm = nn.Conv2d(20, num_classes, kernel_size=1)
        self.wh = nn.Conv2d(20, 2, kernel_size=1)
        self.reg = nn.Conv2d(20, 2, kernel_size=1)


    def forward(self, x):
        x = self.first_conv(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.layer6(x)
        x = self.layer7(x)
        x = self.last_conv(x)

        y = x.view(x.shape[0], -1, 128, 128)
        z = {}
        z['hm'] = self.hm(y)
        z['wh'] = self.wh(y)
        z['reg'] = self.reg(y)
        return [z]

if __name__ == '__main__':
    heads = {'hm': 10, 'wh': 2, 'reg': 2}
    model = MobileNetV2(heads)

    input = torch.randn(1, 3, 512, 512)
    out = model(input)
    print(out.shape)

5、MobileNetV2在CenterNet目标检测落地情况

（1）训练情况

训练loss，mobilenetV1在batch_size=16时最少达到4.0左右，而mobileNetV2在batch_size=16时最少达到0.5以下。与DLASeg的效果基本接近。

（2）目标检测效果

检测效果也与DLASeg基本接近

（3）模型参数量

DLASeg为2000W个左右

MobileNetV1为320W个左右

MobileNetV2为430W个左右，总模型大小为17M

（4）CPU运行时间

DLASeg为1.2s

MobileNetV1为250ms

MobileNetV2为600ms

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