[人工智能]MobileNet V1代码和总结

引入深度可分离卷积代替原有的常规卷积

由于常规卷积会融合通道信息和空间信息，因此要代替常规卷积不仅要考虑轻量化还要考虑是否融合了通道信息和空间信息。

深度可分离卷积由深度卷积和点卷积构成，其中，深度卷积只考虑空间的相关性，只进行空间信息的融合，点卷积只考虑通道相关性，只进行通道信息的融合。
!](https://img-blog.csdnimg.cn/6a086be0b3cb4328823bed92e18eeea0.png?x-oss-process=image/watermark,type_d3F5LXplbmhlaQ,shadow_50,text_Q1NETiBA5ZCR5LiK55qE6Zi_6bmP,size_15,color_FFFFFF,t_70,g_se,x_16)

深度卷积（DW Conv):仅考虑空间的相关性

1. 卷积核的channel仅为1
2. 输入特征矩阵=卷积核个数=输出特征矩阵：即groups = channels
3. 计算量：

KxKxCinxHxW

其中，K为卷积核尺寸，Cin为输入通道数，H,W为输出特征图尺寸。其中由于输入通道数等于输出通道数，因此不乘Cout
如下图（上为常规，下为DW conv）：

点卷积（PW Conv）：仅考虑通道的相关性

由于DW卷积中只考虑了空间信息，通道之间没有没有信息交流，因此加入点卷积，来进行通道信息混合。即1X1卷积
计算量：CinxCoutxHxW

计算量

因此深度可分离卷积的计算量为：

KxKxCinxHxW+CinxCoutxHxW

常规卷积计算量：

CinxKxKxCoutxHxW

相比之下，在k=3时，计算量仅为常规的1/9。

代码：

import torch
import torch.nn as nn

class CONV_BN_RELU(nn.Module):
    def __init__(self,in_channel,ou_channel,kernel_size,stride,padding,groups = 1):
        super(CONV_BN_RELU, self).__init__()
        self.conv = nn.Sequential(nn.Conv2d(in_channel,ou_channel,kernel_size,stride,padding = padding,groups=groups),
                                  nn.BatchNorm2d(ou_channel),
                                  nn.ReLU())
    def forward(self,x):
        return self.conv(x)

class BLOCK(nn.Module):
    def __init__(self,inchannel,out_channel,stride):
        super(BLOCK, self).__init__()
        self.conv = nn.Sequential(
            CONV_BN_RELU(inchannel,inchannel,kernel_size=3,stride = stride,padding = 3//2,groups = inchannel),
            CONV_BN_RELU(inchannel, out_channel, kernel_size=1, stride=1,padding = 0)
        )

    def forward(self,x):
        return self.conv(x)



class Mobilenet_v1(nn.Module):
    def __init__(self,in_channels,classes,alpha):
        super(Mobilenet_v1, self).__init__()
        channels = [32,64,128,256,512,1024]
        channels = [int(i*alpha) for i in channels]
        self.conv1 = CONV_BN_RELU(in_channels,channels[0],3,2,padding = 3//2)

        self.block = BLOCK
        self.stage = nn.Sequential(
            self.block(channels[0], channels[1], 1),
            self.block(channels[1], channels[2], 2),
            self.block(channels[2], channels[2], 1),
            self.block(channels[2], channels[3], 2),
            self.block(channels[3], channels[3], 1),
            self.block(channels[3], channels[4], 2),
            self._make_stage(5,channels[4],channels[4],stride=1),
            self.block(channels[4], channels[5], 2),
            self.block(channels[5], channels[5], 1),
        )
        self.pool = nn.AvgPool2d(7)
        self.fc= nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(channels[-1],classes),
            nn.Softmax()
        )

        for m in self.modules():
            if isinstance(m,nn.Conv2d):
                nn.init.kaiming_normal_(m.weight,mode= 'fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m,nn.Linear):
                nn.init.normal_(m.weight,0,0.01)
                nn.init.zeros_(m.bias)
            elif isinstance(m,nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)



    def _make_stage(self,num_stage,inchannel,ouchannels,stride):
        strides = [stride]+[1]*(num_stage-1)
        layer = []
        for i in range(num_stage):
           layer.append(self.block(inchannel,ouchannels,strides[i]))
        return nn.Sequential(*layer)

    def forward(self,x):
        x = self.conv1(x)
        x = self.stage(x)
        x = self.pool(x)
        x = x.view(x.size(0),-1)
        x = self.fc(x)
        return x

if __name__ == '__main__':
    input = torch.empty(1,3,224,224)
    m = Mobilenet_v1(3,10,0.5)
    out = m(input)
    print(out)

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