DenseNet笔记

1、DenseNet的优势

• 减轻梯度消失问题
• 增强特征的传递
• 充分利用特征
• 减小了参数量

 

2、网络结构公式

对于每个DenseBlock中的每个层,

[x0,x1,…,xl-1]表示将0到l-1层的输出feature map作concatenation。concatenation是作通道的合并，就像Inception那样。而前面resnet是作值的相加，通道数是不变的。Hl包括BN，ReLU和3*3的卷积。

而在ResNet中的每个残差块，

 

3、Growth Rate

指的是DenseBlock中每个非线性变换Hl(BN，ReLU和3*3的卷积)的输出，这个输出与输入Concate.一个DenseBlock的输出=输入+Hl数×growth_rate。在要给DenseBlock中，Feature Map的size保持不变。

 

4、Bottleneck

这个组件位于DenseBlock中，当一个DenseBlock包含的非线性变换Hl较多时（如nHl=48）,此时的grow rate为k=32，那么第48层的输入变成input+47×32,这是一个很大的数，若是不用bottleneck进行降维，那么计算量很大。

所以，使用4×k个1x1卷积进行降维。使得3×3线性变换的输入通道变成4×k。同时，bottleneck起到特征融合的效果。

 

5、Transition

这个组件位于DenseBlock之间，使用1×1卷积进行降维，降维后的通道数为input_channels*reduction. 参数reduction默认为0.5，后接池化层进行下采样，减少Feature Map 分辨率。

 

6、网络结构

 

7、代码实现（Pytorch）

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class Bottleneck(nn.Module):
    def __init__(self,nChannels,growthRate):
        super(Bottleneck,self).__init__()
        interChannels = 4*growthRate
        self.bn1 = nn.BatchNorm2d(nChannels)
        self.conv1 = nn.Conv2d(nChannels,interChannels,kernel_size=1,
                               stride=1,bias=False)
        self.bn2 = nn.BatchNorm2d(interChannels)
        self.conv2 = nn.Conv2d(interChannels,growthRate,kernel_size=3,
                               stride=1,padding=1,bias=False)

    def forward(self, *input):
        #先进行BN（pytorch的BN已经包含了Scale）,而后进行relu,conv1起到bottleneck的做用
        out = self.conv1(F.relu(self.bn1(input)))
        out = self.conv2(F.relu(self.bn2(out)))
        out = torch.cat(input,out)
        return out


class SingleLayer(nn.Module):
    def __init__(self,nChannels,growthRate):
        super(SingleLayer,self).__init__()
        self.bn1 = nn.BatchNorm2d(nChannels)
        self.conv1 = nn.Conv2d(nChannels,growthRate,kernel_size=3,
                               padding=1,bias=False)

    def forward(self, *input):
        out = self.conv1(F.relu(self.bn1(input)))
        out = torch.cat(input,out)
        return out

class Transition(nn.Module):
    def __int__(self,nChannels,nOutChannels):
        super(Transition,self).__init__()

        self.bn1 = nn.BatchNorm2d(nChannels)
        self.conv1 = nn.Conv2d(nChannels,nOutChannels,kernel_size=1,bias=False)

    def forward(self, *input):
        out = self.conv1(F.relu(self.bn1(input)))
        out = F.avg_pool2d(out,2)
        return out

class DenseNet(nn.Module):
    def __init__(self,growthRate,depth,reduction,nClasses,bottleneck):
        super(DenseNet,self).__init__()
        #DenseBlock中非线性变换模块的个数
        nNoneLinears = (depth-4)//3
        if bottleneck:
            nNoneLinears //=2

        nChannels = 2*growthRate
        self.conv1 = nn.Conv2d(3,nChannels,kernel_size=3,padding=1,bias=False)
        self.denseblock1 = self._make_dense(nChannels,growthRate,nNoneLinears,bottleneck)
        nChannels += nNoneLinears*growthRate
        nOutChannels = int(math.floor(nChannels*reduction))        #向下取整
        self.transition1 = Transition(nChannels,nOutChannels)

        nChannels = nOutChannels
        self.denseblock2 = self._make_dense(nChannels,growthRate,nNoneLinears,bottleneck)
        nChannels += nNoneLinears*growthRate
        nOutChannels = int(math.floor(nChannels*reduction))
        self.transition2 = Transition(nChannels, nOutChannels)

        nChannels = nOutChannels
        self.denseblock3 = self._make_dense(nChannels, growthRate, nNoneLinears, bottleneck)
        nChannels += nNoneLinears * growthRate

        self.bn1 = nn.BatchNorm2d(nChannels)
        self.fc = nn.Linear(nChannels,nClasses)

        #参数初始化
        for m in self.modules():
            if isinstance(m,nn.Conv2d):
                n = m.kernel_size[0]*m.kernel_size[1]*m.out_channels
                m.weight.data.normal_(0,math.sqrt(2./n))
            elif isinstance(m,nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m,nn.Linear):
                m.bias.data.zero_()

    def _make_dense(self,nChannels,growthRate,nDenseBlocks,bottleneck):
        layers = []
        for i in range(int(nDenseBlocks)):
            if bottleneck:
                layers.append(Bottleneck(nChannels,growthRate))
            else:
                layers.append(SingleLayer(nChannels,growthRate))
        nChannels+=growthRate
        return nn.Sequential(*layers)

    def forward(self, *input):
        out = self.conv1(input)
        out = self.transition1(self.denseblock1(out))
        out = self.transition2(self.denseblock2(out))
        out = self.denseblock3(out)
        out = torch.squeeze(F.avg_pool2d(F.relu(self.bn1(out)),8))
        out = F.log_softmax(self.fc(out))
        return out