DeepLabv1

论文地址：Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

其实挺烦看这种远古论文的，引用的算法现在都不太常见，使用的措辞也和现在不太一样。该论文主要引入了空洞卷积(Astrous/Dilated Convolution)和条件随机场(Conditional Random Field, CRF)。

空洞卷积，顾名思义，即是在卷积核权重之间注入空洞，使用小卷积核的计算量获得大卷积核的感受野。（如理解有误请邮件指正）

空洞卷积比传统卷积多一个参数为采样率(dilation rate)，表示一个卷积核中采样的间隔。

条件随机场涉及到很多机器学习的知识，学起来比较耗时间，而且在后来的DeepLab版本中被取代，所以此处暂略，有机会再补上。

DeepLabv2

论文地址：DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

比起v1，v2的主要改动是增加了带孔空间金字塔池化(ASPP)模块，其思想来源于SPPnet。但是文中对ASPP的阐述非常少，完全没有讲清楚ASPP的机制，只能通过论文中的图片和网上的博客来猜。

_H7RQ3P@__TYL_4_87Z0H05.png

可以看出，ASPP使用了几种不同采样率的空洞卷积，对一张特征图得出多个分支后，最终concat到一起。可以借助代码理解一下这部分：

#without bn version
class ASPP(nn.Module):
    def __init__(self, in_channel=512, depth=256):
        super(ASPP,self).__init__()
        self.mean = nn.AdaptiveAvgPool2d((1, 1)) #(1,1)means ouput_dim
        self.conv = nn.Conv2d(in_channel, depth, 1, 1)
        self.atrous_block1 = nn.Conv2d(in_channel, depth, 1, 1)
        self.atrous_block6 = nn.Conv2d(in_channel, depth, 3, 1, padding=6, dilation=6)
        self.atrous_block12 = nn.Conv2d(in_channel, depth, 3, 1, padding=12, dilation=12)
        self.atrous_block18 = nn.Conv2d(in_channel, depth, 3, 1, padding=18, dilation=18)
        self.conv_1x1_output = nn.Conv2d(depth * 5, depth, 1, 1)
 
    def forward(self, x):
        size = x.shape[2:]
 
        image_features = self.mean(x)
        image_features = self.conv(image_features)
        image_features = F.upsample(image_features, size=size, mode='bilinear')
 
        atrous_block1 = self.atrous_block1(x)
        atrous_block6 = self.atrous_block6(x)
        atrous_block12 = self.atrous_block12(x)
        atrous_block18 = self.atrous_block18(x)
 
        net = self.conv_1x1_output(torch.cat([image_features, atrous_block1, atrous_block6,
                                              atrous_block12, atrous_block18], dim=1))
        return net