目录
一、介绍
二、使用方法
1.推理
2.训练
三、MPRNet结构
1.整体结构
2.CAB(Channel Attention Block)
3.Stage1 Encoder
4.Stage2 Encoder
5.Decoder
6.SAM(Supervised Attention Module)
7.ORSNet(Original Resolution Subnetwork)
四、损失函数
1.CharbonnierLoss
2.EdgeLoss
文章来源地址https://www.toymoban.com/news/detail-577315.html
一、介绍
论文地址:https://arxiv.org/pdf/2102.02808.pdf
代码地址:http://github.com/swz30/MPRNet
恢复图像任务,需要在空间细节和高级上下文特征之间取得复杂的平衡。于是作者设计了一个多阶段的模型,模型首先使用编解码器架构来学习上下文的特征,然后将它们与保留局部信息的高分辨率分支结合起来。
打个比方,我要修复一张蛇的图片,编解码器负责提取高级上下文特征,告诉模型要在蛇身上“画”鳞片,而不是羽毛或其他东西;然后高分辨率分支负责细化鳞片的图案。
MPRNet细节很多,但最主要的创新还是“多阶段”,模型共有三个阶段,前两个阶段是编解码器子网络,用来学习较大感受野的上下文特征,最后一个阶段是高分辨率分支,用于在最终的输出图像中构建所需的纹理。作者给出了Deblurring、Denoising、Deraining三个任务的项目,三个项目的backbone是一样的,只是参数规模有所不同(Deblurring>Denoising>Deraining),下面我们以最大的Deblurring为例进行介绍。
二、使用方法
MPRNet项目分为Deblurring、Denoising和Deraining 三个子项目。作者没有用稀奇古怪的库,也没用高级的编程技巧,非常适合拿来研究学习,使用方法也很简单,几句话技能说完。
1.推理
(1)下载预训练模型:预训练模型分别存在三个子项目的pretrained_models文件夹,下载地址在每个pretrained_models文件夹的 README.md中,需要科学上网,我放在了网盘里:
链接:https://pan.baidu.com/s/1sxfidMvlU_pIeO5zD1tKZg 提取码:faye
(2)准备测试图片:将退化图片放在目录samples/input/中
(3)执行demo.py
# 执行Deblurring
python demo.py --task Deblurring
# 执行Denoising
python demo.py --task Denoising
# 执行Deraining
python demo.py --task Deraining
(4)结果放在目录samples/output/中。
2.训练
(1)根据Dataset文件夹内的README.md文件中的地址下载数据集。
(2)查看training.yml是否需要修改,主要是最后的数据集地址。
(3)执行训练
python train.py
三、MPRNet结构
我将按照官方代码实现来介绍模型结构,一些重要模块的划分可能跟论文有区别,但是整体结构是一样的。
1.整体结构
MPRNet官方给出的结构图如下:
图1
这个图总体概括了MPRNet的结构,但是很多细节没有表现出来,通过阅读代码我给出更加详细的模型结构介绍。下面的图中输入统一512x512,我们以Deblurring为例,并且batch_size=1。
整体结构图如下:
图2
图中的三个Input都是原图,整个模型三个阶段,整体流程如下:
1.1 输入图片采用multi-patch方式分成四份,分成左上、右上、左下、右下;
1.2 每个patch经过一个3x3的卷积扩充维度,为的是后面能提取更丰富的特征信息;
1.3 经过CAB(Channel Attention Block),利用注意力机制提取每个维度上的特征;
1.4 Encoder,编码三种尺度的图像特征,提取多尺度上下文特征,同时也是提取更深层的语义特征;
1.5 合并深特征,将四个batch的同尺度特征合并成左右两个尺度,送入Decoder;
1.6 Decoder,提取合并后的每个尺度的特征;
1.7 输入图片采用multi-patch方式分成两份,分成左、右;
1.8 将左右两个batch分别与Stage1 Decoder输出的大尺度特征图送入SAM(Supervised Attention Module),SAM在训练的时候可以利用GT为当前阶段的恢复过程提供有用的控制信号;
1.9 SAM的输出分成两部分,一部分是第二次输入的原图特征,它将继续下面的流程;一部分用于训练时的Stage1输出,可以利用GT更快更好的让模型收敛。
2.0 经过Stage2的卷积扩充通道和CAB操作,将Stage1中的Decoder前后的特征送入Stage2的Encoder。
2.2 经过和Stage1相似的Decoder,也产生两个部分的输出,一部分继续Stage3,一部分输出与GT算损失;
3.1 Stage3的原图输入不在切分,目的是利用完整的上下文信息恢复图片细节。
3.2 将原图经过卷积做升维处理;
3.3 将Stage2中的Decoder前后的特征送入Stage3的ORSNet(Original Resolution Subnetwork),ORSNet不使用任何降采样操作,并生成空间丰富的高分辨率特征。
3.4 最后经过一个卷积将维度降为3,输出。
代码实现:
#位置:MPRNet.py
class MPRNet(nn.Module):
def __init__(self, in_c=3, out_c=3, n_feat=96, scale_unetfeats=48, scale_orsnetfeats=32, num_cab=8, kernel_size=3, reduction=4, bias=False):
super(MPRNet, self).__init__()
act=nn.PReLU()
self.shallow_feat1 = nn.Sequential(conv(in_c, n_feat, kernel_size, bias=bias), CAB(n_feat,kernel_size, reduction, bias=bias, act=act))
self.shallow_feat2 = nn.Sequential(conv(in_c, n_feat, kernel_size, bias=bias), CAB(n_feat,kernel_size, reduction, bias=bias, act=act))
self.shallow_feat3 = nn.Sequential(conv(in_c, n_feat, kernel_size, bias=bias), CAB(n_feat,kernel_size, reduction, bias=bias, act=act))
# Cross Stage Feature Fusion (CSFF)
self.stage1_encoder = Encoder(n_feat, kernel_size, reduction, act, bias, scale_unetfeats, csff=False)
self.stage1_decoder = Decoder(n_feat, kernel_size, reduction, act, bias, scale_unetfeats)
self.stage2_encoder = Encoder(n_feat, kernel_size, reduction, act, bias, scale_unetfeats, csff=True)
self.stage2_decoder = Decoder(n_feat, kernel_size, reduction, act, bias, scale_unetfeats)
self.stage3_orsnet = ORSNet(n_feat, scale_orsnetfeats, kernel_size, reduction, act, bias, scale_unetfeats, num_cab)
self.sam12 = SAM(n_feat, kernel_size=1, bias=bias)
self.sam23 = SAM(n_feat, kernel_size=1, bias=bias)
self.concat12 = conv(n_feat*2, n_feat, kernel_size, bias=bias)
self.concat23 = conv(n_feat*2, n_feat+scale_orsnetfeats, kernel_size, bias=bias)
self.tail = conv(n_feat+scale_orsnetfeats, out_c, kernel_size, bias=bias)
def forward(self, x3_img):
# Original-resolution Image for Stage 3
H = x3_img.size(2)
W = x3_img.size(3)
# Multi-Patch Hierarchy: Split Image into four non-overlapping patches
# Two Patches for Stage 2
x2top_img = x3_img[:,:,0:int(H/2),:]
x2bot_img = x3_img[:,:,int(H/2):H,:]
# Four Patches for Stage 1
x1ltop_img = x2top_img[:,:,:,0:int(W/2)]
x1rtop_img = x2top_img[:,:,:,int(W/2):W]
x1lbot_img = x2bot_img[:,:,:,0:int(W/2)]
x1rbot_img = x2bot_img[:,:,:,int(W/2):W]
##-------------------------------------------
##-------------- Stage 1---------------------
##-------------------------------------------
## Compute Shallow Features
x1ltop = self.shallow_feat1(x1ltop_img)
x1rtop = self.shallow_feat1(x1rtop_img)
x1lbot = self.shallow_feat1(x1lbot_img)
x1rbot = self.shallow_feat1(x1rbot_img)
## Process features of all 4 patches with Encoder of Stage 1
feat1_ltop = self.stage1_encoder(x1ltop)
feat1_rtop = self.stage1_encoder(x1rtop)
feat1_lbot = self.stage1_encoder(x1lbot)
feat1_rbot = self.stage1_encoder(x1rbot)
## Concat deep features
feat1_top = [torch.cat((k,v), 3) for k,v in zip(feat1_ltop,feat1_rtop)]
feat1_bot = [torch.cat((k,v), 3) for k,v in zip(feat1_lbot,feat1_rbot)]
## Pass features through Decoder of Stage 1
res1_top = self.stage1_decoder(feat1_top)
res1_bot = self.stage1_decoder(feat1_bot)
## Apply Supervised Attention Module (SAM)
x2top_samfeats, stage1_img_top = self.sam12(res1_top[0], x2top_img)
x2bot_samfeats, stage1_img_bot = self.sam12(res1_bot[0], x2bot_img)
## Output image at Stage 1
stage1_img = torch.cat([stage1_img_top, stage1_img_bot],2)
##-------------------------------------------
##-------------- Stage 2---------------------
##-------------------------------------------
## Compute Shallow Features
x2top = self.shallow_feat2(x2top_img)
x2bot = self.shallow_feat2(x2bot_img)
## Concatenate SAM features of Stage 1 with shallow features of Stage 2
x2top_cat = self.concat12(torch.cat([x2top, x2top_samfeats], 1))
x2bot_cat = self.concat12(torch.cat([x2bot, x2bot_samfeats], 1))
## Process features of both patches with Encoder of Stage 2
feat2_top = self.stage2_encoder(x2top_cat, feat1_top, res1_top)
feat2_bot = self.stage2_encoder(x2bot_cat, feat1_bot, res1_bot)
## Concat deep features
feat2 = [torch.cat((k,v), 2) for k,v in zip(feat2_top,feat2_bot)]
## Pass features through Decoder of Stage 2
res2 = self.stage2_decoder(feat2)
## Apply SAM
x3_samfeats, stage2_img = self.sam23(res2[0], x3_img)
##-------------------------------------------
##-------------- Stage 3---------------------
##-------------------------------------------
## Compute Shallow Features
x3 = self.shallow_feat3(x3_img)
## Concatenate SAM features of Stage 2 with shallow features of Stage 3
x3_cat = self.concat23(torch.cat([x3, x3_samfeats], 1))
x3_cat = self.stage3_orsnet(x3_cat, feat2, res2)
stage3_img = self.tail(x3_cat)
return [stage3_img+x3_img, stage2_img, stage1_img]
图中还有一些模块细节没有表现出来,下面我将详细介绍。
2.CAB(Channel Attention Block)
顾名思义,CAB就是利用注意力机制提取每个通道的特征,输出输入特征图形状不变,结构图如下:
图3
可以看到,经过了两个卷积和GAP之后得到了一个概率图(就是那个残差边),在经过两个卷积和Sigmoid之后与概率图相乘,就实现了一个通道注意力机制。
代码实现:
# 位置MPRNet.py
## Channel Attention Layer
class CALayer(nn.Module):
def __init__(self, channel, reduction=16, bias=False):
super(CALayer, self).__init__()
# global average pooling: feature --> point
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# feature channel downscale and upscale --> channel weight
self.conv_du = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
nn.ReLU(inplace=True),
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv_du(y)
return x * y
##########################################################################
## Channel Attention Block (CAB)
class CAB(nn.Module):
def __init__(self, n_feat, kernel_size, reduction, bias, act):
super(CAB, self).__init__()
modules_body = []
modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
modules_body.append(act)
modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
self.CA = CALayer(n_feat, reduction, bias=bias)
self.body = nn.Sequential(*modules_body)
def forward(self, x):
res = self.body(x)
res = self.CA(res)
res += x
return res
3.Stage1 Encoder
Stage1和Stage1的Encoder有一些区别,所以分开介绍。Stage1 Encoder有一个输入和三个不同尺度的输出,为的是提取三个尺度的特征并为下面的尺度融合流程做准备;其中有多个CAB结构,可以更好的提取通道特征;下采样通过粗暴的Downsample实现,结构如下:
图4
代码实现:
# 位置MPRNet.py
class Encoder(nn.Module):
def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, csff):
super(Encoder, self).__init__()
self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.encoder_level2 = [CAB(n_feat+scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.encoder_level3 = [CAB(n_feat+(scale_unetfeats*2), kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.encoder_level1 = nn.Sequential(*self.encoder_level1)
self.encoder_level2 = nn.Sequential(*self.encoder_level2)
self.encoder_level3 = nn.Sequential(*self.encoder_level3)
self.down12 = DownSample(n_feat, scale_unetfeats)
self.down23 = DownSample(n_feat+scale_unetfeats, scale_unetfeats)
# Cross Stage Feature Fusion (CSFF)
if csff:
self.csff_enc1 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=bias)
self.csff_enc2 = nn.Conv2d(n_feat+scale_unetfeats, n_feat+scale_unetfeats, kernel_size=1, bias=bias)
self.csff_enc3 = nn.Conv2d(n_feat+(scale_unetfeats*2), n_feat+(scale_unetfeats*2), kernel_size=1, bias=bias)
self.csff_dec1 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=bias)
self.csff_dec2 = nn.Conv2d(n_feat+scale_unetfeats, n_feat+scale_unetfeats, kernel_size=1, bias=bias)
self.csff_dec3 = nn.Conv2d(n_feat+(scale_unetfeats*2), n_feat+(scale_unetfeats*2), kernel_size=1, bias=bias)
def forward(self, x, encoder_outs=None, decoder_outs=None):
enc1 = self.encoder_level1(x)
if (encoder_outs is not None) and (decoder_outs is not None):
enc1 = enc1 + self.csff_enc1(encoder_outs[0]) + self.csff_dec1(decoder_outs[0])
x = self.down12(enc1)
enc2 = self.encoder_level2(x)
if (encoder_outs is not None) and (decoder_outs is not None):
enc2 = enc2 + self.csff_enc2(encoder_outs[1]) + self.csff_dec2(decoder_outs[1])
x = self.down23(enc2)
enc3 = self.encoder_level3(x)
if (encoder_outs is not None) and (decoder_outs is not None):
enc3 = enc3 + self.csff_enc3(encoder_outs[2]) + self.csff_dec3(decoder_outs[2])
return [enc1, enc2, enc3]
4.Stage2 Encoder
Stage2 Encoder输入为三个,分别为上一层的输出和Stage1中的Decoder前后的特征。主流程(也就是左面竖着的那一列)和Stage1 Encoder是一样的。增加的两个输入,每个输入又分为三个尺度,每个尺度经过一个卷积层,然后相同尺度的特征图做特征融合,输出,结构如下:
图5
5.Decoder
两个阶段的Decoder结构是一样的,所以放在一起说,有三个不用尺度的输入;通过CAB提取特征;小尺度特征通过上采样变大,通过卷积使通道变小;小尺度的特征图shape最终变成跟大尺度一样,通过残差边实现特征融合,结构如下:
图6
代码实现:
# 位置:MPRNet.py
class Decoder(nn.Module):
def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats):
super(Decoder, self).__init__()
self.decoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.decoder_level2 = [CAB(n_feat+scale_unetfeats, kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.decoder_level3 = [CAB(n_feat+(scale_unetfeats*2), kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.decoder_level1 = nn.Sequential(*self.decoder_level1)
self.decoder_level2 = nn.Sequential(*self.decoder_level2)
self.decoder_level3 = nn.Sequential(*self.decoder_level3)
self.skip_attn1 = CAB(n_feat, kernel_size, reduction, bias=bias, act=act)
self.skip_attn2 = CAB(n_feat+scale_unetfeats, kernel_size, reduction, bias=bias, act=act)
self.up21 = SkipUpSample(n_feat, scale_unetfeats)
self.up32 = SkipUpSample(n_feat+scale_unetfeats, scale_unetfeats)
def forward(self, outs):
enc1, enc2, enc3 = outs
dec3 = self.decoder_level3(enc3)
x = self.up32(dec3, self.skip_attn2(enc2))
dec2 = self.decoder_level2(x)
x = self.up21(dec2, self.skip_attn1(enc1))
dec1 = self.decoder_level1(x)
return [dec1,dec2,dec3]
6.SAM(Supervised Attention Module)
SAM出现在两个阶段间,有两个输入,将上一层特征和原图作为输入,提升了特征提取的性能,,SAM作为有监督的注意模块,使用注意力图强力筛选了跨阶段间的有用特征。有两个输出,一个是经过了注意力机制的特征图,为下面的流程提供特征;一个是3通道的图片特征,为了训练阶段输出,结构如下:
图7
代码位置:
# 位置MPRNet.py
## Supervised Attention Module
class SAM(nn.Module):
def __init__(self, n_feat, kernel_size, bias):
super(SAM, self).__init__()
self.conv1 = conv(n_feat, n_feat, kernel_size, bias=bias)
self.conv2 = conv(n_feat, 3, kernel_size, bias=bias)
self.conv3 = conv(3, n_feat, kernel_size, bias=bias)
def forward(self, x, x_img):
x1 = self.conv1(x)
img = self.conv2(x) + x_img
x2 = torch.sigmoid(self.conv3(img))
x1 = x1*x2
x1 = x1+x
return x1, img
7.ORSNet(Original Resolution Subnetwork)
为了保留输入图像的细节,模型在最后一阶段引入了原始分辨率的子网(ORSNet:Original Resolution Subnetwork)。ORSNet不使用任何降采样操作,并生成空间丰富的高分辨率特征。它由多个原始分辨率块(BRB)组成,是模型的最后阶段,结构如下:
图8
可以看到,输入为三个,分别为上一层的输出和Stage2中的Decoder前后的特征。后两个输入,每个输入又分为三个尺度,三个尺度的通道数都先变成96,然后在变成128;小尺度的size都变成和大尺度一样,最后做特征融合融合前会经过ORB(Original Resolution Block)模块。
ORB由一连串的CAB组成,还有一个大的残差边,结构如下:
图9
代码实现:
# 位置MPRNet.py
## Original Resolution Block (ORB)
class ORB(nn.Module):
def __init__(self, n_feat, kernel_size, reduction, act, bias, num_cab):
super(ORB, self).__init__()
modules_body = []
modules_body = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(num_cab)]
modules_body.append(conv(n_feat, n_feat, kernel_size))
self.body = nn.Sequential(*modules_body)
def forward(self, x):
res = self.body(x)
res += x
return res
##########################################################################
class ORSNet(nn.Module):
def __init__(self, n_feat, scale_orsnetfeats, kernel_size, reduction, act, bias, scale_unetfeats, num_cab):
super(ORSNet, self).__init__()
self.orb1 = ORB(n_feat+scale_orsnetfeats, kernel_size, reduction, act, bias, num_cab)
self.orb2 = ORB(n_feat+scale_orsnetfeats, kernel_size, reduction, act, bias, num_cab)
self.orb3 = ORB(n_feat+scale_orsnetfeats, kernel_size, reduction, act, bias, num_cab)
self.up_enc1 = UpSample(n_feat, scale_unetfeats)
self.up_dec1 = UpSample(n_feat, scale_unetfeats)
self.up_enc2 = nn.Sequential(UpSample(n_feat+scale_unetfeats, scale_unetfeats), UpSample(n_feat, scale_unetfeats))
self.up_dec2 = nn.Sequential(UpSample(n_feat+scale_unetfeats, scale_unetfeats), UpSample(n_feat, scale_unetfeats))
self.conv_enc1 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
self.conv_enc2 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
self.conv_enc3 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
self.conv_dec1 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
self.conv_dec2 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
self.conv_dec3 = nn.Conv2d(n_feat, n_feat+scale_orsnetfeats, kernel_size=1, bias=bias)
def forward(self, x, encoder_outs, decoder_outs):
x = self.orb1(x)
x = x + self.conv_enc1(encoder_outs[0]) + self.conv_dec1(decoder_outs[0])
x = self.orb2(x)
x = x + self.conv_enc2(self.up_enc1(encoder_outs[1])) + self.conv_dec2(self.up_dec1(decoder_outs[1]))
x = self.orb3(x)
x = x + self.conv_enc3(self.up_enc2(encoder_outs[2])) + self.conv_dec3(self.up_dec2(decoder_outs[2]))
return x
四、损失函数
MPRNet主要使用了两个损失函数CharbonnierLoss和EdgeLoss,公式如下:
其中累加是因为训练的时候三个阶段都有输出,都需要个GT计算损失(如图2的三个output);该模型不是直接预测恢复的图像,而是预测残差图像,添加退化的输入图像得到:
Deblurring和Deraining两个任务CharbonnierLoss和EdgeLoss做了加权求和,比例1:0.05;只使用了CharbonnierLoss,我感觉是因为这里使用的噪声是某种分布(入高斯分布、泊松分布)的噪声,不会引起剧烈的边缘差异,所以Denoising没有使用EdgeLoss。
下面简单介绍一下两种损失。
1.CharbonnierLoss
公式如下:
CharbonnierLoss在零点附近由于常数的存在,梯度不会变成零,避免梯度消失。函数曲线近似L1损失,相比L2损失而言,对异常值不敏感,避免过分放大误差。
代码实现:
# 位置losses.py
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=1e-3):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
# loss = torch.sum(torch.sqrt(diff * diff + self.eps))
loss = torch.mean(torch.sqrt((diff * diff) + (self.eps*self.eps)))
return loss
2.EdgeLoss
L1或者L2损失注重的是全局,没有很好地考虑一些显著特征的影响, 而显著的结构和纹理信息与人的主观感知效果高度相关,是不能忽视的。
边缘损失主要考虑纹理部分的差异,可以很好地考虑高频的纹理结构信息, 提高生成图像的细节表现,公示如下:
其中表示Laplacian边缘检测中的核函数,表示对做边缘检测,公式中其他部分和CharbonnierLoss类似。
代码实现:
# 位置losses.py
class EdgeLoss(nn.Module):
def __init__(self):
super(EdgeLoss, self).__init__()
k = torch.Tensor([[.05, .25, .4, .25, .05]])
self.kernel = torch.matmul(k.t(),k).unsqueeze(0).repeat(3,1,1,1)
if torch.cuda.is_available():
self.kernel = self.kernel.cuda()
self.loss = CharbonnierLoss()
def conv_gauss(self, img):
n_channels, _, kw, kh = self.kernel.shape
img = F.pad(img, (kw//2, kh//2, kw//2, kh//2), mode='replicate')
return F.conv2d(img, self.kernel, groups=n_channels)
def laplacian_kernel(self, current):
filtered = self.conv_gauss(current) # filter
down = filtered[:,:,::2,::2] # downsample
new_filter = torch.zeros_like(filtered)
new_filter[:,:,::2,::2] = down*4 # upsample
filtered = self.conv_gauss(new_filter) # filter
diff = current - filtered
return diff
def forward(self, x, y):
loss = self.loss(self.laplacian_kernel(x), self.laplacian_kernel(y))
return loss
MPRNet的主要的内容就介绍到这,主要是backbone的创新,其他部分中规中矩,关注不迷路。
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